IJIEEB Vol. 13, No. 6, Dec. 2021
Cover page and Table of Contents: PDF (size: 639KB)
Organizational culture is the dominant value or habit that becomes the driving force for an organization. Values, thoughts, and symbols based on Islam that influence the norms of behavior, attitudes, habits, and habits of a person in various fields become the culture of Islamic organizations that are believed to be true. The fundamental problem in this research is that organizations do not understand what the dominant organizational culture is today, how effective the system users are with these cultural conditions, and whether the current level of effectiveness of the dominant culture follows Islamic values. Previous research shows the relationship between organizational culture and effectiveness, while this study adds new variables, and proves the relationship of organizational culture dominance model with system effectiveness based on Islamic values on practical contribution, and creates a new model on theoretical contribution. The research phase begins with finding the current dominant organizational culture using the Organizational Culture Assessment Instrument and the Cultural Values Framework Instrument. Next, look for the relationship between the current dominant organizational culture and the effectiveness of the system by using the Delone and Mclean Is Success Model. Then combine the second model based on Islamic values using Structural Equation Modeling (SEM) - Partial least square (PLS). The result of this research is that there is a relationship between the dominance of the current organizational culture and the effectiveness of information systems. This is because the dominant organization is a clan that is familial in reaching an agreement, commitment between members in communicating, increasing the intensity of members in using information systems, systems that are easy to reach and use, and the quality of information systems that are easy, effective, and accountable. Meanwhile, the dominance of organizational culture on the effectiveness of information systems based on Islamic values has not been fully proven. This means that the dominance of organizational culture on the effectiveness of the Islamic values information system still needs to be adjusted to the four indicators on the Islamic values variable showing the closeness of values.[...] Read more.
Dyslexia is a learning disability which causes difficulty in an individual to read, write and spell and do simple mathematical calculations. It affects almost 10% of the global population and detecting it early is paramount for its effective handling. There are many different methods to detect the risk of Dyslexia. Some of these methods are using assessment tools, handwriting recognition, expert psychological help and also using the eye movement data recorded while reading. One of the other convenient and easy ways of detecting risk of dyslexia is to make an individual participate in a simple game related to phonological awareness, syllabic awareness, auditory discrimination, lexical awareness, visual working memory, and many more and recording the observations. The proposed research work presents an effective way of predicing the risk of dyslexia with high accuracy and reliability. It uses a dataset made available from the kaggle repository to predict the risk of dyslexia using various machine learning algorithms. Also it is observed that the dataset has an unequal distribution of positive and negative cases and so the classification accuracy is compromised if used directly. The proposed research work uses three resampling techniques to reduce the imbalance in the dataset. The resampling techniques used are undersampling using near-miss algorithm, oversampling using SMOTE and ADASYN. After applying the undersampling near-miss algorithm, best accuracy was given by SVC classifier with the value of 81.63%. All the other classifiers used in the experiment produced accuracy in the range of 64% to 79.08%. After using the oversampling algorithm SMOTE, the classifiers produced very good results in the evaluation metrics of accuracy,CV score, F1 Score and recall. The maximum accuracy was given by RandomForest with a value of 96.37% and closely followed by XGBBoosting and GradientBoosting with an accuracy of 95.14%. Decision tree, SVC and ADABoost got an accuracy of 91.26%, 93.36% and 93.48% respectively. Even the values of CV score, F1 and recall were considerably high for all these classifiers. After applying the oversampling technique of ADASYN, RandomForest algorithm generated maximum accuracy of 96.25%. Between the two oversampling techniques, SMOTE algorithm performed slightly better in producing better evaluation metrics than ADASYN. The proposed system has very high reliability and so can be effectively used for detecting the risk of dyslexia.[...] Read more.
Manual checking of attendance may lead to inconsistency of data inputs and may generate unreliable attendance result. Hence, Radio Frequency Identification (RFID) system has been developed to solve this problem, but it allows only checking student’s attendance as they enter and exit the school premise only. In consequence, teachers in every subject still need to check and monitor students’ attendance manually. Nevertheless, due to a usual large number of students entering and existing the school premise as they are tapping their RFID card, there is always a possibility of proxy attendance. Thus, Mobile-Based Attendance Monitoring System Using Face Tagging Technology (MBAMSUFTT) was developed to provide an attendance monitoring system through biometric authentication such as face recognition. The system serves as a tool for teachers to check and monitor student’s attendance in most reliable and accurate way using their smart phones. The MBAMSUFTT generates attendance report intended for close monitoring and printing of student’s attendance result. But the reliability of the attendance result (output) of the system depends on the quality of picture (input) sent by the user. Camera specification, ambiance lighting condition, and proper position of students while taking photo is exclusively required. The server and the mobile part can only run together if Wireless Fidelity is on, otherwise, monitoring will not be executed.
As a developmental research, this study used the Agile Model based on System Development Life Cycle (SDLC) intended for building a project that can adapt to change requests quickly. The MBAMSUFTT was evaluated based on the ISO/IEC 25010; MBAMSUFTT’s software quality characteristics by the IT experts, and its functionality, performance efficiency, and usability by the teachers. The analysis of the data revealed that the MBAMSUFTT serves its intended purpose in checking and monitoring students’ attendance per subject area with more accurate and reliable attendance results and has also met the ISO software quality standards.
Inpainting is a task undertaken to fill in damaged or missing parts of an image or video frame, with believable content. The aim of this operation is to realistically complete images or frames of videos for a variety of applications such as conservation and restoration of art, editing images and videos for aesthetic purposes, but might cause malpractices such as evidence tampering. From the image and video editing perspective, inpainting is used mainly in the context of generating content to fill the gaps left after removing a particular object from the image or the video. Video Inpainting, an extension of Image Inpainting, is a much more challenging task due to the constraint added by the time dimension. Several techniques do exist that achieve the task of removing an object from a given video, but they are still in a nascent stage. The major objective of this paper is to study the available approaches of inpainting and propose a solution to the limitations of existing inpainting techniques. After studying existing inpainting techniques, we realized that most of them make use of a ground truth frame to generate plausible results. A 'ground truth' frame is an image without the target object or in other words, an image that provides maximum information about the background, which is then used to fill spaces after object removal. In this paper, we propose an approach where there is no requirement of a 'ground truth' frame, provided that the video has enough contexts available about the background that is to be recreated. We would be using frames from the video in hand, to gather context for the background. As the position of the target object to be removed will vary from one frame to the next, each subsequent frame will reveal the region that was initially behind the object, and provide more information about the background as a whole. Later, we have also discussed the potential limitations of our approach and some workarounds for the same, while showing the direction for further research.[...] Read more.