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

作者： Afrin, Sumaiya Abdi, Khalid 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

In recent times, many streetlights have been wasting energy by staying on unnecessarily during the evenings and nights. This not only harms the environment but also costs a lot of money. The goal of Smart Street Lighting is to stop this wasteful energy use and make better use of resources. While other researchers have worked on this issue, we have a specific aim: to create a smart street lighting system that only turns on when it's needed and turns off when it's not. To achieve this, we're using laser sensors, ZigBee wireless technology, and a powerful microprocessor. Our system identifies when there are vehicles on the road using sensors. It then turns on the streetlights only where they are needed. This saves energy by not lighting empty areas. We can control and monitor each streetlight remotely through ZigBee technology, and everything is connected to a computer that makes the streetlights work smarter. Our sensors are good at detecting objects and people with an accuracy of 89.87 percent. This innovation can reduce electricity consumption by 38.1 percent and cut lighting maintenance costs by 48 percent. © 2024 IEEE.

关键词： Zigbee

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An Area Coverage Aware Clustering Based Solution for MCMS Problem in Directional Sensor Networks 6

An Area Coverage Aware Clustering Based Solution for MCMS Pr...

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6th International Conference on Sustainable Technologies for Industry 5.0, STI 2024

作者： Muntaha, Maisha Al Amin Hossain, Md. Hasan, Fahim Yousuf Siam, Md. Abu Sharmin, Selina Pathan, Md. Naimul Nakshi, Sagufta Sabah Green University of Bangladesh Dept of Computer Science and Engineering Dhaka Bangladesh Jagannath University Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331531973

Wireless Sensor Networks (WSNs) are increasingly employed in applications requiring high reliability, such as industrial monitoring, military operations, and structural health evaluations. One of the main research issues in Directional Wireless Sensor Networks (DSNs) is area coverage. The area coverage problem, also known as MCMS, is a well-known fundamental problem which involves maximizing the sensing area coverage percentage of perception area with the minimum possible sensors. Also, area coverage is a significant difficulty for Directional Wireless Sensor Networks (DSNs). DSNs, unlike omnidirectional sensors, require careful selection of both sensor nodes and detection orientations to maximize coverage. This paper suggests a grid-based method for choosing the cluster head (CH) and setting up its communication sector to get the best area coverage, thus we propose an area coverage aware grid based clustering (ACAC) solution. In this study, we aim to achieve two main objectives: first, selecting CH to collect and transmit data from the sensor to the sink node. Second, choosing feasible communication sector based on the grid count values among multiple sectors. Furthermore, we demonstrate the effectiveness of our algorithms through existing work. © 2024 IEEE.

关键词： Sensor nodes

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Classification and Featurization of Motherboard Components Using YOLO Through Machine Learning

Classification and Featurization of Motherboard Components U...

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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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A Serendipity Recommendation Method for Book Categories Using BERT to Strengthen the Web Service of the Book

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Journal of Web engineering 2025年第2期24卷 199-216页

作者： Kim, Youngmo Kim, Seok-Yoon Park, Byeongchan Dept. of Computer Science and Engineering Soongsil University South Korea

In the field of book search, research on a web service-based user-customized book recommendation system is being conducted to respond to increasingly diverse user requirements. The collaborative filtering algorithm, which is mainly used for book recommendation, has a problem in that it is difficult to reflect the user’s recent interest without considering the changes in preference over time, and the user’s satisfaction decreases because it repeatedly recommends only similar items. In this paper, we propose a book recommendation method using category similarity based on deep learning. The proposed method is to predict books to be used next time by inputting users’ past and current book usage history through BERT, a natural language processing model, and to recommend popular books in other categories with high similarity to the predicted book category in the BERT model to reflect serendipity. This method reflects serendipity, which can lead to users’ recent interests and practical preferences, so that recommendation accuracy and user satisfaction can be satisfied at the same time. © 2025 River Publishers.

关键词： BERT book category recommendation serendipity Web service

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Cloud Based Predictive Maintenance System 11

Cloud Based Predictive Maintenance System

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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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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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NLP Powered Restaurant Chatbot for Exceptional Customer Engagement 2

NLP Powered Restaurant Chatbot for Exceptional Customer Enga...

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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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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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Satellite Communication Vulnerabilities and Threat Landscape

Satellite Communication Vulnerabilities and Threat Landscape

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Khare, Shanu Talwandi, Navjot Singh Chandigarh University Dept of Computer Science Engineering Punjab India Chandigarh University Dept of Apex Computer Science Engineering Punjab India

ISBN: (纸本)9798331540821

Satellite communication (satcom) has become an integral part of modern society, providing essential services in various sectors such as military, aviation, maritime, and emergency response. Despite its importance, satcom systems remain vulnerable to numerous threats that could result in significant consequences. This paper examines the vulnerabilities and threat landscape associated with satellite communication. Several factors contribute to the susceptibility of satcom systems, including their reliance on radio frequency signals, which can be easily intercepted, jammed, or spoofed. Additionally, satcom equipment and infrastructure may lack adequate security features, making them easy targets for cybercriminals. Potential adversaries include state-sponsored actors, hacktivists, terrorists, and criminal organizations seeking financial gain or causing chaos. These groups have varying levels of motivation, resources, and expertise, enabling them to develop sophisticated attack methods. Some common satcom exploitation techniques include signal jamming, eavesdropping, hijacking, and cyberattacks targeting ground stations, satellites, or user terminals. The impact of satcom vulnerabilities varies depending on the sector but generally includes service disruptions, loss of sensitive information, safety risks, and economic losses. To address these challenges, it's crucial to implement robust security measures, such as encryption, authentication, network segmentation, regular software updates, and employee training. Furthermore, international cooperation and collaboration between public and private entities are necessary to ensure effective threat intelligence sharing and incident response. © 2024 IEEE.

关键词： International cooperation

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Unravelling the Enigma of Polycystic Ovary Syndrome (PCOS): Using MLAlgorithms 2

Unravelling the Enigma of Polycystic Ovary Syndrome (PCOS): ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Juneja, Sonam Bhati, Bhoopesh Singh Atwal, Shikha Anand, Monika Maiti, Souvik Chandigarh University Dept. of Computer Science and Engineering Gharuan India Iiit Dept. of Computer Science and Engineering Sonepat India

ISBN: (纸本)9798350371055

The difficulties and ramifications of PCOS, which affects a sizable portion of women who are of reproductive age, are discussed in this review. The article covers the various clinical manifestations of PCOS, how it affects both reproductive and non-reproductive health, and how it's linked to psychological distress and metabolic disorders. It highlights how critical lifestyle modifications, early detection, and accurate diagnosis are. Additionally, the study presents machine learning techniques for PCOS detection, demonstrating the effectiveness of techniques like Random Forests, CNN, and SVM. An innovative CDSS that uses the Red Deer Algorithm shows encouraging accuracy. The necessity of continued research, diversified datasets, and cooperative efforts to enhance PCOS detection at the nexus of technology and healthcare is highlighted in the abstract's conclusion. © 2024 IEEE.

关键词： Deep Learning KNN Machine Learning PCOS Random Forest SVM

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