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检索条件"任意字段=International Conference on Learning and Teaching in Computing and Engineering"

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Dry Power Transformer Fault Diagnosis Using Deep learning: A Review

Dry Power Transformer Fault Diagnosis Using Deep Learning: A...

引用

2024 international conference on Sustainable Power and Energy, ICSPE 2024

作者： Tiwari, Priyanka Singh, Shweta Singh, Vinod Kumar Maharishi University of Information Technology Lucknow Dept of Electrical Engineering UP India SR Group of Institutions Dept. of Electrical Engineering UP Jhansi India

ISBN: (纸本)9798350366051

Dry power transformers to be most effectively used in industrial, commercial, and even utility applications. They are ideal for both low voltage and medium ones hence assuring comfort in providing the needs of utility company. In this article, we summarize the applications of deep learning on vibration-based diagnosis of deformed core detection in dry power transformer based on systematic review from related literature. The perspective of data-driven, that is to say integrating various technologies related with deep learning into traditional on-the-spot fault diagnosis, especially vibration analysis and acoustic signal processing technology;infrared thermal imaging technique coupled with typical diagnostic sinkhole transformation matrix algorithm by using neural network computing. Next, we consider the impact of emerging research in these areas on transformer reliability and discuss several examples from recent literature that show how intelligent systems can be used to improve this. © 2024 IEEE.

关键词： Thermography (imaging)

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Enhancing Detection of Breast Cancer Through a Novel Framework: A Machine learning Approach with Explainable AI Interpretation 1

Enhancing Detection of Breast Cancer Through a Novel Framewo...

引用

1st international conference for Women in computing, InCoWoCo 2024

作者： Vidyashree, R. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798331518943

Breast cancer, the most prevalent cancer among women, significantly contributes to increased mortality rates and ranks as the second leading cause of cancer-related deaths in women. Early detection is crucial for effective treatment, yet traditional diagnostic approaches often involve multiple laboratory tests supervised by medical experts. To mitigate human error and expedite breast cancer detection, there is a pressing need for automated systems capable of accurate and timely diagnosis. This study aims to investigate the application of machine learning (ML) techniques in classifying breast cancer using features from digitized images of fine-needle aspiration (FNA) breast mass samples. Machine learning methods are instrumental in addressing this imperative. The study conducts a comparative analysis of various ML classification methods, including Logistic Regression, Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Trees, Bagging-Boosting, and ensemble models, using a breast cancer dataset to establish a novel framework. The performance of each algorithm is assessed based on metrics such as accuracy, precision, recall, and F1 score. The findings underscore the effectiveness of ensemble classifier models, achieving a notable accuracy rate of 98.87%. Additionally, the integration of explainable Artificial Intelligence enhances the interpretability of the models by providing valuable insights into their decision-making processes. © 2024 IEEE.

关键词： Contrastive learning

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Enabling Technologies in IoMT Smart Healthcare: A Survey 10

Enabling Technologies in IoMT Smart Healthcare: A Survey

引用

10th international conference on Communication and Signal Processing, ICCSP 2024

作者： Shivagangatharani, B. Rabiya, M. Syed National Engineering College Department of Computer Science and Engineering K.R. Nagar Kovilpatti India

ISBN: (纸本)9798350353068

The Internet of Medical Things (IoMT) is a network connected electronic smart device or Things without any involvement from humans. That makes the Healthcare system dynamic and customized one. The healthcare system can enhance the quality, accessibility, availability and affordability by using IoMT with numerous technologies such as Wireless Body Area Network (WBAN) for patients' physical data gathering, wireless communication for data transforming, big data for diagnosing, cloud computing for large storage, Fog computing to minimize the communication cost, blockchain technology provides a better protection for user sensitive data, machine learning and artificial intelligence use in each stage. In this paper, provide an in-depth study on the use of the aforementioned technologies in intelligent healthcare systems. In this article, the latest studies of the recent healthcare systems are discussed the uses, difficulties, and developing technology of smart healthcare systems. © 2024 IEEE.

关键词： Digital storage

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In-Depth Investigation of Artificial Intelligence - Blockchain Integration Strategies for Security Enhancement 7

In-Depth Investigation of Artificial Intelligence - Blockcha...

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Jothy, C.R. Judith, J.E. Noorul Islam Centre for Higher Education Computer Science and Engineering Tamilnadu Kanyakumari India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372816

Blockchain being a distributed ledger preserves data in a highly secured environment which cannot be modified even by the user who created the data. This security aspect makes the blockchain technology popular in recent years and was adopted by many organizations to secure their data. The other technology which is very popular recent times is the Artificial Intelligence (AI). Nowadays, day to day life activities are tied up with the use of AI. Knowingly or unknowingly we are using AI in various circumstances. As the world becomes digitalized almost in all aspects, security threats are also has been growing in different dimensions. This paper makes a deep survey on various research articles which integrates AI and blockchain technology to improve the overall security. This paper also analyses the various methodologies and the machine learning algorithms used by different researchers to secure data on a blockchain so that the highly secured data can be automated with the integration of AI. If proper integration techniques were implemented with appropriate Machine learning algorithms the security will be much improved. This paper concludes by providing a path way to the upcoming researchers to move their research based on the problems identified by comparing various research articles. © 2024 IEEE.

关键词： Adversarial machine learning

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An Accuracy Analysis Of Classical And Quantum Machine learning Algorithms Using Different Datasets 2

An Accuracy Analysis Of Classical And Quantum Machine Learni...

引用

2nd IEEE international conference on Networking and Communications, ICNWC 2024

作者： Bhaskaran, P. Prasanna, S. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350365269

Classical machine learning has practical significant advancements and extensive acceptance across various domains, enabling the growth of precise predictive models. The purpose of this work is to examine the accuracy gained by various machine learning algorithms, both classical and quantum. In addition, the study delves into the fundamental ideas and principles that form the backbone of quantum machine learning algorithms, such as quantum data representation, quantum feature mapping, and quantum optimization strategies. Experiments use classical machine learning models trained and tested on traditional hardware to measure evaluation metrics like classification accuracy, precision, recall, and F1-score for quantum machine learning models executed on quantum computing platforms or simulators. In quantum SVM, the seed dataset obtained 100% accuracy, and the supermarket dataset obtained 69% accuracy when compared to classical SVM. The findings imply that, under some conditions, quantum machine learning algorithms show the capacity to beat their conventional counterparts in terms of accuracy. © 2024 IEEE.

关键词： Quantum entanglement

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Anemia prediction and classification of all classes with and without anemia patients using a machine learning model

Anemia prediction and classification of all classes with and...

引用

2024 IEEE international conference on computing, Applications and Systems, COMPAS 2024

作者： Hirok, Md Kamruzzaman Rahman, Sadekur Parvin, Masuma Daffodil International University Department of Computer Science and Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331529765

Anemia has emerged as a significant public health issue, typically caused by various factors such as nutritional deficiencies, chronic diseases, and genetic conditions. Identifying individuals at risk for developing anemia presents a major challenge for doctors and public health experts, as different types of anemia are distinguished by unique characteristics. While a simple, affordable, and widely available laboratory test, the complete blood count (CBC), is commonly used to diagnose anemia, it cannot differentiate between the various types. Therefore, the goal of this study is to develop a machine learning model to predict and classify all types of anemia using machine learning algorithms. In medical science, accurate prognosis and classification are vital for halting disease progression and detecting early affected areas. Machine learning (ML) techniques are frequently employed to predict and classify disease susceptibility with high accuracy, serving as a valuable tool for doctors and specialists. In this work, four machine learning algorithms were trained on a dataset to diagnose anemia and non-anemia patients: support vector machine, random forest, decision tree, and extreme gradient boosting classifier models. The XGBoost classifier outperformed the others, utilizing 25 out of 29 features with an accuracy of 0.9998, a precision of 0.9969, a recall of 0.9951, and an F1-score of 0.9960. © 2024 IEEE.

关键词： Adversarial machine learning

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Ship Target Radiated Noise Modulation Spectrum Processing: Integration with Deep learning Methods

Ship Target Radiated Noise Modulation Spectrum Processing: I...

引用

2023 international conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Mu, Lin Science and Technology on Underwater Test and Control Laboratory Dalian China

ISBN: (纸本)9781510671881

The modulation spectrum processing of ship target radiated noise is an important means of detecting, tracking, and recognizing targets in naval operations. Improving the processing performance of modulation spectrum is crucial for enhancing naval combat capabilities. This paper combines traditional modulation spectrum processing methods with deep learning methods and proposes a modulation spectrum feature enhancement method based on deep learning. The experimental results show that, the method can effectively improve the clarity and extraction quality of the line spectrum in the modulation spectrum time-frequency map. © 2023 SPIE. All rights reserved.

关键词： Deep learning

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Forecasting New Mobile Site Location Suitability Using Machine learning 8

Forecasting New Mobile Site Location Suitability Using Machi...

引用

8th international conference on Data Science and Machine learning Applications, CDMA 2025

作者： Rabbani, Zeeshan Ebrahim Hosseini, Seyed Pervez Chattha, Shahbaz Alandjani, Gasim Abosaq, Nasser Abdul Majid, Mazlina School of Information Technology Whitecliffe College Auckland New Zealand School of Information Technology Whitecliffe College Christchurch New Zealand School of Information Technology Whitecliffe College Wellington New Zealand Yanbu Industrial College Computer Science and Engineering Yanbu Saudi Arabia Universi Malaysia Pahang Al-Sultan Faculty of Computing Abdullah Pekan Malaysia

ISBN: (纸本)9798331539696

The increasing density of mobile networks has made the identification of optimal new coverage sites a critical challenge for mobile operators. Traditional methods relying on manual inspections and council plans are labour-intensive and insufficient. This study addresses this issue by leveraging data science and machine learning techniques to predict the suitability of new mobile cell sites. Utilizing data from Radio Spectrum Management New Zealand and Stats New Zealand, the study employs Random Forest as Machine learning model. Key factors considered include existing operator information and population density. The methodology integrates data cleaning, feature engineering, and model training, with validation performed using Power BI. Results demonstrate an accuracy of 84.62% for the Random Forest model, highlighting the importance of geographical location and population density in site suitability predictions. This approach will streamline the site selection process and enhance service quality, positioning operators as leaders in underserved areas. © 2025 IEEE.

关键词： Random forests

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Steganography in the Quantum Era 6

Steganography in the Quantum Era

引用

6th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Bharatwaj, Aksaj Kumar Hasabnis, Amey R Dept of Physics Greater Noida201314 India Dept of Basic Studies Madhya Pradesh Mhow453441 India

ISBN: (纸本)9798350306613

The advancement of Quantum information and quantum computing has led to an increase in algorithms and new methods to exploit quantum computers in various domains, such as breaking cryptography, Quantum Machine learning, Quantum Eigensolvers for materials modeling etc. One such way of control-ling quantum computers has led to the inception and emergence of Quantum Image Processing and Quantum Steganography. Steganography has been widely used for secure communication in the past. With the evolution of quantum computing, the exploitation of qubits for image-based steganography has gained attention from academics, government, and military stakeholders alike to improve their respective secure communication channels. This paper aims to discuss a preliminary application of Quantum Steganography using IBM's open source software development kit Qiskit [9] in the Quantum Era. © 2024 IEEE.

关键词： post-quantum cryptography quantum image processing quantum security Quantum steganography quanytum computing

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Investigating the Applications of Artificial Intelligence in Enhancing Virtual Personal Assistants

Investigating the Applications of Artificial Intelligence in...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Praveena, K. Patel, Jyoti Manjunatha Dutt, Amit Khan, Irfan Alkhafaji, Mohammed Ayad Institute of Aeronautical Engineering Hyderabad Dundigal India GyanGanga Institute of Technology and Sciences DoEC M.P Jabalpur India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

AI is becoming a part of our daily lives. One of the most visible AI applications is the VPA. One research suggests AI may improve VPAs in many ways. It examines their merits and downsides and how they may affect technology usage. AI has made VPAs smart, situation-aware pals instead of task-oriented aids. Language processing, machine learning, and deep learning influence this development. VPAs learn what individuals say and do using these methods. AI helps VPAs classify and analyze massive data sets. Their importance has grown in our everyday lives. Right now, privacy and safety matter most. AI-driven encryption and recognition make VPAs safer. AI approaches eliminating prejudice in VPA replies, ensuring fair and unbiased interactions. learning each user's likes, dislikes, and scenario helps VPAs specialize. These adjustments may not address all problems. AI should grasp regular phrases, make clearer conclusions, and tackle data usage issues in society. The research seeks to identify and solve these issues. Finally, AI has improved virtual personal assistants' safety, intelligence, and usability. With the progression of AI, VPAs will become more prevalent. AI research must be perpetually guided by social and private concerns. Exploring the potential of AI to enhance VPAs could have a transformative impact on human-computer interactions and our daily lives. © 2024 IEEE.

关键词： Adversarial machine learning

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