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Cyber Security and Applications 2025年

作者： A. Jayanthiladevi Jayapandian Natarajan KP Arjun L. Godlin Atlas M. Arvindhan Daniel Arockiam Professor Department of Computer Engineering Marwadi University Rajkot Gujarat India Associate Professor Department of Computer Science and Engineering School of Engineering and Technology Christ University Kengeri Campus Bangalore Karnataka India Department of Computer Science and Engineering GITAM University Bengaluru Campus Karnataka India Associate Professor Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu Assistant Professor Amity School of Engineering & Technology Computer Science and Engineering Gwalior Madhya Pradesh India Associate Professor CSE Amity University Madhya Pradesh Gwalior 474005 India

The modern technology network plays an important role. Artificial intelligence to provide a higher solution for network security. As 7G networks emerge, virtual therapy platforms have begun to take center stage, offering fast, real-time interaction and high-definition immersive experiences. While the convergence of security in health care and mobility is in many ways even more integrated into our lives, it is fraught with cybersecurity risks. This article proposes an AI-based cybersecurity framework for a 7 G-based virtual therapy platform. The framework employs advanced machine learning (ML) algorithms, predictive analytics, and adaptive threat intelligence to protect the confidentiality, integrity, and availability of data. The article covers the core building blocks like anomaly detection, secure 7 G-based virtual therapy platform authentication protocols, and privacy-preserving techniques. AI-GN gives an overview of attack mitigation using AI with a comparative analysis of existing 5G/6G frameworks and solutions and their unique aspects needed for 7G environments. This article mainly focuses on threat detection using a deep learning method, also it aims to provide a continuous authentication process. The proposed model response time is decreased by nearly 30 percent, at the same time detection rate is increased to 98.5 percent in a 7G network.

关键词： AI-based cyber security framework Adaptive threat intelligence Anomaly detection Predictive analytics Threat detection

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Toward a Secure Wireless Sensor Network in Intrusion Detection System Utilizing Central-Smoothing Hypergraph Neural Network Optimized With Clouded Leopard Optimization Algorithm

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International Journal of Communication Systems 2025年第11期38卷

作者： Geerthik S Ramachandran A Jegan J Ishwarya M. V Associate Professor Department of Information Technology Agni College of Technology Chennai India Associate Professor Department of Artificial Intelligence & Data Science Saveetha Engineering College Thandalam Chennai India Assistant Professor Department of Computer Science and Engineering School of Computing SRM Institute Of Science And Technology Tiruchirappalli (Deemed To Be University) Trichy Tamilnadu India Associate Professor Department of Artificial Intelligence & Data Science Agni College of Technology Chennai India

Wireless sensor networks (WSNs) is the essential component of wireless technology that provides effective solutions for various monitoring applications. WSN is vulnerable to several security risks as intrusions, attacks, and suspicious activities. Therefore, this paper proposes a Secure WSN in intrusion detection system using Central-Smoothing Hypergraph Neural Network Optimized with Clouded Leopard Optimization Algorithm (WSN-IDS-CSHGNN-CLOA). Here, the input data is taken from WSN-DS database. The gathered data is pre-processed by regularized bias-aware ensemble Kalman filter (RBAEKF) for data cleaning and normalization. The pre-processed data is given into feature selection using Memetic Salp Swarm Optimization Algorithm (MSSOA) to select optimal features. The selected features are given into CSHGNN for classifying the IDS as denial of service (DoS), black hole, gray hole, flooding, and scheduling attacks in WSN. The CLOA is implemented to optimize the hyperparameters of CSHGNN. The performance of the proposed WSN-IDS-CSHGNN-CLOA approach attains 24.39%, 35.71%, and 25.55% higher accuracy; 24.44%, 34.28%, and 14.44% higher precision when compared to the existing techniques.

关键词： Central-Smoothing Hypergraph Neural Network Clouded Leopard Optimization Algorithm Memetic Salp Swarm Optimization Algorithm regularized bias-aware ensemble Kalman filter wireless sensor network

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Photovoltaic Powered Fuzzy Algorithm for IoT-Monitored Four Switch Induction Motor Drive

Photovoltaic Powered Fuzzy Algorithm for IoT-Monitored Four ...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： S. Jayaprakash T. Kanthimathi N. Rathika P. Sathyanathan C. Srinivasan Associate Professor School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Electrical and Electronics Engineering Mahendra Institute of Technology Tiruchengode Tamil Nadu India Department of Electrical and Electronics Engineering VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

There has been a lot of focus on the potential benefits to the industry of combining Renewable Energy Sources (RES) with Internet of Things (IoT) technology. The conventional motor-driven control system often uses traditional power sources and has poor continuous tracking capabilities, which limits its energy economy and control accuracy. This research introduces a unique method for enhanced energy efficiency and control by integrating a fuzzy algorithm, a photovoltaic (PV) power source, IoT-monitored four-switch induction motor (IM) drive. A fuzzy algorithm is used to improve the motor drive system. According to input factors like torque, load conditions and speed, the fuzzy logic controller (FLC) modifies the motor driving settings. Because of the adaptive properties of the fuzzy algorithm, the IM may be controlled effectively, improving system performance. Additionally, the IoT monitoring feature allows for remote control and real-time data collecting, improving system performance and issue detection. MATLAB/Simulink is employed to demonstrate the obtained results of the developed system. The results of this research may be used in a variety of fields, including robots, electric cars, and industrial automation powered by renewable energy.

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AI Framework for Keys Instruction and Music Notation

AI Framework for Keys Instruction and Music Notation

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Krishnaraj Rao N S Smitha GL P. S. Ramesh Nandhini T J P. Nagaveni A Balaji Assistant Professor Department of Information Science and Engineering Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte Karnataka India Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Science (SIMATS) Chennai India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Saveetha School of Enginnering Saveetha Institute of Medical and Technical Science – SIMATS Saveetha University Chennai India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of IT Panimalar Engineering College

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The study of artificial intelligence focuses on the principles that guide all human intellectual endeavors. The integration of several academic disciplines, including as computer science, philosophy, politics, linguistics, systems engineering, neurophysiology, and behavioral science, has allowed it to grow. This article will describe how to build an intelligent machine, consisting of both hardware and software, that can replicate and exceed the abilities of the human brain in order to handle complicated issues that need specialized knowledge. It will specifically examine how to more effectively use artificial intelligence support systems into piano instruction. An Introduction, an oral language and music-assisted education system based on the ARM and SA algorithm, is proposed by this study in order to ascertain the implementation principle guiding the functioning of the piano automation recording system. It also delves deeply into the theory and operation of automated music recording systems, compiling and evaluating data that these systems generate via internal databases. is an effort to improve music education via the use of artificial intelligence support systems. The results of the experiment show that the training system for the automated notation algorithms for playing the piano and songs has a 56.81 and fast growing machine learning auxiliary technology utilization rate. Thus, we may infer that using an AI support system is necessary while teaching performance piano and automated notation.

关键词： Training Machine learning algorithms education Music Machine learning Software Recording Multiple signal classification Telecommunication computing Artificial intelligence

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Optimizing smart grid flexibility with a hybrid MINLP framework for renewable integration in urban energy systems

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Energy Reports 2025年 14卷 508-523页

作者： Sameer Algburi Omer Al-Dulaimi Hassan Falah Fakhruldeen Shakhlo Isametdinova I.B. Sapaev Saiful Islam Quadri Noorulhasan Naveed Ayodele Lasisi Israa alhani Qusay Hassan Doaa H. Khalaf Michael Ssebunya Feryal Ibrahim Jabbar College of Engineering Al-Kitab University Kirkuk 36015 Iraq Electrical Technical College Al-Farahidi University Baghdad Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Baghdad 10011 Iraq Associate professor Kimyo International University in Tashkent Shota Rustaveli street 156 Тashkent 100121 Uzbekistan Head of the department Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University Tashkent Uzbekistan Civil Engineering Department College of Engineering King Khalid University Abha 61421 Saudi Arabia Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Mazaya University College Iraq College of Engineering University of Deyala Diyala Iraq Design Department Al-Turath University College Baghdad Iraq Medical Physics Department College of Sciences Al-Mustaqbal University Babil 51001 Iraq

Urban grids face the challenge of expanding renewable deployment while curbing emissions and minimizing the capital burden of network reinforcements, all of which depend on effective flexibility integration. A hybrid optimization framework is introduced, combining Mixed-Integer Nonlinear Programming with a reformulated MILP structure to jointly size photovoltaic systems, battery storage, staged network upgrades, and the dynamic participation of electric vehicles as both load and distributed storage. Thousands of EV constraints are consolidated through a polytope-based approach, and reinforcement costs are captured using a piece-wise linear model tailored to feeder capacity increments. Application to the Tuwaiq Smart City network, covering 3780 households, employs one-minute resolution data for 2024 to benchmark five operational schemes: No Flexibility, Demand Response, Smart Charging, Vehicle-to-Grid, and Integrated Decentralised Energy Management (IDEM). Compared with the baseline, IDEM achieves a 43.8 % reduction in annualised system cost, 46 % decrease in peak imports, and capacity cuts of 75 % and 82 % for PV and storage respectively, alongside a 65 % drop in grid integration expenses. A Monte Carlo test of 150 runs confirms cost stability within ±6 %, validating the robustness of layered flexibility under stochastic solar and mobility profiles. Solving across a full-year span is achieved within minutes on standard hardware, confirming the framework’s practical value for strategic energy planning

关键词： BESS Battery Energy Storage System DER Distributed Energy Resources DR Demand Response EV Electric Vehicle IDEM Integrated Decentralized Energy Management MILP Mixed-Integer Linear Programming NF No Flexibility PV Photovoltaic SC Smart Charging V2G Vehicle-to-Grid E_ i , e_ i+1 Interval of demand A _PV , A_B, Factors for PV system and BESS B _BESS Factor for BESS B _op Factor for operational costs B _PV Factor for PV System C Matrix of coefficients for linear inequalities C _active Available energy for use C _B BESS capacity C _cap Maximum capacity of the EV battery C _depart Required energy at time of departure C _EV Electric vehicle energy c _i , d_ i Coefficients in cost function C _init Initial stored energy c _PV , c_B Cost Coefficients for PV System and BESS C b0 Initial battery capacity at the start of the time period C b Current battery capacity C bmax Maximum capacity of the battery D Set of all time periods d Right-Hand side of linear inequalities D _peakt Peak demand D Set of all time periods considered E _eff Efficiency of power electronics E _S Energy Capacity of BESS Ein EVt Energy charged by the EV Eout BESSt Energy discharged by the BESS Eout EVt Energy discharged by the EV F _op Operational expenses FSPs Service providers g D_peak Cost function for peak demand g _sum Sum of costs not depending on sizing variables G gridt Power exchanged with the grid G pvt Power generated by the PV system I t Solar Irradiance K _derate Derate factor for PV system L t Load or the energy consumption l′ Unit price of PV equipment M Identity matrix MINLP Mixed-Integer Non-Linear Programming NOCT Modified nominal operating cell temperature Q _comb Combined polytope Q _gridt Grid Power Q _i Individual polytopes Q _max Maximum Grid Power Rt Charging/Discharging rate R_ min , R_ max Min/Max charging/discharging rates R c t , R dt Rates of charging and discharging Rct Rate of charging at time t R cdL Minimum charging rate R cdU Maximum charging/discharging rate R dt Rate of discharging at time t S _efft Efficiency ratio o

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RETRACTED ARTICLE: ALBAE feature extraction based lung pneumonia and cancer classification

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Soft Computing 2023年第2期28卷 589-589页

作者： M Braveen S Nachiyappan R Seetha K Anusha A Ahilan A Prasanth A Jeyam Assistant professor senior School of Computer Science and Engineering Vellore institute of technology Chennai Tamil Nadu India Associate Professor School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Associate Professor School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Associate Professor Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India Assistant Professor Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering Sriperumbudur India Assistant Professor Computer Science and Engineering Lord Jegannath College of Engineering and Technology Kanyakumari Tamil Nadu 629402 India

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Development of Autonomous Robots for Industrial and Domestic Applications

Development of Autonomous Robots for Industrial and Domestic...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： P. Ramadevi VijayaBhaskar Sadu Veeresh Fuskele Siva Nagaiah Bolleddu T. Ch. Anil Kumar M. Kalyan Chakravarthi Department of Computer Science Engineering (Cyber Security) Institute of Aeronautical Engineering Hyderabad Telangana India Department of Mechanical Engineering Jawaharlal Nehru Technological University Kakinada Andhra Pradesh India Department of Mechanical Engineering BabulalTarabai Institute of Research and Technology Sironja Madhya Pradesh India Associate Professor of English and Coordinator KL-CDOE Koneru Lakshmaiah Education Foundation - Deemed to be University Guntur Andhra Pradesh India Department of Mechanical Engineering Vignan's Foundation for Science Technology and Research Guntur Andhra Pradesh India School of Electronics Engineering VIT-AP University Amaravathi Andhra Pradesh India

This research study investigates the potential design of autonomous robots for developing the domestic and industrial applications. The system underwent rigorous testing in both commercial and residential settings, demonstrating exceptional performance in navigation and achieving near-perfect scores in manipulation. Autonomous robots possess the capacity to enhance productivity, reduce expenses, and enhance safety in various scenarios, as emphasized in the literature. The report also outlines potential avenues for future research, which encompass the development of enhanced sensors and perceptual systems, collaborative systems, and advanced decision-making algorithms. In general, this study constitutes a crucial contribution to the expanding corpus of information pertaining to self-governing robots and will facilitate their prospective conception, advancement, and integration.

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Effect of geometric heterogeneity using an auxiliary device on the accuracy of complete arch implant scanning: An in vitro study of different clinical simulations

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Journal of Prosthetic Dentistry 2023年

作者： Abdelrehim, Aly Sulaiman, Eshamsul Sofian, Hazrina Salleh, Nosizana Mohd Graduate student Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya (UM) Kuala Lumpur Malaysia Associate Professor Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya (UM) Kuala Lumpur Malaysia Senior Lecturer Department of Software Engineering Faculty of Computer Science & Information Technology Universiti Malaya (UM) Kuala Lumpur Malaysia

Statement of problem: Intraoral scanning of implants supporting complete arch prostheses is limited because of the lack of geometric heterogeneity and unique reference points, creating inherent errors in the image stitching process by the scanner software program. Purpose: The purpose of this in vitro study was to evaluate the significance of geometric heterogeneity on complete arch implant scanning by using a novel auxiliary geometric device. Three different clinical simulations were tested to assess its significance. The study also assessed whether scans produced using the auxiliary device would meet a clinically acceptable threshold. Material and methods: A total of 60 scans (n=20) were performed using an intraoral scanner in 3 different clinical simulations: 2 parallel implants, 4 parallel implants, and 4 implants with a 30-degree posterior angulation of the distal implants. Scanning alternated between using the auxiliary geometric scanning device (test groups;4IP+, 4IA+, 2IP+) and not using the device (control groups;4IP-, 4IA-, 2IP-). A reference scan for each model was prepared from a high precision laboratory scanner. The scans were analyzed for accuracy in 3-dimensional deviation, interimplant distance deviation, and angular deviation by using an inspection software program. The effect of the auxiliary device was statistically analyzed by comparing scans of the same group using the paired t test for normally distributed data and the Wilcoxon Signed Rank test when data were not normally distributed (α=.05). Results: Significant effects of the auxiliary geometric device were found in 3-dimensional, distance and angular deviations (P<.05). Scans performed using the device were significantly more accurate in most implant positions (P<.05). Linear and angular deviations were clinically acceptable for all test groups. However, the deviations were above the clinically acceptable threshold for the control groups. Conclusions: Using an auxiliary geometric device sig

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Learning analytics using deep learning techniques for efficiently managing educational institutes

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Materials Today: Proceedings 2022年 51卷 2317-2320页

作者： Ravi Kishore Veluri Indrajit Patra Mohd Naved Veduri Veera Prasad Myla M. Arcinas Shehab Mohamed Beram Abhishek Raghuvanshi Aditya Engineering College(A) Surampalem India An Independent Researcher PhD from NIT Durgapur West Bengal India Department of Business Analytics Jagannath University Delhi-NCR India Associate Professor Behavioral Science Department De La Salle University Philippines Research Scholar Department of Computing and Information Systems Sunway University Malaysia Department of Computer Engineering Mahakal Institute of Technology Ujjain India

Increasing numbers of higher education institutions see themselves as service providers, catering primarily to the needs of its students. The improvement of student performance is a top priority for universities. It is critical to first assess the present situation of the students before designing a program to improve their performance. Higher education administrators face a significant problem in predicting a student's future success. The goal of this study is to learn what factors influence college students' decision on a major. It will be possible to forecast students' behavior, attitudes, and performance with the use of predictive tools and procedures. Predicting student performance ahead of time makes it possible to take proactive measures to raise achievement levels. To obtain a high education standard, several attempts have been made to forecast student performance. However the accuracy of these predictions falls short of the desired level of excellence. Machine learning approaches including Artificial Neural Network, Nave Bayes, and SVM are being studied. A University Data Set from UCI Machinery is used in the experimental investigation.

关键词： Learning analytics Deep learning educational data mining Machine learning Student performance Classification Prediction

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Reinforcement learning: A novel approach towards drug discovery

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AIP Conference Proceedings 2023年第1期2477卷

作者： Nishath Ansari Amjan Shaik Patluri Harika Boga Varasree K. Purnachand Saikumar Tara Assistant Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Assistant Professor Department of Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Assistant Professor Department of Information Technology Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Associate Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Associate Professor Department of Electronics and Communication Engineering CMR Technical campus Hyderabad Telangana India

Reinforcement learning has been giving new strategies to ad-lib in the fields of medication and the drug industry. It is probably the best strategy among all the AI strategies. Reinforcement learning has been compelling to discover strategies that could treat the immense number of deadly infections. In this paper, a detailed new methodology about what Reinforcement Leaning and what is drug revelation and what are various techniques utilized in Reinforcement learning, how Reinforcement learning could be utilized in different utilization of medication plan such as target identification, hit discovery, hit to lead, lead optimization. The consequence of the paper illustrates the summarized data of different approaches for the development and discovery of drugs. A clear illustration of various work done in the field of drug discovery, discuss different pros and cons of Reinforcement learning as well emphasize primary challenges need to handle and how to defeat them in the future.

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