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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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machine learning Algorithms for Intrusion Detection Performance Evaluation and Comparative Analysis 4

Machine Learning Algorithms for Intrusion Detection Performa...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Irfan, B. Md. Poornima, V. Mohana Kumar, S. Aswal, Upendra Singh Krishnamoorthy, N. Maranan, Ramya Nalsar University of Law Department of Information Technology Telengana Hyderabad500101 India VISTAS Pallavaram Department of Computer Science Chennai India Ramaiah Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru560054 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Vellore Institute of Technology Vellore Campus Department of Software Systems and Engineering Tamilnadu Vellore632014 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai600124 India

ISBN: (纸本)9798350300888

The security of computer networks is increasingly difficult to maintain due to the rising complexity and frequency of cyber-attacks. Important tools for finding and neutralizing these dangers are intrusion detection systems. This study sets out to do a thorough examination and comparison of the efficacy of several machine learning algorithms for use in intrusion detection. This research study evaluates the efficacy of several machine learning algorithms in correctly categorizing instances of network traffic as normal or invasive via extensive experiments performed on representative datasets. Algorithms like random forests, decision trees, SVMs, DL models and NNs are all being tested and rated. Effectiveness is measured and compared using a variety of performance indicators including accuracy, recall, precision, false positive rate, and F1-score. The results of this study emphasize the potential of deep learning models and Random Forests for use in intrusion detection and add to the body of knowledge around machine learning methods for this task. Professionals in the field of network security might use the results to their advantage when building intrusion detection systems. Future research areas are also mentioned, which will hopefully lead to even greater improvements in the field and safer, more reliable intrusion detection systems. © 2023 IEEE.

关键词： Decision trees

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Water Quality Classification Using machine learning 8

Water Quality Classification Using Machine Learning

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8th IEEE international conference on Recent Advances and Innovations in engineering, ICRAIE 2023

作者： Arifin, Fikri Firas Tajul Idrus, Zanariah Halim, Shamimi A. Ahmarofi, Ahmad Afif Ahmad, Khairul Adilah Shah Alam College of Computing Informatics and Mathematics Selangor Shah Alam Malaysia Kedah Branch College of Computing Informatics and Mathematics Merbok Kedah Malaysia

ISBN: (纸本)9798350315516

Water quality is crucial as it directly affects the ecosystem and human health. However, current water quality classification methods are inefficient because they do not compare prediction accuracy between machine learning methods. In this regard, the objective of this study is to classify water quality based on the proposed machine learning tools. To fulfill that, a preliminary study was conducted by collecting related information in the research domain through articles, electronic books, and online databases. The data collection for the prototype's dataset was obtained from an electronic book published by the Pakistan Council of Research in Water Resources 2021. Subsequently, the data pre-processing phase was conducted by using WEKA software which includes the crucial steps to transform the data into a cleaner format and make the model more accurate. The model for each technique was developed using Python in Jupyter Notebook. The results of the accuracy score for each model were also conducted in this phase. The findings of this research show that the Decision Tree model performs excellently with an accuracy of 97.37% compared to the Support Vector machine and K-Nearest Neighbour models, with an accuracy of 95.69% and 74.72%, respectively. Consequently, implementing a multi-class classification system can help future researchers classify more accurately and reduce the misclassification of water quality. © 2023 IEEE.

关键词： Decision trees

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ML Based Automated Assistance System for Efficient Crowd Control A detailed investigation 3

ML Based Automated Assistance System for Efficient Crowd Con...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Kumar, Ashwini Parikh, Swapnil Arka Jain University Department of Engineering& It Jharkhand Jamshedpur India Pit Parul University Department of Cse Gujarat Vadodara India

ISBN: (纸本)9798350399264

This section covers ML and automated assistance systems for crowd control. ML-based artificial intelligence (AI) lets software programmes enhance prediction accuracy without being designed to. ML techniques forecast correct output using past data. The next step towards truly automated beings is to develop methods for understanding and managing the currently pervasive community info gadgets since data is so important. In this part, we'll discuss ML's automatic crowd control. Data availability is expanding learning for application across sectors. Computer vision has helped medical diagnosis, data presentation and processing, science, and research. Computer hardware, electronics, internet content, and cyber security have used these methods. The machine learning-based automatic assistance system for efficient cored management can now be concluded. © 2023 IEEE.

关键词： Data acquisition

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Attention-based deep learning approach for CSI feedback under 5G TDL channel

Attention-based deep learning approach for CSI feedback unde...

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2023 international conference on machine learning and Automation, conf-MLA 2023

作者： Peng, Hanli School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

In 5G communication systems, accurate channel state information (CSI) is indispensable for signal detection and regulation at the base station side. However, frequent CSI feedback from users leads to excessive system overhead. To tackle this challenge, this paper puts forward a novel deep learning framework - HCNet based on attention mechanism and autoencoder, aiming to efficiently compress and reconstruct the high-dimensional time-varying CSI matrices in an end-to-end approach. The proposed framework incorporates a self-attention module in the encoder to explicitly capture global dependencies within the CSI matrix. Meanwhile, the decoder adopts gated recurrent unit networks to fully exploit inter-feature correlations and redundancy. To evaluate the performance, simulations are conducted using datasets conforming to current 5G time-varying TDL channel models. Results demonstrate superior performance over existing deep learning based feedback networks. Specifically, the proposed framework can reduce the normalized mean square error of CSI reconstruction by 4 dB under various compression ratios which confirms the effectiveness of the attention-enhanced autoencoder structure for compressive CSI sensing and feedback in practical dynamic communication systems. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Channel state information

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A Comparison of Source Code Representation Methods to Predict Vulnerability Inducing Code Changes 18

A Comparison of Source Code Representation Methods to Predic...

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

作者： Halepmollasi, Rusen Hanifi, Khadija Fouladi, Ramin F. Tosun, Ayse Istanbul Tech Univ Istanbul Turkiye TUBITAK Informat & Informat Secur Res Ctr Kocaeli Turkiye Ericsson Secur Res Istanbul Turkiye

ISBN: (纸本)9789897586477

Vulnerability prediction is a data-driven process that utilizes previous vulnerability records and their associated fixes in software development projects. Vulnerability records are rarely observed compared to other defects, even in large projects, and are usually not directly linked to the related code changes in the bug tracking system. Thus, preparing a vulnerability dataset and building a predicting model is quite challenging. There exist many studies proposing software metrics-based or embedding/token-based approaches to predict software vulnerabilities over code changes. In this study, we aim to compare the performance of two different approaches in predicting code changes that induce vulnerabilities. While the first approach is based on an aggregation of software metrics, the second approach is based on embedding representation of the source code using an Abstract Syntax Tree and skip-gram techniques. We employed Deep learning and popular machine learning algorithms to predict vulnerability-inducing code changes. We report our empirical analysis over code changes on the publicly available SmartSHARK dataset that we extended by adding real vulnerability data. software metrics-based code representation method shows a better classification performance than embedding-based code representation method in terms of recall, precision and F1-Score.

关键词： software Vulnerabilities software Metrics Embeddings Abstract Syntax Tree

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COVID-19 Fake News Detection Model on Social Media Data Using machine learning Techniques 8

COVID-19 Fake News Detection Model on Social Media Data Usin...

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8th IEEE international conference on software engineering and Computer Systems, ICSECS 2023

作者： Kai Xuan, Kelvin Liew Bhuiyan, Mohaiminul Islam Kamarudin, Nur Shazwani Nasir, Ahmad Fakhri Ab. Toh Abdullah, Muhammad Zulfahmi Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pahang Malaysia

ISBN: (纸本)9798350310931

Social media sites like Instagram, Twitter, and Facebook have become indispensable parts of the daily routine. These social media sites are powerful instruments for spreading the news, photographs, and other sorts of information. However, since the emergence of the COVID-19 pandemic in December 2019, many articles and headlines concerning the COVID-19 epidemic have surfaced on social media. Social media is frequently used to disseminate fraudulent material or information. This disinformation may confuse consumers, perhaps causing worry. It is hard to counter the widespread dissemination of disinformation. As a result, it is critical to develop a model for recognizing fake news in the news stream. The dataset, which would be a synthesis of COVID-19-related news from numerous social media and news sources, is utilized for categorization in this work. Markers are retrieved from unstructured textual data gathered from a variety of sources. Then, to eliminate the computational burden of analyzing all of the features in the dataset, feature selection is done. Finally, to categorize the COVID -19 related dataset, multiple cutting-edge machine-learning algorithms were trained. Support Vector machine (SVM), Naïve Bayes (NB), and Decision Tree (DT) are the machine learning models presented. Finally, numerous measures are used to evaluate these algorithms such as accuracy, precision, recall, and F1 score. The Decision Tress algorithm reported the highest accuracy of 100% compared to the Support Vector machine 98.7% and Naïve Bayes 96.3%. © 2023 IEEE.

关键词： COVID-19

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26th international conference on Fundamental Approaches to software engineering, FASE 2023, held as part of the 26th European Joint conferences on Theory and Practice of software, ETAPS 2023

26th International Conference on Fundamental Approaches to S...

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26th international conference on Fundamental Approaches to software engineering, FASE 2023, held as part of the 26th European Joint conferences on Theory and Practice of software, ETAPS 2023

ISBN: (纸本)9783031308253

The proceedings contain 18 papers. The special focus in this conference is on Fundamental Approaches to software engineering. The topics include: Parallel Program Analysis via Range Splitting;runtime Enforcement Using Knowledge Bases;specification and Validation of Normative Rules for Autonomous Agents;towards Log Slicing;vamos: Middleware for Best-Effort Third-Party Monitoring;yet Another Model! A Study on Model’s Similarities for Defect and Code Smells;software Testing: 5th Comparative Evaluation: Test-Comp 2023;FuSeBMC_IA: Interval Analysis and Methods for Test Case Generation: (Competition Contribution);A Modeling Concept for Formal Verification of OS-Based Compositional software;compositional Automata learning of Synchronous Systems;concolic Testing of Front-end JavaScript;democratizing Quality-Based machine learning Development through Extended Feature Models;Efficient Bounded Exhaustive Input Generation from Program APIs;feature-Guided Analysis of Neural Networks;JavaBIP meets VerCors: Towards the Safety of Concurrent software Systems in Java;model-based Player Experience Testing with Emotion Pattern Verification.

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A Sequential-based Deep learning Model for Dry Beans Classification

A Sequential-based Deep Learning Model for Dry Beans Classif...

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2023 international conference on Smart Computing and Application, ICSCA 2023

作者： Sujatha, R. Chatterjee, Jyotir Moy Rohith, A. Ramadan, Rabie A. Vellore Institute of Technology Software and Systems Engineering Vellore India Information Technology Lord Buddha Education Foundation Kathmandu Nepal Cairo University Computer Engineering Giza Egypt College of Computer Science and Engineering Hail University Hail Saudi Arabia

ISBN: (纸本)9798350347050

People take various types of cereals every day in their regular meals, but most people do not know their importance while consuming them. Each cereal has its benefit. One such cereal content is dry beans. Dry beans are a variety of beans that are produced in pods. These dry beans can be cooked and eaten, and it has numerous proteins and vitamins and are highly beneficial for our health. It also provides excellent immunity. Each bean has its characteristics, which can be identified with its unique features. These can be oval or kidney-shaped or even without a proper shape. According to their shape and features, they are classified separately. These nutrient-rich beans have to be classified based on their shapes. Human eyes sometimes may oversee or misclassify these tiny cereals for classification. This work involves the classification of 7 such dry bean varieties utilizing a deep learning context. The dataset utilized in this paper includes 13611 dry bean data for seven different UCI machine learning repositories. In this work, we have taken seven different categorical labels: the dry bean varieties. The classification is done using deep learning techniques, and here we have utilized the Keras Sequential Algorithm for the classification. It is a supervised learning concept of machine learning used to predict two or more categorical labels. With the help of the deep learning approach, these dry beans are classified and obtained an accuracy of 94.88%. © 2023 IEEE.

关键词： Deep learning

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REGRESSION-BASED SURROGATE MODEL FOR RAPID PREDICTION OF TEMPERATURE EVOLUTION IN A MICROSCALE SELECTIVE LASER SINTERING SYSTEM 18

REGRESSION-BASED SURROGATE MODEL FOR RAPID PREDICTION OF TEM...

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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： Grose, Joshua Annaluru, Ramakrishna Sai Foong, C. S. Cullinan, Michael Univ Texas Austin Austin TX 78712 USA NXP Semicond Austin TX USA

ISBN: (纸本)9780791887240

Existing metal Additive Manufacturing (AM) tools suffer from limitations on the minimum feature sizes of their producible parts. The Microscale Selective Laser Sintering ( mu-SLS) system directly addresses this restriction with a minimum part resolution on the order of a single micrometer. However, the production of parts at the micrometer scale is unreliable due to unwanted heat transfer in the nanoparticle powder bed. As a result, Finite Element thermal models are developed to predict the temperature evolution within the particle bed during sintering. These thermal models are not only computationally expensive, but also must be integrated into an iterative control framework to optimize the digital mask used to selectively control laser power, making it critical to enable quick temperature predictions. Therefore, this paper proposes a regression-based machine learning model to facilitate accurate and rapid predictions of the thermal evolution within the mu-SLS particle bed and subsequent optimization of the system input parameters. The regression model presented in this work uses an "Element-by-Element" approach, where machine learning models are trained on individual finite elements to learn the relationship between the conditions experienced by the element at the current timestep and the element's temperature at the subsequent timestep. An existing bed-scale thermal model of the mu-SLS system is used to generate element by element training data for various regression models. Key features for each element include the temperature and applied volumetric heat generation of the selected element and its surrounding elements at the current timestep. Additional features are included that represent the spatial distribution of laser energy relative to the selected element. This feature vector is used to predict the temperature of the given element at the next timestep. Using this dataset, a feed-forward Artificial Neural Network with 3 hidden layers is used to predict the

关键词： machine learning Surrogate Regression Finite Element Thermal Laser Sintering Additive Manufacturing

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software Bug Count Prediction Model Based on software Source Code 14

Software Bug Count Prediction Model Based on Software Source...

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14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kumar, Rakesh Chaturvedi, Amrita Department of Computer Science and Engineering Varanasi India

ISBN: (纸本)9798350335095

software bug prediction (SBP) model is a classification model that predicts buggy or non-buggy modules of software projects, while the software bug count prediction (SBCP) model is a regression model that predicts the precise number of bugs in each module. So, SBCP model is a more useful model for a software tester to rank the software modules. Many SBP models are developed using object-oriented (OO) software metrics (PROMISE datasets) and simplified PROMISE source code (SPSC) datasets (DSs). But, no SBCP model was developed using SPSC dataset. To fill this gap, we created SPSC DSs using features extracted from the software source code's Abstract Syntax Trees (ASTs). Further, we developed SBCV models using Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) over 8 SPSC DSs. The experimental results are compared with the six standard machine learning (ML) algorithms and two deep learning (DL) models (LSTM and CNN) implemented over 8 PROMISE DSs. We have applied a non-parametric statistical test (Nemenyi test) to show the significance of the proposed SBCP model. The proposed improved CNN model produces a better mean absolute error (MAE) of 0.50 and a mean relative error (MRE) of 0.19 averaged over eight SPSC DSs. © 2023 IEEE.

关键词： Long short-term memory

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