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 Assoc. Prof.Mohd Arfian Ismail, Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Malaysia Email: arfian@ump.edu.my Assist. Prof. Dr.Mohammad Aljanabi, Department of Computer, College of Education, Al-Iraqia University, Iraq Email:mohammad.khaleel@aliraqia.edu.iq Assist.Prof.Dr.Hijaz Ahmad, Near East University, Nicosia, Cyprus Email: hijaz.ahmad@neu.edu.tr 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.
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
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
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
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.
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
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
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
An Adaptive Activity Cycling Technique for Energy Management in Wireless Sensor Networks (WSNs)
Wireless Sensor Networks (WSNs) consist of large number of small nodes that sense the surround environment and transmit the data to the central collection points. Since these nodes rely on batteries as power source, the effective power management considered as a vital to ensuring that the network continues to operate over long periods. In this research, we offer innovative adaptive activity cycling technique aims to enhance power management in Wireless Sensor Networks. The suggested technique rely on duty cycling concept, where the node is turned on for a certain period and turned off for another period to save energy. Our approach is characterized by dynamically adjusting the on and off periods based on changing network conditions. The adaptive cycling setup according network traffic, so that, in the low traffic cases, the off periods is increased to save power, while these periods decreases in the high traffic cases to ensure transmit data efficiently. In addition, the technique, regard remaining node battery level, which ensure node continue for longer possible period by modifying adapting cycles based on remaining battery level. Simulation tools NS3 and MATLAB are used to evaluate the performance of innovative technique. The results showed, the technique achieve significant enhancements in term energy consumption efficiency comparing with traditional techniques. Furthermore, the technology was able to maintain the quality of service in terms of response time reduction and increase packet delivery ratio. In conclusion, this research demonstrates that using adaptive activity cycling technique can contribute significantly in terms of prolong network lifetime and enhance energy efficiency in Wireless Sensor Networks without sacrificing the quality of service. This technology is promising and can be used in a variety of applications like procession agriculture, environmental monitoring, health care, which support the effectiveness and sustainability of wireless sensor networks in different environments.
Copyright (c) 2024 Hayder Khudhair https://creativecommons.org/licenses/by/4.0
Predicting the Optimal Treatment for Diseases Using Whale Optimization Algorithm
Recently, there has been a significant research focus on addressing disease prevalence, especially in dealing with the complexities of big data associated with disease data. The challenge is achieving high accuracy due to missing value problems in big data. This study aims to use AI techniques to develop a system that predicates the optimal solutions for disease, regardless of the type of disease, i.e. the system can be applied to any type of disease. The approach involves handling missing values and normalizing disease datasets. The Whale Optimization Algorithm (WOA) will be used to improve predictions for effective disease treatments. We obtained good results for predicting the appropriate treatment for the disease in the proposed research, compared to the results obtained when applying the PSO algorithm before development in state of earlier, where the results obtained in the proposed research had higher accuracy than the results in in state of earlier at high iterations starting from 200 iterations and also had a lower error rate.:
Copyright (c) 2024 Sudad Abed, Zuhal Adel Madlool https://creativecommons.org/licenses/by/4.0
Leveraging Social Engineering Techniques for Ethical Purposes: An Approach to Develop Fake Android App for Collecting Valuable Data Discreetly
Social engineering techniques are often viewed negatively due to their association with deceptive practices. However, these techniques can also be utilized ethically, as many cybersecurity professionals do, particularly when evaluating vulnerabilities and testing security defenses. This paper presents EDC (Ethical Data Collector), an Android application that utilizes social engineering techniques to discreetly collect valuable data from an Android device for ethical purposes. EDC employs deception through a simulated UI (fake) to engage the target for a period, while secretly collecting data such as device information, active phone number, and images in the background, then sending them to a designated server via the internet. The researcher argues that EDC could help identify inexperienced cybercriminals or extortionists without complex efforts or significant cost, provided that its capabilities are judiciously utilized and subject to proper controls and oversight. EDC's development methodology emphasizes understanding the target's personality, predilections, and preferences to tailor the app experience as required for attracting the target to install and run the application. The paper describes the core functions and workflows for collecting and sending data. Additionally, permissions handling has been addressed as being critical for enabling EDC to collect the required information. Testing on Android emulators demonstrated that the EDC's APK file size is 4 MB, and data collection and transmission processes functioned as intended across various Android versions. The minimum SDK version required to run EDC is level 16. The total estimated time to complete the fake UI process (cumulative user engagement time) is 57-60 seconds, where each activity takes 8 seconds based on the adopted development approach.
Copyright (c) 2024 Hussein Abdulkhaleq Saleh alsharya https://creativecommons.org/licenses/by/4.0
New Heuristics Method for Malicious URLs Detection Using Machine Learning
Malicious URLs are a very prominent, dangerous form of cyber threats in view of the fact that they can enable many evils like phishing attacks, malware distribution, and several other kinds of cyber fraud. The techniques of detection conventionally applied are based on blacklisting and heuristic analyses, which are gradually becoming inefficient against sophisticated, rapidly evolving threats. In this paper, the authors present various machine learning techniques applied in malicious URL detection. In the present paper, we will look at three machine learning models: Logistic Regression, Random Forest, and Support Vector Machines. We used a methodology that involved collecting data and feature extraction, training a model, then evaluating its performance with different metrics such as accuracy, precision, recall, and F1-score. We implemented and optimized three models—Logistic Regression, Random Forest, and Support Vector Machines (SVM)—based on the literature available that indicates the effectiveness of these models. Logistic Regression shows promising results to detect the malicious URLs, according to Vanitha and Vinodhini. Random Forest models are found to be very robust and accurate according to Cui et al. and Vanhoenshoven et al., SVM models are evidenced to have very high accuracy according to Manjeri et al., Further works on deep learning models emphasized their potentials. In our study, the optimized Random Forest model in our case showed the best performance, and its training accuracy was 99%, while validation accuracy was 90.5%, also logistic Regression and SVM achieved training accuracy was 89.31%, while validation accuracy was 90.5%. All the optimization processes, model performances, and integration into the real-time cybersecurity infrastructures, along with the strengths and limitations, are discussed in this paper. The paper will, therefore, discuss the benefits and challenges for each model in this aspect—emphasizing continuous updating of the models and integrating them into real-time cybersecurity infrastructures.
Malicious URLs, Machine Learning, Cybersecurity, Logistic Regression, Random Forest, Support Vector Machines
Copyright (c) 2024 Maher Kassem Hasan https://creativecommons.org/licenses/by/4.0
Reconfigurable Intelligent Surfaces Between the Reality and Imagination
One interesting idea that could improve wireless communication systems is reconfigurable intelligent surfaces or RIS. To control the spread of wireless signals, RIS uses a plethora of tiny, inexpensive, and customizable reflecting devices. Signal strength, energy consumption, spectrum efficiency, and adaptability to changes in the wireless environment can all be improved by altering the phase shift of the reflecting elements. This is how RIS works. 5G and beyond, the IoT, and smart cities are just a few of the many potential uses for RIS. More study is required before this technology can reach its full potential since it is still in its infancy. Still, RIS is going to be big in the next generation of wireless networks. A great deal of excitement has enveloped the whole 6G development industry. There has been no shortage of grandiose and frequently unfounded assertions that, in reality, the creation of a full-duplex 5G technology that can introduce new services is just around the corner, even though self-interference cancellation schemes for full-duplex communications and the difficulties of building full-duplex base stations have long been known.
Copyright (c) 2024 Asst. Prof. Haider ALRikabi, Adheed Sallomi, Hasan Khazaal, Ahmed Magdy, Iryna Svyd; IVAN OBOD https://creativecommons.org/licenses/by/4.0
Improving Network Security: An Intelligent IDS with RNN-LSTM and Grey Wolf Optimization
While this dependence on interconnected computer networks and the web requires robust cybersecurity. Cyber threats have been met with solutions like Intrusion Detection Systems (IDS). IDS are commonly rule-based, and very often use either signature-based or heuristic approaches to detect intrusions. Therefore, for such we recommend an IDS that merges the Grey Wolf Optimization (GWO) algorithm and Recurrent Neural Networks with Long Short-Term Memory (RNN-LSTM). RNN-LSTM to Handle Dynamic Network data, but not provided enough complain details in model training. Based on the behavior of grey wolf, an optimization technique GWO is implemented for intrusion detection to enhance accuracy and minimize false alarm in RNN-LSTM. Preprocess and segment network data with creating RNN-LSTM model for considering the dependence of our dataset Our approach improves the IDS performance by optimizing hyperparameters such as hidden layers, units, learning rates using GWO. The architecture of this RNN-LSTM with GWO IDS provides capable and responsive intrusion detection, training on previous data to be able to detect new threats. Made for network security by combining deep learning and optimization, tests reached 99.5% accurate. The research advances IDSs, addressing the limitations of traditional systems, and underscores the potential of AI and optimization in complex network security. This study demonstrates the promise of RNN-LSTM and GWO for creating robust, adaptive intrusion detection systems in intricate network environments.
Copyright (c) 2024 murtadha ali https://creativecommons.org/licenses/by/4.0