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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是61-70 订阅

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Parkinsons Disease Progression Prediction using Advanced machine learning Techniques

Parkinsons Disease Progression Prediction using Advanced Mac...

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Bhattacharya, Tania Thomas, K.T. Mathew, Lian University Department of Data Science Pune India

ISBN: (纸本)9798350378092

Parkinson's disease (PD) is a neurodegenerative condition that affects people over time and significantly lowers their quality of life. Patients with PD experience both motor and non-motor symptoms. Through clinical evaluation, the Unified Parkinson's Disease Rating Scale (UPDRS) is used to quantify the severity of Parkinson's disease. No definitive diagnostic tests for PD currently exist. Emerging machine learning techniques show potential to forecast future UPDRS scores for making informed medical decisions and enable better disease management. This paper studies research leveraging proteomic data to forecast PD prognosis, focusing on advanced machine learning techniques like CatBoost Regressor, ElasticNet, XGBoost Regressor, RandomForest Regressor, ExtraTrees Regressor and DecisionTree Regressor. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

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Quantum machine learning Algorithms for Big data Analytics in Cyber Security 1

引用

1st international conference on Intelligent computing and Big data Analytics

作者： Das, Surajit Vishwakarma, Santosh Rao, S. Ashish Reddy, N. Darshini B V Raju Inst Technol Dept AI&DS Narsapur Telangana India

ISBN: (数字)9783031746826

ISBN: (纸本)9783031746819;9783031746826

The article, entitled "Quantum machine learning Algorithms for Big data Analytics in Cyber Security," offers a pioneering investigation into the convergence of quantum computing, machine learning, and cyber security. With the increasing volume and complexity of data in the field of cyber security, traditional computing methods are struggling to efficiently and effectively handle and analyze large datasets. This study explores the revolutionary capacity of quantum machine learning algorithms to change big data analytics in the field of cyber security. Quantum machine learning algorithms utilize the distinct characteristics of quantum computing, such as superposition, entanglement, and quantum parallelism, to provide exceptional skills in identifying patterns, detecting anomalies, and making predictions in the field of cyber security. This paper provides a thorough examination of quantum machine learning methods, such as quantum neural networks, quantum support vector machines, and quantum clustering algorithms. It aims to clarify the theoretical principles and real-world applications of quantum-enhanced algorithms for big data analytics. Through the utilization of quantum computers' computing capabilities, researchers and practitioners may get access to novel insights, detect emerging threats, and effectively reduce cyber risks with unmatched speed and accuracy. The abstract closes by emphasizing the revolutionary capacity of quantum machine learning algorithms to tackle the changing obstacles of cyber security in a progressively networked and data-driven society.

关键词： Quantum computing machine learning Big data Analytics Cyber Security Quantum machine learning Algorithms Pattern Recognition

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Market Segmentation Using machine learning and data Analysis: A Comprehensive Study 15

Market Segmentation Using Machine Learning and Data Analysis...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Ajay Dhanda, Namrata Tiwari, Sharda Verma, Rajat Amity University Uttar Pradesh Amity School of Engineering and Technology Lucknow India Babu Banarasi Das Northern India Institute of Technology Department of Computer Science and Engineering Lucknow India Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350370249

This research explores the integration of machine learning into market segmentation, a strategy traditionally reliant on demographic, geographical, and psychographic traits. With the rise of big data, machine learning offers enhanced precision by uncovering intricate consumer behavior patterns often missed by conventional methods. The study involves reviewing relevant literature, preprocessing datasets, and developing machine learning models to evaluate their effectiveness in segmenting markets. Findings indicate that machine learning significantly improves segmentation accuracy, enabling businesses to predict behaviors and tailor marketing strategies more effectively. This research contributes by highlighting machine learning's potential to transform market segmentation and drive business growth. Future work should focus on real-world implementation of these models to validate their practicality and impact. © 2024 IEEE.

关键词： consumer behavior customer insights machine learning market segmentation predictive analytics targeted marketing

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Enhancing Disease Diagnosis with machine learning Based Symtom Prediction

Enhancing Disease Diagnosis with Machine Learning Based Symt...

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2024 international conference on Emerging Innovations and Advanced computing, INNOCOMP 2024

作者： Priyanka, S. Keerthika, S. Santhiya, S. Malar, S. Jenesha Malavika, A. Babu, T. Dinesh Kongu Engineering College Department of Artificial Intelligence Tamil Nadu India Kongu Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350376470

Effective symptom-based disease prediction is essential for prompt patient care and management. Investigating the use of machine learning and data mining in the analysis of the rapidly expanding medical data. The new method makes use of decision tree for trait-based subgrouping, Random Forest for increased precision, support vector machine for managing complicated datasets, K-nearest neighbor for similarity-based predictions, and logistic regression for binary categorization. These techniques provide important insights for precise forecasting by revealing hidden patterns in vast amounts of disease-related data. The dataset: symptom and severity shows promise for preventative healthcare by effectively predicting diseases by taking into account a variety of symptoms. © 2024 IEEE.

关键词： Adversarial machine learning

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Analyzing Role of Artificial Intelligence in Project Management and Investment Risk: A CiteSpace Insight 24

Analyzing Role of Artificial Intelligence in Project Managem...

引用

16th international conference on machine learning and computing (ICMLC)

作者： Lai, Shuang Zhang, Shaoqian Hassan, Abual Mushtaq, Ray Tahir Northwestern Polytech Univ Sch Publ Policy & Adm Xian Peoples R China Univ Kebangsaan Malaysia Dept Econ & Management Bangi Malaysia Gdansk Univ Technol Fac Mech Engn & Ship Technol Gdansk Poland Northwestern Polytech Univ Sch Mech Engn Xian 710072 Peoples R China

ISBN: (纸本)9798400709234

The current research work explores the artificial intelligence and sustainability impact on investment risk and project management based on trend analysis from 2017 to 2022. Deakin University;the University of London or other key contributors and institutions that contribute to this field is identified by a rigorous analysis of one hundred nineteen articles from Web of science. In it, a considerable part of the world is shown with meaningful contributions from China, Korea and the United States. This research presents the changing AI phenomenon in risk management for investments and especially notes that there is increasing connection with sustainability. The results of this work emphasize the role of AI in renovating traditional risk paradigms and suggest appropriate international cooperation. The study thus not only uncovers the current state of knowledge but also positions itself as a starting point for future research in AI-driven investment risk management, providing valuable perspectives to both science and practice.

关键词： Investment Risk Sustainable Management Artificial Intelligence CiteSpace Sustainability machine learning data science

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Predictive Analytics for Crop Management: Leveraging machine learning for Yield Analysis 2

Predictive Analytics for Crop Management: Leveraging Machine...

引用

2nd international conference on Advanced computing and Communication Technologies, ICACCTech 2024

作者： Singh, Inderjeet Sharma, Muskan Tiwari, Yashs Chandigarh University Department of Computer Science Punjab Mohali India DSMNRU IET Department of Computer Science U.P. Lucknow India

ISBN: (纸本)9798331519056

Precision farming is being disrupted by predictive analytics and machine learning, which add precision on yield forecast level using historical data about the field. To this end, in this study a product-specific machine learning system was created to assess differences across diverse agricultural data types such as weather conditions, soil properties and crop health indicators. The purpose of the study is to find out key drivers that impact cereal production and build reliable predictive models which could predict yield accurately. Using sophisticated algorithms and data-driven insights, this work offers practical suggestions to farmers who take measures to enhance farming methods with a view of scaling up agricultural production. Ultimately, the aim is to advance decision making so that such data can be used for precision agriculture and better crop management strategies with a high degree of sustainability. This particular paper out but how machine learning is used to make leaps and integration in agricultural analytics to predict the yield which may contribute towards overall crop management optimization. © 2024 IEEE.

关键词： Adversarial machine learning

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Analyzing Employee Retention Factors using machine learning 4

Analyzing Employee Retention Factors using Machine Learning

引用

4th international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rahman, Moshiur Islam, Md Rashedul Bala, Partho Sattar, Abdus Daffodil International University Department of Management Information System Daffodil Smart City Dhaka Bangladesh Daffodil Institute of Information and Technology Department of Computer Science and Engineering Dhanmondi Dhaka Bangladesh Bangabandhu Sheikh Mujibur Rahman Science and Technology University Department of Computer Science and Engineering Dhaka8100 Bangladesh Daffodil International University Department of Computer Science and Engineering Daffodil Smart City Dhaka Bangladesh

ISBN: (纸本)9798350343670

This research proposal aims to employ machine learning techniques to analyze employee retention factors in Software Companies, recognizing its crucial role in organizational success and the potential costs of high turnover rates. Through Watson Analytics' advanced analytics capabilities, the study seeks to identify key factors contributing to employee attrition and retention, culminating in the development of a predictive model using machine learning algorithms. The outcomes are expected to include actionable recommendations to improve retention strategies, insights into the relative importance of different factors, and the creation of a data- driven, proactive talent management approach for Software Companies. By empowering organizations to retain top talent and fostering a positive work environment, this research envisions a transformative impact on long-term success and performance. © 2024 IEEE.

关键词： Artificial Intelligence data science Deep learning machine learning Monitoring Smartphone Sensors

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machine learning Approaches for Accurate Energy Content Prediction in Foods Using Nutritional data 2

Machine Learning Approaches for Accurate Energy Content Pred...

引用

2nd international conference on computing and data Analytics, ICCDA 2024

作者： Wickramasinghe, Nishan Hasan, Raza Ahmad, Shakeel Mahmood, Salman Solent University Department of Science and Engineering Southampton United Kingdom Nazeer Hussain University Department of Computer Science Karachi Pakistan

ISBN: (纸本)9798331542573

This study marks a step toward more effectively translating nutritional information to inform public health policy as well as individual dietary choices. Motivated by the increase in diet-related health issues, this research aims to analyze a comprehensive nutritional dataset to uncover valuable insights. Using the USDA National Nutrient database, the study employs data preprocessing to clean the data, exploratory data analysis to identify hidden patterns, and various machine learning models to predict nutritional values. The results demonstrate the usefulness of these models in explaining the composition data and highlight a range of trends and relationships within the observed amounts. The discussion emphasizes that these findings could be instrumental in guiding health professionals and policymakers toward healthier dietary guidelines. The significance of this research lies in its potential to advance public health through more sophisticated nutritional recommendations. © 2024 IEEE.

关键词： data Analytics Food Nutrition machine learning

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Utilizing Deep learning and machine learning Methods to Forecast Market Performance 6

Utilizing Deep Learning and Machine Learning Methods to Fore...

引用

6th international conference on Intelligent computing in data sciences, ICDS 2024

作者： El Mahjouby, Mohamed El Fahssi, Khalid Bennani, Mohamed Taj Lamrini, Mohamed El Far, Mohamed Faculty of Science Dhar El Mahraz Department of Computer Science Fez Morocco

ISBN: (纸本)9798350351200

Forecasting the stock market has become one of the most challenging problems for the AI research community. Stock market investment strategies are complex and depend on analyzing amounts of data. Recently, machine learning techniques have increasingly evaluated and enhanced market predictions compared to traditional methods. This interest is driven by the time-dependent nature of stock prices, making their predictions essential in data mining and a subject of significant interest and effort over the past decades. This challenge remains difficult due to the intrinsic characteristics of time series data, such as its high dimensionality, large volume, and constant updates. Researchers have explored machine learning and deep learning methods to improve the effectiveness of traditional approaches. In this document, we aim to forecast stock market performance at the end of the trading day by applying various machine learning algorithms to two datasets, 'CoinMarketCap' and 'CryptoCurrency,' and analyzing the prediction outcomes of these models. © 2024 IEEE.

关键词： Adversarial machine learning

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A Detailed Overview of Quantum computing machine learning Techniques

A Detailed Overview of Quantum Computing Machine Learning Te...

引用

2024 international conference on Communication, Computer sciences and Engineering, IC3SE 2024

作者： Aggarwal, Ada Singh, S Vikram Bansal, Saloni Bhutani, Vijay Jammu & Kashmir India Amity School Of Engineering & Technology Amity University Uttar Pradesh Greater Noida India Gla University Department Of Computer Engineering And Applications Uttar Pradesh India

ISBN: (纸本)9798350366846

The computing sector has undergone radical changes in the last few decades. Conventional computers use binary (1s and 0s) numbers to perform tasks that are specified by the user. Using the concepts of quantum physics, a unique method known as 'quantum computing' addresses problems that are too complicated for conventional computing devices. Two of the scientific fields with the fastest growth rates in today's time are machine learning and quantum computing. Researchers have recently initiated investigations into the ways in which conventional machine learning methods can be improved by quantum computing. Quantum machine learning comprises hybrid approaches that combine quantum and classical algorithms, using quantum methods to evaluate quantum states rather than traditional data. Current data science techniques could be significantly improved by quantum algorithms. In this work, we summarize the contributions made by different researchers in the domain of Quantum learning and then look at some of the methods related to its real-world uses. © 2024 IEEE.

关键词： machine learning

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