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Communication and Information Technology (AICCIT), Al-Sadiq International Conference on

作者： Nor Syafiqah I.M. Husin Salama A. Mostafa Mustafa Musa Jaber Saraswathy Shamini Gunasekaran Ali H. Al-Shakarchi Nejood Faisal Abdulsattar Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Department of Medical Instruments Engineering Techniques Al-turath University Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-farahidi University Baghdad Iraq Department of Computing College of Computing & Informatics Universiti Tenaga Nasional Selangor Malaysia College of Information Technology Ninevah University Mosul Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna Iraq

This paper attempts to use machine learning algorithms to estimate the energy consumption of appliances in a smart home environment. This work aims to promote awareness among smart home systems and users about their appliances’ energy consumption and guide them toward energy-saving practices. To achieve this, three machine learning algorithms, namely Decision Forest (DF), Boosted Decision Tree (BDT), and Linear Regression (LR), were chosen for regression tasks to estimate the energy consumption of several appliances accurately. The time-series datasets, namely appliance energy prediction datasets, are used for training and testing the algorithms. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, which comprises six processing phases, was employed in this work. The test is performed by utilizing 10-fold cross-validation. The results obtained assess the models’ performance in predicting the appliances’ energy consumption. The experimental results indicate that the three models exhibit varying degrees of accuracy in predicting energy consumption, as measured by their respective R-squared values. Among the three models, the random forest model exhibited superior performance by achieving the highest R2 values of 0.62 and 0.54 during the training and testing phases, respectively.

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Nature-Inspired Level Set Segmentation Model for 3D-MRI Brain Tumor Detection

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computers, Materials & Continua 2021年第7期68卷 961-981页

作者： Oday Ali Hassen Sarmad Omar Abter Ansam A.Abdulhussein Saad M.Darwish Yasmine M.Ibrahim Walaa Sheta Department of Computer Techniques Engineering Imam Al-Kadhum College(IKC)WasitIraq University of Information Technology and Communications Media Technology Engineering DepartmentIraq University of Information Technology and Communications Baghdad10081Iraq Department of Information Technology Institute of Graduate Studies and ResearchAlexandria University63 Horreya AvenueEl–ShatbyAlexandria21526Egypt Informatics Research Institute City of Scientific Research and Technology ApplicationsBorg EL Arab21934Egypt

Medical image segmentation has consistently been a significant topic of research and a prominent goal,particularly in computer *** tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy *** prevent or minimize manual segmentation error,automated tumor segmentation,and detection became the most demanding process for radiologists and physicians as the tumor often has complex *** methods for detection and segmentation presently exist,but all lack high *** paper’s key contribution focuses on evaluating machine learning techniques that are supposed to reduce the effect of frequently found issues in brain tumor ***,attention concentrated on the challenges related to level set *** study proposed in this paper uses the Population-based Artificial Bee Colony Clustering(P-ABCC)methodology to reliably collect initial contour points,which helps minimize the number of iterations and segmentation errors of the level-set *** proposed model measures cluster centroids(ABC populations)and uses a level-set approach to resolve contour differences as brain tumors vary as they have irregular form,structure,and *** suggested model comprises of three major steps:first,pre-processing to separate the brain from the head and improves contrast ***,P-ABCC is used to obtain tumor edges that are utilized as an initial MRI sequence *** level-set segmentation is then used to detect tumor regions from all volume slices with fewer *** suggest improved model efficiency compared to state-of-the-art methods for both datasets BRATS 2019 and BRATS *** BRATS 2019,dice progress was achieved for Entire Tumor(WT),Tumor Center(TC),and Improved Tumor(ET)by 0.03%,0.03%,and 0.01%*** BRATS 2017,an increase in precision for WT was reac

关键词： 3D-MRI tumor diagnosis bio-inspired clustering ABC optimization multimodal detection

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Resource and Energy-Efficient Configuration of IEEE 802.11ah Networks under Rayleigh Channels

Resource and Energy-Efficient Configuration of IEEE 802.11ah...

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IEEE Middle East and North Africa Communications Conference (MENACOMM)

作者： Hamid Taramit Luis Orozco-Barbosa Abdelkrim Haqiq Hassan First University of Settat Faculty of Sciences and Techniques Computer Networks Mobility and Modeling Laboratory: IR2M Settat Morocco Albacete Research Institute of Informatics Universidad de Castilla-La Mancha Albacete Spain

ISBN: (数字)9781665494113

ISBN: (纸本)9781665494120

The Restricted Access Window (RAW) mechanism included in the IEEE 802.11ah standard is one of the promising solutions to meet the requirements of Internet of Things (IoT) applications. It splits channel time into RAW slots and limits channel access to only a group of stations at a time. In this paper, we consider an IEEE 802.11ah network operating under Rayleigh fading channel with capture effect, and we develop efficient schemes to optimize the gain of the RAW mechanism. We propose an algorithm to identify the best cases for the RAW slot duration. Then, we deploy this result to develop a resource-efficient scheme to reconfigure a RAW consisting of several RAW slots. The new configuration eliminates the extra time wasted during the RAW period, resulting in higher throughput and energy efficiency. We present numerical results and simulations to validate our findings and analyze the impact of several parameters. The proposed RAW reconfiguration scheme significantly increases RAW performance by up to 21% for throughput and up to 8% for energy efficiency. It further optimizes the channel allocation resources, providing a channel gain ratio of up to 19%.

关键词： Urban areas Rayleigh channels Channel allocation Throughput Energy efficiency Internet of Things Resource management

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Adaptive Channel Allocation for Wi-Fi HaLow Networks

Adaptive Channel Allocation for Wi-Fi HaLow Networks

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IEEE Conference on Standards for Communications and Networking (CSCN)

作者： Hamid Taramit Luis Orozco Barbosa Abdelkrim Haqiq Faculty of Sciences and Techniques Computer Networks Mobility and Modeling Laboratory: IR2M Hassan First University of Settat Settat Morocco Universidad de Castilla-La Mancha Albacete Research Institute of Informatics Albacete Spain

ISBN: (纸本)9781665476225

The Restricted Access Window (RAW) introduced in IEEE 802.11ah (marketed as Wi-Fi HaLow) is a key feature to address large-scale and dense IoT applications, where stations (STAs) are split into groups and access the channel periodically. In this work, we propose an Adaptive Channel Allocation (ACA) scheme for the RAW mechanism given the assigned STAs, their load, and their locations. Our proposal yields the successful delivery of all STAs’ packets with an improvement in channel usage of up to 80%.

关键词： Adaptive systems Channel allocation Resource management Proposals Internet of Things Wireless fidelity Standards

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High isolation and gain metamaterial based MIMO antenna for 5G millimiter wave applications

High isolation and gain metamaterial based MIMO antenna for ...

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Circuit, Systems and Communication (ICCSC), International Conference on

作者： Ouafae Elalaouy Bilal Aghoutane Mohammed El Ghzaoui Sudipta Das Jaouad Foshi Hanan El Faylali Faculty of Sciences and techniques Errachidia University of Moulay Ismail Errachidia Morocco Department of Computer Science Informatics Systems and Optimization Laboratory Faculty of Sciences IbnTofail University Kenitra Morroco Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fes Morocco Department of Electronics and Communication Engineering IMPS College of Engineering and Technology Malda W.B India

ISBN: (数字)9798350365306

ISBN: (纸本)9798350365313

In the contemporary era, harnessing millimeter-wave frequencies emerges as a compelling strategy to fulfill the swift data transfer requirements for $\mathbf{5 G}$ communications. Consequently, this study involves designing a metamaterial-based multiple-input multiple-output (MIMO) antenna with a focus on achieving high isolation and gain within the millimeter-wave spectrum. The suggested MIMO is printed on a Rogers RT5800 measuring $18 \times 38 \times 0.8 \mathrm{~mm}^{3}$. The single antenna element demonstrates a broad frequency coverage from 27.2 to $\mathbf{2 8. 7 2} \mathbf{G H z}$. To elevate the antenna’s effectiveness, a two-port MIMO configuration is implemented by introducing a second antenna element parallel to benchmark antenna. However, the isolation between the two elements is deemed satisfactory. To address this, a $4 \times 1$ metamaterial unit cell is introduced, significantly enhancing isolation from 31 dB to 39 dB at 28 GHz. Furthermore, it improves the gain by 0.5 dBi at 28 GHz, elevating it from 8.5 to 9 dBi. The collective features of the suggested two ports MIMO antenna, encompassing its compactness and simplicity, position it as an excellent option for upcoming portable and shrink devices in Internet of Things uses.

关键词： Performance evaluation Mutual coupling Millimeter wave devices Millimeter wave measurements Metamaterials Millimeter wave integrated circuits Broadband antennas

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Design and Simulation of an Affordable Vehicle Speed Detection System

Design and Simulation of an Affordable Vehicle Speed Detecti...

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Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), International Conference on

作者： Hassaan Th. H. Thabet Aqeel A. Al-Hilali Mohammed G. S. Al-safi Thabit H. Thabit Firas M. Mahmood Dept. of Computer Technology Engineering Al-Hadba University College Mosul Iraq Dept. of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Dept. of Accounting Al-Esraa University College Baghdad Iraq Dept. of Computer Engineering and Information Ninevah University Mosul Iraq Dept. of Informatics and Software Engineering Cihan University-Erbil Kurdistan Region Iraq

The objective of this paper is to detect the over speed of vehicles on the roads. Although maximum speed limit signs exist on the roads, many accidents keep on because of high-speed driving. It is necessary to solve these problems through automation techniques. This paper describes a speed detection system for vehicles, the traffic police can easily check the speed of vehicles by using this system. This system consists of a PLC (Programmable Logic Controller) LOGO! V8.3, two Laser sensors, an HMI (Human Machine Interface), a speed camera, and a LOGO! CMR (Communication Module Radio). The detected speed is displayed on the HMI display, moreover, if a vehicle crosses the limited speed, this system displays the over-speed on HMI, and the speed camera is activated to capture an image of that vehicle. Also, an SMS (Short Messages Service) will be sent by the CMR to the traffic authority informing them about the violation to take the necessary legal actions. The system can also store the collected data for documentation purposes. The authors describe a simple, affordable technique for monitoring vehicles' speeds in this paper.

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Multi-Criteria Decision Making for Machine Learning Algorithms Using AHP-VIKOR techniques: Case Study Adult Autism Diagnosis

Multi-Criteria Decision Making for Machine Learning Algorith...

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International Conference on Engineering Technology and their Applications (IICETA)

作者： Mustafa Mohammed Jassim Manar Hassan Ali Amer S. Elamer Mustafa Musa Jaber Mohammed Q. Mohammed Ahmed Alkhayyat Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Iraqi Commission for Computers and Informatics Informatics Institute for Postgraduate Studies Baghdad Iraq Department of Computer Science Dijlah University Collage Baghdad Department of Information Technology Al-Esraa University College Baghdad College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

autism spectrum disorder is known as a neurodevelopmental disorder with sensory problems. Many infected adults die frequently due to the psychological conditions they live in. Machine learning helps doctors detect cases of autism spectrum disorders in adults. It has proven very successful in this field. In this study, after pre-processing and solving the data class imbalance problem the data we proposed to use MCDM methods to know prioritization and the best method to diagnose autism between four algorithms (SVM, Random Forest, Neural Network, and KNN), after obtaining the classification results we utilized AHP method to assign weights to each classification performance metrics based on expert assessment then he used both the obtained weights and the classification results to enter them into the VIKOR method to make a decision based on the weighted table where we would get the ranking of the algorithms. The random Forest method scores the best in this classification for diagnosing autism, with an accuracy rate of 0.9957 and sensitivity 0.9841.

关键词： Support vector machines Autism Machine learning algorithms Sensitivity Neural networks Psychology Medical services

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 23

Convolutional Neural Network Based Deep Neural Network Model...

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Proceedings of the 7th International Conference on Future Networks and Distributed Systems

作者： Ahmed Mahmood Khudhur Mohammed Hasan Alwan Qayssar Al Omairi Omar Abdulkareem Mahmood Yousif I Hammadi Mohammed Saleh Ali Muthanna Ahmed Aziz Ammar Muthanna Information Technology Department Kirkuk University Iraq Department of Communications Engineering University of Diyala Iraq Department of Computer Engineering University of Diyala Iraq Dep. of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Institute of Computer Technologies and Information Security Southern Federal University Russia Department of computer science Benha university Egypt and International Business Management Department Tashkent State University of Economics Uzbekistan Department of Telecommunication Networks and Data Transmission The Bonch Bruevich Saint Petersburg State University of Telecommunications Russia and Department of Applied Probability and Informatics Peoples Friendship University of Russia Russia

ISBN: (纸本)9798400709036

Сardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources.

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Data Dams: A Novel Framework for Regulating and Managing Data Flow in Large-Scale Systems

arXiv

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arXiv 2025年

作者： Bouke, Mohamed Aly Abdullah, Azizol Cengiz, Korhan Ivković, Nikola Mihaljević, Ivan Mohamud, Mudathir Ahmed Kowrina, Ahmed Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia Department of Electrical-Electronics Engineering Biruni University Istanbul Turkey Faculty of Organization and Informatics University of Zagreb Pavlinska 2 Varaždin42000 Croatia Faculty of Computing SIMAD University Mogadishu252 Somalia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang43400 Malaysia Département d’Informatique et Mathématiques Faculté des Sciences et Techniques Université de Nouakchott Al Aasriya Nouakchott880 Mauritania

In the era of big data, managing dynamic data flows efficiently is crucial as traditional storage models struggle with real-time regulation and risk overflow. This paper introduces Data Dams, a novel framework designed to optimize data inflow, storage, and outflow by dynamically adjusting flow rates to prevent congestion while maximizing resource utilization. Inspired by physical dam mechanisms, the framework employs intelligent sluice controls and predictive analytics to regulate data flow based on system conditions such as bandwidth availability, processing capacity, and security constraints. Simulation results demonstrate that the Data Dam significantly reduces average storage levels (371.68 vs. 426.27 units) and increases total outflow (7999.99 vs. 7748.76 units) compared to static baseline models. By ensuring stable and adaptive outflow rates under fluctuating data loads, this approach enhances system efficiency, mitigates overflow risks, and outperforms existing static flow control strategies. The proposed framework presents a scalable solution for dynamic data management in large-scale distributed systems, paving the way for more resilient and efficient real-time processing architectures. © 2025, CC BY.

关键词： Queueing theory

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Equitable and Fair Performance Evaluation of Whale Optimization Algorithm

arXiv

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arXiv 2023年

作者： Hassan, Bryar A. Rashid, Tarik A. Ahmed, Aram Qader, Shko M. Majidpour, Jaffer Abdalla, Mohmad Hussein Tayfor, Noor Hamarashid, Hozan K. Sidqi, Haval Noori, Kaniaw A. Ramadhan, Awf Abdulrahmam Department of Computer Science College of Science Charmo University Chamchamal KRI Sulaimani46023 Iraq Department of Computer Science and Engineering University of Kurdistan Hewler KRI Iraq Department of Information Technology College of Science and Technology University of Human Development Sulaimani Iraq Computer Science Institute Sulaimani Polytechnic University Information Technology Department Sulaimani Iraq The Department of Computer Science Kurdistan Technical Institute Sulaimani Iraq Department of Computer Science University of Raparin Sulaimani Iraq Department of Information Technology Kurdistan Technical Institute Sulaimani Iraq Information Technology Department Computer Science Institute Sulaimani Polytechnic University Sulaimani46001 Iraq Technical College of informatics Sulaimani Polytechnic University Sulaimani46001 Iraq Department of Database Technology Technical College of Informatics Sulaimani Polytechnic University Sulaimani Iraq Public Health Department College of Health and Medical Techniques - Shekhan Duhok Polytechnic University Duhok Iraq

It is essential that all algorithms are exhaustively, somewhat, and intelligently evaluated. Nonetheless, evaluating the effectiveness of optimization algorithms equitably and fairly is not an easy process for various reasons. Choosing and initializing essential parameters, such as the size issues of the search area for each method and the number of iterations required to reduce the issues, might be particularly challenging. As a result, this chapter aims to contrast the Whale Optimization Algorithm (WOA) with the most recent algorithms on a selected set of benchmark problems with varying benchmark function hardness scores and initial control parameters comparable problem dimensions and search space. When solving a wide range of numerical optimization problems with varying difficulty scores, dimensions, and search areas, the experimental findings suggest that WOA may be statistically superior or inferior to the preceding algorithms referencing convergence speed, running time, and memory utilization. © 2023, CC BY.

关键词： Benchmarking

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