International Journal of Computer Applications in Technology (92 papers in press)
Simulation and visualisation approach for accidents in chemical plants
by Feng Ting-Fan, Tan Jing, Liu Jin, Deng Wensheng
Abstract: A new general approach to lay the foundation for building a more effective and real-time evacuation system for accidents in chemical plants is presented. In this work, we build the mathematical models and realise automatic grid generating based on the physical models stored in advance with several algorithms in jMonkeyEngine environment. Meanwhile, the results of the simulation data through finite difference method (FDM) are visualised coupling with the physical models. Taking fire as an example, including fire with single and multiple ignition sources, shows the feasibility of the presented approach. Furthermore, a coarse alarm and evacuation system from fire have been developed with a multiple SceneNode and roam system, which also includes the making and importing of the physical models. However, to improve the accuracy of the mathematical models, adaptability and refinement of the grids and universality of the evacuation system is the direction of efforts.
Keywords: simulation; chemical accidents; alarm and evacuation system; jMonkeyEngine.
Detecting occluded faces in unconstrained crowd digital pictures
by Chandana Withana, S. Janahiram, Abeer Alsadoon, A.M.S. Rahma
Abstract: Face detection and recognition mechanisms, a concept known as face detection, are widely used in various multimedia and security devices. There are significant numbers of studies into face recognition, particularly for image processing and computer vision. However, there remain significant challenges in existing systems owing to limitations behind algorithms. Viola Jones and Cascade Classifier are considered the best algorithms from existing systems. They can detect faces in an unconstrained crowd scene with half and full face detection methods. However, limitations of these systems are affecting accuracy and processing time. This project proposes a solution called Viola Jones and Cascade (VJaC), based on the study of current systems, features and limitations. This system considered three main factors: processing time, accuracy and training. These factors are tested on different sample images, and compared with current systems.
Keywords: face detection; unconstrained crowd digital pictures; face recognition.
Parameter optimization and experimental verification of the positive-negative pressure pulse automatic control system for plug removal
by Zhenfu Ma, Yuankai Song, Kai Zhang
Abstract: The effective application of water injection wells in oilfield operations plays an important role in improving the production quality of the oilfield and the actual revenue of oilfield enterprises. Aiming at the current problems of mineralisation blockage and scaling in oil production, this paper designs a positive-negative pressure pulse automatic control system for plug removal. First, it studies the main equipment for the implementation of positive-negative pulse automatic control, and then designs the technical principles, control logic and implementation methods of positive pressure pulse, negative pressure pulse, and positive-negative coupling control. Next, the control effect is verified by the automatic control experiment platform and the control parameters are optimised. Finally, the optimised control effect is verified by experiment using the control platform.
Keywords: oil well scaling and plugging; positive-negative pressure pulse; automatic control; Kingview.
Research on rotating high frequency current injection method based on LC resonance network
by Li-ping Zhong
Abstract: The high frequency signal injection method is an effective method to realise the sensorless detection of the rotor position of the permanent magnet synchronous motor (PMSM), which can estimate any position of the rotor in the whole speed range. When the frequency of the injected signal is high, the rotating high frequency current injection method can obtain more significant signal with rotor position information for PMSM with small salient characteristics, so it can obtain higher position estimation accuracy. However, the traditional method of injecting high-frequency current needs a current source inverter or a complex control mode, which is difficult to apply in practice. The drive circuit based on LC resonant network can convert the resonant voltage signal into the current signal, thus the driving voltage and rotating high-frequency current signals can be simultaneously provided to the PMSM through the voltage source inverter, which greatly facilitates the extraction and processing of rotor position information and improves the sensorless detection accuracy of rotor position. The experimental results verified the effectiveness of the circuit and method.
Keywords: LC resonant network; rotating high frequency current injection; high frequency negative sequence component; double PI regulator.
An improved 2-OPT optimisation scheme for Hamilton loops
by Bo Sun, Shicai Liu, Yongquan You, Chuanxiang Ren
Abstract: Aiming at the problem that the traditional traffic route planning has a single target strategy and cannot be adjusted according to the actual situation, a multi-lane planning model based on Hamilton loops is proposed, which uses the characteristics of Hamilton loops to cover all nodes in the set and uses the nearest neighbour algorithm to obtain the initial loop. The OPT algorithm optimises the initial loop, avoids the problem that the 2-OPT algorithm cannot find the optimal loop due to randomness, and improves the efficiency of the 2-OPT algorithm. Considering road congestion and other situations, the design introduces speed parameters and comprehensive influence factors to obtain different choices and obtain the optimal solution of the Hamilton loop in different situations, which proves the effectiveness and feasibility of the algorithm. The optimised model can give different path planning schemes according to different actual situations and can find the shortest time path and the shortest path, making the Hamilton loop model more practical. The model uses the adjacency matrix to visually represent city nodes. The simulation results verify the introduction of variable parameters, such as speed influence factors, to meet different planning needs, achieve more targeted route planning, improve the path planning scheme, and improve people's travel efficiency.
Keywords: Hamilton loop; 2-OPT algorithm; path planning.
Automatically optimised stereoscopic camera control based on an assessment of 3D video quality of experience
by Dawei Lu, Xiaoguang Huang, Zhi Li, Zhao Zhang
Abstract: Stereoscopic 3D (S3D) technologies have gained significant attention owing to their widely applications. However, producing high-quality S3D content is still a challenging task that requires careful handling to achieve the artistic intent and maintain visual comfort. In this study, we present an automatically controlled stereoscopic camera controller that specifically addresses the challenges in S3D content production. The key idea that distinguishes our method from the existing work is that our method aims to predict the 3D quality of experience (QoE) in the production stage so that the optimised camera parameters can be obtained automatically. To this end, considering two interconstraint indicators, i.e., visual comfort and perceived depth, we collect and label a dataset of S3D video scene clips and generate a 3D video QoE assessment model that can guide the optimisation of the stereoscopic camera parameters. We describe how to implement our system into a modern production pipeline that has been used in some projects, including commercial ones. The experimental results, including the user studies, demonstrate that our system enhances the perceived depth without creating visual fatigue and that our controller can make the production of S3D content easier and more efficient.
Keywords: stereoscopic 3D; camera control; visual comfort; perceived depth; 3D quality of experience.
Feature vector sharing and scale comprehensive optimisation for target detection in smart neighbourhood governance and monitoring
by Jianmin Liu
Abstract: This article proposes feature vector sharing and a scale comprehensive optimisation strategy of image target detection and recognition method of complex street maximum suppression based on the calculation of the corresponding feature area corresponding to the feature map and complete eigenvector. Based on this, this article also combines a fine-tuning method based on transfer learning generalisation, which is suitable for non-convex optimisation and high-dimensional space. First, the method described above implements the optimal rectangular selection box competition based on the scale comprehensive optimisation strategy, and selects the selection box that can reflect the core essence of the target in each classification set. Then, this article realises the model of detecting an image target in a complex neighbourhood, which improves the accuracy and robustness. Furthermore, we experimentally demonstrate that the accuracy and robustness of our proposed method are superior to those of conventional methods.
Keywords: feature vector sharing; scale comprehensive optimisation; neighbourhood images; smart neighbourhood monitoring.
An identity-based integrity verification scheme for cloud storage in 5G environment
by Zuodong Wu, Jianwei Zhang, Zengyu Cai
Abstract: In order to solve the security problems such as leakage and tampering of sensitive data in cloud storage, the previous schemes usually sacrificed communication efficiency for higher security, which caused serious computing overhead. Therefore, in this paper, we adopt the idea of Chinese Commercial Cryptographic SM9 and SM3 algorithms, and regard user identity identification as interference factor and verification mechanism, so as to propose an identity-based integrity verification scheme for cloud storage. Furthermore, we also give the security analysis under the assumption of Diffie-Hellman and discrete logarithm problem on elliptic curve. Finally, simulation results show that our scheme can not only verify the integrity of sensitive data correctly, but also resist common malicious attacks. Especially in terms of efficiency, our scheme can effectively reduce the storage and computing burden, space and time cost. This will have a certain guiding significance for the privacy protection of cloud storage in 5G environment.
Keywords: cloud storage; Chinese Commercial Cryptographic; SM9; SM3; identification; Diffie-Hellman; discrete logarithm; integrity; privacy protection; 5G.
An accessibility learning system for higher integrated education of hearing-impaired students' technology
by Jian Zhao, Liu Wang, Lijuan Shi, Zhejun Kuang, Di Zhao
Abstract: Integrated education for the disabled is the embodiment of social equity and social progress, and China has carried out pilot work of integrated higher education for deaf students for four years. However,in the actual teaching process, it is found that the learning efficiency and effect of deaf students are quite different from that of healthy students. This difference is mainly caused by hearing impairment. Hearing-impaired students are accustomed to understanding the semantic meaning mainly through lip language. However,the amount of information of lip language of hearing-impaired students decreases according to distance, shielding and other interference reasons, which leads to obstacles to the semantic understanding of course teaching. An accessibility learning system for higher integrated education of hearing-impaired students is established to help them to capture the auxiliary information of teachers lip language and voice. It is also evaluated to explore its practical application effect. The evaluation results show that this system can help hearing-impaired students to better understand the semantic meaning of the course and integrate into the integrated education classroom.
Keywords: hearing-impaired students; integrated education; accessibility learning Ssystem.
Measurement of sentence similarity based on constituency parsing and dilated convolution
by MingYu Ji, ChenLong Wang, Gang Liu
Abstract: Measurement of sentence similarity is widely used in the field of natural language processing, the current mainstream method is based on neural network similarity model. In actual application, the method of neural network has some disadvantages. On the one hand, when sentences are input into the neural network, the problem of semantic loss is caused by the interception and zero-filling operation of the sentence that is too long or too short. On the other hand, it ignores the semantic relation between interval words. Thus, this paper proposes a method of measuring sentence similarity based on constituency parsing and dilated convolution, by using constituency parsing to design rules to reduce unimportant semantic components of long sentences and to supplement important semantic components of short sentences. In addition, the receptive fields in sentence dimension and word vector dimension are dilated to capture the semantic association of the two-dimensional interval words. Finally, the method is verified on two datasets.
Keywords: sentence similarity; neural network; constituency parsing; semantic component; dilated convolution.
Hybrid image denoising based on region division
by Yong Tian, Jing Wang, Yunfeng Zhang
Abstract: This paper proposes an efficient hybrid framework for image denoising, in which the advantages of different denoising methods are effectively incorporated by using the region prior knowledge. In detail, the input noisy image is first divided into a large number of overlapping patches followed by extraction of speed-up robust feature (SURF), and then the noisy patches are classified into two categories based on twin support vector machine (TWSVM). The texture patches are enhanced via gradient histogram preservation (GHP) while flat patches can be re-established using Block Matching Three-Dimensional Filtering (BM3D). Finally, the re-established images can be acquired by fusing the processing results of the two kinds of patch. To evaluate the effectiveness of the presented method, we conduct experiments on standard image datasets and compare the performance with other outstanding denoising approaches. Experimental results show that the presented method achieves better results, especially in containing textures and edges compared with existing image denoising methods.
Keywords: image denoising; hybrid framework; twin support vector machine; gradient histogram preservation; block matching three-dimensional filtering.
Predictive analytics for spam email classification using machine learning techniques
by Pradeep Kumar
Abstract: Automated text classification is the most widely used approach to manage an enormous amount of unstructured text data in digital forms, which is continuously increasing across the globe. Machine learning techniques are applied for automatic email filtering effectively to detect the spam mail and prevent them from delivering into the user's inbox. This paper used logistic regression, k-nearest neighbors, naive Bayes, decision trees, AdaBoost, ANNs, and SVMs for spam email classification. All the classifiers are learned, and the performance is measured in terms of precision, recall, and accuracy using a set of systematic experiments conducted on the Spambase dataset extracted from the UCI Machine Learning Repository. The effectiveness of each model was empirically illustrated to find a better and viable alternative model. The quantitative performance analysis of supervised and hybrid learning techniques is presented in detail. Experimental results indicate that ensemble methods outperform in terms of accuracy compared with other methods applied.
Keywords: text analytics; feature selection; predictive modelling; spam filtering; machine learning techniques.
Mechanical analysis and simulation of colliding damage of castor capsule
by Junming Hou, Yong Yang, Jingbo Bai, Hongjie Zhu
Abstract: Mechanical damage in the process of castor capsule shelling is the main problem for castors. In the paper, the effects of collision speed, moisture, collision material curvature and thickness on the collision deformation and equivalent stress of castor capsule are discussed. Based on the Hertz collision theory and dynamic equations, we established a hybrid collision mechanics model of castor capsule, and used finite element method on the simulation of its collision and shelling process. The results show that as the collision continues, the velocity and acceleration of the collision are nonlinear. Based on the optimal space filling (OSF) and multi-objective genetic algorithm (MOGA), the related parameters are determined. The shelling process model is established, and the damage of castor was studied. According to the response surface optimisation (RSO) method, the optimal parameter ratio of the castor capsule shell breaking was obtained. The results were that the hull part curvature was 340 mm, the shelling part thickness was 1.6 mm, the castor capsule capsule elastic modulus was 46.03 MPa and the collision speed was 6.96 m/s. The maximum equivalent stress at this time was 0.28 MPa, and the total deformation was 28.52 mm. The paper can provide reference for the optimal design of castor capsule shelling equipment.
Keywords: castor capsule; collision damage; simulation; genetic algorithm; response surface optimisation.
Design and implementation of a real-time digital signal processing system using PIC24 microcontroller and wireless GUI control
by Shensheng Tang, Alex Stangl, Manish Ale Magar, Shubham K C
Abstract: In this paper, a real-time digital signal processing (DSP) system is designed and implemented by using a PIC24 microcontroller circuit and a C# GUI application running on PC. The wireless communication between the PIC24 subsystem and the GUI subsystem is implemented via Bluetooth modules on the subsystems. The DSP system first digitises an input square signal of a certain frequency through an on-chip ADC of the PIC24 microcontroller, then uses different FIR digital filters to extract certain harmonics of the input signal, and outputs it as a sinusoidal signal to an on-chip DAC as well as sending the sampled data and filtered data over Bluetooth to the GUI. The GUI, besides plotting the input and output waveforms, can provide a means of controlling all functionalities of the system through a developed communication protocol. The design and implementation for the proposed DSP system are successfully demonstrated by experimental results. The hardware and software co-design method can be extended to other industrial applications and used as a good paradigm of engineering education for college students.
Keywords: digital signal processing (DSP); PIC24 microcontroller; ADC; DAC; C programming; C# programming; GUI; FIR filter; Bluetooth.
Design of MIMO QFT fractional control based on intelligent fractional PID? controller combined with decentralised and centralised FBLFD prefilter: application to SCARA robot
by Asma Aribi, Najah Yousfi, Nabil Derbel
Abstract: The aim of this paper is to propose new robust controls for multivariable parametric uncertain systems and to validate their efficiency in robot path tracking. An automated fractional multivariable quantitative feedback theory is developed. The principle is to obtain desired performances on the basis of controllers and prefilters without using a loop-shaping process. The proposed approach has benefited from the robustness of fractional control. Indeed, the method is based on a combination of an intelligent fractional PID? controller with both diagonal and non-diagonal frequency band limited fractional differentiator. A bi-objective optimisation based on genetic algorithm is used to find the controller parameters. The developed methodologies are applied to a SCARA robot model and the findings highlight the robustness of the designed controller and the success of the diagonal prefilter to eliminate loop interactions.
Keywords: FBLFD prefilter; fractional controller; bi-objective optimisation; multivariable systems; path tracking; quantitative feedback theory; SCARA robot.
FPGA design and circuit implementation of a new four-dimensional multistable hyperchaotic system with coexisting attractors
by Sundarapandian Vaidyanathan, Esteban Tlelo-Cuautle, Aceng Sambas, Leutcho Gervais Dolvis, Omar Guillén-Fernández
Abstract: This research work focuses upon the FPGA design and electronic circuit implementation of a new multistable four-dimensional hyperchaotic system. This work starts with the dynamics and phase plots of a new four-dimensional hyperchaos system. A detailed bifurcation analysis is carried out for the new system, and special properties such as multistability with coexisting attractors are reported for the system. An electronic circuit model using MultiSim of the new hyperchaos system is designed. Finally, an FPGA-based design of the new system is performed by applying two numerical methods. Circuit simulation and FPGA design of the new hyperchaos system are very useful for practical applications of the system.
Keywords: hyperchaos; hyperchaotic system; multistability; circuit simulation; FPGA design.
Hierarchical smart Routing protocol for wireless sensor networks
by Amine Kardi, Rachid Zagrouba
Abstract: This paper proposes a new hierarchical routing protocol dedicated to wireless sensor networks. Several solutions have been proposed, but an optimal one, which can be applied in different situations, still remains non-existent. Our proposal takes advantage of existing solutions and proposed a new radial cluster head (CH) selection algorithm to ensure the QoS, good load balancing and enhance the network lifetime. A performance analysis of our proposal with LEACH, Mod_LEACH and M-GEAR protocols is presented in this paper comparing these protocols using different metrics, such as network lifetime, throughput, stability and instability periods, remaining energy, network overhead. The simulations show that our solution outperforms existing solutions owing to the radial architecture proposed for CH selection and the use of two transmission levels for intra- and inter-cluster communication. Obtained results show that our proposal improves the network lifetime by 113% compared with LEACH protocol and optimises the energy consumption by more than 62% in comparison with M-GEAR.
Keywords: routing protocol; wireless sensor networks; hierarchical routing protocol; wireless sensor network; cluster head.
Real time sign languages character recognition
by Sari Awwad, Sahar Idwan, Hasan Gharaibeh
Abstract: Deaf people face many challenges to communicate with the hearing world. Many studies and industrial solutions have come up with interpretations from sign language to normal text, but most of them are limited either to static images for letters or to a static animated character playing the word in motion. Therefore, this research worked on enhancing the feasible algorithms that have been used, such as image detection, image processing techniques and image translation methods. The contribution consists of two parts, the first one is using our dataset, and the second one is proposing a new solution by extracting surf features after using image filtering based on the existing methods to accelerate the translation process for the long sentences. Experimental results over our dataset show a distinguished accuracy compared with other studies in terms of efficiency time and recognition rate of sign language character recognition.
Keywords: sign language dataset; image filtering; surf features; M-SVM2; sign character recognition.
Variable neighborhood search based algorithm to solve the minimum back-walk-free latency problem
by Ban Bang
Abstract: The Minimum Back-Walk-Free Latency Problem (MBLP) is an extension of the Minimum Latency Problem (MLP). It aims to find a tour with minimum latency while ignoring the back-walking costs. One of the justifications for ignoring the back-walking costs is in applications of message broadcasting for mobile devices. Obviously, MBLP is an NP-hard problem because it is a generation of MLP, hence a metaheuristic needs to be developed to provide near-optimal solutions within a short computation time. However, the main issue of metaheuristics is that they fall into local optima in some cases since the search space of the problem is combinatorial explosion. In order to overcome this drawback, we propose a metaheuristic algorithm that is mainly based on Variable Neighborhood Search (VNS) and shaking techniques to solve the problem. The aim of VNS is to generate diverse neighborhoods by using various neighborhood searches while shaking techniques allow it to guide the search towards an unexplored part of the solution space. Moreover, a technique called Adaptive Memory (AM) is applied to address this problem to balance between the diversity and intensification. The computational results show that the effectiveness and efficiency of the proposed algorithm are comparable with the current state-of-the-art algorithms.
Keywords: MBLP; metaheuristic; VNS.
Pedestrian detection algorithm based on improved single shot MultiBox detector
by Dawei Liu, Shang Gao, Wanda Chi, Di Fan
Abstract: The algorithm proposed in this paper introduces the deeply separable fusion hierarchical feature model into the backbone network of Single Shot MultiBox Detector (SSD) algorithm, which reduces the complexity of the model by using depthwise separable convolution and uses hierarchical structure fusion to enhance features. We add a decode-encoder structure between the backbone network and the additional feature layer to propagate the high-level semantic feature downward through decoding, and then integrate the feature with the shallow local detail feature. At the same time, the encoder is used to transfer more advanced semantic features and output the fused shallow features for pedestrian detection, which improves the classification and detection ability of the model. The miss rate of the improved algorithm in this paper is as low as 9.68% on the INRIA dataset.
Keywords: level feature fusion; decode-encoder; pedestrian detection; semantic features; single shot MultiBox detector; depthwise separable convolution.
An information real-time synchronisation system across different media based on position encoding for mobile computing
by Jingxian Zhou, Guangming Zheng, Baobao Zhang, Chunbo Liu, Zhaojun Gu
Abstract: Information should be spread accurately, synchronised in real time and shared at low cost, especially when distributed and stored in different media concurrently. In the education field, traditional paper writing and multimedia equipment input are often combined to achieve a better teaching result, which is a typical scenario that information must be synchronised across different media. Interactive paper system has been proved to successfully realise information synchronisation in real time. However, existing solutions have some deficiencies, such as poor reproducibility, delay in recognition, or positioning error. What is more serious is that most of the solutions are expensive and not suitable for large-scale promotion. In order to solve the problem of information fusion across different media at low cost, an information real-time synchronisation system based on SONIX Code was designed and implemented innovatively. In this paper, we redesign the size of the code and code unit, and the decode algorithm. Firstly, the design and encoding scheme of dot matrix coded paper based on virtual raster is introduced, and some important parameters including pixel width, feature point size, encoding atom size, and encoding unit size are determined. Then the decoding process is described in detail as five steps: image noise reduction, encoding unit determination, encoding unit rotation correction, encoding unit coordinate decoding and coordinate offset correction. Finally, the system is demonstrated comprehensively, and experiments on an A4 paper were conducted. Besides, a practical test among 350 users has been carried out. All of the results proved this system had good performance on visual effect, writing fluency and positioning accuracy.
Keywords: information parallel storage; position encoding; real-time synchronisation; intelligent hardware system.
Map simulation of a dog's behaviour using population density of probabilistic model
by Jirawat Jiwattanakul, Chawapat Youngjitikornkun, Worapan Kusakunniran, Anuwat Wiratsudakul, Weerapong Thanapongtharm, Kansuda Leelahapongsathon
Abstract: To understand relations in an ecosystem, it is important to gather and characterise information about living creatures and their habitats. This paper focuses on a dog's behaviour, since it is in a great concern where dogs are environmentally and physically correlated to human life. We therefore develop a simulator to demonstrate dogs' behaviors considering their individual and group habits. This could be further used in a prediction process of a widespread occurrence of any possible kind of diseases via dogs' transmission that is created to be purposefully expandable for disease control. The proposed system is developed using Unity and Mapbox SDK, in order to provide a spatial implementation for the simulation of such dogs' movements. In addition, the normal distribution, the kernel density method and the probabilistic model are applied to model movement behaviour, world interaction and behaviour rates, respectively. The simulation is validated on an area of Saibai, located in the north-western of Torres Strait islands, Australia. This reports a median tie-strength of 0.0106 which is slightly different from the value calculated from the GPS information of 0.0113. The relative error is only 6.19%. Then, the simulation is applied to three cities in Thailand, including Khon Kaen, Songkhla, and Prachuap Khiri Khan, with the dog populations of 145.86, 74.70, 76.54, respectively. The calculated tie-strengths are 0.054, 0.039, and 0.042 for the three cities, respectively. They are all higher than the calculated tie-strength of Saibai, owing to the significantly higher average numbers of dogs and group distances. Consequently, the contact rates between any pair of dogs are also higher due to the fact that the dogs' communities in Thailand have larger connections either between dogs or between their communities, when compared with Saibai.
Keywords: dog simulation; Unity; Mapbox SDK; probabilistic model; kernel density method; dog behaviour.
Determining solubility of CO2 in aqueous brine systems via hybrid smart strategies
by Tofigh Sayahi, Afshin Tatar, Alireza Rostami, Amin Anbaz, Khalil Shahbazi
Abstract: Carbon Capture and Storage (CCS) helps the reduction of atmospheric CO2 emission, and increases the oil recovery factor. Solubility of CO2 in water/brine is an important parameter for successful development of the CCS process; however, there exists a large gap to establish a rigorous approach to estimate solubility of CO2 in water/brine. Machine learning as a strong approach can properly tackle such issue in this field of study. In current study, Radial Basis Function Neural Network (RBF-NN) and Least Squares Support Vector Machine (LSSVM) were established for estimation of equilibrium CO2-water/brine solubility as a function of salt molecular weight, temperature, salt molality and pressure. A reliable database was gathered from the open source literature, and split into two groups of testing and training subsets. Optimal structure of the proposed RBF-NN technique and the tuning coefficients of LSSVM model were determined by means of the well-known evolutionary method known as Cuckoo Optimisation Algorithm (COA). Parametric and visual statistics were used to assess the reliability of the suggested models in this study. Accordingly, the proposed approaches here can accurately prognosticate the equilibrium CO2-water/brine solubility with a determination factor (R2) of 0.9966 and average absolute relative deviation percent (AARD%) of 2.5885% for COA-LSSVM, and AARD% = 3.8832% and R2 = 0.9962 for COA-RBF-NN; therefore, the proposed COA-LSSVM gives more accurate results for equilibrium CO2-water/brine solubility. It is understood that the salt molality is most affecting variable on the output prediction based on sensitivity analysis. Williams outliers detection technique reveals that a small number of data (less than 3%) are outliers and also the predictions of the best model proposed here accommodate inside the feasibility region. The proposed methods are of enormous help for engineers and scientists working on CO2 injection into underground formations from both economic and environmental points of view.
Keywords: equilibrium CO2-water/brine; solubility; least squares support vector machine; carbon capture and storage; outliers analysis; radial basis function neural network.
Self-attention-based sentiment analysis with effective embedding techniques
by Soubraylu Sivakumar, Ratnavel Rajalakshmi
Abstract: Corporate companies and organisations use sentiment analysis to find opinions from the review to enhance customer relationship management and to increase the revenue from their product or brand. Sentiment analysis is the process of computationally extracting, identifying and categorising the user-generated opinionated data to determine the writer's attitude towards a particular product, topic, or services. The problem of sparse vector representation exists in handling the large-scale data and also semantics of words are not considered in many existing works. Effective word embedding techniques improve the task of sentiment analysis by overcoming the above two problems. Especially to rate a movie, the keywords need to be identified from a movie review, so an attention mechanism is introduced. It is a challenging task, when the review is expressed in multiple sentences. In this scenario, the entire sentence needs to be considered instead of individual words to determine the sentiment from multiple sentences. To achieve this task, we have proposed a novel long short term memory (LSTM)-based deep learning architecture that applies a sentence embedding using the universal sentence encoder along with an attention layer. To evaluate the proposed approach, we have carried out various experiments using both word-embedding and sentence-embedding with LSTM architecture on benchmark collection IMDB dataset. From the experimental results, it is observed that the proposed sentence-embedding with self-attention LSTM method is significantly better than the other word-embedding based approaches, with an improvement of 5%, and we have achieved an F1 score of 89.12% for our approach.
Keywords: sentiment analysis; universal sentence encoder; long short term memory; word embedding; GloVe; self-attention.
Real-time robust tracking with part-based and spatio-temporal context
by Yanxia Wei, Zhen Jiang, Junfeng Xiao, Xinli Xu
Abstract: Owing to the significant and excellent performance of correlation filters in the aspect of computation convenience, correlation filter-based trackers have become increasingly popular in the visual object tracking community. However, complete or partial occlusion is one of the major factors that seriously impact the tracking performance in visual tracking. To address this issue, we propose a novel tracking algorithm that perfectly integrates the results from the global correlation and local correlation filters for estimating the more accurate position of target. Then, we introduce the occlusion detection mechanism to eliminate the occlusion impact on the final position of object. In addition, our proposed tracker employs the spatial geometric constraints among the global object and local patches of object for preserving the structure integration of object. For verifying our method, we conduct extensive qualitative and quantitative experiments on challenging benchmark image sequences.
Keywords: tracking; correlation filter; occlusion; part-based strategy; spatial geometric constraint.
Maximum power production operation of doubly fed induction generator wind turbine using adaptive neural network and conventional controllers
by Hazem Hassan Ali, Ghada Saeed Elbasuony, Nashwa Ahmad Kamal
Abstract: Production of maximum power based on control of the Rotor Side Converter (RSC) of Doubly Fed Induction Generator (DFIG) wind turbine direct axis current is necessary in order to accomplish fast reaching to the maximum power point and protect the working parts in RSC from high current overshoot. An assessment study between adaptive Neural Network (NN) and conventional Proportional Integral (PI) controllers for control of RSC direct axis current is introduced in this paper. NN controller based LevenbergMarquardt backpropagation (LMBP) is designed for its training to mainly control RSC direct axis current. Also, RSC direct axis current is estimated through using PI controller that is used to control the speed of DFIG according to optimum tip speed ratio obtained by genetic algorithm. The simulation results demonstrated that RSC based NN controller are better than RSC based conventional speed regulator in protecting RSC parts from high current overshoot.
Keywords: DFIG wind turbine; MPPT; optimisation; NN controller; conventional controller.
A hierarchical network embedding method based on network partitioning
by Xiankun Zhang, Yunbin Ma, Xuexiong Luo, Junlong Kang
Abstract: In order to analyse the real-world information network more effectively, we propose a hierarchical network embedding method based on network partitioning, NPHNE. NPHNE is a nested algorithm that it can be combined with existing baseline algorithms to enhance their representation. NPHNE consists of two parts: graph abstracting and embedding propagation. The process of NPHNE is as follows: firstly, modularity is used to pre-determine the network partition, the purpose is to constrain the maximum number of levels. Then, based on the hybrid collapsing method, a series of abstract graphs with successively smaller scales are constructed. The representation of the coarsest abstract graph is learned by the existing baseline algorithms. Finally, the representation is propagated and refined level by level from the coarsest abstract graph to the original graph. We evaluate NPHNE on multi-label classification tasks on citation networks and social networks. The experimental results demonstrate that the maximum performance gains over the baseline algorithm by up to 29.1% Macro-F1 score.
Keywords: hierarchical network embedding; network partitioning; modularity; multi-label classification.
What are students thinking and feeling? Understanding them from social data mining
by Hua Zhao, Yang Zuo, Chunming Xu, Hengzhong Li
Abstract: Students' digital footprints on social media shed light into their personal experiences. Mining the results of these social data is useful for educators to understand students' mood swings and provide corresponding helps. But owing to the sharp increase of social data, analysing these data manually is impossible. In this paper, we focus on Chinese college students, and explore a method to better understand them based on social data mining. The method firstly collects the social data related to students, creates a hierarchy category system based on data contents analysis; secondly, it proposes a simple but effective multi-class classification method to classify the data into several appropriate categories; finally, it carries out the sentiment analysis of each one, and then looks deep into their emotion evolutionary process. Experiment results show that postgraduate entrance exam, final exam and other professional certificate exams are three prominent concerns of students, and they express worry about them.
Keywords: social data mining; classification; sentiment analysis; education.
Analysis of web browser for digital forensics investigation
by AlOwaimer AlOwaimer, Shailendra Mishra
Abstract: In today's digitalised world a lot of information is getting online, the size of online data is getting huge day by day and thus emerges the field of data science. The gateway for surfing on the internet is a web browser when there is so much huge size of data it also makes it vulnerable to the people have malicious intentions. Security is being compromised and thus makes the data vulnerable. A browser can be exploited by an internal malicious user and the main hindrance comes when all the browsing data is deleted. A forensics investigation needs to extract all the pieces of evidence, such as like history, cookies, URL, sessions and saved passwords from the cloud space provided by the browser. The research method is a mix of qualitative and quantitative. A scenario is created in a virtual environment in which there is a victim machine whose browser is exploited. Dumpzilla and bulk extractor forensics tools were used to capture its history, URLs, cookies, sessions, add-ons, and extensions, and SQLite database is used in auxiliary with these tools to get the required information. The extracted information is analysed to find the malicious user. Two different platforms are used for the authentication and verification of pieces of evidence collected. The culprit is caught based on matching web browsing activities from the victim machine and another machine in the same place as the victim machine.
Keywords: digital forensics; web browser forensics; forensic investigation; digital evidence.
IoT data security with DNA-genetic algorithm using blockchain technology
by Sultan Saad Alshamrani, Amjath Basha
Abstract: The Internet of Things (IoT) is an on-demand technology that is used in different applications and also includes different sensors, embedded devices and some other objects related to the internet. IoT devices are mainly designed for gathering diverse kinds of data from different sources and transferring data in digitalised form. Moreover, data security is an important issue in IoT technology, which affects the privacy of data. The main objective of this to handle large amounts of IoT data with cryptographic technique for transmission of data with high security, to enhance anonymity, security, and reliability of IoT, to establish consensus and trust over decentralised networks by addressing difficulties in trustless environment, to enable agreement between unique users by using a consensus mechanism in blockchain in a decentralised manner. A new lightweight encryption technique called DNA-GA (deoxyribonucleic acid genetic algorithm) has been proposed for resource-restricted IoT devices which gives high level sensing of data security. Blockchain is another method proposed in this research to store data in the form of blocks using a hash function where the respective user alone can decrypt the data with the gedecomposinerated hash key, so that intruders are not able to hack the original data. Several experiments were conducted with different kinds of sensed data for our proposed method, and the result is compared with the existing method. The results show that the proposed method outperforms the existing method in handling a large amount of IoT data by reducing encryption and decryption time and provide a high level of security to protect the data.
Keywords: DNA; GA; blockchain; IoT; hash function; decentralised network.
An analytical review of texture feature extraction approaches
by Mohammad Reza Keyvanpour, Shokofeh Vahidian, Zahra Mirzakhani
Abstract: Image registration has been an essential task in computer vision and image processing. There are many applications of image registration, for instance, in medical systems. Image registration consists of four main steps, including features extraction, feature matching, transform model estimation, and resampling and transformation. The feature extraction step makes the image registration process more accurate. Despite a large number of survey articles on texture feature extraction approaches, a comprehensive classification of approaches is still required, which also identifies the strengths and weaknesses of each approach. Therefore, the novelty of this paper is that our analytical framework includes three major components: a complete classification of texture feature extraction approaches, using crucial evaluation criteria to present an analytical and qualitative comparison between each approach, which simplifies the accurate selection of the proposed approaches for the intended application. Our framework can also lead to the development of texture feature extraction approaches in future research of scientists.
Keywords: image registration; feature extraction approaches; challenges; benefits; analytical framework.
Data science based landscape ecology for traditional village landscape protection
by Tong Liu, Shijun Wang, Zi Wang, Bingxin Li, Sulin Guo, Baowei Wei
Abstract: With the revitalisation of rural areas and the advancement of new rural construction, at present, the traditional village protection has received more attention. The optimisation of village layout is an essential issue in the protection of traditional villages, which is not only an objective demand for rural development, but also an important way to protect and avoid damage to the ecological and landscape patterns of the original villages. With the increase in the development of traditional villages, the general scale, shape of traditional villages have found profound changes. The phenomenon of village landscape convergence and original landscape destruction occurs from time to time. In the new situation, the traditional village protection faces new opportunities and challenges. By using landscape ecology theory to guide the preservation of traditional villages, the integrity, authenticity and continuity of traditional villages can be maintained. This article takes Yueqing Town, Bailong Village and Shixian Town, Shuinan Village in Yanbian Prefecture, Jilin Province, as the research objects, and applies the landscape pattern index to quantitatively analyse its overall landscape. On this basis, the village landscape optimisation and preservation strategy is proposed, which has certain reference value for the preservation and reuse of traditional villages in Jilin Province.
Keywords: landscape ecology; traditional village protection; landscape protection; landscape pattern index.
A new model of transformer operation state evaluation based on analytic hierarchy process and association rule mining
by Zhenyu Zhou, Haifeng Ye, Huigang Xu
Abstract: In order to establish a transformer state evaluation model for power grid operation and maintenance management, based on the aging mechanism of transformer, a state risk evaluation method based on the analytic hierarchy process (AHP) and the association rule mining is proposed. Based on the big data analysis of the actual state quantity of the transformer, the subjective weight coefficient of different state quantity is determined by the analytic hierarchy process. The objective weight coefficient of the comprehensive state quantity is determined by the association rules mining. And the fusion of the subjective weight coefficient and the objective weight coefficient is completed according to the weight coefficient fusion technology of the mean square deviation method. The practical results show that the model in this paper can evaluate the operation state of transformer comprehensively and accurately.
Keywords: Transformer; Aging mechanism; State evaluation model; AHP; Association rules mining
Comprehensive survey of user behaviour analysis on social networking sites
by Pramod Bide, Sudhir Dhage
Abstract: Social networking sites play an important role in every person’s life. Users start expressing their emotions online whenever any humanitarian or crisis like event occurs. A lot of sub events are stirred up and the internet gets flooded with people tweeting/posting their opinions. Identifying user behaviours, their content and their interaction with others can help in event prediction, cross event detection, user preferences, etc. For these reasons, our research process was divided into studying user behaviour with respect to content-centric, probabilistic approach and a hybrid incorporating the two. We further investigate the existence of multiple OSN’s and how they affect user behaviour. The purpose of this paper is to investigate the existing research methodologies and techniques along with discussion and comparative studies. User behaviour analysis is carried out based on content centric, probabilistic and hybrid approach. content centric Analysis dealt with analysis of the content posted which gives rise to varied applications such as gender prediction, malicious users, real-time user prefer-ences, emotional content influence on users etc. It is observed that in Probabilistic approach, most of the papers addressed, employed the clustering mechanisms followed by probability distribution for the analysis of user behaviour
Keywords: Social Media; User behaviour; Content centric features; Probabilistic features; Hybrid features.
Moth Optimization Algorithm with local search for the permutation flow shop scheduling problem
by Anmar Abuhamdah, Malek Alzaqebah, Sana Jawarneh, Ahmad Althunibat, Mustafa Banikhalaf
Abstract: This work investigates the use of the Moth-Flame Optimization (MFO) algorithm in solving the permutation flow shop scheduling problem (PFSSP) and proposes further optimizations. MFO is a population-based approach that simulates the behaviour of real moths by exploiting the search space randomly without employing any local searches that may stick in local optima. Therefore, we propose a Hybrid Moth Optimization Algorithm (HMOA) that is embedded within a local search to better exploit the search space. HMOA entails employing three search procedures to intensify and diversify the search space in order to prevent the algorithm's becoming trapped in local optima. Furthermore, HMOA adaptively selects the search procedure based on improvement ranks. In order to evaluate the performances of MFO and HMOA, we perform a comparison against other approaches drawn from the literature. Experimental results demonstrate that HMOA is able to produce better-quality solutions and outperforms many other approaches on the Taillard benchmark, which is used as a test domain.
Keywords: Flow shop scheduling; Flow shop scheduling problem (FSSP); Makespan; Moth-flame Optimization Algorithm; Local Search; Adaptive Moth Optimization Algorithm.
Detection and Mitigation of Attacks in SDN Based IoT network using SVM
by Shailendra Mishra
Abstract: Adapting software defined networking(SDN) raises many challenges includes scalability and security on the internet of things (IoT) network. The centralized SDN controller in IoT -SDN network is responsible for managing the critical network\'s operations. Growing network size increases resulting in the network load in the controller and facing security challenges such as cascade failures of controllers, unauthorized access to the controllers, configuration issues, distributed denial of service (DDoS) attacks. The DDoS attack is one of the most acute threats in the present scenario, and the attacker can exploit the vulnerabilities that are located mostly in the control plane. In the previous research studies, authors have found some strategies and proposed some solutions. The attack scenario and security of multiple controller networks are simulated and evaluated in this research. Simulation has been conducted in Mininet-SDN emulator, hosting OS was Ubuntu Linux, Wireshark was used for analyzing the network traffic, support vector machines were used to classify the traffic flows. DDoS attacks were detected, and mitigation has been done using a support vector machine learning-based approach. The results show that the support vector machine\'s sensitivity, specificity, and accuracy are excellent in the range of 98.7 % to 98.8 %. Security solutions are fast and effective in mitigating DDoS attacks.
Keywords: Software Defined Networking(SDN), Distributed Denial of Service Attack(DDoS), Support Vector Machine (SVM), DDoS mitigation
A Neural Adaptive Level Set Method for Wildland Forest Fire Tracking
by Aymen Mouelhi, Moez Bouchouicha, Mounir Sayadi, Eric Moreau
Abstract: Tracking of smoke and fire in videos can provide helpful regional measures to evaluate precisely damages caused by fires. In security applications, real-time video segmentation of both fire and smoke regions represents a crucial operation to avoid disaster. In this paper, we propose a robust tracking method for fire regions in forest wildfire videos using neural pixel classification approach combined with a non linear adaptive level set method based on the Bayesian rule. Firstly, an estimation function is built with chromatic and statistical features using linear discriminant analysis and a trained multilayer neural network in order to get a preliminary fire localization in each frame. This function is used to compute initial curve and the level set evolution parameters providing fast refined fire segmentation in each processed frame. The experimental results of the proposed method proves its accuracy and robustness when tested on different varieties of wildfire-smoke scenarios.
Keywords: Fire detection; linear discriminant analysis; neural networks; active contour; level set; Bayesian criterion.
Robust Tracking of Moving Hand in Coloured Video Acquired Through Simple Camera
by Richa Golash, Yogendra Kumar Jain
Abstract: Interaction of human with a machine using dynamic hand gestures has become one of the interesting yet challenging areas in many aspects. A hand is a non-rigid, subtle object that moves with varying speed and in an undefined path. Additionally, real-time backgrounds are not stable. RGB data of moving hand are sensitive to light variation, camera-view, and randomness in behavior thus continuous detection and localization of hand region in RGB images is strenuous. Some researchers prefer advanced cameras to avoid the mentioned problems and some apply deep learning in their techniques. The first solution puts limitations on the environment and increases the cost of application. The second solution requires a large database for initial training of the deep neural network architecture. In this paper, we have provided a unique solution which combines the benefits of Scale-invariant feature transform (SIFT) features with automatic feature extraction mechanism of Region-based convolutional neural network (R-CNN) a deep learning network, for robust tracking of moving hand in colored video acquired through a camera which does not have very high resolution. The efficiency of the proposed methodology is 96.84 % in the simple background and 94.73% in complex background. The comparative analysis of the proposed system with contemporary techniques using RGB images shows that initial hand detection using R-CNN and then tracking using SIFT is capable of tracking hand movement with high accuracy in unconditional background. In the future, the method can be implemented to design user-friendly and economical natural user interfaces.
Keywords: Computer vision, Deep learning, R-CNN, Visual object recognition, Feature extraction, Scale invariant feature transform (SIFT), Visual object tracking.
A Service-Based Software Architecture for Enabling the Electronic Health Record Storage Using Blockchain
by Ítalo Lima, André Araujo, Rychard Souza, Henrique Couto, Valéria Times
Abstract: The healthcare sector requires computational solutions with reliable authenticity features for the storage and retrieval of electronic health record data. To address this important issue, this article proposes a service-based software architecture to extract data from different legacy databases, standardize the patient’s clinical data requirements and store data using different Blockchain technologies. To achieve this, a software architecture has been designed to guarantee the independence of data storage technology. In addition, a data meta-schema and a set of mapping rules have been specified to store and organize clinical data following an international health standard. To validate the solution presented, the real world scenario of a Brazilian healthcare institution has been used to evaluate the data extraction, standardization and storage capabilities in two Blockchain platforms widely used in the information technology market.
Keywords: Health Information Systems; Blockchain; Software Architecture; Electronic Health Record.
Special Issue on: Computational Intelligence and Applications
Intelligent game-based learning: an effective learning model approach
by Tanzila Saba
Abstract: Game-Based Learning (GBL) broadly refers to the use of video games applications to support teaching and learning processes. This research focuses on the concept of GBL in the context of stimulating interest in the field of computer science education specifically. In contrast to theoretical learning, GBL is a practical learning approach that is meant to teach and be enjoyed at the same time. Additionally, a GBL model with visual features has been proposed and tested. Promising feedback has received from learners through the post conducted surveys. The research findings exhibit that GBL is an effective methodology in transferring knowledge, enhancing learning, and making the learning a more enjoyable process in computer science studies than just the theoretical approach.
Keywords: binary games; game-based learning; logical games; theoretical learning.
Special Issue on: Modelling and Expert Intelligence for Traditional Chinese Medicine Diagnosis and Knowledge Base Development
Application research on quantitative prediction of TCM syndrome differentiation based on ensemble learning
by Huaixin Liang, Xin Yang, Shaoxiong Li, Siheng Chen, Xiaoqing Zhang
Abstract: A quantitative prediction method for TCM syndrome element identification based on ensemble learning is proposed. Four comparative experiments were designed. Firstly, eight mainstream learners were used to perform the regression prediction based on the symptoms and syndrome values using the quantitative data of clinical TCM syndrome differentiation. Secondly, five learners with excellent prediction performance were selected to design three integrated learners including homogeneous static integrated learner, heterogeneous static integrated learner and dynamic one, where the heterogeneous integrator used as the learner weight coefficient to weigh up its significance. By comparing the MAE, MSE and R² of the three ensemble learning methods in the four syndrome differentiation groups, it is found that the regression effect based on heterogeneous ensemble learning is the best (MAE: 0.012, MSE: 4.55E-04, R²: 0.733), and the principal sequential evaluation of syndrome elements gained relatively matching degree, which had proved the feasibility of application on the method proposed in the quantitative prediction of clinical TCM syndromes.
Keywords: ensemble learning; syndrome differentiation; regression prediction; multi-objective learning; traditional Chinese medicine; syndrome element model; quantitative prediction; artificial intelligence; clinical auxiliary diagnosis.
Formal concept attribute partial-order structure diagram and applications
by Ren Yunli
Abstract: Formal-Concept Attribute Partial-Order Structure Diagram (FC-APOSD) is proposed on the basis of formal concept analysis and Attribute Partial-Order Structure Diagram (APOSD) theory, which inherits the advantage of good visualisation of APOSD and can reveal relationships among formal concepts at the same time. This paper focuses on the mathematical description of FC-APOSD, how to use FC-APOSD to explore knowledge from a formal context and the applications of FC-APOSD. Specifically, our work mainly includes the following points: 1. FC-APOSD is described in mathematical formal language; 2. After giving a necessary and sufficient condition for an attribute set of a node to be the intension of a formal concept, a method to find pseudo-intensions using FC-APOSD is provided; 3. The relationship among objects is discussed using groups and subgroups in FC-APOSD; 4. A method to find exclusive attributes from a formal context is provided by using FC-APOSD; 5. With the help of classical Chinese medicinal formulae related to emotional diseases from 'Treatise on Febrile and Miscellaneous Diseases', the syndrome-treatment pattern of emotional diseases is explored using the FC-APOSD method.
Keywords: formal concept analysis; attribute partial-order structure diagram; knowledge discovery; syndrome-treatment pattern.
Research on the regular pattern of Professor Saimei Li using Chinese medicine alone in treating middle-aged type 2 diabetes mellitus on the basis of partial order structure theory
by Sunjing Xu, Yizhao Hao, Yat Tung Li, Saimei Li
Abstract: The method of knowledge discovery based on partial order structure theory has been applied to the study of Traditional Chinese Medicine. In this research, the information of symptoms, prescriptions, and herbs from 50 effective cases of middle-aged type 2 Diabetes Mellitus (T2DM) patients treated by Professor Saimei Li with Chinese Medicine was processed with formal contexts, and then the corresponding partially ordered diagrams were obtained for the exploration of the medication rules. The results showed that the common symptoms of the middle-aged patients are mainly of heat syndrome. In regard of the treatment, the main prescription formula is Lizhong Wan combined with Gegen Qin Lian Tang for middle-aged patients. The combination of warm and cold formula aims at warming the spleen and dispelling the pathogenic wind, clearing pathogenic heat and eliminating dampness. This research can promote the inheritance and innovative development of Chinese medicine in an effective way.
Keywords: middle-aged type 2 diabetes mellitus; partial order structure theory; knowledge discovery; Saimei Li.
Knowledge discovery for spleen yang deficiency syndrome based on attribute partial order structure diagram
by Hui Meng, Xiaoying Han
Abstract: Syndromes and medications in Traditional Chinese Medicine (TCM) have been studied by advanced information technology in the modernisation of TCM, promoting the development of knowledge discovery in TCM. In this paper, based on the method of Attribute Partial Order Structure Diagram (APOSD), syndrome-symptom APOSD and prescription-herb APOSD are constructed for spleen yang deficiency syndrome, which is a common syndrome in TCM. The common symptoms and specific symptoms of spleen yang deficiency syndrome are extracted from the syndrome-symptom APOSD. The association rules among herbs in the prescriptions for treating spleen yang deficiency syndrome are visualised on the prescription-herb APOSD, and herb pairs and herb groups are extracted. Compared with Apriori algorithm, APOSD not only obtains important association rules, but also shows the compositions of prescriptions in the diagram. APOSD provides a new scientific research method for TCM.
Keywords: attribute partial order structure diagram; knowledge discovery; spleen yang deficiency syndrome; symptom patterns; prescription compatibility.
Effects of low frequency somatosensory music on heart rate and skin temperature in healthy people
by Baohong Mi, Lixin Ren, Jialin Song
Abstract: With the rapid development of biology, brain science, psychoanalysis, etc., the biological effects of low frequency somatosensory music on the human body are gradually being discovered. However, its objective evaluation system is not yet sound. In this paper, heart rate and skin temperature of healthy people are monitored by heart rate acquisition equipment and medical infrared thermal imager in different modes of low frequency somatosensory music. Results showed that there was a significant difference in heart rate between experimental groups and control group (p < 0.05), and the skin temperature also had significant difference between experimental groups and control group (p < 0.01), but there was no significant difference in music frequency change and skin temperature change (p > 0.05). Conclusion: low frequency somatosensory music can stimulate parasympathetic nerve and change heart rate of healthy people, and it also can be used to change the metabolic state of the human body by regulating mental activities, and then reduce skin temperature. This paper proposes a new objective evaluation method for low frequency somatosensory music therapy.
Keywords: low frequency somatosensory music; infrared thermal imaging; heart rate; skin temperature.
Statistical analysis for user group of opposing traditional Chinese medicine in Weibo
by Li Hao, Zhang Bingzhu, An Xuzhao, Ma Xingguang, Shen Junhui
Abstract: With Western culture and science being widely accepted in China, Traditional Chinese Medicine (TCM) becomes a controversy. Therefore, it is very important to study the public's views on Traditional Chinese Medicine. The rapid development of online social networks, such as Sina Weibo, can quickly and easily provide a sample for emotional analysis. In this research, firstly, Sina Weibo's users of Traditional Chinese Medicine were collected, and their blogs were obtained. The blogs were automatically marked as supporting Traditional Chinese Medicine or opposing Traditional Chinese Medicine based on user tags. Then, blogs about Traditional Chinese Medicine were selected by using the Chinese word segmentation tool and Traditional Chinese Medicine dictionary. Finally, the Chinese word segmentation tool was used to count the words and find highest frequency of words in the blogs of opposing Traditional Chinese Medicine to explore hot topics and propose corresponding suggestions for the development of Traditional Chinese Medicine.
Keywords: emotional analysis; Chinese word segmentation tool; Traditional Chinese Medicine; Weibo; user tag; hot topics.
State Chinese medicine theory based on the mathematical description TCM principles and modelling of complex human system
by Jialin Song, Wenxue Hong, Cunguo Yu, Xiaoyun Wu, Jingbin Wang, Cunfang Zheng
Abstract: Based on the essence of Traditional Chinese Medicine, the theory of State Chinese Medicine (SCM) proposed and established profoundly reveals that Traditional Chinese Medicine is state medicine. SCM fully expresses the main characteristics of 'holistic concept' and 'syndrome differentiation and treatment' of TCM, which is the description of TCM theory under the vision of modern science. The establishment of SCM theory can make more non-TCM professionals understand TCM, master the core principles of TCM theory, and use modern science and technology to develop TCM to serve their own health and public health. It will promote the formalised mathematical description of the principles of traditional Chinese medicine, and promote the measurable, estimable, computable and quantifiable evaluation of human health states. It will be expected that a high degree of integration of modern science and technology with Traditional Chinese Medicine could be achieved, and make contributions to human health and the progress of Traditional Chinese Medicine.
Keywords: SCM; state Chinese medicine; computable model; modelling of complex human system; syndrome differentiation and treatment.
The physical pattern evaluation and identification method of infrared thermal image of human health state in Traditional Chinese Medicine
by Wenxue Hong, Cunfang Zheng, Baohong Mi, Cunguo Yu, Wenzheng Zhang, Jingbin Wang
Abstract: In view of the lack of objective detection methods for human health in traditional Chinese medicine (TCM), this paper proposes a method based on infrared thermal image sign mode evaluation and identification. From the perspective of data collection, three classification methods are proposed. From medical point of view, the theory of infrared thermography is as follows: governing exterior to infer interior, recognise the whole through observation of the small parts, master both permanence and change. From the perspective of basic methods, health assessment is mainly based on the human symmetry, uniformity, thermal sequence and three-dimensional information. On the basis of the evolution of human health in TCM, this paper proposes the technical route of evaluation and identification of infrared thermal image sign modes. This paper is of great significance to establish an objective method for the evaluation and identification of infrared physical signs based on human health of TCM.
Keywords: traditional Chinese medicine; infrared thermal image; evaluation and identification; human health state.
A novel classification tree based on local minimum Gini index and attribute partial order structure diagram
by Cunfang Zheng
Abstract: Decision tree is not only an important machine learning method, but also the basis of ensemble learning methods such as random forest and deep forest. Based on the theory of Formal Concept Analysis (FCA) and Attribute Partial Order Structure Diagram (APOSD), a new decision tree for classification is proposed in this paper. Firstly, the local minimum of Gini index is used to complete the data granulation, and the Formal Decision Mode Information Table (FDMIT) is constructed. Then, the Attribute Partial Order Classification Tree (APOCT) is generated based on APOSD to complete the pattern recognition and rule extraction. The method of APOCT separates the process of granulation and visualisation, and the granulation process is easy to parallelise and efficient. The experimental results show that APOCT is effective.
Keywords: classification tree; decision tree; partial order; Gini index; data granulation; formal concept analysis; knowledge discovery.
Assumption of constructing intelligent recommend model of diabetic Chinese patent medicines
by Chaonan Liu, Yuzhou Liu, Enliang Yan, Jianfeng Fang
Abstract: Responding to the urgent requirements for rational use of diabetic TCM patent medicines, this study comprehensively, objectively and accurately reveals the "treatment-formula-drug-dosage-property" knowledge of Chinese patent medicines on the basis of TCM principles, EBM, clinical practice and partial order theory. It establishes a multi-level, partial-order visualised expression method for TCM treatment in diabetes, and constructs an intelligent recommendation model of diabetic Chinese patent medicines, which provides technological approaches for promoting rational use of Chinese patent medicines. The completed results prove that this method could effectively find out practical guiding knowledge and give reasonable suggestions for drug use.
Keywords: diabetes; Chinese patent medicines; machine learning; knowledge discovery.
Special Issue on: Advanced Big Data and Artificial Intelligence Technologies for Edge Computing
An improved hybrid error control path tracking intelligent algorithm for omnidirectional AGV on ROS
by Yaqiu Liu, Hui Jing
Abstract: In order to improve the accuracy and stability of intelligent omnidirectional AGV path tracking based on mecanum wheels, an improved intelligent hybrid error control path tracking method is proposed. The method combines the angular velocity of the intelligent AGV vehicle with the error correction of longitudinal velocity as the coupling estimation error. The coupling estimation error and the improved pure tracking algorithm are combined as the lateral control of the intelligent AGV car, while the PID control is used as the vertical control to further reduce the error interference. The ROS simulation results showed that compared with the tracking effect of the traditional pure tracking algorithm, the tracking path of the improved intelligent hybrid error control path tracking algorithm is closer to the real path, which greatly improves the trajectory deviation phenomenon, and the path tracking accuracy and stability are significantly improved.
Keywords: mecanum wheel; path tracking; improved hybrid error control; coupling estimation error; intelligent omnidirectional AGV; pure pursuit; ROS.
Path discovery approach for mobile data gathering in wireless sensor networks
by A.N.U. Raj, Shiva Prakash
Abstract: Data gathering is one of the most important fundamental tasks in wireless sensor networks (WSNs). It consumes large amounts of energy of the sensor nodes, which reduces the network lifetime. So, mobile sinks or mobile collectors are often used to collect data from the sensor nodes in WSNs. But the main challenge is to discover the optimal path for a mobile sink that reduces the energy consumption of the sensor nodes. We propose a Modified Travelling Path Planning (MTPP) algorithm to find the shortest travelling path for a mobile sink that reduces the data latency as well as energy consumption of the sensor node. This method provides an effective data gathering mechanism for the mobile sink. In this method, the mobile sink has to traverse along the chord of the communication circle of the nodes. In this approach, the travelling path is the sum of chords of all the communication range and line segments between them. After that we apply a B-spline smoothing curve method over it. The effectiveness of proposed method is verified through mathematical proving and in MATLAB. This method is used to find the best possible path for a mobile sink or collector that will enhance the network lifetime as well as reduce the energy consumption of the sensor node.
Keywords: data gathering; wireless sensor network; mobile sink; network lifetime.
Efficient synergetic filtering in big datasets using neural network technique
by B. Mukunthan
Abstract: Presently, great accomplishment on speech-recognition, computer-vision and natural-language processing has been achieved by deep neural networks. To tackle the major trouble in synergetic- or collaborative-filtering is the idea of hidden feedback. In this task we concentrated intensively on the techniques based on neural networks. Although a few recent researchers have employed deep learning, they mostly used it to sculpt auxiliary facts, along with textual metaphors of objects and acoustic capabilities of musics. When it involves the major aspect in synergetic filtering, the communication between customer and object capabilities, they still resorted to matrix factorisation and implemented a core product on the hidden capabilities of customers and objects. We present a popular framework named Artificial Neural Synergetic Filtering (ANSF) to substitute the core makeup with a neural design which could be very efficient to analyse data with a random feature. ANSF is ordinary and can specifically popularise matrix-factorisation beneath its framework. To improvise ANSF modelling with non-linearities we propose to leverage a multi-layer perceptron to investigate customer-object communication functions. In-depth experiments on actual-global databases display big improvement of our proposed ANSF over the latest techniques. Investigational results show that the application of core layers of artificial neural networks gives improved overall performance.
Keywords: synergetic filtering; big data; matrix factorisation; deep neural network; multi-layer perceptron.
A system architecture for intelligent agriculture based on edge computing
by Li Liu, Qian Wang, Bo-qun Li
Abstract: Agricultural informatisation has been a major aspect in the field of agriculture owing to advancement in communication technologies. Many studies have been conducted to assist production, such as Wireless Sensor Network (WSN) and cloud computing. However, few studies have been focused on the severe requirements of delay and energy consumption of the mobile node. In order to improve network performance, edge computing is introduced into our system architecture for intelligent agriculture. Mobile Edge Computing (MEC) is a distributed computing architecture to offload some tasks to the edge of core network. We adopt a hierarchical structure made of three layers: physical perception layer, information service layer and intelligent application layer. In addition, we further confirm an offloading model for intelligent agriculture. In this article, the state-of-the-art development in intelligent agriculture and current approach of its key technology are discussed. The potential opportunities and challenges of the proposed architecture are presented as well.
Keywords: edge computing; WSN; mobile edge computing; intelligent agriculture.
Computer image analysis for various shading factors segmentation in forest canopy using convolutional neural networks
by Liangkuan Zhu, Jingyu Wang, Kexin Li
Abstract: Determination of the various parts in the forest canopy images is critical because it reflects a variety of parameters for plant population growth in forest ecosystems. Recently, deep learning has become one of the most promising techniques in machine learning for image analysis. However, only a few studies on applications in forestry information fields are published. This study presents the use of deep learning for the detection of various shading factors in hemispherical photographs of the forest canopy. First, a forest canopy hemispherical photographs dataset that can be used for the research of related algorithms is constructed. Based on FCN, the dilated convolution layers and multi-scale feature fusion are used to improve the accuracy of forest canopy image segmentation. Furthermore, the conditional random field (CRF) is adopted to optimise the results. The loss function is used to control the selection of different features of the model. Finally, experiments show that this method can achieve automatic segmentation of the sky, leaves, and trunks of forest canopy images. Compared with the FCN model, the average pixel accuracy of the improved FCN model is improved by 9.11%, and it has good robustness.
Keywords: forest canopy image; image segmentation; full convolutional neural network; dilated convolution; multi-feature fusion; conditional random field.
Special Issue on: Computational Advances in Healthcare Solutions
The Application of Plug-and-Play ADMM Framework and BM3D Denoiser for Compressed Sensing MR Image Reconstruction
by Xiaojun Yuan, Mingfeng Jiang, Lingyan Zhu, Yang Li, Yongming Li, Pin Wang, Tie-Qiang Li
Abstract: Compressed Sensing Magnetic Resonance Imaging (CS-MRI) is an effective technique to reduce MRI data acquisition time. There is currently growing interest in using alternating direction method of multiplier (ADMM) for CS-MRI reconstruction. In this paper, we propose a flexible plug-and-play framework to incorporate the block matching 3D (BM3D) denoising algorithm as prior into the plug-and-play ADMM reconstruction procedure for CS-MRI reconstruction, termed BM3D Plug-and-play ADMM (BPA) method. We investigated the performance of the proposed BPA method for the construction of highly under-sampled MRI data of two different sampling masks. Compared with other widely used CS-MRI reconstruction methods, such as, PANO, BM3D-IT, BM3D-MRI and BM3D-AMP-MRI, the proposed framework can reconstruct highly under-sampled CS-MRI data with improved gains in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).
Keywords: MR image reconstruction; Plug-and-Play ADMM; denoising algorithm; compressed sensing
UNIFICATION OF FIREFLY ALGORITHM WITH DENSITY BASED SPATIAL CLUSTERING FOR SEGMENTATION OF MEDICAL IMAGES
by Bandana Bali, Brij Mohan Singh
Abstract: This paper proposes a computer-aided approach for brain image segmentation to figure out various characteristics of digital images which are responsible for the identification of brain tumor with MRI images. The proposed Density-Based Spatial Clustering Fused with Firefly (DB-FF) method is based on Density-Based Spatial Clustering and Firefly Algorithm which have an significant places in nature-inspired computing techniques. In this research, the solutions of the firefly algorithm have been improved by the density-based spatial clustering algorithm and a soft computing criterion has also been used as a fitness function. The proposed method has been tested on commonly used images from Harvard Whole Brain Atlas and the results of this method have been compared with other standard benchmarks from the survey. The proposed DB-FF method achieved better segmentation as compare to standard segmentation quality metrics such as normalized peak signal to noise, normalized root square mean error and structural similarity index metric. Matlab has been used for implementation and observation. The result demonstrates that the proposed method has a better and robust performance as compared with the existing MRI segmentation models.
Keywords: Brain Tumor Detection, Data Clustering Technique, Firefly Algorithm, Image segmentation
Alzheimer Disease diagnosis based on feature extraction using Optimized Crow Search Algorithm and Deep Learning
by Sonal Bansal, Aditya Rustagi, Anupam Kumar
Abstract: Alzheimer’ Disease (AD) is long-lasting, progressive, cognitive disorder of degenerative nature and one of the most common reason to cause the dementia. Dementia leads to decline in thinking capacity, unable to handle the behavioural and social skills that disrupts the normal functioning of the person’s ability. Conventional methods of assessing the symptoms and information from a close family member is being recorded to analyse the effect of the Alzheimer and its stage. The Neuroimaging is one of the best methods being used by neurologists and doctors for Alzheimer’s disease. MRI are being used around the world for the diagnosis of the disease and provide insights to the brain and its functioning. With the advancement in the area of Machine Learning, the application to various medical images like MRI, CT-SCAN is on the rise and has become a major discipline of research among the experts and analysts. Existing methods of feature extraction from an image involve CNN, which provides a large number of feature sets that require great computation power as well as time to evaluate them using traditional machine learning or any deep learning algorithm. Consequently, we propose an Optimized Crow Search Algorithm (OCSA) for early diagnosis of AD which when applied to the raw MRI image features, yields a highly representative dense embedding of the same. The mapping learned between this embedding and the image labels resulted in diagnosing 98.62% of AD patients’ dataset correctly.
Keywords: Alzheimer’s Disease; Magnetic resonance images; Evolutionary Algorithm, Feature extractions; Intelligent computer-aided diagnosis systems; Medical imaging; Medical informatics
An Intelligent COVID-19 Classification Model using Optimal Gray-Level Co-Occurrence Matrix Features with Extreme Learning Machine
by PAVAN KUMAR PARUCHURI, Gomathy V, Anna Devi E, Shweta Sankhwar, Lakshmanaprabu SK
Abstract: In recent times, earlier diagnosis of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Chest computed tomography (CT) images are found to be a reliable, helpful and faster method for the classification of COVID-19. Since the chest CT image diagnosis requires medical experts and more time, an automated intelligent model needs to be developed for effective COVID-19 diagnosis. This paper presents a new automated COVID-19 diagnosis model using optimal grey level cooccurrence matrix (GLCM) based feature extraction and extreme learning machine (ELM) based classification. The input chest images undergo preprocessing to improve the image quality. Next, the optimal GLCM features are derived by the use of Elephant Herd Optimization (EHO) algorithm. Then, the ELM model is applied to perform the classification task. The performance of the OGLCM-ELM model has been validated using the benchmark dataset and the experimental outcome ensured the superior performance of the proposed model over the compared methods. The proposed OGLCM-ELM model has achieved maximum sensitivity of 89.56%, specificity of 90.45%, F-score of 90.13% and accuracy of 90.69%.
Keywords: COVID-19, Disease diagnosis, Feature extraction, Classification, Deep learning
Med-Net: A Novel Approach to ECG Anomaly Detection Using LSTM Auto encoders
by Koustav Dutta, Rasmita Lenka, Soumya Ranjan Nayak, Asimananda Khandual, Akash Kumar Bhoi
Abstract: Time Series data is generated in various sectors of day to day life. Among all, the time series data plays vital role in medical domain analysis. In this specific context, various continuous time series dependent EEG and ECG signals are the most important. Till date, heavy reliance is on the doctors regarding the manual analysis of these signals for understanding, monitoring and detecting the anomaly is cumbersome. Thus, this paper proposes a highly novel approach to analyze and detect ECG signals for tracking of anomalies using Hybrid Deep Learning Architectures (HDLA). The proposed scheme achieves by implementing self-supervised pattern recognition according to the mechanism of Long Short Term memory networks (LSTM) in terms of auto Encoder and Decoder. Finally, the proposed scheme is tested on Physionet dataset. The outcome of model can also handle noise associated with ECG-based time series signal, it’s achieved accuracy and solved the over fitting problems.
Keywords: Bio-signals; Encoder; Decoder; LSTM; ECG; Anomaly; Time Series; Reconstruction Error.
Multimodality Medical Image Fusion Based on Non Sub Sampled Contourlet Transform
by VELMURUGAN SUBBIAH PARVATHY, SIVAKUMAR POTHIRAJ, JENYFAL SAMPSON
Abstract: The fusion of medical images is the mainly essential and effective technique for disease analysis. We have provided a Non Sub sampled Contourlet Transform (NSCT) image fusion technique is utilized Neuro Fuzzy with Binary Cuckoo Search (NFBCS) and the Slap Swarm optimization (SSO) method. Here we successfully fused the Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images and creates a single merged image, which provides a new integrated diagnostic method. Initial, two unique sets of images, for example, MRI and CT, were considered for the fusion procedure. These pairs of images are initially used for NSCT to generate the image to divide the high frequency module and the low frequency module. The mixing policies are used here to generate and combine high and low frequencies. Compared to other existing techniques, the results of the proposed technical tests show better processing efficiency and deliver results on subjective and objective evaluation criteria. This is particularly advantageous for accurate clinical analysis of the disease.
Keywords: Magnetic Resonance Imaging, Computed Tomography, Neuro Fuzzy, Binary Cuckoo Search, Slap Swarm Optimization
Efficient Detection of Supraventricular Tachycardia by Machine Learning Techniques
by Monalisa Mohanty, Asit Subudhi, Mihir Narayan Mohanty
Abstract: Supra ventricular tachycardia (SVT) refers to abnormally fast heartbeat that arises because of the improper electrical activity in the upper chamber of the heart. Though SVT cases now a days are taken as a less hazardous diseases, however recurrent incidents may deteriorate the heart muscle over a period of time. Tachycardia usually refers to a quick rise in the heart rate which is of more than 100 beats per minute. SVT is a kind of arrhythmias which is based on an abnormal heartbeat. Electrocardiogram (ECG) is one of the most significant diagnostic tools used for the recognition of the health of a heart. The increasing number of heart patients has made essential progress in the techniques of automatic detection for detecting the numerous kinds of abnormalities or the arrhythmias of the heart to reduce the pressure and share the load of the physicians. ECG recordings have been acquired from MIT-BIH supraventricular arrhythmia database (SVDB) of the Physionet repository. Each record consists of ST, N and VF rhythm with a duration of 30 minutes length. Then using various techniques, a set of features has been extracted for ST,N and VF and finally fed into classifier like logistic model tree (LMT) and multi layer perceptron (MLP) to classify the ECG signals.
Keywords: Tachycardia; Supraventricular tachycardia; arrhythmias; decision tree classifiers
Application and Evaluation of Classification Model to Detect Autistic Spectrum Disorders in Children
by Hrudaya Kumar Tripathy, Pradeep Kumar Mallick, Sushruta Mishra
Abstract: Autism Spectrum Disorder (ASD) affected child faces significant difficulties in social interaction (i.e., communication the language, understanding emotional states of others, thinking and behavior together, etc.). So there is an eminent requirement of a real-time and easy-to-access diagnostic model to identify autism during the initial phase of occurrence to assist medical experts' clinical help. Presently efficient cure for autism does not exist. A reliable detection model will help provide better therapy, thereby supporting autistic children to continue a better life. The work deals with the autistic dataset's efficient categorization using various classifiers like Naive Bayes, Neural network, and Random Forest. At the same time, Python is a programming tool to determine algorithms with optimum accuracy by multiple simulations. An autistic dataset from the UCI repository was used for the research study. It is utilized to build a model where parents of a suspected autistic child can detect autism by providing their answers to some particular questions relating to autism characteristics. The implementation result indicated that the random forest classifier gave the optimal performance. A 97.5% classification accuracy rate was generated using a random forest algorithm. An RMSE value of 0.676 was observed. A very minimal 1.16 sec execution time was recorded using the autistic dataset. 92.8%, 92.6%, 90.8%, and 91.5% are the recorded values of mean accuracy, precision, recall, and f-score metrics. Thus it can be concluded that autistic disorder detection using random forest classifiers generated an optimal performance.
Keywords: Autism Syndrome Disorder; Naive Bayes; Neural network; Random Forest; Logistic Regression.
Performance analysis of surrounding cylindrical gate all around nanowire transistor for biomedical application
by Amit Agarwal, Prashanta Chandra Pradhan, Bibhu Prasad Swain
Abstract: Transistors have been used for more than a decade for fast and accurate detection in biomedical fields such as biomaterials sensing devices. There have been many researchers working on biosensor devices, but we are more focused on the deep micron transistor device, which exhibits high sensitivity and accurate results. This paper presents a highly sensitive, more accurate and faster device using a silicon on insulator based cylindrical surrounding gate all around nanowire (SCGAA-NW) transistor. This proposed device can be used for biomedical applications, e.g. diabetes sensor, gas sensor, pressure sensor and different substances present in the blood or environment by setting and analysing proper physical parameter of the device. In this paper, we have varied different physical parameters, i.e. channel material (i.e. SiN, CdS, GaN, ZnO, GaP, Si, GaAs, Ge), oxide material (i.e. SiO2, Si3N4, Al2O3, Er2O3, Y2O3, HfO2, Ta2O5, La2O3), channel radius (1-10 nm), oxide thickness (1-10 nm), concentration of different materials on the sensor acting as gate to source voltage, drain to source voltage (-0.5 V to 0.5 V), and channel doping (10^7 to 10^14) for the most suitable biomedical application in different environments. Analytical modelling of the SCGAA-NW transistor has been done by solving the 1-D Poisson equation, using Gauss law and parabolic approximation method. Also, we have shown the mathematical equation that relates to the impact on gate to source voltage owing to use of sensor material. In this paper, we have investigated that with changes in physical parameters of the device, there is impact on the potential at the channel surface. We have implemented the SCGAA-NW transistor model and plotted the potential at the channel surface with different device parameters graphs using Matlab Simulator.
Keywords: biosensor; biomedical application; gate all around nanowire; MOSFET; cylindrical channel; potential at the channel surface.
Fuzzy logic system for diabetic eye morbidity prediction
by Tejas V. Bhatt, Raksha K. Patel, Himal B. Chitara, Gonçalo Marques, Akash Kumar Bhoi
Abstract: Diabetes is a common chronic disease; the number of people who are affected by this health problem is increasing worldwide, leading to a high cost for healthcare systems. Therefore, the main contribution of this paper is to present a fuzzy logic system for diabetic eye morbidity prediction. This work is divided into two parts. The first part is the examination of eye vision by the ophthalmologist and also other examinations such as postprandial blood sugar, hypotension, cholesterol, and duration of diabetes. The second part is the analysis of 400 patients medical records collected in 2019. The fuzzy system proposed for prediction of diabetes retinopathy provides reliable accuracy for eye-vision threatening and eye morbidity. The proposed fuzzy system has five input parameters and one output parameter, which predicts diabetic neuropathy. The input parameters are random blood sugar, postprandial blood sugar, hypotension, cholesterol, and eye vision. The output parameter is the morbidity in diabetic retinopathies, which are non-proliferative, proliferative, and clinically significant macular edema. The proposed system is designed to support the endocrinologist and ophthalmologist in the diagnosis of diabetic retinopathy.
Keywords: artificial intelligence; blood sugar; cholesterol; diabetic retinopathy; fuzzy set theory.
Detection of bifurcations and crossover points from retinal vasculature map using modified windows feature-point detection Approach
by Meenu Garg, Sheifali Gupta, Soumya Ranjan Nayak
Abstract: Identification of feature points such as bifurcation points and crossover points in retinal fundus images is useful for predicting the various cardiovascular diseases. In this paper, a new approach called the Modified Window Feature-Point Detection (MWFD) is proposed to identify the vascular feature points in the fundus image. The MWFD technique makes use of two different windows, 3 x 3 and 5 x 5, with alternative vessel pixel property for the detection of all feature points. In this method, firstly, skeletonisation is done to obtain a one-pixel width vasculature map for the identification of feature points. Then spur removal operation is performed to reduce the error generated due to skeletonisation. After that, two windows with alternative vessel pixel property are applied on the skeletonised vasculature map to improve the identification of feature points. Also, adjacent feature points are removed by checking the 8-adjacency between them. This paper also resolves the problem of conversion of one crossover point into two bifurcation points generated due to skeletonisation. The simulation is performed on the DRIVE database using MATLAB software. Simulation results show quantitative improvements in detection of feature points from retinal fundus image by increasing the number of true positives and reducing false positives and false negatives. These results provide an efficient and reliable technique for analysis of various retinal structures.
Keywords: fundus image; retinal vasculature map; feature point; bifurcation point; crossover point; retinal detachment.
Survivability prediction of patients suffering hepatocellular carcinoma using diverse classifier ensemble of grafted decision tree
by Ranjit Panigrahi, Moumita Pramanik, Udit Kumar Chakraborty, Akash Kumar Bhoi
Abstract: The mortality rate of patients who have cancer is the second highest cause of death around the globe. Hepatocellular carcinoma (HCC), a type of liver cancer, is one such cause of death. Though the survivability probability of patients is very rare, a mechanism to predict chances of survival will provide a great aid to the medical practitioners who treat patients suffering from HCC. In this article, two state of the art survivability prediction schemes have been proposed separately for male and female subjects suffering HCC. The prediction engine employs Feature Selection Via concave minimisation (FSV) feature ranking and the Sigmis feature selection scheme to extract limited features of both male and female subjects, and an ensemble of decision tree grafting mechanism successfully predicts the chances of survivability of HCC patients. The gender-specific survivability prediction engine is the first-ever such prediction model for the diagnosis of HCC.
Keywords: hepatocellular carcinoma; liver cancer detection; Sigmis; FSV; machine learning.
Decision-making on the existence of soft exudates in diabetic retinopathy
by A. Reyana, V.T. Krishnaprasath, Sandeep Kautish, Ranjit Panigrahi, Mahaboob Shaik
Abstract: Medical image analysis is recognised to be the most important research area for diagnosis and screening of a wide range of medical problems. The commonly discussed diabetic retinopathy has become a vital factor of serious eye diseases leading to eye blindness. Diabetes mellitus is a metabolic disorder that is rapidly increasing worldwide. The major cause of Diabetic Retinopathy (DR) is the increase in blood insulin levels. The DR lesion thresholds inferred have protective effects and have no benefits for patients. The prediction of micro aneurysms from the fundus images is still the major challenge. The formation of micro aneurysms is the initial sign of DR to reduce the risk of non-proliferated DR. With this in mind, there exists a need for a diagnostic system for early detection of DR to be used by the ophthalmologist to identify different types of lesion, such as hemorrhages and exudates. This paper presents a new approach to detect and classify exudates in coloured retinal images, eliminating the replication of exudates by removing the optical disc region. Our research aimed to extend the current knowledge of several image processing techniques, including image enhancement, segmentation, classification, and registration for early diagnosis preventing visual impairment and blindness.
Keywords: diabetic retinopathy; homogeneity; segmentation; blindness.
Prediction of abnormal hepatic regions using ROI thresholding based segmentation and deep learning based classification
by Shubham Kamlesh Shah, Ruby Mishra, Bhabani Mishra, Om Pandey
Abstract: This paper proposes a novel Computer-Aided Diagnosis System (CADS) model using Artificial Intelligence (AI) to segment liver form abdomen CT scan. Deep Learning Convolutional Neural Network (DL-CNN) model is proposed to train the program to classify normal and abnormal liver images. For training dataset generation, another novel Region of Interest (ROI) based thresholding image processing technique is proposed. DL-CNN network is also compared with the basic CNN model to understand the difference between basic and deep learning networks. The basic CNN model yielded an accuracy of 50.00% while the DL-CNN model achieved an accuracy of 98.75%. It is also compared with other existing models, including AlexNet and adaBoostM1, and with classifiers such as na
Keywords: computer-aided diagnosis; DL-CNN; liver segmentation; image processing.
Orthogonal matching pursuit-based feature selection for motor-imagery EEG signal classification
by Rajdeep Chatterjee, Ankita Chatterjee
Abstract: This paper focuses on a framework that uses a small number of features to obtain high-quality classification accuracy of left/right-hand movement motor-imagery EEG signal. Motor-imagery EEG signal is filtered, and suitable features are extracted using a temporal sliding window-based approach. These features extracted from overlapping and non-overlapping approaches are further compared based on three different types of feature extraction technique: band power, wavelet energy entropy, and adaptive autoregressive model. The overlapping segments with wavelet energy entropy provide the best classification accuracy over other alternatives. The obtained classification accuracy is 91.43%, the highest ever reported accuracy for BCI Competition II dataset III. Subsequently, the orthogonal matching pursuit technique is used to select the subset of most discriminating features from the entire feature-set. It reduces the computation cost but still retains the quality of the classification results with only 1.43% information loss (that is, 90% classification accuracy), whereas the features-set size reduction is 75% for the same. It is found that the wavelet energy entropy technique performs consistently well in all the variants of our experiments and obtains a mean accuracy difference of 0.95% only.
Keywords: brain-computer interface; EEG; ensemble learning; orthogonal matching pursuit; motor imagery.
Remote homology detection using GA and NSGA-II on physicochemical properties
by Mukti Routray, Niranjan Kumar Ray
Abstract: Remote homology detection at amino acid level is a complex problem in the area of computational biology. Any pair of protein sequences are said to be homologous if they share common ancestry, have similar three-dimensional structures and exhibit similar functional similarity between proteins. These similarities can be detected using various laboratory techniques, such as X-ray crystallography, NMR spectroscopy etc. But these techniques are costly and time consuming. With the rapid growth in technological advancement, numerous sequences are generated day by day. Homology prediction of these sequences using laboratory techniques is becoming a tedious task. Hence there is a need to use machine learning algorithms to predict the homology of these unannotated protein sequences, which can save time and cost. This work is divided into three phases. Initially, the features are extracted from protein sequences using Principal Component Analysis (PCA) to build a chromosome set with representative features of each protein based on physicochemical properties. The second stage involves a genetic algorithm for the construction of a set of chromosomes for classification based on PCA and initialises the classifier to build up an error matrix. The third stage uses NSGA-II, crossover and mutation, and tournament selection for the next set of chromosomes. The output of this experiment is a set of minimum classification error values and minimum number of features used for classification of protein families. This technique is applied on the UniProt and SCOP 1.53 benchmark datasets. This approach tends to give superior accuracy over the profile based methods.
Keywords: principal component analysis; feature selection and classification; genetic algorithm; profile-based methods.
Special Issue on: Intelligent Healthcare Systems for Sustainable Development
Dermoscopic Image Segmentation Method Based on Convolutional Neural Networks
by Dang N. H. Thanh, Le Thi Thanh, Ugur Erkan, Aditya Khamparia, V B Surya Prasath
Abstract: In this paper, we present an efficient dermoscopic image segmentation method base on the linearization of Gamma-correction, and convolutional neural networks. Linearization of Gamma-correction is helpful to enhance low-intensity regions of skin lesion areas. Therefore, postprocessing tasks can work more effectively. The proposed convolutional neural network architecture for the segmentation method bases on the VGG-19 network. The acquired training results are convenient to apply the semantic segmentation method. Experimental results are conducted on the public ISIC-2017 dataset. To assess the quality of obtained segmentations, we make use of standard error metrics such as the Jaccard and Dice which are based on the overlap with ground truth, along with other measures such as the accuracy, sensitivity, and specificity. Moreover, we provide a comparison of our segmentation results with other similar methods. From experimental results, we infer that our method obtains excellent results in all the metrics and obtains competitive performance over other current and state of the art models for dermoscopic image segmentation.
Keywords: Dermoscopic Images, Deep CNNs, Machine Learning, Skin Lesions, Image Segmentation, Skin Cancer
Prediction of Diabetic Patients Using Various Machine Learning Techniques
by Shalli RANI, Manpreet Kaur, Deepali Gupta, Amit Kumar Manocha
Abstract: Growth of technology and digitization of several areas has made the world more successful in reaching to the solutions of the remote problems. Large amount of health records is also available in digital storage. Machine learning plays an important role for uncovering the health issues from the digital records or for diagnosis of various diseases. In this paper, we are presenting the basic introduction of recommender system (RS) with respect to diabetic patients after the rigorous review of already present literature. An experiment analysis is performed in Python with the help of machine learning classifiers such as Logistic Regression, Averaged Perception, Bayes Point, Boosted Decision Tree, Neural Network, Decision Forest, Two class Support Vector Machine and Locally Deep SVM on Pima Indian Diabetes Database. We conducted an experiment on 23K diabetic patients’ dataset. Based on the all classifiers results, it reveals the Logistic Regression performs best over all other classifiers with an accuracy of 78% and predicting the accurate results in specificity of 92%
Keywords: Collaborative Filtering, Diabetic Patients, Diabetic Mellitus, Machine Learning
Multisensor Fusion Approach: A Case Study on Human Physiological Factor-based Emotion Recognition and Classification
by Reyana A, Vijayalakshmi P., Sandeep Kautish
Abstract: In people’s daily life human emotion plays an essential role, the mental state accompanied with physiological changes. Experts have always seen that monitoring the perception of emotional changes at an early stage is a matter of concern before flattering serious. Within the next few years, emotion recognition and classification destined to become an important component in human-machine interaction. Today medical field has a great deal in using physiological signals for detection of heart sounds and identifying heart diseases. Thus the parameters temperature and heartbeat can identify the major health risks. The paper calls into question to take a new look at the development of an emotion recognition system using physiological signals. In this context, the signals are obtained from the body sensors such as muscle pressure sensor, heartbeat sensor, accelerometer, and capacitive sensor. The emotions observed are happy (excited), sad, angry, and neutral (relaxed). The results of the proposed system shows an accuracy percentage for the emotional states, happy 80%, sad 70%, angry 90%, and neutral 100%.
Keywords: emotion; recognition; multisensor fusion; body sensors; mental state
LabVIEW Based Cardiac Risk Assessment of Fetal ECG Signal Extracted from Maternal Abdominal Signal
by Prabhjot Kaur, Lillie Dewan
Abstract: In recent years, the inclination toward the automated analysis of the fetal ECG signal has become a trend. Mathematical computational processing of abdominal fetal ECG has proved to be beneficial in the crucial diagnosis of complex cardiac diseases. To arrive at the diagnosis, a cardiologist needs to observe the variations critically in the duration and amplitude of different waves and segments of the ECG. In the case of a fetus, a preliminary diagnosis of these deviations helps to have a valid and appropriate intervention, which may otherwise result in permanent damage to the brain and nervous system.
For a reason mentioned above, the Fetal cardiac signal has been efficiently extracted from a composite abdominal signal in this paper. The signal extraction has been accomplished in the LabVIEW environment using Independent Component Analysis (ICA) approach, implemented after the application of hybrid filters, employed for removing noise and artifacts within the signal taken from PhysioNet Database. By proper selection of the cut-off frequency of filters, the denoised signal is approximately 99% accurate. Statistical features like the signal to noise ratio, standard deviation, error, and accuracy have been computed besides morphological features including heart rate, time and amplitude of QRS complex with a duration of the PR interval, RR interval, and QT interval. Results obtained demonstrate that the implementation of ICA for fetal ECG signal extraction helps to determine fetal heart rate accurately with low computational complexity. The performance of the proposed algorithm has also been explored in the case of twin pregnancy. The estimated heart rate is comparable to the actual heart rate, which validates the algorithm’s accuracy. The results also indicate the feasibility of real-time application of data acquisition and analysis.
Keywords: Electrocardiogram, Independent Component Analysis, sinus rhythm, tachycardia, bradycardia, denoising filters, signal to noise ratio, standard deviation, accuracy, LabVIEW.
Impact of Feature Extraction Techniques on Cardiac Arrhythmia Classification: Experimental Approach
by Manisha , Sanjeev Kumar Dhull, Krishna Kant Singh
Abstract: This paper provides comparative analysis of state-of-the-art feature extraction techniques in context of ECG arrhythmia classification. In addition, the authors examine a linear heuristic function LW-index as an indirect measure for separability of feature sets. Seven feature sets are extracted using state-of-the-art feature extraction techniques. These include Temporal features, Morphological features, EMD based features, Wavelet Transform based features, DCT features, Hjorth Parameters, and Convolutional features respectively. The feature sets’ performance is evaluated using SVM classifier. The experimental set up is designed to classify ECG signals into four types of arrhythmic beats which are normal (N), ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB) and fusion beat (F). A PSO based feature selection method is used for dimensionality reduction utilizing LW-index as cost function. The results validate the hypothesis that convolutional features have better discrimination capability as compared to other state-of-the-art features. This paper can resolve the hassles for new researchers related to performance efficacy of individual feature extraction techniques. The work offers an inexpensive methodology and measure to indirectly evaluate and compare the performance of feature sets.
Keywords: ECG, Feature Extraction, Validity Index, Feature Selection, CNN, PSO, DWT, DCT, Hjorth Parameters, EMD, Temporal Features, MIT-BIH Database, SVM.
IoT based Automatic Intravenous Fluid Monitoring System for Smart Medical Environment
by Harsha Chauhan, Vishal Verma, Deepali Gupta, Sheifali Gupta
Abstract: Over the last few years, hospitals and other health care centers are adopting advancements done in many sophisticated technologies in order to assure the fast recovery of patients. Almost in all hospitals, a caretaker/ nurse is responsible for the monitoring of intravenous fluid levels. Usually most of the caretakers forget to change the bottle at the correct time due to their busy schedule, as a result of which the patient may face problems of reverse flow of blood towards the bottle. To overcome this critical issue, authors have proposed an IoT based automatic intravenous fluid monitoring system. The proposed device consists of Arduino UNO (i.e. ATMega328 microcontroller), Liquid Crystal Display, Solenoid Actuator, Force Sensitive Resistor 0.5”, ESP8266, a buzzer and LED lights. The authors have used FSR (Force Sensitive Resistor) sensor to monitor the weight of bottle. With the installation of proposed device, the constant need of monitoring will be reduced by the staff, especially during night hours, thus decreasing the chance to harm the patient and increase the accuracy of health care in hospitals. Also, this system will avoid the fatal risk of air embolisms entering the patient’s blood stream which leads to immediate death. To analyze the performance of the proposed system, the authors have done a sample testing. The authors have taken time as a parameter to analyze how much time the IV fluid is taking to get empty. The results have shown a promising future aspect of the proposed device in order to enhance the healthcare services in upcoming days.
Keywords: Drip monitoring system, IoT, Healthcare, IV Fluid, Wearable Electronics, ESP8266, FSR Sensor
Special Issue on: The Significance of Machine Learning for COVID-19
Analysis of some topological nodes using the adaptive control based on 9-D, hypothesis theoretical to COVID-19
by Abdulsattar Abdullah Hamad , M.Lellis Thivagar, K Martin Sagayam
Abstract: This work is an extension and based on a new model previously proposed where the Hamiltonian, synchronization, Lyapunov expansion, equilibrium, and stability of the proposed model for the same authors were analyzed. In this paper we will present a broader analysis to develop receiving network nodes faster. The analysis and study have demonstrated how to determine the basic structure and content of the Sym in theory, an attempt to identify objects that have a fundamental engineering role for the model after confirming the performance and results, we can suggest it to determine the spread of Coronavirus.
Keywords: Lu, Hamiltonian, synchronization, Lyapunov expansion, equilibrium, topological nodes.
Simple Program for Computing Objective Optical Properties of Magnetic Lenses
by R.Y. J Al-Salih, Abdullah E. M. AlAbdulla, Ezaldeen Mahmood Abdalla Alkattan
Abstract: The paper describes the basic features of a new program denoted MELOP (Magnetic Electron Lens Optical Properties), primarily intended for providing free, simple to calculate the objective focal properties of rotationally-symmetric electron magnetic lenses with the presence of its axial magnetic field distribution. The calculation is done by solving the paraxial ray equation using the fourth-order Runge-Kutta formula. For specific beam voltage, the program computes the excitation parameter, the object or image plane, the objective principal plane, the objective focal length, the objective magnification, the spherical aberration coefficient, the chromatic aberration coefficient, and the magnetic flux density at the object or image plane, these parameters are solved for zero, low, high or infinite magnification condition. The program can handle instantaneously plotting the variations of the calculated parameters relatively. In addition to that, the data can be transferred to xlsx or txt file format.
Keywords: Electron lenses design, Electron objective focal properties, Forth-order Range - Kutta formula.
The Impact of Oil Exports on Consumer Imports in the Iraqi Economy and Covid-19 Period, theoretical study
by Mustafa Kamil Rasheed, ALI MAHDI ABBAS AL-BAIRMANI , ABIR MOHAMMED JASIM AL-HUSSAINI
Abstract: Exports and imports of foreign trade are widely considered to be the most important contribution to the economic development of society. Especially, the potential and competitiveness of exports are realized, result from that an import capacity that supports growth and balance in all economic sectors. Particularly, the foreign exchange revenues come up with increasing exports, which tend to finance investment projects as well as encourage the importation of developed means of production that contribute to increase productivity and achieve economic efficiency, but this is rarely achieved in developing countries including Iraq. In spite of the high amount of oil exports, there are a large proportion of revenues from these exports go towards import consumer goods, accordingly, that do not create a stimulating environment for production and investment. On the contrary, they stimulate the investment multiplier in the exporting partner countries, which stimulates the investment activity of them. The hypothesis of the study refers to the direct relationship between oil exports and consumer imports disrupt the economy and output, and weaken its performance. The most important finding of the study is that oil exports in Iraq directly link with consumer imports, which led to the loss of the Iraqi economys financial resource and stimulating economic activity. The study recommends that the need to adopt economic diversification to overcome the unilateral Iraqi economy, as well as the optimal use of financial resources to support the national economy.
Keywords: Oil Exports, Consumer Imports, Total Exports, Production Activities, Covid-19
Impact parameters evaluation to Nano Al2O3 dielectric in wire cut-electrical release machining of beneath COVID -19 environment
by farook abed, Azwan bin sapit, Saad Kariem Shather
Abstract: The present paper, focuses on wire electric dis-charge machine of beneath COVID -19 environment. It can be considered as an attempt to develop models of response variables, Using a different liquid, one of which is nanoparticle (AL2O3), in the use of a ratio of (2 mg) and the function of Comparison in both cases they are; the rate of material removing, in the wire electric dis-charge machine process using the response surface methodology. The pilot plan is based on the concept of the Box-Behnken, and the study conveys the six main parameters. To evaluate the value of the advanced model, ANOVA was applied; the test results support the validity and suitability of the advanced RSM model. Optimum settings for the parameters are improved Work safety under beneath COVID -19 environments.
Keywords: wire electric dis-charge machine, titanium, MRR, RSM, COVID -19.
ANALYSIS OF CONVOLUTIONAL RECURRENT NEURAL NETWORK CLASSIFIER FOR COVID-19 SYMPTOMS OVER COMPUTERISED TOMOGRAPHY IMAGES
by srihari kannan, N Yuvaraj, Barzan Abdulazeez Idrees, Arulprakash P., Vijayakumar Ranganathan, Udayakumar E., Dhinakar, P.
Abstract: In this paper, a Convolutional Recurrent Neural Network (CRNN) model is designed to classify the patients with COVID-19 infections. The CRNN model is designed to identify the Computerised Tomography (CT) images. The processing of CRNN is modelled with input image processing and feature extraction using CNN and prediction by RNN model that quickens the entire process. The simulation is carried with a set of 226 CT images by varying the training-testing accuracy on a 10 fold cross-validation. The accuracy in estimating the image samples are increased with increased training data. The results of the simulation show that the proposed method has higher accuracy and reduced MSE with higher training data than other methods.
Keywords: Image classification;covid-19;Medical Imaging;Convolutional Recurrent Neural Network;fold cross validation;
AN EMPIRICAL VALIDATION OF LEARN FROM HOME (LFH): A case of Covid-19 catalysed Online Distance Learning (ODL) in India and Morocco.
by Gabriel A. Ogunmola, wegayehu Enbeyele , Wissale
Abstract: The world as we know it has changed over a short period of time, with the rise and spread ofrnthe deadly novel Corona virus known as Covid – 19 the world will never be the same again. This study explores the devastating effects of the novel virus pandemic, the resulting lockdown, thus the need to transform the offline classroom into an online classroom. It explores and describes the numerous online teaching platforms, study materials, techniques, and technologies being used to ensure that educating the students does not stop. Furthermore, it identifies the platforms, technologies which can be used to conduct online examination in a safe environment devoid of cheating. Additionally, it explores the challenges facing the deployment of online teaching methods. On the basis of literature review, a framework was proposed to deliver superior online classroom experience for the students, so that online classroom is as effective as or even better than offline classrooms. The identified variables were empirically tested with the aid of a structured questionnaire; there were 340 numbers of respondents who were purposefully sampled. The result indicates that students prefer online teaching when such sessions are enhanced with multimedia presentations. The study recommends that instructors need to train in the use of Technology Enhanced Learning (TEL) if LFH is going to be effective.rn
Keywords: COVID – 19, online classroom, Zoom, lock-down, MOOC, iCloud, Proportional Odds Model (POM)
An Empirical Study on COVID-19 for Social Contact Tracing on Classification Perspective
by Mohammed Gouse Galety, Elham Tahsin Yasin, Abdellah Behri Awol, Lubab Talib
Abstract: The staggering emergency of COVID-19 is a pandemic and irresistible one without the antibody and cure. This uprising issue needs the preventive controls through creation of awareness and implementation of the contact tracing process. The procedure of contact tracing is to determine the inflamed individual or the men and women who're contacted with inflamed human beings to be indexed and dealt with carefully. This device has its usage to lessen the infections with the information described at the infectious disease and to reduce the spreadsheet of the infections and inflamed human beings with the precautionary measures via way of means of creating the awareness. Awareness creation demands the various tools for its implication whereas social media networking is a superb knowledge set of nowadays market coverage of maximum sociology of the planet to see, analyze and interpret the presently on the market knowledge with the support of classifiers and applied math learning ways of Artificial Intelligence (AI). Here the authors have derived the obtainable data with its analysis and roaring contract tracing through the usage of social media dataset similarly as applied math learning ways of AI to determine the infected COVID -19 and infers the adequate action and preventive measures for the reduction of the expansion of COVID – 19 infections by the controller's segment of the said method.
Keywords: COVID-19, Infectious, Preventive controls, awareness, social media, Contact tracing, Artificial Intelligence (AI)
ANALYSIS OF COVID-19 PANDEMIC AND FORECASTING USING MACHINE LEARNING MODELS
by Ekansh Chauhan, Manpreet Sirswal, Deepak Gupta, Ashish Khanna, Aditya Khamparia
Abstract: The coronavirus pandemic is rapid and universal, menacing thousands of lives and economy. The full analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is very imperative as a deciding factor for the remedic actions. Machine learning is being used in every sphere to fight the coronavirus, be it understanding the biology of virus in time, be it diagnosing the patients or be it drug and vaccine development. It is also critical to predict the pandemic lifetime so to decide on opportune and remedic activities. Being able to accurately forecast the fate of an epidemic is a critical but difficult task. In this paper, based on public data available of the world and India, the estimation of pandemic parameters and the ten days ahead forecast of the coronavirus cases is proposed using Prophet, Polynomial Regression, Auto Arima and Support Vector Machine (SVM). The performance of all the models were motivating. MAE and RMSE of polynomial regression and SVM were convincingly low. Polynomial regression has predicted the highest number of cases for India and the lowest number of cases for the world, which depicts that according to polynomial regression the daily cases are going to spike in India and decline a little in the world. Prophet has forecasted the lowest number of cases for India and the highest number of cases in the world, after SVM. The results of Arima are closest to the average of combined results by all of the four models. The only limitation is the lack of enough data, which creates the high uncertainty in the result of forecast. The only limitation is the lack of enough data, which creates the high uncertainty in the result of forecast. The four factors, i.e. growth factor, growth ratio, growth rate and second derivative for the growth of coronavirus in the United States (U.S.) and India will also be calculated and compared. Several theories revolving around the origin of coronavirus are also discussed and submitted in this paper. Under optimistic predictions, the results show that the pandemic in some countries is going to terminate soon, while in some countries it is going to increase at an alarming rate and overall rate of growth of the coronavirus cases is decreasing in both U.S. and India.
Keywords: COVID-19, Machine learning, Novel coronavirus, Classification, Technology
A Statistical Analysis for COVID-19 as a Contact Tracing Approach & Social Networking Communication Management
by Abdulsattar A. hamad, Anasuya Swain, Suneeta Satpathy, Saibal Dutta
Abstract: The devastating crisis of COVID-19 is a pandemic and infectious one without the vaccine and remedy. This uprising issue needs the preventive controls through creation of awareness and implementation of the contact tracing process. The process of contract tracing is to determine the infected person or the persons who are contacted with infected people to be listed and treated carefully. This tool has its usage to reduce the infections with the details description on the infectious disease and to minimize the spread sheet of the infections and infected people with the precautionary measures by creating the awareness. Awareness creation demands the different tools for its implication whereas social media networking is a very good data set of today market coverage of maximum demography of the world to determine, analyse and interpret the current available data with the support of classifiers and statistical learning methods of Artificial Intelligence (AI). Here the authors have derived the available information with its analysis and successful contract tracing through the usage of social media dataset as well as statistical learning methods of Artificial Intelligence to determine the infected COVID -19 and infers the adequate action and preventive measures for the reduction of the growth of COVID – 19 infections by the controllers segment of the said method.
Keywords: (COVID-19, Infectious, Preventive controls, awareness, social media, Contact tracing, Artificial Intelligence (AI)
The Degree of Applying Electronic learning in the Gifted School / Nineveh in Iraq and what Management provided to the students and its Relationship to Qualitative Education under Coronavirus (COVID-19) Pandemic.
by Ahmed S. Al-Obeidi, Nawar A. Sultan, Anas R. Obaid, Abdulsattar A. hamad
Abstract: this paper we discussed the most important pillars on which e-learning on the success of the distance learning process in a Gifted school in Nineveh. Through this study, we were able to identify the methods of conducting distance education under the information technology system and on the work and learning environment used in electronic learning. Increasing the efficiency of the educational institution through distance electronic learning just as the basics of building an e-learning system in various educational institutions.\r\nThe types of programs and the most famous of which are in the application of e-learning in educational institutions in general and in the Gifted School in particular were also discussed. A comparison was made between the two in terms of method and accuracy of using these programs.\r\n
Keywords: Electronic learning ; Gifted School ; Coronavirus-19 ; Distance Education ; Hypothetical education
DESIGN AND ANALYSIS ON MOLECULAR LEVEL BIOMEDICAL EVENT TRIGGER EXTRACTION USING RECURRENT NEURAL NETWORK BASED PARTICLE SWARM OPTIMIZATION FOR COVID-19 RESEARCH
by Devendra Kumar R N, Arvind Chakrapani, Srihari Kannan
Abstract: In this paper, the rich extracted feature sets are fed to the deep learning classifier that estimates the optimal extraction of lung molecule triggered events for covid-19 infections. The feature extraction is carried out using Recurrent Neural Network (RNN) that effectively extracts the features from the rich datasets. Secondly, a particle swarm optimization (PSO) algorithm is utilized to classify the extracted features of covid-19 infections. The rule set for feature extractor is supplied by the fuzzy logic rule set. The simulation shows that the RNN-PSO, which is the combination of two different algorithms offer improved performance than other machine learning classifiers. rnrn
Keywords: Event Triggers; Covid-19;Lung Molecules; Feature Extraction; Classification; PSO; RNN;
Multivariate Economic Analysis of the Government Policies and COVID-19 on Financial Sector
by Monika Mangla, Nonita Sharma, Sourabh Yadav, Vaishali Mehta, Deepti Kakkar, PRABHAKAR KANDUKURI
Abstract: The whole world is experiencing a sudden pandemic outbreak of COVID-19. In the absence of any specific treatment or vaccine, social distancing has proved to be an effective strategy in containing the outbreak. However, this has led to disruption in trade, travel, commerce by halting manufacturing, industries, the closing of corporate offices, and all other sundry activities. The alarming pace of the virus spread and the increased uncertainty is quite concerning to the leading financial stakeholders. This has led to the customers, investors, and foreign trading partners fleeing away from new investments. Global markets plummeted, leading to erosion of more than the US $6 trillion within just one week from 24 to 28 February 2020. During the same week, the S&P 500 index alone experienced a loss of more than $5 trillion in the US, while other top 10 companies in the S&P 500 suffered a combined loss of more than $1.4 trillion. This manuscript performs multivariate analysis of the financial markets during the COVID-19 period and thus correlates its impact on the worldwide economy. An empirical evaluation of the effect of containment policies on financial activity, stock market indices, purchasing manager index, and commodity prices are also carried out. The obtained results reveal that the number of lockdown days, fiscal stimulus, and overseas travel ban significantly influences the level of economic activity.
Keywords: Coronavirus; COVID-19; Financial Sector; Forecasting; Multivariate Analysis; NIFTY-Indices; Pandemic, Regression Model; Stringency Index
COVID-19 suspected person detection and identification using thermal imaging based Closed Circuit Television (CCTV) camera and tracking using drone in Internet of Things
by Pawan Singh Mehra, Yogita Bisht Mehra, Arvind Dagur, Anshu Kumar Dwivedi, M.N. Doja, Aatif Jamshed
Abstract: COVID-19 has emerged as world-wide health concern where human to human transmission is described with an incubation period of 2-10 days. It is contagion by droplet and contaminated surfaces like hands. The sole way to detect a suspected person being infected with COVID-19 without COVID-19 testing kit is through thermal scanner. Since the disease is spreading at a vast rate, not only it is very hard to check or scan every individual manually but also there are chances of transmission of COVID-19 to the inspecting person. In this paper, we have propound a system where the suspected person can be easily detected and identified for COVID-19 by using thermal imaging based closed circuit television. The thermal imaging based closed circuit television will automatically scan the person in the vicinity and capture video/image of the suspected person. The system will raise an alarm in vicinity so that people in that area can distance themselves from each other. The recorded video/image will be forwarded to base station and information about the suspected person will be fetched from the server. Meanwhile the drones will be used for tracking the suspected person until the nodal medical team diagnose the suspected person for confirmation. The proposed system can contribute significantly for curbing the rate of infected COVID-19 persons and prevent further spread of this pandemic disease.
Keywords: Corona virus; COVID-19; Face recognition; Drone; Internet of Things; Automation; Deep Learning.
Machine Learning Based Classification: An Analysis Based on COVID-19 Transmission Electron Microscopy Images
by Kalyan Kumar Jena, Sourav Kumar Bhoi, Soumya Ranjan Nayak, Chinmaya Ranjan Pattanaik
Abstract: Virus is a type of microorganism which provides adverse effect on the human society. Viruses replicate itself within the human cells rapidly. Currently, the effects of very dangerous infectious viruses are a major issue throughout the globe. Coronavirus (CV) is considered as one of the dangerous infectious viruses for the entire world. So, it is very important to detect and classify this type of virus at the initial stage so that preventive measures can be taken as early as possible. In this work, machine learning (ML) based approach is used for the type classification of CV such as alpha CV (ACV), beta CV (BCV) and gamma CV (GCV). The ML based approach mainly focuses on several classification techniques such as support vector machine (SVM), Random Forest (RF), AdaBoost (AB) and Decision Tree (DT) techniques by processing several CV images (CVIs). The performance of these techniques is analysed using classification accuracy (CA) performance metric. The simulation of this work is carried out using Orange3-3.24.1.
Keywords: COVID-19, Machine Learning, TEM CVIs, Support Vector Machine, Random Forest, AdaBoost, Decision Tree
An Ensemble Approach to Forecast COVID-19 Incidences using Linear and Non-Linear Statistical Models
by Asmita Mahajan, Nonita Sharma, Firas Husham AlMukhtar, Monika Mangla, Krishna Pal Sharma, Rajneesh Rani
Abstract: Coronavirus 2019, also known as COVID-19, is currently a global epidemic. This pandemic has infected more than 100 countries all over the globe and is continuously spreading and endangering the human species. Researchers are perpetually trying to discover a permanent antidote for the virus, but presently no particular medication is available. As a result, health sectors worldwide are experiencing an unexpected rise in cases each day. Hence, it becomes necessary to predict the spread of disease so as to enable public health sectors improve their control capabilities in order to mitigate the spread of the infections. This manuscript proposes a stacked ensemble model for accurately forecasting the future occurrences of COVID-19. The proposed ensemble model uses Exponential Smoothing (ETS), Auto Regressive Integrated Moving Average (ARIMA), and Neural Network Autoregression (NNAR) as the base models. Each base model is trained individually on the disease dataset, whose regress values are then used to train the Multi-layer perceptron (MLP) model. The stacked model gives better predictions compared to all the other four forecasting models. It is validated that the proposed model outperforms the base models. This validation is established through error metrics like Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results conclude that the ensemble model is highly robust and reliable in forecasting future COVID incidences in comparison to other statistical time series models.
Keywords: Coronavirus; COVID; Pandemic; Forecasting; Ensemble; Stacking; Auto Regressive Integrated Moving Average; Exponential Smoothing; Neural Network; Multi Layer Perceptron.