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: Prof. Dr. Hassanain ghani Hameed Hassan Al-Hussaini Managing Editor: Assist Prof. Dr. Alyaa Abdulhussein Al-Joda 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.
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.
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
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
Adaptive non-Hermitian DCO-GFDM Modulation Using ML for Indoor VLC Systems
Visible light communication (VLC) has appeared as an attractive wireless technology for indoor data transmission, but its performance is restricted by such a fluctuating environment as the indoor channel caused by obstacles, reflections, and ambient sunlight interference. The conventional direct current-biased optical generalized frequency division multiplexing (DCO-GFDM) requires a Hermitian symmetry to generate real values, which are restricted by the light source. Hence, the size of the IFFT/FFT and the pulse shaping filter must be doubled, which leads to higher complexity and power consumption. This paper introduces a machine learning (ML)- based adaptive strategy for non-Hermitian DCO-GFDM (NH-DCO-GFDM), which was proposed in our prior study. This strategy is developed to change the modulation parameters according to the channel conditions dynamically. We employ random forest regression models trained on simulation-generated datasets to predict critical performance metrics, including bit error rate (BER) and peak-to-average power ratio (PAPR). The proposed algorithm works to reduce the BER under a specific threshold. Then, it optimizes the modulation parameters via a cost function that maximizes throughput while controlling complexity and PAPR. The results indicate that this adaptive system significantly improves reliability and achieves BER below the threshold at a lower SNR (9 dB), providing higher throughput. Thus, the proposed method offers an effective solution for robust, high-throughput VLC that can trade with such dynamic indoor environments.
A Bio-Inspired Algorithm for Zero-Day SQL Injection Detection via Slime Mould Classifier
With the continuous development of the cybersecurity field, detecting unknown or zero-day attacks remains a significant challenge due to their unpredictable behavior. This paper proposes a novel biologically inspired approach to detecting unknown SQL Injection (SQLi) attacks using the Slime Mould Algorithm (SMA). This method leverages the adaptive and heuristic capabilities of the SMA to detect unknown attacks. A binary classification model was developed and trained on a benchmark dataset containing both natural queries and diverse SQLi attack vectors, including Out-of-Band, Boolean-based, Time-based, and Union-based injections. To ensure robustness and generalization, K-Fold cross-validation was employed. The SMA-based model demonstrated superior detection capabilities, particularly in identifying zero-day attacks that deviate from known patterns. The experimental results reveal promising detection rates across all attack types: 89.33% for Out-of-Band-based SQLi, 97.89% for Boolean-based SQLi, 90.27% for Time-based SQLi, and 96.69% for Union-based SQLi, and 91.51% for Error-based SQLi. These results underline the effectiveness of SMA in generalizing beyond seen data, a critical advantage in dynamic threat environments. Compared to traditional machine learning models, the SMA-based classifier achieved higher accuracy and F1 scores, confirming its potential as a powerful tool for web application security.
Slime Mould Algorithm, Zero-Day Attack, SQL Injection, Web Security
Feature-Centric Framework for Network Traffic Management and Optimization
While complex network development and dynamic traffic are still developing, there has been great optimization and management of network traffic. This paper will develop a feature-based approach based on intelligent selection and reduction to improve traffic flow management, optimization of resource utilization, and enhancement of network security. While methods of statistical correlation and clustering retain only the relevant features in the system, irrelevant ones are filtered out and operational efficiency is optimized along with the decision-making process. Advanced correlation algorithms are proposed in a cross-selection set, such as Spearman and Kendall, besides dendrogram-based clustering analysis. This makes the solution robust for designing and monitoring modern network environments. It can be seen from the experimental results that the proposed framework can remove redundancy and enhance the feature interpretability, scalability, and adaptability to domain-specific network requirements. Furthermore, the framework proves its efficiency in capturing nonlinear feature relationships, making it robust for real-world network applications.
Network Feature Selection, Spearman correlation, Kendall correlation, Dendrogram clustering
Performance Analysis of the Speck Cryptography Algorithm
Encryption algorithms such as the block cipher techniques were built in particular to guarantee security on limited systems, whose design clarity is vital. The multiplicity of the planned use cases, however, needs flexibility in execution. In the building of cryptosystems, simplicity, security, and flexibility are continuous but incompatible aims. The requirement for a lightweight block cipher algorithm that supports a broad range of systems, architectures, and block/key sizes efficiently has been developing recently. In 2013, the National Security Agency (NSA) recommended the block ciphers SIMON and SPECK. Different block sizes, including 16, 32, and 64 bits, and key sizes, such as 64, 96, and 128 bits, are supported by SIMON and SPECK. In this research, we evaluate the performance of lightweight SPECK cryptographic block ciphers utilizing multiple criteria such as execution time, throughput, and energy usage. We utilize two distinct Intel CPUs to assess the Speck Cryptography technique. Three keys of sizes 128, 192, and 256 are employed in this article to compare the performance criteria. The obtained results over two different Intel processors show a linear and non-linear relationship to the key size. In other words, the increase in key length increases the execution time and energy consumption, while throughput results decrease. Moreover, the NIST statistical randomness test is implemented for all speck versions to compare them in term of the security level.
Speck cipher, Cryptography, Throughput, Energy consumption
Using the Doppler Effect to Improve Automatic Landing of Drones in Remote Geographical Environments
Drones are currently being used more and more to deliver postal goods across short, medium, and long distances. Unmanned aerial vehicles (UAVs), especially those with vertical take-off and landing (VTOL) capabilities, have the potential to be used in transportation services due to almost unrestricted access to areas without significant airspace errors. So drones are very important. Part of the process of implementing transportation services. These days, the worldwide Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) help UAVs throughout the flight phase. However, GNSS+INS's imprecision renders it irrelevant for landing with take off, entailing the assistance of human drone operators along such stages. Unfortunately, the accuracy of the navigation technologies currently in use is insufficient to locate drones. Because of this, full automation of the landing technique is Not feasible. Since the study describes the design of the autonomous system, this paper offers a solution to this issue. Automatic landing approaches are made possible by Doppler-effect-based navigation algorithms as a sophisticated accuracy evaluation. Simulation studies serve as the foundation for the vertical take off and landing (VTOL) solution. The proposed frequency hopping modulation with the Doppler effect adaptive control model has been designed and implemented to simulate the successful landing of a typical drone structure. Experimental results have shown excellent metrics for frequency hopping signal of (20-50) MHz carrier frequency and 150 hops/sec specified to UAV speed at 80 m/s having Doppler effect with N=1024 and 2024 samples. The obtained results show that the received control signals for Doppler effects match the results of the adaptive compensator, which indicates an enhancement in the take-off and landing operations of the drone model. The observed improvement was up to 98%, with an error rate of less than 0.2%, with reception power ranging from 40 to 100 dB at the operating frequencies of the path model
Global Navigation Satellite System (GNSS)
Boosting Audience Targeting Effectiveness in Digital Advertising Campaigns with Artificial Intelligence (AI): Review
Improving the effectiveness of audience targeting in digital advertising campaigns is a key factor for companies’ success in the digital age. With the rapid development of artificial intelligence technology, these campaigns have become more precise and engaging for the target audience. This paper reviews the importance of exploiting artificial intelligence tools to improve the effectiveness of digital advertising campaigns and e-marketing through improving personal targeting, big data analysis, and content personalization. The study includes a review of previous research on this topic and the various artificial intelligence tools used in this field, such as machine learning, big data analysis, and recommendation systems used in recommending products to users online while providing recommendations for improving digital targeting strategies.
Artificial Intelligence (AI), Audience Targeting, Digital Advertising, Company, Recommendation system E-commerce, E-Marketing, Revenue, Customer Engagement
A Comprehensive Review of Deep Learning Techniques for Detecting and Classifying Cataracts and Glaucoma
Artificial intelligence (AI) and deep learning have recently attracted a lot of attention as potential tools for the early detection of ocular diseases. Reason being, these innovations have the potential to substantially enhance therapeutic options and healing rates. With the use of algorithms built using massive medical datasets, a number of common eye disorders may now be detected automatically. These include myopia, diabetic retinopathy, macular degeneration, cataracts, and glaucoma. On a worldwide basis, cataracts and glaucoma are two of the leading causes of permanent visual loss. However, testing the viability of AI for the diagnosis of these two illnesses has been sparse in comparison to other age-related eye problems. An extensive review of deep learning algorithms for glaucoma and cataract detection using slit-lamp and fundus images and other relevant data is presented in this paper. As an added bonus, it explores state-of-the-art algorithms and models, preprocessing methods, retinal image databases, and performance evaluation standards. The main foci of this research are cataract diagnosis and glaucoma detection and classification. via the dissemination of new scientific knowledge to researchers and ophthalmologists via this comprehensive review, this study aspires to contribute to the progress of ophthalmology and the field overall.
Eye disease, Cataracts, Glaucoma, Deep Learning (DL), AI.
Optimization AVR Response Based on Intelligent Cheetah Algorithm
This research paper is interested in studying how to obtain the best response to the (AVR) Automatic Voltage Regulator, which is an essential device because of its clear effect on maintaining the stability of the voltage in various circumstances through researching to find the best values for the parameters of the Proportional Integral Derivative (PID) which are (Kp, Ki, Kd). After several experiments and studies by researchers to improve the AVR response, we discuss here another new study by applying the cheetah Optimization (CO) algorithm and knowing the extent of its ability to find the best solutions by comparing results by time limits, namely delay time (td), rise time (tr), peak time(tp), stalling time(ts), and Overshot (OS) as well as determining fitness function for Integral Squirrel Error (ISE). Where excellent results were obtained when implementing the (CO) algorithm when comparing the values of time limiters and error criteria, it is also worth mentioning that one of the most important properties of this algorithm is the speed of performance, as it is within the limits of seconds, so that it is the fastest algorithm among other algorithms.
Automatic Voltage Regulator (AVR), Cheetah Optimization (CO), Integral square of error (ISE), and Proportional Integral Derivative (PID)
A Systematic Method for Tackling Data Encryption Standard Vulnerabilities
Data Encryption Standard (DES) is a broadly used symmetric-key algorithm for ciphering digital data. However, there are several vulnerabilities related to this technique, including key distribution, namely, the key must be securely passed between the associated parties, and susceptibility, that is, the DES is defenceless against brute force and cryptanalysis attacks upon the key and the message, respectively. This paper attempts to tackle the first vulnerability by using the Diffie-Hellman protocol, as it enables the parties to interchange unshared secret. In addition, the second vulnerability is alleviated through: (1) performing the Columnar Transposition Cipher on the message before DES, (2) replacing the classical shifting process of the key with a type of shifter called Barrel. These improvements are vital for safeguarding against breaches and sophisticated attacks. The findings of conducted experiments have revealed that the proposed method satisfies the key and plaintext avalanche with a 50% output bit flipped in the ciphertext, and the average time required to crack passwords handled by the enhanced DES is more than that for classical DES. Therefore, the proposed method of this paper has raised the security requirements of DES.
Data Encryption Standard (DES); Diffie-Hellman Protocol; Columnar Transposition Cipher; Barrel Shifter
Robotics Applications in Healthcare and Industry Sectors: Review
The detailed inspection surveys the literature surveys the significant influences that robotics has been on many different fields, like healthcare and industry. This study takes a look at the advantages and the challenges of robots in different industries, following their development from simple devices into complex and very intelligent components today. The research demonstrates how robots have been developed from traditional manufacturing to more effective types that are used in healthcare and industry by depending on academic research, technical progress and real data. The use of robots has dramatically increased the efficiency of production in the industrial field, eliminating the error limits which resulted in cost savings as well as maximum quality articles. Many usages that, appear for robots, like those in healthcare for the monitoring and diagnostics of patients, as well as increase the precision of surgical operations. This full change explains the robot’s potential to transform the care of patients as a whole. Also, many studies focused on the robot’s growing importance in the service sector. This article confirms the robot’s potential to transform multiple industries to be under the control of robots to obtain more precision, quality and improved production in various fields
Robotics; Technology Impact; Industry; Healthcare
Classification of Water Bodies Using Sentinel-2 Image and Artificial Neural Network
This research aims to classify water bodies in Iraq using Sentinel-2A satellite images and artificial neural networks (ANNs) techniques. This satellite provides high-resolution multispectral imaging data, which helps neural networks to distinguish water from other features such as lakes, rivers, and reservoirs. An ANN model was trained using pre-labelled datasets to recognize the distinctive patterns of water. In this research, a new index called Water Body Index (WBI) is introduced to extract water bodies, which is based on the difference between six spectral bands of Sentinel-2A: blue (B), red (R), green (G), near infrared (NIR), short infrared 1 (SWIR 1), and short infrared 2 (SWIR 2). Using the neural net library in R I obtained a new linear equation that serves as the index. The new index concept had two distinct proposed versions. WBI-C (complete version). WBI-R (reduced version). The distributions of these new indices were compared with four other previously used indices: NDWI, MNDWI, MUWI-C, and MUWI-R. The results showed that WBI-C and WBI-R were more efficient in classifying water compared to vegetation, buildings, and other elements. The total average accuracy rates were: WBI-R: 99.6%, WBI-C: 99.8%, NDWI: 99.6%, MNDWI: 98.7%, MUWI-C: 98.9%, MUWI-R: 99%. As for the average Kappa Coefficient: WBI-R: 95.4%, WBI-C: 97.7%, NDWI: 85%, MNDWI: 87%, MUWI-C: 87% , MUWI-R: 89%. As for the average F-score: WBI-R: 97.7%, WBI-C: 98.9%, NDWI: 97%, MNDWI: 93.7%, MUWI-C: 93%, MUWI-R: 94%. These results show that WBI-C and WBI-R outperformed other indices in classifying water bodies with high accuracy.
Water Index; Sentinel-2; ANN; Machine Learning; Remote Sensing
DLFER: Deep Learning-based Cascaded Approach for Facial Emotion Recognition
Facial expression identification has garnered considerable attention in recent years owing to its extensive applicability across various domains, including human-computer interaction, market research, and healthcare. The primary objective of Facial Emotion Recognition (FER) is to correlate various facial expressions with their corresponding emotional states. Advancements in deep learning have significantly enhanced the recognition accuracy of FER technology relative to conventional approaches. This study seeks to improve the accuracy for facial emotion identification by introducing a Deep Learning Cascaded Network (DLFER) founded on the EF-FER1 and EF-FER2 architectures. The hyperparameters of the EfficientNet-B3 network were fine-tuned to improve facial expression representation and classification. The trials utilized the widely recognized Facial Expression Recognition 2013 (FER2013) dataset, comprising 35,887 greyscale photos of faces, each linked to one of seven specific emotions. The model's performance was assessed using accuracy, precision, recall, and F1 score. A comparative analysis was also performed with similar modern studies. The trials demonstrated that the Cascaded Network (DLFER) attained a classification accuracy of 82.09%, surpassing that of state-of-the-art models.
Deep learning ; Facial emotion recognition (FER) ; transfer learing ; EfficientNet-B3; Fine-tuning model
Unveiling Data Science Salary Trends (2021-2023) for Sustainability: A Comprehensive Analysis of Work Experience, Job Titles, and Company Location
Sustainability refers to the ability to maintain balance and continuity over the long term, whether in the environment, in the economy or in society, so one of the highest-paid professions in the world is data science because this profession is concerned with providing companies with tools and systems with a machine learning system, such as the grade estimation system for universities and the system for extracting thousands of pieces of data to make decisions. Data science salaries vary across different fields and depend on factors such as skills, programming experience, and certifications held. In recent years, experts have considered the job of data scientists to be the most attractive. However, people with data science experience seem to be rare. This study aims to highlight data science salary trends for the years 2021–2023 for Sustainability. Focusing on various aspects of employment, including work experience, job titles, and company locations, this data set provides valuable insights into salary distributions within the industry. The research is based on a dataset taken from the open-source ‘Kaggle’ and an online survey of various sites. This study showed that the level of salaries for data analysts has increased significantly in 2023. People with the highest salaries are experienced professionals who make the most money. The study also showed that salaries in US dollars are the highest, with experienced contractors earning 416,000.
Data Science, Salary, Job titles, Company locations, Visualization
A Review of Indoor Li-Fi Receiver Challenges: Mathematical Models and Testing Approaches
This work presents a general and introductory review of Visible light communication (VLC) systems represent an effective alternative to conventional wireless communications, providing a fast means of transmitting data in indoor environments. However, the dynamic nature of wireless optical channels poses several constraints that must be taken into account during design, especially at the receiver side. Among the most prominent of these challenges are those related to receiver movement and orientation, which may cause issues related to channel gain and signal to noise ratio (SNR).Since the channel changes continuously over time due to several reasons, including user movement and environmental factors such as dimming during use, accompanied by interference from other ambient lights, researchers are seeking to find accurate and continuous methods for estimation. In addition to the problems that come with relying on line-of-sight (LOS), it imposes limitations and challenges related to signal degradation due to unavoidable obstacles, and thus more efficient nonlinear techniques such as non-line-of-sight (NLOS). Advanced models have been proposed to solve receiver problems, including advanced receiver motion models, such as those for user device orientation based on experimental measurements. Device orientation statistics are presented for both sitting and walking activities, and other random path models based on light direction are considered with the importance of taking into account receiver orientation and motion patterns to improve performance. Also, deep learning methods are effective in improving resource allocation and signal detection to enhance flexibility especially in complex indoor environments. In this paper, the latest developments related to the problems and innovative solutions to ensure reliable communication in visible light communication (VLC) networks are discussed.
Receiver orientation; signal to noise ratio (SNR); Visible Light Communication (VLC); line-of-sight (LOS); non-line-of-sight (NLOS); Deep learning
A Hybrid Wireless Sensor Network for Smart Agriculture
The integration of Wireless Sensor Networks (WSNs) in agriculture has transformed precision farming, enabling monitoring and management of crops. However, traditional WSNs face challenges related to coverage, scalability, and adaptability to varying conditions. This study introduces a WSN architecture that combines terrestrial and aerial (drone-based) sensor nodes to enhance the efficiency of smart agriculture. While terrestrial nodes collect ground-level data, aerial nodes offer a perspective that can swiftly respond to changing environmental conditions. This combined approach optimizes resource allocation, enhances data accuracy, and ensures coverage. By conducting simulations and field tests, we assess the performance of the WSN in terms of coverage, energy efficiency, and throughput. The results indicate that hybrid WSNs significantly improve precision agriculture capabilities by providing a solution for real-time monitoring, efficient resource utilization, and timely decision-making. This study sets the stage for advancing cutting-edge farming technologies to meet the increasing need for productive agricultural practices.
HWSNs; Smart Agriculture; Precision Farming
Enhancing FSO Communication Performance with UAV Technologies
Integrating unmanned aerial vehicles (UAVs) into a relay-based free space optical (FSO) communication system has several blessings that incorporate dealing with turbulence-induced atmospheric scintillation due to their flexible mobility. This study extensively assesses a UAV-FSO system that is based on a decode-and-forward dual-hop strategy with multiple transmission sources. Through the incorporation of the Gamma-Gamma (GG) distribution to measure factors such as atmospheric losses, atmospheric turbulence, angle-of-arrival fluctuations, and pointing errors, a faithful formula for the probability density function (PDF) of the total channel gain is derived. A mathematical representation for the average bit error rate (BER) of the system is also derived and later on, validated through extensive Monte Carlo simulations. The simulation outcomes spotlight model robustness across a range of operational conditions. These results underline the enhanced efficiency and versatility of the UAV-assisted FSO systems that can manage various data sources, and consequently, they can be quite applicable in the case of surveillance and mobile network communications.
FSO communication, UAVs relay, BER, Pointing Error
Enhancing Efficiency of Digital Filters via Artificial Intelligence Algorithms
The "infinite impulse response" (IIR) digital filter was developed using the hybrid optimization artificial intelligence (AI) algorithm. At AI algorithm D-PSO, which stands for the "particle swarm optimization" dynamic topology, we make use of an approach that is both efficient and simple to memorize. The researchers investigated strategies to maximize the permissible ripples in a passband, the stopband, and the transition band while minimizing the nonlinear mean square error (MSE) between an initial filter response and the required filter response in those bands. Using the approach resulted in a considerable reduction in the square error rates of both the passband and the stopband, according to the findings of an experiment. The IIR digital filter square error values with the D-PSO are superior to those of digital filters using evolutionary AI algorithms such as the dynamic inertia PSO (DI-PSO) or the modified dynamic inertia PSO (MDI-PSO); this allows the IIR digital filter to achieve better results because the square error values depend on the sequence in which the filters are applied. Therefore, compared to digital filters and coefficients created by evolutionary ways, the ones produced by the suggested method are more precise and stable than those produced by evolutionary AI approaches.
Artificial Intelligence Algorithms, IIR digital filter, Particle swarm optimization (PSO) Algorithm