Al-Furat Journal of Innovations in Electronics and Computer Engineering

The use of strong encryption and hiding techniques is essential for secure digital communication. The aim of this article is to give a thorough study of advanced steganography methods designed especially for embedding information in Arabic text, which may be sensitive in nature and needs to be transmitted securely. This review is focused on four main criteria; embedding rate, invisibility, robustness against detection, and text quality. We conducted a comprehensive survey of text steganography and examined its distinct characteristics, including the nature of the script, accents, and linguistic rules. Several researchers have utilized these distinct characteristics of Arabic writing for developing a number of techniques for embedding information. This article gives a detailed analysis of the strengths, weaknesses, possible applications, and major challenges. The conclusions of this review can help researchers and practitioners to select an ideal method according to their own needs regarding storage capacity, security, and text quality. Moreover, the article underscores the need for giving priority to Arabic text steganography which has become highly important as the use of hidden communication increases in Arabic-speaking communities, while also preserving the cultural and linguistic integrity.

The proliferation of digitalization in the realm of information has paved the way for numerous types of multimedia data to be attacked. Hence, the security of information has become a critical concern. Over the years, image steganography has been widely acknowledged as one of the most effective techniques for securing confidential information. This methodology comprises the integration of sensitive information into a cover image via the manipulation of specific pixels, resulting in the formation of a steganography image. However, it is imperative to note that such manipulations could be identified by steganalysis algorithms, thus undermining the security of the message. To address the aforementioned concern, the notion of coverless image steganography has been introduced, which serves as a promising solution. The coverless image steganography technique entails the establishment of a mapping correlation between the content of the clandestine message and the cover images, while leaving the cover images unaltered. This review article meticulously scrutinizes the recent studies on coverless image steganography, thereby furnishing significant and profound insights into the techniques.

Deploying wireless sensor networks (WSN) is one of the areas with the most significant research since the methods employed can have a big impact on the system's overall performance and the amount of energy the sensors in it consume. Thus, an effective deployment problem solution should not only "enhance its performance" but also save the energy needed to extend the lifetime of WSN. Finding an optimal solution for most deployment problems with limited computational resources is challenging, particularly for NP-hard optimization problems. By searching for a nearly optimal solution with constrained computational resources in a reasonable amount of time, metaheuristics present an alternative to exhaustive search and deterministic algorithms in solving these optimization problems. This paper classifies the deployment techniques of sensor networks and the metaheuristic algorithms.

The study investigates the development and improvement of hybrid renewable energy systems for a residential residence in Babylon, Iraq, utilising the HOMER programme. The project aims to determine the most efficient and cost-effective energy system design for residential usage, in response to the growing global need for sustainable and renewable energy sources. The process entailed examining different permutations of solar photovoltaic (PV) panels, wind turbines, and battery storage devices. According to the results, an off-grid photovoltaic (PV) and battery system is the most cost-effective option, with the lowest upfront investment and ongoing expenses. Nevertheless, the integration of wind energy, although enhancing the dependability and variety of the system's energy sources, results in higher upfront and ongoing costs. These findings offer vital information for politicians and engineers to implement renewable energy solutions that are customised to Iraq's specific meteorological and economic circumstances.

All existing communications systems require high-speed data transmission rates, as all services today demand high transmission speeds and data transmission dependability, and communications are an essential aspect of daily life. One of the most important modulation techniques used in both wired and wireless communication networks is Orthogonal Frequency Division Multiplexing (OFDM). It is a cutting-edge technology for achieving high-speed digital communication. In this work, compression Images by Discrete Wavelet Transform and transmission over DWT-OFDM, and used different baseband modulation schemes such as Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Key (QPSK), Quadrature amplitude modulation (16QAM), and (64QAM) has been considered. From numerical simulation results, it is observed that produced better quality images at the receiver when the Quarter Amplitude Modulation is 16QAM and 64QAM with high SNR.

Cloud computing is one of the networks that has attracted more people's attention than other networks in today's world, and the reason for this is that it stores data in itself. This network consists of different computers that are scattered in different places and each user will be able to be a member in this space by registering in it. Among the many important issues related to the environment is the issue of security, which has become a very complex challenge. In order to secure networks, tools have been created, and one of these tools is the use of artificial intelligence methods to establish security in this environment. In most of the researches carried out in this field, either the duration of the algorithm execution was very long or the researches did not have enough accuracy, so article, we tried to detect the penetration in cloud computing. This was done in two different scenarios for feature selection and reduction. In one scenario, we reduced the dimensions using the fundamental analysis method, and in the other scenario, we did this using the bat meta-heuristic algorithm. In both scenarios, we used self-encrypting neural network for classification, and the structure of this network was the same in both scenarios. The reason for using these two scenarios was that we want to compare the accuracy obtained and the execution time of the algorithm for the dimensionality reduction method with principal component analysis and meta-heuristic algorithm. , the obtained results showed that the accuracy of the dimension reduction method with the meta-heuristic algorithm was more accurate than the principal component analysis method, but its execution time was approximately 13 minutes more than the principal component analysis method.

The development of heterogeneous networks (HetNets), in the dynamic field of wireless communications still depends critically on the improvement of power control and interference management. An original sectoring technique to address the problems with Non-orthogonal Multiple Access (NOMA) systems such power allocation and interference management is presented in this study. It attempts to greatly lower inter-user and co-tier interference, increasing network efficiency and user fairness, by combining a strategic sectoring approach, round-robin channel allocation, and an inverse route loss power allocation framework. In the present work, the Bit Error Rate (BER) performance in a simulated two-tier NOMA-enabled HetNet is analysed using Monte Carlo simulation. The findings show that, especially at higher signal-to-noise ratios (SNR), sectoring considerably lowers BER by around 50% compared to the non-sectoring situation. The results of this work emphasize the need of sectoring in optimizing the efficiency of future wireless network infrastructures and have wide-ranging implications for the deployment of 5G networks and beyond. The findings back up the idea of combining sector-based designs to increase spectrum and energy efficiency, hence increasing the reliability and resilience of future communication systems

This study provides an integrated control strategy with proportional derivative (PD) and sliding mode control (SMC) for a quadrotor. For inner loop control, the SMC, a robust nonlinear control method, is used to increase the system's resistance to uncertainty and shocks. By using high-speed switching in variable structure control, it reduces tracking mistakes. In the meantime, the PD control concentrates on tracking a helical path while controlling the outer loop for trajectory tracking. The PD and SMC parameters (k1, k2, and k3) could not be manually adjusted to produce acceptable results at first. As a result, this work presents an improvement by optimizing these control parameters using the Wild Horse Optimization (WHO) algorithm, a modern and cutting-edge optimization technique. This improvement shows good results in improving the quadrotor's tracking precision and stability, and it also greatly increases system performance. All simulations were performed in MATLAB, which facilitated detailed analysis and validation of the proposed control strategy.

In the modern world, Q&A systems are essential for promoting better communication between people and technology. These systems play an important role in collecting information quickly and efficiently, and this leads to great progress in learning, teaching and development in many areas of life. Using deep learning techniques, this research addresses the problem of excellent prediction of the questions that need to be answered. We created a question-and-answer system using "Bidirectional Long Short-Term Memory (BiLSTM)", a modern neural network known for its accuracy and results in text analysis and natural language understanding. This technique is more effective in understanding questions and producing very accurate answers because of its special ability to pay attention to preceding and following information in a sentence. Preprocessing was used to remove unnecessary, unimportant and time-consuming data. The "Stanford Question Answering Dataset version 2 (SQuAD 2.0)" was used, which is considered one of the important datasets used in the field of machine learning and natural language processing. The following evaluation metrics were used to evaluate the model’s performance: “Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), Recall, Precision, Loss, F1 Score, and Exact Match (EM). The results, based on 150 epochs (EPOGs) and 128 batch sizes with a cleaned dataset split into 70% training and 30% test/validation (15% each), are as follows: "Precision (0.966), Loss (0.591), F1-score (0.966), Recall (0.967), EM (0.967), MRR (0.918), MAP (0.776), and accuracy (0.966)". Interestingly, the highest performance was observed when using the accuracy measure.

With the demands increasingly imposed by many new applications on existing networks, long-reaching high data rate links are crucial. One of the ideal ways to deal with such an advanced telecommunications system is to use Dense Wavelength Division Multiplexing (DWDM). This work proposes an optical communication system based on 32-channel DWDM and designed for a high data speed of 40 Gbps per channel. In addition, this study investigates different dispersion and attenuation compensation mechanisms. The primary aim is to extend the transmission distance of a high data rate DWDM system. On the other hand, this study aims to find the best parameters setting for the 32-DWDM channels by comparing the performance of numerous optimization algorithms to achieve better performance compared to prior studies. This paper presents a novel method to improve various system characteristics, particularly the signal-to-noise ratio, as the main goal of improving the system's performance in general and making it suitable to become a mainstay for networks with high data rates by determining the optimal values for essential DWDM system parameters. To realize this, Optimization techniques are applied to multiple critical variables such as filter bandwidth, input power, and dispersion modulation. Optisystem program is utilized to simulate the proposed system and analyze the variables that play a vital role in raising the performance to a higher level. Three different algorithms for dispersion compensation and optimal power levels are also compared and implemented. The optimized scheme proposed demonstrates the achievability of reaching long distances with a data rate of 1.28 Tbps. The results of system performance in this study push the DWDM technique to be a candidate for high-speed backbone optical fiber link offering an interesting practical solution for the growing network.

Due to the tremendous development in the wireless communication field and the wide requirements of current generations, such as high data rates, extremely low power consumption, and high performance, it is necessary to think about techniques and methods that present improvement to meet the growing requirements. The introduction of the Generalized Frequency Division Multiplexing (GFDM) technique was one of the proposed methods; it is one of the candidate schemes for the current generations and beyond. GFDM was presented as an improved method over other multiplexing techniques, as it provided an improvement in the field of Out Of Band (OOB) and Peak Average Power Ratio (PAPR). At the same time, it provided a lower performance in BER with respect to SNR. In this paper, methods of enhancement were presented based on Matlab to improve the performance of the GFDM system, which was improved by adding a coding system based on Polar Coding (PC). The proposed method is presented to improve the performance of GFDM by using the Genetic Algorithm (GA) to improve the performance of the Pulse shaping Filter (PSF) in GFDM. The channel estimation is accomplished by means of the LS method, and the Artificial Neural Network (ANN) based on feed-forward backpropagation is used to enhance the estimation process. The proposed Genetic Algorithm Artificial Neural Network Polar Code Interleaver GFDM (GNPI-GFDM) presents an enhancement over the traditional GFDM of more than 3.5dB at BER=10-4 over the Rayleigh fading channel.

The urgent need to improve the efficiency and precision of analyzing images in the field of magnetic resonance imaging (MRI) has led to the rise of transformer networks, as a groundbreaking solution. This review delves deeply into how transformer networksre used in classifying MRI images summarizing studies to showcase the progress made and challenges faced in this evolving area. With their abilities transformers excel at analyzing MRI images by highlighting both details and functional complexities while effectively capturing both global and local nuances using their advanced handling of long distance connections. Additionally their adaptability allows for processing of input data in sizes along with the capability to break down processing and analysis tasks. By providing researchers with an insight into transformers and their crucial role in enhancing medical imaging methods this research aims to lay the groundwork, for advancements in this critical field.

This study studies the impact of activation functions in the field of machine learning and deep learning in general and especially on medical images for different aims such as classification, clustering, feature engineering. Important components that add nonlinearity and allow networks to learn intricate patterns are activation extraction, training and etc. The study first starts by explaining various activation function types that are commonly used in NN applications and fields. Subsequently, a comprehensive comparative analysis is conducted, to evaluate how activation functions perform in terms of accuracy and their impact on speed convergence. Understanding how activation functions impact the categorization of medical imagery is crucial to the study's findings. In additions, the study illustrates overview of which activation functions yield optimal results. The Main results contribute how to select best activation functions that suits most accurate and efficient medical image classification based on this overview, any researcher can choose best activation function after reading this overview.

Cardiomyopathy encompasses various types and refers to a condition affecting the heart muscle, which impairs the organ's normal pumping function. When diagnosing cardiomyopathy, it is crucial to identify the specific type of the disease. This paper outlines the benefits and implications of a new classification system for cardiomyopathy, highlighting its potential to improve diagnosis and treatment, as well as its impact on research and clinical trials. The methodology section details the research design, data collection methods, and sample selection process. The data analysis section explains the techniques used for data processing and statistical analysis. Finally, the evaluation and validation section describes the expert review process and the testing and validation methods applied. This research adopts an interpretive paradigm, which emphasizes the importance of individual experience in understanding social realities. The statistical analysis assessed the diagnostic capability of genetic testing across genders using ROC curves, revealing strong performance for both males (AUC=.856, p < .001) and females (AUC=.835, p < .001), surpassing pure chance. A one-way ANOVA test was conducted to examine age differences among four genetic patient groups. The assumption of homogeneity of variance was satisfied, as indicated by Levene's test for equality of variances, F(3,156) = 1.277, p > .05. Significant age differences were found among the groups, F(3,156) = 5.537, p < .005. An evidence-based classification system would enhance diagnostic accuracy, treatment efficacy, and research quality, thereby improving outcomes for patients with cardiomyopathy.

In this paper, a novel design based on a dual-mode ring resonator, the filtering balun, feeding the printed dipole antenna, the filter balun antenna (FBA), is presented. One port on the structure allows the signals to be phase-shifted 180 degrees from one unbalanced port to two balanced signals. The suggested designs use a single dual-mode ring resonator to operate as both a filter and a balun at the same time. Compact designs are achieved by applying feeding to the second layer. Another unique characteristic of the filtering balun is its ability to alter, based on perturbation size, the phase error and magnitude imbalance of the two balanced signals. With the use of the Advanced Design System (ADS) software, numerous structures have been developed, modeled, and evaluated. To achieve the highest performance, every section of the design is assessed and optimized. Because the substrate is a lossy type (FR4), the realized transmission coefficients, which are poorer than expected ideal values, make the suggested design a strong contender for narrow-band wireless applications. For S2,1 and S3,1, the signal is routed through two vias and fed into a dipole antenna in the opposite phase by employing the zero iteration for filtering the balun in the first layer to condense with the second layers. The antenna structure is a printed dipole antenna operating at 2.4 GHz.

This comprehensive exploration delves into the innovative field of smart inverter design, with a particular focus on the role of artificial intelligence (AI) in enhancing the integration of Distributed Energy Resources (DERs) into power grids. The rapid evolution of energy systems and the increasing reliance on renewable energy sources have necessitated the development of more sophisticated technologies for power system management. Smart inverters, at the heart of this transformation, are crucial for integrating DERs like solar panels and wind turbines into existing power grids. The integration of AI into smart inverters represents a significant advancement in managing and optimizing these complex systems. This study demonstrates how these advanced technologies can lead to substantial improvements in inverter reliability, power distribution efficiency, and overall management of renewable energy sources. Realistic models highlight the resilience of smart inverters to fluctuating grid conditions and their ability to maintain system reliability and efficiency. Furthermore, the study critically assesses the current challenges facing this field, such as the need for standardized communication protocols, robust cybersecurity measures against digital threats, and the ongoing requirement for innovation to keep pace with rapid technological advancements. Finally, the exploration forecasts future developments and areas that will further enhance smart inverter technology, emphasizing the essential role of continued research and development in fully realizing AI's potential to transform power grids. The examination concludes by highlighting the promising future of AI in smart inverter design, particularly in contributing to the development of more efficient, reliable, and sustainable energy systems.

The major challenge of data authenticity is how to check for image fraud, which creates a huge problem for the credibility of visual media. In this paper, we propose a method to investigate the performance of a Multilayer Perceptron (MLP) to extract the fraud images, this network is a class of supervised Artificial Neural Network (ANN. The proposal model applies MLP model to allocate extracted image features in order to distinguish them between real and modified contents. The examined features are included within statistical matrices, analysis of histogram space, and possible inequality that may arise during modifications. The proposed MLP was trained with dataset that contains both real and fraudulent images, thus allowing the model to extract knowledge from the original patterns that differentiate between those two classes. The model's performance was validated with several metrics, including accuracy, precision, and computational cost. Furthermore, this paper presents comparisons against traditional methods that were examined in the procedure. The finding of this work enhances the model with improved image fraud detection by showcasing the capabilities of MLPs within 162.59 seconds to 86% detection, while the base algorithm in 205.92 seconds succeeded in recognizing 82%.

This paper presents the development and effectiveness of deep and machine learning techniques in forecasting stock market trends. This paper review focus on key developments from 2020 to 2024 involves the integration of ensemble learning, sentiment analysis from social media, and various predictive algorithms such as LSTM, CNN, and ANN. These techniques improves predication accuracy by efficient processing large data groups and detect nonlinear patterns in stock price trends. in addition, the paper presented background about various machine learning algorithms such as KNN, naive Bayes, linear regression decision trees, SVM, random forests and deep learning models such as neural network, LSTM and CNN .the findings of this study to providing valuable insights to researchers and practitioners who aim to improve investment strategies and improve predictive accuracy By looking at the appropriate algorithm.

In recent years, humans rely heavily on space Satellites are especially for communications, military defence, intelligence, science and Trade. As a result of the development of artificial intelligence technology, especially machine learning and deep learning, Time can be saved by using satellite images, which can offer a wealth of large-scale information on the Earth's surfaces quickly. Advanced image processing techniques have been used to improve the resolution of acquired objects with the emergence of sensors that give satellite images. Research on ways to apply it in various fields has been actively conducted. This paper summarizes research activities using machine learning using a search engine to survey the number of research proposals submitted in 2013 and 2024 and the number of papers published in the past five years, although the purpose of applying machine learning as there was a relatively large number of studies involving learning Automated. There has also been increasing interest in new methods such as reinforcement learning.

In the realm of medical imaging, segmenting and classifying medical images is essential for helping medical professionals diagnose and treat a variety of medical disorders. This review discusses artificial intelligence (AI) techniques. AI systems have shown impressive accuracy and efficiency in identifying and quantifying elements in medical images, such as MRI, CT, and X-rays, by utilizing deep learning techniques, in particular convolutional neural network (CNNs). We describe CNN design and training for medical image segmentation and classification, emphasizing the usefulness of CNNs in identifying and defining diseased regions and anatomical features. Even with obstacles like data privacy, requiring sizable annotated datasets, and requiring model interpretability, further research and development in AL-driven medical images analysis has promise for improving clinical decision-making and diagnostic accuracy. Future research in this area should concentrate on improving the generalizability and resilience of AI models by using methods like data augmentation, transfer learning, and the creation of more complex network topologies. Furthering the area also requires guaranteeing ethical concerns, enhancing data-sharing mechanisms, and encouraging cooperation between medical practitioners and AI researchers.

Death rates across the globe are often linked to respiratory illnesses, with severe conditions like­ chronic obstructive pulmonary disease (COPD) and asthma being the primary culprits. Early detection of the­se diseases in their initial stages is more crucial than we may realize. The ancient diagnostic technique of lung auscultation, where a stethoscope is placed on the lungs, is renowned but also has inherent limitations and susceptibility to data distortion due to environmental variables. This led to the deve­lopment of modern solutions, born out of necessity, to address these challenges innovative methods that harness the power of deep learning algorithms to capture respiratory sounds more accurately. The International Conference on Biomedical and Health Informatics (ICBHI) dataset, containing lung sound recordings, is available to the machine learning community for research and development. Leveraging machine learning and deep learning techniques, with the latter being a subset of machine learning, such as convolutional neural networks, has enabled more accurate diagnoses compared to traditional auscultation methods. These advanced algorithms have achieved impre­ssive voice classification accuracy rates, outperforming conventional approaches. The fusion of cutting-edge technology and medical expertise has the potential to revolutionize respiratory disease detection and management. Scientific investigations and research have demonstrated that when utilizing )ICBHI 2017( data set, its precision varies from 42% to 90%.The goal of this article is to review articles related to the use of deep learning algorithms, which are combined in some articles with other machine learning algorithms, and the way they deal with the ICBHI 2017 dataset.

The rise of the internet and intelligent gadgets has increased platform traffic, collecting data to detect user preferences. Researchers need help finding relevant material due to the rapid proliferation of academic papers across fields. Academic article recommendation systems (ARSs) help solve this problem by suggesting articles based on research interests and needs. Academic article recommendation algorithms help navigate the vast scholarly literature. Researchers, students, and academics utilize them to find articles that match their interests and study emphasis in enormous databases. This review covers recommendation algorithms, data sources, assessment metrics, and user interfaces in ARS research. This survey also examines new trends and research directions, such as advanced machine learning and semantic analysis.

Image segmentation is a crucial technique for processing images. It is a challenging task to process images, and the quality of the segmentation process affects the following assignments, which include classification, object recognition, feature extraction, and object detection. It's a significant phase of a system for computer vision. Image segmentation is the basic problem in many applications for image processing. Over time, image segmentation has gotten more challenging due to its extensive use in numerous applications. It is the procedure of segmenting the image into various areas by using a specific technique. There are many different ways for image segmentation. A new information parameter with a threshold basis for segmenting images using the genetic algorithm. Due to its ability to calculate the ideal number of segmentation regions, we employed the Genetic Algorithm. In this work, a novel approach built upon a genetic algorithm is used to solve the image segmentation problem by utilizing the thresholding concept. The suggested method uses a genetic algorithm to identify the evolutionary best segmented image based on a threshold that is based on new information. We presented the results of our experiments using the suggested method on various grayscale images in the last section. By using parameters used to evaluate image segmentation quality (PSNR, MSE, SC), we notice the results are good.

This paper presents a hybrid communication system combining optical code division multiple access (OCDMA) and multicarrier millimeter-wave (MM-Wave) architecture operating at 35, 55, 75, and 95 GHz connected to a home network center (HNC) and the end user (EU). Hybrid Link reduces congestion on complex RF networks by combining optical fiber backhaul with radio links. The OptiSystem simulation demonstrates successful data transmission and transmission capacity at 40 Gbps over various distances. Furthermore, this new 40G-MM wave OCDMA system that takes advantage of a two-dimensional single-weighted zero cross-correlation (2D-SWZCC) system with a multiplexing scheme of polarization wavelength multiplies the same N channels to M OCDMA code of the MM-Wave system that dramatically increased scalability. The comparative analysis shows that the proposed 2D-SWZCC hybrid algorithm outperforms 1D-SWZCC regarding scalability. This study contributes to developing communication algorithms with high data rates and provides promising solutions for method limitations and scalability in hybrid-MM-Wave-OCDMA architectures.

Road safety is one of the most important conditions for public driving safety around the world. This is done by monitoring the roads periodically to detect defects that can appear due to several factors affecting the asphalt, such as weather and accidents. Traditional road inspection methods are often time-consuming, labor-intensive and expensive due to the length of the methods. Deep learning (DL) has emerged as a promising technology for automated detection and classification of road defects, offering the potential for faster, more accurate, and less expensive inspections. This review paper provides a comprehensive overview of the state-of-the-art methods working on the principle of artificial intelligence for detecting defects on road surfaces. We will also discuss the different types of defect monitoring methods that differ from one method to another and furthermore, we will show the different datasets from oldest to newest with all their details that can be useful in DL. A review is then performed of various blind learning architectures and algorithms that have been used for defect detection and classification, incorporating generative adversarial networks (GANs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs). Finally, we will provide a comparative analysis of the reviewed algorithms and methods by presenting the metrics used to choose the best technique, which is what these paper aims to do.

Millimeter Wave (mmWave) frequencies, a factor of the electromagnetic spectrum, provide a varied array of frequency bands that can be connected for the development of 5G mobile communication technologies. These frequency bands proposal a substantial view for advancing data transfer rates and network capacity inside the area of wireless communication. However, the employment of mmWave technology faces numerous challenges and obstacles that must be fixed. One of the key challenges relates to propagation losses, which have the possible to disturb the effectiveness of signal propagation over unlimited distances. Furthermore, accomplishing accurate beam alignment represents a vital obstacle in guaranteeing precise signal transmission between transmitters and receivers, thus optimizing the total efficiency of 5G infrastructures. An additional difficulty to be overcame is the existence of signal reflection paths, which may result in interference and signal decline if not effectively managed. In conflict, technique similar to beamforming policies is vital progression revealing the potential of mmWave which is utilize many antennas operating together and automatically instruction and intensive the signal to the selected recipient with notable precision. In addition, participating phased antenna arrays as improvements in antenna technology and reduced antennas and integrated circuits is extra advancement in 5G technology. Furthermore, scattering applications challenging high precision as a result of the high-frequency nature of mmWave and realizing multi-gigabit-per-second has the beneficial comprise the possible for maximum-capacity wireless broadcast at high data rates. The determination of these challenges is authoritative for revealing the comprehensive potential of mmWave technology and exploiting on the advantages it can offer to the realm field of mobile communications.

Emotion detection in Arabic text in social media has gained significant attention due to the increasing use of social media platforms in the Arab world. It is a special case of affective computing that relies on natural language processing techniques to recognize emotions from written text. This article provides a comprehensive review of different approaches used in developing models for emotion detection in Arab textual data in social media. Social media have a substantial impact on societies and have contributed significantly to many fields (marketing, recommendation systems, etc.). Arabic language texts on social networking sites in all their forms (posts - comments - messages - any other form) express the personal feelings of the person concerned; if they are understood correctly, the understanding of the written text will be correct. Scientific research has recently increased in this field to determine people's feelings through their writing of texts. In addition, we have highlighted the utilization of deep learning architectures in the improvement of the performances of models for different types of deep learning models.

The adoption of IoT technology in universities has significantly improved campus security, emergency response, and control systems, particularly through IoT-based surveillance and fire detection systems. Traditional fire detection systems suffer from high false alarm rates, leading to unnecessary resource allocation. This paper proposes an intelligent fire detection system leveraging IoT and adaptive fuzzy systems to enhance accuracy and reduce false alarms. The system uses sensors to collect real-time data, which is analyzed by an Adaptive Neuro-Fuzzy Inference System (ANFIS) to determine alarm levels based on input severity. ANFIS combines fuzzy logic and neural networks, enabling learning and adaptation. Data analyzed by ANFIS is sent to ThingSpeak channels for real-time monitoring. When a fire is confirmed, alerts with fire location, timestamp, and severity are sent via SMS through GSM to the fire management system. The system utilizes cost-effective, small-sized sensors, ensuring repeatable solutions. After training, the ANFIS system achieved 99.59% accuracy, significantly reducing false alarms. It demonstrated faster detection times by reducing the fire detection rate by 28% and maintaining high detection accuracy with fewer sensors, training inputs, and epochs. Utilizing IoT and ANFIS technologies, the system integrates efficiency, speed, and reliability, protecting lives and property and highlighting the importance of safety and technology in serving humanity.

Load balancing is essential within the cloud. Distributing the workload over many servers permits for a more efficient machine. Optimal resource use, decreased time to response, and more advantageous gadget performance are all results. Among the numerous challenges that traditional methods of accomplishing equilibrium come upon are problems with adaptability and the unpredictability of cloud structures. This survey focuses on the demanding situations associated with load balancing in cloud computing and proposes the usage of specialized algorithms as an answer. It determines the most reliable alternatives for facts placement in the cloud. Meta-heuristic algorithms draw concept from herbal approaches, which includes the foraging behavior of ants or the hunting strategies of predators. These algorithms excel at rapidly finding super answers in situations where conventional optimization algorithms fail to get great results. Various strategies have been proposed to attain paintings equilibrium in cloud computing. Evidence has shown that these techniques can enhance work balance in cloud computing.

In many industries, particularly electronics, photonic devices are indispensable. However, the diffraction limit and miniaturization are the two problems that have hampered the development of photonics devices. These problems are resolved by plasmonic devices, which enable nanophononics and nanodevices. One of the primary all-optical demultiplexer components utilized in an all-optical Athematic Logic Unit (ALU), which is regarded as the basic building block for all-optical computers, is a plasmonic demultiplexer This paper provides a new design for an elliptical ring insulator-metal-insulator (IMI) plasmonic waveguide-based all-optical demultiplexer (Demux). The suggested device has a small footprint (300 nm × 250 nm) and works at a 1550 nm wavelength. Demux has a 0.5 transmission threshold between logic zero and logic one states. Transmission (T), extinction ratio (contrast ratio (CR)), modulation depth (MD), and insertion loss (IL) are the four metrics that best explain the performance of the plasmonic Demux. The suggested structural dimensions are outstanding and optimal based on the values of MD for Demux which is (95.87%). The device's maximum transmission efficiency is 56.84%. The suggested plasmonic Demux structure makes a substantial contribution to all-optical signal processing Nano-circuits and nano-photonics integrated circuits. Using COMSOL Multiphysics, we simulate the proposed plasmonic Demux using the Finite Element Method (FEM).

The OPF difficulties are characterized by the presence of four distinct objective functions., which are: active power losses, fuel operating costs and voltage variations. The volatile and variable nature of distributed generator (DG) sources contributes to the convolution of the optimum power flow (OPF) issue. This paper employed bat algorithms to tackle the issue of optimum power flow (OPF) and PSO algorithm to optimal allocation of DG. The goal was to enhance the effectiveness of energy use, environmental sustainability, and cost efficiency of the electricity grid. The test cases include a wide range of scenarios, including the existence of Distributed Generation (DG), different locations of DG within the network, various kinds of load expansion, and occasional breakdowns of transmission lines. These possibilities are considered in order to address the problem presented by the suggested solution OPF. The incidents are assessed utilizing bus systems, such as the 30 bus system in MATLAB.

The problem of edge detection represents one of the most elementary assignments in image processing, providing an essential base for all further study and interpretation of the visual data analysis. This paper proposed an enhanced version of the Ant Colony Optimization (ACO) algorithm for edge detection. The following paper tries to compare the Proposed ACO method with the conventional techniques of edge detection like Canny, Prewitt, and Sobel, using various quantitative metrics like Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Entropy, Natural Image Quality Evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) applied over different images. The datasets for this evaluation are considered as a standard Cameraman, a biological cell, and MRI image, with and without noise, considering the ranges of complexities and textures. The results of our study prove the competencies of ACO's algorithm. In some cases, it stands out against standard algorithms for MSE and PSNR values and maintains high Entropy values, suggesting the robustness of detail-keeping in an image. Further, the quality assessment of the images by using NIQE and BRISQUE shows the ability of ACO to maintain a natural appearance post-edge detection. In this regard, the study highlights that the proposed ACO is an effective method for edge detection in varying image conditions, and, in doing so, it even validates the effectiveness of bio-inspired algorithms in image processing domains.

The increasing demand for electricity, the expansion of complex power networks, and the operation of power systems near their capacity have heightened the risk of power outages. Consequently, control room operators must develop comprehensive restoration plans to minimize recovery time. Key steps in power system restoration include starting non-black-start units with black-start units and initiating load pick-up. This process requires identifying the shortest route between nodes to expedite repairs. This research presents an expert plan employing the Modified Cuckoo Search algorithm (MCSA) to determine optimal pathways. Additionally, it explores the optimal allocation of distributed generators (DG) to enhance the system's voltage profile and reduce power losses using particle swarm optimization (PSO). The results demonstrate that DG significantly reduces losses during recovery. The study prioritizes the restoration of DGs with strong black start capability, considering network and DG constraints. The effectiveness of the proposed restoration approach is validated through MATLAB simulations on the IEEE 39-bus system.

A considerable number of people get their news from electronic sources. As there was a great increase in the usage of these platforms, more and more people are consuming the news from social media sources along with other websites. Thus, a variety of sites and sources has grown exponentially, allowing for easy and fast distribution of false information. Such deliberately generated lies fixing to deceive both the person and society are referred to as fake news Since the media plays an essential role in presenting fake information that alters public opinion and makes members of society take responsibility for unsupported facts. Currently the widespread of social media has worsened the level of fake news dissemination. To assess our methodology, we used popular machine learning classifiers such as Support Vector Machine (SVM), Random forest (RF), Logistic Regression(LR), Naive Bayes(NB), Gradient Boosting, AdaBoost, K-nearest neighbor (KNN), Decision tree (DT), and Extreme Gradient Boosting (XGBoost). We constructed a multi-model false news detection system using the Majority Voting approach, using the previously discussed classifiers to provide more accurate findings. Our strategy achieved an accuracy, of 0.9977%, recall of 0.998%, precision of 0.997%, and 0.997% of F1-measure, with a loss of 0.0022 according to the trial data. The assessment reveals that, in comparison to individual learning strategies, the Majority Voting approach produced more outcomes that were deemed acceptable.

The ever-increasing and ever-demanding growth of the field of cryptanalysis imposes a continuous requirement for algorithm modifications. This paper introduces modifications in widely-used RC5 block cipher to cater to the increasing security demands, i.e. dynamic S-box generation and magic square-based key expansion. The main purpose is to improve the complexity and linearity of the algorithm as a result making it resistant to newly-devised attacks. A small (3 × 3) magic square is taken for generating dynamic S-boxes and constants to add unpredictable randomness during the key expansion phase. SHA-256 hash function is used for S-boxes generation meanwhile properties of magic square are exploited for constants derivation purpose. Proposed changes resulted in improved security against differential, linear, and algebraic attacks respectively meanwhile efficiency is still sustained so that this modified version can be well-suited for recent applications that necessitate strong encryption services.

Watermarks are commonly used to protect the ownership and copyright of digital media. However, there are legitimate scenarios where watermark removal is necessary. Recent advancements in deep learning have led to the development of sophisticated techniques for both detecting and removing watermarks. this research provides a summary of methods for detecting and removing Regenerative Adversarial Networks (GANs) are one noteworthy method. It is possible to train GANs to recognize watermark patterns and produce unwatermarked versions of watermarked content. One such method, which uses GANs to find and remove watermarks in deep neural networks (DNNs), has been demonstrated to be successful even when it comes to DNN watermarks that are based on backdoors. Another method makes use of deep neural networks' U-structure, which is highly effective in translating images. A comprehensive model like the AdvancedUnet has been developed to concurrently extract and remove visual watermarks. This model uses a deep-supervised hybrid loss to direct the network in learning the transformation between the watermarked input and the clean ground truth. It also integrates efficient modules to extend the architecture's depth without appreciably increasing computing costs.