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Energy Efficient Unequal Fault Tolerance Clustering Approach

computer Systems Science & engineering 2023年第5期45卷 1971-1983页

作者： Sowjanya Ramisetty Divya Anand Kavita Sahil Verma NZ Jhanjhi Mehedi Masud Mohammed Baz Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadIndia School of Computer Science and Engineering Lovely Professional UniversityPhagwaraIndia Department of Computer Science and Engineering Chandigarh UniversityMohaliIndia Department of Computer Science and Engineering Tailor’s UniversityMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia

For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network *** saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor *** of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to *** increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor *** Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster *** data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the ***,the MCH overhead *** the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.

关键词： Energy-efficiency unequal fault tolerant clustering approach particle swarm optimization master data aggregator energy efficient time division multiple access optimal nodes

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Early-stage stroke prediction based on Parkinson and wrinkles using deep learning

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Neural Computing and Applications 2024年第30期36卷 18781-18805页

作者： Haritha, T. Babu, A. V. Santhosh Department of Information Technology Vivekanandha College of Engineering for Women Tamilnadu Tiruchengode India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tamilnadu Tiruchengode India

The application of noninvasive methods to enhance healthcare systems has been facilitated by the development of new technology. Among the four major cardiovascular diseases, stroke is one of the deadliest and potentially fatal, but, if detected early enough, a patient's life may be spared. Most stroke research has centered on MRI and CT scans for uncomplicated categorization. This medical approach (imaging) is costly, time-consuming and needs the utilization of complex technology. To make up for these shortcomings, however, there has been a lot of interest in adopting noninvasive, measurable EEGs. Nevertheless, the raw data should be classified before the proper characteristics can be formed, both the forecasting algorithms and the analytical techniques demand time. As a result, this work proposes a deep learning-based model that aims to predict the chance of stroke at an early stage utilizing Parkinson's disease and wrinkles as markers. A patient may have a stroke disease if they are diagnosed with both Parkinson's disease and wrinkles. To the best of our knowledge, this research is the first to use these biomarkers to predict the risk of having a stroke. The proposed model achieves a higher accuracy of 94.7% on the considered dataset. Additionally, the recommended model was evaluated and tested in terms of loss, training time, accuracy, recall, and F1-score versus the other existing models. With less price and pain than present testing approaches, these discoveries are predicted to result in major enhancements in the early detection of strokes. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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A survey of video violence detection

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Cyber-Physical Systems 2023年第1期9卷 1-24页

作者： Yao, Huiling Hu, Xing School of Optical-Electrical Information and Computer Engineering University of Shanghai for Science and Technology Shanghai China

As one of the important applications of intelligent video surveillance, violent behaviour detection (VioBD) plays a crucial role in public security and safety. As a particular type of behaviour recognition, VioBD aims to identify whether the behaviours that occurred in the scene is aggressive, such as fighting and assault. To comprehensively analyse the current state and predict the future trend of VioBD research, we survey the existing approaches of VioBD in this work. First, we briefly introduce the basic principle and the challenges of VioBD;Then, we category the existing approaches according to their framework, including the traditional framework, end-to-end deep learning framework, and hybrid deep learning framework. Finally, we introduce the public datasets for evaluating the performance of VioBD approaches and compare their performances on these datasets. Besides, we also summarise the open problems in VioBD and predict its future trends. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Surveys

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Exploring diversity and time-aware recommendations: an LSTM-DNN model with novel bidirectional dynamic time warping algorithm

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Soft Computing 2025年第4期29卷 2003-2013页

作者： Li, Te Chen, Liqiong Sun, Huaiying Hou, Mengxia Lei, Yunjie Zhi, Kaiwen School of Computer and Engineering East China University of Science and Technology Meilong road Shanghai200237 China School of Computer Science and Information Engineering Shanghai Institute of Technology Haiquan road Shanghai201400 China School of Chemistry and Environmental Engineering Shanghai Institute of Technology Haiquan road Shanghai201400 China

With the advent of the Web 3.0 era, the amount and types of data in the network have sharply increased, and the application scenarios of recommendation algorithms are continuously expanding. Location recommendation has gradually become one of the popular application scenarios in recommendation algorithms. Traditional recommendation algorithms not only ignore the temporal attribute of data when recommending information to users, but also blindly pursue the recommendation accuracy, which will cause certain "information cocoon room" problems. Therefore, this article treats user historical data as a time series and proposes an LSTM-DNN model based on the novel bidirectional Dynamic Time Warping (DTW) algorithm. Firstly, in response to the issue of different users consuming different amounts of information, this article proposes a novel bidirectional DTW algorithm to calculate the similarity between different users. Secondly, this article supplements the user dataset from three perspectives: "utilization" and "exploration" of information, and spatiotemporal attributes of data, which alleviates the problem of data sparsity and cold start in the dataset to a certain extent. Moreover, it effectively enhances the diversity of recommendation results. Finally, this paper constructs an Long Short-Term Memory-Deep Neural Networks (LSTM-DNN) to dynamically obtain user interests and preferences, and proposes a new metric Cumulative Self-System Diversity (CSSD) to measure the diversity of algorithm recommendation results. Experiments have shown that the model effectively enhances the diversity of recommendation results while ensuring recommendation accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Time series

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Leveraging Journaling File System for Prompt Secure Deletion on Interlaced Recording Drives

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IEEE Transactions on Emerging Topics in Computing 2023年第3期11卷 619-634页

作者： Chen, Shuo-Han Huang, Kuo-Hao National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

With the growing awareness of secure computation, more and more users want to make their digital footprints securely deleted and irrecoverable after updating or removing files on storage devices. To achieve the effect of secure deletion, overwritten-based secure deletion techniques have been proposed to overwrite invalidated storage space with scrambled data content. Nevertheless, overwritten-based secure deletion requires explicit write requests for rewriting invalidated storage space with random data, thus incurring additional internal write traffic on storage devices. Recently, this inefficient aspect of secure deletion is exacerbated by the emergence of high-density interlaced magnetic recording (IMR) technology. IMR technology enhances the storage density of hard disk drives by interlacing narrower top tracks on wider bottom tracks. Owing to the interlaced track layout, securely erasing invalidated data on bottom tracks interferes with adjacent top tracks and track rewrites are required to preserve valid data on top tracks;thus, overwritten-based secure deletion on IMR drives significantly enlarges the internal write traffic and impairs the secure deletion efficiency. In this paper, instead of directly alleviating the constraint induced by the interlaced track layout, we propose leveraging the interlaced track layout of IMR drives in combination with the journaling data stream of journaling file systems to enable prompt secure data deletion while minimizing the write traffic of secure deletion operations. Experimental results suggest that the performance improvement achieve by the proposed prompt secure deletion (P-SD) strategy is 51.84% on average, when compared with the previous TrackLace approach on IMR drives. © 2013 IEEE.

关键词： Hard disk storage

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Multi‐feature fusion and memory‐based mobile robot target tracking system

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IET Cyber-Systems and Robotics 2024年第3期6卷 62-73页

作者： Hanqing Sun Shijie Zhang Qingle Quan College of Information Engineering Henan University of Animal Husbandry and EconomyZhengzhouHenanChina College of Information Technology and Computer Science University of the CordillerasBaguioPhilippines College of Electrical Engineering Henan University of TechnologyZhengzhouHenanChina

In crowded settings,mobile robots face challenges like target disappearance and occlusion,impacting tracking *** existing optimisations,tracking in complex environments remains *** address this issue,the authors propose a tailored visual navigation tracking system for crowded *** target disappearance,an autonomous navigation strategy based on target coordinates,utilising a path memory bank for intelligent search and re‐tracking is *** significantly enhances tracking *** handle target occlusion,the system relies on appearance features extracted by a target detection network and a feature memory bank for enhanced *** control technology ensures robust target tracking by fully utilising appearance information and motion characteristics,even in occluded *** testing on the OTB100 dataset validates the system's effectiveness in addressing target tracking challenges in diverse crowded environments,affirming algorithm robustness.

关键词： autonomous navigation Kalman filtering memory bank mobile robots proportional‐integral‐derivative

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Designing an Ultrasonic-Assisted Machining System by Using a Network Mapping Fusion–Convolutional Neuro-Fuzzy Network Model

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IEEE Transactions on Industrial Informatics 2024年第7期20卷 1-12页

作者： Lin, Cheng-Jian Jhang, Jyun-Yu Lin, Xue-Qian the Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung 411030 Taiwan the Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung 404336 Taiwan

In the field of machining, product quality must meet customer specifications. In general, surface roughness is an essential indicator of machining quality. Low surface roughness correlates with increased fatigue strength and corrosion resistance. However, the main factor that affects surface roughness is the selection of the machining parameters. When different parameters are combined, the resulting machining quality varies. Therefore, to achieve the desired machining quality, appropriate machining parameters must be selected. In this study, an ultrasonic-assisted machining system (UAMS) was designed to help users determine the machining parameters and machine SiC materials. To establish a prediction model for surface roughness, a novel network mapping fusion (NMF) convolutional neuro-fuzzy network (CNFN) model was used in the designed UAMS. The differential evolution algorithm was then used to search for optimized machining parameters. To explain the prediction model, which can help analyze the factors that have the greatest influence on surface roughness, a Shapley additive explanations method is proposed. The proposed NMF–CNFN model was more accurate than were the other deep learning models and exhibited a MAPE of 1.98%. When optimized machining parameters were selected, the desired surface roughness was obtained, thereby confirming the effectiveness and accuracy of the proposed UAMS. Moreover, the proposed model was implemented in a field-programmable gate array (FPGA) to reduce its power consumption and increase its computational performance. Experimental results indicated that the computational speed of the FPGA was 99.64%and 99.16%higher than those of the CPU and GPU, respectively. IEEE

关键词： Convolutional neuro-fuzzy network (CNFN) differential evolution (DE) algorithm neuro-fuzzy network Shapley additive explanations (SHAP) ultrasonic-assisted machining system (UAMS)

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Pyram: a robust and attack-resistant perceptual image hashing using pyramid histogram of gradients

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International Journal of information technology (Singapore) 2024年第8期16卷 5331-5349页

作者： Neog, Prabhakar Sharma Roy, Moumita Sangale, Tejas Jain, Nandita Dahal, Aditi Patgiri, Ripon Thounaojam, Dalton Meitei Department of Computer Science and Engineering National Institute of Technology Silchar Department of Computer Science and Information Technology Siksha ‘O’ Anusandhan University

Perceptual image hashing is a significant and time-effective method for recognizing images within extensive databases, focusing on achieving two key objectives: robustness and discrimination. The right balance between these two aspects remains a significant challenge in contemporary hashing research. Moreover, many image hashing algorithms face limitations when performing satisfactorily against specific image processing attacks, such as rotation. With this in mind, a rotation correction based perceptual image hashing system is designed called as Pyram using a pyramid histogram of gradients (PHOG). The system exploits the properties of log polar transform for invariance to geometric distortions and then the PHOG in blocks for generating the final hash vector. In computing hash similarity, the correlation is employed as the metric of choice. The trade-off between robustness and discrimination is evaluated on benchmark databases against single and double attacks. Furthermore, the comprehensive experiments further confirm that the PHOG method consistently delivers better accuracy than its state-of-the-art methods. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Log-Polar Transform Perceptual image hashing PHOG

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Computation of Graph Fourier Transform Centrality Using Graph Filter

IEEE Open Journal of Circuits and Systems

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IEEE Open Journal of Circuits and Systems 2024年 5卷 69-80页

作者： Tseng, Chien-Cheng Lee, Su-Ling National Kaohsiung University of Science and Technology Department of Computer and Communication Engineering Kaohsiung824005 Taiwan Chang Jung Christian University Department of Computer Science and Information Engineering Tainan711301 Taiwan

In this paper, the computation of graph Fourier transform centrality (GFTC) of complex network using graph filter is presented. For conventional computation method, it needs to use the non-sparse transform matrix of graph Fourier transform (GFT) to compute GFTC scores. To reduce the computational complexity of GFTC, a linear algebra method based on Frobenius norm of error matrix is applied to convert the spectral-domain GFTC computation task to vertex-domain one such that GFTC can be computed by using polynomial graph filtering method. There are two kinds of designs of graph filters to be studied. One is the graph-aware method;the other is the graph-unaware method. The computational complexity comparison and experimental results show that the proposed graph filter method is more computationally efficient than conventional GFT method because the sparsity of Laplacian matrix is used in the implementation structure. Finally, the centrality computations of social network, metro network and sensor network are used to demonstrate the effectiveness of the proposed GFTC computation method using graph filter. © 2020 IEEE.

关键词： Complex networks

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A Computational Model for Driver Risk Evaluation and Crash Prediction Using Contextual Data from On-board Telematics

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Recent Advances in computer Science and Communications 2022年第4期15卷 620-635页

作者： Jain, Neetika Mittal, Sangeeta Department of Computer Science & Engineering and Information Technology Faculty of Engineering Jaypee Institute of Information Technology Noida India

Introduction: Vehicle crashes can be hazardous to public safety and may cause infrastructure damage. Risky driving significantly raises the possibility of the occurrence of a vehicle crash. As per statistics by the World Health Organization (WHO), approximately 1.35 million people are involved in road traffic crashes resulting in loss of life or physical disability. WHO attributes events like over-speeding, drunken driving, distracted driving, dilapidated road infrastructure and unsafe practices such as non-use of helmets and seatbelts to road traffic accidents. As these driving events negatively affect driving quality and enhance the risk of a vehicle crash, they are termed as negative driving attributes. Methods: A multi-level hierarchical fuzzy rules-based computational model has been designed to capture risky driving by a driver as a driving risk index. Data from the onboard telematics device and vehicle controller area network is used for capturing the required information in a naturalistic way during actual driving conditions. Fuzzy rules-based aggregation and inference mechanisms have been designed to alert about the possibility of a crash due to the onset of risky driving. Results: On-board telematics data of 3213 sub-trips of 19 drivers has been utilized to learn long term risky driving attributes. Furthermore, the current trip assessment of these drivers demonstrates the efficacy of the proposed model in correctly modeling the driving risk index of all of them, including 7 drivers who were involved in a crash after the monitored trip. Conclusion: In this work, risky driving behavior has been associated not just with rash driving but also other contextual data like driver’s long-term risk aptitude and environmental context such as type of roads, traffic volume and weather conditions. Trip-wise risky driving behavior of six out of seven drivers, who had met with a crash during that trip, was correctly predicted during evaluation. Similarly, for the other 12

关键词： Telematics

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