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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDsAAI 2023

作者： Gobinathan, N. Ponnusamy, R. V.R.S. College of Engineering and Technology Department of Computer Science and Engineering India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348910

A pivotal acquisition model designed to formulate control strategies directly from high-level perceptual data using reinforcement learning is introduced by us. The training of this model, a convolutional neural network, was done using a different Q-learning algorithm. An output is produced in the form of a value function that indicates anticipated future earnings after processing raw image inputs. Recent demonstrations of cutting-edge performance in reinforcement learning tasks have been made by these neural networks, which leverage Deep Q-Networks (DQNs) based on the Q-Learning algorithm. The planning process for DQNs is significantly different from that of standard deep neural networks, resulting in significant challenges in specific reinforcement learning scenarios. © 2023 IEEE.

关键词： Reinforcement learning

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YOLO version v1 to v8 Comprehensive Review 7

YOLO Version v1 to v8 Comprehensive Review

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Mulajkar, Ruta Yede, sanjay H.V.P.M.'S Degree College of Physical Education Computer Science And Engineering Amravati India

ISBN: (纸本)9798350359299

Real time object detection methods are useful in assessing different situation like theft, military activities, health care sectors etc. YOLO is one of the fastest deep learning techniques for object detection. In this research study comprehensive survey is done for different versions of YOLO object detection technique which is very popular nowadays. YOLO version 1 to 8 is surveyed in this research study;comparative study is done based on dataset used, YOLO architecture, mAP achieved, strength and limitation. the major changes done in architecture is also elaborated in this research study. © 2024 IEEE.

关键词： Object detection

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Diagnosis of Diabetic Retinopathy Using Hierarchical Feature Learning and Modified CNN with ACAF 4

Diagnosis of Diabetic Retinopathy Using Hierarchical Feature...

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4th International Conference on sustainable Expert systems, ICsEs 2024

作者： sankarananth, s. Uma Maheswari, G. Arun Kumar, b. Nagasuresh, M. Arun Kumar, R. Karthiga, M. Bamnari Amman Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Sathyamangalam India Dr. Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Karpagam Academy of Higher Education Department of Artificial intelligence and Data Science Tamilnadu Coimbatore India Karpagam Institute of Technology Department of Information Technology Tamilnadu Coimbatore India V.S.B Engineering College Department of Electrical and Electronics Engineering Tamilnadu Karur India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Diabetic retinopathy (DR) remains one of the leading causes of vision loss and blindness today, making the need for accurate and reliable detection techniques paramount. This work presents a new method for feature extraction through combining two methods namely;vsLM and Wavelet CNN. The approach is complemented by a modified CNN model that is equipped with an AdaptiveConvolutional ActivationFunction (ACAF) for classification. As it was mentioned before, the vsLM technique creates detailed retinal maps, and thus can be effective for the detection of critical DR indicators such as microaneurysms, haemorrhages, and exudates. Wavelet CNN enhances the feature extraction procedure, taking into consideration both spatial and spectral features of the retinal images. The resolution provided by the proposed CNN model with ACAF demonstrates outstanding classification results with accuracy rates of 97.12% on DiaretDb0 dataset, 99. 12% on the public EyePACs Kaggle dataset and 99.15% on the DRIvE dataset and 98.12% on the CHAsE dataset. These findings highlight the importance of using the feature extraction and classification approach for improving the accuracy of DR detection and classification. The findings point to severe advancements in the diagnostic tools of DR and the effect of these enhancements in the pragmatic aspect of identify and handling of DR. © 2024 IEEE.

关键词： Convolutional neural networks

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Posture Detection of Heart Disease Using Multi-Head Attention vision Hybrid(MHAvH)Model

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computers, Materials & Continua 2024年第5期79卷 2673-2696页

作者： Hina Naz Zuping Zhang Mohammed Al-Habib Fuad A.Awwad Emad A.A.Ismail Zaid Ali Khan School of Computer Science and Engineering Central South UniversityChangsha410003China Department of Quantitative analysis College of Business AdministrationKing Saud UniversityP.O.Box 71115Riyadh11587Saudi Arabia Electrical and Computer Engineering University of VictoriaVictoriaV9A1B8Canada

Cardiovascular disease is the leading cause of death *** disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as ***,minimizing the death ratio can be achieved by early detection of heart attack(HA)*** the United states alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four ***,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives *** objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their *** address these challenges,we employ deep learning *** have utilized a vision transformer(viT)to address this ***,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product ***,since transformer performance typically relies on large-scale or adequate data,adapting viT for smaller datasets is more *** response,we propose a three-in-one steam model,theMulti-Head Attention vision Hybrid(MHAvH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and vGG-16,renowned for their robust feature extraction *** incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGb images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack *** the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original dat

关键词： Image analysis posture of heart attack(PHA)detection hybrid features vGG-16 ResNet-50 vision transformer advance multi-head attention layer

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spatio-Temporal service Analysis in Multi-Layer Non-Terrestrial Networks

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Journal of Communications and Information Networks 2024年第1期9卷 43-55页

作者： Qian Chen Weixiao Meng shuai Han Cheng Li School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China School of Engineering Science Simon Fraser UniversityBurnabyBC V5A 1S6Canada Department of Electrical and Computer Engineering Memorial UniversitySt.John’sNL A1B 3X5Canada

In the advent of the 6G era, non-terrestrial networks (NTN) with expansive coverage are being increasingly recognized as a vital supplement to cellular networks for facilitating seamless communication. The intricate interplay between network performance and service quality necessitates a thorough investigation into the modeling and analysis of services for efficient construction of *** studies on service analysis, predominantly focused on terrestrial networks,fall short in addressing the unique challenges posed by NTN,particularly those related to platform distribution and antenna gain modeling. This deficiency in research,coupled with the varying preferences of users for different network types, forms the basis of this study. This paper explores the spatio-temporal characteristics of services within a multi-layered NTN *** this context,the spatial distribution of the platforms is modeled using a binomial point process, and the antennas are characterized by a sectorized beam pattern. We derive the closed-form expressions for the association probability,the number of accessed users, and the arrival rate of services with certain delay requirements towards different types of NTN. simulation results are provided to evaluate the influence of various parameters on the association probability, the number of accessed users, and the total arrival rate of services. The number of satellites can be determined to achieve the optimal system utility,balancing the accessed services, offloading effects, and launching costs. This initial investigation lays the groundwork for further theoretical progress in the service analysis and platform deployment of NTN.

关键词： association strategy non-terrestrial networks service analysis stochastic geometry system utility

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A Multi-Layer Encryption and Anomaly Detection Framework for securing sCTP Data Transmission

A Multi-Layer Encryption and Anomaly Detection Framework for...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： venkadesh, P. Divya, s.v. Ponnaian, Geetha saju Raj, T. Ninisha Nels, s. subash Kumar, K. Department of AI&DS V.S.B College of Engineering Technical Campus Coimbatore642109 India Department of CSE V.S.B College of Engineering Technical Campus Coimbatore642109 India Department of CSE Saveetha University Chennai600077 India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai600062 India Department of CSE Noorul Islam Centre for Higher Education Kumaracoil629180 India

ISBN: (纸本)9798331510299

stream Control Transmission Protocol (sCTP)is an efficient protocol that can replace TCP/UDP in *** it is initially used in IP networks, it performs better in multitasking and multi-homing environments because of its incredible *** to this, sCTP gains attention by the researchers for utilizing it in data communication similar to TCP/***, its popularity is hindered due to several security issues like Dos attack, chunk bundling, lack of confidentiality, man-in -the middle attack *** a secure and reliable data communication using sCTP is essential. The research focuses on proposing a steganographic algorithm to analyze and verify the hidden areas in sCTP, aiming to detect unauthorized access. The proposed method enhances the integrity and security of sCTP for data transmission compared to existing methods and achieves a reasonable throughput and delay. © 2024 IEEE.

关键词： Data transmission Information Hiding Multi-homing sCTP steganography

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Ensemble Learning Applications and visualizations Taxonomy of blockchain Data 2

Ensemble Learning Applications and Visualizations Taxonomy o...

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2nd International Conference on Data science and Information system, ICDsIs 2024

作者： Kurada, Ramachandra Rao Ramu, Y. silpa, N. Rao, v.v.R. Maheswara Pattem, sunil Pallavi, L. Shri Vishnu Engineering College For Women Department Of Cse Bhimavaram India B V Raju Institute Of Technology Department Of Computer Science And Engineering Narsapur India

ISBN: (纸本)9798350381665

Trading particularly in blockchains are being hailed as a disruptive force in the finance sector and are instrumental as a monumental task. Ensemble Learning (EL) techniques have an enduring legacy in such aspects, as they combine multiple models to enhance performance and increase accuracy in predictive modeling. This study meticulously unfolds two distinct sections intended to document of the observations the vision of model building to uphold the unprecedented values of blockchains and embark visual indepth understanding of information. From this perspective, this transcript devolves the prime objective into a deeper understanding of blockchain data with EL methods to peruse the concealed knowledge in the blocks. Data visualization of blockchain data objectively stands as a service look at history seemingly with a characteristic of immense and self-restraint value to users. Hence, the subsequent objective bestows to present the epitomization of cryptocurrency websites envies with the economic renaissance pontificated in blockchains. © 2024 IEEE.

关键词： blockchain

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Effective segmentation and Classification of brain Tumour MRI Images Using YOLOv8 Technique

Effective Segmentation and Classification of Brain Tumour MR...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOvA 2024

作者： Taradale, Aniket Prabhu Kattimani, suvarna Laxmikant Department of Computer Science and Engineering B.L.D.E.A’s V.P. Dr. P.G. Halakatti College of Engineering and Technology Karnataka Vijayapura India

ISBN: (纸本)9798331505134

brain tumour classification is crucial for prompt diagnosis and life-saving treatment. A novel approach employing You Only Look Once (YOLO) technology for brain tumour segmentation and classification We used pre-existing data for tumour diagnosis and categorised in four main types: meningioma, glioma, pituitary, and no tumours. In diagnosis, segmentation is necessary to identify regions containing tumours seen in medical images such as MRI scans. Our method offers use of YOLO's real-time product search capabilities and makes them relevant for clinical use. The method achieves 95% F1-score, 96.2% precision, 93.6% recall, and 97.2% mAP50 in identifying and segmenting tumour regions while using a large dataset of MRI scans as training. This method is superior to traditional segmentation methods as it provides a fast and accurate target detection method. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Triangular Mesh and Neural Network for Object search based Cluster Centre Descriptor

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sN computer science 2024年第8期5卷 1171页

作者： Hosur, Ravi Mahendrakar, Pavan Hiremath, Ananda Noola, Daneshwari Department of Computer Science and Engineering BMS Institute of Technology and Management Doddaballapur Main Road Avalahalli Yalahanka Karnataka Bengaluru India Department of Computer Science and Engineering BLDEA’s V.P. Dr. P.G. Halakatti College Engineering and Technology Karnataka Vijayapur India Department of Information Science and Engineering BLDEA’s V.P. Dr. P.G. Halakatti College Engineering and Technology Karnataka Vijayapur India

since 3D models and printing have become more and more widespread, 3D object recognition has gained appeal. It might be challenging to locate the needed object in a large database of 3D objects of different kinds. several methods have been put forth in the past to effectively identify and detect 3D objects based on their shape data. These techniques describe 3D objects in a high-dimensional feature plane either by using geometric shape features or by creating a 3D shape model from a 2D shape model. One of the disadvantages of 3D descriptors is that their high memory requirements make recognition speed a major difficulty. We provide a unique 3D object search method based on 3D object identification to get around the problem. The process reads the 3D vertices from a CAD file, samples the triangular meshes from below, locates the center of each triangle, and then projects the points onto the front and top views of the YZ and XY planes, respectively, in two dimensions. Ten clusters are formed from the points in each plane, and the feature descriptor for the three-dimensional object is derived from the cluster centers. The shape is indexed using this description. In order to extract the descriptor from CAD files, the system looks for objects in a designated directory. A feed-forward neural network is then used to classify the descriptor. We demonstrate that the proposed method outperforms the existing shape descriptor-based methods in terms of speed 139 ms, classification accuracy of 98.3%, and search time. © The Author(s), under exclusive licence to springer Nature singapore Pte Ltd. 2024.

关键词： Centre discriptor Content based neural network Triangular mesh

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An Efficient Wireless sensor Network based Intrusion Detection system 2

An Efficient Wireless Sensor Network based Intrusion Detecti...

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2nd IEEE International Conference on Communication, security and Artificial Intelligence, ICCsAI 2023

作者： Rajassekharan, Dinesh Ramachandran, v. sharma, Dilip Kumar Palanisamy, Thirumoorthy Myilsam, Y. varatharaj Poonia, Ramesh Chandra Peninsula College Faculty of Computer Science Malaysia Gitam School of Computing Gitam Deemed to Be University Department of Computer Science and Engineering Bengaluru561203 India Jaypee University of Engineering and Technology Department of Mathematics Madhya Pradesh Guna473226 India Nandha Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India V.S.B College of Engineering Department of Electrical and Electronics Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of Computer Science Delhi-NCR Uttar Pradesh Ghaziabad201003 India

ISBN: (纸本)9798350369960

Wireless sensor Networks (WsNs) are widely used in diverse applications due to their cost-effectiveness and versatility. However, they face substantial difficulties because of their innate resource constraints and susceptibility to security attacks. A possible method to improve the security of WsNs is clustering-based intrusion detection and responding mechanisms. An in-depth analysis of the clustering-based intrusion detection and response method for WsNs is presented in this study. The suggested method efficiently uses data mining and machine learning techniques to identify unusual behaviour and probable intrusions. The system effectively analyses data inside clusters by grouping sensor Nodes (sN) into clusters, allowing it to differentiate between legitimate patterns and insecure activity. The network may respond promptly to identified breaches and react to the responsive mechanism, which reduces their impact and protects network integrity. The proposed Mathematically Modified Gene Populated spectral Clustering based Intrusion Detection system and Responsive Mechanism (MMMMGPsC-IDs-RM) is compared with existing state-of-art techniques, and MMMMGPsC-IDs-RM outperforms with the highest detection rate of 96%. © 2023 IEEE.

关键词： IDs machine learning responsive mechanism spectral clustering WsN

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