International Journal of Computational Science and Engineering (59 papers in press)
Implicit emotional tendency recognition based on disconnected recurrent neural networks
by Yiting Yan, Zhenghong Xiao, Zhenyu Xuan, Yangjia Ou
Abstract: Implicit sentiment orientation recognition classifies emotions. The development of the internet has diversified the information presented by text data. In most cases, text information is positive, negative, or neutral. However, the inaccurate participle, the lack of standard complete sentuation lexicon, and the negation of words bring difficulty in implicit emotional recognition. The text data also contain rich and fine-grained information and thus become a difficult research point in natural language processing. This study proposes a hierarchical disconnected recurrent neural network to overcome the problem of lack of emotional information in implicit sentiment sentence recognition. The network encodes the words and characters in the sentence by using the disconnected recurrent neural network and fuses the context information of the implicit sentiment sentence through the hierarchical structure. By using the context information, the capsule network is used to construct different fine-grained context information for extracting high-level feature information and provide additional semantic information for emotion recognition. This way improves the accuracy of implicit emotion recognition. Experimental results prove that the model is better than some current mainstream models. The F1 value reaches 81.5%, which is 2 to 3 percentage points higher than those of the current mainstream models.
Keywords: hierarchical interrupted circulation network; implicit emotion; capsule network; sentiment orientation identification.
An intelligent block matching approach for localisation of copy-move forgery in digital images
by Gulivindala Suresh, Chanamallu Srinivasa Rao
Abstract: Block-based Copy-Move Forgery Detection (CMFD) methods work with features from overlapping blocks. As overlapping blocks are involved, thresholds related to similarity and the physical distances are defined to identify the duplicated regions. However, these thresholds are controlled manually in localising the forged regions. In order to overcome this, an intelligent block matching approach for localisation is proposed using Colour and Texture Features (CTF) through Firefly algorithm. Investigation of the proposed CTF method is carried out on a standard database, which achieved an average true detection rate of 0.98 and an average false detection rate of 0.07. The proposed CTF method is robust against brightness change, colour reduction, blurring, contrast adjustment attacks, and additive white Gaussian noise. Performance analysis of the CTF method validates its superiority over other existing methods.
Keywords: digital forensics; copy-move forgery detection; intelligent block matching; firefly algorithm.
Discrete stationary wavelet transform and SVD-based digital image watermarking for improved security
by Rajakumar Chellappan, S. Satheeskumaran, C. Venkatesan, S. Saravanan
Abstract: Digital image watermarking plays an important role in digital content protection and security related applications. Embedding watermark is helpful to identify the copyright of an image or ownership of the digital multimedia content. Both the grey images and colour images are used in digital image watermarking. In this work, discrete stationary wavelet transform and singular value decomposition (SVD) are used to embed watermark into an image. One colour image and one watermark image are considered here for watermarking. Three-level wavelet decomposition and SVD are applied and the watermarked image is tested under various attacks, such as noise attacks, filtering attacks and geometric transformations. The proposed work exhibits good robustness against these attacks, and the simulation results show that proposed approach is better than the existing methods in terms of bit error rate, normalised cross correlation coefficient and peak signal to noise ratio.
Keywords: digital image watermarking; discrete stationary wavelet transform;; wavelet decomposition; singular value decomposition; peak signal to noise ratio.
Disaster Management Using D2D Communication with ANFIS Genetic Algorithm Based CH Selection and Efficient Routing by Seagull Optimization
by Lithungo K. Murry, R. Kumar, Themrichon Tuithung
Abstract: The next generation networks and public safety strategies in communications are at a crossroads in order to render best applications and solutions to tackle disaster management proficiently. There are three major challenges and problems considered in this paper: (i) disproportionate disaster management scheduling among bottom-up and top-down strategies; (ii) greater attention on the disaster emergency reaction phase and the absence of management in the complete disaster management series; and (iii) arrangement deficiency of a long-term reclamation procedure, which results in stakeholder resilience and low level community. In this paper, a new strategy is proposed for disaster management. A hybrid Adaptive Neuro-Fuzzy Inference Network based Genetic Algorithm (D2D ANFIS-GA) used for selecting cluster heads, and for the efficient routing the Seagull Optimization Algorithm (SOA) is used. Implementation is done in the MATLAB platform. The performance metrics, such as energy use, average battery lifetime, battery lifetime probability, average residual energy, delivery probability, and overhead ratio, has used to evaluate the performance. Experimental results are compared with two existing approaches, Epidemic and FINDER. Our proposed approach gives better results.
Keywords: disaster management; adaptive neuro-fuzzy inference network; residual energy; device-to-device communication; seagull optimisation algorithm.
Design and implementation of chicken egg incubator for hatching using IoT
by Niranjan Lakshmappa, C. Venkatesan, Suhas A R, S. Satheeskumaran, Aaquib Nawaz S
Abstract: Egg fertilisation is one of the major factors to be considered in poultry farms. This paper describes a smart incubation system designed to combine the IoT technology with the smartphone in order to make the system more convenient to the user in monitoring and operation of the incubation system. The incubator is designed first with both the setter and the hatcher in one unit and incorporating both still air incubation and forced air incubation, which is controlled and monitored by the controller keeping in mind the four factors temperature, humidity, ventilation and egg turning system. Here we are setting with three different temperatures for the experimental purpose at 36.5oC, 37.5oC and 38oC. The environment is maintained the same in all three cases and the best temperature for the incubation of the chicken eggs is noted.
Keywords: IoT; poultry farms; embryo; brooder; hatchery; Blynk App.
Application of light gradient boosting machine in mine water inrush source type online discriminant
by Yang Yong, Li Jing, Zhang Jing, Liu Yang, Zhao Li, Guo Ruxue
Abstract: Water inrush is a kind of mine geological disaster that threatens mining safety. Type recognition of water inrush sources is an effective auxiliary method to forecast water inrush disaster. Compared with the current hydrochemistry methodology, it spends a large amount of time on sample collection. Considering this problem, it is urgent to propose a novel method to discriminate water inrush source type online, and further to obtain much more time for evacuation before the disaster. The paper proposes an in-situ mine water sources discrimination model based on Light Gradient Boosting Machine (LightGBM). This method combines Light Gradient Boosting (LGB) with the Decision Tree (DT) to improve the network's integrated learning ability and enhance model generalisation. The data were collected from in-situ sensors such as pH, conductivity, Ca, Na, Mg and CO3 components in different water bodies of LiJiaZui Coal Mine in HuaiNan. The results illustrate that the accuracy of the proposed method achieves 99.63% to recognise water sources in the mine. Thus, the proposed discriminant model is a timely and an effective online way to recognise source types of water in mines.
Keywords: water inrush source; light gradient boosting machine; online water sources discrimination.
Unmanned surface vehicle adaptive decision model for changing weather
by Han Zhang, Xinzhi Wang, Xiangfeng Luo, Shaorong Xie, Shixiong Zhu
Abstract: The autonomous decision-making capability of an unmanned surface vehicle (USV) is the basis for many tasks, such as obstacle avoidance, tracking and navigation. Most of the works ignore the variability of the scene when making behavioral decisions. For example, traditional decision-making methods are not adaptable to dynamic environments, especially changing weather that a USV is likely to encounter. In order to solve the low adaptability problem of a USV using a single decision model for autonomous decision-making in changing weather, we propose an adaptive model based on human memory cognitive process. It uses deep learning algorithms to classify weather and uses reinforcement learning algorithms to make decisions. Simulated experiments are carried out on USV obstacle avoidance decision task in the Unity3D ocean scene to test our model. Experiments show that our model's decision-making accuracy in changing weather is 27% higher than using only a single decision model.
Keywords: brain memory cognitive process; reinforcement learning; weather classification; adaptive model.
FACF: fuzzy areas-based collaborative filtering for point-of-interest recommendation
by Ive Tourinho, Tatiane Rios
Abstract: Several online social networks collect information from their users' interactions (co-tagging of photos, co-rating of products, etc.) producing a large amount of activity-based data. As a consequence, this kind of information is used by these social networks to provide their users with recommendations about new products or friends. Moreover, Recommendation Systems (RS) are able to predict a persons activity with no special infrastructure or hardware, such as RFID tags, or by using video and audio. In that sense, we propose a technique to provide personalised Points-of-Interest (POI) recommendations for users of Location-Based Social Networks (LBSN). Our technique assumes users' preferences can be characterised by their visited locations, which is shared by them on LBSN, collaboratively exposing important features as, for instance, Areas-of-Interest (AOI) and POI popularity. Therefore, our technique, named Fuzzy Areas-based Collaborative Filtering (FACF), uses users' activities to model their preferences and recommend the next visits to them. We have performed experiments over two real LBSN datasets and the obtained results have shown our technique outperforms location collaborative filtering at almost all of the experimental evaluation. Therefore, by fuzzy clustering of AOI, FACF is suitable to check the popularity of POI to improve POI recommendations.
Keywords: recommendation systems; fuzzy clustering; location; points-of-interest.
ELBA-NoC: ensemble learning-based accelerator for 2D and 3D network-on-chip architectures
by Anil Kumar, Basavaraj Talawar
Abstract: Networks-on-Chip (NoCs) have emerged as a scalable alternative to traditional
bus and point-to-point architectures. The overall performance of NoCs become highly
sensitive as the number of cores increases. Research on NoCs and development thus play
a key role in the design of hundreds to thousands of cores in the near future. Simulation is one of the main tools used in NoC for analysing and testing new architectures. To achieve the best performance vs. cost tradeoff, simulations are important for both the interconnect designer as well as the system designer. Software simulators are too slow for evaluating medium and large scale NoCs. This paper presents a learning framework which can be used to analyse the performance, area and power parameters of 2D and 3D NoC architectures which is fast, accurate and reliable. This framework is named as Ensemble Learning-Based Accelerator (ELBA-NoC) which is built using the random forest regression algorithm to predict parameters of NoCs considering different synthetic traffic patterns. On 2D, 3D Mesh, Torus and Cmesh NoC architectures, ELBA-NoC was tested and the results obtained were compared with the extensively used cycle-accurate Booksim NoC simulator. Experiments with different virtual channels, traffic patterns and injection rates were performed by varying topology sizes. The framework showed an approximate prediction error of less than 5% and an overall speedup of up to 16K
Keywords: network-on-chip; 2D NoC; 3D NoC; performance modelling; machine learning; regression; ensemble learning; random forest; Booksim; router; traffic pattern.
A blockchain-based authority management framework in traceability systems
by Jiangfeng Li, Yifan Yu, Shili Hu, Yang Shi, Shengjie Zhao, Chenxi Zhang
Abstract: The frequent occurrence of product quality and food safety incidents in recent years has greatly lost the trust of consumers. Traceability systems are developed to trace status of products in processes of production, transportation, and sales. However, the tracing data stored in the traceability systems' centralised database can be tampered. In this paper, a blockchain-based authority management framework for traceability systems is proposed. Tracing data are stored on Hyperledger Fabric and InterPlanetary File System (IPFS) to reduce data storage space and improve data privacy protection on blockchain. In the framework, using the Role Based Access Control (RBAC) mechanism, a blockchain-based RBAC model is presented by defining entities, functions, and rules. Additionally, components in four layers are designed in the framework. Strategies of operation flows are presented to achieve authority management in business applications. The framework not only guarantees the integrity of tracing data, but also prevents confidential information from being leaked. Compared with existing approaches, experiments show that the framework performs better in time and storage.
Keywords: blockchain; authority management; RBAC model; Hyperledger Fabric; IPFS;
The analysis of stego image visual quality for a data-hiding scheme based on a two-layer turtle shell matrix
by Ji-Hwei Horng, Xiao-zhu Xie, Chin-Chen Chang
Abstract: In 2018, Xie et al. proposed a novel data-hiding scheme based on a two-layer turtle shell matrix. In their scheme, they claimed that their stego image visual quality is superior to that of the state-of-the-art methods, regardless of the features of cover images. In this research note, we make a theoretical analysis of stego image quality for Xie et al.'s method based on the symmetrical characteristic of the matrix. Furthermore, we found that their simulation outcomes do not reveal the fact that their embedding capacity is larger than those of the data-hiding methods proposed previously under the same stego image visual quality. More simulations are made to reveal that our experimental outcomes coincide with the results of our theoretical analysis. Furthermore, the experimental results made by Xie et al. have also been corrected.
Keywords: theoretical analysis; two-layer turtle shell; data hiding.
Coupling model based on grey relational analysis and stepwise discriminant analysis for subsidence discrimination of foundations in soft clay areas
by Bo Li, Nian Liu, Wei Wang
Abstract: We selected grey correlation and stepwise discriminant analyses as basic models, proposed a coupling discrimination method, and designed and established a coupling discriminant model of the foundation subsidence in soft soil areas to address the challenges in the discriminant analysis of the seismic subsidence grade of soft soil. In this model, samples for discrimination, and reference samples were first analysed by indicator relations analysis. Seismic subsidence grades were ranked according to the correlation, and discriminant grades were screened. Finally, the seismic subsidence grades of the samples that met the criteria were confirmed with stepwise discriminant analysis. Actual sample data were calculated, and the discriminant results were compared and analysed with the traditional model to verify the applicability and accuracy of the coupling model. We obtained good evaluation results, which provided a new method for discriminant analysis of soft soil seismic subsidence grades.
Keywords: soft clay; grey relational analysis; stepwise discriminant analysis; coupling model.
Efficient self-adaptive access control for personal medical data in emergency setting
by Yifan Wang, Jianfeng Wang
Abstract: The notion of access control allows data owners to outsource their data to cloud servers, while encouraging the sharing of data with legally authorised users. Note that the traditional access control techniques only allow authorised users to access the sharing data. However, it is intractable to obtain the required data when the data owner encounters some emergency circumstances, such as medical first-aid. Recently, Yang et al. proposed a self-adaptive access control scheme, which can ensure secure data sharing in both normal and emergency medical scenarios. However, their construction needs to involve an emergency contact person. We argue that their scheme suffers from two weaknesses: (i) it is vulnerable to single point of failure when the emergency contact person is offline, (ii) the two-cloud model brings extra computation and communication overhead. To overcome the above shortcomings, we present a new efficient self-adaptive medical data access control by integrating fuzzy identity-based encryption and convergent encryption. Specifically, our proposed construction can achieve patients' data access by their fingerprint in emergency setting. Furthermore, the proposed scheme supports cross-user data deduplication and improves the performance of the system by convergent encryption. Experiment results show that our scheme has advantage in efficiency.
Keywords: self-adaptive access control; privacy-preserving; medical data storage; secure deduplication.
Edge servers placement in mobile edge computing using stochastic Petri nets
by Daniel Carvalho, Francisco Airton Silva
Abstract: Mobile Edge Computing (MEC) is a network architecture that takes advantage of cloud computing features (such as high availability and elasticity) and makes use of computational resources available at the edge of the network in order to enhance the mobile user experience by decreasing the service latency. MEC solutions need to dynamically allocate the requests as close as possible to their users. However, the request placement depends not only on the geographical location of the servers, but also on their requirements. Based on this fact, this paper proposes a Stochastic Petri Net (SPN) model to represent a MEC scenario and analyses its performance, focusing on the parameters that can directly impact the service Mean Response Time (MRT) and resource use level. In order to present the applicability of our work, we propose three case studies with numerical analysis using real-world values. The main objective is to provide a practical guide to help infrastructure administrators to adapt their architectures, finding a trade-off between MRT and level of resource usage.
Keywords: mobile edge computing; internet of things; stochastic models; server placement.
Application of particle swarm optimisation for coverage estimation in software testing
by Boopathi Muthusamy, Sujatha Ramalingam, C. Senthil Kumar
Abstract: A Markov approach for test case generation and code coverage estimation using particle swarm optimisation is proposed. Initially, the dd-graph is taken from control flow graph of the software code by joining decision to decision. In the dd-graph, the sequences of independent paths are identified using c-uses and p-uses based on set theory approach and compared with cyclomatic complexity. Automatic test cases are generated and the nature of the test cases are integer, float Boolean variables. Using this initial test suite, the code coverage summary is generated using gcov code coverage analysis tool, and the branch probability percentage is considered as TPM values with respect to each branch in the dd-graph. Path coverage is used as a fitness function which is the product of node coverage and TPM values. This algorithm is iterated until it reaches 100% code coverage among each independent test path. The randomness of the proposed approach is compared with genetic algorithm.
Keywords: particle swarm optimisation; dd-graph; mixed data type variables; branch percentage; TPM-based fitness function; most critical paths.
Enhancing user and transaction privacy in bitcoin with unlinkable coin mixing scheme
by Albert Kofi Kwansah Ansah, Daniel Adu-Gyamfi
Abstract: The concept of coin mixing is significant in blockchain and achieves anonymity and has merited application in bitcoin. Albeit, several coin mixing schemes have been proposed, we point out that they either hoard input transactions and address mapping or do not fully satisfy all requirements of practical anonymity. This paper proposes a coin mixing scheme (mixing countersignature scheme, ring signature, and coin mixing approach) that allows users to transact business untraceably and unlinkably without having to trust a third party to ensure coins are safe. Our proposed novel countersignature scheme simulation results prove the countersignature schemes correctness with an average running time of 4s using PBC Type A.80. The schemes security and privacy are met with standard ring signature, ECDSA unforgeability and our countersignature. We demonstrated the efficiency of the mixing scheme using Bitcoins core regtest mode to set up a private Bitcoin network. The mix takes 80, 160, 320, 640, 800 secs to service 500, 1000, 2000, 4000, 5000 users respectively. It was observed that the number of users scales linearly with average running time.
Keywords: bitcoin blockchain; user and transaction privacy; coin mixing; bilinear pairing and ECDSA; ring signature.
NO2 pollutant concentration forecasting for air quality monitoring by using an optimised deep learning bidirectional GRU model
by Shilpa Sonawani, Kailas Patil, Prawit Chumchu
Abstract: Air pollution is the most crucial environmental problem to be handled as it has adverse effects on human health and agriculture, and is also responsible for climate change and global warming. Several observations have warned about the level of increase in the pollutant nitrogen dioxide (NO2) in the atmosphere in many regions. Studies have also shown that nitrogen dioxide pollutant is associated with diseases such as diabetes mellitus, hypertension, stroke, chronic obstructive pulmonary disease (COPD), asthma, bronchitis, and pneumonia, and its high level can lead to death due to asphyxiation from fluid in the lungs. It can also have negative effect on vegetation, leading to reduced growth and damage to leaves. Considering its devastating effects, to estimate and monitor NO2 concentration an optimised bidirectional GRU model is proposed. It is evaluated for its performance with other models, such as timeseries methods, sklearn machine learning regression methods, AUTOML frameworks and all advanced and hybrid deep learning techniques. The model is further optimised for the number of features, number of neurons, number of lookbacks and epoches. It is implemented on a real time dataset of Pune city in India. This model is helpful to government and central authorities to prevent excessive pollution levels and their adverse effects, and for smart homes for controlling pollution levels.
Keywords: air pollution; air quality; AUTOML; bidirectional GRU; deep learning; nitrogen dioxide; NO2; timeseries forecasting.
A knowledge elicitation framework in ranking healthcare providers using rough set with formal concept analysis
by Arati Mohapatro, S.K. Mahendran, Tapan Kumar Das
Abstract: A comparison of healthcare institutions by ranking involves generating their relative scores based on the infrastructure, process and other quality dynamics. Being a top-ranking institute depends on the overall score secured against the hospital quality parameters that are being assessed for ranking. However, the parameters are not equally important when it comes to ranking. Hence, the objective of this research is to explore the parameters that are vital as they significantly influence the ranking score. In this paper, a hybrid model is presented for knowledge extraction, which employs techniques of rough set on intuitionistic fuzzy approximation space (RSIFAS) for classification, Learning from Examples Module 2 (LEM2) algorithm for generating decision rules, and formal concept analysis (FCA) for attribute exploration. The model is discussed using AHA US News score data for cancer specialisation. The result signifies the connection between quality attributes and ranking. Finally, the leading attribute and its particular values are identified for different states of ranking.
Keywords: rough set with intuitionistic fuzzy approximation space; formal concept analysis; hospital ranking; knowledge mining; attribute exploration.
Real-time segmentation of weeds in cornfields based on depthwise separable convolution residual network
by Hao Guo, Shengsheng Wang
Abstract: Traditional artificial spraying of pesticides not only leads to greater use of pesticides but also environmental pollution. However, intelligent weeding devices can identify weeds and crops through sensing devices for selective spraying, which will effectively reduce the use of pesticides. The accurate and efficient identification method of crops and weeds is crucial to the development of the mechanised weeding model. To improve the segmentation exactitude and real-time performance of crops and weeds, we propose a lightweight network based on the Encoder-Decoder architecture, namely, SResNet. The shuffle-split-separable-residual block was employed to compress the model and increase the number of network layers at the same time, thereby extracting more abundant pixel category information. Besides, the model was optimised by a weighted cross-entropy loss function due to the imbalance of pixel ratios of background, crops, and weeds. The results of the experiment prove that the method presented can greatly improve the segmentation accuracy and real-time segmentation speed on the corns and weeds dataset.
Keywords: weed segmentation; convolutional network; residual network; machine vision; image recognition.
Array manifold matching algorithm based on fourth-order cumulant for 2D DOA estimation with two parallel nested arrays
by Sheng Liu, Jing Zhao, Yu Zhang
Abstract: In this paper, a two-dimensional (2D) direction-of-arrival (DOA) estimation algorithm with two parallel nested arrays is developed. Firstly, a constructor method for fourth-order cumulant (FOC) matrices is given according to the distribution of sensors. Then a pre-existing DOA estimation technique is firstly used to estimate the elevation angles and an improved unilateral array manifold matching (AMM) algorithm is used to estimate the azimuth angles. Compared with some classical 2D DOA estimation algorithms, the proposed algorithm has much better estimation performance, particularly in the case of low SNR environment. Compared with some traditional FOC-based algorithm, the proposed algorithm has higher estimation precision. Simulation results can illustrate the validity of proposed algorithm.
Keywords: DOA estimation; fourth-order cumulant; array manifold matching; two parallel nested arrays.
Feature weighting for naive Bayes using multi-objective artificial bee colony algorithm
by Abhilasha Chaudhuri, Tirath Sahu
Abstract: Naive Bayes (NB) is a widely used classifier in the field of machine learning. However, its conditional independence assumption does not hold true in real-world applications. In the literature, various feature-weighting approaches have attempted to alleviate this assumption. Almost all of these approaches consider the relationship between feature-class (relevancy) and feature-feature (redundancy) independently, to determine the weights of features. We argue that these two relationships are mutually dependent and both cannot be improved simultaneously, i.e. form a trade-off. Multi-objective optimisation (MOO) techniques are used to solve these types of problem. In this paper, we propose a new paradigm to determine the feature weight. Feature weighting is formulated as an MOO problem to balance the trade-off between relevancy and redundancy. Multi-objective Artificial Bee Colony based feature weighting technique for na
Keywords: naive Bayes; feature weighting; multi-objective optimisation; artificial bee colony.
Hyperspectral endmember extraction using Pearson's correlation coefficient
by Dharambhai Shah, Tanish Zaveri
Abstract: Hyperspectral unmixing is a source separation problem. The spectral unmixing process is simply the composition of the three-step chain (subspace identification, endmember extraction and abundance estimation). A critical step in this chain is endmember extraction, which finds endmembers from the image for the estimation of abundances. In this paper, a novel framework is proposed that uses the concept of Pearsons correlation coefficient and convex geometry. The novel framework extracts endmembers from the convex set of the two bands extracted using Pearsons correlation coefficient so it is named as PCGE (Pearsons correlation coefficient based Convex Geometry for Endmember extraction). This PCGE framework is different from other commonly used frameworks owing to there being only two bands convex geometry, which means that the computation time for the proposed framework is less. The proposed framework is applied to a synthetic dataset and four popular real hyperspectral datasets. In the simulation results, the proposed framework is compared with other popular frameworks based on standard evaluation parameters (spectral angle error, spectral information divergence, normalised cross-correlation). It has been observed from the simulation results that the proposed framework outperforms popular frameworks. It has been also observed that the proposed framework takes less time than others for extracting endmembers.
Keywords: endmember extraction; hyperspectral image; Pearson’s correlation coefficient; spectral unmixing.
A dynamic slicing based approach for affective SBFL technique
by Debolina Ghosh, Jagannath Singh
Abstract: Fault finding is an activity to locate the fault or bug present in a software. It is a time-consuming job and needs much more effort if done manually. Hence, automated fault localisation is always in high demand, which reduces the human effort and also makes the task more accurate. Among different existing debugging techniques, spectrum-based debugging is the most efficient for automated fault localisation. Dynamic program slicing is an another technique that can reduce the debugging time by reducing the unaffected source codes depending on slicing criteria. In this paper, we present a spectrum-based fault localisation technique by using dynamic slicing. Context-sensitive slicing is used to diminish the fault localisation time and makes the process more effective. SBFL metrics are used in the sliced program to find the suspiciousness score of individual program statements. The efficiency of the proposed approach is evaluated on three open-source programs. From the results, we notice that owing to dynamic slicing the technique takes less time to find the suspiciousness score of individual statements in the sliced program compared with the original program. We have also observed that the programmer needs to inspect less source code to detect the buggy statement. The results indicate that the proposed approach outperforms the pure spectrum-based fault localisation techniques.
Keywords: program slicing; spectrum-based fault localisation; statistical formula; Java; context-sensitive slicing.
Open data integration model using a polystore system for large scale scientific data archives in astronomy
by Shashank Shrestha, Manoj Poudel, Rashmi Sarode, Wanming Chu, Subhash Bhalla
Abstract: Polystore systems have been recently proposed as a new data integration model to provide integrated access to heterogeneous data stores through a unified single query language. Recently, there is a growing interest in the database community to manage large scale unstructured data from multiple heterogeneous data stores. Special attention is given to this problem owing to growth in the size of data, the speed of increment of data and the emergence of various data types in different scientific data archives. Moreover, astronomy as a scientific domain produces huge amounts of data which are stored in the data archives provided by NASA and their subsidiaries. The data type mostly consists of images, unstructured texts and structured (relations, key-values). This paper articulates the problems of integrating multiple data stores to manage heterogeneous data and presents a polystore architecture as a solution. A method of managing a local data store and communicating with a remote cloud data store with the help of a web-based query system is defined.
Keywords: astronomical data; heterogeneous data; data integration; workflow system.
Adaptive online learning for classification under concept drift
by Kanu Goel, Shalini Batra
Abstract: In machine learning and predictive analytics, the underlying data distributions tend to change with the course of time known as concept drift. Accurate labelling in case of supervised learning algorithms is essential to build consistent ensemble models. However, several real-world applications suffer from drifting data concepts, which leads to deterioration in performance of prediction systems. To tackle these challenges, we study various concept drift handling approaches that identify major types of drift pattern in drifting data streams, such as abrupt, gradual and recurring. This study also highlights the need for adaptive algorithms and demonstrates comparison of various state-of-the-art drift handling techniques by analysing their classification accuracy on artificially generated drifting data streams and real datasets.
Keywords: concept drift; ensemble learning; classification; non-stationary; adaptive algorithms; machine learning.
Flexible human motion transition via hybrid deep neural network and quadruple-like structure learning
by Shu-Juan Peng, Liang-Yu Zhang, Xin Liu
Abstract: Skeletal motion transition is of crucial importance to the animation creation. In this paper, we propose a hybrid deep learning framework that allows for efficient human motion transition. First, we integrate a convolutional restricted Boltzmann machine with deep belief network to extract the spatio-temporal features of each motion style, featuring on appropriate detection of transition points. Then, a quadruple-like data structure is exploited for motion graph building, motion splitting and indexing. Accordingly, the similar frames fulfilling the transition segments can be efficiently retrieved. Meanwhile, the transition length is reasonably computed according to the average speed of the motion joints. As a result, different kinds of diverse motions can be well transited with satisfactory performance. The experimental results show that the proposed transition approach brings substantial improvements over the state-of-the-art methods.
Keywords: skeletal motion transition; hybrid deep learning; convolutional restricted Boltzmann machine; quadruple-like data structure.
Flow-based machine learning approach for slow HTTP distributed denial of service attack classification
by Muraleedharan Navarikuth, Janet B.
Abstract: Distributed Denial of Service (DDoS) attack is one of the common threats to the availability of services on the internet. The DDoS attacks evolve from volumetric attack to slow DDoS. Unlike the volumetric DDoS attack, the slow DDoS traffic rate looks similar to the normal traffic. Hence, it is difficult to detect using traditional security mechanism. In this paper, we propose a flow-based classification model for slow HTTP DDoS traffic. The important flow level features were selected using the CICIDS2017 dataset. The impact of time, packet length and transmission rate for slow DDoS are analysed. Using the selected features, three classification models were trained and evaluated using two benchmark datasets. The results obtained reveal the proposed classifiers can achieve higher accuracy of 0.997 using RF classifiers. A comparison of the results obtained with state-of-the-art approaches shows that the proposed approach can improve the detection rate by 19.7%.
Keywords: denial of service; Slow DDoS; application layer DoS; machine learning; network flow; slow HTTP DDoS; slow loris; slow read.
A decision system based on intelligent perception and decision for scene ventilation safety
by Jingzhao Li, Tengfei Li
Abstract: There are many hidden safety hazards in the mine ventilation process that cannot be dealt with in time. It is because the type of coal mine and its mining conditions are complex and changeable, and the safety decision-making level is low when coal mine ventilation is abnormal. To solve these problems, this paper presents a decision system for scene ventilation safety based on intelligent perception and decision. First, grey correlation analysis and rough set theory are used to reduce the decision table horizontally and vertically. Then, the reduced data is input into the mine ventilation safety decision model based on the improved capsule network to make ventilation safety decision. Experimental results show that this system can significantly improve the accuracy of mine ventilation safety decisions, has the characteristics of strong information perception ability and accurate decisions, and provides an important guarantee for mine ventilation safety.
Keywords: grey correlation analysis;rough set; mine ventilation;capsule network; attribute reduction; intelligent decision making.
Satellite image fusion using undecimated rotated wavelet transform
by Rishikesh Tambe, Sanjay Talbar, Satishkumar Chavan
Abstract: This paper presents two satellite image fusion algorithms namely decimated/subsampled rotated wavelet transform (SSRWT) and undecimated/non-subsampled rotated wavelet transform (NSRWT) using 2D rotated wavelet filters for extracting relevant and pragmatic information from MS and PAN images. Three major visual artefacts, colour distortion, shifting effects and shift distortion, are identified in the fused images obtained using SSRWT which are addressed by using NSRWT. The proposed NSRWT algorithm preserves spatial and spectral features of the source MS and PAN images, resulting in a fused image with better fusion performance. The final fused image provides richer information (in terms of spatial and spectral quality) than that of the original input images. The experimental results strongly reveal that undecimated fusion algorithm not only performs better than decimated fusion algorithm but also improves spatial and spectral quality of the fused images.
Keywords: satellite image fusion; feature extraction; rotated wavelet filters; subsampled rotated wavelet transform; nonsubsampled rotated wavelet transform ; MS images; PAN images; shift distortion; shifting effect; fusion metrics.
SE-SqueezeNet: SqueezeNet extension with Squeeze-and-Excitation block
by Supasit Kajkamhaeng, Chantana Chantrapornchai
Abstract: Convolutional neural networks have been popularly used for image recognition tasks. They are built based on the stack of convolutional operations to extract hierarchical features from images. It is known that the deep convolutional neural network can yield high recognition accuracy while training it can be very time-consuming. AlexNet was one of the very first networks shown to be effective for an image classification task. It contains only five convolutional layers and three fully connected layers. However, owing to its large kernel sizes and fully connected layers, the training time is significant. SqueezeNet has been known as a small network that yields the same performance as AlexNet (Krizhevsky et al., 2012). The key element in the network is the Fire module that contains squeeze and expand filters which can reduce the number of parameters significantly. Based on SqueezeNet, we are interested in supplementing other modules that can further improve the performance. The Squeeze-and-Excitation (SE) module yielded promising results in ILSVRC2017. In this paper, we explore the effective insertion of the SE module into SqueezeNet. The methodology and pattern of module insertion have been explored. Further, we propose to combine the residual operation and SE modules to improve accuracy. The effects on size and accuracy are reported. The experimental results for evaluating the module insertion are shown on the popular image classification datasets, including CIFAR-100 and ILSVRC2012. The results show improvements on CIFAR-100 and ILSVRC2012 top-1 accuracy by 1.55% and 3.32% respectively, while the model size is enlarged up to 16% and 10% for CIFAR-100 and ILSVRC2012, respectively.
Keywords: convolutional neural network; deep learning; image classification; residual network; SENet; SqueezeNet.
Semantic techniques for discovering architectural patterns in building information models
by Beniamino Di Martino, Mariangela Graziano
Abstract: Architectural patterns, a concept devised by the Viennese architect Christoper Alexander, have inspired the world of patterns, especially in software engineering. Two objectives are addressed in this work: to realise a semantic representation of Alexanders patterns by developing an OWL ontology, and to devise a rule-based system for discovering the patterns into a concrete building model, represented in IFC format, one of the pillars of the BIM (Building Information Modelling) approach now widely spread in the architectural and civil engineering design world. This paper shows how semantic and logical inference rules have been applied to discover Alexanders pattern on a generic IFC model of a building.
Keywords: semantic technology; architectural patterns; building information modelling; IFC.
Multi-label software bug categorisation based on fuzzy similarity
by Rama Ranjan Panda, Naresh Kumar Nagwani
Abstract: The quality and cost of the software depend on the timely detection of software bugs. For better quality and low-cost development, the bug fixing time should be minimised which is possible only after understanding the bugs and their root cause. Categorisation of the software bugs helps in their understanding and effective management, and improves various software development activities such as triaging and quick resolution of the reported bugs. Because of the modular software development approach and multi-skilled development teams, it is possible that one software bug can affect multiple modules and there can be multiple developers who can fix the newly reported bugs. Multi-label categorisation of the software bugs can play a significant role in handling this situation, as in practice one bug can belong to multiple categories and there can be multiple developers for a software bug. Fuzzy logic and fuzzy similarity techniques can be very helpful for understanding the belongingness of the software bugs in multiple categories in real-life scenarios. Since most of the software bug attributes are textual in nature, in this paper a multi-label fuzzy similarity based text categorisation technique is presented for effective categorisation of software bugs in multiple categories. In the presented approach, the fuzzy similarity between a pair of software bugs is computed and, based on a user-defined threshold value, the bugs are categorised into multiple categories. Experiments are performed on available benchmark software bug datasets, and the performance of the proposed multi-label classifier technique is evaluated using parameters such as F1 score, BEP and Hamming loss.
Keywords: software bug mining; software bug classification; fuzzy similarity; multi-label classification; software bug repository.
Development of an intrusion detection system using mining and machine learning techniques to estimate denial of service malware
by Revathy Ganapathy, Sathish Kumar Palani
Abstract: A denial of service is one of the main types of cyber security attack which allows trespassers to outbreak many of the services such as failures in data, botnet in the system or network environment that makes the systems very slow. Consequently,
prevention of legalised users for accessing the services in the system is a major issue. Intrusion Detection System (IDS) techniques play a very important role for detecting and preventing mechanism that eradicate the issues made by hackers in the network environments. In this research, we describe different data mining techniques that can be used to handle different kinds of network attack. We present a model that incorporates an skilled, dedicated IDS for classifying attacks in the system. In this paper, three machine learning techniques are used for classification problems, such as decision tree classifier, gradient boosting classifier, and K nearest neighbour classifier, to find the metric values of false negative rate, accuracy, F score and prediction time. Through this paper we analyse that among all the three algorithms, the decision tree classifier and voting classifier is the best method, which has shorter prediction time and better accuracy of 99.86% to 99.9% which makes the model better along with greater performance. We estimate the solution via KDD cup 99 datasets (normal and malicious). The proposed investigational outcome shows high accuracy level and shorter prediction time. Moreover, the relationship is presented between existing approaches and the proposed approach in terms of metrics calculating namely accuracy, precision and recall, for detecting denial of service attacks and distinguishing malicious from normal data.
Keywords: denial of service; machine learning techniques; statistical analysis; false negative rate; intrusion detection system.
Performance evaluation of smart cooperative traffic lights in VANETs
by Láecio Rodrigues, Firmino Neto, Glauber Gonçalves, André Soares, Francisco Airton Silva
Abstract: Vehicular Ad-Hoc Network (VANET) is an emerging new type of network, consisting of vehicles as mobile nodes and temporary communication links among these nodes. One of the crucial topics in VANETs is related to how to use traffic lights to optimise vehicle mobility. The traffic lights can work cooperatively to reduce traffic jams by communicating with the vehicles. However, the architecture of smart traffic lights offers challenges related to network latency restrictions and resource constraints.
This paper presents a performance evaluation of a cooperative smart traffic light using a stochastic Petri net (SPN) model. The proposed model can calculate the mean response time, resource use, and the number of requests discarded. Three case studies are presented to illustrate how useful the model can be. Besides, we conduct real experiments to validate the proposed model by using micro-controllers (Raspberry Pi) that emulate traffic lights. The model is highly flexible, allowing developers and system administrators to calibrate eighteen parameters.
Keywords: analytical modelling; mean response time; vehicular ad hoc networks; stochastic Petri net.
Blockchain for committing peer-to-peer transactions using distributed ledger technologies
by Rashmi P. Sarode
Abstract: Blockchain consists of networks of successive blocks that are interconnected to each other by references to their former block. These form a chain. Blockchain technology creates a database-like support for creation of digital ledgers, in order to support distributed transactions. The adoption of blockchain in real-world applications poses many challenges. This study aims to understand the method, its characteristics as well as the implementation concepts of blockchain systems in terms of distributed transactions over web resources. The study also examines the current trends and issues in the use of blockchain in many large-scale public utility applications in e-commerce.
Keywords: blockchain; bitcoin; peer-to-peer transactions; cloud-based databases; web services; e-commerce.
CEMP-IR: a novel location-aware cache invalidation and replacement policy
by Ajay Kumar Gupta, Udai Shanker
Abstract: Earlier mobile client cache invalidation-replacement policies used in the location-based information system are not appropriate if the path for the client movement is changing rapidly. Further, previous cache invalidation-replacement policies show high server overhead in terms of processing costs. Therefore, the objective of this work is to solve the aforementioned challenges by developing a novel effective approach for predicting the future path for the user movement by the use of mobility Markov chain and matrix created to estimate the future movement path (FMP) used in the revised spatio-temporal cost estimation of a data item in cache replacement for contribution to the cache hit ratio improvement. The user-predicted FMP is further used in optimal sub-polygon selection for reducing the storage overhead in cache invalidation valid scope representation. A client-server queuing model is used for simulation of CEMP-IR in location-based services (LBS). Analytical results show significant caching performance improvement compared with previous policies, such as Manhattan, FAR, PPRRP, SPMC-CRP, and CEFAB for LBS.
Keywords: LBS; location-based computing; context prediction; context-aware systems; context-aware mobility.
Evaluation of feature selection techniques on network traffic for comparing model accuracy
by Prabhjot Kaur, Amit Awasthi, Anchit Bijalwan
Abstract: The accuracy and performance of any machine learning model are highly dependent on the number of qualitative features taken into consideration while training the model. The selection of qualitative features depends on the considerate choice of feature selection technique. In this study, feature selection is performed using different techniques, such as information gain, gini decrease, chi2 and FCBF, on the same dataset, and subsequently, the accuracy has been measured. The results showed that the FCBF method has dramatically reduced the number of features and moderated the accuracy over other feature selection methods.
Keywords: feature selection; FCBF; network traffic; chi2; gini decrease; information gain.
A novel method of mental fatigue detection based on CNN and LSTM
by Shaohan Zhang, Zhenchang Zhang, Zelong Chen, Shaowei Lin, Ziyan Xie
Abstract: Mental fatigue is a state that may occur owing to excessive work or long-term stress. Electroencephalogram (EEG) is considered a reliable standard for mental fatigue detection. The existing EEG fatigue detection methods mainly use traditional machine learning models to classify mental fatigue after manual feature extraction. However, manual feature extraction is difficult and complicated. The quality of feature extraction largely determines the quality of the model. In this article, we collected EEG signals from 30 medical staff. The wavelet threshold denoising method was then applied to the measured EEG signal data to denoise the original EEG data. And, a method based on a Convolution and Long Short-Term Memory (CNN+LSTM) neural network to determine the fatigue state of medical staff. The extensive experiment on the established data set clearly proves the advancement of our proposed algorithm compared with other neural network based methods. Compared with the existing DNN, CNN and LSTM, the proposed model can quickly learn the information before and after the time series, so as to obtain higher classification accuracy.
Keywords: mental fatigue detection; wavelet threshold denoising; CNN+LSTM.
A computational semantic information retrieval model for Vietnamese texts
by Tuyen Thi-Thanh Do, Dang Tuan Nguyen
Abstract: Semantic information retrieval systems for text document aim at retrieving text documents having semantic contents relevant to the query. Semantic representation of text can be a vector or a dependency graph, depending on the approach of the semantic analysis. This paper proposes a model of semantic information retrieval for Vietnamese to retrieve similar texts to a query. In the proposed system, the semantic analysis is to identify the semantic dependency graph of sentences, and the retrieving process computes the relevance of text document with these semantic dependency graphs. In order to identify the semantic dependency graph of a sentence, the transformation rules are studied to apply on dependency parse using a lexicon ontology for Vietnamese. For ranking retrieval results, the JaccardTanimoto distance is applied to the ranking function. The evaluation shows that the proposed model has higher MAP (0.4045) than that of BM25 model (0.3825) and of TF.IDF model (0.3688).
Keywords: semantic information retrieval; lexicon ontology; dependency graph; semantic distance.
Counterexample generation in CPS model checking based on ARSG algorithm
by Mingguang Hu, Zining Cao
Abstract: With the rapid development of software and physical devices, Cyber-Physical Systems(CPS) are widely adopted in many application areas. It is difficult to detect defects in CPS models owing to the complexities involved in the software and physical systems. Counterexample generation in CPS model checking is of interest because it is able to find defects in CPS models efficiently. In many studies, robustness-guided counterexample generation of CPS is investigated by various optimisation methods, which falsifies the given properties of a CPS. In this work, we combine the genetic algorithm with acceptance-rejection technique based on the neighborhood of input sequence space and create a novel algorithm ARSG. We claim that the ARSG algorithm can find counterexamples more quickly and accurately, its idea is similar to 'exploration-exploitation' in reinforcement learning. Finally, we demonstrate the effectiveness of our technique on the automatic transmission controller.
Keywords: cyber-physical systems; signal temporal logic; counterexample generation; acceptance-rejection technique; genetic algorithm.
A hybrid heuristic algorithm for optimising SLA satisfaction in cloud computing
by Yongxuan Sang, Zhongwen Li, Tien-Hsiung Weng, Bo Wang
Abstract: Task scheduling is a one of key techniques for the effective and reliable resource usage of cloud computing. In this paper, we design a hybrid heuristic scheduling that employed Particle Swarm Optimisation (PSO) and least accumulated slack time first to respectively address the problems of the assignment of tasks to servers and of the task scheduling for multi-core servers, to maximise the service level agreement (SLA) satisfaction with resource efficiency improvement, for task execution in heterogeneous clouds with deadline constraints. Experimental results show that our method can complete up to 112.5% more tasks, compared with several classical and state-of-art task scheduling methods.
Keywords: cloud computing; hybrid heuristic; SLA; task scheduling; PSO.
Detection of denial of service using a cascaded multi-classifier
by Avneet Dhingra, Monika Sachdeva
Abstract: This paper proposes a cascaded multi-classifier Two-Phase Intrusion Detection (TP-ID) approach that can be trained to monitor incoming traffic for any suspicious data. It addresses the issue of efficient detection of intrusion in traffic and further classifies the traffic as DDoS attack or flash event. Features portraying the behaviour of normal, DDoS attack, and flash event are extracted from historical data obtained after merging CAIDA07, SlowDoS2016, CIC-IDS-2017, and World Cup 1998 benchmark datasets available online, along with the commercial dataset for e-shopping assistant website. Information gain is applied to rank and select the most relevant features. TP-ID applies supervised learning algorithms in the two phases. Each phase tests the set of classifiers, the best of which is chosen for building a model. The performance of the system is evaluated using the detection rate, false-positive rate, mean absolute percentage error, and classification rate. The proposed approach classifies the traffic anomalies with a 99% detection rate, 0.43% FPR, and 99.51% classification rate.
Keywords: intrusion detection; denial of service; DDoS attack; multi-classifier; flash event; detection rate; false-positive rate; network security; machine learning; supervised learning algorithm.
Application of convolution neural network in web query session mining for personalised web search
by Suruchi Chawla
Abstract: In this paper, a deep learning Convolution Neural Network (CNN) is applied in web query session mining for effective personalised web search. In this research, CNN extracts high-level continuous clicked document/query concept vector for semantic clustering of documents. The CNN model is trained to generate document/query concept vector based on clickthrough web query session data. Training of CNN is done using backpropagation based on stochastic gradient descent maximising the likelihood of a relevant document given a user search query. During web search, search query concept vector is generated and compared with semantic cluster means to select the most similar cluster for web document recommendations. The experimental results were analysed based on average precision of search results and loss function computed during training of CNN. The improvement in precision of search results as well as decrease in loss value proves CNN to be effective in capturing semantics of web user query sessions for effective information retrieval.
Keywords: convolution neural network; deep learning; personalised web search; search engines; clustering; information retrieval.
Prediction of consumer preference for the bottom of the pyramid using EEG-based deep model
by Debadrita Panda, Debashis Das Chakladar, Tanmoy Dasgupta
Abstract: Emotion detection using electroencephalogram (EEG) signals has gained widespread acceptance in consumer preference studies. It has been observed that emotion classification using brain signals has great potential over rating-based quantitative analysis. In the consumer segment, the Bottom of the Pyramid (BoP) people also have been considered as an essential consumer base. This study aims to classify consumer preferences while visualising advertisements for the BoP consumers. Four types of consumer preference (most like, like, dislike, most dislike) have been classified while visualising different advertisements. A robust long short-term memory (LSTM)based deep neural network model has been developed for classifying consumer preferences using the EEG signal. The proposed model has achieved 94.18% classification accuracy. The proposed model has attained a significant improvement of 11.71% and 3.24% in terms of classification accuracy over other machine learning classifiers (support vector machine and random forest), respectively. This study aims to add a significant contribution to the research domain of consumer behaviour, as it provides a guideline about the consumer preferences of the BoPs after seeing the online advertisements.
Keywords: neuromarketing; deep learning; EEG; bottom of the pyramid; consumer behaviour.
Cost-effective and mobility-aware cooperative resource allocation framework for vehicular service delivery in the vehicular cloud networks
by Mahfuzulhoq Chowdhury
Abstract: The cloud-empowered vehicular networks have been identified as a promising paradigm to improve the latency of vehicular application by transferring the large computation tasks of the vehicular application to clouds for processing. Note that existing cloud empowered vehicular networks cannot adequately meet the very low latency requirements of real-time vehicular applications owing to the absence of a suitable resource allocation algorithm. However, at present, most of the existing works on resource allocation in vehicular cloud networks do not take into account the heterogenous cloud resources, heterogeneous vehicular services, SLA requirements, vehicles mobility, cost-effectiveness, different transmission strategies, workload processing and data transfer delay along with waiting delay, and both cloud and network resource allocation. To address these issues, this paper proposes a cost-effective and mobility-aware cooperative resource allocation (CEMC) method that aims to reduce the overall vehicular service result delivery delay for multiple intelligently connected vehicle applications over cloud empowered vehicular networks. The method considers inter-cluster resource awareness, both workload processing and communication latency, mobility awareness, SLA requirements, different types of power consumption and pricing cost, different network and cloud resources, proper scheduling order, best fit resource selection by taking account of cooperation among roadside units, vehicles, cloud servers, and mobile devices. This paper also presents three different variants of our proposed framework. The performance of our proposed scheme, along with the variants, is evaluated through service result delivery delay, throughput, service finishing ratio, normalised power consumption cost, net pricing cost, waiting delay, and average time gain. The evaluation results show superiority of the proposed CEMC framework in terms of delivery delay, throughput, and pricing cost among all the variants of the proposed framework
Keywords: vehicular cloud; resource allocation; heterogeneous computing; vehicular service result delivery; best fit allocation; service finishing ratio; communication delay.
The FRCK clustering algorithm for determining cluster number and removing outliers automatically
by Yubin Guo, Yuhang Wu
Abstract: the clustering algorithm is one of the most popular unsupervised algorithms for data grouping. The K-means algorithm is a popular clustering algorithm for its simplicity, ease of implementation and efficiency. But for K-means algorithm, the optical cluster number is difficult to predict, while it is sensitive to outliers. In this paper, we divided outliers into two types, and then prompt a clustering algorithm to remove the two types of outliers and calculate the optimal cluster number in each clustering iteration. The algorithm is a fusion of rough clustering and K-means, abbreviated as FRCK algorithm. In the FRCK algorithm, outliers are removed precisely, therefore the optical cluster number can be more accurate, and the quality of the clustering result can be heightened accordingly. This algorithm is proven effective by experiment.
Keywords: clustering algorithm; number of clusters; outliers; rough clustering.
Gene selection and classification combining information gain ratio with fruit fly optimization algorithm for single-cell RNA-seq data
by Jie Zhang, Junhong Junhong, Xiani Yang, Jianming Liu
Abstract: There are a wide range of genes in single-cell data, but some of them are not beneficial to classification. In order to eliminate these redundant genes and select beneficial genes, this study first uses the information gain (IG) to select some genes coarsely, then uses the modified fruit fly optimisation algorithm (FOA) to choose the relevant genes refinedly from the subsets after performing IG. The proposed algorithm makes full use of respective advantages of the IG and FOA, and is abbreviated as IGFOA. The proposed algorithm is implemented on multiple scRNA-seq datasets with various numbers of cells and genes, and the obtained results validate that the IGFOA can select effectively some superior genes and acquire good classification performance.
Keywords: single-cell; scRNA-seq; gene selection; fruit fly optimisation; information gain.
ECC-based lightweight mutual authentication protocol for fog-enabled IoT system using three-way authentication procedure
by Upendra Verma, Diwakar Bhardwaj
Abstract: Internet of Things (IoT) devices may be easily compromised and incapable of defending and securing themselves owing to their resource-constrained nature. Hence, the integration of devices with a resource-rich pool such as fog is required. This integration provides expected growth in delay-sensitive IoT applications. In this context, authentication plays a vital role. In this paper, we propose a new and anonymous mutual authentication protocol for fog-enabled IoT based on elliptic curve cryptography. The proposed protocol achieves mutual authentication between a device and the fog server with the help of a cloud server, called three-way authentication procedure. Security analyses of the proposed protocol show that it is robust against several attacks. Moreover, the performance of the proposed protocol has been evaluated and compared with other related protocols in terms of communication and storage cost. Security analyses and performance analyses reveal that the proposed authentication protocol attains better security than related protocols.
Keywords: elliptic curve cryptography; mutual authentication; fog server; three-way authentication procedure; delay-sensitive IoT applications.
Real-time lidar and radar fusion for road-object detection and tracking
by Wael Farag
Abstract: In this paper, a real-time road-object detection and tracking (LR_ODT) method for autonomous driving is proposed. The method is based on the fusion of lidar and radar measurement data, where they are installed on the autonomous car, and a customised Unscented Kalman Filter (UKF) is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the autonomous car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and uses highly optimised math and optimisation libraries for the best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the UKF fusion is compared with that of the Extended Kalman Filter fusion (EKF) showing its superiority. The UKF has outperformed the EKF on all test cases and all the state variable levels (-24% average RMSE). The employed fusion technique shows how outstanding is the improvement in tracking performance compared with the use of a single device (-29% RMES with lidar and -38% RMSE with radar).
Keywords: sensor fusion; Kalman filter; object detection; object tracking; ADAS; autonomous driving.
A novel audio steganography technique integrated with a symmetric cryptography: a protection mechanism for secure data outsourcing
by Shirole Rashmi, Jyothi K
Abstract: Data transfer over the internet is a very common process and security of this data is everyones responsibility. Many techniques are available to secure such confidential data. Among them, cryptography and steganography are two major subdisciplines of information security. The aim of this paper is to maintain the imperceptibility and provide better security. The goals of this paper are to be achieved through (1) the use of variable sample selection method for audio samples, which enhances the security with better audio quality; (2) deploying one of the most secure algorithms, blowfish, which is used to secure data from eavesdropping. The performance of the proposed work is compared with the traditional LSB algorithm for audio. The proposed system is the best approach for enhancing the security and retaining the good quality of the cover object.
Keywords: data hiding; cryptography; blowfish; AES; steganography; LSB; PSNR.
Local-constraint transformer network for stock movement prediction
by Hu Jincheng
Abstract: Stock movement prediction is to predict the future movements of stocks for investment, which is challenging both for research and industry. Typically, stock movement is predicted based on financial news. However, existing prediction methods based on financial news directly use models for natural language processing, such as recurrent neural networks and transformer network, which are still difficult to effectively process the key local information in financial news. To address this issue, Local-constraint Transformer Network (LTN) is proposed in this paper for stock movement prediction. LTN leverages transformer network with local constraint to encode the financial news, which can increase the attention weights of key local information. Moreover, since there are more difficult samples in financial news which are hard to learn, this paper further proposes a difficult-sample-balance loss function to train the network. This paper also researches the combination of financial news and stock price data for prediction. Experiments demonstrate that the proposed model outperforms several powerful existing methods on the datasets, and the stock price data can assist to improve the prediction.
Keywords: stock movement prediction; short-term dependence; transformer network; difficult sample.
Constructive system for double-spend data detection and prevention in inter- and intra-block of blockchain
by Vijayalakshmi Jayaraman, Murugan Annamalai
Abstract: Currently, our global financial market faces lots of trouble owing to migration from fiat currency to cryptocurrency and its underlying blockchain technology. Blockchain provides trust in a decentralised way for storing, managing, and retrieving transactions. The double-spending issue arises owing to the erroneous transaction verification mechanism in the blockchain. Research has shown that transaction malleability, such as double-spending, creates millions of bitcoin losses to the owners as well as to a few bitcoin exchanges. This research aims to detect and prevent the double-spending of bitcoins in single and multiple blocks. In this context, double-spend data in a single block is identified using the DPL2A method. Further, the original transaction from the double-spend transaction list is identified using the ACRT method, which acts as a prevention of double-spend in a forthcoming occurrence. Similarly, double-spend data in multiple blocks are identified using MBDTD along with the Cognizant Merkle tree. Finally, a system named F2DP is constructed to detect and prevent the double-spend data in inter- and intra-blocks of the blockchain. The result indicates these methods will act best for double-spend detection and prevention with a limited set of transaction records. Further research is needed to increase the scalability of transaction records.
Keywords: cryptocurrency; bitcoin; double-spending; UTXO; Merkle.
Special Issue on: Recent Advancements in Machine Learning Techniques for Big Data and Cloud Computing
Research of the micro grid renewable energy control system
based on renewable related data mining and forecasting technology
by Lin Yue, Yao-jun Qu, Yan-xia Song, Kanae Shunshoku, Jing Bai
Abstract: The output power of renewable energy has the characteristics of random fluctuation and
instability, which have a harmful effect on stability of renewable power grids and causes the problem of low usage ratio on renewable energy output power. Thus, this paper proposes a method to predict the output power of renewable energy based on data mining technology. Firstly, the renewable generation power prediction accuracy of three different algorithms, linear regression, decision tree and random forest, is obtained and compared. Secondly, by applying the prediction result to the power dispatch control system, grid-connected renewable power will be consumed by grid-connected load to improve the usage ratio of renewable power. A simulation model and experiment platform are established to verify and analyse the prediction usefulness. The experiment shows that the prediction accuracy of the random forest algorithm is the highest. The tendency of renewable energy output power within a period can be calculated by using data mining technology, and the designed experiment platform system can adjust the working state automatically by following the instruction from the data mining result, which can increase the usage ratio of renewable energy output power and improve the stability of renewable power grid.
Keywords: data mining; micro grid; renewable energy.
Research on advertising content recognition based on convolutional neural network and recurrent neural network
by Xiaomei Liu, Fazhi Qi
Abstract: The problem tackled in this paper is to identify the text advertisement information published by users in a medium-sized social networking website. First, the text is segmented, and then the text is transformed into sequence tensor by using a word vector representation method, which is input into the deep neural network. Compared with other neural networks, RNN is good at processing training samples with continuous input sequence, and the length of the sequence is different. Although RNN can theoretically solve the training of sequential data beautifully, it has the problem of gradient disappearance, so it is a special LSTM based on RNN model that is widely used in practice. In the experiment, the convolutional neural network is used to process text sequence, and time is regarded as a spatial dimension. Finally, the paper briefly introduces the use of universal language model fine-tuning for text classification.
Keywords: RNN; LSTM; CNN; word vector; text classification.
Special Issue on: Intelligent Self-Learning Algorithms with Deep Embedded Clustering
Application of virtual numerical control technology in cam processing
by Linjun Li
Abstract: Numerical control (NC) machining is an important processing method in the machinery manufacturing industry. In most cases, as the final processing procedure, NC machining directly determines the quality of the finished product. As the key components needed in many industries such as automobile, internal combustion engine, national defense and so on, the precision and efficiency of the cam processing have a direct impact on the quality, life and energy saving standard of the engine and related products. This paper takes the cam NC grinding machining as the research object, takes the optimisation and intelligentisation of processing technology as the goal and uses the virtual NC technology to develop a process intelligent optimisation and NC machining software platform specifically for cam NC grinding machining. The software platform has machine tool library, grinding wheel library, material library, coolant reservoir library, process accessory library and other basic technology libraries, and it also has process example library, meta-knowledge rule library, forecast model library and other process intelligent libraries. With the support of database, the software platform can realise intelligent optimisation and automatic NC machining programming of cam grinding process plan. Because the software platform involves many research contents, this paper mainly focuses on the modelling of the motion process of the NC machining process system, the architecture of the intelligent platform software of the cam NC machining, and the virtual NC machining simulation of the process system. Therefore, the study of this paper is of great significance.
Keywords: cam grinding; numerical control grinding; intelligent platform software; process problems; virtual grinding.
Research on oil painting effect based on image edge numerical analysis
by Yansong Zhang
Abstract: With the continuous development of the technology of non-photorealistic rendering, the effect of oil painting on image is increasing. The traditional oil painting effect is not satisfactory enough to satisfy people's needs. Therefore, this paper puts forward the research of oil painting effect based on image edge numerical analysis, and constructs a corresponding algorithm for image edge numerical analysis and detection. Through the comparison experiment with traditional algorithm oil painting results, the conclusion is drawn that the algorithm proposed in this paper can accurately analyse and detect the image edge, and the final rendering effect is more natural and more smooth than the traditional algorithm oil painting effect.
Keywords: image; edge value; analysis; oil painting effect.
Special Issue on: Computational Intelligence in Data Science
Topologisation of the situation geographical image in the aspect of control of local transport and economic activity
by Sergei Bidenko, Sergei Chernyi, Yuri Nikolashin, Evgeniy Borodin, Denis Milyakov
Abstract: The specific features of cartographic images are considered from the point of view of procedures for assessing the situation in the area of maritime transport activity and spatial planning. The tasks of spatial analysis are highlighted, requiring a transition from cartographic to topological mapping of geographic reality. The existing anamorphic techniques, their classification as well as their advantages and disadvantages, are considered. Models for constructing anamorphosis of the terrain for topologising the geoimage of real situation have been developed. An algorithm based on affine transformation, based on the distortion of the boundaries of the area relative to the centre of mass of the region, is proposed. A comparison of the proposed algorithm with the applicable Gastner-Newman algorithm is given.
Keywords: maritime territorial activity; territorial situation; analysis and assessment of the situation; base map; geospace; geoobject; anamorphosing; cartoid; anamorphosis.
Special Issue on: Cloud Computing and Networking for Intelligent Data Analytics in Smart City
Real time ECG signal preprocessing and neuro-fuzzy-based CHD risk prediction
by S. Satheeskumaran, C. Venkatesan, S. Saravanan
Abstract: Coronary heart disease (CHD) is a major chronic disease that is directly responsible for myocardial infarction. Heart rate variability (HRV) has been used for the prediction of CHD risk in human beings. In this work, neuro-fuzzy-based CHD risk prediction is performed after performing preprocessing and HRV feature extraction. The preprocessing is used to remove high frequency noise, which is modelled as white Gaussian noise. The real time ECG signal acquisition, preprocessing and HRV feature extraction are performed using NI LabVIEW and DAQ board. A 30 seconds recording of the ECG signal was selected in both smokers and non-smokers. Various statistical parameters are extracted from HRV to predict CHD risk among the subjects. The HRV extracted signals are classified into normal and CHD-risky subjects using neuro-fuzzy classifier. The classification performance of the neuro-fuzzy classifier is compared with the ANN, KNN, and decision tree classifiers.
Keywords: electrocardiogram; Gaussian noise; wavelet transform; heart rate variability; neuro-fuzzy technique; coronary heart disease.
Optimised fuzzy clustering-based resource scheduling and dynamic load-balancing algorithm for fog computing environment
by Bikash Sarma, R. Kumar, Themrichon Tuithung
Abstract: The influential and standard tool known as fog computing performs applications of the Internet of Things (IoT) and it is an extended version of cloud computing. In the network of edge, the applications of IoT are possibly implemented by the fog computing, which is an emerging technology in the cloud computing infrastructure. The unique technology in fog computing is the resource-scheduling process. The load on the cloud is minimised by the resource allocation of the fog-based computing method. Maximisation of throughput, optimisation of available resources, response time reduction, and elimination of overload of single resource are the goals of a load-balancing algorithm. This paper suggests an Optimised Fuzzy Clustering Based Resource Scheduling and Dynamic Load Balancing (OFCRS-DLB) procedure for resource scheduling and load balancing in fog computing. For resource scheduling, this paper recommends an enhanced form of Fast Fuzzy C-means (FFCM) with the Crow Search Optimisation (CSO) algorithm in fog computing. Finally, the loads or requests are balanced by applying the scalability decision technique in the load-balancing algorithm. The proposed method is evaluated based on some standard measures, including response time, processing time, latency ratio, reliability, resource use, and energy consumption. The proficiency of the recommended technique is obtained by comparing with other evolutionary methods.
Keywords: fog computing; fast fuzzy C-means clustering; crow search optimisation algorithm; scalability decision for load balancing.