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Predicting Student Performance in a Design Entrepreneurship and Leadership Course: Leveraging Academic Metrics
No full textPredicting and understanding student performance is a critical challenge for design education
programs, as it enables educators to identify at-risk students, allocate resources effectively, and
enhance teaching strategies to support student success. This study investigates the use of academic
metrics to develop a predictive model for student performance in a "Design Entrepreneurship and
Leadership" course at Effat University. The academic record data collected includes student
attendance, quiz grades, midterm exam grades, final exam grades, and assignment project grades.
Using a multiple linear regression approach, this research examined the relative influence of these
factors on the students' final course performance. The results indicate that quiz grades, midterm
exam grades, and assignment project grades were the strongest predictors of student success, while
attendance grades also contributed significantly to the model. The proposed predictive model
provides valuable insights for design educators and program administrators. By understanding the
key drivers of student performance, they can identify at-risk students early on and implement
targeted interventions to improve learning outcomes. Additionally, the findings can inform
curriculum development, assessment practices, and the allocation of resources within design
program’s other courses. This study contributes to the limited but growing body of research on
predicting student performance in design-focused courses. By leveraging academic metrics, the
researchers demonstrate a rigorous and data-driven approach to forecasting student success, which
can be adapted and applied to a variety of design education contexts. The findings have important
implications for enhancing the quality and effectiveness of design curricula, ultimately preparing
students for the complex challenges they will face in the professional world.N
Attitude and Altitude Nonlinear Control Regulation of a Quadcopter Using Quaternion Representation
No full textControlling a quadcopter is a challenging task because of the inherent high nonlinearity of a quadcopter system. In this paper, a new quaternion based nonlinear feedback controller for attitude and altitude regulation of a quadcopter is proposed. The dynamic model of the quadcopter is derived using Newton and Euler equations. The proposed controller is established based on a feedback linearization technique to control and regulate the quadcopter. Global asymptotic stability of the designed controller is verified using Lyapunov stability criterion. A comparison of the proposed controller performance and that of the state-of-the-art quadcopter controllers is performed to ensure the effectiveness of the proposed model. The efficiency of the proposed controller is clearly shown when the quadcopter is in or near a corner pose. Simulations are performed to assess the transient and steady state performance. Steady State Error ( Ess ) and Max Error ( EM ) are used as evaluation metrics of the proposed model performance
The Effectiveness of Nostalgic Marketing on the Market Offerings in Saudi Arabia
No full textAs the market offerings are increasing and new deferent alternatives of these offerings are being introduced to the market every day, marketers are coming up with deferent ways and concepts to promote their products or services. The deferent ways and concepts that are used in promotion help in differentiating between the offerings that the market has. This research aims to understand the meaning of nostalgic marketing and its effect on the market of Saudi Arabia. This study will highlight the importance of nostalgic marketing and the respond that it generates in the market of Saudi Arabia. The target population will be the population of Jeddah. Survey method will be used where the survey participants will be more than 100. The survey will be carried out using digital survey forms e.g.: iPad, tablet etc. The findings of this research According to the report, tailored advertisements are the most effective nostalgic stimulus in Saudi Arabia, followed by joyful memories, communal nostalgia, and cultural nostalgia, with digital nostalgia being the least successful nostalgic notion employed in this market.
When the five nostalgia triggers are applied in marketing, they might elicit three different emotions from the Saudi market: emotional, cognitive, and behavioral reactions. These reactions can occur as a single reaction or all three at the same time from the same commercial, and they can come as a and out come from the market after multiple advertisements were placed in it. According to the poll, joyful memories and communal nostalgia stimuli will elicit an emotional response the majority of the time, whereas tailored advertisements, cultural nostalgia, and digital nostalgia will elicit a cognitive response
A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks
No full textToday's advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustibl
The era of advanced machine learning and deep learning algorithms for malware detection
No full textSoftware has been the essential element to computers in today's digital era. Unfortunately, it has experienced challenges from various types of malware, which are designed for sabotage, criminal money-making, and information theft. To protect the gadgets from malware, numerous malware detection algorithms have been proposed. In the olden days there were shallow learning algorithms, and in recent years there are deep learning algorithms. With the availability of big data for training of model and affordable and high-performance computing services, deep learning has demonstrated its superiority in many smart city applications, in terms of accuracy, error rate, etc. This chapter intends to conduct a systematic review on the latest development of deep learning algorithms for malware detection. Some future research directions are suggested for further exploration