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

共 5113 条记录，以下是131-140 订阅

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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...

引用

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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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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Research on Short and Long-Term Prediction of New Energy Generation Power Based on vAE-GAN

Research on Short and Long-Term Prediction of New Energy Gen...

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Smart Grid and Artificial Intelligence (SGAI), International conference on

作者： Wenbo Zhang Jun Wei Hua Wang Fanglin Guo Rong Yang Digitization Division State Grid Gansu Electric Power Company Lanzhou China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

New energy generation power is affected by multiple factors such as meteorological conditions, equipment status and grid dispatching demand, and its output shows strong volatility, intermittency and randomness, which brings great challenges to grid dispatching. In this paper, a new short and long-term prediction method combining variational Autoencoder (v AE) and Generative Adversarial Nets (GAN) is proposed to solve the shortcomings of traditional models in accuracy and generalization ability. Firstly, key features highly related to power generation are extracted from multi-dimensional meteorological and load data through mutual information (MI) to ensure the stability of model input. Secondly, vAE is used to perform latent distribution learning and feature coding on historical data, and the future power sequence is generated by combining the adversarial training mechanism of GAN generator and discriminator, so as to continuously optimize the authenticity and accuracy of prediction results. The effectiveness of the proposed method is verified in short and long-term prediction tasks. Compared with BiLSTM, LightGBM, Informer and other mainstream models, the Mean Absolute Percentage Error (MAP E) of short-term prediction is reduced to 2.46%, and the coefficient of determination is increased to 0.977. The MAPE of long-term prediction is 3.37%, and the coefficient of determination is 0.961. The results show that the proposed method not only significantly improves the prediction accuracy and stability, but also has good generalization ability, which can provide reliable support for the scheduling optimization and planning decision-making of new energy generation power, and provides an innovative method exploration for new energy generation power prediction.

关键词： Accuracy Autoencoders Time series analysis Predictive models Power system stability Feature extraction Stability analysis Optimization Mutual information Meteorology

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Reducing Complexity in Drug-Target Interaction Studies: Framework for Descriptor Subset Selection using Deep Clustering and Classification

Reducing Complexity in Drug-Target Interaction Studies: Fram...

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Ambient Intelligence in Health Care (ICAIHC), International conference on

作者： Jyothy v A Maya L Pai Sandesh E PA Jaleel U C A Department of CS & IT School of Computing Amrita Vishwa Vidyapeetham Kochi India UL Research Centre UL Cyber Park Calicut Kozhikode India Open Source Pharma Foundation Bengaluru India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

The plethora of publicly available molecular descriptors poses a potential risk of encountering the 'curse of dimensionality,' which can significantly complicate the resolution of high-dimensional challenges such as drug discovery rooted in chemical space. In drug-target interaction analysis, the comprehensive RDKit database comprising more than 20,000 descriptors contributes to an enormous complexity in selecting, reducing, and building models. To alleviate this complexity, meticulous subset selection, evaluation, and validation of clustering and classification analyses are imperative. The study focuses on the relative importance of various molecular descriptors in drug-target interactions for three immune targets: Collagenase-3 (MMP-13), STAT1-alpha/beta isoform alpha (STAT-1), and NF-kB p105 subunit isoform1 (NF-kB). The study utilized a Self-Organizing Map (SOM) with K-means clustering, a Cluster-Generative Adversarial Network (ClusterGAN) based DL clustering algorithm, and a Fully Connected Neural Network (FCNN) for classification tasks. Experiments were conducted utilizing the entire RDKit descriptor set, RDKit descriptors subjected to Principal Component analysis (PCA), and domain-specific subsets. The findings revealed that, in clustering tasks, domain-based subset selection marginally enhances performance when sufficient data are available. Conversely, classification accuracy is adversely impacted by subset selection and dimensionality reduction.

关键词： Self-organizing feature maps Dimensionality reduction Analytical models Biological system modeling Complexity theory Space exploration Drug discovery Chemicals Immune system Principal component analysis

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Fossa Optimization Algorithm with Convolution Neural Network Based Brain Tumor Segmentation and Classification in MRI Images

Fossa Optimization Algorithm with Convolution Neural Network...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International conference on

作者： veeresh Shivaprakash Ramy Read Hossain Aruna T M Tatiraju.v.Rajani Kanth Sir M Visvesvaraya College of Engineering Raichur India Affiliated to Visvesvaraya Technological University Belagavi India Government Engineering College Raichur India Visvesvaraya Technological University Belagavi India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India TVR Consulting Services Private Limited Gajularamaram Hyderabad India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Magnetic Resonance Image (MRI) brain tumor segmentation is critical and significant in a clinical field that assist in diagnosis, complete growth prediction and tumor mass rates required for patients. The complexity in segmenting brain tumors are challenging due to the various structures, shapes, locations, visual characteristics, frequency, texture and contrast. To overcome this issue, this research proposes a Fossa Optimization Algorithm (FOA) with Convolution Neural Network (CNN) for brain tumor classification. The Geodesic Active Contour (GAC) is used for segmentation which identifies tumor boundaries precisely in noisy and intricate data. FOA utilizes dynamic search strategy by its hunting behavior, balancing exploration and exploitation effectively. Its adaptive step size enables better convergence when avoiding local optima. Furthermore, its ability to capture edges and patterns makes it effective for classification. CNN are better to diverse in scale, noise and rotation because of its shared weights and local connectivity reduce the complexity thereby leading to higher accuracy and quick processing in classification. The FOA-CNN obtains 99.92% accuracy, 99.20% f1-score, 99.07% sensitivity and 99.34% precision for BRATS2020 dataset which is superior than Spatial Transformer Network and Non-local Attention Mechanism (STN-NAM).

关键词： Image segmentation Accuracy Sensitivity Convolution Brain tumors Search problems Classification algorithms Complexity theory Optimization Biological neural networks

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visual Recognition and Recommendation System for Cultural Tourism Attractions Based on Deep Learning 1st

Visual Recognition and Recommendation System for Cultural To...

引用

1st International conference on Pattern analysis and Machine Intelligence, ICPAMI 2024

作者： Li, Si Yang, Xiaoqi Guangzhou College of Commerce Guangzhou China

ISBN: (纸本)9789819633487

This article designs and constructs a deep prediction model that combines deep neural networks and Transformer encoders. Build a prediction model using deep neural networks and Transformer encoders. Due to the powerful feature extraction ability of deep neural networks and their ability to fuse heterogeneous data from multiple sources, they are used to process basic information about users and scenic spots. The Transformer encoder is used to process user comment data, modeling long-distance dependencies between words in sentences through position encoding and self attention at the encoding end, thereby fully mining the comprehensive features of comment data and obtaining more potential user interest features. The features of the last three networks are input into the factorization machine to further learn their potential interaction relationships, thereby improving the prediction accuracy of the model and alleviating the problem of data sparsity. Finally, the effectiveness and accuracy of this recommendation model were verified through experimental comparison with traditional recommendation models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Enhanced Monitoring and Security Measures with video analysis

Enhanced Monitoring and Security Measures with Video Analysi...

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

作者： v. Esther Jyothi G. Rakesh vipparla Aruna Shobana Gorintla Yalanati Ayyappa U Ganesh Naidu Department of Computer Applications Velagapudi Ramakrishna Siddhartha Engineering College Deemed to be University Vijayawada A.P. India Department of Computer Science and Engineering NRI Institute of Technology (Autonomous) Pothavarapadu A.P India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu India B V Raju Institute of Technology Narsapur Telangana India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

In modern surveillance, activities have increasingly become dependent on the continuous observation offered by CCTv systems. Still, with massive amounts of video data generated in a minute, sifting through this information manually to pick up any anomalies would be extremely burdensome and nearly impossible without significant human labor and vigilant watchfulness. CNN and CLIP models changed everything with regards to the working of surveillance systems. It makes use of CNN's capability for video frame processing and analysis to detect and classify these activities in the frames as either normal or suspicious. At the same time, it improves this by adding the CLIP model's capability of understanding textual descriptions and visual content together for better nuance detection in suspicious activities. This methodology transforms video surveillance by breaking video streams into frames and analyzing the behavior and interactions of persons in those frames. The synergy of CNN and CLIP models not only ensures real-time, efficient, and accurate surveillance but also minimizes dependency on extensive manual labor for monitoring. This paper aims to provide a contribution toward developing better security infrastructures in public spaces, transportation hubs, and private facilities. The introduction of this anomaly-detection mechanism can hugely improve the capability of implemented surveillance systems with a proactive response based on real-time detection and response capabilities against suspicious activities.

关键词： visualization Transportation Transforms Manuals Streaming media video surveillance Real-time systems Security Feeds Streams

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OFDM For Underwater Acoustic Communication Using FPGA

OFDM For Underwater Acoustic Communication Using FPGA

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

作者： D. Malathi R. S. Prakalya T. Rithuvarshini v. Raghav S. Umamaheswari Department of ECE Kongu Engineering College Erode India Department of ECE Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Underwater wireless communication is of prime importance in ocean research, environmental monitoring, and deep-sea exploration. However, it is greatly affected by signal loss, limited bandwidth, and multipath interference, which complicates reliable data transfer. There has been recent interest in a promising solution that is less susceptible to multipath fading and can accommodate higher data rates. To achieve effective performance underwater, one needs robust error correction. In this project, we have fixed our focus on polar coded Orthogonal frequency division multiplexing OFDM - a system tailored toward underwater communication - incorporating Polar Codes with OFDM. Introduced by Erdal Arıkan, Polar Codes are known for their ability to achieve near-capacity performance and efficiency in encoding and decoding processes. A Polar Coded OFDM system to construct and analyze with the objective of improving the communication reliability and efficiency under different conditions of underwater environments. We have designed a simulation environment by emulating all the underwater communication challenges, including signal attenuation, noise, Doppler shifts, and multipath effects, and our system will evaluate all aspects of the designed system along with bit error rate, data rate, and power efficiency. Our results will be compared to those of conventional uncoded OFDM systems as well as other error-correcting techniques such as Turbo and low density parity check LDPC codes. Ultimately, we hope to produce a communication system that operates with greater reliability and efficiency in the challenging oceanic environment.

关键词： Wireless communication Underwater communication Phase modulation OFDM Bit error rate Parity check codes Reliability Polar codes Underwater acoustics Time-domain analysis

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ElephantGuardNet: A Comprehensive Solution for Elephant Conservation and Human-Elephant Conflict Reduction

ElephantGuardNet: A Comprehensive Solution for Elephant Cons...

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

作者： S. Naveen R. Dhamotharan S. Sahebzathi R. Santhana Krishnan v. vinoth Kumar P. Sundaravadivel Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Krishnankoil India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The ElephantGuardNet system is an advanced technological framework designed to monitor and protect elephants while minimizing human-elephant conflict. The system leverages a combination of real-time data collection, AI-driven analysis, and IoT sensors to track elephant movements across vast territories. By integrating wildlife camera traps, drones, satellite imagery, and IoT sensors such as infrared motion detectors and seismic sensors, the system provides continuous monitoring of elephant activity, enabling proactive management of potential risks. A custom dataset, ElephantMovementTracker, compiled from diverse data sources, forms the foundation for the model's training. Preprocessing techniques such as data normalization, augmentation, and noise filtering enhance the dataset's quality and ensure its suitability for deep learning applications. EfficientNet B7 and YOLO are employed for image and real-time detection tasks, respectively, while 3D CNNs are used for video analytics, enabling accurate tracking of dynamic movement patterns. GRU-based models predict future elephant movements, helping assess the likelihood of conflict. XGBoost is utilized for classification tasks, evaluating the risk of human-elephant conflict. The system is deployed on AWS and incorporates NvIDIA Jetson Nano for real-time detection and automated alerting via SMS and email. IoT sensors and automated deterrent systems, including sound emitters and drones, further mitigate conflict risk. The system's effectiveness in reducing human-elephant conflicts and providing a scalable solution for elephant conservation demonstrates the potential of AI and IoT integration in wildlife protection efforts, offering a comprehensive approach to both conservation and human-elephant coexistence.

关键词： Deep learning YOLO Accuracy Tracking visual analytics Wildlife Predictive models Real-time systems Sensor systems Drones

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Using Underwater Hyperspectral Imaging to Increase Feature Detection and Improve Under-Ice Navigation

Using Underwater Hyperspectral Imaging to Increase Feature D...

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International Symposium on Underwater Technology

作者： Leonard Günzel Janina Osanen Håvard Snefjellå Løvås Geir Johnsen Alberto García Ortiz Martin Ludvigsen Marine Technology NTNU Trondheim Norway Marine Biology NTNU Trondheim Norway Integrated Digital Systems University of Bremen Bremen Germany

ISBN: (数字)9798331534189

ISBN: (纸本)9798331534196

Extensive sea ice in Arctic marine regions has hindered indepth exploration and research. To overcome this frontier, underwater robots are increasingly used to explore beneath the ice cover. While this enables research over a broader area, it increases the risk of losing valuable vehicles under the ice, placing greater emphasis on the navigational capabilities of the robots. While inertial and acoustical navigation methods share limitations for operating under the ice, visual navigation can utilize optical features for position estimation at a very fine resolution. However, the underside of ice tends to lack visible features. To overcome this, we propose integrating a novel Underwater Hyperspectral Imager. This technology captures detailed spectral information, revealing features and patterns invisible to traditional imaging methods. To test this, a Remotely Operated vehicle was equipped with an upward-pointing hyperspectral imager. Three Arctic expeditions were conducted, capturing under-ice data including an Arctic ice algae bloom. To determine whether the hyperspectral imager provides improved navigation capabilities, we created five spectral transformations utilizing the spectral quantity of the hyperspectral domain by either increasing the number of layers fed into the detection algorithm (5-Band, 10-Band and 20-band) or by transforming the spectra using Principal Component analysis (PCA) and the Pearson correlation. We then compared these to four transformations which resembled the behaviour of an RGB/grayscale equivalent. We found that the hyperspectral methods showed an improvement in precision. These improvements in precision highlight the potential of hyperspectral imagina for robust navigation in challenging under-ice environments.

关键词： Integrated optics visualization Navigation Feature detection Estimation Gray-scale Optical imaging Arctic Hyperspectral imaging Principal component analysis

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