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5th Inter.a.ional Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Logapriya, E. Surendran, r. Poornima, D. Kartheesan, L. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

This study presents a deep learning architecture designed to monitor.a.woman's menstr.a. cycle and provide personalized dietary suggestions to improve health. The system employs complex models, including CNNs, rNNs, Transformers, T-GNNs, VAEs, and GANs, to predict the intensity of symptoms, determine the phases of the menstr.a. cycle, and offer personalized nutritional advice. By analysing data from menstr.a. diaries, food consumption, and symptom tracking, the method establishes a strong association between hormonal fluctuations, symptoms, and nutritional needs. Model accuracy is enhanced through data preprocessing techniques, including imputation, nor.a.ization, and augmentation, which provide high-quality training inputs. research shows that Transformer models outperform other.a.pr.a.hes in phase recognition (96% accuracy) and phase-aware nutritional recommendation accuracy. This research highlights how deep learning can transform women's healthcare by providing personalized nutritional recommendations based on data that align with the phases of a woman's menstr.a. cycle, ultimately contributing to improved health for women. © 2024 IEEE.

关键词： Generative adver.a.ial networks

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Deep Learning-Based Intrusion Detection System for Internet of Things Networks for Enhancing Security Against Cyber.a.tacks

Deep Learning-Based Intrusion Detection System for Internet ...

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Inter.a.ional Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Shar.a. Preeti Hasan, Dler Salih Marthandan, T. Singh, Jagendra Chaku, Shweta Tiwari, Mohit Department of Computer Science Engineering Raj Kumar Goel Institute of Technology AKTU University Lucknow India Department of Computer Science and Information Technology University of Salahaddin Erbil Iraq Electronics and Communication Engineering K S R Institute for Engineering and Technology Tiruchengode India School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of Computer Science Engineering Inderprastha Engineering College Ghaziabad AKTU Lucknow India Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

ISBN: (纸本)9789819986606

Deep learning algorithms are used in this research to propose a novel appr.a.h to intrusion detection in Internet of Things (IoT) networks. The suggested intrusion detection system employs a six-layered deep neural network architecture, which is augmented with a feature extraction module to examine network packet data and identify dangerous activities. The system was evaluated against a large dataset that comprised the following five primary attack types encountered in IoT environments: Blackhole Attacks, Opportunistic Attacks, Distributed Denial of Service (DDoS) Attacks, Wormhole Attacks, and Sinkhole Attacks. Perfor.a.ce evaluation metrics such as precision, recall, accuracy, specificity, and F1 Score were used to evaluate the system's effectiveness in recognizing and categorizing attacks. The data demonstrate that across various forms of assault, accuracy and recall rates vary from 93 to 96.4%. The F1 Score demonstrates balanced perfor.a.ce, highlighting the system's ability to eliminate false positives and false negatives. The feature extraction module considerably improved the dataset by adding essential network packet attributes such as source and destination IP addresses, session duration, and transmission rates, incr.a.ing the overall accuracy of the intrusion detection system. The utility of the proposed deep learning model in dealing with diverse attack scenarios is shown by its ability to detect and neutralize various intrusion attempts. This research extends intrusion detection methodologies by presenting a robust and intelligent solution to safeguard critical data and resources in IoT networks. Continuous research and development, on the other hand, are essential in r.a.-world IoT deployments to handle new attack vectors and provide system adaptability to dynamic network situations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially impor.a.t to evaluate and determine the particularly Weather.a.tribute(WA),which is directly related to the detection reliability and *** this paper.a.strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation appr.a.hes include competing AEF-based predicted value inter.a.s,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular.a.F,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs alr.a.y evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for.a.more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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Pose invar.a.t non-frontal 2D, 2.5D face detection and recognition technique

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Inter.a.ional Jour.a. of Infor.a.ion technology (Singapore) 2023年第5期15卷 2603-2611页

作者： Thavani, Shiva Shar.a. Sahil Kumar, Vijay Computer Science and Engineering Department Thapar Institute of Engineering and Technology Punjab Patiala India Computer Science and Engineering Department Punjab Engineering College Chandigarh India Information Technology Department Dr. B.R. Ambedkar National Institute of Technology Punjab Jalandhar India

In this paper, the Pose Invar.a.t Identity recognition (PIIr) technique is proposed. It measures face resemblance using facial landmarks, which are further vigorous to pose disparity. The proposed technique utilizes the concepts of face frontalization and discriminative learning techniques. 3D Morphable Model is used in both techniques for pose invar.a.t. The proposed technique is validated on CAS-PEAL face dataset. Experimental results signify that the proposed technique attains authentication accuracy of 90.24% on face datasets with 30° pan-angle. The proposed technique is further.a.plied on 2.5D face dataset with var.a.t facial positions and expressions. results reveal that the proposed method can identify the face accurately. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： 2.5D depth-based face recognition Face reconstruction Feature extraction Non-frontal face recognition Pose-invar.a.t identity recognition

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Intelligent IoT-Driven Advanced Predictive Maintenance System for Industr.a. Applications

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SN computer Science 2025年第2期6卷 1-24页

作者： Dhinakaran, D. Edwin raja, S. Velselvi, r. Purushotham, N. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of CSE School of Computing Mohan Babu University Tirupati India

Predictive Maintenance (PdM) aims to ensure the continuous operation of high-risk industr.a. systems. This challenge is especially critical in environments where equipment failure can cause major financial losses and disrupt operations. Traditional PdM appr.a.hes, while fairly effective, often fall short in accurately predicting failures due to their reliance on simple statistical methods and predefined rules. The proposed work introduces an Intelligent IoT-Driven Advanced Predictive Maintenance System (APdM) that addresses these limitations. By integrating advanced machine learning techniques, including supervised and reinforcement learning, with IoT technologies to enhance predictive accuracy as well as the operational efficiency. The system leverages Explainable AI (XAI) to ensure transparency in decision-making and employs federated learning for distributed model training, preserving data privacy across multiple edge devices. The proposed APdM system was evaluated using the Kaggle "Air Compressor Predictive Maintenance" Dataset, which provided extensive sensor data from heavy vehicle operations. Comparative analysis against four prevailing PdM appr.a.hes—Traditional PdM, Decision Support System-PdM, Genetic Algorithm-based PdM, and AI-based PdM—demonstrated the superior perfor.a.ce of our system. The results show that the APdM system, powered by intelligent IoT integration, diminishes the Mean Error Percentage (MEP) by up to 38.4% compared to traditional methods and decr.a.es the Mean Absolute Error (MAE) by 36.7% relative to DSS-PdM. Additionally, the system achieves a 78% improvement in Symmetric Mean Absolute Percentage Error (SMAPE) over GA-PdM, underscoring its robust perfor.a.ce in r.a.-world industr.a. scenarios. These outcomes validate the effectiveness of the proposed APdM system, marking a significant improvement in the field of predictive maintenance through the intelligent integration of IoT technologies. © The Author(s), under exclusive licence to S

关键词： Explainable AI IoT Machine learning Predictive maintenance Supervised and reinforcement learning

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Blockchain-based knowledge-aware semantic communications for remote driving image transmission

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Digital Communications and Networks 2025年第2期11卷 317-325页

作者： Yangfei Lin Tutomu Murase Yusheng Ji Wugedele Bao Lei Zhong Jie Li Department of Computer and Network Engineering University of Electro-CommunicationsChofuTokyo 182-8585Japan Information Technology Center Nagoya UniversityNagoya 464-8601Japan National Institute of Informatics(NII) Tokyo 101-8430Japan Hohhot Minzu College Hohhot 010051China Toyota Motor Corporation Tokyo 112-8701Japan Department of Computer Science and Engineering Shanghai Jiaotong UniversityShanghai 200240China

remote driving,an emergent technology enabling remote operations of vehicles,presents a significant challenge in transmitting large volumes of image data to a central *** requirement outpaces the capacity of traditional communication *** tackle this,we propose a novel framework using semantic communications,through a region of interest semantic segmentation method,to reduce the communication costs by transmitting meaningful semantic infor.a.ion rather than bit-wise *** solve the knowledge base inconsistencies inherent in semantic communications,we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge *** system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient ***,the implementation of blockchain sharding handles differentiated knowledge bases for various tasks,thus boosting overall blockchain *** results show a gr.a. reduction in latency by sharding and an incr.a.e in model accuracy,confirming our framework's effectiveness.

关键词： Semantic communication remote driving Semantic segmentation Blockchain Knowledge base management

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Exploring Deep Learning Methods for computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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computer Modeling in engineering & Sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this paper.a. be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide r.a.ers with context and examples of how these techniques can be applied to specific ar.a..A curated list of popular datasets and a brief description of them are also included for the benefit of r.a.ers.

关键词： Neural network machine vision classification object detection deep learning

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VresNet: A Deep Learning Architecture for Image Inpainting of Irregular Damaged Images

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SN computer Science 2024年第2期5卷 1-16页

作者： Shar.a. Sariva rani, rajneesh Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar 144011 India

In computer vision, image inpainting is a famous problem to automatically reconstruct the damaged part of the image according to the undamaged portion of an image. Inpainting irregular damaged ar.a. in the image is still challenging. Deep learning-based techniques have given us a fantastic perfor.a.ce over the last few years. In this paper, we propose VresNet, a deep-learning appr.a.h for image inpainting, inspired by U-Net architecture and the residual framework. Since deeper neural networks are extra hard to train, the superficial convolution block in U-Net architecture is replaced by the residual learning block in the proposed appr.a.h to simplify the training of deeper neural networks. To develop an effective and adaptable model, an extensive series of experiments was conducted using the Paris-Street-View dataset. Our proposed method achieved notable results, including a PSNr of 20.65, an SSIM of 0.65, an L1 Loss of 6.90, and a total loss (L _Total) of 0.30 on the Paris-Street-View dataset. These outcomes clearly demonstrate the superior perfor.a.ce of our model when compared to other techniques. The paper presents both qualitative and quantitative comparisons to provide a comprehensive assessment of our.a.pr.a.h. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Image inpainting Irregular mask residual learning U-net

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r.a. Time Facial Emotion recognition using Deep Learning and CNN

Real Time Facial Emotion Recognition using Deep Learning and...

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2023 Inter.a.ional Conference on computer Communication and informatics, ICCCI 2023

作者： Lokeshnaik, S.K. Punitha, A. Vijayakarthik, P. Kiran, Ajmeera Dhangar, A. Narsimha reddy, B.Jathin Sudheeksha, M. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Annamalai University Department of Computer Science and Engineering TamilNadu India R.L.Jalappa Institute of Technology Dodabalapur Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350348217

In this universe, a person's mood may be r.a. from their facial expressions. But one thing is for sure: every expression is a way that people communicate their emotions. Despite the fact that every person has a unique set of facial features and a range of emotions, artificial intelligence (AI)technology allows us to recognize these emotions. A face emotion-recognition AI is built using a variety of techniques, one of which is deep learning to build a convolutional neural network (CNN) to train the data set and obtain high prediction accuracy. recognizing seven of the fundamental facial emotions out of hundreds is the major goal here. The most common facial emotions are those of happiness, sadness, neutrality, anger, surprise, fear, and disgust. There are plenty of ways to achieve this facial emotion recognition like using deep face algorithms, machine learning, by AI techniques, by Tensor flow etc. The accuracy of our model is between 70% to 80%. It is per.a.s the simplest and efficient appr.a.h of all of them. © 2023 IEEE.

关键词： Face recognition

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MOPSO-driven optimization for sustainable retrofitting: balancing time, cost, and environmental impacts

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Asian Jour.a. of Civil engineering 2025年第5期26卷 2165-2178页

作者： Singh, rekha Sangeeta, Viswanadham Manjunath, T.C. Singh, Prachi Bishoyi, Sasanka Sekhar Pati, Aditya Kumar Department of Civil Engineering Raj Kumar Goel Institute of Technology Ghaziabad India Computer Science and Engineering GITAM Deemed to Be University Visakhapatnam India Department of Computer Science & Engineering Rajarajeswari College of Engineering Bengaluru India Department of Civil Engineering Madhav Institute of Technology and Science Gwalior India Department of Mathematics NIST University Berhampur India Department of Mathematics Centurion University of Technology and Management Gajapati R. Sitapur India

retrofitting projects play a critical role in enhancing the sustainability of existing structures, yet balancing time, cost, and environmental impact remains a significant challenge for decision-makers. This study introduces a Multi-Objective Particle Swarm Optimization (MOPSO) appr.a.h to achieve optimal trade-offs among these competing objectives. By leveraging MOPSO’s capability to explore Pareto-efficient solutions, the research provides a robust framework for sustainable decision-making in retrofitting projects. The model evaluates project scenarios based on key metrics such as time efficiency, cost minimization, and reduced carbon emissions. Through the application of MOPSO, the study generates a range of viable solutions, enabling project managers to make informed decisions tailored to specific sustainability goals. A case study is conducted to validate the model’s effectiveness, comparing its perfor.a.ce with conventional optimization techniques. The results demonstrate that MOPSO excels in balancing multiple objectives, delivering superior outcomes in sustainability metrics. This research contributes to the advancement of sustainable construction practices by offering a practical tool for optimizing retrofitting projects in alignment with environmental and economic priorities. The findings provide valuable insights for practitioners seeking to integrate sustainability into decision-making processes, addressing the pressing need for environmentally conscious infrastructure development. © The Author(s), under exclusive licence to Springer Nature Switzer.a.d AG 2025.

关键词： MOPSO Multi Objective optimization Sustainable construction practices Sustainable retrofitting Time-cost-environmental trade-off

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