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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Saranya, G. Tamilvizhi, T. Tharun, S.V. Surendran, R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Panimalar Engineering College 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

ISBN: (纸本)9798350396638

Emotions are the best indicators of the actions of humans in advance. It is of great advantage in the current smart world. Prediction of these emotions can be able to sense the current mood of the driver and control the smart automobile accordingly and can be used in the case of chatting with customers using AI devices. This can be done by extracting the features including Mel-frequency cepstral coefficients (MFCCs) of the respective emotions and training the learning model using the classification algorithm Convolutional Neural Networks (CNN) and eventually the model can predict the emotion by comparing the newly retrieved features and the features of the training dataset and classify them accordingly. © 2023 IEEE.

关键词： Large dataset

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A deep learning model framework for diabetic retinopathy detection

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International Journal of Networking and Virtual Organisations 2022年第2期27卷 107-124页

作者： Padmapriya, M. Pasupathy, S. Sumathi, R. Punitha, V. Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Department of Computer Science and Engineering Annamalai University Tamilnadu Annamalai Nagar India Department of Information Technology Saranathan College of Engineering Tamilnadu India

Diabetic retinopathy (DR) is the typical diabetic eye issue and a main reason of blindness around the world. As per the International Diabetes Federation (IDF), the rates of diabetes would rise to 552 million by 2034. Breakthroughs in computer science techniques inclusive of artificial intelligence (AI) and deep learning (DL) have multiplied opportunities for early detection of DR. This indicates that the likelihood of a patient's healing will improve, and the risk of eyesight loss could be minimised in due course. A deep learning model (ResNet) for medical DR detection was examined in this article. The dataset of Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 was used to train and test the DL model. To demonstrate the vitality of the chosen ResNet model, performance measures and testing accuracy like recall, precision, and F1 score were determined. The modified ResNet model attained a testing accuracy of around 84% even with only a few dataset images. Also, training time and computational complexity were reduced with this simpler model. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Statistical tests

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CX-R Classification Using DCNN Method 2

CX-R Classification Using DCNN Method

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Ananya, R. Prathibhavani, P.M. Venugopal, K.R. University of Visvesvaraya College of Engineering Department of Computer Science Bangalore India

ISBN: (纸本)9798350320923

The virus SARS-CoV2 was identified in late 2019. Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety. Deep Learning (DL) is anticipated to be the most excellent strategy for reliably predicting COVID-19. Convolutional Neural Networks(CNNs) have achieved successful outcomes particularly in categorization and analyzing of medical image data. This work proposes a Deep CNN(DCNN) method for the classification of CX-R(Chest X-Ray) images in prediction of COVID-19. The dataset is preprocessed under many phases with different techniques for creating effective training dataset for the DCNN model to achieve best performance. This is done to deal various complexities like availability of very small sized imbalanced dataset with quality issues. In the first instance, model is trained using the train dataset. Then the model is tested for a separate validate X-ray image dataset and Confusion matrix is displayed. Up to 98.3% Accuracy is obtained, when proposed model was tested using the validate dataset. The Accuracy and Loss graph is plotted for the same. Later, random image prediction is made from prediction dataset which include both COVID and Normal X-rays. Other important performance metrics like F1 score, Recall, Precision for the model is displayed. © 2023 IEEE.

关键词： COVID-19

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Evaluation of Machine-Learning Based Approaches for Early Detection of Breast Cancer Tumor

Evaluation of Machine-Learning Based Approaches for Early De...

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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Mohammad, Fares Al-Haran, Bader Doush, Iyad Abu Kandil, Marwa College of Engineering and Applied Science American University of Kuwait Kuwait Kuwait Computer Science Department Yarmouk University Irbid Jordan

ISBN: (纸本)9798331533137

Chances of survival can be significantly improved through early detection of breast cancer. Traditional breast cancer detection techniques require experts to identify the physical features of breast tumors from medical tests. However, the limited number of medical experts will leave many women at risk of developing higher stages of cancer. Large amount of data can be efficiently analysed using Machine Learning (ML) algorithms to recognise abnormal patterns;thus providing an early detection tool for breast cancer. This paper examines the outcome of six distinct ML algorithms in classifying breast tumors to either benign tumor (B), or malignant tumor (M). The models of ML utilized Wisconsin Breast cancer dataset that contains 569 rows of data and 32 features for training. The models were evaluated by the ROC curve, the confusion matrix, precision, recall, accuracy, and F1 score values. The experimental results proved that SVM and KNN models achieved the highest accuracy of 98.24%, and F1 score of 98% for SVM and 99% for KNN. This indicates that SVM and KNN models handled the data’s complexity effectively and performed accurate prediction of breast tumor type. ©2024 IEEE.

关键词： Lung cancer

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Deep fake Video Detection Using Unsupervised Learning Models: Review 2

Deep fake Video Detection Using Unsupervised Learning Models...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Jyothi, B.N. Jabbar, M.A. JNTUH Department of Computer Science and Engineering Vardhaman College of Engineering Shamshabad India Department of CSM Vardhaman College of Engineering Shamshabad India

ISBN: (纸本)9798350305418

The recent decade has been the most emerging for technical advancements in computer and communication fields. These advancements have made the task of automation much cheaper and available to every other individual at minimum or free of cost. Deep Fakes are one such advancement in the field of image processing. Deep fakes are images or videos developed using deep learning models to create fake images or videos. Although there are advantages of these deep fakes like visual training, powerful simulation, etc... There are also demerits or disadvantages highly possible with these technological advancements such as creating content that never existed, making a person speak something which one has not spoken at all. These mentioned possibilities lead to person identification theft, spreading of false or misinformation, etc... This paper is an attempt to investigate the proposed detection techniques for deep fakes. It focuses on unsupervised deep fake detection techniques and the role of ensemble models for detection. © 2023 IEEE.

关键词： an ensemble of model deep fake detection Deep fakes Deep Learning unsupervised learning

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Revolutionizing Healthcare With Cloud Computing: The Impact of Clinical Decision Support Algorithm 1

Revolutionizing Healthcare With Cloud Computing: The Impact ...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Lenin, J. Komathi, A. Vijayan, Hima Rathinam, Anantha Raman Kasthuri, A. Srinivasan, C. Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India Nadar Saraswathi College of Arts and Science College Department of Computer Science Tamilnadu Theni India S.A. Engineering College Department of Information Technology Tamil Nadu Chennai India Kl University Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350330915

The integration of cutting-edge technology, such as cloud computing and Clinical Decision Support (CDS) algorithms, is radically altering the healthcare system. This research digs into how Inference Engines, Bayesian Networks, Machine Learning Algorithms, and Natural Language Processing (NLP) have all played critical roles in reshaping the healthcare industry. Medical professionals may more efficiently use CDS algorithms thanks to cloud-based technologies that streamline data storage, sharing, and processing. Decision-making is aided by Inference Engines because they provide organized insights based on preset criteria. This is where Bayesian Networks come in, with their ability to represent complicated, probabilistic interactions among variables for exact diagnoses and risk assessment. Machine learning algorithms improve predictive analysis by identifying trends in large datasets, paving the way for more individualized approaches to medical care. Chatbots and sentiment analysis are only two examples of how NLP is transforming the doctor-patient relationship by teaching computers to understand human language. Those in the medical field may greatly increase their diagnostic precision, treatment efficiency, and patient outcomes by adopting these innovations. © 2023 IEEE.

关键词： Bayesian Networks Clinical Decision Support Cloud Computing data-driven Healthcare Healthcare Transformation Inference Engines Machine Learning Algorithms Natural Language Processing Patient-Centric Solutions Personalized Medicine

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Comprehensive Earth System Analysis for Advancing Community Resilience: Integrating Remote Sensing with Hybrid GAN-RNN Techniques

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 788-803页

作者： Sundar, Ganapathy Patchaiammal, Pitchaimuthu Lakkshmanan, Ajanthaa Ramya, Saminathan Pallavi, Lanke Thiagarajan, Ranganathan Department of Computer Applications Sindhi College Chennai India Department of Computing Technologies SRM Institute of Science and Technology Kattankaluthur Chengalpattu District India Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamil Nadu Erode India Department of CSE B V Raju Institute Of Technology Narsapur India Department of Information Technology Prathyusha Engineering College Thiruvallur India

Effective disaster prediction is essential for climate resilience, especially in areas prone to extreme weather events. Conventional models often face challenges in predicting complex or uncommon events, largely due to data scarcity and the difficulty of modeling temporal dependencies in sequential data. This paper introduces an innovative hybrid GAN-RNN model that leverages remote sensing and IoT sensor data to improve disaster prediction accuracy. By combining generative adversarial networks (GANs) for generating synthetic data and recurrent neural networks (RNNs) for recognizing temporal patterns, this model forms a potent predictive tool. Achieving a remarkable testing accuracy of 98.16%, the hybrid GAN-RNN model significantly surpasses traditional predictive methods. Comprehensive evaluations demonstrate its enhanced capability to predict complex and rare events effectively, establishing it as a crucial resource for disaster risk management and environmental monitoring. This research advances AI-driven methodologies in climate resilience, offering a solid framework for future innovations in the field. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： AI-driven solutions Climate resilience data augmentation Disaster prediction Environmental monitoring Hybrid GAN-RNN IoT sensors Remote sensing Synthetic data Temporal pattern recognition

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Random Forests for Predicting Diabetes Progression and Complications 3

Random Forests for Predicting Diabetes Progression and Compl...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Ranganathan, Chitra Sabapathy Nandhini, R. Nisha, K. Sumathi, G. Sudhakar, G. Srinivasan, C. Mphasis Corporation Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

The main objective of the presented work in this paper is to use Random Forests as an alternative biomarker for predicting the development of diabetes. Identifying high-risk patients is crucial for improving patient outcomes and reducing healthcare expenses. The proposed system improves diabetes treatment via the use of predictive analytics, which promotes preventative and early intervention-focused healthcare practices. It helps manage the risks associated with diabetes by using machine learning, which is part of a larger movement towards precision medicine. This method shows promise to enhance long-term health outcomes, optimize resource allocation, and provide personalized treatment to patients. Findings from the Pima Indians Diabetes database quantify the risks of diabetes and patient characteristics. Blood pressure, glucose, insulin, and body mass index are all taken from each patient. The dangers of retinopathy, neuropathy, and nephropathy are also numerically encoded, with 0 indicating minimal risk, 1 medium risk, and 2 high risks. HbA1c is also included. The blood pressure ranges from 115/78 to 130/85mmHgA1c levels, the body mass index (BMI) from 26 to 32 mm, and the glucose level from 110 to 140 mm Hg. © 2024 IEEE.

关键词： Blood pressure

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Proposed Framework for Modifications to Artificial Intelligence/Machine Learning (Ai/Ml)-Based Software as A Medical Device (SAMD) 7

Proposed Framework for Modifications to Artificial Intellige...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Shrivastava, Anurag Jain, Upma Al-Farouni, Mohammed Kumar, Yogendra Anandhi, R.J. Bhavana, Munugapati Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Electrical Engineering Mathura India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

The rapid adoption of software as a medical device (SAMD) driven by artificial intelligence and machine learning has brought about a fundamental shift in the medical industry. This shift has the potential to greatly improve clinical outcomes and the quality of care provided to patients. This shift has been responsible for a number of key achievements made in recent times. When seen in this context, the proposed legal framework for revisions to the AI/ML-SAMD appears as an essential response to the malleability of these technologies. To successfully navigate the tough process of modifying AI/ML-SAMD with the assistance of this framework. It does this by taking into consideration the need for rapid regulatory scrutiny and making an attempt to combine the promotion of innovation with the simultaneous preservation of patient safety. In other words, it ensures that patient safety is protected while also encouraging innovation. This abstract provides a summary of the fundamental components of the framework, as well as a discussion of the significance of those components with regard to fostering the development of moral AI/ML-SAMD within the context of the healthcare ecosystem. The healthcare sector is undergoing a change as a direct result of artificial intelligence and machine learning, which are improving patient outcomes, diagnostic accuracy, and treatment options. The research emphasizes the significance of specific AI and ML applications as well as the sector's embrace of this paradigm-shifting technology. In addition, the regulatory framework that has been presented is an important step towards guaranteeing the safe use of AI and ML in the medical field. © 2024 IEEE.

关键词： Artificial Intelligence Compliance Guidelines Healthcare Innovation Machine Learning Modifications Patient Safety Regulatory Framework Software as a Medical Device

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Secure Multi-Party Computation for Collaborative data Analysis in Network Security 2

Secure Multi-Party Computation for Collaborative Data Analys...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Tamilselvi, P. Lathika, V. Jayachitra, S. Arunkumar, S. Balasubramani, M. Kalaichelvi, V. Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Department of Cse Salem India Psna College of Engineering and Technology Department of Ece Dindigul India Coimbatore Institute of Engineering and Technology Department of Ece Coimbatore India Vsb Engineering College Department of Computer Science and Engineering Karur India Sastra Deemed to Be University Srinivasa Ramanujan Centre Department of Cse Kumbakonam India

ISBN: (纸本)9798350306415

Within the scope of this research, we provide a Secure Multi-Party Computation (SMPC) architecture that has been adapted specifically for the purpose of collaborative data analysis in network protection. Significant improvements have been made to the framework in terms of performance metrics, security, and applicability in the real world. When measured against a baseline, comparative analysis showed a notable reduction of 25% in execution time, a decrease of 20% in communication overhead, and a drop of 16.67% in computational complexity. These enhancements highlight the framework's efficiency advantages, which are vital for maximising responsiveness and scalability in collaborative efforts to improve network security. As a result of the security evaluation, which demonstrates a shift from moderate to high privacy preservation, strong resilience to assaults, and low sensitivity to parameters, the suggested framework has been shown to be reliable. Real-world application scenarios further emphasise its adaptability, showing that its influence on threat intelligence sharing is moderate, while its impact on collaborative incident response is significant, and its impact on vulnerability analysis is low. These findings, when taken as a whole, establish the SMPC framework as a useful resource for organisations that want to improve the effectiveness of their joint efforts to secure networked environments. The research places a strong emphasis on the practical importance of the framework, which helps to build enhanced trust, collaboration, and resilience against developing cyber threats. Assessments of scalability, improvements to cryptographic protocols, and interaction with newly developing technology are some potential future topics for study. Advanced threat analysis may also benefit. © 2024 IEEE.

关键词： Collaborative data Analysis Cryptographic Protocols Network Security Privacy Preservation Secure Multi-Party Computation (SMPC)

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