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检索条件"任意字段=6th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2023"

共 265 条记录，以下是11-20 订阅

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Proceedings - 2023 6th international conference of Women in data Science at Prince Sultan University, WiDS-PSU 2023

Proceedings - 2023 6th International Conference of Women in ...

引用

6th international conference of Women in data Science at Prince Sultan University, WiDS-PSU 2023

ISBN: (纸本)9781665477239

the proceedings contain 40 papers. the topics discussed include: data science approach for climate change in Saudi Arabia: trend analysis;visualization approach to forecasting retail business;text mining to analyze mammogram screening results for breast cancer patients in Saudi Arabia;implementation of an effective framework in merging cybersecurity and software engineering;alarm rationalization for cybersecurity monitoring;a comparative assessment of accomplishment of sustainable development goals in tropical region;properties and future of the skew Kalman filters;web scraping for data analytics: a beautiful soup implementation;and a machine learning approach to identifying facial masks in real time.

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Towards Abalone Differentiation through machine learning 1

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5th international conference on applied machine learning and data analytics, amlda 2022

作者： Barrera-Hernandez, Ruben Barrera-Soto, Viridiana Martinez-Rodriguez, Jose L. Rios-Alvarado, Ana B. Ortiz-Rodriguez, Fernando Multidisciplinary Academic Unit Reynosa-Rodhe Autonomous University of Tamaulipas Reynosa Mexico Faculty of Engineering and Science Autonomous University of Tamaulipas Victoria Mexico

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

Abalone is a marine species of mollusks of a great lineage with different properties for human consumption. Abalone farming is supported by productive and industrial infrastructure in several countries, including Mexico, where polyculture is being experimented with to optimize operating costs. One of the tasks of the producers is to identify the sex of each abalone to apply certain measures for its growth or to take steps for its preservation. However, this task can be challenging for those just starting the process. this paper presents a case study for classifying abalone sex using machine learning algorithms. A methodology is presented that involves the analysis and processing of information to train and configure four different classification algorithms to recognize three types of classes: male, female, and immature. the results demonstrate the impact of using linear classifiers for this task. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Keratoconus Classification Using Feature Selection and machine learning Approach 1

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5th international conference on applied machine learning and data analytics, amlda 2022

作者： Smily Yeya Jothi, E. Anitha, J. Hemanth, Jude Department of Biomedical Instrumentation Engineering Avinashilingam Institute for Home Science and Higher Education for Women Coimbatore India Department of Electronics and Communication Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

the development and design of new tools that can help identify the disease at an early stage may aid in preventing or delaying the progression of the disease. the preservation of young populations’ vision is crucial because Keratoconus (KC) typically affects people at puberty, with children being the most affected. the goal of this research is to identify the most pertinent variables in relation to the various keratoconus classifiers employed in the Harvard dataverse keratoconus dataset. Out of 3162 observations, a total of 446 parameters are examined by 3 feature selection techniques, Sequential Forward, and Backward Selection (SFS and SBS), Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO). the prediction is done through machine learning algorithms. the experimental results implied that Sequential forward selection and PSO provided the highest classification accuracy by the application of Negative Correlation based Deep Ensemble Network (NC-DEN), with an accuracy of 96.14%. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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2023 6th international conference on Signal Processing and Information Security, ICSPIS 2023

2023 6th International Conference on Signal Processing and I...

引用

6th international conference on Signal Processing and Information Security, ICSPIS 2023

ISBN: (纸本)9798350329599

the proceedings contain 44 papers. the topics discussed include: blockchain-enabled DNS: enhancing security and mitigating attacks in domain name systems;deep learning-based human tracking, face mask and social distance monitoring, systems using YOLOv5;IMU-based human activity recognition using machine learning and deep learning models;enhancing brain tumor aid diagnosis with augmented reality rendering-based 6 DoF object pose estimation;secure key exchange scheme and blockchain-oriented data protection in the internet of vehicles;an intelligent edge-deployable indoor air quality monitoring and activity recognition approach;DiffT: a novel approach for privacy preserving data analytics;using data integration platform for effective location-aware service development platform;hybrid whale-mud-ring optimization for precise color skin cancer image segmentation;and an extensive evaluation of plant disease detection using diverse machine learning approaches.

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Understanding and Managing Employee Attrition: Strategies for Retaining Talent 6

Understanding and Managing Employee Attrition: Strategies fo...

引用

6th IEEE international conference on Advances in Science and Technology, ICAST 2023

作者： Phadke, Ghanashyam Chadha, Aryan Bharmal, Haider Pathare, Sahil K.J. Somaiya Institute of Technology Department of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9798350359817

In this study, we address the issue of employee attrition by employing a synergistic approach that amalgamates Power BI analytics and advanced machine learning techniques. Our methodology includes a meticulous analysis of historical employee data using Power BI, uncovering subtle attrition trends, influential variables, and pertinent demographic insights. Subsequently, we engineer a predictive machine learning model that forecasts forthcoming attrition patterns and proficiently classifies employees at high risk of departure. the model's precision is meticulously refined through rigorous feature engineering and judicious algorithm selection like Decision Trees, Gaussian NB, Random Forest, XGBoost. To actualize these insights, we develop a user-friendly Flask web application that seamlessly integrates the analytical and predictive processes. this intuitive application empowers users to input employee details and promptly receive accurate attrition predictions. Our innovative amalgamation of analytics, machine learning, and user-centric web deployment establishes a comprehensive and efficient solution. By arming organizations with actionable insights and preemptive strategies, we significantly contribute to ameliorating employee retention, fostering engagement, and augmenting overall organizational efficacy. © 2023 IEEE.

关键词： Logistic regression

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Predictive Analysis of NBA Game Outcomes through machine learning 6

Predictive Analysis of NBA Game Outcomes through Machine Lea...

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6th international conference on machine learning and machine Intelligence, MLMI 2023

作者： Wang, Junwen Department of Physics University of California Santa Barbara United States

ISBN: (纸本)9798400709456

this study delved into the realm of sports analytics, employing machine learning techniques to predict the outcomes of NBA games based on player performance and team statistics. through meticulous data collection, filtering, and model comparison, we gained insights into the factors that significantly impact game results. Logistic Regression, Support Vector machines, Deep Neural Networks (DNN) and Random Forest models were rigorously evaluated, showcasing the power of advanced algorithms in uncovering intricate patterns within the data. the structured methodology of this study provides a versatile framework applicable to various sports analytics scenarios. the study shows the better performance of DNN and Random Forest in predicting the game results and the importance of field goal percentage in the predicting. Limitations still exist in this work, including data quality and model constraints;however, the findings have immediate implications for sports professionals seeking actionable insights. As we look ahead, this research underscores the potential for future advancements, encouraging exploration into more sophisticated algorithms, deeper feature analysis, and the integration of temporal patterns for comprehensive predictive accuracy. © 2023 Owner/Author.

关键词： Predictive analytics

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the Predictive Modelling of Student Academic Performance Using machine learning Approaches 6th

The Predictive Modelling of Student Academic Performance Usi...

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6th international conference on Soft Computing and data Mining (SCDM)

作者： Rahman, Nurul Habibah Abdul Sulaiman, Sahimel Azwal Ramli, Nor Azuana Univ Islam Pahang Sultan Ahmad Shah Dept Business Fac Management & Informat Jalan Gambang Kuantan 25150 Pahang Malaysia Univ Malaysia Pahang Ctr Math Sci Kuantan Pahang 26300 Malaysia

ISBN: (纸本)9783031669644;9783031669651

In the field of education, machine learning techniques have been applied in numerous studies covering a wide range of topics, including student enrollment, graduation forecasts, failure rates, retention, and academic performance. the use of predictive analytics in machine learning offers valuable insights to educators, potentially aiding in the improvement of students' outcomes through the analysis of historical data. However, based on a review of existing literature, research focusing on the use of machine learning and predictive analytics to enhance student performance in Malaysian higher education remains limited, specifically among Islamic Universities. the primary objective of this study is to create the most effective predictive model for forecasting students' final grades by employing machine learning methods such as Multinomial Logistic Regression, Decision Trees, Random Forest, K-Nearest Neighbors, Naive Bayes, and Support Vector machine. this research utilizes a dataset comprising student records from the Business Statistics course at Universiti Islam Pahang Sultan Ahmad Shah, spanning from 2013 to 2022. the findings reveal that the Decision Tree model is the most accurate, with a 0.60 accuracy rate in predicting students' performance levels. this optimal model is instrumental in enabling lecturers to identify students at risk of failing at an early stage.

关键词： machine learning Predictive models Students' performance Education

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Crops Recommendation: An Extensive Review and Comparative Study of machine learning Methods 6

Crops Recommendation: An Extensive Review and Comparative St...

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6th IEEE international conference on Advances in Science and Technology, ICAST 2023

作者： Sankpal, Parth Kundlik Khedekar, Aditya Sandeep Bhardwaj, Shubham Devanand Kadu, Rupali K.J. Somaiya Institute of Technology Department of Electronics and Telecommunication Engineering Mumbai India

ISBN: (纸本)9798350359817

the project seeks to transform the agricultural supply chain through the development of a web application that empowers farmers to sell their produce directly to warehouse owners and retailers, bypassing the constraints imposed by intermediaries. this initiative aims to bridge the gap between farmers and buyers, promoting fairness, efficiency, and transparency within the trading ecosystem. In traditional agricultural trading systems, intermediaries often diminish farmers' profits and obscure pricing transparency. the platform addresses these challenges by enabling farmers to create profiles, list their agricultural products for sale, and engage directly with warehouse owners and retailers. Additionally, the data analytics tools offer valuable market insights. this methodology outlines the key steps involved in the development of the platform, encompassing front-end and back-end development, data analytics, and the expected outcomes of empowering farmers, enhancing transparency, improving efficiency, and promoting user engagement. © 2023 IEEE.

关键词： data analytics

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2023 IEEE 6th international conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Chen, Kexin International School Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350313444

Artificial Intelligence technologies have been developed rapidly in the past 20 years and have been widely applied in various industries. machine learning, as the most important way to achieve artificial intelligence, is evolving at a huge speed. Keeping track of the regular development is quite a difficult work, especially for beginners. In this paper, we are going to briefly discuss about the most popular machine learning algorithms and their variants or improved versions. these algorithms are usually classified as supervised learning or unsupervised learning methods, or other grouping methods based on functionality. Deep learning is covered because deep learning methods have led to revolutionary progress in computer vision and machine learning. We also talk about the industries that these models can be applied in. this article is intend to provide basic references of these commonly used algorithms in the field for beginners, help them to get a general understanding of the algorithms and the scenarios, and guide to select appropriate algorithm or model to start solving a practical data problem. © 2023 IEEE.

关键词： Algorithm Artificial Neural Network Deep learning machine learning

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Empowering Smart Cities with Edge Computing-Based IoT Systems: A Focus on data analytics and machine learning Techniques 6th

Empowering Smart Cities with Edge Computing-Based IoT System...

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6th international conference on Recent Innovations in Computing, ICRIC 2023

作者： Nizam, Mohd Khairul Goyal, S.B. Verma, Chaman Illés, Zoltán City University Petaling Jaya46100 Malaysia Department of Media and Educational Informatics Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9789819728381

the rise of the Internet of things (IoT) has led to the development of smart cities, where various systems and devices are interconnected and constantly generating large amounts of data. Edge computing, which enables the processing and analysis of data closer to the source, has emerged as a promising solution to handle the data deluge in smart cities. this research aims to develop a data analytics framework that utilizes machine learning techniques for edge computing-based IoT systems in smart cities. the proposed framework will be designed to handle the unique challenges of IoT data, such as heterogeneity, volume, velocity, and variety. the framework will also enable real-time analytics and decision-making at the edge, which is critical for many smart city applications, such as traffic management, energy management, and public safety. the research will involve the evaluation of various machine learning algorithms for edge computing-based IoT systems and the development of a prototype system to demonstrate the effectiveness of the proposed framework. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Edge computing

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