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Classification and Featurization of Motherboard Components Using YOLO Through Machine Learning

Classification and Featurization of Motherboard Components U...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Rahman Fahim, Md. Imadur Mahazabin Senthia, Sabrina Ferdows, Rubayea Ahmed, Fuad IUBAT-International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331529765

The role of technology is vital and can be observed through the 5th industrial revolution. As a matter of fact, the impact is so severe, it can be felt almost everywhere. As technology advances, one of the most promising frontiers lies in the realm of Artificial Intelligence (AI), with machine learning at its forefront. Object detection is a crucial such part with applications in robotics and image analysis. This paper presents a comprehensive study on vision AI, focusing on the detection of motherboard components utilizing the YOLOv8 object detection framework. The results demonstrate the effectiveness and real time effectiveness of YOLOv8 in terms of motherboard object detection. Simultaneously, this work validates the feasibility of employing YOLOv8 for in-depth analysis and object detection on various circuit boards. This will allow many automated devices to repair circuit boards with the help of artificial intelligence. Consequently, allowing robots to detect flaws and repair integrated hardware components in situations considered difficult for humans. © 2024 IEEE.

关键词： Robot vision

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Advancements in Flood Mapping Methodologies: A Comprehensive Review 6

Advancements in Flood Mapping Methodologies: A Comprehensive...

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Chandran, Divya V Anitha, J. Chandran, Divya V Vijayakumar, Amrutha Raveendran, Anjana Karunya Institute of Technology and Sciences Dept.of Computer Science Engineering Coimbatore India Adi Shankara Institute of Engineering and Technology Dept. of Electronics and Communication Engineering Kalady India

ISBN: (纸本)9798350381689

This review aims to comprehensively assess the recent advancements in flood mapping methodologies and technologies. By analyzing diverse techniques and approaches employed in flood mapping, this review seeks to present an in-depth overview of the current landscape of this field. The primary focus is on evaluating the strengths and limitations inherent in various flood mapping methods. Special attention will be given to exploring the integration of remote sensing technologies and geographic information systems (GIS) in flood mapping. Additionally, the review will delve into the transformative impact of machine learning and artificial intelligence on enhancing the precision and efficiency of flood mapping processes. Recent developments in flood mapping have significantly elevated the accuracy and reliability of the techniques employed. The integration of AI technologies with remote sensing methods has notably contributed to this advancement. Conventional approaches to flood mapping have traditionally relied on multiple methodologies to comprehend potential flood impacts. However, these methods often prove laborious, resource-intensive, and may lack access to real-time data, posing limitations for precise flood mapping and prediction. These identified limitations underscore the critical need for the development of more robust flood mapping techniques. Addressing these challenges is essential for enhancing flood risk assessment, improving management strategies, and ensuring better preparedness for future flood events. © 2024 IEEE.

关键词： Floods

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Cloud Based Predictive Maintenance System 11

Cloud Based Predictive Maintenance System

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Saini, Nikesh Yadav, Anup Lal Rahman, Ataur Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350350357

Maintenance is a critical aspect of modern industrial operations, as it ensures the reliability and longevity of equipment while minimising unplanned downtime. Traditional, schedule-based maintenance approaches are often inefficient and costly and fail to harness the full potential of data-driven insights. In response, cloud-based predictive maintenance systems have emerged as a transformative solution, leveraging the power of the cloud, data analytics, and machine learning to enable proactive maintenance strategies. This research paper explores the architecture, advantages, challenges, and potential applications of cloud-based predictive maintenance systems. We delve into the underlying technology, the role of the cloud in data storage and processing, and the use of machine learning algorithms for predictive analytics. We discuss how real-time monitoring and analysis of sensor data can help anticipate equipment failures, reduce operational costs, and increase overall efficiency. © 2024 IEEE.

关键词： Data Analytics

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A New Power Efficient, Wide-Range PWM-Based MPPT Circuit for Ultra-Low Power Energy Harvesters 66

A New Power Efficient, Wide-Range PWM-Based MPPT Circuit for...

引用

2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Abedi, Mostafa Shrivastava, Aatmesh Northeastern University Dept.of Electrical and Computer Engineering Boston United States

ISBN: (纸本)9798350302103

This paper presents a Pulse Width Modulation (PWM)-based Maximum Power Point Tracking (MPPT) approach suitable for low-power DC energy harvesting systems. A wide-range, low-power PWM scheme is achieved by incorporating one half-circuit of a single thyristor delay element. The proposed Thyristor-based PWM circuit achieves a wide duty cycle range of 2% to 60% while consuming 23nW to 80n W, making it an energy-efficient solution for various applications. The energy harvester attains a peak tracking efficiency of 99.9 % and above 99% for a wide input power range. An integrated Power Change Detector (PCD) is proposed, which saves power by activating the MPPT circuit only when needed, reducing power consumption to 125nW. The performance is verified through simulation using 65nm CMOS technology. © 2023 IEEE.

关键词： Boost Converter Energy Harvesting MPPT Power Change Detector PWM Thyristor Tracking Efficiency

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Effective Machine Learning Model for Disease Recognition and Categorization of Mango Leaves

Effective Machine Learning Model for Disease Recognition and...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Kumar, Sandeep Gupta, Bhupesh Grover, Richa Chhabra, Mohit Computer Science & Engineering Dept. Ambala Mullana India Panipat Institute Of Engineering & Technology Computer Science & Engineering Dept. India

ISBN: (纸本)9798350306446

Maintaining the health and production of mango farms depends on the early detection of illnesses in mango trees. Despite the vital role agriculture plays, it remains an industry receiving limited attention from the machine-learning community. A noteworthy challenge is the lack of standardized and openly accessible agricultural datasets, impeding researchers from fully leveraging the potential of advanced computational prediction tools. The study focuses on employing deep learning and image processing techniques for diagnosing prevalent mango leaf diseases in India. The goal is to enhance both the quality and quantity of agricultural production by implementing effective disease control measures upon identifying plant diseases. The automation of plant disease detection proves particularly beneficial, reducing the monitoring efforts required by large farms. Rapid identification of leaf diseases is crucial, given that leaves serve as a plant's primary source of nutrition. Notably, none of the seven different mango leaf illnesses that have been observed in India have ever been the subject of earlier research. In this study, we provide a lightweight Resnet50 model that can reliably classify healthy mango leaves and the seven discovered illnesses on mango leaves. The performance of our proposed lightweight Resnet-50 model is evaluated against many pre-trained models, such as Exception, Resnet101, and VGG16. The findings reveal that the lightweight Resnet-50 model achieves the highest testing accuracy, reaching an impressive 95%. This underscores the model's effectiveness in automating the identification of mango leaf diseases, offering a promising solution for improving agricultural practices in India. © 2024 IEEE.

关键词： Diseases

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Optimizing AODV Routing Protocol to Improve Quality of Service Performance for V2V Communication

Optimizing AODV Routing Protocol to Improve Quality of Servi...

引用

2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Bintoro, Ketut Bayu Yogha Priyambodo, Tri Kuntoro Musthofa, Muhammad Nurul Gadjah Mada University Faculty of Mathematics and Computer Sciences Dept.of Computer Science and Electronics Yogyakarta Indonesia Universitas Trilogi Faculty of Sciences Technology and Design Dept. of Informatic Engineering Jakarta Indonesia

ISBN: (纸本)9798350364101

The study focuses on Quality of Service (QoS) issues in learning automata-based AODV (LA-AODV) for V2V communication protocols. It analyzes and compares the LAAODV protocol with the original AODV protocol in dynamic traffic situations. The study evaluates critical metrics such as the number of vehicles, average speed, congestion spots, and vehicle time loss across various traffic scenarios, including smooth traffic, steady flow traffic, and heavy traffic. NS3 and SUMO are used as essential tools for this evaluation. The study finds that the LA-AODV protocol performs better in smooth traffic conditions than AODV. It demonstrates a 38.27% higher packet delivery ratio (PDR), an 85.26% lower packet loss ratio (PLR), and a remarkable 56.62% increase in average throughput compared to AODV. Both protocols exhibit similar performance in steady flow traffic with high PDRs (97.60% and 96.87%, respectively) and low PLRs (2% and 3%, respectively). However, AODV outshines LA-AODV with a higher PDR (97.60%)) than LA-AODV (96.87%) in heavy traffic. LA-AODV, on the other hand, excels in average throughput by 17.24% in heavy traffic. The study offers valuable insights for improving V2V communication protocols and enhancing communication efficiency to handle dynamic and traffic situations. © 2024 IEEE.

关键词： Quality of service

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Deep Learning-Based Flood Detection System Using Semantic Segmentation 7

Deep Learning-Based Flood Detection System Using Semantic Se...

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Chandran, Divya V Anitha, J. Anusree, A.R. Nair, Gowri Karunya Institute of Technology and Sciences Dept.of Computer Science Engineering Coimbatore India Adi Shankara Institute of Engineering and Technology Dept. Electronics and Communication Engineering Ernakulam India

ISBN: (纸本)9798350372816

Floods cause immense damage worldwide, resulting in significant financial losses and loss of life. Existing flood detection methods often face limitations, including delays and errors due to manual data interpretation and limited coverage. In this study, we propose a novel flood detection system that leverages deep learning techniques for semantic segmentation. Our system employs a U-Net architecture to analyze aerial and satellite images, identifying flood-affected areas at a pixel level. Trained on a diverse dataset of labeled flood images from various regions, our model is both robust and adaptable. Notably, our system achieves an Intersection over Union (IoU) score of 0.85, surpassing traditional methods in accuracy. This advanced system offers the potential to provide real-time flood information, enhancing disaster response and management. With real-time flood mapping, emergency services can better understand the disaster's scope and plan rescue operations more effectively. Improved emergency response and resource management become possible with timely, precise data on flooded areas, allowing for informed decisions regarding evacuation, relief distribution, and infrastructure maintenance. Future work will focus on integrating additional data sources, such as weather conditions and water flow rates, to further enhance system performance. © 2024 IEEE.

关键词： Semantic Segmentation

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NLP Powered Restaurant Chatbot for Exceptional Customer Engagement 2

NLP Powered Restaurant Chatbot for Exceptional Customer Enga...

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Goyal, Diya Guru Prasad, M.S. Garg, Ishika Tewari, Shashank Pandu N, K. Asha, P.N. Dept. of Computer Science and Engineering Dehradun India Srinivas University Institute of Engineering & Technology Dept. of Computer Science and Engineering Surathkal India REVA University School of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350371314

Technology is very volatile in nature, in a way that it keeps on changing daily. Restaurants are no different in the effects of this changing scenario. The primary objective of this research paper is to explore the historical trajectory of restaurants, analyze the current trends shaping the industry, and delve into the potential of chatbot integration to overcome existing challenges. Chatbots can play a pivotal role in enhancing the performance of businesses as per the research done. An integrated Dialog Flow application has been proposed, incorporating Rule-based grammar matching and M.L matching algorithms. This proposed work is trying to make NLP-based chatbots which may expedite the endeavor of businesses to produce and become profitable. © 2024 IEEE.

关键词： Python

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A Novel Approach using Machine Learning and NLP for Revolutionizing Pali Manuscript Conservation 4

A Novel Approach using Machine Learning and NLP for Revoluti...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Gudadhe, Sangita R. Bardekar, Aashish A. Ranit, Amitkumar B. Sipna COET Amravati Dept. of Computer Science & Engineering Amravati India Prof. Ram Meghe Cem Dept.of Civil Engineering Badanera Amravati India

ISBN: (纸本)9798331540364

The preservation and analysis of ancient Pali manuscripts are crucial for understanding India's cultural heritage. However, the unique shapes of Pali characters present significant challenges for accurate identification and classification. This research proposes a novel system that utilizes machine learning, image processing, and natural language processing (NLP) to convert Pali characters into understandable formats and decipher the Pali Prakrit language. By combining computer science and linguistics, the proposed model aims to effectively extract data from Pali literature. The proposed model was validated using the Government of India dataset, demonstrating improved character recognition accuracy and reduced memory footprint. This research contributes to the preservation of ancient Pali manuscripts and facilitates the study and understanding of this valuable cultural heritage. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparative Study of Machine Learning and Deep Learning Models for Food Waste Detection Using Image Processing

Comparative Study of Machine Learning and Deep Learning Mode...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Rajagopal, R. Chaithanya, K. Sonth, Ankita Kallappagoudar, Prasanna Basanagouda Mohammadishaq Nadaf, Mohammadzaid Ananda Theertha, H. Dept of Computer Science Alliance College of Engineering and Design Alliance University Bangalore India Bangalore India Alliance College of Engineering and Design Dept of Computer Science Bangalore India Alliance College of Engineering and Design Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331529833

For the purpose of reducing financial losses and environmental impacts, food waste detection is essential. Inorder to detect food waste using image processing, this study investigates the efficiency of several machine learning algorithms, including Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), Random Forest, and Inception Model (IV3). A set of images of food are then processed. Before each algorithm is used for classification, pre-processing methods like segmentation and feature extraction are used. © 2025 IEEE.

关键词： k-nearest neighbors

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