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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Rajululi, G. Govinda Geetha, R. Bebitha, J. Basha, SK Mahaboob Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science & Engineering Chennai India R.M.K. Engineering College Department of Computer Science & Engineering Chennai India Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Department of Computer Science & Engineering Chennai India TKR College of Engineering and Technology Department of Computer Science & Engineering Telangana India

ISBN: (纸本)9798331534950

Ransomware is becoming a more serious threat to cybersecurity, and conventional detection techniques frequently fail to maintain up with its changing strategies. To overcome this difficulty, this research proposes an extreme learning machine (ElM) framework enhances the system the efficiency in threat identification. The Static and dynamic feature of files is identified using ElM for analyzing their behavior patterns. The Seagull Optimization Algorithm (SGO) is employed for feature selection refining the dataset by eliminating redundant characteristics, to improve detection process. The system model trained with a Support Vector Machine (SVM) to determine which files are malicious and which are safe. The ElM-SGO-SVM framework is evaluated using performance metrics for identify ransomware threats and it has efficacy in early ransomware detection and mitigation by showing that it greatly outperforms existing techniques. However, this proposed method strengthens the cybersecurity defenses against ransomware threat and ensures better protection for important digital threats. © 2024 IEEE.

关键词： Adversarial machine learning

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An Intelligent Big data Security Framework Based on AEFS-KENN Algorithms for the detection of Cyber-Attacks from Smart Grid Systems

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Big data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine learning(Ml) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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deep learning-Based Robust Hybrid Approaches for Brain Tumor Classification in Magnetic Resonance Images

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Journal of The Institution of Engineers (India): Series B 2023年 1-15页

作者： Krishna, S. Rama Ravela, Chittibabu Mantena, Srihari Varma Sirajuddin, Mohammad deol, G. Joel Sunny Hyderabad India Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University Vaddeswaram India Department of Computer Science and Engineering SRKR Engineering College Bhimavaram India Department of Information Technology Kallam Haranadhareddy Institute of Technology Dasarapalem India Department of Computer Science and Engineering Kallam Haranadhareddy Institute of Technology Andhra Pradesh Dasarapalem India

Classification of brain images is a very challenging problem among the most helpful and commonly employed procedures in the medical system. deep learning, a subset of artificial intelligence, has pioneered new techniques for automatic medical image investigation. The goal of this study was to create two deep learning-based hybrid models to segment and classify brain MRI data. The first hybrid model is a combination of Fully Convolutional Networks and Residual Networks for segmentation and classification. The second is a hybrid of SegNet for segmentation and MobileNet for classification. The analysis is based on a labelled dataset containing images of glioma, meningioma, pituitary, and no tumor. The hybrid models proposed above are built using Python. The suggested models exhibit a high accuracy of 93.9% and 91.3% for a brain MRI dataset, respectively. Furthermore, the proposed models were compared and evaluated against other existing hybrid models in terms of dice score, Positive predictive value, False predictive value, precision, recall, specificity, and F1 score. As a result, the proposed deep-learning technique can assist doctors as well as radiologists in detecting brain tumors early. © 2023, The Institution of Engineers (India).

关键词： Magnetic resonance imaging

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Experimental Insights into the Modeling and Control of Heater Systems Using discrete PId Controllers 1

Experimental Insights into the Modeling and Control of Heate...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Shah Ankit, K. Anilkumar, Markana Patel, Hardik R. L. D. College of Engineering Instrumenation & Control Engineering Department Gujarat Ahmedabad India Pandit Deendayal Energy University Department of Electrical Engineering Gujarat Gandhinagar India Government Engineering College Instrumenation & Control Engineering Department Gujarat Rajkot India

ISBN: (纸本)9798331520540

This research presents the real-time implementation of discrete PI and PId control algorithms on a Heater System, with the system model identified through open-loop experiments using the Scilab platform. A First-Order Plus dead Time model is fitted to the experimental data, and controller parameters are determined using the tuning method. The study investigates the impact of disturbances, modeled by a dC fan, on temperature control and compares the performance of different control strategies. The PId and PId with filter controllers show improved disturbance rejection, faster response, and reduced steady-state error, offering valuable insights for selecting the most effective control algorithm for temperature control in such systems. © 2025 IEEE.

关键词： disturbance rejection

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Performance-Based Capacity Allocation of dERs to Improve Aggregated Flexibility Provision

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IEEE Transactions on Smart Grid 2025年第4期16卷 2968-2990页

作者： Hamidi, Amir Hamzeh, Mohsen Bozorg, Mokhtar Cherkaoui, Rachid School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Institute of Energy HEIG-VD Yverdon-les-Bains Switzerland Power System Group École Polytechnique Fédérale de Lausanne Lausanne Switzerland

Importance of aggregating distributed energy resources (dERs) capacity to procure high-performance frequency regulation services ensuring adequate flexibility for renewable-rich power systems is increasing. Meanwhile, disregarding response performance variety arising from different technical characteristics and inherent uncertainties of dERs managed by an aggregator adversely impacts aggregated response (AR) from three viewpoints: technical feasibility of AR delivery, economic profit-making and fairness of capacity allocation. To tackle these challenges, this paper introduces a new probabilistic flexibility response index (FRI) to integrally evaluate dERs' performance considering four factors: structure, control, communication, aggregation and uncertainties and interactions of these factors. Also, a multi-objective stochastic bidding strategy framework in the day-ahead energy and regulation markets is developed, in which the dERs capacity allocation is devised using the evaluation of their FRIs as the second objective. This framework lies within a mixed-integer linear programming model, considering uncertainties of resources, communication systems, AR, and market prices. The multi-objective structure of problem is handled through augmented ϵ -constraint and Fuzzy decision-making approaches. To validate the efficiency, scalability, and generality of the developed framework, it is implemented on three aggregators of different numbers and types of dERs participating in the ERCOT market context. The results show fundamental improvements regarding three viewpoints. © 2010-2012 IEEE.

关键词： Mixed-integer linear programming

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Med-tech device security through advanced server cryptography

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International Journal of Information Technology (Singapore) 2025年第2期17卷 1015-1022页

作者： Sharan, Preeta Yasmeen, M.S. Nagesh, N. Parimi, Pranav Chetan, V. Roy, Sandip Kumar Department of Electronics and Communication (R & D) The Oxford College of Engineering Bangalore India Department of Electronics and Communication Engineering The Oxford College of Engineering Bangalore India Department of Computer Science Engineering Amrita Vishwa Vidyapeetham Bangalore India Research and Development The Oxford College of Engineering Bangalore India S P Jain School of Global Management Dubai United Arab Emirates

To protect secrecy without compromising data, the study outlines a strong security framework for communications between mobile devices and servers and recommends encryption as the best option. Traditional encryption techniques are not immediately transferable due to the limitations of e-health data, including its size, redundancy, and bandwidth especially when patient data is moving across public networks. Patient privacy may be compromised as a result of this security flaw. Strong encryption measures must be put in place in the healthcare industry to combat this and ensure that confidential patient data may only be retrieved securely and by authorised personnel, protecting data integrity and privacy. To strengthen communication in the healthcare industry, the study presents a new, simplified encryption and decryption technique. We carefully examine, evaluate, and then benchmark this technique against accepted encryption standards. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： data Security Healthcare device security RSA algorithm Secured communication

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deep learning-Based Automated System for Multi-Classification of Plant diseases in Real-Time Environments

Deep Learning-Based Automated System for Multi-Classificatio...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Aruna, K.B. Prema, S. Sureka, V. Sudha, l. Sasikala, V. S.A. Engineering College Computer Science And Engineering Department Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute Of Science And Technology Computer Science And Engineering Department Chennai India Sri Sairam Engineering College Department Of Electronic And Communication Engineering India

ISBN: (纸本)9798350352689

Agriculture provides food for the world's expanding population. Several developing nations are going through this period, and agriculture has been developed to fulfil the rising food demand. While agriculture seeks to feed the world's rapidly growing population, plant disease reduces the amount and quality of food, fiber, and biofuel crops. Although losses can be severe, they normally account for 42% of the yield of the six most important food crops. Managing illnesses costs farmers sometimes without enough professional assistance, which has unfavorable outcomes including pollution and subpar disease control. deep learning algorithms are helping farmers all around the world identify and categories the illness. In order to identify disease in plant leaves, it employs techniques like Convolutional Neural Networks (CNN). This research discussed the preset and user specified models that were employed to identify diseases in plant leaves. We are transmitting the driver circuit to power the pump motors to Arduino Uno using UART protocols. determine the different soil types using an NPK sensor. At the end, we are using Arduino to control the motor model with a server motor. © 2024 IEEE.

关键词： Convolutional neural networks

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Hybrid Reptile-Snake Optimizer Based Channel Selection for Enhancing Alzheimer’s disease detection

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Journal of Bionic engineering 2025年第2期22卷 884-900页

作者： digambar Puri Pramod Kachare Smith Khare Ibrahim Al-Shourbaji Abdoh Jabbari Abdalla Alameen Department of Electronics and Telecommunication Ramrao Adik Institute of TechnologyD.Y.Patil CampusNavi-MumbaiMaharashtra400706India Maersk Mc-Kinney Moller Institute Faculty of EngineeringUniversity of Southern DenmarkOdenseDenmark Department of Electrical and Electronics Engineering Jazan UniversityJazan45142Saudi Arabia Department of Computer Science University of HertfordshireHatfieldUK Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam Bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia

The global incidence of Alzheimer's disease(Ad)is on a swift *** Electroencephalogram(EEG)signals is an effective tool for the identification of Ad and its initial Mild Cognitive Impairment(MCI)stage using machine learning *** of Ad using EEG involves multi-channel ***,the use of multiple channels may impact the classification performance due to data redundancy and *** this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for Ad and MCI detection based on decomposition *** Mode decomposition(EMd),low-Complexity Orthogonal Wavelet Filter Banks(lCOWFB),Variational Mode decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG *** extracted thirty-four features from each subband of EEG ***,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel *** effectiveness of this model is assessed by two publicly accessible Ad EEG *** accuracy of 99.22% was achieved for binary classification from RSO with EMd using 4(out of 16)EEG ***,the RSO with lCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)*** performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing Ad detection techniques.

关键词： Alzheimer's disease Brain disorder Electroencephalogram Reptile Search Algorithm Snake Optimizer Optimization

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Secure digital Identity using Non-Fungible Tokens 5

Secure Digital Identity using Non-Fungible Tokens

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Agarkhed, Jayashree Patil, Spoorti S. Raza, Syed Yaseen Patil, Venkatesh P. D. A. College of Engineering Department of Computer Science and Engineering Kalaburagi India

ISBN: (纸本)9798350321425

digital identity management is a significant aspect of online security, with traditional methods presenting several challenges. The introduction of blockchain technology provides a promising solution to traditional digital identity challenges. The observed drawback of Blockchain based identity is scalability and interoperability. To overcome these problems, this research study proposes an NFT-based digital identity management system that prioritizes self-sovereign identity by leveraging the properties of NFTs, such as its novelty and immutability. This research study provides a detailed description of the system's workflow. The proposed system is built on the strengths of existing Blockchain based digital identity management systems while addressing their limitations. Overall, the proposed system provides an alternate approach to solve the challenges of traditional digital identity management. © 2023 IEEE.

关键词： Blockchain

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Breast Calcifications and Histopathological Analysis on Tumour detection by CNN

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computer Systems Science & engineering 2023年第1期44卷 595-612页

作者： d.Banumathy Osamah Ibrahim Khalaf Carlos Andrés Tavera Romero P.Vishnu Raja dilip Kumar Sharma Department of Computer Science and Engineering Paavai Engineering CollegePachalNamakkal637018Tamil NaduIndia Al-Nahrain University Al-Nahrain Nanorenewable Energy Research CenterBaghdadIraq COMBA R&D Laboratory Faculty of EngineeringUniversidad Santiago de Cali76001CaliColombia Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226Madhya PradeshIndia

The most salient argument that needs to be addressed universally is Early Breast Cancer detection(EBCd),which helps people live longer *** computer-Aided detection(CAds)/computer-Aided diagnosis(CAdx)sys-tem is indeed a software automation tool developed to assist the health profes-sions in Breast Cancer detection and diagnosis(BCdd)and minimise mortality by the use of medical histopathological image classification in much less *** paper purposes of examining the accuracy of the Convolutional Neural Network(CNN),which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early iden-tification of breast cell malignancies formation of masses and Breast microcalci-fications on the *** we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network(ResNet50)for Breast Cancer detection and diagnosis,to obtain the discriminative localization,Convolutional Neural Network with Class Activation Map(CAM)has also been used to perform breast microcalcifications detection tofind a specific class in the Histopathological *** test results indicate that this method performed almost 225.15%better at determining the exact location of disease(discriminative localization)through breast microcalci-fications ***50 seems to have the highest level of accuracy for images of Benign Tumour(BT)/Malignant Tumour(MT)cases at 97.11%.ResNet50’s average accuracy for pre-trained Convolutional Neural Network is 94.17%.

关键词： computer-Aided detection breast cancer detection convolutional neural network class activation map computer-aided diagnosis

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