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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Naveenkumar, N. Jayanthi, L.N. Shadaksharappa, B. Ramesh, S. Babuji, R. Rajmohan, M. Department of Computer Science and Engineering Muthayammal Engineering College Tamil Nadu Namakkal India Tamil Nadu Chennai India Department of Computer Science and Engineering Sri Sairam College of Engineering Anekal Karnataka Bengaluru India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur. Tamil Nadu Chennai India Department of Electronics and Communication Engineering R.M.K. Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai India

ISBN: (纸本)9798350361155

Optimization of passenger matching to improve service efficiency and customer happiness is both possible and difficult in the ride-hailing sector, which has grown rapidly. This paper proposes optimizing ride-hailing passenger matching using IoT and reinforcement learning (RL). To gather real-time traffic, passengers, and vehicle data, integrate Internet of Things (IoT) sensors into ride-hailing vehicles. Using IoT data, an RL agent dynamically assigns passengers to drivers. Journey duration, driver proximity, and passenger preferences are considered by the RL model to match passengers. This technique improves passenger matching efficiency and reduces wait times and journey durations via thorough simulations and real-world trials. Our findings show that the IoT-integrated RL system outperforms heuristics-based systems, making ride-hailing more smooth and rewarding for passengers and drivers. It explores the scalability and practical application of our approach on current ride-hailing platforms, emphasizing the potential for considerable service quality and operational cost savings. It addresses IoT data privacy and security problems and recommends solutions to protect user data. © 2024 IEEE.

关键词： Efficiency

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Cardiacnet: Cardiac Arrhythmia Detection and Classification Using Unsupervised Learning Based Optimal Feature Selection with Custom CNN Model

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第2期48卷 147-158页

作者： Srinivas, Kalyanapu Ch, Vijayalakshmi Borra, Subba Reddy Raju, K. Srujan Rao, Ganga Rama Koteswara Satyanarayana, K.N.V. Kumar, Pala Mahesh Department of Artificial Intelligence and Data Science Seshadri Rao Gudlavalleru Engineering College Andhra Pradesh Gudlavalleru India Department of Electronics and Communication Engineering St. Peter’s Engineering College Maisammaguda Telangana Hyderabad India Maisammaguda Telangana Hyderabad India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Medchal Road Telangana Hyderabad India Department of Computer Science and Information Technology Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India Department of Electronics and Communication Engineering Sagi Rama Krishnam Raju Engineering College Andhra Pradesh Bhimavaram India SAK Informatics Telangana Hyderabad India

The irregularity in the heartbeats caused cardiac arrhythmia, which resulted in serious health problems. This cardiac arrhythmia is monitored by electrocardiogram (ECG) signals. As a result, an accurate and timely analysis of ECG data can prevent serious health problems. However, the conventional manual prediction systems and artificial intelligence (AI) methods failed to detect cardiac arrhythmia because they failed to extract the deep salient features from the ECG dataset. So, this research work implements a model named CardiacNet, which is used to identify and classify cardiac arrhythmias from an MIT-BIH-based dataset. Initially, the pre-processing operation is performed to remove the non-linearities from the dataset. Then, unsupervised machine learning algorithm-based principal component analysis (UML-PCA) is used to extract the features of the pre-processed dataset. Further, the optimal feature selection operation is carried out using improved Harris Hawk's optimization (IHHO), which is a naturally inspired model. Moreover, a customized convolutional neural network (CCNN) model performs the classification of various cardiac arrhythmia diseases using IHHO features. The simulation results show that the proposed CardiacNet resulted in an accuracy of 97.57%, sensitivity of 98.29%, specificity of 97.97%, F-measure of 97.40%, precision of 98.66%, Matthew's correlation coefficient (MCC) of 98.17%, dice of 98.96%, and Jaccard of 97.12%. The performance comparisons show that the proposed CardiacNet resulted in improved metrics over all existing methods. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Advanced Malware Detection Methods for Polymorphic Virus Identification 5

Advanced Malware Detection Methods for Polymorphic Virus Ide...

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5th IEEE International Conference on Communication, computing and Industry 6.0, C2I6 2024

作者： Anil, D. Shreeshayana, R. Kiran, B. Preethi Uvce Bangalore University Department of Computer Science and Engineering Bengaluru India Atme College of Engineering Department of Electrical and Electronics Engineering Mysuru India Pes University Department of Computer Science and Engineering Bengaluru India Reva University School of Computing and Information Technology Bengaluru India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379884

Polymorphic viruses pose a significant challenge to traditional malware detection methods due to their ability to modify their code structure with each infection, effectively evading signature-based detection. This paper explores advanced malware detection methods tailored for identifying polymorphic viruses, focusing on the integration of machine learning, deep learning and hybrid approaches. Through a comprehensive analysis of static and dynamic features extracted from executables and URLs, this study employs classifiers such as Random Forests and Support Vector Machines (SVMs) to enhance detection accuracy. The SVM classifier demonstrated superior performance, achieving a detection accuracy of 96.23% compared to the 93.67% accuracy of the Random Forest classifier. This research highlights the potential of combining static and dynamic analysis with machine learning techniques to address the limitations of traditional methods, offering a robust framework for detecting and mitigating polymorphic and zero-day threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep-CNWO: a deep-chaotic nature whale optimization algorithm for early prediction of blood pressure disorder in smart healthcare settings

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Neural computing and Applications 2024年第24期36卷 15117-15136页

作者： Motwani, Anand Shukla, Piyush Kumar Pawar, Mahesh Arya, Monika Jain, Paras School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan MP Sehore466114 India Department of Computer Science & amp Engineering University Institute of Technology RGPV MP Bhopal462033 India Department of Computer Science & amp Engineering CMR Engineering College Kandlakoya Medchal Telangana Hyderabad501401 India Department of Information Technology University Institute of Technology RGPV MP Bhopal462033 India

The integration of cloud and edge computing, along with machine learning, plays a vital role in the development of efficient healthcare systems in smart cities. However, machine and deep learning (DL) models are prone to delayed convergence and Type-I and Type-II errors due to data vastness and high degree imbalance. To overcome the shortcomings of previous frameworks, this work aims to propose an optimization method with DL, ‘Deep-Chaotic Nature Whale Optimization’ (Deep-CNWO) for early prediction of Blood Pressure disorders among patients under at-home supervision. A simplex search algorithm is integrated to improve the update mechanism of whale optimization algorithm (WOA), thereby creating a CNWO algorithm. The purpose of this hybrid optimization is to increase the accuracy and efficiency of DL models. Leveraging the power of DL and CNWO, this method (Deep-CNWO) provides an effective solution for early detection and proactive management of a chronic disease in at-home healthcare settings. We collected relevant data from clinical studies, including vital signs and patient contextual information, to train and evaluate the deep-CNWO model. The CNWO optimization approach has been used to improve the predictive performance and convergence of DL models. Experiments performed on imbalanced datasets using deep-CNWO have given 99.90% accuracy. The average F-score for emergency cases has improved by 22%, while the average accuracy has increased by 5.72% across all three classes, compared to the results reported in previous related work. Deep-CNWO improves the convergence of DL and reduces Type-I and Type-II errors. The experimental results demonstrate the efficacy of our proposed method for remote patient monitoring and highlight its potential for quick intervention during emergencies. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization

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Secure Data Sharing with Confidentiality,Integrity and Access Control in Cloud Environment

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computer Systems Science & engineering 2022年第2期40卷 779-793页

作者： V.Rajkumar M.Prakash V.Vennila Department of Computer Science and Engineering Krishnasamy College of Engineering and Technology(Affiliated to Anna UniversityChennai)Cuddalore607109India Department of Computer Science and Engineering School of ComputingSRM Institute of Science&TechnologyKattankulathur603203India Department of Computer Science and Engineering K.S.R.College of Engineering(Affiliated to Anna UniversityChennai)Tiruchengode637215India

Cloud storage is an incipient technology in today’s *** of security in cloud environment is one of the primary challenges faced these *** scenario poses new security issues and it forms the crux of the current *** current study proposes Secure Interactional Proof System(SIPS)to address this *** methodology has a few key essential components listed herewith to strengthen the security such as authentication,confidentiality,access control,integrity and the group of components such as AVK Scheme(Access List,Verifier and Key Generator).It is challenging for every user to prove their identity to the verifier who maintains the access *** is conducted by following Gulliou-Quisquater protocol which determines the security level of the user in multi-step authentication ***,RSA algorithm performs the key generation process while the proposed methodology provides data integrity as well as confidentiality using asymmetric *** methodological operations such as time consumption have been used as performance evaluators in the proposed SIPS *** proposed solution provides a secure system for firm data sharing in cloud environment with confidentiality,authentication and access *** Timed Petri(STPN)Net evaluation tool was used to verify and prove the formal analysis of SIPS *** evidence established the effectiveness of the proposed methodology in secure data sharing in cloud environment.

关键词： Secure interactional proof system access control multi-step authentication Gulliou-Quisquater protocol

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A Deep Learning Approach to Mesh Segmentation

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computer Modeling in engineering & Sciences 2023年第5期135卷 1745-1763页

作者： Abubakar Sulaiman Gezawa Qicong Wang Haruna Chiroma Yunqi Lei Department of Computer Science School of InformaticsXiamen UniversityXiamen361005China Shenzen Research Institute Xiamen UniversityShenzen518000China College of Computer Science and Engineering University of Hafr Al BatinHafr Albatin39524Saudi Arabia

In the shape analysis community,decomposing a 3D shape intomeaningful parts has become a topic of interest.3D model segmentation is largely used in tasks such as shape deformation,shape partial matching,skeleton extraction,shape correspondence,shape annotation and texture *** approaches have attempted to provide better segmentation solutions;however,the majority of the previous techniques used handcrafted features,which are usually focused on a particular attribute of 3Dobjects and so are difficult to *** this paper,we propose a three-stage approach for using Multi-view recurrent neural network to automatically segment a 3D shape into visually meaningful *** first stage involves normalizing and scaling a 3D model to fit within the unit sphere and rendering the object into different *** viewpoints,on the other hand,might not have been associated,and a 3D region could correlate into totally distinct outcomes depending on the *** address this,we ran each view through(shared weights)CNN and Bolster block in order to create a probability boundary *** Bolster block simulates the area relationships between different views,which helps to improve and refine the *** stage two,the feature maps generated in the previous step are correlated using a Recurrent Neural network to obtain compatible fine detail responses for each ***,a layer that is fully connected is used to return coherent edges,which are then back project to 3D objects to produce the final *** on the Princeton Segmentation Benchmark dataset show that our proposed method is effective for mesh segmentation tasks.

关键词： Deep learning mesh segmentation 3D shape shape features

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Health Monitoring Transforming Using IoT: A Review 5

Health Monitoring Transforming Using IoT: A Review

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Nidhi Rawat, Bhupender Singh Srivastava, Animesh Garg, Navin Coer University College of Smart Computing Uttarakhand Roorkee India Graphic Era Hill University Department of Computer Science & Engineering Uttarakhand Dehradun India Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350383522

The power of the Internet to manage healthcare is transforming the healthcare industry. With the advent of Internet of Things technology, healthcare providers can now collect real-time data from patients and send them live health assessments. This creative approach can improve patient outcomes, reduce healthcare costs, and strengthen preventive services. IoT-compliant healthcare systems include wearable technology, sensors and connected healthcare systems. These devices collect various physiological data such as body temperature, heart rate, blood pressure and activity level. The information is then securely transmitted to nurses or specialists so they can monitor the patient's condition remotely. Personal and regular maintenance is one of the most important benefits of online health monitoring. The information is then securely transmitted to nurses or specialists so they can monitor the patient's condition remotely. Personal and regular maintenance is one of the most important benefits of online health monitoring. Health care providers can anticipate changes in patients and health indicators and respond quickly. This treatment can be especially useful for patients who need post-operative care, have chronic diseases or are elderly. Additionally, it encourages patient empowerment and engagement in healthcare services through IoT-based health management. Patients can track their progress, actively participate in their treatment plans, and receive personalized feedback. This participation improves patient adherence and creates a sense of control. Furthermore, combining IoT data with artificial intelligence and machine learning algorithms enables advanced analytics and predictive modeling. Healthcare professionals will also benefit from these features. © 2024 IEEE.

关键词： Health monitoring Healthcare providers Remote Monitoring Revolutionizing healthcare Sensors Wearable tech

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CRAYM: Neural Field Optimization via Camera RAY Matching 38

CRAYM: Neural Field Optimization via Camera RAY Matching

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lin, Liqiang Wu, Wenpeng Fu, Chi-Wing Zhang, Hao Huang, Hui College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Computing Science Simon Fraser University Canada

We introduce camera ray matching (CRAYM) into the joint optimization of camera poses and neural fields from multi-view images. The optimized field, referred to as a feature volume, can be "probed" by the camera rays for novel view synthesis (NVS) and 3D geometry reconstruction. One key reason for matching camera rays, instead of pixels as in prior works, is that the camera rays can be parameterized by the feature volume to carry both geometric and photometric information. Multi-view consistencies involving the camera rays and scene rendering can be naturally integrated into the joint optimization and network training, to impose physically meaningful constraints to improve the final quality of both the geometric reconstruction and photorealistic rendering. We formulate our per-ray optimization and matched ray coherence by focusing on camera rays passing through keypoints in the input images to elevate both the efficiency and accuracy of scene correspondences. Accumulated ray features along the feature volume provide a means to discount the coherence constraint amid erroneous ray matching. We demonstrate the effectiveness of CRAYM for both NVS and geometry reconstruction, over dense- or sparse-view settings, with qualitative and quantitative comparisons to state-of-the-art alternatives. © 2024 Neural information processing systems foundation. All rights reserved.

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Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

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computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt Abdulwahab Alazeb Naif Al Mudawi Mohammad Alonazi Nouf Abdullah Almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： Semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter SURF

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Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

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computers, Materials & Continua 2024年第8期80卷 2785-2813页

作者： Wufei Wu Javad Hassannataj Joloudari Senthil Kumar Jagatheesaperumal Kandala N.V.P.SRajesh Silvia Gaftandzhieva Sadiq Hussain Rahimullah Rabih Najibullah Haqjoo Mobeen Nazar Hamed Vahdat-Nejad Rositsa Doneva School of Information Engineering Nanchang UniversityNanchang330031China Department of Computer Engineering Technical and Vocational University(TVU)Tehran***Iran Department of Computer Engineering University of BirjandBirjand***Iran Department of Computer Engineering Babol BranchIslamic Azad UniversityBabol***Iran Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005India School of Electronics Engineering VIT-AP UniversityNear Vijayawada522237India Faculty of Mathematics and Informatics University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria Examination Branch Dibrugarh UniversityDibrugarh786004India Department of Computer Engineering Faculty of EngineeringUniversity of BirjandBirjand***Iran Faculty of Electrical and Computer Engineering University of BirjandBirjand***Iran Malaysian Institute of Information Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Physics and Technology University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)*** functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic *** these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle *** paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly ***-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by *** systems can autonomously create specific models based on network data to differentiate between regular traffic and *** these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational *** evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and *** review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV *** examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

关键词： Cyber-attacks internet of things internet of vehicles intrusion detection system

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