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检索条件"任意字段=4th International Conference on Machine Learning and Soft Computing, ICMLSC 2020"

共 564 条记录，以下是81-90 订阅

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Diabetic Condition Classification using Supervised and Unsupervised machine learning Techniques: A Use Case for HealthTech Industry 4

Diabetic Condition Classification using Supervised and Unsup...

引用

4th IEEE international conference on Smart Technologies in computing, Electrical and Electronics, ICSTCEE 2023

作者： Bongale, Anupkumar M. Biradar, Santoshkumar Pise, Priya Suke, Amita Priyadarshan Uke, Nilesh Dharrao, Deepak S. Symbiosis Institute Of Technology Department Of Aiml Pune Campus Pune India G H Raisoni College Of Engineering And Management Department Of Cyber Security And Data Science Pune Wagholi India Indira College Of Engineering And Management Department Of Ai And Ds Pune India Trinity Academy Of Engineering Pune India Symbiosis Institute Of Technology Department Of Cse Pune Campus Pune India

ISBN: (纸本)9798350384628

Diabetes is a very underestimated and, in many cases, an ignored disease. People do not understand the seriousness of diabetes. Many people think diabetes is just something because of which one cannot consume sweets. this, however, is not true. Diabetes is a very serious problem where symptoms can range from being very thirsty all the time to even death. there are many cases where diabetic people had to get their leg, or some other limb amputated because of it. thus, people have to be made cognizant of diabetes' seriousness. this article dives deep into this disease and lists the different types of diabetes, and the way people can know if they are diabetic. Also, there is an in-depth literature review of research papers ranging from 2002-2020. Furthermore, a deep dive in the project of diabetes prediction is present. the techniques used to classify into 'diabetic' or 'non-diabetic' are Random Forest, Logistic Regression and K Nearest Neighbor algorithms which all belong to the supervised machine learning algorithms. Logistic Regression uses sigmoid function to classify into different classes, Random Forest approach uses multiple Decision Trees and the majority decision of them all for the final output. Moreover, K Nearest Neighbor technique uses the concept of Euclidean Distance to decide which class is suitable for any new coming datapoint. © 2023 IEEE.

关键词： Decision trees

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A Sublinear-Regret Reinforcement learning Algorithm on Constrained Markov Decision Processes with reset action 2020

A Sublinear-Regret Reinforcement Learning Algorithm on Const...

引用

4th international conference on machine learning and soft computing (icmlsc)

作者： Watanabe, Takashi Sakuragawa, Takashi Kyoto Univ Grad Sch Human & Environm Studies Kyoto Japan

ISBN: (纸本)9781450376310

In this paper, we study model-based reinforcement learning in an unknown constrained Markov Decision Processes (CMDPs) with reset action. We propose an algorithm, Constrained-UCRL, which uses confidence interval like UCRL2, and solves linear programming problem to compute policy at the start of each episode. We show that Constrained-UCRL achieves sublinear regret bounds (O) over tilde (SA(1/2)T(3/4)) up to logarithmic factors with high probability for both the gain and the constraint violations.

关键词： reinforcement learning constrained Markov decision processes regret analysis expected average-reward criterion

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A Closet Fit Algorithm to Recover Missing Data in Data Mining

A Closet Fit Algorithm to Recover Missing Data in Data Minin...

引用

soft computing for Security Applications (ICSCSA), international conference on

作者： Nidhi S Bhavsar Khushbu Madhav University Rajsthan

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Missing data can lead to inaccurate results and bad decisions. the proposed Closet Fit Algorithm(CFA) solves this problem by identifying the closest match for missing data, Refining the match until it's the best fit, working well with different types of data and missing values, improving data quality for better analysis and decision-making. Key benefits of A Closet fit Algorithm are improved accuracy, easy to use, works with different data types, helps make better decisions.

关键词： machine learning algorithms Accuracy Sensitivity Refining Noise machine learning Imputation Data mining Security Reliability

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Design and Development of Attendance System Using Face Recognition

Design and Development of Attendance System Using Face Recog...

引用

soft computing for Security Applications (ICSCSA), international conference on

作者： Abhay Krishna Nandana Manoj Subbulakshmi T Bachelor in Computer Science Vellore Institute of Technology Chennai India Vellore Institute of Technology Chennai India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

the face identification technology-based attendance system represented in this paper is a novel design. the new system is an alternative to conventional more reliable, secure, and user-friendly. methods the key components are a robust face detection and recognition algorithm, a database management system, and an intuitive interface. the system achieves high accuracy, scalability, and privacy compliance by utilizing machine learning. the real-time performance testing results show that it can be successfully used in various scenarios, thus becoming a significant breakthrough in the automated attendance management system.

关键词： Privacy machine learning algorithms Face recognition Scalability machine learning Real-time systems Face detection Security Reliability Testing

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machine learning Technique for Improvised and Automated Diagnosis of soft Tissue Tumor 4

Machine Learning Technique for Improvised and Automated Diag...

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4th international conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Keerthana, S. Kumar, J R Dinesh Priyadharsini, K. Pavithra, M. Pavithra, D. Engineering Sri Krishna College of Engineering and Technology Department of Electronics and Communication Coimbatore India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

soft Tissue Tumor (STT) are malignancies that are formed in the connective, supportive and surrounding tissues of the body. Because of their modest frequency throughout the person's body and enormous variation, they look diverse when seen via MRI Scans. these tumors are frequently misdiagnosed as Struma nodose, lymphadenopathy and fibrous adenoma mammie. the diagnostics error has substantial unfavorable influence on patient's healthcare. Several machine learning (ML) algorithms have been developed to diagnose cancers, but none have satisfactorily addressed the problem of misdiagnosis. Moreover, similar research that have presented models for evaluating such cancers typically ignore heterogeneity and data size. In response, this research study proposes a novel system of training technique that combines methods for data pre-processing records and for function transition. the process has different strategies to minimize bias and destabilizing variation based on classification tests. the technique is based on the datasets collected at Nur Hidayah Hospital in Yogyakarta, Indonesia, and shows a significant advancement over earlier studies. these findings show that ML approaches shall be used to improve the efficiency and efficacy of STT diagnostics self-driving decision-making procedures. the proposed Decision Tree method performs the classification with maximum accuracy of 98%, Area Under Curve of 94.4% and F1 score of 98.3%. © 2022 IEEE.

关键词： Decision trees

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Named Entity Recognition Method for Fault Knowledge based on Deep learning 2020

Named Entity Recognition Method for Fault Knowledge based on...

引用

4th international conference on machine learning and soft computing (icmlsc)

作者： Chen, Zhicheng Liu, Xiaobao Yin, Yanchao Lu, Hongbiao Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming Yunnan Peoples R China

ISBN: (纸本)9781450376310

Aiming at the problem that fault text is difficult to be directly parsed and utilized, a fault knowledge extraction method is proposed based on deep learning method. Firstly, the characteristics of fault knowledge are analyzed, and a fault knowledge extraction model is established based on multi-layer neural network. Finally, the presented model is discussed comprehensively from the extraction accuracy, recall rate and F1 value, which proves the feasibility of the method. the unstructured text data is used to provide reference for fault diagnosis and prediction.

关键词： Fault text characteristics Characteristic dependence Knowledge extraction Deep learning

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Automatic Mapping of Deciduous and Evergreen Forest by Using machine learning and Satellite Imagery 4th

Automatic Mapping of Deciduous and Evergreen Forest by Using...

引用

4th international conference on soft computing and its Engineering Applications (icsoftComp)

作者： Saini, Rashmi Singh, Suraj Verma, Shashi Kant Hatture, Sanjeevakumar M. GB Pant Inst Engn & Technol Pauri Garhwal 246196 India Basaveshwar Engn Coll Bagalkot 587103 India

ISBN: (纸本)9783031276088;9783031276095

the existence of forests is crucial to the sustainability of life on earth. Automatic forest mapping is necessary to obtain accurate information about the deforestation rate, quantifying, monitoring and mapping. Such information is essential for various schemes to save forest. European satellite Sentinel-2 provide data at thirteen spectral band along with three different spatial resolution level. this satellite data is freely available having medium spatial resolution and faster revisit time, which makes it suitable choice for forest mapping. the objective of this study is automatic mapping of Deciduous and Evergreen Forest by using Sentinel-2 imagery (single date data) in district Dehradun, Uttarakhand, India. Two efficient machine learning (ML) approaches have been used for the classification i.e., Random Forest (RF) and k-Nearest Neighbor (k-NN). Sentinel-2 satellite spectral bands (10 m spatial resolution) namely Near-infrared (NIR), visible light band (Blue, Green and Red) are stacked for classification. In this study, overall classification accuracy attained by RF and k-NN is 81.52% (kappa value of 0.759) and 80.84% (kappa value of 0.751) respectively. Results indicates that both classifiers performed well, however, RF achieved slightly higher (+0.68%) accuracy as compared to k-NN classifier. It is found that RF obtained User Accuracy (UA) and Producer Accuracy (PA) 77.53% and 82.85% respectively for Deciduous Forest. Whereas, for Evergreen Forest UA and PA is 82.26% and 77.95% respectively. On the other hand, k-NN achieved UA of 77.33% and PA of 82.25%. For Evergreen Forest attained UA and PA of 81.81% and 76.95% respectively. Results demonstrated that Sentinel-2 multispectral satellite data is highly suitable for mapping of forests.

关键词： Sentinel-2 Forest mapping k-Nearest Neighbor (k-NN) Random Forest (RF) Land Use Land Cover (LULC)

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Churn Prediction using Ensemble learning 2020

Churn Prediction using Ensemble Learning

引用

4th international conference on machine learning and soft computing (icmlsc)

作者： Wang, Xing Khang Nguyen Nguyen, Binh P. Victoria Univ Wellington Sch Math & Stat Wellington New Zealand Ibm Vietnam Hanoi Vietnam

ISBN: (纸本)9781450376310

With a wealth of information on hand from the Internet, customers now can easily identify and switch to alternatives. In addition to this, a consensus has been reached that the cost of securing new customers is substantially higher than the cost of retaining the current customers. therefore, customer retention has become an essential part of operating strategy for any organisation. Churn prediction is a practice of data analysis on the historical data, which is aiming to predict if a customer will be leaving the business or not in advance. A wide range of algorithms have been proposed for churn prediction in the past, however there is no agreement on choosing the best one. therefore, this study presents a comparative study of the most widely used classification methods on the problem of customer churning in the telecommunication sector. the main goal of this study is to analyse and benchmark the performance of some widely used classification algorithms on a public dataset.

关键词： churn prediction data science machine learning ensemble

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Discovered changes in rice occupation with satellite images based on random forest approach 2020

Discovered changes in rice occupation with satellite images ...

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4th international conference on machine learning and soft computing (icmlsc)

作者： Hiep Xuan Huynh Ky Minh Nguyen Khoa Duc Nguyen Huong Hoang Luong Tran, Nghi C. Linh thuy thi Nguyen Tan Minh Tran Phuong Truc thi Pham Simona Niculescu Can Tho Univ Coll Informat & Commun Technol Can Tho City Vietnam Fsoft Can Tho Stock Co Can Tho City Vietnam FPT Univ Can Tho City Vietnam Natl Cent Univ Taoyuan Taiwan Inst Univ Europeen Mer Plouzane France

ISBN: (纸本)9781450376310

Although agricultural production contributed a significant share of Vietnam's total production, the advancement and proficiency of remote sensing are still narrowly applied in this sector. Recent years, by the open access to satellite products of sufficient characteristics, the agriculture with satellite images supporting is being boosted. this paper focuses on identifying land use and its long-term changes in the selected regions of the Mekong Delta.

关键词： Rice satellite images remote sensing regions of interest random forest classification machine learning.

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A Comprehensive Analysis on Integrating machine learning in Agriculture Practices

A Comprehensive Analysis on Integrating Machine Learning in ...

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soft computing for Security Applications (ICSCSA), international conference on

作者： K. Mahendran Madhavan R Sakthivel M Department of ECE Saveetha Engineering College

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

this research investigates the integration of machine learning (ML) into traditional agricultural practices to address the challenges posed by conventional methods. the study begins by examining the limitations of traditional farming techniques, such as labour-intensive processes, low efficiency, and susceptibility to environmental factors. Subsequently, this study reviews the existing research that has successfully implemented technology in various agricultural domains, including precision farming, crop monitoring, and yield prediction. the core focus of the research lies in exploring the potential of ML to automate agricultural tasks. A detailed explanation is provided on how ML models can be developed and deployed to optimize agriculture processes such as irrigation scheduling, monitoring and harvesting. the study highlights the benefits of ML-driven automation, including increased productivity, reduced costs, and improved sustainability. the research concludes by highlighting the transformative potential of ML in reshaping the agriculture and ensuring food security for future generations.

关键词： Productivity Costs Crops Food security machine learning Agriculture Resource management Sustainable development Monitoring Farming

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