Wasit Journal of Computer and Mathematics Science
Journal title Wasit Journal of Computer and Mathematics Science Indexing Online ISSN Print ISSN 2788-5879 2788-5887 Frequency 4 issues per year DOI prefix 10.31185 Editor in Chief Managing Editor of Computer Science section Managing Editor of Mathematics Section Organized by College of Computer and Information Technology – University of Wasit, Iraq Citation Analysis <a 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&sid=5c0034fa290bab752bde2d8e817fdd35&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&editSaveSearch=&sessionSearchId=5c0034fa290bab752bde2d8e817fdd35&limit=10">Scopus | Google Scholar Acceptance Rate: 40% Review Speed: 90 days Before submission, You have to make sure that your paper is prepared using the WJCMS paper Template, or Latex Template 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.
Hybrid Framework for Multi-Disease Chest X-Ray Diagnosis Using Vision Transformers with Label Noise Correction and Uncertainty Calibration
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.
Chest X-ray, Vision Transformer, label noise, uncertainty calibration, multi-disease diagnosis, medical image analysis.
Copyright (c) 2025 bahaa mohammed, Nor Azura Husin https://creativecommons.org/licenses/by/4.0
Fairness-Aware Mutual Information for Feature Selection in Employee Attrition Prediction
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.
Feature selection, Mutual‑Information, Equal Opportunity Difference (EOD) ,Fairness aware
Copyright (c) 2025 omar shakir https://creativecommons.org/licenses/by/4.0
Stable Numerical Solution of an Elliptic PDE Inverse Problem Subject to Incomplete Boundary Conditions
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.
Copyright (c) 2025 عباس يحيى لعيبي https://creativecommons.org/licenses/by/4.0
Multitype Convolutional Networks for Improved Iris Classification
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.
Copyright (c) 2025 Firas Muneam Bachay https://creativecommons.org/licenses/by/4.0
Brain Tumor Classification of Medical Images based on Transfer Pre-trained Learning Models
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.
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
Enhancing IoT Network Security Through Deep Learning-Based Intrusion Detection
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.
machine learning techniques, deep neural networks, IoT security, LLT attack detection, intrusion detection systems
Copyright (c) 2025 MOHAMMED FAWWAZ ALI MOHAMMED FAWWAZ ALI https://creativecommons.org/licenses/by/4.0
Cyberbullying Detection in Arabic Text Using Different Deep Learning Approaches
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
cyberbullying detection, deep learning, text mining
Copyright (c) 2025 Humera Shaziya https://creativecommons.org/licenses/by/4.0
A New Conjugate Gradient for Efficient Unconstrained Optimization with Robust Descent Guarantees
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.
Unconstrained optimization, Descent and Sufficient descent condition, Global convergent.
Copyright (c) 2025 Hussein Saleem Ahmed https://creativecommons.org/licenses/by/4.0
Boosting Malware Detection with AlexNet and Optimized Neural Networks Using the Grasshopper Algorithm
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.
Copyright (c) 2025 Mohammed Aswad https://creativecommons.org/licenses/by/4.0
Blockchain Scalability Analysis and Improvement of Bitcoin Network through Enhanced Transaction Adjournment Techniques
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 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.
Blockchain, BlockSim, SupplyChain, Bitcoin, Litecoin, Dogecoin, and Peercoin.
Copyright (c) 2025 Umar Danjuma Maiwada https://creativecommons.org/licenses/by/4.0
Evaluation Study for Celiac Disease Diagnosing by using Deep Learning Techniques
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.
Artificial Intelligence, Deep Learning, Computer-Aided Diagnosis (CAD), Celiac Disease
Copyright (c) 2025 asra ali https://creativecommons.org/licenses/by/4.0
Low-Cost Vehicle Tracking System Based Web Application
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.
Copyright (c) 2025 Nabeel ALI AL BACHACHEE, Hakim Adil Kadhim https://creativecommons.org/licenses/by/4.0
Some approaches to solving fuzzy linear fractional programming
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.
Copyright (c) 2025 Waleed Mohammed Elaibi Elaibi, Hadi Nasseri https://creativecommons.org/licenses/by/4.0
Categorization of Celebrity Photos Based on Deep Machine Learning for Feature Extraction and Classification
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.
Copyright (c) 2025 Salwa Shakir Baawi, Farah Jawad Al-Ghanim, Nisreen Ryadh Hamza https://creativecommons.org/licenses/by/4.0
Feature Fusion for Improved Skin Cancer Diagnosis Using Support Vector Machines
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.
Keywords: Extraction Feature, Feature Fusion Techniques, Medical Image Classification, Skin Cancer Diagnosis,
Copyright (c) 2025 ali fahim https://creativecommons.org/licenses/by/4.0
Model-Driven Engineering for Adaptive Software Systems in Dynamic Environments
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.
Driven Engineering (MDE); Runtime Adaptation; Self-Adaptive Systems; MAPE-K Loop; Model Transformation; Domain-Specific Modeling; Evaluation Metrics; Metamodel Evolution; Adaptive Architecture; AI in Software Adaptation.
Generalized (theta, theta)- Semi-derivations of Prime Near-rings
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.
Copyright (c) 2025 mohd Shahoodh, Omar Kareem Ali https://creativecommons.org/licenses/by/4.0
Brain age prediction from MRI images based on a convolutional neural network with MRMR feature selection layer
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.
Copyright (c) 2025 Mustafa Hatem Al Ghariri, Seyed Omid Shahdi https://creativecommons.org/licenses/by/4.0
Internet of Things Optimal Routing based on Markov-Reinforcement Learning Algorithm
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.
Copyright (c) 2024 Jamal M.Ali Radha, Raza Y. Abdulrahman https://creativecommons.org/licenses/by/4.0
Optimized Path Planning and Scheduling in Robotic Mobile Fulfillment Systems Using Ant Colony Optimization and Streamlit Visualization
Context: In the age of rapid e-commerce growth; the Robotic Mobile Fulfillment Systems (RMFS) have become the major trend in warehouse automation. These systems involve the use of self- governed mobile chares to collect shelves as well as orders for deliveries with regard to optimization of task allocation and with reduced expenses. However, in a manner to implement such systems, one needs to find enhanced algorithms pertaining to resource mapping and the planning of movement of robots in sensitive environments. Problem Statement: Despite RMFS have certain challenges especially when it comes to the distribution of tasks and the overall distances that employees have to cover. Objective: The main goal of this paper is to propose a new compound optimization model based on RL-ACO to optimize the RMFS’s task assignment and navigation. Also, the direction of the study is to investigate how such methods can be applied to real-life warehouse automation and how effective such methods can be on a large scale. Methodology: This research introduces a new optimization model for RMFS selection which integrates reinforcement learning with Ant Colony Optimization (ACO). Specifically, a real gym environment was created to perform the order assignment and training in the way of robotic movement. Reinforcement Learning (RL) models were trained with Proximal Policy Optimization (PPO) for improving the dynamic control of robots and ACO was used for computing optimal shelf trajectories. The performance was also measured by policy gradient loss, travelled distance and time taken to complete the tasks. Results: The proposed framework showed potential in enhancing the efficiency of tasks required and the travel distances involved. In each of the RL models used the shortest paths were identified and the best route was determined to have a total distance of 102.91 units. Also, other values such as, value function loss and policy gradient loss showed learning and convergence in iterations. To build a global solution, ACO integration went a step forward in enabling route optimization through effective combinatorial problems solving. Implications: This research offers a practical, generalizable and flexible approach for the improvement of the operations of RMFS and thinking for warehouse automation.
Multi-Robot Systems, Autonomous Navigation, Path Planning, Adaptive Control, Mobile Robot Coordination, Energy Efficiency.
Copyright (c) 2024 Isam Sadeq Rasham https://creativecommons.org/licenses/by/4.0
this paper present a new type of operator , referred to as the fuzzy soft class (Q) operator , which operator on a complex Hilbert space . we examin several advantageous properties that are exclusive this operator class. Additionally , we explore an extended version of several fuzzy soft class Q operator in this work , introduce fuzzy soft (a,b)class (Q) and fuzzy soft (a,b)normal operator. we also provide some theorems of operations related to these notions.
FS-class(Q) operator, FS(α,β)-normal operator, and fuzzy soft(α,β)-class(Q) operator
Copyright (c) 2024 Hanan Khalid https://creativecommons.org/licenses/by/4.0
Advanced Malware Detection: Integrating Convolutional Neural Networks with LSTM RNNs for Enhanced Security
Malware, or malicious software, is a serious threat to people, businesses, and the cybersecurity environment as a whole. Its purpose is to disrupt, damage, or obtain unauthorized access to computer systems. The ability to accurately classify and identify different types of malware is very important in developing effective defense mechanisms and reducing possible risks In order to classify malware from photos, this paper presents a novel approach that combines the capabilities of an LSTM architecture with the convolutional neural network AlexNet. We began with preprocessing the data, which included resizing the images for compatibility with the network architecture. Then, we used AlexNet to extract powerful and meaningful features from the malware images. Although we extracted 1,000 features, we trimmed the list to 120 features using linear discriminant analysis for more efficient and effective classification. Finally, we trained an LSTM network with the extracted features. The images used in our experiments contained malware from nine different families. To evaluate the performance of our proposed approach, we conducted experiments on the MaliMG dataset, which includes a diverse range of malware samples. The obtained results show the effectiveness of the proposed method. The training accuracy reached a significant value of 99.80%, which shows the ability of our model to accurately learn patterns and features of malware images. Moreover, the evaluation of the test dataset yielded a remarkable accuracy of 99.49%, which highlights the robustness and generalizability of our approach.
Copyright (c) 2024 Balsam Ridha Habeeb Alsaedi https://creativecommons.org/licenses/by/4.0
Optimizing Poisson-Lindley Parameter Estimation: LQM and Reliability Analysis Applied to Guinea Pig Survival Data
The main objective of this work is to estimate the scale parameter of the Poisson Lindley distribution by means of multiple approaches, such as Poisson Linear Quantile-Moment and Maximum Likelihood. Based on mean square error criteria(MSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC), Linear Quantile-Moment is the most efficient estimator among these techniques. The study focuses on reliability analysis and investigates the probability functions of the distribution to create a theoretical framework for parameter estimation by Using R programming language for in-depth analysis. Through simulation and real data analysis, several estimation techniques are compared and contrasted, demonstrating the superiority of the Linear Quantile Moment approach in terms of accuracy and model fit. The Poisson Lindley Distribution parameter estimation is improved in this work, which has implications for environmental research, finance, and epidemiology. Moreover, variance estimates for the known parameters and the related Kolmogorov–Smirnov (K–S) statistics, along with their corresponding p-values for the Poisson-Lindley Distribution (PLD), are analyzed using actual data on guinea pig survival times under various tubercle bacilli dosages. An observation indicating a strong fit with the optimal estimator with (LQM=4.190217) and the lowest MSE (78.71956) is made in light of the small K–S distance and the significant p-value for the test.
Poisson Lindley, Reliability, Linear Quantile-Moment
Copyright (c) 2024 Sameera Othman https://creativecommons.org/licenses/by/4.0
Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments
The Internet of Things (IoT) is no longer limited to single personalities, but rather, it is a perceptions that has widely increased and spread in some applications or fields. The mechanism for communicating between IoT devices similarity works as traditional communication between hosts. However, the growing use of IoT has been gaining the interest of a growing number of attackers. Hence, a number of researchers are attempting to build an intrusion detection system utilizing machine learning and deep learning algorithms. In this work, a novel attack detection model is proposed by superimposing Whale Optimization Algorithm and Bidirectional Long Short-Term Memory (WB-LSTM) together. There are numerous deep learning competencies, but LSTM is one of the ones used to interpret big data or time series data. But, it is not easy to find what is the best weights for LSTMs in order to directly achieve performance. The LSTM results were 99.1%. Hence, in this work, we introduce the WOA-LSTM hybrid model, that utilizes WOA for finding the optimal weights for a network based on LSTM, and is used to detect the IoT attacks. The 99.98% was obtained from the WOA-LSTM hybrid model.
CICIoT2023 Dataset, Feature scaling, IoT Security, Deep learning, LSTM Models, Whale optimization algorithm (WOA), Detect IoT Attacks.
Copyright (c) 2024 Hanan Abbas Mohammad https://creativecommons.org/licenses/by/4.0
Secure Image Reconstruction using Deep Learning-based Autoencoder with Integrated Encryption Layers
This study presents an autoencoder model designed for secure image reconstruction through the integration of encryption and decryption layers within its framework. The major goal is to achieve more effective image reconstruction while safeguarding data integrity. A convolutional neural network (CNN) is first utilized as the primary architecture, attaining a reconstruction accuracy of 90.63% with 2.3737x losses. This brought an opportunity for further improvement, and thus we propose the improved model with the integration of CNN and bidirectional gated recurrent unit (BiGRU) as hybrid model. The integration of CNN-BiGRU leverages the feature extraction advantage of CNN and the temporal processing ability of BiGRU to a great improvement of reconstruction accuracy, reaching 95.57% and validation accuracy stabilizing around 0.85 at the end of training. The model exhibits great accuracy without significant overfitting, thus acquiring robust characteristics crucial for precise image reconstruction. In this work, the hybrid model outperforms the conventional CNN-only architectures for secure image reconstruction and can thus be considered a potential approach when high fidelity with security is required in processing image data.
Bidirectional gated recurrent neural network (BiGRU)
Copyright (c) 2024 Wurood Abd Ali https://creativecommons.org/licenses/by/4.0
Computational intelligence in the identification of Covid-19 patients by using KNN-SVM Classifier
Initiatives to mitigate the persistent coronavirus disease 2019 (COVID-19) crisis shown that quick, sensitive, and extensive screening is essential for managing the present epidemic and future pandemics. This virus seeks to infect the lungs by generating white, patchy opacities inside them. This research presents an advanced methodology employing deep learning techniques for the analysis of medical pictures pertaining to respiratory disorders. This experiment included two data sets, the initial one including normal lungs sourced from the Kaggle data pool. We acquired the anomalous lungs from https://github.com/muhammedtalo/COVID-19. We applied Principal Component Analysis (PCA) and Histogram of Gradients (HOG) as extract features. while we conducted a classification process using K nearest neighbors (KNN) and Support Vector Machine (SVM) algorithms . Results showed that the classification accuracy with SVM for Covid-19 identification is 88.54% while with KNN is 82.31%
Covid-19, Chest X-rays, AI, Neural classifiers
Copyright (c) 2024 shaymaa adnan https://creativecommons.org/licenses/by/4.0
Cloud-Based Service for Fingerprint Image Matching
Fingerprint matching is one of the most important services that related to the issue of identifying individuals, as it can be used to verify the validity of contracts, documents or in other security fields such as criminal investigations. Fingerprints are also one of the most common and accurate forms, as they are easy to obtain, it also has a unique features and cannot be identical to any individuals. The proposed service exploits the advantages provided by cloud computing, such as ease of use, availability of the service anytime and anywhere, and no need to own expensive devices with high specifications, in addition to being low cost. In this service, all fingerprint image preprocessing operations and images enhancement, in addition to the matching, verification and decision-making process are done with the help of tools which provided by cloud computing environment. While the client's role in this service is limited to feeding the system with the fingerprint to be matched. The experimental results proved that this service has achieved high accuracy in matching fingerprint images, in addition to greatly facilitating the matching process for the user, and it can be used anytime and anywhere thanks to the cloud environment facilities adopted by this service.
Copyright (c) 2024 Ethar Abdul Wahhab Hachim https://creativecommons.org/licenses/by/4.0
Email Spam Detection Using a Hybrid Approach of Feedforward Neural Network and Penguin Optimization Algorithm
The electronic communication of today is beset by unwanted junk messages. These are not just a nuisance; they waste our time and energy and clog up our inboxes, which are supposed to be, in many ways, our digital front doors. But how do we keep them from coming? Well, as today's article makes clear, the situation is hardly straightforward. There are various methods that can be used, both traditional and modern, and each has its own set of pros and cons. One method, however, stands out in today's article as one that we think might work well in practice, the good old-fashioned neural network. The method's novelty derives from its use of neural networks, a type of machine learning, in conjunction with the POA. The POA is a powerful optimization algorithm that can perform very well when fine-tuning the weights and biases of a neural network. We therefore used the POA to optimize these two crucial components of our model and achieved a surprising level of accuracy—98.7%. This figure surpasses the next nearest competitor by 2% and demonstrates the efficacy of our method in spam detection while also highlighting the POA's potential in this field. The overall proposed method pushes the spam-detection field forward, but it also demonstrates something even broader: the power of hybrid approaches in machine learning. Neural networks are great—but you have to train them properly, and the use of a particle swarm for that purpose is a neat idea. But in the end, a hybrid spam filter is still a spam filter, and this one does what it's supposed to do better than many rivaling methods.
Detection of unsolicited messages using machine learning
Neural Networks and the Penguin Optimization Algorithm (POA)
Copyright (c) 2024 Layth Al-busultan https://creativecommons.org/licenses/by/4.0
Evaluating End-to-End Delay in Road-Based Routing Protocols for VANETs with Snake Optimization
Vehicular Ad hoc Networks (VANETs) require efficient routing protocols due to their dynamic network topologies and high mobility. This paper proposes a Road-Based Vehicular Traffic (RBVT), which combines reactive (RBVT-R) and proactive (RBVT-P) routing protocols to improved the performance. We evaluate the impact of RBVT on Average End-to-End Delay (AEED) by comparing it with existing protocols such as AODV, DSR, GPSR, OLSR, DSDV, and WRP. Furthermore, we integrate Snake Optimization Algorithm (SOA) with RBVT to optimize routing and reduce AEED. Simulations demonstrate that SOA-RBVT significantly reduces AEED compared to baseline protocols, especially in dense VANET scenarios.
Copyright (c) 2024 Hanadi Hassan karim Al-Maliki, Hamid AL-Asadi https://creativecommons.org/licenses/by/4.0
Texture Analysis and Classification using Local Binary Patterns and Statistical Features
Texture analysis and classification are crucial in many applications such as medical applications, satellite imagery analysis, and so on. This paper presents a new technique that incorporates the LBP with the HOG to improve the texture analysis and classification. LBP is famous for finding out the local texture descriptors by comparing the pixel intensities it deals with and can challenge light variations and image transformations. On the other hand, there is HOG which emphasizes edge and gradient information to detect the orientation and shape of a texture in a given image. While LBP describes the local micro-patterns HOG gives the perspective of the global gradient orientation. The combined use of local and global texture information is helpful in the presented problem when prioritizing the recognition of complex texture patterns that cannot be deciphered by a single algorithm. To demonstrate the efficiency of the proposed hybrid method, several experiments are performed on several texture databases. According to the findings, the proposed LBP-HOG strategy proves more beneficial than basic LBP and HOG techniques concerning the classification rate and computing time. The combination method shows improved performance in terms of agreement with the reference of fine and coarse level of texture descriptions along with providing large-scale description of the texture which results in the improved discriminatory power of the texture classes.
Image Processing, Texture Analysis, Local Binary Patterns, Histogram of Oriented Gradients, Feature Extraction.
Copyright (c) 2024 Hasan Maher https://creativecommons.org/licenses/by/4.0