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Research on intelligent water valve fault classification method based on machine learning 4

Research on intelligent water valve fault classification met...

引用

4th international conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2023

作者： Wu, Huaikun Hao, Weisha Li, Xiangyu Gao, Hongbiao Zhang, Jianbin Guo, Huaizhou Hefei General Machinery Research Institute Co. Ltd Hefei230031 China Hefei University of Technology Hefei230009 China

ISBN: (纸本)9798350329490

Water valves are crucial components for controlling fluid flow in smart city pipeline networks. Identifying the types of faults in water valves, which are widely distributed and numerous, and timely judging and warning their operational status, will greatly improve the efficiency of water systems and significantly reduce the risk of accidents. Based on various datasets of water valve parameters, we simulated the normal operation data of the valve, as well as valve clogging, external leakage, and positioner supply pressure drop using DAMADICS. We then compared classification algorithms such as KNN, BP, SVM, etc. The KNN classification algorithm, which is suitable for online monitoring of valves, has been selected. © 2023 IEEE.

关键词： Support vector machines

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A Systematic Review of IoT Malware Detection using machine learning 10

A Systematic Review of IoT Malware Detection using Machine L...

引用

17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Sharma, Badal Kumar, Rajendra Kumar, Anuj Chhabra, Megha Chaturvedi, Saumya School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9789380544472

The Internet of Things (IoT) is spreading quickly across the globe, yet its security is insufficient. The results are encouraging for using machine learning to identify malware on these frequently unattended devices. Among all infections found in mobile networks, the Internet of Things was the target of more than 30% of them. Such detection in a single IoT node has been done yet, though, because IoT nodes often have insufficient processing. This study explores the feasibility of malware detection in a single IoT device by scaling machine learning methods to allow a single IoT device to perform near-real- time network traffic anomaly detection and label packets as malware or benign. Using one of the machine learning techniques, the suggested software for categorizing data points from the IoT-23 dataset can be implemented on an ESP32 device. When completely deployed, this could imply that IoT devices would be able to decide for themselves whether a network connection is a part of a malware attack or a regular connection. Interconnected devices and terminals are used as a lucrative target by cybercriminals, especially those who have the backing of the government or other national state actors. Vector malware application is a frequent form of assault. Using the operational code sequence or functional code of the device, the learning Things and learning technique seeks out harmful online applications on the Internet. This paper uses deep learning approach to convert the Opcodes into vector space and categorize them into dangerous and malicious Applications. This paper present evidence for the efficiency of our proposed approach against malware detection and garbage Code insertion assaults. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

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Automatic Extraction of software Requirements Using machine learning 7th

Automatic Extraction of Software Requirements Using Machine ...

引用

7th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

作者： Apte, Siddharth Honrao, Yash Shinde, Rohan Talele, Pratvina Phalnikar, Rashmi School of Computer Engineering and Technology Dr. Vishwanath Karad MIT World Peace University Pune India

ISBN: (纸本)9789819937578

System requirement specification (SRS) documents specify the client’s requirements and specifications in the software systems. Requirement engineering is a mandatory phase of the software Development Life Cycle (SDLC) that includes defining, documenting, and maintaining system requirements. As the complexity increases it becomes difficult to categorize and prioritize the various software Requirements (SR). Different natural language processing methods such as tokenization, lemmatization is used in the text pre-processing phase followed by Term frequency-inverse document frequency (TF-IDF). The aim of this research is to compare existing machine learning algorithms to evaluate which algorithm is able to efficiently classify the system requirements. The algorithms are assessed on two parameters precision and accuracy. The results obtained showed Decision Tree (DT) could identify all types of requirements except portability. But the accuracy of Support Vector machine (SVM) is highest with 78.57% for the publicly available dataset than that of DT which has an accuracy of 61.42% © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Decision trees

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Batch Mode Deep Active learning for Regression on Graph Data

Batch Mode Deep Active Learning for Regression on Graph Data

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2023 IEEE international conference on Big Data, BigData 2023

作者： Samoaa, Peter Aronsson, Linus Leitner, Philipp Chehreghani, Morteza Haghir Chalmers University of Technology Data Science and AI Division Gothenburg Sweden Chalmers University of Technology Interaction Design and Software Engineering Gothenburg Sweden

ISBN: (纸本)9798350324457

Acquiring labelled data for machine learning tasks, for example, for software performance prediction, remains a resource-intensive task. This study extends our previous work by introducing a batch-mode deep active learning approach tailored for regression in graph-structured data. Our framework leverages the source code conversion into Flow Augmented-AST graphs (FA-AST), subsequently utilizing both supervised and unsupervised graph embeddings. In contrast to single-instance querying, the batch-mode paradigm adaptively selects clusters of unlabeled data for labelling. We deploy an array of base kernels, kernel transformations, and selection methods, informed by both Bayesian and non-Bayesian strategies, to enhance the sample efficiency of neural network regression. Our experimental evaluation, conducted on multiple real-world software performance datasets, demonstrates the efficacy of the batch mode deep active learning approach in achieving robust performance with a reduced labelling budget. The methodology scales effectively to larger datasets and requires minimal alterations to existing neural network architectures. © 2023 IEEE.

关键词： Active learning Deep learning Graph Neural Network Kernels

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software Platform for CTG Classification Using Artificial Intelligence 7

Software Platform for CTG Classification Using Artificial In...

引用

7th international conference on Advances in Biomedical engineering, ICABME 2023

作者： Christelle, Margossian Sandy, Rihana Holy Spirit University of Kaslik Biomedical Engineering Department Jounieh Lebanon

ISBN: (纸本)9798350325850

Pregnancy is a critical period in every woman's life, and it comes with its own risks and downsides that she must look out for, such as pre-term labor. Unfortunately, maternal and fetal morbidity and mortality have been significant issues in the past decades that have yet to be resolved. Despite the evolving medicine and technology, tackling the dangers of pregnancy is still a challenge even though there are a lot of new devices used for monitoring. The target of this project is to build an algorithm that allows the signal processing and feature extraction of uterine contraction signals outputted from the Cardiotocography device, as well as a machine learning algorithm that classifies these signals into pathological, suspicious, and normal cases. These signals and features were collected from a trusted database and used to undergo signal processing before executing the machine learning step. Several algorithms were tested in this work including KNN, CNN, LMNN, and BNN where the Bayesian model exhibited the highest performance results with the input data. The input was also manipulated using the Chi squared approach to yield ameliorated results and analyzed based on significance. © 2023 IEEE.

关键词： MATLAB

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Understanding Fairness Requirements for ML-based software 31

Understanding Fairness Requirements for ML-based Software

引用

31st IEEE international Requirements engineering conference, RE 2023

作者： Baresi, Luciano Criscuolo, Chiara Ghezzi, Carlo Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingegneria Milan Italy

ISBN: (纸本)9798350326895

Today's technologies are becoming more and more pervasive and advanced software systems can replace human beings in many different tasks. This is especially true in the case of automated decision-making systems based on machine learning (ML). Important ethical implications arise when such decision systems are used in sensitive contexts (e.g., justice or loans). The elicitation of these implications, that is, of the ethical requirements behind ML-based systems is a new challenge we must address to avoid societal risks. This is particularly urgent for fairness since this notion lacks a precise and commonly accepted definition, thus hampering its assessment. This paper aims to give a comprehensive definition of fairness, present a unified taxonomy of alternative interpretations, define a new decision tree that can guide the choice of the correct interpretation, and carry out a preliminary assessment with experiments in a real-world context. © 2023 IEEE.

关键词： machine learning

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Prediction of software Vulnerabilities Using Random Forest Regressor 2nd

Prediction of Software Vulnerabilities Using Random Forest R...

引用

2nd international conference on Information Technology, InCITe 2022

作者： Kamal, Navirah Raheja, Supriya Amity University Sector 125 Noida201313 India

ISBN: (纸本)9789811973451

The launch of new software technologies with new features helps the developer in software development but it can be prone to vulnerabilities. software vulnerabilities are still a critical issue for software security as they can negatively impact the organization and the end user. To mitigate this problem, various techniques have been adopted, machine learning is one of them. The main objective of this paper is to predict the severity of software vulnerabilities using a random forest regressor algorithm. To evaluate the performance, two other machine learning algorithms are also implemented for the same task. A dataset of the National Vulnerabilities database is used for the present work. The efficacy of the models has been evaluated and compared using four different performance metrics namely mean absolute error, mean square error, root mean square error, and R2 score. Random forest regressor performed the best out of the applied machine learning algorithms with a root mean square error of 0.01945. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Decision trees

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Detection of Sensors Fault Using Singular Value Decomposition Technique in Power Plant

Detection of Sensors Fault Using Singular Value Decompositio...

引用

2023 international conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Helen, R. Nikei Antony Miekle, A. Thenmozhi, T. Suhail Ahmed, A. Aravindh, R. Saveetha Engineering College Department of Medical Electronics Chennai India Thiagarajar College of Engineering Eee Department Madurai India

ISBN: (纸本)9798350300857

Temperature sensors are critical components in power plants, and their accurate operation is essential for efficient plant performance and safety. However, temperature sensors are susceptible to various faults such as offset, and sensor failure, which can lead to incorrect temperature readings and affect the overall plant operation. Therefore, it is crucial to detect and diagnose temperature sensor faults to secure the safety and dependability of the plant. Integrating machine learning methods into existing sensor systems for fault detection poses significant challenges. Seamless integration with data acquisition systems and sensor networks is essential but can be complex due to differences in data formats and communication protocols. Real-time processing requirements may necessitate optimization for low latency. Ensuring that machine learning models run efficiently on embedded, or edge devices can also be challenging. Furthermore, integrating the output of machine learning models into decision-making processes or control systems requires careful consideration to prevent false alarms or missed faults. This study proposes the implementation of Singular Value Decomposition (SVD) for detecting sensor faults using Lab *** method utilizes SVD to extract feature vectors from sensor data, which are then compared to reference vectors to identify faulty sensors. The Lab Viewplatform is used to develop the software application for processing the sensor data and performing the SVD analysis. The proposed method offers a reliable and efficient approach to detecting sensor faults in industrial systems, potentially improving system reliability and reducing downtime. © 2023 IEEE.

关键词： Singular value decomposition

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Gray-box Adversarial Attack of Deep Reinforcement learning-based Trading Agents 22

Gray-box Adversarial Attack of Deep Reinforcement Learning-b...

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22nd IEEE international conference on machine learning and Applications, ICMLA 2023

作者： Ataiefard, Foozhan Hemmati, Hadi University of Calgary Electrical and Software Engineering Calgary Canada York University Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350345346

In recent years, deep reinforcement learning (Deep RL) has been successfully implemented as a smart agent in many systems such as complex games, self-driving cars, and chat-bots. One of the interesting use cases of Deep RL is its application as an automated stock trading agent. In general, any automated trading agent is prone to manipulations by adversaries in the trading environment. Thus studying their robustness is vital for their success in practice. However, typical mechanism to study RL robustness, which is based on white-box gradient-based adversarial sample generation techniques (like FGSM), is obsolete for this use case, since the models are protected behind secure international exchange APIs, such as NASDAQ. In this research, we demonstrate that a 'gray-box' approach for attacking a Deep RL-based trading agent is possible by trading in the same stock market, with no extra access to the trading agent. In our proposed approach, an adversary agent uses a hybrid Deep Neural Network as its policy consisting of Convolutional layers and fully-connected layers. On average, over three simulated trading market configurations, the adversary policy proposed in this research is able to reduce the reward values by 214.17%, which results in reducing the potential profits of the baseline by 139.4%, ensemble method by 93.7%, and an automated trading software developed by our industrial partner by 85.5%, while consuming significantly less budget than the victims (427.77%, 187.16%, and 66.97%, respectively). © 2023 IEEE.

关键词： Adversarial At-tacks Automated Trading Deep Reinforcement learning Robustness

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Gender Classification of Social Network Text Using Natural Language Processing and machine learning Approaches 20

Gender Classification of Social Network Text Using Natural L...

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

作者： Jintawatsakoon, Supanat Poonsawat, Ekkapob College of Digital Innovation Technology Rangsit University Pathum Thani Thailand True Digital Group Co. Ltd. Department of Data Analytics Bangkok Thailand

ISBN: (纸本)9798350300505

Gender is a crucial consideration in many fields of study. In the era of social networks, massive volumes of data are gathered and processed, enabling us to use this data for a variety of purposes. Our objective to build a gender classification model based on Thai text using natural language processing (NLP) and a machine learning approach. We collected the data on social media websites using web scraping. TF-IDF and n-gram were applied for feature extraction tasks. Logistic Regression, Naïve Bayes, and Random Forest have implemented classification models. Accuracy, precision, recall, and f1 score are used as evaluation metrics and demonstrate that the Logistic Regression model trained on the features derived from data received from texts longer than 200 words produces the best outcome. The dataset is available at https://***/supanat/***. © 2023 IEEE.

关键词： Logistic regression

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