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Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： Sahar Ebadinezhad Nooshin Nooraei Nia Nasratullah Shirzad Nwabueze Kenneth Osemeha Department of Computer Information Systems Computer Information Systems Research and Technology Center (CISRTC) Near East University Nicosia Turkey Artificial Intelligence Near East University Nicosia North Cyprus Software Engineering Department Near East University Nicosia North Cyprus

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Due to The lack of comparison studies and practical applications of RNN, LSTM, and hybrid RNN-LSTM models for intrusion detection systems, especially when managing class imbalances in complex network datasets, represents a gap in the literature. This study investigates the application of deep learning techniques to improve the detection capabilities of IDS. For this study, we applied and assessed Recurrent Neural Network, Long Short-Term Memory, and Hybrid RNN-LSTM models on a UNSW-NB15 dataset, and to redress class imbalance we trained and validated our models using synthetic minority over-sampling. The RNN-LSTM model was shown to perform the best with a 9 4. 0 0 % accuracy in comparison to other models. These experimental results signify the possibility of enhancing IDS performance and a dependable method of detecting various network intrusions with the use of hybrid models comprised of RNN & LSTM.

关键词： Deep learning Recurrent neural networks Accuracy Computational modeling Intrusion detection computer architecture Network security Hyperparameter optimization Network intrusion Long short term memory

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MQTT based monitoring and control system for remote stations in automotive industry

MQTT based monitoring and control system for remote stations...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Denis Ivanović Josip Job Marko Puntarić Marijan Herceg CX TTTech-Auto Osijek Croatia Department of Software Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia Department of Communications Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

In this paper is presented implementation of a lightweight supervisory control and data acquisition system for remote test stations as a part of a larger test house in the automotive industry. It is necessary to allow remote set up and monitoring of the test stations, which means defining the structure of a system with numerous test stations as endpoints. The test stations are meant to be virtual machines but can be both virtual and physical machines. A communication protocol must be used that enables this within the organization’s network, regardless of actual distance and location. The parameters of the test stations to be monitored can be defined according to the needs and purpose. Even when creating your own tool, this function is not unique, i.e. on one test station parameters of operating system resources such as memory usage can be monitored, while on another test station logged-in users or running programs, reported system errors and others must be monitored. Data storage is one of the mandatory functions of this system. This process allows viewing data throughout history and more detailed analysis of events. There are numerous benefits that can be drawn and used from the data stored in the database. It is necessary to pay attention to the data stored as well as the scope and retention throughout history. The correct and secure setup of a database is one of the prerequisites for any serious system that processes data. Appropriate views should be created for users involved in the data monitoring process using all the elements mentioned so far. Creating a visually acceptable and easy-to-use interface that serves the main purpose is the final presentation of the entire system to users. A complete system leads to higher productivity, profitability and better organization of the company. It also allows project managers to better plan the use and occupancy of test stations. At any time, they have insight into the status of individual test stations, their activitie

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Architecting Digital Twins for Intelligent Transportation systems

Architecting Digital Twins for Intelligent Transportation Sy...

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IEEE International Conference on software Architecture Workshops (ICSAW)

作者： Hiya Bhatt Sahil Karthik Vaidhyanathan Rahul Biju Deepak Gangadharan Ramona Trestian Purav Shah Software Engineering Research Centre IIIT Hyderabad India Computer Systems Group IIIT Hyderabad India Faculty of Science and Technology Middlesex University London

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.

关键词： Adaptation models software architecture Transportation computer architecture Predictive models Traffic control Real-time systems Digital twins Safety Predictive analytics

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Location Intelligence Based Smart E-Commerce Platform for Residential Real-Estate Industry 3

Location Intelligence Based Smart E-Commerce Platform for Re...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Balasooriya, Tharindu Kavishka, Lakindu Dananjana, Isini Yumna, Maryam Thelijjagoda, Samantha Attanayaka, Buddhima Marasinghe, Lanil Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Sliit Business School Malabe Sri Lanka Department of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Sysco Labs Colombo Sri Lanka

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Making the decision to purchase or invest in real estate can be a very crucial process due to its high financial risk. The purchasing decision of residential real estate properties can be even more decisive because, apart from the financial risk, the choice of a property can have a great impact on the future lifestyle of the buyer. When considering residential real estate, one major factor to be considered is the property location. This research sought to determine the applicability of modern technologies such as location intelligence and machine learning in the development of an e-commerce system that may assist users in making optimal residential real estate location decisions. Third-party web APIs were used to obtain location data, and as a result of the study, methodologies were defined to convert location data into meaningful insights by using statistical methods like weighted sum and the analytical hierarchy process. The functionalities in the proposed system have been designed, considering the roles of both buyers and sellers in the real estate business. In the proposed system, the location quality index framework provides overall insights on the location, the personalized insights and alternative locations are generated via the personal preference-based suitability analyzer and the price prediction system provides the current and future price fluctuations. The usefulness of image processing technologies and machine learning for making the sellers' journey easier on a real estate platform has also been assessed. © 2022 IEEE.

关键词： Decision making

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

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Enhancing Classroom Engagement using Real-Time Student Attention Monitoring with YOLOv9 10

Enhancing Classroom Engagement using Real-Time Student Atten...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Gunawan, Teddy Surya Muzarudin, Muhammad Amirul Faiz Kartiwi, Mira Yusoff, Nelidya Md International Islamic University Malaysia Electrical and Computer Engineering Department Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Universiti Teknologi Malaysia Faculty of Artificial Intelligence Kuala Lumpur51400 Malaysia

ISBN: (纸本)9798350365283

This research investigates the utilization of YOLOv9 in classrooms to monitor student attention in real time, thereby addressing the challenge of sustaining student engagement. The inability of conventional teaching methods to maintain students' attention frequently necessitates the implementation of dynamic modifications to instructional strategies. The goal is to create a system that accurately identifies students' attention levels. The system was trained and tested on annotated datasets using the YOLOv9c and YOLOv9e models, resulting in precision, recall, and mAP scores that demonstrated high effectiveness. A dashboard from Streamlit offered real-Time and cumulative insights, which allowed for immediate pedagogical adjustments. The results showed that YOLOv9c had a higher precision (0.876) and YOLOv9e had a superior recall, underscoring their respective strengths. However, despite a few misclassifications, the system's robust performance suggests its potential as a transformative educational tool. Future enhancements will focus on integrating additional data sources, reducing misclassifications, and improving algorithm scalability and accuracy to foster a more adaptive learning environment and showcase the system's potential for diverse educational applications. © 2024 IEEE.

关键词： Students

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A Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient Health

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computers, Materials & Continua 2023年第5期75卷 4553-4571页

作者： Md.Moddassir Alam Md Mottahir Alam Muhammad Moinuddin Mohammad Tauheed Ahmad Jabir Hakami Anis Ahmad Chaudhary Asif Irshad Khan Tauheed Khan Mohd Department of Health Information Management and Technology University of Hafr Al-BatinSaudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing AbdulazizJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems King Abdul Aziz UniversityJeddahSaudi Arabia College of Medicine King Khalid UniversityAbhaSaudi Arabia Department of Physics College of ScienceJazan UniversityP.O.Box.114Jazan45142Saudi Arabia Department of Biology College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11623Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Math and Computer Science Augustana CollegeIL61201United States

Artificial Intelligence(AI)is finding increasing application in healthcare *** learning systems are utilized for monitoring patient health through the use of IoT sensor,which keep track of the physiological state by way of various health ***,early detection of any disease or derangement can aid doctors in saving patients’***,there are some challenges associated with predicting health status using the common algorithms,such as time requirements,chances of errors,and improper *** propose an Artificial Krill Herd based on the Random Forest(AKHRF)technique for monitoring patients’health and eliciting an optimal prescription based on their health *** begin with,various patient datasets were collected and trained into the system using IoT *** a result,the framework developed includes four processes:preprocessing,feature extraction,classification,and result ***,preprocessing removes errors,noise,and missing values from the dataset,whereas feature extraction extracts the relevant ***,in the classification layer,we updated the fitness function of the krill herd to classify the patient’s health status and also generate a *** found that the results fromthe proposed framework are comparable to the results from other state-of-the-art techniques in terms of sensitivity,specificity,Area under the Curve(AUC),accuracy,precision,recall,and F-measure.

关键词： Healthcare system health monitoring clinical decision support internet of things artificial intelligence machine learning diagnosis

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Path Selection Optimization Algorithms for Mobile Agent Based on Push-All-Data Strategy

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第10期19卷 112-128页

作者： Alzyoud, Faisal Tarawneh, Monther Al-Shrouf, Faiz Almaghthawi, Ahmed Alrehaili, Meaad Kanaker, Hasan Department of Computer Science Isra University Amman Jordan Department of Information Technology College of Information Technology and Communications Tafila Technical University Tafila Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Computer Science College of Science & Art at Mahayil King Khalid University Abha62529 Saudi Arabia Department of Computer Sciences and Artificial Intelligence Collège of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Information Security Faculty of Information Technology Petra University Amman Jordan

With the advent of 5G and 6G technologies and the growing ubiquity of the Internet, the Mobile Agent (MA) paradigm is increasingly seen as a promising alternative to the conventional client-server model. MAs, which are software entities capable of moving and processing data across different systems, offer potential efficiencies in data management. However, their operation in dynamic and mobile environments can lead to challenges, such as incomplete or delayed tasks. This study addresses these issues by focusing on reducing the relocation time of MAs. A numerical procedure and a streamlining strategy were developed to expedite the transfer of an agent from the source to the target hub. Utilizing the itinerary design pattern and the Ant Colony Optimization (ACO) algorithm, implemented via the Java Agent Development Framework (JADE), this study sought the most efficient path for the MA. The proposed algorithm demonstrated a significant improvement, selecting the optimal path in just 271.511 seconds. This performance represents a substantial enhancement over previous approaches using the master-slave design pattern with either the Genetic Algorithm (GA) or the Node Compression Algorithm (NCA). The implications of this improvement are far-reaching, potentially enhancing the efficiency and reliability of data management systems in a variety of applications. © 2025 by the authors of this article. Published under CC-BY.

关键词： information management

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Coordinated PI-based frequency deviation control of isolated hybrid microgrid: An online multi-agent tuning approach via reinforcement learning

Coordinated PI-based frequency deviation control of isolated...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Nosrati, K. Tepljakov, A. Petlenkov, E. Levron, Y. Skiparev, V. Belikov, J. Tallinn University of Technology Department of Computer Systems Tallinn Estonia Technion Faculty of Electrical Engineering Haifa Israel Tallinn University of Technology Department of Software Science Tallinn Estonia

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

Numerous remote area applications welcome standalone renewable energy power generation systems or isolated microgrids (MGs). Due to the nature of solar and wind energy, the frequency deviation control (FDC) in hybrid MGs has become more complicated and critical than the conventional grid for power quality purposes. By using a coordination control strategy between a double-layered capacitor and a fuel cell, our mission here is to design a FDC system based on the PI controller which is tuned by an artificial neural network (ANN) in a multi-agent structure. To achieve this aim, a reinforcement learning technique is applied to train the ANN-based tuners. The performance of the proposed FDC system has been verified under different conditions by using real data to demonstrate the stability and robustness of the proposed controller. © 2022 IEEE.

关键词： Reinforcement learning

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Modified Dragonfly Optimization with Machine Learning Based Arabic Text Recognition

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computers, Materials & Continua 2023年第8期76卷 1537-1554页

作者： Badriyya BAl-onazi Najm Alotaibi Jaber SAlzahrani Hussain Alshahrani Mohamed Ahmed Elfaki Radwa Marzouk Mahmoud Othman Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or *** the number of labels is limited to one,the task becomes single-label text *** Arabic texts include unstructured information also like English texts,and that is understandable for machine learning(ML)techniques,the text is changed and demonstrated by numerical *** recent times,the dominant method for natural language processing(NLP)tasks is recurrent neural network(RNN),in general,long short termmemory(LSTM)and convolutional neural network(CNN).Deep learning(DL)models are currently presented for deriving a massive amount of text deep features to an optimum performance from distinct domains such as text detection,medical image analysis,and so *** paper introduces aModified Dragonfly Optimization with Extreme Learning Machine for Text Representation and Recognition(MDFO-EMTRR)model *** presentedMDFO-EMTRR technique mainly concentrates on the recognition and classification of the Arabic *** achieve this,theMDFO-EMTRRtechnique encompasses data pre-processing to transform the input data into compatible ***,the ELM model is utilized for the representation and recognition of the Arabic *** last,the MDFO algorithm was exploited for optimal tuning of the parameters related to the ELM method and thereby accomplish enhanced classifier *** experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%.

关键词： Arabic corpus dragonfly algorithm machine learning text mining extreme learning machine

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