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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Anusha, Dasari Matta, Durga Satish kumar, Dudla Anil Prabhakar, Dudla Department of Electronics and Instrumentation Engineering Velagapudi Ramakrishna Siddhartha Engineering College Deemed to be University Vijayawada520007 India Department of Computer Science and Engineering Vishnu Institute of Technology Bhimavaram534202 India Department of Computer Science and Engineering Lakireddy Balli Reddy College of Engineering Mylavaram521230 India Department of Electronics and Communication Engineering Seshadri Rao Gudlavalleru Engineering College Gudlavalleru521356 India

ISBN: (纸本)9783031770807

Enforcing traffic laws in a densely populated nation like India is challenging. However, many motorcyclists continue to put their lives in danger by without wearing protective headgear. The traffic police have an extremely time-consuming job in keeping tabs on all the cars on the road. CCTVs are commonplace in several of the more populous places. There is already the technology in place to make motorcycle riders’ tracking and monitoring more streamlined. This study proposes a technique for identifying triple riding, helmetless motorcyclists and retrieving the vehicle identification number of any motorbike driven by an unprotected rider. The YOLO (You Look Only Once) approach is used to identify the triple riding, biker’s helmet and the bike’s licence plate. Modeled on the COCO dataset, YOLO has the ability to identify 80 different classes. YOLO learned its stuff using inputs like helmet images and licence plate data. This technique yields excellent outcomes. Identifying triple riding, helmets and licence plates is done with great precision. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Bicycles

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Internet of Things Assisted Human Detection and Rescue Robot: A Game Changer in the Disaster Environment 7

Internet of Things Assisted Human Detection and Rescue Robot...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Akhtar, Md Jawed Singh, Manmohan Suneel, Sajja Sutar, Anand Nayaki, A. Periya Khan, Nikhat Raza Ies College of Technology Department of Computer Science & Engg Bhopal India Ies College of Technology Department of Computer Science And Engineering Mp Bhopal India Hyderabad Telangana Dundigal500043 India Bharati Vidyapeeh College of Engineering Dept of Electronics And Telecommunication Navi Mumbai India Velammal College of Engineering And Technology Department of Information Technology Tamilnadu Madurai India Ies College of Technology Department of Computer Science And Engineering Bhopal India

ISBN: (纸本)9798350359299

In the realm of disaster response, the integration of Internet of Things (IoT) technologies has paved the way for innovative solutions to enhance human detection and rescue operations. This research work introduces a revolutionary approach through the development of the Challenging Human Detection and Rescue Robot (CHDRR). This robot incorporates advanced sensors, including the MQ6 Gas Sensor, LM335 Temperature Sensor, Humidity Sensor, and a Passive Infrared (PIR) Human Body Detection sensor, collectively forming a comprehensive sensory suite. The CHDRR leverages the power of IoT to enable real-time data collection, analysis, and communication, transforming disaster response capabilities. The MQ6 Gas Sensor ensures the detection of hazardous gases in the environment, providing crucial information for safe rescue operations. The LM335 Temperature Sensor and Humidity Sensor contribute to environmental monitoring, allowing the robot to adapt its strategies based on prevailing conditions. The PIR Human Body Detection sensor serves as a key element in the CHDRR's human detection capabilities. Through sophisticated algorithms and machine learning, the robot can identify and locate human presence amidst challenging disaster scenarios. The fusion of these sensors with IoT technology empowers the CHDRR to make informed decisions autonomously, optimizing its efficiency in dynamically changing environments. © 2024 IEEE.

关键词： Internet of things

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A novel convolution neural network architecture with fully connected network for efficient speech emotion recognition system

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International Journal of Information technology (Singapore) 2025年 1-9页

作者： Singh, Vandana Prasad, Swati Electronics and Communication Engineering Department Birla Institute of Technology Mesra Jharkhand Ranchi 835215 India Computer Science & Engineering Birla Institute of Technology Mesra (Noida Off Campus) Uttar Pradesh Noida 201301 India

Speech emotion recognition determines the emotion of a speaker utilizing only his/her spoken utterance. This research faces difficulty in achieving high speech emotion recognition accuracy due to differences in speaker’s emotional intensity. This study has proposed a novel 1D CNN architecture integrated with fully connected layers which is able to capture the emotion characteristics in speech efficiently for addressing the above mentioned issue. Experiments conducted on RAVDESS dataset which consists of two levels of speaker emotion intensity, namely, ‘strong’ and ‘normal’ have shown better overall performance for the proposed architecture over the Baseline. The proposed method has achieved 4.1% improvement in average speech emotion recognition accuracy compared to the Baseline across 8 different emotions. Further the proposed method has demonstrated superior performance for recognizing neutral & sad emotions. It has achieved 14 percent and 9 percent higher speech emotion recognition accuracy over the Basline method for neutral and sad emotions, respectively. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Convolutional neural network Data augmentation Fully connected network Human–computer interaction RAVDESS Speech emotion recognition

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

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

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International Conference on Electrical and electronics engineering, ICEEE 2023

作者： Sharma, 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 approach 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. Performance 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 performance, 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, increasing 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 real-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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A Hybrid CNN-LSTM-PSO Framework for Enhanced Cybersecurity Threat Detection and Classification

A Hybrid CNN-LSTM-PSO Framework for Enhanced Cybersecurity T...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Akilandeswari, S. Soumya, T.R. Sumathi, S. Sushith, Mishmala Brinda, V. Rajan, S. E.G.S. Pillay Engineering College Department of Artificial Intelligence & Data Science Nagapattinam611002 India Prathyusha Engineering College Department of Computer Science Engineering Tiruvallur602025 India Velammal Engineering College Department of Electronics and Communication Engineering Chennai600066 India Adithya institute of Technology Department of Information Technology Kurumbapalayam India K.Ramakrishnan College Of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode638012 India

ISBN: (纸本)9798331509828

With the advancement of digital era comes an increased value for cybersecurity, and these need to be dealt with using advanced techniques. Deep learning - metaheuristic optimization hybrid model for advanced threat detection and mitigation is presented in this paper. With a Convolutional Neural Network (CNN) for feature extraction and Long Short-Term Memory (LSTM) for sequence analysis, the proposed framework integrates all the modules in order to provide robust cyber-attack detection. In order to optimize the parameters of the model, a Particle Swarm Optimization (PSO) algorithm is used in order to achieve better classification accuracy and faster computation. Benchmark datasets are used for extensive experiments, where existing methods outperformed in terms of precision, recall, and Fl-score. The results demonstrate that with detected flagged FPs and the hybrid model, the increased detection rates are achieved without the reduction in true negatives, rendering it a practical solution for real world applications. This research adds to the field by providing a solution to cybersecurity problems that is scalable and adaptable to the present and beyond. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhanced Route Optimization for Wireless Networks Using Meta-Heuristic engineering

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computer Systems Science & engineering 2022年第10期43卷 17-26页

作者： S.Navaneetha Krishnan P.Sundara Vadivel D.Yuvaraj T.Satyanarayana Murthy Sree Jagadeesh Malla S.Nachiyappan S.Shanmuga Priya Department of Electronics and Communication Engineering SACS MAVMM Engineering CollegeMaduraiIndia Department of Computer Science and Engineering SACS MAVMM Engineering CollegeMaduraiIndia Department of Computer Science Cihan University-DuhokKurdistan RegionIraq Department of Computer Science and Engineering Bapatla Engineering College BapatlaAndhra PradeshIndia Department of Computer Application National Institute of TechnologyThiruchirapalliIndia School of Computer Science and Engineering Vellore Institute of TechnologyChennaiIndia Department of Computer Science and Engineering M.I.E.T Engineering CollegeTrichyIndia

Wireless Sensor Networks(WSN)are commonly used to observe and monitor precise *** consist of a large number of inexpensive sensor nodes that have been separated and distributed in different *** base station received the amount of data collected by the numerous *** current developments designate that the attentFgion in applications of WSNs has been increased and extended to a very large *** Trust-Based Adaptive Acknowledgement(TRAACK)Intrusion-Detection System for Wireless Sensor Networks(WSN)is described based on the number of active positive deliveries and The Kalman filter used in Modified Particle Swarm Optimization(MPSO)has been proposed to predict knot *** were run for non-malicious networks(0%malicious)and different percentages of malicious nodes were *** findings suggest that the proposed method TRAACK Modified Particle Swarm Optimization(MPSO)packet delivery rate outperforms TRAACKPSO by 3.3%with 0%malicious ***,the packet delivery rate of TRAACKMPSO is 30%malicious,3.5%better than TRAACKPSO in WSN.

关键词： Trust-based adaptive acknowledgment(TRAACK) modified particle swarm optimization(MPSO) intrusion detection system(IDS) kalman filter

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Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

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computers, Materials & Continua 2024年第7期80卷 139-161页

作者： Roseline Oluwaseun Ogundokun Joseph Bamidele Awotunde Hakeem Babalola Akande Cheng-Chi Lee Agbotiname Lucky Imoize Department of Multimedia Engineering Kaunas University of TechnologyKaunas44249Lithuania Department of Computer Science Landmark UniversityOmu Aran251103Nigeria Department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Telecommunication Science University of IlorinIlorin230003Nigeria Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Fintech and Blockchain Research CenterAsia UniversityTaichung City41354TaiwanChina Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr UniversityBochum44801Germany

Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to *** leads to increased *** biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational ***,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is *** pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory *** proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and *** contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable *** model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class *** the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is *** testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,a

关键词： Retinal images ocular disorder deep transfer learning disease identification mobile device

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Improving the review classification of Google apps using combined feature embedding and deep convolutional neural network model

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4257-4272页

作者： Aslam, Naila Alzamzami, Ohoud Xia, Kewen Sadiq, Saima Umer, Muhammad Bisogni, Carmen Ashraf, Imran School of Electronics and Information Engineering Hebei University of Technology Tianjin China Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Computer Science Information Technology The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Salerno Fisciano Italy Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Online reviews play an integral part in making mobile applications stand out from the large number of applications available on the Google Play store. Predominantly, users consider posted reviews for appropriate app selection. Manual categorization of such reviews is both inefficient and time-consuming. Therefore, automatic analysis of the sentiments of such reviews provides fast suggestions for new users and facilitates their selection of the appropriate app. However, data imbalance is a major challenge for performing class prediction of such reviews as their distribution is sparse and often leads to low accuracy. This work proposes a framework to overcome this limitation. Extensive experiments are performed using the original and balanced data with the synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). Additionally, deep learning and machine learning models are evaluated using FastText, FastText Subword, global vector (GloVe), and their combinations for word representation. Baseline machine learning models, including random forest, extra tree classifier, gradient boosting, Naive Bayes, logistic regression (LR), stochastic gradient descent (SGD), and voting classifier (VC) that combines LR and SGD, are used for comparison. The outcomes show that the convolutional neural network using a combination of word embedding techniques produces the most accurate results. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolutional neural networks

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Real-Time Bus Driver Monitoring System Using Machine Learning 2

Real-Time Bus Driver Monitoring System Using Machine Learnin...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Kadu, Anil Dharashive, Rajnandini Nimje, Rachit Rajvaidya, Arya Palkar, Sanket Gaikwad, Vijay Vishwakarma Institute of Technology Department of Instrumentation and Control Pune India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Vishwakarma Institute of Technology Department of Electronics and Telecommunication Pune India

ISBN: (纸本)9798331519056

In recent years, the importance of ensuring road safety has intensified, prompting the development of advanced driver monitoring systems. This paper presents a real-time bus driver monitoring system utilizing a Raspberry Pi, OpenCV, a RaspiCam and machine learning techniques. The system aims to enhance passenger safety by detecting driver fatigue and distraction, which are major contributors to road accidents. The proposed solution leverages the Raspberry Pi's processing capabilities and the RaspiCam's imaging features to continuously capture and analyze the driver's facial expressions and eye movements. OpenCV's robust image processing algorithms are employed to identify signs of drowsiness and inattentiveness. The innovative aspects of the proposed solution include the use of Eye Aspect Ratio (EAR) for drowsiness detection and Mouth Aspect Ratio (MAR) for detecting yawning. Both the EAR and MAR are calculated through real-time facial landmark analysis. The system issues alerts when unsafe driving behavior is detected, thereby allowing for timely intervention. The proposed monitoring system is cost-effective and scalable. It offers a promising approach to improving public transportation safety by mitigating the risks associated with driver fatigue and distraction. Experimental results showed an accuracy of 92% for fatigue detection and 88% for distraction detection. This demonstrates the system's effectiveness in real-time detection and its potential for deployment in commercial bus fleets. © 2024 IEEE.

关键词： Bus drivers

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Towards Trustworthy Networks: A Collaborative Federated Learning Paradigm for Security Enhancement 12th

Towards Trustworthy Networks: A Collaborative Federated Lear...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Dhabliya, Ritika Senthil Ganesh, R. Rizvi, Nuzhat Agarwal, Kshitiz Deshmukh, Gopal B. Dongare, Sonali Prashant Researcher Connect Nagpur India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India Pune India Electronics & Communication Engineering Arya College of Engineering Jaipur India Department of Computer Engineering VIIT Pune India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9789819601387

In today’s interconnected environment, it is crucial to strengthen network security in order to protect against constantly evolving threats. This study aims to contribute to this important goal by proposing a paradigm of collaborative federated learning as a method to improve security. The proposed framework, enhanced by the implementation of Federated Stochastic Gradient Descent (FedSGD), seeks to create reliable networks by attaining a desired accuracy level of 99.17%. The study involves a thorough investigation of the collaborative federated learning model, including its complex architecture and the smooth incorporation of FedSGD. The research explores various data sources and focuses on the complexities of preprocessing techniques to enhance model training. The user provides a clear explanation of the experimental setup, including the datasets, parameters, and configurations used for training. The chosen evaluation metrics guarantee a comprehensive evaluation of the model’s performance. The text presents the implementation details of collaborative federated learning deployment with FedSGD integration, explaining the complexities involved. The study proposes strategies to overcome potential obstacles that arise from the inherent challenges of implementing such an innovative approach. The achieved outcomes demonstrate the exceptional capability of the model, attaining an impressive accuracy of 99.17%. The model’s effectiveness in real-world scenarios and resilience against adversarial threats are supported by rigorous robustness and reliability analyses. The following discussion analyses the importance of the attained precision, elucidating its implications for the security of the network. Taking into account possible constraints, the research presents suggestions for future investigations and improvements. To summarize, this research strongly supports the implementation of collaborative federated learning with FedSGD as a crucial measure to establish reliable net

关键词： Federated learning

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