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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Anand, L. Padmalal, S. Seetha, J. Juliana, R. Naveen Kumar, P.S. Parasa, Gayatri Srm Institute of Science and Technology School of Computing Department of Networking and Communications Kattanguluthur India Mangalam College of Engg Department of Computer Science and Engineering Kerala India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Loyola-ICAM College of Engineering and Technology Department of Information Technology Chennai India St. Ann's College of Engineering and Technology Department of CSE-DATA Science AP Chirala India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9781665462167

Microcomputers and medical devices with signal transceivers that operate on a specific radio display constitute the backbone of wireless sensor networks (WS Ns) that monitor environmental conditions (temperature, pressure, light, vibration levels, location). It is widely used in WAN sensor networks because of its flexible design and low setup fees. The u200b touch network allows for the connection of up to 65,000 devices, while the Intelligent sensors on other wireless networks are used to transfer data ports and assign wireless networks. Since the price of wireless solutions has been decreasing, and their functional capabilities have been growing, they are gradually replacing wired ones in telemetry data gathering systems and long- distance detecting communication. A deep learning model was used in this investigation to prevent the sensor nodes from manipulating data. Sensor nodes include a lot of parameters and estimations. If these projected data values are altered, network performance will suffer, and the node's lifetime will be reduced. data security became a priority when the sensor nodes were distributed. This new method is 98.82% more efficient than the previous one. © 2023 IEEE.

关键词： Sensor nodes

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ODLCP: A Novel Methodology to Predict Immune system Strength by Using Optimized Deep Learning and Classification Principle

ODLCP: A Novel Methodology to Predict Immune System Strength...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Sahunthala, S. Jayasri, R. Neela, K. Ashwini, R. Raut, S.P. Amer, A. Rajalakshmi Institute of Technology Department of Ai&ds Chennai India St. Joseph's college of Engineering Department of Artificial Intelligence and Data Science Chennai India Sri Sairam Engineering College Department of It Chennai India S.A. Engineering College Department of Ece Chennai India Yeshwantrao Chavan College of Engineering Department of Civil Engineering Maharashtra Nagpur India Zarqa University Department of Energy Engineer Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Immune responses that are mediated by antibodies are essential to the functioning of the immune system in vertebrates. Due to the fact that antibodies have the ability to connect to antigens, they are employed widely in the treatment of cancer as well as other dangerous ailments. The process of experimentally finding antibody-antigen interactions is an essential stage in antibody therapy. This process is typically laborious, costly, and time-consuming. Because of the continued reliance on multidimensional structures, the deployment of computational methodologies that have been recommended for screening potential antibodies continues to be restricted. We introduce a unique deep learning strategy called Optimized Deep Learning and Classification Principle (ODLCP) for accurate immune strength prediction in this paper. In order to evaluate the effectiveness of this method, we cross-validate it with a more conventional learning method known as Convolutional Neural Network (CNN). By incorporating a wide range of clinical and immunological data, our goal is to develop a prediction model that is capable of accurately determining the strength of an individual's immune system. It is possible that the proposed paradigm would revolutionize healthcare since it will make it possible to develop individualized treatments that will improve immune health and allow for the early detection of disorders connected to immunological processes. © 2024 IEEE.

关键词： Convolutional neural networks

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The Necessary Requirements for Utilizing AI in data Access Setting 15

The Necessary Requirements for Utilizing AI in Data Access S...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Swapnil, K. Sharma, Bhuvnesh Singh, Manpreet Yadav, Rakesh Kumar Devaraj Verma, C. Umarani, K. Vishwakarma Institute of Technology Department of Artificial Intelligence &data Science Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi University of Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

AI has been an increasing number of adopted in records get admission to settings to improve the overall performance of statistics get admission to and extract significant insights, using extended productiveness and value-effectiveness. However, the success implementation of AI calls for positive essential requirements to be met, together with facts accuracy, connectivity, security, and scalability. First, facts accuracy is critical for AI applications in facts get entry to settings. Information gathered need to be of excessive quality and accuracy, as AI working on erroneous facts can yield unreliable and probably harmful effects. Records should be of suitable granularity, taking pictures the most relevant elements, to make certain a complete and steady training performance.2nd, linked statistics assets, networks, and services are had to leverage the capability of AI in information get entry to settings. This gives an interconnected and up to date dataset at scale, while requiring included answers for uninterrupted get entry to. Moreover, single sign-on and alertness orchestration abilities are vital to decorate user revel in. © 2024 IEEE.

关键词： Connectivity Gathered Increasing Productiveness Security

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A 2.86-TOPS/W CMCB based Edge ML and RO-PUF engine for IoT based nano-electronic material applications

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Materials Today: Proceedings 2022年 62卷 1780-1784页

作者： Rajasekar, P. Rama prasad Reddy, M. Deepak, Karanam Balamurugan, K. Amudha, S. Vignesh, C.J. Department of Data Science and Business Systems SRM Institute of Science and Technology Tamilnadu India Head of the Department and Professor Department of Electrical and Electronics Engineering G.pullaiah College of Engineering and Technology India Department of EEE G.Pullaiah College of Engineering and Technology Andhra Pradesh Kurnool518452 India Department of Electrical and Electronics Engineering Sri Ramakrishna Engineering College Tamil Nadu Coimbatore India SRM Institute of Science and Technology Tamilnadu India KPR Institute of Engineering And Technology Tamilnadu Coimbatore India

Energy-Effective Machine Learning and Ring Oscillator (RO) Physical Untraceable (PUF) is an IoT care effort that diminishes information move limit and ensures estimations and search interface protection (IoT). Independent contraption. This test gives preparing on Edge machines and PUF machines for IoT errands. This planning utilizes the current glass beam (CMCB), which is the focal circuit vertical to the two capacitors, to diminish the space of the concede line by a variable of48.5. Another element of expansion style has been proposed to broaden the weight matrix factors past a genuine authorized organization, while keeping the expense of clothing and energy low. Paper development and total is performed by CMCB with current discriminative styles and two-venture change. The means proposed give a disappointment rate of6.34 to the MNIST plate acknowledgment issue with an energy attainability of2.86 covers/W. PUF gives a unique harmony disappointment rate (BER) of2.3 in everywhere and the CRP is upgraded with ternary information sources. This mode has developed incomprehensibly, with a space of4.17 × for taking care of all solicitations/reactions (CRP). Extremely low-10–59 m 2/CRP. © 2021

关键词： Internet of things

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Cloud Computing based Smart Traffic Management system with Priority Switching for Health Care Services 2

Cloud Computing based Smart Traffic Management System with P...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Shiny, X.S. Asha Ravikumar, D. Chinnasamy, A. Hemavathi, S. Department of Computer Science and Engineering Telangana Hyderabad India Kings Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Chennai India Sri Sairam Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665456302

Regular cities can be transformed into intelligent structures by leveraging information and communication technologies. Innovative city development could be significantly impacted by the Internet of Things paradigm, commonly called cloud computing. All urban locations have a lot of traffic. In this study, Internet-of- Things (IoT) based system is proposed for health care services to organize and to establish the traffic signaling and pick up a route under which road congestion can be administrated. Vehicle accident contraventions are identified by the traffic officers using an online service that is hierarchically carefully monitored or constrained. However, the suggested approach is generic and may be utilized in any major metropolis without losing its generality. Traffic lights linked to cameras in metropolitan areas can be upgraded by connecting to IoT. During a pandemic, this approach is precious. The police can easily regulate traffic from their homes using their cell phones and identify defaulters. The suggested technique aids in the separation of ambulance and rescue engines from ordinary traffic. © 2023 IEEE.

关键词： Internet of things

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： data integrity

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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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CancerAI: A Deep Learning Framework for Ovarian Cancer Prediction 5

CancerAI: A Deep Learning Framework for Ovarian Cancer Predi...

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5th International Conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Revathy, G. Maheswari, P. Uma Madhavi, S. Vijay Anand, P. Begum, R. Vajubunnisa Jasmin, H. Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Engineering Department of Computer Science and Business Systems Trichy India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Chennai India Cmr Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Justice Basheer Ahmed Sayeed College for Women Affiliated by University of Madras Department of Electronics and Communication Science Teynampet Chennai India

ISBN: (纸本)9798350379945

Ovarian cancer is the fifth most common cause of death from cancer in women. This is mainly because it is often diagnosed at a late stage, as the earliest symptoms are unclear and inconsistent. Existing diagnostic techniques, such as biomarkers, biopsies, and imaging tests, have notable drawbacks such as reliance on subjective interpretation, inconsistency across different observers, and time-consuming testing procedures. This research study introduces an innovative deep learning framework that employs a convolutional neural network (CNN) algorithm to accurately predict and diagnose ovarian cancer, thereby overcoming the existing constraints. The CNN was trained using a dataset of histopathology images. The dataset was partitioned into test and training subsets to enhance the model's performance. The algorithm demonstrated an impressive accuracy rate of 96%, successfully detecting 97.12% of malignant instances and precisely categorizing 95.02% of healthy cells. This method effectively mitigates the challenges related to human expert evaluation, including elevated rates of misclassification and variability across different observers, while also reducing the time required for analysis. The results underscore the capability of this CNN-based technique to offer a more precise, effective, and dependable strategy for forecasting and detecting ovarian cancer. Subsequent investigations will prioritize the integration of new breakthroughs in deep learning to further amplify the efficacy of the suggested approach. © 2024 IEEE.

关键词： Lung cancer

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Enhancing Residential Security with AI-Powered Intrusion Detection systems

Enhancing Residential Security with AI-Powered Intrusion Det...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Jeneetha Jebanazer, J. Prabha, B.Vishnu Yasotha, B. Jaisudha, J. Senthilkumar, C. Pandi, V.Samuthira Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Jeppiaar Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (纸本)9798350313987

Using Intrusion Detection systems (IDS) powered by artificial intelligence is presented in the proposed work as a novel method for enhancing residential security. The overarching goal of the study is to design, develop, and evaluate a system that employs artificial intelligence techniques for real-time detection and prevention of unauthorized access in response to the rising demand for such measures. Using anomaly detection, neural networks, and decision trees, which are all examples of machine learning algorithms that benefit from the incorporation of data from multiple sensors, the proposed system guarantees the accurate identification of suspicious activities. Proposed work examines large datasets and compares them to conventional security measures to demonstrate the system's superior performance and prospective impact on reducing home intrusions. Proposed work contributes to the field of residential security by proposing a dependable, adaptable, and intelligent method for protecting homes against the ever-changing types of infiltration threats that exist today. © 2023 IEEE.

关键词： Anomaly Detection Artificial Intelligence Convolutional Neural Network Home Protection Intrusion Detection Sensor Integration

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