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Influencer Marketing to Youth: The Impact of Instagram Influencer on Healthy Food Choices among Youth
No full textHealthy food choices have become a trend among youth as they are more concerned about their health. The increasing role of social media in affecting the behavioural change of today's society can be seen as social media has become an integral part of most people worldwide. The rise of influencer marketing on social media platforms is affecting the consumer's eating behaviour. However, previous studies have shown contradictory results regarding influencers' impact on youth's healthy eating behaviour. Scholars suggest that Instagram has a definitive effect in encouraging healthy eating behaviour among youth, but there is a lack of research that mainly focuses on Instagram's influencer marketing and its impact on eating behaviour. This study aims to focus on the roles of influencer marketing on Instagram in influencing youth's healthy eating behaviour. The Healthy Food Promotion Model is reviewed to explain the underlying mechanism of healthy food promotion. A qualitative research method was applied to collect the data. Intensive interviews were conducted with six Instagram users in Malaysia via Zoom Meeting. The study concludes that Instagram influencer marketing plays a vital role in influencing the healthy eating behaviour of youth. Future researchers are advised to conduct longitudinal studies on this research to adapt it to the continuous evolution of social media platforms. It is also to explore other possibilities of social media influencer marketing in influencing youth's healthy eating behaviour.
A Lung Cancer Detection with Pre-Trained CNN Models
No full textLung cancer is a common cancer in Malaysia, affecting the majority of male citizens. The early detection of lung cancer will decrease its death rate. The only way to detect lung cancer is with a CT scan, and it also requires the doctor to check the scan to confirm the disease. In another way, the computer's support for the detection and diagnosis tool will assist doctors in determining lung cancer more accurately and efficiently. There are three main objectives for this research work. The first target is to study state-of-the-art research work to detect and recognize lung cancer from CT scan images. Then, the article will aim to adopt pre-trained convolutional neural network models in lung cancer detection. It also evaluates the performance of convolutional models on lung cancer imagery data. Then, the pre-trained models with a few added layers and modifications to parameters such as epochs, batch size, optimizer, etc. to conduct model training in this article. After that, Python Pylidc is used in image pre-processing to filter the dataset. Overall, pre-trained models such as ResNet-50, VGG-16, Xception, and MobileNet achieve above-state-of-the-art performance in classifying lung cancer from CT scan images in the range of 78% to 86% accuracy. The best detection accuracy result is the pre-trained VGG-16 model with the addition of some fully connected layers, 16 batch sizes, and the Adam optimizer, which achieved 86.71%
Comparison of Machine Learning Methods for Calories Burn Prediction
No full textThis paper focuses on the prediction of calories burned during exercise using machine learning techniques. Due to a growing number of obesity and overweight people, a healthy lifestyle must be adopted and maintained. This study explores and compares several machine learning regression models namely LightGBM, XGBoost, Random Forest, Ridge, Linear, Lasso, and Logistic to assess their calories burned prediction performance that can be used in systems such as fitness recommender systems supporting a healthy lifestyle. Our findings show that the LightGBM for predicting calorie burn has a good accuracy of 1.27 mean absolute error, giving users reliable recommendations. The proposed system has a good potential in assisting users in reaching their fitness objectives by offering precise and tailored advice
Classroom Environment Analysis Via Internet of Things
No full textIn this era of rapid technological advancement, the potential of the digital age has opened up numerous possibilities for our society. However, despite these advancements, traditional classrooms still lack the necessary technology to create an optimal learning environment for students. Consequently, students may struggle to effectively acquire knowledge within classrooms. This paper aims to conduct a classroom environment analysis using Internet of Things technology to gather data and uncover valuable insights. The proposed solution involves an embedded system for controlling and monitoring the classroom environment, as well as exporting historical data for further research. By ensuring accurate data collection, this paper seeks to facilitate meaningful improvements in the classroom environment, aligning with the principle of "garbage in, garbage out" in computer science
Investigation on Understanding the Numeracy Capacity of Intellectual Disabled Students using Enabling Technology Tools: Web Application, AR and UI/UX
No full textThe population of individuals with intellectual disabilities (ID) is increasing, necessitating assistance with a wide range of daily activities. Acquiring and assessing numeracy and communication skills are critical for this demographic, requiring tools and techniques tailored to their specific needs. Effective educational tools must employ multi-modal and multi-sensory approaches to cater to diverse learning styles and incorporate assistive technological solutions. Despite the availability of numerous tools, there is a need to enhance their utility and effectiveness. This study aims to identify and refine the requirements for an innovative educational tool that employs Two-dimensional (2D) and Augmented Reality (AR) technologies. To achieve this, we conducted semi-structured interviews and surveys with teachers working with students with ID, gaining insights into the current solutions, advantages, and limitations. Additionally, we used physical props as design probes in a co-design methodology to better understand and elicit the true needs of individuals with ID. The findings from this research will inform the development of a 2D/AR tool designed to make learning mathematics more engaging and effective for individuals with ID, contributing to the advancement of inclusive education practices. Enabling Technology plays a significant role in the numeracy ability among people with ID. Generative AI and Explainable AI shall further improve learning ability in the years to come
Review on Development of Digital Twins for Predicting, Mitigating Faults and Defects in Solar Plants
No full textAbstract – The thought of digital twins has gained substantial attention in recent years due to its potential to transform various industries, including renewable energy. Digital twins involve the creation of virtual models that mirror the behaviour and characteristics of real-world physical systems. In the perspective of solar plants, digital twins have emerged as a promising tool to enhance performance monitoring, predictive maintenance, and overall operational efficiency. Digital twin engineering, characterized by its dynamic data modelling of industrial assets, offers a disruptive technology capable of adapting to real-time changes in the environment and operations. This living model can predict future infrastructure behaviour and proactively identify potential issues within the physical system. The article highlights the essential components of the digital twin ecosystem, such as sensor technologies, the Industrial Internet of Things, simulation, modelling, and machine learning, underscoring their relevance in predictive maintenance applications. This review provides an in-extensive review of the development and application of digital twins for predicting and mitigating faults and defects in solar power plants. It opens with a look at current developments, underlining the rising focus on digital twins for optimizing solar farms. It begins with an overview of existing solutions in the field, highlighting the growing interest in leveraging digital twin technology to enhance solar plant operations. Additionally, the article outlines the implementation stage of a prototype digital twin for a solar power plant.
Manuscript received: 17 May 2024 |Revised: 22 June 2024 | Accepted: 13 July 2024 | Published: : 30 Sep 202
Rebung Garden : Blooming Dreams: DOI: https://doi.org/10.33093/ijomfa.2024.5.1.1
No full textRebung Garden, a family business established in 2019, has grown into a thriving enterprise dedicated to cultivating and selling rose plants. The garden's expertise lies in not only growing exquisite roses but also providing comprehensive plant care solutions using organic practices. In order to address the challenge of limited rose availability during the Covid-19 pandemic, Rebung Garden started cultivating their own-root roses, ensuring a steady supply and maintaining quality and variety. However, the garden faces additional challenges such as low brand awareness and operating within limited space
Determinants of consumers' intention to purchase and switch to products of bio-waste: Potential support for a closed-loop supply chain
No full textThe impact of urbanisation and increasing number of populations have become major factors that lead to negative impact toward the environment. Although waste management has been practiced by most of the marketers, the problem of waste is still not well-managed. Therefore, this study is conducted with the aim to examine the impacts of consumers’ perception towards products of bio-waste and to understand consumers’ purchasing behaviour. The paper adopted multiple regression analysis in investigating consumers’ perception towards bio-waste products and consumers’ purchasing behaviour variables. The self-administered surveys were randomly disseminated to Penang consumers, where 99 responses were collected. The result shows a positive relationship between consumers’ perception towards bio-waste and purchase behaviour variables, which is consistent with the theory of planned behaviour. Thus, this study caters several implications and recommendations to the scholars, industrial practitioners and policymakers regarding consumers’ perception and purchasing behaviour towards bio-waste products
Rice Leaf Nitrogen Content Estimation Through A Methodological Framework Using Single-Sensor Multispectral Images
No full textUsing non-destructive evaluation tools based on imaging techniques, including single-sensor multispectral cameras, provides a cost-effective solution for optimizing rice nitrogen fertilization through site-specific nutrient management. However, their accuracy and precision have been identified as areas for improvement. This study aims to develop a methodology to improve the accuracy of estimations through field experiments. It utilizes multispectral images captured by MAPIR Survey3W Orange Cyan Near-Infrared and MAPIR Survey3W Red Edge cameras. The Normalized Difference Vegetation Index and Red Edge values derived from these images are correlated with Soil Plant Analysis Development values to assess rice nitrogen levels. A prediction model is then built using the Support Vector Regression algorithm. Findings from the experiments underscore the importance of addressing shadow effects, integrating the dataset on light intensity and image capture time, conducting radiometric calibration, filtering outlier data, employing image segmentation, and utilizing nonlinear Canova tests to enhance estimation accuracy. By configuring the Support Vector Regression model with RBF kernel, gamma set to 1.24, and epsilon set to 0.1, the R2 of the train data and validation data reaches 0.851, and 0.840 respectively. Meanwhile, the R2 of the test data achieves 0.793 with a mean absolute percentage error of 3.49 % and a root mean square error of 1.70. These findings underscore the potential of the proposed methodology to improve the estimation of rice nitrogen status based on single-sensor multispectral images, paving the way for more effective nutrient management strategies in rice cultivation.
Manuscript Received: 29 December 2023, Accepted: 18 March 2024, Published: 15 September 2024, ORCiD: 0000-0002-7899-875
CRATSM: An Effective Hybridization of Deep Neural Models for Customer Retention Prediction in the Telecom Industry
No full textIn the dynamic field of Customer Retention Prediction (CRP), strategic marketing and promotion efforts targeting specific customers are crucial. Understanding customer behavior and identifying churn indicators are vital for devising effective retention strategies. However, identifying customers likely to terminate services presents a challenge, leading to data imbalance issues. Existing CRP studies using Machine Learning (ML) techniques and data imbalance methods face problems such as overfitting and computational complexity. Similarly, recent CRP studies employing Deep Learning (DL) approaches rely on data sampling techniques, which can result in overfitting and a lack of cost sensitivity. Additionally, DL approaches struggle with slow convergence and get stuck in local minima. This paper introduces an effective hybrid of Deep Learning (DL) classifiers focusing on cost-metric integration to address data imbalance issues and period-shift Cosine Annealing Learning Rate (ps-CALR) to accelerate model training, ultimately enhancing performance. Three Telecom datasets, namely IBM, Iranian, and Orange, were used to assess the model performance. Empirical findings show that the hybrid DL classifiers significantly improved CRP over conventional ML. This paper contributes methodological advancements and practical insights for effective customer retention in the telecom industry.
Manuscript Received: 8 April 2024, Accepted: 5 June 2024, Published: 15 September 2024, ORCiD: 0000-0001-5539-195