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Development and Validation of Autotronic Training Module for Automobile Technology Students in Polytechnics in Southern Nigeria
No full textA research was carried out to create and verify an autotronic training module for students studying vehicle technology at polytechnics located in Southern Nigeria. The design used for the project was Research and Development (R&D). An observation has been made that the curriculum of polytechnics in Nigeria lacks sufficient substance on autotronics technology. As a result, lecturers have challenges in fully imparting the abilities that are essential for the professional world. Undoubtedly, there is now a disparity between the training that craftsmen receive and the skills that are demanded by industries. The research was carried out in the southern region of Nigeria. The research focused on a specific demographic of 1,443 respondents. All the lecturers teaching automobile technology were included in the study, and a purposive sampling technique was used to select 75 automobile technicians from the region. A grand total of 122 questionnaires were distributed to the respondents with the assistance of five research assistants, one hailing from each state. The researcher created an 86-item questionnaire called the Autotronic Training Module Questionnaire (ATMQ). The 5-point Likert scale includes answer alternatives such as Highly Appropriate (HA) - 5, Appropriate (A) - 4, Moderately Appropriate (MA) - 3, Inappropriate (I) - 2, and Highly Inappropriate (HI) - 1, respectively. The tools (Questionnaire and Multiple Choice Questions) underwent face and content validity assessment by three (3) experts. The instrument's internal consistency was assessed using Cronbach Alpha reliability, resulting in a coefficient of .88. The collected data were analysed using the mean and standard deviation to address the study objectives. Additionally, the hypotheses were tested using Analysis of Covariance (ANCOVA). A criteria mean of 3.5 or above was considered 'acceptable', while anything below was considered 'inappropriate'. In addition, the F-calculated (F-cal) ratio was compared to the .05 probability level of significance for each hypothesis. If the F-ratio is lower than the .05 probability level of significance, the null hypothesis was rejected; otherwise, it was accepted. The research concluded that the goals, materials, training facilities, training method, instructors' activities, students' activities, and assessment procedures are suitable for incorporation into the autotronic training module in polytechnics in Southern Nigeria
Development of Robot Feature for Stunting Analysis Using Long-Short Term Memory (LSTM) Algorithm
No full textStunting prevalence in Indonesia persists as a significant challenge necessitating concerted efforts from all stakeholders. We developed robot for stunting analysis using deep learning algorithm. It aligns with the Sustainable Development Goal (SDG) agenda, specifically targeting SDG 3, which focuses on ensuring good health and well-being for all. Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) designed to overcome the vanishing gradient problem in traditional RNNs. In general, either LSTM can be used in analysis. This study aims to classify stunting based on age and height using LSTM. The LSTM model was trained with 50 epochs using datasets collected from the health office and robots. The evaluation results show training accuracy of 96.65% and training validation of 96.61%, with precision, recall and f1-score varying with relevance to the f1-score and support value. This research illustrates the potential for using data classification methods in stunting diagnosis. However, it is necessary to adjust parameters and increase the amount of training data to improve model performance. With good convergence at epoch 50, these results show the model's ability to classify stunting based on age and height. However, further validation and testing on larger datasets is needed to thoroughly test the reliability and generalization of the model. This research can contribute to the development of deep learning regarding robots as a means of testing stunting. This research provides initial evidence of the potential of stunting classification methods using robots. However, parameter adjustments and increasing the amount of training data need to be done to improve the overall model performance
Performance Evaluation on E-Commerce Recommender System based on KNN, SVD, CoClustering and Ensemble Approaches
No full textE-commerce recommender systems (RS) nowadays are essential for promoting products. These systems are expected to offer personalized recommendations for users based on the user preference. This can be achieved by employing cutting-edge technology such as artificial intelligence (AI) and machine learning (ML). Tailored recommendations for users can boost user experience in using the application and hence increase income as well as the reputation of a company. The purpose of this study is to investigate popular ML methods for e-commerce recommendation and study the potential of ensemble methods to combine the strengths of individual approaches. These recommendations are derived from a multitude of factors, including users' prior purchases, browsing history, demographic information, and others. To forecast the interests and preferences of users, several techniques are chosen to be investigated in this study, which include Singular Value Decomposition (SVD), k-Nearest Neighbor Baseline (KNN Baseline) and CoClustering. In addition, several evaluation metrics including the fraction of concordant pairs (FCP), mean absolute error (MAE), root mean square error (RMSE) and normalized discounted cumulative gain (NDCG) will be used to assess how well different techniques work. To provide a better understanding, the outcomes produced in this study will be incorporated into a graphical user interface (GUI)
Sibling Discrimination Using Linear Fusion on Deep Learning Face Recognition Models
No full textFacial recognition technology has revolutionised human identification, providing a non-invasive alternative to traditional biometric methods like signatures and voice recognition. The integration of deep learning has significantly enhanced the accuracy and adaptability of these systems, now widely used in criminal identification, access control, and security. Initial research focused on recognising full-frontal facial features, but recent advancements have tackled the challenge of identifying partially visible faces, a scenario that often reduces recognition accuracy. This study aims to identify siblings based on facial features, particularly in cases where only partial features like eyes, nose, or mouth are visible. Utilising advanced deep learning models such as VGG19, VGG16, VGGFace, and FaceNet, the research introduces a framework to differentiate between sibling images effectively. To boost discrimination accuracy, the framework employs a linear fusion technique that merges insights from all the models used. The methodology involves preprocessing image pairs, extracting embeddings with pre-trained models, and integrating information through linear fusion. Evaluation metrics, including confusion matrix analysis, assess the framework's robustness and precision. Custom datasets of cropped sibling facial areas form the experimental basis, testing the models under various conditions like different facial poses and cropped regions. Model selection emphasises accuracy and extensive training on large datasets to ensure reliable performance in distinguishing subtle facial differences. Experimental results show that combining multiple models' outputs using linear fusion improves the accuracy and realism of sibling discrimination based on facial features. Findings indicate a minimum accuracy of 96% across different facial regions. Although this is slightly lower than the accuracy achieved by a single model like VGG16 with full-frontal poses, the fusion approach provides a more realistic outcome by incorporating insights from all four models. This underscores the potential of advanced deep learning techniques in enhancing facial recognition systems for practical applications
Editorial: Artificial Intelligence and Cybersecurity in Pervasive Computing
No full textPervasive computing, or ubiquitous computing, is rapidly increasing in capacity and capabilities. With the Internet of Things (IoT) becoming an integral part of daily life and the growing availability of edge computing resources, automation guided by data is advancing applications in healthcare, manufacturing, automotive, and other areas. It's natural that pervasive computing will intersect with artificial intelligence (AI) and cybersecurity. AI can improve detection, prediction, and anticipative responses to human needs, while cybersecurity addresses topics like misuse prevention, ethics, policies, and governance. This issue features seven articles on these intersections, including four AI articles exploring natural language processing and computer vision, and three cybersecurity articles covering cryptography, medical devices, and maritime security
Enhancing MIMO Capacity Through Space-Time Coding: Analysis And Design Framework
No full textSpace-time coding combines time and space to generate codewords, transmitting signals in both time and space domains. This leads to not only diversity and coding gains but also reduces the impact of multipath fading, resulting in high spectral efficiency. This paper examines the challenges in implementing space-time coding to enhance the capacity of MIMO systems. It analyzes the principle, design objectives, and criteria of space-time coding, providing a basic design framework, based on the space-time coding (STC) system model. .STC and MIMO have proven to be effective in improving system capacity, reliability, and overall performance in wireless communication. They have been applied in various fields such as wireless local area networks (WLANs), cellular networks, satellite communication, and wireless body area networks (WBANs). The use of space-time coding in 5G massive multi-antenna technology improves diversity and gain, resulting in higher communication throughput, leading to increased research in the field
A Marker Free Visual-based Home Rehabilitation Framework
No full textAdhesive capsulitis or more commonly known as frozen shoulder, is a familiar occurrence for adults aged above 40 caused by the inflammation of the connective tissues surrounding the shoulder joint. There are different severity of adhesive capsulitis but patients afflicted with frozen shoulder typically will experience stiffness, severe pain, and reduced range of motion (ROM) for the shoulder. No matter the course of treatment being non-steroidal anti-inflammatory drugs (NSAIDs) or steroid injections, which can help reduce the inflammation and reduce pain, in order to restore ROM for the afflicted shoulder joint, rehabilitation exercises need to be performed. Even without the current climate where medical workers are severely overworked, physical therapists are in short order especially for developing countries like Malaysia. A remedy for this situation would be to deploy home rehabilitation instead. This would be a way for patients to get proper rehabilitation exercises in between visits to the clinic to meet the physical therapist. This can also reduce the frequency of in-clinic visits while still allowing the patient to progress in the rehabilitation of their afflicted shoulder joint. Though home rehabilitation seems like a clear solution, it does come with its own set of challenges. How open will the patients themselves be to utilizing a home rehabilitation system? In light of that, this paper proposes a home rehabilitation framework focusing on a marker free visual-based implementation using the Microsoft Kinect camera. The framework will measure the impact of variables such as capability, motivation and opportunity on the adoption rate of the home rehabilitation. Cronbach’s Alpha tests were conducted to ascertain the reliability of the variables used in the framework
Development of AI-Enabled Contactless Visitor Access Monitoring System
No full textAbstract - This research focuses on developing an AI-enabled Contactless Visitors Access Monitoring System. The monitoring system integrated a facial recognition system with a real-time database. Visitors registered themselves through an online registration form. This research developed and compared two different facial recognition systems. The first facial recognition system integrated the dlib model with the face recognition library, while the second integrated the FaceNet model with the Haar Cascade Classifier. Twenty facial images were collected. The researcher found out that the facial recognition system with FaceNet has higher accuracy of 82% while the has 76% of accuracy. The value of EER obtained for FaceNet is at 51% with an allowed threshold of 0.52. This research found that the accuracy of the facial recognition system could be affected by different conditions, such as the visitors’ facial features, the distance between the camera and the face, and the illumination condition of the test environment. The number of images does not affect the speed and the accuracy of the facial recognition system in this research due to the small number of images.
Manuscript received: 12 May 2023 | Revised: 31st July 2023 | Accepted: 1st September 2023 | Published: 30 September 202
TRANSMISSION DYNAMICS OF SMOKING - A MATHEMATICAL MODEL: TRANSMISSION DYNAMICS OF SMOKING
No full textSmoking is a disastrous habit that exposes smokers at a greater chance of cardiovascular and blood vessel ailments.Also, it can cause many diseases like cancer, asthma,strokes etc., to smokers. It plays a major role in one of the economic issues of a country. This articledeals with the transmissiondynamics of smoking addiction of population in a country. To analyse thesmoking addiction among adolescents population, a nonlinear mathematical model is developed here.In this model, the four compartments viz., Susceptible male, Susceptible female, Chain Smokers andSmokers but not chain smokers are considered to study the transmission dynamics of smoking addiction.The deterministic model for smoking addiction is formulated to exhibit the smokers free equilibriumandsmokers equilibrium. Also, the reproductive number of the model is calculated to analyze the spread ofsmoking habit. The local stability of these equilibrium points is also analyzed analytically and numerically.
Manuscript received: 28 May 2023 | Revised: 20 July 2023 | Accepted: 10 August 2023 | Published: 30 September 202
An Analysis of Factors Affecting Malaysia’s Youth Unemployment Rate: DOI: https://doi.org/10.33093/ijomfa.2023.4.1.5
No full textThis study aims to identify if macroeconomics-related indicators consisting of gross domestic product (GDP), inflation, population and economic shocks are related in the Malaysia’s youth unemployment. The youth unemployment rate and its determinants’ data from an extended period of 1982 to 2020 were obtained from the Department of Statistics Malaysia (DOSM). By employing the autoregressive distributed lag (ARDL) analysis, findings show that Gross Domestic Product and Population significantly affect Youth Unemployment in the long-run. These findings indicate that the Malaysian government should develop youth-specific strategies to combat youth unemployment, with Technical and Vocational Education Training (TVET) quality and availability being one of the key initiatives. Emergency income support for youth starting in the job market ought to be provided as well, to assist them during economic shocks. Future research that includes foreign direct investment and gender variables together with youth unemployment data utilised in the current study is recommended