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

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Proceedings - 2023 international conference on Power, Electrical engineering, Electronics and Control, PEEEC 2023

Proceedings - 2023 International Conference on Power, Electr...

引用

2023 international conference on Power, Electrical engineering, Electronics and Control, PEEEC 2023

ISBN: (纸本)9798350329124

The proceedings contain 190 papers. The topics discussed include: construction of network security situation assessment model based on improved ant colony algorithm;optimization of software engineering supervision based on machine learning algorithm;control system of multi-function grid-connected inverter for distributed power grid based on intelligent optimization algorithm;intelligent dispatching model of power system based on improved genetic algorithm;a mathematical model of sound pressure attenuation for optimizing distribution of orchestra instruments;digital analysis of automotive equipment based on-machine learning;design of intelligent management platform for electric valve by calculation information;analysis on Halo characteristics and thermal effect of AC transmission line;construction of intelligent mining visualization centralized control system based on neural network and genetic algorithm;and research on automatic start-up and debugging method of electrical engineering equipment based on Apriori algorithm.

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An analysis on Keylogger Attack and Detection based on machine learning

An analysis on Keylogger Attack and Detection based on Machi...

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2023 international conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICEconf 2023

作者： Balakrishnan, Yeshaswini Renjith, P.N. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350334357

A keylogger attack is a type of cyberattack that involves the use of a software program to record keystrokes on a target device. Attacks of this kind can be used to steal sensitive data, including credit card numbers and login credentials. Keylogger attacks are often targeted at specific individuals or organizations, and the attackers may have prior knowledge of the target's systems and configuration. There are a variety of ways to carry out a keylogger attack, and the attacker's choice of method will depend on the type of information they are trying to steal. For example, an attacker may install a hardware keylogger on the target's computer in order to record every keystroke made. As an alternative, the attacker might develop malicious software that captures keystrokes and transmits them to a distant server. Keylogger attacks are difficult to detect, as the keylogger software can be disguised as a legitimate program or run in the background without the user's knowledge. However, there are some signs that a keylogger attack may be taking place, such as unexpected activity on the computer or unusual network traffic. The best way to protect against keylogger attacks is to use a reputable antivirus program and to keep all software up to date. Furthermore, users need to be cautious while opening attachments or clicking on hyperlinks that come from unknown sources. This paper focuses on the evolution of technology over time, as well as the implementation and observation of keylogger attacks. © 2023 IEEE.

关键词： machine learning

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machine learning Techniques for Water Quality Classification of Thailand's Rivers 20

Machine Learning Techniques for Water Quality Classification...

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20th international Joint conference on Computer Science and software engineering, JCSSE 2023

作者： Sirikarin, Keereeluk Khonthapagdee, Subhorn Srinakharinwirot University Faculty of Science Department of Computer Science Bangkok Thailand

ISBN: (纸本)9798350300505

Water is necessary for human consumption. To ensure that water is safe, a monitoring system for water quality is required. One part of the system is to be able to predict the water quality class. Using data collected from the Pollution Control Department of Thailand from 2009 to 2021, we compared four machine learning approaches for classifying water quality classes in four main rivers in Thailand: the Ping, Wang, Yom, and Nan rivers. Random Forest, Extreme Gradient Boosting (XGBoost), Logistic Regression, and Support Vector machine were used in this study. Moreover, synthetic minority oversampling technique (SMOTE) and Random oversampling, two strategies for dealing with imbalanced data, were also used to improve classification F1 score. This study found that XGBoost with SMOTE achieved the highest score, and BOD was the most important feature in classifying water quality. © 2023 IEEE.

关键词： Water quality

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GPTCloneBench: A comprehensive benchmark of semantic clones and cross-language clones using GPT-3 model and SemanticCloneBench 39

GPTCloneBench: A comprehensive benchmark of semantic clones ...

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39th IEEE international conference on software Maintenance and Evolution (ICSME)

作者： Alam, Ajmain I. Roy, Palash R. Al-Omari, Farouq Roy, Chanchal K. Roy, Banani Schneider, Kevin A. Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada

ISBN: (纸本)9798350327830

With the emergence of machine learning, there has been a surge in leveraging its capabilities for problem-solving across various domains. In the code clone realm, the identification of type-4 or semantic clones has emerged as a crucial yet challenging task. Researchers aim to utilize machine learning to tackle this challenge, often relying on the BigCloneBench dataset. However, it's worth noting that BigCloneBench, originally not designed for semantic clone detection, presents several limitations that hinder its suitability as a comprehensive training dataset for this specific purpose. Furthermore, CLCDSA dataset suffers from a lack of reusable examples aligning with real-world software systems, rendering it inadequate for cross-language clone detection approaches. In this work, we present a comprehensive semantic clone and cross-language clone benchmark, GPTCloneBench (1) by exploiting SemanticCloneBench and OpenAI's GPT-3 model. In particular, using code fragments from SemanticCloneBench as sample inputs along with appropriate prompt engineering for GPT-3 model, we generate semantic and cross-language clones for these specific fragments and then conduct a combination of extensive manual analysis, tool-assisted filtering, functionality testing and automated validation in building the benchmark. From 79,928 clone pairs of GPT-3 output, we created a benchmark with 37,149 true semantic clone pairs, 19,288 false semantic pairs(Type-1/Type-2), and 20,770 cross-language clones across four languages (Java, C, C#, and Python). Our benchmark is 15-fold larger than SemanticCloneBench, has more functional code examples for software systems and programming language support than CLCDSA, and overcomes BigCloneBench's qualities, quantification, and language variety limitations. GPTCloneBench can be found here(1).

关键词： software Clone SemanticCloneBench GPT-3 Language Model machine learning Cross Language Clone Semantic Clone BigCloneBench

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3rd international conference on Electrical, Computer and Communication engineering, ECCE 2023

3rd International Conference on Electrical, Computer and Com...

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3rd international conference on Electrical, Computer and Communication engineering, ECCE 2023

ISBN: (纸本)9798350345360

The proceedings contain 137 papers. The topics discussed include: sexual harassment detection using machine learning and deep learning techniques for Bangla text;optimization strategies for micro-grid energy management and scheduling systems by sine cosine algorithm;efficient hardware and software co-design for EEG signal classification based on extreme learning machine;measurement and modeling of GaAs based nano-pHEMT: small signal to large signal analysis;Alzheimer’s disease classification from 2D MRI brain scans using convolutional neural networks;faulty classes prediction in object-oriented programming using composed Dagging technique;interpretable multi labeled Bengali toxic comments classification using deep learning;protein structure prediction in structural genomics without alignment using support vector machine with fuzzy logic;and impact of transmission line capacity expansion on electricity operation cost and selling price.

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PURLTL: Mining LTL Specification from Imperfect Traces in Testing 38

PURLTL: Mining LTL Specification from Imperfect Traces in Te...

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38th IEEE/ACM international conference on Automated software engineering (ASE)

作者： Peng, Bo Liang, Pingjia Han, Tingchen Luo, Weilin Du, Jianfeng Wan, Hai Ye, Rongzhen Zheng, Yuhang Sun Yat Sen Univ Guangzhou Peoples R China Guangdong Univ Foreign Studies Pazhou Lab Guangzhou Peoples R China

ISBN: (纸本)9798350329964

Formal specifications are widely used in software testing approaches, while writing such specifications is a time-consuming job. Recently, a number of methods have been proposed to mine specifications from execution traces, typically in the form of linear temporal logic (LTL). However, existing works have the following disadvantages: (1) ignoring the negative impact of imperfect traces, which come from partial profiling, missing context information, or buggy programs;(2) relying on templates, resulting in limited expressiveness;(3) requesting negative traces, which are usually unavailable in practice. In this paper, we propose PURLTL, which is able to mine arbitrary LTL specifications from imperfect traces. To alleviate the search space explosion and the wrong search bias, we propose a neural-based method to search LTL formulae, which, intuitively, simulates LTL path checking through differentiable parameter operations. To solve the problem of lacking negative traces, we transform the problem into learning from positive and unlabeled samples, by means of data augmentation and applying positive and unlabeled learning to the training process. Experiments show that our approach surpasses the previous start-of-the-art (SOTA) approach by a large margin. Besides, the results suggest that our approach is not only robust with imperfect traces, but also does not rely on formula templates.

关键词： specification mining machine learning

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A Systematic Mapping Study of machine learning Techniques Applied to software Traceability

A Systematic Mapping Study of Machine Learning Techniques Ap...

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2023 IEEE international conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Bangchao Li, Xingfu Wan, Hongyan Deng, Yang School of Computer Science and Artificial Intelligence Engineering Research Center of Hubei Province for Clothing Information Wuhan Textile University Wuhan China

ISBN: (纸本)9798350337020

Context: software traceability (ST) refers to capturing associations in various artifacts. A growing interest has been in applying machine learning (ML) techniques to ST. Objective: The purpose of this work is to present a comprehensive review of the state-of-the-art progress on the intersection of ML and ST. Method: A systematic mapping study (SMS) is conducted. A total of 965 citations are retrieved from 2013 to 2022, among which 37 studies are selected as primary studies. Result: 32 ML technologies and 9 enhancement strategies for generating trace links have been identified. Besides, 90 datasets and 16 measures have been summarized, which are applied to evaluate the efficacy of the ML-based tracing techniques. The overall reproducibility of these primary studies is at a medium level. Conclusion: We have found that ML is playing a positive role in improving the accuracy and efficiency of ST. However, there are still some challenges such as reproducibility. Hence, researchers are suggested to pay more attention to standardization to improve the reproducibility of studies. © 2023 IEEE.

关键词： machine learning reproducibility software traceability systematic mapping study

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learning-Based Traffic Classification for software-Defined Networks 8th

Learning-Based Traffic Classification for Software-Defined N...

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8th international conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Panni, Priyanka Budihal, Suneeta V. KLE Technological University Hubli India

ISBN: (纸本)9789819784219

Recent advancements in linked devices and industrial automation have created a large demand for network resources. The amount of traffic created by these technologies is so great that traditional networks are becoming less capable of handling it. Network automation is made possible by machine learning (ML) applications using software Defined Networking (SDN), which simultaneously presents a programmable, reconfigurable, and scalable networking solution. This SDN system can address problems with conventional approaches to categorize network traffic and assign resources. The SDN controller will collect network data that data analytic techniques may analyze and use ML models to tailor network management. The paper proposes to analyze network data, apply ML to classify network traffic, and integrate the model into LabVIEW. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Predicting the Risks of Brain Stroke Using machine learning Models and Artificial Neural Network

Predicting the Risks of Brain Stroke Using Machine Learning ...

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

作者： Nahid, Nafiz Hossain, Mehrab Rifat, Ariful Islam Khan Raisa, Meher Durdana Islam, Ashraful Sylhet Engineering College Department of Computer Science and Engineering Sylhet Bangladesh American International University Department of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Department of Software Engineering Dhaka Bangladesh University of Regina Department of Computer Science Regina Canada

ISBN: (纸本)9798350303452

The primary cause of death and disability worldwide is stroke. Accurate prediction models and identification of stroke risk factors can aid in early intervention and preventive measures. In this study, an approach based on machine learning algorithms, including Logistic Regression (LR), Support Vector machine (SVM), K-Nearest Neighbors (KNN), AdaBoost (AdB), Gradient Boosting (GB), Decision Tree (DT), and a unique Artificial Neural Network (ANN) model, is proposed to predict stroke risk at four different levels. To address the issue of imbalanced dataset, Synthetic Minority Over-sampling Technique coupled with Edited Nearest Neighbors (SMOTEENN) was used. 11 features were selected for training the models out of the initial 12 features, and the data-set was divided into an 80-20 train-test ratio for model assessment. The Decision Tree (DT) model achieved an accuracy of 98.53%. Additionally, a 1000 epoch Artificial Neural Network (ANN) model with various batch sizes was created;128 produced the best results. Stochastic Gradient Descent (SGD), Adam, and RMSprop were examined along with other optimizers, however SGD produced the highest accuracy rate of 99.21%. The results suggest that utilizing the selected features and the balanced data-set, machine learning algorithms such as the unique CNN model can accurately predict stroke risk at various levels. These models have the potential to aid in early detection of stroke risk and guide preventive interventions, thus improving patient care and outcomes. Further testing and implementation of these models in real clinical settings are warranted. © 2023 IEEE.

关键词： Decision trees

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Cross comparison of COVID-19 fake news detection machine learning models 17

Cross comparison of COVID-19 fake news detection machine lea...

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17th international conference on Open Source Systems and Technologies, ICOSST 2023

作者： Qadees, Muhammad Hannan, Abdul Islamabad Department of Computer & Software Engineering Pakistan

ISBN: (纸本)9798350381320

The quick advancement of technology in internet communication and social media platforms eased several problems during the COVID-19 outbreak. It was, however, used to spread untruths and misinformation regarding the illness and the immunization. In this study, it is examined whether machine-learning algorithms (Naive Bayesian, Random Forest, Logistic Regression, Decision Tree, and Support Vector machine, as well as Gradient Boost, Bagging, AdaBoost, Stochastic Gradient Descent, and Multi-layer Perceptron) can automatically classify and point out fake news text about the COVID-19 pandemic posted on social media platforms. The 'COVID19-FNIR DATASET' was used to train, test, and fine-Tune machine learning models in order to predict the sentiment class of each fake news item on COVID-19. The results were assessed using a variety of evaluation metrics (confusion matrix, classification rate, true positives rate, etc.). The findings collected demonstrate an extremely high level of accuracy when compared to other models. © 2023 IEEE.

关键词： COVID-19

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