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Feedback Control Based on Trust for Varied Automation Transparency 1

Feedback Control Based on Trust for Varied Automation Transp...

1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Dbritto, Ceres Lopes, Joel Bandyopadhyay, Krishnan Banerji, Nandan Mishra, Ishani Kandpal, Ankit St Louis United States Redmond United States Dayananda Sagar College of Engineering Department of Medical Electronics Engineering Karnataka Bengaluru India Sikkim Manipal Institute of Technology Sikkim Manipal University Department of Computer Science and Engineering Sikkim India New Horizon college of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560103 India Bipin Tripathi Kumaon Institute of Technology Department of ECE Almora Dwarahat India

ISBN: (纸本)9798331518578

Interactions between people and automation depend heavily on human confidence in the latter. Lack of trust can cause people to not use automation, while too much trust can cause them to put their faith in a flawed autonomous system, which could have detrimental effects on them. Transparency and trust in automation are vital for the usefulness and security of human-robotization frameworks. The majority of the research that has been done on the trust that human operators have in robotics and automation has been an ongoing variable that has received little attention. paid to how trust changes over time. In order to fill this research void, we offer a study that examines how trust is dynamic. Using real-time trust measurements, we first discovered options to quantify trust of entirety, which we described as a metric that considers trust throughout the entire human-automation interaction experience. In this study, a feedback control strategy for powerfully changing mechanization straightforwardness and transparency concurring on client trust is analysed. The proposed approach tweaks mechanical way of behaving and correspondence by incorporating an input control component with a trust assessment model. The adequacy of the feedback control framework in improving errand execution and trust adjustment under different levels of computerization straightforwardness and transparency is checked utilizing MATLAB reenactments. © 2025 IEEE.

关键词： Human robot interaction

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Predicting Traffic Patterns Using V2X Communication Data

Predicting Traffic Patterns Using V2X Communication Data

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Kaur, Mansimran Jaiswal, Om Parikh, Jolly Chakravarty, Sumit Saini, Dharmender Bharati Vidyapeeth's College of Engineering Department of Computer Science & Engineering New Delhi India Kennesaw State University Department of Electrical Engineering Kennesaw United States

ISBN: (纸本)9798331504847

The aim of this study is to investigate whether vehicle to vehicle (V2V) communication data can be used to understand and forecast traffic patterns. The V2AIX V2X dataset contains over 230,000 messages collected from over 1,800 vehicles and roadside units in public road traffic, extracting instantaneous features, such as speed, bearing, and acceleration. Two unsupervised machine learning techniques of K-Means and Agglomerative Clustering were used to uncover hidden traffic patterns. They also construct methods that identify distinct clusters of vehicles travelling on particular routes in the same or opposite directions, travelling through intersections or curved road segments, and travelling in alternate or parallel road segments. The results suggest that clustering V2V data could improve traffic prediction models and lead to useful traffic management and optimization strategies. © 2025 IEEE.

关键词： Highway traffic control

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Care Compass: Monitoring made easy, Care made effective

Care Compass: Monitoring made easy, Care made effective

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Prabal Manhas Joydeep Chandra Ramanjot Kaur Rashi Sahay Computer Science & Engineering Chandigarh University Punjab India Dept.of Computer Science and Engineering Chandigarh University Mohali Punjab Computer Science & Engineering Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Traditional healthcare focused on hospitals and clinics proved out to be inadequate, especially during the COVID-19 pandemic and various emergency crisis, Care Compass, is an innovative and affordable smart wearable device aimed at addressing critical health challenges faced by today’s youth. The 21 st century witnessed a large-scale transformation with the integration of Internet of Things and smart devices ranging from healthcare to manufacturing sector. The device integrates smart IoT devices and other technologies such as cloud computing platforms, health management mobile application with machine learning algorithms, offering real-time personalized health recommendations and continuous monitoring of vital health parameters. Emphasis is placed on accessibility and affordability, making Care Compass a viable solution for a broader demographic. The integration of Care Compass with clinical workflows is facilitated through collaboration with healthcare providers, enhancing the utility and effectiveness of the device in real-world applications. The results from initial testing demonstrate the potential of Care Compass to significantly improve health outcomes and quality of life for young individuals.

关键词： Heart rate Cloud computing Printed circuits Real-time systems Safety Mobile applications Internet of Things Compass Wearable devices Monitoring

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IoT-Driven Smart Farming with Machine Learning for Sustainable Food Systems

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Procedia computer science 2025年 260卷 552-560页

作者： Sanjana Murgod Tanushree Kabbur Bibijan Matte Vaibhav Mujumdar Meenaxi M Raikar Department of Computer Science and Engineering KLE Technological University Hubli Karnataka India

Integrating the Internet of Things (IoT) and machine learning (ML) technologies in agriculture, commonly called smart farming, is revolutionizing the sector by enhancing productivity, efficiency, and sustainability. This paper explores the application of IoT-driven smart farming using machine learning for sustainable agricultural practices. The system introduces an efficient Soil Moisture Detection System utilizing IoT Technology, revolutionizing modern farming practices. By continuously monitoring crucial parameters such as soil moisture, temperature, and humidity in real-time, the system ensures seamless data transmission to a centralized server. Additionally, integrating motion detection capabilities enhances security measures and promptly alerts farmers to environmental changes. The dataset consisting of 100,000 rows is generated to facilitate the development and training of five ML models to predict soil moisture trends. Decision Trees achieved an accuracy rate of 99.98%, while Random Forests achieved 99.99%. The integration of these predictive models empowers farmers with actionable insights for precise irrigation scheduling and optimal crop yield optimization. These models provide actionable insights for precise irrigation scheduling and optimal crop yield optimization. Field tests have confirmed the efficacy of this approach, demonstrating significant improvements in irrigation efficiency and subsequent crop yields. Thus, the proposed system represents a substantial advancement in leveraging the synergistic potential of IoT and ML technologies to foster sustainable agricultural practices.

关键词： Precision Agriculture Crop Management Soil Moisture Detection Blynk Integration Motion Detection (PIR Sensor)

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Invariant Shape Representation Learning for Image Classification

Invariant Shape Representation Learning for Image Classifica...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Tonmoy Hossain Jing Ma Jundong Li Miaomiao Zhang Computer Science University of Virginia Case Western Reserve University Electrical and Computer Engineering

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Geometric shape features have been widely used as strong predictors for image classification. Nevertheless, most existing classifiers such as deep neural networks (DNNs) directly leverage the statistical correlations between these shape features and target variables. However, these correlations can often be spurious and unstable across different environments (e.g., in different age groups, certain types of brain changes have unstable relations with neurodegenerative disease); hence leading to biased or inaccurate predictions. In this paper, we introduce a novel framework that for the first time develops invariant shape representation learning (ISRL) to further strengthen the robustness of image classifiers. In contrast to existing approaches that mainly derive features in the image space, our model ISRL is designed to jointly capture invariant features in latent shape spaces parameterized by deformable transformations. To achieve this goal, we develop a new learning paradigm based on invariant risk minimization (IRM) to learn invariant representations of image and shape features across multiple training distributions/environments. By embedding the features that are invariant with regard to target variables in different environments, our model consistently offers more accurate predictions. We validate our method by performing classification tasks on both simulated 2D images, real 3D brain and cine cardiovascular magnetic resonance images (MRIs). Our code is publicly available at https://***/tonmoy-hossain/ISRL.

关键词： Representation learning Training Deformable models Correlation Three-dimensional displays Shape Magnetic resonance imaging Brain modeling Data models Image classification

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Assessing the Significance of Machine Learning in Forecasting Energy Recovery from Waste

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Procedia computer science 2025年 252卷 623-632页

作者： Nirzona Binta Badal Fahmida Anjum Ismail Mahmud Nur Tithi Paul Ahmed Wasif Reza Department of Computer Science and Engineering East West University Dhaka-1219 Bangladesh

Accurate estimating energy recovery from trash is vital for optimizing the Waste-to-Energy (WTE) mechanisms essential in tackling global waste management processes and energy sustainability issues. This paper analyzes a synthetically expanded dataset generated with the help of SMOTE techniques to compare the performance of four machine learning (ML) models, Decision Tree Regression, Random Forest Regression, CatBoost Regression, and XGBoost Regression. Here, the dataset contains important waste parameters like composition, moisture content, and treatment procedures that help the models forecast energy output with high precision—datasets and evaluate additional machine learning techniques to boost prediction accuracy in industrial WTE systems further. Performance indicators like MAE, RMSE, MAPE, and R² scores have been assessed here to identify each model’s accuracy and computational efficiency. The final result of the analysis states that the ensemble based models, more precisely XG-Boost and CatBoost, outperformed the simpler ones like Decision Tree, where CatBoost achieved the best R² value of 0.9893 and the minimum MAPE of 12.90 percent. Though using little extra storage, CatBoost showed great performance. The obtained results bring useful insights into efficient model selection for WTE applications. Further studies shall therefore be exclusively focused on validating this study’s results in real life conditions.

关键词： Waste-to-Energy Machine Learning Energy Recovery Prediction CatBoost Comparative Analysis

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Img2Neuro: brain-trained neural activity encoders for enhanced object recognition

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Machine Learning: science and Technology 2025年第2期6卷

作者： Aboelnaga, Mona A. El-Kharashi, Mohamed W. Eldawlatly, Seif Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt

For decades, the brain’s visual pathway has inspired machine and deep learning models, yet these models oversimplify the brain’s complex visual processing. This is manifested in the significant superiority of the brain in comparison to the developed models in terms of the accuracy and the amount of data needed for training. Therefore, rather than using the brain as an inspiration, in this paper, we introduce Img2Neuro;a convolutional neural network model feature extractor that predicts the visual brain’s response to images by encoding neural activity. Img2Neuro is trained on natural scene images paired with single-neuron recordings from the visual cortex and thalamus of mice and monkeys. We explore the feasibility of using Img2Neuro as a feature extractor for object recognition, where the output of Img2Neuro in response to unseen images is used as input to classifiers with the task of recognizing the object in the image. We evaluated our approach on three benchmark datasets;namely, MNIST, Fashion-MNIST, and Cifar10. In our experiments, we examined the classification performance when Img2Neuro is used as a feature extractor compared to using the images as direct input to the classifier, using five different classifiers;namely, linear discriminant analysis, perceptron, logistic regression, ridge classifier, and a single-layer neural network. The results demonstrate superior performance when using Img2Neuro in most datasets and across all classifiers, reaching an enhancement in accuracy of 9% on the MNIST dataset, 2% on FashionMNIST, and 18% on Cifar10 in some cases compared to using raw images as an input in the classifiers. The performance enhancements suggest that brain-trained encoders can effectively capture image features for object recognition tasks. By leveraging neural response data, Img2Neuro demonstrates a promising avenue for bridging the gap between biological and artificial visual processing, ultimately leading to novel strategies for improving state-of-t

关键词： Discriminant analysis

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AI-Based Optimization Method for Efficient Placement of VNF in Cloud-Edge Computing 3rd

AI-Based Optimization Method for Efficient Placement of VNF ...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Sabyasachi, Abadhan Saumya Kumari, Sangeeta Sahoo, Biswa Mohan School of Computing Science and Engineering Galgotias University U.P. Greater Noida203201 India School of Computer Science Engineering and Technology Bennett University U.P. Greater Noida201310 India

ISBN: (纸本)9789819726707

The efficient placement of Virtual Network Functions (VNFs) has emerged as a critical challenge in cloud-edge computing environments due to the growing demand for low-latency and high-bandwidth services. The present study investigates diverse optimization techniques for Virtual Network Function (VNF) placement with the objective of reducing latency, optimizing resource allocation, and improving overall network performance. This article presents a comprehensive examination of cloud-edge computing, emphasizing the significance of virtual network function (VNF) placement optimization. We propose a Remora Optimization Algorithm for Virtual Network Function (ROAVNF) placement technique is a revolutionary way to improve the Service Function Chains (SFCs) sequential layout. The utilization of maximum computer resources by the ROAVNF method has been demonstrated to enhance performance in comparison to state-of-the-art methods. This has been evidenced through extensive simulation on edge network, which has been evaluated across five metrics, namely energy consumption, throughput, resource cost, execution time, and end-to-end delay. The comparative analysis has revealed that the energy consumption rate of the proposed algorithm surpasses that of GA, BGWO, and OCPS by 56.36%, 29.34%, and 11.78%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Image Captioning Using Deep Learning: Bridging the Gap between Vision and Natural Language Processing

Image Captioning Using Deep Learning: Bridging the Gap betwe...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Mudholkar, Rachana Ravindra Srivastava, Kingshuk Ahammed, Javeed Tajne, Niket Anap, Sharayu Kavitha, R. PCET's Pimpri Chinchwad College of Engineering and Research Computer Engineering Pune India Vivekananda Global University Sector 36 Computer Science & Engineering Rajasthan Jaipur303012 India Narasaraopeta Engineering College Department of Electronics & Communication Engineering Andhra Pradesh Narasaraopet522601 India Siu Symbiosis School for Online and Digital Learning Hill Base Campus Maharashtra Pune412115 India Autonomous Cs and DS G H Raisoni College of Engineering and Management Maharashtra Pune India Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering Erode638401 India

ISBN: (纸本)9798331508685

The process of writing educational captions for photos has evolved dramatically in recent years thanks to developments in deep learning technologies. The issue of fully understanding image content and creating logical, contextually relevant captions remains a serious difficulty despite notable progress. Image captioning is a crucial task that can be applied to editing tools, virtual assistants, image indexing, and assistance for people with disabilities. AI and Natural Language Processing (NLP) meet up in picture or image subtitling/caption, which is the test of making clear normal language phrases for pictures. Since profound learning strategies, Using Regional Neural Networks (RNNs) and Convolutions Neural Networking (CNN) for image extraction, which are as often as possible upgraded by consideration instruments, have been taken on, this interdisciplinary test has seen critical progressions. Text summarization, template construction, and information retrieval are the three main activities included in the suggested model. The both directions long short-term storage method is used to locate the written content utilizing every word in a phrase, extract the knowledge, and finally incorporate it within the semantically vector. This paper highlights the importance of deep learning as a whole while also examining the development of image inscribing, its important methodology, and state-of-the-art techniques. It additionally discusses the hardships, uses, and expected ways in this field. © 2025 IEEE.

关键词： Semantics

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Learning the Hub Graphical Lasso Model with the Structured Sparsity via an Efficient Algorithm

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Journal of Scientific Computing 2025年第3期103卷 1-31页

作者： Wang, Chengjing Tang, Peipei He, Wenling Lin, Meixia School of Mathematics Southwest Jiaotong University Chengdu China School of Computer and Computing Science Hangzhou City University Hangzhou China Engineering Systems and Design Singapore University of Technology and Design Singapore Singapore

Graphical models have exhibited their performance in numerous tasks ranging from biological analysis to recommender systems. However, graphical models with hub nodes are computationally difficult to fit, particularly when the dimension of the data is large. To efficiently estimate the hub graphical models, we introduce a two-phase algorithm. The proposed algorithm first generates a good initial point via a dual alternating direction method of multipliers (ADMM), and then warm starts a semismooth Newton (SSN) based augmented Lagrangian method (ALM) to compute a solution that is accurate enough for practical tasks. We fully excavate the sparsity structure of the generalized Jacobian arising from the hubs in the graphical models, which ensures that the algorithm can obtain a nice solution very efficiently. Comprehensive experiments on both synthetic data and real data show that it obviously outperforms the existing state-of-the-art algorithms. In particular, in some high dimensional tasks, it can save more than 70% of the execution time, meanwhile still achieves a high-quality estimation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Lagrange multipliers

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