The Journal of Technology Research (JTR) is a peer-reviewed, open-access scientific journal, published in both print and electronic formats by the College of Industrial Technology – Misurata. The journal covers all fields of engineering and technology, serving as a scientific platform for the dissemination of knowledge. Since all submissions undergo rigorous review by highly qualified specialists, the published research is distinguished by the novelty of its information, the diversity of its topics, and the accuracy, integrity, and objectivity of its contributions. This makes the journal a valuable reference for researchers and an essential resource for researchers across different academic levels. This guide outlines the policies and internal regulations of the Journal of Technology Research. Vision: Leadership and excellence in publishing technical and industrial research locally, regionally, and internationally, serving the community and advancing the scientific research environment. Mission: The Journal of Technical Research is committed to publishing original, peer-reviewed scientific research across various technical, engineering, and industrial disciplines, while promoting creativity and innovation according to the highest standards of scientific publishing. Objectives: 1. Publish original, peer-reviewed research in technical, engineering, and industrial fields. 2. Enhance the position of College of Industrial Technology – Misurata. as a distinguished center for applied scientific research. 3. Provide a scientific platform for researchers to share their ideas and contributions in support of sustainable development. 4. Continuously improve the quality of research and publication services in line with the latest scientific and technological advancements. 5. Raise the level of scientific publishing in accordance with local and international standards.
College of Industrial Technology - Misurata
Artificial Intelligence–Based Fuzzy Logic to Determine Admission of First Year Primary School Student
Students are typically admitted to the first grade of primary school upon reaching the age of six years. This policy, however, often results in the exclusion of children who are just a few weeks shy of turning six, despite possessing the mental and physical capabilities required for admission. Hence, it is crucial to reconsider these criteria during the admission process. This paper introduces a software model designed to calculate the acceptance rate of students. The model incorporates fuzzy logic and artificial intelligence, employing the Mamdani model to consider age along with mental and physical criteria as inputs to the system. Specific threshold values, indicative of actual values for each input, have been established and incorporated during the system's design. These values, derived from insights provided by educational experts, are then used by the system to process criteria entered by end-users. Subsequently, the system determines the acceptance decision based on the processed criteria.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
An Informatics-Based Statistical Study for Estimating the Impact of Quranic Symbols on Data Compression
This paper included a statistical analysis of information in the electronic version of the Medina Mushaf with the narration of Qalun on the authority of Nafi’, where the symbols used in writing the Holy Quran were identified, and they were divided into four groups according to the similarity in the drawing method and the function performed by these symbols. The main concepts of information theory were used to measure the information content of each of these symbols, and calculate the source entropy of the Holy Quran. The results were placed in illustrative tables that help estimate the degree of impact of each symbol in the data compression process .
Inspection the Fuel Spray Pattern of The Injector Nozzle in Turboprop Engine by Using Fuel Testing Device
This study deals experimental study to inspect the fuel spray pattern in fuel injector nozzle in turboprop engine type PT6A-60 SERIES produced by Pratt & Whitney Canada by using fuel testing device. due to incomplete combustion which is mainly occurred due to incorrect shape of fuel spray, it results in carbon formation inside the combustion chamber, which negatively effects on the fuel injector and compressor turbine (CT) vane. The cleaning procedure to fuel injector is done by two methods which are handling clean and ultrasonic clean device. The testing is done by using fuel injector testing machine which is use fuel to check spray pattern. This machine was made due to the need for it by the maintenance team, and the actual machine used by the manufacturer was not available. This test was carried out at different conditions which is the first at the start of operation at 20 psi, the second at low speed, 30 psi pressure, and the third at maximum speed at high pressure 60 psi. The case 01 and 02 have been taken at the time of maintenance check of the fuel injector in 15/12/2022, while the case 03 and 04 were taken also randomly from the same engine but in different time at 09/03/2023. From obtained results, most of the cases were acceptable and does not need to re-cleaning after testing, except case 04 was not acceptable and need to clean by ultrasonic device.
Experimental Study of Innovative Design for Solar Still Desalination
Solar still is a cost-effective and sustainable desalination process since it uses abundant solar energy to lower energy-related expenditures. For the first time, this study provides a new concept of free water production employing Solar Still with a built-in parabolic trough collector (PTC). The suggested solution is based on integrating a conventional solar still (CSS) and a PTC supported by an absorbent plate that serves as a thermal storage medium for the systems. The effect of integrating PTC on conventional single slope solar still as well as performance is being explored experimentally. The results show that the maximum cumulative productivity per hour for the seven-hour work period is 921 ml, and the highest overall productivity is 2451 ml and the maximum efficiency per hour of the modified Solar Still 44.9 %.
A Comparison of Data Hiding in Digital Colour Images Using the Improved Least Significant Bit Algorithm Based on the Three Color Channels with (2:2:4) and (2:3:3) Arrangements
The study aimed to introduce the concept of steganography, which is the science of concealing secret information within other media such as text, video, audio, or images. In image steganography, the digital image serves as a cover image in which the secret message is hidden, and the resulting image after embedding is known as the stego image. The work relied on the Improved Least Significant Bit (Improved LSB) technique by embedding data inside an RGB colour image in a way that preserves image quality while providing larger storage capacity for hidden information. The hiding process was implemented using the same algorithm in two modes: (2:2:4) and (2:3:3). In the first mode, 2 bits of secret data were hidden in the least significant part of the red channel (R), 2 bits in the green channel (G), and 4 bits in the blue channel (B). In the second mode, 2 bits were hidden in the red channel, while 3 bits were hidden in each of the green and blue channels. This algorithm is based on color theory, which states that the human eye is more sensitive to red and green than to blue. The results confirmed successful data hiding in both cases without noticeable distortion or visible artifacts, and the hidden data was retrieved with no loss, without requiring the original image or a location map. The implementation was carried out in MATLAB, where a program was designed for data embedding and extraction. Results of both methods were compared by evaluating image quality using Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Bit Error Rate (BER). Additionally, histograms were generated to illustrate color distribution differences between the original and stego images.
Artificial Intelligence and Machine Learning: Part 1
This article explores the evolution of artificial intelligence (AI) from early rule-based systems to contemporary data-driven approaches. Traditional models faced limitations due to their inability to fully represent complex realities and the lack of sufficient data and computational power. The rise of machine learning and artificial neural networks marked a significant shift, relying on large datasets and advanced processing capabilities rather than novel mathematics. The study emphasizes artificial neural networks, inspired by the human brain, which consist of layers of artificial neurons learning by adjusting internal weights. A practical example illustrates predicting transformer failures using operational data, highlighting common challenges such as under fitting and overfitting. Deep learning, with its multi layered architectures, enables the extraction of complex relationships from data, supporting applications in image, text, and speech processing. The article also addresses ethical challenges in AI, including unemployment, bias, critical decision-making, and deep fake generation. It stresses that AI itself is neutral, and its impact depends on how it is applied. When harnessed responsibly, AI can significantly benefit society in healthcare, education, governance, and even religious and cultural initiatives. The paper concludes by advocating for societal preparedness, strengthening education, and preserving cultural identity to mitigate potential risks while embracing the transformative potential of AI technologies.
Nonlinear Estimation of Incident Solar Irradiance on Finland Using Discrete-time Extended Kalman Smoothers
A huge source of energy lies within the incident solar heat and light from the sun,especially for colder territories found in the Nordics such as Finland. For a country situated with close proximity to the North pole, Finland suffers from chilling conditions most of the year in addition to the withheld Sun light. Except during the very short seasons of spring and summer when the Sun remains in the sky for almost 22 hours per day on average. Thus, solar heat flux and solar irradiation become essential energy sources between April and September annually. However, solar irradiance is a physical quantity that is highly affected by probabilistic uncertainty found in many weather conditions, solar behavior throughout the year, time of the day, and the geographical location. In this article, we propose a solar irradiance estimation method base on the Extended Kalman algorithms. The experiment was carried out between the years 2014-2016 in which we installed solar radiation sensors underground and on a rooftop inside the campus of Vaasa University. The readings were collected via an embedded system specifically designed for the endeavors of this experiment. The results showed that the algorithm was able to predict the incident solar irradiance on the city of Vaasa (Finland) with an acceptable accuracy range.
Computer Vision–Based Object Tracking System Assisted by a PID Controller
The study aimed to identify the applications of artificial intelligence that can be benefited from in the development of the video and surveillance camera system. There are challenges and problems related to the following aspects (monitoring process - identifying shapes - tracking them. And by employing some artificial intelligence applications in the monitoring process, such as object recognition and tracking systems, where the tasks of object recognition and tracking in video surveillance and monitoring systems are a key element, and color detection to track the target with real-time video sequences. The system described in this paper is on a camera attached to a Raspberry Pi board, containing an image processing algorithm (Haarcascade) that first detects the object and then tracks it. Color detection is generally a key stage in most image processing applications, if the application is based on color information. To monitor the object in the video, a built-in panel is adopted to monitor the activity of the object of interest based on the Raspberry PI panel. A real-time execution software method is implemented to track the object of interest. Animated and recognized using the Python programming language with OpenCV libraries. The two algorithms are tested and compared to prove the robustness of the object detection algorithm.
Laboratory Study on the Effect of CNC Plasma Cutting Process Parameters on Product Quality
CNC Plasma Cutting Technology is one of the most important cutting techniques that are widely used in the metal industries, due to its high ability to cut complex designs with great accuracy and low production cost. The quality of the products of this technology mainly depends on the rational control of the process parameters. In this study the effect of process parameters such as cutting speed, cutting current, and standoff distance on Kerf width and dross formation rate has been investigated using Taguchi method. The obtained results indicate that the cutting speed dominates both quality criteria with an impact rate of more than 57%, followed by cutting current which was around 12%, while the nozzle height showed very low impact on the quality criteria. Furthermore, the results of the conformation experiment indicate a high convergence between model and practical results in case of crack width and a lower level of congruence in case of the slag rate formation model due to narrow of testing levels.
Optimization of plasma cutting parameters
Predicting Sales Volume Using Multiple Linear Regression
The process of sales forecasting is of strategic importance in industrial enterprises, and is also a complex process, especially when choosing the explanatory variables that have the greatest impact on the volume of sales. The problem of the study, is that the traditional methods of forecasting sales used by many Libyan organizations are ineffective, and most of them depend on practical experience and are not based on scientific foundations, which leads to an imbalance between the volume of production and the volume of sales. This results in the accumulation of products, if the quantity of production is greater than the quantity of sales, or the loss of sales in the event that the quantity of the product is less than the quantity of sales, and this results in a loss in customers. This study aims to use the multiple linear regression method to predict the sales volume of the investment complex for construction materials industry (INCOMA) in Misurata by developing a mathematical model using the SPSS (Statistical Package for the Social Sciences) software. The most important independent (interpretive) variables were identified in the study, as advertising, price, and quality were identified as variables with the greatest impact on sales. The results of the study showed that the method that was adopted in the study was able to predict sales effectively, as the independent variables were interpreted by 85.5%. The results also showed that the F value was 41.03 at a significant level, which indicates that the model is statistically significant, as was the t value for all. The independent variables have a significant level of less than 5%, which indicates that they are statistically significant, except for the quality variable, which was not statistically significant. The results also portrayed that the variable with the largest influence was the advertising variable, followed by the price variable then the quality variable, based on the value of t. The Durbin Watson test to detect autocorrelation was also statistically significant, the distribution of the residuals was a normal distribution and through the results. The results also showed that the average error of forecasting model for a period of thirty six months was 6.89%, and it is considered a low percentage
Study of the Spatial Distribution of Ambulance Stations in the City of Bani Walid
Early Diagnosis of Autism in Children Using Convolutional Neural Networks (CNNs)
The use of artificial intelligence (AI) alongside medical skills has witnessed significant growth in recent years, leading to impressive results in classification and processing, while facilitating the work of medical staff. Moreover, there has been an urgent need for software tools to assist in the early classification of diseases. In response to this need, this paper aims to develop a bidirectional neural network model with a web application interface for diagnosing Autism Spectrum Disorder (ASD) in children. The methodology employed relies on machine learning techniques for the early detection of this disorder, which includes collecting a comprehensive dataset containing 6,000 images, adjusting the image dimensions to focus on the facial area, and extracting morphological features associated with the disorder's symptoms. Edge detection techniques were applied, and images were segmented into recognizable parts. Subsequently, the model was trained using these features, and its performance was evaluated using metrics such as accuracy and F1 score. The proposed model achieved an accuracy ranging from 70% to 76% and an F1 score ranging from 75% to 79%, indicating its ability to classify cases of Autism Spectrum Disorder with high precision.The web application provides a user-friendly and accessible interface to utilize the diagnostic model, facilitating the screening and evaluation process. However, it is important to emphasize that the Convolutional Neural Network (CNN) model is a supportive tool for diagnosis and not a definitive solution. While it can assist in identifying potential cases of Autism Spectrum Disorder, the findings highlight the importance of expert involvement in diagnosis. Recommendations include expanding the dataset, applying data augmentation techniques, and training specialists to use the model and interpret its results correctly
Making Noise Reduction in Medical Imaging More Accessible: Machine Learning and Traditional Techniques
Medical imaging is essential for diagnosis and treatment and is often plagued by noise, which can obscure important details. This study presents the effectiveness of traditional noise reduction techniques including Gaussian, Median, and Wiener filters, and compares them to machine learning methods such as automatic denoising encoders (DAE). We used a dataset consisting of grayscale medical images and simulated different noise scenarios—specifically Gaussian, Salt-and-Pepper, and Poisson—to reflect real-world conditions. The outputs were evaluated using key performance metrics: maximum signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). By evaluating these techniques, we aim to highlight the strengths and weaknesses of preserving essential image details while effectively reducing noise. The results indicate that automatic denoising encoders not only outperform noise reduction but also preserve important information more effectively than traditional methods. This research underscores the potential of integrating machine learning techniques into the medical imaging workflow to increase diagnostic clarity and reliability. As healthcare continues to evolve with technological advances, the role of advanced noise reduction techniques has become increasingly important in improving the quality of medical images and ultimately supporting better patient outcomes. The results of this study could pave the way for future research into hybrid methods that combine traditional filtering with machine learning strategies, further enhancing the capabilities of medical imaging technologies. Future work will uncover larger datasets and noise patterns to validate and improve the results, ensuring that medical professionals have the most reliable imaging tools.
Application Designed for Detecting Foetal Deformities and Congenital Defects by Comparing Ultrasound Images Using ORB Algorithm
This research aims to design and implement an Application that significantly aids in early detection of fetal deformities and congenital defects by comparing ultrasound images taken for the case with images stored in a large database using artificial intelligence. Additionally, some pregnant women face risks of fetal deformities, necessitating early detection to reduce health impacts and expedite intervention. Moreover, the application addresses the issue of relying on traditional screenings, which are often prone to human errors. By employing artificial intelligence techniques to analyze images, using the ORB algorithm because it is fast and unaffected by noise, lighting, and rotation, the application enhances diagnostic accuracy and assists healthcare professionals in making appropriate medical decisions. The research objectives include early diagnosis, supporting healthcare professionals, and reducing medical costs for patients.
Development of a System for the Central Blood Bank
Demand for access to information about the blood bank management system is rising day by day due to the growing demands for blood. The traditional system alone cannot help in facilitating the needs of the community at large and may result in critical delays in responding to emergencies. To handle this problem, an integrated blood bank management system has been developed based on a comprehensive and updated database. This system will allow users to get immediate results or lists compiled according to their specific criteria, such as geographic location and blood type, to manage inventories, donations, and campaigns. These efforts are geared to raise the need for blood donation and allow the community to participate in donation campaigns, creating better social cohesion and humanitarian consciousness. Through such donations, people can become heroes by saving the lives of others; hence, these activities are of essence in enhancing community interaction. It can locate blood bags, and when a patient requiring blood units arrives, the system will be linked to a search page where they can view the available donors. In addition, when a donor enters the site, their information is captured and analyzed instantly to come up with potential donors regarding age and disease-free body conditions The research will help improve the management of blood banks by adopting modern technologies, such as MySQL for data storage and HTML5, CSS, and JavaScript for web development. This enhances the efficiency of the system and ensures that critical information is accessed quickly and accurately. Hopefully, this will improve users' experiences and enhance the sustainability of blood supplies in the community
Machine Learning in Head and Neck Cancer: Clinical Prospects and Future Directions
The American Joint Committee on Cancer (AJCC) Tumor-Nodal-Metastasis (TNM) staging system has been widely used for planning of treatment strategies. However, for an individual patient, it is ineffective for predicting outcome due to its inability to consider other tumor- and patient-related risk factors. To this end, a tool that considers these factors together to accurately predict patients’ outcomes would be pertinent. Objectives: This study aimed at examining the potential of a collaborative machine learning (cML)-based approach in estimating the overall survival of oral cancer patients. We compared the performance of cML with voting ensemble-based machine learning model. The prognostic significance of the clinicopathological parameters used to develop the model was examined using permutation feature importance. Furthermore, we examined some of the factors that can hinder the recommendation of machine learning (ML) models for further clinical evaluations. Methodology: The clinicopathological information of 9439 oral cancer patients were extracted from the Surveillance, Epidemiology, and End Results database, United States. Altogether, three machine learning (ML) models – voting ensemble, stacked ensemble, and extreme gradient boosting were combined to form a cluster of cML models. The performance of the cML was compared with the best performing individual ML algorithm in terms of accuracy. Results: The performance accuracy of voting ensemble, stacked ensemble, and extreme gradient boosting was 70.2%, 69.2%, and Place figures are at the bottom, while the tables are at the top, with the necessity of sequencing the numbering of the figures and tables as shown in TABLE 1. and Fig. 1. below 69.6%, respectively. When the cML and voting ensemble were randomly validated with 50 cases from the temporal validation cohort, they showed comparable performance. In terms of future importance, the most significant features were age of the patient at diagnosis, T stage, tumor grade, marital status, gender, primary site, surgery, N stage, radiation treatment, ethnicity, chemotherapy, and M stage. Discussion and Conclusions: The idea of the cML is to consider the unique properties of each of the ML models in making final predictions. Thus, representing a paradigm shift from individualism to cooperativism in the quest for personalized estimation of outcome for oral cancer patients. Of note, rather than competition among participating models, they cooperate to offer reliability to the final prediction made by the cML. Lack of independent geographic validation, model generalization and rigidity, and explainability are some of the factors identified that limit the recommendation of ML models for further clinical evaluation. The cML approach may aid in determining individualized treatment for oral cancer patients.
Head and Neck Squamous Cell Carcinoma (HNSCC)
Enhancing Patient Care Through Preparing an AI Expertise in Libya: A Postgraduate Program on AI in Healthcare
The Libyan healthcare system is struggling with many problems, like a short workforce, limited access to care, and fewer resources to run the hospitals and medical canters. In Libya, these challenges are more due to the increased demand for quality services and the city's population. Aims: Thus, there is an urgent need for quick solutions like Artificial Intelligence (AI) tools and resources that may help to increase healthcare productivity by managing ongoing issues like hospital aiting time and patient schedules and more help with operational management through appropriate workforce allocations. Objectives: To start using AI tools, the study will focus on the importance of training healthcare professionals through a post graduate degree to ensure a proper understanding of how AI is implemented in healthcare and how to manage its challenges. A mixed research qualitative approach searching on the Google engine and the National Institution of Health (NIH) website and a comprehensive class curriculum was searched based on the international benchmarks that teach a master's degree in AI healthcare. A master's degree program was prepared to target newly graduated professionals focusing on three core areas: medical practice issues, technology applications, and AI model projects. There is an urgent need in Libya for a postgraduate program to prepare professionals to be experts in using AI in healthcare. The postgraduate degree is expected to help healthcare professionals improve patient care, mainly when adapting AI dependent health services.
Use of Rank Order Clustering Algorithm for Part-Machine Grouping in Cellular Manufacturing
The Rank Order Clustering (ROC) algorithm, developed by J.R. King, has undergone significant advancements over the last five decades and is widely utilized in diverse fields, including manufacturing, for the grouping of machines and parts. This study investigates the utilization of the ROC algorithm in cellular manufacturing systems (CMS) to optimize the creation of machine cells and part families, with the objective of enhancing production efficiency. The research presents a comprehensive analysis employing a binary part-machine matrix and utilizes Microsoft Excel for data manipulation. By iteratively rearranging rows and columns based on binary values, the ROC algorithm effectively clusters machines and parts into intersecting machine cells and non-intersecting part families. A case study involving a 16×10 binary part-machine matrix demonstrates the practical implementation of the ROC algorithm. The findings indicate that while the ROC algorithm provides a structured approach to cell formation, its effectiveness may vary. This research highlights the potential of the ROC algorithm in improving manufacturing layout optimization and process management, thereby it paving the way for further advancements in manufacturing systems.
A Laboratory Study on the Effect of Process Parameters on the Quality of Low-Carbon Steel Cutting Using Optical Laser Technology.
The utilization of laser cutting methodologies has witnessed a widespread adoption in the local industrial sector recently, thereby emphasizing the necessity of elucidating this technology and elucidating effective strategies for enhancing and elucidating the outcomes of this procedure. This investigation was undertaken with the aim of enhancing the precision of the cutting process through laser technology by ascertaining the optimal values for key parameters associated with this procedure, specifically the cutting speed, laser power, and beam focal length. Moreover, a mathematical model was developed to depict &nbsp; the correlation between the process parameters and the quality of its results, including the reduction of bottom slag formation, kerf width, and bevel angle. The research relied on the utilization of the MiniTab-18 software to design the experiment and analyze its findings utilizing the Taguchi method, encompassing the response table, independent and interaction parameter effects charts, and analysis of; variance table. Furthermore, the regression model technique was employed to derive the mathematical model capable of forecasting the criteria values based on the presumed parameter levels. Additionally, contour plots were implemented to illustrate the interaction impact of the parameters. The study outcomes underscored that cutting speed exerts the most significant influence on the three process quality criteria (slag formation, inclination angle, and kerf width), followed by focal length concerning slag formation and kerf width. However, beam power demonstrated a greater impact than focal length in relation to the inclination angle. The study concluded by identifying the optimal parameter values for attaining superior quality based on each considered criterion.
Applications of Waiting Lines Models for Improving the Performance Levels of Banking Service System
In this study, the theory of waiting queues was applied to improve the quality of banking services provided by Jumhouria Bank, Ahmed Al-Sharif Branch. By using a single- row waiting queue model and several service centers with one service stage (MM/s), and to determine the requirements for building the model, the study focused on banking service centers, where the research relied on studying two periods (the first period is first half of the month and the second period is second half of the month), according to the bank’s system, which usually adopts five service centers in the first half of the month and three service centers in the second half of the month. The importance of the study lies in that it addresses the problem of the unwanted waiting phenomenon that most industrial and service institutions suffer from. The aim of study is to develop a model that optimizes the waiting line quantitatively. The service rate and arrival rate were calculated and queuing equations were applied using Quantitative Methods (QM) software (Waiting Lines). The study concluded that the system operates under a stable state, where the value of the utilization rate, which is the probability that the system will be busy in the first period, is 79%, the probability that the system will be empty is 1%, the probability that the system will be busy in the second period is 62%, and the probability is 13% that the system is empty in the second period.
Remote Laser Welding: The Future of Automotive Manufacturing
Global Automobile industry has adopted laser technology as early as 1973 into its manufacturing process, starting from Ford Motor Co. use of laser welding system for underbody operation over its assembly line. In modern times the solid- state laser is gaining popularity and evolving very fast. Laser technology has become integral part in automotive industry right from laser cutting to laser welding in gear parts welding, manufacturing air-bags and welding engine components like injection values, welding tailored blanks and welding hydro formed parts. Companies like Volkswagen AG. have been using lasers to produce its car bodies and now has over 800 high-power laser systems placed in its manufacturing plants globally. The same goes for Audi AG., BMW AG., Nissan Motor Co. Ltd., Toyota Motor Corp., Hyundai Motor Co., and Volvo as they have been using lasers for joining and welding metals for many years.The purpose of this paper is to illustrate the technology of remote laser welding instead of traditional methods in the automotive industry. (Photonics, 2024)
Copyright (c) 2024 Journal of Technology Research https://creativecommons.org/licenses/by-nc/4.0
Case Study: Cost Analysis and Emission and Analysis of a 100mw Grid-Connected Wind Energy System Using Retscreen Expert Simulation Software
This research demonstrates the feasibility of using wind energy as a clean source for electricity generation. The study was conducted at a wind farm in the eastern city of Misrata, using the RETScreen program for performance and cost evaluation. Wind is considered a enewable and non-polluting source, and the research aims to compare the efficiency of the wind farm with gas-fired power plants. The RETScreen program was used to determine the suitable location for establishing a wind farm based on the appropriate wind speed and selecting the appropriate turbine from the program's library. Multiple results were obtained, including speed, power, and energy curves. The research also investigated the connection of the wind farm to the public electricity grid and analyzed the costs and carbon emissions associated with the farm.
The Transformative Power of AI in Healthcare
Recent advancements in Artificial Intelligence (AI) have created considerable prospects for disruptive changes in various fields, including healthcare. As one of the largest contributors to government expenditures, the healthcare sector stands to benefit greatly from AI-driven business models that can enhance its efficiency in both quantity and quality. These benefits include improving decision-making capabilities, optimizing patient outcomes, and redefining medical roles. This paper explores the anticipated transition brought about by AI in healthcare, examines the challenges associated with this shift, and proposes strategies to overcome them.
Artificial Intelligence, AI, Machine Learning, Gen AI, Healthcare.
Exploring Knowledge, Attitudes, and Practices of Dentists in Libya on Tele dentistry: A Cross-Sectional Online Survey
Teledentistry (TD) combines digital technology with dentistry, letting dentists and patients communicate remotely. As it becomes more important, it can help dentists understand new requirements and gain the skills needed to provide good dental care for their community. Aim: to assess the knowledge, attitudes, and practices of Libyan dentists regarding TD. Materials and method. This cross-sectional study involved 257 dentists in Libya. They were provided with a self-administered questionnaire through Google Forms online to collect information on their awareness, usage patterns, and perceptions of TD. Results: 257 dentists from different Libyan cities joined the study. About 65.5% were female and around 34.5% were male. Most participants (79%) had a bachelor's degree. indicated that 62% of dentists were aware of it as a tool for telemedicine either solely or in combination with other tools. While 78% of participants expressed confidence in TD's ability to protect patient privacy, nearly 80% raised concerns about the reliability of patient-provided information. A Significant association in awareness was observed based on academic qualification (p ≤ 0.05) indicating a positive correlation. Key challenges highlighted by participants included information accuracy, limitations in physically examining the patient, and patient cooperation.
Tele dentistry, dentists, attitude, questionnaire, Libya.
Development of an Automated System for Brain Tumor Detection Using Machine Learning Techniques
In today’s rapidly evolving technology landscape, machine learning is transforming medical diagnostics, offering new hope to patients and healthcare providers alike. Brain tumors are one of the most serious health challenges that need accurate and early detection to improve treatment outcomes and increase survival rates. This research details the thorough development as well as the thorough implementation of an advanced automated brain tumor detection system, coupled with a classification system, using MATLAB. Using advanced image processing techniques along with machine learning algorithms, the system identifies approximately 90% of tumors as either benign or malignant. MRI image preprocessing begins the process. During this stage, techniques such as denoising and skull stripping are used to improve image quality and isolate brain regions. Precise tumor boundaries are determined via edge detection and further improved through precise morphological refinements to guarantee accuracy. Convolutional neural networks CNNs automatically extract features. This eliminates manual feature selection because the networks learn important patterns from the data. The highly accurate CNN classifier successfully and efficiently distinguishes between benign and malignant tumors using these learned patterns. This system's extraordinary speed, along with its outstanding ease of use, distinguishes it from others. Its fast MRI scan processing—about three seconds per image—makes it ideal for real-time clinical use. An intuitive graphical user interface (GUI) is featured in the system, allowing many radiologists to easily upload many MRI images, view several segmented tumor regions, in addition to access many clear classification results. This work demonstrates how the combination of advanced image processing and machine learning can enhance diagnostic accuracy and efficiency. The system has the potential to support radiologists by reducing diagnostic variability and enabling more informed decisions. Future improvements could include expanding the scope of datasets, incorporating multimodality imaging techniques, and testing the system in diverse clinical settings to further validate its effectiveness and applicability.
Brain Tumor Detection, Convolutional Neural Networks, Automated Diagnostic System, MRI, Image Processing, Tumor Classification, Medical Imaging, Machine Learning.
Development of Data Model for Computerized Information System at a Clinic
Databases are integral part of all computerized information systems (CIS). Well-designed database is an important requirement for having a useful and effective CIS. Database analysis and design are essential phases of database development process that would lead to the database creation. This project looks at developing a conceptual data model that can represent a prospective database for supporting a selected healthcare processes at Al-Keesh clinic in Benghazi. Enacting such development process defines the targeted processes and their challenges as well as their data requirements.Methods: requirement analysis is one task of the database development process where data is collected from the field then analysed to produce a conceptual data model for the database. Data are collected via various technique including interviews, document analysis, and work observations which all were used on this project.Results: requirement analysis defined process descriptions for three targeted healthcare processes at the clinic. These descriptions provide an understanding about the database system context and identified a number of challenges that would be resolved by the prospective database. Data redundancy, lack of clear data structure, and fragmented data documentation were some of the findings revealed about the targeted processes. The requirements analysis also defined the conceptual data model which represented the specification of the data content of the database. The produced ERD illustrates 16 entities specific to the targeted processes along their complete attributes as well as the relationship among these entities Conclusions: database analysis and design activities provide the guidelines for establishing the database specifications including the conceptual data model. A conceptual data model that represents local data requirements is essential for developing a CIS that is relevant and useful to the users. These activities also revealed the targeted processes issues and challenges which allow the system analyst to contemplate the adequate technical solution to address them.
Database design, database model, conceptual model, medical database, data modeling.
Innovative Solutions for Glucose Monitoring: A Non-Invasive Approach for Diabetes Management Review
This review explores a pioneering technique for measuring glucose levels, addressing a critical need for millions, especially diabetics. Conventional methods, which rely on painful and potentially dangerous blood tests, have long been a daily challenge, particularly for children and individuals with specific blood conditions. In contrast, the technique we introduce offers a ground-breaking, pain-free solution by measuring glucose levels from saliva. By establishing a reliable correlation between salivary and blood glucose, this approach offers a game-changing way to monitor glucose levels with ease and accuracy, revolutionizing diabetes care. The device employs advanced enzyme-based biofluid sensors, providing precise and efficient glucose measurements without the need for invasive procedures. Beyond real-time glucose analysis, the system goes a step further by storing critical data and generating detailed monthly reports, accessible through a dedicated smartphone application. This feature empowers patients to monitor their health continuously, facilitating seamless communication with healthcare providers and enabling personalized care. Furthermore, we present a robust mathematical model and simulation, which demonstrates the relationship between blood and saliva glucose levels through nonlinear equations. The device’s performance is validated by comparing simulation results developed using MATLAB and Simulink with analytical solutions, offering deeper insights into its functionality and This represents a revolution in oral diagnosis of diabetes. This innovative approach can reshape diabetes mellitus management by providing a non-invasive, accurate,andpatient-friendly alternative to traditional blood glucose testing. The advancements discussed in this paper signal a promising future in glucose monitoring improving the lives of diabetics and offering new hope in the ongoing battle against diabetes.
Glucose levels, Salivary, Blood glucose, Advanced enzyme, Biofluid sensors.
The Internet of Things Applications: Its Effects, Roles, and Implications in Libyan Healthcare System
Libya is more shifting to digital transformation. Using Ubiquitous Computing (Ubicomp) by Integrating Internet of Things (IoT) technologies and mobile computing (MC) in the Libyan healthcare system (LHS) represents a transform shift with tangible benefits. Merging IoT, which brings connectivity of the network and their devices, low-power consumption, Miniaturization, and biosensors with mobile computing, which brings remote health monitoring, real-time locating, personalized healthcare, better patient-doctor interaction. This merger will have dramatic improvement by enabling patients, devices, doctors, sensors, health providers, and supply chains to communicate and interact with each other seamlessly and autonomously, without requiring explicit human intervention. This paper presents some of the innovations in IoT technologies on a global scale. Based on our previous works, the authors discuss the roles, effects, and implications of IoT technologies inclusion in the digital transformation of LHS. Also, the authors aim to inspire healthcare providers, policymakers, and researchers in Libya to collaborate in developing and using IoT in their digital transformations of LHS.
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