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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211-220 订阅

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Impact of Deep Feature Synthesis on Deep learning in Electronic Transaction Fraud Detection 3

Impact of Deep Feature Synthesis on Deep Learning in Electro...

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3rd IEEE international conference on software engineering and Artificial Intelligence, SEAI 2023

作者： Uddin, Mohammed Najim Azad, Salahuddin Hossain, Md Rahat Chugh, Ritesh CQUniversity School of Engineering and Technology Rockhampton Australia CQUniversity School of Engineering and Technology Melbourne Australia

ISBN: (纸本)9798350337952

Electronic transaction fraud has been a severe threat in recent years, causing substantial financial losses and devaluing the reputation of financial institutions. Various machine learning and deep learning models have been cited in the literature to detect electronic transaction fraud effectively. Feature selection is crucial in enhancing the performance of a machine learning model. Automatic feature engineering techniques outweigh the manual feature selection process regarding the time spent and adapting to the changing nature of the dataset. However, to the best of our knowledge, the impact of state-of-the-art feature selection techniques, such as Deep Feature Synthesis (DFS), on the performance of deep learning-based electronic transaction fraud detection has not been thoroughly studied. Our study uses the DFS algorithm to generate features automatically from the labelled credit card transaction dataset collected from open-source sites. The data is fed into a baseline deep learning model, i.e., Convolutional Neural Networks (CNN), in two separate cases- with DFS and without DFS. A comparison of results from both cases shows that CNN with DFS outperforms the standalone CNN significantly in terms of accuracy, precision, recall, and F1 scores. © 2023 IEEE.

关键词： Convolutional neural networks

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A Review on Quantum machine learning 6th

A Review on Quantum Machine Learning

引用

6th international conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Verma, Toran Kishore Kumar, Botcha Rajendar, Janga Kumara Swamy, B. CMR Engineering College Hyderabad India

ISBN: (纸本)9789819971367

Due to the increasing power of computers and the algorithmic advances that have occurred in the field of machine learning, techniques for analyzing data have become more powerful tools. Quantum computers could perform better than classical ones on certain tasks. The development of machine learning software using quantum computers is a major area of interest in the field. Researchers are working on developing methods that could allow them to perform faster than traditional computers. Although, several algorithms had developed that can act as the building blocks for quantum machine learning programs, the challenges in implementing them are still immense. In this review, we are trying to collaborate on the advanced concepts regarding Quantum machine learning and summarize the prospects in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural networks

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PYEVOLVE: Automating Frequent Code Changes in Python ML Systems 23

PYEVOLVE: Automating Frequent Code Changes in Python ML Syst...

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

作者： Dilhara, Malinda Dig, Danny Ketkar, Ameya Univ Colorado Boulder Boulder CO 80309 USA Univ Colorado Boulder JetBrains Res Boulder CO USA Uber Technol Inc San Francisco CA USA

ISBN: (纸本)9781665457019

Because of the naturalness of software and the rapid evolution of machine learning (ML) techniques, frequently repeated code change patterns (CPATs) occur often. They range from simple API migrations to changes involving several complex control structures such as for loops. While manually performing CPATs is tedious, the current state-of-the-art techniques for inferring transformation rules are not advanced enough to handle unseen variants of complex CPATs, resulting in a low recall rate. In this paper we present a novel, automated workflow that mines CPATs, infers the transformation rules, and then transplants them automatically to new target sites. We designed, implemented, evaluated and released this in a tool, PYEVOLVE. At its core is a novel data-flow, control-flow aware transformation rule inference engine. Our technique allows us to advance the state-of-the-art for transformation-by-example tools;without it, 70% of the code changes that PYEVOLVE transforms would not be possible to automate. Our thorough empirical evaluation of over 40,000 transformations shows 97% precision and 94% recall. By accepting 90% of CPATs generated by PYEVOLVE in famous open-source projects, developers confirmed its changes are useful.

关键词： machine learning Program synthesis Program transformation Programming by example Python Repetitive code changes Transformation by Example

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software Cost Estimation: A Literature Review and Current Trends 3

Software Cost Estimation: A Literature Review and Current Tr...

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

作者： Singh, Suneeta Kumar, Kuldeep Dr B R Ambedkar National Institute of Technology Jalandhar Department of Computer Science and Engineering Punjab Jalandhar India National Institute of Technology Kurukshetra Department of Computer Engineering Haryana Kurukshetra India

ISBN: (纸本)9798350300710

software cost estimation is a challenging and complex task during software development. It directs project managers and developers to analyze and predict costs at the beginning of the software development life cycle. The most important job for developing software projects is correctly estimating cost, time duration, and needed effort. This paper aims to review different models used for software cost estimation, including algorithmic, non-algorithmic, and learning-oriented models, which have been published over the last ten years (i.e., from 2011 to 2022). The comparison is done based on the methods, selected datasets, and metrics used in different techniques. We observed that machine learning-based models perform better compared to other existing methods. However, to the best of our knowledge, analysis, and literature survey, we found that no technique in the literature provides a fit-all solution. © 2023 IEEE.

关键词： Cost estimating

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Proceedings - 2023 IEEE/ACM 45th international conference on software engineering: software engineering in Society, ICSE-SEIS 2023

Proceedings - 2023 IEEE/ACM 45th International Conference on...

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45th IEEE/ACM international conference on software engineering: software engineering in Society, ICSE-SEIS 2023

ISBN: (纸本)9798350322613

The proceedings contain 20 papers. The topics discussed include: software engineering as the linchpin of responsible ai;one adapter for all programming languages? adapter tuning for code search and summarization;keeping pace with ever-increasing data: towards continual learning of code intelligence models;detecting JVM JIT compiler bugs via exploring two-dimensional input spaces;validating SMT solvers via skeleton enumeration empowered by historical bug-triggering inputs;regression fuzzing for deep learning systems;the untold story of code refactoring customizations in practice;a comprehensive study of real-world bugs in machine learning model optimization;evaluating the impact of experimental assumptions in automated fault localization;locating framework-specific crashing faults with compact and explainable candidate set;and how do we read formal claims? eye-tracking and the cognition of proofs about algorithms.

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Object Detection for Visually Impaired 21

Object Detection for Visually Impaired

引用

21st OITS international conference on Information Technology, OCIT 2023

作者： Singh, Raj Jethani, Laksh Sanghvi, Shubh Patil, Ratnesh Mandal, Nabanita Thadomal Shahani Engineering College Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350358230

In this project, we are evaluating the efficacy of utilizing deep learning and convolutional neural networks to support individuals who have visual disabilities. Our objective is to create a mobile or web application that aids people with visual impairments in their daily activities by assisting them in recognizing objects within their proximity. This application leverages recent advancements in machine learning, particularly deep learning in conjunction with transfer learning. By employing convolutional neural networks, the approach becomes less reliant on specific characteristics. The process begins by capturing an image or video through the camera. The algorithm identifies the objects present and transmits the anticipated object names to text-To-speech software. Subsequently, this software converts the names into auditory output, providing a means for visually impaired individuals to perceive the information. © 2023 IEEE.

关键词： Convolutional neural networks

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Text Mining Studies of software Repository Contents 18

Text Mining Studies of Software Repository Contents

引用

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

作者： Dobrzynski, Bartosz Sosnowski, Janusz Warsaw Univ Technol Inst Comp Sci Ul Nowowiejska 15-19 Warsaw Poland

ISBN: (纸本)9789897586477

Issue tracking systems comprise data which are useful in evaluating or improving software development processes. Revealing and interpreting this information is a challenging problem which needs appropriate algorithms and tools. For this purpose, we use text mining schemes adapted to the specificity of the software repository. They base on a detailed analysis of the used dictionaries which comprise Natural Language Words (NLW) and are enhanced with specialized entities in issue descriptions (e.g., emails, code snippets, technical names). They are defined with specially developed regular expressions. The pre-processed texts are submitted to original text mining algorithms (machine learning). This approach has been verified in commercial and open-source projects and showed possible development improvements.

关键词： Issue Tracking System Issues and Comment Classification software Repository Text Mining

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FPGA Interfaced ECG Signal Processing Based on machine learning Classifier

FPGA Interfaced ECG Signal Processing Based on Machine Learn...

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2023 international conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Jeya Malar, R. Mary Mettilda, J. Mageshbabu, M. Siva Nagini, R. Grace, Lily Saron Gireesha, B. Kings Engineering College Department of Information Technology Chennai India Ppg Institute of Technology Department of Electronics and Communication Engineering Coimbatore India SIMATS Saveetha School of Engineering Department of Electronics and Communication Engineering Thandalam Chennai India Universal College of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Vallioor India Kings Engineering College Department of Electronics and Communication Engineering Chennai India University College of Science Tumkur University Department of Electronics Karnataka572103 India

ISBN: (纸本)9798350393378

This study aims to propose an FPGA-oriented ECG signal processing system architecture. The method identifies FPGA complexes and classifies beats as either preterm or typical contractions of the ventricular wall. For pulse classification, an element of the Open Source ECG Analysis software is used, and for FPGA complexity verification, a method based on a phase-space image of an ECG signal is used. Artificial intelligence (AI) is increasingly serving as the basis for autonomous and independent systems. One of the most popular machine learning techniques among many categorization strategies is the Support Vector machine (SVM) classification. We may further improve the SVM classifier's accuracy and classification process by using convex optimization techniques to identify the best solution. Numerous machine learning techniques, such as SVM classification, need a lot of processing power since they are compute-and data-intensive. Additionally, a number of machine learning algorithms have made it into incorporated and handheld gadgets, which have very particular demands. © 2023 IEEE.

关键词： Electrocardiograms

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Design of a perceptual-coupled plasma simulation system based on COMSOL 4

Design of a perceptual-coupled plasma simulation system base...

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4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Huang, Rui Wang, Ying Wang, Hui Liu, Yunlong School of Electrical and Information Engineering Heilongjiang University of Technology Heilongjiang Province Jixi City China School of Modern Manufacturing Engineering Heilongjiang University of Technology Heilongjiang Province Jixi City China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

By studying the basic principle of ICP and the characteristics of low-temperature plasma, the advantages of using a plasma metal cylinder discharge chamber are discussed, and the influence of skin effect on discharge effect is deeply investigated. COMSOL multi-physics finite element software is used to analyze the physical characteristics of the sensitively coupled plasma. The results show that COMSOL has high feasibility and accuracy in numerical simulation of the perceptual-coupled plasma, which provides theoretical and technical support for the application of plasma in material processing, environmental purification, and other fields. © 2024 SPIE.

关键词： Temperature

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UTILIZING VISION-BASED OBJECT DETECTION ALGORITHMS IN RECOGNIZING UNCOMMON OPERATING CONDITIONS FOR CNC MILLING machine 18

UTILIZING VISION-BASED OBJECT DETECTION ALGORITHMS IN RECOGN...

引用

ASME 18th Int Mfg Sci and Engn conf held jointly with the 51st N Amer Res conf / Japan-Soc-of-Mech-Engineers Int conf on Leading Edge Mfg/Mat and Proc

作者： Choudhari, Sahil J. Sujay, B. J. Singh, Swarit Anand Desai, K. A. Indian Inst Technol Dept Mech Engn Jodhpur Rajasthan India

ISBN: (纸本)9780791887240

Although Computer Numerical Control (CNC) machines were designed to perform tasks with the least human intervention, operator involvement is mandatory to ensure fault-free operations. Numerous technological solutions utilizing Artificial Intelligence, sensor fusion, Internet of Things (IoT), machine vision, etc., have been developed for process, component, and machine monitoring to impart smartness and autonomous operating abilities. The primary focus of these solutions is to monitor process faults such as tool wear, chatter, static deflections, and cutting forces to assist the operator in minimizing the consequences. The present work develops a vision-based solution for identifying uncommon process abnormalities like improper coolant flow, chip clogging, and tool breakage during CNC milling. The proposed solution replicates the task of a machine operator in identifying these faults and assists in fault-free operations. The study explores the feasibility of utilizing classical and deep learning-based object detection algorithms while developing these solutions. The classical image processing algorithm is ineffective during dynamic process conditions. The deep learning-based algorithm, with an average precision of about 0.75, showed proficiency in abnormalities detection. A Graphical User Interface (GUI) has been developed and integrated with the CNC milling machine to provide an interactive in-process monitoring tool. It is demonstrated that the proposed solution can reduce dependence on a machine operator while monitoring these faults.

关键词： CNC machine Industry 4.0 Object Detection machine Vision Image Processing Deep learning

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