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Towards AI-assisted digital twins for smart railways: preliminary guideline and reference architecture

Journal of Reliable Intelligent Environments 2023年第3期9卷 303-317页

作者： De Donato, Lorenzo Dirnfeld, Ruth Somma, Alessandra De Benedictis, Alessandra Flammini, Francesco Marrone, Stefano Saman Azari, Mehdi Vittorini, Valeria Department of Electrical Engineering and Information Technology University of Naples Federico II Via Claudio 21 Naples80125 Italy Department of Computer Science and Media Technology Linnaeus University Växjö Sweden School of Innovation Design and Engineering Mälardalen University Eskilstuna Sweden

In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow. © 2023, The Author(s).

关键词： Cyber Physical System

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Bismuth-Based Double Perovskites for Solar-Powered EVs using ResNet and Long Short-Term Memory 3

Bismuth-Based Double Perovskites for Solar-Powered EVs using...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kiran, C.H. Gupta, Raj Kumar Kumar, Neeraj Devi, Rukmani Bhoopathy, V. Kathiravan, A. Mijar India Sardar Vallabhbhai Patel College Department of Physics Ara India Doon Institute of Engineering and Technology Department of Electrical Engineering Rishikesh India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Science Chennai India Sree Rama Engineering College Department of Computer Science and Engineering Tirupathi India Rajiv Gandhi College of Engineering and Technology Department of Electrical and Electronics Engineering Puducherry India

ISBN: (纸本)9798331508456

As people rely more on cars for transportation, greenhouse gas emissions and ozone layer depletion rise. For shorter rides, solar-powered e-bikes are affordable and environmentally friendly. This proposed approached a ResLSTM hybrid model with LSTM. The methodology involves preprocessing, feature extraction, and model training. Remove duplicates and missing values before k-means grouping and training. Consistency requires scaling, normalizing, and standardizing features. With an average accuracy of 93.45%, the ResLSTM model surpassed current approaches. The recommended method effectively estimates solar energy and vehicle energy consumption to maximize energy use for solar-powered e-bikes. The results suggest that ResLSTM can provide efficient and eco-friendly transportation options. Resolving data anomalies and ensuring accurate projections helps solar-powered electric bikes become more popular as a sustainable transportation choice. © 2025 IEEE.

关键词： Electric bikes

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A Novel Approach on Advanced Fruit Disease Identification Using RCNN

A Novel Approach on Advanced Fruit Disease Identification Us...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Kumar, R. Boopathi, P. Rajan, M. Sundar Bangalore India Shri Nehru College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India Arba Minch Institute of Technology Arba Minch University Faculty of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798331509828

The early diagnosis of diseases in fruits holds immense importance for agricultural industries, as it directly impacts production quality and quantity. This study introduces a novel approach utilizing Recursive Convolutional Neural Network (RCNN) for automated citrus plant disease detection. Through a comprehensive methodology involving data collection, image pre-processing, segmentation, feature extraction using Linear Principal Component Analysis (LPCA), and classification, the proposed RCNN algorithm showcases promising results. Experimental evaluation demonstrates the efficacy of the RCNN-based approach, with precision averaging at 0.929, recall at 0.946, accuracy at 0.945, F-measure at 0.943, and minimal time complexity. Compared to traditional manual inspection methods, RCNN offers a more accurate and efficient solution, significantly reducing the burden on farmers and experts while enhancing fruit disease detection capabilities. Overall, the proposed RCNN-based approach presents a significant advancement in fruit disease identification, offering precise and timely detection to mitigate agricultural losses. With its superior performance metrics and streamlined process, RCNN stands as a promising tool for revolutionizing fruit disease diagnosis and improving agricultural productivity. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhancing Septic Shock Detection through Interpretable Machine Learning

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computer Modeling in engineering & Sciences 2024年第12期141卷 2501-2525页

作者： Md Mahfuzur Rahman Md Solaiman Chowdhury Mohammad Shorfuzzaman Lutful Karim Md Shafiullah Farag Azzedin Department of Information and Computer Science King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Department of Electrical and Computer Engineering North South UniversityDhaka1229Bangladesh Department of Computer Science Taif UniversityTaif21974Saudi Arabia College of Applied Arts and Technology Seneca PolytechnicTorontoM2J 2X5Canada Department of Control&Instrumentation Engineering King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia

This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health *** traditional methods,which often lack transparency in decision-making,our approach focuses on early detection,offering a proactive strategy to mitigate the risks of *** integrating advanced machine learning algorithms with interpretability techniques,our method not only provides accurate predictions but also offers clear insights into the factors influencing the model’s ***,we introduce a preference-based matching algorithm to evaluate disease severity,enabling timely interventions guided by the analysis *** innovative integration significantly enhances the effectiveness of our *** leverage a clinical health dataset comprising 1,552,210 Electronic Health Records(EHR)to train our interpretable machine learning models within a cloud computing *** techniques like feature importance analysis and model-agnostic interpretability tools,we aim to clarify the crucial indicators contributing to septic shock *** transparency not only assists healthcare professionals in comprehending the model’s predictions but also facilitates the integration of our system into existing clinical *** validate the effectiveness of our interpretable models using the same dataset,achieving an impressive accuracy rate exceeding 98%through the application of oversampling *** findings of this study hold significant implications for the advancement of more effective and transparent diagnostic tools in the critical domain of sepsis management.

关键词： Sepsis prediction machine learning cloud computing

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Behavioral Changes Based Trust Computation in Mutli-Agent Predictive Maintenance Applications 4

Behavioral Changes Based Trust Computation in Mutli-Agent Pr...

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4th International Conference on Advanced Research in Computing, ICARC 2024

作者： Premarathne, Uthpala Subodhani Rajasingham, Shalini The Open University of Sri Lanka Department of Electrical and Computer Engineering Nawala Sri Lanka The Open University of Sri Lanka Center for Education Technology and Media Nawala Sri Lanka

ISBN: (纸本)9798350384864

Predictive maintenance practices rely on reliable scheduling and allocation of resources to sustain seamless operations. Advent of agent technology has increased the avenues to manage efficient schedules in distributed industry settings. This paper proposes a multi-agent architecture with behavior based trust computations and reinforcement learning to effectively manage multiple schedule assignments. To formalize the schedule maintenance problem, bigraph was used to model the centralized resource allocations among multiple schedules. Reinforcement learning algorithms was used to compute and update trust values based on the agent behaviors. Results reveal that the cooperation among agents helps to maintain a variation in assignments to less than 40%. © 2024 IEEE.

关键词： Reinforcement learning

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An Effective Detection of Ransomware Using Improved Long Short-Term Memory 3

An Effective Detection of Ransomware Using Improved Long Sho...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Vijayalakshmi, V. Fallahhusein, Muhamed Balamurugan, D. Suresh, T. Muthu Krishnan, K. Lakshmi Bangaru Arts and Science College Department of Computer Science with Artificial Intelligence Melmaruvathur India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Sona College of Technology Department of Computer Science and Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering Technology Department of Electrical and Electronics Engineering Mamallapuram India

ISBN: (纸本)9798331508456

Ransomware detection focuses on recognizing the unusual system activities which indicate an unauthorized data access and restriction. It continuously monitors the system behavior continuously to detect potential threats before significant damage occurs. By identifying anomalies and patterns, it effectively distinguishes ransomware attacks from normal operation. However, Ransomware attacks cause a serious threat because of far-reaching effects and its zero-day variants are even more hazardous. This research proposes Improved Long Short-Term Memory (LSTM) to detect the ransomware effectively and accurate detection of Ransomware. The proposed improved LSTM learns the sequential patterns in system behavior and detects subtle abnormalities over time. The enhanced model improves detection accuracy by learning past data and adapts to new attack variants including zero-day threats. By identifying anomalies, the proposed method enables timely detection and reduces the impact of ransomware attacks. To select the appropriate features, the Dung Beetle Optimization (DBO) is used to optimize the feature subset for better detection performance. The proposed improved LSTM obtains a better accuracy of 95% when compared to existing method like cost sensitive Pareto Ensemble classifier (CSPE-R). © 2025 IEEE.

关键词： Zero-day attack

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Affective gaming using adaptive speed controlled by biofeedback 25

Affective gaming using adaptive speed controlled by biofeedb...

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25th International Conference on Multimodal Interaction, ICMI 2023 Companion

作者： Frachi, Yann Chanel, Guillaume Barthet, Mathieu Media and Arts Technology Cdt School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Sims Lab Computer Science Department University of Geneva Switzerland

ISBN: (纸本)9798400703218

This work is part of a larger project exploring how affective computing can support the design of player-adaptive video games. We investigate how controlling some of the game mechanics using biofeedback affects physiological reactions, performance, and the experience of the player. More specifically, we assess how different game speeds affect player physiological responses and game performance. We developed a game prototype with Unity1 which includes a biofeedback loop system based on the level of physiological activation through skin resistance (SKR) measured with a smart wristband. In two conditions, the player moving speed was driven by SKR, to increase (respectively decrease) speed when the player is less activated (SKR decreases). A control condition was also used where player speed is not affected by SKR. We collected and synchronized biosignals (heart rate [HR], skin temperature [SKT] and SKR), and game information, such as the total time to complete a level, the number of ennemy collisions, and their timestamps. Additionally, emotional profiling (TIPI, I-Panas-SF), measured using a Likert scale in a post-task questionnaire, and semi-open questions about the game experience were used. The results show that SKR was significantly higher in the speed down condition, and game performance improved in the speed up condition. Study collected data involved 13 participants (10 males, 3 females) aged from 18 to 50 (M = 24.30, SD = 9.00). Most of the participants felt engaged with the game (M = 6.46, SD = 0.96) and their level of immersion was not affected by wearing the prototype smartband. Thematic analysis (TA) revealed that the game speed impacted the participants stress levels such as high speed was more stressful than hypothesized;many participants described game level-specific effects in which they felt that their speed of movement reflected their level of stress or relaxation. Slowing down the participants indeed increased the participant stress levels, but coun

关键词： Human computer interaction

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Predicting Electricity Consumption in Commercial and Residential Buildings Using A-CNN-LSTM Model

Predicting Electricity Consumption in Commercial and Residen...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Babu, Uppara Raghu Kumar, Neeraj Padmaja, Kantimahanti Bhoopathy, V. Alam, Mohammad Shabbir Devi, S. Rukmani Srinivasa Ramanujan Institute of Technology Bk Samudram Mandal Anantapur India Doon Institute of Engineering and Technology Department of Electrical Engineering Rishikesh India Geetanjali College of Engineering Hyderabad India Sree Rama Engineering College Department of Computer Science and Engineering Tirupathi India Jazan University Department of Computer Science Jizan Saudi Arabia Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India

ISBN: (纸本)9798350360660

In this proposed approach an A-CNN-LSTM model that, over the medium to long term, can forecast, down to the hourly level, the patterns of electricity use in commercial and residential buildings. Institutional and residential buildings account for the vast bulk of America's overall energy usage. Thanks to developments in smart technologies and sensors, there is a need to forecast the medium- to long-term hourly electricity consumption of home and business buildings. This is to help with decision-making for operations, demand response strategies, and distributed generation system installations. To achieve the best possible results during model training, feature selection, and preprocessing, it is essential to adhere strictly to this strategy. Since some data on daily precipitation was lost during pre-processing, the Lagrange interpolation method was used to fill in the gaps. Feature selection makes use of PSO-K-means clustering technique. An A-CNN-LSTM was employed for the purpose of training the model. Traditional methods, such as LSTM and CNN, are surpassed by the suggested approach. Applying the strategy resulted in a 93.15 % improvement in accuracy. © 2024 IEEE.

关键词： K-means clustering

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IoT-Enabled Secure Energy Management System in Smart Meters with CRFN: A Novel Approach to Billing and Theft Prevention

IoT-Enabled Secure Energy Management System in Smart Meters ...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suganya, R. Punitha, S. Subalakshmi, N. Kavitha, K. Jeyakarthic, M. Raak College of Engineering and Technology Department of Computer Science and Engineering Puducherry India D. G. Govt. Arts College for Women Department of Computer Science Tamilnadu Mayiladuthurai India Annamalai University Department of Computer and Information Science Tamilnadu Chidambaram India Annamalai University Department of Electrical & Electronics Engineering Chidambaram India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

In response to the challenges posed by missed electricity bill payments and unauthorized power consumption, this study introduces enhancements to existing smart energy meter systems. The proposed improvements include the integration of advanced security features and automation capabilities, such as refined theft control mechanisms and optimized communication protocols between energy providers and consumers. Leveraging IoT technology, the system enables real-time energy monitoring, automatic billing, and timely payment reminders via SMS. Additionally, innovative theft prevention measures, including automated power disconnection for overdue accounts and theft detection systems, bolster security and reduce losses. The integration of the Convolutive Recurrent Fusion Network (CRFN) algorithm significantly enhances the system's theft detection and prevention capabilities. The CRFN detects anomalies in power consumption that may indicate theft, such as sudden spikes or drops in usage, through advanced pattern recognition. Upon detecting potential theft, the system can trigger automated responses like alerts or power disconnection, reducing the need for manual intervention. The incorporation of a magnetic reed switch further enhances efficiency by automatically controlling power access for consumers who have not paid their bills by the due date. By addressing these challenges and integrating advancements, the proposed framework aims to create a more efficient, secure, and sustainable energy management solution, ensuring accurate billing, preventing unauthorized usage, and promoting environmental responsibility. © 2024 IEEE.

关键词： Reed relays

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An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

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Intelligent Automation & Soft Computing 2023年第1期35卷 755-767页

作者： Ziad M.Ali Ahmed M.Galal Salem Alkhalaf Imran Khan College of Engineering at Wadi Addawaser Prince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia Electrical Engineering Department Aswan Faculty of EngineeringAswan UniversityAswan81542Egypt Production Engineering and Mechanical Design Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer College of Science and Arts in Ar RassQassim UniversityAr RassSaudi Arabia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan

The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids andenergy management on the load side. Microgrid can effectively solve this problemby using its regulation and flexibility, and is considered to be an ideal *** traditional method of computing total transfer capability is difficult due tothe central integration of wind farms. As a result, the differential evolutionextreme learning machine is offered as a data mining approach for extractingoperating rules for the total transfer capability of tie-lines in wind-integratedpower systems. K-medoids clustering under the two-dimensional “wind power-load consumption” feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, aknowledge base for total transfer capability operating rule mining is ***, a novel method is used to filter redundant characteristics and find featuresthat are closely associated to the total transfer capability in order to decrease theultra-high dimensionality of operational features. Finally, by feeding the trainingdata into the proposed algorithm, the total transfer capability operation rules arederived from the knowledge base. It can be seen that, the proposed algorithmcan optimize the system performance with good accuracy and generality, according to numerical data.

关键词： Load forecasting distribution network machine learning renewable energy

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