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2025 International Russian Smart Industry Conference, SmartIndustryCon 2025

作者： Trapezin, Andrey Filianin, Ivan ITMO University Faculty of Programming and Infocommunications St. Petersburg Russia ITMO University Faculty of Software Engineering and Computer Engineering St. Petersburg Russia

ISBN: (纸本)9798331511241

The current research work addresses the problem of automating the delivery of machine learning models from MLflow to Kubernetes infrastructure. To solve the mentioned problem, a Kubernetes operator has been developed to automate the delivery of machine learning models to production by integrating MLflow for model tracking and Seldon Core for model serving. The developed operator allows data scientists to deploy models while maintaining the familiar MLflow environment. The operator's automatic deployment triggers upon tagging models in MLflow, greatly simplifying engineers' tasks and minimizing the need for manual infrastructure configuration. By automating configuration tasks and optimizing deployment workflows, the solution achieves a 40-50% reduction in model time to deployment (TTD) metric compared to manual processes and decreases error rates from 15% to around 3%. The practical relevance of the work is that it simplifies collaboration between data and infrastructure teams by providing a unified deployment framework, resulting in faster, more reliable, and automated integration of machine learning models into an organisation's business processes. © 2025 IEEE.

关键词： engineering education

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Xgboost-Based Multi-Steps Cybersecurity Attacks Detection Model

Xgboost-Based Multi-Steps Cybersecurity Attacks Detection Mo...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdulfattah E. Ba Alawi Ferhat Bozkurt Faruk Baturalp Faculty of Engineering and IT Software Engineering Taiz University Taiz Yemen Faculty of Engineering Computer Engineering Ataturk University Erzurum Turkey

With the rapid growth of software and networks, the rate of cyber-attacks has increased rapidly. As a result, the demand for a dependable and suitable Intrusion Detection System (IDS) solution for safeguarding devices and networks has become essential. Nevertheless, in order to accurately detect the activities of new kinds of crimes, particularly tasking-step incidents, an effective IDS requires an accurate and up-to-date dataset. In this study, MSCAD is used which comes with multi-step attack tasking: the initial attack is for password-cracking type, and the subsequent one is a volume-driven Distributed Denial of Service (DDoS) attack. The dataset used (MSCAD) contains five types of internet attacks including Port Scan Traffic, App-based DDoS, Volume-based DDoS, Web Crawling, and Password Cracking (Brute Force). Nine algorithms including Gaussian Naive Bayes, Bournuli Naive Bayes, Decision Tree, K-Nearest Neighbors, Catboost, XGB, and Random Forest were employed as classifiers. The accuracy rate reached over 99.9% in terms of accuracy and AUC-ROC.

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Experimental Study on Surface Integrity of Ti6Al4V by Broaching

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International Journal of engineering, Transactions B: Applications 2022年第2期35卷 481-492页

作者： Khanjanzadeh, P. Amirabadi, H. Sadri, J. Department of Mechanical Engineering University of Birjand Birjand Iran Faculty of Engineering University of Neyshabur Neyshabur Iran Department of Computer Science and Software Engineering Concordia University Montreal Canada Camp in Labs Foundation 1 Westmount Square Bureau 1001 WestmountQC Canada

The performance of many parts in the airplane, aircraft engine and biomedical implants is highly related to their fatigue life, which is clearly depend on the condition of their surface integrity. The geometry parameters of broaching tools have an important influence on the surface integrity after broaching Ti6Al4V alloy. Therefore, this research studies the surface integrity of Ti6Al4V by broaching. The surface integrity is studied at different geometric parameters such as rake angles (α, and α 0), clearance angles (β, and β0), and radius of the cutting edge (r0) in two last teeth of the broaching tool that perform chipping. The broached surface integrity is assessed in cases of surface roughness, microstructural, residual stresses, and micro hardness. These results show that the sample broached by tool number 1 (α = 18.4, β = 3.1, α0 = 45, β0 = 9, r0 = 0.02) had higher surface integrity because it was the smoothest surface and the thinnest deformed layer among the other samples. Since the main criterions in selection of the optimal tool are to create the smoothest surface and the least deformed layer depth in the broached sample, tool number 1(α = 18.4, β = 3.1, α0 = 45, β0 = 9, r0 = 0.02) is suggest ed as the optimal tool. © 2022 Materials and Energy Research Center. All rights reserved.

关键词： Titanium alloys

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Research and Development of Kubernetes Operator for Machine Learning Pipelines

Research and Development of Kubernetes Operator for Machine ...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Andrey Trapezin Ivan Filianin Faculty of Programming and Infocommunications ITMO University St. Petersburg Russia Faculty of Software Engineering and Computer Engineering ITMO University St. Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

关键词： Production systems Error analysis Machine learning Manuals Tagging Data models Reliability Research and development Load modeling Resilience

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CNN-Based Colon Cancer Recognition Model

CNN-Based Colon Cancer Recognition Model

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdulfattah E. Ba Alawi Ferhat Bozkurt Software Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Computer Engineering Faculty of Engineering Ataturk University Erzurum Turkey

Digital pathology is being used extensively for the diagnosis of tumors. Disappointingly, the existing approaches are still constrained whenever confronted with a resolution, a size of images, and a lack of extensively cleaned datasets. Further, the recognition accuracy mostly does not reach high scores. In terms of Deep Learning (DL) approaches' capacity to handle extensive applications, such an approach appears to be an absorbing solution for both categorization and tissue segmentation in histopathology images. The present study concentrates on the application of deep learning models in the classification of the context of histopathology data and the recognition of colon cancer. In this, cutting-edge Fully Convolutional Network (CNN) models such as DenseNet121, EfficientNetB7, EfficientNetB1, EfficientNetB2, and DenseNet201 have been evaluated for the recognition of Colon Cancer. The assessment of the algorithms used for the proposed CNN-based colon cancer detection model ensures reliable classification findings with EfficientNetB2 attaining up to 99.9994% in terms of accuracy.

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A Study on New EPC-MPSI for an Inputless External Party to Compute the Intersection of Multiple Private Datasets

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IET Communications 2025年第1期19卷

作者： Kim, Ki-Hwan Seo, Dae-Hee Lee, Im-Yeong Kim, Su-Hyun Department of Software Convergence Soonchunhyang University Asan Korea Republic of Faculty of Artificial Intelligence and Data Engineering Sangmyung University Seoul Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan Korea Republic of

Private set intersection (PSI) is a privacy-preserving scheme that computes the intersection of two datasets without leaking any other information. Additionally, there is multiparty private set intersection (MPSI) to extend the number of parties for computing the intersection of multiple private datasets. In the traditional PSI and MPSI studies, protocol parties input their private datasets, and one or all of them can compute the intersection. However, there are some scenarios where an inputless external party requires the intersection between private datasets of other parties. Thus, the external party PSI protocols have been recently studied for applications such as pandemic contact tracing, computing human genome information and evaluating policy effects. However, they are limited in applications because the external party can compute the intersection of two datasets. In this paper, we propose a new external party compute-MPSI (EPC-MPSI) protocols that allow an external party to compute the intersection of multiple datasets. We provide the extension of the number of parties and solve the limitation of prior external party PSI protocols. In addition, we analyze the correctness, security and the efficiency in terms of communication and computation costs compared to the prior traditional MPSI protocols. © 2025 The Author(s). IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Sensitive data

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Classifying Psychiatric Patients Using Machine Learning 25

Classifying Psychiatric Patients Using Machine Learning

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alrjoob, Hmmam M. Alazaidah, Raed Batyha, Radwan Khafajeh, Hayel Elsoud, Esraa Abu Saeb Al-Sherideh, Ala'a Elrashidi, Ali Alqaraleh, Muhyeeddin Amer, Waleed Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan Zarqa University Faculty of Information Technology Department Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Cyper Security Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Department of Software Engineering Zarqa Jordan Zarqa University Faculty on Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798331540012

Psychological disorders are considered chronic illnesses that affect a wide range of populations. Some studies in the United States indicate that one in every eight individuals is affected by a psychological disorder. Additionally, individuals with a psychological disorder often have an impact on the nature of the lives of those around them. It is beneficial to detect the illness in its early stages to increase the likelihood of recovery. In this research, thirteen classification algorithms are used. Each algorithm has an interesting approach. It can be used to analyze several datasets where three datasets specific to Psychiatric patients were selected. The datasets have been chosen from UCI Machine Learning Repository. The datasets were re-analyzed after only 50% of the characteristics were approved after being evaluated by the several feature selection techniques. The main objective of this research is to find the best classifier and feature selection strategy in analyzing datasets of Psychiatric patients. After conducting numerous comparisons, it became evident that, the best classifier for the Autism-Adult-Data dataset are Bagging and Random Forest. As for the best learning strategy, it was found to be Functions and Trees, the best feature selection was obtained using Symmetrical Uncourt Attribute Evaluation. Both IBK and KStar classifier proved to be the best classifier for the Parkinson's dataset, Lazy demonstrated to be the most effective learning strategy, Correlation Attribute Evaluation technique yielded the best feature selection. Logistic emerged as the best classifier for planning-relax dataset, the best learning strategy was Bayes, the best feature selection was found to be Symmetrical Uncert Feature Selection. © 2024 IEEE.

关键词： Contrastive Learning

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User Acceptance of New HCI Technologies 6

User Acceptance of New HCI Technologies

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6th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2023

作者： Katona, Jozsef Kovari, Attila Institute of Electronics and Communication Systems Obuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Digital Technology Eszterházy Károly Catholic University Faculty of Computer Science Eger Hungary Institute of Engineering University of Dunaujvaros Department of Natural Sciences Dunaujvaros Budapest Hungary

ISBN: (纸本)9798350328752

Human-computer interaction (HCI) is an evolving field of research that focuses on understanding and improving the communication and interaction between humans and computers. Over the past decades, we have seen many significant advances in this field, which have contributed to the widespread adoption and integration of technology into our daily lives. HCI research and development aims to design information technology systems to meet the needs and preferences of users. Usability, efficiency, accessibility and user satisfaction are important considerations in HCI design. This paper presents the results of a pilot study on user acceptance of HCIs. The results show that users are positive about and willing to use HCIs. © 2023 IEEE.

关键词： Human computer interaction

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Automation of Monitoring and Optimization of Docker Containers Using Artificial Intelligence 24

Automation of Monitoring and Optimization of Docker Containe...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Stanišić, Sava Luković, Vanja Belotić, Branislav Faculty of Technical Sciences Department of Computer and Software Engineering Čačak Serbia Faculty of Technical Sciences Department of Information Technologies Čačak Serbia

ISBN: (纸本)9798331515799

The increasing adoption of Docker containers in modern software development requires effective monitoring and optimization to ensure high performance and efficient resource utilization. This paper provides an overview of the various tools and technologies that leverage artificial intelligence (AI) to automate the monitoring and optimization of Docker containers. The authors explore the capabilities of AI-driven tools such as machine learning-based anomaly detection, predictive analytics, and automated resource management. By examining these tools, the authors highlight their benefits in enhancing container performance, scalability, and availability. Through comparative analysis, the strengths and limitations of each tool and their impact on container management are discussed. The findings underscore the potential of AI to revolutionize container monitoring and optimization, leading to more efficient and robust IT infrastructure. © 2025 IEEE.

关键词： Resource allocation

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Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan 25

Prediction of Hypertension Disease Using Machine Learning Te...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Samara, Ghassan Katrawi, Anwar Hadi, Wael Al-Safarini, Maram Y. Al-Mamoori, Farah Elrashidi, Ali Alqaraleh, Muhyeeddin Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan Petra University Faculty of It Information Security Department Amman Jordan Zarqa University Faculty of Pharmacy Department of Pharmaceutical Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of It Department of Software Engineering Zarqa Jordan Applied Science Private University Facultty of Information Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Hypertension is one of the most common diseases in Jordan. It is one of the main reasons of death among Jordanian adult citizens. Worldwide, nearly 13% of all deaths are due to Hypertension with nearly 8 million deaths yearly. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that Random Forest, Random Committee, and MultilayerPerceptron are the best classification models to handle the task of the early prediction of Hypertension in Jordan. © 2024 IEEE.

关键词： Prediction models

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