Using genetic algorithm for optimal sizing of stand-alone hybrid energy system

When planning a hybrid energy system (HES) that incorporates both renewable and non-renewable energy sources—those that rely on fossil fuels—the primary considerations are the total cost of the system and the CO? emissions. In this paper, we will investigate the typical hybrid energy system (HES) that incorporates both renewable and non-renewable energy sources involving a detailed simulation process that may require specific inputs, models, and data. Then, we employed dual optimization methods: genetic algorithm (GA) and particle swarm optimization (PSO). The consequences of GA and PSO execution in the bus timetabling problem depict that the GA algorithm is better at finding the optimal solution in terms of accuracy and iteration. Additionally, the GA algorithm is also superior to the straightforwardness of the techniques used. So, in this work, we employed a Genetic Algorithm Optimization (GA)–-based optimal sizing technique for HES configurations that include sustainability wind turbines (WTs), battery storage (BS), and diesel generators (DGs). HES improved power delivery to a rural community in the Wasit Province, Iraq, situated at 46° - 36° and 32° - 31° in the country's southeastern central region. Throughout the project's 25-year lifespan, the optimization primarily aims to minimize the total cost (CT) and total CO? emissions (ECO2T). The outcomes demonstrate that the GA algorithm may, with continuous electricity supply, minimize the objectives while meeting the load demand

Information and Communication Technology and its Impact on Improving the Quality of Engineering Education Systems

The emergence of the remarkable phenomenon of information and communication technology (ICT) in the last two decades of the twentieth century, and its integration into the formal education systems of leading countries, has expanded learning opportunities and facilitated easy access to educational resources. Due to the vast amount of information available, there is a growing emphasis on information management. This approach allows students to enhance their learning by utilizing various tools and visual aids. These tools help in teaching and training by engaging students’ different senses, making learning more realistic, practical, and enjoyable. The quality of education and the effectiveness of educational systems are among the most important concerns for educational developers, and decision-makers in any country. The areas of education is one of the fields that has undergone fundamental changes with the emergence of information technology. Information technology has been recognized as an effective tool in the learning and teaching process. In this research, we will discuss the role of ICT and its impact on enhancing the quality of education systems. The results demonstrate that ICT plays an effective role in the design, planning, implementation, learning, educational evaluation, and structure of education. This includes aspects such as timing, suitability, accuracy, adequacy, realism, speed of transmission, learning accuracy cost reduction, and educational effectiveness. Based on the aforementioned points, educational institutions must offer a suitable framework for integrating ICT into education through thorough planning

E-learning in the Cloud Computing Environment: Features, Architecture, Challenges and Solutions

The need to constantly and consistently improve the quality and quantity of the educational system is essential. E-learning has emerged from the rapid cycle of change and the expansion of new technologies. Advances in information technology have increased network bandwidth, data access speed, and reduced data storage costs. In recent years, the implementation of cloud computing in educational settings has garnered the interest of major companies, leading to substantial investments in this area. Cloud computing improves engineering education by providing an environment that can be accessed from anywhere and allowing access to educational resources on demand. Cloud computing is a term used to describe the provision of hosting services on the Internet. It is predicted to be the next generation of information technology architecture and offers great potential to enhance productivity and reduce costs. Cloud service providers offer their processing and memory resources to users. By paying for the use of these resources, users can access them for their calculations and processing anytime and anywhere. Cloud computing provides the ability to increase productivity, save information technology resources, and enhance computing power, converting processing power into a tool with constant access capabilities. The use of cloud computing in a system that supports remote education has its own set of characteristics and requires a unique strategy. Students can access a wide variety of instructional engineering materials at any time and from any location, thanks to cloud computing. Additionally, they can share their materials with other community members. The use of cloud computing in e-learning offers several advantages, such as unlimited computing resources, high scalability, and reduced costs associated with e-learning. An improvement in the quality of teaching and learning is achieved through the use of flexible cloud computing, which offers a variety of resources for educators and students. In light of this, the current research presents cloud computing technology as a suitable and superior option for e-learning systems

Gender Recognition of Human from Face Images Using Multi-Class Support Vector Machine (SVM) Classifiers

In the realm of robotics and interactive systems, gender recognition is a crucial problem. Considering the several uses it has in security, web search, human-computer interactions, etc., gender recognition from facial photos has garnered a lot of attention. The need to use and enhance gender recognition techniques is felt more strongly today due to a significant development in the design of facial recognition systems. Relatively speaking to other approaches, the progress gained in this area thus far is not exceptional. Thus, a novel method has been adopted in this study to improve accuracy in comparison to earlier research. To create the best rate of accuracy and efficiency in the suggested method of this research, we choose a minimal set of characteristics. Testing on the FERET and UTK-Face datasets reveals that our suggested algorithm has a lower degree of inaccuracy. In this article, the input image of the person's face is pre-processed to extract the right features from the face once the person's face has been recognized. Gender separation is achieved using Multi-class Support Vector Machine (SVM) Classifiers after features from normalized images have been extracted using Histogram Oriented Gradient (HOG), Gabor Filters, and Speeded Up Robust Features (SURF), as well as their combination to select the most appropriate feature from them as input for gender classification. As a feature reduction feature, the Principal Component Analysis (PCA) algorithm is also employed. Using the proposed approach, 98.75% gender recognition precision has been accomplished on the FERET database and a runtime performance of 0.4 Sec. on the UTK-Face database, 97.43% gender recognition accuracy has been accomplished and a runtime performance of 0.5 Sec

Enhancement of the Fifth Generation of Wireless Communication by Using a Search Optimization Algorithm

The fifth generation of cellular networks (5G) is seeing a rapid expansion, and energy efficiency (EE) is a hot topic of discussion. It has been found that both EE and maximum spectral efficiency (SE) are desired. They are conflicting objectives, meaning that maximizing one will decrease the other. To tackle this issue, strategies for spectrum and energy optimization have been proposed, as well as green communication plans that aim to minimize the tradeoff between SE and EE. Research has been conducted on EE-oriented resource allocations to reduce energy usage while ensuring high-quality results. To do this, the Crow Search Optimization Algorithm (CSA) has been used. Simulation results have demonstrated that this proposed method is effective in finding the most suitable solution

Smart Learning based on Moodle E-learning Platform and Digital Skills for University Students

The education system is facing new challenges and requirements as a result of the rapid and complex changes in the world. It has become necessary to change and improve the current system for online learning to keep pace with these changes. E-learning is of increasing importance in higher education and learning communities as standard components in many courses. Especially with the great development in technology, which the Corona pandemic had a great impact on its acceleration and increase in dealing with it. So the COVID-19 affected the whole world also affected education, so education is no longer limited to a specific place and time but it is now possible at any time and place. So even education has changed from face-to-face education to distance education. In this paper, Moodle is used to design an open-source e-learning platform that supports both the student and the teacher by providing services that facilitate teaching, and educational-related administrative tasks and effectiveness. In addition, to know how it affects digital skills which considered one of the most important things related to the Internet recently. The findings obtained as a result of the research are given in detail in the results section