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Predicting Diabetes Risk: A Comparative Analysis of Machine Learning Algorithms 1

Predicting Diabetes Risk: A Comparative Analysis of Machine ...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Thakur, Hardeo Kumar Sahu, Dinesh Prasad Chaturvedi, Rajnish Kumar Jha, Govind Kumar Kumari, Divya School of Computer Science Engineering and Technology Bennett University Greater Noida India Government Engineering College Department of CSE Bihar Munger India USICT GGSIPU Delhi India

ISBN: (纸本)9798331518578

Nowadays, diabetes mellitus is a worldwide health concern that is becoming more common. Therefore, for better early detection techniques (Machine Learning) is need of hour. Six machine learning algorithms are applied in this article to forecast the likelihood of diabetes in three stages: prediabetes, diabetes, and no diabetes. Three metrics i.e., recall, F1 score and precision are applied to evaluate the algorithms performance. Additionally, we have used the Synthetic Minority Oversampling Technique (SMOTE) for improving class imbalance. The results show that different algorithms and stages of diabetes function differently, which emphasizes the necessity of choosing algorithms according to the demands of certain applications. © 2025 IEEE.

关键词： Contrastive Learning

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Fuzzy Logic and Fog Computing in Electronic Health Records Data Privacy: A Comprehensive Review

Fuzzy Logic and Fog Computing in Electronic Health Records D...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Devendra Singh Mohan Varun Bansal Anil Kumar Shobhit University Saharanpur India Computer Science and Engineering Shobhit University Saharanpur India Computer Science and Engineering GCET India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The potential to improve healthcare services has drawn attention to the use of fuzzy logic and fog computing into electronic health records (EHRs). This study offers a thorough analysis of the research on Fog Computing (FC) enabled Fuzzy Logic c-based EHR systems. While fog computing allows for real-time data processing and decentralized storage near end users, fuzzy logic provides a reliable method for handling ambiguities in patient data, diagnoses, and decision-making. When combined, these technologies enhance data security, scalability, and system responsiveness. This research paper includes major approaches, architectures and important applications such as in cloud computing fog-based solutions reduces bandwidth and latency related issues with the help of fuzzy logic how clinical decision support system improves. In this study we list the future research possibilities to address these type problems and also explains obstacles such as complexity of computing, interoperability and privacy of data. These type of study assists academics and practitioners to creating effective, intelligent and safe EHRs’ system. In this research paper privacy preservation of EHRs addressed by integration of fuzzy logic with fog computing.

关键词： Fuzzy logic Decision support systems Electric potential Reviews Data security Medical services Real-time systems Electronic medical records Interoperability Edge computing

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Overview of Security Approaches for Quantum Computing in the Current Era 3

Overview of Security Approaches for Quantum Computing in the...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Hansora, Pinal Prajapati, Viral Thakkar, Amit Rathod, Ajaysinh Mannanuddin, Khaja Alsarmi, Haitham Said Naseer Cspit Charusat Cse Department Changa388421 India Snpit It Department Umrakh India Department of Computer Science & Design Gujarat Vasad India Sr University School of Cs & Ai Department of Computer Science & Engineering Hasanparthy Warangal India Sohar International Bank Muscat Oman

ISBN: (纸本)9798331509675

Quantum computing are breakthroughs aimed to revolutionize several sectors, although they also present considerable cybersecurity threats. Quantum computers can manage quite greater quantities of information than the most powerful conventional supercomputers and has the capability to do complex computations. Despite being in the early stages of development, quantum computers have the potential to be regarded as cybersecurity concerns. Quantum computing is a phenomenon that should be seen as a technology primed to facilitate significant innovations in the future, while also presenting different concerns. The quantum cybersecurity threat encompasses data breaches of sensitive health and financial information, challenges to the integrity of digital assets, and vulnerabilities in the encryption supporting cryptocurrencies, among other possible risks. This review paper focuses on quantum computing and its effects on different security methods in the present generation. It covers the quantum computer threats to cryptography, post quantum cryptography and a comparison of different approaches to cryptography. This work also analyzes machine learning and AI in quantum security and its challenges and trends for the future. The literature review has incorporated references from more than 25 reputed Scopus-listed journal and conference proceeding. © 2025 IEEE.

关键词： Cybersecurity

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A Genetic Algorithm-based Feature Selection Technique to Predict Diabetes in Machine Learning

A Genetic Algorithm-based Feature Selection Technique to Pre...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Tamim Wasif Fardin Amin Arpon Computer Science and Engineering North Western University Khulna Bangladesh Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Diabetes is a complex and lifelong disease that happens to someone with high blood glucose or blood sugar. The cause of Diabetes can be genetics, being overweight, and inactivity is a cause of Diabetes. If Diabetes is not in control, the patient must go to the doctor for a long time. However, machine learning approaches can reduce it. Therefore, in this paper, this paper executed seven classifiers to detect Diabetes. The classifiers are Random Forest, XGBoost (Extreme Gradient Boosting), K Nearest Neighbor, Support Vector Machine, Neural Network, Naïve Bayes, and MOEFC (Multi-Objective Evolutionary Fuzzy Classifier) with two algorithms NSGA-II and ENORA. This paper used those classifiers and tried to optimize the accuracy. For optimization, this paper used some feature selection techniques. This paper used filter selection, wrapper, and recursive feature elimination approaches. Using the SVM classifier, this paper implemented cross-validation, which is k-fold, and got the highest accuracy of 77.6% using the SVM classifier. The features have been sorted according to their importance using the filter approach. This work achieved the highest accuracy of 79.87% from the filter approach using the SVM classifier. Also, it got the highest accuracy of 77.52% from the SVM classifier's wrapper approach. Then, by applying the recursive feature elimination approach, the research got the highest accuracy of 79.87% using the SVM classifier. Then, by applying MOEFC, the research got the highest accuracy of 83.043%. This work got the highest accuracy among all the processes by executing the MOEFC classifier, 83.043%, with four out of nine features using the genetic search approach of feature selection. This paper has been compared with some existing methods and is more accurate.

关键词： Support vector machines Accuracy Neural networks Nearest neighbor methods Feature extraction Genetics Diabetes Random forests Optimization Genetic algorithms

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An Efficient Machine Learning Framework for Panic Disorder Detection: Addressing Data Imbalance with SMOTE to Reduce False Negatives

An Efficient Machine Learning Framework for Panic Disorder D...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Nabanita Saha Joya Sagar Biswas Sadia Jannat Mitu Rabea Khatun Md Manowarul Islam Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

One common mental health condition that can seriously impair daily functioning is panic disorder. It is a type of anxiety disorder characterized by frequent, unplanned panic attacks. Effective treatment requires early and precise detection. With objective, data-driven methods, machine learning (ML) presents a promising way to improve panic disorder detection in early stages and supports broader public health initiatives aimed at mental health awareness and treatment accessibility. This study presents an efficient machine learning framework designed to enhance panic disorder detection by reducing the false negative rate (FNR) through the integration of Synthetic Minority Over-sampling Technique (SMOTE). The proposal employs four ML algoithms including Logistic Regression, Random Forest, K-nearest Neighbor and Decision Tree ensuring that the model is trained effectively on minority class instances using SMOTE. Our results demonstrate a significant reduction in FNR, facilitating earlier detection and timely intervention for patients. We achieved around 71%-92% reduction in the false negative rate, leading to a substantial enhancement in the overall diagnostic performance of the model including precision upto 34%, recall upto 91%, f1-score upto 81% for different ML algorithms. Thus, the importance of this research lies in enhancing diagnostic performance and improving patient outcomes, facilitating a more effective and empathetic approach to mental health diagnosis.

关键词： Accuracy Computational modeling Anxiety disorders Mental health Nearest neighbor methods Safety Proposals Decision trees Public healthcare Random forests

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Application and improvement of algorithms in machine learning based loan review system 24

Application and improvement of algorithms in machine learnin...

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2024 International Conference on Economic Data Analytics and Artificial Intelligence, EDAI 2024

作者： Rao, Guoyong Hu, Peiyi Liu, Bo School of Information Engineering Jingdezhen University Jiangxi Jingdezhen China Department of Computer Science University of Utah Salt Lake City United States

ISBN: (纸本)9798400713255

This study developed an advanced credit scoring system through the innovative use of an oil consumption dataset. The project incorporates a decision tree model to create a predictive model for determining delinquency rates between loans. The development of the system required the solution of several new challenges, including the methodological phase of feature induction-improving the decision tree model to produce significant results. This optimisation not only significantly improved the efficiency of the loan review process, but also led to a significant reduction in the delinquency rate from 7.8% to 2.6%. In addition, the approval rate increased dramatically from less than 17 per cent to more than 45 per cent. In addition, the summary section explores the application of embedded algorithms to further improve the system, resulting in an overall performance improvement of approximately 5 per cent. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease Prediction and Online Consultation for Accessible Medical Support 2

AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease P...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Al Jabri, Jaber Saleh Salim Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman SRM Institute of Science and Technology Kattankulathur Department of Computational Intelligence Tamil Nadu India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798331505745

Improvements in patient care and early disease detection possibilities are two of the ways artificial intelligence is transforming the healthcare industry. Alzheimer's disease, a neurodegenerative disease that worsens with time, is among the most difficult diseases to exactly identify in early stages. Patients thus delay in treatment and experience a decline in their quality of life. Research is still under progress, thus knowledge about how AI-driven prediction models could be combined with readily available healthcare services remains lacking. This work presents a special artificial intelligence-powered system consisting of DenseNet-based deep learning for Alzheimer's disease prediction with an online consultation platform. The proposed approach analyzes brain MRI data and classifies Alzheimer's stages with higher accuracy using a DenseNet architecture, well-known for its exceptional performance in the field of feature extraction. Comprising three thousand magnetic resonance imaging (MRI) images, the dataset has been preprocessed for noise reduction and tuned for models performance. At 98.5%, the DenseNet model exceeded other methods including VGGNet and ResNet in accuracy. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Entropy Based Transparent and Secure Watermarking Approach Using Arnold Transform 3rd

Entropy Based Transparent and Secure Watermarking Approach U...

引用

3rd International Conference on Machine Intelligence and Smart Systems, MISS 2023

作者： Patsariya, Sanjay Dixit, Manish Department of Computer Science and Engineering RGPV MP Bhopal India Department of Computer Science and Engineering MITS MP Gwalior India

ISBN: (纸本)9783031691140

Due to speedy development in internet technology, peoples have usually dependent upon internet enabled digital equipment’s in a wide range of application. As a result, different challenges relating to information security and authentication are brought up with multimedia objects in both the civil and defense sectors. The use of watermarking technology eliminates the need for an overheard as in traditional encryption by integrating it directly into various frames. As a result, it is effective when employed for data encryption that require restricted resources, as those engaged in the Internet of Things (IoT). The projected study illustrates a spatial domain approach using the concept of entropy for digital watermarking grounded on LSB substitution and Arnold transform. Original Image chunk keeping highest entropy value is employed for watermark implantation. In addition, the perceptual excellence, robustness and security of the watermarked images produced by the proposed framework have been compared to certain cutting-edge watermarking techniques using image quality parameters namely MSE, PSNR, SSIM NPCR, UACI and NCC. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Watermarking

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BC-SBOM: Blockchain-based SBOM Management System

BC-SBOM: Blockchain-based SBOM Management System

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International Conference on Advanced Communication Technology (ICACT)

作者： Ahyun Song Euiseong Seo Heeyoul Kim Department of Computer Science and Engineering Sungkyunkwan University Republic of Korea Division of Computer Science and Engineering Kyonggi University Republic of Korea

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

This paper presents BC-SBOM, a novel blockchain-based system designed to enhance the management of Software Bills of Materials (SBOMs). By leveraging blockchain technology, BC-SBOM ensures secure storage and sharing of SBOMs, while providing a comprehensive global view of dependencies among software components. The system also supports rapid propagation of alerts for newly discovered vulnerabilities, thereby increasing responsiveness to potential threats. Offering superior reliability, transparency, and availability compared to traditional SBOM tools, BC-SBOM aims to significantly improve the management of complex software systems and contribute to the advancement of software security practices.

关键词： Bills of materials Software systems Communications technology Blockchains Software reliability Security Secure storage

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Using Social Media as LMS in Terms of Teaching, Evaluating and Course Management in the Perspective of Bangladesh and the Feasibility of it’s Integration

Using Social Media as LMS in Terms of Teaching, Evaluating a...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Anika Tasnim Islam Tahmid Tahmid Md. Rifat Hasan S.M. Nahid Hasan Computer Science and Engineering Fareast International University Dhaka Bangladesh Computer Science and Engineering Universität Innsbruck Innsbruck Austria

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Learning online has become the most evolving content and approach frontier in a new method of education. In recent times, a well-featured learning management system software is a necessary contrivance for every person associated with an online education system. Some of them use social media as a method of education which doesn’t help them a lot and sometimes fails in serving its purpose of providing quality functioning. From the perspective of Bangladesh, there are many kinds of people here. So, their point of view and essential requirements also differ from one another. So, there should exist a convenient and effective online educational platform or software. Its primary concern could be serving people from all walks of life in a simple, not very difficult, and uncomplicated way. This study has come up with the concept of integrating Learning Management System(LMS) features into social media platforms which means social media like Facebook, YouTube, etc. There will be an option for studying purposes. It will be very beneficial not only to the students but also to the teachers who are in search of a superior online education platform. The study concludes with the statement that considering some conditions and constraints, it is possible to build this proposed integrated system with the assistance of social media platforms

关键词： Surveys Learning management systems Electric potential Video on demand Social networking (online) Statistical analysis Education Buildings Software Web sites

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