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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3243 条记录，以下是531-540 订阅

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Application of the Semantic Segmentation Method in the Assessment of Forest Carbon Balance 16

Application of the Semantic Segmentation Method in the Asses...

引用

16th international conference Management of Large-Scale System Development, MLSD 2023

作者： Tulokhonova, Inna Mikhaylova, Svetlana Danilova, Soelma East Siberia State University of Technology and Management Department of Software Engineering and Artificial Intelligence Ulan-Ude Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350337907

This article focuses on the problem of human impact on the natural environment and its solution by machine learning methods. The concept of carbon balance is key in assessing climate change on the planet, which is especially important in connection with global warming. The main causes of global warming are associated with anthropogenic emissions of carbon dioxide as a result of agricultural production, land use and burning of fossil fuels. One of the approaches to mitigate this environmental problem is to estimate the carbon balance based on neural network semantic segmentation of forests of terrestrial ecosystems. The proposed convolutional neural network (CNN) model is based on the U-Net architecture. This architecture surpasses traditional machine learning methods in solving computer vision and pattern recognition problems, and is also quite easy to learn. The input data of the model is a set of satellite images of the forest area. The images were obtained using the Sentinel-2 satellite, which provides high spatial resolution and a large number of spectral channels. A software prototype has been developed to implement the CNN model. Training experiments and calculations on changes in the carbon balance in forests led to the conclusion that the results of the study will contribute to the balanced management of reforestation and carbon deposition. © 2023 IEEE.

关键词： Global warming

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software Development Methodologies: Analysis and Classification 5

Software Development Methodologies: Analysis and Classificat...

引用

5th international Youth conference on Radio Electronics, Electrical and Power engineering, REEPE 2023

作者： Gurianov, Dmitrii A. Myshenkov, Konstantin S. Terekhov, Valery I. Bauman Moscow State Technical University Moscow Russia

ISBN: (纸本)9798350399523

This paper discusses software development life cycle models and development methodologies created on their basis. The place of methodologies and their role in the software development management process is determined, the differences and relationships between them are identified, as well as the degree of influence on the success of the project. A review of the criteria for comparing software development methodologies has been carried out. The existing methods of single-criteria and two-criteria classification of methodologies are analyzed, and their shortcomings are identified. New methods of single-criteria classification that eliminate the identified shortcomings are presented. A new multi-criteria classification based on four criteria has been developed, offering a more complete and broader hierarchical distribution of life cycle models and software development methodologies. Existing methods for selecting a software development methodology are considered, as well as an own method based on a retrospective analysis and machine learning methods presented. Improving methods of choosing a methodology is imperative to achieve the targets of Sustainable Development Goals (SDGs) Goal 9. © 2023 IEEE.

关键词： Classification (of information)

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Fault Detection and Diagnosis in Grid-Connected PV Systems Under Irradiance Variations 3

Fault Detection and Diagnosis in Grid-Connected PV Systems U...

引用

3rd international conference on Energy, Power and Electrical engineering, EPEE 2023

作者： Li, Jincan Han, Tao Li, Jingshun Li, Bintao Sha, Peidong Qin, Zongtao Sun, Qiankai Guangxi Power Grid Co. Ltd. Nanning China Yantai Haiyi Software Co. Ltd. Nanning China

ISBN: (纸本)9798350318180

Photovoltaic energy holds great promise. However, the energy output of photovoltaic systems is closely linked to solar irradiance. Monitoring irradiance variability is crucial for ensuring the stable operation and improved generation of these systems. Within the field of fault detection and diagnosis, researchers hold different perspectives on the impact of irradiance variability. To gain a deeper understanding of the dynamic model of photovoltaic systems under various operating conditions, this paper introduces the influence of irradiance variability and proposes an efficient fault detection and diagnosis method based on machine learning and deep learning techniques. This method accurately identifies the healthy and faulty operating states of photovoltaic systems and exhibits inspiring results in simulated data studies. The significance of this research lies in the incorporation of irradiance variability into photovoltaic system monitoring, as well as the realization of efficient fault detection and diagnosis through the utilization of machine learning and deep learning techniques. As a result, the stability and reliability of photovoltaic systems are enhanced, thereby fully harnessing the potential of photovoltaic energy as a renewable energy source. © 2023 IEEE.

关键词： Fault detection and diagnosis Irradiance Photovoltaic energy

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A Novel Invoice Automation System 5

A Novel Invoice Automation System

引用

5th IEEE international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2023

作者： Jena, Pradeep Kumar Dash, Amrit Kumar Maharana, Dharitri Palai, Charulata National Institute of Science and Technology Department of Computer Science and Engineering Odisha Berhampur761008 India

ISBN: (纸本)9798350338287

OCR has been employed since the inception of the machine learning era, but it charged a lot of effort and money to develop. OCR models with improved accuracy have been produced numerous benefits to recent advancements in machine learning and deep learning that involved models and methodologies. Receiving invoices using a software tool to read and analyse them, record the processed data in an accounts payable system, match it with purchase orders and receipts created in connection with the invoice, process the invoice, and release payments to the appropriate party. Optical character recognition (OCR) is a method for extracting text from scanned documents and images. The receipt or document must be clear and language should be readable in order to extract such data effectively and more accurately. Here the proposed model is primarily focusing on pre-processing of the receipts so that the information from these can be easily comprehensible by the model and extraction accuracy will be escalated. The techniques associated with the proposed model are OpenCV, tesseract and google OCR to compare the accuracy after proposed pre-processing methods. The accuracy of OCR extraction after enhancement of the receipts is 20-30% higher than normal OCR output. © 2023 IEEE.

关键词： Image enhancement

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Evolution of Research on Adaptive Gamification: A Bibliometric Study

Evolution of Research on Adaptive Gamification: A Bibliometr...

引用

2023 international conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Vijay, Margret Jayan, J.P. Department of Computer Science Tamil Nadu India Department of Software Engineering Tamil Nadu India

ISBN: (纸本)9798350333244

This study explores the evolving landscape of adaptive gamification-related research from 2014 to 2022. A comprehensive analysis of 1110 documents from journals and books was conducted to investigate publication trends, prominent countries, authors, and the development of knowledge in the field. The findings reveal a consistent growth in publications, with an annual growth rate of 26.89%. Gamification emerges as a dominant cluster, consistently exhibiting high centrality and density scores, indicating its strong interconnectedness and influence within the research network. The country-wise analysis demonstrates a diverse global research landscape, with the United States contributing the highest number of publications, followed by Spain and Germany. Prominent authors from universities and institutions worldwide have made significant contributions to the field. The thematic evolution demonstrates a shift from collaborative learning to gamification and online learning, followed by a focus on technology-driven themes and the emergence of machine learning as a prominent area of research. The increasing relevance of machine learning indicates its growing significance in the domain of adaptive gamification. These findings contribute to the knowledge base of adaptive gamification and offer insights for researchers in shaping the future of gamified learning environments. © 2023 IEEE.

关键词： Knowledge based systems

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Efficient Regression with Feature Selection Based on L12-Norm 4

Efficient Regression with Feature Selection Based on L12-Nor...

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4th IEEE international conference on Pattern Recognition and machine learning, PRML 2023

作者： Lei, Yu Li, Qin You, Jane Shenzhen Institute of Information Technology School of Software Engineering Shenzhen China Xidian University School of Telecommunications Engineering Xi'an China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (纸本)9798350324303

Feature selection is one of the most important components of many machine learning applications. Most of the existing sparse coding-based methods only concerned with the regularization of the transformation matrix, but ignoring the sparsity of projected features in the intrinsic feature space. In this paper, we introduce a novel regularization with 12-norm, called class specificity distribution term. It aims to ensure that samples with the same label could obey a specific distribution, while data from the different categories should have different distributions in the intrinsic feature space. Then we propose a novel embedded feature selection framework based on regression and class specificity distribution term measured by 12-norm, termed RL12. An efficient augmented Lagrange multipliers based optimization algorithm is proposed to solve the 12-norm regularization iteratively. Experimental results demonstrate the effectiveness of our method. © 2023 IEEE.

关键词： Feature Selection

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machine learning and Data Privacy in Human Resource Management 4

Machine Learning and Data Privacy in Human Resource Manageme...

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4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Manoharan, Geetha Jalaja, V. Sathe, Manoj A. Neetika Lourens, Melanie Suresh, K. School of Business Sr University Telangana Warangal India Reva University Bengaluru560064 India Pes Modern Institute of Business Studies Computer Science Pune India School of Management Ims Ghaziabad India Durban University of Technology South Africa Sri Venkateswara College of Engineering TamilNadu Chennai India

ISBN: (纸本)9798350393248

The incorporation of machine learning (ML) for recruiting has been one of the primary subjects in human resources because ML software investigated the first recruitment software and discovered that utilising technology enhances their productivity at work, speed, and makes the method simpler. In addition to developing and testing a prototype of the system for the activities it is intended to accomplish, this study intends to highlight the significance of employing Al in the recruitment process for the Indian Police Academy. This article has three goals: to evaluate the necessity for ML in the organization's hiring procedures, to evaluate the degrees of adoption of this technology, and, ultimately, to look at the incidence of complaints during such important organisational activities. Additionally, in order to increase the validity and trustworthiness of the study's findings, a survey research design is used, as well as a triangulation of qualitative and quantitative approaches. © 2023 IEEE.

关键词： Human resource management

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An Intelligent Model to Predict Cardiovascular Disease using machine learning Techniques 25

An Intelligent Model to Predict Cardiovascular Disease using...

引用

25th international Multi Topic conference, INMIC 2023

作者： Sadaf, Zawaria Khawaja, Sajid Gul Akram, Muhammad Usman National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350317701

Human life is the most important asset of human beings. Every year millions of people lose their lives to cardiovascular diseases. It is a group of diseases related to blood vessels and the heart. The chances of developing cardiovascular diseases in a person can be controlled by reducing some risk factors that cause them. If they are predicted timely in patients, the patients can take decisions and make changes to their lifestyles, and consequently reduce the risk of developing cardiovascular diseases. The proposed model gave very promising results. It has proven to be very efficient in predicting cardiovascular disease in a person using the Gradient Boosting Tree algorithm. The model had 78.78%, 76.78%, 81.10%, 82.43%, and 18.90% accuracy, sensitivity, specificity, miss rate, and precision, respectively. Moreover, the fallout, LR+, LR-, and NPV were 18.90%, 4.06, 3.82, and 75.16% respectively. The classification time was 13 milliseconds per record and the detection time was approximately 0.2137 seconds per record. The proposed model also outperformed various well-known machine learning algorithms and state-of-the-art models. © 2023 IEEE.

关键词： machine learning

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Seeing Through Sound: Object Detection and Distance Analyzer for the Visually Impaired Using Audio Feedback

Seeing Through Sound: Object Detection and Distance Analyzer...

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2023 Annual international conference on Emerging Research Areas: international conference on Intelligent Systems, AICERA/ICIS 2023

作者： Abraham, Anishamol Issac, Bini M. Roy, Allen Thoppil, Abhijith J Raj, Aswin Jacob, Daan Mathews, Christy K. Amal Jyothi College of Engineering Dept of Cse Kottayam India

ISBN: (纸本)9798350303452

Blind Aid is a technology designed specifically for visually impaired individuals, aiming to assist them in navigating their environment. Through a combination of hardware and software, users are provided with real-time auditory information about their surroundings, including details about nearby obstacles. The hardware component of the system consists of a small and portable device equipped with sensors that track and assess the user's surroundings, along with a speaker for delivering audio output. The software component utilizes machine learning algorithms to interpret and process the sensor data, enabling users to receive concise and accurate audio descriptions of their environment. By enabling simpler and more confident navigation, Blind Aid significantly contributes to enhancing the independence and overall quality of life for people with visual impairments. © 2023 IEEE.

关键词： Object detection

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Implementation of Extreme learning machine and software as a Service for Recognition of Tamil Handwritten words 3

Implementation of Extreme Learning Machine and Software as a...

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3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Naveen, P. Nithyasai, S. Nataraj, B. Sri Eshwar College of Engineering Ece Department Coimbatore India Kathir College of Engineering Ece Department Coimbatore India Sri Ramakrishna College of Engg Ece Department Coimbatore India

ISBN: (纸本)9798350300710

The conventional cloud computing paradigm has spurred the development of innovative architectures for next-generation cloud computing. These novel designs can effectively manage vast amounts of data, which was previously beyond the capabilities of conventional architectures. To tackle this challenge, feed forward neural network algorithms have emerged as a cutting-edge solution for processing massive data sets. As a result, they are now widely employed for solving various tasks, particularly in the pitch of recognition. One promising research application in the domain of postal automation is the recognition of handwritten town names. To address this challenge, we have employed an Extreme learning machine to enhance the concert of feed forward neural networks. Our approach has yielded superior results compared to state-of-the-art algorithms. © 2023 IEEE.

关键词： Network architecture

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