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ADVANCED ATTACK MITIGATION IN IOT GATEWAY PROTOCOLS

computers & Security 2025年 157卷

作者： K. Praveen Kumar Dr. N. Suresh Kumar Assistant Professor Department of Information Technology Kakatiya Institute of Technology and Science Warangal (KITSW) Telangana India Research Scholar Department of Computer Science and Engineering GITAM University Visakhapatnam Andhra Pradesh India Associate Professor Department of Computer Science Engineering GITAM Institute of Technology GITAM (Deemed to be University) Visakhapatnam India

With the increasing number of users on the internet, numerous cyberattacks are becoming more and more common. Proper detection of these attacks by Intrusion Detection Systems (IDS) is extremely important, particularly for IoT networks. Deep learning methods have proved to be very promising for enhancing IDS performance. This paper presents an end-to-end system for attack detection and prevention in IoT networks with the use of data augmentation, preprocessing, feature extraction, and deep machine learning algorithms. The class imbalance is resolved using the Enhanced Synthetic Minority Over-Sampling Technique (ESMOTE), and preprocessing operations normalize and clean the data for improved model performance. Feature extraction involves statistical features and Shannon entropy-based features, which are fused and sent through a feature selection process. A new 2D-LICM hyper-chaotic map combined with Walrus Optimization (2D-LICMHy-CM_WO) is used to enhance feature selection through enhanced search diversity, convergence rate, and eliminating redundancy. The Dense Convolutional Spatial Attention-based Enhanced Bi-GRU (DCSAtten_EBi-GRU) effectively extracts attack pattern dependencies for precise detection, and an Enhanced Double Deep Q-Learning Network (DoubleDQN) offers dynamic adaptive real-time countermeasures. Experimental findings prove that the proposed solution can obtain a 99.6% detection accuracy with an F1-score of 0.98 and outperforms current IDS models in false positive rate and detection time.

关键词： 2D-LICM hyper-chaotic map with Walrus Optimization (2D-LICMHy-CM_WO) Dense Convolutional Spatial Attention-based Enhanced Bi-GRU (DCSAtten_EBi-GRU) Double Deep Q-Learning Network (DoubleDQN) Enhanced Synthetic Minority Over-Sampling Technique (ESMOTE)

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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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Advanced skin lesion segmentation and classification using adaptive contextual GLCM and deep learning hybrid models

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Egyptian Informatics Journal 2025年 30卷

作者： Vinit Kumar D.L. Shanthi Tummala Ranga Babu Narendra Kumar Rakesh Kumar Godi Dr Arun G Professor Galgotias College of Engineering and Technology Greater Noida India Assistant Professor Department of Computer Science and Engineering BMS Institute of Technology and Management Bangalore Karnataka India Professor Electronics & Communication Engineering R.V.R. & J.C.College of Engineering India Associate Professor Department of CSE Amity University Jharkhand Ranchi Jharkhand India Department of Computer Science Central University of Karnataka Kalaburagi 585367 India Assistant Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India

One of the most common and dangerous types of cancer, skin cancer, especially melanoma, is distinguished by its quick spread and high death rate if left untreated. It is impossible to overestimate the significance of early detection and precise diagnosis as they are essential for successful treatment and greatly raise patient survival rates. To improve skin lesion segmentation and classification, this work presents a state-of-the-art image analysis technique that uses hybrid deep learning models in conjunction with the Adaptive Contextual Gray Level Co-occurrence Matrix (GLCM). DeepLabV3 + is used to segment the skin lesion from pre-processed input images with high accuracy. A Long Short-Term Memory (LSTM) network trained on characteristics collected by the Adaptive Contextual GLCM is included to further evaluate the segmented images. Because of this special combination, the model can accurately capture contextual information and tiny texture differences, both of which are essential for differentiating between benign and malignant skin lesions. Our suggested approach performs very well in tasks involving both segmentation and classification. In particular, it attains a remarkable 98.34 % accuracy, 99.13 % specificity, 97.25 % sensitivity, 97.15 % Dice coefficient, and 98.6 % F1 score. These findings demonstrate the method’s stability and dependability, which makes it a potentially useful instrument for raising the detection accuracy of skin cancer. This approach has the potential to transform early detection procedures by providing improved diagnostic accuracy and resilience, eventually contributing to better patient outcomes and more effective management of skin cancer cases.

关键词： Skin cancer Machine Learning Deeplabv3 Adaptive Contextual Gray Level Co-occurrence Matrix Long Short-Term Memory

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Optimizing Hindi Paragraph Summarization through PageRank Method

Optimizing Hindi Paragraph Summarization through PageRank Me...

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International Conference on Edge Computing and Applications (ICECAA)

作者： Sheshang Degadwala Dhairya Vyas Khush Nilaykumar Patel Mukesh Soni Pavitar Parkash Singh Ramya Maranan Associate Professor & Head of Computer Engineering Department Sigma University Vadodara Gujarat India The Maharaja Sayajirao University of Baroda Vadodara Gujarat India Charotar University of Science and Technology Anand Gujarat Department of CSE University Centre for Research & Development Chandigarh University Mohali Punjab India Lovely Professional University Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

This research paper presents a novel approach to optimize Hindi paragraph summarization using the PageRank method. With the increasing volume of Hindi language content on the internet, there is a growing need for effective summarization techniques tailored specifically for Hindi text. The proposed method leverages the inherent structure and connectivity of Hindi paragraphs to extract the most important sentences for summarization. The PageRank algorithm is applied to construct a rank representation of the text, with sentences as nodes and their co-occurrence patterns as edges. By calculating PageRank scores iteratively, the algorithm identifies the most central and significant sentences in the paragraph. Linguistic features such as sentence length, position, and content relevance are incorporated to enhance the quality of the summaries. Additionally, the algorithm dynamically adjusts sentence importance based on their relations with other sentences rank. Experimental results on a comprehensive dataset demonstrate that the PageRank-based approach outperforms existing summarization techniques in terms of informativeness and coherence, as evidenced by higher similarity score. The proposed method offers an effective solution for optimizing Hindi paragraph summarization, facilitating the management and comprehension of Hindi text in various applications.

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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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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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Introducing Service Learning To Under Graduate engineering Students Through EPICS

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Procedia computer science 2020年 172卷 688-695页

作者： Santosh Madeva Naik Surendra Bandi Hema Mahajan Assistant Professor Department of Mechanical Engineering HITAM Hyderabad Telangana 501401 India Associate Professor Department of Computer Science Engineering HITAM Hyderabad Telangana 501401 India Assistant Professor Department of Computer Science Engineering HITAM Hyderabad Telangana 501401 India

This paper describes the introduction of doing engineering through the "engineering Project in Community Service (EPICS)" and the impact of a community-based learning approach on student motivation. Students get practical hands-on experience while working with non-profit organizations. Now a day’s students find it difficult to learn in the traditional way of classroom teaching chalk and board. They want to work on real-time problems with community partners by applying engineering knowledge. EPICS program helps in grooming the students’ skills in design, communication, teamwork, and develop leadership skills, facing the challenges, social awareness, multidisciplinary knowledge, solution-driven approach towards a problem and maintaining social relationships. EPICS is one of the IUCEE (Indo Universal Collaboration for engineering Education) initiative where students are exposed to study the community needs and frame an engineering problem to work on further.

关键词： EPICS Community partner Design process Team work Products

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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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Proposed Selection Technique of Evolutionary Algorithm and its implementation for Combinatorial Problems

Proposed Selection Technique of Evolutionary Algorithm and i...

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Advances in Computing, Communication & Materials (ICACCM), 2020 International Conference on

作者： Rajiv Kumar Minakshi Memoria Computer Science and Engineering Uttaranchal University Dehradun India Associate professor Computer Science and Engineering Uttaranchal University Dehradun India

ISBN: (数字)9781728197852

ISBN: (纸本)9781728197869

The present paper proposed new selection techniques of the evolutionary algorithm. The nature of the evolutionary algorithm is probabilistic and randomized. Evolutionary algorithm work on Charles Darwin's principle of natural selection. This algorithm can be applied to various optimization problems, such as scheduling, traveling salesman problems, Routing problems, or combinatorial problems. Evolutionary algorithms include Genetic algorithm, Memetic algorithm. The performance of the evolutionary algorithm depends upon its operators, such as selection techniques, crossover operators, mutation operator, and its parameter setting. The main focus of this paper is selection techniques. The proposed algorithm has been successfully implemented for the CPU scheduling problem. Experimental results show better results.

关键词： Genetic algorithms Sociology Statistics Evolutionary computation Processor scheduling Biological cells Scheduling

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IoT-based shrewd crop field MPA system

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AIP Conference Proceedings 2025年第1期3224卷

作者： Himanshu Agarwal Shweta Agarwal Utkarsh Mishra Vanshika Chaudhary Vanshita Singh Shaurya Chauhan Associate Professor Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad (U.P.)-India Assistant Professor Department of Mathematics Ramabai Ambedkar Government Degree College Gajraula Amroha (U.P.)-India UG Scholars Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad (U.P.)-India

Context: Agriculture stands as a pivotal driver of economic progress within a nation, yet the realm of technical advancements within this sector remains distressingly neglected by a multitude of governments. While farmers contribute untiring efforts to tend to their fields, substantial time is squandered on tasks like irrigation and safeguarding crops from birds and animal threats [1, 2]. This unwavering dedication often exacts a toll on farmers’ health, leading to ailments and respiratory issues stemming from exposure to noxious gases emitted by certain crops. Extensive research endeavours [3-9] have been undertaken to alleviate the burdens faced by farmers. These efforts, however, frequently culminate in singular applications such as automated irrigation systems or electric perimeters for crop protection. A subset of researchers has also delved into probing the prevalence of harmful gases across agricultural fields. This paper proposes an innovative approach to address these challenges through the utilization of Internet of Things (IoT) technology. Objective: The proposed solution canters on a NodeMCU powered intelligent crop field Monitoring-Protection-Alert (MPA) system, which serves as a technical path to revolutionize farming practices. The core objective underpinning the proposed system is the acquisition of real-time insights emanating from the crop field. This critical initiative empowers farmers with timely and accurate data, enabling them to make informed and precise decisions pertaining to their agricultural domain. Methods: By seamlessly integrating various sensors, the system detects the presence of birds, animals, and noxious gases in real-time. Furthermore, it enhances crop productivity by continuously monitoring soil parameters, including temperature and moisture levels, thereby optimizing irrigation processes. For seamless communication, the system is fortified with a GSM module that promptly alerts farmers about potential threats to their crops.

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