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intelligent Video Surveillance System 4

Intelligent Video Surveillance System

引用

4th international conference on Expert Clouds and Applications, ICOECA 2024

作者： Aryan, Ansh Singh, Dharya Pratap Kumar, Nitish Amity University Dept. of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350385793

this research study provides the utilization of an intelligent video surveillance system, a sophisticated technology that integrates video cameras, sensors, and cutting-edge software to monitor and analyze activities within a designated area or locale. By seamlessly combining compute r vision and machine learning techniques, it goes beyond conventional video surveillance installations to provide real-time insights along with automated alerts. intelligent video surveillance systems harness the power of machine learning and computer vision algorithms to analyze video feeds in real-time, empowering them to detect, track, and comprehend objects, individuals, and occurrences captured within the video frames. Furthermore, these systems can recognize a variety of objects, including cars, people, animals, and particular objects, even in complex and crowded settings. they also offer a variety of analytical tools and insights that are very helpful to organizations and security operations, such as traffic patterns, heatmaps, and other data. this research study provides a thorough explanation of the modern uses of machine learning in the field of surveillance systems. Two crucial issue statements are addressed by it in practice: the identification of human falls and the detection of auto accidents. this study's main goal is to demonstrate an end-to-end system that will act as a beacon for law enforcement and medical personnel, giving them the ability to quickly identify traffic accidents and serious injuries and guarantee that the necessary assistance is provided on time. © 2024 IEEE.

关键词： Deep learning

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the method of utilizing prompt engineering to train large language models for the intelligent expression of affair-elements 11

The method of utilizing prompt engineering to train large la...

引用

11th international conference on Information Technology and Quantitative Management, ITQM 2024

作者： Liu, Zequan Wang, Longhao Li, Xingsen School of Electromechanical Engineering Guangdong University of Technology Guangzhou510006 China Institute of Extenics and Innovation Methods Guangdong University of Technology Guangzhou510006 China

this paper proposes the concept of applying large language models to the expression of extenics affair-elements with the aim of enhancing the accuracy and standardisation of computerised expression of extenics affair-elements. the feasibility of using prompt engineering to train large language models for intelligent expression of affair-elements is also verified. Firstly, the concepts and structured expressions of extenics and affair-elements are inputted into the large language model. Secondly, a training method for prompt engineering is designed, integrating few-shot prompts, thought-chain prompts, and system-2-attention prompts. then, the feedback from the large language model's expression of affair-elements is evaluated in terms of standardisation and accuracy, and the prompts are continuously modified to interact with the large language model for iterative learning in expressing affair-elements. Finally, through the use of the case study of Kimi, the capacity of the large language model to express extenics affair-elements in a standardised manner following training is evaluated, thereby confirming the feasibility of this method. © 2024 the Authors.

关键词： Natural language processing systems

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Aloe Vera Leaf Diseases Pathology: Harnessing Federated learning CNNs for Enhanced Detection 11

Aloe Vera Leaf Diseases Pathology: Harnessing Federated Lear...

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： thapliyal, Nitin thapliyal, Siddhant Singh, Abhay Narayan Kukreja, Vinay Mehta, Shiva Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Uttar Pradesh Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350357

this research paper proposes a novel technique that utilizes the federated learning framework and convolutional neural networks (CNN) to identify aloe vera leaf diseases. the study targets five main species of aloe vera leaf diseases using data from clients in about five countries. the ultimate goal is to use federated learning techniques for aggregation while achieving data privacy and minimizing the need for centralized data when the model is rigorously tested using precision, recall, and F1-score. Support and accuracy metrics to calculate its performance the clients effectively detected disease to different degrees, which indicates a geographically heterogeneous manifestation of the said condition. the performance metrics across the clients were as follows: Precision, recall F1-score and accuracy of 81.54%, 81.59 %, and 80% were demonstrated by the client. the precision, recall, F1-score and accuracy of the client rj_2 were 79.03% and 79.19%, respectively, as well as a total of precisely the precision, recall f-score, and accuracy scores for the client rj_3 were 87.05%, followed by 87.10%, followed up with 87.07%, and finally ending at 95%. Secondly, the investigation focused on evaluating federated averaging for model aggregation using local client data and comparing it with global aggregate data. the analysis revealed a progressive enhancement in model accuracy and reliability, as evidenced by the average performance metrics: Macro averages were 81.56% (rj_1) and 90.02% (rj_5). the weighted average scores ranged from 81.63% (rj_1) to 90.01% (rj_5). the micro averages went from 81.59% (rj_1) to 90.02% (rj_5). these findings support the model's ability to respond to region-specific diseases and improve its performance by employing federated learning. the approach is instrumental in agricultural settings where the data are fragmented and heterogeneous. © 2024 IEEE.

关键词： data privacy

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intelligent Monitoring and Control of Power Systems Based on Artificial Intelligence Technology 11th

Intelligent Monitoring and Control of Power Systems Based on...

引用

11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Wang, Lina Xu, Weidong Li, Xiaoxia Dongguan Power Supply Bureau of Guangdong Power Grid Corporation Guangdong Dongguan523000 China

intelligent monitoring and control of the power system plays an important role in saving the operating efficiency of the power system. However, the current intelligent monitoring and control of the power system has the problems of low stability, poor safety and insufficient reliability. Starting from artificial intelligence technology, the paper applies artificial intelligence Artificial Neural Network (ANN) and designs a power system intelligent monitoring and control model to adapt to the needs of smart power systems. the model applies adaptive learning to the power system through data analysis and pattern recognition methods. Experimental results show that the application of artificial neural networks in power systems can improve the real-time performance and accuracy of power systems, and its fault prediction accuracy can reach up to 98.9%. At the same time, it improves the system's data processing capabilities and adaptive capabilities. © 2024 the Authors. Published by Elsevier B.V.

关键词： Artificial Intelligence Technology intelligent Detection and Control Power Systems System Security

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PROGRESSIVE VORONOI DIAGRAM SUBDIVISION ENABLES ACCURATE data-FREE CLASS-INCREMENTAL learning 11

PROGRESSIVE VORONOI DIAGRAM SUBDIVISION ENABLES ACCURATE DAT...

引用

11th international conference on learning Representations, ICLR 2023

作者： Ma, Chunwei Ji, Zhanghexuan Huang, Ziyun Shen, Yan Gao, Mingchen Xu, Jinhui Department of Computer Science and Engineering University at Buffalo United States Computer Science and Software Engineering Penn State Erie United States

data-free Class-incremental learning (CIL) is a challenging problem because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep Neural Networks (DNNs). In this paper, we present iVoro, a novel framework derived from computational geometry. We found Voronoi Diagram (VD), a classical model for space subdivision, is especially powerful for solving the CIL problem, because VD itself can be constructed favorably in an incremental manner - the newly added sites (classes) will only affect the proximate classes, making the non-contiguous classes hardly forgettable. Furthermore, we bridge DNN and VD using Power Diagram Reduction, and show that the VD structure can be progressively refined along the phases using a divide-and-conquer algorithm. Moreover, our VD construction is not restricted to the deep feature space, but is also applicable to multiple intermediate feature spaces, promoting VD to be multilayer VD that efficiently captures multi-grained features from DNN. Importantly, iVoro is also capable of handling uncertainty-aware test-time Voronoi cell assignment and has exhibited high correlations between geometric uncertainty and predictive accuracy (up to ∼0.9). Putting everything together, iVoro achieves up to 25.26%, 37.09%, and 33.21% improvements on CIFAR-100, TinyImageNet, and ImageNet-Subset, respectively, compared to the state-of-the-art non-exemplar CIL approaches. In conclusion, iVoro enables highly accurate, privacy-preserving, and geometrically interpretable CIL that is particularly useful when cross-phase data sharing is forbidden, e.g. in medical applications. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Deep neural networks

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Structured Process on FL for Big data Analysis 6

Structured Process on FL for Big Data Analysis

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Govindaram, Anitha Prasath, J.S. Jayasakthi, K. Rajkumar, N. Porkodi, G. Jose Anand, A. Thandalam Chennai602105 India Department of Computer Science and Engineering Sapthagiri NPS University Chikkasandra Karnataka Bengaluru560057 India Department of ECE KCG College of Technology Karapakkam Tamil Nadu Chennai600097 India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvalluvar Tamil Nadu Avadi600062 India Department of Computer Science and Business Systems Vel Tech Multi Tech Dr. Rangaranjan Dr.Sakunthala Engineering College Avadi Tamil Nadu Thiruvallur600062 India

ISBN: (纸本)9798331522667

the integration of the Internet of things (IoT) into various aspects of daily life is accelerated by the proliferation of cognitive capabilities and AI-driven applications. Traditional automated systems rely on centralized data storage and dispensation, which may be feasible in specific application scenarios. However, the increasing flexibility of recent IoT networks and rising concerns over data confidentiality are challenging this approach. Federated learning (FL), a decentralized collaborative AI technique, allows machine learning to occur directly on external IoT devices without the need for data to be centrally aggregated or shared. this paves the way for a wide variety of intelligent IoT applications to be developed. this article presents a thorough analysis of the along with a detailed analysis of the convergence of these two fields. After that, a full analysis of how FL is currently being utilized in several essential applications related to the IoT is provided. these include smart health, mobility, Unmanned Aerial Vehicles (UAVs), infrastructure improvements, and smart industries. In total, 49 publications from the years 2015-2022 were reviewed here. Finally, discuss future study challenges with issues and exposed questions about data with IoT. © 2025 IEEE.

关键词： Federated learning

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automated Sewer Pipe Defect Classification Using Inspection Robot Imagery and Deep Neural Network 12

Automated Sewer Pipe Defect Classification Using Inspection ...

引用

12th RSI international conference on Robotics and Mechatronics, ICRoM 2024

作者： Goharinezhad, Saleh Hadi, Alireza Mirzaei, Sayeh School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology University of Tehran Advanced Service Robots Laboratory Department of Mechatronics Tehran Iran School of Engineering Science College of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9798331529734

Sewer systems play a critical role in transporting wastewater. Efficient maintenance requires accurate identification of sewer pipe defects, traditionally performed through manual interpretation of Closed-Circuit Television (CCTV) images from robot inspections. this manual process is time-consuming, labor-intensive, and susceptible to human error. While conventional computer vision methods (machine learning methods) have been explored, they often involve extensive image pre-processing and intricate feature extraction. In this study, we propose an automated approach for classifying sewer pipe defects in Iran using deep learning, specifically leveraging the ResNet50 architecture for defect classification. We applied this deep convolutional neural network (CNN) model to automatically detect three common defect types as root intrusions, deposits, and open joints within a dataset 4630 images extracted from CCTV inspection videos of sewer pipes. Our model achieved an average testing accuracy of 96%, highlighting its potential for automating the interpretation of sewer CCTV footage. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep learning-enabled vision-based pavement crack detection 11

Deep learning-enabled vision-based pavement crack detection

引用

11th international conference on Structural Health Monitoring of intelligent Infrastructure, SHMII 2022

作者： Ranyal, Eshta Sadhu, Ayan Jain, Kamal Department of Civil Engineering IIT Roorkee India Department of Civil and Environmental Engineering Western University LondonON Canada

Cracks in pavements are common distress affecting the safety and longevity of transportation infrastructure. automated pavement monitoring for cracks is a pliable solution with access to high-performance computing and memory resources. Vision-based deep learning (DL) techniques promise economic and automated results using commercially available cameras. To maximize the advantage of DL methods, this article proposes a vision-based method employing an optimized and regularized RetinaNet convolutional neural network (CNN) for detecting concrete cracks in pavements. the designed CNN is trained on a subset of the SDNET2018 dataset with an F1 score of 94%. the evaluation metrics of the model promise a realistic solution for detecting cracks in concrete pavements. © 2022 international conference on Structural Health Monitoring of intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： Convolutional neural networks

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Adapting to Evasive Tactics through Resilient Adversarial Machine learning for Malware Detection 18

Adapting to Evasive Tactics through Resilient Adversarial Ma...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Krishna, G. Bala Sravan Kumar, G. Ramachandra, Mummadi Sampurnima Pattem, K. Rani, D. Sandhya Kakarla, Geeta Cvr College of Engineering Department of Computer Science Engg. Rangareddy Ibrahimpatnam India Cvr College of Engineering Department of Computer Science Engg.-Cyber Security Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Hyderabad India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Cyber Security Hyderabad India

ISBN: (纸本)9789380544519

this paper presents the Adaptive Resilience-based Convolutional Network (ARCNet), a sophisticated machine learning framework specifically designed to detect advanced, evasive malware. ARCNet combines convolutional and recurrent neural networks, making it highly adaptable to changing cyber threats. Its core components, the Adversarial learning Module (ALM), Predictive Analysis Engine (PAE), and Dynamic Adaptation System (DAS), significantly boost its detection power. Tests using a synthetic dataset show ARCNet's superiority over traditional models like the Support Vector Machine (SVM). It achieved 95.2% accuracy under normal conditions (compared to SVM's 89.4%) and maintained 92.5% accuracy even during adversarial attacks (against SVM's 80.3%). Notably, ARCNet's detection rates improved from 78.5% to 86.7% in five months after integrating the DAS. these results confirm ARCNet's efficiency in tackling complex malware challenges, contributing greatly to cybersecurity. the study underscores the importance of evolving and enhancing machine learning methods to keep pace with the rapidly changing landscape of cyber threats. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Block chain Blockchains Challenges of Block chain Consensus algorithm data privacy Digital signatures Distributed ledger Faces Organizations Smart contracts

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Revolutionizing in Agriculture: Federated CNN Models for Sunflower Leaf Diseases 11

Revolutionizing in Agriculture: Federated CNN Models for Sun...

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Joshi, Jyoti Aeri, Manisha Kukreja, Vinay Mehta, Shiva Graphic Era Hill University Dehradun Computer Science and Engineering Uttarakhand India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350357

With the advent of an age when the reason for adopting precision agriculture to ensure essential food security and economic sustainability is foremost, the precise recognition and tattooing of sunflower leaf disease is an inauspicious part of agricultural study. In a learning process characterized by interconnectedness, collaboration, and data protection and confidentiality maintenance, the five participants (xc_1 to xc_5) have applied different data representations to the model. the summary explains our approach to analytical matters, citing the influence of global and local data analyzed via the trained AI model using federated averaging. the most considerable average accuracy was observed for all clients at xc_5, 95.71%, whereas the first version was much lower at xc_1, only 88.76%. It demonstrated clear progress from the eventual to the initial stages of the model learning without bias towards certain classes. Weighted averages are essential to the analysis of the datasets where there are imbalances. With xc_1, the weighted average starts at 89.15 and reaches the top with xc_5 of 95.72. the fact that it is more accurate and adaptable when categorizing common diseases is demonstrated by the improved precision that is illustrated in general. the micro average is the resultant average that depicts the general model accuracy, while the weight averages are the averages based on the weight. To be specific, the accuracy for sample 1 reached 89.13%, and sample 5 overviewed the accuracy level and reached 95.72%. the core of the work is federated averaging, which transforms the world's local neighborhood analytics into a universal understanding of the world. It refers to the mix of local training for separate models inside and outside the globe. © 2024 IEEE.

关键词： data privacy

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