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1st International conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Song, Xiaolu Zaozhuang Vocational College of Science & Technology College of Mechanical and Electrical Engineering China

ISBN: (纸本)9798331529246

The rapid advancement of industrial automation has led to increased reliance on intelligent control systems for optimizing mechanical processes. Traditional automation methods often struggle with dynamic environmental conditions, system uncertainties, and real-time decision-making challenges. Many existing approaches, such as PID controllers and conventional neural networks, fail to effectively handle sequential dependencies in time-series data, resulting in suboptimal system performance. To address these limitations, this research presents an advanced mechanical automation control framework based on a Bidirectional Long Short-Term Memory (BiLSTM) algorithm. The BiLSTM model enhances automation efficiency by capturing long-range dependencies in sequential data, improving predictive accuracy and system adaptability. By leveraging bidirectional processing, the model effectively learns past and future context, leading to superior performance in control precision and response time. Experimental evaluations demonstrate that the proposed framework achieves an accuracy of 98.2%, significantly outperforming conventional automation control techniques in terms of robustness, adaptability, and computational efficiency. This study contributes to the development of intelligent, data-driven mechanical automation systems that align with Industry 4.0 objectives and enhance operational reliability. © 2025 ieee.

关键词： Three term control systems

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Neural Contextual Adaptation (NCA): For Unsupervised learning in Dynamic Environments 3

Neural Contextual Adaptation (NCA): For Unsupervised Learnin...

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3rd International conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Praveen Kumar, G.D. Sanjan, S. Suraj, P. Rohan, U.A. Srinivas, R. Thanush, V. Visvesvaraya Technological University P.E.S College of Engineering Department of Computer Science and Engineering Mandya India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

In recent years, artificial intelligence (AI) has made significant strides in various domains, primarily driven by supervised learning techniques requiring vast amounts of labeled data. However, real-world applications, particularly those involving rapidly changing environments, demand models capable of autonomous adaptation without the need for extensive labeled datasets. In this paper, we present a novel framework for unsupervised learning that allows AI models to dynamically adapt in real-time to changing environments. Our approach leverages a combination of self-organizing maps and contrastive predictive coding to enhance model flexibility and adaptability across diverse settings. We introduce an architecture that incorporates temporal data processing and on-the-fly adaptation mechanisms, enabling the model to learn and generalize from evolving data distributions. Through extensive practical assessments and experimentation, we demonstrate our model's ability to outperform existing unsupervised approaches in real-time object detection and decision-making scenarios. The results, which show a significant reduction in model retraining time and increased adaptability to novel environments, suggest that our framework can be applied to various fields including robotics, autonomous vehicles, and dynamic control systems. © 2025 ieee.

关键词： Self-supervised learning

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A Review on Mixed Reality in Education, Portable Solutions for Overcoming Accessibility Challenges 3

A Review on Mixed Reality in Education, Portable Solutions f...

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3rd International conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nedusezhiyan, Roopa Alfred, Marie Celia Rajes Kannan, S. Pavithra, S. Department of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

Mixed reality revolutionized education by creating comprehensive and dynamic educational settings that foster student participation and understanding. Through a comparative analysis of Mixed Reality applications in fields such as STEM, medical training, and vocational education, this work identifies critical limitations including high costs, internet dependency, and the lack of localized content, particularly in rural and underserved areas. This work presents a novel strategy using Portable Mixed Reality learning Pods (MRLPs) to get around these obstacles. These compact and energy efficient units integrate affordable hardware, renewable energy systems, AI-driven personalized learning, and culturally adaptive content. By bridging the gaps in accessibility and infrastructure, Portable Mixed Reality learning Pods offer a scalable and sustainable pathway to democratize education and ensure equitable learning opportunities for all. © 2025 ieee.

关键词： Federated learning

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Understanding the Aftermath of COVID-19: A data-driven Exploration of Health Effects 3

Understanding the Aftermath of COVID-19: A Data-Driven Explo...

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3rd International conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Selvaraj, Arikumar K. Naveen Akash, P. Venkata Swamy, B. Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798331527549

The COVID-19 pandemic has ravaged public health in a manner that is unprecedented: millions of infections and fatalities worldwide. Now that the immediate crisis seems to recede, there is growing concern over long-term health consequences faced by survivors, commonly referred to as "long COVID." This study aims to systematically explore the multifaceted health effects experienced by individuals who have recovered from COVID-19, focusing on physical, mental, and cognitive health issues that have emerged in the aftermath of the infection. Employing a data-driven approach, the collected extensive survey data from a diverse cohort of COVID-19 survivors to assess their post-recovery health status. The survey included questions targeting various health dimensions, such as fatigue, pain, anxiety, depression, and cognitive function. To analyze the data, utilized several machine learning algorithms to identify significant health complications and to determine the interrelationships among various health factors. © 2025 ieee.

关键词： COVID-19

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TrafficCAM: A Versatile dataset for Traffic Flow Segmentation

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2025年第2期26卷 2747-2759页

作者： Deng, Zhongying Cheng, Yanqi Liu, Lihao Wang, Shujun Ke, Rihuan Schonlieb, Carola-Bibiane Aviles-Rivero, Angelica I. Univ Cambridge DAMTP Cambridge CB3 0WA England Hong Kong Polytech Univ Dept Biomed Engn Hong Kong Peoples R China Univ Bristol Sch Math Bristol BS8 1UG England Tsinghua Univ Yau Math Sci Ctr Beijing 100084 Peoples R China

Traffic flow analysis is revolutionising traffic management. By leveraging traffic flow data, traffic control bureaus could provide drivers with real-time alerts, advising the fastest routes and therefore optimising transportation logistics and reducing congestion. The existing traffic flow datasets have two major limitations. They feature a limited number of classes, usually limited to one type of vehicle, and the scarcity of unlabelled data. In this paper, we introduce a new benchmark traffic flow image dataset called TrafficCAM. Our dataset distinguishes itself by two major highlights. Firstly, TrafficCAM provides both pixel-level and instance-level semantic labelling along with a large range of types of vehicles and pedestrians. It is composed of a large and diverse set of video sequences recorded in streets from eight Indian cities with stationary cameras. Secondly, TrafficCAM aims to establish a new benchmark for developing fully-supervised tasks, and importantly, semi-supervised learning techniques. It is the first dataset that provides a vast amount of unlabelled data, helping to better capture traffic flow qualification under a low-cost annotation requirement. More precisely, our dataset has 4,364 image frames with semantic and instance annotations along with 58,689 unlabelled image frames. We validate our new dataset through a large and comprehensive range of experiments on several state-of-the-art approaches under four different settings: fully-supervised semantic and instance segmentation, and semi-supervised semantic and instance segmentation tasks. Our benchmark dataset and official toolkit are released at https://***/TrafficCAM/.

关键词： Cameras Annotations Urban areas Semantics Benchmark testing Vehicle-to-everything Traffic control Vehicles Real-time systems Pedestrians TrafficCAM dataset traffic flow analysis semantic segmentation instance segmentation semi-supervised learning

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Maximizing Utility and Quality in Smart Healthcare with Incentive driven Hierarchical Federated learning 17

Maximizing Utility and Quality in Smart Healthcare with Ince...

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17th International conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Singh, Himanshu Patidar, Sourabh Pratap, Ajay Yasumoto, Keiichi Department of Computer Science and Engineering Varanasi India Nara Institute of Science and Technology Nara Japan

ISBN: (纸本)9798331531195

Recent development of federated learning based smart healthcare techniques facilitates distributed processing of patient data while protecting privacy. However, two significant challenges can notably degrade model learning performance. Firstly, patients often show reluctance to participate in model training due to a lack of incentives. Secondly, the quality of updated models can change significantly due to different aspects such as the size and training data quality. The incorporation of low-quality updates can subsequently degrade global model quality in FL. Considering the above scenarios, this paper formulates a utility maximization problem keeping model quality function and budget into deliberation. To solve the formulated problem, this paper proposes association algorithm utilizing Utility Maximization and Payment (UMaxP) algorithm based on matching approach to select Wireless Body Area Networks (WBANs) and hospitals in a way that increases the utility and improves model quality using Hierarchical FL (HFL). In UMaxP, the allocation of patients to hospitals is optimized to maximize both the sum of local model qualities and the overall utility of the patients. The effectiveness of the proposed algorithmic model is demonstrated through theoretical analysis, experimental validation, and comparative evaluation with various methods using real-world data. © 2025 ieee.

关键词： Federated learning

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Real-World Implementation of Offline Model-Free Reinforcement learning for Thermostat control

Real-World Implementation of Offline Model-Free Reinforcemen...

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2025 ieee International conference on Mechatronics, ICM 2025

作者： Ghane, Sara Elmaz, Furkan Jacobs, Stef Huybrechts, Thomas Verhaert, Ivan Mercelis, Siegfried Mannens, Erik University of Antwerp - imec IDLab - Faculty of Applied Engineering Antwerp Belgium University of Antwerp EMIB Faculty of Applied Engineering Antwerp Belgium University of Antwerp - imec IDLab Department of Computer Science Antwerp Belgium

ISBN: (纸本)9798331533892

This study introduces a model-free, offline Reinforcement learning (RL) approach for optimizing the thermostat control in heating systems. Specifically, historical data from a real-world building was used to train the RL agent that is based on the Dueling Double Deep Q-Network (DQN) algorithm. The RL agent's objective is to balance thermal comfort with energy savings, by controlling indoor temperature set points. Traditional rule-based thermostat control methods often depend on fixed schedules and lack adaptability to varying conditions, resulting in energy inefficiencies. Training the RL model offline eliminates the requirement for live interaction, thereby preventing potential disruptions to occupant comfort. During evaluation, the RL model achieved an 18.66% reduction in energy use compared to a rule-based controller, while maintaining recommended indoor temperature levels. © 2025 ieee.

关键词： Reinforcement learning

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Adaptive Deep learning with Batch Feature Re-Engineering and Differential Dynamical systems

Adaptive Deep Learning with Batch Feature Re-Engineering and...

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2025 ieee SoutheastCon, SoutheastCon 2025

作者： Chen, Ruixin Li, David Katz School of Science and Health Yeshiva University New YorkNY United States

ISBN: (纸本)9798331504847

This paper presents a novel deep learning frame-work that integrates class information directly into batch feature engineering to enhance model interpretability and adaptability. Unlike conventional machine learning models, this approach introduces an innovative training paradigm and evaluation methodology, enabling the model to adapt seamlessly to new data distributions without requiring re-training. By coupling the power of differential equation dynamics simulation, the proposed framework bridges the gap between data-driven learning and mechanistic modeling, offering robust predictive capabilities. A case study on infectious disease modeling demonstrates the effectiveness of this approach, showcasing superior performance compared to traditional methods in handling evolving data distributions and capturing complex system dynamics. This work lays the foundation for adaptive modeling in dynamic, real-world scenarios. © 2025 ieee.

关键词： Reusability

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Beyond Playlists: AI-driven Emotion-Based Music Recommendation systems 2

Beyond Playlists: AI-Driven Emotion-Based Music Recommendati...

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2nd International conference on Machine learning and Autonomous systems, ICMLAS 2025

作者： Shiralaskar, Hrishikesh Mendhe, Sameer Datta Meghe Institute of Higher Education md Research Department of Computer Science & Engineering Faculty of Engineering & Technology Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Department of Computer Science & Design Faculty of Engineering & Technology Maharashtra India

ISBN: (纸本)9798331505745

Music recommendation systems have evolved from simple playlist curation techniques to sophisticated AI-driven models capable of analyzing human emotions for personalized song suggestions. Emotion-based music recommendation systems integrate advanced machine learning, deep learning, and artificial intelligence techniques to detect users' emotional states through various input modes, including facial expressions, speech tone, text analysis, and physiological signals. These systems aim to enhance user experience by dynamically selecting music that aligns with real-time emotions, thereby improving mood regulation and mental well-being. This review explores the methodologies employed in emotion-based music recommendation systems, including deep learning architectures, facial recognition techniques, and sentiment analysis models. It also discusses the challenges associated with real-time emotion detection, data privacy concerns, cross-cultural differences in music perception, and system adaptability. Furthermore, the paper highlights emerging trends and future research directions, such as multimodal emotion detection, hybrid AI models, reinforcement learning, and ethical AI frameworks. Addressing these challenges and advancements will be crucial in developing more robust, accurate, and user-centric emotion-based music recommendation systems. © 2025 ieee.

关键词： Deep learning

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Federated learning and LSTM-Based Approach for Privacy-Preserving Workforce Management in Global Human Resource systems 1

Federated Learning and LSTM-Based Approach for Privacy-Prese...

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1st International conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Latha, B. Madhavi Devi, Sistla Suneetha Thrimurthulu, V. Chenniappan, Loganathan Swetha, Devulapally Saravanakumar, R. Sarojini Naidu Vanita Maha Vidyalaya Exhibition Grounds Nampally Hyderabad India Narsee Monjee Institute of Management Studies Hyderabad India Mlr Institute of Technology Department of Cse Dundigal Hyderabad India University of Technology and Applied Sciences College of Economics & Business Administration Ibri Oman Vaagdevi Engineering College Department of Cse Hyderabad India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Ece Chennai India

ISBN: (纸本)9798331529246

In the data-driven world of today, safeguarding personal information is not only required by law but also essential to upholding operational integrity and confidence. The increasing reliance of firms on data to inform decisions and streamline HR procedures makes this strategy imperative. Traditional methods had problems with scalability and privacy when working with sensitive and varied data sources. The suggested work offers an innovative approach that protects data privacy and permits safe, by combining Federated learning with LSTM (Long Short-Term Memory). Federated learning preserves secrecy and complies with privacy requirements. The study's findings show a notable increase in performance, attaining a 99.1% accuracy rate. This increased accuracy demonstrates the method's capacity to uphold strict data privacy rules while offering trustworthy and practical labor management insights. The implementation of the study is done using Python The study opens the door for further developments in safe and effective labor management systems. © 2025 ieee.

关键词： Differential privacy

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