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A Review on Rural Women’s Entrepreneurship Using machine learning Models 3rd

A Review on Rural Women’s Entrepreneurship Using Machine Le...

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3rd international conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

作者： Pareek, Shivani Bhatnagar, Vaibhav Poonia, Ramesh Chandra Sharma, Shilpa Samanta, Debabrata Department of Computer Applications Manipal University Jaipur Rajasthan Jaipur India Delhi NCR Uttar Pradesh Ghaziabad India Rochester Institute of Technology – RIT Global Campus n.n. Germia Campus Pristina Kosovo10000

ISBN: (纸本)9789819926015

Rural women’s entrepreneurship has contributed significantly to the country’s economy. Entrepreneurship rates have fluctuated in recent years, according to a variety of reasons including economic, social, and cultural influences. Therefore, machine learning models are used to assess the features to make better business decisions. In this research paper, papers from 2009 to 2022 were studied and found that machine learning models are being used to improve women’s entrepreneurship. In this paper, nine machine learning models have been described in detail which include multiple regression, lasso regression, logistic regression, decision tree, Naive Bayes, clustering, classification, deep learning, artificial neural network, etc. In the study of all these models, it was found how accurately this model has been used in women’s entrepreneurship work. It has been observed that by using different machine learning models with the data acquired from rural entrepreneurship, women entrepreneurs may use a new way of understanding the dynamics of rural entrepreneurship. Various machine learning models have been studied to improve rural development for women working in rural areas. Thus, we have proposed a comparative study of various machine learning models to predict entrepreneurship-based data. The findings of this study may be used to assess how rural women entrepreneurs may change the decisions made in several domains, such as making use of different economic policies and promoting the long-term viability of women entrepreneurs for the country’s economic growth. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Rural areas

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Based on Multidimensional Data Analysis Technology in the application of Professional Literacy 3

Based on Multidimensional Data Analysis Technology in the Ap...

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3rd international conference on Electronics and Communication;Network and computer Technology (ECNCT)

作者： Cui, Yuanling Zou, Xiaoqiong Beijing Polytech Beijing 100176 Peoples R China Beijing Polytech Beijing 101585 Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

With the development of big data technology and the arrival of the information age, people's production, life and learning have undergone many changes. "Multidimensional data analysis" as a very convenient and efficient analysis mode, has been widely popularized. computer technology can effectively promote the correct construction of occupational literacy model.

关键词： Enterprise Staff Occupational Literacy Model computer

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An Effective Ensemble learning Method for Fault Diagnosis of Photovoltaic Arrays 3rd

An Effective Ensemble Learning Method for Fault Diagnosis of...

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3rd international conference on Electronic Engineering and Renewable Energy, ICEERE 2022

作者： Mellit, Adel Boubaker, Sahbi Department of Electronics Faculty of Science and Technology University of Jijel Jijel Algeria Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9789811962226

In this paper, a novel fault diagnosis method for photovoltaic (PV) arrays is proposed. The method combines three machine learning (ML) algorithms: the first one is an unsupervised ML algorithm (principal component analysis, ‘PCA’) used for features reduction;the second one is a kind of a recurrent neural networks (long short-term memory, ‘LSTM’) employed to predict the PV power;and the third one is a supervised ensemble learning categorical boosting algorithm (CatBoost), used mainly for fault classification improvement. Four types of faults (dust deposit on PV modules surface, partial shading effect, shunted diode and open-circuit diode) are examined in this study. Results showed the effectiveness of the proposed method in terms of detection and classification accuracy, 98.70% and 98.58% respectively, and demonstrate the superiority of this method over other investigated ML algorithms (e.g. decision tree, support vector machine, k-nearest neighbours, and random forest). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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An Initial Framework for Adaptive Serious Games Based on a Systematic Literature Review 3rd

An Initial Framework for Adaptive Serious Games Based on a S...

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3rd international conference on Technology and Innovation in learning, Teaching and Education (TECH-EDU)

作者： Antonio de Araujo Pistono, Alvaro Marcos Pinto Santos, Arnaldo Manuel Vieira Baptista, Ricardo Jose Univ Tras Os Montes & Alto Douro Quinta Prados P-5000801 Vila Real Portugal Univ Aberta P-1269001 Lisbon Portugal

ISBN: (纸本)9783031229176;9783031229183

Serious Games have been used in professional training to increase employee engagement and improve the results of training initiatives. This work intends to investigate the influence of game elements, in adaptable Serious Games, according to the users' interactions, on the increase of engagement in the game itself and, as the main goal, on the learning results and the transfer of the acquired knowledge and practised skills to the daily work activities. Using the Design Science Research - DSR methodology, this study aims to develop a framework for the development and evaluation of Serious Games to improve the user experience, the learning outcomes, the transfer of knowledge to work situations, and the application of the skills practised in the game in real professional scenarios. This paper presents an initial Framework for Adaptive Serious Games derived from a systematic literature review. The next steps in this investigation are pointed out following the DSR methodology.

关键词： Framework Adaptive Serious games Professional training learning outcomes

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application of machine learning Methods for Annotating Boundaries of Meshes of Perineuronal Nets 1

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3rd international conference on Information Technologies and Intelligent Decision Making Systems, ITIDMS 2023

作者： Egorchev, Anton Kashipov, Aidar Lipachev, Nikita Derzhavin, Dmitry Chiсkrin, Dmitry Aganov, Albert Paveliev, Mikhail Federal University 18 Kremlin Street Kazan420008 Russia University of Helsinki 8 Haartmaninkatu Helsinki00290 Finland

ISBN: (数字)9783031603181

ISBN: (纸本)9783031603174

The article explores the use of neural networks to solve the problem of determining boundaries of meshes of perineuronal nets. The confocal stacks’ image layers of rat brains are used as initial data. This article presents a comparison of two alternative methods to solving the problem. The first method is based on the generation of boundaries through the use of a neural network based on the DCGAN architecture. The second method is based on solving the problem of semantic segmentation using a neural network based on the U-Net architecture, that is widely used in biomedicine. For both neural networks, architectural changes are presented to achieve greater generalizability of the models. Some learning strategies are considered to solve the problem of overfitting that is typical for small samples. Both solutions showed results comparable in quality to the semi-automatic algorithm. A solution based on the U-Net architecture provides good tools for further solving the problem of boundary ambiguity and allows customizing the algorithm for various criteria for boundaries of the perineuronal nets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Generative adversarial networks

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machine learning Techniques Applied of Land Use—Land Cover (LULC) Image Classification: Research Avenues Challenges with Issues 3rd

Machine Learning Techniques Applied of Land Use—Land Cover ...

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3rd international conference on Mobile Radio Communications and 5G Networks, MRCN 2022

作者： Thakur, Reena Panse, Prashant Department of Computer Science and Engineering Medi-Caps University Indore India

ISBN: (纸本)9789811979811

An easy-to-use programming environment, open access to satellite data, and access to high-end consumer computation power has made it very easy to align remote sensing and machine learning during the new era. A variety of remote sensing applications have utilized publicly available data. Land use (LU) image classification has become vitally important in the natural environment because of the expansion of some global changes relating to the temperament of the earth. Therefore, researchers should investigate this area more deeply. This paper presents a complete review to help out the researchers to carry on with the land use/land cover for image classification process, as there are limited numbers of review articles to assist them. The purpose of this paper is to discuss the classification of satellite images using mainly employed machine learning algorithms. We discuss the general process of LU/LC based on multi-image classification, as well as the challenges and issues faced by researchers. Only a few studies evaluate machine learning algorithms for image classification using openly available data, however. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Remote sensing

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An Expanded Study of the application of Deep learning Models in Energy Consumption Prediction 3rd

An Expanded Study of the Application of Deep Learning Models...

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3rd EAI international conference on Data and Information in Online Environments (DIONE)

作者： Amaral, Leonardo Santos de Araujo, Gustavo Medeiros Moraes, Ricardo de Oliveira Villela, Paula Monteiro Univ Estadual Montes Claros UNIMONTES Ave Prof Rui BragaS-N Vila Mauriceia Montes Claros MG Brazil Univ Fed Santa Catarina UFSC S-N Trindade Florianopolis SC Brazil

ISBN: (纸本)9783031223235;9783031223242

The time series of electrical loads are complex, influenced by multiple variables (endogenous and exogenous), display non-linear behavior and have multiple seasonality with daily, weekly and annual cycles. This paper addresses the main aspects of demand forecast modeling from time series and applies machine learning techniques for this type of problem. The results indicate that through an amplified model including the selection of variables, seasonality representation technique selection, appropriate choice of model for database (deep or shallow) and its calibration, it's possible to archive better results with an acceptable computational cost. In the conclusion, suggestions for the continuity of the study are presented.

关键词： Forecast Energy Demand Deep learning

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Quantum Computing and machine learning Algorithms-A Review 3

Quantum Computing and Machine Learning Algorithms-A Review

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3rd international conference on Innovations in computer Science and Software Engineering, ICONICS 2022

作者： Manan, Iram Rehman, Faisal Sharif, Hanan Riaz, Naveed Atif, Muhammad Aqeel, Muhammad Khawaja Fareed University of Engineering and Information Technology Department of Information Technology Rahim Yar Khan Pakistan Lahore Leads University Department of Computer Science & Information Technology Lahore Pakistan NUST Department of Computer Science and Information Technology Islamabad Pakistan

ISBN: (纸本)9781665461412

The goal of machine learning (ML) is to develop models that automatically learn from the past without being explicitly programmed. ML has numerous applications, such as pattern recognition, forecasting upcoming trends, and judgement. It can handle massive amounts of huge vectors and tensors representing multidimensional data. To handle these processes on traditional computers, enormous time and computational resources are required. Unlike traditional computers, which use binary bits to compute, quantum computers (Q.C.) use qubits, which can simultaneously hold 0 and 1 combinations through superposition and entanglement. This makes *** an excellent choice for implementing ML algorithms because they are skilled at handling and post-processing large tensors. Although many of the models used for ML on quantum computers are based on concepts from their classical computing counterparts, the use of quantum computers has made them the better of the two. This paper assess the speed and complexity benefits of using quantum computers and gives a general overview of the state of knowledge regarding the use of ML on Q.C. © 2022 IEEE.

关键词： Quantum entanglement

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Smart Use of machine learning in Heart Disease Identification 3

Smart Use of Machine Learning in Heart Disease Identificatio...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Raman, Ramakrishnan Shintre, Vaijanath Joshi, Kapil Prasanthi, G.S.L.B.V. Kaleem, Afshan Rajput, Kapil Symbiosis Institute of Business Management Maharashtra Pune India Sanjay Ghodawat University Department of Electronics Engg Maharashtra Kolhapur India Uttaranchal University Uttaranchal Institute of Technology India Muffakham Jah College of Engineering and Technology India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

Heart patients are increasing in number on a daily basis, and each year in every region of the globe, a significant number of people lose their lives unexpectedly as a result of sudden heart *** a result of this, it is essential to diagnose cardiovascular illness at an early stage in order to avert the death of It is necessary for the area of medicine to make use of some kind of technology-based application in order to identify cardiac patients with greater precision and in much shorter time. It is possible to effectively identify heart illness by making use of data mining methods, and there are a considerable number of people suffering from heart disease who are now being treated in hospitals all over the globe. Data mining is the practice of sifting through enormous amounts of raw data in search of knowledge or information that can be put to good use. During the prediction analysis, many machine learning algorithms are put to use in order to locate useful patterns and make accurate projections on forthcoming events or trends. Through the use of a reworked machine learning algorithm, this research study will attempt to forecast the risk that individuals would suffer from coronary heart disease. Before being classified, the incoming data go through a number of processes, including preprocessing, grouping, and the selection of useful features. Four different algorithms, including random forest (RF), K Nearest Neighbor (KNN), Particle swarm optimization (PSO) and logistic regression are combined in order to find out what kind of cardiac disease the patient *** enhance performance and shorten the amount of time needed for training, this method removes features from the cardiac dataset that are not relevant to the problem at hand. The random forest approach is the one that finishes off this procedure. In order to group all of the data points that are considered to be outliers, KNN are optimized using PSO algorithm. In the end, a method known as logistic regres

关键词： Forecasting

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Prediction of Diseases Using Different machine learning Approaches 3

Prediction of Diseases Using Different Machine Learning Appr...

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Gupta, Anish Gupta, Manish Kumar Apex Institute of Technology Chandigarh University Department of Computer Science & Engineering Punjab Mohali India Buddha Institute of Technology GIDA Department of Computer Science & Engineering Gorakhpur India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Every year, as the number of patients and diseases increases, the medical system becomes overburdened and, in many nations, expensive. The majority of the condition necessitates a visit with a doctor in order to be treated. With enough data, disease prediction using an algorithm can be simple and inexpensive. Predicting disease based on symptoms is an important element of treatment. In our project, we attempted to accurately forecast an illness based on the patient's symptoms. For this, we utilized four different algorithms and achieved 92-95 percent accuracy. A system like this has a lot of potential in future medical therapy. We've also created an interactive interface to help you interact with the data. © 2022 IEEE.

关键词： Forecasting

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