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2nd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2023

作者： Srinivasan, V. Parthasarathy, M. Kumar, M. Pavin Vaishnavi, S. Sri Ramakrishna Engineering College Department of Mechanical Engineering Coimbatore India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350381979

Ladle monitoring systems are essential for optimizing steel melting operations. They improve material and equipment flow, reduce wait times, increase overall production, enable quick problem response, and ensure steel quality. Traditional ladle tracking methods utilizing sensors require a wide frequency band for data transmission. However, sensor technology has limitations such as high cost, limited range, signal interference, poor integration, wear, and tear. Thus, an additional layer of security is needed. To address these issues, a proposed solution suggests implementing a machine vision system for ladle tracking and management in steel factories. This vision-based system uses a live video captured by a camera as input to detect the ladle. The template matching algorithm is employed to compare the ladle template with the image's covered window, allowing the system to identify the ladle's ID and the zone where it is located. Various techniques for template matching are compared, and the most suitable one for the application is selected based on the results. The matched templates are stored in a database using Python SQLite3. The ladle tracking positions can be accessed through a web application, which also provides the capability to track individual ladles. The results demonstrate that implementing a vision-based tracking system for ladles in steel factories offers optimal outcomes with minimal hardware expenses. © 2023 IEEE.

关键词： Object detection

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Diabetes Prediction Using Machine Learning Techniques: A Case Study from Jordan 25

Diabetes Prediction Using Machine Learning Techniques: A Cas...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Batyha, Radwan Samara, Ghassan Al Henawi, Esraa Al-Safarini, Maram Y. Samhan, Ahmad A. Elrashidi, Ali Alluwaici, Mo'Ath Al-Omari, Deana It Zarqa University Department of Data Science Ai Faculty Zarqa Jordan Private University Department of Computer Science Faculty of It Applied Science Amman Jordan Zarqa University Department of Computer Science Faculty of It Zarqa Jordan Zarqa University Department of Software Engineering Faculty of It Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Diabetes is one of the most common diseases in Jordan. It is the main reason of death among Jordanian adult citizens. Worldwide, 48% of all deaths are due to Diabetes occurred before the age of 70 years. 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 BayesNet and NaiveBayes are the best classification models to handle the task of the early prediction of Diabetes in Jordan © 2024 IEEE.

关键词： Adversarial machine learning

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Proposed Biometric Security System Based on Deep Learning and Chaos Algorithms

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computers, Materials & Continua 2023年第2期74卷 3515-3537页

作者： Iman Almomani Walid El-Shafai Aala AlKhayer Albandari Alsumayt Sumayh S.Aljameel Khalid Alissa Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Computer Science Department King Abdullah II School of Information TechnologyThe University of Jordan11942Jordan Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Applied CollegeImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Computer Science Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Nowadays,there is tremendous growth in biometric authentication and cybersecurity ***,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private ***,designing and implementing robust security algorithms for users’biometrics is still a hot research area to be *** work presents a powerful biometric security system(BSS)to protect different biometric modalities such as faces,iris,and *** proposed BSSmodel is based on hybridizing auto-encoder(AE)network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their *** employed AE network is unsupervised deep learning(DL)structure used in the proposed BSS model to extract main biometric *** obtained features are utilized to generate two random chaos *** first random chaos matrix is used to permute the pixels of biometric *** contrast,the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional(2D)chaotic logisticmap(CLM)*** assess the efficiency of the proposed BSS,(1)different standardized color and grayscale images of the examined fingerprint,faces,and iris biometrics were used(2)comprehensive security and recognition evaluation metrics were *** assessment results have proven the authentication and robustness superiority of the proposed BSSmodel compared to other existing *** example,the proposed BSS succeeds in getting a high area under the receiver operating characteristic(AROC)value that reached 99.97%and low rates of 0.00137,0.00148,and 3516 CMC,2023,vol.74,no.20.00157 for equal error rate(EER),false reject rate(FRR),and a false accept rate(FAR),respectively.

关键词： Biometric security deep learning AE network 2D CLM cybersecurity and authentication applications feature extraction unsupervised learning

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Machine Learning Approaches in Cybersecurity to Enhance Security in Future Network Technologies

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SN computer science 2025年第4期6卷 1-18页

作者： Ahmad, Sultan Haque, Md. Alimul Abdeljaber, Hikmat A. M. Eljialy, A.E.M. Nazeer, Jabeen Mishra, B.K. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Mohali Punjab Gharuan 140413 India Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Department of Physics Veer Kunwar Singh University Ara 802301 India

As technology continues to evolve rapidly, cybersecurity has become a critical global concern. The increasing sophistication of cyber threats poses significant risks to individuals, businesses, and governments. To combat these threats, cybersecurity tools play a crucial role in monitoring, detecting, and mitigating security risks in digital environments. These tools ensure data protection, prevent unauthorized access, and safeguard sensitive information. However, traditional cybersecurity approaches are struggling to keep pace with emerging cyber-attacks. In response, machine learning (ML) has emerged as a powerful solution for enhancing cybersecurity strategies. ML algorithms enable organizations to analyze large datasets, identify anomalies, and predict potential threats with greater accuracy. Cybersecurity tools, integrated with ML, act as the final line of defense against attacks such as data breaches, identity theft, and system intrusions. This research explores the application of ML in selecting and optimizing cybersecurity models for enterprise ICT systems. It also emphasizes the growing demand for skilled professionals who can develop and implement ML-based security solutions. By examining current trends and future possibilities, this study provides valuable insights into the role of ML in strengthening cybersecurity measures and enhancing overall digital protection for organizations worldwide. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber security Cybercrime Data analysis ML algorithms Network security Threats

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Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

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computer Systems science & engineering 2023年第9期46卷 3121-3139页

作者： Khaled M.Alalayah Fatma S.Alrayes Jaber S.Alzahrani Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial *** Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production *** industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network *** various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of *** is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT ***,the existing Deep Learning(DL)can be employed to identify anonymous ***,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT *** presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful *** HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of ***,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the ***,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN *** proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： Industrial IoT deep learning network security intrusion detection system attribute selection smart factory

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Performance Evaluation of SDN Controllers: RYU and POX for WBAN-based Healthcare Applications

Performance Evaluation of SDN Controllers: RYU and POX for W...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Alfaify, Lama Alnajem, Nujud Alanzi, Haya Almutiri, Rawan Alotaibi, Areej Alhazri, Nourah Alqahtani, Awatif College of Applied Studies and Community Service King Saud University Computer Science and Engineering Department Saudi Arabia

ISBN: (纸本)9798350369359

Wireless Body Area Networks (WBANs) have made it easier for healthcare workers and patients to monitor patients' status continuously in real time. WBANs have complex and diverse network structures;thus, management and control can be challenging. Therefore, considering emerging Software-defined networks (SDN) with WBANs is a promising approach since SDN implements a new network management and design approach. The SDN concept is used in this study to create more adaptable and dynamic network architectures for WBANs. The study focuses on comparing the performance of two SDN controllers, POX and Ryu. Mininet is used as an open-source simulation tool, to construct network topologies. The performance of the controllers is evaluated based on bandwidth, throughput, and round-trip time metrics for networks. The study finds that the choice of network controller can significantly impact network performance is crucial for optimizing network performance. The study provides valuable insights into the performance of SDN-based WBANs using POX and Ryu controllers and highlights the importance of selecting the appropriate network controller for a given network architecture. © 2023 IEEE.

关键词： Network architecture

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Explainability of Leverage Points Exploration for Customer Churn Prediction

Explainability of Leverage Points Exploration for Customer C...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Wang, Danny Y. C. Jordanger, Lars Arne Lin, Jerry Chun-Wei Western Norway University of Applied Sciences Electrical Engineering and Mathematical Sciences Department of Computer Science Bergen Norway

ISBN: (纸本)9798350324457

Customer churn is a most crucial challenge faced by various industries, especially for the telecommunications sector since it causes over 5 - 6 times cost to keep customers. Thus, customer churn prediction has experienced substantial expansion. While a lot of prediction models are becoming increasingly accurate, the capability to interpret these models and perform causal analysis has become a new challenge. Identifying the key reasons or patterns that truly influence customer churn is vital for enabling industries to make significant improvements. This study then introduced a comprehensive framework. That integrates causal graphs and explainable models such as SHAP and Shapley flow, and concepts from system dynamics to explore customer churn patterns and leverage points. Experimental results indicates that the designed model is more explainable and interpretable to explore the customer behaviors for customer churn prediction. © 2023 IEEE.

关键词： Customer Churn Prediction (CCP) Explainable Artificial Intelligence (XAI) Leverage Points SHapley Additive exPlanations (SHAP)

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Maintenance Optimization of Dormant Systems Submitted to Failure Based PM and Imperfect CM.

Maintenance Optimization of Dormant Systems Submitted to Fai...

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2023 International Conference on Information and Digital Technologies, IDT 2023

作者： Bris, Radim Jahoda, Pavel Vsb - Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Applied Mathematics Czech Republic

ISBN: (纸本)9798350305869

This paper deals with the exploration of a system which acts on demand in case of emergency matter. To verify functionality, each component of the system must be regularly inspected with a specified inspection interval and tested to find latent failures. A failure-based preventive maintenance (PM) is considered, in context with the imperfect corrective maintenance model. This means that PM brings full renew which is realized after a beforehand setting the number of failures. If a failure occurs, it is detected during the first follow-up inspection and the reparation process starts. The imperfect corrective maintenance model is intended where each repair deteriorates the system life-time whose probability distribution is gradually altered through rising failure rate. Reliability mathematics for unavailability analysis is briefly demonstrated. The new renewal process model, including the failure-based PM, has been termed as a real ageing process. The imperfect corrective maintenance as well as increasing length of the inspection interval result in unwanted increase of system unavailability function which can be eliminated as well as optimized by the properly selected failure-based PM applied to all system components. If the PM is fixed due to cost, inspection intervals of system components can be optimized to keep the maximal value of the unavailability function in its original level. Sensitivity analysis applied to the length of the optimal inspection intervals with regard to gradually changing PM is demonstrated. This optimization process is demonstrated on a four-component system adopted from references. © 2023 IEEE.

关键词： Preventive maintenance

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Automatic Speech Recognition Techniques for Transcription of Thai Traditional Medicine Texts 21

Automatic Speech Recognition Techniques for Transcription of...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Popun, Jettasic Lee, Wilaiporn Prayote, Akara King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9798350377088

Currently, Automatic Speech Recognition (ASR) technology is widely used for communication convenience in converting speech to text. Initial surveys reveal that existing studies on ASR, both in Thai and foreign languages, focus on contemporary language communication, lacking support for specialized vocabulary. Traditional Thai Medicine contain ancient medical terminology recorded in various media such as palm leaves, parchment, notebooks, and inscriptions. Centuries of textual preservation have led to text degradation. Given the suitability of ASR study to accommodate medical terminology and ancient languages, it is proposed to apply it to convert original Traditional Thai Medicine texts into contemporary language, aiding experts in reading and interpreting them. ASR involves converting speech signals into text. In this study, Hidden Markov Model techniques using Kaldi are employed. Due to the absence of a Thai Traditional Medicine vocabulary repository, a dataset comprising text and speech is developed, consisting of 900 training sentences and 100 test sentences randomly selected from recordings of 10 Thai Traditional Medicine experts. Offline evaluation methods are employed, resulting in improved accuracy rates. Future efforts will focus on expanding the speech dataset to enhance recognition accuracy further. This study lays the groundwork for efficiently extracting knowledge from Traditional Thai Medicine prescriptions, ensuring the preservation of this valuable heritage for future generations. © 2024 IEEE.

关键词： Hidden Markov models

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Accurate and Fast Phase Compensation of Dynamic Samples in Digital Holographic Microscopy

Accurate and Fast Phase Compensation of Dynamic Samples in D...

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2023 Digital Holography and Three-Dimensional Imaging, DH 2023 in Optica Imaging Congress - Part of Imaging and applied Optics Congress 2023

作者： Obando-Vásquez, Sofía Doblas, Ana Trujillo, Carlos Applied Optics Group School of Applied Science and Engineering Universidad EAFIT Medellín Colombia Department of Electrical and Computer Engineering The University of Memphis TN United States

We present a fast algorithm for accurate phase compensation of holographic videos of dynamic phenomena recorded in a digital holographic microscope. The proposal is tested with human sperm and red blood cell samples. ... 详细信息

ISBN: (纸本)9781957171289

关键词： Holography

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