Designing Ray-Pointing using Real hand and Touch-based in Handheld Augmented Reality for Object Selection

Augmented Reality (AR) have been widely explored worldwide for their potential as a technology that enhances information representation. As technology progresses, smartphones (handheld devices) now have sophisticated processors and cameras for capturing static photographs and video, as well as a variety of sensors for tracking the user's position, orientation, and motion. Hence, this paper would discuss a finger-ray pointing technique in real-time for interaction in handheld AR and comparing the technique with the conventional technique in handheld, touch-screen interaction. The aim of this paper is to explore the ray pointing interaction in handheld AR for 3D object selection. Previous works in handheld AR and also covers Mixed Reality (MR) have been recapped.

Hybrid Encryption for Messages’ Confidentiality in SOSE-Based IOT Service Systems

Internet of Things (IOT) is an essential paradigm where devices are interconnected into network. The operations of these devices can be through service-oriented software engineering (SOSE) principles for efficient service provision. SOSE is an important software development method for flexible, agile, loose-coupled, heterogeneous and interoperable applications. Despite all these benefits, its adoption for IOT services is slow due to security challenges. The security challenge of integration of IOT with service-oriented architecture (SOA) is man-in-the-middle attack on the messages exchanged. The transport layer security (TLS) creates a secured socket channel between the client and server. This is efficient in securing messages exchanged at the transport layer only. SOSE-based IOT systems needs an end-to-end security to handle its vulnerabilities. This integration enables interoperability of heterogeneous devices, but renders the system vulnerable to passive attacks. The confidentiality problem is hereby addressed by message level hybrid encryption. This is by encrypting the messages by AES for efficiency. However, to enable end-to-end security, the key sharing problem of advanced encryption standard (AES) is handled by RSA public key encryption. The results shows that this solution addressed data contents security and credentials security privacy issues. Furthermore, the solution enables end-to- end security of interaction in SOSE-based IOT systems.

Training Evaluation Models for Skill-Based E-learning System: A Systematic Literature Review

Training evaluation can be defined as a way of measuring how well users learn and adapt to a system or software. Various methods have been developed to carry out training evaluations of systems or software over the past few decades. A systematic literature review report on the assessment training model was conducted to give different views on the usability aspects of the proposed approach. This study provides a current systematic review of training evaluation on skill-based system or software. The particular purpose of the review is to explore the research as preliminary step that helps in choosing the right type of training evaluation model for skill-based E-learning system or software. There is a lack of appropriate models available through the specific gaps in literature and finding especially for skill-based E-learning system evaluation.

An Improved Blockchain Technique for Secure Land Registration Data Records

The land is a very valuable asset for any government. It’s government job to ensure that the land registration and transfer are done without any fraud, good speed and transparency. The current land registration method employed by the governments are not open to frauds, hacks, and corruption of land records. Fraud is one of the major problems in land registration methods. In this study, the goal is to develop the framework by incorporating the blockchain technique that secures the land data during the land registration and transfer phases by preventing the fraud. The use of blockchain gives us the transparent, decentralized and robust infrastructure to build our framework upon. The blockchain technology is implemented with the asymmetric keys encryption/decryption that securely stores the land registration/transfer data. The data is held using encrypting with the public key of the landowner and storing a hash of the data. The use of the cryptographic function of hashing using SHA. The comparison of using SHA 256 and SHA 512 is given and discussed. The dataset used to compare results is created using 200 records of JSON objects with each object being identical for both SHA256 and SHA512 to remove data bias. The proposed framework with the SHA 512 performed 29% faster than the SHA 256. The results indicate our proposed framework performing better than the works proposed in current research land registration techniques.

Study on Gender Identification Based on Audio Recordings Using Gaussian Mixture Model and Mel Frequency Cepstrum Coefficient Technique

Speaker recognition is an ability to identify speaker’s characteristics based from spoken language. The purpose of this study is to identify gender of speakers based on audio recordings. The objective of this study is to evaluate the accuracy rate of this technique to differentiate the gender and also to determine the performance rate to classify even when using self-acquired recordings. Audio forensics uses voice recordings as part of evidence to solve cases. This study is mainly conducted to provide an easier technique to identify the unknown speaker characteristics in forensic field. This experiment is fulfilled by training the pattern classifier using gender dependent data. In order to train the model, a speech database is obtained from an online speech corpus comprising of both male and female speakers. During the testing phase, apart from the data from speech corpus, audio recordings of UTM students will too be used to determine the accuracy rate of this speaker identification experiment. As for the technique to run this experiment, Mel Frequency Cepstrum Coefficient (MFCC) algorithm is used to extract the features from speech data while Gaussian Mixture Model (GMM) is used to model the gender identifier. Noise removal was not used for any speech data in this experiment. Python software is used to extract using MFCC coefficients and model the behavior using GMM technique. Experiment results show that GMM-MFCC technique can identify gender regardless of language but with varying accuracy rate.

Visual Analytics Design for Students Assessment Representation based on Supervised Learning Algorithms

Visual Analytics is very effective in many applications especially in education field and improved the decision making on enhancing the student assessment. Student assessment has become very important and is identified as a systematic process that measures and collects data such as marks and scores in a manner that enables the educator to analyze the achievement of the intended learning outcomes. The objective of this study is to investigate the suitable visual analytics design to represent the student assessment data with the suitable interaction techniques of the visual analytics approach. sheet. There are six types of analytical models, such as the Generalized Linear Model, Deep Learning, Decision Tree Model, Random Forest Model, Gradient Boosted Model, and Support Vector Machine were used to conduct this research. Our experimental results show that the Decision Tree Models were the fastest way to optimize the result. The Gradient Boosted Model was the best performance to optimize the result.

Classification of Attention Deficit Hyperactivity Disorder using Variational Autoencoder

Attention Deficit Hyperactivity Disorder (ADHD) categorize as one of the typical neurodevelopmental and mental disorders. Over the years, researchers have identified ADHD as a complicated disorder since it is not directly tested with a standard medical test such as a blood or urine test on the early-stage diagnosis. Apart from the physical symptoms of ADHD, clinical data of ADHD patients show that most of them have learning problems. Therefore, functional Magnetic Resonance Imaging (fMRI) is considered the most suitable method to determine functional activity in the brain region to understand brain disorders of ADHD. One of the ways to diagnose ADHD is by using deep learning techniques, which can increase the accuracy of predicting ADHD using the fMRI dataset. Past attempts of classifying ADHD based on functional connectivity coefficient using the Deep Neural Network (DNN) result in 95% accuracy. As Variational Autoencoder (VAE) is the most popular in extracting high-level data, this model is applied in this study. This study aims to enhance the performance of VAE to increase the accuracy in classifying ADHD using fMRI data based on functional connectivity analysis. The preprocessed fMRI dataset is used for decomposition to find the region of interest (ROI), followed by Independent Component Analysis (ICA) that calculates the correlation between brain regions and creates functional connectivity matrices for each subject. As a result, the VAE model achieved an accuracy of 75% on classifying ADHD.

Fraudulent Detection Model Using Machine Learning Techniques for Unstructured Supplementary Service Data

The increase in mobile phones accessibility and technological advancement in almost every corner of the world has shaped how banks offer financial service. Such services were extended to low-end customers without a smartphone providing Alternative Banking Channels (ABCs) service, rendering regular financial service same as those on smartphones. One of the services of this ABC’s is Unstructured Supplementary Service Data (USSD), two-way communication between mobile phones and applications, which is used to render financial services all from the bank accounts linked for this USSD service. Fraudsters have taken advantage of innocent customers on this channel to carry out fraudulent activities with high impart of fraudulent there is still not an implemented fraud detection model to detect this fraud activities. This paper is an investigation into fraud detection model using machine learning techniques for Unstructured Supplementary Service Data based on short-term memory. Statistical features were derived by aggregating customers activities using a short window size to improve the model performance on selected machine learning classifiers, employing the best set of features to improve the model performance. Based on the results obtained, the proposed Fraudulent detection model demonstrated that with the appropriate machine learning techniques for USSD, best performance was achieved with Random forest having the best result of 100% across all its performance measure, KNeighbors was second in performance measure having an average of 99% across all its performance measure while Gradient boosting was third in its performance measure, its achieved accuracy is 91.94%, precession is 86%, recall is 100% and f1 score is 92.54%. Result obtained shows two of the selected machine learning random forest and decision tree are best fit for the fraud detection in this model. With the right features derived and an appropriate machine learning algorithm, the proposed model offers the best fraud detection accuracy.

Timetable Scheduling System using Genetic Algorithm for School of Computing (tsuGA)

Current timetable scheduling system in School of Computing(SC), Universiti Teknologi Malaysia(UTM) is done manually which consumes time and human effort. In this project, a Genetic Algorithm (GA) approach is proposed to aid the timetable scheduling process. GA is a heuristic search algorithm which finds the best solution based on current individual characteristics. Using GA and scheduling info such as rooms available and timeslots needed, it is shown that scheduling can be done more efficiently, with less time, effort and errors. As a testbed, a web application is developed to maintain records needed and generate timetables. Introduction of GA helps in generating a timetable automatically based on information such as rooms, subjects, lecturers, student group and timeslot. GA reduces human error and human efforts in the timetable scheduling process.

Preventing Enterprise Resource Planning Failure Through an Enhanced Approach to Solve Ineffective Communication

Enterprise Resource Planning (ERP) is a widely known type of software that eases the managerial aspect in enterprises. It increases their efficiency and productivity which helps them to exponentially grow in a short span of time compared to organizations that are not using it. However, as much as productive it is, implementing it does not often succeed. Majority of ERP implementations ends up failing due to different types of factors. Spotting the light on technical aspects showed that several factors contribute to this failure. Starting from pre-implementation phase with Business Process Reengineering (BPR) execution failure, or during the implementation phase due to miscommunication or incapable project members. The research amount in this field, particularly in critical failure factors is not sufficient to learn from and avoid future implementations, hence this topic provides insights about this specific issue. Quantitative method is used to analyse the data collected from a survey questionnaire for those who got involved in ERP or BPR implementations. The research process goes through objectives from problem identification to an in-detail explanation about its causes and effects, to how it is going to be addressed, how the data is going to be collected and analysed, and finally the proposed approach with a technical evaluation for it. The final objective of the research results in developing an approach that minimises the negative contribution of two failure factors, poor BPR and ineffective communication on the mentioned implementations, or prevent them entirely. The reason these two were chosen were due to their high occurrence frequency and lack of research regarding why they are considered failure factors. Concluding the research, the mentioned enhanced approach is being evaluated showing its potential to solve these factors, as they are relying on each other, with additional suggestions to further improve the approach in future work.