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An Innovative Approach Towards Efficient Diabetes Mellitus Prediction Using Federated Forest

An Innovative Approach Towards Efficient Diabetes Mellitus P...

引用

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Madhu, Gifsy De, Anurag Pal, Gautam School of Computer Science and Engg. VIT-AP University AP Amaravati India Tripura Institute of Technology Computer Science & Engineering Dept. Tripura Narsingarh India

ISBN: (纸本)9798331540821

In this study, we discuss a method for diagnosing diabetes mellitus that combines the Random Forest algorithm with Federated Learning. The global model preserves data privacy while guaranteeing accurate and dependable diagnostics by combining local models rather than data. The diabetes dataset, sourced from Mendeley Data, is preprocessed to address class imbalance through the Synthetic Minority Oversampling Technique (SMOTE). The dataset is further augmented using Conditional Generative Adversarial Networks (CGAN) to introduce non-lID variations while splitting the data. Our global model is evaluated and compared across different node configurations, with the optimal performance achieved using six nodes. The results show impressive performance characteristics with 98.18% accuracy, 99.09% specificity, 98.18% sensitivity, and an ROC AUC of 1.00. These findings indicate that our Federated Forest based approach significantly enhances the diagnostic capability for Diabetes Mellitus while maintaining data privacy and integrity. © 2024 IEEE.

关键词： Federated learning

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Advanced Flood Risk Prediction Leveraging Geospatial Satellite Insights

Advanced Flood Risk Prediction Leveraging Geospatial Satelli...

引用

2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Singh, Soumya Tiwari, Ashish Amity University Uttar Pradesh Lucknow Campus Dept. of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798331507541

Accurate flood risk prediction is critical for disaster management, particularly given the increased unpredictability of weather patterns caused by climate change. This work applies to a 1-day-ahead flood forecast model that combines geospatial data, including Landsat and Sentinel-1 SAR observations, with machine learning methods. The results show great prediction accuracy, with a correlation coefficient. Surface water conditions and soil surface moisture are both significant predictors, accounting, respectively. The model accurately anticipated inundation patterns during the peak and recession stages of the Idai flood, with results closely matching satellite-derived data. This method emphasizes the need of merging machine learning and remote sensing to provide actionable flood risk assessments, resulting in improved disaster planning and resilience methods. © 2025 IEEE.

关键词： Risk management

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Real Time Facial Emotion Recognition using Deep Learning and CNN

Real Time Facial Emotion Recognition using Deep Learning and...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Lokeshnaik, S.K. Punitha, A. Vijayakarthik, P. Kiran, Ajmeera Dhangar, A. Narsimha Reddy, B.Jathin Sudheeksha, M. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Annamalai University Department of Computer Science and Engineering TamilNadu India R.L.Jalappa Institute of Technology Dodabalapur Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350348217

In this universe, a person's mood may be read from their facial expressions. But one thing is for sure: every expression is a way that people communicate their emotions. Despite the fact that every person has a unique set of facial features and a range of emotions, artificial intelligence (AI)technology allows us to recognize these emotions. A face emotion-recognition AI is built using a variety of techniques, one of which is deep learning to build a convolutional neural network (CNN) to train the data set and obtain high prediction accuracy. Recognizing seven of the fundamental facial emotions out of hundreds is the major goal here. The most common facial emotions are those of happiness, sadness, neutrality, anger, surprise, fear, and disgust. There are plenty of ways to achieve this facial emotion recognition like using deep face algorithms, machine learning, by AI techniques, by Tensor flow etc. The accuracy of our model is between 70% to 80%. It is perhaps the simplest and efficient approach of all of them. © 2023 IEEE.

关键词： Face recognition

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A Comprehensive End-to-End Solution for Web Security with Cryptography, Multi-Factor Authentication, and Secure Communication 3

A Comprehensive End-to-End Solution for Web Security with Cr...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Kanderi Johith Sai, Kandlapalli Aravind Reddy, Adapala Dharshan Reddy, Chinthamreddy Pranay Daiwik Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

In today’s digital landscape, securing sensitive information has become an essential priority in the modern digital era for both individuals and organizations. This project introduces a comprehensive web application that integrates advanced encryption and decryption functionalities alongside multifactor authentication (MFA) and secure payment processing. The application supports various cryptographic algorithms, including AES, DES, Triple DES, Blowfish, etc., allowing users to securely encrypt and decrypt data through dedicated routes accessed over secure HTTPS connections. To further enhance security, robust multifactor authentication mechanisms, such as OTP-based challenge-response and QR-code-enabled 2FA, have been implemented to prevent unauthorized access to sensitive operations. The platform also incorporates PGP encryption powered by Keybase, enabling users to securely exchange encrypted emails. Additionally, a secure payment gateway is integrated using Stripe, ensuring sensitive payment data is encrypted and handled safely. With a strong focus on data integrity, user privacy, and modern web security standards, this project delivers an all-encompassing solution for safeguarding sensitive information and facilitating secure communication. By addressing common vulnerabilities and security risks, the platform stands as a reliable and scalable tool for data protection. © 2025 IEEE.

关键词： Authentication Protocol

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Re-Cognition I: An Inspection of Learner's Cognitive Well-Being, Pursuant to Contemporary Period, Facilitates the Academic Life Cycle 6

Re-Cognition I: An Inspection of Learner's Cognitive Well-Be...

引用

6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ganguly, Souvik Pati Mandal, Kailash Guha, Debasis Dr. B.C. Roy Engineering College Dept. of Computer Application Durgapur India National Institute of Technology Dept. of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350306613

Recommender systems enhance the user experience by integrating factual and behavioral practices, enhancing learning strategies, and granting academic opportunities based on learners subjective interests. Cognitive intelligence, a learner's capability to interpret and synthesize cognitive abilities, is crucial to assemble effective recommendations. However, a student encounters diverse adversities in his or her academic life, whether it is his or her academic pacers or outward circumstances. As a result, a learner consequently experienced severe academic setbacks. Thus, this paper proposes a novel approach to identify cognitive deficits of learners in a school or college before recommending any scholastic content or pertaining in any academic activity. We have employed a modified Bi-directional Gated Recurrent Unit (Bi-GRU) method in conjunction with Shuffled Frog Leaping Algorithm (SFLA). Our approach overcomes some of the shortcomings of conventional Bi-GRU method in choosing the right scaling parameters during training. Bi-GRU method is opted for detecting the cognitive deficits of a learner and SFLA is applied over Bi-GRU in order to hyper-Tune the parameters. The proposed method is compared with a few cutting-edge methods, and the experimental result outperforms the other methods in training, validation, and testing accuracies, achieving 99.34%, 98.98%, and 99.19% respectively. © 2024 IEEE.

关键词： Bi-directional GRU Cognitive Deficits Cognitive Equilibrium Learner's Mental State Meta-heuristic Optimization SFLA

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Classification of Structures and Monuments based on Indian Architectural Styles 3

Classification of Structures and Monuments based on Indian A...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Ganapathi, Ananya Saravanan, Deivanai Raghavan, Sneha T Jyotsna, C. Aiswariya Milan, K. Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331523893

Recognizing architectural styles is essential for preserving & understanding cultural heritage, as it helps categorize & document diverse structures, highlighting their historical, cultural & artistic value. The study addresses the gap between technology & heritage by developing a framework for classifying 8 architectural styles: Buddhist, Indo Islamic, Rajput, Dravidian, Hindu, Sikh, British & Modern. Using the HOG method for feature extraction, the models capture intricate architectural details. Classification is done using machine learning models like Logistic Regression, Random Forest & XG-Boost, as well as advanced deep learning models such as DenseNet & InceptionV3. DenseNet achieves the highest accuracy of 86%, followed by InceptionV3 at 79%, showing the capability of deep learning in handling complex visual data. The study not only compares model performance but also provides a scalable method for architectural style classification that contributes to the heritage preservation. © 2025 IEEE.

关键词： Photointerpretation

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Integrating Artificial Intelligence and Educational Technology for Inclusive and Ethical Education Practices 3

Integrating Artificial Intelligence and Educational Technolo...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ilayaraja, Lathika Yukthika, S.S. Prabakaran, Subramani Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

AI and EdTech are transforming education by making it more accessible, personalized, and assessable, even in low and middle income countries (LMICs) facing resource constraints. However, challenges come with unequal access, issues in data privacy, and bias in AI model creations. As AI continues to become more popular, the issue of whether the work made by the students will be original. This paper focuses on the impact of artificial intelligence on learning in both its ethical and practical dimensions while highlighting the themes of inclusivity, fairness, and authenticity. Guidelines and policies for responsible AI in integration will be proposed further, alongside teacher training. Results should depict that this moral framework, policies, and teacher education should aid in ensuring that the implications of AI are positive and fruitful for learning. Ideal AI guidelines would foster an educational system that is inclusive, transparent, and considers uniqueness and innovation. © 2025 IEEE.

关键词： Federated learning

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A Scalable Privacy Preservation Block Chain-Based Method for Smart Healthcare System 3

A Scalable Privacy Preservation Block Chain-Based Method for...

引用

3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Dongre, Shubhangi Saxena, Kanak Raghuwanshi, Sandeep Dept. of Computer Science and Engineering Sati Vidisha India Synechron Pvt. Ltd India

ISBN: (纸本)9798331542375

In this study, we propose a scalable privacypreserving blockchain mechanism for smart healthcare systems. The rapid digitization of medical data has made security, scalability, and privacy vital. This approach minimizes risks associated with sensitive healthcare information by utilizing the decentralized architecture of blockchain to create a secure, transparent, and efficient healthcare information management platform. The system showed an increase in transaction throughput, a reduction of the latency, and an enhancement in energy efficiency compared with traditional approaches through simulations. Moreover, state-of-the-art cryptographic approaches provided strong privacy protection by reducing the risk of data leaks and malicious access. Smart contract implementation further optimized operations by facilitating secure and automated healthcare workflows. This method solves the growing issues in the area of managing healthcare data while delivering a much more scalable, secure, and patient-centric solution for contemporary healthcare systems. © 2025 IEEE.

关键词： Sensitive data

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Real-time Posture Correction System in sitting using Deep Learning Techniques 14

Real-time Posture Correction System in sitting using Deep Le...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Dewang, Rupesh Kumar Ali, Abid Kumar, Divya Ranvijay Kumar, Atul Omar, Sanskar Dept. Computer Science & Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj India

ISBN: (纸本)9798331531935

In today’s workplace, desk employees often spend long hours sitting in front of computers, leading to issues such as poor posture, back pain, neck strain, and other musculoskeletal problems. This research presents a solution using wearable devices and computer vision technology to tackle these health concerns. The system monitors the user’s body position in real-time by analyzing images captured by the wearable device. It calculates body angles and relevant data to evaluate posture and offers corrective feedback when poor posture is detected. Maintaining good posture is important not only for physical well-being but also for enhancing productivity. Recognizing this, researchers and developers are creating technological solutions to help desk workers develop healthier posture habits throughout the workday. This paper introduces a system designed to promote better posture using computer vision. The camera capture real-time images of the user, and the system uses algorithms to identify key body points and calculate posture angles. If improper posture is identified, t he s ystem p rovides t he u ser w ith guidance to make adjustments, helping prevent posture-related health *** technology offers a practical approach to reducing the physical strain associated with prolonged sitting, benefiting both individual health and workplace efficiency. © 2025 IEEE.

关键词： Desk Workers Posture Correction Wearable Devices

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Fraud Detection in Financial Transactions using Machine Learning - A Comparative Study 3

Fraud Detection in Financial Transactions using Machine Lear...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Jayanthkumar, Avati Sathwik, Mayakuntla Sai Reddy, Shagam Srinivasa Kumar, Donadula Mounish Samir, Chougule Saish Patni, Jagdish Chandra Bahadure, Nilesh Kumar, Rohit Alliance School of Advanced Computing Alliance University Dept. of Computer Science Bengaluru India School of Technology Gsfc University Department of Computer Science and Engineering Vadodara India

ISBN: (纸本)9798331518592

Data analysis techniques using machine learning approaches are the most prominent when we are considering the online fraud, it may be a credit card fraud or any other online fraud in the banking system. Using Machine Learning techniques, we can easily manage the information of fake transactions by extensive data analysis of time, location and amount in the real time. This paper gives the comprehensive study of the changing scene of the fraud discovery in monetary exchanges, investigating the most recent patterns, advances, and techniques utilized by financial institutions and other administrative bodies. An extensive review of various machine learning techniques has been done to find out the effective solution to protect the online fraud. © 2024 IEEE.

关键词： Adversarial machine learning

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