Iraqi Journal of Science

Staphylococcus aureus (S. aureus) represents one of the most prevalent patho-gens responsible for food poisoning because of its capacity for producing staphylo-coccal enterotoxin. The main goal of this investigation was to determine the pres-ence of S. aureus and evaluate its enterotoxin genes in randomly selected food han-dlers from different restaurants and cafeterias in Baghdad City, Iraq. A total of 325 nasal swabs were collected from healthy food handlers and examined for the pres-ence of S. aureus and classical enterotoxin genes. The strains were determined using confirmatory biochemical, morphological, and molecular (nuc gene) techniques. Five Staphylococcal enterotoxin genes, including sea, seb, sec, sed, and see, were screened using multiplex polymerase chain reaction. Out of the 325 samples, 30.15% harbored S. aureus pathogens that were confirmed by the existence of nuc gene in all isolates. 46.93% of isolates were positive for one or more classical genes. 22.44% of these isolates harbored a single enterotoxin gene. The prevalent single gene was sea (9.1%), followed by seb (6.1%), sec (4%) and lastly, sed (3%). The see gene was not observed in any of the isolates. 14 (14.28%), 6 (6.1%) and 4 (4%) ex-hibited two, three, or four genes, respectively. The sea + seb (5.1%) and sea + sec (4%) were the most frequent combinations. The current data conclusively demon-strated a high rate of enterotoxigenic S. aureus nasal carriage among the food han-dlers studied. Thus, comprehensive and continual evaluation of hygienic protocols and infection prevention plans is strongly recommended. Finally, nasal decoloniza-tion could be one of the most effective ways to minimize staphylococcal food poi-soning and protect restaurant customers.

Polycystic ovary syndrome (PCOS) comprises a multiplex hormone-based disorder that is typically accompanied by several complications, including irregularity of menses, excessiveness of hair growth in face and body, acne, obesity, diminished fertility, and higher probability of developing diabetes. Many hormones and enzymes may undergo changes in PCOS patients. This study involved 138 women who were separated into two groups: PCOS women (n=90) with an age range of (22-45) years, and apparently healthy women (n=48) representing the control group with age being matched to the patient group. All patients attended to Al-Kadhimiya Teaching Hospital, Department of Obstetrical and Gynecology and diagnosed by a consultant physician. Following peripheral blood withdrawal (5 ml) from participants, serum was isolated and placed in an Eppendorf tube until used. ELISA kit was used to determine serum levels of some biochemical parameters (Lactate dehydrogenase, cortisol, dopamine, zinc, and vitamin D3). The results showed that LDH level was significantly elevated (P<0.0001) in PCOS patients (2.55±0.70) compared with the control (1.16±0.18). While, significantly lower levels of cortisol, dopamine, zinc and vit D3 (P<0.0001) were demonstrated in PCOS group (4.30±1.62, 48.45±26.27, 5.21±1.85 and 271.19±36.07, respectively), compared with the control (9.40±0.76, 252.52±47.47, 8.26 ±1.42, and 328.82±21.33, respectively). The study found an elevation in the serum level of LDH in PCOS patients compared to control, while cortisol, dopamine, zinc, and Vit D3 recorded a significant decrease in patients as compare to control.

Hyaluronidase is an enzyme that primarily breaks down hyaluronic acid, thereby disrupting the structural integrity of the extracellular matrix (ECM) found in connective tissues, which is considered a virulence factor. Peganum is a genus of plants in the family Nitrariaceae that contains a number of alkaloids, including harmaline and harmine, which have been used in traditional medicine. This study aimed to investigate the effects of a plant-active compound (peganin) isolated from a plant sample (Peganum harmala) as an inhibitor of hyaluronidase. In total, 120 skin samples were randomly isolated from infected burns. Isolates with highly hydrolyzed zones were identified. The Turbidity Reduction Assay was used to assess cell growth and hyaluronidase activity spectrophotometrically. The results showed that 100 out of 120 clinical samples grew bacteria when cultured on a blood agar medium. Also, it was recognized that, out of the 100 isolates, 56 (56%) belonged to Staphylococcus aureus bacteria and had an inhibitory zone of hyaluronidase with a range of 5-26mm; the inhibitory zone was increasing over time.

Antibiotic resistance is a significant problem for which new treatments are required. There is growing concern about Fosfomycin resistance in Escherichia coli; understanding this resistance will help develop effective treatment strategies and preserve the efficacy of antibiotics. This study aimed to investigate the synergistic effects of Eugenol and Fosfomycin against Uropathogenic Escherichia coli (UPEC) and their possible potential as co-treatment. This study identified and isolated UPEC isolates from urine samples, with 63.6% being identified as UPEC. Antibiotic susceptibility tests showed that 99.3% of the UPEC isolates were resistant to multiple types of antibiotics [multidrug-resistant (MDR)]. The study examined the antibacterial properties of Fosfomycin and Eugenol using the parameters of agar well diffusion, minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), fractional inhibitory concentration index (FICI), and time-kill curves. The MIC for Fosfomycin was 512-1024 μg/mL and for Eugenol was 1.25-5 μg/mL, while the MBC for Eugenol was 5-10 μg/mL and for Fosfomycin was 2048 μg/mL. Synergistic effects were considerable, where the addition of Eugenol at 1/4 MIC concentration resulted in 1/8 MIC of Fosfomycin. . The highest bactericidal activity for most UPEC isolates was recorded at 4-8 hours using Eugenol, 8-12 hours using Fosfomycin, and 4-8 hours using co-treatment. The study also used molecular assays to identify the expression levels of Fosfomycin resistance genes (murA, glpT, and cyaA) under different treatments, which revealed positive espression in all the isolates. The results showed variable gene expression changes in response to the different treatments. In conclusion, this study shows that Eugenol and Fosfomycin co-treatment is effective against UPEC.

The convective heat transfer and nanofluid flow through porous media within the Photovoltaic/Thermal system (PV/T) under maximum air temperatures of 10, 25, and 52 οC in Baghdad city were investigated numerically. The PV/T system was filled with porous media and nanofluid, where the porous media consisted of open-cell aluminum foam (6101-T6 alloy) with a pore density of 20 PPI and a porosity of 0.9353-0.92%. The nanofluids were composed of aluminum oxide (Al2O3) and silicon dioxide (SiO2) nanoparticles suspended in water and ethylene glycol (EG) with a volume fraction of 1-3 % and a nanoparticle diameter of 25 nm. A high-efficiency solar panel with a thermal collector was used at tilt angles of 0ο, 10ο, 30ο, 45ο, and 90ο. The Finite-Volume Method (FVM) was employed to solve the governing equations of momentum, continuity, and energy. The study examined the effects of porous media and nanofluid properties, as well as tilt angles, on the performance of the PV/T system. The findings indicate that the highest heat transfer coefficient was attained when employing Al2O3-water nanofluids and aluminum foam at a PV/T tilt angle of 90ο. The heat transfer coefficient increased with the PV/T tilt angle, nanoparticle volume fraction, and Reynolds number, while it demonstrated a decrease with porosity. An increase in the Reynolds number from 600 to 1700 resulted in a 6% enhancement in the heat transfer coefficient. Conversely, an increase in porosity led to an approximate 1% decrease in the average heat transfer coefficients. Using water as the base fluid improved the heat transfer coefficient by 6% compared to EG. Additionally, as the tilt angle increased from 0⁰ to 90⁰, the heat transfer coefficient experienced a 2.5% enhancement. The combination of nanofluid with aluminum foam improved the thermal efficiency compared to nanofluid and base fluids alone. Moreover, the thermal efficiency remained consistent with an increase in heat flux.

This study aims to determine the ability of glass sand to absorb chromium from aqueous solutions. Glass sand samples were collected from the Rutba Formation (Cenomanian age) in the Western Desert of Iraq. Numerous laboratory experiments were conducted to test the effects of time and concentration on adsorption. For a duration of 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 minutes, four initial chromium concentrations (20, 40, 60, and 80) were tested. Glass sand to chromium solution ratio was 2 grams to 30 ml. Total dissolved solids (TDS), electrical conductivity (EC), and pH values were measured at 30ºC. The equilibrium time is determined as 20 min in most concentrations. The equation of Freundlich showed that the adsorption intensity (n) is equal to 0.0603, the constant of the Freundlich equation (Kf) = 59.06, Langmuir equation showed the sorption capacity (Qo) equivalent to 1.0989 mg/g, (KL) is 0.0487. The absorption of chromium was modelled using both the Langmuir and Freundlich equations. It was discovered that glass sand had the highest efficiency in chromium adsorption for solutions with a concentration of 20, reaching 74.54, and clarified how glass sand material could benefit from reducing the chromium concentrations from aqueous solutions protecting the environment.

Future hydraulic thermal substances made of hybrid nanofluids have a lot of potentials to fulfill the strict applied thermal energy management specifications of cutting-edge heat flux tricks. Due to synergistic thermal effects and general hydrothermal characteristics, it is necessary to examine the enhanced heat-transfer properties of nanofluids based on hybrid nanomaterials in various advances for the thermal management of machines with advanced heat flux. In a variety of corrugated casings filled with water and a Cu−Al2O3/water hybrid nanofluid, the effects of a horizontal magnetic field on natural convection heat transfer have been studied. Finite element techniques (FEM) have been employed to solve the generated dimensionless partial differential equations of the imposed nanofluids. On three distinct wave-shaped heated walls, the streamlines, heat flow, and average Nusselt number for the base fluid and the hybrid nanofluids were plotted and examined. The outcome demonstrates how the device\'s undulating walls and magnetic field strength have a major impact on heat transfer and heat flow.

The current work aims to study the effect of an inclined magnetohydro dynamics peristaltic transport of a non-Newtonian hyperbolic tangent fluid in a non-uniform channel via porous medium. The nonlinear governing equations regarding hyperbolic tangent fluid have been studied and solved analytically with the use of a regular perturbation approach under considerations of a long wavelength as well as low Reynolds number. All computational results are also discussed graphically using the MATHEMATICA program. The mathematical expressions for axial velocity and stream functions have been derived analytically. Different physical parameters have been shown graphically and discussed it's effect. It is established that increasing the Hartmann number and the parameter of porosity will increase the velocity and the quantity of boluses. While the size of boluses decreases by increasing the magnetic field inclination angle. Moreover, Some mathematical and engineering aspects of non-Newtonian fluids are investigated in the current analysis.

The purpose of this study is to present a new concepts on a module ???? over a ring, these concepts is called ????∗-small sub module, ????∗-hollow module which present generalizations of the small submodule and hollow module, respectively. Key characteristics of these concepts such as the image and direct sum of ????∗-small sub module, ????∗-hollow module are ????∗-small submodule, ????∗-hollow submodule, respectively.

In this paper, we explore and introduce a novel study employing a new operator denoted as ????????,????,????,????,????,????,????????,???? within the field of geometric function theory, particularly in the context of sandwich theorems. We derive results pertaining to differential subordination and superordination for this new formula operator. Additionally, we establish specific sandwich theorems.

Mathematics and the applied sciences both heavily rely on fixed point (FP) theory. Additionally, this theory has several applications in integral equations and differential equations to guarantee the solutions' existence and uniqueness. FP theory relies mainly on the Banach contraction principle. Since this idea first appeared, it has gained a lot of attention and there has been a lot of development in this field. In this paper, the concept of generalized Kannan-type(G????????) mapping is presented in intuitionistic fuzzy metric space(IFM space), and the FP theory is proven. The results contain extensions of FP theory in IFM-space which include the Caccioppoli FP theorem. Additionally, an instance is provided to illustrate the practical significance of the research's results

Let A be a 2- torsion free prime Γ− ring, and L be a square closed Lie ideal of A i.e. ????????????∈???? for all ????∈???? and ????∈Γ, suppose that ???? is an endomorphism of A, and f is a right ????− centralizer which is not the identity map on L, satisfying ????([????,????]????)=∓[????,????]????( or ????(????????????)=∓????????????) for all ????,????∈???? ,????∈Γ, then ????⊆????(????).

In this paper, we modify an SEIVR model concerning COVID-19 from the first-order system into a multi-fractional order system of differential equations. We also find an approximate solution by using the Sumudu Adomian decomposition method. Furthermore, we try to give qualitative results rather than qualitative results. Numerical simulation is given through a table and graphs which show the efficiency of the method.

Voice encryption is crucial to protecting data during transmission and storage due to the widespread problem of document fabrication and forgery. Various methods have been used to protect important data, including encryption. Voice data encryption is a way to encrypt important messages within transmissions. Therefore, it needs a strong and efficient encryption algorithm, using the standard encryption algorithm known as Rijndeal. This method is reliable, secure and efficient, because the Rijndael algorithm is characterized by its key lengths and blocks of different sizes. The Rijndael algorithm is a method of decrypting audio files, making it a powerful tool for protecting sensitive information. This paper presents a method for encoding WAV audio files using the Rijndael algorithm that is compatible with many audio formats. This study converts audio data into a 256-bit audio sample that has been used to encode using the Rijndael algorithm, as this algorithm is known for its robustness and has won many awards. The working principle of this algorithm is to create multiple blocks with different sizes and keys (128 bits, 192 bits or 256 bits). The algorithm creates keys for the purpose of encryption, and they are combined into the audio files using the Rijndael algorithm and decrypted that has been noted as the return of the original text. The goal of this proposed methodology is to protect audio data from attacking people and reduce the chances of this data being disclosed. This methodology is used for the purpose of transferring, storing and keeping confidential audio files securely with security aspects. One of the most important features of this research is the strength of the encryption, which is represented by the Rigndael algorithm, which led to the file being broken and restored at a record rate of up to 100%. It also provides a cutting-edge and innovative methodology. For the purpose of securing, transmitting, storing and protecting audio data in all modern digital fields.

This research aims to study the behavior of solutions of second-order neutral differential equations with periodic coefficients. Some necessary and sufficient conditions have been obtained that classify all solutions of these equations into three categories: either oscillatory, non-oscillatory, and convergent to zero, or non-oscillatory and divergent. The extent to which periodic coefficients influence the occurrence of oscillation, convergence, or divergence for each solution has been explained. The equation under consideration contained a variable delay and a constant delays in which the coefficients are periodic. Not much previous research has discussed the oscillation of solutions of second-order neutral equations with periodic coefficients. In each case, some illustrative examples have been provided that illustrate the ease of achieving the conditions for the obtained results.

The peduli-lindungi (Care-Protect) application is an official application used by the Indonesian government to monitor crowds of people who are active in closed and public spaces. The purpose of this application is to facilitate actions that can be taken by the government to prevent the spread of COVID-19. One of the actions is user tracing. In this study, an analysis was carried out on data originating from the Peduli-lindungi Application, which is confidential. Some of the data fields used are still protected by the government. To get accurate data analysis results, preprocessing techniques are carried out on Peduli-lindungi data to produce new datasets as observational data material. The results obtained show that 4073 data points were successfully observed based on the population identification number (NIK) or that there was a reduction in the number of observation data points by 2.83% from the Peduli-lindungi source data. The exploratory data analysis (EDA) method and data visualization techniques using Scimago Graphica and Orange Data Mining were successfully applied to this study. The analysis results obtained show that as many as 13% of the number of people based on NIK do not comply with government regulations on traveling or entering public places, as indicated by the color status in the Peduli-lindungi application, namely red and black. As many as 60% of them do their activities by entering shopping centers, namely malls. Based on the results of the observations and data analysis that have been carried out in this study, it can be concluded that the MALL location is a cluster that must be the center of attention for the government.

The deepfake videos were spread in the last few years and were created by different deepfake techniques (i.e., faceswap, face2face, etc.). These techniques have a terrible impact on society and would give anyone a chance to create videos with fake faces. The objective of this paper was to develop a model that detects deepfake videos to reduce their negative impact. Two hybrid models of machine learning and deep learning were proposed. The first model used a convolutional neural network (CNN) for feature extraction and different machine learning classifiers (support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), logistic regression, and naive bayes (NB)). In contrast, the second model used a transfer learning concept developed by the VGG16 (Visual Geometry Group) pre-trained model and the same machine learning classifiers as the first model. Both models were evaluated on the FaceForensics++ video dataset, which includes four different deepfake techniques (Deepfake, Faceswap, Face2Face, and Neuraltexture). The results showed good accuracy, which proved the effectiveness of the proposed models, which may be used as a detection deepfake application. While the first model can obtain the highest accuracy with the SVM classifier on the four deepfake techniques sequentially: 0.96, 0.87, 0.90, and 0.64. In contrast, the second model achieved the highest accuracy with the KNN classifier on Deepfake and Face Swap techniques: 0.95 and 0.91, and with SVM on Face2Face and Neural Texture techniques: 0.86 and 0.77.

Media platforms have become essential for staying informed about events and activities around the globe. While there has been research on identifying news in English, detecting it in Arabic has been relatively overlooked. The unique linguistic characteristics and diverse slang expressions in Arabic have contributed to a scarcity of studies in this area. This research examines the effectiveness of deep learning algorithms in identifying fake news, specifically in the Arabic language. In this study, Global Vectors for Word Representation (GloVe) were used to capture the semantic relationships between words in order to improve the performance of the models. Furthermore, the utilization of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) algorithms has shown potential in the field of neural networks for text classification purposes. The study also delves into incorporating a trained Arabic BERT (Bidirectional Encoded Representations from Transformers) model, which is widely recognized for its outstanding performance, in various natural language processing tasks. The current research utilizes the Arabic Fake News Dataset (AFND). It is a large, fully annotated dataset of Arabic fake news. The results demonstrated that, among all the investigated algorithms, Arabic BERT achieved accuracy with a score of 0.98 on the dataset. According to the results obtained, LSTM and BiLSTM achieved scores of 0.94 and 0.93, respectively, implementing GloVe word embeddings. This research showcases the effectiveness of the Arabic BERT model, alongside the LSTM and BiLSTM models, in detecting information. It highlights the contribution of BERT in enhancing accuracy when dealing with the identification and mitigation of challenges related to identifying news in Arabic-language contexts.

The Internet of Things (IoT) refers to a network comprised of interconnected items, including computing devices and digital gadgets. Cloud-based IoT infrastructures are vulnerable to distributed denial of service (DDoS) attacks. A DDoS attack has the potential to incapacitate a server for an extended duration, resulting in service disruptions as a consequence of overwhelming system resources. This research presents a novel framework for mitigating DDoS attacks in IoT networks. The proposed system leverages the fog-cloud architecture to provide efficient, lightweight, and precise attack mitigation. Notably, the mitigation process is executed at the fog layer. The suggested fog layer uses Particle Swarm Optimization (PSO) to make allocating resources easier, which makes it possible for the mitigation framework to be set up quickly. This approach addresses the challenges associated with resource management on resource-constrained IoT devices. The mitigation framework uses the Fitness Leader Optimization (FLO) approach to construct a trained database, taking into consideration factors such as the needed time, the size of the request, and the number of created requests. The FLO system employs multilayer perceptron (MLP), k-nearest neighbors (KNN), and support vector machine (SVM) classification algorithms to effectively mitigate the assault. The results of this study show that adding classification algorithms to our framework made it easier to test networks for Internet of Things (IoT) devices, especially when the Particle Swarm Optimization (PSO) method was used together. The mitigation framework demonstrates a minimized fitness value of 0.284556 seconds, showcasing enhanced resource utilization and processing time optimization for IoT nodes and servers in a distributed fog environment. The total average of resource utilization is improved to 6.0850%, processing time is decreased to 17.0397, and fitness value is decreased to 0.0258 seconds in the proposed DDoS attack mitigation system. The machine learning classification model achieves high accuracy, with SVM leading at 99.6785% compared to others, emphasizing the robustness of the proposed framework in securing IoT networks.

A migraine is a neurological disorder that causes severe headaches. Although it is not life-threatening, it greatly affects people's lives. Migraine classification is considered a complicated research area. Early detection and accurate classification help in determining the appropriate care, speeding recovery from the disease, and avoiding its effects. So, developing a migraine classification system is very necessary. The aim of this work is to suggest a migraine classification model based on a stochastic gradient-boosting ensemble algorithm. First, the dataset is pre-processed. Adaptive Synthetic (ADASYN) is used to balance a migraine dataset that contains seven groups of migraine patients. Twenty-three migraine attributes were extracted from 400 patients. The ANOVA feature selection method is applied to select the most relevant features. Four experiments have been carried out based on apply/not apply ADASYN or ANOVA. The SGB model is trained using the number of hyperparameter tunings used in the four experiments in order to improve model performance. The SGB model is evaluated in terms of precision, recall, f1-score, and accuracy. The results showed that SGB achieved outstanding outcomes in the fourth experiment when applying ADASYN and ANOVA. The model achieved 97.01% accuracy, 0.970905 precision, 0.966250 recall, and a 0.967278 f1-score.

An older people's disease known as frailty occurs when an elderly individual exhibits heightened susceptibility to little stimuli. An individual who is feeble experiences limited ability to grasp, an unintended reduction in weight, a feeling of happiness, a sluggish gait, and trouble standing. The detection of frailty phenomenology is one of the greatest developments in geriatric medicine in the past 20 years. Frailty develops into an impairment if left unchecked. Clinical decision support systems are used to help professionals, or physicians, make more informed and complex decisions. Thus, in order to assist the board in providing healthcare effortlessly in controlling the frailty conditions and, usually, to identify the class of acquiring a medical condition (frailty) and offer an appropriate procedure therapy based on what can be placed to the perfect use, an appropriate support system has been set up using doctors' expertise and information mining extraction structure. In order to categorize this condition and analyze their respective efficacy and rectification rates, we suggest using the Decision Trees, C4.5, ID3, Random Forests (RF), Logistic Regression (LR), and CART algorithms. With a decrease in undertriaging elderly patients who are seriously ill, predictive machine learning assessment demonstrated superior discriminating capacity for predicting medical results and attitudes. The present research identifies an effective combination of six characteristics—unintentional reduction in weight, sluggish weak strength or tiredness, frequent chair current stands, and age—that assist in predicting death with precision and F1 score using predictive machine learning techniques. In order to obtain consistently high accuracy throughout the characteristics of frailty illness detection, numerous algorithms based on machine learning are being examined. Five cases of rigorous testing validate the excellent predictive accuracy of the suggested framework. This research would hasten the process of making decisions in healthcare organizations so that targeted therapies can be administered promptly and precisely.

Image steganography involves concealing various forms of information, such as text, images, or videos, within an existing image. The hidden data is embedded in a manner that remains imperceptible to human observers. In this paper, a stego image that successfully hides the secret information and renders it undetectable to others is created by embedding a hidden message into an original image. The secret message may be text (the doctor’s report of the patient’s case) or maybe a secret image (chest x-ray) or encrypted secret image to increase security and guarantee the patient’s privacy. The cover images may be brain, breast, and lung images. An enhanced LSB technique is introduced, incorporating common LSB and conditional MSB, with a focus on the lowest energy (blue channel) in the RGB spectrum. This approach aims to maintain image quality and imperceptibility. The innovation lies in strategically combining conditional MSB with the selective use of the blue channel, enhancing security. The method demonstrates superior concealment of secret information, surpassing conventional LSB techniques. Additionally, the Structural Similarity Index (SSIM) reports a high value of 0.998, affirming the technique's success in concealing information while preserving image quality. Performance metrics show positive results due to minimal changes (2 LSB bits at most). The experiments demonstrated positive outcomes in comparison to prior studies in this field.

In recent years, there has been a growing popularity of autonomous vehicles due to their significant impact on society. One of the key tasks of autonomous vehicles is accurate pedestrian detection, which plays a vital role in preventing accidents. However, accurately detecting and tracking pedestrians under various environmental circumstances poses a significant challenge. In this paper, an efficient model for pedestrian detection is proposed by integrating three modules: You Only Look Once version 8 (YOLOv8) for object segmentation, Histogram of Oriented Gradients (HOG) for feature extraction, and Custom Convolutional Neural Network (CNN) for classification and detection. For tracking purposes, a simple online and real-time tracking (SORT) algorithm is used to track pedestrians in consecutive frames. Extensive experiments were conducted using the EPFL dataset. By leveraging the strengths of these modules, the proposed model aims to improve the accuracy and performance of pedestrian detection and tracking. The experimental results demonstrate the remarkable capability of the suggested model to detect and track pedestrians, achieving an accuracy rate of 93.34% even in challenging weather scenes.

Noise reduction is a significant field that appears in many aspects of life. There are many methods for speech noise reduction, especially for stationary noise. This paper talks about a new way to lower speech noise that combines the Log-MMSE (Logarithmic Minimum Mean Square Error) algorithm, which is used to improve speech signals that have been messed up by noise, with an adaptive Wiener filter with a decision-directed (DD) approach. This filter lowers musical noise and predicts the time-varying noise spectrum, which results in a better signal-to-noise ratio. The frame delay issue resulting from DD was resolved by utilizing the Two-Step Noise Reduction (TSNR) technique to reconstruct the harmonic structure of the voice signal that was distorted or missed during the processing and boost speech quality and intelligibility in loud circumstances. Harmonic Regeneration Noise Reduction (HRNR) was used. In this paper, we will investigate these methods in the field of stationary and non-stationary noise. The proposed method was evaluated using different techniques like SNR, perceptual evaluation of speech quality (PESQ), and short-time objective intelligibility (STOI). The proposed approach produced acceptable results in PESQ and STOI, with a considerable increase in the percentage of (SNR), where the percentage of our improvement reached 63.49% in the results of (SNR).

Extremism detection is an important area of natural language processing (NLP). It is used to detect hate speech, sectarianism, and terrorism on social media. This field has been discussed and studied in many international languages, especially Arabic and English, as many studies touched on languages in particular, but dialects were not addressed even though users of social networking sites write in their dialect. One of the most difficult Arabic dialects is the Iraqi dialect. Because the Iraqi dialect has few sources on the Internet regarding available data that can be used by researchers, this research aims to detect extremism in Iraqi texts using machine learning. The data was pre-processed by deleting suffixes and prefixes for Iraqi words, deleting repeated letters in the word, and deleting Iraqi stop words. Pre-trained embedding as well as embedding using Gensim Word2vec and FastText were used to represent the words in the embedding step. Also, four learning classifiers were used: Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Gaussian Naive Bayes (GNB). The experiments were conducted on two Iraqi datasets collected from social media platforms related to extremism: the Iraqi Facebook Comments Dataset (IFCD) and the Iraqi Tweets Dataset (ITD). The performance of all models was evaluated using accuracy, macro-average precision, macro-average recall, and macro-average F1-score; the best F1-score is 0.9521, while recall and precision are 0.95 and 0.955, respectively. In addition, the models presented in this research were tested on an Iraqi data set related to hate speech available on the Internet, and the results obtained were compared with the results of the work that provided this data set.

Brain tumors pose a significant global health concern, imposing substantial social and economic burdens. Accurate classification of tumor types (gliomas, meningiomas, and pituitary tumors) from MRI data is essential for aiding radiologists and avoiding invasive biopsies. This paper presents a new method for brain tumor classification that has excellent accuracy when compared with existing methods. The proposed method provides improvements for feature extraction and classification. This new method comprises MRI image preprocessing, feature extraction, and image classification. Preprocessing includes resizing and data augmentation. Following this, features are extracted from MRI images using a mixed transform approach involving a mix of methods like DWT (Discrete Wavelet Transform), FAN (FAN Transform), and DCT (Discrete Cosine Transform). Classification employs the ResNet34 model. Where the outcomes demonstrate a training accuracy of 0.9439, a validation accuracy of 0.9195, and a best accuracy of 0.9397 when distinguishing between different brain tumor tissues in MRI images.

Over two decades, this study analyzes drought trends in Diyala Province, Iraq, and It utilizes remote sensing data and rainfall analysis to evaluate the severity and frequency of drought in 2002, 2005, 2010, 2015, 2020, and 2022. The research maps drought distributions in Diyala Province using satellite images from Landsat 7, Landsat 8, and Landsat 9 and analyzes the vegetation health index (VHI), soil moisture index (SMI), and average rainfall data. Results showed increased drought intensity: severe drought expanded from 48% in 2005 to 50% in 2022, and arid conditions increased from 1.1% in 2002 to 17.1% in 2022, as per SMI. Moreover, rainfall analysis reveals varying patterns, with 2022 experiencing lower average rainfall (4.215 mm) and higher variability (1.4633 mm standard deviation) compared to 2015 (6.735 mm, 1.1227 mm). The discoveries, pivotal for agriculture and water management, underscore the necessity of consistent monitoring and adaptable strategies for addressing shifting climate patterns.

Kirkuk province needs a comprehensive and specialized strategy to address these difficulties and identify the most suitable solar energy solutions to reduce its ecological impact and improve its energy security and economic sustainability in future years. This study aims to present a framework for solar energy farm site selection working with Geographic Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) techniques surrounding Kirkuk province, Iraq. Several spatial data sets were integrated in two steps to locate the best locations for solar energy farms. The first stage was to identify land availability with the elimination of constraints areas such as distance from major roads, distance from bodies of water, urban areas, distance from electric power transmission lines, and distance from industrial locations. The second stage was to choose the essential criteria, weigh them using an Analytical Hierarchy Process (AHP), and finally analyze the criteria using GIS to determine the best sites for solar energy farms. The available land results indicated their distribution in three regions with large areas, which are the north-east, north-west, and south-east, with a total area estimated at 40.182 km2, in addition to separate small areas of about 1.698 km2 in the rest regions of the study area. The highly suitable lands were dispersed throughout the northwest and north of the research region, covering a total area of 7.4304 km2, while medium suitable lands, indicated by the yellow color, are spread out in the southeast, with a total area estimated at 2.9376 km2. The low suitable lands cover some small areas south of the research area, shown in green, and cover an area of 1.8432 km2.

Geographical Information Systems (GIS) provides a set of instruments and techniques to explain and display different geographical data, and ArcGIS Model Builders consider a very beneficial technique to represent these procedures in a very effective and influential manner. This paper indicates that using the ArcGIS tool Model Builder is the preferred technique to display the flux of procedures, in which the model's design is highly preferable to decrease the time consumed to apply any procedure. One model was used to explain the best methodology to resolve the issue, which deals with dust storm detection over Iraq using MODIS data. This study Facilitate Terra and Aqua MODIS satellite images; the first case was Terra / MODIS during the first half of the day with NDDI and IDDI, while the second case was Aqua/ MODIS during the second half of the day with the three thermal indices MEDI, BTV, TB beside IDDI. This study displays the effectiveness of these indices in two cases to show their validity in detecting dust storms compared to synoptic data over the study area. Besides, it displays ArcGIS Model Builder's importance in applying these Indices more efficiently and smoothly to benefit from it in serving society and the scientific community.

The study seeks to assess the efficacy of utilizing a Back Propagation Artificial Neural Network (BPANN) as an alternative to conventional methodologies for 2D coordinate transformation. Converting the previously used reference system in Iraq to the global system is one of the main requirements that play a main role in various applications requiring the standardization of geographic referencing for different data, including geospatial applications, remote sensing, image processing, and more. This necessitates researching and finding the most suitable methods to ensure high accuracy in the results. The Karbala 1979 (Clark 1880) of the Iraqi national geodetic network and the Iraqi Geospatial Reference System (IGRS) were currently used as geodetic reference frames in Iraq. In the context of transitioning between the local system (Karbala 1979) and the global system, specifically IGRS (ITRF2000 at epoch 1997.0), a crucial procedure involves the conversion of two-dimensional (2D) coordinate data. This conversion employed the BPANN method and conventional techniques such as affine and polynomial. The outcomes derived from the BPANN methodology are juxtaposed against those from the affine and polynomial methodologies. The findings reveal that the effective implementation of each approach within this study is contingent upon the spatial arrangement of the control points leveraged to develop the transformation model. Additionally, the root mean square error (RMSE) of BPANN ranging from 3 to 5 cm closely aligns with the outcomes achieved through affine and polynomial methods.