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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Nugraha, Ade Chandra Supangkat, Suhono Harso Nugraha, Igb Baskara Handoko, Yunendar Aryo Purwadinata, Awan Hermawan Saputro, Dimas Wisnu Bandung State of Polytechnic School of Electrical Engineering and Informatics Bandung Institute of Technology Department of Computer Engineering and Informatics Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Smart City and Community Innovation Center Bandung Indonesia Bandung Institute of Technology Faculty of Mechanical and Aerospace Engineering Bandung Indonesia PT Kereta Api Indonesia Managing Director of Infrastructure Bandung Indonesia Bandung State of Polytechnic Department of Computer Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350339543

Predicting the value of railroad track geometry is needed to reduce the cost and time of rail condition measurements that have been carried out periodically every three months by railroad operators in Indonesia (PT Kereta Api Indonesia/PT KAI). Four track geometry parameters - vertical profile, horizontal alignment, twist, and gauge - are the reference measurements that will be mapped with alternative measurement results using accelerometers installed on regular trains (in-service trains). In this paper, eight prediction algorithms from the machine learning model are applied to map the vertical profile with 73 kilometers of measurement data in the Jakarta to Padalarang railroad segment, precisely on the Jatinegara-Cikampek measuring trip conducted from April 2019 to June 2022. The evaluation performance of the prediction results has been measured with the GadientBoosting Regressor algorithm is the best algorithm with MSE = 0.791, R2 = 0.507 and feature importance on the Train speed and Az attributes. The research can be continued to detect the correlation of three other track geometry parameters referring to the dataset of rail geometry condition measurement results through accelerometers on regular trains (in-service trains). © 2023 IEEE.

关键词： Forecasting

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QoS-Aware Load Balancing Scheme in Dense Wi-Fi 6 WLANs 1

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8th EAI International Conference on Industrial Networks and Intelligent Systems, INISCOM 2022

作者： Ak, Elif Canberk, Berk Computer Engineering Department Istanbul Technical University Istanbul Turkey Artificial Intelligence and Data Engineering Department Computer and Informatics Faculty Istanbul Technical University Istanbul Turkey

ISBN: (数字)9783031088780

ISBN: (纸本)9783031088773

Nowadays, increasing mobile and wireless usage demand accelerates new enhancements on IEEE 802.11 family to provide concurrent transmission and high efficiency. Thus, IEEE 802.11ax is released with the aim to improve overall throughput in dense WLANs considering the overlapping channel interference. However, the current WLANs suffer from uneven load distribution among the Access Points (AP). Because a station simply connects to the AP with the highest received signal strength indication (RSSI) in a legacy association way. And RSSI might not be the only criteria in the current WLANs due to the variety of application requirements and high date rates. Therefore, we propose a complete association scheme called Access Controller (AC) to manage admission, association, and adaptive carrier sensitivity control as a whole. The station from an overloaded AP is handed to a less loaded AP by considering various crucial metrics such as packet delivery ratio, fairness, RSSI, throughput, and round trip time. After effective load balancing, we adaptively tune the carrier sensitivity threshold to enhance concurrent transmission in IEEE 802.11ax WLANs. The results show that the proposed AC scheme outperforms in terms of fairness and per-station throughput in dense WiFi6 scenarios with over 500 stations. © 2022, ICST Institute for computer Sciences, Social informatics and Telecommunications engineering.

关键词： Wireless local area networks (WLAN)

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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study 4

The Correlation Between Architecture Smells, Package Stabili...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Benidris, Mrwan Elfallah, Ehab A. Omar Cheema, Marwa Aslam Alsma, Mohammed Salah Addin Kadiki, Tareq Ziad Elbarsha, Abdelfattah University of Benghazi Faculty of Information Technology Dep. of Computer Science Libya Libyan International Medical University Faculty of Information Technology Dept. of Software Engineering Libya University of Benghazi Faculty of Public Health Health Informatics Department Libya

ISBN: (纸本)9798350372632

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-depth research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic Dependency and Unstable Dependency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable Dependency and reusability. Finally, we found a slight negative correlation between package stability and reusability. © 2024 IEEE.

关键词： Reusability

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Detection System for Lung Tumours Based on CT Scan Images Using Cellular Automata

Detection System for Lung Tumours Based on CT Scan Images Us...

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2024 International Conference on Electrical and Information Technology, IEIT 2024

作者： Latif Hayati, Fierly Harsono, Tri Yuniarti, Heny Ferriastuti, Widiana Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Universitas Airlangga Faculty of Medicine Dr. Soetomo General Academic Hospital Department of Radiology Surabaya Indonesia

ISBN: (纸本)9798331516888

Lung tumours are masses of tissue that arise abnormally in the lung organ. In the field of medicine, One means of detecting and analyzing lung tumours is to use Computed Tomography (CT) scan images, where doctors see with the naked eye on the image and make a diagnosis. The existence of a lung tumour area detection system using a computer program is needed as a companion system in diagnosing. This research develops a lung tumour detection system using Cellular Automata (CA) with the system input is CT scan image. The quality of CT Scan image is improved through pre-processing, then segmentation is performed to separate the lung object from the background. Binaryization is applied to the segmented image to clarify the presence of lung tumour area. The detection process by CA is conducted on the binaryized image. The experiment was implemented on 20 CT scan images for normal lung and 20 other images for abnormal lung obtained from The Lung Image Database Concortium (LIDC) and Image Database Resource Initiative (IDRI). Analyzing the experimental data using performance score, it was found that the accuracy of detection was 95%, 100% precision, 90% sensitivity, and 100% specificity. These conditions indicate that the detection system has good reliability. © 2024 IEEE.

关键词： Lung cancer

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving engineering Design Problems

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computer Modeling in engineering & Sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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Brain Tumour Segmentation in MRI Data Using Gray Level Co-Occurrence Matrix

Brain Tumour Segmentation in MRI Data Using Gray Level Co-Oc...

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2024 International Conference on Electrical and Information Technology, IEIT 2024

作者： Al Ghifari, Muhammad Zidni Ferriastuti, Widiana Harsono, Tri Sigit, Riyanto Hayati, Fierly Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Dr. Soetomo General Academic Hospital Airlangga University Faculty of Medicine Department of Radiology Surabaya Indonesia

ISBN: (纸本)9798331516888

In the medical field, the image segmentation process is very important as the first step to diagnose a disease. Currently, brain tumors are one of the most life-threatening diseases. To detect brain tumors more accurately in less time, many techniques have been proposed using image segmentation. This research will present a brain tumour segmentation method on brain MRI data using GLCM (Grey Level Co-Occurrence Matrix). The input data in this system is the planned MRI data obtained from a hospital and Open Source MRI data in the form of grey level which is calculated using GLCM. Calculation using GLCM which is to find the maximum probability among the grey values of surrounding cells. The result of the GLCM calculation produces texture values (Texture Features). The combination of the intensity value in the MRI data and its texture value as an input parameter in the segmentation process. The method used to segment the tumour is thresholding by identifying contours on the MRI image. Both intensity-based and texture-based images are subjected to the same segmentation method. The segmentation result in this study is the categorization of cell areas affected by tumours and normal cells. To see the performance of the system built in this study using DSC (Dice Similarity Coefficient) and JC (Jaccard Coefficient) presented in the form of a percentage. Tests were conducted with 25 MRI data with two types of tumours namely glioblastoma and meningioma. Evaluation using Dice Similarity and Jaccard methods showed a small difference in accuracy between texture and intensity images. The texture image has an average Dice Similarity of 82.81% and Jaccard of 88.40%. While the intensity image has an average Dice Similarity of 80.35% and Jaccard 87.22%. In conclusion, the GLCMCA method provides slightly higher and more precise segmentation accuracy. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Millimeter-Wave Gap Waveguide-Based Broadband Slant-Polarized Slot Array Antenna 19

Millimeter-Wave Gap Waveguide-Based Broadband Slant-Polarize...

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19th European Conference on Antennas and Propagation, EuCAP 2025

作者： Zarifi, Davood Farahbakhsh, Ali Mrozowski, Michal Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Microwave and Antenna Engineering Gdansk 80-233 Poland University of Kashan School of Electrical and Computer Engineering Kashan Iran Graduate University of Kerman Department of Electrical and Computer Engineering Kerman Iran

ISBN: (纸本)9788831299107

This research deals with the design of a broadband, slant-polarized antenna array for 26-40 GHz frequency band. The design employs gap waveguide technology to minimize unwanted energy leakage, ensuring high gain and efficiency. In this configuration, the radiating slots are angled at 45°, and a two-layer feed network, utilizing compact folded E-plane groove gap waveguide power dividers, is used to energize the slots. Key advantages of the proposed antenna array include its low profile, low loss, wide bandwidth, and suitability for millimeter-wave applications. © 2025 European Association on Antennas and Propagation.

关键词： gap waveguide technology slant polariztion slot array antenna

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Public Roads Management and Visualisation

Public Roads Management and Visualisation

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International Symposium on Applied Machine Intelligence and informatics (SAMI)

作者： Jaroslav Marko Anton Baláź Department of Computer and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

The paper deals with the issue of collecting and displaying geographic information about roads in Slovakia with emphasis on information about obstacles on roads. The main objective is to design a solution for collecting and displaying the roads based on the analysis of existing systems and identifying the requirements for an optimal design. The paper starts with an analysis of available solutions and the development of requirements for the most favourable design, followed by the planning of an optimal solution for collecting and displaying road obstruction data. The focus of the paper is on the development of a method for creating and modifying geographic obstacle information, the design of a textual and graphical display of the information, and the design of a three-level model for user access to the application. The paper concludes with a discussion of the proposed solution, comparing the requirements for the optimal design with the developed design.

关键词： Roads Planning Object recognition informatics Machine intelligence

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Action Localization and Recognition through Unsupervised I3D and TSN 3

Action Localization and Recognition through Unsupervised I3D...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Umran, Munzir Muchtar, Kahlil Abidin, Taufik Fuadi Arnia, Fitri Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Universitas Syiah Kuala Faculty of Mathematics and Natural Sciences Department of Informatics Banda Aceh Indonesia

ISBN: (纸本)9798350321487

Since CCTV technology was widely introduced as a video surveillance system, it has become very popular due to increased security concerns. However, detecting suspicious behavior is still done manually by humans, which is inefficient in terms of accuracy and manpower (prone to distraction and fatigue). Many studies use the entire frame of a video to recognize human action, whereas in this study first localize the area of object movement using the unsupervised inflated 3D (I3D) network which produces several frames that have been determined as the region of interest (ROI) from the total frames in the video. Then the collection of ROI frames is generated into a video which will then be classified to recognize human action through the Temporal Segment Networks (TSN). For efficiency purposes, the proposed pipeline can be run separately in the edge (for localization), and cloud environment (for recognition), respectively. The results show that there are 54 classes of ROI video data sets that have an average human action recognition prediction similarity rate above 84.6% which is the average of the prediction similarity rate of the entire ROI video data. In terms of prediction accuracy, 37 classes of data (36.63%) are better than the original class, including 25 classes of data (24.75%) above the threshold, and the processing speed of classification with ROI video data is on average 5 times faster than full-frame video data. © 2023 IEEE.

关键词： Forecasting

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Deep Reinforcement Learning-Based Framework for Enhancing Cybersecurity

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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