Journal of Techniques

Customer opinions and reviews play a vital role in marketing expansion. Big companies worldwide assign a lot of their efforts to analyzing customers’ feedback to keep track of their needs. Natural Language Processing (NLP) is widely used to analyze such review texts. Arabic customer analysis and classification also began to gain researchers’ attention due to the wide range of Arabic language speakers. Working with Arabic Language is a very challenging task because of the orthographic nature of Arabic. Also, customers often write their reviews dialectically, diverting from standard Arabic. This study presents a method to classify Arabic customer reviews using four classifiers (K-nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (RL), and Naïve Bayes (NB)). The classification phase uses three stemming techniques (Snowball, Khoja, and Tashaphyne). The HARD dataset is adopted to perform the experiments. The results stated that the stemming methods could enhance classification performance despite the complexity of Arabic scripts and dialects, where the best accuracy of the results was 91% when using SVM and LR with Snowball Stemmer. This work sheds light on utilizing and investigating more machine learning (ML) techniques and evaluating the results.

The Internet of Things (IoT) and Artificial Intelligence (AI), particularly Machine Learning (ML), have both seen significant advancements in recent years, which has resulted in significant leaps forward in the development of health monitoring systems. Patients may now be prevented, diagnosed, and monitored remotely and at home, eliminating the need to go to health and treatment centers or spend a significant amount of money doing so. This is made possible by advancements in technology. Deep learning has been the primary focus of this research as it relates to the development of a remote health monitoring system for the diagnosis of diabetes. In the system that has been suggested, improvements have been made to both the precision of the detection and the swiftness of the data processing. The Neighbourhood Component Analysis-Convolutional Neural Network (NCA-CNN) approach that we have presented involves two stages: the first stage involves picking the most important features from all of the data, and the second stage involves categorizing the chosen features. The NCA algorithm is a mathematical method that rates the characteristics based on the results of an analysis of the data and picks the most significant aspects. After that, the most salient characteristics are categorized by a deep convolutional neural network, and an accurate diagnosis of the condition is accomplished. According to the findings that were collected, the accuracy of the approach that was suggested is 97.12%.

In pragmatic courses, graduate students are required to submit programming assignments, which have been susceptible to various forms of plagiarism. Detecting counterfeited code in an academic setting is of paramount importance, given the prevalence of publications and papers. Plagiarism, defined as the unauthorized replication of written work without proper acknowledgment, has become a critical concern with the advent of information and communication technology (ICT) and the widespread availability of scholarly publications online. However, the extensive use of freeware text editors has posed challenges in detecting source code plagiarism. Numerous studies have investigated algorithms for revealing different types of plagiarism and detecting source code plagiarism. In this research, we propose an innovative strategy that combines TF-IDF (Term Frequency-Inverse Document Frequency) modifications with K-means clustering, achieving a remarkable precision rate of 99.2%. Additionally, we explore the hierarchical clustering method, which estimates an even higher precision rate of 99.5% compared to previous techniques. To implement our approach, we utilize the Python programming language along with relevant libraries, providing a robust and efficient system for source code plagiarism detection in student assignment submissions.

In this paper, the results of our research were presented into the sliding wear behaviors of reinforced epoxy composites
made with 3D networked carbon fibers ornamented with nanowires of cobalt oxides (NWRs@CFs). Composites are made
using the cast-in-place method. Under different normal loads and sliding velocities, these composites\' wear and friction
coefficient performances are assessed. Tests were performed at 400 rpm spindle speed, 10, 20, and 30 N normal load, and
a duration of 300 seconds. Pure epoxy exhibits wear rates of 0.449, 0.481, and 0.501 * 10-5 mm3/Nm at 10, 20, and 30N.
Conversely, 3D CFs/epoxy composites exhibit lower wear rates (0.334, 0.360, and 0.390 * 10-5 mm3/Nm) at the same
pressure. The epoxy composites of NWRs@CFs wear less, measuring 2.1, 3.5, and 0.4 * 10-5 mm3/Nm under applied
loads. The effects of speed on the tribological characteristics were also studied, the friction coefficients for 400, 800, and
600 rpm at 10 N. Pure Epoxy has fraction coefficients of 0.449, 0.494, and 0.552µ at 400, 800, and 600 rpm. In comparison,
three-dimensional carbon fibers and epoxy composites show reduced wear rates (0.334, 0.376, and 0.304 µ) under identical
loads. Epoxy composites of NWRs@CFs have friction coefficients of 0.261, 0.304, and 0.332µ. Pure epoxy has high wear,
indicating less friction resistance.

High Impedance Fault (HIF) detection in distribution networks is challenging for protection engineers, mainly because HIFs possess unique characteristics, including non-linearity, asymmetry, randomness, and relatively low fault current levels compared to the feeder load current. In this regard, the study proposes an approach to detect HIFs in a radial distribution feeder based on the spectrum analysis of current signals at the substation bus. The proposed method comprises two stages: signal decomposition and feature extraction. Fast Fourier Transform (FFT) is utilized for signal decomposition, followed by feature extraction. These features are subsequently used as input to an artificial neural network (ANN) to distinguish HIF from non-HIF events, such as linear and non-linear load switching, capacitor bank switching, and transformer energization. The proposed method\'s efficacy is rigorously evaluated under various dynamic conditions, demonstrating that the method can detect and differentiate HIFs from non-fault events with a high detection rate and high accuracy of 99.3%, irrespective of the HIF location and fault resistance.

DC-DC converters are an important class of power electronics due to their wide use in various applications as sources of efficient power supplies. They step down or step up the applied voltage so that it is always either lower or higher than the supplied voltage. This is crucial in power delivery and portable systems, especially in battery-operated systems. The purpose of the paper is to investigate how the efficiency of Buck-Boost converters improves by using sliding mode control when operating under different conditions. The work aims to develop a control strategy that increases the efficiency and reliability of Buck-Boost converters, employed in a myriad of power electronics applications. The research focuses on a sliding mode control approach to overcome the challenges of nonlinear dynamics and susceptibility to external disturbances. The methodology involves studying the behavior of the converter under different conditions such as changes in loads, input voltage variations, and reference voltage changes. The study uses theoretical modeling and simulation to evaluate the concept of sliding mode in addressing the challenges for improved efficiency. Such investigations show how sliding mode control improves efficiency. SMC approach reduces the response time by 5%, improves efficiency by 3%, and enhances overall stability under fluctuating conditions. The use of sliding mode control enhances the converters against disturbances and provides an efficient voltage regulator. The research is useful to the field as it offers more insights into the control strategy that significantly improves the performance of converters concerning efficiency and stabilization.

One of the most important operations in agriculture is tillage, which has a great contribution to increasing crop yield and aeration of the soil and, ultimately, root development. Among the important tools for tillage are rotary rotators that are exposed to high and variable dynamic loads, so they are designed to have both the greatest contribution to soil agitation and high strength; It is necessary. Therefore, in this research, an L-shaped blade sample was designed for a rotator using a Biomimetic method. Due to the extensive research that has been done in the world in the field of manufacturing different types of rotators with a rotating agitator mechanism, therefore, it is necessary to study and optimize the rotator blades according to Iranian conditions. In this study, the studied variables include dependent and independent variables. Dependent variables include von Mises stress and arm displacement, and independent variables include the dimensions and geometric parameters of the Shape of the Rotator Blades. The idea for the blade design was inspired by the paw of a mole rat. Six blades were designed by simple method and biomimetic method and then by finite element method. Effects of blade angle change and design method on stress distribution, maximum displacement, maximum energy absorbed in the blade, reliability and under 400 N checked out. The results showed that in all designed models, the values of stress, displacement and energy absorbed were lower in the biomimetic design mode. As the blade angle increased, the values of the above parameters also decreased. Finally, the best model for having the least von Mises stress and the highest reliability was the blade with a Biomimetic design and a 20-degree angle.

The growth of cities around the world creates difficulties for governments due to the strain it puts on resources and the negative impact on the environment and living conditions for residents. The Kirkuk Governorate has experienced significant urban growth in recent years, causing various environmental, economic, and social issues. the study aims to monitor the growth of Kirkuk Governorate from 1990-2020 using multi-spectral Landsat images to better understand urban development. Landsat-5 TM for 1990, Landsat-7 ETM+ for 2000, Landsat-5 TM for 2010, and Landsat-8 OLI for 2020 were used for this study. The Supervised Maximum Likelihood algorithm was utilized to categorize images into urban and non-urban areas, with accuracy rates ranging from 91.25% to 96.66% between 1990 and 2020. Post-classification change detection analysis was also conducted to observe changes in urban growth, revealing that the studied city expanded from 46.96 km2 to 193.25 km2 over 30 years. Over the past thirty years, the urban area of Kirkuk Governorate has increased while the non-urban area has decreased. This is due to factors such as population growth, political changes, and economic development. This study provides valuable information about this urban growth and can be used as a foundation for future studies on the region\'s socioeconomic and environmental changes.

Hydroxyapatite (HA) scaffolds produced by the accumulation of HA fibers were separately treated hydrothermally in three calcium phosphate solutions to form coatings of different micro/nano-structures. Different micro/nano-structure and morphologies have been regulated on the surface of treated HA scaffolds. Plate-like compromise flower-like morphology was obtained with solution 1 (Ca-sufficient) i.e., ratio: Ca/Ca=1%; Ca/P=1.67. Full coatings (flower-like) morphology treated after Cu-doped coating solution 2 (Cu/(Cu+Ca) = 5%; ratio: (Cu+Ca)/P = 1.67). Furthermore, partial coatings (flower-like) morphology fabricated with solution 3 (Ca-deficient and Cu-replacement), i.e., ratio: Ca/Ca=0.95%; Ca/P=1.58. The results showed the effect of hydrothermal coatings on HA scaffolds. Cultured human endothelial cells spread and proliferated better on the treated HA scaffolds than on the uncoated scaffolds, suggesting a potential effect of calcium phosphate surface morphology on endothelial cell response. Thus, it can provide an appropriate micro/nano-structure approach supporting angiogenesis capacity, which is a necessity to accelerate the time of bone healing and regeneration.

Wind turbines have gained popularity as one of the efficient micro grid energy generation sources over the years. However, wind energy yield has been greatly impacted by the wind farm wake effect, especially when the size of the wind farm becomes very large. One way to address this challenge is through wind turbine arrangements and their scalability. Developing effective means of optimizing wind farm configurations is a major concern for energy communities. This has continuously led to increased power generation and a corresponding cost reduction. As such, this research article developed an optimal large-scale offshore wind turbine placement based on wind farm layout design. The system developed focuses on wind turbine placement in wind farm layouts that have a negative influence on the capital investment due to the increasing wake effect thereby reducing energy production. A multi-objective function consisting of wake effect and component costs within the wind farm is formulated. After-which a cuckoo search algorithm technique is employed in the wind farm model to optimize the optimization problem (minimizing the wake effect while improving power output and cost). Four test case scenarios were considered when implementing the wind model. The results obtained from the developed scheme were compared with those obtained when other optimization techniques were used, using power output and cost as performance metrics. The obtained power output and cost for the test case scenario in the ideal wind turbine position on the wind farm are 18288.3KW, 19680.1KW, 18879.9KW, 21105.8KW and 26.9, 28.7, 26.9, and 29.8KW, respectively. This shows that the result obtained from the developed scheme outperformed that obtained from PSO and that of WOA.