Al-Furat Journal of Innovations in Electronics and Computer Engineering
The Al-Furat Journal for Innovations in Electronics and Computer Engineering (FJIECE) is a peer-reviewed scientific journal dedicated to publishing high-quality research in the fields of electronics, computer engineering, artificial intelligence, and information technology. The journal serves as a platform for researchers, academics, and industry professionals to share innovative ideas, cutting-edge developments, and practical applications that contribute to the advancement of these disciplines. Editorial Team: Editor-in-Chief: Assist. Prof. Dr. Alyaa Abdulhussein Al-Joda Managing Editor: Assist. Prof. Dr. Ghufran M. Hatem Editorial Board Members: Experts and researchers from leading institutions specializing in electronics, communication, computer engineering, AI, and IT. Reviewers: A diverse panel of scholars ensuring a rigorous double-blind peer review process. FJIECE is committed to upholding the highest academic standards, fostering collaboration, and promoting scientific excellence in electronic and computer engineering research.
Design of SIW Antenna Array Using FSS for 5G (mmWave) Applications: A Review
This review paper provides a glimpse of the recent developments of Substrate Integrated Waveguide (SIW) technology integrated with Frequency Selective Surfaces (FSS) antenna arrays to be implemented over the fifth-generation (5G) system operating within the millimeter-wave (mmWave) regime. The review is primarily based on papers that were generated within the timeframe between the years 2020 and 2025 and some foundational research works that embody the background theoretical knowledge. The document encapsulates the core ideas of the SIW and the FSS technologies and the application of the antenna array to improve radiation performance. The latest works have additionally proven the effectiveness of exponential-tapered FSS configurations and multilayer wideband filters, which are used in increasing gain and interference reduction at sub-6 GHz and at mmWave frequencies. Investigations carried out in the area of microstrip–FSS hybrids and endfire SIW arrays [3] verify that integration methods of this kind are essential for high-gain low-profile antennas applicable in terminal and base station 5G applications. Stacking technologies that make system design compact and efficient are also introduced. The application of technologies to enhance gain, improve the bandwidth, and design radiation patterns is analyzed extensively. An exhaustive comparison of open-loop designs that have been made public is presented, focusing on engineering benefits, design challenges, new research directions, and potential utilization within terminal and base station equipment within 5G systems.
SIW Antenna, Antenna Array, Frequency Selective Surface (FSS), 5G mmWave, Substrate Integrated Waveguide
Copyright (c) 2026 Al-Furat Journal of Innovations in Electronics and Computer Engineering
Artificial Intelligence for Power Consumption Optimization in Reconfigurable Intelligent Surface-Assisted Communication Systems
Intelligent and flexible resource management systems will be required as Sixth-Generation (6G) wireless networks advance in order to minimize energy consumption and safeguard against security risks. To reduce power consumption in active symbiotic secure communication networks, this study uses Reconfigurable Intelligent Surfaces (RIS) to improve the performance of Successive Interference Cancellation (SIC). Because it offers reliable, scalable, and nearly perfect solutions to this complex, non-convex, multi-variable optimization problem, artificial intelligence is essential. We use the Educational Competition Optimizer (ECO), a unique metaheuristic algorithm based on human cognition, to determine the optimal values for the beamforming vector and the active RIS reflection coefficient matrix. The algorithm finds a good balance between exploration and exploitation by simulating how competition changes over time in elementary, middle, and high school. The simulation results show that the proposed AI-driven approach uses 89.0% less power than traditional passive RIS, 52.5% less power than active RIS with random phases, 12.8% less power than Alternating Optimization with Successive Convex Approximation (AO SCA), 10.1% less power than hybrid Particle Swarm Optimization Grey Wolf Optimizer (PSO GWO), and 4.7% less power than hybrid Deep Neural Network Genetic Algorithm Deep Reinforcement Learning (DNN GA DRL). It also meets all security and reliability requirements with 100% feasibility. These results show that AI has the power to change how we communicate in the future by developing systems that are safe, energy-efficient, and long-lasting.
Alternating Optimization Educational Competition, Optimizer Energy Efficiency, Metaheuristic Optimization, Optimization Algorithm, Reconfigurable Intelligent Surface
Metaheuristic-Based Observer Design for Electromagnetic and Load Torque Estimation in PMSMs: A Comprehensive Review
Permanent Magnet Synchronous Motors (PMSMs) are widely used in electric vehicles and robotics due to their high efficiency and torque density. However, accurate torque and speed estimation without mechanical sensors remains a major challenge. To address this, observer-based techniques such as EKF, UKF, MRAS, SMO, and Luenberger observers have been extensively applied, along with adaptive, nonlinear, and AI-based observers. Recent research trends focus on load torque observers for their superior disturbance rejection under dynamic conditions. Moreover, metaheuristic optimization algorithms such as PSO, GA, and GWO have been utilized to auto-tune observer and controller parameters, enhancing estimation accuracy and dynamic performance. Modern control strategies like Field-Oriented Control (FOC) and Model Predictive Control (MPC) benefit from these optimized estimates, achieving higher efficiency and reduced torque ripple. Despite these advances, most studies rely on simulation, highlighting the need for real-time implementation and hardware validation, particularly in electric vehicle drive systems.
PMSM, LTOs, Metaheuristic Optimization, FOC, MPC
A semi-Automatic Annotation with a Novel Deep Learning Model for Terrorism Tweets
Abstract: Terrorist groups are resorting more to social networks to promote their operations through Twitter. In order to win new members, these groups carry out radical propaganda by posting content. Sentiment based natural language processing methods are widely used in practice to preprocess such posts or tweets. Semi-automatic annotation approach was used: the first set of labels were created automatically with the help of Zero-Shot classifier (ZSc) which text to category classification after which random tweets were manually verified. The labeled dataset was then lastly used to train the model. This paper gives a developed system of terrorism-content classification utilizing the Cross-linguistic Language Model RoBERTa Large (XLM-R Large) to categorize information in the Arabic and the English language in Twitter using structured and labeled information. The accuracy of our system that involved the combination of automated labeling and deep learning was 85% and it was successful in identifying extremist and terrorist materials
Keywords: classification, deep learning , Annotation , Labeling, Xlm-R Large
Comprehensive Review of Holographic Antenna Beamforming for Next Generation (6G) Wireless Communications
Holographic beam forming antennas can be used to create high-capacity wireless networks by increasing spectral efficiency, thereby reducing interference, especially in 5G networks and beyond. The holographic beam-forming antennas can be used to create a high-efficiency communication network by utilizing advanced signal processing techniques that create adaptive beam patterns. The review aims to review the state-of-the-art technology for holographic beamforming antennas and identify its uniqueness compared with conventional beamforming technology. The uniqueness of this technology is its capacity for controlling electromagnetic waves. The review will also look into the main principles and advantages of this technology and its future prospects. The study reviews existing literature to compare holographic beamforming technology with other technologies, such as conventional phased arrays and massive MIMO to identify its specific advantages and disadvantages. The advantages of high-frequency communications using holographic beamforming can be attributed to its precise capabilities resulting from its flexible design, simple hardware requirements, and beam steering capabilities. Empirical studies on holographic beamforming antennas identified specific advantages to include its flexible beam control system, reduced power requirements, and accurate tracking capabilities to reduce delay times for communications. The realization of holographic beamforming systems depends on resolving three main obstacles, which include high frequency propagation loss, fabrication constraints, and energy efficiency concerns. Future investigators need to concentrate on antenna optimization while developing improved fabrication methods and smart control systems to improve performance and scalability for practical 6G applications. The complete realization of holographic beamforming potential in next generation wireless communications depends on resolving the existing challenges.
HBF; 6G Wireless Communications; Beam Steering; THz Communications
Automated and Robust Brain Skull Stripping using Optimized Pre-processing and a Refined Residual U-Net Framework
In this study, a method for skull stripping referred to as the 3D Enhanced Residual U-Net is introduced. The approach combines the traditional U-Net with an enhancement mechanism designed to improve both the effectiveness and processing speed of the U-Net. An anisotropic diffusion filter (ADF) reduces noise in MRI images while maintaining the edges of present objects. This is accompanied by skull stripping to eliminate non-brain matter and contrast enhancement to elevate the visual quality. The architectural adaptations allow for rapid and stable training. As brain images vary significantly from subject to subject, a deep learning approach will account for these differences leading to consistent skull stripping results. Neurofeedback Skull Stripping (NFBS) dataset was used for the proposed model formulation. The results from the experiments show that the proposed approach is effective and practical compared to previous methods. This method obtained an impressive sensitivity rate of 0.9974, DSC of 1.0000, a specificity of 0.9983, an IOU of 0.9831, an accuracy percentage of 0.9969, and a precision of 0.9961, showing an actual ability to differentiate the distinct parts of the brain and the skull.
Deep learning, Medical Image, Skull stripping, MRI, Residual units, NFBS dataset, U-Net.
Analysis of The Technical Architecture of Modern Mobile Communication Networks and Its Impact on Data Transmission Efficiency
The primary objective of this research is to identify the structure of modern communication networks, particularly 5G, and their efficiency in transmitting and receiving data in a practical manner. The research employed a network simulation method using the srsRAN Project 23.11, the Samsung Galaxy S23, and LimeSDR Mini v2. The research concluded that the new technology improved data transmission speed and reduced latency by 74.6% (9.7 milliseconds). It also accelerated data downloads by 455%, reaching speeds of up to 512 Mbps. The network architecture supports Massive MIMO, Beamforming, and an All-IP Core, making it a suitable technology for use in many applications, such as remote surgery.
Communication networks, data transmission, communication network architecture
Transfer-Learning for Cross-Weighted MRI Liver Cirrhosis Segmentation Using CirrMRI600+
Accurate and fully automated liver segmentation has the potential to greatly help in the improvement of disease detection and the personalization of treatments. Liver cirrhosis is the final stage of liver disease and is characterised by fibrosis and the remodeling of the liver, which increases the mortality rate. Magnetic resonance imaging (MRI), particularly T1- and T2-weighted sequences, provides a powerful non-invasive tool for assessing liver structure and pathology. However, manual delineation of cirrhotic liver regions is both time-consuming and prone to human error, with performance heavily dependent on the pathologist’s expertise. Reliable segmentation is further complicated by pronounced morphological distortions and heterogeneous signal characteristics associated with cirrhosis. In this study, we address these challenges using a fully convolutional ResNet-UNet framework that employs cross-weighted transfer learning, retrained on T1-weighted MRI data to capture general anatomical representations and fine-tuned on T2-weighted images to adapt to contrast and pathological variability, using the CirrMRI600+ dataset. The proposed model achieved the highest Dice coefficient of 0.89 and mIoU scores of 0.81 on the testing set, surpassing the performance of training a single ResNet-UNet on T1 and T2 separately, without transfer learning. This cross-weighted training paradigm demonstrates the effectiveness of contrast-domain pretraining for robust and efficient liver segmentation, paving the way for multimodal MRI integration in hepatic disease assessment and advancing progress toward automated cirrhosis staging and personalised therapeutic planning.
Transfer learning, Liver Cirrhosis segmentation ResNet-Unet , AI
Smart Bin Concept for Effectively Waste Management
Effective waste management, which includes processing garbage and reducing the amount of uncontrolled refuse areas, is the first step toward improving the environment. Waste classification and then recycling them into alternatives as raw materials could improve the environment, including the quality of air and water. Using smart waste bins is one of the proposed solutions to deal with waste management processes. This work suggests an affordable design for smart waste trash, which could be considered an intelligent solution for waste management. The system suggests using three color-coded bins. Each bin is customized for specific types of waste, which are food waste, papers and metal cans or plastic waste. Mechanical sensors are used for each bin in order to weigh it and notify the workers that the trash is full. The other components of the suggested design are a solar panel and remote control system. The solar panel is used for lighting and to power surveillance cameras. The remote-control system is necessary to monitor the whole operation of waste management. The proposed system is recommended to be used in specific areas of society, such as universities, schools, hospitals, and malls, as a first step and then generalize it to include the whole city and even the public areas. Also, in order to feature the need for such smart waste bins in different places of the city, a questionnaire is proposed, and a survey is undertaken as part of this work. Hence, including smart waste management systems could ensure a significant advancement in urban planning. A significant acceptance has been gained for the suggested design, and the majority of participants have encouraged using those smart bins.
smart cities, smart bins, waste management systems, sensors
Neural-Aided State Estimation for Nonlinear Vehicle Tracking Using KalmanNet and GRU.
Accurate tracking of vehicles is a key problem in autonomous driving, especially when the maneuvers are complex and/or under high noise sensing measurements. Traditional model-based estimators, such as the EKF, can easily be affected by linearization errors and are dependent on precise mathematical modelling and carefully calibrated noise characteristics. In this paper, we propose an enhanced learning-aided state estimator called KalmanNet GRU that maintains the recursive property of Kalman filtering and introduces a neural dynamics learner based on a GRU (gated recurrent unit) cell and a learnable Kalman gain in order to achieve robustness against nonlinearities and measurement noises. The performance of the proposed approach is demonstrated via the HDVT problem with nonlinear range–bearing radar measurements at an inverse sensing noise level of 20 dB. The experimental results reveal that KalmanNet significantly outperforms the EKF as it can attain an average test MSE of −7.91 dB in contrast to 9.85 dB resulted by the EKF, which means achieving nearly 17.76 dB improvement in performance. The model proposed also has a stable estimation performance and yet holds an acceptable processing time for real-time autonomous navigation. These results verify the utility of recursive filtering by data-driven dynamics learning for nonlinear state estimation in ITS.
KalmanNet Gated Recurrent Unit (GRU) Autonomous Vehicle Tracking Nonlinear State Estimation Deep Learning
Deep Learning Techniques For Massive MIMO Detection Algorithms: A Review
Massive Multiple-Input Multiple-Output (MIMO) is a critical Technique for next-generation communication systems, as it significantly improves spectrum efficiency and system capacity, that must meet user performance and quality of service requirements(QoS). However the detection of signal in massive MIMO systems remains challenging due to the limited performance of traditional algorithms and high computational complexity. Over recent years deep learning has been introduced as a method for improving signal detection algorithms in massive MIMO systems .This study provides a systematic review of Deep neural network-based detection techniques for massive MIMO systems .A systematic literature review was conducted, drawing on recent studies selected from major scientific databases and research published between 2016 and 2025, with a focus on widely adopted detection frameworks. A unified comparison was performed using key criteria, including bit error rate (BER) performance, computational complexity and practical feasibility. The reviewed methods were categorized as data-driven, model-driven, and hybrid approaches, allowing for the organization of analyses according to their design principles. The results indicate that model-driven and hybrid techniques enhanced trade-off between detection accuracy and computational complexity, while purely data-driven methods require intensive training and exhibit limited generalize ability under varying channel conditions. Compared to existing surveys, this work provides a more standardized classification and a more accurate comparative framework, highlighting current research trends and identifying existing challenges, such as scalability for large-scale systems ,effective training strategies and robustness to realistic channel environments.
Massive MIMO , Deep Learning , 5G , Detection , Convolutional neural network.
Detecting DoS Attacks in Sensor Networks Using Deep Learning for Improved Cybersecurity
Wireless Sensor Networks (WSNs) constitute an attractive target as they are increasingly being incorporated into Internet of Things (IoT) applications, where they are vulnerable to the types of security threats, such as Denial of Service (DoS) attacks. These attacks are hard to detect to ensure the dependability and functionality of the WSNs. This paper suggests an improved machine learning (ML) and deep learning (DL) solution to detect DoS through the WSN-DS dataset. The methodology contains data preprocessing, feature and target definition, label encoding, dimension reduction through Principal Component Analysis (PCA), data balancing using SMOTE and train-test splitting. The ML models, including the Logistic Regression, Random Forest, Gradient Boosting, Decision Trees, Extra Trees, Naive Bayes, K-Nearest Neighbors, AdaBoost, LightGBM, XGBoost, and CatBoost are hyperparameter-optimized with Ant Colony Optimization (ACO). MLP, CNN, RNN, LSTM, GRU models are also tuned with ACO on the number of epochs and the batch size. Analysis in terms of accuracy, precision, recall, F1-score, AUC, specificity, and MCC reveals that the optimized approach performs much better in identifying DoSs and can therefore be adapted to increase the security of WSNs and enable IoT intrusion detection studies.
Wireless Sensor Networks WSN, Internet of Things IoT; Denial of Service DoS attack Intrusion Detection.
Metaheuristic Optimization in Machine Learning for Cyber Attack Detection: A Review of Techniques and Applications
The speed of cyber-attacks and the increasing complexity of cyber theft has made intrusion detection a focal point in today's cybersecurity environment. The present paper is an overview of metaheuristic optimization techniques to detect cyber-attacks using machine learning. The purpose of this survey is to review the use of metaheuristic algorithms in the improvement of intrusion detection systems (IDSs) in terms of feature selection, parameter optimization, and multi-objective optimization. This survey covers the main work that uses single-objective and multi-objective metaheuristic algorithms such as evolutionary algorithms, swarm intelligence algorithms, and hybrid metaheuristic optimization approaches. The papers are reviewed in terms of their use for IDS tasks, use of single-objective and/or multi-objective optimization, and their performance using benchmarking datasets. The survey shows some of the trends: metaheuristic algorithms are highly effective for feature selection, classification performance, and a combination of detection rate and false alarm rate. And hybrid solutions that use metaheuristic algorithms and machine learning / deep learning algorithms, such as Convolutional Neural Networks (CNN) and Gradient Boosting, are more effective than single solutions. The advantages are clear, yet the research also intersects some of the challenges related to the methods, including scalability, efficiency, interpretability, and adversarial attack resistance. Based on these insights, future research will focus on developing adaptive and scalable optimisation approaches, integrating optimisation approaches into real-time and distributed systems, and designing interpretable and robust IDS. This survey offers a practical perspective on the metaheuristic optimisation for cyber-attack detection techniques, and the use of metaheuristic optimisation in designing efficient and scalable IDS
Intrusion Detection Systems Cyber-attack detection Metaheuristic optimization Feature selection Multi-objective optimization
Different Structures of mmWave MIMO Array Antennas for 5G Applications: A Review
This review of literature outlines advances in antenna design based on 5G millimeter-wave (mm-wave) technology and establishes that it has the potential to meet previously unrealized data rates, very low latency, and spectral efficiency, and meet the growing needs of the current wireless network. The current condition of the mm-wave technology and the antenna array development, and what they can do in the 5G applications, have been mentioned in the review. It also presents the ideas of more advanced antenna design, including dielectric resonator antennas, with multiple inputs and multiple outputs, and a single-layer MIMO antenna, which can operate over a great variety of frequencies. Microstrip array antennas in this regard are important, especially at 28 and 38 GHz, since they have the ability to achieve high gain and provide high precision in beamforming to reduce serious propagation losses at mm-wave frequencies. Moreover, they are very small, inexpensive, and can be combined with printed circuit technologies, and thus they suit well in real-life 5G applications. These antenna arrays maximize spectral efficiency and system capacity as they are able to support advanced MIMO techniques and therefore can maximize communication performance and reliability in congested cities.
A Comprehensive Review of Hand Gesture Recognition: Vision-Based vs. Wearable Sensor Approach
This comprehensive review systematically compares vision-based and wearable sensor approaches for hand gesture recognition (HGR), addressing a critical gap in existing literature that often treats these modalities in isolation. While previous surveys have predominantly focused on either vision-based methods or sensor-based approaches separately, this review provides an integrated analysis of both paradigms, highlighting their complementary strengths and limitations across the entire HGR pipeline: from data acquisition and preprocessing to feature extraction and classification. We examine diverse datasets including UCI MYO Thalami and RGB-based collections, analyzing preprocessing techniques (data augmentation, noise reduction, normalization) and both traditional machine learning (SVM, ANN, KNN) and deep learning methods (CNN, RNN, LSTM). Our comparative analysis reveals that sensor-based methods excel in controlled environments with precise motion capture, while vision-based approaches offer greater usability at the cost of environmental sensitivity. This review contributes a unified framework for selecting appropriate HGR technologies based on application requirements, computational constraints, and user needs, particularly in healthcare rehabilitation, virtual reality, and assistive technologies for hearing-impaired communities
Gesture Recognition, Machine Learning, Deep Learning, Vision data, Wearable sensor
A Comparative Study of YOLO Architectures with AI-Based Data Augmentation for Automated Platelet Estimation in Thrombocytopenic Patients
Thrombocytopenia, a hematological disorder characterized by an abnormally low platelet count, demands diagnostic methods that are both rapid and precise. Conventional manual estimation of platelets from blood smears is notoriously labor-intensive and suffers from significant inter-observer variability. This study presents a systematic comparison of various YOLO object detection architectures, enhanced by advanced AI-based data augmentation, for the automated quantification of platelets in thrombocytopenic patients. We evaluated YOLOv5, YOLOv7, and YOLOv8 on a dataset of 1,500 annotated blood smear images from patients with confirmed low platelet counts. To overcome data scarcity, we implemented sophisticated augmentation techniques, including Generative Adversarial Networks (GANs) and neural style transfer. Our results establish the superior performance of YOLOv8, particularly when trained on GAN-generated samples, achieving a mean Average Precision (mAP@0.5) of 96.2%, a precision of 97.1%, and a recall of 95.8%. Crucially, the model sustained high accuracy across varying platelet densities and exhibited an almost perfect correlation (r = 0.98) with manual expert counts. This methodology lays the groundwork for a reliable, automated platelet assessment tool with the potential to significantly advance diagnostic and monitoring protocols for thrombocytopenia in clinical practice.
YOLO Architectures, Platelet Estimation, Thrombocytopenia, Data Augmentation, Deep Learning
Copyright (c) 2025 Al-Furat Journal of Innovations in Electronics and Computer Engineering
Design and Performance Evaluation of An Off-Grid Photovoltaic System Based on Weekday Load Profiles for Rural Schools: A Case Study in Babil, Iraq
This study highlights the design, simulation, and performance assessment of a standalone photovoltaic system developed for a rural educational facility in Babil, Iraq, under actual site conditions, the work fulfilling a novel weekday only for the electrical requirements of Al-Nasra Primary School, situated adjacent to the Al-Musayyib Grand Irrigation Project. The system was modeled and analyzed using PVsyst software, utilizing a realistic weekday-only load profile that simulates school operations for five hours daily (08:00–13:00), five days a week. The entire daily energy requirement was assessed at 62.0 kWh, encompassing classrooms, administrative offices, and heating and cooling systems. The results of the simulation indicated that the designed system produced a yearly energy output of 21,306 kWh, with 15,927 kWh effectively employed to meet the load and 4,295 kWh recorded as surplus energy. The system showed the performance ratio (PR) of 62.0%, solar fraction of 99.72% and a load loss of 0.6%. The battery bank maintained a state of health exceeding 91.7%, with an estimated lifespan of 12 years. These results verify the technical viability and dependability of a suitably sized standalone photovoltaic system in fulfilling the distinct operating requirements of a rural school. Furthermore, the setting up of a pragmatic weekday-only load profile yields a more precise evaluation of system performance, preventing oversizing and promoting improved energy management strategies for schools in off-grid or locations with an unsteady power grid. The weather data employed for the simulation was obtained from the PVsyst linked Meteonorm 8.0 data for the Al-Musayyib region.
Solar Energy, Standalone Photovoltaic System, off-grid design for school, Load Profile, Performance Ratio, PVsyst simulation Software, Babylon/Iraq
Artificial Intelligence and Medical Imaging: A Pathway to Sustainable, Data-Driven Healthcare
The global healthcare sector stands at a critical juncture, facing unprecedented challenges including aging populations, rising costs, workforce shortages, and the environmental burden of medical waste. Concurrently, it is experiencing a digital revolution, generating vast repositories of data, particularly medical images which constitute nearly 90% of all healthcare data. This paper posits that the strategic integration of Artificial Intelligence (AI) and advanced medical image processing technologies is not merely an incremental improvement but a foundational paradigm shift essential for achieving sustainable, data-driven healthcare. We move beyond technical performance metrics to explore a holistic framework where AI-driven imaging enhances all three pillars of sustainability: economic (through improved efficiency and reduced waste), social (through equitable access and improved outcomes), and environmental (through optimized resource use). This paper provides a detailed analysis of current applications in radiology, pathology, ophthalmology, and cardiology, supported by expanded clinical examples and data tables. We critically examine the "green" implications of computational costs, the ethical imperatives of bias mitigation, and the practical pathways for implementation. Ultimately, we argue that an AI augmented, image centric approach is the most viable pathway to a healthcare system that is not only more accurate and efficient but also profoundly more sustainable and equitable for future generations.
A Hybrid Deep Learning Model to Detect Advanced Man in the Middle Attacks in the Internet of Things Networks
Highly developed Man-in-the-Middle (MITM) attacks in Internet of Things (IoT) networks present a significant security risk because they can avoid protocol violations but introduce a small amount of behavioral deviation such that more traditional intrusion detection systems cannot detect it. This research will solve this issue by suggesting a hybrid neural network that learns spatial, temporal and contextual features of IoT traffic to classify multi-class MITM attacks. The model combines CNN-LSTM, Transformer-BiLSTM, and CNN-Transformer models to collectively model local feature correlations, sequential dynamics, and long-range dependencies. Large-scale experiments show that performance has improved on all models following training, with the CNN-Transformer having the highest overall performance, with Macro F1-score of 97.72, accuracy of 98.00 and AUC of 98.41, and lower inter-class confusion and better stability through repetitions. The findings attest to the idea that hybrid systems that consist of convolutional feature generation and attention-based global modeling offer strong and balanced detection of advanced variants of MITM attacks under heterogeneous IoT conditions, which justifies their appropriateness in the context of implementing effective IoT security in practice
IoT Security, MITM Attacks, Intrusion Detection System, Hybrid Deep Learning, CNN-Transformer
Abstract. Noise is undesired information in an image that appears during image capturing, transmission, coding, and processing steps, negatively impacting visual quality and obscuring important details. Image denoising is therefore a central problem in both image processing and computer vision, aiming to suppress such distortions while preserving essential structural information. Our paper aims to shed light on recent research in image denoising techniques, evaluate the performance of prominent methods, highlight research gaps, and identify areas for further study.
Noise; Gaussian noise; Salt and Pepper; CNNs; GANs
Optimization Techniques for Spectrum Sharing between MIMO Radar and Wireless Communication System: A Review
For more than a decade, the need to share the frequency spectrum between radar and wireless communication systems has emerged due to the massive increase in demand for communication services and the availability of underutilized radar bands. At the same time, spectrum sharing between radar and wireless communication systems faces the problem of interference that affects the performance of both systems, which has attracted researchers to find effective solutions to reduce interference in an environment where both systems share the radio spectrum. In this paper, we review the most prominent optimization methods that have been used to share the frequency spectrum and reduce interference between radar and wireless communication systems.
Spectrum Sharing; MIMO Radar; Communication System; Optimization Techniques; Interference Mitigation.
Copyright (c) 2020 Al-Furat Journal of Innovations in Electronics and Computer Engineering
Design a Novel Fully Reconfigurable Nonreciprocal Quasi Reflectionless Microstrip Diplexer
In this paper, a novel design of a multifunctional quasi-reflectionless non-reciprocal diplexer is proposed. The reflection less diplexer is necessary to overcome many problems in various parts of communication systems that result from reflective unwanted signals from passive elements. in addition, the nonreciprocal diplexer has many advantages in RF systems as interference elimination. The proposed diplexer consists of two pseudocombline BPFs that work in two channels (2.2, 2.6) GHz, respectively. A T-shaped structure is used in the proposed QR diplexer design in three parts (an absorptive part for each BPF and a matching part between the two BPFs). Also the Spatial Temporal Modulation (STM) is used for achieving the nonreciprocity property for the diplexer. Furthermore, the proposed model achieved a high level of absorption for undesired reflected signals, where undesirable waves in the 1.5–4 GHz frequency band were efficiently absorbed. The proposed QR diplexer demonstrates a wide range of suppression for unwanted signals as well as significant separation between the RX and TX ports. All simulations are performed by Keysight ADS software and fabricated by a CNC machine. The results of the proposed work show good agreement between simulation and fabrication.
multi-functional diplexer; reflectionless; non-reciprocal; diplexer; STM; reconfigurable diplexer
Detection Diabetic Retinopathy Illnesses Using a Combination of Xception and NASNetMobile
Convolutional Neural Networks (CNNs) are one of the various forms of deep learning that have become effective tools in computer vision. Deep Neural Networks (DNNs) with a lot of parameters have changed the field of machine learning, and their influence is especially noticeable in network architecture. CNNs are very good at image classification problems because of their ability to concentrate on objects in images and extract information using spatial relationships. This work presents a novel CNN model for image classification based on transfer learning that integrates two advanced architectures: Xception and NASNetMobile. Using Xception and NASNetMobile, the model uses photographs with specified dimensions, often known as "best windowing of images," as input to categorize the images into two groups. To avoid overfitting issues in CNN, a dropout layer is introduced after the outputs of these designs are concatenated using a concatenate layer. This study assessed the proposed model for an exceptionally challenging dataset associated with diabetic retinal disease. This dataset, named “Diabetic Retinopathy 224*224 Grayscale images” from the “APTOS 2019 Blindness Detection” dataset on Kaggle, contains 3662 images, with 1875 depicting abnormal cases and the remaining 1805 representing normal cases. In this test, the model performed exceptionally well, with an accuracy of 97.50%.
Convolutional Neural Network, Image Classification, Transfer Learning
Accelerating Neural Networks on FPGA via Tan-Sigmoid Approximation
. This research examines the execution of feed-forward artificial neural networks on field-programmable gate arrays (FPGAs), addressing the challenge of limited hardware resources that restrict network size and hinder the direct execution of nonlinear activation functions. The proposed approach reduces resource usage through data normalization and restructuring, while approximating the tan-sigmoid function via a logarithmic method. Implemented on the Xilinx Spartan-3A (3sd3400afg676-4) platform, the design achieves a 3% optimization in slices and a 2% optimization in lookup tables (LUTs), demonstrating improved efficiency, enabling larger and more cost-effective neural networks on a single FPGA chip.
Neural networks (NNs), field-programmable gate arrays (FPGAs), data normalization, tan-sigmoid implementation, and restructuring approach
Classifying ECG Signals with an Effective Machine Learning Method
The goal is to compare deep learning and classical methods for automated ECG beat classification in a rigorous inter-patient evaluation. Band-pass filtering, baseline correction, Kalman denoising, and QRS detection using the Pan-Tompkins algorithm were applied to 48 half-hour, two-lead recordings from 47 subjects from the MIT-BIH Arrhythmia Database. Discrete wavelet transforms features fed a tree-based baseline (random forest, RF) following R-peak segmentation were compared to a convolutional neural network (CNN). Per-class precision/recall/F1, macro-F1, and ROC-AUC/PR-AUC with 95% bootstrap CIs were reported. In a four-class setup (atrial fibrillation, normal, other rhythms, and noise), RF obtained macro-F1 ≈ 0.70 and CNN macro-F1 ≈ 0.59. AF↔Other, Noise↔Other, and Noise↔AF were the most commonly confused terms. In inter-patient evaluation, RF serves as a competitive baseline. Comparability across studies is improved by PR-centric evaluation, standardized reporting, and clear preprocessing, especially when there is a class imbalance.
classification; Machine learning; Arrhythmia detection; Deep learning; Signal processing.
Enhancing Data Security in WLAN Networks Using a Hybrid Cryptographic System
Wireless Local Area Networks (WLANs) are one of the fastest growing technologies today. In recent years, WLANs have become one of the most adopted technologies. Nevertheless, wireless communications presents a huge security risk. As WLAN is susceptible to many attacks, encryption methods are the building block of WLAN security. However, it can be difficult to develop a practical system that provides high security but also has decent performance. The research proposes a new solution for a dual hybrid encryption method namely Advanced Encryption Standard with Elliptic Curve Cryptography (AES–ECC) and Advanced Encryption Standard with Rivest–Shamir–Adleman (AES–RSA). The paper compares two hybrid approaches to develop a unified framework. So, the key novelty is to combine the two approaches under the same framework. In the method proposed, symmetric encryption (AES) secures the sent data while asymmetric encryption (ECC or RSA) encrypts and securely sent an important secret key. The main contribution of this paper is to offers equal characterization of AES–ECC and the AES–RSA hybrids in WLAN environments as well as to provide an unbiased comparison of their results and security, resources usage and processing time. Experimental testing shows how effective and practical this framework is for high-level security and performance of WLAN digital communications.
WLAN, Symmetric encryption, Asymmetric encryption, Hybrid cryptography, AES algorithm, REA algorithm, ECC algorithm, Throughput
Energy-Efficient Routing in Wireless Sensor Networks: A Comprehensive Review of Machine Learning, Optimization, Clustering, and Duty Cycling Techniques
Wireless Sensor Networks (WSNs) are widely used in various applications, yet they remain limited by the energy constraints of sensor nodes. This review paper explores and compares recent advancements in energy-efficient routing strategies across four major categories: Q-Learning-based routing, traditional optimization algorithms, clustering-based protocols, and sleep scheduling techniques. Each approach is analyzed in terms of its methodology, simulation environment, performance metrics, and limitations. Q-Learning techniques provide adaptive and intelligent routing decisions but often lack real-world deployment. Traditional algorithms such as Ant Colony Optimization (ACO) and Whale Optimization Algorithm (WOA) offer reliable clustering but adapt poorly to dynamic environments. Clustering-based protocols, especially those integrating fuzzy logic or quantum methods, show strong results in simulations but assume static and homogeneous nodes. Sleep scheduling and duty cycling protocols significantly reduce idle energy waste, yet are rarely integrated with routing layers. The review identifies that most current protocols are evaluated only through simulations and face challenges such as congestion near sinks, lack of cross-layer integration, and scalability under heterogeneous conditions. Future research should focus on building lightweight, real-time learning frameworks that jointly optimize routing, clustering, and sleep scheduling in practical deployments.
Wireless Sensor Networks; Energy Efficiency; Q-Learning; Sleep Scheduling; Duty Cycling; Network Lifetime
A Mechanism for Combining Hill Cipher and Steganography Techniques in DNA Sequences to Enhance Information Security
This research integrates the Hill Cipher encryption algorithm with DNA-based steganography as a form of dual protection. The plugins and modules mask the cryptex as DNA sequences. The proposed methods presented the full accuracy of the data recovery with low time and full recovery. The encryption and decryption steps are of low time and full recovery. The effective masking and absence of the cryptex within the cover DNA, as nominal 50%, and the absence of the cryptex confirm the absence of cryptex and detection. The proposed model gives an extremely high potential for secure communication, in high confidentiality environments, like in the exchange of medical and governmental data.
Information Security, Hill Cipher, Steganography, DNA Sequences"
Analyzing Academic Networks at Kufa University Using Social Network and Text Mining Techniques
The negative aspects of the academic social networks, such as ResearchGate (so shallow measures as co-authorship and citation numbers), is where the present paper proposes a hybrid data analysis model in which more valuable data sets of scholarly collaboration at University of Kufa are pursued. Research A network was created among scholars who were interested in research at the University of Kufa. To identify semantic clusters, the K-Means clustering algorithm was used on the TF-IDF vectors using K-Means and dimensionality reduction using SVD, and structural communities using the Louvain algorithm. The most suitable value of k was assessed with the help of the Elbow method. Findings indicated that the K-Means clustering performed better and the Louvain worse on maximum Silhouette score and Calinski-Harabasz score and integrated semantic- cohesion in research interest networks. These will be the structural and semantic analysis tools that will give additional measurements and the Louvain and K-Means functions that will determine the topological form and theme relations respectively when applied to the semantic material. The article warrants the existence of hybrid analytic models, feasibility of designing comparative academic suggestion systems, which have structural location as well as semantic proximity.
K-Means clustering; TF-IDF; Academic Social Network ; Community Detection; Clustering Algorithm.
A Survey on Face Spoof Attack Detection Using Multimodal Machine Learning Approaches
Face anti-spoofing (FAS) has lately received more attention because of its critical function in protecting face recognition systems against presentation assaults (PAs). With the advent of more realistic face recognition systems of various sorts, early-stage face anti-spoofing approaches based on hand-crafted characteristics have become untrustworthy due to their limited representativeness. With the advent of large-scale academic datasets over the last decade, deep learning-based face anti-spoofing algorithms have outperformed and dominated the area. However, most existing evaluations in this field rely on hand-crafted features, which are now outdated and hinder progress in the face anti-spoofing research community. To inspire future research, we give the first complete analysis of recent advances in deep learning-based face anti-spoofing systems. This article addresses numerous innovative and significant components: 1) In addition to binary label supervision (e.g., "0" for truth versus "1" for face recognition systems), we also examine cutting-edge approaches using pixel-level supervision (e.g., pseudo depth map). 2) In addition to traditional evaluation within the dataset, we collect and analyze cutting-edge methods specifically designed for domain generalization and open-set FAS analysis; and 3) In addition to commercial RGB cameras, we summarize deep learning applications under multimodal (e.g., depth and infrared) or specialized (e.g., light field and flash) sensor categories. We end our survey by stressing existing outstanding challenges and future opportunities.
Deep Learning; Face Anti-Spoofing; Machine Learning; convolutional neural network