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Research on the Application of Machine learning in Photovoltaic Power Station 3

Research on the Application of Machine Learning in Photovolt...

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3rd International Academic Exchange conference on Science and Technology Innovation, IAECST 2021

作者： Wang, Jiapeng Xi'An Jiaotong-liverpool University International Business School Suzhou Suzhou215000 China

ISBN: (纸本)9781665402675

With the increasingly prominent environmental problems, clean energy is gradually replacing fossil energy and changing the existing energy structure is the only way to achieve sustainable development in the future. This paper mainly studies the application of machine learning in photovoltaic power stations from three aspects of photovoltaic forecasting, data monitoring and fault detection, and aims to explore the development direction of machine learning in the next stage. Though study these parts, the paper discusses the current monitoring methods of photovoltaic forecasting data, and comes to the development path of in-depth data mining, multi-source data acquisition and multi-system integration. It is concluded that the comprehensive application of multiple prediction methods and monitoring methods will be the key to improve the accuracy of prediction in the future, including the intelligent detection method, unintelligent detection method and UAV (unmanned aerial vehicle) monitor method. © 2021 IEEE.

关键词： Machine learning

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Optimizing the Efficiency of Computational Platforms: Traditional vs. Containerized Setups

Optimizing the Efficiency of Computational Platforms: Tradit...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： Haranath Rakshit Subhasis Banerjee Department of Computer and System Sciences Siksha Bhavana Visva-Bharati Bolpur(Santiniketan) India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Optimizing computational platforms in digital labs is vital for education, research, and enterprise applications. Traditional setups face resource inefficiencies, scalability constraints, and configuration conflicts, hindering performance. This paper compares traditional and containerized setups using Docker and Kubernetes, evaluating metrics such as CPU usage, memory consumption, latency, and setup time. Results reveal containerization reduces CPU usage by 23%, memory by 34%, latency by 40%, and setup time by 80%, while improving scalability by 140%. Proxy containers and orchestration effectively resolve MySQL plugin mismatches, ensuring uninterrupted operation and efficient resource utilization. These findings underscore containerization’s role in enhancing digital lab efficiency, scalability, and adaptability. Future research will focus on AI-driven orchestration techniques and advanced network policies to address challenges in multi-container environments.

关键词： Measurement Scalability Memory management Education Microservice architectures Containers Resource management intelligent systems optimization Faces

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A Study of Lung Cancer Prediction Using Machine learning Algorithms

A Study of Lung Cancer Prediction Using Machine Learning Alg...

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Electronic and Electrical Engineering and intelligent System (ICE3IS), International conference on

作者： Chinmoy Modak Md Ahnaf Shahriyar Md Siam Taluckder Md Sabbirul Haque Md Abu Sayed Department of Computer Science Florida Polytechnic University Lakeland Florida College of Technology and Engineering Westcliff University Irvine CA Phillip M. Drayer Department of Electrical Engineering Lamar University Beaumont Texas IEEE Professional Community Institute of Electrical and Electronics Engineers New York NY Department of Professional Security Studies New Jersey City University Jersey City New Jersey

Lung cancer is the leading cause of death in the USA It is caused for various reasons, such as malignant tumors that grow spontaneously in the lung and spread to other parts of the body, where they can be devastating. Other than that, there are so many external reasons why smoking is the primary factor that gradually creates lung problems that lead to lung cancer. However, we can prevent this deadly cancer by detecting it at an early stage. A machine learning-based detection model has the potential to detect lung cancer, which helps to predict the early stage of cancer. So, we aim to build some powerful machine learning algorithms to predict this cancer that can help physicians make decisions about the diagnosis process of a particular patient, whether the patient needs a high or a regular diagnosis level. This can reduce the treatment cost of a patient because the physician knows from the result how many doses are needed for a patient, so it can avoid unnecessary treatment, which saves money and brings an efficient diagnosis system. We aimed to build a sustainable model that could predict the disease accurately, and finally, our models, such as XGboost, achieved the highest value of other models. According to the accuracy level, XGboost performed up to 95.92%. Decision Tree, Random Forest, Logistic Regression, and Naïve Bayes achieved 91.50%, 93.32%, 57.41%, and 90.32% accuracy, respectively.

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AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool for Non-Invasive Medical Diagnostics

AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool fo...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Aniruddha Ghosh Sayan Snigdha Pal Sumedha Das Spandan Bhattacharya Arijit Bhattacharya Nilanjana De Sudipta Sahana Computer Science and Engineering(Artificial Intelligence and Machine Learning) Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata West Bengal India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Thermography is an innovative technique that creates a heatmap of different portions of the human body by using infrared (IR) radiation where color scales vary for various temperature regions. This work's scope aims to extend thermal imaging applications with machine learning algorithms in conjunction with Azure API services to analyze thermal images and return a preliminary diagnosis. The project consists of generating heatmaps for better visualization results, analyzing them through image processing techniques, and feeding the information to Azure’s Custom Vision API to detect anomalies and classify health conditions and finally producing a proper report. This research allows individuals and healthcare professionals to have a quick, non-invasive diagnostic tool that empowers health monitoring and preventive care. It combines the capabilities of contemporary technologies with uses directed toward health practice, leading to inimical patient outcomes and true practices.

关键词： Visualization Machine learning algorithms Image color analysis Infrared heating Infrared imaging Thermal analysis Medical diagnosis Predictive analytics intelligent systems Monitoring

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Design of an FPGA-Based Smart Block Handling System

Design of an FPGA-Based Smart Block Handling System

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Smart City Challenges & Outcomes for Urban Transformation (SCOUT)

作者： Chenxu Li Ronghui Kang Fuyuan Song Ruolin Ran Yuanlin Gao Yanning Zhao Jiawen Ren Yifei Wang China University of Petroleum

ISBN: (数字)9798331528638

ISBN: (纸本)9798331528645

This paper presents the design of an intelligent robot handling system based on vision servo, aimed at improving the level of industrial automation and production efficiency. The system adopts a modular architecture, including three core modules: vision perception, trajectory planning, and motion control. The vision perception module utilizes deep learning algorithms for rapid identification and precise localization of objects; the trajectory planning module is based on an improved RRT algorithm to generate optimal paths; and the motion control module employs an adaptive PID control strategy to ensure system stability and accuracy. Experimental results show that the system performs excellently under various load conditions, demonstrating superior handling efficiency and positioning accuracy. Long-term continuous operation tests confirm the system's reliability and stability. This design offers an efficient, precise, and stable automation solution for the field of intelligent manufacturing, with broad application prospects.

关键词： Deep learning Automation Accuracy Trajectory planning Visual servoing Manufacturing Object recognition Servomotors intelligent robots Visual perception

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A Review on Analyzing and Predicting the State of Cancer Disease using Machine learning Algorithms

A Review on Analyzing and Predicting the State of Cancer Dis...

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International conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Babu Selvaraj M. Aruna Department of Computer Science And Engineering SRM Institute of Science and Technology Chengalpattu Distric Kattankulathur Tamil Nadu Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu District Kattankulathur Tamil Nadu

Cancer represents a chronic anomaly induced by mutations within genes that regulate the functioning of body cells, particularly their growth and replication. Over 100 variations of cancer exist, each named after the tissue or organs in which they originate. Cancer stands as the leading global cause of death. Timely detection and prognosis of cancer have become imperative, aiding subsequent medical intervention for patients. This research offers a comparison of machine learning, deep learning, and optimization strategies for cancer detection. Numerous researchers have focused on cancer diagnosis and prognosis, with each approach exhibiting distinct accuracy rates influenced by specific circumstances, tools, and datasets. Navigating challenges associated with obtaining high-quality labeled datasets, addressing the diverse nature of cancer, and ensuring the ethical integration of machine learning into clinical workflows are essential obstacles. Additionally, dealing with class imbalance, extracting features from intricate biological data, ensuring dependable validation, and integrating various data sources pose crucial challenges for developing robust algorithms in cancer analysis. The main aim of this study is to evaluate various existing Machine learning and optimization approaches, identifying the most suitable methods to accommodate extensive datasets with high prediction accuracy. This revision primarily aims to showcase previous research on machine-learning techniques employed for cancer detection. The study presents comprehensive information for novices wishing to explore machine learning methods and establish a foundation in deep learning.

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Traffic Classification For Botnet Detection Using Deep learning

Traffic Classification For Botnet Detection Using Deep Learn...

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International conference on Smart Cities, Automation & intelligent Computing Systems (ICON-SONICS)

作者： Md. Sagar Hossen Tohari Ahmad Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

In this modern world, we are naturally dependent on technology, which is constantly evolving. At the same time, our fear of data fraud is increasing every day. The ever-increasing number of attacks on our servers indicates that people have learned to adapt to new technologies. In addition, the number of botnet attacks is not insignificant, and botnets threaten computer networks. This can impact security systems, including malware distribution, phishing, spamming, and click fraud. Due to their harmful effects, botnets must be identified as soon as possible. Due to the dynamic character of botnets, however, detection has proven difficult. In this proposed method, deep learning is used to analyze and predict botnet data, and model performance metrics such as accuracy, precision, Recall, and F1 score are compared to prior work. In the proposed procedure, patterns are also discussed by analyzing botnet data. Various scenarios and sensors separate the NCC and NCC-2 datasets. The NCC dataset is separated into 13 scenarios, while the NCC-2 dataset is separated into 3 sensors. The dataset is preprocessed using the clean method, the null value handling method, the imbalance method, and the feature selection method to extract the essential characteristics. The dataset is then trained using LSTM, GRU, and Bi-LSTM models, and 99.93% accuracy is achieved.

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A Comprehensive Review of intelligent Voice Disorder Diagnosis: Techniques, Databases, and Machine learning Approaches

A Comprehensive Review of intelligent Voice Disorder Diagnos...

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Embedded Systems and Artificial Intelligence (ESAI), International conference on

作者： Kanza Boujraine Naouar Laaidi Abderrahim Ezzine Hassan Satori Computer Science Faculty of Sciences Dhar MahrazSidi Mohammed Ben Abdallah University Fez Morocco

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

The diagnosis and monitoring of voice-affecting disorders using machine learning (ML) approaches have garnered significant attention globally. This review highlights the current state of research in this domain, identifying a growing interest in leveraging ML techniques for voice disorder diagnosis. However, it reveals several limitations, such as the inconsistent use of datasets across studies and a predominant focus on diagnostic accuracy, often overlooking long-term surveillance of specific disorders. Despite these shortcomings, this review provides valuable insights into the potential of ML-based voice disorder detection and emphasizes the need for broader research efforts. Expanding access to diverse datasets and addressing disorder-specific monitoring will be crucial to advancing clinical applications in this field.

关键词： Surveys Pathology Adaptation models Accuracy Reviews Databases Surveillance Speech recognition Standardization Reinforcement learning

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Design of a Real-Time Crime Monitoring System using Machine learning

Design of a Real-Time Crime Monitoring System using Machine ...

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intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： A. Krishnaveni C. Manikandan Department of Computer Science Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Crime in public areas poses risks to individuals and society at large. Traditional crime prevention methods face significant hurdles in detecting and tracking crime. Furthermore, the influx of large volumes of data overwhelms security systems and personnel making security enforcement harder. However, technological innovations have positively influenced different fields, including public safety and crime prevention. This study introduces an innovative system for crime prediction and analysis using machine learning, designed to assist law enforcement in identifying criminals in public spaces. The system enables authorities to input detailed information about criminals, including facial data, which is used to train an advanced recognition model. The system's ability to provide accurate and timely alerts enhances the efficiency of law enforcement operations. This framework leverages state-of-the-art face recognition technology to offer a scalable and robust solution for crime prevention. By integrating machine learning algorithms with intelligent surveillance systems, the proposed approach addresses the growing need for proactive measures to ensure public safety. Further, this system also highlights the potential of technology to transform traditional crime detection methods, paving the way for smarter and more responsive law enforcement practices. Finally, the proposed system attained a 92% face detection accuracy score which was higher than existing approaches.

关键词： Accuracy Recurrent neural networks Law enforcement Face recognition Surveillance Prevention and mitigation Speech recognition Real-time systems Public security Data models

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BDSIC 2021 - 2021 3rd International conference on Big-Data Service and intelligent Computation

BDSIC 2021 - 2021 3rd International Conference on Big-Data S...

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3rd International conference on Big-Data Service and intelligent Computation, BDSIC 2021

ISBN: (纸本)9781450390552

The proceedings contain 14 papers. The topics discussed include: who will win: cowboy drifter or Poirot? regulation-mode-based search strategies in changing environments;reversible data hiding in encrypted images based on Chinese remainder theorem and secret sharing mechanism;automatic smart device identification based on web fingerprint and neural network;machine learning-powered mobile app for predicting used car prices;research on key algorithms of intelligent recommendation system for retired soldiers’ employment training;research on intelligent monitoring and maintenance of civil air defense alarm system based on Internet of things;and research on strategic decision-making of government, medical institutions and patients in dual-referral system based on multi-party evolutionary game.

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