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A Scoping Review of Artificial Intelligence Research Trends in Mobile Applications
No full textOver the past decade, mobile devices have become an integral part of our daily routines, offering a broad spectrum of applications that enhance everyday tasks. As more people adopt smartphones, developers are increasingly focusing on improving app quality, particularly by incorporating artificial intelligence (AI) features. This growing trend has led to a surge of interest from both researchers and industry experts, who are aiming to explore AI integration in sectors such as healthcare, education, agriculture, and e-commerce. This study conducts a thorough review of AI applications on mobile platforms by analysing 98 scholarly articles published between 2014 and 2024 from databases including Scopus, IEEE Explore, and Science Direct. After screening for relevance, 50 articles were selected for in-depth evaluation. The findings show a significant emphasis on healthcare, which accounted for 38% of the reviewed studies, followed by agriculture at 30% and education at 18%. This advancement is in line with societal demands because AI-powered mobile apps improve vital industries like healthcare, agriculture, education, and corporate operations by offering predictive analytics. Notably, machine learning (ML) techniques were prominent, used in 66% of the articles, while deep learning (DL) appeared in 16%. The review also highlights convolutional neural networks (CNN) as a key algorithm, present in 56% of the studies. These insights demonstrate the profound influence of AI on mobile app development and point to emerging trends and future research opportunities in this field. The need for cross-platform AI development has increased dramatically as AI continues to transform mobile technology. This strategy is essential to the scalability, accessibility, and effectiveness of the larger mobile app ecosystem since AI-enabled apps are designed to function flawlessly across a variety of mobile operating systems (iOS, Android, etc.)
Area-Based Conservation Approach in the Sundarbans and Saint Martin's Island of Bangladesh: Prospects and Challenges
No full textArea-based conservation is regarded as a viable approach to conserve biodiversity. The Convention of Biological Diversity 1992 (CBD) is the key international instrument creating binding obligations for state parties. With the influence of CBD and other international instruments, Bangladesh has adopted new laws, policies, and strategies and thereby seems to have departed from the traditional approach to conservation and embraced the new approach to conservation in line with CBD. This study assesses Bangladesh’s conservation approach regarding the Sundarbans Mangrove Forest and Saint Martin’s Island—two critically important ecological sites and concludes that despite various conservation measures taken by the Government of Bangladesh, the biodiversity of these two sites is in decline and that these two critically important ecological sites require a well-functioning area-based conservation approach. It identifies the reasons behind the failure of Bangladesh’s conservation efforts regarding these two sites and finds out the key factors contributing to this failure. In doing so, it emphasizes qualitative elements of conservation, such as effective and equitable management, ecological representativeness, connectivity, integration into wider land and seascapes, etc. Bangladesh has already taken the first steps towards a proper conservation approach in theory; it is now high time to bring them into proper practice
Indo-Bangladesh Transboundary Water-Sharing of the Ganges and Teesta Rivers: Through the Lens of International Law and Practice
No full textThough international water law emphasises ensuring equitable and sustainable utilisation of water resources by all riparian states, most often transboundary rivers are used selfishly and unsustainably by upstream countries. Bangladesh and India, two neighbours in South Asia, share 54 rivers and Bangladesh stands as a downstream country for all of them. Amongst all the rivers, the Ganges and the Teesta are the most contested ones and this article has investigated the issues surrounding their sharing and utilisation. More specifically, the article has analysed the contested Farakka Barrage and bilateral arrangements especially the Ganges Water-Sharing Treaty, 1996, and related issues on the touchstone of existing legal architecture and jurisprudence. Also, the existing no-agreement situation of the Teesta River has been analysed in view of international law and practice. The author considers the Ganges Water-Sharing Treaty, 1996 as a milestone in the mutual relationship between India and Bangladesh, but at the same time suggests further improvement in line with international legal norms and practices. As regards the Teesta, the article argues that India’s approach towards the Teesta River reflects a total disregard for the principle of equitable and reasonable utilisation and the principle of no-significant harm
Deep Learning Approaches to Autocorrelation Function and Signal-to-Noise Ratio Estimation in Noisy Images
No full textAccurate estimation of signal-to-noise ratio (SNR) in Scanning Electron Microscopy (SEM) is crucial because it evaluates the image quality. SEM images faced a challenge whereby Gaussian noise commonly appears in the images. Thus, researchers have developed several methods to estimate the SNR value. With the introduction of deep learning, most of the limitations in the classical methods can be addressed. This paper proposes a novel deep learning, CNN-based Calibration Map Network (CalibNet) to estimate the SNR value from SEM images using a calibration map between classical SNR and autocorrelation function SNR. The architecture consists of convolutional layers, rectified linear unit (ReLU) activations, max-pooling layers, adaptive pooling, and a regression head to predict the SNR value correctly. The proposed model is trained, validated and tested on two SEM images, the Biofilm SEM dataset (67 images) and the NFFA-EUROPE SEM dataset (961 images). Each image was artificially corrupted with Gaussian noise variance ranging from 0.001 to 0.01 to simulate realistic SEM imaging conditions. The proposed model was compared with Classical SNR, Autocorrelation Function (ACF), Nearest Neighbour (NN)-ACF, First-Order Linear Interpolation (LI)-ACF, and Quadratic-Sigmoid (Quarsig)-ACF methods. The results show that CalibNet outperformed all the classical methods in terms of mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and R-squared (R²). Statistical analyses further confirmed that CalibNet predictions closely align with the Classical SNR values. Future work includes exploring more advanced model architectures, alternative calibration techniques, and real-time SNR estimation applications
Editor's Preview: Robotics and Automation, Computer Science, and Artificial Intelligence
No full textThis thematic issue highlights the convergence of robotics and automation, computer science, and artificial intelligence as the foundation of modern intelligent systems. Advances in robotic design, control systems, and autonomous operation continue to expand the capabilities of automated technologies across diverse sectors. Concurrently, developments in computer science—including algorithmic innovation, distributed systems, and secure computing—provide the essential infrastructure that supports complex and scalable digital solutions. Artificial intelligence strengthens this ecosystem through machine learning, deep learning, and data-driven decision-making, enabling enhanced perception, prediction, and adaptability. Together, these fields drive transformative progress in industrial automation, smart systems, human–robot collaboration, and intelligent applications. This issue brings together research contributions that address theoretical advancements, practical implementations, and interdisciplinary perspectives, offering insight into the evolving landscape of intelligent and autonomous technologies
Exploring Activities of Daily Living Among the Elderly through Machine Learning Techniques
No full textActivities of daily living (ADLs) is a term that is used to describe the activities performed in everyday life that involves the motion of the human body such as eating, walking, and sitting. ADLs can be used to determine the state of elderly people as a decline in ADL performance will generally mean a decline in the human body. It can act as an early indicator if an elderly person is experiencing underlying illness or health issue. This project aims to detect five different ADLs which are eating, cooking, sweeping, walking, and sitting and standing. A dataset was collected from twenty individuals performing each ADL at two different angles, a front view and a side view. A computer vision-based human pose estimation technique is used to extract the human body keypoints. These keypoint values are then processed and fit into multiple deep learning models for analysis. In this study, five different deep learning models namely LSTM, Bi-LSTM, CNN, RNN and Transformer models have been evaluated. The performance of each model is analysed and discussed. It was determined that the CNN model performed the best achieving a categorical accuracy of 82.86%.
Manuscript received: 14 Sep 2024 | Revised:4 Dec 2024 | Accepted: 11 Dec 2024 | Published: 31 Mar 202
Decade-Long Analysis of Sustainable Development Goals Compliance and Financial Performance Tiers in All Banking Companies Listed on Bursa Malaysia: DOI: https://doi.org/10.33093/ijomfa.2025.6.1.2
No full textThis study examines the relationship between Sustainable Development Goals (SDGs) compliance and financial performance in Malaysian banks from 2013 to 2022. Using advanced machine learning techniques, including Support Vector Machines, Decision Trees, K-nearest Neighbours, Extra Trees, Gradient Boosting, and Random Forests, banks were classified into financial performance tiers. Gradient Boosting was the most effective, achieving 80% accuracy in categorising medium and low-performance tiers. Significant correlations were found between SDGs 10 and 15, as well as financial metrics like market capitalisation and asset turnover. These findings highlight the benefits of integrating specific SDGs into banking strategies and the need for supportive policy frameworks, contributing to a deeper understanding of sustainable banking practices
Factors Affecting Vietnamese Young People’s Impulsive Purchasing Intention on Live-Streaming Commerce: DOI: https://doi.org/10.33093/ijomfa.2025.6.1.11
No full textThis paper examines the factors affecting Vietnamese young people’s impulsive purchasing intention in the live-streaming commerce environment. Adopting the S-O-R research model with influencing factors is the Streamer’s Attractiveness (AT), Streamer’s Trustworthiness (TR), Streamer’s Expertise (EP), Perceived Price (PP), Product Usefulness (PU), and Facility Condition (FC). The mediating variables, or the O elements, are Perceived Enjoyment (PCE) and Perceived Usefulness (PCU). Lastly, the response that buyers deliver is Impulsive Purchasing Intention (IPI). The research focused on young Vietnamese people, particularly Millennials and Generation Z, born from 1980 to 2006 (18 to 44 years old). The quantitative research used a snowball sampling technique with a total of 291 qualified surveys. The data was processed and analyzed with the assistance of SPSS version 22 and SmartPLS 4. After thorough analysis, it is proven that perceived enjoyment and perceived usefulness have a positive influence on impulsive purchasing intention; streamer’s attractiveness and trustworthiness, as well as perceived price, have a positive impact on perceived enjoyment and indirectly impact impulsive purchasing intention; product usefulness and facility condition have a positive influence on perceived usefulness and indirectly impact impulsive purchasing intention. However, the streamer’s expertise and perceived price do not impact perceived enjoyment and usefulness, respectively. The study found that impulsive buying intention is often triggered by emotional arousal, yet consumers still care about product quality and usefulness. The proposed model has been verified in the Vietnamese context and delivers practical insights for companies and marketers
Effects of Research and Development Subsidies on Research and Development Investment and Firm Performance: Evidence From High-Tech Firms in China: DOI: https://doi.org/10.33093/ijomfa.2025.6.2.2
No full textOver the past decade, the Chinese government has actively promoting research and development (R&D) in high-tech industries to foster scientific and technological innovation and address economic slowdown. Against this backdrop, this study explores how the interaction between government R&D subsidies and R&D investment influences the performance of high-tech firms in China. Specifically, it investigates (i) the relationship between government R&D subsidies and firm performance, (ii) the relationship between government R&D subsidies and R&D investment, and (iii) the mediating role of R&D investment in the relationship between government R&D subsidies and firm performance. Analysing 773 listed high-tech firms from 2018 to 2021, this study finds that direct government R&D subsidies and tax incentives do not significantly influence firm performance. However, direct subsidies indirectly reduce firm performance by stimulating R&D investments that are resource-intensive and risky in nature. This mediating effect is not observed in the case of tax incentives. The findings provide insights into enhancing the effectiveness of R&D subsidies in promoting R&D investment and firm performance in high-tech industries
From Anxiety to Action: Understanding Financial Worries and Strategies to Boost Insurance Take-Up: DOI: https://doi.org/10.33093/ijomfa.2025.6.2.12
No full textIn developing countries, inadequate insurance and financial planning have led experts to call for government intervention to avoid potential financial crises. In Malaysia, where increasing insurance uptake is a national priority, this study examines how education and socioeconomic factors influence financial concerns. Using a regression model, this study analyses data from 1,000 survey responses from Malaysia in the Global Findex database, examining the effects of education, income, gender, age, and urbanicity on various financial worries, including retirement, medical expenses, monthly bills, and education costs. Findings show that higher education levels are associated with reduced financial anxiety around medical expenses and monthly bills, while income and age significantly impact all financial worry areas. The study highlights the potential of incorporating insurance awareness and financial literacy into education to improve financial well-being and build a more resilient society. The novelty of this study highlights the need for targeted insurance policies that address the specific financial concerns of diverse socio-economic groups