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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Bisht, Girish Singh Gobi, N. Kapse, Vinod Mansiram Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

The primary objective of this research is to get a comprehensive understanding of Cloud Computing ontologies, their applications, and their specific areas of interest. The proposed research methodology for this systematic review comprises a well-defined research topic, criteria for including and excluding studies, and a comprehensive search strategy to identify relevant source papers for the review. The chosen research is categorized into four main areas: elucidation of cloud resources and services, elaboration of cloud security, clarity of cloud interoperability, and understanding of cloud services discovery and selection. Utilizing ontologies to improve the security and smooth integration of cloud computing services uncovers various challenges and identifies areas for more research. © 2024 IEEE.

关键词： Cloud Computing Cloud service ontology systematic review

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Agroculture Web Application

Agroculture Web Application

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Sangeetha, M. Sumithra, A. Sneha, R. Suganya Devi, M. Vishnu Nivasini, J. SNS College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350348217

Agriculture is the backbone of our country. Mostly 60-70% of people in India depends either directly or indirectly on Agriculture for food for their own living. As India is also an agriculture dependent country, it is the major platform for India's development. Even though, Agriculture is the main occupation of India farmers have both advantages and disadvantages too. Farmers usually invest most of their money on agriculture. But they were not correctly rewarded to their hard work. They only get less amount of money compared to the money they invested on it. Ssome farmers who were new to agriculture may also don't know much about agriculture like which pesticides need to be used on certain crops, how many times the crops should be irrigated per month, how many months it will take for cultivation or maybe how it should be cultivated. So, the vital idea of our project is to create an web application which will help the farmers to solve these problems for selling their goods as it is a trust-worthy and beneficial way for farmers. It would be profitable for both the buyer's side and the seller's side. © 2023 IEEE.

关键词： Crops

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Gesture Recognition using Convolutional Neural Network 2

Gesture Recognition using Convolutional Neural Network

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2nd International Conference on Advances in Computational Intelligence and Communication, ICACIC 2023

作者： Shanmuga Priya, G. Nitisha Sree, V. Magisha, K. Pooviga, S. Sri Krishna College of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350318456

Hand gesture detection, which is adaptable and user-friendly, is one of the most active study areas in the field of human-computer interfaces. Using the gesture recognition system, a system is designed that can be deployed to communicate with disabled people or to administer a product. Excellent internal consistency commonalities in hand gesture postures, slight variances, non-uniform surroundings, with user-specific hand form and shape variations all offer considerable obstacles to creating an efficient hand gesture detection mechanism. In this article a hand gesture detection method which is user independent is examined using custom characteristics such convolutional neural networks (CNN). A technology known as Gesture Recognition uses mathematical algorithms to interpret human gestures. The dataset is collected and stored in the form of files and folders. Using CNN algorithm, the collected dataset is compared with the threshold value of the given input and thus the result is presented in the form of text or voice. Gesture Recognition System is an inventive user interface that eliminates the challenges associated with using remote controls for home appliances by replacing the remote-control system with hand gestures. Deep learning and computer vision are two tools that can be used to further the cause. Using the suggested method, an authentic implementation of gesture - based system is constructed and evaluated. © 2023 IEEE.

关键词： Remote control

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Acceleration Object Detection using FPGA platform based on Improved CNN 10

Acceleration Object Detection using FPGA platform based on I...

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10th International Conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Salehzadeh, Mohammad Hossein Monfared, Mohammad Hamidi Rostami, Vahid Salamati, Mahdi Department of Computer and Information Technology Engineering Science and Research Tehran Branch Islamic Azad University Tehran Iran Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran

ISBN: (纸本)9798350348873

Deep neural networks have emerged as a leading approach for object detection in recent years. However, their intensive computational requirements often make them unsuitable for embedded systems. The TinyYolo network is a widely-used deep neural network is designed to be simpler and smaller compared to its counterparts. Yet, it still presents challenges in FPGA implementations due to its large number of nodes. In this research, we focus on adapting the TinyYolo network for FPGA implementation. Our modified network comprises four deep neural sub-networks. One serves as the input, two as intermediate networks that extract both local and global features and the final one aggregates the extracted features. Based on these aggregated features, the target objects are detected. Our adaptation offers several advantages including Hardware-based implementation leading to increased computing power, Significant power savings compared to software implementations and Optimal parallel processing, power efficiency and memory usage making it suitable for embedded applications across various domains Our network implemented on the ZU3EG A484 chip achieved a processing speed of 58 FPS and consumed 4.105 W making it competitive with similar systems. ©2024 IEEE.

关键词： Object recognition

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DEVELOPMENT OF AI-BASED POSTURE MONITORING SYSTEM TO ASSIST YOGA TRAINING

DEVELOPMENT OF AI-BASED POSTURE MONITORING SYSTEM TO ASSIST ...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Anitha Elavarasi, S. Ankit Kumar, P. Jayanthi, J. Department of Computer Science and Engineering Sona College of Technology Salem India

ISBN: (纸本)9798350305708

In the modern era of remote fitness and wellness, maintaining correct posture during exercises and yoga practices is vital for maximizing the benefits and preventing injuries. However, monitoring one's posture can be challenging, often leading to improper form and reduced effectiveness. To address this issue, this project presents an innovative real-time posture monitoring system using computer vision and pose estimation techniques. The system employs the Mediapipe library to detect and analyze the user's body posture through a camera feed. It begins by capturing a reference posture from an image and calculating its landmark coordinates. These coordinates serve as the baseline for correct posture. As the user performs exercises in front of the camera, their live posture is continuously evaluated against the reference posture using Euclidean distance as a similarity metric. A crucial contribution of this project lies in its ability to provide immediate feedback to the user regarding their posture. The system visualizes landmarks on the user's body, highlighting correct and incorrect positions in distinct colors. If the similarity score between the live posture and the reference posture surpasses a certain threshold, the landmarks appear in green, indicating good posture. Conversely, red landmarks indicate discrepancies, prompting the user to adjust their form. Experimental results showcase the effectiveness of the proposed system in real-time posture assessment. Users can actively observe their posture and make necessary adjustments, fostering improved exercise outcomes and reduced risk of injuries. By offering a convenient and immediate feedback mechanism, this project bridges the gap between remote fitness practices and professional guidance, ultimately promoting safer and more effective workout routines. © 2023 IEEE.

关键词： Artificial Intelligence Deep learning Mediapipe online Yoga trainer

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JEDI: Joint and Effective Privacy Preserving Outsourced Set Intersection and Data Integration Protocols

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IEEE Transactions on information Forensics and Security 2023年 18卷 4504-4514页

作者： Chen, Yu-Chi Huang, Kuan-Chun National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106 Taiwan Telecom Technology Center New Taipei City220 Taiwan

In the era of big data with an ever-increasing amount of data, the demand for data processing is increasing, and various computing service agreements have also been created. While data are widely used, data providers worry that their privacy will be infringed, so the security of the data has also received more attention. Private set intersection (PSI) allows two data-owning parties to jointly compute the intersection of private datasets. The party cannot obtain any other information except for the intersection results. However, Bloom filter (BF) is a naive solution that can be used to potentially obtain PSI, but the verifiability of using BF is usually a concern for effectiveness. Hence, garbled Bloom filter is indeed expected to overcome this concern in two-party setting. We aim for the outsourced scenario where there are many data-owning parties in distributed setting. These parties would like to jointly obtain the set intersection in a private manner supported by an outsourced cloud, and further to obtain data integration. The main challenges are that the previous solutions cannot be directly transformed into an outsourced scenario. In this paper, the proposed joint and effective privacy preserving outsourced set intersection and data integration protocols are called JEDI. They are generic constructions. We start by introducing a basic outsourced PSI protocol from the cryptographic building blocks and new BF and GBF techniques. Then, we modify the basic protocol and propose the full-fledged scheme to achieve an outsourced multi-party PSI protocol. Finally, we analyze the security and execution efficiency aspects to ensure usability. © 2023 IEEE.

关键词： Data integration

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Evaluation of the Explainable AI-NLP Framework for Text Categorization

Evaluation of the Explainable AI-NLP Framework for Text Cate...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Singh, Amar Preet Gupta, Gaurav Soumya, K. Noida Institute Of Engineering And Technology Department Of Electrical & Electronics Engineering Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India

ISBN: (纸本)9798350366846

Although there has been a decrease in interpretability, significant progress has lately been achieved in developing state-of-the-art models. This assessment provides a thorough analysis of Explainable AI (XAI) within the framework of Natural Language Processing (NLP). This study examines the fundamental classification of descriptions in addition to the many techniques for producing and displaying explanations. The purpose of the survey is to provide a comprehensive study of the many approaches and strategies used to clarify the models' predictions, making it a helpful tool for people who develop NLP models. Additionally, we draw attention to the gaps in the current literature and urge further investigation into potential avenues for this crucial field of study. An experiment is also conducted and the results are discussed in this article. © 2024 IEEE.

关键词： Natural language processing systems

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Enhancing Accessibility for Visually Impaired on Integrating Object, Distance and Face Detection 8

Enhancing Accessibility for Visually Impaired on Integrating...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Selvarathi, C. Kannan, R. Muthumanickam, K. Pandiaraja, P. Venkateswaran, T.S. Arunraj, R. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamilnadu Chennai600 062 India Saranathan College of Engineering Department of Computer Science and Business Systems Tamilnadu Tiruchirapalli620 012 India Kongunadu College of Engineering and Technology Department of Information Technology Tamil Nadu Trichy621215 India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Tamilnadu Karur639113 India K. Ramakrishnan college of Engineering Department of Information Technology Tamilnadu Samayapuram621112 India

ISBN: (纸本)9798350376425

This research presents a novel assistive technology system designed to empower visually impaired individuals by providing enhanced object, facial, and distance detection capabilities. The system aims to replace traditional electronic travel aids with a more comprehensive and cost-effective solution. By combining object and facial recognition with distance detection, the system enables users to navigate their surroundings independently and confidently. Additionally, the system incorporates threat detection capabilities, alerting users to potential dangers and triggering SOS alerts to contacts or authorities. This innovative system offers a more reliable and accessible solution for visually impaired individuals, providing them with the freedom and independence to navigate their daily lives with greater confidence and security. © 2024 IEEE.

关键词： Assistive technology

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Influence of AI and MAS in Enhancing Energy Efficiency of 6G Communications

Influence of AI and MAS in Enhancing Energy Efficiency of 6G...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Negi, Divya Prakash, Febin Gupta, Anjali Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Electronical And Electronics Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350366846

Next-generation Internet of Things (IoT) is progressing rapidly due to the introduction of beyond 5G (B5G) and the approaching arrival of 6G, which have improved the dependability, productivity, and profitability of both industrial and personal operations. Nevertheless, data overload is frequently caused by the abundance of wireless sensors in commercial 6G devices. Data mining gathers useful information in order to solve this. Meanwhile, in the 6G architecture, edge computing enables automated equipment operations and intelligent decision-making in real-time. Even so, despite the drawbacks of sensor-based energy sources, the integration of many technologies has led to an increase in energy. Using a multi-agent system (MAS) approach, a system model for industrial wireless sensor networks in 6G applications has been presented in order to combat this. Sensor node clustering locates and forecasts the locations of principal nodes by employing distributed artificial intelligence (DAI). Convolutional neural networks (CNN) and back-propagation neural networks (BPNN) are used in optimisation. Allocating resources to individual nodes efficiently is made possible by the analysis of inter-cluster correlations. The outcomes of the simulation show how well the approach works to reduce resource waste, increase overall energy efficiency, and protect important data. This strategy improves network performance in industrial 6G applications while simultaneously addressing concerns about energy consumption. © 2024 IEEE.

关键词： Multi agent systems

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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