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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 3232 条记录，以下是311-320 订阅

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machine learning Applications and the use of Big Data to Manage the Operation of SMEs 2

Machine Learning Applications and the use of Big Data to Man...

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2nd IEEE international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Mohd, Noor Kumar, Indrajeet Agrawal, Avinash J. Graphic Era Deemed to Be University Uttarakhand Dehradun India Graphic Era Hill University Uttarakhand Dehradun India ShriRamdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9798350369960

Managing big data is a trending concept for industrial development. machine learning helps to increase the sales rate of the firm improving the quality of the production process in the SMEs. Training and skill development managed by technological tools and big data analytics ensure the progress of the firm providing information security. Big data help to design the operations of the firm analyzing the market condition and customer demand. Furthermore, the data safety issues create barriers to the technological advancement of the firm, and the managers need to update and monitor the software development to increase consumer confidentiality in SMEs. © 2023 IEEE.

关键词： advancement of technology Big data analytics decision making Industrial development machine learning operation system project management SMEs

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Web Platform for Job Recommendation Based on machine learning 18

Web Platform for Job Recommendation Based on Machine Learnin...

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18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Marin, Iuliana Amel, Hanoosh Univ Politehn Bucuresti Fac Engn Foreign Languages Splaiul Independentei 313 Bucharest Romania Directorate Almuthanna Educ Minist Educ Muthanna Iraq

ISBN: (纸本)9789897586477

After three years of dealing with a global medical catastrophe, our society is attempting to re-establish normalcy. While companies are still struggling to get back on track, workers have grown afraid to seek new jobs, either because they offer low pay or an uncertain schedule. The result is a disconnected environment that does not merge, even though it appears to. The proposed approach creates a suitable recommender system for those looking for jobs in data science. The first-hand information is gathered by collecting ***'s data science job listings, analysing the top talents that employers value, and generating job ideas by matching a user's skills to openings that have been listed. This process of job suggestion would assist the user in concentrating on the positions where he has the greatest chance of succeeding rather than applying to every position in the system. With the aid of this recommendation system, a recruiter's burden would be decreased because it lowers the quantity of undesirable prospects.

关键词： Jobs Skills Recruitment Platform Recommendations machine learning

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Comparison of machine learning Techniques for Grade Classification of Exported Cavendish Bananas

Comparison of Machine Learning Techniques for Grade Classifi...

引用

2023 IEEE international conference on Data and software engineering, ICoDSE 2023

作者： Prihantoro, Junanto Anbiya, Dhika Rizki Prihantoro, Galuh Nashrullah, Erwin Cahya, Zaid Riza Research Center for Energy Conversion and Conservation National Agency of Research and Innovation Jakarta Indonesia Research Center for Electronics National Agency of Research and Innovation Jakarta Indonesia

ISBN: (纸本)9798350381382

The development of artificial intelligence technology has multiplied and penetrated all fields. One example is using artificial intelligence in a classification technique to determine the grading of bananas based on their quality. Banana grading is divided into three output classes: export quality, local quality, and defects. The banana classification input data is obtained by measuring the length and weight of the bananas and their color, which refers to their ripeness. The machine learning algorithm obtains test results by dividing three types of dataset separation ratios, namely 60% training data, 40% testing data (60:40), 70% training data, 30% testing data (70:30), and 80% training data, and 20% testing data (80:20). The Support Vector machine (SVM) algorithm gets the best accuracy results of 0.84 at 80:20 ratio conditions. Based on the test results, the classification process with the highest accuracy uses the SVM algorithm. Thus, it is recommended for use as a classifier in classifying cavendish bananas for export purposes. copy;2023 IEEE. © 2023 IEEE.

关键词： Grading

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Hybrid Deep learning Approach for Nonfunctional software Requirements Classifications 3

Hybrid Deep Learning Approach for Nonfunctional Software Req...

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3rd international conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Rahman, Kiramat Ghani, Anwar Ahmad, Rashid Sajjad, Syed Haseeb International Islamic University Dept. of Software Engineering Islamabad Pakistan Big Data Research Center Jeju National University Jeju Korea Republic of Inernational Islamic University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350332391

This paper explores the concept of algorithmic hybridization, which involves combining various machine learning (ML) algorithms to enhance performance by utilizing the benefits of both simultaneously. This study presents a framework that utilizes a combination of long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) with artificial neural networks (ANN) to classify non-functional requirements (NFR). The task of NFR classification is challenging due to the scarcity of supervised learning data. The effectiveness of the proposed approach was assessed by comparing the performance of our integrated model with that of single LSTM and BiLSTM models. To conduct this evaluation, we combined two datasets consisting of 1000 non-functional requirements (NFR). The experimental findings revealed that the proposed approach is effective as the hybrid models exhibited better precision, recall, and F-1 score compared to its counterparts. © 2023 IEEE.

关键词： Requirements engineering

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MEG: Multi-objective Ensemble Generation for software Defect Prediction (HOP GECCO'23)

MEG: Multi-objective Ensemble Generation for Software Defect...

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Genetic and Evolutionary Computation conference (GECCO)

作者： Moussa, Rebecca Guizzo, Giovani Sarro, Federica UCL London England

ISBN: (纸本)9798400701207

This Hot-off-the-Press abstract aims at disseminating our recent work titled "MEG: Multi-objective Ensemble Generation for software Defect Prediction" published in the proceedings of the 16th ACM/IEEE international Symposium on Empirical software engineering and Measurement (ESEM) [4]. We believe this work is of interest for the GECCO community as it proposes a novel way to automatically generate ensemble machine learning models leveraging the power of evolutionary computation: MEG introduces the concept of whole-ensemble generation as opposed to the well known Pareto-ensemble generation. While we evaluate the effectiveness of MEG for software Defect Prediction in our work, MEG can be applied to any classification or regression problem and we invite both researchers and practitioners to further explore its effectiveness for other application domains. To this end, we have made MEG's source code publicly available.

关键词： Multi-objective Ensamble Search-Based software engineering

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Towards Utilizing Natural Language Processing Techniques to Assist in software engineering Tasks 23

Towards Utilizing Natural Language Processing Techniques to ...

引用

45th IEEE/ACM international conference on software engineering (ICSE)

作者： Ding, Zishuo Concordia Univ Montreal PQ Canada

ISBN: (纸本)9798350322637

machine learning-based approaches have been widely used to address natural language processing (NLP) problems. Considering the similarity between natural language text and source code, researchers have been working on applying techniques from NLP to deal with code. On the other hand, source code and natural language are by nature different. For example, code is highly structured and executable. Thus, directly applying the NLP techniques may not be optimal, and how to effectively optimize these NLP techniques to adapt to software engineering (SE) tasks remains a challenge. Therefore, to tackle the challenge, in this dissertation, we focus on two research directions: 1) distributed code representations, and 2) logging statements, which are two important intersections between the natural language and source code. For distributed code representations, we first discuss the limitations of existing code embedding techniques, and then, we propose a novel approach to learn more generalizable code embeddings in a task-agnostic manner. For logging statements, we first propose an automated deep learning-based approach to automatically generate accurate logging texts by translating the related source code into short textual descriptions. Then, we make the first attempt to comprehensively study the temporal relations between logging and its corresponding source code, which is later used to detect issues in logging statements. We anticipate that our study can provide useful suggestions and support to developers in utilizing NLP techniques to assist in SE tasks.

关键词： code embeddings logging natural language processing

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Power Grid Maintenance Cost Prediction Based on machine learning Method

Power Grid Maintenance Cost Prediction Based on Machine Lear...

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2023 IEEE international conference on Sensors, Electronics and Computer engineering, ICSECE 2023

作者： Yu, Zhijian Zhou, Wei Zhu, Zhenbin Hu, Haiyan Power Co. Ltd Taizhou Power Supply Company State Grid Zhejiang Electric Taizhou China Power Co. Ltd Luqiao District Power Supply Company State Grid Zhejiang Electric Taizhou China

ISBN: (纸本)9798350313734

This paper designs an easy-to-use intelligent substation monitoring and fault detection platform. The software realizes the integration of the 3D model of the substation and the 3D perspective of the panoramic monitoring data. Make the working status and monitoring data of the substation clearer. The maintenance items of the substation are classified and applied to the analysis of horizontal and vertical data, and the method based on machine learning is used to estimate the maintenance cost. Through the interactive inspection of the original data, the original training mode can be accurately corrected. This method adopts the genetic optimization method to correct and improve the initial value and the threshold point of the machine learning. Therefore, an improved algorithm for repair and maintenance costs based on BP neural network is proposed. Taking a substation in a city as an example, the operation and maintenance costs of the power grid are estimated. The comparison shows that this method can effectively improve the prediction accuracy of the power system, and it has certain reference significance for the power grid to allocate maintenance costs in the power distribution system. © 2023 IEEE.

关键词： machine learning

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Comparative Analysis of machine learning Models for Data Traffic Anomaly Detection Systems in Intrusion Detection Systems

Comparative Analysis of Machine Learning Models for Data Tra...

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2023 international conference on Information Technology and Computing, ICITCOM 2023

作者： Panuntun, Luthfi Hakim Amaranggani, Nabilah Nawang Adhitya, Syafrizal Ananta Amiruddin Rosdiana, Sri Politeknik Siber Dan Sandi Negara Department of Cryptographic Engineering Bogor Indonesia

ISBN: (纸本)9798350359633

Cyberattacks can happen at any time and have many different goals. It might take the shape of ransomware, DOS attacks, or even malicious software. Its existence is undoubtedly a reason for concern since a continuous internet network might result in data flooding and a decline in the network's performance. In this paper, we try to develop an application of machine learning to analyze data traffic in the network. As we know, in a data traffic network, there are various types of data traffic in units of time. Analyzing each incoming network is very time-consuming, which makes it inefficient and ineffective so we try to develop an automated machine that can detect data traffic, or in other terms an Intrusion Detection System. By analyzing the types of attack variables, the KDD 1999 dataset provided one of the key components for constructing an IDS system. There are seven common types of supervised machine learning models. The machine learning model is designed to investigate the various types of attacks contained in the KDD 1999 dataset. The LGBM model is the most accurate of the seven models. © 2023 IEEE.

关键词： Malware

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C-V2X Link Decision with machine learning

C-V2X Link Decision with Machine Learning

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2023 international conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Sokmaz, Omer Yazar, Ahmet Eskisehir Osmangazi University Department of Computer Engineering Eskisehir Turkey Eskisehir Osmangazi University Department of Software Engineering Eskisehir Turkey

ISBN: (纸本)9798350302523

Vehicle-to-everything (V2X) communications standards have started to reach a maturity in recent years. The evolution period of 5G communications systems also accelerates the development of V2X concept. In this period, cellular V2X (C-V2X) standards are enhanced by the 3GPP organization. In this paper, a novel approach is proposed to decide on a communications link for two different modes (Mode 3 and Mode 4) of C-V2X systems. The proposed approach is developed in an environmental-aware perspective based on machine learning (ML) techniques. © 2023 IEEE.

关键词： Vehicle to Everything

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AI-Powered Smart and Personalized Education Platform

AI-Powered Smart and Personalized Education Platform

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2023 international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Dunusinghe, A.V. Ranasinghe, T.K.S.A. Gamage, J.G.A.C.H. Perera, K.G.D.T. Thelijjagoda, Samantha Gunatilake, Poojani Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Research and International Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350319200

An AI assistant addresses online education's shortcomings, focusing on personalized learning and career guidance. It provides tailored recommendations, assesses performance, suggests subjects, and crafts learning plans. Objectives include accurate grade prediction, subject stream advice, customized assessments, and personalized strategies. The study shows data analytics and machine learning forecast grades, while multi-tenancy architecture improves data handling. Personalized assessments consider both academics and soft skills. machine learning promises accurate subject stream predictions, and engagement detection aids in optimal learning. This AI system holds potential in reshaping online education, but ongoing improvements are vital for enhanced learning. © 2023 IEEE.

关键词： career guidance learning strategies machine learning multi-tenancy Online education personalized assessment personalized learning subject stream recommendation

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