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This paper aims to provide clear and appropriate evidence of factors that trigger student engagement of flipped classroom (FC) learning in higher education (HE), based on an example of a fully flip...

Determinants of bank performance, profitability and distress have been extensively assessed by modelling the microeconomic and macroeconomic factors. These factors while important are inadequate, otherwise the financial crises would not have been so devastating that led to bank closures and bankruptcies across the globe. For example the CAMEL bank rating model that forms the basis of central bank regulators has proven insufficient. This research applies the EAGLES framework that was built on the CAMELS bank rating model. The study focussed on 48 banks across Asia Pacific between 2012 to 2018. In particular the study shall highlight the strategic response quotient (SRQ) of the EAGLES framework. The SRQ is derived from the computation of the interaction of four financial data components, namely, interest income, fee-based income, interest cost and operational expenses. The paper found that the identified data components predicted and presented bank stability and profitability with a unique perspective.

In this study, we propose to diagnose facial nerve palsy using Support Vector Machines (SVMs) and Emergent Self-Organizing Map (ESOM). This research seeks to analyze facial palsy domain using facial features and grade the degree of nerve damage based on the House-Brackmann score. Traditional diagnostic approaches involve a medical doctor recording a thorough history of a patient and determining the onset of paralysis, rate of progression and so on. The most important step is to assess the degree of voluntary movement of the facial nerves and document the grade of facial paralysis using House- Brackmann score. The significance of the work is the attempt to understand the diagnosis and grading processes using semi-supervised learning with the aim of automating the process. The value of the research is in identifying and documenting the limited literature seen in this area. The use of automated diagnosis and grading greatly reduces the duration of medical examination and increases the consistency, because many palsy images are stored to provide benchmark references for comparative purposes. The proposed automated diagnosis and grading are computationally efficient. This automated process makes it ideal for remote diagnosis and examination of facial palsy. The profiling of a large number of facial images are captured using mobile phones and digital cameras.

Rapid penetration of Internet in the world has provided a significant amount of data on people and societies. The data captured has increased exponentially. This poses great opportunity as well as challenges. In this paper, global health surveillance and epidemic predictions of potentially life threatening diseases are considered. These emerging technologies provide new opportunities for ASEAN entrepreneurs. The clustering methodology applied to geospatial data shows great potential for epidemic detections. Its applications include health surveillance and detection of epidemics of infectious diseases such as Ebola and Flu. However, the current methodologies mainly focus on clustering given datasets, but not their dynamics on how the size of clusters changes overtime. Another problem is that computational efficiency is still an issue for big data analysis: when data is updated frequently and there is too much data for a single computer to handle efficiently and cost effectively. In this paper, we propose an improved DBSCAN algorithm that greatly reduces the computational complexity of DBSCAN and allows parallel processing for big data analysis. We report on the experimental results of UPDBSCAN. The test results show that the improved algorithm is 81 times faster than DBSCAN for n = 20,000 with a fraction of additional storage requirement.

Short- and medium-term predictions of stock prices have been important problems in financial analysis. In the past, various different approaches have been used including statistical analysis, fundamental analysis, and more recently advanced approaches that use machine learning and data mining techniques. However, most of existing algorithms do not incorporate all available information of the market. Using more informative and relevant data, prediction results will better reflect market reality. This would benefit in reducing the inaccuracy of predicting due to randomness in stock prices, using trend rather than a single stock price variation. For instance, some stock prices are correlated and/or dependent with/on each other and market mood. In this paper, we review the existing techniques of stock prices and time series predictions, and the classification and clustering methods. Based on the literature analysis, we propose a method for incorporating-related stock trend information: clustering-related companies using machine learning approaches. We report on a preliminary analysis results using monthly adjusted closing prices of 100 companies collected over a 15-month period.

In the context of B2B (Business To Business) transactions, identifying the potential customers or business partners is essential. Due to the availability of massive amount of data in the Internet, we can now use sophisticated data mining methods to automate the task of discovering potential customers much faster and more effective than ever before. It is important for ASEAN entrepreneurs to take advantage of this new technology. One approach is to analyse geographical distributions of patent firms in US and analyse their trading patterns in order to identify potential business partners for oversea patent firms. In this paper, we propose a method of analysing the geospatial patterns of patent business relations. In particular, we propose a method of finding good quality clusters of patent firms who are actively dealing with other countries. A comparative study of clustering algorithms has been done to find the best clustering algorithm that is suitable for geospatial analysis of business relations.

There are still a large number of unbanked people in Indonesia, particularly, in the rural areas. Without proper financial services, there can be no economy as banking is essential for any modern economy. Without economy, the livelihood of rural communities will remain poor. One reason for the large number of unbanked population in the rural areas is the cost of opening bank branches for low-income earners. It may not be cost-effective for banks to open branches in rural Indonesia. The number of customers may not be sufficient to cover the operating cost. This paper discusses digital banking services, such as online banking, SMS banking, and mobile banking. These new technologies could reduce the operating costs of banking services and allow the banks to provide financial services in the rural areas. This could potentially help the unbanked population have access to financial services, revitalize their local economy, and eventually improve the livelihood of rural communities, adding to the range of policy instruments to reduce rural poverty in Indonesia.

IT plays an enormous role in creating conditions for faster socioeconomic development of a nation. Computerized data analysis methods can provide vital information for many businesses to make informed decisions in time and stay competitive. Frequent pattern (FP) mining is a data mining approach for discovering useful market data automatically from seemingly abundant data that are difficult to analyze using conventional statistical approaches. FP approaches have been widely accepted in developed countries for market data analysis and consumer behavior analysis, but seldom evaluated or applied in developing countries, such as Indonesia, due to limited access to data in the regions. However, such data would be tremendously useful for investors and financial institutions who are planning to invest in those developing countries. For this reason, we have collected large itemset transaction data from Indonesia for over 2 years and devised FP mining approaches to explore the quality of the data and extract useful data for foreign and local investors. For the first time, this paper reports the FP analysis results, new analysis approaches for this emerging market using novel datasets. In particular, we show that the average number of items that are bought together frequently in a retail shop in Indonesia is three. This kind of information is useful for formulating various marketing strategies, but difficult to obtain using conventional statistical approaches.

Bank rating is as old as bank regulatory authorities. The most common approach to bank rating is on-site examination. The bank supervision authority conducts an audit of physical bank records and documents and other observations. In particular, the on-site examination looks at the bank’s assets, capital structure, management ability and professionalism of the bank practices. A summary rating is assigned based on this examination. This approach was reported in 1974 in a publication of the Federal Reserve Bank of New York. This rating system was called into question for the first time during the Asian Financial Crisis (AFC) of the 1997 that affected South Korea, Thailand, and Indonesia. IMF painfully restructured the South Korean economy. Thailand suffered a severe outflow of funds and banks collapsed. The entire Indonesian banking system had to be revamped. Then the global financial crisis struck in 2007–2008, where the western world was hit harder than the AFC that led to the collapse of venerable financial institutions such as Lehman Bros and Bear Sterns. This begs the question: why bank ratings did not, or cannot, forewarn imminent failures in the banking system. In 2009, it was reported that banks were usually rated higher if they had greater capitalization, larger assets, and higher return on assets and equity. In 2013, in a most scathing attack on bank ratings, it stated that most failing banks had enjoyed investment status ratings just before they defaulted. This paper revisits the age-old CAMEL model and its derivatives, and a more recent EAGLES framework that alerted the Asian Financial Crisis of the 1990s. Based on the experiences of two crises in the last two decades, what lessons did we learned, especially on the early warning signals, contagion effect, information transparency and the availability of technology processes to collect and harness critical information.

This chapter reports a new simulated smart learning environment, called Mobile Collaborative Experiential Learning (MCEL). MCEL provides automated, continuous, personalized, and formative feedback. Students interact with the simulated learning system via simple text messages using mobile devices in order to change the state of the system to a desired state over time. In the process, students engage in complex problem solving activities, and the system provides continuous formative assessment to help the students achieve learning objectives. Unlike conventional summative assessment approaches, students acquire set competency levels during their journey to achieving certain goals while interacting with the simulated system. MCEL allows instructors easily define learning journeys using event-condition-action (ECA) rules and reliable structured-text parser. A pilot study of MCEL is conducted and evaluated on a group of twenty Masters-in-IT students. Their participation is observed and a survey is conducted. The evaluation results show that MCEL supports Bigg’s constructive alignment in curriculum design, contextualized experimental learning, and personalized formative learning.

Global cell-phone ownership has surpassed over 5 billion. The proliferation of cell phones offers an unprecedented opportunity for aid organizations and governments in developing countries for providing affordable medical services for everyone. The available standardized interfaces of low-cost cell-phones allow us to create powerful medical diagnostics systems. For instance, digital cameras of cell phones now provide easy to use interfaces for capturing useful information of various medical conditions. However, photographic images often contain private and sensitive personal information in its raw form thus considered unsuitable for many available online services. Therefore, there is a need for a computational algorithm for extracting anonymous, de-identified, digital features from captured images for assessing medical conditions and general personal wellbeing. We present a de-identified feature generation method, called Gaussian Hamming Distance (GHD). We show that GHD features are significantly correlated with personal wellbeing. Its low computational complexity makes it ideal to be used with low-cost mobile devices. Its prediction power is suitable for providing a variety of online services including recommending useful health information for improving general wellbeing.

In this chapter we would like to share new emerging technologies that are changing the way we access medical care. Despite the rural development and modernization of the way of life, the cost burden of medical care and access to medical care has become a central concern for both developed and underdeveloped countries. The number of people admitted to hospitals and medical expenditures have doubled over the past 10 years. Since the invention of radio, television, and computer, various attempts such as telemedicine have been made for improving access to medical care. Telemedicine is still being actively developed and utilized in developed countries, but its main purpose is to allow medical specialists to remotely diagnose or operate on patients. However, our focus here is the other aspect of accessibility: reducing the cost of medical care by using computerized medical services. We can broadly categorize computerized health care services into four categories: (1) tele-health, (2) automated diagnosis and assessment of health, (3) online health support, and (4) health information management systems. We review the latter three approaches: automated health care systems, highlighting the potential power of each in solving current health care problems.

In recent years there is intense criticism of the exploitative interest rates charged to borrowers of microfinance institutions (MFIs). Development specialists and researchers have found incidences of usurious lending rates being levied upon the unbanked population, ironically in an attempt to lift them out of poverty. The borrowers suffer greater indebtedness and take extreme measures to repay while others just defaulted. One important reason for the high lending interest rates is the transaction cost or sometimes called administrative cost or operating expenses. It presupposes that high lending interest rates are necessary to cover the transaction and administrative expenses (TAE) of providing many small loans and monitoring many small borrowers. Thus this paper aims to identify the cost drivers and the cost of the MFI transactions and administration by using activity based costing (ABC). Thereafter a mobility solution is designed to replace the manual processes to lower the TAE, which in turn reduces lending rates charged to MFI borrowers. A pilot project has been started in Indonesia to test the hypothesis.

Respiratory conditions, such as pneumonia, cold, flu, and bronchitis, are still the leading causes of child mortality in the world. One solution for alleviating this problem is to develop affordable respiratory assessment methods using computerized respiratory-sound analysis. This chapter illustrates how computers can be used to automatically diagnose various respiratory health problems. We use an enhanced perceptual and cepstral feature set (PerCepD) for analysing breathing sounds (BSs) for identification and classification of respiratory health problems. Classification models are developed using support vector machine (SVM) and artificial neural network (ANN) to achieve automatic detection from BS data. The high detection accuracy results validate the performance of the proposed feature sets and classification models. The experimental results also demonstrate that the high accuracy of the pathological BS data can provide reliable diagnostic suggestions for breath disorders, such as flu, pneumonia and bronchitis.

Supporting diverse and rapidly changing learning styles of new digital age generations is a major hurdle in the age of massification of education markets. The institutions must now utilize the unprecedented network speed and mobile technology to stimulating learning environments for the new digital age generations. Institutions need to accommodate different learning styles of students often comprising of working professionals, different age groups and students from cultural backgrounds. Researchers have been trying solve these challenges through flexible learning and blended learning approaches. Faced with new digital age generations, unprecedented network speed and mobile technology, researchers now frequently quote mobilized curriculum, social-interaction oriented learning, personalized learning and collaborative mobile learning. This chapter reviews relevant education models, learning styles, literature, and technology to help identify new emerging research problems and research methodologies in m-learning.

One of the challenges in providing medical services over the network connections is maintaining authenticity and integrity of the data that are transmitted over the network and stored on remote servers. To provide critical medical services, data must be free from tempering. Medical data often also contain sensitive information, and therefore require a method to prevent unauthorized access to sensitive information of patients. In this chapter, we illustrate how the methods developed for the protection of copyright can be used to achieve this. In particular, we illustrate how a watermarking scheme can be used for protecting breath sounds transmitted over the internet. We illustrate how this method can be used to insert encrypted source and identity information in breath sounds while maintaining significant biological signals for proper diagnosis. The method can be applied to any types of data including image, video, and sound data. We show experimental results to demonstrate that the proposed watermarking scheme obtains good robustness against common manipulation attacks and preserves imperceptivity. The performance comparison results verify that the scheme outperforms existing approaches in terms of robustness and imperceptibility.

Banking is essential to modern economies, yet nearly half of the world’s people are unbanked and underserved by banks. This is mainly due to the high operating costs of banks that are passed on to retail customers, driving away customers who are not able to afford the high service costs. Without having access to the financial system the unbanked and rural poor have little means to engage in mainstream economic activities. In this chapter, we present a low-cost mobile phone-based core-banking system. This system was designed to provide retail banking services to the world’s unbanked population and rural poor of 2.2 billion based on a cashless banking system that reduces operating cost and lending risks and therefore lower the current exorbitant rates charged on retail lending and micro-credit. Our analysis and evaluation shows that the mobile core-banking system on a low-cost mobile device (an Android tablet) is able to serve over 15,000 micro-banking customers. The system is therefore suitable for retail banks or microfinance institutions.

According to UNESCO’s final figures for 2009, since 2000 the number of international students has increased by 75 %. The number of international students in Singapore is also expected to grow at 10 % annual rate reaching 150,000 in 2015. This number of students, according to a government’s foresees, is worth USD 5 billion and contributes to 2 % of GDP. As a result, there are various great opportunities coming up for higher learning education in Singapore. Singapore has become one of the most favourite destinations for international students for higher education; its growth rate is surpassing the traditionally favoured Western countries, such as US. International students have also become an important economic factor. According to former researches related to education, students are usually influenced by the surrounding sources of information as well as their own experiences. Besides, institutional factors such as program type, reputation, location, and facilities are also considered when students choose their higher learning institutes. Other studies also recognized the extreme importance of several country factors such as living condition, and safety that may affect students when they consider a destination for their study abroad. This chapter aims to examine the sources of information that international students in Singapore consider in order to choose Singapore as their study abroad destination. Additionally, the study also compares the importance of country factors and institutional factors. We have surveyed 234 students who are currently studying in Singapore to find out the competitive advantage of Singapore Tertiary Education. The results show that recommendations from friends and teachers have major influence on students’ decision in choosing their schools (higher education) and location of education, followed by country factors, such as personal security. Surprisingly, schools’ promotional activities and agents have limited influence on students’ decision making.

We propose a mobile collaborative experiential learning system that students can access via mobile devices or any interactive devices connected to the Internet. The approach departs from conventional summative assessment approaches, such as reports submitted at the end of their learning sessions. Students interact with the learning system via mobile devices in order to change the state of a simulated learning lab to a desired state over time anywhere anytime to solve problems or achieve their goals. Students acquire set competency levels during their journey to achieving their goals interacting with the learning environment via mobile devices. The main objectives are to provide ownership of their journey, experiential, contextualized and formative assessment at low cost. The system features SMS/MMS biding of WSDL, SOA 2.0, and Event- Condition-Action (ECA) process modeling for easy integration with existing systems.

Banking is essential to modern economies and nearly half of the world's people are unbanked and underserved by banks. The high operating costs of banks passed on to retail customers are driving them away who otherwise should be in the financial system. Without having access to the financial system the unbanked and rural poor have little means to engage in mainstream economic activities. A mobile phone-based banking system was designed to provide retail banking services to the world's unbanked population and rural poor of 2.2 billion based on a cashless banking system that reduces operating cost and reduces lending risks and therefore can lower the current exorbitant rates charged on retail lending and micro-credit. A core-banking server on a low-cost mobile device (an Android tablet) could serve over 15,000 micro-banking customers. The system definitely appears to be suitable for most retail banks or microfinance institutions.

The UN has predicted that cell-phone ownership will reach 5 billion in 2010. This proliferation of cell phones and connectivity offers an unprecedented opportunity to access vast populations, including previously hard-to-reach populations in rural areas and mountainous zones and underserved populations. Cell phones now can provide capabilities for the developing world that includes text, image processing and image displays. The available standardized interfaces can be leveraged to create powerful systems. In particular, digital cameras of cell phones provide easy to use interfaces for capturing useful information on the general well-being and emotive features of individuals. However, photographic images contain private and sensitive personal information in its raw form and thus considered unsuitable for online services. Therefore, there is a need for a computational algorithm for extracting anonymous digital features (for example, Hamming distance) from captured facial expression images for estimating different states of well-being. We have developed computer algorithms predicting well-being states from anonymous facial expression features. The research outcome can be used in a variety of online services including suggesting useful health information to improve general well-being.

A health social network is an online information service which facilitates information sharing between closely related members of a community. The main means of finding patients with similar health conditions has been based on labor-intensive methods, such as searching the Internet. Also, because of the privacy and security issues of health information systems, it is often difficult to find patients who can support each other in the community. Over the years, many automated recommender systems have been developed for social networking. We propose a social networking framework for patient care, where health service providers facilitate social links between parents using similarities of mental health conditions. A machine learning approach was developed to automatically generate keywords for mental health descriptions that can be used to screen for mental health conditions and to then group individuals with similar mental health conditions. Keywords are generated from sources such as conversations on online forums. These keywords are then used to identify similarities between mental health descriptions, in order to recommend a community of related patients.

The empowerment of women micro entrepreneurs is necessary for the holistic social-economic development of a nation. The aim of this paper is to address the gap in the study of formal financial access to rural women micro-entrepreneurs in Indonesia. This study will contribute further in the pool of academic resource for micro-financing organisations intending to create formal financial products for this niche market. Meta-analytic research methodology was employed to review relevant literature, in order to assemble supporting data relating to women’s constraints for access to formal micro-finance for women. As a result, a hypothesis was devised that gender inequality is the underlying constraint for rural women entrepreneurs to access formal microfinance. Evidential data was reviewed for factors pertaining to gender inequality index, general and financial education status, and social-cultural norms of rural Indonesian women. In conclusion, the authors found that gender inequality issues require further attention by micro financers during the design process of successful micro-financial products. In addition, relevant recommendations were formulated such as aesthetic redesign of financial outlets, reduction in lead time and costs for micro-financial transactions, and micro-insurance. Further research is required to conduct a test of the generated hypothesis at the location under discussion.

In this paper, we present a mobile phone-based banking system, called ACMB (Affective Cashless Mobile Banking), for microfinance. ACMB is designed to provide banking services to the world’s unbanked population of 2.2 billion. To enable interoperability between various microfinance institutions over a heterogeneous network of mobile devices, cell-phone networks and internet services, we define MSDL (Microfinance Service Definition Language) based on WSDL (Web Service Definition Language). MSDL includes a binding for SMS for service queries and utilization. To ensure that the banking service provides an acceptable level of usability and user experience, ACMB incorporates a wellbehaved service interface, which informs the design to create affective banking services based on human emotion models. ACMB was implemented on an Android tablet and evaluated with 147 participants, who performed 804 transactions and exchanged 2,412 SMS messages over a three hour testing period. The results suggest that an ACMB core-banking server on a low-cost mobile device can serve over 15,000 microfinance customers. Therefore, the systems appears to be suitable for most microfinance institutions.

According to UNESCO’s final figures for 2009, since 2000 the number of international students has increased by 75 %, about 8 % annual growth rate. The number of international students in Singapore is also expected to grow at 10 % annual rate reaching 150,000 in 2015. Singapore has become one of most favorite destinations for international students for higher education, its growth rate surpassing traditionally favored Western countries, such as US. International students have also become an important economic factor. The total economic contribution from international students studying in Singapore is about 2 % of Singapore GDP. We have surveyed 234 students who are currently studying in Singapore to find out the competitive advantage of Singapore Tertiary Education. The results show that recommendations from friends and teachers have major influences on students’ decision in choosing schools and countries, followed by country factors, such as personal security. Surprisingly, schools’ promotional activities and agents have limited influence on students’ decision making.

MFIs have a high interest rate burden due to the small amount per transaction of microcredit and inevitably high operating cost per transaction. To ensure financial viability and to expand the depth and breadth of their operations, MFIs have to adopt cost recovery interest rates on microcredit, hence, MFIs have to charge interest rate high enough, usually substantially higher than the bank loan risk free interest rate. The major factors determining the interest rate on microcredit are the cost of funds, operating costs, loan loss cost and capital for business expansion. To illustrate the impacts of the above factors on interest rate, we present a summary of the current cost structures of microfinance institutes (MFIs) in three Southeast Asia countries, Cambodia, Vietnam, and Indonesia. Then, we review existing studies for the roles of mobile technologies for lowering the interest burden.

The financial inclusion of women micro entrepreneurs is both important and necessary for the holistic socio-economic development of a nation. This paper aims to address the gap in the study of the processes of financial inclusion by women micro-entrepreneurs in Indonesia. The research will contribute to the pool of academic resources that pertains to financial inclusion, micro credit, and women entrepreneurship, intending to improve the processes and benchmarking for financial inclusion initiatives. There is an extensive literature research plus in situ research conducted in Indonesia. There is also significant evidence of factors pertaining to gender inequality, which is a result of financial inclusion of women. Both desk research and field research found financial exclusion is linked to financial education, cultural norms of rural Indonesia and high banking cost and mobile payments. In conclusion, the authors recommend a better processes in designing financial products, improving financial education, lowering cost of banking especially in the use of mobile payment technology, and instituting micro-insurance. Further research is recommended to generate hypotheses relating to financial inclusion and women entrepreneurship in Indonesia.

In recent years, information and communication technologies ICT impacted greatly socioeconomic development in developing countries. Encouraged by the development of rural IT network projects in countries like India, Sri Lanka, Bangladesh and Philippines, the government of Vietnam focused on IT infrastructure and capacity building efforts. Under the framework of public administration reform in Ministry of Agriculture & Rural Development MARD project scheme, a study was conducted in setting up Rural Telecentres RTs to provide information to farmers in Vietnam. Farmers' opinions and their needs in relation to agricultural and rural issues were considered in establishing 13 RTs in rural communities. The post-implementation survey indicated that the RTs fulfilled their objectives in improving the livelihoods of rural farmers. This paper presents the experiences and lessons learnt from the RTs project particularly in strengthening arguments in favour of national level ICT projects in rural development.

Management information systems deliver information—the right information to the right person, in the right form, at the right time. Such a system is an asset for planning and control. The key determinants for successfully implementing a system are described with reference to commercial banking.

The main focus of the study is an exploratory empirical investigation on quantitative determinants of bank profitability in South East Asia. The study examines the existence of any structural relationship between key financial ratios and bank profitability, which is measured by the return on assets. The study also provides explanations to the differences in effects of profitability determinants in the five South East Asia countries, namely Indonesia, Malaysia, Philippines, Singapore and Thailand. A total of 121 locally incorporated commercial banks were included in the sample. A multivariate regression analysis was performed for data collated over 6 years, from 1992 to 1997. ​Master of Business

The purpose of this research is to analyse the relationship between capital and profitability for banks in the three regions, Asia, Europe and the United States for two years, 1992 and 1993, that are ranked among the Top 1000 banks in the world by The Banker. It takes the lead from Berger (1995) who found that capital-asset ratios have positive effects on profitability. Besides the direct impact of capital on profitability through lower interest expenses, this study recognises that capital has an indirect impact on profitability through its effects on the bank's volume of assets. The bank's volume of assets may increase when capital increase but the ultimate profitability of the bank depends largely on the composition and pricing of the assets which in turn depend on factors like competition, regulation, management's strategy and the general economic environment affecting the level of interest rates. Master of Business

This study arose from the current interest of our government to encourage local businesses to invest overseas. With this drive to encourage overseas business ventures, project analysis and appraisal have gained importance. The motivation of our study resulted from preliminary contacts with some local businessmen who indicated that they have reservations on their part to venture abroad. These perceptual differences between the theories postulated by academics and practitioners with regards to approaches on project analysis and appraisal is also looked into in this paper. Master of Business Administration (Accountancy)