Forthcoming and Online First Articles

International Journal of Computer Applications in Technology

International Journal of Computer Applications in Technology (IJCAT)

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International Journal of Computer Applications in Technology (102 papers in press)

Regular Issues

  • Convolutional neural network model for an intelligent solution to crack detection in pavement images   Order a copy of this article
    by Aaron Rababaah, James Wolfer 
    Abstract: This paper presents a deep learning solution using convolutional neural networks for pavement crack detection. The advancements in machine learning and machine vision open new opportunities for researchers to explore the power of deep learning instead of classical machine learning to solve old and new problems. We propose a convolutional neural network model to detect cracks in pavement. Our solution is based on a multi-layer model that encompasses a raw image input layer, convolutional layers, activation layers, max-pooling layers, a flattening layer, and a multi-perceptron neural network as classification layers. Matlab was our development platform to create and test the solution. Five hundred sample images were collected from publicly-available sources. Sixteen different experiments were conducted to determine the best configuration for the proposed model in terms of the number of features. The results of the experiments suggest that the proposed model is effective with a detection accuracy of 96.6% when correctly configured.
    Keywords: deep learning; convolutional neural networks; pavement images; crack classification; machine vision.

  • DDoS attack detection and defence mechanism based on second-order exponential smoothing the Holt model   Order a copy of this article
    by Rachana Patil 
    Abstract: Technological progress and digitisation are greatly assisted by the growth of the internet. The web has now become a national asset, and all national security relies on it as well. But these emerging developments have also brought with them unparalleled network threats. Among them, a strong and more powerful attack on the internet is a distributed denial of service attack. This work proposes a novel framework for DDoS detection. To detect anomalous variations in mean distance values, the technique of second order exponential smoothing (Holts method) is used. In the proposed context, the DDoS defence module is based on the principle of the rate limitation of incoming traffic on the basis of bandwidth and demand rates from a device connected. The experimental verification of the proposed method is done using NS2 simulator, and the results are evaluated with detection rate, throughput, and false positive rate.
    Keywords: DDoS attack; rate limiting; network security; Holt model.

  • Cyber attacks visualisation and prediction in complex multi-stage networks   Order a copy of this article
    by Shailendra Mishra, Waleed Bander Alotaibi, Mohammed AlShehri, Sharad Saxena 
    Abstract: In network security, various protocols exist, but these cannot be said to be secure. Many hackers and illegal agents try to take advantage of the vulnerabilities through various incremental penetrations that can compromise critical systems. The conventional tools available for this purpose are not enough to handle things as desired. Risks are always present with dynamically evolving networks and are very likely to lead to serious incidents. This research work proposes a model to visualise and predict cyber attacks in complex, multilayered networks. All the available network security conditions and the possible places where an attacker can exploit the system are summarised. The vulnerability-based multi-graph technique for the attacker is presented. Also, an attack graph algorithm is proposed, leading to identifying all the vulnerable paths that can be used to harden the network by placing sensors at the desired locations and is used for vulnerability assessment of multi-stage cyber attacks.
    Keywords: network vulnerability; attack graph; adjacency matrix; clustering technique; cyber defence.

  • Overheat protection for motor crane hoist using internet of things   Order a copy of this article
    by Paduloh Paduloh, Rifki Muhendra 
    Abstract: Crane hoist is a material-moving tool and work tool for the production process. The hoist often stops suddenly owing to overheating; this condition impacts the production process and safety. This study aims to design a safety device to anticipate the occurrence of overheating in the hoist. The research began with brainstorming, prioritising AHP and designing products using QFD, and designing systems using UML. The product is designed to use a microcontroller, Arduino, fan, and GSM to control the motor temperature and to transmit temperature information to the user. The motor cooler will operate if there is a notification of a motor temperature rise. The novelty of this research lies in the decision-making system, product design, and information system design so that this research can produce a safety device that suits company needs and is easy to operate. This tool is also able to prevent overheating effectively.
    Keywords: hoist crane; QFD; UML; Arduino; IoT.

  • Important extrema points extraction-based data aggregation approach for elongating the WSN lifetime   Order a copy of this article
    by Ali Kadhum M. Al-Qurabat, Hussein M. Salman, Abd Alnasir Riyadh Finjan 
    Abstract: Energy conservation is one of the most basic problems of wireless sensor networks. The energy of sensor nodes is limited, so effective energy usage is important. Data aggregation helps to minimize the volume of data communicated across the network while preserving information quality and decreasing energy waste, thereby enhancing the lifetime of the network. In this paper, we propose a data aggregation approach based on the important extrema points extraction for elongating the WSN lifetime (IEEDA). Rather than transmitting all the set of collected measures at the end of every time period, we propose transmitting the extracted important extrema measures of sensor nodes. Using real-world data sets with radically different properties, we tested our method against two protocols ATP and PFF. The proposed method resulted in a reduction in the amount of the following: data remaining up to 95%, data sent up to 80%, and energy consumed up to 77%.
    Keywords: data aggregation; important extrema points; lifetime; WSN.

  • Digital forensics evidence management based on proxy re-encryption   Order a copy of this article
    by Rachana Patil 
    Abstract: The growing world of digitisation has given rise to cybercrimes. Digital forensics is the process of collecting lawful evidence. Such evidence plays a very crucial role in the court of law to demonstrate the fact explicitly against the crime of the suspect. To ensure the admissibility of evidence in the court of law during trials, it is important to maintain evidence using a proper evidence management system. This paper proposes the use of a unidirectional multi-hop proxy re-encryption scheme for authority delegation. This system will help in securely delegating access to digital evidence. The re-encryption scheme provides a clearer perception of security and validates the usefulness of proxy re-encryption as a method of adding access control to a secure evidence management system. The correctness analysis of the proposed scheme is validated by using BAN logic. The security analysis using the AVISPA tool shows that the proposed scheme is safe against various security attacks.
    Keywords: digital forensics; evidence management; proxy re-encryption; cybercrime; BAN logic; AVISPA.

  • A serious game for the responsible use of fossil fuel-powered vehicles   Order a copy of this article
    by Francisco Javier Moreno Arboleda, Javier Esteban Parra Romero, Agnieszka Szczesna 
    Abstract: Serious gaming has gained increasing prominence in climate change communication, and provides an opportunity to engage people in topics related to environmental protection. This paper presents the design and evaluation of a serious game that concerns pollution generated by fossil fuel-powered vehicles. Serious games might be an effective and motivational tool in that field. The games intention is to motivate the participants to change towards sustainable lifestyles. To achieve this goal, an enhanced game design methodology was proposed and a serious mini-game prototype was developed
    Keywords: serious gaming; game design; sustainable transport; vehicular pollution.

  • Biased compensation adaptive gradient algorithm for rational model with time-delay using self-organising maps   Order a copy of this article
    by Yanxin Zhang, Jing Chen, Yan Pu 
    Abstract: This paper develops a biased compensation adaptive gradient descent algorithm for rational models with unknown time-delay. Owing to the unknown time-delay, traditional identification methods cannot be directly applied for such models. To overcome this difficulty, the self-organised maps are proposed, which can obtain the estimates of the time-delay based on the residual errors. Then, an adaptive gradient descent algorithm is introduced to obtain the parameter estimates. Compared with the traditional gradient descent and redundant rule-based methods, the proposed method has two advantages: (1) each element in the parameter vector has its own step-size, thus it is more effective than the traditional gradient descent method; (2) the number of the unknown parameters is unchanged, therefore, it has needs less computational effort than the redundant rule-based method. Finally, a simulation experiment is given to show the excellent accuracy of the proposed algorithm.
    Keywords: self-organised maps; biased compensation; adaptive gradient descent; parameter estimation; time-delay.

  • FPGA-based DFT system design, optimisation and implementation using high-level synthesis   Order a copy of this article
    by Shensheng Tang, Monali Sinare, Yi Xie 
    Abstract: In this paper, a discrete Fourier transform (DFT) algorithm is designed and optimized for the FPGA implementation using the Xilinx VIVADO High-Level Synthesis (HLS) tool. The DFT algorithm is written by C++ programming and simulated for functional verification in the HLS and MATLAB. For hardware validation, the DFT module is packaged as an IP core and tested in a VIVADO project. A Xilinx SDK application written by C language is developed and used for testing the DFT module on a Zynq FPGA development board, ZedBoard. For visualization of the DFT magnitude spectrum generated in FPGA, a GUI is developed by C# programming and related commands/data can be communicated between the GUI and ZedBoard over the serial port. Experimental results are presented with discussion. The DFT module design, optimization and implementation as well as the VIVADO project development methods can be extended to other FPGA applications.
    Keywords: FPGA; DFT; IP core; VIVADO HLS; C/C++; Verilog; C#; optimisation; hardware validation.

  • QSFN:QoS aware fog node provisioning in fog computing   Order a copy of this article
    by Ashish Chandak, Niranjan Kumar Ray, Deepak Puthal 
    Abstract: The quantity of IoT gadgets is consistently expanding and these gadgets are delay-sensitive and require a speedy reaction. These gadgets are connected with a cloud for the computation of requests yet there may be a delay in computation. To beat the present circumstance, fog nodes are kept at the edge of the IoT gadgets to perform speedy calculation for delay-delicate applications. IoT devices generate tremendous number of tasks and if the task number is increased then the number of fog nodes needs to be increased for immediate processing. Here QoS aware fog node (QSFN) provisioning algorithm has been proposed in which the number of fog nodes is automatically increased based on CPU utilization, queue length, and the number of available resources. We evaluate the performance of QoS-aware fog node provisioning with Bee Swarm and Concurrent algorithm based on makespan, average execution, flowtime, success execution rate, and average response time. Simulation results demonstrate that the proposed QSFN algorithm performs better in comparison with other algorithms.
    Keywords: fog computing; scalability; IoT; fog node.

  • Design of a FPGA accelerator for the FIVE fuzzy interpolation method   Order a copy of this article
    by Roland Bartók, József Vásárhelyi 
    Abstract: Complex control systems are difficult to describe with mathematical models owing to the complexity of the system. The control of these systems can be solved with a solution that does not require accurate knowledge of the system model. The most common solution in such cases is the use of neural networks. With complex description of rule-based behaviour, complex control system can also be implemented. Using fuzzy logic is an effective way to describe the behaviour of a system. The problems encountered when using classical fuzzy logic are avoided by using a fuzzy interpolation method such as the Fuzzy Interpolation in Vague Environment (FIVE) method. Compared with classic fuzzy logic, FIVE has the advantage of computing acceleration, through algorithm parallelisation, which is important in the case of real-time calculations and in tuning procedures. The paper presents a flexible parameterisable hardware-accelerator implementation of the FIVE method using field programmable gate array.
    Keywords: fuzzy; FPGA; hardware accelerator; etorobotics; robotics; behaviour-based control.

  • Two-Degree-of-Freedom Tilt Integral Derivative Controller based Firefly Optimization for Automatic Generation Control of Restructured Power System   Order a copy of this article
    by G. Tulasichandra Sekhar, Ramana Pilla, Ahmad Taher Azar, Mudadla Dhananjaya 
    Abstract: The present work proposes a two degree of freedom tilt integral derivative (2- DOFTID) controller tuned with a firefly algorithm (FA) for a two-area automatic generation control (AGC) power system. Initially, a standard two-area power system is tested to show the superior output of the proposed controller relative to other control strategies. After that, the 2-DOFTID controller is continued for the next test system i.e. restructured power system as a secondary controller. Further, the operation of the Unified Power Flow Controller (UPFC) is analyzed by assimilation in the tie-line. In addition, the redox flow battery (RFB) is used in area-1 to track the frequency variation and thus increases the system's transient responses. Finally, a robustness study was carried out to analyze the capacity of the proposed controller in a poolco-based transaction with the UPFC and RFB system integration by different system parameters and to consider unpredicted load disturbances.
    Keywords: Automatic Generation Control (AGC); Firefly Algorithm (FA); Random Load Disturbance; Redox Flow Battery (RFB); Two Degree of Freedom Tilt Integral Derivative (2-DOFTID) controller; Unified Power Flow Controller (UPFC).

  • Multi-label legal text classification with BiLSTM and Attention layer   Order a copy of this article
    by Liriam Enamoto, Andre Santos, Ricardo Maia, Li Weigang, Geraldo Pereira Rocha Filho 
    Abstract: Like many other knowledge fields, the legal area has experienced an information-overloaded scenario. However, to extract data from legal documents is a challenge owing to the complexity of legal concepts and terms. This work aims to address Bidirectional Long Short-Term Memory (BiLSTM) to perform Portuguese legal text classification to solve such challenges. The proposed model is a shallow network with one BiLSTM layer and one Attention layer trained over two small datasets extracted from two Brazilian courts: the Superior Labour Court (TST) and 1st Region Labour Court. The experimental results show that combining the BiLSTM layer and the Attention layer for long judicial texts helps to capture the past and future contexts and extract multiple tags. As the main contribution of this research, the proposed model can quickly process multi-label and multi-class datasets and adapt to new contexts in different languages.
    Keywords: legal text; multi-label; text classification; BiLSTM; Attention layer.

  • Network intrusion detection using fusion features and convolutional bidirectional recurrent neural network   Order a copy of this article
    by Jagruthi H, Kavitha C, Manjunath Mulimani 
    Abstract: In this paper, novel fusion features to train Convolutional Bidirectional Recurrent Neural Network (CBRNN) are proposed for network intrusion detection. UNSW-NB15 dataset's attack behaviors (input features) are fused with their first and second-order derivatives at different stages to get fusion features. In this work, we have taken architectural advantage and combine both Convolutional Neural Network (CNN) and bidirectional Long Sort-Term Memory (LSTM) as Recurrent Neural Network (RNN) to get CBRNN. The input features and their first and second-order derivatives are fused and considered as input to CNN and this fusion is known as early fusion. Outputs of the CNN layers are fused and used as input to the bidirectional LSTM, this fusion is known as late fusion. The performance of the early and late fusion features is evaluated on the publicly available UNSW-NB15 dataset. Results show that late fusion features are more suitable for intrusion detection and outperform the state-of-the-art approaches with average recognition accuracies of 98.00% and 91.50% for binary and multiclass classification configurations, respectively.
    Keywords: intrusion detection; fusion features; convolutional neural network; bidirectional long short-term memory; convolutional bidirectional recurrent neural network; UNSW-NB15 dataset.

  • A brief survey for person re-identification based on deep learning   Order a copy of this article
    by Li Liu, Xi Li, Xuemei Lei 
    Abstract: Person re-identification (Re-ID) has been paid more attention due to its wide application in intelligent surveillance systems. Finding the same person from other non-overlapping cameras when a specific image of the pedestrian is given, which is a challenging problem for the reason of viewpoint variation, clothes changing, low resolution, etc. In this paper, we motivate reviewing for deep learning-based methods of Person Re-ID. We present a detailed survey of the state of the art in terms of the description and analysis of supervised-based and unsupervised-based network and their performance evaluation in the commonly used datasets. Finally, we analyze the challenging problems and discuss future works in this area.
    Keywords: person re-identification; deep learning; literature survey; evaluation metric.

  • Parameter identification of fractional order CARMA model based on least squares principle   Order a copy of this article
    by Jiali Rui, Junhong Li 
    Abstract: The fractional order model is more accurate than the integer order model when describing the actual system. This paper studies the fractional order CARMA model identification, and derives the identification expression of the fractional order CARMA model through the definition of Gr
    Keywords: fractional order model; parameter estimation; system identification; least squares; colored noise.

  • On the use of emerging decentralised technologies for supporting software factories coopetition   Order a copy of this article
    by Fábio Paulo Basso, Diego Kreutz, Carlos Molina-Jiménez, Rafael Z. Frantz 
    Abstract: Besides the recent adoption of outsourcing and open source business models, software factory is still a centralised process. In spite of the advantages of centralisation, it is widely accepted that decentralised systems are better alternatives; for instance, they are more scalable and reliable, and more suitable for market sharing. The implementation of decentralised software factories demands solution to several technical challenges that conventional technology cannot solve. Emerging decentralised technologies (e.g., blockchain and smart contracts) can help to solve these challenges. Decentralised technologies are only emerging, consequently, its potential use to support software factories is a new research avenue. To cast some light on this emerging topic, in this article we provide an analysis of some centralized and decentralized architectures and raise research questions that need attention from the perspective of architecture selection. To frame the discussion, we focus our attention on software architectures for Model Driven Engineering, Asset Specifications and Integration Tools, resulting in a characterization and analysis of possibilities for implementation of heterogeneous blockchain-oriented repositories.
    Keywords: MDE; software ecosystems; smart contracts; systems of systems; blockchain; MDE as a service; pivot language.

  • A novel approach for decision support system in cricket using machine learning   Order a copy of this article
    by Sudan Jha, Sidheswar Routray, Hikmat A. M. Abdeljaber, Sultan Ahmad 
    Abstract: In shorter format of cricket, the choice of a bowler has three main parameters namely: economy, strike rate and dot balls delivered. In most of the cases, the most hitting parameters are economy rate and number of wickets taken, which again are inter related with the dot balls delivered. This paper presents a survey operational linear approach which comparative analyse the above-cited three parameters and suggests a solution-based approach to choose a best bowler in Playing Eleven with highest preference to the dot balls delivered. The bowler with highest dot ball delivered is considered as highest preference bowler. The inter-relationship among these parameters are established based on collected data. The proposed indicator is proved useful while making decisions. A software-based architecture is also proposed relating to decision support system for selecting a bowler in playing eleven using past data.
    Keywords: Twenty20 match; cricket; bowler selection; indicator; parameter; decision tree.

  • Apple image fusion algorithm based on binocular acquisition system   Order a copy of this article
    by Liqun Liu, Yubo Zhou, Renyuan Gu 
    Abstract: To solve the problem that single natural scene image acquisition information in orchard cannot meet the requirements of accurate fruit recognition and target positioning, a new apple image fusion algorithm based on the binocular acquisition system named New Non-subsampled Contourlet Transform algorithm is proposed to obtain a high-quality fusion image. The binocular acquisition system is constructed with the Time of Flight industrial camera and the color camera. In order to achieve a better fusion effect, a parameter optimization algorithm based on an Artificial Bee Colony algorithm with discard strategy for brightness saliency function is proposed to optimize the low-frequency component parameters of the new fusion coefficient rule. The experiments were taken on series of apple images under three sunlight conditions in the orchard. The experimental results show that the six evaluation indicators obtained by the new algorithm achieve the expected fusion image effect under three different types of sunlight conditions.
    Keywords: binocular acquisition system; time of flight; camera calibration; image fusion.

  • A flexible mobile application for image classification using deep learning: a case study on COVID-19 and X-ray images   Order a copy of this article
    by Omar Andres Carmona Cortes 
    Abstract: This paper proposes a flexible mobile application for embedding any CNN-image-based classification model, providing a computer application to assist health professionals. Two approaches are suggested: an embedded offline and a running online model via web API. To preset the applicability of the mobile software, we used a CNN COVID-19 classification based on X-ray images as a case study. Still, any other image-based classification application could have been used. We used a popular Kaggle database consisting of 7178 X-ray images divided into three classes: normal, COVID-19, and viral pneumonia. We tested 14 state-of-art CNNs to decided which one to embed. The VGG16 achieved the best performance metrics; therefore, the VGG16 was embedded. The software production methodology was applied based on the built model, class diagram, use cases, and execution flow, besides designing a web API to execute the back-end classification model.
    Keywords: mobile application; Medicine 4.0; CNN; COVID-19; X-ray.

  • Stacking-based modelling for improved over-indebtedness predictions   Order a copy of this article
    by Suleiman Ali Alsaif, Adel Hidri, Minyar Sassi Hidri 
    Abstract: In a world now starkly divided into pre- and post-COVID times, it's imperative to examine the impact of this public health crisis on the banking functions - particularly over-indebtedness risks. In this work, a flexible analytics-based model is proposed to improve the banking process of detecting customers who are likely to have difficulty in managing their debt. The proposed model assists the banks in improving their predictions. The proposed meta-model extracts information from existing data to determine patterns and predict future outcomes and trends. We test and evaluate a large variety of Machine Learning Algorithms (MLAs) by using new techniques like feature selection. Moreover, models of previous months are combined in order to build a meta-model representing several months using the stacked generalization technique. The new model will identify 91% of the customers potentially unable to repay their debt six months ahead and enable the bank to implement targeted collections strategies.
    Keywords: over-indebtedness; predictive analytics; machine Learning; features selection; stacked generalisation.

  • FIRE ANT OPTIMIZATION FEATURE SELECTION METHOD FOR BREAST CANCER PREDICTION   Order a copy of this article
    by Vijayalakshmi S, MohanaPriya D, Poonguzhali Narasingam 
    Abstract: Breast cancer is common disease in the todays world and many techniques are used to extract the cancer cell from the breast image. Most of the systems use features extracted from the images and these images are selected using feature selection techniques. The feature selection techniques helps to a greater level in removal of irrelevant data from huge amount of data and fine tune the identification process and accuracy of relevant data. But still the prediction accuracy and number of the features selection factors are not yielding 100% prediction result. This work, Nearest Density Fire Ant (NDFA) is proposed for breast cancer prediction and used for feature selection techniques. This technique is used for diagnosing and producing results compared to the previous methods such as Random Forest, Ant Colony and Genetic Algorithm. The proposed techniques are inspected on Wisconsin Breast Cancer Database (WBCD) and Breast Cancer Wisconsin WDBC datasets. The experimental result shows that proposed NDFA using Fire Ant optimization technique produces better results in prediction and diagnosing of Breast Cancer.
    Keywords: breast cancer; feature selection classification; nearest neighbour search; fire ant; optimisation.

  • Multi-scale super-pixels-based passive forensics for copy-move forgery using local characteristics   Order a copy of this article
    by Wensheng Yan, Hong Bing Lv 
    Abstract: Copy-move forgery is the most common kind of tampering technique for digital images. This paper presents a novel hybrid approach, which uses the speeded-up robust features (SURF) point and characteristics of local feature regions (LFR) matching. First, the multi-scale super-pixels algorithm adaptively divides the suspected image into irregular blocks according to the texture level of host images. Then, the improved SURF detector is adopted in extracting feature points from each super-pixel and the feature point threshold is related to the entropy of each super-pixel block. Next, LFRs are defined, and a robust feature descriptor is extracted from each LFR as a vector field. Last, the matching LFRs are found by using Euclidean locality sensitive hashing; the removal of falsely matched pairs is realised by using the random sample consensus (RANSAC) algorithm. Comparing with the leading-edge block-matching methods and point-based methods, our method can produce far better detection results.
    Keywords: digital image forensics; multi-scale super-pixels; local feature region; polar harmonic transform; duplicated region detection.

  • Prediction of the right crop for the right soil and recommendation of fertiliser use by machine learning algorithm   Order a copy of this article
    by Rubini PE, Kavitha P 
    Abstract: Crop production is a crucial aspect of farming and it depends on many factors like soil nutrients, fertiliser usage, water resources, etc. The critical factor for effective agriculture is soil. The composition of soil varies from one land to another which muddles the farmers to choose the appropriate crop for their farmland. The proposed study focuses on recommending the right crop for the right soil and also signifies the required composition of fertiliser. Nonetheless, the work requires analysis of a huge volume of data which can be accomplished by applying five machine learning techniques and to enhance the accuracy and precision in the prediction of the crop, the solution of all these algorithms is integrated into a proposed model through ensemble learning which provides the aggregated output i.e., the recommended crop and fertiliser dosage. The intention of the proposed model is to improvise farmer's growth by increasing their productivity and profit.
    Keywords: agriculture; machine learning; fertiliser use; ensemble learning; prediction; crop productivity.

  • A multivocal literature mapping on mobile compatibility testing   Order a copy of this article
    by Isabel K. Villanes, Andre T. Endo, Arilo C. Dias-Neto 
    Abstract: Today's mobile market is composed of multiple brands and device models with different characteristics. In addition, mobile apps can present particular behaviors when running on these diverse devices. Mobile compatibility testing is a quality assurance task aimed at ensuring that apps run correctly on all devices. However, it is infeasible to test all devices on the market. This paper aims to identify and categorise the research evidence that has been published in mobile compatibility testing. We conducted a Multivocal Literature Mapping (MLM) divided into two phases: a systematic mapping study guided by the snowballing approach and a grey literature review. Results have evinced a need for continuous monitoring of API evolution and operating systems' updates. Those changes have an immediate impact on mobile app compatibility and device selection for testing. There is still room for new studies to understand the mobile app characteristics to improve methods for selection devices.
    Keywords: compatibility testing; mobile testing; Android fragmentation; mobile device selection.

  • Wind turbine signal fault diagnosis using deep neural networks-inspired model   Order a copy of this article
    by Aaron Rababaah 
    Abstract: This work presents a deep neural network-inspired solution to intelligent signal fault diagnosis for wind turbine gearbox systems. A 1D convolution deep neural network architecture is proposed, constructed and validated. The proposed model was constructed of 1D signal for the input layer, 10 different learned kernels as signal features, convolution layer, activation layer using rectified linear unit function, max-pooling layer, flatten layer and traditional multi-perceptron neural network for classification with soft-max class assignment. The data was acquired from real-world experiments conducted on real wind turbine gearboxes and archived by the National Renewable Energy Labs of the USA Department of Energy. Ten independent experiments were conducted on 2,400,000 data points, and the proposed model produced a mean classification accuracy of 96.14% for normal signals with a standard deviation of 0.0027 and a mean classification accuracy of 99.87% for faulty signals with a standard deviation of 0.0016.
    Keywords: deep neural networks; fault signal diagnosis; wind turbine; gearbox; signal processing; deep learning; convolutional neural networks; signal features.

  • Research on the evolution of public opinion and topic recognition based on multi-source data mining   Order a copy of this article
    by Zeguo Qiu, Baiyan He 
    Abstract: In the era of rapid network development, the Internet has become the main medium for the spread of online public opinion. Users can express their views on hot issues through text, pictures, and videos anytime and anywhere, analyze the evolving trend of netizens emotions in emergencies to discover the law of public opinion evolution and potential risks, and provide decision support for public opinion guidance and control. In this paper, python is used to preprocess the collected text data, and proposes a method based on spectral clustering algorithm, using the Latent Dirichlet Allocation to extract text topics. From the perspective of multi-language and multi-source data, it mines high-value public opinion topics in online public opinion, and uses data visualization methods to study the emotional tendencies of netizens. The research results can clearly reveal the content of discussion and the emotional attitude of netizens in the period of public opinion transmission.
    Keywords: internet public opinion; sentiment analysis; multi-source data; visualisation; natural language processing.

  • A novel mixed resilience engineering and macro ergonomics framework for performance evaluation of an insurance company   Order a copy of this article
    by Mehrdad Sarani, Mojtaba Hamid, Mahdi Bastan, Mina Samieinasab 
    Abstract: Insurance industry has always attracted the attention of researchers because the improved performance of this industry can have a tremendous impact on other manufacturing and service sectors. Therefore, the evaluation and efficiency of active departments in this service area are of great significance. This study evaluated the performance of an insurance company based on resilience engineering (RE) and macro ergonomics (ME) and indicators using quantitative and qualitative approaches. For this purpose, after designing a questionnaire and having it completed by 65 experts, the company's performance was analysed using Data Envelopment Analysis (DEA), statistical methods, and sensitivity analysis methods. Results showed that considering resilience engineering and macro ergonomics can improve the company's performance. Also, it was found that preparedness and information flow indicators were the most important and least important factors influencing the company's performance, respectively. The proposed framework can be used as an effective tool by managers to boost efficiency.
    Keywords: performance evaluation; resilience engineering; macro ergonomic; data envelopment analysis; insurance company.

  • Hyper-chaos based image encryption and cryptanalysis on a non-RGB channel   Order a copy of this article
    by Jayashree Karmakar, Subhashish Pal, Mrinal Mandal 
    Abstract: Till date, a lot of research works have been published proposing an image encryption mechanism. However, the cryptanalysis of several research works shows their limitations besides fulfilling security-test by the NPCR, UACI, entropy, etc. This work proposes a novel hyper-chaos-based encryption technique that uses multiple layers of diffusion and scrambling based on a highly sensitive hyper-chaotic sequence. The multiple layers of scrambling (pixel-scrambling, local-global bit-scrambling (LGBS)) are performed with a different chaotic sequence that gives extra security against the intruder. Besides using the widely used RGB channel, we chose the YCbCr-channel. The higher informative Y channel (luminance channel) has gone through more layers of diffusion and scrambling . In contrast, the other channel (lesser informative chroma channels i.e, Cb, Cr) go through comparatively lesser steps. It reduces the computational complexity compared to other state-of-the-art works without compromising security. Various widely used security-testing parameters examine the encryption strength. Furthermore, the resistivity of the proposed mechanism against the intruder is examined by the cryptanalysis. We use the hyper-chaotic key-dependent pixel diffusion and different scramblings. The scrambling methods include local bit scrambling (LBS), Global Bit Scrambling (GBS), and pixel scrambling using Arnold Cat Map (ACM). It makes the algorithm highly resistive against attacks, and the experimental results fulfill the authors' claim. A comparative study with some recent and related works shows the superiority of the proposed algorithm in terms of security and computational load.
    Keywords: Hyper-chaos; encryption; non-RGB channel; cryptanalysis; decryption.

  • A mathematical model to compute makespan for application integration processes using graph theory approach   Order a copy of this article
    by Sandra Neuckamp, Rafael Z. Frantz, Fabricia Roos-Frantz, Sandro Sawicki 
    Abstract: The performance of integration platforms depends on the performance of execution models implemented for execution engines. Integration processes can be divided into two levels: process execution and resource management. In the first level, we considered the processing time of tasks; as for the second level, we only analyzed elements of management and allocation of computational resources. Our proposal aims to identify the elements present at these levels that impact the process execution time, so we develop a mathematical model which determines makespan analytically in integration processes using a Directed Acyclic Graph (DAG). The proposed mathematical model can be used not only to compute makespan, but also to identify the optimal number of computational threads to execute an integration process with the lowest possible makespan. This mathematical model is validated by a Coffee Shop integration process, which is a well-known benchmark to evaluate and compare integration platforms.
    Keywords: enterprise application integration; runtime system; mathematical modelling; integration platforms; graph theory.

  • Canny optimisation of the dynamic image colour automatic segmentation algorithm   Order a copy of this article
    by Song Li 
    Abstract: Aiming at the current dynamic image colour automatic segmentation algorithm in image segmentation, there are problems of missing image edge information, poor clarity, large impact noise, and low image segmentation accuracy. A Canny-optimized automatic colour segmentation algorithm for dynamic image was proposed. By the Laplace operator test, a switch-type median filter was used to filter out the impact noise in the dynamic image. The Canny operator was optimized by concavity method, and the gradient amplitude histogram of dynamic image was analysed. Adaptive double threshold was selected according to image features. After weighted fusion of edge image by using primary colour feature, and dynamic image colour edge image segmentation was completed. The results show that the proposed algorithm has complete image edge information and good segmentation effect, and can effectively filter out impact noise and improve the accuracy of image segmentation.
    Keywords: Canny optimisation; dynamic image; automatic segmentation; weighted fusion; the edge.

  • A CNN approach for online metal can end rivet inspection   Order a copy of this article
    by Maurício Stivanello, Juliano Masson, Marcelo Stemmer 
    Abstract: Can end rivet fracture is an important defect type that may arise during the manufacturing of metal cans used in the food industry. Thus, an inspection procedure must be performed to remove the defective can ends from the production line. Previous approaches have demonstrated the possibility of performing an automated inspection. However, these approaches faced limitations associated with description and classification as they employed classical techniques. In this paper, a new machine vision-based method for online can end rivet inspection is described. In the proposed method the rivets are localized by using blob analysis, while the description and classification are entrusted to a convolutional neural network. The experiments carried out using images acquired under real conditions of use demonstrate that the proposed approach outperforms the results obtained in previous works.
    Keywords: automated inspection; convolutional neural network; fracture detection; machine vision; pull tab rivet.

  • Integrated encoding and scheduling protocol for improving the lifetime in wireless sensor networks   Order a copy of this article
    by Lina Waleed Jawad, Ali Kadhum Idrees 
    Abstract: In this paper, we propose an Integrated Encoding and Scheduling (InES) protocol for extending the lifetime in wireless sensor nodes in IoT networks. InES protocol works on two levels: sensor nodes and the gateway. The sensor nodes capture the data and compress them using either Modified Run Length Encoding (MRLE) or Huffman Encoding (HE). In the gateway level, InES applies the agglomeration clustering approach to produce the best schedule of sensor nodes that stay active in the next period. The sensor nodes are clustered based on the received vectors of data into groups, and one sensor will be elected in each group to stay active in next period. The simulation results were conducted to prove the effectiveness of the proposed InES protocol, where it reduced the transmitted data from 94.3 up to 98.8, decreased the consumed energy from 99.5 up to 99.8, and decreased the percentage of lost data from 5.3% up to 21.1% compared with the PFF.
    Keywords: sensor networks; IoT; scheduling; clustering; data compression; network lifetime.

  • Research on image to illustration translation method based on CycleGAN   Order a copy of this article
    by Yuhan Wei, Mingyu Ji, Jian Lv, Xinhai Zhang 
    Abstract: Aiming at the problem that balance between abstract style and original painting content is not enough in traditional image style migration, this paper puts forword an improved method that based on traditional CyclicConsistent Generation Adversarial Networks (CycleGAN), which enables generator to subsample feature map of each residual layer, and uses skip link and upsampling to merge low level feature and advanced feature. Then image averaging operation is used to enhance contrast of generated images, and weighted mean filter is used to filter out redundant details. Finally, gray level of the image is reduced in order to improve its abstraction. The experimental results show that with CycleGAN, DualGAN, CartoonGAN compared three methods of migration image style, this model not only improves the image style of abstract degree, also better retain original image content, significantly improve the processing and transfer the balance between the content of original painting abstract style.
    Keywords: GAN; image to image translation; illustrations to generate; deep learning.

  • Study on the law of the structure parameters' influence on thermal deformation of magnetic poles of magnetic-liquid double suspension bearing   Order a copy of this article
    by Liwen Chen, Dianrong Gao, Jianhua Zhao, Jisheng Zhao 
    Abstract: Magnetic-LiquidDouble Suspension Bearing (MLDSB) is suitable for seawater desalination high-pressure pump, aerospace and other fields. Magnetic poles are the source of electromagnetic levitation force, which directly determine bearing capacity and control accuracy. The temperature rise and thermal deformation of magnetic poles are influenced by structural factors. The influence of structural parameters on temperature rise and thermal deformation of magnetic poles is studied. The results indicate that with the number of magnetic poles increase, the fluid cooling efficiency is improved, the thermal deformation of magnetic poles basically unchanged. As the number of turns in the coil increases, the temperature and thermal deformation of magnetic poles show a linear increasing trend. Through orthogonal test range and variance analysis, the effects of structural parameters on the degree of temperature rise and thermal deformation are detected.
    Keywords: magnetic-liquid double suspension bearing; electromagnetic suspension; hydrostatic bearing; liquid-solid-thermal coupling; structure parameters; orthogonal experiment.

  • A novel spread spectrum underwater acoustic communication system for the reduction of inter-symbol interference   Order a copy of this article
    by Yan Li, Ning Jia, Jianchun Huang, Biao Liu, Shengming Guo 
    Abstract: A novel spread spectrum underwater acoustic (UWA) communication system is proposed to suppress the inter-symbol interference (ISI) caused by the long time-delay spread underwater acoustic multipath channels in this paper. The interleaved hybrid spread spectrum (IHSS) communication method is combined with M-ary code shift spread spectrum and interleaving method, which has an excellent communication performance when the time-delay spread of the UWA channels is larger than the period of pseudo-noise sequences in the spread spectrum system. The BER performance of the proposed system and the conventional M-ary code shift spread spectrum underwater communication system (CMCSSS) is evaluated through computer simulations and the sea trial. The results demonstrate the proposed system can effectively suppress ISI and has a better communication quality.
    Keywords: underwater acoustic communication; long time-delay spread; interleaved hybrid spread spectrum.

  • The optimisation of the travelling salesman problem based on parallel ant colony algorithm   Order a copy of this article
    by Amin Jarrah, Ali Al Bataineh, Abedalmuhdi Almomany 
    Abstract: General search algorithms offer some solutions to solve and avoid the constraints of the finding shortest path problem. Ant colony optimisation (ACO) is a metaheuristic search-based and probabilistic searching technique for an optimal path. However, ACO is computationally intensive and may not achieve the desired performance. Thus, a parallel implementation of the ACO for the travelling salesman problem is proposed and implemented on FPGA platform. Different optimisation techniques are adopted and applied to enhance the performance of the algorithm. The overall results have shown a 28-fold improvement in performance due to the applied optimisation techniques with different numbers of ants and iterations. The speedup is improved by increasing the number of iterations/ants, which further validated the proposed method. The network on chip methodology was also adopted to connect the ants where a comprehensive analysis of FPGA chip traffic is modelled and emulated to get the best architecture.
    Keywords: ant colony optimisation; FPGA; TSP problem; high level synthesis; parallel architecture; optimisation techniques.

  • Transformer for long-term effect estimation   Order a copy of this article
    by Shengjia Cui, Xianglong Qi, Xiao Wang, Chen Zeng 
    Abstract: Determining the long-term causal effects as well as making decisions in a timely fashion are the most challenging and significant tasks in A/B tests. The key challenge is that long-term and short-term effects may be different, especially in an online controlled experiment that a low-qualified ads strategy with high short-term revenue may hurt users' service experience in the future. We propose a Transformer on Long-Term Effect (TLTE) method to encode the short-term latent features and predict the outcome of the long-term to estimate the long-term effects. TLTE uses the transformer structure to capture the reasoning feature, considering the global information by the self-attention mechanism. Extensive experiments are conducted on public day-level datasets and our collected large-scale hourly dataset, providing flexible and sensitive analysis.
    Keywords: long term; transformer; causal effect.

  • PUID: persona user intention detection   Order a copy of this article
    by Shengjia Cui, Xianglong Qi, Xiao Wang, Chen Zeng 
    Abstract: Although user intention detection has been widely studied, existing researches suffer inferior performance when only using the semantic features of query and neglecting the personalized user attributes. A key challenge is that the same or similar queries among users who possess different social positions can be inferred from different intentions. Therefore, we propose a novel task that user attributes are introduced as additional personalised features in user intention detection besides the semantic information of queries, named Persona User Intention Detection (PUID). We collected the query log with corresponding user attributes on the professional search engine to construct a large-scale user intention dataset. Then, we propose a Persona-Augmented Hypergraph Neural Network (PAHG) for PUID consequently. Extensive experiments are conducted on several state-of-the-art methods and our method.
    Keywords: intent detection; hypergraph learning; large-scale modelling.

  • Multi-objective enhanced imperialistic competitive method for multi-criteria engineering issues   Order a copy of this article
    by Nejlaoui Mohamed 
    Abstract: Optimising multi-criteria engineering issues using a multi-objective evolutionary algorithm has received a lot of attention in recent years. In this work, a multi-objective enhanced version of the imperialist competitive algorithm (ICA), named (MOEICA), was developed and investigated to handle multi-criteria constrained engineering problems. A new strategy for colonies progress towards their imperialists, which is called enhanced assimilation, is implemented to ameliorate the efficiency of the algorithm to achieve the global optima. Moreover, in contrast to ICA, the proposed algorithm integrates the Pareto dominance strategy to store the Pareto optimal solutions of multiple conflicting functions. Two performance metrics are used to evaluate the performance of the proposed algorithm; a) convergence to the to the true Pareto-optimal set and b) diversities of optimal solutions. The obtained results show that for both benchmark functions and multi-objective engineering issues, the MOEICA outperforms other common techniques in terms of convergence characteristics and global search capability.
    Keywords: MOEICA; multi-criteria problems; con?icting objectives; engineering optimisation; high dimensions.

  • A contemporary review on soft computing techniques for thyroid disorder identification and detection   Order a copy of this article
    by Rajshree Srivastava, Pardeep Kumar 
    Abstract: This paper reviews various soft computing techniques for the identification and detection of thyroid disorder. The papers are extracted from databases such as Science Direct, ACM etc. More than 400 papers published in the last decade were downloaded. The inclusion criteria include papers from SCI/Scopus databases. Soft computing techniques are divided into three categories, namely simple, improved and hybrid techniques. Out of 113 papers, 51%, 37% and 12% papers are based on simple, improved and hybrid soft computing techniques. This review will answer the following research questions for thyroid disorder: (1) various soft computing techniques; (2) reasons to adopt soft computing techniques; (3) existing research gaps; (4) feature selection techniques; (5) different performance metrics. It sheds light on less attention of researchers on hybrid soft computing techniques for thyroid nodule disorder. It also provides future research directions to develop novel techniques for thyroid disorder.
    Keywords: simple techniques; improved techniques; hybrid techniques; thyroid disorder; identification; detection; feature selection; soft computing.

  • Role-playing gamification-based educator career promotion system   Order a copy of this article
    by Tubagus Mohammad Akhriza, Indah Dwi Mumpuni 
    Abstract: This article offers a solution to a long-standing problem in higher education institutions in Indonesia, where the number of associate professors and professors is still low. Lack of motivation is suspected to be one of the causes. HRD management often arbitrarily applies a reward and punishment system to increase motivation, where the assumption is that educators can be regulated through material rewards; however, this actually causes educators to feel less humanised. On the other hand, rewarding and punishing are gamification mechanics that should bring a fun game atmosphere, not a feeling of insecurity in educators when completing a task. This article proposes a gamification approach to Indonesian Educator Career Promotion System (ECPS) as a solution, by redefining gamification mechanics that channels the educator's interest in completing tasks. The implementation of the gamified ECPS prototype shows the diverse interests of educators, in contrast to the assumptions of the traditional system.
    Keywords: educator career; gamification; higher education; role-playing game.

  • A clustering allocation and scheduling analysis approach for multiprocessor- dependent real time tasks   Order a copy of this article
    by Faten Mrabet, Walid Karamti, Adel Mahfoudhi 
    Abstract: The ultimate objective in this paper is to propose a new method for dependent tasks clustering by considering both the inter-tasks communication cost, the inter-clusters communication cost (inter-calculation units), the precedence impact, and the execution cost. The optimal Munkres assignment algorithm is used for an optimal total execution cost. Task deadlines and their imposed precedence obligations are taken into consideration to lead a fast and safe exact scheduling analysis of each partition separately, while giving pertinent feedback. Experimental results highlight the effectiveness of the proposed approach by comparing it with optimal ones. The outcome shows better results in the total execution cost and gives exact scheduling analysis results.
    Keywords: multiprocessor real-time systems; dependent tasks; clustering and allocation; heterogeneous multiprocessor architecture; partitioned scheduling; scheduling analysis.

  • Simulation of photovoltaic systems artificial intelligence controller based on fuzzy perturbation algorithms   Order a copy of this article
    by Wei Liming, Li Kaikai, Wu Yangyun 
    Abstract: Solar energy is popular because it is clean and non-polluting. However, solar power generations efficiency restricts the development of the photovoltaic industry. To improve this problem, the research of maximum power point tracking (MPPT) algorithm is discussed by scholars. The paper proposes an artificial intelligence algorithm that is composed of fuzzy logic and perturbation observation method, so as to achieve the goal of MPPT. In the process of approaching the maximum power point, the optimal variable-steps size at each moment is adjusted by the fuzzy controller, which imitates the human brain. The simulation is carried out in Matlab/Simulink and is compared with the traditional perturbation observation algorithm. The result shows that the power loss of the artificial intelligence algorithm is significantly less than that of the traditional algorithm, and its strain ability is stronger when the environment changes abruptly, and the time to reach the maximum power point is effectively reduced.
    Keywords: photovoltaic system; MPPT; Fuzzy perturbation algorithm; perturbation observation.

  • Intelligent machine vision model for building architectural style classification based on deep learning   Order a copy of this article
    by Aaron Rababaah, Alaa Rababah 
    Abstract: This paper presents an intelligent model for building architectural style classification. Image classification of architectural style is challenging to traditional machine vision methods. The main challenge in these systems is the feature extraction phase as there are many visual features in these styles that need to be extracted, refined and optimized. All these operations are done at the researcher discretion in traditional Machine Learning (ML) models. The advancements of ML to Deep Learning (DL) made automation of all the challenging operations possible. We constructed a machine vision model based on DL to investigate the effectiveness of DL in the classification problem at hand. A publicly available annotated dataset was used to train and validate the proposed model. The dataset consists of more than 5000 images of eight different architectural styles. The experimental results showed that the proposed model is reliable as it produced a classification accuracy of 95.44%.
    Keywords: architectural styles classification; machine intelligence; machine vision; deep learning; feature extraction; impact of number kernels/features.

  • Assistive typing technologies: a new method based on binary sequence   Order a copy of this article
    by Luiz Augusto Costa, Geraldo Filho, Rodrigo Bonacin, Rodolfo Meneguette, Vinícius Gonçalves 
    Abstract: Users with severe motor difficulties may experience problems when interacting with traditional computing devices. This article addresses these problems by setting out a new method that involves interacting through binary sequences. A hardware and software system was designed based on this method. This system enables users to interact using binary movements that are triggered by clicking a single button. Comparative tests were carried out between the proposed method and Switch Control, an assistive technology embedded in iOS devices for the interaction of users with motor difficulties. In terms of typing speed, the results show that the proposed methodology proved to be more efficient in all the tested cases, and it was, on average, 22% more productive than the Switch Control.
    Keywords: communication; binary sequence; assistive technology; simulation; motor disabilities.

  • Modelling and verification of enterprise application integration processes through coloured Petri nets   Order a copy of this article
    by Alexsandro Q. Lencina, Fabricia Roos-Frantz, Rafael Z. Frantz, Sandro Sawicki 
    Abstract: Integration processes are considered workflow processes that can be modelled using a process modelling language. Integration platforms usually offer a modelling language based on the well-known integration patterns. To automatically verify the logical correctness of such processes, they must be formally specified. Although there is a proposal in the literature that translate the integration patterns documented by Hohpe and Woolf into coloured Petri nets, it targets conceptual pattern definitions and does not cover all integration patterns found in actual implementation of integration platforms. More complex conceptual patterns usually have to be adapted when realised by a specific integration platform. We show how an actual modelling language that implements integration patterns can be translated into coloured Petri nets and its formal properties be verified. We intend to inspire researchers interested in the verification of models designed with other platform dependent message-based languages that also realise the same integration patterns.
    Keywords: coloured Petri nets; integration patterns; integration process modelling; model verification.

  • A practical approach for the porting of the Ravenscar profile from ADA to C: method, rules adaptation and supporting tools   Order a copy of this article
    by Claudia Rinaldi, Fabio Romano, Paolo Serri, Luca Tiberi 
    Abstract: While conceiving with hard real time systems, determinism is the main requirement that must be satisfied in order for properly predict their behavior as required by their definition. For achieving this purpose an available solution is restricting Ada language tasking features to the Ada Ravenscar profile subset. This paper presents a solution to apply the Ravenscar profile concepts in systems where the tasking management is based on RTEMS real-time operating system, and C and Ada languages are used together. Moreover, a SW tool to automatically check the compliance with Ravenscar is proposed and the outcomes of some experimental activities proving the effectiveness of the SW tool are discussed
    Keywords: Ravenscar profile; Ada; C; RTEMS.

  • Implementation of an IoT System for Environment Monitoring and Remote Web Control using ARM Mbed Cloud and GUI
    by Shensheng Tang, Yi Zheng 
    Abstract: This paper implements an IoT system for environment monitoring and remote web control using ARM Mbed cloud. The embedded system used for environment monitoring is implemented using an ARM Cortex-M4 core-based STM32L4 series development board integrated with multiple sensors. The sensed data (i.e., temperature, relative humidity and atmospheric pressure) can be wirelessly sent to the Mbed cloud managed by the Pelion device management platform. The data values can also be sent to a graphical user interface (GUI) with user authentication. We develop three application appliances that can be controlled remotely over Internet through the Pelion platform. The proposed IoT system has been successfully implemented on the STM32L4 series development board with the main application program developed using C++ and the GUI developed by C# programming. The work of hardware and software co-design can be a practical paradigm of engineering education for IoT hobbyists and college students.
    Keywords: IoT; ARM Mbed Cloud; Pelion Device Management Platform; GUI; C++; C#; WiFi; Environment Monitoring; Web Control.

  • A method for evaluating the relationship strength of group users based on co-occurrence   Order a copy of this article
    by X.U. Jingke, XIAO Fei 
    Abstract: At present, the inference of users' social relations based on check-in data has become a hot topic. However, the existing methods to evaluate users relationship strength mainly concentrated between two users, and group users contain greater information and use value. This paper focuses on the co-occurrence phenomenon that group users are in the same time and space, and research group users relationship strength problem with co-occurrence method and min-max method based on entropy. The real check-in data sets are used to carry out sufficient experiments on the two proposed evaluation methods. The experimental results show that the min-max method can better adapt to the sparse problem of check-in data and reduce the influence of coincidence. Compared with the basic co-occurrence method, it has certain stability in groups of different sizes, and the larger the group size is, the more obvious its advantages are.
    Keywords: check-in data; group users; relationship strength; co-occurrence in time and space.

  • Effects of Big Data Analytics Capability on Performance of Internet Enterprises: Chain Mediating Effects of Strategic Flexibility and Strategic Innovation
    by Hua Zhang, Lifang Wang, Hongji Yang, Chunyuan Yu, Fubin Xia, Xinzhe Xue 
    Abstract: The recent interest in big data has led many companies to develop big data analytics capability (BDAC) in order to enhance firm performance (FP). However, BDAC pays off for some companies but not for others. It appears that very few have achieved a big impact through big data. To address this challenge, this study proposes a BDAC model drawing on the resource-based theory and the dynamic capability theory. In order to carry out the research, this paper takes Chinese Internet enterprises as the research object and obtains survey data from 629 employees through questionnaires. Through the test of the proposed chain mediation model using bootstrapped, it is found that: (1) big data analytics capability has significant positive influences on firm performance of Internet enterprises. (2) Strategic flexibility and strategic innovation play chain mediating roles on the path joining big data analytics capability and firm performance.
    Keywords: big data analytics capability; firm performance; Internet enterprise; strategic flexibility; strategic innovation;chain mediating effects.

  • Optimisation of energy consumption in cloud video surveillance centre based on monitoring and placement of virtual machines   Order a copy of this article
    by Majid Heidary, Ehsan Sadeghi Pour, Azad Noori, Maedeh Abedini Bagha 
    Abstract: Cloud computing is one of the most popular computational models, which requires plenty of physical devices where services are provided based on user demand; A majority of data centres need plenty of energy consumption which has become a challenge in recent years. Regarding cloud video surveillance as a contemporary research field of cloud computing and big data, the service, due to the high demand for monitoring remote places, continually consumes surplus energy to process the high-volume data. This study considers the importance of energy consumption in cloud video surveillance, and it has been tried to increase the efficiency of servers concerning energy usage. The proposed method employs virtual machine placement in two steps, including monitoring and placement, to reduce energy consumption and increase the efficiency of servers. Implementation results in Cloudsim showed that it reduces energy consumption and increases resource efficiency.
    Keywords: cloud video surveillance; virtual machine placement; energy.

  • APP test system: a case study of calculus   Order a copy of this article
    by Ting-sheng Weng 
    Abstract: COVID-19 has had a broad impact on society, and a profound impact on education, thus, distance online courses are seen as a way to continue schooling during the pandemic. This study employed Android Studio to develop an APP calculus learning test system which can be used for self-review exercises and allows students to make good use of mobile apps to conduct post-learning and self-testing of calculus at home, and immediately determine their learning results. In addition, through back-end access, teachers can view students' learning scores and the number of wrong and correct questions, and thus, know the effect of individual students' self-review. During the COVID-19 pandemic, as teachers and students cannot interact in class at school, teachers can use the APP calculus learning test system to provide distance remedial teaching to students who fall behind during the course.
    Keywords: APP; test system; calculus; distance learning; COVID-19.

  • Visual-inertial fusion positioning and mapping method based on point-line features   Order a copy of this article
    by Qinghua Feng 
    Abstract: In order to solve the problem of current visual SLAM (Simultaneous Localization And Mapping, SLAM) based on point feature technique in a structured or weak texture environment exist the problem of the strong dependence and weak noise immunity. This paper proposes a method of simultaneous location and map building based on the information of the visual point, line features and inertial information fusion. Firstly, feature points and feature lines are extracted by camera and fused with inertial information. Then recover the object's motion state in real time according to the information. The evaluations on the public dataset of EuRoc shows that the Root Mean Square Error (RMSE) is 0.152m and the method has excellent accuracy, robustness and timeliness.
    Keywords: SLAM; line feature; inertial information; recover; computer vision.

  • An underwater vehicle odometry scheme based on visual-inertial fusion   Order a copy of this article
    by Yufan Wang 
    Abstract: In order to solve the problems of underwater vehicle inaccurate odometry caused by uneven illumination, current floating and blurred vision in underwater vehicle perception, this paper proposes an underwater vehicle odometry scheme based on visual-inertial fusion. The camera and IMU are first calibrated through the calibration board to solve the parameters of the device, and the accuracy of the pose can be ensured. Then, the visual-inertial odometry (VIO) system of underwater vehicle is designed to complete the fusion of camera information and IMU data. At the same time, an underwater shadow detection and removal algorithm is proposed to optimise the underwater information acquisition of the robot. Finally, this scheme is tested in various underwater data sets and different mainstream algorithms. The experimental results prove that the accuracy of the proposed method, and it has good robustness and pervasiveness in complex underwater scenes.
    Keywords: feature extraction; visual-inertial fusion; monocular camera; inertial measurement unit.

  • Lightweight crop pest identification algorithm under natural background   Order a copy of this article
    by Dong Benzhi, Wang Yaqi, Xu Dali 
    Abstract: Aiming at the problem of poor detection effect and low recognition accuracy of small target insects under the background of complex natural environment, proposes an improved Yolo v5s insect recognition algorithm. The channel attention mechanism is embedded in the backbone network. The adaptive spatial feature fusion (ASFF) structure is introduced in the PANet part, and dynamic weight parameters is used to assign different weights to feature maps of different scales, Finally, we change the loss function and non-maximum suppression strategy to improve the accuracy of bounding box positioning and the speed of regression. Experimental results show that the improved algorithm has a final average accuracy (mAP@0.5) of 97.8% in the D0 dataset and an average detection speed of 13.66 ms per image, which is more suitable for deployment in mobile and embedded devices to achieve real-time detection.
    Keywords: insect recognition; lightweight convolutional neural network; channel attention mechanism; adaptive spatial feature fusion.

  • Enhancing the accuracy of transformer-based embeddings for sentiment analysis in social big data   Order a copy of this article
    by Wiem Zemzem, Moncef Tagina 
    Abstract: Social media have opened a venue for online users to post and share their opinions in different life aspects, which leads to big data. As a result, sentiment analysis has become a fast-growing field of research in natural language processing (NLP) owing to its central role in analysing the public's opinion in many areas, including advertising, business, and marketing. This study proposes a transformer-based approach, which integrates contextualized words with Part-Of-Speech (POS) embedding. Then, the enhanced word vector is forwarded to a hybrid deep learning architecture combining a Convolutional Neural Network (CNN) and a Bidirectional Long Short Term Memory (BiLSTM) to discover the post's sentiment. Extensive experiments on four review datasets from diverse domains demonstrate that the proposed method outperforms other machine learning approaches in terms of accuracy.
    Keywords: deep learning; sentiment analysis; word embedding; big data; natural language processing.

  • Methodology of aircraft structural design optimisation   Order a copy of this article
    by Nihong Yang 
    Abstract: Aircraft structural design normally aims to achieve the lightest weight while meeting aircraft performance requirements. Various optimisation methods and approaches are used in structural design optimisation. This paper reviews the aircraft structural optimisation process and methods currently applied in aircraft structural design, including structural analysis and optimisation process, FEA modelling techniques and optimisation algorithms. It is seen that a two-step global local approach is extensively applied in aircraft structural optimisation. Submodelling technique is often used in the global local optimisation, whereas superelement technique can provide accurate solutions. Gradient based optimisation algorithms are suitable for optimisation problems with continuous variables such as size and distance of metallic aircraft structures, whilst genetic algorithms and particle swarm optimisation algorithms are often used for optimisation problems with discrete variables such as laminate composite thickness and stacking sequence.
    Keywords: aircraft structures; design optimisation; FEA modelling techniques; laminated composites; numerical modelling; optimisation algorithms.

  • Unsupervised machine learning schemes for cooperative spectrum sensing in cognitive radio   Order a copy of this article
    by RAJENDRA YELALWAR, YERRAM RAVINDER 
    Abstract: The major challenge in the development of recent wireless technology is spectrum scarcity which is addressed by introducing the Cognitive Radio (CR) technique. In CR, spectrum sensing is the most critical task that senses the surrounding environment to detect the presence of a primary User (PU) in the target spectrum. This paper proposes the machine learning (ML) enabled Cooperative Spectrum Sensing (CSS) approaches where the application of clustering algorithms for the eigenvalue based CSS under different fading channel conditions is explored. The sensing performance is analysed with different PUs, signal features, Signal to Noise Ratio (SNR) values, and channel conditions. Secondly, this work proposes the novel clustering based CSS framework for Non-orthogonal Multiple Access (NOMA) signal detection. The system performance is measured in terms of sensing accuracy and Receiver Operating Curve (ROC). The simulation results ensure the effectiveness of the proposed clustering based CSS framework compared to the existing work in terms of improved accuracy which is observed to be 92.5% for K means clustering based CSS framework for NOMA
    Keywords: spectrum sensing; machine learning; K-means; K-medoids; agglomerative; NOMA.

  • FPGA implementation and Multisim simulation of a new four-dimensional two-scroll hyperchaotic system with coexisting attractors   Order a copy of this article
    by Sundarapandian Vaidyanathan, Esteban Tlelo-Cuautle, Khaled Benkouider, Aceng Sambas, Ciro Fabian Bermudez-Marquez, Samy Abdelwahab Safaan 
    Abstract: Field-programmable gate array (FPGA) design of a new four-dimensional two-scroll hyperchaotic system is investigated in this work. A detailed system modelling of the new system with a hyperchaotic attractor begins this work with phase plots, which is followed by a bifurcation study of the new system. Special dynamic properties such as multistability and symmetry are also investigated for the new system. Using Multisim software, a circuit model is designed and simulated for the new hyperchaotic system. FPGA design and Multisim simulation of the new system enable practical applications in science and engineering. The implementation of the FPGA design in this work is carried out by applying two numerical schemes, viz. Forward Euler and Trapezoidal methods. Experimental attractors observed in the oscilloscope show good match with the Matlab signal plots.The FPGA hardware resources are detailed for both numerical methods.
    Keywords: hyperchaos; bifurcation; symmetry; phase plots; hyperchaotic system;rnparameters; stability; multistability; circuit model; FPGA implementation.

  • A study of integration application based on 5G/8K/AI/VR for the activation of intangible cultural heritage   Order a copy of this article
    by Lu Zhang, Shaojun Ji, Meiyu Shi 
    Abstract: Within the culture and tourism field, the activation of intangible cultural heritage has been a hot topic in recent years. A major way of doing that is through leveraging various cutting-edge technologies, in particular, 5G/8K/AI/VR. However, due to the diversity of intangible culture and inescapable relationships among different technologies, it is necessary to consider the integration application of the advanced technologies. There is a trend that the said technologies are often used to construct the immersive experience. A related embodied cognition theory is referred in this paper to build the theoretical basis. Then, a detailed analysis of the functions and features of 5G/8K/AI/VR is followed. Based on the research efforts, several integration application scenarios are summarized through case study for the activation of intangible cultural heritage.
    Keywords: integration application; 5G/8K/AI/VR; activation; intangible cultural heritage.

  • A systematic mapping study on IoT-based software systems for precision agriculture   Order a copy of this article
    by Vinícius Lopes, Cleiton Silva, Dayana Gonçalves, Roberto Oliveira, Renato Bulcão-Neto, Mohamad Kassab, Valdemar Graciano-Neto 
    Abstract: Context: Agriculture is often pressured to adopt new technologies so that production rate can be accordingly increased. The Internet of Things (IoT) has played an essential role in modernizing the agricultural practices once it can support monitoring and automated decisions on planting. Objective: Given the ascending adoption of IoT in agriculture, the main goal of this study is reporting collected evidence from the literature and summarize how IoT systems have been used to support several activities in the agriculture domain. Method: We adopted the systematic mapping study (SMS) procedure. We designed a search string executed in two Web search engines: Scopus and Embrapa repositories. We selected a total of 35 primary studies that either propose or evaluate IoT-based systems in the agriculture domain. Results: Results suggest that, although there are different platform solutions such as Web-based systems, Web-and-mobile-based systems, and mobile systems targeting activities such as planting monitoring and irrigation, there are still several opportunities in the area, such as (i) the conception of automated decision-making processes and supporting technologies for agriculture recurrent activities, (ii) adoption of edge computing and machine learning for information processing and automation, respectively, and (iii) proposition of solutions towards reducing production costs and ecological impacts.
    Keywords: software system; internet of things; precision agriculture; 4.0 agriculture, smart agriculture; mapping study.

  • Experimental Study for Makespan Reduction in Enterprise Application Integration Processes Using Bio-Inspired Algorithms
    by Maira S. Brigo, Fernando Parahyba, Rafael Z. Frantz, Sandro Sawicki, Fabricia Roos-Frantz 
    Abstract: Enterprise Application Integration area seeks to support the companies' business processes by enabling data and functionality of the applications to become reusable. Integration platforms are tools that develop and execute integration processes. This execution is done by a key component of the platforms called run-time system; that said, the performance from integration processes heavily depends on the efficiency of the run-time system. The task-based execution model implemented by the run-time system can use a strategy based on local pools to store computational threads associated with each task that make up the workflow of the integration process, to execute them. The challenge in this strategy is to evenly distribute the threads in each pool, minimising the makespan. We propose a experimental study, which uses two meta-heuristics to find the best distribution with the optimal number of threads. We compared both Particle Swarm Optimisation and Cat Swarm Optimisation, with the latter showing better results.
    Keywords: makespan; task-based; run-time system; optimisation; integration platforms; integration process; meta-heuristics; particle swarm optimisation; cat swarm optimisation; threads

  • Risk Assessment of Construction Safety of Prefabricated Building Hoisting Based on Cloud Model-Entropy Method
    by Chengkuan Fang, Chunling Zhong, Yunlong Zhang 
    Abstract: In order to ensure the safety of assembly building hoisting construction, the safety risk assessment index and model of assembly construction are studied. Based on assembly building hoisting construction characteristics, accident inducement, field investigation, and literature research, the safety risk assessment model of assembly building hoisting construction based on the cloud model and entropy weight method is constructed. Using the cloud model effectively reduces the fuzziness and randomness of risk assessment data, and then using the entropy weight method, the weight coefficient of the evaluation index is given, which effectively avoids the subjectivity of expert weighting. The model is applied to engineering examples, and good results are achieved. The results show that the risk level evaluation results of the model are consistent with the field risk level, indicating that the model can objectively and accurately evaluate the risk level of assembly building hoisting construction.
    Keywords: prefabricated building; hoisting; cloud model; entropy method; risk assessment.

  • DL-RED: A RED-Based Algorithm for Routers
    by Samuel Hassan, A. Rufai, C. Ajaegbu, F. Ayankoya 
    Abstract: Keeping the average queue size small (which will in turn, offers a minimized delay performance) is regarded an important goal of Active Queue Management (AQM) algorithms implemented in Internet routers. The Random Early Detection (RED) algorithm is unable to achieve this desired goal. In this paper, we present an enhanced Random Early Detection (RED) algorithm, named Double Linear RED (DL-RED) which utilizes a linear packet dropping function for a light - and moderate - network traffic load scenarios and another linear packet dropping function for a heavy network traffic load scenario. The effectiveness of DL-RED was evaluated and compared with RED and Nonlinear RED (NLRED) algorithms using ns-3 simulation tool. Experimental results proved that DL-RED clearly performed better than RED and NLRED with reference to delay and throughput. A little effort is required to amend the packet dropping probability profile of RED's algorithm implementation with DL-RED algorithm. Therefore, RED can be easily replaced/upgraded in Internet routers with DL-RED.
    Keywords: AQM; Congestion control; Delay; DL-RED; Simulation.

  • Improving Recentness of the ICT Book Recommendation using an Adaptive Rules-based Recommender System
    by Mochammad Husni, Tubagus Mohammad Akhriza, Sarifuddin Madenda, Eri Prasetyo Wibowo 
    Abstract: The traditional library book recommendation system (RS) has limitations where all recommendations only refer to internal book borrowing transactions; while the development of science and technology, especially in the field of ICT, has exceeded the theme of the recommended books. As a result, the recentness of the recommendations is questionable. As a solution, this article proposes an adaptive-rules-based book RS while at the same time introducing a dimension to measure the quality of recommendations namely recentness. It measures how up-to-date the recommended book theme is, against a set of trending themes extracted from external publications. An experiment was conducted to measure the book recommendations generated by the new RS in a library, compared to a collection of recent publications in the IEEE Xplore database. At first, the recentness of the recommendation was only around 23.5% - 57.1%, but it was successfully increased to 47.6% - 76.2% by the proposed RS.
    Keywords: association rule; library; recommendation quality; recommendation system

  • Cooperative Game amongst Prefabricated Building Chain Stakeholders based on Improved the Shapley Value Method
    by Qi Zhao, Chunling Zhong 
    Abstract: The cooperation among stakeholders at all nodes is the premise for the stable operation of the prefabricated building chain. Such cooperation is the key factor to the stable development of the prefabricated building industry. The cooperation among stakeholders cannot be separated from a reasonable and effective benefit distribution mechanism. Firstly, the prefabricated building chain is defined to analyze the cooperative relationship among stakeholders. Secondly, the study investigates the influencing factors of interest distribution among all stakeholders of the prefabricating project and puts forward the distribution principles. Finally, profit distribution using the Shapley value method of the prefabricated building chain model is performed on the basis of cooperative game theory. At the same time, it considers the influencing factors of prefabricated industrial chain profit distribution using the entropy weight method to improve the model. Particularly, it improves the rationality and objectivity of the profit distribution model and makes up for the theoretical vacancy of benefit distribution in the prefabricated construction industry chain.
    Keywords: prefabricated building industry chain; stakeholder; Shapley value method; entropy weight method; cooperative game

  • A Comprehensive Review of Clustering Approaches for Energy Efficiency in Wireless Sensor Networks
    by Wesal Bassem Nedham, Ali Kadhum M. Al-Qurabat 
    Abstract: Wireless Sensor Networks (WSNs) have become more popular in recent years due to their vast range of applications. The utilization of WSNs is an absolute requirement for future revolutionary domains such as smart cities, the Internet of Things, or ecological fields, where hundreds or thousands of sensor nodes are placed. Moreover, because WSNs are energy-constrained networks, implementing energy-aware protocols is critical. Hierarchical techniques enhance network performance and extend network lifetime in large-scale WSNs. Within a WSN, hierarchy is achieved by dividing the network into sub-networks known as clusters, which are directed by Cluster Heads (CH). Clustering is the most common energy-efficient approach, and it offers several benefits, like reduced latency, scalability, lifetime, and energy efficiency. This study presents a detailed assessment of several clustering techniques, together with their aims, features, etc. Furthermore, clustering techniques are classified and evaluated based on numerous cluster features, cluster head at-tributes, and clustering procedures.
    Keywords: Wireless Sensor Networks; Energy Consumption; Energy-Efficiency; Clustering Techniques; IoT.

  • A novel shape-based Time Series classification with SAX-Ensemble
    by Mariem TAKTAK, Slim TRIKI 
    Abstract: Since the first publication of the Symbolic-Aggregate approXimation (SAX), a lot of extension with novel SAX-distance measure are published. Each of them attempts to integrate additional statistical features in order to improve original SAX average-based feature. Each SAX-feature has its own distance function which quantify the (dis)similarity between two Time Series (TS). However, none of them can fit the overall shape-characteristics of a TS and give the superiority to an individual SAX-based classifier. In order to combine the prediction of each single SAX-based classifier, we propose a collection of several SAX-feature to compose a shape-based ensemble for TS classification. The proposed SAX-Ensemble scheme is applied on a multiple domain representation of the TS where the diversity of collected SAX-feature make the setting of the SAX-discretization parameters a challenging task especially for a long TS data or a large training dataset. In order to avoid a time-consuming of either grid search or expensive optimization algorithm, we instead apply a data-aware or data-agnostic parameters setting technique. Experimental results on real TS database show that the performance of the proposed SAX-Ensemble with data-aware technique exceeded the SAX-based classifiers with more flexible and realistic parameters estimation.
    Keywords: Time Series data; Symbolic Aggregate approXimation; Shape-based classification

  • A Concrete Product Derivation in Software Product Line Engineering: A Practical Approach
    by Karam Ignaim, Khalid Alkharabsheh, André L. Ferreira, João M. Fernandes 
    Abstract: Software Product Lines (SPLs) support the development of a full family of products through systematic reuse of shared assets. Product derivation is a key activity in SPL engineering and one of the primary issues that an SPL faces. This paper presents a practical approach that supports an automated derivation of a product from an SPL. The SPL is derived from a family of products that originated from a non structured approach to manage variability. The automated derivation approach is based on the use of product configurations and Feature Models (FMs) refactoring. The approach was deployed and evaluated with a real-world SPL in the automotive domain. The result reveals that the approach derives a product in an automated and successful way.
    Keywords: Software Product Line; Product Derivation, Feature Models, Product Configuration; Refactoring

  • A Comprehensive Review of the Electroencephalography Data Analytics
    by Marwa saieed khlief, Ali Kadhum Idrees 
    Abstract: This paper proposes a comprehensive review of Electroencephalography (EEG) data analytics. The EEG signal definition and the analysis process are presented. The public EEG datasets that were utilized by the researchers are explored. EEG data acquisition methods are investigated. This paper covers and summarises the work and techniques that have been done to compress EEG data. Significant approaches for feature extraction for EEG signal processing are illustrated. The collected features are then utilized to classify signals based on their properties. Machine learning techniques have become very important in this field in recent years because of their incredible ability to assess complicated volumes of data. Therefore, machine learning and deep learning for EEG data have been introduced. For researchers interested in EEG data analysis, this work can serve as a basic strategy and a roadmap.
    Keywords: Electroencephalography (EEG); EEG signal processing; Data Compression; machine learning; deep learning.

  • Performance Analysis of Machine Learning Algorithms Applied to Network Intrusion Detection
    by Minyar Sassi Hidri, Suleiman Ali Alsaif, Adel Hidri 
    Abstract: Despite enormous efforts by researchers, Intrusion Detection Systems (IDSs) still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. Recently, machine learning-based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. Most of them cannot perform well with large-scale or even real-time data, while the rest cannot track down evolving malicious attacks, thus putting a huge void in existing solutions. The proposed approach is an attempt to explore the possibility of developing an IDS which analyzes raw network data in the form of network traffic files or server logs allowing us to simulate a real environment to accomplish testing and evaluation. Thanks to several conducted experiments, we were able to demonstrate that it is possible to improve the overall performance of learning algorithms in the field of network security by model biasing.
    Keywords: Machine learning; Intrusion detection system; Malicious attacks; Model biasing; Network traffic.

  • MODEL AND SIMULATION OF TRACKING MOTION FOR ACCURATE WELDING USING A UNIVERSAL COBOT
    by Ha Quang Thinh Ngo, Thanh Trung Thai, Hao An Nguyen, Vo Nhut Quang Mo, Quang Long Le, Tuan Anh Vu, Aly Mach 
    Abstract: Welding is a process that requires the skill and time of a professionally-trained welder. However, this welding performance is not highly productive. Therefore, in this paper, the control approach of a 6-DOF collaborative robot (Cobot) arm is investigated in the application of a MIG (metal inert gas). By studying the trajectory generation algorithm, the Cobot can be used to track the welding curve. A theoretical model of the 6-DOF Cobot arm is successfully established in 3D space. Then, the results to validate the method according to the welding trajectory are presented in numerical simulations. It can be clearly seen that our approach shows great position-controlled ability. From these achievements, the applications of this Cobot are expected to be implemented in many industrial fields.
    Keywords: robot arm; cobot; automatic welding; position control; motion tracking

  • Improving the routing performance in optical networks by an optimized BFS algorithm
    by Juliano Antônio da Silva, Djeisson Hoffmann Thomas, Fernando Cesar de Castro 
    Abstract: The procedure of routing and wavelength assignment became a limiting factor for new applications in optical networks (ONs) due to the faster dynamic and greater unpredictability of service requests. The need for quick service recovery in the event of failures is also a crucial aspect which depends on this procedure. The present work proposes a new assignment of alternative routes able to comply to any data service request in ONs. The assignment procedure is based on the distinction by low correlation between paths, on the available bandwidth of different paths and on the latency for the establishment of communication between network nodes. In such context, an improvement of the Breadth-First Search algorithm (BFS) is proposed and tested with parameters from a real world, resulting in a good performance to find alternative routes and to classify them according to the best score obtainable, independently of the source or destiny nodes.
    Keywords: Optical networks. Optical network performance optimization. Routing in optical networks. RWA. Routing Algorithms.

  • Prescribed-time leader-following group consensus for linear multi-agent systems with delays
    by Chen Xin, Dai Lingfei, Guo Liuxiao, Yang Yongqing 
    Abstract: This paper challenges the problem of prescribed time group consensus for linear multi-agent systems with delays. By designing the delayed protocols based on prescribed time scaling function, the multi-agent systems can realize group consensus in any preset convergence time, which is independent of both the initial conditions and system parameters. Most existing results on finite or fixed time bipartite consensus, require the considered structurally balanced of strongly connected signed networks. In this paper, prescribed time group consensus, including bipartite consensus as its special item, can be achieved without the assumption of signed balanced networks. In addition, group consensus according to different delays are also presented by resorting the Lyapunov stability and algebraic graph theory. Simulations illustrate the validity and correction of the proposed protocols.
    Keywords: Prescribed-time; Time delay; Group consensus; Multi-agent systems.

Special Issue on: Signal and Information Processing in Sensor and Transducer Systems

  • Development and application of PD spatial location system in distributing substation   Order a copy of this article
    by Fang Peng, Hong-yu Zhou, Xiao-ming Zhao 
    Abstract: Partial discharge is an important cause of insulation deterioration of distribution network equipment. Due to the variety of distribution network equipment, the location of discharge source is always a technical difficulty in engineering. In this paper, through the research of UHF PD spatial location technology, a system for spatial location of discharge source in distribution room is developed. The UHF sensor, acquisition and processing module and analysis and diagnosis module for the location of discharge source in distribution room are designed. The results of laboratory test and actual operation show that the system has the advantages of high detection sensitivity, high location accuracy and high operation reliability. It can be used for effective monitoring and timely warning of PD defects in distribution room, which helps to improve the power supply reliability of distribution network system.
    Keywords: distributing substation; partial discharge; spatial location; sensor; online monitoring.

  • Finger knuckle print verification by fusing invariant texture and structure scores   Order a copy of this article
    by Chaa Mourad, Zahid Akhtar, Sehar Uroosa 
    Abstract: Finger Knuckle Print (FKP) biometric traits for person recognition have recently gained much attention from both the research community and industry, owing to their distinctive features and higher usability or user friendliness. In this paper, a reliable and robust personal identification approach using FKP is presented. The proposed framework merges two types of matching scores extracted from structure and texture images. The region covariances algorithm (RCA) has been employed in the presented method to extract the structure and the texture images from each FKP captured image. Gabor filter bank and Kernel Fisher Discriminant (KFD) methods have been used to obtain distinctive feature vectors. Finally, the Cosine Mahalanobis distance similarity metric is used for classification. Experimental analyses were performed on the Hong Kong Polytechnic University (PolyU) FKP database. Experimental results show that our proposed system achieves better results than prior state-of-the-art systems. In addition, fused scores using the weighted sum rule in the proposed framework renders very good performance compared with min, max, and simple sum rules.
    Keywords: biometric system; FKP-based person recognition; Gabor filter; region covariances algorithm; kernel Fisher discriminant.

  • Research on cable partial discharge detection and location system based on optical fibre timing   Order a copy of this article
    by Jian-jun Zhang, Fang Peng, An-ming Xie, Yang Fei 
    Abstract: Partial discharge (PD) is an important index to reflect the running state of cable. According to the characteristics and propagation mechanism of cable partial discharge signal, a cable partial discharge detection and location system based on optical fibre time synchronization technology and travelling wave double terminal location principle is developed. The system has high detection sensitivity, high reliability, real-time detection, diagnosis and positioning of cable discharge power supply. The experimental results show that the positioning accuracy of the cable partial discharge source can be effectively improved based on the fibre timing and double terminal positioning technology, and the positioning accuracy can reach 1%; the method studied in this paper can meet the requirements of the accurate location of the partial discharge source of the cable, Gil and other equipment.
    Keywords: cable; partial discharge; optical fibre timing; double terminal positioning; online monitoring.

Special Issue on: Computer Applications in Technology and their Role in Education with Respect to Economic Impact

  • Efficient residential load forecasting using a deep learning approach   Order a copy of this article
    by Rida Mubashar, Mazhar Javed Awan, Muhammad Ahsan, Awais Yasin, Vishwa Pratap Singh 
    Abstract: A reliable and efficient smart grid depends on smart meters that are used for tracking electricity usage and provides accurate, granular information that can be used for forecasting power loads. Residential load forecasting is indispensable, since smart meters can now be deployed at the residential level for collecting historical data consumption of residents. The proposed method is tested and validated through available real world data sets. A comparison of LSTM is then made with two traditionally available techniques, ARIMA and exponential smoothing. Real data from 12 houses over a period of 3 months is used to inspect and validate the accuracy of load forecasts performed using three mentioned techniques. LSTM models, owing to their higher capability of memorising large datasets, establish their usefulness in time series based predictions.
    Keywords: short term load forecast; residential load; power system planning; LSTM; exponential smoothing; ARIMA; deep learning.

  • Fake profile recognition using big data analytics in social media platforms   Order a copy of this article
    by Mazhar Javed 
    Abstract: Online social media platforms today have many more users. This has led to increased fake profile trends, which is harming both social and business entities as fraudsters use images of people for creating new fake profiles. However, most of those proposed methods are outdated and arent accurate enough with an average accuracy of 83%. Our proposed solution for this problem is a Spark ML based project that can predict fake profiles with higher accuracy than other present methods of profile recognition. Our project consists of Spark ML libraries including Random Forest Classifier and other plotting tools. We have described our proposed model diagram and tried to depict our results in graphical representations like confusion matrix, learning curve and ROC plot for better understanding. Research findings through this project illustrate that this proposed system has accuracy of 93% in finding fake profiles over social media platforms. There is 7% false positive rate in which our system fails to correctly identify a fake profile.
    Keywords: fake profile; social media; big data; machine learning; Spark.

  • Towards an enhanced user experience with critical system interfaces in Middle-Eastern countries: a case study of usability evaluation of Saudi Arabias weather interface system (Arsad)   Order a copy of this article
    by Abdulrahman Khamaj 
    Abstract: Access to weather forecasts is well adopted by the public in Western countries. However, in Saudi Arabia (KSA), the use of weather forecasts for administering safety precautions and planning daily activities is still not at an acceptable level owing to the lack of easily accessible weather platforms. Recently, the Saudi Presidency of Meteorology and Environment has launched the first governmental smartphone weather application, Arsad. However, the usability of the Arsad systems interface design is still unknown. Through user testing and a questionnaire, this study examined all Arsads embedded features and design aspects. The analyses highlighted several usability issues and recommendations to be considered in the redesign phases. This research will contribute to the usability body of knowledge of weather interface systems, as well as offer opportunities for users and providers to work together to enhance the accessibility and usability of weather system interfaces in KSA and other Middle-Eastern countries.
    Keywords: smartphone applications; usability; weather forecasts; user experience.

  • Comparative study of satellite multispectral image data processing with Map Reduce and classification algorithm   Order a copy of this article
    by Ch Rajya Lakshmi, Katta Subba Rao, R. Rajeswara Rao 
    Abstract: Now that big data has amassed a significant amount of data, it is available in both structured and unstructured formats. Unstructured data processing is difficult to generate by individuals (e.g. Twitter data) or even sensors (e.g. satellites, videos) with data sizes ranging from gigabytes to terabytes and petabytes. The term 'big data' is being used to describe a growing number of items. We can easily analyse and classify different trends in unstructured datasets if the right analytical approach is used, while keeping data quality and size in mind. Early warning forecasts, which are based on satellite imagery and radar sensor data, are a major problem in the real world. The number of space objects derived from such data querying is a complex task. To obtain a better understanding of big data, a proper architecture for the analysis of various classifications of satellite imagery patterns using Hadoop technology should be proposed. Different classification methods for different satellite imagery pattern classification methods are segregated in the proposed architecture, and Google's Map Reduce C4.5 algorithm for successful classification is proposed for both time efficiency and scalability results to increase the performance of classification patterns and an increasing amount of datasets. The focus of this study is on NASA satellite data, Twitter data, and weather forecasting.
    Keywords: C4.5 algorithm; satellite image; Hadoop; Google’s Map Reduce; big data.

  • Management techniques and methods of total quality management implementation in management institutions.   Order a copy of this article
    by Pritidhara Hota, Bhagirathi Nayak, Sunil Mishra, Pratima Sarangi 
    Abstract: This study explored the implementation and barriers of the internal stakeholder Total Quality Management (TQM) activities and of various performance measures. In this analysis, we used a methodology-based approach to the stakeholders who were the unit of the survey. The sample has been chosen from Top Management, Students, Faculty, Non-teaching Staffs, Alumni and Principal of Management Institutes in Odisha. We developed a questionaire with 64 accessible questions, and 10 major factors were collected, with an acceptable answering rate of 49.4%. The primary research elements Principal Components Analysis (PCA) and ANOVA test have been performed. This study showed that different TQM activities influence different performance results significantly. Results showed that lack of employee engagement, knowledge of employees and dedication, inadequate structure, and lack of resources, were the key challenges that management organisations face with internal stakeholders. Management institutes with all their variables to enhance efficiency should continue implementation of TQM.
    Keywords: TQM; stakeholders; ANOVA; PCA.

  • Improving software performance by automatic test cases through genetic algorithm   Order a copy of this article
    by Sudeshna Chakraborty, Vijay Bhanudas Gujar, Tanupriya Choudhury, Bhupesh Dewangan 
    Abstract: Software testing is a vital part of software development. One would like to decrease work and get the most out of the number of faults detected. For optimization problems, test case production is used. Program checking for major problems in regular production trials has a known sufficiency importance factor. Generating test cases automatically will decrease the price and working time considerably. Experiment case information produced without any human interface by using genetic algorithm and random testing is compared with genetic algorithms. Observation is random testing limitations are solved by genetic algorithms. We have implemented these test cases and tested them in real time environments, and the outcomes show good performance.
    Keywords: routine test case generation; correspondence class partitioning; arbitrary testing.

  • Application of hazard identification and risk assessment for reducing occupational accidents in firework industries with specific reference to Sivakasi   Order a copy of this article
    by Indumathi N, Ramalakshmi R 
    Abstract: Occupational accidents should be avoided because they have very negative consequences on both industries and employees. In India, Sivakasi is the second largest fireworks manufacturing sector. Every workplace task that involves a chemical process has the potential to cause an accident. Identifying the hazards is essential to reduce accidents and explosions. Hazard Identification and Risk Assessment (HIRA) can be used as a risk assessment method to help users identify hazards and estimate the risk associated with each one. This study aims to look into the possible hazards and incidents that may occur in the fireworks industry, and to improve occupational safety and wellness by the techniques of HIRA and F-test. HIRA identified the industrial risk zones based on the task as high (43%), medium (36%), and low (21%). Through the failure detection analysis, it achieved 84% reduction of risk priority number through the prevention and mitigation program.
    Keywords: occupational accidents; hazard identification; risk assessment; fireworks industry; safety; chemical hazards.

  • Flight web searches analytics through big data   Order a copy of this article
    by Amna Khalil, Mazhar Javed Awan, Awais Yasin, Vishwa Pratap Singh, Hafiz Muhammad Faisal Shehzad 
    Abstract: The flight search is considered one of the biggest searches on the World Wide Web. This study aims to establish an effective prediction model from a huge dataset. This article offers a linear regression model to forecast flight searches using the big data framework SparkML library and statistics. Experiments on realistic datasets of domestic airports reveal that the suggested model's accuracy is close to 90% using the big data framework. Our research provides an efficient flight web search engine, which can manage through big data.
    Keywords: flights databases; search query; World Wide Web search engines; content-based retrieval.

  • Application of information technology to e-commerce   Order a copy of this article
    by Nasser Binsaif 
    Abstract: Nations are accelerating the application of technology to e-commerce because of its impact at all times, just as the use of computer applications in electronic shopping is one of the most important pillars of trade. E-commerce is the process of buying and selling services and goods over the internet. Generally, data or money is also transferred over the internet, which is an electronic network. In an online business environment, all information is displayed and payment is also made online. The current situation and the digital world have embraced the e-commerce facility. This article discusses the potential behaviours of e-commerce in the Kingdom of Saudi Arabia. The article analyses perspective and e-commerce in the Kingdom of Saudi Arabia. Fraudulent activities are encountered during transactions and purchases. The difficulties are addressed through security policies and the technology is promoted through social activities.
    Keywords: e-commerce; business; consumer; security policy; threat; social media.

  • The electrical circuit of a new seven-dimensional system with twenty-one boundaries and the phenomenon of complete synchronisation   Order a copy of this article
    by Khaled Mohammed Al-Hamad, Anas Romaih Obaid, Ahmed S. Al-Obeidi 
    Abstract: One the most important properties of dynamic systems is the synchronisation phenomenon. In this document, we obtain nonlinear control unit results in which to synchronise two equivalent 7D structures. We used linearisation and Lyapunov as analytical ways and linearisation way doesnt require Lyapunov function updating, thus it is effective to achieve synchronisation phenomena with better results than Lyapunov way. The two ways were used, and eventually we obtained such similarity to the error of the dynamic system. Digital emulation was applied to the mathematical system with control and error of the dynamic system. The digital excellent results were close to those of the two ways mentioned before. We studied three synchronisation phenomena (complete, incomplete, and hybrid) according to Lyapunov and linearisation ways, and compared their results. Finally, we applied the current system and present it in new attractor and compared these results with other similar ones.
    Keywords: chaos; Lyapunov; linearisation; projective synchronization; nonlinear dynamic system.

  • An empirical study for customer relationship management in the banking sector using machine learning techniques   Order a copy of this article
    by Guru Prasad Dash, Bhagirathi Nayak 
    Abstract: Customer creation is necessary for a new bank and as well as retention is an existing bank that is more productive and cost-effective. Indian bankers' CRM aims to develop and retain their customers and to view a whole organizational structure as a fully integrated attempt to seek, build, and meet the needs of customers. The deposit and innovation capacity is extremely low in rural areas, but immense in urban regions because the majority of the potential product scheme is well known. It was assumed that analysis and CRM in the banking sector were appropriate in these circumstances. The study is an analytical survey using machine learning techniques aimed at investigating the technological progress faced by commercial banks, and how far banks have gained to create a better performance of the financial sector of public and private sector banks.
    Keywords: CRM; financial sector; machine learning.

  • Data visualization using augmented reality for an education system   Order a copy of this article
    by Sumit Hirve, Pradeep Reddy CH 
    Abstract: Data is present in abundance in the present world whether it is education, e-commerce, defence or many other sectors of the nation, but the actual benefit arises when information is extracted from such huge data which can be used in educational, scientific, or commercial fields. Data visualisation plays an important role in understanding what exactly the data is while processing it and inferring results out of it. Students, being the building and budding blocks of the nation, at a very ripe age are exposed to abundant data so this paper is focused on improving the educational system so that students can get a proper understanding of concepts by visualising the matter at hand. The main objective of the paper lies in innovating educational technology by introducing augmented reality into the data visualisation process.
    Keywords: augmented reality; 3-D visualisation; Hololens; education system; Unity; Visual Studio.

  • An expert system based IoT system for minimisation of air pollution in developing countries   Order a copy of this article
    by Sudan Jha, Sidheswar Routray, Sultan Ahmad 
    Abstract: Carbon dioxide (CO2), nitrogen dioxide (NO2), suphur dioxide (SO2) are the major contributing sources to environmental pollution in developing countries. Therefore, measuring and controlling them are significant to human health. This paper proposes a new approach using Internet-of-Things (IoT) to measure and control air pollution in developing countries. Various sensors related to temperature, humidity, and smoke have been used to collect data. These data are sent to the central server through an access point. Numerous techniques have been used on these stored data to investigate the increase in the levels of pollution, temperature, and other parameters that cause air pollution. If they are above danger levels, then an alert signal will be sent to the whole city, and the precautionary measures that need to be taken by the citizens are broadcast. The proposed IoT model has shown superior performance compared with related works concerning factors such as CO2 and NO2.
    Keywords: pollution; pollutants; sensors; internet of things; temperature; controller.

  • Design of QoS on data collection in wireless sensor network for automation process   Order a copy of this article
    by Ghaida Muttashar Abdulsaheb, Hassan Jaleel Hassan, Osamah Ibrahim Khalaf 
    Abstract: Wireless sensor networks (WSN) facilitate the analysis of the universe with an unprecedented resolution. WSN is a network that connects many low-cost, low-powered sensor nodes that are capable of sensing/actuating and can interact with one another over short distances through a low-power radio. This article aims to provide strategies for enhancing and maintaining Quality of Service (QoS) in WSN communication. As a result, we implemented a classification-based multi-layer WSN stack prototype that takes CDC applications into account. This method decreases the increasing point load by control messages, resulting in higher application throughput. In the presence of reasonably large unicast data traffic, the CDC multi-layer stack achieves 85.16% throughput while running at less than 12.14% delay. While WSN is the transmission technique and automation is the implementation scenario, the existing methods can be extended to certain other WSN and scenarios with related communication patterns and QoS issues.
    Keywords: WSN; QoS; throughput; end-to-end delay; network traffic.

Special Issue on: Edge Computing and Artificial Intelligence Driven Technologies for Education Improvement

  • AI-BASED ARTIST STYLE APPRECIATION: FOLK ART IN THE CENTRAL PLAINS ORIENTED IMPLEMENTATION PLATFORM
    by Wei Tian 
    Abstract: The folk art in the Central Plains is rich in variety and has strong vitality. The culture of the Central Plains has a long history, and folk art is considered to be a representative type of folk culture in the Central Plains. Given this, this study proposes an image of folk art in the Central Plains style transfer algorithm with salient region reservation by fast style transfer algorithm. By introducing saliency detection network to generate saliency map of composite image and content image, which is helpful to improve the quality of stylized image. Experiments show that the stylized image generated by the algorithm proposed in this paper not only has satisfactory color and texture, but also reserves salient regions in the content image, which is conducive to the appreciation of the folk art in the Central Plains.
    Keywords: Folk art; Central Plains; AI; Image style transfer; Stylized image

  • IoT for Smart English Education: AI-based Personalized Learning Resource Recommendation Algorithm
    by Fang Wang 
    Abstract: The development of smart English education provides talent support for national economic and social development, solves the difficult problems in current education and enhances national competence. At present, Internet of Things (IoT) is constant maturing of continuous development of information technology, providing a solid technical foundation for the development of smart English education. IoT mainly senses and collects data through multi-sensors, uses a variety of communication technologies to establish a network to realize the connection between things and things. Data collection through IoT is an important source of big data in English education. IoT is the basis for smart English education, learning, management and effective collection of educational data. Artificial intelligence analyzes the learning resources collected by multi-sensors to make recommendation decisions. In this study, an attention-based deep collaborative learning resource recommendation model is proposed by integrating the attention mechanism into the learning resource recommendation algorithm.
    Keywords: Smart English education; IoT; AI; Sensor; Recommendation model

  • Sports injury detection mechanism based on multi-sensor fusion
    by Zun Liu, Xue Han, Yi Yang, Wei Wang, Fuquan Liu 
    Abstract: Cardiovascular disease and muscle damage caused by human sports injuries are increasing. Therefore, it is of great research significance to greatly reduce the damage to the human body through sports injury monitoring, detecting the degree of injury, and taking effective measures in time. In this paper, we propose a sports injury detection mechanism based on multi-sensor fusion. First, we determine the motion area of the human body by computing the cumulative difference between frames. Second, we utilize wavelet method and AutoEncoder model to design two feature extraction methods. Third, we combine the extracted features to detect the sports injuries. The experimental results verify the effectiveness of our proposed method.
    Keywords: Sports injury detection; Multi-sensor fusion; Neural network; Feature extraction

  • A Novel LightGBM-Based Industrial Internet Intrusion Detection Method
    by Zhiqiang Lv 
    Abstract: This paper proposes an Active Learning-based Intrusion Detection System. The system introduces expert annotation into the intrusion detection process, and combines the active learning query strategy with LightGBM to solve the problem of low accuracy of the intrusion detection system when the training samples are scarce. First, the process of data preprocessing is applied. Features are extracted from the traffic, and the borderline SMOTE method is introduced to improve the samples distribution. Then, the LightGBM algorithm is adopted for feature selection to reduce the data dimension. Next, the most valuable training samples are selected and labeled by human experts. The training samples are then fed into the classifier, while the Bayesian optimization is applied to optimize the hyperparameters of the classification model. Finally, a set of experiments are performed to evaluate the performance of our method.
    Keywords: Industrial Internet; intrusion detection; active learning; LightGBM

  • A Novel Neural Network based 3D Animation Model Classification Method
    by Ximan Shi 
    Abstract: The rapid development of information technology has also brought new vitality to art design. The 3D animation model making is a new multimedia technology based on computer technology. In order to efficiently organize and utilize the 3D model resources, researchers focus on how to achieve effective retrieval and classification. In order to realize the recognition and classification of 3D models, a novel network model called 3DSmallPCapsNet is proposed in this paper based on the feature that Capsule Network (CapsNet) exploits vector neurons to store feature space information. The proposed method can extract more representative features while reducing the model complexity. To evaluate our method, three different methods which are MeshNet, Shape-DNA and GPS-embedding respectively are compared. The experimental results on datasets SHREC10 and SHREC15 show that the proposed method has better performance.
    Keywords: 3D model classification; Capsule Network (CapsNet); pooling; animation model.

  • An Edge Computing based Evaluation and Optimization of Online Higher Vocational Education Mechanism
    by Jingtang Jia 
    Abstract: As a skill education and employment education, higher vocational education must comprehensively improve students' various skills, so that they can fully adapt to the social economic development and the actual needs of the post after graduation. However, the outbreak of COVID-19 disrupted students' normal life, making online teaching the main teaching mode. Online teaching mainly takes mobile devices as terminals, and the number of devices at the edge of the network and the data are growing rapidly. In this case, the centralized processing mode with cloud computing model as the core will not be able to efficiently process the data generated by edge devices. In this paper, an improved binary particle swarm optimization algorithm is proposed to study the offloading and execution sequence of user computing tasks. Simulation results reveal that the proposed method outperforms the-state-of-the-art algorithms in terms of convergence, low latency and energy consumption.
    Keywords: dge computing; higher vocational education; task offloading; particle swarm optimization

  • Multidimensional Meteorological Data Analysis Based on Machine Learning
    by Jianxin Wang, Geng Li 
    Abstract: Multidimensional meteorological data has very important application scenarios, and how to effectively analyze and use it is a challenging problem. This paper proposes a multi-dimensional meteorological data analysis method based on an improved Bayesian neural network. This paper considers the example of wind power forecasting for wind farms. The input data can be dived into two categories, which are the multidimensional meteorological data and historical data of wind power. First, the raw multidimensional meteorological data are preprocessed using principal component analysis (PCA). Then, the processed meteorological data and historical wind power data are fed through the Long- and Short-Term Memory (LSTM) network to achieve data feature extraction and further data dimensionality reduction. At last, they are input to the improved Bayesian neural network to achieve data fitting. This paper selects the data of 12 wind farms in a certain region of China for simulation experiments. Our proposed method is compared with BP-Neural Network and Support Vector Machine (SVM) to evaluate its performance. The experimental results show that the method proposed in this paper has good performance.
    Keywords: Meteorological data; Multidimensional data; Bayesian neural network; Wind power forecast.

  • Energy-saving smart city: an edge computing-based renovation and upgrading scheme for old residential areas
    by Zhi Zhao 
    Abstract: The renovation of old communities has become an important issue in the current development of new urbanization. The development of edge computing provides a powerful pillar for the energy-saving renovation of old residential areas. Accurately predicting the electricity usage can provide a more personalized electricity consumption plan for the users of the community, thus making the overall energy saving possible. Therefore, we propose a power prediction model based on the stacking model to provide a strategy for saving power and energy in old communities. First, we adopt the word2vec algorithm to extract the discrete feature word vector and to capture the co-occurrence relationship from the discrete feature. Second, we adopt a neural network model to perform feature extraction on for continuous features. Third, we design a power prediction model based on the stacking model by using XGBoost algorithm, LightGBM algorithm, and linear regression. The experimental results prove that the method proposed in this paper has good prediction performance.
    Keywords: Energy-saving; Smart city; Neural network; Feature extraction

  • A Novel Deep Learning Driven Robot Path Planning Strategy: Q-learning Approach
    by Junli Hu 
    Abstract: As the basis of mobile navigation technology, path planning has attracted the attention of the majority of scholars. In this paper, the deep learning framework is integrated into Q-learning, and a Deep Q-Network (DQN) algorithm based on memory optimized mechanism is designed to improve the convergence of DQN, so that the robot can carry out good path planning in complex environment. Experimental results show that the proposed method has a good performance in path planning. Experimental results show that in the case of multi-round training using the algorithm proposed in this paper, the path planning steps of the robot are the shortest, and the total training time and single round time of the robot in path planning are also the shortest.
    Keywords: robot; path planning; deep learning; DQN; memory

  • Machine Learning for English Teaching: A Novel Evaluation Method
    by Yang Yang 
    Abstract: This paper proposes a novel oral English scoring system based on machine learning. The system can be deployed on the end side (mobile app) through the Internet and can be used to assist teachers in evaluating students' oral English pronunciation, fluency and the tunnel degree. An attention based LSTM (Long Short-Term Memory) neural network is employed in the paper, which can process and analyze speech signals effectively. Meantime a large amount of training data is collected for network training. We compare the novel oral English evaluation system and the experts’ evaluation results. The verification results show that the oral English evaluation system based on machine learning not only can achieve the ability of the English experts, but also has higher accuracy and can identify more oral pronunciation problems.
    Keywords: College English teaching; Internet; Oral English; Machine Learning; LSTM; attention