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A Fast Response Fault Detection Technique for HVAC Transmission Line Using Machine Learning

A Fast Response Fault Detection Technique for HVAC Transmiss...

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International Conference on Power Systems (ICPS)

作者： Fahim Muntasir Wasif Al Wazed Wasi Mahdee Nafis M. Mofazzal Hossain Dept. of Computer Science and Engineering BRAC University Dhaka Bangladesh Dept. of Electrical and Electronic Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Dept. of Electronics and Telecommunication Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Dept. of Electrical and Electronic Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh

High voltage alternating current (HVAC) transmission lines are the only way to transmit electrical energy from the power plant to the utilities. There are several types of transmission line faults that can interrupt the power supply, sometimes even resulting in national blackout. To ensure a highly reliable power supply system, detecting the faults in the quickest of time is crucial. This research used machine learning and neural network algorithm to find five different types of faults using data from a real power transmission line in Bangladesh. Apart from the accuracy of the methods, the focus was given on the response time in identifying the faults. The two boosting methods CatBoost and XGBoost stood out as the most precise among all the algorithms, achieving excellent accuracy and detection time in between 20 ms to 30 ms. The response time of all the methods was found to be in millisecond range, making them perfect for not only long term faults, but also to be implied in transient fault analysis.

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Exploring the Semantic Complexity of Adjective-Noun Collocations between Uzbek and English for Improved Machine Translation

Exploring the Semantic Complexity of Adjective-Noun Collocat...

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International Conference on computer Science and engineering (UBMK)

作者： Nilufar Abdurakhmonova Nargiza Shamieva Eşref Adali Dept. of Computational and applied linguistics National University of Uzbekistan Tashkent Uzbekistan Dept. of Foreign language and Literature National Univ. of Uzbekistan Tashkent Uzbekistan Computer Engineering and Informatics Faculty Istanbul Technical University Istanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

The bilingual thesaurus of Uzbek-English adjectives is a field that has not been studied well explored yet for machine translation. Several websites and electronic dictionaries created nearby show that not enough semantically close adjectives are included in them. Despite extensive research on the meaning the adjectives in both languages, there remains a lack of understanding about the relationship between the adjective itself and the noun, which has an absolutely new connotation in collaboration. Our result shows that a recent bilingual thesaurus demonstrates how one adjective could acquire numerous new synonyms while combining with different nouns. The meanings of words, in both languages have been extensively studied, Little is understood about the dynamic connection between these words and the nouns they describe. The complexity of how adjectives pair with nouns is evident in how this relationship leads to meanings and connotations. Our study aims to bridge this gap by demonstrating how a bilingual thesaurus can capture the meanings and variations of adjectives when combined with nouns. The findings indicate that depending on the noun it accompanies a contemporary bilingual thesaurus can generate synonyms, for a single adjective.

关键词： Vocabulary Dictionaries Sensitivity Semantics Linguistics Thesauri Complexity theory Machine translation Cultural differences Probes

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Developing a Python-Based QR Code Generation System

Developing a Python-Based QR Code Generation System

引用

Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Priyanka Kaushik Shamna Parveen S Shalu Sucheta Panda Swati Agarwal P. R. Sukanya Sridevi Dept. of CSE Chandigarh University Chandigarh India Dept. of CSE Dhanlaxmi Srinivasan College Of Engineering Coimbatore India Dept. of Computer S. & Technology Manav Rachna University Faridabad India Dept. of Computing and IT Usha Martin University Jharkhand India Dept. of Mathematics Usha Martin University Jharkhand India Dept. of Computer Science Meenakshi A.H.E.R Chennai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

This project entails importing necessary modules, creating the main window, and obtaining user inputs such as text or URL, the location to store the QR code, the name of the QR code, and its size. The paper delves into the underlying principles of QR code technology, covering aspects such as its structure, error correction capabilities, and applications across various industries. It highlights how Python serves as a powerful, flexible, and user-friendly tool for generating QR codes. This project is designed to be utilized by software developers, researchers, and enthusiasts, enabling them to gain a comprehensive understanding of QR code generation and to create their own customized QR code generation applications. By providing a detailed technical guide for QR code generation using Python, this research paper makes a significant contribution to the field of QR code technology.

关键词： Industries Uniform resource locators QR codes User interfaces Programming Generators Software Reliability Standards Python

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Real-time Human Movement Recognition and Interaction in Virtual Fitness using Image Recognition and Motion Analysis

Real-time Human Movement Recognition and Interaction in Virt...

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International Conference on Awareness Science and technology (iCAST)

作者： ChunHsiu Yeh Wei-Cheng Shen Chi-Wei Ma Qiu-Tong Yeh Chung-Wei Kuo Jong-Shin Chen Dept. of Information Engineering and Computer Science Feng Chia University Taichung Taiwan ROC. College of Computer Science Jimei University Xiamen China Dept. of Information Communication and Engineering ChaoYang University of Technology Taichung Taiwan ROC.

This research focuses on the application of image recognition, motion detection, and artificial intelligence techniques to achieve real-time identification and analysis of human movements in a dynamic manner. The primary goal is to establish a seamless association between captured human movements and corresponding actions performed by virtual avatars. The implementation involves the utilization of image processing methodologies from python's OpenCV and pygame libraries. MediaPipe technology is employed to capture users' images and facilitate accurate detection of specific movement patterns. By calculating the angles between users' limbs and torso, the correctness of certain movements can be assessed. The research is practically developed and applied to a simulated fitness system, where an interactive exercise game platform is devised. The system is characterized by its engaging user interface, enhancing the ease and enjoyment of fitness activities. The seamless integration of interactive virtual avatars and real users within physical activities serves to stimulate greater participation, particularly among the elderly. The amalgamation of image recognition and motion detection technologies presents opportunities for combining fitness and entertainment in diverse life scenarios, offering an enhanced experience of physical exercise and enjoyment.

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Review on Architectures of Convolutional Neural Network

Review on Architectures of Convolutional Neural Network

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Disruptive Technologies (ICDT), International Conference on

作者： Sachin Chawla Deepa Gupta Sanjeev Kumar Pippal Dept. Amity School of Engineering and Technology Amity University Greater Noida UP India Dept. Amity Institute of Information Technology Amity University Noida UP India Dept. Computer Science-Artificial Intelligence G.L Bajaj Institute of Technology and Management Greater Noida UP India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This review provides a broad overview of CNN architectures, focusing on the crucial building blocks and layered structures that form complete networks. It traces the evolution of CNNs, summarizing key publications and highlighting the diverse array of architectures that have emerged in recent years. While simplifying some technical details, the review maintains conceptual rigor to ensure a thorough understanding of CNN principles and development. This review serves as a valuable resource for those seeking to navigate the complexities of CNNs, offering insights into their operational principles, architectural innovations, and potential applications beyond the realm of computer Vision.

关键词： Training Technological innovation computer vision Reviews Architecture Buildings computer architecture Self-supervised learning Time measurement Convolutional neural networks

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Stakeholders Sentiment Analysis Using Twitter Data on NEP 2020: A Case Study Approach

Stakeholders Sentiment Analysis Using Twitter Data on NEP 20...

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International Conference on Network Applications Protocols and Services (NETAPPS)

作者： Shivaleela Arlimatti Walid Elbreiki Suhaidi Hassan Suresh D. Mane Ganesh Rathod Dept of Computer Science & Engineering Tatyasaheb Kore Institute of Engineering & Technology Warana University Warananagar Kolhapur Dept. of Computer Science College of Computer Technology Banghazi Libya Internetworks Research Laborotary School of Computing University Utara Malaysia Sintok Malaysia Dr. D. Y. Patil Pratisthan's College of Engineering Salokhenagar Kolhapur India Dept of Computer Science & Engineering Dr. D. Y. Patil Pratisthan's College of Engineering Salokhenagar Kolhapur India

ISBN: (数字)9798331519513

ISBN: (纸本)9798331519520

The National Education Policy 2020 (NEP2020) is a comprehensive framework aimed at transforming the education system in India. This paper analyzes NEP2020 and its details envisioned in promoting educational excellence and addressing the needs of a rapidly changing world. The educated elite have access to the information on salient features and hence are in a position to give their free and fair opinion. Thus, this work is based on twitter wherein it utilizes a dataset of 18000 tweets, focusing on discussions and opinions related to the NEP 2020. The tweets are preprocessed and then labeled with sentiment categories such as positive, negative, or neutral. A decision tree classifier is trained on the preprocessed and labeled dataset to predict the sentiment of new tweets. The performance of the classifier is assessed using accuracy, recall, F1 score, and precision metrics, providing insights into the effectiveness of the sentiment analysis approach. The decision tree classifier achieves promising performance in accurately predicting sentiment categories. It is observed that over 55% of tweets are positive towards the policy, 17% are negative and 28% are neutral towards the policy with the study being 92% accurate. The findings of this work can be used by policymakers and education stakeholders in understanding public sentiment and making informed decisions regarding the NEP 2020.

关键词： Measurement Sentiment analysis Accuracy Protocols Social networking (online) Education Blogs Stakeholders Decision trees Reliability

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Automated Weed Species Classification in Rice Cultivation Using Deep Learning

Automated Weed Species Classification in Rice Cultivation Us...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Hamdadur Rahman Mahbub Alam Khan Shakil Khan Zishad Hossain Limon Md Ismail Hossain Siddiqui Shimanto Krishna Chakroborty Shamim Forhad Abdullah Al-Sakib Sheikh Shemanto Afridi Shafiur Rahman Dept. of Management of Information System International American University CA USA Dept. of Management Information System Pacific State University CA USA Dept. of Business Analytics International American University CA USA Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Engineering/Industrial Management Westcliff University Irvine CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University CA USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Weeds in rice fields compete for essential resources such as water, nutrients, and light, leading to reduced crop yields, increased production costs, and environmental concerns. Traditional methods of weed detection are often labor-intensive, subjective, and inefficient, highlighting the need for automated approaches in precision agriculture. This study introduces an ensemble model designed for robust and accurate classification of rice weeds, addressing the shortcomings of conventional methods and enhancing detection scalability. The proposed ensemble model combines four deep learning architectures: EfficientNet-V2, SqueezeNet, InceptionResNet-v2, and DenseNet-201. By leveraging the complementary strengths of these models, we aim to improve performance. The study utilizes the Rice Field Weed Detection Dataset, which contains 3,632 high-resolution images that have been augmented to a total of 21,792 samples. Advanced preprocessing techniques such as resizing, normalization, and augmentation are applied to ensure effective feature extraction. Our model achieved a remarkable classification accuracy of 99.51%, significantly surpassing individual models and other state-of-the-art approaches. In addition, we developed a web-based application for real-time weed detection, providing a user-friendly platform for farmers and agricultural professionals to implement timely weed management strategies. This system supports sustainable farming by reducing the need for excessive herbicide use and promoting environmentally friendly practices. This research highlights the potential of ensemble deep learning for precise agricultural weed detection. Future work will focus on integrating diverse datasets and developing mobile applications to enhance accessibility and scalability.

关键词： Deep learning Accuracy Scalability Transfer learning Feature extraction Real-time systems Mobile applications Crop yield Water resources Farming

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Enhanced QSAR Modeling for Drug Discovery: Leveraging Advanced Computational Tools and Techniques

Enhanced QSAR Modeling for Drug Discovery: Leveraging Advanc...

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IEEE Region 10 Symposium

作者： Mogalluru Chidhvilas Tanay Rahul Rathnam Pamba Vamshi Krishna Mrinal Devnath Narinder Singh Punn Dept. of Information Technology ABV-IIITM Gwalior M.P. India Dept. of Computer Science and Engineering Centre for Biomedical Research ABV-IIITM Gwalior M.P. India

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

In this research, we present a comprehensive Quan-titative Structure-Activity Relationship (QSAR) methodology uti-lizing advanced computational tools and techniques for drug discovery targeting the SARS 3-C like proteinase. By integrating data retrieval from the ChEMBL database, calculation of Lipin-ski parameters, molecular fingerprint generation using PaDEL, and Gaussian Process Regressor modeling, we aimed to enhance the predictive accuracy and reliability of QSAR models. Our results demonstrate the effectiveness of the proposed method-ology in accurately predicting compound bioactivity against the target protein. The Gaussian Process Regressor, with its probabilistic modeling capabilities and flexibility in capturing complex nonlinear relationships, proved to be a suitable choice for modeling intricate interactions between chemical structures and bioactivities. This approach offers a promising foundation for future research and advancements in computational drug design, potentially accelerating the development of novel therapeutic agents for various diseases.

关键词： Proteins Drugs Computational modeling Gaussian processes Predictive models Fingerprint recognition Probabilistic logic Data models Drug discovery Reliability

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Analysis and Research on Students' Learning Behavior Based on Lag Sequence Analysis 4

Analysis and Research on Students' Learning Behavior Based o...

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4th International Conference on computer Information Science and Artificial Intelligence, CISAI 2021

作者： Han, Jiazheng Wei, Zhengang Ocean University of China Dept. of Computer Application Technology Qingdao China Ocean University of China Dept. of Software Engineering Qingdao China

ISBN: (纸本)9781665406925

with the effective practical application of data mining technology in other fields, many researchers have used data mining technology to analyze the learning process data in the field of education and teaching, and explore the hidden and potentially valuable information and knowledge. This paper mainly mines the association in students' learning behavior data through lag sequence analysis algorithm (LSA), and shows learners' learning trajectory, willingness and cognitive process in the learning process. Help to grasp the potential behavior mode of students' learning and effectively guide the development and implementation of follow-up teaching work. © 2021 IEEE.

关键词： Data mining

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Reinforcement Learning for Unrelated Parallel Machine Scheduling with Release Dates, Setup Times, and Machine Eligibility

Reinforcement Learning for Unrelated Parallel Machine Schedu...

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Simulation Winter Conference

作者： Sang-Hyun Cho Hyun-Jung Kim Lars Mönch Dept. of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Dept. of Mathematics and Computer Science University of Hagen Universitätsstraße 1 Hagen GERMANY

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

This paper presents a novel approach for solving unrelated parallel machine scheduling problems through reinforcement learning. Notably, we consider three main constraints: release date, machine eligibility, and sequence- and machine-dependent setup time to minimize total weighted tardiness. Our work presents a new graph representation for solving the problem and utilizes graph neural networks combined with reinforcement learning. Experimental results show that our proposed method outperforms traditional dispatching rules and an apparent tardiness cost-based algorithm. Furthermore, since we represent and solve the problem using graphs, our method can be used regardless of the number of jobs or machines once trained.

关键词： Metalearning Adaptation models Heuristic algorithms Stochastic processes Reinforcement learning Parallel machines Dynamic scheduling Size measurement Dispatching Graph neural networks

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