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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Sairam, D.V. Tamilselvi, M. Devi, S Gayathri Bhuvaneswari, R. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350317558

Improvements in aircraft detection are necessary to improve surveillance. This work investigates the exact detection and classification of airplanes using YOLOv8 in conjunction with Synthetic Aperture Radar (SAR) photos. SAR imaging performs well in dimly lit and unfavorable environments. The objective is to use YOLOv8's object detection to identify aircraft kinds and categories in SAR pictures. A variety of SAR datasets are gathered, preprocessed, and trained on the model to allow for a thorough assessment of YOLOv8's performance in differentiating between different types of aircraft. Measures that assess model performance across aircraft classes include mean average precision (mAP), recall, and loss. The results demonstrate the effectiveness of YOLOv8 and show promising results in terms of speed and precision. This work drives progress in aerial surveillance by highlighting the robustness of SAR when combined with cutting edge deep learning for aircraft taxonomy. The achieved 98.5% mean average precision (mAP) attests to exceptional performance in aircraft detection and categorization from SAR images, reinforcing the utility of this integrated approach in bolstering security systems. © 2024 IEEE.

关键词： Aerospace Surveillance Aircraft Detection Aircraft Taxonomy Classification Deep Learning Imaging Techniques Machine Learning Synthetic Aperture Radar (SAR)

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Marine Propulsion Health Monitoring: Integrating Neural Networks and IoT Sensor Fusion in Predictive Maintenance 2

Marine Propulsion Health Monitoring: Integrating Neural Netw...

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2nd IEEE International Conference on Device Intelligence, computing and Communication Technologies, DICCT 2024

作者： Anitha, K. Vijayakumar, R. Gnana Jeslin, J. Elangovan, K. Jagadeeswaran, M. Srinivasan, C. School of Computing Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Dhanalakshmi Srinivasan Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Vinayaka Missison's Kirupanandha Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha University Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798350372847

The maritime sector is shifting towards predictive maintenance to improve marine propulsion system dependability and efficiency. This research introduces neural networks and IoT sensor fusion for marine propulsion health monitoring. Real-time operational data is collected by a sophisticated sensor array spanning crucial propulsion system components. Fusing sensor data using modern IoT algorithms gives a comprehensive overview of system health. The suggested technique uses neural networks for predictive maintenance. A deep learning model analyses sensor-fused data to detect flaws or performance deterioration. Training the neural network on past data from various operating situations allows it to adapt and forecast faults. The model's capacity to learn and develop improves its vessel operating state adaptation. Neural networks and IoT sensor fusion offer early defect identification and dynamic maintenance schedules. Low downtime, operating expenses, and marine propulsion system longevity are achieved using this strategy. Case studies and simulations indicate that the suggested system can predict and avoid significant failures, making it suitable for marine use. © 2024 IEEE.

关键词： Fault detection

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AIDEN - Artificial Intelligence and Digital Entity Network

AIDEN - Artificial Intelligence and Digital Entity Network

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Megha V Kawad Sudhanshu Himanshu Makwana P. Kirubanantham Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

In the past few years, we have witnessed a rapid surge in technological advancements, which has significantly accelerated the evolution of Artificial Intelligence (AI) systems [1], [2]. This has fundamentally transformed the manner in which humans and machines communicate and interact with each other [3], [4]. The advent of advanced AI technologies has paved the way for the development of personal AI assistants that can emulate human intelligence and behavior [5], [6]. Our paper provides an in-depth exploration into the design and execution of ‘AIDEN: Artificial Intelligence and Digital Entity Network’. AIDEN is an innovative project that aims to create a personal AI assistant. The primary objective of AIDEN is to enhance user experiences by facilitating interactions that are as natural and human-like as possible [7], [8]. This groundbreaking project represents a significant stride in the field of AI, pushing the boundaries of what machines can achieve in terms of mimicking human intelligence and interaction [9], [10].

关键词： Measurement Accuracy Computational modeling Human intelligence Predictive models Data models Artificial intelligence

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection 24

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Yifan Xiang, Haolong Zhang, Xuyun Xu, Xiaolong Fan, Wei Zhang, Qin Qi, Lianyong School of Software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia University of Oxford Uffield Department of Women's & Reproductive Health United Kingdom College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering China

ISBN: (纸本)9798331506681

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods. © 2024 IEEE.

关键词： Anomaly Detection Efficient FR clustering Parallel Algorithm

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AModified Search and Rescue Optimization Based Node Localization Technique inWSN

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Computers, Materials & Continua 2022年第1期70卷 1229-1245页

作者： Suma Sira Jacob K.Muthumayil M.Kavitha Lijo Jacob Varghese M.Ilayaraja Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Wireless sensor network(WSN)is an emerging technology which find useful in several application areas such as healthcare,environmentalmonitoring,border surveillance,*** issues that exist in the designing of WSN are node localization,coverage,energy efficiency,security,and so *** spite of the issues,node localization is considered an important issue,which intends to calculate the coordinate points of unknown nodes with the assistance of *** efficiency of the WSN can be considerably influenced by the node localization ***,this paper presents a modified search and rescue optimization based node localization technique(MSRONLT)*** major aim of theMSRO-NLT technique is to determine the positioning of the unknown nodes in *** the traditional search and rescue optimization(SRO)algorithm suffers from the local optima problemwith an increase in number of iterations,MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the *** application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO *** order to validate the effective node localization performance of the MSRO-NLT algorithm,a set of simulations were performed to highlight the supremacy of the presented model.A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.

关键词： Node localization WSN chaotic map search and rescue optimization algorithm localization error

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Evaluation of Natural Language Used to Software Engineer based on Chatbot

Evaluation of Natural Language Used to Software Engineer bas...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Balamurugan, J. Subramanyam, Naguru Mani, D. Sharada Basi Reddy, A. St. Joseph's College of Engineering Department of Mba Chennai India Audisankara College of Engineering and Technology Department of Computer Science and Engineering Gudur India Qis College of Engineering and Technology Vengamukkapalem Department It Ongole India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798350317060

The field of natural language processing (NLP), which aims to represent and analyze human language digitally, has recently attracted much interest. Machine learning has found several uses, including translation, spam filtering, data mining, stigmatization, medicine, and answering questions online. After introducing the background and development of NLP, it breaks down into four distinct periods by describing the various stages of NLP and the building blocks of Natural Language Generation (NLG). The numerous applications of NLP, current trends, and problems are then presented, the state of the art in great depth. At the end of the paper, it explores several existing NLP datasets, models, and assessment measures. © 2023 IEEE.

关键词： Machine learning

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Cyberbullying Image Classification using Artificial Intelligence for Safer Online Platform

Cyberbullying Image Classification using Artificial Intellig...

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Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： M. Vamshi Krishna R. Asish Verma P. Kirubanantham Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Kattankulathur India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

The toll that cyberbullying takes on victims' life is incalculable because a person's approach to it is highly individualized. For some victims, the message might be harsh, but for others, it might be normal. It is quite difficult to find reliable content because of the ambiguities in cyberbullying communications. Several research works have reportedly used a text- content-based methodology to answer this question. However, picture-based cyberbullying detection is now receiving less attention. Enhancing a methodology that permits the prevention of social media posts on image-based cyberbullying issues is the aim of this job. This study provides support for an automated version that searches a malicious social media network for a cyber-symbol image, relying just on switch mastery. By applying fashion knowledge, it is feasible to infer hidden contextual functions from texts containing cyberbullying. Our test includes two photo datasets: pictures of cyberbullying and pictures of non-bullying. The datasets might be helpful to Destiny researchers in expanding their research. It's challenging to find a pleasant and suitable version to recognize bullying photographs, therefore experiment with both transfer the experiment's findings showed that switch learning models are an excellent method for predicting conversations that are only based on images and involve cyberbullying.

关键词： Training Accuracy Image recognition Computational modeling Cyberbullying Switches Safety

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Comprehensive Methods for Secure Medical Image Transfer: Encryption and Standardization Techniques

Comprehensive Methods for Secure Medical Image Transfer: Enc...

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Science, technology, engineering and Management (ICSTEM), International Conference on

作者： K. Priyadharshini Hitesh Nandi Harsh Parmar Dept. of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The primary goal of the process is to securely transfer medical images. This involves utilizing MRI datasets for processing and applying resizing and filtering techniques such as Conv2D and Gaussian blur to standardize the images. Additionally, an attack model is developed to enhance information security, starting with a PGD attack to access model weights and followed by a DAG attack to increase loss functions. Encryption is then performed using a Chaos-based encryption model to create a chaotic map and the Henon map for image conversion. A randomly generated key encryption method is utilized to generate a key for secure transfer. The encrypted image is displayed, and in the receiver section, the process is reversed for decryption by inputting the key generated in the encryption model. Finally, the original medical image is retrieved along with parameters such as MSE, MAE, RMSE, and F2 score to assess the accuracy of the recovered image and ensure its correctness.

关键词： Accuracy Filtering Magnetic resonance imaging Information security Standardization Receivers Encryption

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computing Challenges of UAV Networks: A Comprehensive Survey

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Computers, Materials & Continua 2024年第11期81卷 1999-2051页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Xianxuan Lin Farman Ali Ahmad Ali AlZubi School of Computer Science&Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science Community CollegeKing Saud UniversityRiyadh11495Saudi Arabia

Devices and networks constantly upgrade,leading to rapid technological ***-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information *** network types,including sensor networks and 5G mobile networks,support this ***,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/*** topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future ***,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility *** also explores the prospects of UAV systems and presents state-of-the-art solutions.

关键词： Autonomous vehicles UAV systems UAV transmission UAV in healthcare future of UAV system security measures

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New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2749-2767页

作者： Shimaa M.Amer Ashraf A.M.Khalaf Amr H.Hussein Salman A.Alqahtani Mostafa H.Dahshan Hossam M.Kassem Electronics and Communications Engineering Department Higher Institute of Engineering and TechnologyKafr ElsheikhEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringHorus University EgyptNew DamiettaEgypt Department of Computer Engineering College of computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computing Mathematics and EngineeringCharles Sturt UniversityBathurstNSW2795Australia

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient SLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme SLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

关键词： Array synthesis convolution process genetic algorithm(GA) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(SLL) quality of service(QOS)

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