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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Venkatesan Rajinikanth Robertas Damaševičius David Taniar Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai TN India Centre of Real Time Computer Systems Kaunas University of Technology Kaunas Lithuania Faculty of Information Technolog Monash University Melbourne VIC Australia

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

The eye is a critical sensory organ in human physiology, and any abnormality in the eye will result in vision problems ranging from mild to severe. Image-guided methods are frequently employed to conduct clinical-level eye health assessments, and retinal fundus imaging (RFI) is among the most frequently employed modalities for eye analysis. This research aims to implement a Deep-learning (DL)-based technique for classifying the Normal/Glaucoma RFI database. This scheme consist following phases, image collection and labelling, feature extraction with EfficientNet, feature optimization using a novel Brownian-Distribution Algorithm (BDA), and binary classification with 3-fold cross validation. Initially, the proposed approach performs the classification with EfficientNet-model features and documents the results. Subsequently, the BDA is employed to optimize the features in order to identify the most significant ones. Serial features-fusion is then implemented to generate a new features vector. The results of this study confirm that this approach is capable of achieving a 100 % accuracy rate when the BDA optimized feature vector is utilized for the classification task. In the future, this application may be utilized to analyze the RFI collected from hospital.

关键词： Glaucoma Accuracy Hospitals Databases Retina Feature extraction Vectors Physiology Classification algorithms Optimization

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A Study on Effects of Synthetic Data for Predicting the Remaining Useful Life of Aluminium Electrolytic Capacitors Using Bagging-Based Ensemble learning 2nd

A Study on Effects of Synthetic Data for Predicting the Rema...

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2nd International Conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2023

作者： Bhattacharyya, Anindya Srijith, K. Behera, R.P. Dasgupta, Arup Chakraborty, R.S. Real Time Systems Division Electronics & Instrumentation Group Indira Gandhi Centre for Atomic Research Tamil Nadu Kalpakkam603102 India Birla Institute of Technology and Science Hyderabad Campus Telangana Secunderabad500078 India Physical Metallurgy Division Materials Characterization Group Metallurgy and Materials Group Indira Gandhi Centre for Atomic Research Tamil Nadu Kalpakkam603102 India Homi Bhabha National Institute Training School Complex Anushaktinagar Mumbai400094 India Department of Computer Science and Engineering IIT Kharagpur Kharagpur721302 India

ISBN: (纸本)9789819995172

Aluminium electrolytic capacitors (AECs) are crucial components used in power supplies, computer motherboards, and variable frequency drives for ripple filtering and energy storage. Their performance is affected by factors like temperature, frequency, ripple current, and ageing. Despite design advancements, AECs remain reliability weak points, demanding thorough analysis. This research proposes a method to predict the Remaining Useful Life (RUL) of AECs under combined temperature and voltage stress. We develop an RUL prediction model using bagged decision trees by ageing 24 AEC samples and tracking degradation via capacitance and ESR changes. Synthetic data strengthens the model due to limited training data. We assess the impact of different minority oversampling methods like SMOTE, SMOTER, and SMOGN on prediction accuracy, using ANOVA to compare outcomes within and between algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Analysis of variance (ANOVA)

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Stability Analysis and Performance Evaluation of Delayed Bilateral Telerobotic systems over a Lossy Communication Channel

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Journal of systems science & Complexity 2021年第1期34卷 157-179页

作者： BAKHSHI Ali ALFI Alireza TALEBI Heidar Ali SOURATGAR Amir Aboulfazl YOUSEFI Mahdi Faculty of Electrical and Robotic Engineering Shahrood University of TechnologyShahrood 36199-95161Iran Power Plants Control Systems Technology Development Plan Department of monitoring and ControlNiroo Research InstituteTehran 14665-517Iran Real-Time and Robotics system Laboratory Faculty of Electrical EngineeringAmirkabir University of TechnologyHafez AvenueTehran 15914Iran Electrical and Computer Engineering Department The University of British Columbia2385East MallVan couverBC V6T 1Z4Canada

The focus of this paper is to address a novel control technique for stability and transparency analysis of bilateral telerobotic systems in the presence of data loss and time delay in the communication channel. Different control strategies have been reported to compensate the effects of time delay in the communication channel;however, most of them result in poor performance under data loss. First, a model for data loss is proposed using a finite series representation of a set of periodic continuous *** improve the performance and data reconstruction, a holder circuits is also introduced. The passivity of the overall system is provided via the wave variable technique based on the proposed model for the data loss. The stability analysis of the system is then derived using the Lyapunov theorem under the time delay and the data loss. Finally, experimental results are given to illustrate the capability of the proposed control technique.

关键词： Lossy channel passivity stability telerobotic transparency

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Impact of Robotic Process Automation on Enterprise Resource Planning systems

Impact of Robotic Process Automation on Enterprise Resource ...

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2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023

作者： Yendluri, Dileep Kumar Ponnala, Jainath Thatikonda, Ramya Kempanna, M. Tatikonda, Reshmi Bhuvanesh, A. University of the Cumberlands Masters in Computer Science United States Jawaharlal Technological University Bachelors in Ece Hyderbad India University of the Cumberlands PhD in Information Technology Williamsburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Central Missouri Masters in Computer Information Systems Warrensburg United States Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350306088

ERP systems are merging their powers to generate a dynamic collaboration. By examining the integration between RPA (Robotic Process Automation) and ERP (Enterprise Resource Planning) systems, we can identify their shared potential for enhancing process flow. Combining automation and ERP integration makes RPA a powerful tool for process management across multiple industries. The paper examines the basics of RPA and its ERP integration, highlighting the advantages of enhanced productivity and fewer errors. Addressing security, compliance, and change management issues simultaneously. In addition to top-down and bottom-up methods, process analysis and selecting suitable processes are covered. Practical examples offer insightful observations on this integration's success. The union of RPA and ERP creates a powerful combination that can elevate operational effectiveness, facilitate strategic insights, and reshape the workplace. © 2023 IEEE.

关键词： Integration

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Risk-Sensitive Safety Filters for Reinforcement learning with Probabilistic Guarantees

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IEEE Transactions on Automatic Control 2025年

作者： Lederer, Armin Noorani, Erfaun Baras, John S. Hirche, Sandra ETH Zurich Learning and Adaptive Systems Group Institute for Machine Learning Department of Computer Science Switzerland University of Maryland Department of Electrical and Computer Engineering Institute for Systems Research (ISR) College Park MD United States Technical University of Munich Chair of Information-oriented Control (ITR) School of Computation Information and Technology Munich 80333 Germany

Humans have the ability to deviate from their natural behavior when necessary, which is a cognitive process called response inhibition. Similar approaches have independently received increasing attention in recent years for ensuring the safety of control. realized using control barrier functions or predictive safety filters, these approaches can effectively ensure the satisfaction of state constraints through an online adaptation of nominal control laws, e.g., obtained through reinforcement learning. While the focus of these realizations of inhibitory control has been on risk-neutral formulations, human studies have shown a tight link between response inhibition and risk attitude. Inspired by this insight, we propose a flexible, risk-sensitive method for inhibitory control. Our method is based on a risk-aware condition for value functions, which guarantees the satisfaction of state constraints. We propose a method for learning these value functions using common techniques from reinforcement learning and derive sufficient conditions for its success. By enforcing the derived safety conditions online using the learned value function, risk-sensitive inhibitory control is effectively achieved. The effectiveness of the developed control scheme is demonstrated in simulations. © 1963-2012 IEEE.

关键词： risk sensitive control Safe reinforcement learning safety filter stochastic systems uncertain systems

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Challenges in Cyber Physical Social systems and Internet of Things

Challenges in Cyber Physical Social Systems and Internet of ...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： S. Revathi J. Raja M Mohanraj K. Malathi Balasubbareddy Mallala R.G. Vidhya Department of Computer Science Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Business Systems Sri Eshwar college of engineering Coimbatore India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of ECE HKBK College of Engineering Bangalore India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

This research analyses the complex dynamics of Cyber-Physical-Social systems (CPSS), encompassing cyber-physical systems, cybersecurity, the Internet of Things (IoT), and social media. By exploring the interactions among these elements, this study analyses the vulnerabilities, risks, and potential benefits within the CPSS framework. This research study analyses the evolving landscape of cybersecurity by considering various aspects such as data integrity, privacy in the IoT era, and the influence of social media on user behaviour and influence dynamics. The study also introduces innovative strategies to enhance CPSS resilience, including methods to mitigate cyber threats and enhance overall system integrity. Additionally, we investigate how social media shapes CPSS behaviours, offering valuable insights into influence dynamics. This research advances the comprehensive understanding of CPSS to support future studies and advancements in the protection and improvement of interconnected systems.

关键词： Data privacy Social networking (online) Shape Media real-time systems Internet of Things Protection Predictive analytics Monitoring Resilience

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Development of Efficient Wireless Sensor Network for IoT Applications 3

Development of Efficient Wireless Sensor Network for IoT App...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Shankar, S. Deepika, G. Devi, G. Ramesh, S. Srivastava, Suraj Kumar, S Sandeep Sri Krishna Arts and Science College Department of Electronics and Communication Systems Tamil Nadu Coimbatore641008 India St. Peter's Engineering College Department of Electronics and Communication Engineering Telangana Hyderabad500100 India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Dindigul624622 India Institute of Artificial Intelligence and Machine Learning Saveetha University Depattment of Computational Intelligence Tamil Nadu Chennai602105 India I.K. Gujral Punjab Technical University Department of Computer Science and Engineering Mohali Campus-1 Punjab Sahibzada Ajit Singh Nagar160055 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9798350322842

This research study provides an overview of the effort that went into developing reliable 'Wireless sensor networks' for IoT applications, including both theoretical study and practical testing. The report also includes the findings of an analysis of variance and regression analysis, which pinpoint the significance of such aspects as network architecture, traffic load, and ambient conditions on the performance of Wireless Sensor Network (WSN). Finally, study provides a discussion on the future research directions, as well as the design and implementation of a Wireless Sensor Network (WSN) for Internet of Things (IoT) applications. © 2023 IEEE.

关键词： Electric power utilization

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A Study of Robustness of a Closed-Loop System for Blood Pressure Control

A Study of Robustness of a Closed-Loop System for Blood Pres...

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American Control Conference (ACC)

作者： Taylor E. Baum Bharadwaj Satchidanandan Munther A. Dahleh Emery N. Brown Electrical Engineering and Computer Science Department Massachusetts Institute of Technology MA USA Department of Electrical Engineering and Computer Science The Laboratory for Information & Decision Systems The Institute for Data Systems and Society at the Massachusetts Institute of Technology MA USA Department of Anesthesia Critical Care and Pain Medicine The Massachusetts General Hospital MA USA The Picower Institute for Learning and Memory The Massachusetts Institute of Technology MA USA The Division of Sleep Medicine Harvard Medical School MA USA

Wide variability of patient response to vasoactive drugs, and mechanical limits of infusion pumps lead to difficulties in achieving robust Closed-Loop Blood Pressure Control (CLBPC). Our goal is to refine a previously proposed CLBPC system which emphasizes the incorporation of mechanistic cardiovascular models, design simple Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) controllers, and assess their robustness to estimation errors in physiological parameters of patients that are expected to vary widely within a patient population. A pharmacokinetic-pharmacodynamic (PK-PD) model that quantifies the impact of drugs, phenylephrine and nicardipine, on a patient’s blood pressure is provided. A state space model is then formulated which incorporates the full dynamics of a patient’s blood pressure. We then implement MPC and PID control to simulate regulation of blood pressure and explore the robustness of such controllers to errors in estimates of patient physiological parameters.

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Artificial intelligence in multimodal learning analytics: A systematic literature review

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computers and Education: Artificial Intelligence 2025年 8卷

作者： Mohammadi, Mehrnoush Tajik, Elham Martinez-Maldonado, Roberto Sadiq, Shazia Tomaszewski, Wojtek Khosravi, Hassan School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia Department of Educational Psychology and Learning Systems Florida State University Tallahassee United States Centre for Learning Analytics at Monash Faculty of Information Technology Monash University Melbourne Australia Australian Research Council Centre of Excellence for Children and Families Over the Life Course The University of Queensland Brisbane Australia Institute for Social Science Research The University of Queensland Brisbane Australia Institute for Teaching and Learning Innovation The University of Queensland Brisbane Australia

The proliferation of educational technologies has generated unprecedented volumes of diverse, multimodal learner data, offering rich insights into learning processes and outcomes. However, leveraging this complex, multimodal data requires advanced analytical methods. While Multimodal learning Analytics (MMLA) offers promise for exploring this data, the potential of Artificial Intelligence (AI) to enhance MMLA remains largely unexplored. This paper bridges these two evolving domains by conducting the first systematic literature review at the intersection of AI and MMLA, analyzing 43 peer-reviewed papers from 11 reputable databases published between 2019 and 2024. The findings indicate a growing trend in AI-enhanced MMLA studies published predominantly in high-quality venues, led by education researchers with a predominant focus on tertiary education targeting diverse stakeholders. Guided by a novel conceptual framework, our analysis highlights the transformative role of AI across the MMLA process, particularly in model learning and feature engineering. However, it also uncovers significant gaps, including limited AI implementation in components requiring deep integration with learning theories, insufficient application of advanced AI techniques, and lack of large-scale studies in authentic learning environments. The review identifies key benefits, such as enhanced personalization and real-time feedback, while also addressing challenges related to ethical considerations, data integration, and scalability. Our study contributes by offering comprehensive recommendations for future research, emphasizing international collaboration, multi-level studies, and ethical AI implementation. These findings advance the theoretical understanding of AI's role in education, providing a foundation for developing sophisticated, interpretable, and scalable AI-enhanced MMLA approaches, potentially revolutionizing personalized learning across diverse educational settings. © 2025

关键词： Educational robots

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Risk Management in Robotic Process Automation

Risk Management in Robotic Process Automation

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Dileep Kumar Yendluri Ramya Thatikonda Reshmi Thatikonda Jainath Ponnala M. Kempanna A. Bhuvanesh Department of Computer Science University of the Cumberlands Williamsburg USA Department of Information Technology University of the Cumberlands Williamsburg USA Department of Computer Information Systems University of Central Missouri Williamsburg USA Department of Electronics and Communication Engineering Jawaharlal Technological University Hyderbad India Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Bangalore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

The paper investigates the nuances of RPA risk management, detailing the difficulties, tendencies, and approaches organizations must adopt to integrate RPA into their operations successfully. From automation to optimization, RPA’s impact on industry workflow is revolutionary. However, RPA introduces a spectrum of technical, operational, business, and compliance risks alongside its benefits. Amidst the intricate landscape of RPA, this paper sheds light on key risks. It offers practical strategies for tackling them, enabling organizations to confidently unlock automation’s transformative power. Trends like advanced analytics fusion, AI-driven risk analysis, and blockchain utilization will likely influence future RPA risk management. In addition, the paper discusses evolving challenges, including ethical issues, breakneck technology evolution, and digital security dangers. Using an in-depth examination of RPA risk management, this paper prepares organizations for the ever-changing automation environment with informed decisions and proactive measures.

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