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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Akiladevi, R. Sardha, S. Shruthi, R. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350395235

The biggest barrier is the financial strain caused by rising electricity costs from using too much power. The security of businesses in the energy sector is threatened by this financial burden, therefore finding creative ways to get through these rough waters is imperative. Moreover, the intricacies involved in the issuance and exchange of energy certificates have intensified the need for enhanced openness and economy. Industry stakeholders are now seriously concerned about these nuances. Our project offers a methodical strategy based on multichain blockchain technology to address these enormous obstacles. invested in the future of the industry. This innovative approach focuses on tokenizing energy assets, optimizing energy management procedures, and providing industry stakeholders with a safe environment. The work on the project in stages, starting with collecting a lot of data. The carefully collect standardized data on power use, expressed in terawatt-hours (TWh), and then improve data quality to make sure the information is reliable and credible. The principal aim is to bring in an era characterized by security, efficiency, and transparency in the administration of energy resources, hence fostering growth and resilience within the energy industry. The ultimate objective is to clear the path for a future in energy asset management marked by openness, security, and efficient operations, all of which will support the growth and sustainability of the sector. © 2024 IEEE.

关键词： Blockchain

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ECG - Driven Heart Disorder Profiler using Machine Learning Techniques 1

ECG - Driven Heart Disorder Profiler using Machine Learning ...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Manimaran, V. Shanthi, N. Aravindhraj, N. Aatarsh, K.M. Adharshini, G. Gokul, P. Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350371802

Right now, heart illness is the driving cause of passing around the world and its frequency is on the rise. To precisely analyze heart illness in its early stages some time recently a heart event occurs, it is troublesome to do so. There is a large amount of data on heart disease available in the healthcare sector such as clinics, hospitals, etc., but it is not easy to process this data to identify hidden patterns. Machine learning techniques can help to transform this medical data into valuable information. Machine learning can be used to design support system that can learn from their experiences. The aim of the research is to furnish the heart illness diagnosis in the most precise way. The proposed work uses machine learning and feature selection algorithms to compute results using the hybrid model. Methods like the Random Forest, Boruta, decision tree, logistic regression (LR), support vector machine (SVM), logistic regression with SVM, and hybrid models that combine logistic regression with SVM and Random Forest were used in this process. The assessment includes both individual and ensemble models and covers all heart disease datasets. Hybrid models are evaluated across multiple datasets using key metrics such as true positive rate, specificity, accuracy. The proposed framework's results are analyzed with following extensive experimentation. The study found that the proposed hybrid machine learning models outperform individual models and approaches in terms of accuracy, sensitivity, and specificity across all heart disease datasets. © 2024 IEEE.

关键词： Neural networks

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Advance Genome Disorder Prediction Model Using Deep Learning Algorithms 3

Advance Genome Disorder Prediction Model Using Deep Learning...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Ponmalar, S. Priyadharshini, K. Pooja, V. Swathi, M. Velammal College of Engineering and Technology Computer Science and Engineering Madurai India

ISBN: (纸本)9798350381931

A comparative analysis of deep learning techniques for the prediction of genetic disease - cystic fibrosis. Cystic fibrosis (CF) is a complex genetic disorder that needs early and accurate diagnosis for effective treatment. To achieve this, we introduce a powerful genome disorder prediction model that uses advanced deep learning techniques. Our model is built on two key algorithms: Residual Neural Network (ResNet) and Extreme Learning Machine (ELM). We collected a comprehensive dataset that includes genetic data, clinical records, and information about the environment. Our goal was to improve the accuracy of CF prediction. After extensive testing and cross-validation, our research shows that our model performs better than traditional methods. This study is a significant step toward improving the diagnosis and personalized management of cystic fibrosis © 2024 IEEE.

关键词： Forecasting

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Deep Learning - Driven RPA Bot for Automated Evaluation of Handwritten Examination Papers Using Gen AI

Deep Learning - Driven RPA Bot for Automated Evaluation of H...

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

作者： Duraimurugan, N. Vaishnavi Sri, S.M. Subhikshaa, S. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331543617

The incorporation of Robotic Process Automation (RPA) and deep learning in the educational evaluation system brings a strong, automated solution for assessing handwritten exam assessments. Educational institutions have exponentially started using RPA systems for monotonous activities like data storage and management, but the capacity extends far beyond these activities. This paper gives an automated solution for evaluating handwritten examination papers in educational institutions by combining advanced deep learning models and RPA (Robotic Process Automation). Using CNNs (Convolutional Neural Networks) and BiLSTM (Bidirectional Long Short-Term Memory) networks, the system converts handwritten responses into machine-readable text, trained to various handwriting styles. For grading, a Gen-AI model is used to assess the answers recognized by CNN and BiLSTM. This provides precise grading that aligns closely with human correction. RPA does end-to-end automation, from scanning and processing papers through deep learning models for text recognition to grading and data management thus reducing human effort, enhancing consistency, saving time and improving efficiency in the evaluation process. This technique streamlines examination, allowing teachers and professors to focus more on personalized instruction and academic development. © 2024 IEEE.

关键词： Convolutional neural networks

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Multimodal Knowledge Graph Completion Using CLIP-Enhanced Hyper-Node Relational Graph Attention Networks 5

Multimodal Knowledge Graph Completion Using CLIP-Enhanced Hy...

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

作者： Shashank, R.B. Swami, Saurabh B. Pai, Akanksha A Mady, Pranamya Thomas, Sharon Moharir, Minal Rv College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350379945

Knowledge graph (KG) completion is a critical task in Artificial Intelligence (AI) focused on deducing absent connections between entities. The diverse nature of data, encompassing text, images, and numerical information, introduces considerable difficulties in precisely forecasting these missing links. Multi-modal KGs are necessary to fully capture the diverse and rich information associated with entities, enhancing applications in search, recommendation, and AI. This study presents a new framework for completing multi-modal knowledge graphs that integrates textual, visual, and numerical data. Pre-trained Contrastive Language-Image Pre-training (CLIP) embeddings are leveraged for textual and visual information and a dense layer is utilized for numerical data, creating a unified entity representation through a low-rank multi-modal fusion approach. The proposed model employs a Hyper-node Relational Graph Attention Network (HRGAT) to effectively aggregate and process multi-modal information. The research shows that this new approach performs significantly better than current models in certain metrics, such as Mean Rank (MR), particularly excelling in lowering the mean rank of head entities, showcasing the enhanced capability and accuracy of the method proposed in completing multi-modal KGs. © 2024 IEEE.

关键词： Knowledge graph

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Review on Blockchain-Enabled Techniques for Secure Storage and Sharing of Patient Healthcare Records 2

Review on Blockchain-Enabled Techniques for Secure Storage a...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Mahmood, Rasmiya George, Vinodu Lbs College of Engineering Department of Computer Science and Engineering Kasaragod India

ISBN: (纸本)9798350328202

The Patient Medical Health Records (PMHR) have always provided a large amount of research data to medical institutions, and pharmaceutical companies and have contributed to the development in medical research. Lack of confidentiality and integrity produces many security problems with sensitive health information and has a significant negative effect on a patient's life. Therefore, securing medical records is much-needed task for the healthcare system. However, the attack's harmfulness, data vastness and complexity have made data protection a complicated mission. Today's healthcare sector use centralized system to record and manage patient data. These may be less secure and subject to misinformation. Therefore, proper and secure management of patient health records is an important task in entire healthcare field. This include secure storing of health records, sharing among various healthcare professionals. In order to overcome the issues regarding the centralized system, various blockchain enabled approaches have been utilized and most of them could result in enhanced confidentiality, high throughput, low processing time, better access management and finally reduced computational overhead. However, still we need more improvements in assessing and addressing the scalability issues considering the increasing volume of data and the growing number of participants in the healthcare ecosystem. Main contribution of this paper is to review the blockchain enabled techniques in securing patient health records and discuss their underlying mechanisms in order to improve confidentiality, security, throughput, processing time and providing effective access control mechanisms. Finally will conclude with realizing a gap for utilizing methodologies for overcoming scalability issues. © 2024 IEEE.

关键词： Blockchain

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Performance Evaluation of Natural Language Processing Algorithms for Sentiment Analysis

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SN computer science 2024年第6期5卷 724页

作者： Silviya, S. H. Annie Faith, S. Julia Seetha, R. Hemalatha, M. Department of Computer Science and Engineering Rajalakshmi Institute of Technology (RIT) Department of Information Technology S.A.Engineering College Department of Information Technology Vellore Institute of Technology Department of Computer Science and Engineering RMD Engineering College

Sentiment analysis can be used to identify if a text’s sentiment is neutral, positive, or negative. One type of natural language processing is sentiment analysis. An interdisciplinary field encompassing linguistics, computer science, and artificial intelligence called “natural language processing” studies how computers and human language interact, with a focus on how to teach computers to process and analyze vast volumes of natural language data. Sentiment analysis, sometimes referred to as opinion mining or emotion artificial intelligence, is the methodical identification, extraction, quantification, and study of affective states and subjective data using natural language processing, text analysis, computational linguistics, and biometrics. Sentiment analysis is frequently used in online and social media platforms, voice of the customer materials (such as reviews and survey replies), and healthcare materials for marketing, customer service, and clinical medicine purposes. This paper discusses different sentiment analysis methods and selects the best approach for a certain task. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Biometrics Computational linguistics Natural language processing Sentiment analysis Text analysis

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A Survey on Fish Detection and Species Recognition 2

A Survey on Fish Detection and Species Recognition

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Sheela, A Jeba Madhan Raj, R. Manoj, E. Roshan, G.K. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

Automated fish identification is essential to overcome the challenges and time constraints associated with manual identification processes. Various techniques are explored, evaluating their performance based on factors like pre-processing methods, significant characteristics, and accuracy. Fish classification is a well-studied problem with applications in diverse fields, including target marketing and government initiatives to manage fish supply and maintain ecological balance. These automated methods play a pivotal role in sectors like commercial fisheries, agriculture, marine science, and the broader industrial fish market, supporting industries such as nutrition and canning factories. One approach involves enhancing fish recognition algorithms based on models like AlexNet, IDNet, SAFNet. Methods such as item-based soft attention mechanisms, reduced structural complexity, and transfer learning have been employed to improve accuracy and reduce training time. Similarly, researchers have applied deep learning algorithms like Mask R-CNN, MobileNet, and Fast RCNN to tackle species identification, length estimation, and object detection in marine environments. These techniques have shown promising results, especially when dealing with overlapping fish or elongated ***, lightweight models have been developed for underwater object detection, considering issues like low visibility, colour distortion, and small target size. Techniques like attentional feature fusion, modified feature pyramid generation, and adaptive anchor generators have been proposed to optimize performance, achieving a balance between accuracy and processing speed. © 2024 IEEE.

关键词： Fish

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Deep Learning for Crop Disease Detection using YOLOv8 15

Deep Learning for Crop Disease Detection using YOLOv8

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

作者： Rakesh Kumar, M. Rengalakshmi, N. Saghana Shree, R.P. Akiladevi, R. Kumar, P. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Agriculture, deeply rooted in India's history, remains as the root of the country's economy and a primary source of employment. Despite the ageold practice, smallholder farmers in India sometimes face problems, especially in getting critical information that would allow them to make knowledgeable farming decisions. In this era of technology, knowledge is the foundation of advancement, farmers productivity and profitability could be impacted due to a lack of widely accessible agricultural knowledge. Crop health is an important aspect in productivity in agriculture. Crop diseases can have major implications for quality and quantity, as well as farmer's livelihoods. This technology aims to assist farmers, increase productivity, and contribute to agriculture's sustainability by providing up-to-date knowledge regarding crop health, disease identification, and optimal farming practices. Crop disease identification used to rely on digital image processing, but new advances in deep learning have exceptionally outperformed previous techniques. In this paper, we focus on the effectiveness of employing deep learning for crop disease identification. Our research is based on a database of images of diseased crop leaf and healthy leaf, with a focus on prevalent crop diseases in India. An application has been created with this ideology in mind to aid farming practices by detecting plant disease. There are two types of input format can be given to the application such as image and video of the diseased plant leaves. YOLO v8 is the deep learning model that is used in this application to effectively identify the disease. © 2024 IEEE.

关键词： Agriculture productivity Crop disease Crop health Deep Learning YOLOv8

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Speech Enhanced Intelligent Search using Deep Learning 1

Speech Enhanced Intelligent Search using Deep Learning

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1st International Conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Florance, G. Shruthi, G.R. Gupta, Aashutosh Abhishek, V. Patil, Amogh Bokka, Siddharth New Horizon College of Engineering Computer Science and Engineering Bengaluru India New Horizon College of Engineering Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350365405

The latest advancements in Deep Learning (DL) techniques have led to a high level of curiosity in the integration ofvoice technology with intelligent search engines. This research project intends to improve users' information retrieval experiences by utilizing DL techniques through the design, implementation, and assessment of a unique Speech-Enhanced Intelligent Search (SEIS) system. To give consumers a more efficient and natural way to engage with search interfaces, the proposed approach seeks to close the space between current speech recognition technologies and traditional text-based search engines. © 2024 IEEE.

关键词： Adversarial machine learning

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