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International Symposium on Underwater Technology

作者： Anish GoyalID Dobromir Iliev Ricardo Guardado Department of Electrical and Computer Engineering Georgia Southern University Statesboro Georgia Department of Computer Science Georgia Institute of Technology Atlanta Georgia Department of Computer Science Brown University Providence Rhode Island

ISBN: (数字)9798331534189

ISBN: (纸本)9798331534196

We propose a biomimetic propulsion system inspired by the spinal motor control mechanisms of zebrafish, allowing the submersible to adapt its propulsion strategy in real time based on real-time feedback from the environment. The design consists of two components: a neurological model and a topological model. The topological model is derived from Navier-Stokes equations to simulate fluid dynamics, coupled with topology optimization methods to optimize the shape and surface interactions of the submersible with water. To evaluate the effectiveness of the system, we tested the system under two control conditions: open-loop configurations and closed configurations. The experimental results demonstrated that the topology and topological mod-els achieved significant improvements in energy efficiency and traversal time, compared to a control set-up. This design allows us to extend the operational range and duration of underwater vehicles, enabling more extensive and sustainable exploration of the ocean floor. This technology has far-reaching implications for ocean exploration, climate research, and underwater robotics. Moreover, the scalability of our design allows for potential applications in larger underwater vehicles as well as in multi-agent systems where multiple underwater vehicles work together.

关键词： Adaptation models Biological system modeling Biomimetics Propulsion Mathematical models Real-time systems Topology Underwater technology Underwater vehicles Sports

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Detecting and Characterising Mobile App Metamorphosis in Google Play Store

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IEEE Transactions on Mobile Computing 2025年

作者： Denipitiyage, D. Silva, B. Gunathilaka, K. Seneviratne, S. Mahanti, A. Seneviratne, A. Chawla, S. University of Sydney School of Computer science Australia University of Moratuwa Department of Computer Science and Engineering Sri Lanka Sydney Australia Qatar Computing Research Institute Qatar

App markets have evolved into highly competitive and dynamic environments for developers. While the traditional app life cycle involves incremental updates for feature enhancements and issue resolution, some apps deviate from this norm by undergoing significant transformations in their use cases or market positioning. We define this previously unstudied phenomenon as 'app metamorphosis.' In this paper, we propose a novel and efficient multi-modal search methodology to identify apps undergoing metamorphosis and apply it to analyse two snapshots of the Google Play Store taken five years apart. Our methodology uncovers various metamorphosis scenarios, including re-births, re-branding, re-purposing, and others, enabling comprehensive characterisation. Although these transformations may register as successful for app developers based on our defined success score metric (e.g., re-branded apps performing approximately 11.3% better than an average top app), we shed light on the concealed security and privacy risks that lurk within, potentially impacting even tech-savvy end-users. © 2002-2012 IEEE.

关键词： Mobile commerce

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Improving Faster R-CNN for Vehicle Detection under Varying Conditions with Domain Adaptation Technique

Improving Faster R-CNN for Vehicle Detection under Varying C...

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Power, Control and Computing Technologies (ICPC2T), Conference on

作者： Hemant Kumar Pushpa Mamoria Deepak Kumar Dewangan Department of Computer Science & Engineering Chhatrapati Shahu Ji Maharaj University Kanpur India Department of Computer Application Chhatrapati Shahu Ji Maharaj University Kanpur India Department of Computer Science & Engineering ABV-IIITM Gwalior India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

In recent years, the rapid progress in autonomous driving technology has transformed the automotive industry, positioning vehicle detection as a vital component to guarantee secure and efficient navigation. This article presents an upgraded Faster R-CNN architecture that incorporates a SelfSupervised Thermal Network (SSTN) to advance vehicle detection, specifically in unfavorable weather conditions. This model is built using MobileNetV3 as its backbone and is enhanced with a Feature Pyramid Network (FPN) for extracting features at multiple scales. Additionally, it employs self-training and pseudo-labeling techniques to facilitate domain adaptation. The model is trained and tested using the DAWN dataset, which includes difficult scenarios such as fog, rain, sandstorms, and snowstorms. Experimental results demonstrate that the improved Faster R-CNN with SSTN significantly outperforms some modern methods, achieving an accuracy of 87.15%, a mean Average Precision (mAP) of 85.20%, and a recall rate of 81.37%. Although it lacks behind the traditional method like HOG-SVM. This approach enhances the reliability of vehicle detection systems in varying environmental conditions, making it a valuable contribution to the development of robust autonomous driving systems.

关键词： Adaptation models Rain Accuracy Storms Vehicle detection Snow Feature extraction Reliability Autonomous vehicles Software development management

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Comprehensive Heart Attack Prediction Model Using Stacked Ensembles and Clinical Feature engineering

Comprehensive Heart Attack Prediction Model Using Stacked En...

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

作者： Anandhi Jayabalan R. Uma S Padmakala Department of Computer Science Engineering Sri Sai Ram Engineering College Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute Of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Cardiovascular disease continues to be a predominant cause of mortality globally, requiring precise and effective strategies for early identification. This work examines the application of clinical datasets to explore ensemble models and machine learning for predicting heart attacks. The dataset includes patient information about factors such as age, diabetics, levels of cholesterol, blood pressure, and smoking status. The Label Encoder is utilized to encode categorical variables, while class imbalance is tackled through the application of the Synthetic Minority Over-sampling Technique (SMOTE). The top five features were identified through the application of feature importance in Random Forest analysis. To enhance prediction accuracy, various classification techniques, including Random Forest (RF), Gradient Boosting (GB), XG Boost, and Light GBM, along with an ensemble Voting Classifier, are utilized. The best model performance was attained by intensive hyperparameter optimization using Grid Search CV. The performance of the proposed system was assessed through a stacked ensemble methodology. The Random Forest model revealed an accuracy of 87.25% and a ROC-AVC of 0.91. The results for Gradient Boosting and XG Boost showed accuracies of 85.60% and 86.80%, accompanied by ROC-AUC scores of 0.89 and 0.90, respectively. The ensemble Voting Classifier enhanced performance, reaching an accuracy of 88.15% and a ROC-AUC of 0.92. The findings indicate that utilizing an ensemble method alongside hyperparameter optimization significantly enhances prediction accuracy and facilitates the early identification of heart attack risks.

关键词： Heart Accuracy Loading Mortality Cardiac arrest Predictive models Hyperparameter optimization Boosting Random forests Optimization

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An Efficient DDoS Attack Detection in SDN using Multi-Feature Selection and Ensemble Learning

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Procedia computer science 2025年 252卷 241-250页

作者： Amit V Kachavimath Narayan D G Department of Master of Computer Applications KLE Technological University Hubballi 580031 India School of Computer Science and Engineering KLE Technological University Hubballi 580031 India

Software-defined networking (SDN) has significantly enhanced network agility by decoupling network control from hardware devices. This architectural shift exposes SDN infrastructures to increased risks from Distributed Denial of Service (DDoS) attacks, which can severely disrupt network services and render them unavailable to legitimate users. Despite the existence of various techniques for detecting specific types of DDoS attacks, there remains a need for more comprehensive work capable of accurately identifying multiple attack categories. We have addressed the gap by proposing a robust DDoS attack detection method in the SDN. We utilize advanced machine learning (ML) algorithms to analyze the InSDN dataset, employing advanced feature extraction techniques to improve detection accuracy and efficiency. Three feature extraction methods, Se-lectKBest, ANOVA F-value scores, and feature importance scores from RandomForest Classifier, were used to select the best ten features from 81. This feature selection, combined with ensemble learning, achieved an accuracy of 99.9%. The Receiver Operating Characteristic (ROC) curve, confusion matrix, and K-fold cross-validation were used to evaluate the performance of the proposed model.

关键词： SDN DDoS Attacks Machine Learrning

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A High-Gain DC-DC Boost Converter for Solar Microgrid Systems Using a Switching Inductor and a Capacitor 1

A High-Gain DC-DC Boost Converter for Solar Microgrid System...

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

作者： Prasanth, S. Priyadarsini, S. Lakshmi, R. Sneha, G. Ajitha, S.S. Saravanakumar, R. Narendra, Konnaiah Gari Unnamalai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Kovilpatti India PSR Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India Siddharth Institute of Engineering and Technology Department of Elecrical and Electroncis Engineering Puttur India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electonics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331518578

This paper outlines the design and implementation of a solar microgrid-specific high-gain DC-DC booster converter that makes use of a variable inductor and capacitors. To improve the suitability of photovoltaic (PV) panels for incorporation into microgrids, the proposed converter efficiently increases their low-voltage outputs. Conventional boost converters can't offer that kind of voltage increase because they lose too much performance and put too much strain on their internal components. The converter increases the voltage gain dramatically while maintaining a high degree of efficiency thanks to a switching inductor-capacitor network. The main breakthrough is an inventive multi-stage energy transfer and storage device that cleverly employs a switching inductor and capacitor. This configuration increases the output voltage while decreasing the voltage and current ripples, which in turn reduces the amount of electromagnetic interference (EMI) and increases the life of the components. Thorough assessments of the converter's working via theoretical modeling and simulation studies confirm its performance across several load and input situations common to solar microgrids. The prototype's testing findings show a significant voltage increase and a peak efficiency of over 95% when compared to traditional boost converters. Moreover, the converter's reliable performance over a wide range of voltage sources and workload scenarios proves its suitability for practical solar applications. As a result, the proposed high-gain boost converter will greatly improve the efficiency and reliability of solar microgrids, leading to a rise in the use of renewable energy sources in DGS. © 2025 IEEE.

关键词： Microgrids

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DashSpace: A Live Collaborative Platform for Immersive and Ubiquitous Analytics

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Borowski, Marcel Butcher, Peter W.S. Kristensen, Janus Bager Petersen, Jonas Oxenbøll Ritsos, Panagiotis D. Klokmose, Clemens N. Elmqvist, Niklas The Department of Computer Science Aarhus University Aarhus Denmark CAVI at Aarhus University Aarhus Denmark The School of Computer Science and Engineering Bangor University Bangor United Kingdom

We introduce DashSpace, a live collaborative immersive and ubiquitous analytics (IA/UA) platform designed for handheld and head-mounted Augmented/Extended Reality (AR/XR) implemented using WebXR and open standards. To bridge the gap between existing web-based visualizations and the immersive analytics setting, DashSpace supports visualizing both legacy D3 and Vega-Lite visualizations on 2D planes, and extruding Vega-Lite specifications into 2.5D. It also supports fully 3D visual representations using the Optomancy grammar. To facilitate authoring new visualizations in immersive XR, the platform provides a visual authoring mechanism where the user groups specification snippets to construct visualizations dynamically. The approach is fully persistent and collaborative, allowing multiple participants—whose presence is shown using 3D avatars and webcam feeds—to interact with the shared space synchronously, both co-located and remotely. We present three examples of DashSpace in action: immersive data analysis in 3D space, synchronous collaboration, and immersive data presentations. © 1995-2012 IEEE.

关键词： Specifications

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Energy Storage System Using Digital Twins with AI and IoT for Efficient Energy Management and Prolonged Battery Life in Electric Vehicles

Energy Storage System Using Digital Twins with AI and IoT fo...

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

作者： M. Sri Lakshmi Vanam Ajay Sarma Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology(Autonomous) Kurnool India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This research proposes an integrated framework of a digital twin, incorporating artificial intelligence and the Internet of Things to optimize energy management and prolong the lifespan of the battery in electric vehicles. Under typical operating conditions, the system exhibits predictive accuracy with mean absolute error relative to actual data equal to 0.042 and root mean square error equal to 0.055. These values reflect improvement in responsiveness of the system since latency has been reduced to 0.12 seconds with feedback response times established at 0.45 seconds. Moreover, it has increased by 15% in energy efficiency while promoting its longevity with a capacity fade rate measured at 0.0025% per cycle. Besides, the model shows strong anomaly detection capability - maintenance alert precision is found to be 95% while detection rate reaches76%. All these results point out the possibility that combining Digital Twin technology with AI and IoT may end up significantly boosting energy management systems for electric vehicles compared to more traditional approaches.

关键词： Energy measurement Electric vehicles Digital twins Batteries Maintenance Time factors Internet of Things Artificial intelligence Root mean square Optimization

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StreamTag: A Platform for Flexible Tagged Data Management 11

StreamTag: A Platform for Flexible Tagged Data Management

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11th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Díaz-Jiménez, David Mata-Mata, Francisco López, José L. Pérez-Cordón, Luis G. Serrano, José-María Martínez-Cruz, Carmen Morcillo-Martínez, Juana M. Verdejo-Espinosa, Ángeles Cuevas-Martínez, Juan C. Viciana-Abad, Raquel Reche-López, Pedro J. Pérez-Lorenzo, José M. Gaitán-Guerrero, Juan F. Espinilla, Macarena Department of Computer Science University of Jaén Jaén23071 Spain Department of Languages and Computer Systems University of Granada Granada18071 Spain Psycology Department Faculty of Social Work University of Jaén Jaén23071 Spain Electrical Engineering Department University of Jaén Jaén23071 Spain Telecommunication Engineering Department University of Jaén Jaén23071 Spain

ISBN: (纸本)9789897587436

This paper presents StreamTag, a platform designed for the efficient management of labeled data in healthcare environments, particularly for activity recognition systems in residential and nursing home settings. Human Activity Recognition (HAR) is crucial for monitoring patient behaviors and supporting personalized care, and this field has evolved significantly with advances in IoT and AI. StreamTag integrates a flexible data labeling structure and a modular architecture, enabling data collection, labeling, and secure management of activity data. The system leverages non-relational databases for scalable data handling, along with secure protocols to ensure data integrity and privacy. This work examines existing approaches in HAR, including data-driven, knowledge-based, and hybrid models, and situates StreamTag as a versatile solution that combines flexible user-controlled labeling with high adaptability for diverse healthcare contexts. Future directions are suggested for enhancing system functionality and integration with more advanced analytical tools. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Information management

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Optimizing Multi-Agent System Swarm Performance Through Explainable AI 11

Optimizing Multi-Agent System Swarm Performance Through Expl...

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11th International Conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Mahmud, Shekhar Kutlu, Mustafa Alan, Alper Turan Military Technological College System Engineering Department Muscat Oman Sakarya University of Applied Sciences Mechatronics Engineering Department Sakarya Turkey International University of Kuwait Computer Science & Cybersecurity Department Kuwait City Kuwait

ISBN: (纸本)9798331509293

Advancements in multi-agent systems (MAS) have enabled swarm-based systems to perform decentralized decision-making and autonomous tasks. However, optimizing their performance while ensuring transparency and interpretability remains a challenge. This paper introduces a framework that combines Bayesian optimization with Explainable Artificial Intelligence (XAI) techniques to enhance both the efficiency and transparency of MAS swarms. The Bayesian optimization framework fine-tunes agent parameters to improve swarm metrics such as energy efficiency, task completion time, and coordination success. The experimental results show significant improvements: a 25% increase in the coordination success rate, a 15% increase in energy efficiency, and a 20 % reduction in task completion time. XAI techniques, including SHAP values, provide interpretable explanations for optimization decisions, improving user trust and understanding. This study demonstrates the efficacy of integrating Bayesian optimization with XAI to create transparent, efficient, and reliable MAS swarms. Future work should address scalability and implications in dynamic environments. © 2025 IEEE.

关键词： Multi agent systems

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