Mesopotamian Journal of Computer Science
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Using Wearable Technology for Context-Aware of Pilgrimage Management System During/Post-Pandemic of Covid-19
Hajj is an annual event placed in Saudi Arabia. It is one of the largest religious gatherings, such that it gathers millions of pilgrims from all around the world. The pandemic of Covid-19 is not over yet, and could likely be part of our life for a long time. Also, it is negatively affecting such mass gatherings. The aim of this research is to facilitate the overall hajj event while maintaining peoples’ health, safety, and security. Therefore, we use a technology that helps to reduce direct contact between pilgrims themselves as well as with other entities, such that human interaction would be at a minimum level. We propose a context-aware framework of Pilgrimage Management System (PMS) using wearable technology, such that, a four-user context-aware PMS framework is integrated wearable devices and mobile applications. The framework considers four groups of users. These include: pilgrims, convoy managers, Saudi authorities, and medical staff. Our context-aware framework fulfils users’ needs where feasible and applicable. It makes hajj rituals safe and efficient so that they become more flexible. As a result, the overall hajj event will be improved. Our proposed framework is not only designed to be used during the pandemic, but also after the pandemic. Moreover, this framework can be applied to any transmission disease that might happen in the future. Hence, the overall system is developed based context-aware model and it can be expanded to accommodate new challenges.
Copyright (c) 2025 Fatina Shukur, Ahmed Al- Fatlawi, Safaa Jasim Mosa, Nasir Ibrahim https://creativecommons.org/licenses/by/4.0
A Review of Image Steganography Based on Metaheuristic Optimization Algorithms
Due to the widespread popularity of digital images on the Internet, image-based steganography has become a widely adopted technique for embedding secret information into everyday visual content. In parallel, steganalysis plays a vital role in digital forensics and information security by seeking to uncover hidden content within these images. Although steganographic techniques—particularly those employing adaptive embedding strategies—have made significant progress, many steganalysis approaches still struggle to generalize effectively across different image types and embedding methods. This contrast highlights the need for more intelligent, flexible, and robust analysis frameworks. This review examines steganographic techniques for digital images and the application of metaheuristic algorithms in steganalysis. These algorithms are employed in tasks such as feature selection and parameter optimization. They can also function as classifiers themselves, thereby enhancing the detection of hidden information. We conducted a structured review of over 100 research articles, categorizing steganographic approaches based on their embedding domain (spatial and frequency) and steganalysis techniques according to the metaheuristic algorithms they utilize. Metaheuristic algorithms have demonstrated significant promise in improving the effectiveness of steganalysis by optimizing both feature selection and classification processes. However, their performance is often influenced by factors such as parameter tuning, initialization strategies, and the quality of extracted features. Recent studies also show a growing trend toward hybrid and ensemble-based techniques, which further enhance detection accuracy and reliability.
Copyright (c) 2025 Fatima Abdulhussain Khalil, Ammar Ali Neamah, Hasanen Alyasiri https://creativecommons.org/licenses/by/4.0
Agentic AI-Enhanced Virtual Reality for Adaptive Immersive Learning Environments
Immersive learning using Virtual Reality (VR) has gained prominence for delivering experiential, engaging education. However, most VR learning environments lack real-time adaptability, personalization, and cognitive responsiveness. This study presents an Agentic AI-enabled VR framework that autonomously adjusts pedagogical content, interaction style, and challenge level based on learner behavior, emotions, and performance feedback. The proposed system integrates a reinforcement learning-based agent with a virtual reality module to form an intelligent tutor capable of independent decision-making. A neuro-symbolic model processes multi-modal feedback (gesture, speech, gaze, performance) to determine context-aware pedagogical strategies. The system employs a self-evolving curriculum logic that adapts in real time. Experiments are conducted using Unity3D integrated with Python-based RL agents and simulated student models. Results demonstrate a 35.7% improvement in learner retention and a 42.1% reduction in cognitive overload compared to traditional static VR systems. The agent successfully personalized 92.3% of scenarios without human intervention. Emotional adaptivity and dynamic pacing showed increased engagement and reduced frustration metrics among diverse learners. Agentic VR represents a paradigm shift in intelligent education systems, enabling autonomous, emotionally-aware, and responsive learning environments. The proposed framework outperforms conventional VR platforms by offering real-time adaptive learning without pre-scripted logic. Integrating reinforcement learning, neuro-symbolic reasoning, and affective feedback into a single VR space results in a novel contribution to adaptive educational technology. The reported metrics were obtained from an administrator-controlled user study of 10 participants (aged 18 - 25 years), all of whom participated in three VR-based learning experiences as described in a controlled evaluative protocol. This study provides a foundation for future work on autonomous agents in educational met verses, special education, and lifelong learning systems.
Copyright (c) 2025 Indra Kishor, Udit Mamodiya , Mohammed Almaayah , Amer Alqutaish , Rami Shehab , Theyazn H. H. Aldhyani https://creativecommons.org/licenses/by/4.0
Lightweight Deep Reinforcement Learning Model for Energy-Efficient Resource Allocation in Edge Computing
A lightweight digital twin model for a single 6G cell operating in the D-band (140 GHz) with a 1 GHz bandwidth is presented in this work with the goal of assessing the cell's capacity, coverage, and terminal time in order to support extended reality (XR) applications. With a tangent dispersion of 3 dB and a path exponent of n = 2.2, the model is based on the free-space loss equation as per ITU-R Recommendation P.525. The instantaneous capacity is determined using the Shannon-Hartley theorem. Three XR sessions are created every minute using a Poisson method, and their durations are determined by an exponential distribution (mean of 120 seconds). In accordance with 3GPP and Ericsson guidelines for normal XR loads, the bit needs per user are randomly selected to fall between 40 and 120 Mb/s. The average coverage was around 92%, the average cell capacity was approximately 5.1 Gb/s, and the edge capacity (lowest quintile) was approximately 230 Mb/s, according to fifty statistical forecasts. Additionally, the 95th percentile round-trip latency was 3.9 ms, which is significantly less than the permitted maximum (10–20 ms) for immersive XR research. These findings suggest that modest XR loads may be supported by a 250-meter cell with a high-gain antenna layout without the need to immediately lower the radius or raise the transmitted power. However, the model remains theoretical and simplified, excluding geometric blockage and cell overlap in complex metropolitan environments.
Copyright (c) 2025 Ghassan A. Abed, Mohanad A. Al-askari https://creativecommons.org/licenses/by/4.0
DGEN: A Dynamic Generative Encryption Network for Adaptive and Secure Image Processing
Cyber-attacks keep growing. Because of that, we need stronger ways to protect pictures. This paper talks about DGEN, a Dynamic Generative Encryption Network. It mixes Generative Adversarial Networks with a key system that can change with context. The method may potentially mean it can adjust itself when new threats appear, instead of a fixed lock like AES. It tries to block brute‑force, statistical tricks, or quantum attacks. The design adds randomness, uses learning, and makes keys that depend on each image. That should give very good security, some flexibility, and keep compute cost low. Tests still ran on several public image sets. Results show DGEN beats AES, chaos tricks, and other GAN ideas. Entropy reached 7.99 bits per pixel, correlation dropped 0.002, and the avalanche effect was 95.4 percent. Encrypting a surveillance frame took 7.5 ms, while the picture quality stayed high, with PSNR 39.7 dB and SSIM 99.2. These numbers suggest the tool can still work in real time and scale up significantly. The study also looks at how DGEN could fit with quantum computers and federated learning, hinting it might be a very big step forward for safe image handling.
Copyright (c) 2025 Mohammed Rajih Jassim, Qusay M. Salih , Ghada Al-Kateb https://creativecommons.org/licenses/by/4.0
Enhanced TEA Algorithm Performance using Affine Transformation and Chaotic Arnold Map
In digital images, protecting sensitive visual information against unauthorized access is considered a critical issue; robust encryption methods are the best solution to preserve such information. This paper introduces a model designed to enhance the performance of the Tiny Encryption Algorithm (TEA) in encrypting images. Two approaches have been suggested for the image cipher process as a preprocessing step before applying the Tiny Encryption Algorithm (TEA). The step mentioned earlier aims to de-correlate and weaken adjacent pixel values as a preparation process before the encryption process. The first approach suggests an Affine transformation for image encryption at two layers, utilizing two different key sets for each layer. The dual encryption process achieves high diffusion and confusion properties for the cipher process. The second approach proposed a chaotic Arnold map before the Tiny Encryption Algorithm (TEA) process. Various statistical measures are used, such as Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Image Quality Index (IQI). For example, the lower PSNR, SSIM, and IQI values indicate better results for test image Lina of the second approach. The obtained results for the previous measures of the second approach are 8.5449, 0.0008, and -0.0061, compared to the first approach, 8.5529, 0.0054, -0.0015, respectively. Moreover, key space and time analysis are used to assess the encryption process. The outcomes show a high-key space 32,768*2128 ) and a slight encryption time of 130 milliseconds for the first approach and 1862 milliseconds for the second approach.
Copyright (c) 2025 Nada Hussein M. Ali, Mays M. Hoobi, Sura Abed Sarab Hussien https://creativecommons.org/licenses/by/4.0
Enhanced IOT Cyber-Attack Detection Using Grey Wolf Optimized Feature Selection and Adaptive SMOTE
The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats. This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrate that GWO reduces features from 32 to 21, thereby enhancing computational efficiency and interpretability without compromising accuracy, while customized SMOTE addresses class imbalance and enhances minority-class detection. The optimized RF and XGBoost models were assessed using accuracy, precision, recall, and F1-score metrics, and achieved 100% accuracy with strong generalization. These results highlight the effectiveness of optimization-based feature selection and data balancing in improving IoT security that is extensible to deep learning and ensemble-based approaches.
Copyright (c) 2025 Sura Abed Sarab Hussien, Mustafa S. Ibrahim Alsumaidaie, Nada Hussein M. Ali https://creativecommons.org/licenses/by/4.0
End-to-End License Plate Detection and Recognition in Iraq Using a Detection Transformer and OCR
Automatic license plate recognition (ALPR) is essential for intelligent transportation systems, traffic monitoring, and law enforcement. Although deep learning has significantly progressed ALPR, the adaptation of these techniques to the specific characteristics of Iraqi plates presents challenges due to the variety of fonts, differing plate dimensions, and intricate real-world driving conditions. This paper presents an end-to-end Automatic License Plate Recognition (ALPR) framework specifically designed for Iraqi license plates. This system integrates a Detection Transformer (DETR) for the identification of plates alongside a Convolutional Recurrent Neural Network (CRNN)-based Optical Character Recognition (OCR) module for the purpose of character recognition. The system is trained and evaluated on a newly developed dataset of 1,000 annotated images representing diverse driving scenarios in Iraq. It achieves a mean Average Precision (mAP@[.5:.95]) of 0.91 for detection and a full-plate OCR accuracy of 93%. The accuracy of DETR surpasses that of YOLOv5, Faster R-CNN, and SSD, as demonstrated by comparative experiments. Conversely, a lightweight transformer (DeiT-Tiny-Det) approaches DETR's performance at faster inference velocities, illustrating a practical trade-off between speed and precision. Ablation studies confirm the importance of robust detection for end-to-end accuracy, while error analysis shows that low-light and character-level confusions remain the main challenges. The results indicate that transformer-based detectors, in conjunction with specialised OCR models, yield dependable region-specific ALPR appropriate for real-world application.
Automatic License Plate Recognition (ALPR)
Copyright (c) 2025 Younis Al-Arbo, Hanaa F Mahmood, Asmaa Alqassab https://creativecommons.org/licenses/by/4.0
Enhancing Routing Efficiency in VANETs by Leveraging 5G to Mitigate Congestion
Transportation networks impact millions of people daily. Their efficiency immediately affects travel time, safety, and environmental sustainability. Unfortunately, various issues hinder the expected performance and efficiency of these networks. Traffic congestion is an up-to-date issue in the urban environment. Fuel consumption is high because travel time has increased, which has a passive environmental impact. Extensive research has been conducted to progress the intelligent transportation systems installed on communication networks and information to treat this congestion. However, there is a significant amount of affront residue in combining real-time data, estimation analytics, and 5G abilities effectively. This paper offers a novel routing algorithm integrating vehicular ad hoc networks with 5G technology to increase routing efficiency and minimize congestion. This routing is named 5G adaptive traffic management (5G-ATM). It collects real-time data from connected vehicles and roadside units to estimate traffic status and congestion. Out of simulations in an urban environment, the proposed 5G-ATM routing significantly progresses over previous routing protocols, such as an ant colony-inspired energy-efficient for optimized link state (AC-OLSR) routing and directional-cache agent-based location-aided (D-CALAR) routing. During rush hours, 5G-ATM shows the lowest traffic congestion events. Moreover, it minimizes average travel times by almost 8% compared to D-CALAR and 21% compared to AC-OLSR. These outcomes suggest that combining vehicular ad hoc networks with 5G technology helps manage traffic more efficiently, providing an efficient pathway and practical transportation systems.
Copyright (c) 2025 Yusor Rafid Bahar Al-Mayouf, Omar Adil Mahdi, Heba Hussain Hadi, Suleman Khan, Mazin Abed Mohammed https://creativecommons.org/licenses/by/4.0
ROI – Enhancing Detection of Citrus Disease Based on YOLOv10
One of the most important fruit crops in the world is citrus. However, some citrus diseases spread rapidly, which is why early detection at an accurate stage is important for timely intervention. YOLO-based object detection models, such as the latest YOLOv10, where small lesions are difficult to identify among noisy backgrounds, have recently been developed, yet their accuracy tends to degrade. Therefore, we proposed a citrus disease detection model by integrating the region of interest (ROI) for object segmentation with the YOLOv10 model, thus addressing the issues of low detection accuracy and slow inference time. The proposed model was trained on an annotated dataset of three major citrus pathologies: anthracnose, citrus canker, and leaf miner infestation. Results showed significant improvements in the performance of our proposed model, which incorporates the ROI mechanism into YOLOv10. Specifically, the ROI-YOLOv10 model achieved high mAP scores, reaching 0.99 and 0.985 during training and validation, respectively, and maintaining high generalization capabilities with a test mAP of 0.984. Precision and recall metrics similarly underline the enhanced accuracy and robustness of ROI-YOLOv10. Compared with previous YOLO-based studies, our model exhibits enhanced accuracy and faster inference times. The incorporation of ROI techniques into the YOLOv10 framework is a highly effective approach for improving agricultural productivity by facilitating early and precise detection of plant disease.
Copyright (c) 2025 Raya N. Ismail, Armaneesa Naaman Hasoon, Israa Rafaa Abdulqader, Salwa Khalid Abdulateef https://creativecommons.org/licenses/by/4.0
Diabetes at a Glance: Assessing AI Strategies for Early Diabetes Detection and Intervention via a Mobile App
Diabetes is a widespread disease worldwide that does not differentiate between children and adults. It also affects the elderly and pregnant women. However, early detection of the disease facilitates its control to avoid the effects resulting from delayed diagnosis. With the emergence of artificial intelligence represented by machine learning techniques and its use in most sectors, accordingly, the adoption of machine learning techniques to help in disease prediction has become a necessity. This study proposes a machine learning algorithm-based approach for diabetes prediction. This study uses three datasets, two of which are private and the other includes the Pima Indians dataset. Six machine-learning models have been used and evaluated in this study including Gaussian Naive Bayes model. Bernoulli Naive Bayes Model, Bagging Regressor Model, Neural Network Architecture, Multilayer perceptron, and SVR. To address the imbalance in classes of the private datasets, the SMOTE technique has been utilized. To analyze the state of the arts, a systematic literature review was conducted. The results showed that the Bagging Regressor algorithm is the best among the used algorithms in terms of the accuracy of the derived results. It achieved an accuracy of 99.79 with SMOTE included and 97.95 without SMOTE. A smart mobile application was developed based on the proposed approach that facilitates clinicians to predict diabetes. This study strengthens the theoretical foundations of machine learning in healthcare by presenting a robust and empirically validated approach for early detection and prediction of diabetes. The findings not only advance academic knowledge but also provide practical guidance for developing AI-based diagnostic tools in clinical settings.
Copyright (c) 2025 Ayad Hameed Mousa , Ibrahim Oday Alrubaye , Mayameen S. Kadhim , Ahmed Dheyaa Radhi, Mudatheer M. Al-Slivani , Rusul Mansoor Al-Amri , Liaw Geok Pheng https://creativecommons.org/licenses/by/4.0
Self-Attention Enhanced Dual BiGRU for Arabic Fake News Detection
The rapid proliferation of social media platforms has greatly amplified the dissemination of fake news, representing significant obstacles to public trust and evidence-based decision-making, particularly for the Arabic-speaking population. Meeting the challenge of Arabic fake news detection is a problem compounded by the complex morphological nature of the language, as well as limited resources. This study presents a hybrid deep learning framework that integrates two Bidirectional Gated Recurrent Units (BiGRUs) along with an attention mechanism for efficiently detecting misinformation in Arabic news. The method leverages FastText word embeddings for disambiguating the intricate semantics of the Arabic language. The model is meticulously crafted to account for the morphological variability and contextual sensitivities of Arabic, with the extensive Arabic fake news dataset (AFND) being used for training and testing. Experimental findings show that our model performs better with an accuracy of 91.92%, outperforming current state-of-the-art approaches. The findings highlight the effectiveness of integrating advanced neural architectures and tailored preprocessing for Arabic, paving the way for more robust and interpretable fake news detection systems in low-resource languages.
Bidirectional gated recurrent unit (BiGRU)
Copyright (c) 2025 Baqer M. Merzah, Jafar Razmara, Jaber Karimpour https://creativecommons.org/licenses/by/4.0
Synthesizing Deception: Countering Large Language Model-Generated Phishing Campaigns through Adaptive Semantic Anomaly Detection
The paper fills in a gap in the literature that demonstrates an insufficient number of sturdy detection schemes that can recognize the small semantic aberrations inherent in LLM-generated deceptive text. Our proposed co-design hybrid model is Semantic Anomaly Detection with Isolation Forest (SADI) model that combines the synergistic mixture of a fine-tuned transformer-based LLM for deep semantic feature extraction with Isolation Forest algorithm that detects anomalies efficiently. This study introduces SADI, an adaptive semantic-anomaly detector for large-language-model phishing emails. Using a corpus of 10 000 messages, SADI attains an F1 score of 0.981 (95 % CI 0.978–0.984) and processes a single message in 18 ms on consumer GPUs. An expanded evaluation against three public benchmarks and a live enterprise feed confirms robustness to prompt variation. Code, data splits, and a reproducible environment file accompany the paper. We also prepared the new, more challenging target dataset, that is phishing attacks synthesized by a variety of state-of-the-art LLMs, denoted LLM-Phish-Synth-2025, with this objective in mind. The results of our experiments on three publicly available data sets and our new corpus show that SADI received a higher F1- score of 0.981 compared to the baseline models, including separate fine-tuned LLMs, by a wide margin. The proposed SADI architecture is the first to combine semantic anomaly detection with adaptive, contamination-aware isolation in the context of LLM-generated phishing, addressing both scalability and evolving attack sophistication. The theoretical impact is the new architecture of architectural fusion that branched semantic anomaly detection and the practical advantage of a more robust defense solution against an ever-evolving threat in cyber space in addition to the provision of a new benchmark dataset to the research community. This approach is an efficient and scalable solution to combat the wave of the phishing campaigns generated by AI.
Copyright (c) 2025 Bekim Fetaji , Debabrata Samanta https://creativecommons.org/licenses/by/4.0
Towards Achieving the UN Sustainable Development Goals: The Role of AI in Municipality Services
In 2015, the United Nations adopted the Sustainable Development Goals (SDGs) to end poverty, protect the planet, and ensure global peace and prosperity by 2030. However, progress has been hindered by challenges like the COVID-19 pandemic, climate change, funding shortages, political instability, and data limitations. Municipal services, crucial to achieving the SDGs, provide essential functions like waste management, healthcare, and public safety. Artificial Intelligence (AI) offers innovative solutions to enhance these services, improving urban sustainability and fostering public-private collaboration. This research examines AI's role in municipal services, analyzing its applications, benefits, challenges, and future potential through case studies and expert insights.
Copyright (c) 2025 Thabit Sultan Mohammed , Karim Mohammed Aljebory, Ahmed Thabit Sultan https://creativecommons.org/licenses/by/4.0
HAZE-IMAGE-DATASET: A Large-Scale Benchmark for Image Dehazing in Variable Fog and Low-Light Conditions
To enhance image dehazing and visual recognition in real-world conditions, we introduce HAZE-IMAGE-DATASET, a large-scale dataset comprising nearly 42,000 images. It is constructed from 1,532 clean images sourced globally and captured using a Samsung smartphone, covering diverse natural and urban scenes. The dataset includes synthetic and real haze variations. Synthetic haze was generated using MATLAB-based atmospheric scattering models with depth maps for 10 fog levels. Colored haze was created using alpha blending (α = 0.4) in six colors: red, green, blue, yellow, white, and black. Low-light conditions were simulated via uniform darkening at 10 levels. Also, 616 real haze images were captured using a steam device to replicate genuine haze characteristics. Benchmarking was performed using MobileNetV2 and GoogLeNet. On a hazy subset, MobileNetV2 improved PSNR from 13.34 dB to 21.77 dB and SSIM from 0.805 to 0.950, Also, GoogleNet achieved a PSNR of 22.38 dB and SSIM of 0.953.Classification experiments using a seven-class subset showed high accuracy: 98.93% with AlexNet and 98.13% with GoogLeNet.The dataset is organized into haze types, levels, and color folders. It serves as a strong foundation for training and evaluating image dehazing and recognition models under different visibility conditions. It is available on GitHub: https://github.com/mustafa1jamal/HAZE-IMAGE-DATASET.
Copyright (c) 2025 Mustafa J. Shahbaz (Author); Ali A. D. Al-Zuky https://creativecommons.org/licenses/by/4.0
Using Machine Learning to Enhance Interaction and Creativity Among Children By Using The Scratch And Mblock Programming Languages and Many Different Kids’ Machine Learning Platforms For Designing A.I Programs
Artificial intelligence (AI) and machine learning (ML) technologies have experienced substantial growth in the last decade, affecting billions of individuals across all facets of contemporary life. This trend of AI's expanding influence is expected to persist. The increasing significance of AI and ML in computer science and society supports the integration of AI and ML principles at an early stage.ML can be made more approachable and interesting for children by utilizing beginner-friendly kids’ programming languages like scratch. We design models for incorporating machine learning techniques using scratch and mblock programming languages to recognize images and text. These models are created using web-based cloud service tools such as "Machine Learning for Kids" and "teachable machine," as well as programming using Scratch 3 programming language and mblock programming language. The proposed methodology is introduced to 5th and 4th grade classrooms in Alhijragroup education in Iraq through provided practical exercises involving the creation of models that can recognize images and text. The survey shows that the best practices for incorporating machine learning into 5th grade classrooms are so that students can actively participate in their own learning and develop their own unique ideas for artificial intelligence (A.I) programs using different platforms. Alhijragroup education students are familiarized with the concept of machine learning through the summer course provided to them by IOTKIDS Company in Iraq. The survey shows that the F parameter is confidence which means every increase in the importance of artificial intelligence (the independent axis X) is linked to a similar increase in the quality of education (the dependent axis Y). The correlation between the two axes is very strong. These output modules are designed by using a special computer equipped with GPU memory Nividia Geforce RTX 2060 6G, IBM SPSS Statistics and Microsoft Excel 2010 programs.
Copyright (c) 2025 Amani Y. Noori https://creativecommons.org/licenses/by/4.0
Real-Time SDN–IoT Integrated Framework for Intelligent Emergency Vehicle Prioritization in Smart Cities
Urban traffic control has become increasingly complex with rising vehicle density, particularly in smart cities. Timely arrival of emergency vehicles is critical, yet existing systems relying on manual transmitters and sirens offer limited range and effectiveness. This paper proposes a real-time intelligent traffic management framework integrating Software-Defined Networking (SDN), Internet of Things (IoT) technologies, and the Edge of Things (EoT)—a paradigm combining edge computing with IoT to enable low-latency processing at the network edge. The framework connects the SUMO traffic simulator and Veins vehicular network framework via TraCI, with the RYU SDN controller dynamically adjusting traffic signals and vehicle routes for emergency vehicle prioritization. Simulation results show that the system reduces emergency vehicle travel delay by 38%, decreases intersection waiting time by 42%, and improves overall traffic throughput by 25% compared to conventional control. These results demonstrate the framework’s scalability, responsiveness, and potential for deployment in real-world smart city infrastructures.
Copyright (c) 2025 Sura F. Ismail https://creativecommons.org/licenses/by/4.0
WOA-COVID-19: Whale Optimization Algorithm for Selection of Multi-Examination Features based on COVID-19 Infections
Since its emergence in late 2019, COVID-19 (Coronavirus Disease 2019) has become one of the most critical global health threats, claiming millions of lives and placing many more at serious risk. The complexity of diagnosing COVID-19 lies in the wide range of clinical and examination features involved, prompting researchers to explore various advanced diagnostic methods. However, one of the main challenges is identifying the most relevant features that can streamline and improve diagnostic accuracy. In this study, we propose a feature selection approach based on the Whale Optimization Algorithm (WOA) to identify key examination indicators associated with COVID-19. We used a dataset of 78 patients that included 25 features, covering demographics, symptoms, vital signs, laboratory findings, and chronic health conditions. The WOA was applied as a single-objective optimization technique to select the most informative features. These selected features were then used with the K-Nearest Neighbors (KNN) algorithm to classify patients into three categories of severity: mild, moderate, and severe. To evaluate the effectiveness of WOA, we compared it against six other well-established metaheuristic algorithms: Particle Swarm Optimization (PSO), Multi-Verse Optimizer (MVO), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Firefly Algorithm (FFA), and the BAT algorithm. Results showed that WOA successfully reduced the feature set from 25 to just 6 key features while achieving a high classification accuracy of 92.5%. It also demonstrated strong robustness, as reflected in its low standard deviation compared to other methods. Overall, the proposed WOA-COVID-19 framework proved to be a highly effective and efficient solution for feature selection in the context of COVID-19 diagnosis.
Copyright (c) 2025 Karrar Hameed Abdulkareem, Mazin Abed Mohammed, Zaid Abdi Alkareem Alyasseri , Dawood Zahi Khutar , Osama Ahmad Alomari https://creativecommons.org/licenses/by/4.0
Automated Video Colorization Techniques for Enhanced Visual Realism and Computational Efficiency
Automatic video colorization remains a challenging computer vision task, particularly when ensuring semantic accuracy and temporal coherence across dynamic, multi-scene content. Existing methods often rely on a single fixed reference image, which fails to adapt to abrupt scene changes or variations in lighting and texture. This study presents a hybrid deep learning framework that dynamically selects multiple reference images per scene using adaptive thresholds derived from the Structural Similarity Index Measure (SSIM) and deep features extracted via a ResNet50 backbone with Generalized Mean Pooling (GeM). The framework integrates three specialized modules pre-processing, reference image processing, and attention-based colorization—operating in the Lab color space before conversion to RGB. Experimental evaluations on the YouTube-8M dataset demonstrate a PSNR of 37.89, SSIM of 0.998, and inference speed of 2.6 FPS with a compact 81 MB model (3.2M parameters). Compared to state-of-the-art methods, the proposed approach achieves superior color fidelity and temporal stability while maintaining efficiency, making it suitable for deployment in resource-constrained environments such as embedded vision and IoT systems.
Trilinear Upsampling Encoder And Decoder
Copyright (c) 2025 Zahoor M. Aydam , Nidhal K. El Abbadi https://creativecommons.org/licenses/by/4.0
ANILA: Adaptive Neuro-Inspired Learning Algorithm for Efficient Machine Learning, AI Optimization, and Healthcare Enhancement
The Adaptive Neuro-Inspired Learning Algorithm (ANILA) offers a breakthrough in the realm of machine learning by drawing inspiration from the biological processes of the human brain. Developed to address limitations in conventional models such as CNNs and RNNs, ANILA enhances real-time responsiveness, energy efficiency, and system adaptability. By emulating neurobiological behaviors particularly sparse coding and synaptic plasticity ANILA allows systems to process data dynamically, adjust to novel inputs without retraining, and scale effectively across environments like IoT and healthcare diagnostics. Performance evaluations highlight significant reductions in latency, increases in energy efficiency (up to 92%), and exceptional adaptability to changing data streams. Despite current constraints linked to neuromorphic hardware and the interpretability of its learning processes, ANILA sets a robust foundation for the development of responsive, scalable, and sustainable AI systems across diverse sectors, particularly in real-time healthcare monitoring and diagnostics.
Copyright (c) 2025 Ismael Khaleel, Wijdan Noaman Marzoog , Ghada Al-Kateb https://creativecommons.org/licenses/by/4.0
Enhancing Cybersecurity in Smart Education with Deep Learning and Computer Vision: A Survey
The rapid digital transformation of education, driven by the widespread adoption of smart devices and online platforms, has ushered in the era of smart education. While this shift enhances learning experiences, it also introduces significant cybersecurity risks that threaten the confidentiality, integrity, and availability of educational resources, student data, and institutional systems. This survey examines how deep learning (DL) and computer vision (CV) techniques can enhance cybersecurity in smart education environments. By reviewing 202 peer-reviewed research papers published between January 2022 and June 2025 across leading publishers such as ACM Digital Library, Frontiers, Wiley Online Library, IGI Global, Nature, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, Taylor & Francis, Sage, BMC, and Google Scholar, the study explores the integration of these advanced technologies to address emerging threats. It highlights the use of DL in intrusion detection, anomaly detection, and biometric authentication to protect digital learning platforms. It also examines how CV techniques, such as facial recognition, behavioral analysis, and emotion detection, enhance security and foster adaptive learning environments. The survey also addresses key challenges, including data quality, model interpretability, computational costs, and ethical considerations. By identifying research gaps and proposing future directions, this survey offers valuable insights for researchers, educators, and policymakers aiming to develop robust, scalable, and ethical AI-driven cybersecurity solutions for smart education.
Copyright (c) 2025 Guma Ali, Aziku Samuel , Maad M. Mijwil, Kholoud Kholoud Al-Mahzoum , Malik Sallam, Ayodeji Olalekan Salau , Indu Bala , Klodian Dhoska , Engin Melekoglu https://creativecommons.org/licenses/by/4.0
A Deep Learning Approach for Identifying Malicious Activities in the Industrial Internet of Things
Data-driven decision-making, real-time connectivity, and automation have transformed industrial operations with the Industrial Internet of Things. However, the integration also introduces substantial cybersecurity vulnerabilities, making IIoT networks a prime target for malicious activities. Cyber threats are evolving and becoming more sophisticated, which makes traditional security mechanisms inadequate. An approach using deep learning to detect malicious activities in IIoT environments is examined. It is investigated whether Deep Feed Forward neural networks, autoencoders, and convolutional neural networks are effective at detecting anomalies and mitigating cyber threats. NSL-KDD and UNSW-NB15 benchmark datasets are used to evaluate the proposed model's accuracy, precision, and detection rates. In addition to strengthening IIoT security, deep learning techniques can also ensure the resilience of industrial infrastructure.
Copyright (c) 2025 Mohammed Amin Almaiah, Fuad Ali El-Qirem, Rami Shehab, Khaled Sulieman Momani https://creativecommons.org/licenses/by/4.0
Development and construction of new scanning antennas with high remote sensing capability for wireless communication systems in the millimeter wavelength range
In this paper, geometric methods and wave optics were used to calculate the basic profiles and characteristics of the lens antennas. The planar reflector arrays were assembled using an iterative method with multiple forward and inverse Fourier transform calculations. Three-dimensional electromagnetic modeling was performed in CST Microwave Studio software to evaluate the technical parameters of the designed antennas. Measurements of the characteristics of fabricated prototypes of far-field scanning antennas were performed using a custom-designed experimental setup. This study focuses on the analysis and development of scanning antennas for use in millimeter-wave wireless communication systems. The researchers aim to develop a lenticular antenna in the 57.24-65.88 GHz band with a high DER and a wide-angle radial scanning function in the azimuth plane and another lenticular antenna system in the 58-62 GHz band with a high DER and a wide scanning sector in the azimuth plane. The researchers also aim to develop a methodology to design flat reflective gratings to provide electron beam scanning in the azimuthal plane. The characteristics of prototypes of these antennas have been studied, and experimental tests have been conducted. The study predicts the realization of scanning antennas in the 57.24-63.72 GHz band using microstrip flat reflector grids.
Copyright (c) 2025 Nagham Habeeb Shakir , Sarah R. Hashim, Ahmed Sileh Gifal, Ahmed Dheyaa Radhi, Alaa.G.K. Alshami , Rusul Mansoor AlAmri, Hussein Mohammed Ali https://creativecommons.org/licenses/by/4.0
Approach for Detecting Face Morphing Attacks Using Convolution Neural Network
The facial morphing method combines at least two images of the face to get a singular altered facial image that exposes the vulnerabilities of face recognition systems (FRS). The extensive implementation of face recognition algorithms, particularly in Automatic Border Control (ABC) systems, has raised apprehensions over potential threats, as modified passports present significant risks to national security. In this paper, a new face morphing attack detection approach has been proposed using two different datasets (StyleGAN and AMSL) for testing and validation. A new model for face morphing attack detection based on a special Convolutional Neural Networks (CNNs) architecture has been used for classifying real and morphing images. Experimental results indicate that our suggested approach is extremely generalized and markedly robust in detecting face morphing attacks produced by different techniques .
Copyright (c) 2025 Essa M. Namis , Khalid Shaker , Sufyan Al-Janabi https://creativecommons.org/licenses/by/4.0
The Next Frontier in Computer Science, Trends and Research Opportunities
Editorial Perspective
Copyright (c) 2025 Mostafa Abdulghafoor Mohammed, Z.T. Al-qaysi, Tahsien Al-Quraishi https://creativecommons.org/licenses/by/4.0
Analysis of climate change in Chelyabinsk and Kurgan: effects of temperature and precipitation from 1990 to 2020 based on CRU data
This study analyzes climate patterns in the Kurgan and Chelyabinsk regions of Russia using high-resolution data from the Climate Research Unit (CRU) between 1990 and 2020. The research focuses on how temperature and precipitation have evolved over time and their impacts on local ecosystems, agriculture, and water resources. Using MATLAB for visualization, temperature and precipitation maps were created for January and July across five time periods to understand the spatial and temporal variations in these regions. The analysis revealed a noticeable increase in temperature, with warmer winters and hotter summers in both regions. Precipitation patterns showed a shift, with a significant decrease in winter precipitation (January) and an increase in summer rainfall (July). These findings highlight the need for targeted regional climate adaptation strategies to cope with changing weather patterns and ensure sustainable management of resources.
Copyright (c) 2025 Irina Potoroko , Ammar Kadi, Ali Subhi Alhumaima https://creativecommons.org/licenses/by/4.0
Securing the Internet of Wetland Things (IoWT) Using Machine and Deep Learning Methods: A Survey
Wetlands are essential ecosystems that provide ecological, hydrological, and economic benefits. However, human activities and climate change are degrading their health and jeopardizing their long-term sustainability. To address these challenges, the Internet of Wetland Things (IoWT) has emerged as an innovative framework integrating advanced sensing, data collection, and communication technologies to monitor and manage wetland ecosystems. Despite its potential, the IoWT faces substantial security and privacy risks, compromising its effectiveness and hindering adoption. This survey explores integrating machine learning (ML) and deep learning (DL) techniques as solutions to address the security threats, vulnerabilities, and challenges inherent in IoWT ecosystems. The survey examines findings from 231 sources, encompassing peer-reviewed journal articles, conference papers, books, book chapters, and websites published between 2020 and 2025. It consolidates insights from prominent platforms such as the Springer Nature, Emerald Insight, ACM Digital Library, Frontiers, Wiley Online Library, SAGE, Taylor & Francis, IGI Global, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, and Google Scholar. Machine learning and DL methods have proven highly effective in detecting adversarial attacks, identifying anomalies, recognizing intrusions, and uncovering man-in-the-middle attacks, which are crucial in securing systems. These techniques also focus on detecting phishing, malware, and DoS/DDoS attacks and identifying insider and advanced persistent threats. They help detect botnet attacks and counteract jamming and spoofing efforts, ensuring comprehensive protection against a wide range of cyber threats. The survey examines case studies and the unique requirements and constraints of IoWT systems, such as limited energy resources, diverse sensor networks, and the need for real-time data processing. It also proposes future directions, such as developing lightweight, energy-efficient algorithms that operate effectively within the constrained environments typical of IoWT applications. Integrating ML and DL methods strengthens IoWT security while protecting and preserving wetlands through intelligent and resilient systems. These findings offer researchers and practitioners valuable insights into the current state of IoWT security, helping them drive and shape future advancements in the field.
Copyright (c) 2025 Guma Ali , Wamusi Robert , Maad M. Mijwil , Malik Sallam, Jenan Ayad , Ioannis Adamopoulos https://creativecommons.org/licenses/by/4.0
Leveraging Artificial Intelligent for Optimized Crop Production: An ANN-Based Approach
To incite modern day crop production and ensure sustainability, exact crop recommendations are key to the process. This study pays significant attention to the need for the use of big data tools in studies involving comprehensive data sets that contain information on soil and other environmental characteristics. The set of data used in this research includes Nitrogen, Phosphorus, and Potassium content coordinated with Temperature, Humidity, pH Value, and Rainfall. Knowing these factors is to make a favorable decision about improving agricultural products yield, availability and management of the resources, as well as general well-being of the crops. Specialized advisory on crop type according to the specific data of the soil is likely to click the concept of yield by dealing with nutrient gaps and variable environments. This approach also guarantees reasonable distribution of the limited resources as well as cutting the cost of production and the impact on the environment through preserving the soils and keeping off the faster pace of nutrient leaching. This method of producing plants also helps in minimizing the utilization of fertilizers and enhanced inputs hence minimizing the possible negative impacts on the environment as well as increasing the lifespan of the field. Furthermore, specific advice also assists the farmers in handling changes in climate patterns in a way that increases crop’s ability to withstand the impacts of more frequent episodes of the extremities and manage the risks implicated in those circumstances. Since climate issue plays significant role in threatening the global food security, the strategic maneuvering capacity which enables to analyze the environment and respond to the changes in a timely manner has gained added importance. This also eliminates the losses likely to be incurred as a result of adverse weather factors through the use of data and empirical analysis, which provides management measures to be adopted in order to protect crops and ensure steady food production. In the end, enhanced decision making regarding the types of crop that do well on the soil can easily enhance farmers’ revenues via; improved yields, improved crop quality, lower input expenses etc. This does not only positively affect the economics of the farming enterprises but it is also a way of supporting the stability of agricultural businesses globally. This has informed a build of an Artificial Neural Network (ANN) model which is capable of analyzing the large and rich dataset of soil information to specify accurate crop recommendation. In the validation of the model, this achieved an accuracy level of 99% as it manifested its ability to provide succor in decision making analysis in the sector of agriculture. In comparison to the conventional algorithms in Machine Learning like Logistic Regression, K-Nearest Neighbors (KNN), Multinomial Naive Bayes algorithms, our ANN model exhibits higher accuracy and consistent performance. This study thus establishes the importance of adopting the higher inclusive machine learning with improved data about soil to enhance a more efficient, resilient, and sustainable agriculture era.
Copyright (c) 2025 Yahya Layth Khaleel, Fadya A. Habeeb, Mustafa Abdulfattah Habeeb, Fatimah N. Ameen https://creativecommons.org/licenses/by/4.0
An optimized model for Liver disease classification based on BPSO Using Machine learning models
Liver disease (LD) is a world health concern that requires accurate diagnostic methods. This study proposes an optimized machine learning model (ML) based on BPSO for LD classification using a shared public dataset from kaggle Indicates to liver patients from India. The paper used six ML models such as Random Forest (RF), Support Vector Machine (SVM), Dummy Classifier (DC), Extra Trees Classifier (ET), K-Nearest Neighbors (KNN), and Logistic Regression (LT) to evaluate the performance. Through observations we detected that ET achieved an accuracy of 79.82%. The BPSO hyperparameter optimization optimized ET to enhance accuracy to reach 85%. The paper used metrics such as accuracy, precision, recall, F1 score, and AUC. The results indicate that ML techniques with optimization have the potential to develop reliable diagnostic tools for LD.
Copyright (c) 2024 El-Sayed M. El-Kenawy , Nima Khodadadi , Abdelhameed Ibrahim , Marwa M. Eid , Ahmed M. Osman , Ahmed M. Elshewey https://creativecommons.org/licenses/by/4.0
Credit Card Fraud Detection based on Deep Learning Models
Credit card fraud detection (FD) protects consumers and financial institutions by identifying suspicious or unauthorized transactions. To improve security and reduce false positives, fraud detection systems can analyze transaction data patterns in real time using advanced machine learning (ML) and deep learning (DL). This paper exploits DL models to detects transactional data which includes anomalies through Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) to verify data and mitigate fraud. The models used precision, recall, F1-score, and AUC on a balanced shared 559856-record Kaggle repository dataset. The RNN model detected anomalies with 99.39% accuracy, 0.9939 F1-score, and excellent recall. RNN shows promise as a real-time anomaly detection method with high performance and low computational cost.
Copyright (c) 2024 El-Sayed M. El-Kenawy , Ahmed Mohamed Zaki , Wei Hong Lim , Abdelhameed Ibrahim , Marwa M. Eid , Ahmed M. Osman , Ahmed M. Elshewey https://creativecommons.org/licenses/by/4.0