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PublicationHalal tourism with the family: destination – local islands of the Maldives(Emerald Publishing, 2025-01)Purpose: This study aims to investigate the attributes of halal tourism with family members by exploring the experiences of Muslims who had travelled with their families to the local islands of the Maldives. This country was chosen as the context of the study as it is a destination with a fully Muslim population, which served as a normative context for studying halal tourism. Design/methodology/approach: A basic qualitative design was adopted as the research methodology, with the data collected through in-depth interviews with the selected Muslim families. Findings: Nine emergent themes unique to the context of halal tourism with family members extend the existing discussion on family tourism and halal tourism. Overall, halal family tourism experience is laden with Islamic family values, characterised by the dimensions of group organisation, safety, practicality, risk management as well as mutual respect and benefit between travellers and providers, and among family members. This experience leads to increased family bonding and the internalisation of Islamic values. Hence, this study highlights halal tourism with family members as a form of dignified tourism. Originality/value: Travel with the family deserves greater academic attention due to the large market size and the distinctive nature of travel undertaken by groups of individuals bonded through familial relationships. To the best of the authors’ knowledge, this study is among the first to explore the attributes of halal tourism with family members, and the normative Islamic context of the local islands of the Maldives assisted in elucidating the emergent themes and values of this form of halal tourism with family members. Halal family tourism, as a nexus of family tourism and halal tourism, offers a huge potential of future research avenue.
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PublicationImproving lung region segmentation based on lazy snapping and clustering for aiding COVID-19 diagnosis(Semarak Ilmu Publishing, 2024-12)The COVID-19 global pandemic, brought on by the rapid spread of the new coronavirus (SARS-CoV-2), has developed into one of the healthcare industry's most significant challenges in recent memory. Early detection of positive patients is essential to prevent the further spread of the COVID-19 virus. Chest x-ray (CXR) images of patients reporting shortness of breath initially led clinicians to suspect the presence of this novel virus. On CXR images, among the alterations detected in the lungs are indications of cloud region, also known as Ground-Glass Opacity. Consequently, the primary objective of this study is to develop a robust segmentation and to acquire an accurate segmented lung region in a CXR image, as this is a necessary step for accurate diagnosis using computer-aided diagnostic systems (CADS). The proposed methodology employs a multi-level segmentation strategy to improve the performance of lung region segmentation, where Lazy Snapping is utilized as pre-segmentation step to automatically remove the bone of the chest area, followed by clustering to achieve the complete segmentation. Furthermore, the advantage of fast k-means (FKM) clustering has also been utilized to obtain the desired lung region. The proposed strategy using Lazy Snapping and FKM was experimented on 150 CXR images and has achieved an average accuracy, sensitivity of and specificity of 92.38%, 85.23% and 96.27%, respectively. Based on the results obtained, this approach demonstrated efficacy in lung segmentation in chest x-ray images and has a significant potential for clinical use.
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PublicationEnvironmental lighting towards growth effect monitoring system of plant factory using ANN(Semarak Ilmu Publishing, 2025-01)Malaysia is currently driven to become another most developed country in the world. Among other priority sector is Food Sustainability. Along the process, our vegetable supply-demand keeps increasing by year. Compared to traditional systems, closed systems or its other name called hydroponic is getting more important for plant production, with artificial light which has many potential advantages, including better quality transplants, shorter production time and less resource use. To gain full profit from it, the quality of vegetables needs to be controlled efficiently. Climate conditions, especially temperature and light intensity, have a significant impact on vegetable growth and yield, as well as nutritional quality. Plant growth and development are influenced by a variety of environmental factors, the most important one is light intensity. Among the problems to be tackled in this research are plant growth manual observation, light intensity variation and abundance of growth-related data to be evaluated manually. Therefore, to solve these problems, the specific type of vegetable used here is lettuce. The proposed methods are, observation of plant growth conducted automatically round the clock in intervals of 15 minutes for the whole month (estimated mature period of lettuce), using images captured. At the same time, the proposed light intensity which is red & white to the ratio of 2:1 (optimum ratio recommended by previous researchers) will be used. The issue of data to be evaluated manually will be solved using Artificial Neural Network (ANN) architecture, in specific Deep Learning. Concisely, the results & analysis shows the research is successfully developed for plant growth monitoring by using artificial neural network which, reached 80% to 90% accuracy in the training and validation session that made the architecture sufficient for determining the growth of the said vegetable. This is indeed foreseen, will highly assist the farmer in better monitoring the growth rate of the plant.
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PublicationUrban farming growth monitoring system using Artificial Neural Network (ANN) and Internet of Things (IOT)(Semarak Ilmu Publishing, 2025-01)As an introduction to this project, the growth-related traits, such as above-ground biomass and leaf area, are critical indicators to characterize the growth of indoor lettuce plants. Currently, non-destructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. It is also one of the problem statements in this project. Based on this project the next problem is manual control of nutrients may cause quality issues to the lettuce plant. If the nutrient supply is too much or less, it will disturb the growth of the lettuce plant either the lettuce plant is dead or stunted. This project is about urban farming growth monitoring system using Artificial Neural Network (ANN) and Internet of Things (IoT). In this project, a method for monitoring the growth of indoor lettuce plants was proposed by using digital images and an ANN using Deep Learning Architecture. DLA is mostly developed by the software of MATLAB or Python to insert and run the coding. DLA is mostly used for image detection, pattern recognition, and natural language processing through the graph for Neural Network. Next, the Internet of Things (IoT) is a medium to store images of indoor lettuce plant growth into the Cloud (Google Drive). Furthermore, it takes indoor lettuce plant images as the input, an ANN was trained to learn the relationship between images and the corresponding growth- related traits with other fixed parameters. The pH level parameters were controlled by other fixed parameters to take the images of indoor lettuce plant growth. The parameters used in this project are temperature and humidity. This helps to compare the results of Artificial Neural Network (ANN), widely adopted methods were also used. Concisely, this project is expected to develop the Deep Learning Architecture using an Artificial Neural Network (ANN) with digital images as a robust tool for the monitoring of the growth of indoor lettuce plants every 30 minutes per day. Generally, focused on an urban farming growth monitoring system using Artificial Neural Network (ANN) and the Internet of Things (IoT).
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PublicationBreast cancer status, grading system, etiology, and challenges in Asia: an updated review(De Gruyter Brill, 2023)The number of breast cancer incidences reported worldwide has increased tremendously over the years. Scoping down to Asia, in 2020, the reported incidences of breast cancer are appalling, comprising 1,026,171 cases, occupying up to 45.4% of cases across the globe. Breast cancer is a non-communicable disease, that emerges in variegated forms, self-subsistent, and the etiology is observed to be multifactorial, dependent on the individual reproductive pattern, hormonal factors, diet, physical activity, lifestyle, and exposure to certain advent procedures. Given this complexity, breast cancer is expected to undergo a persistent increment in the number of incidences in near future, exacerbating the public health quality, regardless of race, ethnicity, geographical subgroups, and socioeconomic. In this review article, the authors examine breast cancer in multiple facets, comprising the updated statistics on breast cancer, typically in Asia; etiology of breast cancer; diagnosis of breast cancer; grading system; and challenges in breast cancer from the country’s income perspective. Realizing the ever-increasing demand for quality treatment, here, the article also contemplates common therapies in breast cancer, such as breast-conserving therapy, mastectomy, postmastectomy radiation therapy, neoadjuvant chemotherapy, axillary surgery, chemotherapy, adjuvant medical therapies, biological and targeted therapies, and endocrine therapy. This review article intended to provide a brief yet broad panoramic view of breast cancer, to readers, ranging from newcomers, existing researchers, and relevant stakeholders in the topic of interest.
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