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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Singh, Aditya Pratap Mishra, Santosh Kumar Nidhi Singh, Jagendra Agrawal, Madhup Joshi, Prakash Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India Bennett University School of Computer Science Engineering & Technology Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science Ghaziabad India

ISBN: (纸本)9798350364569

In this paper, we investigated deep learning models for automated segmentation of liver and tumor regions from CT images to improve the diagnostic accu-racy as well as treatment planning in case of liver cancer. The research used a dataset of 3200 CT scans, with about 70% serving as the testing set and 30% for training. Preprocessing steps like Auto Windowing HU (Hounsfield Units) and Histogram Equalization to retain better clarity of the images. Based on the metrics of performance such as accuracy, Dice coefficient, IoU(Intersection over Union), precision and recall were calculated used three types of CNN models (ResUNet, VGG16 and VGG19). Its hybrid architecture combining U-Net and ResNet, called as ResUNet indicated better results with an accuracy of 97.3%, against VGG16 at 93.5% and VGG19 at 91.5%. The model also demonstrated good data loss reduction from 0.35 to about 0.10 over the course of a total for around an epoch angle in enduring caching past phase. VGG16 and VGG19 outperformed the baseline performances with reduced data losses while higher accuracies, although their convergence rate was slightly slower than ResUNet. The results confirmed the high precision of liver/tumor segmentation that ResUNet provides, which would be useful for clinical practice to achieve sufficient diagnostic improvement. The architecture of ResUNet specifically designed for the segmentation tasks in medical imaging from this study, demonstrates to be a versatile framework favoring its potential integration into automated tools within oncological diagnostics and could thus radically transform liver cancer detection. © 2024 IEEE.

关键词： computerized tomography

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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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A novel approach towards tracing the parents of orphaned children and dead bodies in cloud and IoT-based distributed environment by integrating DNA databank with Aadhar and FIR databases

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International Journal of Cloud Computing 2022年第5-6期11卷 456-479页

作者： Mishra, Kamta Nath Mishra, Ved Prakash Jugran, Arun Goel, Shivam Raj, Ankur Department of Computer Science and Engineering Birla Institute of Technology Jharkhand Mesra India Department of Computer Science and Engineering Amity University Dubai United Arab Emirates

An Aadhar card is an exceptional and bona-fide character card in India which is being utilised as a legitimate personality verification for a wide range of everyday exchanges including deal, buy, opening financial balance, air tickets, train tickets, transport tickets, and for getting advantages of legislature of India and express government’s plans. An Aadhar card of an individual incorporates fingerprints, thumb-prints, iris pictures (left eye and right eye), and face picture. The authors believe that it is the basic right of every child/person to know the names of his/her biological parents. But, it is unfortunate that the orphaned children are not able to meet with their parents, even with the technological advancement of this earth, there is still the feeling helplessness. The hardware and software implementation of proposed technique clearly reveals that it is technically feasible to trace the parents of all orphaned children whose parents are alive. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Internet of things

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Diabetic Prediction using Feature Selection based Random Forest and Fine Tuned K-Nearest Neighbor Classifier Algorithm-A Design Thinking Approach 4

Diabetic Prediction using Feature Selection based Random For...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Ramya, S. Vijayaraghavan, T. Kalaivani, D. Department of Computer Science India Sns College of Technology Department of Computer Science and Engineering India Dr.SNS Rajalakshmi College of Arts and Science Department of Computer Technology Coimbatore India

ISBN: (纸本)9798350300093

In low- and middle-income nations today, diabetes affects the majority of the population, according to a World Health organization (WHO) research. The WHO report suggested that 80% of the deaths would be due to the diabetes from 2016 to 2030. However, the current method continues to provide findings that are erroneous, which has a substantial negative impact on performance. To overcome the abovementioned issue, in this work, Random Forest (RF) algorithm and Fine tuned K-Nearest Neighbor (FKNN) classifier algorithm is proposed. Pre-processing, feature selection, and classification are the three primary stages of this project. Initially, preprocessing is performing for improving the final dataset results more accurately. Preprocessing is the process of cleaning the database into correct format. In order to choose more relevant and useful data from the dataset, the feature selection is then carried out utilizing the RF algorithm. It also minimizes the risk of over fitting with minimum features. Finally, diabetic prediction and classification is done by using FKNN classifier algorithm is used for categorizing items in the feature space based on training samples that are the most similar to the objects being classified. According to the experimental results, the suggested RF+FKNN method outperforms the current algorithms in accuracy, precision, recall, and f-measure. © 2023 IEEE.

关键词： Classification (of information)

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Blockchain-Based Diagnostic Certificate System with Privacy Protection 28th

Blockchain-Based Diagnostic Certificate System with Privacy ...

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28th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2023

作者： Lin, Chun-Li Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan

ISBN: (纸本)9789819717101

Blockchain represents a novel form of information technology application. It offers promising potential for future transactions and practical applications, particularly through the implementation of smart contracts. To address the issue of the time and expenses incurred by patients when obtaining a diagnosis certificate from various hospitals or clinics, blockchain technology is leveraged to generate diagnostic certificates via smart contracts on the Ethereum platform. This application harnesses blockchain features to ameliorate the identified problems, and it also presents solutions for the various operational processes involved in doctors recording diagnostic certificates and patients accessing their diagnostic records. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart contract

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AI Based Criminal Detection and Recognition System for Public Safety and Security using novel CriminalNet-228 4th

AI Based Criminal Detection and Recognition System for Publi...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Siddesh, G.M. Department of Computer Science and Engineering Ramiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819726103

The recent surge in public space criminal activities underscores the need for an efficient system to promptly detect, recognize, and track criminals. Existing AI-based criminal detection literature, while insightful, has limitations such as the complexity to analyze video data, time, and speed of accuracy when it comes to training the algorithms that call for further advancements. Hence proposed AI-driven system addresses this demand, aiming to automate criminal identification, equipping law enforcement with a potent tool for proactive crime prevention and resolution. The system utilizes the innovative "CriminalNet-228" Convolutional Neural Network (CNN) architecture, meticulously trained on a vast criminal image dataset for enhanced detection accuracy. To bolster face detection, additional computational resources like parallel processing and distributed computing are employed, enabling real-time analysis of extensive CCTV footage. Notably, the system tracks identified criminals, providing law enforcement with real-time situational awareness. CriminalNet-228 achieves an impressive overall Mean Average Precision (mAP) of 0.65 and excels in detecting facial features as small as 4 × 4 pixels, demonstrating its detail-oriented recognition capabilities. When evaluating Proposed CriminalNet-228 in comparison to established state-of-the-art techniques like Fast-RCNN, Yolov7, and AlexNet, it surpassed them in terms of precision, recall, f-measure, and accuracy, achieving an impressive accuracy rate of 99.2%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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Electronic health records protection strategy by using blockchain approach

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Multimedia Tools and Applications 2024年第39期83卷 86883-86894页

作者： Kathole, Atul B. Patil, Sonali D. Kumbhare, Savita Vhatkar, Kapil Kshirsagar, Aniruddha P. Kimbahune, Vinod V. Computer Engineering Dr. D. Y. Patil Institute of Technology Pune India IT Dept. Pimpri Chinchwad College of Engineering Pune India Computer Science and Engineering Department K.B.P College of Engineering Satara Satara India

Electronic health records (EHRs) provide significant challenges for medical organizations due to the digitalization of traditional medical information. When using EHRs, both doctors and patients are accustomed to spending considerable time conducting the necessary queries;nevertheless, the acquired data is not always related to the sought one, and access may be revoked if this is the case. The exchange and maintenance of medical health records are among the most crucial tasks in the healthcare system. The integrity of health records is crucial, as is maintaining patient anonymity, and either would have devastating effects in the event of a breach. Therefore, it is one of the most important reasons to keep electronic health records safe. Modern healthcare infrastructure is notoriously difficult to use, wasteful, and fraught with problems related to data integrity, security, and privacy. Better EHR management and monitoring, however, has the potential to lessen the severity of the problems with security and complexity. The blockchain's potential, with its decentralized and trustworthy nature in the healthcare industry, makes this a reality. Problems with data management and the way information is validated and disseminated are at the heart of the healthcare delivery system's inherent difficulties. Improved access to medication tracking, hospital assets, drug systems, patient information, and so on is the primary benefit of employing blockchain technology in healthcare data management. Because of how important it is to have access to a patient's medical history when prescribing medication, blockchain technology has the potential to significantly enhance the current healthcare delivery system. Therefore, it is critical to develop a blockchain-based system for verifying and protecting medical records. The primary objective of this article is to create a permissioned blockchain-based Ciphertext Policy-Attribute-based encryption system to protect privacy and regulate acces

关键词： Blockchain

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Shallow Tuned DenseNet: A Lightweight Convolutional Neural Network Approach for Enhanced Skin Lesion Recognition 2

Shallow Tuned DenseNet: A Lightweight Convolutional Neural N...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Ahmmed, Tomal Tahsin Faruk, Md. Farukuzzarnan Srizon, Azmain Yakin Mahedy Hasan, S.M. Khansur, Md. Nasif Efat, Anwar Hossain Hossain, Md. Rakib Al Mamun, Md. Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331517984

Skin lesions are irregularities on the skin, often harmless, though some may progress to skin cancer. Detecting them early is crucial for successful treatment and prevention of cancer development. Traditional detection methods involve significant expenses and testing. This study introduces an auto-mated system for classifying skin lesions using a Shallow Tuned DenseNet (STDN), a compact Convolutional Neural Network (CNN) model. We employ Transfer Learning (TL) by fine-tuning a pretrained DenseNet-169 model with additional lightweight layers, allowing the STDN to adapt to the PH2 dataset's specific characteristics while remaining efficient. Initially, we utilize data augmentation techniques to address class imbalances within the dataset, improving model performance by reducing bias towards the majority class. The evaluation demonstrates a promising hundred percent accuracy with perfect F1-score and specificity. We attribute these results to effective transfer learning utilization, enhanced tuning, and partially to data augmentation, which mitigates overfitting and biases, facilitating robust feature extraction. Furthermore, we employ Grad-CAM (Gradient-weighted Class Activation Mapping) to visualize and interpret the model's predictions, enhancing trust and comprehension. The proposed STDN model makes a significant contribution by achieving high accuracy with a lightweight design, offering a potentially cost-effective and efficient solution for automated skin lesion detection, thereby aiding in early cancer prevention. © 2024 IEEE.

关键词： Convolutional neural networks

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A Mango Disease Prediction for Smart Agiculture using Machine Learning Algorithms 8

A Mango Disease Prediction for Smart Agiculture using Machin...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Lavanya, K. Packialatha, A. Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Chennai Tamilnadu Pallavaram India

ISBN: (纸本)9798350376425

The country's economy relies heavily on agriculture, and the health of the crops is essential to its success. Crop diseases that go undetected can cost the agriculture industry. Thus early detection and identification are crucial. It is possible to avoid crop diseases from destroying the harvest if they are accurately diagnosed and detected. Because healthy and diseased plants appear identical in their early stages, farmers cannot distinguish between the two by watching the crop leaf. India exports vast mangoes, making it an economically and environmentally significant fruit. About 1500 mango species are grown in India, with over 1000 commercial types. There are a lot of diseases that harm mangoes, affecting their look, taste, and economy. The "Prediction of Disease in Mango Fruit Crops"A complex warning system that uses machine learning and IoT. One of the main objectives is to develop a system that can forecast disease outbreaks on mango fruit harvests using historical weather information and crop yield Mango trees in India are plagued by a fungus called Anthracnose, which is the most frequent disease of its kind. Anthracnose, a highly contagious fungus, requires a quick and accurate method of diagnosis. As a result, an in-depth examination of the plants is essential before initiating any control measures. This study examines machine learning(ML) and deep learning (DL) strategies for detecting and classifying mango plant diseases. The performance of ML and DL-based classification models for mango crops and their datasets and feature extraction approachesare examined in this work. Finally, a variety of issues involved in plant disease identification are explored. © 2024 IEEE.

关键词： Smart agriculture

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Advanced Machine Learning Techniques for Early Diagnosis of Diabetic Macular Edema 8

Advanced Machine Learning Techniques for Early Diagnosis of ...

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8th International Conference on Electronics, Materials engineering and Nano-technology, IEMENTech 2025

作者： Muthuselvi, M. Duvvuri, Suneel Kumar Geetha Rani, K. Surendran, R. Balamurugan, K.S. Karpaga Vinayaga College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chengalpattu India Department of Computer Science and Applications Andhra Pradesh Rajahmundry India Department of Computer Science and Engineering Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331520762

One consequence of diabetic retinopathy is diabetic macular edema (DME), a condition that effects the eyes of individuals with diabetes. More than 28 million people been affecting by diabetic macular edema due to diabetes, late diagnose of diabetic macular edema leads to vision loss. Available method with deep learning algorithms and artificial intelligence (AI) for prediction of diabetic macular edema is not effective way for diagnosing in early. Proposed a method with XGBoost algorithm for the classification, pre-processing, extraction and detection of a disease by fundus samples. This method resulted in 0.992, 0.989, 0.991 and 0.990 for accuracy, Recall, precision and F1-score respectively. © 2025 IEEE.

关键词： Deep learning

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