A heuristic-based, multineural network (MNN) image analysis as a solution to the problematical diagnosis of hydatidiform mole (HM) is presented. HM presents as tumors in placental cell structures, many of which exhibit premalignant phenotypes (choriocarcinoma and other conditions). HM is commonly found in women under age 17 or over 35 and can be partial HM or complete HM. Appropriate treatment is determined by correct categorization into PHM or CHM, a difficult task even for expert pathologists. Image analysis combined with pattern recognition techniques has been applied to the problem, based on 15 or 17 image features. The use of limited data for training and validation set was optimized using a k -fold validation technique allowing performance measurement of different MNN configurations. The MNN technique performed better than human experts at the categorization for both the 15- and 17-feature data, promising greater diagnostic consistency, and further improvements with the availability of larger datasets.

Historical information on herbal medicines is underexploited and this is particularly true of the important resources of Arabic herbal medicines. Current research into Arabic medicinal plants as alternative medicine is limited and there is a lack of accurate translations and interpretations of herbal medicine texts. This research focuses on an investigation of Arabic herbal medicinal plants in relation to the problem of obesity. This paper demonstrates how text mining can help extract relevant concepts associated with Arabic herbal plants and obesity in order to discover associations between the herbal medicinal ingredients and obesity symptoms.

Stock Market (SM) is a significant sector of countries' economy and represents a crucial role in the growth of their commerce and industry. Hence, discovering efficient ways to analyse and visualise stock market data is considered a significant issue in modern finance. The use of Data Mining (DM) techniques to predict stock market has been extensively studied using historical market prices but such approaches are constrained to make assessments within the scope of existing information, and thus they are not able to model any random behaviour of stock market or provide causes behind events. One area of limited success in stock market prediction comes from textual data, which is a rich source of information and analysing it may provide better understanding of random behaviours of the market. Text Mining (TM) combined with Random Forest (RF) algorithm offers a novel approach to study critical indicators, which contribute to the prediction of stock market abnormal movements. A Stock Market Random Forest-Text Mining system (SMRF-TM) is developed to mine the critical indicators related to the 2009 Dubai stock market debt standstill. Random forest is applied to classify the extracted features into a set of semantic classes, thus extending current approaches from three to eight classes: critical down, down, neutral, up, critical up, economic, social and political. The study demonstrates that Random Forest has outperformed the other classifiers and has achieved the best accuracy in classifying the bigram features extracted from the corpus.

Stock Market (SM) is believed to be a significant sector of a free market economy as it plays a crucial role in the growth of commerce and industry of a country. The increasing importance of SMs and their direct influence on economy were the main reasons for analysing SM movements. The need to determine early warning indicators for SM crisis has been the focus of study by many economists and politicians. Whilst most research into the identification of these critical indicators applied data mining to uncover hidden knowledge, very few attempted to adopt a text mining approach. This paper demonstrates how text mining combined with Random Forest algorithm can offer a novel approach to the extraction of critical indicators, and classification of related news articles. The findings of this study extend the current classification of critical indicators from three to eight classes; it also show that Random Forest can outperform other classifiers and produce high accuracy.

The area of knowledge management, the SECI mode in particular, has great value in terms of enriching patients’ knowledge about their diseases and its complications. Despite its effectiveness, the application of knowledge management in the healthcare sector in the Kingdom of Saudi Arabia seems deficient, leading to insufficient practice of self-management and education of different prevalent diseases in the Kingdom. Moreover, the SECI model seems to be only focusing in the conversion of human knowledge and ignore knowledge stored in databases and other technological means. In this paper, we propose a framework to support diabetic patients and healthcare professionals in the Kingdom of Saudi Arabia to self-manage their disease. Data mining and the SECI model can provide effective mechanisms to support people with diabetes mellitus. The area of data mining has long been utilised to discover useful knowledge whereas the SECI model facilitates knowledge conversion between tacit and explicit knowledge among different individuals. The paper also investigates the possibilities of applying the model in the web environment and reviews the tools available in the internet that can apply the four modes of the SECI model. This review helps in providing a new median for knowledge management by addressing several cultural obstacles in the Kingdom.

As natural language processing spans many different disciplines, it is sometimes difficult to understand the contributions and the challenges that each of them presents. This book explores the special relationship between natural language processing and cognitive science, and the contribution of computer science to these two fields. It is based on the recent research papers submitted at the international workshops of Natural Language and Cognitive Science (NLPCS) which was launched in 2004 in an effort to bring together natural language researchers, computer scientists, and cognitive and linguistic scientists to collaborate together and advance research in natural language processing. The chapters cover areas related to language understanding, language generation, word association, word sense disambiguation, word predictability, text production and authorship attribution. This book will be relevant to students and researchers interested in the interdisciplinary nature of language processing. Discusses the problems and issues that researchers face, providing an opportunity for developers of NLP systems to learn from cognitive scientists, cognitive linguistics and neurolinguistics Provides a valuable opportunity to link the study of natural language processing to the understanding of the cognitive processes of the brain

Despite emerging evidence about the benefits of telemedicine, there are still many barriers and challenges to its adoption. Its adoption is often cited as a failed project because 75% of them are abandoned or 'failed outright' and this percentage increases to 90% in developing countries. The literature has clarified that there is neither one-size-fit-all framework nor best-practice solution for all ICT innovations or for all countries. Barriers and challenges in adopting and implementing one ICT innovation in a given country/organisation may not be similar not for the same ICT innovation in another country/organisation nor for another ICT innovation in the same country/organisation.To the best of our knowledge, no comprehensive scientific study has investigated these challenges and barriers in all Healthcare Facilities (HCFs) across the Kingdom of Saudi Arabia (KSA). This research, which is undertaken based on the Saudi Telemedicine Network roadmap and in collaboration with the Saudi Ministry of Health (MOH), is aimed at identifying the principle predictive challenges and barriers in the context of the KSA, and understanding the perspective of the decision makers of each HCF type, sector, and location. Three theories are used to underpin this research: the Unified Theory of Acceptance and Use of Technology (UTAUT), the Technology Organisation Environment (TOE) theoretical framework, and the Evaluating Telemedicine Systems Success Model (ETSSM). This study applies a three sequential-phase approach by using three mixed methods (i.e., literature review, interviews, and questionnaires) in order to utilise the source triangulation and the data comparison analysis technique. The findings of this study show that the top three influential barriers to adopt and implement telemedicine by the HCF decision makers are: (i) the availability of adequate sustainable financial support to implement, operate, and maintain the telemedicine system, (ii) ensuring conformity of telemedicine services with core mission, vision, needs and constraints of the HCF, and (iii) the reimbursement for telemedicine services. (C) 2016 King Saud Bin Abdulaziz University for Health Sciences. All rights reserved.

Many boundaries are hindering successful utilisation of e-health in the Kingdom of Saudi Arabia (KSA). We have previously proposed an integrated framework of knowledge management and knowledge discovery to overcome barriers of e-health in KSA. Our proposed framework facilitates diabetes self-management for diabetic citizens in the Kingdom. In this paper, we will investigate and rank the barriers of e-health in KSA from the prospective of three stakeholders. We designed a questionnaire which constituted of items related to eight different e-health barriers and its associated sub-barriers. Citizens participated in 51 items related to six barriers. Healthcare professionals answered 83 items related to eight barriers. IT specialists participated in 74 items related to six barriers. Within each group of respondents, we compared the mean scores for each factor and sub-factor. The highest possible score for the mean was 5.00 and the lowest was 0.00 where the higher the mean score was the more the barrier constituted an obstacle for e-health in KSA. Citizens ranked the connectivity of information system as the top barrier with the mean of 4.0 whereas the least barrier was the cultural barriers with the mean score of 3.1. Healthcare professionals ranked the connectivity of information systems as the top barriers with the mean score of 3.5 whereas the least barrier was the technical expertise and computer skills with the mean score of 2.2. The top ranked barrier from the perspective of IT specialists was the medication safety with the mean score of 3.5 and the least ranked barrier was security and privacy with the mean score of 2.2. The results showed consistency with the literature review. Our proposed framework will contribute to the successful implementation of e-health initiatives and assist citizens in KSA to self- manage diabetes.

The advances in artificial intelligence and the post-Google interests in information retrieval, in the recent decades, have made large-scale processing of human language data possible and produced impressive results in many language processing tasks. However, the wealth and the multilingualism of digital corpora have generated additional challenges for language processing and language technology. To overcome some of the challenges an adequate theory of this complex human language processing system is needed to integrate scientific knowledge from the fields of cognitive science and cognitive neuroscience, in particular. Over the last few years, emerging applications of NLP have taken a cognitive science perspective recognising that the modelling of the language processing is simply too complex to be addressed within a single discipline. This paper provides a synopsis of the latest emerging trends, methodologies, and applications in NLP with a cognitive science perspective, contributed by the researchers, practitioners, and doctoral students to the international workshops in Natural Language processing and Cognitive Science (NLPCS).

This paper outlines some of the challenges that currently face healthcare systems in Kingdom of Saudi Arabia (KSA). Increasing and continuing demand for healthcare services is aggravated by a critical shortage of health human resources (HHR) and healthcare facilities (HCFs) especially in rural areas. In 2013, 17.8% of the population lived in rural and remote areas with a huge disparity in HCFs distribution, and 76% of physicians and 44.7% of nurses are non-Saudis. Current studies have shown the potential of telemedicine to alleviate these challenges. The use of telemedicine has been adopted and the telemedicine roadmap has been developed by the Ministry of Health (MOH) in KSA in collaboration with Canada Health Infoway (Infoway). This roadmap has identified many barriers and challenges likely to face the implementation of telemedicine in KSA. This paper describes a holistic framework to address these challenges and to assess telemedicine applications in order to assist decision makers of HCFs in KSA. The proposed framework is developed in collaboration with the National eHealth Strategy and Change Management Office in the Ministry of Health (MOH) and Prince Mohammad Medical City (PMMC) in KSA.

Outsourcing is a widespread practice in the modern global economy, with decisions motivated by expectation of various advantages of which cost reduction is often the primary factor. Recent trends have been towards Business Process Outsourcing (BPO) and offshore outsourcing, and more recently Knowledge Process Outsourcing (KPO) has emerged as an established area of the market. Despite the potential attractions, there are a range of risks associated with outsourcing, and these are demonstrated by the substantial number of arrangements that fail to meet expectations. For example, Glick (2004) cites research by Gartner that indicated £4bn was wasted on poorly managed contracts by European businesses in the previous year. This highlights the business significance of outsourcing decisions and shows that the impact of major outsourcing decisions is likely to be strategic in that the competitive position of a company is influenced by the outcomes. The argument for use of a systematic decision support system to assist with outsourcing decisions is persuasive. The Holistic Approach Business, Information, Organisation (HABIO) Framework offers a holistic approach which takes account of interrelated strategies and complex considerations that may vary according to the circumstances and priorities of the decision-maker. It also facilitates monitoring decisions over the life of the outsourcing arrangements, thereby supporting timely anticipation and response to significant changes in the economic, political, or social environment. The HABIO model could be used in consideration of intra-national outsourcing decisions to areas such as North Staffordshire where decline in industries traditionally associated with the locality created needs and opportunities for regeneration.

Falls which affect the musculoskeletal system are the leading cause of injury in people over 65 years. To address the growing problem of falls in an ageing society and to support and improve the healthcare service provided, a diagnostic tool is required. This study proposes a new approach to analyse and diagnose the risks associated with elderly falling by applying K-means clustering to cluster and assess the fall risks data of elderly Thai people, captured using motion capture technology. These clusters are mapped into two-dimensional space using self-organising map (SOM). The resulting 95.45% accuracy suggests that the two-stage clustering technique is applicable and useful in managing fall risks which can then be included in decision support system to assist physiotherapists, in recommending a customised rehabilitation programme.

To continue development as a leading Southeast Asian economy, and meet the needs of a knowledge-based society, Thailand must improve English language proficiency. This paper presents an innovative knowledge sharing framework targeting software engineering students' written English skill, by integrating four learning theories; constructionism, cognitive learning, cone of experience and the learning pyramid. Together, these theories promote 'learning by doing' as the most effective way to improve students' English. Results show statistically significant improvements in students' English and suggest the new framework can change the dominant memorisation and recall methods in Thai education to reach higher levels of learning.

Molar pregnancy (also known as hydatidiform mole, hydatid mole, gestational trophoblastic disease) represents forms of abnormal conception caused by defective fertilisation resulting in excess expression of paternal genes in placental tissue. There are two forms of hydatidiform mole: complete (diploid androgenetic) and partial (paternal triploid), the distinction between which is important for determining appropriate prognosis and management of patients. Both complete and partial hydatidiform moles are associated with increased risk of development of malignant gestational trophoblastic tumours, the risk being much greater for complete hydatidiform moles. Whilst in most cases the diagnosis of these moles can be reliably achieved on morphological histological assessment, these represent a continuing diagnostic problem for histopathologists since in early pregnancy complete hydatidiform moles, partial hydatidiform moles and non-molar hydropic miscarriages may be difficult to distinguish. In this paper, we propose a computational image analysis approach guided by the knowledge of expert pathologists in identifying essential distinguishing morphological criteria. The approach, which combines Fuzzy C-Means clustering with hue, saturation and value colour space, shows promising results as it is able to classify successfully the villi into appropriate regions, namely trophoblast and stroma, and extract areas of blood. However, because of the marked variations in size, shape and outline of the villi, and trophoblast proliferation, both within and between cases, the analysis shows that there is no single criteria which can reliably classify these products of conception and a combination of criteria is required.

In 2008 the global older population (aged 65 and over) was estimated to be about 7% of the world's population; it is estimated to reach 2 billion by 2050 accounting for 22% of the world total population. This demographic trend sets new challenges to health services and policies, and imposes a significant financial and social burden on economies as a whole. The decline in musculoskeletal system in the aging population is the biggest cause of injury death in older people who require effective physiotherapy treatment. However the current demand for physiotherapists outstrips current supply. To address this problem this paper described an innovative approach which consists of a screening risk assessment of the elderly which can be combined with a case based reasoning system to support physiotherapist in managing the care of the elderly following a fall. Although the study is based on a small cohort of the Thai population it is believed that this approach can address the shortage of physiotherapists and bring a significant improvement in the health care of older people. (C) 2012 Elsevier Ltd. All rights reserved.