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检索条件"任意字段=Conference on Visual Data Exploration and Analysis V"

共 5114 条记录，以下是121-130 订阅

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Developing a Tool for Predicting Changes in Parameters of Bioimpedance analysis of Human Body Composition

Developing a Tool for Predicting Changes in Parameters of Bi...

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Digital Signal Processing and its Applications (DSPA), International conference on

作者： vladimir A. Kolesnikov Svetlana P. Shchelykalina Dmitry v. Nikolaev Marina v. Gavrik Development dept. SRC Medas Moscow Russia Biomedical faculty Pirogov Russian National Research Medical University Moscow Russia The Ivanovo Regional Clinical Hospital Ivanovo Ivanovo Region Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The parameters of bioimpedance analysis of human body composition (BIA) are investigated to diagnose current medical conditions and assess the efficacy of therapeutic interventions. In some cases, it is important to predict changes in these parameters during ongoing treatment or rehabilitation. The purpose of this study was to develop a graphical tool for the visual assessment of the most probable movements of data points representing current BIA parameter pair values on plots of two-dimensional bioimpedance analysis of human body composition (2DBIA). The baseline data for the development of 2DBIA references were derived from records of 1,083,275 patients aged 5 to 80 years, who underwent body composition assessments at health centres between 2009 and 2015. The benchmarks for predicting the direction of changes in 2DBIA plots can be either the rectangular domains of the medical norm or the boundaries of selected reference centiles (e.g., the 50th percentile) for the population in question. To evaluate the adequacy of predicting BIA parameter values, the study utilised data from 130 adult patients at the Ivanovo Regional Clinical Hospital (Ivanovo, Ivanovo Region, Russian Federation) who were monitored for fat reduction and had at least three BIA measurements. The median interval between consecutive observations was 37 [33; 48] days, with an interquartile range of 33 to 48 days, and the median number of observations was 5 [4; 8]. A total of 798 observations were recorded. The percentage of points on the 2DBIA plot that fell outside the predicted region ranged from 0.6% in the Total Body Water/Basal Metabolic Rate pair to 11.7% in the Fat Mass Index/Fat-Free Mass Index pair. Points falling outside the prediction zone almost always indicated non-compliance with the prescribed treatment regimen by the patients.

关键词： visualization Medical conditions Hospitals Bioimpedance Digital signal processing Fats Indexes Biomedical monitoring Monitoring Medical diagnostic imaging

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Step-Wise Working Status for Next-Generation Smart Cities: Integrating Fog and Edge Computing Solutions

Step-Wise Working Status for Next-Generation Smart Cities: I...

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2025 International conference on visual Analytics and data visualization, ICvADv 2025

作者： Beevi, L Sherin Nehru, v. Joe Prathap, P.M. Dani, W. vinil R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Avadi Chennai India St. Peter's College of Engineering and Technology Department of Eee Avadi India

ISBN: (纸本)9798331521394

In the fast changing environment of smart cities, combining fog and edge computing with powerful IoT devices like Hikvision cameras is revolutionising urban administration and public safety. Hikvision, a video surveillance systems pioneer, offers a variety of IP and analogue cameras, thermal imaging, and intelligent software platforms used for traffic management, public safety, environmental monitoring, and infrastructure security. These technologies, which combine high-definition video quality, advanced image processing, and cognitive analytics, enable real-time monitoring, event detection, and quick responses. Traffic cameras collect real-time data for vehicle counts and congestion management, while environmental sensors monitor air, water, and noise levels to aid in health warnings and pollution control. Smart meters and wearable health gadgets monitor energy, water, and health indicators to optimise resource management. Parking sensors and waste management sensors boost parking and rubbish collection efficiency. The integration of fog and edge computing processes data locally, lowering latency and bandwidth utilisation, while fog nodes aggregate data for thorough analysis. Cloud storage enables long-term data analytics, which improves decision-making for city planners and emergency responders. Hikvision's software, which includes HikCentral and ivMS-4200, combines video surveillance, access control, and intelligent traffic management to create a comprehensive urban security framework. These systems are crucial to public safety because they provide real-time monitoring, facial recognition, and motion detection. For city planners, such innovations provide essential insights for urban growth and sustainability programs. The integration of Hikvision video surveillance with fog and edge computing is a game-changing approach to smart city management, improving urban safety and efficiency while improving inhabitants' quality of life. © 2025 IEEE.

关键词： Water pollution control

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2nd International conference on Cognitive Mobility, COGMOB 2023

2nd International Conference on Cognitive Mobility, COGMOB 2...

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2nd International conference on Cognitive Mobility, COGMOB 2023

ISBN: (纸本)9783031876196

The proceedings contain 34 papers. The special focus in this conference is on Cognitive Mobility. The topics include: Estimation of Thermodynamic and Emission Characteristics of a Sustainable Hydrogen-Gasoline Engine Through Simulation;parking Reservation Service Development Based on Driving Behaviour in Urban Areas;socio-Cognitive Sciences at the Service of Urban Planning: Towards a City for All for Children with Medical Complexity;failure Rate analysis of Driverless Car Reliability Using Different Confidence Intervals;evolutionary Reinforcement Learning Based Approaches for Solving the Traveling Salesman Problem;Adapting RL Control Policies to Changing Dynamics for Improved Robustness;comparison of Single and Multi-agent Reinforcement Learning for Highway Driving;variable Speed Limit Control for Highway Scenarios a Multi-agent Reinforcement Learning Based Approach;automatic Segmentation of Road Surface Points Using Shallow Neural Network from 3D Colored Point Cloud data;An Experimental Study of NOx Reduction with Superheated Steam Injection in Evaporative Burner Using Heating Oil;seasonal Impact on Cycling Accidents: Exploring Cognitive Abilities and Black Spot Distribution;Downloading Modern vehicles SD Card data for Forensics Examination – A Case Study;pressure Map Sensitivity analysis of Open Cathode Proton Exchange Membrane Fuel Cell;analysis of In-vehicle Information Systems from a Cognitive Perspective;classification of Traffic Accident Severity Using Machine Learning Models;examining the Role of Distracting Factors and Cognitive Elements in Pedestrian Safety;Applying DNN Model for v2X Communication Anomaly Detection;A Short-Term Approach for Enhancing Efficiency in Agglomerated BEv Charging Areas;production of Fuels from Renewables via Methanol;eye Tracking Examination of the Influence of visual Smog on Drivers in Urban Environments.

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Detection of Emergency vehicles in Traffic and Assign Traffic Free Path Using Deep Learning

Detection of Emergency Vehicles in Traffic and Assign Traffi...

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Sentiment analysis and Deep Learning (ICSADL), International conference on

作者： Sriharsha vikruthi Tejesh Reddy Singasani v T Ram Pavan Kumar M P v v S D Nagendrudu Ch Raghavendra R. Sahith Dept of CSE B V Raju Institute of Technology Narsapur Medak Telangana India University of Cumberlands Lousiville KY Dept of Computer Science Kakaraparti Bhavanarayana College Vijayawada Andhrapradesh India Department of Artificial Intelligence and Machine Learning Aditya Univeristy Suramplaem India Dept of CSE(Data Science) KKR & KSR Institute of Technology and Sciences Guntur Andhra Pradesh Dept of Computer Science and Engineering CVR College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Too much of Traffic jams are in a highly populated country. Sometimes emergency vehicles such as fire-fighter and ambulance get stuck in traffic, putting lives at threat in many cases. Priority should be given to special vehicles and their way should be cleared. However, traffic police find it difficult or sometimes impossible to handle with it. Emergency vehicles have visual and audio warning devices help them to overtake traffic by demanding as little space as possible from other vehicles. Many death happen due to delay in doctors, ambulance and fire engine vehicles while reaching their destinations. The traditional systems didn't provide path free route accurately. Therefore, by using detection of emergency vehicles in traffic and assign traffic free path using deep learning system, emergency vehicles can get free path or fast route by observing the traffic cameras. So, those emergency vehicles can reach the location on time by using RNN system. Therefore, the main objective is to improve this system for better results interms of accuracy, detection rate and precision.

关键词： Deep learning Space vehicles visualization Sentiment analysis Recurrent neural networks Accuracy Law enforcement Roads Medical services Automobiles

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Real-Time Cyberbullying Detection Across Multimodal Platforms Using XLNET and Deep CNN

Real-Time Cyberbullying Detection Across Multimodal Platform...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： Suresh Kumar K Abi C Asmitha v Dharshine S Department of Information Technology Sri Krishna College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Cyberbullying has emerged as a primary societal concern with the widespread use of social media and online communication systems. The developing complexity of virtual interactions, together with text messages, social media comments, and images with embedded textual content, makes computerized detection way more challenging and complex. Conventional methods frequently fail to adequately convey the complex and dynamic character of cyberbullying practices. Here, we recommend a robust solution which makes use of Deep Convolutional Neural Networks (Deep CNNs) for potent classification of messages and XLNet for feature extraction. This can process a variety of input types, including textual and visual information, to enhance the identification of bullying patterns more efficient by integrating these contemporary system learning techniques. This framework's ability to detect various forms of online movement makes it more effective across multiple modalities thus contributing to the development of a safer and more inclusive online environment through corrective action for all users.

关键词： Training Sentiment analysis Adaptation models visualization Text analysis Cyberbullying Feature extraction Real-time systems data models Convolutional neural networks

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S-Shaped versus v-Shaped Transfer Functions for Salp Swarm Algorithm in Sentiment analysis Feature Selection Problem

S-Shaped Versus V-Shaped Transfer Functions for Salp Swarm A...

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Advanced Innovations in Smart Cities (ICAISC), International conference on

作者： Dinar Ajeng Kristiyanti Akhmad Hairul Umam Program Study of Information System Universitas Multimedia Nusantara Tangerang Indonesia Program Study of Communication Science Tanri Abeng University Jakarta Indonesia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

This paper proposed an advanced method to enhance sentiment analysis (SA) for large-scale datasets. Given the complexity of SA, especially when dealing with high-dimensional data rich in features, effective feature selection (FS) becomes crucial to manage and reduce the data's dimensionality. Conventional machine learning techniques often fall short in this regard, highlighting the need for more sophisticated FS algorithms. In this study, we propose an improved bio-inspired optimization technique known as the Salp Swarm Algorithm (SSA) as a wrapper method for feature selection to enhance sentiment analysis of Indonesian-language tweets. Feature selection is inherently an NP-hard problem, making meta-heuristic algorithms like SSA a more efficient choice than exact methods. However, SSA typically faces challenges in terms of slow convergence, particularly in binary problems and large datasets. To address this, we introduce eight SSA variants, each employing S-shaped and v-shaped transfer functions to map the continuous search space into a discrete one, thus facilitating efficient feature selection. The proposed SSA-based approach is benchmarked against existing metaheuristic algorithms, including both Ant Lion Optimization (ALO) and Particle Swarm Optimization (PSO). Comparative experiments demonstrate that the enhanced SSA method outperforms both PSO and ALO, showing superior performance in terms of classification accuracy and processing time. Specifically, the experimental results reveal that the SSA combined with the v2-shaped transfer function achieved in just 5.16 seconds using the Support vector Machine (SvM) classifier, with an accuracy of 89.76%. Similarly, the SSA integrated with the S1-shaped transfer function closely follows, completing the processing in 4.38 seconds with an accuracy of 89.43%. These findings indicate that the proposed SSA-based feature selection approach efficiently explores the feature space, selects the most informative features, and sig

关键词： Support vector machines Sentiment analysis Technological innovation Accuracy Machine learning algorithms Metaheuristics Transfer functions Feature extraction Space exploration Classification algorithms

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Fire Detection and Autonomous Evacuation Guidance System

Fire Detection and Autonomous Evacuation Guidance System

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Sandyarani vadlamudi D Roja Ramani S Preetham Ganesh Dagadi Nipun Goyal Harshavardhan v Mantri Department of AIML New Horizon College of Engineering Bengaluru India Department of CSE New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Effective fire emergency responses are critical to ensuring safety and minimizing damage. This research exhibits an innovative fire detection and evacuation system that utilizes deep-learning and real-time video analysis to improve response times and efficiency. Utilizing convolution neural nets (CNNs), the system is able to detect fires with high precision and minimal delay, while dynamically generating evacuation plans depending on the fire's location. The system integrates advanced machine learning techniques to analyse building layouts and determine the safest, shortest evacuation routes. This information is conveyed to occupants via a public address system, enabling a streamlined and well-coordinated evacuation process. By combining real-time fire detection with intelligent route optimization, the system offers a significant advancement over traditional fire safety technologies. The paper also addresses key challenges such as processing latency and the system's ability to operate effectively in real-world conditions. Experimental results demonstrate the reliability of the CNN model—Inception v3 for fire recognition and Dijkstra algorithm for route planning—in both accuracy and speed aspects. The system's ability to handle complex visual data while maintaining operational efficiency is a highlight of this research. Along with technical details, the research explores the practical implications of implementing this system in various environments. Key challenges include maintaining low latency in video streaming and fire detection, as well as ensuring the effectiveness of the escape route analysis in complex environments. Future improvements focus on scalability, with plans to expand the system's capabilities to support multi-camera setups and larger buildings, making it adaptable for widespread use.

关键词： visualization Scalability Buildings Neural networks Streaming media Real-time systems Convolutional neural networks Time factors Reliability Optimization

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A software ecosystem for multi-level provenance management in large-scale scientific workflows for AI applications 24

A software ecosystem for multi-level provenance management i...

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Proceedings of the SC '24 Workshops of the International conference on High Performance Computing, Network, Storage, and analysis

作者： G. Padovani v. Anantharaj L. Sacco T. Kurihana M. Bunino K. Tsolaki M. Girone F. Antonio C. Sopranzetti M. Fronza S. Fiore University of Trento Trento Italy Computational Sciences and Engineering Directorate Oak Ridge National Laboratory USA CERN Geneva Switzerland Euro-Mediterranean Center on Climate Change Foundation Lecce Italy

ISBN: (纸本)9798350355543

Scientific workflows and provenance are two faces of the same medal. While the former addresses the coordinated execution of multiple tasks over a set of computational resources, the latter relates to the historical record of data from its original sources. This paper highlights the importance of tracking multi-level provenance metadata in complex, AI-based scientific workflows as a way to (i) foster and (ii) expand documentation of experiments, (iii) enable reproducibility, (iv) address interpretability of the results, (v) facilitate performance bottlenecks diagnosis, and (vi) advance provenance exploration and analysis opportunities.

关键词： ML tasks

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Step-Wise Working Status for Next-Generation Smart Cities: Integrating Fog and Edge Computing Solutions

Step-Wise Working Status for Next-Generation Smart Cities: I...

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visual Analytics and data visualization (ICvADv), International conference on

作者： L Sherin Beevi v. Nehru P. M. Joe Prathap W. vinil Dani Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai INDIA Department of Computer Science and Engineering Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Chennai Department of EEE St. Peter's College of Engineering and Technology Avadi INDIA

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

关键词： Cloud computing Smart cities video surveillance Cameras Real-time systems Public security Internet of Things Intelligent sensors Water resources Edge computing

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A CLIP-based video Question Answering framework with Explainable AI

A CLIP-based Video Question Answering framework with Explain...

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IEEE Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Jeshmol P J Binsu C. Kovoor Division of Information Technology School of Engineering Cochin Univesity of Science and Technology Kochi Kerala

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

video analytics utilizing video Question Answering (videoQA) techniques to develop AI systems capable of comprehending and responding to user-specific questions from the content of the custom video datasets. The proposed system detects objects from the custom video datasets using YOLO v8, a deep learning-based object detection algorithm driven by the provided questions. The SORT algorithm helps to track detected objects throughout the entire video. Also, significant frames are extracted from the input questions utilizing a pre-trained model Contrastive Language-Image Pretraining (CLIP) specialized for analyzing video clips. The proposed model comprises video processing where the raw video data is analyzed and transformed for further analysis. Subsequently, questions are encoded and processed facilitating the integration of natural language understanding into the system. The proposed model integrates video comprehension with questions and enables it to correlate the content of the videos with the queries posed by the user. Finally, the model generates answers to the questions asked, accompanied by the selection of the most pertinent frame from the video based on similarity scores. The explainable LIME algorithm enhances the interpretability of the frame with the highest similarity score. This selection process ensures that the provided answers are grounded in the relevant visual context, enhancing the overall quality and relevance of the responses generated by the system. The model exhibited superior performance when applied to specialized custom video datasets.

关键词： YOLO Measurement Deep learning Computer science Analytical models Accuracy Explainable AI visual analytics Question answering (information retrieval) data models

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