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Blockchain and ML-Based Framework for Diabetes Assessment of Patients in Telesurgery System 3

Blockchain and ML-Based Framework for Diabetes Assessment of...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Patel, Yogi Tank, Denisha Gupta, Rajesh Kakkar, Riya Jadav, Nilesh Kumar Gohil, Lata Tanwar, Sudeep Institute of Technology Nirma University Department of Computer Science and Engineering India

ISBN: (纸本)9798350319125

With the advent of innovative technologies, smart healthcare services such as telemedicine and telesurgery adopted across the globe due to their prevalent benefits which helps to perform the surgery in an efficient and reliable manner. However, before the surgeries can be performed on the patient, the patient's health is to be monitored to declare the patient fit for the surgery. Therefore, we have proposed a blockchain-based diabetes detection scheme using machine learning. Our proposed framework leverages blockchain technology to safeguard sensitive data against potential malware threats. We have employed the Interplanetary File System (IPFS) protocol that improves the cost-efficiency of the proposed framework for reliable patient health monitoring. Moreover, we have implemented a random forest (RF) model to predict diabetes in a patient with an improved accuracy when compared to the other machine learning models. Finally, to assess the performance of the proposed framework, we have compared the performance of the proposed framework using RF based on various factors like accuracy, precision, recall, log-loss, and F1-score. © 2023 IEEE.

关键词： Blockchain IPFS Machine learning Random forest Telesurgery

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Revolutionizing Machine Learning Security: The Role of Quantum-Enhanced Federated Learning 2

Revolutionizing Machine Learning Security: The Role of Quant...

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

作者： Kannan, E. Ravikumar, S. Carmel Mary Belinda, M.J. Vijay, K. Vathani, Anstey Kannan, Sriram Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology Department of Civil Engineering India Fidelity Investments Data Engineering Chennai India

ISBN: (纸本)9798331534950

In today's age of collaborative machine learning where data sharing happens across organizations and privacy becomes a real issue. Old-school methods of securing systems work, but tend to lack against popular new attacks. The research uses a novel approach that uses quantum computing to reimagine what privacy means. An article which deals with the combination of quantum technologies and federated learning systems, specifically discussing how it may set a new standard for data privacy. The plan is to use quantum key distribution and entanglement-based protocols in order to fortify the defences of collaborative machine learning systems against adversarial attacks or unauthorized access, according a consortium statement. The approach to quantum -enhanced federated learning presented provides a solution not only with secure improvements but also implementing more extreme circumstances in decentralised learning scenarios, such as challenges due to different data available and regulatory demands. This paper provides a theoretical benchmark for quantum federated learning and focuses on the underlying concepts leading to excellent security guarantees. Applications and simulations show that this framework can be used in practice with effectiveness. Furthermore, this work benchmarks the scalability of quantum federated learning for different classical and common machine-learning models. The results are a major step towards the goal of making more sophisticated privacy solutions available and allow to create secure, private-first workspace for collaborative machine learning. By leveraging the latest in quantum computing along with specialized tactics designed to combat rapidly evolving data protection threats, this approach will provide a strong foundation for future applications of shared machine learning within distributed quantum environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Review of Artificial Intelligence methods for detecting cancer in medical image processing 3

Review of Artificial Intelligence methods for detecting canc...

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

作者： Sarkar, Priya Saha, Ashim National Institute of Technology Agaratala Computer Science and Engineering Agartala India

ISBN: (纸本)9781665462167

Cancer is the unchecked spread of aberrant cells throughout the body. Cancer is a general word for a set of diseases brought on by the growth of abnormal cells in various bodily parts. Lung cancer, breast cancer, skin cancer, oral cancer, colon cancer, and prostate cancer are just a few of the more than a hundred different forms of cancer. Delays in treatment might result in major health problems and even death. This research reviews strategies for detecting liver, brain, and lung cancer using image processing. Automated and computer-aided detection systems (CAD) with artificial intelligence are the approaches utilized for detection, and these systems are good at processing big datasets to produce accurate and effective results in the detection of cancer. To be compatible with AI, these processing systems must overcome numerous obstacles, including picture capture, pre-processing, data management, segmentation, and classification algorithms. The numerous picture acquisition and segmentation approaches are reviewed in this work. These methods are now essential to meet the needs of the expanding patient population and to improve the healthcare system. © 2023 IEEE.

关键词： Diseases

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Security Issues and Workable Solutions in the Context of Sensor Cloud

Security Issues and Workable Solutions in the Context of Sen...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Mandal, Sanjeev Kumar Gupta, Manali Sharma, Brajehdra Bangalore India Department of Computer Science & Business System Noida Institute Of Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Tula's Institute Dehradun India

ISBN: (纸本)9798350355338

Notable tech companies such as Facebook, Microsoft, Apple, Google, and a number of game companies have launched bold plans to bring the metaverse to life. It is inevitable that in the years to come, virtual worlds will change significantly as a result of the metaverse ecosystem's ongoing enhancement and the rapid advancement of new technology. Nonetheless, a great deal of work still has to be done before the metaverse can be incorporated into our everyday lives and physical reality. This paper explores the metaverse's rising importance in the area of medicine and looks at its technological roadmap. We see a metaverse having the potential to drastically change intelligent healthcare facilities in the future, ushering in a new age of creative healthcare solutions. © 2024 IEEE.

关键词： Modeling languages

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DocumentQA-Leveraging LayoutLMv3 for Next-Level Question Answering

DocumentQA-Leveraging LayoutLMv3 for Next-Level Question Ans...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Chanti, S. Harika, P. Venkata Vinay, V. Guna Sri Charan, P. Ajad, Sk. Computer Science and Engineering Gmr Institute of Technology Andhra Pradesh India

ISBN: (纸本)9798350300857

With the increasing prevalence of digital documents in various domains, the demand for efficient and accurate question-answering (QA) systems has grown significantly. Traditional QA models primarily focus on text-based content, but the complex structure and visual elements present in documents pose unique challenges. This project explores the integration of LayoutLMv3, an advanced language and vision model, to elevate document question answering to the next level. The key role of LayoutLMv3 in document question answering (QA) lies in its ability to handle complex documents that contain not only textual information but also intricate visual elements like tables, images, and graphs. Traditional QA models typically focus on processing plain text and may struggle to comprehend the spatial arrangement and structure of documents. However, documents often contain vital information in their layout, which, if overlooked, can lead to inaccurate or incomplete answers. LayoutLMv3 overcomes this limitation by integrating both language and vision components into a single model. Through comprehensive tests on a variety of document datasets, this study shows the efficiency of DocumentQA. This study also analyzes the LayoutLMv3's interpretability and how it affects the way questions are answered. Through visualization techniques, various insights are gained on how the model analyses the document's layout, text, and interactions to generate accurate answers. The implications of this research extend beyond document QA, as LayoutLMv3's unique fusion of language and vision has broader applications in information retrieval, natural language processing, and document understanding tasks. The performance of LayoutLMv3 over LayoutLmv2 is also compared in this work showing efficient results. © 2023 IEEE.

关键词： Natural language processing systems

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Hybrid Cloud Strategies: Balancing the Benefits of Public and Private Clouds for Optimal Performance and Security 4

Hybrid Cloud Strategies: Balancing the Benefits of Public an...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Babu, B. Eswar Prasad, Ganesula Satya Vara Sarma, P.K.V. Subbaraya Neelima, Sanugula Vidya Jyothi Institute of Technology Department of Information Technology Hyderabad India Vidya Jyothi Institute of Technology Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350352931

This research focuses on exploring the different strategies for adopting the hybrid cloud that offers the best performance, the minimum cost and the strongest security using some complex equations. The research uses K Means Cluster;Support Vector Machine, Random Forest and Gradient Boosting on fifty hybrid cloud configurations. Performance analysis indicated that the configurations in Cluster 1 had response rate of 50 ms and through puts of 500 TPS was higher than that of Cluster 2 and 3. Cost evaluation revealed that the Configuration C was the most efficient in terms of cost since it cost $80,000 per annum while Configuration A cost $220,000 per annum and Configuration B cost $120,000 per annum With security analysis;it was evident that Configuration A had a high level of compliance measure with a compliance score of 95 and recorded 2 incidents compared to Configuration C which had high risk level and low compliance score therefore it was best. The conclusion essentially stresses the need for the optimization of performance, the minimization of cost and security issues in the adoption of the hybrid cloud, and this argumentation matches with the existing body of knowledge. © 2024 IEEE.

关键词： Cloud security

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DifEvoDenseFed: Differential Evolution Optimization Based DenseNet Federated Model in Alzheimer's detection

DifEvoDenseFed: Differential Evolution Optimization Based De...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Ghosh, Ananya Krishnamoorthy, Parthiban School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350319088

DifEvoDenseFed is introduced as an innovative solution for Alzheimer's disease prediction, leveraging the combined strengths of Differential Evolution Optimization (DifEvo), DenseNet, and Federated Learning (Fed). The urgency of Alzheimer's as a global health concern necessitates accurate early detection for effective intervention. In this advanced model, DenseNet forms the cornerstone, capitalizing on its proficiency in image-based tasks. At the client level, Differential Evolution Optimization (DifEvo) fine-tunes model parameters locally, adapting to the unique characteristics of each dataset. This individualized optimization enhances precision and ensures adaptability across diverse data sources. The collaboration between multiple clients is orchestrated by Federated Learning, preserving data privacy and decentralizing the learning process. Clients retain control of their sensitive medical data, reducing privacy risks associated with centralized systems. This decentralized approach improves scalability and fault tolerance. The synergy of these components culminates in a robust and accurate Alzheimer's prediction model. Local Differential Evolution (DifEvo) optimizations refine parameters, which are aggregated at a central server to iteratively enhance the global model. This process ensures ongoing model improvement while minimizing communication overhead. Moreover, the versatility of this approach extends beyond Alzheimer's prediction, making it suitable for various medical image analysis tasks. It fosters community collaboration among healthcare institutions, promoting a collective effort to combat Alzheimer's disease. It is important to note that the model's effectiveness is contingent upon data quality, system design, and DifEvo's optimization capabilities. Rigorous validation and evaluation are essential to measure its real-world impact. 'DifEvoDenseFed' marks a significant stride in early Alzheimer's detection, privacy-preserving AI in healthcare, and collab

关键词： Forecasting

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Sentiment Analysis using Long Short-Term Memory 14

Sentiment Analysis using Long Short-Term Memory

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Prabha, B. Maheshwari, S. Durgadevi, P. SRM Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350335095

Sentiment analysis is becoming increasingly beneficial to monitor social media, allowing us to obtain insight into public opinion surrounding certain topics. Long Short-Term Memory (LSTM) has been widely applied in sentiment analysis tasks due to its ability to model the sequential nature of text data and capture contextual information. By considering the ordering of words and their dependencies, LSTM can better understand the sentiment expressed in a sentence or document. The proposed work focusing on identifying the opinion of the text utilizing Machine Learning procedures like LSTM. We propose LSTM based system to characterize Food Review data. The audits dataset is taken from the Amazon fine food reviews dataset. An audit here will be characterized given the memory of an LSTM cell state in the neural network. In this study, we are doing the effect of dropout layer, Comparison of batch, activation function, optimization algorithm, Single and Multi LSTM layer. © 2023 IEEE.

关键词： Sentiment analysis

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A Deep Learning Model-Based Neural Network Framework for Diabetic Foot Ulcer Classification 5

A Deep Learning Model-Based Neural Network Framework for Dia...

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

作者： Shanmugam, P. Kumar, P.K. Manoj Hussain, Mohammad Manzoor Revathi, R. Deepa, P. Sakthivelu, U. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Subbalakshmi Lakshmipathy College of Science Department of Computer Science Tamil Nadu Madurai India B v Raju Institute of Technology Department of Computer Science and Engineering Medak Telangana Narsapur India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Texcity Arts and Science College Department of Computer Science Madukkarai Market Coimbatore India College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350379945

Diabetic Foot Ulcer (DFU) is a severe complication of diabetes, frequently resulting in amputation and having a significant impact on patients' quality of life. Early and accurate DFU classification is essential for prompt and efficient therapy. This study introduces an innovative neural network framework that utilizes deep learning techniques to categorize DFU based on thermograms. The proposed framework employs the pre-trained EfficientNet-B0 deep learning model, well-known for its exceptional performance in image classification tasks. The framework underwent training and assessment using a dataset sourced from Kaggle, consisting of 1866 colored images. Among them, 890 images depict plantar thermograms with negative DFU, whereas 976 images depict foot thermograms with positive DFU. The proposed framework demonstrated a higher accuracy of 98.71, outperforming previous approaches by a wide margin. The findings demonstrate the ability of deep learning to categorize DFU, paving the way for the development of automated testing techniques to assist healthcare practitioners in the timely identification and treatment of this incapacitating ailment. © 2024 IEEE.

关键词： Ability testing

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Development and Evaluation of a Speech-Recognized Voice Bot

Development and Evaluation of a Speech-Recognized Voice Bot

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Babu, K. Roobini, M.S. Gopirajan, P.V. Sadagopan, S. Anand, Rishi Rohit, B.S. SRM Institute of Science & Technology Dept of Computational Intelligence India Sathyabama Institute of Science & Technology Dept of Computer Science & Engineering India

ISBN: (纸本)9798350319200

In recent years, there have been significant advancements in speech recognition technology, which have opened up new possibilities for voice-controlled applications and devices. This report presents the design, development, and evaluation of a Speech-Recognized Voice Bot (SRVB) that aims to enhance user interactions with technology through natural language processing. The SRVB utilizes cutting-edge speech recognition algorithms and machine learning techniques to convert spoken language into actionable commands. The methodology involves training a deep learning model on a large dataset of speech samples and conducting user experiments to assess its performance. The results indicate that the SRVB demonstrates promising accuracy and usability, making it a valuable tool for a wide range of applications. © 2023 IEEE.

关键词： Machine learning techniques NLP Speech-Recognized Voice Bot

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