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Introduction and Development of Cyber Physical Systems in Smart Grid with blockchain technology to Enhance Sustainability 2

Introduction and Development of Cyber Physical Systems in Sm...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Valarmathi, P. Manoj, G. Das, Sandip Kumar Oommen, Sujo Tiwari, Mohit B. S Abdur Rahman Crescent Institute Department of Computer Science & Engineering Tamil Nadu India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Management Studies Tamilnadu Chennai India School of Electrical Engineering Odisha Bhubaneswar751024 India Reva University School of Electrical and Electronics Engineering Karnataka Bangalore560064 India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350325799

The need for increased sustainability and efficiency in the energy industry has led to a major increase in interest in the development of cyber-physical systems (CPS) in the smart grid domain in recent years. In the context of the smart grid, the term "CPS"refers to a network of physical objects, sensors, and communication systems that are interconnected and allow for the smooth exchange of data and control signals to optimize the production, distribution, and use of electricity. Demand response, energy management, and grid optimization are just a few of the smart grid applications that can be successfully implemented by utilizing CPS's capabilities. On the other side, blockchain technology provides a decentralized and secure framework for logging and validating transactions among numerous organizations. Since it is distributed, there is no need for middlemen, transparency is improved, and data integrity and immutability are all guaranteed. These qualities make blockchain an excellent choice for tackling the issues that the smart grid is facing, such as data privacy, security, and stakeholder confidence. There are many advantages to integrating blockchain and CPS in the smart grid. First and foremost, a decentralized and democratized energy market is made possible by blockchain, which enables safe and effective peer-to-peer energy trade among prosumers. This increases sustainability by encouraging the use of renewable energy sources and reducing dependency on centralized power generation. In conclusion, there is enormous potential for improving sustainability through the smart grid's integration of blockchain technology with cyber-physical systems. © 2023 IEEE.

关键词： Cyber Physical System

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Deep Learning-based Diagnosis System for Liver Cancer Through Multimodal Determination for Early Treatment

Deep Learning-Based Diagnosis System for Liver Cancer Throug...

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Artificial Intelligence and Emerging technology (Global AI Summit), International Conference on

作者： N. Sabiyath Fatima Ramendra Singh Akuthota Swathi Amar Choudhary Azhar Ahmed Chhaya Sharma Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science & Technology Chennai India Department of Computer Science and Engineering (IoT) Raj Kumar Goel Institute of Technology Ghaziabad India Department of Computer Science and Engineering CVR College of Engineering Mangalpalli Rangareddy India Department Electronics and Communication Engineering Alliance College of Engineering and Design (ACED) Alliance University Bengaluru India Department of Computer Science and Engineering KCC Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The present research provides an analysis of the development and implementation of a deep learning-based diagnosis system for liver cancer. The provided system was developed using a dataset encompassing 2300 sensor readings, including imaging, clinical, and genomic data. A range of deep learning features was implemented to improve the early detection of liver cancer such as CNN, VGG 16, VGG 19, and Inception V3. The data were divided into two categories: one is for training, and another used for testing. The results of the data analysis have shown that VGG 19 provides the highest diagnostic accuracy of 98.76 % , with VGG 16, Inception V3 and CNN displaying the levels of 94.5%, 93.4%, and 90.25%, respectively. The Model performance analysis was also augmented at the application of precision, recall, and F1 score indicators, as well as using AUC-ROC values which highlighted VGG 19 as the most effective algorithm. Another method of evaluation was the application of confusion matrices for plotting the results based on the number of true positive, true negative, false positive, and false negative detections, where all the samples from the dataset were added to each model calculation. The obtained results have shown the importance of multimodal data for the increase of diagnosis accuracy and revealed the promising potential of advanced computation techniques in medical analytics. In such a way, it can be stated that the developed deep learning models contribute to the ability of healthcare specialists in the definition of liver cancer at early stages, which allows timely interventions and facilitates the development of a range of personalized treatment protocols. The application of such algorithms has the potential to reshape liver cancer diagnostics, where major tasks are associated with further improvement and validation of the developed algorithms based on clinical case studies.

关键词： Deep learning Training Liver cancer Analytical models Accuracy Computational modeling Confusion matrices computer architecture Feature extraction Testing

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Cloud Computing-Enhanced and Raspberry Pi-powered Solution for Remote Ear, Nose, and Throat Consultations with AI-Driven Diagnostics 5

Cloud Computing-Enhanced and Raspberry Pi-powered Solution f...

引用

5th IEEE International Conference for Emerging technology, INCET 2024

作者： Giriprasad, S. Karpagam, T. Nalini, V. Mishra, Nilamadhab Mohankumar, N. Malathi, N. Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Tamil Nadu Chennai India Department of Mathematics Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Tamil Nadu Chennai India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350361155

The cutting-edge telemedicine program presents a game-changing alternative for patients seeking ENT (ear, nose, and throat) consultations from a distant location. Utilizing the capabilities of Raspberry Pi devices, powerful artificial intelligence, and cloud computing, the system allows otoscopic tests to be performed without interruption while securely transmitting data to the cloud. On a web-based platform, interactive patient-professional consultations are made possible via real-time communication, which is made possible by WebRTC. Cloud services are very important because of their ability to provide scalable storage as well as AI-driven diagnostics for accurate evaluations. The system complies with the requirements for healthcare compliance, emphasizes security and privacy, and features encryption from beginning to finish. This integrated approach promises scalability, reliability, and advanced diagnostic capabilities, and it reshapes the landscape of remote ENT care with accessible and technologically advanced healthcare solutions. With a user-friendly interface for patients and a cloud-hosted application for healthcare professionals, this integrated approach provides patients with advanced healthcare solutions. © 2024 IEEE.

关键词： Artificial intelligence

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brain Tumor Detection using MRI Image Processing techniques

Brain Tumor Detection using MRI Image Processing Techniques

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2024 IEEE International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Jadhav, Ambaji S. Pawar, Rashmi Hattaraki, Sunilkumar M. bagali, Shwetha Patil, Rajeshwari S. Patil, bhagyashree Department of Elecrtical and Electronics Engineering B.L.D.E. A’s V.P. Dr. P.G.H. College of Engg. and Tech. Vijayapura India Department of Electronics and Communication Engineering B.L.D.E. A’s V.P. Dr. P.G.H. College of Engg. and Tech. Vijayapura India Department of Computer Science and Engineering Sri Krishna Institute of Technology Bengaluru India

ISBN: (纸本)9798331505134

Medical image processing is highly developed and critical in the medical field, encompassing methods like X-rays, MRI, and CT scans. These techniques help to identify minute defects in the human body. brain tumors, which disrupt normal brain function, are detected through MRI using segmentation, feature extraction, and classification, processes that are time-consuming and depend on the expertise of clinical professionals. computer-aided technology addresses these challenges. This work aims to diagnose brain tumors accurately and quickly using MRI images. Image preprocessing is done using CLAHE and median filter to enhance contrast and eliminate noise. Thresholding is used for segmentation and a lightweight U-net is used for tumor segmentation, achieving high accuracy and efficiency without extensive training data. Finally, an MSD-CNN classifies the tumor, capturing both local and global features for better image recognition. Our proposed model outperforms over existing methods in terms of accuracy, robustness, and efficiency in brain tumor detection. © 2024 IEEE.

关键词： Median filters

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Implying Fuzzy Set for Computing Agricultural Vulnerability 2

Implying Fuzzy Set for Computing Agricultural Vulnerability

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2nd International Conference on Computing and Information technology, ICCIT 2022

作者： Amudhavalli, P. Sherubha, P. Ponmani, S. Ahamed, S. Aarif Muthuraja, M. B S Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Vandalur Chennai India Karpagam College of Engineering Department of Information Technology Coimbatore India Rajalakshmi Engineering College Tamilnadu Chennai India Vel Tech Rangarajan Dr. Sagunthala Rd Institute of Science and Technology Department of Computer Science and Engineering Chennai India Department of Computer Science and Engineering Erode India

ISBN: (纸本)9781665436052

Climatic variations may impacts the intensity and frequency of external weather events that may have stronger influence towards agriculture-based ecosystems. In some regional areas, this eco-system may vary due to farming practices, ecological environment, intrinsic factors that may influences event vulnerability. The connection among external events and vulnerabilities of eco-system are categorized by experts, farmers, researchers and various scientists. Moreover, the knowledge eco-system-based vulnerabilities should be considered. Some disciplinary modelling was constructed to map agricultural vulnerability based expert system with combination of geographical information system and fuzzy inference system. This approach was utilized to evaluate vulnerabilities of major eco-systems. Vulnerabilities in cultivation based on heavy rain and vulnerabilities due to drought. This approach is more flexible and may identify diverse factors based on vulnerabilities. This offers essential tools to identify potential source of adaptations with agricultural system. © 2022 IEEE.

关键词： Cultivation

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Smart Water Management in Agriculture: Enhancing Crop Productivity with Restricted boltzmann Machine 1

Smart Water Management in Agriculture: Enhancing Crop Produc...

引用

1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Gore, K.b. Chidambararaj, N. Kartheesan, L. Ghamande, Manasi Vyankatesh Sarode, Ghanasham C. Kalra, Gourav SNJB's Ls K. B. Jain College of Engineering Department of Mechanical Engineering Nashik India St. Joseph's College of Engineering Department of Electrical and Electronics Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Technology Department of Engineering Science and Humanities Pune India Dr. D.Y. Patil Institute of Technology Department of Civil Engineering Pune India Department of Mechanical Engineering Ambala India

ISBN: (纸本)9798350352931

Effective water management in agriculture is crucial for sustainable food production in the face of water scarcity and climate change. The focus of this literature is on new methods and technology that aim to maximize water usage in agricultural activities and improve irrigation efficiency. Efficient water management practices are critical for increasing agricultural yields while lowering water wastage. by implementing water-saving measures and utilizing efficient irrigation systems, farmers can enhance yields while reducing pressure on scarce freshwater resources. Preprocessing, feature selection, and model training are all part of the suggested method. During the preprocessing phase, an Adaptive Inverse Normalization Layer is employed. The RFE methodology relies on a ranking algorithm that sorts the dataset from best to worst according to the attributes that are considered most important. Throughout training, the proposed approach utilized the RbM model without interruption. With a 93.21% average accuracy, this cutting-edge approach surpasses both DbN and KNN. © 2024 IEEE.

关键词： Irrigation

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Deep Learning Cerebral Splitting: Adaptation for Spine Elimination

Deep Learning Cerebral Splitting: Adaptation for Spine Elimi...

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

作者： M. balasubramani b. J. Job Karuna Sagar K. C. Rajheshwari E. Saranya b. Aishwarya P. Murugeswari Assistant Professor Department of Computer Science and Engineering V.S.B. Engineering College Professor Department of Computer Science and Engineering Joginpally B.R Engineering College Hyderabad India Department of Computer Science and Engineering Sona College of Technology Assistant Professor Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Assistant Professor Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Electronics and Communication Engineering Theni Kammavar Sangam College of Technology

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The posterior fossa volume and the total intracranial volume are important factors to consider when doing a brain volumetric analysis using structural electromagnetic resonance imaging. The thorough segmentation of the whole cortex offers a method for non-invasively evaluating different brain regions. In addition, the extraction of skulls in a way that maintains anonymity is increasingly being used as a means of disseminating large amounts of data obtained from brain images. Therefore, generalizing machine learning brain regions for intracranial measurements after skull removal is an interesting task. However, data collecting is difficult due to the small number of handwritten and whole-brain atlases marked with his TICV/PFV. In this study, we use U-Net tiles to estimate intracranial volume and perform whole-brain segmentation with and without cranial characteristics simultaneously. In the absence of individually recorded total cortical volume, a transfer learning approach has been shown to estimate extra her TICV and PFV labeling during whole-brain delineation on T1-weighted MRI. More specifically, U-Net tiles are pre-trained using a big brain COLOR atlas generated using multi-atlas segmentation, and do not include his PFV labels. The pre-trained model is improved to learn more His TICV and PFV labels using the limited brain COLOR atlas. We also elaborate on his method for processing MR images of the brain in the absence of a skull. The results show that our method produces reasonable segmentation and size estimation results in terms of average contact distance, cubic similarity index, and total volumetric similarity for both brains within and outside the skull. is showing off. This technology is currently available as open source to the general public.

关键词： Deep learning Image segmentation Image color analysis Magnetic resonance imaging Transfer learning Pipelines Estimation Skull Resonance Telecommunication computing

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Advancing HRM with AI Powered Recruitment Performance Management and Employee Engagement Using One Class SVMs 3

Advancing HRM with AI Powered Recruitment Performance Manage...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： banu, Rafiya Tripathi, Ruchi Sharma, Saumya Chenniappan, Loganathan Chandrasekaran, K. Umanesan, R. B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Commerce Chennai India SRM-IST Faculty of Management Studies Ghaziabad India The Northcap University Department of Management & Commerce Gurugram India Business Administration IBRI Oman K.S.R College of Engineering Department of Mechanical Engineering Tiruchengode India School of Computing Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508456

AI is making labour-intensive HRM operations like hiring, performance review, and employee engagement more efficient and effective. The incorporation of AI technology in HRM could change several sectors. Through efficient applicant sourcing, screening, and selection, AI-driven solutions simplify hiring processes and ensure fair and accurate picks. Performance management using AI-powered solutions provides data-driven feedback and customized development plans to align individual progress with organizational goals. Predictive analytics improve employee engagement by personalizing experiences and meeting needs. The method focuses on preprocessing and model training. Data are cleansed, ordinal and categorical variables encoded, scaling characteristics and engineering features applied, and dimensional homogeneity assured before processing. For model training, the simple and robust one-class SVM technique is used. One-class SVM model outperformed others with 95.28%. This method worked best compared to GAN and GRU models. AI could improve HRM by improving decision-making and offering data-driven solutions. The findings show that the proposed model outperforms current options, providing the framework for HRM AI applications. © 2025 IEEE.

关键词： Human resource management

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Text recognition by air drawing 4

Text recognition by air drawing

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4th International Conference on Computational Intelligence and Communication technologies, CCICT 2021

作者： Patel, Jay Mehta, Umang Panchal, Kevin Tailor, Dev Zanzmera, Devam Pandit Deendayal Energy University B.Tech in Computer Science and Engineering Gandhinagar India

ISBN: (纸本)9781665423922

In this paper, the text drawn by the user in the air is captured by the computer's camera, followed by the identification of that text. So, the video camera will be turned on at the time of capturing the written text. Now the particular object is defined based on its colour to detect a movement done by the user. The colour is captured by the lower and upper bound of HSV (Hue Saturation Value), which finally leads to object detection at every instant. Lastly, the text will be recognized by the trained model. The model is trained by CNN (Convolution Neural Network) with an accuracy of 98.64% (training) and 98.24% (testing). For completion of the project, OpenCV, python programming language, and its libraries are used. This project requires only a camera and a defined object. © 2021 IEEE.

关键词： Python

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DeepMind Trainer: Harnessing Cognitive Neural Networks on Raspberry Pi for Advanced Cognitive Enhancement Games 2

DeepMind Trainer: Harnessing Cognitive Neural Networks on Ra...

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2nd International Conference on Advancement in Computation and computer technologies, InCACCT 2024

作者： Abirami, P. Antony Joseph Rajan, D. Vivekanandan, T. Saillaja, V. Mohankumar, N. Rajmohan, M. Velammal Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sakunthala Engineering College Vel Tech Multi Tech Dr Rangarajan Dr Department of Mathematics Tamil Nadu Chennai India SRM Institute of Science and Technology Faculty of Management Studies Tamil Nadu Kattankulathur Chennai India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371314

The presentation summarizes a groundbreaking cognitive training program designed specifically for those who suffer from cognitive impairments or neurodevelopmental abnormalities. The work creates a dynamic platform for immersive and adaptable cognitive training games by combining cutting-edge Convolutional Neural Networks (CNNs) with Raspberry Pi technology. Memory, focus, and ability to think creatively are all qualities directly targeted by these games. The CNN model, trained on a broad dataset, interprets real-time user inputs, delivering quick feedback and altering game complexity depending on an individual's performance. The incorporation of Raspberry Pi guarantees a solution that is both affordable and easily accessible. The capacity of the system to redefine cognitive rehabilitation by providing a solution that is individualizable, interactive, and broadly deployable is the potential source of the system's influence. The goal of the work is to make a substantial contribution to the field, which will eventually be to the advantage of users, caregivers, and healthcare professionals and ultimately improve the cognitive well-being of those coping with cognitive issues. © 2024 IEEE.

关键词： Convolutional neural networks

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