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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 3233 条记录，以下是491-500 订阅

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5th international conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

5th International Conference on Entrepreneurship, Innovation...

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5th international conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

ISBN: (纸本)9789819716814

The proceedings contain 41 papers. The special focus in this conference is on Entrepreneurship, Innovation, and Leadership. The topics include: A Study on Indian Digital Banking Online Customer Experience;a Comparative Analysis and Statistical Inference of Diabetes Cases;impact of Gamification on Student learning: An Empirical Evidence;Measuring Impact of Generative AI in software Development and Innovation;Sustainability in the Digital Era: Case Study of a Security Equipment Manufacturing SME;Using Monte Carlo Methods for Retirement Simulations of the 401K and IRA;a Novel Classification Scheme for Credit Card Fraud Detection Using Data Mining;Analyzing the Impact of UPI on Supply Chain Performance: A Natural Language Processing Approach with Generative Pre-trained Transformers;mental Health: Prediction and Analysis of Anxiety, Depression, Stress and Happiness Using machine learning;detecting Diabetes Retinopathy Through machine learning;extracting Relevant Features for software Transplantation;robust Method for Accessing IoT Devices and Blockchain for Secure Data Management;comparative Analysis of Financial Datasets Predictive Modeling Using Feature Extraction Methods;Cancer Detection Techniques: An Overview of Traditional and AI-Based Methods and Their Comparative Analysis;The Role of Generative Artificial Intelligence (GAI) in Education: A Detailed Review for Enhanced learning Experiences;employing machine learning Models in Prediction of Harmful Gases from Agri-Waste;emerging Trends and Perspectives on Challenges and Opportunities in Cloud Computing: A Systematic Literature Review;tagTrackr: A Smart Asset Tracking Solution;Assessing the Effectiveness of the ELSA App for English Language learning of Indian Native Speakers;safeMeds: Electronic Health Records Using Blockchain;depression Detection Using Linear Regression Model.

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Risks’ Attribute Values Evaluation in software engineering by Monte Carlo Simulation 13

Risks’ Attribute Values Evaluation in Software Engineering ...

引用

13th IEEE international conference on Electronics and Information Technologies, ELIT 2023

作者： Lyashkevych, Maria Lyashkevych, Vasyl Shuvar, Roman System Design Department Ivan Franko Lviv National University Lviv Ukraine

ISBN: (纸本)9798350383096

It is a good practice in the industry to carry out a risk assessment even before the stages of software production because we can prevent the costs of both human and material resources. In the era of the success of artificial intelligence with machine learning, it is very important to have accumulated expert knowledge on the basis of which we fuel datasets. Usually, it is very difficult to obtain numerical values of risk attributes, and especially to determine their importance among others. The values of risk attributes can be balanced in various ways, one of which is Monte Carlo simulation, which is presented in the publication. © 2023 IEEE.

关键词： Risk assessment

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Architecture, Tools, and DSLs for Developing Conversational Agents: An Overview 14

Architecture, Tools, and DSLs for Developing Conversational ...

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14th international conference on Emerging Ubiquitous Systems and Pervasive Networks / 13th international conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2023

作者： Ouaddi, Charaf Benaddi, Lamya Jakimi, Abdeslam Software Engineering and Information Systems Engineering Team FST Errachidia University of Moulay Ismail Meknes50050 Morocco

Conversational agents (CA) are software programs that can converse with users using natural language. They are now widely used in various domains, such as tourism, healthcare, and others, to perform tasks and provide permanent assistance to users by interacting with them in natural language. The development of such applications is a task that requires expertise in several fields, such as software engineering, machine learning, deep learning, and natural language processing (NLP). However, several platforms and frameworks on the market facilitate the building of CA, such as Dialogflow, Rasa, and others. Recently, several research studies have proposed solutions to reduce the workload of developers and designers by offering their model-driven development approaches using domain-specific languages (DSLs), which facilitate the automation of the development of CA. This work aims to provide an Overview of CA to identify and describe their architecture and the details of its key components. and discuss the tools and technologies for their development. At the same time, discover the research topics that focus on using DSLs for model-driven development to automate and speed up the creation of these agents and discover approaches and technologies employed to implement each of these DSLs. © 2024 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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Binary Decomposition for Multi-Class Classification Problems: Development and Applications

Binary Decomposition for Multi-Class Classification Problems...

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2023 international conference on machine learning and Cybernetics, ICMLC 2023

作者： Li, Peiwei Liu, Han College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798350303780

Binary decomposition of a multi-class classification problem is a widely used method in the field of machine learning, which involves using an ensemble of binary classifiers to undertake multi-class classification tasks. The motivation of binary decomposition lies in two main aspects. Firstly, some learning algorithms such as support vector machine can not achieve directly learning a multi-class classifier. Secondly, in some cases, the adoption of binary decomposition for training an ensemble of binary classifiers can even achieve better performance than the direct learning of a multi-class classifier. There are some existing methods that can be applied in achieving binary decomposition and have been proven to be effective. Some of these methods may also be applied in other tasks beyond classification. This paper presents a comprehensive review of the development and applications of binary decomposition methods. Specifically, these methods are put into two categories, namely, ordered and unordered decomposition, which each is introduced in terms of the essence and strategies of decomposition. These methods are also compared through theoretical analysis of their effectiveness and efficiency. On the basis of the comparative analysis, we suggest some future directions towards achieving further advances in binary decomposition. © 2023 IEEE.

关键词： Classification (of information)

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CC: Causality-Aware Coverage Criterion for Deep Neural Networks 23

CC: Causality-Aware Coverage Criterion for Deep Neural Netwo...

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

作者： Ji, Zhenlan Ma, Pingchuan Yuan, Yuanyuan Wang, Shuai Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781665457019

Deep neural network (DNN) testing approaches have grown fast in recent years to test the correctness and robustness of DNNs. In particular, DNN coverage criteria are frequently used to evaluate the quality of a test suite, and a number of coverage criteria based on neuron-wise, layer-wise, and path-/trace-wise coverage patterns have been published to date. However, we see that existing criteria are insufficient to represent how one neuron would influence subsequent neurons;hence, we lack a concept of how neurons, when functioning as causes and effects, might jointly make a DNN prediction. Given recent advances in interpreting DNN internals using causal inference, we present the first causality-aware DNN coverage criterion, which evaluates a test suite by quantifying the extent to which the suite provides new causal relations for testing DNNs. Performing standard causal inference on DNNs presents both theoretical and practical hurdles. We introduce CC (causal coverage), a practical and efficient coverage criterion that integrates a set of optimizations using DNN domain-specific knowledge. We illustrate the efficacy of CC using diverse, real-world inputs and adversarial inputs, such as adversarial examples (AEs) and backdoor inputs. We demonstrate that CC outperforms previous DNN criteria under various settings with moderate cost.

关键词： Causality Analysis machine learning testing software engineering

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Forecasting User Payment Behavior Using machine learning 1

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6th international conference on Applied machine learning and Data Analytics, AMLDA 2023

作者： Vora, Deepali Choudhary, Rupali Kumar, Aman Kadam, Payal Lavale Pune India College of Engineering Pune India

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

The payment prediction model is a software application that uses machine learning algorithms to predict future payments based on historical data. These models are crucial for organizations like credit card companies, banks, and other financial institutions that depend on recurring payments from their clients businesses may determine the possibility that consumers will miss their payments by utilizing payment prediction models, and they can then take action to either avoid or lessen the effects of any prospective non-payment. This system works on real-time data, data goes to different stages including data collection and storage, data pre-processing and cleaning, feature engineering, and model development. The system uses machine learning algorithms such as linear regression, decision trees, or XG boost to predict future payments based on historical payment data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： User interfaces

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An Approach for intrusion detection using machine learning algorithms 10

An Approach for intrusion detection using machine learning a...

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10th international conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Chentoufi, Oumaima Alloug, Ilyas Chougdali, Khalid Ibn Tofail University Engineering Science Laboratory Ensa Kenitra Kenitra Morocco

ISBN: (纸本)9798350329674

Individuals and organizations face threats from attackers that constantly attempt to collect information by exploiting their vulnerabilities and obtain unauthorized access to the network. One way of protecting the system is by using an Intrusion detection System (IDS) alongside other security measures like firewalls, antivirus software, and access controls. An intrusion detection system, which is a system that can detect and recognize attacks on a network, is an approach that has been tried, tested, and tweaked to detect attacks as effectively as possible. Therefore, in this work, we will propose an approach using either a feature extraction method or a feature selection algorithm and other machine learning algorithms to provide a new way of intrusion detection, this approach will be applied to the NSL KDD data set. © 2023 IEEE.

关键词： Intrusion detection

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Pseudocode to Code Based on Adaptive Global and Local Information 30

Pseudocode to Code Based on Adaptive Global and Local Inform...

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30th IEEE international conference on software Analysis, Evolution and Reengineering (SANER)

作者： Yu, Qianqian Huang, Zhangjin Gu, Naijie Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China

ISBN: (纸本)9781665452786

The pseudocode-to-code task has two main stages: code translation and search synthesis. It faces two main challenges: First, the generated candidate code pieces need to be more accurate. Second, there are problems with search efficiency and accuracy. To address the above challenges, this work proposes a novel approach: For the encoder of code translation, a new multi-scale pyramid feature extractor is proposed to obtain multi-scale local information, which is combined with the global information to improve the accuracy of code translation. For the search synthesis stage, this paper designs the intra-line attention, the inter-line attention, and the code-errMsg attention, which are adaptively integrated with the graph attention to effectively fuse global and local information. Under a budget of 100 program compilations, our final model, AGL-Code, outperforms the previous state-of-the-art models, achieving 46.1%/63.5% synthesis success rate on the TestP/TestW of the SPoC dataset, respectively.

关键词： Code generation Program repair machine learning software engineering Program comprehension

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Rain Predictive Model using machine learning Techniques

Rain Predictive Model using Machine learning Techniques

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2023 international Multi-Disciplinary conference in Emerging Research Trends, IMCERT 2023

作者： Muneer, Muhammad Shahbaz Nabeel Mustafa, Syed Muhammad Zehra, Syeda Sundus Maqsood, Haniya NED University of Engineering and Technology Dpertment of Computational Finance Karachi Pakistan Sir Syed Uuniversity of Engineering and Technology Dept. of Software Engineering Karachi Pakistan NED University of Engineering and Technology Department of Computer and Information Systems Engineering Karachi Pakistan

ISBN: (纸本)9798350347708

Climate is rapidly changing around the world. Over time, there have been significant changes in the weather. Rainfall is now erratic due to climate change. The frequency of extreme weather events like droughts and floods has increased due to climate change, necessitating the need for more precise and timely rainfall forecasts. For strategic reasons including agriculture, water resource management, and architectural design, rain forecasting is crucial. The naturally occurring non-stationary component in the rainfall time series impairs model performance for practical hydrologists and drought risk assessors. We present a rain predicting model based on machine learning to address the forecasting issue. In our work, we predict the possibility of rain the next day on the basis of last 10 years' data. The variables that were calculated during the experiments were humidity, pressure, evaporation, sunshine, rainfall, and so on. Random Forest gave the 90% accuracy with 0.904 Area under Curve, highest out of all the algorithms. The model's performance will significantly aid in the rain forecast. © 2023 IEEE.

关键词： Rain

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Current Perspectives and Virtualisation solutions with SDN for IoT 2

Current Perspectives and Virtualisation solutions with SDN f...

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2nd international conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Bhardwaj, Shanu Kumar, Shailender Girdhar, Ashish Department of Computer Science and Engineering Delhi Technological University Delhi India Department of Computer Science and Applications Kurukshetra University Kurukshetra India

ISBN: (纸本)9798350305418

During the past years, IoT has acquired a lot of consideration since it incorporates intelligent gadgets which empower many applications that work in our day-to-day existence. Due to this the rising number of clients and the interest in more different and specific applications have prompted a tremendous expansion in the network traffic. Managing different traffic requests from various applications is a difficult task for the current traditional networking architecture. Therefore, the paper provides a thorough analysis of the SDN and various other technologies-based network virtualization methods as well as current perspectives for the IoT. The representation provides the working of various up-going technologies such as machine learning, Edge Computing, and Virtualization with SDN to betray the performance of the SDN applications in today’s world. © 2023 IEEE.

关键词： edge computing internet of things machine learning network traffic software-defined networking

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