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共 267 条记录，以下是141-150 订阅

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intelligent Parking System to Prevent Unauthorized Parking of Vehicles Using IoT and R-CNN with YOLO

Intelligent Parking System to Prevent Unauthorized Parking o...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sushma Vispute Manasi Potdar Pranjal Pawara Shreya Patil Satvika Patil Dinesh Bhaskar Ahirrao Dept. of Computer Engineering Pimpri Chinchwad College Of Engineering Pune India KCES's College Of Engineering and Management Jalgaon India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In recent years., the proliferation of private vehicles has led to a significant challenge in finding adequate parking space., with unauthorized parking becoming a prevalent issue in many residential societies. To address this problem., we propose a novel solution that integrates machine learning techniques and motion detection technology to effectively manage parking spaces and curb unauthorized parking. Our approach utilizes advanced machine learning models such as RCNN for object detection and YOLOv8 for license plate detection., complemented by optical character recognition (OCR) for character recognition. Additionally., the ESP32-CAM webcam module is employed to capture real-time images., ensuring seamless integration with our system. This combination of technologies allows for the accurate detection of unauthorized parking instances in real time., providing efficient management of parking spaces within societies. Key features of the system include automated vehicle identification., real-time monitoring., and notification alerts., all of which contribute to a robust verification system for resident and guest vehicles. By leveraging these advanced technologies., our solution not only streamlines the parking process but also promotes a more organized and peaceful living environment. This innovative approach addresses a critical issue in urban settings., paving the way for more efficient and sustainable parking management practices.

关键词： YOLO Space vehicles Accuracy Webcams Machine learning Real-time systems Internet of Things Character recognition Reliability License plate recognition

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Dynamic Weight Optimization for Health Index Construction in Mass Spectrometer Ion Sources

Dynamic Weight Optimization for Health Index Construction in...

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intelligent Manufacturing, Advanced Sensing and Big data (IMASBD), international conference on

作者： Yang Miao Zehao Li Sen Wang Qi Huang College of Mechanical & Energy Engineering Beijing University of Technology Beijing China

ISBN: (数字)9798331524456

ISBN: (纸本)9798331524463

This paper presents a deep learning-based method for assessing the health status of industrial equipment. The proposed Real-time Adaptive Dynamic Weighted Fusion (RADWF) model integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for extracting spatiotemporal features. Additionally, a dynamic weight optimization mechanism is employed to improve the accuracy and robustness of the Health Index (HI) predictions and fault early warning. The paper details the CNN and LSTM components of the feature extraction module and elaborates on the data preprocessing steps, emphasizing ideal Value Normalization's role in ensuring data consistency. The RADWF model is also compared against traditional machine learning models and other dynamic weight optimization techniques, demonstrating superior performance across multiple metrics. Experimental results confirm that RADWF not only captures degradation trends effectively but also suppresses short-term fluctuations, ensuring stable predictions. These findings provide theoretical support and practical insights for intelligent health management in industrial and aerospace equipment.

关键词： Adaptation models Accuracy Power system dynamics Aerodynamics Feature extraction Real-time systems Indexes Convolutional neural networks Optimization Long short term memory

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Colorectal Cancer Detection based on Integrated Graph Convolutional Networks and Shift Window Transformer

Colorectal Cancer Detection based on Integrated Graph Convol...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Gayathri M D. Thamarai Selvi S. Kalaiselvi S. Santhi Department of computer science and engineering National Engineering College Kovilpatti Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Advanced predictive analytics is required to improve early identification and patient outcomes for colorectal cancer (CRC), which is still a major worldwide health concern. Using deep learning and graph-based methods, this study suggests a new prediction framework that makes use of Graph Convolutional Networks (GCNs) and a Shifted Window Transformer architecture by means of thorough pre-processing, which includes graph creation and normalization, we establish a strong input structure for our model. To improve feature extraction from histopathology pictures, the Shifted Window Transformer-which is well-known for its effectiveness in capturing long-range dependencies-is incorporated. The model is able to utilize both individual cellular properties and their interrelations thanks to GCNs that learn from the graph structure. By automatically identifying colorectal cancer in its early stages, this prediction system seeks to do away with the requirement for human identification by medical professionals and allow for prompt treatment without awaiting a formal diagnosis. This work aims to improve early-stage (stage 1) detection using our suggested automated models because it is challenging to detect CRC in stage 0. This innovative strategy promises prompt interventions that will enhance patient care and clinical results.

关键词： Deep learning Adaptation models Accuracy Sensitivity Graph convolutional networks Computational modeling Colorectal cancer Transformers Feature extraction Real-time systems

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Multi-Agent Systems for Autonomous IoT Network Management Using Distributed Reinforcement learning 3

Multi-Agent Systems for Autonomous IoT Network Management Us...

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3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Neelamegam, G. Venkatesan, Rajaram Ramya, S.R. Ramya, R.S. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak VSB College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Builders Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tirupur India Dr NGP Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems. © 2025 IEEE.

关键词： Resource allocation

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Deep learning-Based Plant Species Identification using VGG16 and Transfer learning: Enhancing Accuracy in Biodiversity and Agricultural Applications

Deep Learning-Based Plant Species Identification using VGG16...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： K Kiran Arpanpreet Kaur School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In this paper, the method of deep learning is applied for plant species identification, with a special emphasis on Convolutional Neural Network VGG16 for feature extraction and classification. Identification of plants is important in matters concerning biological diversity, agriculture as well as management and utilization of resources but the conventional methods used in identification are time consuming and sometimes inaccurate. Machine learning specifically deep learning is a more suitable replacement by offering a fast and accurate identity recognition. The research work works with a Kaggle dataset comprising of 24881 images of four plant species categorized into twenty-two with the objectives of increasing efficiency of the classification. We used transfer learning with weights from the ImageNet data set and obtained accuracy greater than 90% on the test data set, which shows the model's ability to identify plant species based on leaf structure. To counter overfitting and enhance model generality, data augmentation methods were employed where the input images were rotated by 30, 45 and 60 degrees, and flipped vertically. The performances suggest that deep learning models, such as VGG 16, yield improved recognition for plant classification than conventional methods provide higher reliability. This research makes a valuable input toward a number of applications related to automated systems for agriculture and conservation, discussing possible uses of AI technique for better penetration of plant species, enhancing conservation goals, and future advancements in the agrarian field. Future research will leverage on these discoveries to make improvements on classification frameworks.

关键词： Deep learning Accuracy Plants (biology) Biological system modeling Transfer learning data augmentation data models Convolutional neural networks Biodiversity Environmental management

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A survey on deep learning based lip-reading techniques 3

A survey on deep learning based lip-reading techniques

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3rd international conference on intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Pujari, Sheetal Sneha, S.K. Vinusha, R. Bhuvaneshwari, P. Yashaswini, C. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9780738111834

Lip reading is a way of using skills and knowledge to understand a speaker's verbal communication by visually interpreting the lip movements of the people. This becomes a tedious task when there are obstructions or background noise in the data. Over the decade, with the buzz of Deep learning there has been an increasing demand for developing systems that can help mankind by converting speech to text. Lip reading systems are those intelligent automated systems which on looking at the face of the user try to comprehend what he/she is trying to convey and represent it visually. This process can be done by using various deep learning algorithms to detect the face, localize the lips, extract the features, train the classifier to detect the lip movement and finally convert into text. This survey paper presents a summary of various deep learning techniques, different datasets, adopted methodologies by highlighting their performance and limitations in the speech and vision applications. © 2021 IEEE.

关键词： Surveys

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Air Pollution Forecasting Using Machine learning with Temporal Fusion Transformer and Graph Neural Networks

Air Pollution Forecasting Using Machine Learning with Tempor...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Subhashree Rath Maneesha P dept. of Computer Science and Engineering New Horizon College Of Engineering Autonomous College Affiliated to VTU Bengaluru

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Air pollution is a significant obstacle to achieving urban sustainability and public health, particularly in rapidly developing regions. Accurate air quality prediction is essential for proactive pollution management and policy formulation. In this study, we employ sophisticated machine learning frameworks, such as Temporal Fusion Transformer (TFT) and Graph Neural Networks (GNNs), to predict air quality levels. TFT is designed to capture temporal dependencies, making it ideal for time-series forecasting, while GNNs model spatial relationships, enabling the analysis of pollutant dispersion across monitoring stations. A synthetic dataset comprising 50 rows was generated, simulating pollutant concentrations (PM2.5, PM10, CO, NO2, SO2) and meteorological variables (temperature, humidity, wind speed). The models were assessed using criteria like $R^data$ , RMSE, and MAE. TFT achieved an $R^data$ of 0.97 and an RMSE of $4.2 \mu ~\mathrm{g} / \mathrm{m}^3$ , significantly outperforming traditional models like Random Forest and XGBoost. GNN provided spatial insights, identifying pollutant hotspots and their dispersion patterns. This research highlights the superior accuracy and scalability of TFT and GNN for air quality prediction. The results underline their potential for real-time monitoring, smart city integration, and proactive environmental management, offering a robust solution for addressing urban air pollution challenges.

关键词： Accuracy Atmospheric modeling Predictive models Transformers Air pollution Graph neural networks Real-time systems Thin film transistors Forecasting Dispersion

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IoT-Enabled Reinforcement learning for Autonomous Robotic Lawn Mowers in Residential Gardens

IoT-Enabled Reinforcement Learning for Autonomous Robotic La...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： K. Helenprabha Raveendra N Amarnath N. Mohankumar B. Gopi N. Aishwarya S. Murugan Department of Electronics and Communication Engineering R.M.D.Engineering College Chennai Tamil Nadu India Project Lead Mphasis Campbell Dr Melissa Texas USA Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Professor and Head - Innovation Center Sona College of Technology Salem Tamil Nadu India Assistant Professor Department of Electrical and Electronics Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed To Be University) Salem Tamil Nadu India Adjunct Professor Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Traditional automated lawn mowers sometimes have difficulties in navigating complex garden designs and changing to changing conditions, which causes ineffective efficiency and limited coverage. This research introduces a new way to improve the efficiency and autonomy of robotic lawn mowers in residential settings by combining Reinforcement learning (RL) algorithms with Internet of Things (IoT) technologies. To help the robotic mower respond appropriately to environmental signals it presents a system that uses IoT sensors to collect data on topography, weather, and vegetation in real-time. It uses RL methods to make the mower better at mowing and navigating over time. With the autonomous mower attaining more accuracy in grass cutting while reducing energy consumption and operating expenses, our trial findings show considerable increases in coverage and efficiency compared to traditional techniques. Building on previous work in smart gardening technology to the body of understanding on IoT enabled autonomous systems for home use. The research addresses inefficient lawn management through the integration of IoT and RL, resulting in adaptive, accurate, and dependable robotic mowing for enhanced energy efficiency and coverage.

关键词： Energy consumption Accuracy Navigation Computational modeling Reinforcement learning Robot sensing systems Real-time systems Energy efficiency Sensors Internet of Things

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A Review on framework to Detect and Diagnose Body System based Cancer using deep learning Techniques

A Review on framework to Detect and Diagnose Body System bas...

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Advance Computing and Innovative Technologies in engineering (ICACITE), international conference on

作者： Hari Prasad Gandikota S. Abirami Department of Computer Science & Engineering Annamalai University Chidambaram Tamilnadu India

Cancer imaging examination is based mainly on doctors’ manual interpretation, which requires professional abilities, clinical experience, and attentiveness. However, as the volume of medical imaging data grows, radiologists face increasing challenges. Artificial Intelligence (AI) detection of Digestive System Cancer (DSC) can provide a solution for automatic picture analysis and assist doctors in achieving high-precision intelligent cancer diagnosis. The primary purpose of this study is to explain the critical research methodologies for AI-based DSC detection, as well as to give meaningful learning and reference for relevant researchers. Meanwhile, it outlines the essential issues with these methodologies and provides better direction for future research. DSC automated categorization, recognition, and segmentation can be improved by utilizing machine learning and deep learning detection techniques, which reduce internal image information that humans find difficult to uncover. Using AI to assist imaging surgeons in diagnosing DSC for multiple organs and high incidence can achieve rapid and effective cancer identification while saving doctors’ diagnosis time. These can be the foundation for better clinical diagnosis, therapeutic planning, and quantitative DSC evaluation.

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A Blockchain Based Decentralized Application System for Vanet FDIA Detection 3rd

A Blockchain Based Decentralized Application System for Vane...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Preeti, Grover Sanjeev Kumar, Prasad School of Computing Science and Engineering Galgotias University Uttar Pradesh NCR Greater Noida India

ISBN: (纸本)9789819726707

The growing prominence of intelligent transportation systems (ITS) has led to increased research on vehicular ad hoc networks (VANETs). Blockchain technology offers promising solutions for ensuring data security, integrity, and immutability within VANETs. However, VANETs remain vulnerable to false data injection attacks (FDIAs), which can significantly disrupt network operations and endanger safety. This paper proposes a novel approach using a blockchain-based decentralized application system (Dapp) for FDIA detection in VANETs. The Dapp leverages smart contracts to manage crucial aspects like data provenance, reputation, and consensus mechanisms. It uses POS consensus algorithm to guarantee data consistency and fault tolerance even in the presence of malicious nodes. To manage the vehicular data, users can access the system via Dapp, an Ethereum—distributed application. By integration of blockchain-based IPFS-Dapps system and AI/ML/big data model with blockchain are aim of the research article for providing the solutions for false data injection attacks (FDIA). We collect the data through Radio Channels, Smart Sensors, Smart Antennas and RSU and than uploaded using Dapp and than we classified nodes from smart contract and finally send it to Ethereum. We used a machine learning SVR algorithm, i.e., vehicular network-based support vector regression (VBSVR) to identify an attacker node. We have written the pseudocode for using IPFS with Ethereum, proposed research methodology includes integration of blockchain and Dapps with smart contract, integration of AI/ML/big data models with blockchain, and integration of ML Oracle under blockchain that are ideal for detection of false injection attacks nodes, and same is logged in IPFS Decentralized Server. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ethereum

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