Wasit Journal of Computer and Mathematics Science
type: channel
description: Journal title Wasit Journal of Computer and Mathematics Science (WJCM) Indexing IASJ – Iraqi Academic Scientific Journals | DOAJ | Google Scholar | Crossref Online ISSN 2788-5887 Print ISSN 2788-5879 Frequency 4 issues per year DOI prefix 10.31185 Editor in Chief Prof. Dr. Abdulhadi Mohammed Managing Editor of Computer Science section Asst. Prof. Dr. Yousif Raad Muhsen Managing Editor of Mathematics Section Prof. Dr. Ali Khalaf Hussain Al-Hachami Organized by College of Computer Science and Information Technology, Wasit University, Iraq Citation Analysis <a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" href="https://www.scopus.com/results/results.uri?sort=plf-f&src=dm&st1=%22wasit+journal+of+computer+and+mathematics+science%22+OR+%22Wasit+J.+Comput.+Math.+Sci.%22&sot=b&sdt=b&sl=99&s=REF%28%22wasit+journal+of+computer+and+mathematics+science%22+OR+%22Wasit+J.+Comput.+Math.+Sci.%22%29&origin=searchbasic">Scopus | Google Scholar Acceptance Rate 40% Average time from submission to publication About 90 days Before submission, You have to make sure that your paper is prepared using the WJCMS paper Template and has been carefully proofread and polished and conformed to the author's guidelines. Online Submissions Do you already have a username/password for WJCMS? GO TO LOGIN Need a username/password? GO TO REGISTRATION Registration and login are required to submit items online and check current submissions' status.
publisher: Wasit University
language: en
publication name: Wasit Journal of Computer and Mathematics Science
issn: 2788-5879
items: An Information Technology System for Measuring Organizational Culture and Predicting Employee Turnover Based on Questionnaire Data and Text Mining
items: Expert Systems Using Fuzzy Logic for the Early Alzheimer’s Disease Diagnosis
items: Innovative New Framework of Complex Q-Fuzzy Set and Its Application
items: A recent study of the Darbaugh problem's stability in partial differential equations using fixed-point theory
items: Design of Reliable Queue Systems in Complicated Logistics Networks by Using Mathematical Programming Methods
items: A Graph-Aware Recurrent Fusion Deep Ensemble Architecture for Fake News Detection
items: Educational Data Mining for Predicting Academic Attrition in Iraqi Universities: A Hybrid KDD-Based Approach using Weighted Euclidean Distance and Neural Architectures
items: Experimental Evaluation of Attention-Based Explainable AI Models for Detecting Zero-Day Threats in (IoT) Systems
items: Hybrid Generalization Open Sets in Generalization Topological Spaces
items: The combined method for solve the constraint optimization problems uses the augmented Lagrangian and line search technique
items: Hybrid Framework for Multi-Disease Chest X-Ray Diagnosis Using Vision Transformers with Label Noise Correction and Uncertainty Calibration
items: Fairness-Aware Mutual Information for Feature Selection in Employee Attrition Prediction
items: Real-Time Emotion Recognition in Human-Robot Interaction Using Deep AI Models
items: Stable Numerical Solution of an Elliptic PDE Inverse Problem Subject to Incomplete Boundary Conditions
items: Multitype Convolutional Networks for Improved Iris Classification
items: Brain Tumor Classification of Medical Images based on Transfer Pre-trained Learning Models
items: Enhancing IoT Network Security Through Deep Learning-Based Intrusion Detection
items: A New Conjugate Gradient for Efficient Unconstrained Optimization with Robust Descent Guarantees
items: Cyberbullying Detection in Arabic Text Using Different Deep Learning Approaches
items: Boosting Malware Detection with AlexNet and Optimized Neural Networks Using the Grasshopper Algorithm
items: Blockchain Scalability Analysis and Improvement of Bitcoin Network through Enhanced Transaction Adjournment Techniques
items: Evaluation Study for Celiac Disease Diagnosing by using Deep Learning Techniques
items: Low-Cost Vehicle Tracking System Based Web Application
items: Some approaches to solving fuzzy linear fractional programming
items: Categorization of Celebrity Photos Based on Deep Machine Learning for Feature Extraction and Classification
items: Feature Fusion for Improved Skin Cancer Diagnosis Using Support Vector Machines
items: Model-Driven Engineering for Adaptive Software Systems in Dynamic Environments
items: Brain age prediction from MRI images based on a convolutional neural network with MRMR feature selection layer
items: Generalized (theta, theta)- Semi-derivations of Prime Near-rings
items: Internet of Things Optimal Routing based on Markov-Reinforcement Learning Algorithm

An Information Technology System for Measuring Organizational Culture and Predicting Employee Turnover Based on Questionnaire Data and Text Mining
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description: Abstract: This paper implements the prediction of employees leaving their company using conventional methods such as surveys and modern techniques like mining textual data. The open-ended responses were analyzed (12,847 words of text in total) resulting in a vocabulary of 1,892 unique words. The words that came up most often in positive reference were “team” (142), “growth” (98) and “resilience” (87); negative terms found included “pressure”, which occurred 156 times, along with injustice (112) and burnout (76). The average sentiment score was 2.8/5, with 34% of responses very negative and only 18% very positive. Various machine learning models have been tested to predict for turnover. If we only use closed-ended questions, though, XGBoost reaches an accuracy score of 78%. But then features for text mining (sentiment scores + keywords) increased accuracy to 87% (+9%), precision:0.85, recall:0.84, AUC-ROC 0.89 Key risk indicators based on text mining included the term “unfair” (turnover risk 4.2x higher), “burnout” (3.8x higher risk) while “recognition” (3.5x lower risk) and growth (2.8 times lower risk). The turnover risk increased by 31% per 1-point decrease in sentiment score. The hybrid XGBoost model with text features performed better than fine-tuned BERT ( accuracy of 84% ). This proposed IT stains enables organization to proactively identify risk oriented employees through linguistic signals in the open-ended feedback allowing timely intervention by HR wherein it improves efficiency.
subject: Computer
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creator: Firas Layth Khaleel Alameen
creator: Farah Amer Abdalaziz
creator: Mohammed Abdulaziz Alsubh
creator: Abdullatif Saleh Alfaqiri
creator: Normala Rahim
creator: Mohammed Nizam Saad
creator: Tengku Siti Meriam Tengku Wook
rights: Copyright (c) 2026 Firas Layth Khaleel Alameen, Farah Amer Abdalaziz, Mohammed Abdulaziz Alsubh, Abdullatif Saleh Alfaqiri, Normala Rahim, Mohammed Nizam Saad, Tengku Siti Meriam Tengku Wook https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 51
ending page: 72
doi: 10.31185/wjcms.522

Expert Systems Using Fuzzy Logic for the Early Alzheimer’s Disease Diagnosis
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description: Alzheimer’s disease continues to be an important international health concern and early, accurate diagnosis is essential for treatment initiation and management of the disease. However, early identification of Alzheimer ’s disease (AD) remains a formidable challenge, mainly attributed to the complex underlying pathology and clinical heterogeneity along with conventional neuroimaging and cognitive tests possessing limited accuracy when applied individually. In this work, we aim to improve the sensitivity and specificity of detection for early-stage AD by developing a hybrid method based on MRI neuroimaging features woven with data from cognitive assessments through using a fuzzy-based expert system combined with machine-learning techniques. Cognitive data were collected from 60 subjects using a structured questionnaire covering multiple cognitive domains and MR-derived structural features, representative of neurodegeneration patterns. In particular, a fuzzy inference system was used to model the uncertainty and to represent nonlinear relationships frequently seen in medical data, then its output data were analyzed by a supervised classifier. Various cross-validation methods were used to validate the performance of the proposed model and an overall accuracy 91.9%, predictive accuracy 92.5%, sensitivity 91.1%F1-score ~91.5% and Area Under the Curve (AUC) AUC = 0.94 with our results defined a high discriminative effect size large to very large supported by Lakehead Bayesian Test. In addition, prodigious find the pressure of statistical significance testing and confidence interval checking implied composites that were references to fortune. . In conclusion, our results indicate that the development of fuzzy expert-based hybrid models merging neuroimaging and cognitive data has great potential for state-of-the-art early accurate classification of individuals with Alzheimer’s disease, thus providing a new promising reliable screening tool useful for clinical decision-makin…
subject: Computer
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creator: Mohammed Mahdi Hashim
creator: Abdullah A. Nahi
creator: Amna Kadhim Ali
creator: Vlad Ciobanu
rights: Copyright (c) 2026 Mohammed Mahdi Hashim, Abdullah A. Nahi, Amna Kadhim Ali, Vlad Ciobanu https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 27
ending page: 43
doi: 10.31185/wjcms.517

Innovative New Framework of Complex Q-Fuzzy Set and Its Application
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description: The Q-fuzzy set (Q-FS) is a powerful general formal framework that generalises the notion of a fuzzy set (FS) when the domain of the FS extends from the universal set X to the product between X and Q. This article presents a novel complex Q-fuzzy set (CQ-FS) notion by extending the range of Q-fuzzy set (Q-FS) values to the unit circle in the complex plane by adding a further term called the phase term. This action characterises CQ-FS’s elements in terms of time. Based on these mathematical tools, we present the practical possibilities for basic operations on this concept, such as intersection, union, and complement, supported by numerical representations with some mathematical properties. Finally, we present a scenario for a decision-making problem in the medical field where our concept plays a dynamic role in representing the problem data, with time being a significant factor in determining symptom severity. To address this problem, we have developed a multi-step mathematical algorithm that helps the user create a simulation system for handling such issues within a decision-making environment.
subject: Mathematics
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creator: Barakat Muhammad
creator: Faisal Al-Sharqi
creator: Mamika Ujianita Romdhini
rights: Copyright (c) 2026 Barakat Muhammad, Faisal Al-Sharqi, Mamika Ujianita Romdhini https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 17
ending page: 26
doi: 10.31185/wjcms.514

A recent study of the Darbaugh problem's stability in partial differential equations using fixed-point theory
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description: The stability analysis of solutions to nonlinear PDEs involves a fundamental and serious problem in applied mathematics and physics. The paper aims for the first time ever to provide a stability analysis for a class of nonlinear initial-boundary value problems called Darbaugh problems. To achieve this goal, we convert the PDE into a fixed-point equation via a nonlinear solution operator so that we can investigate several kinds of stability interested in Hyers-Ulam and Lagrange stability using the fixed-point theories of Banach Contraction Principle, Krasnoselskii’s Theorem and Schaefer’s Theorem, by using a variety of suitable function spaces, especially Banach algebras such as C(X), and Sobolev spaces to make use of their topological and multiplicative properties. New stability theorems are proved. One of them states that if the nonlinearity satisfies a Lipschitz-type condition where the constant can be taken sufficiently small relative to the norm of the linear solution operator then the problem has uniform Hyers-Ulam stability. Another one states that if the operator is decomposable into contractive and compact parts then we get Lagrange stability. Finally, uniform a priori bound gives bounded-input-bounded-output stability. In conclusion, the fixed-point method gives rise to explicit conditions for stability that can be verified algebraically. This provides a unified and robust alternative to the classical energy methods and adds analytical techniques to our toolbox of methods for the analysis of nonlinear PDEs.
subject: Mathematics
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creator: Noora Jaafar
rights: Copyright (c) 2026 Noora Jaafar https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 73
ending page: 94
doi: 10.31185/wjcms.503

Design of Reliable Queue Systems in Complicated Logistics Networks by Using Mathematical Programming Methods
type: item
description: How to construct strong queuing schemes in a network logistics setting using mathematical programming will be considered in this paper. As global supply chains get increasingly complex, Effective management of queues at logistics hubs is crucial for business performance. This work provides a unifying structure which combines queueing models and mathematical programming methods to minimize waiting time, maximize capacity utilization, and optimizing the allocation of resources (e.g., servers) in an unknown demand environment. We employ both analytical models (e.g. M/M/c queueing systems) and simulation techniques developed by means of WinQSP software to explore performance measures under varying circumstances. For illustration, sortation systems of a realistic distribution center network have been placed in simulated. The computational results also show that our technology can reduce the average waiting time by 42% and increase the system's utilization rate by 28% compared to traditional methods by applying mathematical programming for optimal server scaling. The findings of the study will enable logistics managers to develop stable queuing systems that are responsive to demand fluctuation and service performance. This study bridges the gap between queuing theory and practical logistics applications, providing managers with actionable insights.
subject: Mathematics
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creator: Hasanain Hamed
rights: Copyright (c) 2026 Hasanain Hamed https://creativecommons.org/licenses/by/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 28
ending page: 37
doi: 10.31185/wjcms.496

A Graph-Aware Recurrent Fusion Deep Ensemble Architecture for Fake News Detection
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description: With the rapid spread of fake news on social media platforms, it has become essential to develop effective detection mechanisms that overcome the limitations of content-only or propagation-only approaches. This study aims to design a unified framework that jointly models textual semantics and diffusion characteristics for improved misinformation detection. To achieve this, we propose GRAFT-FND, a Graph-Aware Recurrent Fusion Deep Ensemble architecture that integrates contextual word embeddings (Word2Vec, BERT, and BERTweet) with recurrent neural networks (RNN/GRU/LSTM/BiLSTM) and graph-based node embedding methods (Node2Vec and DeepWalk) within a fusion-aware learning module. Extensive experiments conducted on the Twitter15 and Twitter16 benchmark datasets using 10-fold cross-validation demonstrate that the proposed framework consistently outperforms baseline and recent state-of-the-art models, with the fusion mechanism and propagation-aware representations contributing significantly to performance improvement. The results indicate that jointly modelling semantic and structural information enhances the ability to capture complex misinformation patterns and improves generalisation across datasets. In conclusion, the proposed framework provides a robust and scalable solution for fake news detection in social media environments. Future work is recommended to investigate adversarial robustness, real-time deployment, and the integration of multimodal data sources.
subject: Computer
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creator: Karrar Kanaan
rights: Copyright (c) 2026 Karrar Kanaan https://creativecommons.org/licenses/by/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 1
ending page: 18
doi: 10.31185/wjcms.500

Educational Data Mining for Predicting Academic Attrition in Iraqi Universities: A Hybrid KDD-Based Approach using Weighted Euclidean Distance and Neural Architectures
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description: Academic attrition within the higher education sector of Iraq is accepted as a notable phenomenon with substantial socio-economic impacts. Within the digital revolution environment of the higher education sector of Iraq, there is a growing need to exploit the student data repositories as a strategic tool. The objective and purpose of this research is to enhance the existing strong framework, based on the Knowledge Discovery in Databases (KDD) process, whereby the risk factors associated with academic attrition can be proactively identified. Through the use of advanced image mining techniques, such as a texture-like matrix, along with a precise prediction model utilizing a Weighted Euclidean Distance with parameters relevant to education, a precise prediction model is now attainable. A comprehensive evaluation of different architectures for machine learning was carried out using a rich dataset of 5,000 students, designed based on the Iraqi context for the educational system. The results show the benefits of using Artificial Neural Network (ANN) methodology with Knowledge Discovery in Databases (KDD) methods, resulting in an accuracy rate of 94.21%. The research provides a strategic framework for Iraqi university administrators seeking to improve student retention using early warning systems
subject: Computer
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creator: mayada jabar
rights: Copyright (c) 2026 mayada jabar https://creativecommons.org/licenses/by/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 9
ending page: 17
doi: 10.31185/wjcms.492

Experimental Evaluation of Attention-Based Explainable AI Models for Detecting Zero-Day Threats in (IoT) Systems
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description: The paper proposes to evaluate attention-based explainable artificial intelligence (XAI) models for their potential to detect zero-day attacks on Internet of Things (IoT) networks. The evaluation will be done by comparing the performance of attention-based XAI models with other baseline models and post-hoc XAI models, considering their trustworthiness as well as their performance. We use a suite of publicly accessible and proprietary IoT intrusion datasets that vary in terms of the underlying protocol, devices, and intrusion types used in the attack scenarios. The study compares attention-based deep models, including sequence- and graph-oriented models, against non-attention-based neural and tree models as baselines. Evaluation criteria are detection performance, faithfulness and stability of the explanations, robustness to domain shift, noise, and adversarial attacks, as well as computational efficiency relevant to edge computing scenarios. In all scenarios, attention-based models achieve superior zero-day detection rates and overall F1-scores compared to the non-attention models, while maintaining competitive latency and resource utilization for edge devices. Explainability of the attention-derived explanations has been shown to be more faithful and stable compared to the post-hoc XAI methods, and they also have better graceful degradation characteristics when faced with domain shifts and adversarial attacks. Attention-based Explainable Intrusion Detection System (IDS) models have promising characteristics of high accuracy, robustness, and interpretability for zero-day threat detection in IoT environments.
subject: Computer
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creator: Zainab Raheem Oleiwi Algraiti
creator: Ahmad Ahmad-Kassem
rights: Copyright (c) 2026 Zainab Raheem Oleiwi Algraiti, Ahmad Ahmad-Kassem https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 1
ending page: 16
doi: 10.31185/wjcms.491

Hybrid Generalization Open Sets in Generalization Topological Spaces
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description: In this paper, we define a new class of generalized open sets in a generalized topological space <!-- [if gte msEquation 12]><m:oMath><m:r>(</m:r><m:r><m:rPr><m:scr m:val="double-struck"/><m:sty m:val="i"/></m:rPr>X</m:r><m:r><m:rPr><m:scr m:val="double-struck"/><m:sty m:val="i"/></m:rPr>,</m:r><m:r><m:rPr><m:scr m:val="script"/><m:sty m:val="i"/></m:rPr>G</m:r><m:r><m:rPr><m:scr m:val="script"/><m:sty m:val="i"/></m:rPr>)</m:r></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 3.5pt; mso-text-raise: -3.5pt; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><!-- [if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f"> <v:stroke joinstyle="miter"/> <v:formulas> <v:f eqn="if lineDrawn pixelLineWidth 0"/> <v:f eqn="sum @0 1 0"/> <v:f eqn="sum 0 0 @1"/> <v:f eqn="prod @2 1 2"/> <v:f eqn="prod @3 21600 pixelWidth"/> <v:f eqn="prod @3 21600 pixelHeight"/> <v:f eqn="sum @0 0 1"/> <v:f eqn="prod @6 1 2"/> <v:f eqn="prod @7 21600 pixelWidth"/> <v:f eqn="sum @8 21600 0"/> <v:f eqn="prod @7 21600 pixelHeight"/> <v:f eqn="sum @10 21600 0"/> </v:formulas> <v:path o:extrusionok="f" gradientshapeok="t" o:connecttype="rect"/> <o:lock v:ext="edit" aspectratio="t"/> </v:shapetype><v:shape id="_x0000_i1025" type="#_x0000_t75" style='width:27.5pt; height:14pt'> <v:imagedata src="file:///C:/Users/HP/AppData/Local/Temp/msohtmlclip1/01/clip_image001.png" o:title="" chromakey="white"/> </v:shape><![endif]--> , referred to as <!-- [if gte msEquation 12]><m:oMath><m:r>h</m:r><m:r>yg</m:r><m:r>-</m:r><m:r>open</m:r><m:r> </m:r></m:oMath><![en…
subject: Mathematics
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creator: Ekram MOHAMMED ABDULLAH
rights: Copyright (c) 2026 Ekram MOHAMMED ABDULLAH https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 44
ending page: 50
doi: 10.31185/wjcms.478

The combined method for solve the constraint optimization problems uses the augmented Lagrangian and line search technique
type: item
description: This paper presents an effective hybrid optimization methodology that combines the Augmented Lagrangian (AL) framework with the Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm to address large-scale constrained problems. The method leverages the AL framework to impose quadratic and linear penalties on the constraints, transforming the constrained problem into a sequence of unconstrained subproblems, driven by the computational asymmetry of decomposition. Then, the L-BFGS method is used to make these smaller problems easier to solve. L-BFGS efficiently approximates the search direction with just operations per iteration by using a two-loop recursion with a limited history of gradient pairs. This avoids the problem of calculating the Hessian matrix. It is therefore very important for large-scale situations. The AL-BFGS design that comes out of this speeds up convergence in the inner minimization loop. The outer loop, on the other hand, keeps updating the Lagrange multipliers and changing the penalty parameter based on the primal and dual residuals. by making an efficient iterative solution and handling the inequality constraints correctly.
subject: Mathematics
  topic: section
creator: MOHAMMED ALI ABBAS
rights: Copyright (c) 2026 MOHAMMED ALI ABBAS https://creativecommons.org/licenses/by/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 47
ending page: 54
doi: 10.31185/wjcms.454

Hybrid Framework for Multi-Disease Chest X-Ray Diagnosis Using Vision Transformers with Label Noise Correction and Uncertainty Calibration
type: item
description: The most accessible radiological technique for thoracic disease detection and diagnosis uses chest X-ray imaging as its primary method. The deployment of automated chest X ray analysis systems faces two main obstacles because of untrustworthy labels in large datasets and unpredictable predictive confidence levels. The research proposes a hybrid system which combines Vision Transformer (ViT) architecture with methods to handle noisy labels and produce accurate probability estimates for multiple disease diagnosis in chest X-ray images. The system trains on CheXpert and NIH ChestX-ray14 datasets while using Co-Teaching and DivideMix noise-handling methods and self-supervised pretraining to enhance feature resistance against supervision errors. The framework uses temperature scaling and Monte Carlo dropout as post-hoc methods to enhance confidence reliability without compromising discriminative performance. The system aims to reach performance levels that match or exceed traditional CNN and standard ViT models in AUROC and mAP and F1 score metrics. The system reduces the effects of untrustworthy labels while generating meaningful confidence scores which doctors can understand. The model produces Grad-CAM++ explanations to assist doctors in understanding its decision-making process. The hybrid system works to develop AI systems which deliver both exact results and safe operational readiness for real-world chest X-ray decision support systems.
subject: Computer
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creator: bahaa mohammed
creator: Nor Azura Husin
rights: Copyright (c) 2025 bahaa mohammed, Nor Azura Husin https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 1
ending page: 9
doi: 10.31185/wjcms.451

Fairness-Aware Mutual Information for Feature Selection in Employee Attrition Prediction
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description: Feature selection is a critical part of creating machine learning models, particularly for touchy areas such as HR, finance, and healthcare. The literature shows that many current methods only review the results based on accuracy while ignoring both fairness and interpretability. This study presents a feature selection method which fuses Mutual Information for assessing feature relevance, while imposing fairness constraints by measuring the Equal Opportunity Difference (EOD) metric. The aim, of this study, is to maximize accuracy, fairness, and interpretability. The methodology was tested on the IBM HR Analytics dataset for six models (Fair Logistic Regression, Explainable Boosting Machines (EBMs), XGBoost, Random Forest, SVM and KNN). Fair Logistic Regression, provided the best overall results with 96% accuracy and little fairness bias (EOD = 0.005). XGBoost and Random Forest had good predictive reliability, evidence of disparity in fairness measures. Moreover, Explainable Boosting Machines (EBMs) met accuracy standards, and have good interpretability, so these models could be used in intervention areas where transparency of the model/construction process is required.
subject: Computer
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creator: omar shakir
rights: Copyright (c) 2025 omar shakir https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 26
ending page: 37
doi: 10.31185/wjcms.404

Real-Time Emotion Recognition in Human-Robot Interaction Using Deep AI Models
type: item
description: Emotion recognition in human-robot interaction (HRI) is important in order to develop socially aware and responsive robotic systems. In this work, a near real-time emotion recognition architecture is introduced which combines deep AI models such as CNN, and recurrent architectures (LSTM/ GRU) for audio and visual-based emotion detection. The system is tested on the benchmark datasets like FER-2013 and RAVDESS. The goal is to provide a strong and scalable approach that can serve as a step toward the full integration of emotionally intelligent robots into everyday life, to encourage empathic, adaptive, and rewarding human-robot interaction. The proposed deep AI model was tested on a dataset comprising 5,000 multimodal samples of human emotional expressions collected in controlled and real-world human-robot interaction scenarios. The dataset included five emotional categories: Happy, Sad, Angry, Neutral, and Surprise. Experimental results show the effectiveness of the proposed system based on the public datasets, and its practical use in the simulated human-robot interaction (HRI) scenario. Also, the proposed approach provides high accuracy and low inference delay, which can support robotic agents to have effective emotion-adaptive behaviors in live-interaction environments.
subject: Computer
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creator: Hiba alaamaidi
rights: Copyright (c) 2026 Hiba alaamaidi https://creativecommons.org/licenses/by/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 38
ending page: 46
doi: 10.31185/wjcms.426

Stable Numerical Solution of an Elliptic PDE Inverse Problem Subject to Incomplete Boundary Conditions
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description: This paper addresses the inverse problem of reconstructing complete steady-state solutions for elliptic partial differential equations when boundary information is incomplete a situation common in electromagnetic, thermal, and geophysical modeling where full Dirichlet or Neumann data are not available. The objective is to develop a numerically stable and reproducible method that reconstructs the interior field and missing boundary trace from partial boundary measurements contaminated by noise. To that end, the inverse task is posed as a Tikhonov-regularized optimization problem in which the unknown Dirichlet trace on the unobserved boundary segment minimizes a least-squares discrepancy between forward-model predictions and boundary observations; the forward PDE is discretized by the finite element method and the gradient of the discrete cost is computed via the adjoint-state method, enabling efficient computation independent of the number of boundary parameters. The optimization uses a nonlinear conjugate-gradient scheme with automated L-curve-based regularization selection. Numerical validation on synthetic test cases including a unit-square Poisson problem and a circular-domain example shows that the proposed approach recovers the interior field and the missing boundary trace with low RMSE and small relative L^2 errors for noise levels up to 5%, whereas unregularized reconstructions suffer severe noise amplification. These results demonstrate that the FEM + adjoint + H¹-Tikhonov methodology provides a practical and stable solution approach for elliptic inverse problems with partial boundary conditions, and that this approach scales to moderate resolution meshes and is potentially extendable for non-smooth targets, nonlinear PDEs, and realistic measurement models.
subject: Mathematics
  topic: section
creator: ABBAS ALDNADOI
rights: Copyright (c) 2025 عباس يحيى لعيبي https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 10
ending page: 25
doi: 10.31185/wjcms.432

Multitype Convolutional Networks for Improved Iris Classification
type: item
description: Iris recognition is an essential biometric modality that has achieved far-reaching adoption in most applications including government ID plans, voter registration and de-duplication, international border crossing, and mobile device authentication. Due to its high sensitivity as a secure, non-invasive human identification technique, iris recognition has various challenges like light variation, specular highlights, motion blur, and image noise to deal with. To overcome these limitations, deep learning models have been employed more widely both for pattern matching and feature extraction so that higher quality and robust detection of complex and highly unique iris textures can be achieved. The current paper presents a successful two-stage iris recognition system. Iris segmentation and detection are achieved in the first stage by using the HoughCircles algorithm and inpainting improved to achieve higher image quality and remove artifacts. The second stage employs a new deep network employing multiple types of convolutional layers like regular, spatial, and depthwise convolutions to execute efficient feature extraction and accurate classification. The proposed model was also validated and tested with the MULBv1 iris image database and achieved promising results with the classification accuracy of 98.21% and F1-score of 98.16%. The results prove the effectiveness of the proposed method as a feasible and practical solution to be used in real-life iris identification systems.
subject: Computer
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creator: Firas Muneam Bachay
rights: Copyright (c) 2025 Firas Muneam Bachay https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 38
ending page: 48
doi: 10.31185/wjcms.436

Brain Tumor Classification of Medical Images based on Transfer Pre-trained Learning Models
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description: Identifying brain tumor types is crucial for accurate diagnoses and appropriate therapeutic planning in medical imaging. The study aims to develop a robust and accurate system for categorizing medical images of brain tumors, utilizing transfer learning from pre-trained neural network models. The purpose is to optimize the efficiency and precision of brain tumor diagnosis, ultimately facilitating prompt and precise medical intervention by clinicians. This endeavor holds immense importance in promptly diagnosing and treating brain tumors. Transfer learning greatly facilitates the classification of different forms of brain tumors visible in medical imaging, enabling the achievement of this target. Utilizing pre-trained neural network models capitalizes on their comprehension of visual attributes and patterns by adjusting to the specific activity with efficiency and accuracy. Through this strategy, the training process is accelerated, and the model is generalized. Medical professionals can rapidly and reliably diagnosing brain tumors from medical imaging via this technique. he results demonstrate that the AlexNet model showed a remarkable performance, with an accuracy rate of 95.60% and high sensitivity and specificity rates.
subject: Computer
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creator: Mohamed H Ghaleb Abdkhaleq Abdkhaleq
creator: Ibraheem Al-Jadir
creator: Loay George
creator: Yuri Yuryevich Gromov
creator: Eliseev Aleksey Igorevich
rights: Copyright (c) 2025 Mohamed H Ghaleb Abdkhaleq Abdkhaleq, Ibraheem Al-Jadir, Loay E. George, Gromov, Yuri Yuryevich, Eliseev Aleksey Igorevich https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 94
ending page: 105
doi: 10.31185/wjcms.388

Enhancing IoT Network Security Through Deep Learning-Based Intrusion Detection
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description: The internet of today is a mainstream aspect of life, with increasingly more devices in the Internet of Things (IoT) ecosystem becoming networked. Although they are leaders in the market today, IoT networks are beset by increasingly more degrading security problems that confuse neatly defined solutions. These weaknesses need to be halted because new threats continue to challenge the resilience of IoT networks. Although the existing methodologies are made to provide more security, there remains untapped potential in machine learning development. Certain of the machine learning and deep learning methods, and benchmark data sets, used for IoT security enhancement are described in this paper. It proposes a new deep learning-based Legitimate Load Testing (LLT) attack detection algorithm implemented in Python and supported by libraries such as TensorFlow, scikit-learn, and Seaborn. Experimental outcomes confirm that the deep learning model improves attack detection precision significantly, which results in more effective countermeasures for protecting IoT networks.
subject: Computer
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creator: MOHAMMED FAWWAZ ALI MOHAMMED FAWWAZ ALI
rights: Copyright (c) 2025 MOHAMMED FAWWAZ ALI MOHAMMED FAWWAZ ALI https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 74
ending page: 85
doi: 10.31185/wjcms.363

A New Conjugate Gradient for Efficient Unconstrained Optimization with Robust Descent Guarantees
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description: The Conjugate Gradient method is a powerful iterative algorithm aims to find the minimum of a function by iteratively searching along conjugate directions. This work presents a nonlinear conjugate gradient approach for unconstrained optimization, resulting from the resolution of a novel optimization problem. The theoretical framework of the proposed method is discussed, and on the application of the descent condition. To evaluate its performance, numerical experiments were conducted, comparing the proposed method against established algorithms () and (Lia and Story) methods. The results demonstrate that the new method not only exhibits enhanced efficiency but also significantly outperforms the () and (Lia and Story) methods in terms of optimization effectiveness. These findings suggest that the proposed approach offers a competitive and promising alternative for solving unconstrained optimization problems.
subject: Mathematics
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creator: Hussein Saleem Ahmed
rights: Copyright (c) 2025 Hussein Saleem Ahmed https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 86
ending page: 93
doi: 10.31185/wjcms.358

Cyberbullying Detection in Arabic Text Using Different Deep Learning Approaches
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description: The rise of social media has enabled the rapid spread of user-generated content across various domains, including advertising, entertainment, politics, and economics. However, this growth has also facilitated the increase of harmful behaviors, notably cyberbullying. Addressing this issue requires advanced emotional and sentiment analysis techniques. In this study, the ArCyC (Arabic Cyberbullying Corpus) were integrated with Twitter data to develop robust models for cyberbullying detection. Several deep learning models have been suggested which are: Deep Neural Net. (DNN), Convolutional Neural Nets. (CNN), Recurrent Neural Net. (RNN), Hybrid CNN+RNN, BERT & AraBERT. Text and Emoji have been experimented in the dataset. Models’ performance was evaluated based on accuracy. Experiments using the ArCyC dataset demonstrated that both textual and symbolic elements contributed significantly to classification accuracy. In contrast, analysis with the ArCyC dataset revealed that textual features had a more dominant influence due to the limited use of emojis. The results underscore the effectiveness of deep learning approaches in detecting cyberbullying within Arabic social media content. AraBERT for text has obtained the highest accuracy equal to 95%, similarly LSTM obtained the same accuracy for both text and emoji’s
subject: Computer
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creator: Humera Shaziya
rights: Copyright (c) 2025 Humera Shaziya https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 45
ending page: 55
doi: 10.31185/wjcms.390

Boosting Malware Detection with AlexNet and Optimized Neural Networks Using the Grasshopper Algorithm
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description: That risk is compounded as more critical infrastructure and systems are being managed by computers in general, connected over the Internet. To combat such nefarious software that can steal data and do a number of other privatively outcomes, you need to be very vigilant and also train all our artificial intelligent tools not just to find the malware per se but all the countless other ways in which meddlers might find their way into your computer or set off some enormously disruptive chain reaction (or series thereof). In this research, it is offering a potent new technique by integration of innovative neural network method with conventional artificial intelligence tool known as "multilayer perceptron (MLP)". For the detection of 25 distinct categories of malwares it is obtained 99.84% accuracy rate in classification through in this hybrid system.
subject: Computer
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creator: Mohammed Aswad
rights: Copyright (c) 2025 Mohammed Aswad https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 28
ending page: 44
doi: 10.31185/wjcms.303

Blockchain Scalability Analysis and Improvement of Bitcoin Network through Enhanced Transaction Adjournment Techniques
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description: Bitcoin is the most popular among the cryptocurrency it includes Litecoin, Dogecoin, and Peercoin. Blockchain is the foundation of cryptocurrencies, most cryptocurrency technologies are decentralized. Despite its benefits, blockchain remains a cutting-edge technology with flaws that can be addressed to increase efficiency. Therefore, this research digs into the topic of scalability in blockchains and presents a comparative analysis of numerous blockchain metrics with real time data. We performed this using a blockchain simulation (BlockSim), and then looked at effective techniques that may be utilized to overcome the limitation by comparing the simulator and real-world circumstances. The second part of this research work proposes an effective algorithm that improves scalability of Bitcoin network through efficient transaction deferment. In this study, we propose an algorithm that improves the current Bitcoin protocols using Inventory messaging (INV) and transaction deferment or adjournment. The deferred transaction relay on the message to carry multiple raw transactions. These improvements are compatible with the existing Bitcoin Network protocols. The improve algorithm was simulated using BlockSim and AnyLogic Multi Paradigm Simulation Engine, the simulators ware configured with 1000 nodes interconnected in a Bitcoin Like Peer-to-Peer network. The result of the simulation shows that the adjourned transaction protocol provides for a controlled reduction in the number of messages required to propagate a transaction at the cost of a modest increase in transaction propagation time. We may manage the tradeoff between the quantity of messages and propagation latency by adjusting the threshold and timeout values, thereby improving the network's overall scalability. Bitcoin is the most popular among the cryptocurrency it includes Litecoin, Dogecoin, and Peercoin. Blockchain is the foundation of cryptocurrencies, most cryptocurrency technologies are decentralized. Despite i…
subject: Computer
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creator: Umar Danjuma Maiwada
rights: Copyright (c) 2025 Umar Danjuma Maiwada https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 27
doi: 10.31185/wjcms.298

Evaluation Study for Celiac Disease Diagnosing by using Deep Learning Techniques
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description: Celiac disease (CD) is the autoimmune reaction that occurs as a result of ingestion of gluten, which results to mucosal injury. Proper screening is especially important here, though the existing diagnostic techniques are frequently expensive, as well as the time-consuming. This review seeks to uncover the future of deep learning techniques in changing the diagnosis of CD through imagery of biopsies. we explore Single Layer ABC-DL architectures, CNN in CD diagnosis, Transfer Learning and Multiple Instance Learning (MIL). In this review, these methods are described with regard to their current capabilities to improve diagnostic accuracy and speed over conventional approaches compared to those used in the construction of the models. Additionally, we describe the prospects and flaws seen in applying DL to medical image analysis and the critical issues of increased requirements for big labeled datasets, data quality, and the interpretability of the model. Last but not least, we discuss the possible future research avenues of the DL applications for CD diagnosis like multi-modal approach and better interpretability mechanisms. This review enable the author to identify the current status of developing deep learning techniques for CD diagnosis and further research direction in this promising field.
subject: Computer
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creator: asra ali
rights: Copyright (c) 2025 asra ali https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 56
ending page: 73
doi: 10.31185/wjcms.373

Low-Cost Vehicle Tracking System Based Web Application
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description: Tracking and monitoring systems are a necessity and are widely used in various industries and fields for security and supervision purposes. Particularly in vehicle monitoring perspectives, it is considered a sophisticated technology that is conducted and designed to monitor and manage the location and status of the vehicles in real-time settings. Different methods are employed to ensure monitoring and tracking, also to enhance robustness and security. These methods include onboard devices, GPS, and cellular networks. Therefore, this paper’s hardware aspect consists of a very reliable, compacted, reasonable priced tracking device mounted on vehicles or any trackable bodies, this device is connected to the web application databases, the web application has databases for users and their information, irregularities, and maps where each user is marked on the map with the real-time position. The proposed system used an Arduino-based ESP8266 microcontroller and a NEO-6M-Ublox GPS receiver module we can receive the latitude and longitude from satellites then send the information via WIFI to a Database where this data will be processed to not only mark the exact real-time location but also saving these locations in case of any irregularities, do other helpful functions. The experimental results have shown that a low-cost tracking system managed by an easy-to-use Web application. Although the error range for the system is about (8-10) meters, depending on weather and terrain, many companies can make great use of such systems, fleet companies, taxi companies, delivery companies, and vehicle rental companies.
subject: Computer
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creator: Hakim Adil Kadhim
creator: Nabeel ALI AL BACHACHEE
rights: Copyright (c) 2025 Nabeel ALI AL BACHACHEE, Hakim Adil Kadhim https://creativecommons.org/licenses/by/4.0
date: 2025-09-30
publication date: 2025-09-30
doi: 10.31185/wjcms.397

Some approaches to solving fuzzy linear fractional programming
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description: An important planning tool is linear fuzzy fractional programming, it is used in various fields such as business, engineering, and others. We are trying to get one of the direct and effective methods that contain some arithmetic operations to obtain the optimal real values through which a multi-objective fuzzy linear programming problem (MOFLFPP) is transformed into a linear programming problem (LPP) through the use of α-cut and MaxMin technique. The field of application is Iraqi Light Industries Company and chose the best products that must be protected which achieved a possible greatest profit ratio to less cost, Where the paper will include two sections, the first is concerned with describing the data and building the mathematical model for the problem (MOFLFPP) related to the research problem. The second section deals with trying to solve the model as well as finding the optimal solution, which represents determining the best and optimal production mix that achieves maximum profits at the lowest costs in light of the restrictions imposed on the production process, which may limit the company’s ability to provide products in the required quantity and the right time. The proposed methodology proved effective in solving multi-objective linear fractional programming problems with fuzzy coefficients (MOLFPP). While the previous approach produced results between (0.19904, 0.3406), our technique improved them to (0.2087, 0.3431), demonstrating higher reliability and efficiency with an ε-optimal unique solution.
subject: Mathematics
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creator: Waleed Mohammed Elaibi Elaibi
creator: Hadi Nasseri
rights: Copyright (c) 2025 Waleed Mohammed Elaibi Elaibi, Hadi Nasseri https://creativecommons.org/licenses/by/4.0
date: 2025-11-15
publication date: 2025-11-15
doi: 10.31185/wjcms.389

Categorization of Celebrity Photos Based on Deep Machine Learning for Feature Extraction and Classification
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description: In feature extraction, images or videos are analyzed to identify facial features before pinpointing the exact target. Significant progress has been made in the realm of feature extraction through deep learning. With deep learning technologies, developers have created algorithms for facial analysis and recognition that enhance accuracy and effectiveness. Consequently, programmers and developers have started to implement facial recognition technology in a wide range of applications to address these challenges. The identification process is particularly difficult due to the abundance of unstructured datasets. In real-world scenarios, identification continues to be quite challenging despite various methods proposed so far. This paper aims to analyze celebrity categorization based on deep features under different conditions. Most algorithms are influenced by these problematic factors, whether intentionally or unintentionally. Six datasets are utilized in this paper: the Open Famous People Dataset, Celeb Identification Dataset, Bollywood Celebrity Localized Dataset (170), Football Player's Dataset, Mini_LFW, and Nepali Celeb Localized Dataset. Most of these images feature celebrities including actors, singers, players, actresses, politicians, and social figures from diverse nationalities and countries. Therefore, this paper offers a combination of results from classification methods: Multilayer Perceptron Classifier (MLP), Decision Tree Classifier (DT), Bootstrap Aggregation Decision Tree Classifier (TreeBagger), Extreme Gradient Boosting Classifier (XGB), and Stochastic Gradient Descent Classification (SGD), alongside deep learning methods for feature extraction (VGGFace2 model pretraining and SENet-50 model architecture) across six different facial datasets. The results demonstrated that the highest accuracy (F-measure, Recall, and Precision) was achieved following the training and testing process in all datasets using the MLP and SGD classifiers.
subject: Computer
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creator: Salwa Shakir Baawi
creator: Farah Jawad Al-Ghanim
creator: Nisreen Ryadh Hamza
rights: Copyright (c) 2025 Salwa Shakir Baawi, Farah Jawad Al-Ghanim, Nisreen Ryadh Hamza https://creativecommons.org/licenses/by/4.0
date: 2025-05-21
publication date: 2025-05-21
starting page: 1
ending page: 16
doi: 10.31185/wjcms.341

Feature Fusion for Improved Skin Cancer Diagnosis Using Support Vector Machines
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description: the early detection and successful treatment of skin cancers, a potent form of cancer, calls for the use of sophisticated diagnostic instruments. This study delves into the use of support vector machines (SVMs), to cope with the inconsistencies occurring among skin lesions, by merging them with feature fusion techniques. SVMs are preferred for this situation, as they are highly effective when it comes to the management of exceedingly dimensional data. Initially, in order to train and enhance the diagnostic capacity of the SVM classifier, a single and all-inclusive single dataset was generated through the analysis, identification and extraction of a wide variety of explanatory features (including colour, texture and shape) from a dataset comprising 10000 dermatoscope skin lesion representations. This was followed by the use of early and late fusion approaches, to generate an extensive dataset of descriptions, for assessing the reliability of the SVM classifier. Finally, the accuracy, precision and recall of the SVM classifier were ascertained by way of an objective dataset, comprising 25 dermatoscope representations of malignant and benign lesions. The accuracy, precision and recall of the SVM classifier are supported by its capacity to distinguish 10 true positives, 12 true negatives, three false positives and zero false negatives. As such, the SVM classifier can be considered effective, for the early detection of skin cancers. The results from this investigation verify that the capacity of SVMs, in terms of skin cancer diagnosis, is greatly improved with the utilization of feature fusion techniques. Also verified through this undertaking, is the effectiveness of innovative computational procedures, for the delivery of dependable medical diagnoses.
subject: Computer
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creator: ali fahim
rights: Copyright (c) 2025 ali fahim https://creativecommons.org/licenses/by/4.0
date: 2025-05-21
publication date: 2025-05-21
starting page: 53
ending page: 58
doi: 10.31185/wjcms.382

Model-Driven Engineering for Adaptive Software Systems in Dynamic Environments
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description: In such an environment, an urgent requirement has been felt for systems that can adapt at runtime to changing situations, as available computing environments are more and more dynamic and complex. The research presented in this paper is about the use of Model Driven Engineering (MDE) for systematic support of managing software adaptability. MDE advocates the use of models as core working entities in software development, allowing you to raise the level of abstraction, transformation and consistency of the adaptive processes. The work presents a full-fledged framework that combines MDE principles together with real-time monitoring, self-configuration strategies, and the MAPE-K feedback loop. By means of theoretical study and cross-domain case comparisons (smart environment, education, transportation, and cloud computing), we show how MDE enables the structural, behavior, and parameter adaptation. The quality of adaptation is evaluated based on a series of metrics, including adaptivity time, model similarity, and transformation delay. The results substantiate the distinctive capability of MDE-based approaches for responsiveness and system correctness in volatile environments and draw attention to important limitations with respect to tool maturity, run-time sync service and scale. The paper concludes by discussing potential future endeavors to drive the field forward, including runtime metamodel evolution, AI-driven model adaptation, and decentralized model-driven infrastructures. The work casts MDE as a suitable and generalizable basis for the development of adaptive systems that exhibit resilience to unanticipated failures, context-awareness, and autonomous adaptation.
subject: Computer
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creator: ali fahim
rights: Copyright (c) 2025 ali fahim https://creativecommons.org/licenses/by/4.0
date: 2025-05-21
publication date: 2025-05-21
starting page: 37
ending page: 52
doi: 10.31185/wjcms.381

Brain age prediction from MRI images based on a convolutional neural network with MRMR feature selection layer
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description: An sophisticated medical technique used to diagnose illnesses and brain disorders including multiple sclerosis, Alzheimer's, and other neurological ailments is the ability to predict the biological age of the brain using MRI pictures. To do this, sophisticated algorithms and neural networks are used to scan MRI brain pictures in order to extract different brain properties, including cortical thickness and volume. The brain ages of individuals are determined by matching their characteristics against MRI imaging data collected from other patients. The research employs a new deep learning model named CNN-MRMR which combines features from the Minimum Redundancy Maximum Relevance (MRMR) feature selection approach and Convolutional Neural Network (CNN) technology. MRI images of human brains are initially processed by the convolutional network to extract age-related characteristics. The feature selection layer uses MRMR algorithm which identifies essential characteristics for a target variable while minimizing feature redundancy to select the optimal feature subset. The system employs a regression layer as the final stage to predict brain age by utilizing the selected characteristics. The proposed method for estimating individual brain age attained a prediction accuracy of 90.3%, outperforming results from comparable research studies.
subject: Computer
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creator: Mustafa Hatem Al Ghariri
creator: Seyed Omid Shahdi
rights: Copyright (c) 2025 Mustafa Hatem Al Ghariri, Seyed Omid Shahdi https://creativecommons.org/licenses/by/4.0
date: 2025-05-21
publication date: 2025-05-21
starting page: 17
ending page: 29
doi: 10.31185/wjcms.296

Generalized (theta, theta)- Semi-derivations of Prime Near-rings
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description: The concept of differential algebra has been initiated before many years ago. This research topic has inspired a lot of authors to its study with different algebraic structures such as rings or semi-rings. Their studies provided many good results in this field some of which by extending from previous works and others by introducing new notion in this direction. In this article a type of rings are called prime near-rings have been considered. In particular, we introduced the idea of generalized (theta,theta)-semi-derivation of prime near-rings and studied the commutativity of such types of rings by using this notion.
subject: Mathematics
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creator: mohd Shahoodh
creator: Omar Kareem Ali
rights: Copyright (c) 2025 mohd Shahoodh, Omar Kareem Ali https://creativecommons.org/licenses/by/4.0
date: 2025-05-21
publication date: 2025-05-21
starting page: 30
ending page: 36
doi: 10.31185/wjcms.307

Internet of Things Optimal Routing based on Markov-Reinforcement Learning Algorithm
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description: By increasing Internet of Things (IoT) development, it will face new challenges due to its application in various fields of science and industry. One of these challenges is the routing problem in communication which has many effects in various parts and the quality criteria of the IoT in communication. In this research, a new approach provide with an effective and optimal method for routing in the IoT, which will use 6LoWPAN as the default protocol. This protocol has qualitative weaknesses and its goal is to improve and extend it as much as possible with the Markov Reinforcement Learning (MRL). Improving power consumption and reducing energy beside quality of services criteria optimization is the main targets of this approach. The results represented the improvement of the proposed approach compared to other similar protocols such as Z-WAVE and Zig Bee and the classic 6LoWPAN protocol.
subject: Computer
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creator: Jamal M.Ali Radha
creator: Raza Y. Abdulrahman
rights: Copyright (c) 2024 Jamal M.Ali Radha, Raza Y. Abdulrahman https://creativecommons.org/licenses/by/4.0
date: 2024-09-30
publication date: 2024-09-30
starting page: 89
ending page: 98
doi: 10.31185/wjcms.271