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Digital Twin-based Flight Dynamics Simulation

Digital Twin-based Flight Dynamics Simulation

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engineering Informatics, IEEE

作者： Geetam Talluri Anand Konjengbam Dept of Electrical Electronics & Communication Engineering Indian Institute of Technology Dharwad Dharwad India Computer Science & Engineering Indian Institute of Technology Dharwad Dharwad India

ISBN: (数字)9798331505776

ISBN: (纸本)9798331505783

Unmanned Aerial Vehicle (UAV) systems face significant challenges during critical flight phases, such as post-liftoff stabilization and mid-flight disturbance response. This paper presents a Virtual Reality (VR) based simulation and control system integrated with a digital twin to enhance the understanding of UAV dynamics. The system uses real-time data from the UAV’s Onboard computer (OBC) to create a dynamic 3D environment where users can interact with flight parameters such as position, rotational dynamics, and vibration amplitude. This approach enables the analysis of UAV performance and stability under changing conditions by allowing real-time adjustments. This immersive system supports research, training, and operational optimization of aerospace systems.

关键词： Vibrations Three-dimensional displays Data visualization Virtual reality Autonomous aerial vehicles Aerodynamics Real-time systems Digital twins Vehicle dynamics Optimization

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Personalized Federated Learning for Smart Healthcare Using SDN-Driven Edge Computing

Personalized Federated Learning for Smart Healthcare Using S...

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IEEE Southeastcon

作者： Deborsi Basu Ritu Chaudhary Uttam Ghosh Raja Datta G. S. Sanyal School of Telecommunications Indian Institute of Technology Kharagpur India Dept. of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur India Dept. of Computer Science and Data Science School of Applied Computational Sciences MMC TN USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The advancement in distributed Federated Learning (FL) techniques is transforming next-generation edge computing, especially in e-Helathcare systems where data integrity and security are critical concerns. Collecting, processing and analyzing such substantial medical data is highly challenging due to their non-identical and versatile nature. Conventional FL approaches often show unsatisfactory and inaccurate results while operating on those data. Traditional FL methods also lack optimal dynamic management mechanisms for edge devices in ehealthcare, which is crucial to ensure that only authorized devices participate in handling any system failures and new registrations. To tackle such important aspects, in this work, we propose a personalized FL technique to handle abrupt label shifts among clients' data. The proposed approach further targets to achieve personalized models for local edge clients. A statistical batch normalization is proposed on customized edge servers, keeping the data specificity intact. To make the proposed system more secure and robust, a Software Defined Network-based (SDN-based) centralized server is used that allows only authorized and efficient edge clients to participate. The proposed comprehensive experiments on two healthcare datasets demonstrate better accuracy than traditional FL methods with enhanced security and privacy.

关键词： Data privacy Federated learning Data integrity Medical services Servers Security Software defined networking Next generation networking Batch normalization Edge computing

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Ensemble Model Classifier in Hybrid CNN to Predict Diabetic Foot Ulcers

Ensemble Model Classifier in Hybrid CNN to Predict Diabetic ...

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International Conference on Computing and Networking technology (ICCNT)

作者： Anil Jyothi Athira Anjana Varshepally Akula Manogna Alakunta Sridhar D. Vivek Dept.of Computer Science Engineering B V Raju Institute of Technology Narsapur Medak Telangana India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Diabetes arises due to decrease in the level of sufficient insulin produced in the human body. The prevalent issue faced by the individuals suffering from diabetes is Diabetic foot ulcer (DFU). Diabetic Feet affected around 1-4% individuals within 6.1% of diabetic population in 2023 globally. In this developing world, computer vision technologies are playing a vital role even in the field of medicine, particularly it can be beneficial in detecting and monitoring diabetes, along with its difficulties such as foot ulcers. Various models in computer vision are used for detecting the foot ulcers by image processing. Here the proposed novel hybrid model detects and monitors the diabetic foot ulcer in a proficient way. The proposed model is a combination of multiple neural network frameworks including EfficientNet as a Convolutional neural network architecture along with InceptionNet, Stacked Sparse Autoencoder (SSAE) and Sparrow Search Optimization (SSO). Every framework helps in performing different tasks in contribution for an efficient and accurate output. By analyzing diabetic foot images, EfficientNet brings out the information related to visual features and Inception-ResNet-v2 is able to capture the specific features as well as patterns from the images. Stacked Sparse Autoencoder (SSAE) can learn about the features in detail. Sparrow Search Optimization (SSO) is an optimization algorithm which can make the models work together optimally moreover, can eliminate unnecessary parameters which results in less memory requirement and makes the model smaller. The proposed model can improve the detection and management of Diabetic Foot ulcers and is expected to be 2 percentage higher than any existing models.

关键词： computer vision Visualization Computational modeling Neural networks Predictive models Diabetes Convolutional neural networks Optimization Monitoring Foot

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Detection of Pre Shoplifting Suspicious Behavior Using Deep Learning

Detection of Pre Shoplifting Suspicious Behavior Using Deep ...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Sujan Shrestha Yoji Taniguchi Tetsuo Tanaka Course of Information and computer sciences Graduate school of Engineering Kanagawa Institute of Tehnology Atsugi Japan Department of Information Artificial Intelligence and Data Science Daiichi Institute of Technology Tokyo Japan Dept. of Information and Computer Sciences Faculty of Information Technology Kanagawa Institute of Technology Atsugi Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

Shoplifting presents a significant challenge for retailers, leading to substantial financial losses and security concerns. Current surveillance systems effectively identify shoplifting incidents postoccurrence; however, the capability to detect and prevent such activities preemptively remains limited. This research focuses on identifying behaviors that likely precede shoplifting events. To this end, we introduce a novel benchmark dataset, specifically designed for the early detection of suspicious behavior that may lead to shoplifting. This dataset is curated by selecting and segmenting relevant sequences from the UCF101-Crime dataset, encompassing suspicious behavior, actual shoplifting incidents, and normal activities. Our approach addresses the limitations of existing datasets where the brief duration of shoplifting events may skew neural network performance. The dataset includes clips of only a few seconds each, for which we propose a robust neural network architecture combining the strengths of convolutional neural networks (CNN) for efficient feature extraction and Bidirectional long short-term memory (BiLSTM) networks for capturing temporal dependencies within action sequences. Our model achieves an impressive classification accuracy of 86.21 % on the newly created dataset.

关键词： Deep learning Recurrent neural networks Accuracy Surveillance Benchmark testing Feature extraction Convolutional neural networks Security Informatics Long short term memory

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Experiences and challenges on testing and validating Network Aware PPDR XR Applications for 5G Advanced networks

Experiences and challenges on testing and validating Network...

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IEEE Future Networks World Forum (FNWF)

作者： Panagiotis Papaioannou Manos Kamarianakis Antonis Protopsaltis Leonidas Perlepes Ioannis Chasiotis Evangelia Milolidaki George Margetis George Papagiannakis Constantine Stephanidis Christos Tranoris Electrical and Computer Eng. Dept. University of Patras Greece ORamaVR S.A. Switzerland & Greece Foundation for Research and Technology - Hellas (FORTH) Institute of Computer Science (ICS) Greece Computer Science Dept. University of Crete Greece Electrical and Computer Eng. Dept. University of Western Macedonia Greece Satways Ltd Greece p-Net Greece

ISBN: (数字)9798350379495

ISBN: (纸本)9798350379501

This paper presents initial findings from the FIDAL project, which tests and validates network-aware PPDR XR applications for 5G advanced networks. Using a 5G testbed, we evaluated use cases highlighting the crucial role of 5G technologies in enhancing emergency response through improved connectivity and low latency. Preliminary results show the potential of 5G features in revolutionizing PPDR services. Our testbed performance evaluation supports realistic, large scale trial preparations, promising improved situational awareness and operational efficiency in critical communications.

关键词： 5G mobile communication Extended reality Bandwidth Emergency services Resource management Low latency communication Testing

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Fall Detection Scheme based on Deep Learning Model for High-Quality Life 3

Fall Detection Scheme based on Deep Learning Model for High-...

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Jayakarthik, R. Srinivasan, Aravindan Goswami, Sohan Shivaranjini Mahaveerakannan, R. Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Dept of Comp Science India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India Technique Polytechnic Institute West Bengal 712102 India Viswam Degree College Department of Computer Science Madanapalle India Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Elderly individuals worldwide have a significant risk of injury or death due to falls. An aged person's mobility, independence, and quality of life can be significantly impacted if he or she falls and does not receive timely assistance. This paper proposes the creation of a low-power network, smart devices, and cloud computing-based fall detection system for senior individuals in indoor spaces as part of this study. 10 volunteers volunteered to have their fall and non-fall movement data collected using a wireless telemetry surface electromyography capture system. Generative Adversarial Network (GAN) models use the effective signal segments as input. Notifications are automatically sent to caregivers of the elderly if a fall is detected, and an alert is triggered. As the final step, the system offers cloud-based services. There is a service that allows healthcare professionals to obtain the fall data for further examination from a medical standpoint. In experiments, fall detection has been found to be highly accurate, precise, and recallable. © 2022 IEEE.

关键词： Big data

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Enhancing Cybersecurity in Internet of Vehicles: A Machine Learning Approach with Explainable AI for Real-Time Threat Detection 25

Enhancing Cybersecurity in Internet of Vehicles: A Machine L...

引用

40th Annual ACM Symposium on Applied Computing, SAC 2025

作者： Patel, Tanish Jhaveri, Rutvij Thakker, Dhavalkumar Verma, Sandeep Ingle, Palash Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India School of Computer Science University of Hull Hull United Kingdom Dept. of Electronics and Communication Engineering Dr BR Ambedkar National Institute of Technology Punjab Jalandhar India Department of Computer and Information Security Sejong University Seoul Korea Republic of

ISBN: (纸本)9798400706295

The proliferation of IoV technologies has revolutionized the use of transport systems to a great level of improvement in safety and efficiency, and convenience to users. On the other hand, increased connectivity has also brought new vulnerabilities, making IoV networks susceptible to a wide range of cyber-attacks. The contribution of this paper is the in-dept. study of the development and evaluation of advanced machine learning (ML) models that detect and classify network anomalies in IoV ecosystems. Several classification models have been studied in our research to achieve high accuracy for discriminating between benign and malicious traffic. This work further harnesses Explainable AI (XAI) methodologies through the LIME framework for enhanced interpretability of models' decision-making processes. Experimental results strongly advocate the strength of Random Forest and XGBoost, proving to be better on the binary and multi-class classification tasks, respectively. Due to resilience, preciseness, and scalability these models are a practical choice in real-world IoV security frameworks. Explainability integrated not only strengthens model reliability but also closes the gap between performance and interoperability in vehicular networks. Copyright © 2025 held by the owner/author(s).

关键词： Intelligent systems

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A Comprehensive Framework for Evaluating Cyber-Physical Threats in Energy Internet

A Comprehensive Framework for Evaluating Cyber-Physical Thre...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Pooja Sahu A. Jeraldine Viji Vandana Roy Lipika Roy Devolla Manogna Somil Vasal Department of Computer Science & Engineering IES College of Technology Bhopal M.P. India Dept. of EEE Mailam Engineering College Villupuram TN India Department of EC GGITS Jabalpur M.P. India Dept. of Computer science and Business Studies GGITS Jabalpur M.P. India Dept. of CSE MLR Institute of Technology Hyderabad Telangana India Dept of Dept of Computer Science & Design GGITS Jabalpur M.P. India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

The proposed Energy Internet threat modeling technique identifies system constraints, components, and resources appropriately. Threat Modeling Algorithm classifies and identifies dangers using STRIDE. It assigns relevance and probability to each system vulnerability. Risk and vulnerability assessments can forecast and influence such scenarios. Grouping hazards by significance affects priorities. To demonstrate threat implications, we design and assess instances. Upon reviewing and revising the threat model, we establish risk ratings and determine their importance. Develop the risk model, inform the necessary stakeholders, and then mitigate the risk. A comprehensive risk assessment ensures that all hazards are effectively managed at the end of the activity. The assessment and management of cyber-physical risks in the energy Internet are implemented to enhance system security and resilience.

关键词： Threat modeling Energy Internet Hazards Classification algorithms Stakeholders Security Risk management Resource management Intelligent systems Resilience

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A Novel Approach for Electricity Load Forecasting using Hybrid Deep Learning Models

A Novel Approach for Electricity Load Forecasting using Hybr...

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Women in Innovation, technology & Entrepreneurship (ICWITE), IEEE International Conference for

作者： Shyam Prasath J Natarajan B Venkatraman K M. Nallusamy M. Revathi T. Muruganantham Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering VIT University Chennai Tamilnadu India Department of Computer Science and Engineering Roever Engineering College Perambalur Tamilnadu India Department of Computer Science and Engineering University College of Engineering Bharathidasan Institute of Technology Campus Anna University Tiruchirappalli Tamilnadu India Dept. of ECE K. Ramakrishnan College of Engineering Tiruchirappalli Tamil Nadu India

The importance of electricity in both daily lives of people and the economy of the country has made it a vital commodity. Accurate forecasting of electricity load is crucial to regulate production and meet demands at different times. This research work uses the electricity load data of Spain which had samples for every hour in a day for four years and opted for the multivariate forecasting approach instead of treating the features as multiple univariate sequences. The data was broken down and windowed with features such as price of electricity, hour of the day, day of the week and so on. Numerous research studies have shown that traditional statistical methods fail to capture the complex relations, however neural networks are able to learn features through extraction of spatial relationships in the time series data. In order to model the dependencies, Recurrent Neural Networks (RNN) which are the base of Long Short-Term Memory Networks (LSTM) are commonly used. The proposed research uses multiple hybrid models such as stacked LSTMs with CNN (Convolutional Neural Networks) with and without skip connections. As shown by previous works this model can support long input sequences as it can be read as sub-sequences by the CNN and pieced together by the RNN thus capturing details and interconnections between multivariate factors. This research work analyzes and compare the performances of these models on complex multivariate time series data by making them predict energy loads for different time periods and calculating errors. This approach also deeply examined the strength and weakness of each of the models and their performance patterns and yields outstanding performance.

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A Comparative Study of Machine Learning Methods for Intrusion Detection

A Comparative Study of Machine Learning Methods for Intrusio...

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International Conf on Electrical and Electronic engineering (ICEEE)

作者： Fadi N. Sibai Abu Asaduzzaman Ahmad Sibai Electrical & Computer Engineering Dept. Gulf University for Science and Technology Mishref Kuwait Electrical & Computer Engineering Dept. Wichita State University Wichita Kansas U.S.A. Computer Science Dept. University of South Florida Tampa FL U.S.A.

In this work, we applied 8 machine learning (ML) techniques to detect intrusions, namely, neural networks, kNN, SVM, random forest, trees, AdaBoost, naive Bayes, and stochastic gradient descent SGD. Using the NSL-KDD data set, these ML techniques were trained and tested to correctly classify the network and operating system records of this dataset into one of 24 possible attacks. The performances of these ML methods were analyzed and compared, with the random forest method performing at the top. To the best of our knowledge, this is the first work on investigating more than 4 ML classifiers on this data set in one single work and using the same set of tools.

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