The objective of this study is to explore the role of artificial intelligence applications (AIA) in education. AI applications provide the solution in many ways to the exponential rise of modern-day challenges, which create difficulties in access to education and learning. They play a significant role in forming social robots (SR), smart learning (SL), and intelligent tutoring systems (ITS) to name a few. The review indicates that the education sector should also embrace the modern methods of teaching and the necessary technology. Looking into the flow, the education sector organizations need to adopt AI technologies as a necessity of the day and education. The study needs to be tested statistically for better understanding and to make the findings more generalized in the future.

The aim of the article is to explore the academic and administrative applications of Artificial Intelligence. Teachers have the main responsibility of teaching in any educational setting. But there are various other tasks to be performed by the teachers as well. Besides academic duty, most of the teacher’s time and educational resources are dedicated to administrative works. Artificial Intelligence Applications (AIA) are not only assisting education academically and administratively but also enhance their effectiveness. AIA provides help to teachers in various types of tasks in the shape of Learning Analytics (LA), Virtual Reality (VR), Grading/Assessments (G/A), and Admissions. It minimizes the administrative tasks of a teacher to invest more in teaching and guiding students. In the current era, where there are a lot of tasks associated with the teaching profession, AIA adds a significant contribution to enhance student learning, minimize the workload of a teacher, grade/assess the students effectively and easily, and to help in a lot of other administrative tasks. The study needs to be quantitatively checked to make it generalized and acceptable.

Deep neural networks have made tremendous strides in the categorization of facial photos in the last several years. Due to the complexity of features, the enormous size of the picture/frame, and the severe inhomogeneity of image data, efficient face image classification using deep convolutional neural networks remains a challenge. Therefore, as data volumes continue to grow, the effective categorization of face photos in a mobile context utilizing advanced deep learning techniques is becoming increasingly important. In the recent past, some Deep Learning (DL) approaches for learning to identify face images have been designed; many of them use convolutional neural networks (CNNs). To address the problem of face mask recognition in facial images, we propose to use a Depthwise Separable Convolution Neural Network based on MobileNet (DWS-based MobileNet). The proposed network utilizes depth-wise separable convolution layers instead of 2D convolution layers. With limited datasets, the DWS-based MobileNet performs exceptionally well. DWS-based MobileNet decreases the number of trainable parameters while enhancing learning performance by adopting a lightweight network. Our technique outperformed the existing state of the art when tested on benchmark datasets. When compared to Full Convolution MobileNet and baseline methods, the results of this study reveal that adopting Depthwise Separable Convolution-based MobileNet significantly improves performance (Acc. = 93.14, Pre. = 92, recall = 92, F-score = 92).

This study highlights a new construction of SEPIC DC-DC converter. The proposed converter aims for some features such as high voltage gain, continuous input current and reduce stress on the power switch. In addition, the circuit construction ensurs the simplicity in design along with signicant cost saving, since its components are readily available and smaller in size compared to the off-shelf components. This type of converter can adjust the DC voltage to maintain its output voltage to be constant. Typically, SEPIC operated in equipment that uses battery and also in wide range input voltage DC power supply. The converter is designed for renewable energy application where it is able to regulate the output voltage of the Photovoltaic (PV). The converter has been analysed based on different switching frequencies and duty cycle. Thus the outcome of the proposed converter can be achieved by using D=0.45 and fs=30 kHz. The proposed converter is supplied by 26V as an input voltage and produces 300V output and gives 94% of efficiency.

Wind turbine is a device that converts kinetic energy of wind into electrical energy. Weak air pressure with an average of 2m/s to 4m/s makes wind turbine implementation in Malaysia becoming difficult and unrealistic. In order to produce a good power and torque, a quality air pressure is needed. Wind turbine performance depends on the incoming wind speed. Fluctuating and low quality air pressure are the significant challenges in wind turbine implementation. There are many types of mechanism and control but the production of output power is still considered low compared to the actual equipment. In a large building, Heat Ventilation Air Conditioning (HVAC) system is highly required to control the indoor temperature and consistent air pressure through ducting. The consistency of air pressure in ducting system make a drive for this research to proposed a wind turbine implementation in ducting system in order to produce electrical energy. In HVAC there are five parameters to be considered in order to identify the air pressure distribution in ducting system. These five parameters are input air pressure, ducting height, distance between blower to the pipe, total effective length and gap between trunk or run out.  In this paper, an analysis of air pressure  effect in a ducting system will be conducted via a development of air pressure prototype. Thousands of experiments are required with regards to a huge number of experiments to be considered. Spesifically a statistical analysis Taguchi Method is used to reduce a number of experiments. Without statistical analysis, 3125 experiments  shall be conducted to identify the air pressure distribution in the ducting system. However, these huge number of experiments are reduced to only 25 experiment by using Taguchi method based on the function of L25 in array orthogonal