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A Survey on machine learning Approaches in Cryptocurrency: Challenges and Opportunities 4

A Survey on Machine Learning Approaches in Cryptocurrency: C...

引用

4th international conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Mujlid, Hana Taif University Department of Computer Engineering Taif Saudi Arabia

ISBN: (纸本)9798350335316

Blockchain research is now heavily centred on cryptocurrency, which has drawn the attention of financial academics. the availability of numerous types of data and a wealth of resources in bitcoin research facilitates the application of machine learning algorithms. However, a through analysis and a complete study of machine learning-based cryptocurrencies need further research. Cryptocurrency price prediction is the most pertinent research topic and the algorithms being used in cryptocurrency research are not unique. Various researcher are using combination of multiple machine learning algorithms. therefore, in this study, the research related to cryptocurrency price prediction using machine learning is summarized along with prominent research challenges related to application of machine learning algorithms in cryptocurrency. © 2023 IEEE.

关键词： Forecasting

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Detection of Malicious URL Websites using machine learning Models 4

Detection of Malicious URL Websites using Machine Learning M...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Reddy, Karri Narendra Venkatesh, Kolapalli Naga Prem Jacob, T. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350300093

In recent years, web applications have become increasingly vulnerable to hacking, with an estimated occurrence of 32,590 attacks every day. However, many web developers and website owners are unprepared to identify and prevent these attacks. Hackers utilize various techniques, including phishing websites to gain unauthorized access or compromise authentic web programmers. this research study examines the web application security and the frequency of attacks on such systems. this study focuses on the most common types of attacks and suggests effective detection methods for preventing them. Recently, the secure coding methodologies and machine learning algorithms are used to detect and block unauthorized access and phishing *** almost all the existing research works, a web application is developed to test several website URLs. the findings suggest that the model is capable of detecting malicious web application attacks. Furthermore, the model compares the performance of several machine learning algorithms for identifying phishing website links and detecting with the best model. © 2023 IEEE.

关键词： Websites

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Artificial Intelligence and learning Techniques in Intelligent Fault Diagnosis 4

Artificial Intelligence and Learning Techniques in Intellige...

引用

4th international conference on computer Science and Network Technology (ICCSNT)

作者： Sun Yuanyuan Guo Lili Wang Yongming Chinese Acad Sci Technol & Engn Ctr Space Utilizat Beijing 100094 Peoples R China Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China

ISBN: (纸本)9781467381734

At present, based on computer and information technology, intelligent diagnosis technology is in rapid development. In this paper, the application of artificial intelligence and learning techniques in intelligent fault diagnosis are demonstrated, such as Rule-Based Reasoning, Case-based Reasoning, Network neural, Fuzzy Logic, Genetic algorithm, Rough set theory, Bayesian network theory, Multi-agents, Reinforcement learning, Support Vector machine. Some kinds of applications are introduced. these intelligent fault diagnosis methods are widely used in complex fault diagnosis system. We will try to use them in our future intelligent fault diagnosis system for space station.

关键词： RBR CBR NN FL GA RST Bayesian network Multi-agents RL SVM

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Empowering Healthcare Systems Using machine learning: Working, Classification and application 4

Empowering Healthcare Systems Using Machine Learning: Workin...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： thakur, Keshav Raj Bhushan, Bharat Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350306118

Empowering Healthcare Systems with machine learning: Mechanisms, Classifications, and applications' examines how machine learning (ML) and healthcare are dynamically combining. It explains how ML algorithms manage healthcare data in detail, classifies ML's many healthcare applications, and emphasizes the significant impact of these applications. ML transforms healthcare systems, improving everything from the accuracy of disease diagnosis to the creation of individualized treatment approaches. To ensure appropriate integration, ethical and legal aspects are emphasized. In addition to redefining patient care, this ML and healthcare synergy improves decision-making and improves patient outcomes. the paper's main contribution is its discussion of an overview of machine learning (ML), which includes its needs, features, many applications and challenges and difficulty of (ML) in health sector. this paper highlights how (ML) have the ability to improve healthcare efficiency in general and the diagnosis and personalization of patient care. But it also acknowledges problems like data accessibility, data quality, high dimensionality, efficiency, and privacy issues that demand careful thought. © 2023 IEEE.

关键词： machine learning

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Derivation of Constituent Problem Characteristics for the application of machine learning Systems 4

Derivation of Constituent Problem Characteristics for the Ap...

引用

4th international conference on Information and computer Technologies (ICICT)

作者： Schuh, Guenther Scholz, Paul Burger, Timon Fraunhofer Inst Prod Technol IPT Aachen Germany Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9781665413992

the increasing digitalization across all business sectors creates ever larger amounts of data. When analyzing this data to extract information, traditional data analysis methods easily meet their limits of performance. Conversely, methods from the machine learning (ML) spectrum promise to be a versatile tool for solving highly complex data-related tasks. Yet, organizations fail to identify relevant applications for ML due to a lack of systematic understanding of the applicability of the technology. this research paper draws upon the assumption that real-world problems exhibit distinctive characteristics that indicate their suitability for the application of ML. A framework for describing those constituent problem characteristics is proposed. Investigating the functional differences between traditional data analysis methods and ML, constituent characteristics are derived based on the distinctive technological abilities of ML. In order to differentiate simple ML methods from advanced ML methods regarding their technological abilities, a framework is presented. We suggest that advanced ML methods such as Deep learning or Transfer learning provide different potential benefits than simple methods such as Decision Trees, therefore necessitating this additional distinction.

关键词： machine learning artificial intelligence statistics data requirements application

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An Empirical Study of Monte Carlo-based Methods in machine learning 4

An Empirical Study of Monte Carlo-based Methods in Machine L...

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4th international conference on Signal Processing and computer Science, SPCS 2023

作者： Lei, Fei Wang, Yan Wang, Lulu Wu, Qiuping School of Finance and Economics Guangdong University of Science and Technology Dongguan China School of Management Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9781510672505

Empirical studies in the field of machine learning based on Monte Carlo methods explore a powerful statistical technique aimed at solving the core problems of uncertainty modeling, model evaluation, and decision making;Monte Carlo methods provide new perspectives and tools for the application of machine learning models through stochastic sampling and simulation;however, Monte Carlo methods are also facing a number of challenges, such as computational efficiency, high-dimensional space problems, and sampling bias, etc.;in order to overcome these problems, this paper explores future research directions that may involve improved sampling methods, innovative applications that incorporate deep learning, etc. therefore, the continuous innovation in this field will provide new opportunities and challenges for the development and application of machine learning models. © 2023 SPIE.

关键词： Monte Carlo methods

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Analysis and Study of Conventional and Advanced Image Segmentation Techniques for machine learning-Based Leaf Disease Detection and Classification 4

Analysis and Study of Conventional and Advanced Image Segmen...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： John, Sreya Rose, P. J. Arul Leena FSH SRMIST KTR Dept of Computer Science Chennai India

ISBN: (纸本)9798350306118

the demand for agricultural crops is constantly rising throughout time. this increment in demand is coupled with many other challenging factors such as climate change, pollution, urbanization etc that affects the quality and quantity of the crop production which can even lead to an economic breakdown. Lately, many studies were done to put an end to the difficulties faced by the agricultural sector. the application of technology in this area has been a pivotal turning point in the conservation of agricultural crops. Consequently, many computer based automated systems were developed to ease the tasks of farmers. Among these innovations, the application of image processing and machine learning has emerged as a breakthrough. By enabling early detection of plant diseases, these tools significantly contribute to maintaining crop health. Segmentation is an important stage in image processing, that is critical in accurately extracting relevant information from images. In this paper, we delve into the significance and advancements of segmentation, highlighting its major role in various applications. Additionally, we have also done a comparative study based on few experiments and observations in the latest improvements of various segmentation techniques. © 2023 IEEE.

关键词： machine learning

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Model Evaluation of Various Supervised machine learning Algorithm for Heart Disease Prediction 7

Model Evaluation of Various Supervised Machine Learning Algo...

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7th international conference on Software Engineering and computer Systems and 4th international conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Hameetha Begum, S. Nisha Rani, S.N. Muscat College Muscat Oman Fatima Michael Engineering College Madurai India

ISBN: (纸本)9781665414074

machine learning (ML) is a type of Artificial Intelligence (Al) that allows the machine to learn from data and become more accurate in predicting outcomes without human intervention. the fundamental idea of machine learning is to mimic the way our brain works. In a variety of application domains, including medicine and healthcare, ML techniques have demonstrated strong prediction abilities. this study evaluated and compared several popular supervised machine learning techniques for predicting heart diseases using medical records from the UCI machine learning repository. the performance of different models, including Support Vector machines (SVM), K-Nearest Neighbor (KNN), and Logistic Regression models, is examined in this paper. the performance of these algorithms was analyzed based on the (Area Under ROC Curve) AUC score. AUC is an evaluation metric that helps us validate our machine learning algorithm how good it is, and the decision is made to implement if the AUC score is more significant than 0.5. the trial result verifies that the Logistic regression algorithm has achieved the highest AUC score of 0.87 compared to other ML algorithms. © 2021 IEEE.

关键词： Support vector machines

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Design and Implementation of a Mobile Teaching-learning Pattern through Atom Publishing Protocol

Design and Implementation of a Mobile Teaching-Learning Patt...

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4th international conference on machine Vision (ICMV) - computer Vision and Image Analysis - Pattern Recognition and Basic Technologies

作者： Wu Yu Tongji Univ Comp Sci & Technol Dept Shanghai 200092 Peoples R China

ISBN: (纸本)9780819490261

the new technologies set the stage for Mobile learning. In this paper, we explored a Mobile Teaching-learning pattern and its advantages. And then we modeled courses with Atom and Atom Publishing Protocol. Grounded on the pattern and modeling, we implemented mobile learning client side with Apple technologies, which could achieve anytime, anywhere learning. And at last, we discussed the application of our system.

关键词： Atompub Mobile Teaching and learning Apple Technology

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Research on Equipment Fault Analysis based on machine learning Algorithm 4

Research on Equipment Fault Analysis based on Machine Learni...

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2023 4th international conference on computer Engineering and Intelligent Control, ICCEIC 2023

作者： Qin, Hua Wang, Pengfei Xu, Linping Unit 92145 of Pla Hongkou Shanghai China Navy Petty Officer Academy Bengbu Anhui China

ISBN: (纸本)9798350308877

this paper builds an intelligent operation and maintenance platform based on machine learning algorithm through using the equipment level and system level fault monitoring data generated in the operation and maintenance process and combining with machine learning data analysis technology. It focuses on solving the difficult problems such as fault feature selection, fault diagnosis model classification, time series trend prediction, etc. the above research provides a decision-making reference for operating the communication links and making equipment maintenance scientifically, which has certain engineering application value and practical significance. © 2023 IEEE.

关键词： Support vector machines

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