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Advancing Healthcare with Federated learning: Strengths, Challenges, and Methodological Concerns 6

Advancing Healthcare with Federated Learning: Strengths, Cha...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Marada, Anusha Farooqui, Yassir A. Chauhan, Ankit Department of Computer Engineering PIET Parul University Gujarat Vadodara India

ISBN: (纸本)9798331522667

Federated learning (FL), which permits decentralized data analysis while protecting patient privacy, is quickly becoming a crucial paradigm in the healthcare industry. FL complies with strict privacy rules like GDPR and HIPAA by enabling machine learning models to train on local datasets without disclosing sensitive data. In order to in vestigate the use of FL in healthcare, this study summarizes the results of several research, with a particular emphasis on wearable technology data, medical imaging, and Electronic Health Records (EHRs). We as sess several algorithms, including Gradient Boosting Machines (GBMs) and Convolutional Neural Networks (CNNs), determining their prowess in managing complex healthcare data as well as the drawbacks they en counter, such as computational demands, high communication overhead, and non-identically Distributed (non-IID) data. conventional assessment methodologies and moral standards. This evaluation outlines the essen tial improvements for FL’s scalability and dependability across varied healthcare contexts, while also highlighting FL’s potential to change healthcare. © 2025 IEEE.

关键词： Federated learning

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An Innovative Deep learning-Based Predictive Maintenance Model for Smart Automotive System

An Innovative Deep Learning-Based Predictive Maintenance Mod...

引用

2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Indumathi, V. Ashokkumar, C. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331509828

This research presents an innovative deep learning-based predictive maintenance model designed for smart automotive systems, utilizing the EnsembleAE-Boost (EAE-Boost) algorithm. The primary objective of the proposed model is to enhance the prediction accuracy and fault detection capabilities of smart vehicles through advanced machine learning techniques. The EAE-Boost algorithm, an ensemble model integrating auto encoders and boosting, is compared with existing algorithms, namely K-Nearest Neighbors (KNN), Deep Neural Networks (DNN), and Extreme Gradient Boosting (XGBoost), based on key simulation metrics, including accuracy, precision, prediction time, and fault detection rate. Simulation results demonstrate that the EAE-Boost algorithm outperforms the existing algorithms, achieving superior performance in all metrics. Notably, the proposed method showed significant improvements in accuracy and fault detection rate, with a reduction in prediction time, highlighting its potential for real-time maintenance applications in smart automotive systems. The findings underscore the effectiveness of the proposed deep learning model in enhancing vehicle reliability, safety, and operational efficiency. © 2025 IEEE.

关键词： Predictive maintenance

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Sentiment Detection in Financial News: A Deep learning Approach to Extreme and Moderate Classification 3

Sentiment Detection in Financial News: A Deep learning Appro...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Didolkar, Amey Lokulwar, Prasad G H Raisoni College of Engineering Computer Science and Engineering Nagpur India

ISBN: (纸本)9798331515911

Today's financial industries need precise, directed news analysis with sentiment identification more than ever in order to forecast possible future moves. This research focuses on developing a robust system for sentiment detection in financial news using a deep learning approach, enabling both extreme and moderate. The data collection has been ensured through the use of web scraping to gather data from multiple reliable financial news sources. To reliably forecast the attitudes encoded in the news items, this dataset has been processed using state-of-the-art deep learning algorithms. The latest technologies in deep learning have been employed, and a comparative analysis of different models has been conducted to identify the most effective one of this specific task. Providing an easy-to-use interface that allows users to obtain news from many sources and their corresponding sentiment analysis is the main goal of this research. With the help of this technology, stakeholders should be able to make well-informed decisions by using the sentiment trends found in financial news. By providing the effectiveness of deep learning models in sentiment identification, the system not only improves user experience by offering sentiment-based news insights but also advances the field of financial news analysis. The deep learning model that was constructed has demonstrated encouraging results in reliably classifying attitudes through extensive testing and evaluations, indicating that it has the potential to be an instrumental tool for financial analysis. For future research in the fields of sentiment identification and financial forecasting, this study establishes a precedent and emphasis the significance of incorporating cutting-edge technologies into news analysis. © 2025 IEEE.

关键词： Contrastive learning

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Towards Early Stroke Prediction: Detecting Hidden Patterns with Data Analytics 4

Towards Early Stroke Prediction: Detecting Hidden Patterns w...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Shinde, Priyanka P. Desai, Varsha P. Mirajkar, Gayatri S. Oza, Kavita S. Government College of Engineering Karad Department of Mca Maharashtra Karad India D y Patil Agriculture and Technical University Computer Science and Engineering Maharashtra Kolhapur India Arvind Gavali College of Engineering Maharashtra Satara India Shivaji University Computer Science Department Maharashtra Kolhapur India

ISBN: (纸本)9798331523923

Soft computing has found wide-ranging applications in diverse fields, including agriculture, finance, commerce, retail, education, healthcare, genetics, and genomics. Among these areas, healthcare is a critical domain that soft computing techniques can profoundly impact. The adoption of machine learning methods in the healthcare industry has steadily increased due to their potential to improve decision-making and uncover valuable patterns. The healthcare sector presents an ideal platform for implementing soft computing techniques, given the massive amounts of data generated daily in health records. Machine learning has demonstrated remarkable effectiveness in manually identifying hidden patterns that are difficult to discern. This capability of machine learning can lead to significant improvements in diagnosis, the development of innovative treatments, cost reduction, and enhanced patient care, making it a compelling choice for integration in the healthcare industry-diseases like heart disease, stroke, lung cancer, and diabetes demand intensive research and investigation. Brain stroke stands out as a severe and potentially fatal condition arising from interrupted blood supply to a portion of the brain. The lack of oxygen and glucose supply leads to the deterioration and eventual death of brain cells. As the second most common disease worldwide, early diagnosis and prediction become paramount for the survival of patients suffering from brain strokes. This research paper delves into utilizing machine learning techniques for early illness identification, primarily focusing on predicting the likelihood of strokes before they occur. The stroke probability dataset, encompassing input variables such as gender, the number of diseases, age, and smoking habits, was sourced from the kaggle database. Various machine learning algorithms were employed to construct prediction models, including Random Forest, KNN, Logistic Regression, SVM, and Decision Trees. Through this work, the

关键词： Diagnosis

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Adaptive Local Neighborhood Window Laplacian Eigenmaps 24

Adaptive Local Neighborhood Window Laplacian Eigenmaps

引用

13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Xu, Jiayi Huang, Qirui Zhao, Yang Pei, Jihong Yang, Xuan College of Electronics and Information Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong China College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong China

ISBN: (纸本)9798400717482

Manifold learning method is a dimensionality reduction method that treats the data in non-Euclidean space as a Euclidean space in a local scope. However, most existing manifold learning methods cannot obtain the true neighborbood relationship of each sample point well. Therefore, we propose a manifold learning method based on adaptive local neighborhood. This method can adaptively select a neighborhood window. It can be approximated as a local Euclidean space based on the sample characteristics. To implement this method, a specific manifold learning method, named Laplacian Eigenmap of Adaptive Local Neighborhood Window (ALNWLE) is proposed. ALNWLE mainly contains two modules, namely the manifold structure learning module (MSLM) and the manifold structure pattern recognition module (MSPR). MSLM constructs an adaptive local neighborhood adjacency matrix to constrain the learning of the manifold structure. MSPR uses the interpolation method to recognize new sample patterns. The adaptive local neighborhood method proposed in this paper is a general method which is applicable to any manifold learning method. © 2024 Copyright held by the owner/author(s).

关键词： Dimensionality reduction

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Exploration of Feature engineering teaching Based on Max-Relevance Min-Redundancy 3

Exploration of Feature Engineering Teaching Based on Max-Rel...

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3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Chen, Zhengguang Ma, Xiaohui Liu, Shuo Feng, Huiyan Yin, Shuxin Xu, Nan College of Information and Electrical Engineering Heilongjiang Bayi Agricultural University Heilongjiang Daqing China

ISBN: (纸本)9798400712692

To meet the requirements of science and engineering personnel training in the new era, based on the teaching content of the feature engineering part of big data in the undergraduate teaching stage, combined with the process of machine learning practice, this paper discusses the course practice of the Max-Relevance Min-Redundancy algorithm in the task-driven teaching mode. The study revolves around teaching content on feature engineering in big data during undergraduate education. Students practice algorithms in specific projects and accumulate practical experience in machine learning. They can better understand and apply the maximum correlation minimum redundancy algorithm to select feature variables, improve the performance of machine learning models, be familiar with the process of Feature engineering, and improve students' ability to solve practical problems and employment competitiveness. The results of teaching practice show that the teaching practice has achieved a good teaching effect in improving students' practical ability and understanding and mastering of feature engineering. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： data processing feature engineering feature selection Machine learning practice teaching

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Rack Sensor Metrics Odyssey: Analyzing Sensor Behavior with Random Forest and kNN for Strategic Job Allocation Insights

Rack Sensor Metrics Odyssey: Analyzing Sensor Behavior with ...

引用

2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： George, Angelina Beena, B.M. Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering India

ISBN: (纸本)9798331529833

Data centers are vital to modern computing but contribute significantly to carbon emissions due to their high energy consumption. To address this, machine learning models can optimize resource allocation by analyzing sensor metrics. In this study, the Random Forest Regressor was chosen for its ability to capture sensor behavior, even showing negative R2 values, aligning with observed patterns. Meanwhile, k-Nearest Neighbors (kNN) emerged as the best model for learning from the same data, despite irregularities, with R2 values ranging between 0.6 and 1. This analysis supports sustainable data center management by enhancing energy efficiency and minimizing environmental impact. Future work will explore the integration of these models into simulation environments. © 2025 IEEE.

关键词： Carbon sequestration

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From Distraction to Interaction: Investigating Learner Engagement Challenges in Virtual Classrooms 12th

From Distraction to Interaction: Investigating Learner Engag...

引用

12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

作者： Irfan, Muhammad Patel, Preeti Hassan, Bilal London Metropolitan University 166-220 Holloway Road London United Kingdom

ISBN: (纸本)9789819601387

Digital transformation in education, the associated advancements in technology, and the pandemic have led to an unprecedented acceleration of virtual learning and teaching, disrupting the educational environment irreversibly. The pandemic, in particular, necessitated a global and swift adaptation by all teaching institutions to virtual learning methods. For universities, the transition to online learning predominantly focused on migrating teaching content, leaving online pedagogy, social interactions, and informal learning areas, largely unattended. Given that virtual classrooms are used consistently in many educational settings, and given that there is a strong correlation between learner engagement and successful outcomes, the ability to effectively monitor, evaluate, and modify that engagement is crucial. This paper investigates the current pedagogical and technological methods that have been proposed to enhance learner engagement. It identifies progress, opportunities, limitations, and barriers that have been observed by researchers in this field through a systematic literature review from studies conducted between 2020 and 2024. Areas that have potential include hybrid combinations of human pose dynamics, facial and eye movement tracking;integrated with predictive modeling capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Smart Agriculture: A Comprehensive Approach to Crop Disease Diagnosis using Offline Multilingual Technologies 6

Smart Agriculture: A Comprehensive Approach to Crop Disease ...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Ashok, K. Mattew, Jimsha K. Chandana, K.S. Maharathi, G.N. Abhilash, G.M. Somani, Nithish Computer Science and Engineering New Horizon College of Engineering Bengaluru India Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331522667

This research study presents a comprehensive multilingual approach to crop disease diagnosis aimed at transforming agricultural practices. The proposed system leverages advanced deep learning models such as ResNet, EfficientNet, and MobileNet to automate plant phenotyping using high-resolution images. Beyond disease identification, it provides intelligent plant parenting recommendations to enhance crop breeding strategies. The system is equipped with multilingual support and offline capabilities, ensuring global accessibility, especially in areas with limited internet access. Additionally, the model delivers outputs by detecting diseases and offering diagnosis in the user’s native language, facilitating better decision-making for farmers. Experimental results from real-world scenarios demonstrate the system’s high accuracy and potential to drive sustainable agriculture, accelerate genetic progress, and improve crop prediction. © 2025 IEEE.

关键词： Smart agriculture

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Empowering Energy Systems: Exploring the Intersection of Artificial Intelligence and Predictive Maintenance 6

Empowering Energy Systems: Exploring the Intersection of Art...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Natarajan, Karthika Tekkali, Chandana Gouri Radhika, Vankayalapati School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Raghu Engineering College Andhra Pradesh Visakhaptnam India and AI & DS VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331522667

This article aims to improving and refining energy processes by applying diverse technologies, strategies, or methodologies of Artificial Intelligence (AI). AI plays an essential role in transforming and optimizing energy systems across various sectors. This article provides how advanced techniques in AI to analyze data, predict necessary issues, and enact proactive maintenance protocols, ultimately contributing to a more resilient and intelligent energy infrastructure. We have used various Machine learning (ML) models to optimize energy consumption by learning from benchmark data i.e., energy consumption of 2020, and adjusting system parameters accordingly, leading to improved efficiency. This article contributed to sustainability, cost-effectiveness, and improved reliability in the generation, distribution, and consumption of energy and also helps in proactive maintenance to minimizing downtime, and improving overall system reliability. By harnessing the power of AI technologies, industries can proactively address challenges and optimize energy resources to meet the growing demands of the future. © 2025 IEEE.

关键词： Predictive maintenance

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