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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Ali Alheeti, Khattab M. Najim, Ali Hamza Satar, Nurhizam Safie Mohd Aliesawi, Salah A. Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq Faculty of Engineering Department of Electrical and Computer Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9798350369106

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks. © 2024 IEEE.

关键词： Aircraft accidents

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Early-Stage Segmentation and Characterization of Brain Tumor

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computers, Materials & Continua 2022年第10期73卷 1001-1017页

作者： Syed Nauyan Rashid Muhammad Hanif Usman Habib Akhtar Khalil Omair Inam Hafeez Ur Rehman Department of Computer Science National University of Computer and Emerging Sciences IslamabadPeshawar CampusPakistan Faculty of Computer Science and Engineering Ghulam Ishaq Khan(GIK)Institute of Engineering Sciences and TechnologyTopiPakistan IFahja Pvt Limited PeshawarPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadPakistan

Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain *** life expectancy of patients diagnosed with gliomas decreases *** gliomas are diagnosed in later stages,resulting in imminent *** average,patients do not survive 14 months after *** only way to minimize the impact of this inevitable disease is through early *** Magnetic Resonance Imaging(MRI)scans,because of their better tissue contrast,are most frequently used to assess the brain *** manual classification of MRI scans takes a reasonable amount of time to classify brain *** this,dealing with MRI scans manually is also cumbersome,thus affects the classification *** eradicate this problem,researchers have come up with automatic and semiautomatic methods that help in the automation of brain tumor classification ***,many techniques have been devised to address this issue,the existing methods still struggle to characterize the enhancing *** is because of low variance in enhancing region which give poor contrast in MRI *** this study,we propose a novel deep learning based method consisting of a series of steps,namely:data pre-processing,patch extraction,patch pre-processing,and a deep learning model with tuned hyper-parameters to classify all types of gliomas with a focus on enhancing *** trained model achieved better results for all glioma classes including the enhancing *** improved performance of our technique can be attributed to several ***,the non-local mean filter in the pre-processing step,improved the image detail while removing irrelevant ***,the architecture we employ can capture the non-linearity of all classes including the enhancing ***,the segmentation scores achieved on the Dice Similarity Coefficient(DSC)metric for normal,necrosis,edema,enhancing and non-enhancing tumor classes are 0.95,0.97,0.91,0.93,0.95;respectively.

关键词： Segmentation CNN characterization brain tumor MRI

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Determining the Critical Factors of Antenatal Care Follow-Up Using Two-Phase Machine Learning 27

Determining the Critical Factors of Antenatal Care Follow-Up...

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27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Bessmertny, Igor A. Enkomaryam, Bidru C. Mamuye, Adane L. Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia University of Gondar Department of Computer Science Gondar Ethiopia

ISBN: (纸本)9798350363708

Determining the critical factors affecting antenatal visits will greatly contribute to reducing maternal and infant mortality. This study, thus attempted to construct a cluster-based predictive model to determine the critical factors of antenatal care follow-up among women in Ethiopia using two-step machine learning techniques. The 5,990 preprocessed obstetric dataset with 13 selected attributes were used that was collected from the Dabat Research Center at the University of Gondar, Ethiopia. To solve the study problem, six cluster models were developed using K-means, Expectation Maximization and Make Density clustering algorithms, which are well-suited for analyzing health datasets, providing scalability, flexibility, robustness to noise, and the ability to handle uneven cluster sizes. K-means out-formers Density based and Expectation Maximization clustering algorithms with minimum number of sum of square errors (17094) and number of iteration (3). Then, output of the K-means cluster model was used as input to segment the dataset and build a total of 21 predictive models using the tree-based J48 classifier, PART and JRip rule generators while adjusting their parameters. The model for predicting antenatal care follow-up is constructed by induction of the truncated PART rule (accuracy of 99.93%) and extracts attributes of interest such as antenatal care attendance, antenatal care location, antenatal care frequency, current vaccination, woman's age, and HIV testing to predict antenatal care follow-up in the study area. © 2024 IEEE.

关键词： Machine learning

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Improving bot detection of fake followers on Twitter via a hybrid B-HC optimisation algorithm 5

Improving bot detection of fake followers on Twitter via a h...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Alzeyadi, Haider Duran, Fecir Informatics Institute Department of Computer Science Gazi University Ankara Turkey Gazi University Engineering Faculty of Technology Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9781665493604

Sophisticated cyber threats are seen on Online Social Networks (OSNs) social media accounts automated to imitate human behaviours has an impactful effect on distorting public thoughts and opinions. OSNs are weaponized to diffuse deception, misinformation, and malicious activities, that forms a serious threat to society. The deceptive nature of imitating human behaviour has become a challenging and crucial task to detect automated accounts (socialbots). This research, however, proposes a hybrid metaheuristic optimisation algorithm for socialbot detection. Specifically, a hybrid B-Hill Climbing (B-HC) optimisation algorithm works in tandem with a k-NN nearest neighbour classifier to accurately select a relevant feature subset. It is applied to be tested for fake followers account on Twitter data. Experimental results showed that the proposed method is better than the traditional and the latest feature selection techniques as well as the rule-set methods. The B-HC alongside with k-NN method achieved promising results using only relevant feature subset. © 2023 IEEE.

关键词： Feature Selection

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Characterization of Traffic in the Internet of Vehicles Using Microscopic Modelling

Characterization of Traffic in the Internet of Vehicles Usin...

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2024 IEEE International Conference on Advanced Telecommunication and Networking Technologies, ATNT 2024

作者： Obeng, Kobby Asare Ariffin, Sharifah H.S. Muhammad, Nor Aishah Baharudin, Muhammad Ariff Universiti Teknologi Malaysia Faculty of Electrical Engineering Malaysia Koforidua Technical University Department of Computer Science Ghana Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Malaysia

ISBN: (纸本)9798350353501

The emergence of the Internet of Vehicles (IoV) as a driver for the new age of Intelligent Transportation Systems (ITS) provides an opportunity for a wide range of services and applications driven by the various interactions that exist between a vehicle and its environment. However, the nature of road networks affects real-time vehicular movements with respect to the departure, delay, speed and arrival of vehicles. In this paper, we present a data-driven, traffic theory and microscopic traffic simulation approach that can be used in modelling the underlying vehicular network of the IoV. Using the traffic status from the A4 highway dataset in Luxembourg, we obtain vehicular volumes from the vehicle flow rate, define thresholds for traffic status using data scaling and employ microscopic modeling to create traffic scenarios. The analysis from the results of the traffic scenarios shows unique vehicular patterns due to the nature of the road network providing insights that ultimately affect the design and implementation of IoV-based services and applications. © 2024 IEEE.

关键词： Internet of things

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Free-Hand-Sketched Logic Circuit Detection Using Vision Transformers 7

Free-Hand-Sketched Logic Circuit Detection Using Vision Tran...

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7th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2025

作者： Eimasry, Noha Aly Fakhr, Mohamed Waleed Maghraby, Fahima A. Arab Academy for Science Technology and Maritime Transport Department of Computer Science Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Arab Academy for Science Technology and Maritime Transport Department of Computer Engineering Cairo Egypt

ISBN: (纸本)9798331531836

This paper investigates the application of Vision Transformers (ViTs), specifically DETR (DEtection TRans-former), for the detection and classification of digital logic gates in hand-sketched digital logic circuits (DLC). A novel dataset of 295 hand-sketched DLC images was developed, capturing all seven fundamental gate types: AND, OR, NOT, NAND, NOR, XOR, and XNOR. The dataset introduces real-world complexities, such as variations in sketching styles, pen types, and paper textures, therefore addressing gaps in existing datasets. The performance of DETR is compared with the state-of-the-art YOLOv8 model under various preprocessing configurations, including grayscale conversion and image resizing. The results show that YOLO achieves higher overall detection accuracy, while DETR demon-strates strengths in capturing spatial relationships and complex patterns in abstract sketches. Additionally, the study analyses the effects of training duration and dataset structure on performance, highlighting the sensitivity of ViTs to preprocessing and spatial feature preservation. This work advances the understanding of transformer-based models for DLC recognition, advancing the way for more effective automated tools in engineering and education. © 2025 IEEE.

关键词： Photointerpretation

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Ordered Clustering-Based Semantic Music Recommender System Using Deep Learning Selection

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computers, Materials & Continua 2025年第5期83卷 3025-3057页

作者： Weitao Ha Sheng Gang Yahya D.Navaei Abubakar S.Gezawa Yaser A.Nanehkaran School of Computer Science and Technology Weinan Normal UniversityWeinan714099China School of Information&Engineering Yancheng Teachers UniversityYancheng224002China Department of Computer and Technology Engineering Qazvin BranchIslamic Azad UniversityQazvin34199-15195Iran School of Information Engineering Sanming UniversitySanming365004China Department ofManagement Information Systems Faculty of Economics and Administrative SciencesCankayaUniversityAnkara06790Türkiye

Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their *** systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide *** improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music *** paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as ***,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural *** network then predicts ratings for unreviewed music by ***,the system analyses user music listening behaviour and music *** popularity can help to address cold start users as ***,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening *** proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each *** these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across *** number of recommended tracks is aligned with each user’s typical listening *** experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain

关键词： Music recommender system order clustering deep learning

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Efficient PV Monitoring with LoRaWAN: Integrating IoT and Smartphone Application for Cost-Effective Solutions 8

Efficient PV Monitoring with LoRaWAN: Integrating IoT and Sm...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Hameed, Bilal Hashim Kurnaz, Sefer Ahmed, Saadaldeen Rashid Altinbas University Faculty of Engineering and Natural Sciences Electrical and Computer Engineering Dept Istanbul Turkey Bayan University Computer Science Department Kurdistan Erbil Iraq College of Engineering Al-Ayen University Artificial Intelligence Engineering Department Thi-Qar Iraq

ISBN: (纸本)9798350354423

This paper presents a low-cost, scalable approach for monitoring photovoltaic (PV) installations, catering to the growing demand for effective monitoring frameworks in renewable energy. The system incorporates sensors to record critical data such as solar panel output, battery status, and ambient variables, addressing cost-effectiveness, scalability, and remote monitoring concerns in PV installations. Data transfer is supported via a LoRaWAN network, providing long-range and low-power connectivity, making it ideal for remote and off-grid PV systems. Field testing results show that the system provides real-time data on PV system performance, enabling preventive maintenance and informed decision-making. The inclusion of LoRaWAN technology improves system scalability and efficiency, providing a path for mass adoption in sustainable energy solutions. The low-cost design makes PV systems accessible to a wide range of users, including those in distant or economically challenged locations. The research describes a realistic and efficient monitoring approach customized to the unique requirements of PV systems, contributing to the greater adoption of sustainable energy solutions by using LoRaWan technology. © 2024 IEEE.

关键词： Decision making

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Optimizing Hybrid Energy Systems: Employing Smart Monitoring Networks with IoT Integration 8

Optimizing Hybrid Energy Systems: Employing Smart Monitoring...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Taher, Anmar Yahya Kurnaz, Sefer Ahmed, Saadaldeen Rashid Altinbas University Faculty of Engineering and Natural Sciences Electrical and Computer Engineering Dept Istanbul Turkey Bayan University Computer Science Department Kurdistan Erbil Iraq College of Engineering Al-Ayen University Artificial Intelligence Engineering Department Thi-Qar Iraq

ISBN: (纸本)9798350354423

For effective and sustainable energy solutions, this paper investigates at a hybrid energy system that uses Internet of Things technologies. Three components make up the system: PV system, an AC turbine voltage source, DC batteries, and ThingSpeak, an IoT analytics program hosted on the cloud that allows for real-time monitoring. In order to optimize performance, the study emphasizes the significance of hybrid energy systems as well as the requirement for improved analytics and monitoring. A detailed description of the experimental setup is given, including the measured parameters. The results demonstrate the hybrid system's adaptability to various loads and input voltages. fluctuations in DC battery and turbine voltages cause fluctuations in AC power and AC current, while the AC voltage stays constant at 224.4 V. For efficient analytics and monitoring, the hybrid energy system must include ThingSpeak. The study emphasizes how the system can adjust to changing conditions and how Internet of Things technologies may assist hybrid energy systems grow more powerful. This research adds to the current discourse on the integration of IoT data for enhanced system optimization and sustainable energy solutions. © 2024 IEEE.

关键词： Hybrid power

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New Delay Doppler Communication Paradigm in 6G Era:A Survey of Orthogonal Time Frequency Space(OTFS)

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China Communications 2023年第6期20卷 1-25页

作者： Weijie Yuan Shuangyang Li Zhiqiang Wei Yuanhao Cui Jiamo Jiang Haijun Zhang Pingzhi Fan Department of Electronic and Electrical Engineering Southern University of Science and TechnologyShenzhenChina Faculty of Electrical Engineering and Computer Science Technical University of BerlinBerlinGermany School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’anChina Department of Communication Engineering Beijing University of Posts and TelecommunicationsBeijingChina China Academy of Information and Communications Technology BeijingChina School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina School of Information Science and Technology Southwest Jiaotong UniversityChengduChina

In the 6G era,Space-Air-Ground Integrated Network(SAGIN)are anticipated to deliver global coverage,necessitating support for a diverse array of emerging applications in high-mobility,hostile *** such conditions,conventional orthogonal frequency division multiplexing(OFDM)modulation,widely employed in cellular and Wi-Fi communication systems,experiences performance degradation due to significant Doppler *** overcome this obstacle,a novel twodimensional(2D)modulation approach,namely orthogonal time frequency space(OTFS),has emerged as a key enabler for future high-mobility use ***,OTFS modulates information within the delay-Doppler(DD)domain,as opposed to the timefrequency(TF)domain utilized by *** offers advantages such as Doppler and delay resilience,reduced signaling latency,a lower peak-to-average ratio(PAPR),and a reduced-complexity *** studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications(ISAC).In this article,we present an in-depth review of OTFS technology in the context of the 6G era,encompassing fundamentals,recent advancements,and future *** objective is to provide a helpful resource for researchers engaged in the field of OTFS.

关键词： OTFS 6G delay-Doppler(DD)domain

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