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检索条件"任意字段=3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems"

共 1251 条记录，以下是41-50 订阅

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Design of a Real-Time Crime Monitoring System using Machine Learning 3

Design of a Real-Time Crime Monitoring System using Machine ...

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3rd International Conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Krishnaveni, A. Manikandan, C. Department ofComputer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (纸本)9798331527549

Crime in public areas poses risks to individuals and society at large. Traditional crime prevention methods face significant hurdles in detecting and tracking crime. Furthermore, the influx of large volumes of data overwhelms security systems and personnel making security enforcement harder. However, technological innovations have positively influenced different fields, including public safety and crime prevention. This study introduces an innovative system for crime prediction and analysis using machine learning, designed to assist law enforcement in identifying criminals in public spaces. The system enables authorities to input detailed information about criminals, including facial data, which is used to train an advanced recognition model. The system’s ability to provide accurate and timely alerts enhances the efficiency of law enforcement operations. This framework leverages state-of-the-art face recognition technology to offer a scalable and robust solution for crime prevention. By integrating machine learning algorithms with intelligent surveillance systems, the proposed approach addresses the growing need for proactive measures to ensure public safety. Further, this system also highlights the potential of technology to transform traditional crime detection methods, paving the way for smarter and more responsive law enforcement practices. Finally, the proposed system attained a 92% face detection accuracy score which was higher than existing approaches. © 2025 ieee.

关键词： Crime prediction exact location facial data machine learning public surveillance recognition model

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IoT based Thermal Management and State of Charge Estimation of LFP battery 3

IoT based Thermal Management and State of Charge Estimation ...

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3rd International Conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Anand, Deepthi Paraag, V. Naik, Sahana Kavana, T.M. Vasudha, Karanam Shubha Rao, K. Bnm Institute of Technology Dept. of Eee Bangalore India

ISBN: (纸本)9798331515911

Efficient storage of electricity is pivotal in modern applications, necessitating the use of batteries. However, enclosing batteries in compact spaces, such as power plants or electric vehicles, often leads to excessive heat generation, diminishing efficiency and longevity. Heat generation primarily stems from the charging and discharging processes. A liquid cooling system, powered by a motor connected to the battery system, circulates fluid externally around the battery layers to manage temperature, optimizing the battery's performance within the ideal range of 25°C to 35°C. Thermal Modelling of the battery is implemented in systems to understand how temperature changes affect their performance, efficiency, and reliability. IoT-based Battery Thermal Management System (IoT-BTMS) is introduced resulting in a sophisticated solution utilizing sensors, communication technologies, and data analytics. This system monitors and regulates battery temperature in real-time, based on sensed data a relay will enable cooling system if temperature values exceed the limit values or when the system becomes unstable, the cooling system continues until the system stabilizes and temperatures fall within limits. © 2025 ieee.

关键词： Cooling systems

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Traffic Incident Detection Algorithm based on Deep Learning: A Statistical Optimization Approach 3

Traffic Incident Detection Algorithm based on Deep Learning:...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Jing, Zhao Heilongjiang Institute of Technology College of Automobile and Traffic Engineering Harbin150050 China

ISBN: (纸本)9798331509675

The continuous evolution of smart transportation has led to the vigorous development of research in the field of intelligent vehicle anomaly detection. This study proposes a new traffic incident detection algorithm based on deep learning and combines it with statistical optimization methods for real-time traffic incident monitoring. This model first uses the pre-trained YOLOv5m-c model to detect vehicles and outputs images with vehicle frames from the video stream. At the same time, from the data analysis level, it determines whether there is abnormal behavior between vehicles based on the characteristics of the vehicle frame. The proposed algorithm improves the effect of abnormal events by optimizing the network structure of the YOLOv5 model. Then, starting from the sensor level, the detection method based on the Internet of Vehicles collects and shares vehicle status information in real time. This analysis process fully considers the task offloading and migration strategy in the edge computing environment. Quantitative experimental results show that the proposed algorithm can effectively improve the detection accuracy of traffic incidents and reduce the false alarm rate in real-time traffic data. © 2025 ieee.

关键词： intelligent systems

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Review on Advancements in Coal Mine Safety and Comprehensive Overview of IoT-based Monitoring systems 3

Review on Advancements in Coal Mine Safety and Comprehensive...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Manonmani, A. Nalina, A. Seethaladevi, S. Yashika, S. Sri Saveetha Engineering College Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Ensuring the safety of miners is a critical priority due to the inherently hazardous conditions in underground mining environments. This research presents a Zigbee-based wireless data acquisition system designed to enhance real-time monitoring of essential environmental parameters such as air quality, temperature, humidity, and toxic gases. Moving beyond conventional methods, the system incorporates an adaptive sensor deployment strategy tailored to the complex and dynamic underground landscape, greatly enhancing data reliability and network resilience. advanced RSSI modeling techniques were applied to validate packet delivery and optimize network performance under changing environmental conditions. Field trials demonstrated that this system achieves exceptional accuracy and efficiency in detecting hazardous conditions, providing a proactive approach to hazard mitigation. The findings highlight the system's ability to transform safety protocols and operational practices in mining with a scalable, robust, and innovative wireless monitoring solution. © 2025 ieee.

关键词： Zigbee

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Early Intervention in Autism Spectrum Disorder: Hybridizing Convolutional Neural Networks with Ensemble Methods for Enhanced Detection Using Facial Imagery 3

Early Intervention in Autism Spectrum Disorder: Hybridizing ...

引用

3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Das, Anupam Pattnaik, Prasant Kumar Bandyopadhyay, Anjan KIIT Deemed to be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798331523893

Autism Spectrum Disorder is a neurodevelopmental condition affecting social communication and behavior. Early and accurate detection is essential for improving long-term outcomes through timely interventions. However, traditional diagnostic methods rely heavily on behavioral observations, which are time-consuming, subjective, and can vary between clinicians. The objective of this study is to propose an automated, objective method for ASD detection using facial image analysis. Specifically, we present a hybrid model that integrates Convolutional Neural Networks for feature extraction with ensemble learning methods - Random Forest and Extreme Gradient Boosting for classification. EfficientNet and MobileNet were used as feature extractors, chosen for their efficiency and accuracy in image classification tasks, while RF and XGBoost were utilized to ensure robust classification. To prevent overfitting and enhance generalization, extensive data augmentation techniques were applied, along with class balancing methods to handle imbalanced data. The proposed model was evaluated on the Autism Image data dataset and achieved an accuracy of 97.35%, with precision and recall values of 95.2% and 95.75%, respectively. These results demonstrate the model's potential in contributing to early ASD detection, which can be an essential tool for clinicians in diagnostic decision-making. © 2025 ieee.

关键词： Autism Spectrum Disorder Convolutional Neural Networks data Augmentation Deep Learning Facial Feature Analysis Imbalanced data Handling Machine Learning Random Forest XGBoost

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Resilient 3D Object Recognition using GR-Net in Sparse Point Clouds 3

Resilient 3D Object Recognition using GR-Net in Sparse Point...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Ghadekar, Premanand Dhame, Pratik Dixit, Soham Patil, Arpit Sanjekar, Rushikesh Shinde, Siddhesh Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India

ISBN: (纸本)9798331523893

Recognizing three-dimensional (3 Dimensonal) objects is crucial for several uses for computer vision like service robots, self-driving cars and surveillance drones to navigate effectively in complex environments. However, existing classification techniques struggle with challenges such as varying resolutions, noisy data, and diverse object poses. Previous studies have highlighted the limitations of point cloud-based methods in handling sparsity, rotation, and positional variance. In this study, we concurrently address these difficulties with a unique technique to 3D object categorization. Our method leverages the graph structure of point clouds and employs a to develop a strong latent representation of 3D objects using a neural network. This representation achieves invariance to rotation, positional shift, and scaling while remaining resilient to point sparsity. Our technique outperforms existing approaches, as evidenced by experimental findings on the ModelNet40 dataset, which show a 45.0% and 38.1% gain in classification accuracy when employing sparse point clouds, respectively, over existing models. © 2025 ieee.

关键词： 3D object recognition GR-Net ModelNet40 Sparse point clouds

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A Patient-Centric Blockchain-Based Framework with Layer-2 Integration for Secure and Scalable Healthcare data Management 3

A Patient-Centric Blockchain-Based Framework with Layer-2 In...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Tarannum, Wajda Abidin, Shafiqul Aligarh Muslim University Department of Computer Science Aligarh India

ISBN: (纸本)9798331523893

Today, personal data is vulnerable in the digital era, especially in the medical sector, where healthcare data is sensitive. Yet, it plays a significant role in deciding the health conditions of patients, doctors, and researchers. Healthcare data is associated with patients' history, adding more value and utility to patients, doctors, and Researchers. These data are usually stored in centralized databases, creating vulnerabilities and leading to cyber-attacks. Furthermore, some healthcare providers rely on third-party contractors for data storage, which can be expensive and unreliable since vendors use diverse security procedures. Ensuring the secure protection of data from cyber threats and breaches is vital. This study aims to design a framework that manages large volumes of data from numerous sources like laboratories and hospitals. As we know, the volume of healthcare data is persistently growing. To address these issues, we propose a blockchain-based Patient-Centric framework with layer-2 solutions for healthcare data management that enhances security, privacy, and transparency in handling sensitive medical information. The concept integrates blockchain technology and the Interplanetary File System (IPFS), a distributed cloud storage system. We created smart contracts in Solidity to promote safe interactions between patients and healthcare professionals, and a React application communicates with them using MetaMask. Layer-2 technology decreases congestion, allowing the system to process a wide range of transactions more effectively while maintaining performance and security. Furthermore, by keeping cryptographic proofs on-chain and huge files off-chain, our strategy improves data security and privacy while lowering storage costs. © 2025 ieee.

关键词： Blockchain Cloud computing Ethereum IPFS Layer 2 Solutions Smart contract

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Performance Enhancement of DBSCAN Density-Based Clustering Algorithm in data Mining 3

Performance Enhancement of DBSCAN Density-Based Clustering A...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Tyagi, Neha Mishra, Rashmi Patel, Sitanshu Kumar Karki, Somraj Amity University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798331523893

In order to facilitate better data analysis, clustering is a prominent data mining approach which separates data into separate categories based on their similarities and distinctions. It's a method of unsupervised learning which may generate a model with a training dataset. Throughout this study, the Euclidian distance and EPS (Epsilon) value has been determined using the DBSCAN technique based on the similarities as well as disparity for the input data. Moreover, Euclidian distance is dynamically determined using the back propagation method;a simulation study is carried out that demonstrates both speed and precision improvements. DBSCAN, or spatial clustering based on density of application with noise, is a common clustering technique for geographic datasets that can locate clusters of any shape and automatically detect noise points. To deal with the problem, an improved DBSCAN technique based on the selection of features (FS-DBSCAN) has been suggested. However, the algorithm proved unstable while handling huge dimensional data. Many simulation via real-world datasets testify the algorithm's efficiency. Comparison of the proposed algorithm to the DBSCAN algorithm demonstrates its benefits. Structures within the collections may be identified by dividing of collections of products into classes and clusters, with a focus on increasing the resemblances of objects within each cluster and lowering the similarities of things belonging to different clusters. © 2025 ieee.

关键词： Accuracy and high dimensional data types Back Propagation Clustering data Mining data mining methods DBSCAN Execution Time feature selection I-DBSCAN machine learning Noise sampling spatial data

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Empowering smart traffic avoidance using Natural language processing 3

Empowering smart traffic avoidance using Natural language pr...

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3rd International Conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Kalaiselvi, V.K.G. Vishnu Chandar, K.R. Vishwa, G. Tirunagari, Sharanya Hariharan, Shanmugasundaram Krishnamoorthy, Murugaperumal Sri Sairam Engineering College Department of Information Technology Tamilnadu India Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798331515911

The integration of Natural Language Processing (NLP) into smart traffic systems offers a novel approach to enhancing real-time traffic management and optimization. This paper explores how NLP can be leveraged to interpret and analyze vast amounts of unstructured traffic-related data, such as social media updates, traffic reports, and incident logs. A key component of this approach is the use of a BERT (Bidirectional Encoder Representations from Transformers) classifier, which enables more accurate extraction and classification of relevant traffic information from textual data. By processing this data in real time, the BERT-based NLP system can provide more precise traffic predictions, identify congestion patterns, and optimize traffic flow dynamically. The study highlights the potential of NLP, particularly through the use of BERT, in improving the efficiency and responsiveness of traffic management systems, ultimately leading to reduced travel times, lower emissions, and enhanced urban mobility. The app also includes user-friendly interfaces that allow drivers and city planners to access real-time updates, personalized route recommendations, and predictive analytics. The system's adaptability ensures that it can respond quickly to unexpected incidents, such as accidents or road closures, further enhancing urban mobility. The study highlights the transformation potential of this app in improving the efficiency and responsiveness of traffic management systems, ultimately leading to reduced travel times, lower emissions, and a significant improvement in overall urban transportation experiences. © 2025 ieee.

关键词： Efficiency

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FPGA based Vehicle Collision Avoidance and Accident Warning using Sobel Operation and Manhattan Distance Metrics 3

FPGA based Vehicle Collision Avoidance and Accident Warning ...

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3rd International Conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Vishal Pranao Amarnath Bhattacharjee, Sanjana Kumar, Harsh Mal, Rupam Ravichandran, Vaarshini Sivasankaran, K. School of Electronics Engineering VIT University Vellore India

ISBN: (纸本)9798331527549

A collision avoidance system in vehicles is a safety technology which is designed to prevent or reduce accidents caused due to any obstacles such as other vehicles or pedestrians. It utilizes cameras, or other sensors to monitor the vehicle's surroundings and alerting the driver to any potential danger. advanced driver assistance systems (ADAS) and collision avoidance works hand in hand as this enhances overall vehicle safety, reduces any driver errors and also contributes to overall safety on road. This paper presents the design and implementation of a real-time collision avoidance system using Field-Programmable Gate Arrays (FPGAs). The proposed system processes the video input from a vehicle's camera and converts the video's frame to grayscale and applies Sobel edge detection on it which helps to calculate the distance between the two vehicles using Manhattan distance formula and hence effectively detects any potential collision. It utilizes FPGA's parallel processing capabilities, reconfigurability and low power to enhance the efficiency and speed of the collision detection and response in autonomous vehicles. The experiments were performed real-time on Altera Cyclone IV-E FPGA which demonstrated effectiveness of the system as the results indicated high accuracy and reliability in distance measurement and collision warning by analyzing parameters like dynamic color variation and background noise reduction for improving the safety and performance in autonomous vehicles. © 2025 ieee.

关键词： advanced driver assistance systems

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