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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Manjunath, K.V. Alam, Intekhab Nidhya, M.S. Verma, Tushar K. School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Attention-primarily based Recurrent Neural Networks (RNNs) are a technique for medical image classification. RNNs can robotically extract temporal and spatial facts from scientific imaging statistics and make sensible decisions. RNNs are especially nicely-perfect for clinical photograph category obligations, cutting-edge their potential to perceive lengthy-term temporal styles, recognize the relationships between distinct medical picture datasets, and enable In this paper, we overview the existing tries to use interest-based RNNs for medical image classification, discuss the contemporary demanding situations and suggest some ability future guidelines for studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Invited Paper - Circuit Partitioning with Reinforcement Learning and Edge-Based Initialization 35

Invited Paper - Circuit Partitioning with Reinforcement Lear...

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35th International Workshop on Rapid System Prototyping, RSP 2024

作者： Cheng, Ka Chuen Siddiqi, Umair F. Grewal, Gary Areibi, Shawki M. University of Guelph School of Computer Science Guelph Canada King Fahd University of Petroleum and Minerals Information Technology Dhahran31261 Saudi Arabia University of Guelph School of Engineering Guelph Canada

ISBN: (纸本)9798331519537

The Fiduccia-Mattheyses-Sanchis (FMS) algorithm is a widely used local search method for K-way circuit partitioning, but it's prone to getting stuck in local minima. Traditionally, this has been addressed by running FMS multiple times with different random initial solutions, hoping for a better result. Building on our previous work with an RL-based local search method that helps FMS avoid these traps, this research explores a new approach: using constructive methods to generate superior initial solutions. We explored two such methods: NDE (node growing algorithm), a commonly used node-based method that maximizes node absorption, and NET (net growing algorithm), an edge-based approach that maximizes net absorption. By integrating NDE and NET with our RL-based local search, we've achieved significant improvements. Experiments on ISPD98/IBM benchmarks demonstrate that an edge-based approach provides higher-quality solutions for larger circuits and larger numbers of partitions. Combining these initial solutions with our RL-based approach further reduces the cutsize generate by the RL-based approach by up to 79.5%. © 2024 IEEE.

关键词： Circuit Partitioning Hypergraph Partitioning Machine Learning Reinforcement Learning

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Fake review detection using transformer-based enhanced LSTM and RoBERTa

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 250-258页

作者： Mohawesh, Rami Bany Salameh, Haythem Jararweh, Yaser Alkhalaileh, Mohannad Maqsood, Sumbal College of Engineering Al Ain University Al Ain United Arab Emirates Hijjawi Faculty of Engineering Technology Yarmouk University Irbid Jordan Department of Computer Science Jordan University of Science and Technology Jordan College of Education Al Ain University Al Ain United Arab Emirates School of Information Technology University of Tasmania TAS Australia

Internet reviews significantly influence consumer purchase decisions across all types of goods and services. However, fake reviews can mislead both customers and businesses. Many machine learning (ML) techniques have been proposed to detect fake reviews, but they often suffer from poor accuracy due to their focus on linguistic features rather than semantic content. This paper presents a novel semantic- and linguistic-aware model for fake review detection that improves accuracy by leveraging advanced transformer architecture. Our model integrates RoBERTa with an LSTM layer, enabling it to capture intricate patterns within fake reviews. Unlike previous methods, our approach enhances the robustness of fake review detection and authentic behavior profiling. Experimental results on semi-real benchmark datasets show that our model significantly outperforms state-of-the-art methods, achieving 96.03 % accuracy on the OpSpam dataset and 93.15 % on the Deception dataset. To further enhance transparency and credibility, we utilize Shapley Additive Explanations (SHAP) and attention techniques to clarify our model's classifications. The empirical findings indicate that our proposed model can offer rational explanations for classifying specific reviews as fake. © 2024

关键词： Fake detection

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Intrusion Detection System in IoT Network by using Metaheuristic Algorithm with Machine Learning Dimensional Reduction Technique 3

Intrusion Detection System in IoT Network by using Metaheuri...

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3rd International Conference on Computing, Analytics and Networks, ICAN 2022

作者： Anusha, Chintam Sravani, A. Anusha, Jetti Lakshmi, Choudari Kumari, Gantla Santoshi Vignan's Institute of Information Technology Computer Science and Engineering Visakhapatnam India GITAM School of Technology Computer Science and Engineering Visakhapatnam India

ISBN: (纸本)9781665499446

The network security is a main problem in any disseminated framework. To providing the safe and secure network we have been proposed anomaly detection system against from the suspicious attacks. The essential goal of this exploration is to plan effective IDS for IoT organization. The interruption discovery assumes a fundamental part in recognizing various assaults on IoT and upgrades the IoT performance. In this paper, one of the metaheuristic algorithms GSO (glow-worm swarm optimization) algorithm add with machine learning technique PCA (principal component analysis) used for providing the IDS. Therefore, the metaheuristic-based intrusion discovery model to recognize malicious activities by utilizing the NSL-KDD dataset. Dimensional reduction technique Principal component analysis is utilized for extraction of features and GSO algorithm is utilized to classify the various class of malicious attacks in the dataset. We have considered some metrics like precision, detection rate, recall, accuracy and FAR are computed. The suggested method evaluated better in each parameter compared with other existing techniques. © 2022 IEEE.

关键词： Principal component analysis

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Energy-Efficient Communication Protocols for IoT Devices 9

Energy-Efficient Communication Protocols for IoT Devices

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Rahamathunnisa, U. Vivekanand, Chettiyar Vani Chithras, T. Sreedevi, E. Kiran, Venneti Rajendiran, M. School of Computer Science Engineering And Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Rmk College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Thiruvallur India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science And Technology Department of Electrical And Electronics Engineering Chennai Tamil Nadu Avadi600062 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram India Andhra Pradesh Surampalem India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

The development and optimisation of energy-efficient communication protocols that are specifically adapted for Internet of Things (IoT) devices is the focus of this research. The proliferation of interoperable bias has given rise to the converse girding energy consumption, which has come a consummate concern in the contemporary period. The primary end of this bid is to probe innovative approaches and protocols with the capacity to reduce the energy consumption of Internet of Effects(IoT) connections. As a result, the bias will retain the capability to attain an extended functional lifetime while coincidently producing a lowered environmental footmark. The presented protocols aim to achieve a result that balances the utilisation of energy coffers with the conservation of dependable communication. A thorough examination of the presently employed protocols, coupled with the perpetration of sophisticated optimisation ways, will be the styles employed to achieve this thing. Theprovides a significant donation to the development and prosecution of communication protocols that effectively address the unique energy constraints encountered by Internet of effects(10T)- connected ecosystems. This contributes to the enhancement of effectiveness and sustainability in the world of networked bias, which is expanding at an accelerated rate. © 2024 IEEE.

关键词： Energy efficiency

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A Three-Stage Impulsive Delay Synchronization 6

A Three-Stage Impulsive Delay Synchronization

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6th International Conference on Electronic engineering and Informatics, EEI 2024

作者： Zhu, Shixiang Luo, Hao Feng, Yuming Onasanya, Babatunde Oluwaseun School of Electronic and Information Engineering Chongqing Three Gorges University Chongqing Wanzhou China School of Computer Science and Engineering Chongqing Three Gorges University Chongqing Wanzhou China University of Ibadan Department of Mathematics Ibadan Nigeria

ISBN: (纸本)9798350353594

In most research on impulsive control, the phenomenon of delay is often overlooked. However, in safetycritical applications involving the synchronization of two systems, transmission delays and information interference inevitably occur during the information transfer process. In this paper, we address the issue of delay synchronization between two systems. We derive stability criteria based on Lyapunov stability, impulsive control theory, and linear matrix inequalities. Subsequently, we design a three-stage impulsive switching controller with time windows. This controller achieves delay synchronization between two chaotic systems. Simulation results confirm the effectiveness of this method in stabilizing the systems. © 2024 IEEE.

关键词： Stability criteria

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Interference elimination in IRS-enabled indoor mmw-D2D communication 24

Interference elimination in IRS-enabled indoor mmw-D2D commu...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Han, Bingjie Chen, Xin Jiao, Libo Guo, Dongchao School of Control Engineering Beijing Information Science and Technology University Beijing China School of Computer Science Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798350319934

In this paper, we study the interference elimination in intelligent reflecting surface (IRS) enabled indoor millimeter-wave device to device (mmw-D2D) communication. In order to improve spectrum utilization and transmission rate, we consider assisting IRS by the anti-phase signal reflection to reduce the co-channel interference in indoor mmw-D2D communication. We establish the system model of the IRS selection and enablement on interference elimatation (IRS-SEIE) problem based on integer programming theory. Then, we prove that IRS-SEIE problem is an NP-hard problem by transforming it into a knapsack problem. Therefore, we propose a heuristic algorithm based on genetic algorithm to obtain the optimal solution. Comparison experiments with particle swarm optimization (PSO), monte carlo (MC) algorithm and without IRS show that proposed algorithm's rate is 24.36%, 48.47% and 103.03% higher than them. And it can also reduce the blocking probability by 25.53%, 45.81% and 62.34%. © 2022 IEEE.

关键词： Millimeter waves

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Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

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computers, Materials & Continua 2023年第5期75卷 3239-3255页

作者： Majdy M.Eltahir Adil Yousif Fadwa Alrowais Mohamed K.Nour Radwa Marzouk Hatim Dafaalla Asma Abbas Hassan Elnour Amira Sayed A.Aziz Manar Ahmed Hamza Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Arts and Science Najran UniversitySharourahSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Applied SciencesKing Khalid UniversityMuhayil63772Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event *** is especially applicable in the case of elderly or disabled people who live self-reliantly in their *** sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during *** is one of the most important problems confronted by older people and people with movement *** previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled ***,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor ***,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s *** this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)*** presented IWODL-FDDP model aims to identify the fall events to assist disabled *** presented IWODLFDDP model applies an image filtering approach to pre-process the ***,the EfficientNet-B0 model is utilized to generate valuable feature vector ***,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall ***,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the *** experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

关键词： Fall detection disabled people deep learning improved whale optimization assisted living

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Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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Soft Computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

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Rapid Single-Flux-Quantum Circuit Routing Algorithm Considering Length Matching

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Jisuanji Yanjiu yu Fazhan/computer Research and Development 2025年第5期62卷 1151-1163页

作者： Liu, Genggeng Yu, Yantao Zhou, Ruping Wei, Rongshan Xu, Ning College of Computer and Data Science Fuzhou University Fuzhou350116 China Fuzhou350116 China College of Physics and Information Engineering Fuzhou University Fuzhou350108 China School of Information Engineering Wuhan University of Technology Wuhan430070 China

The high frequency characteristics of rapid single-flux-quantum (RSFQ) circuits poses a great challenge to circuit layout design. In order to solve the circuit delay problem caused by the high frequency characteristics of RSFQ, delay elements such as passive transmission line can be used in the routing stage. The delay of a passive transmission line is roughly proportional to its length, and the power consumption of the passive transmission line does not increase with the increase of the wirelength, so length matching routing is a crucial problem for RSFQ circuits. Therefore, we propose an efficient RSFQ circuit routing algorithm considering length matching, including the following key strategies: 1) when generating the initial path, a method of detour routing is presented to meet the partial length matching of passive transmission lines without changing the initial routing space;2) an iterative resource insertion algorithm based on region-awareness is utilized to reduce the area of additional resources needed to be added;3) a length-matching driven routing algorithm considering blocking cost is designed, which improves the resource utilization of routing space. Experimental results show that, compared with existing multi-terminal routing algorithms, the proposed algorithm reduces the area required for routing by 8% and the running time by 36%, thus achieving fast and high-quality routing results. © 2025 science Press. All rights reserved.

关键词： Electric delay lines

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