BIG DATA ALGORITHMS AND PREDICTION: BINGOS AND RISKY ZONES IN SHARIA STOCK MARKET INDEX

Each country with a stock exchange normally calculates various indexes. So is the case for Malaysia’s Kuala Lumpur Stock exchange (KLSE). FTSE BURSA Malaysia EMAS Sharia price index (FTBMEMA) is one of its Sharia indexes. In an effort to find which other indices may forecast this Sharia index, we selected 23 relevant indexes and two exchange rates. Momentum indicators for short, medium and long term have been calculated for the variables. The objective of this study is to find predictive indicators for FTBMEMA out of the population of 188 original and derived variables. Difficulty arises in reducing the number of variables for regression or other predictive models like neural networks. In this preliminary study, data mining attribute selection algorithms along with cross validation criteria have been used, through the use of Java class library Weka (JCLW), for reducing the number to statistically relevant variables for our regression estimation in an effort to forecast various performance parameters for FTBMEMA like performing either in a mean performance range, having jackpots and bingos or falling into danger zones. Provided the extent of the required predictive accuracy, the results may bring additional insights for diversifying and hedging various types of investment portfolios as well as for maximizing returns by portfolio managers

Architecture and Design of Medical Processor Units for Medical Networks

This paper introduces analogical and deductive methodologies for the design medical processor units (MPUs). From the study of evolution of numerous earlier processors, we derive the basis for the architecture of MPUs. These specialized processors perform unique medical functions encoded as medical operational codes (mopcs). From a pragmatic perspective, MPUs function very close to CPUs. Both processors have unique operation codes that command the hardware to perform a distinct chain of subprocesses upon operands and generate a specific result unique to the opcode and the operand(s). In medical environments, MPU decodes the mopcs and executes a series of medical sub-processes and sends out secondary commands to the medical machine. Whereas operands in a typical computer system are numerical and logical entities, the operands in medical machine are objects such as such as patients, blood samples, tissues, operating rooms, medical staff, medical bills, patient payments, etc. We follow the functional overlap between the two processes and evolve the design of medical computer systems and networks.Comment: 17 page

Knowledge extraction of financial derivatives options in the maturity with data science techniques

To improve the level of support in information systems and quality of services by questioning the daily routine of a team using a set of financial evidence has been an interesting and challenging problem for many researcher and decision maker professionals. As part of a well-known investment bank that deals financial instruments like European-style options derivatives, operational teams are well aware that the focus of their work are around the evolution on pricing until the expiry moment. The choice of knowing more about financial derivatives options, especially in the maturity period, was made after a long process of study on economics and financial concepts in a certain institution. A special attention was given in subjects where information technology teams have less knowledge, which are the mathematical operation of derivative financial options and their implications in financial terms. As well, the identification of areas of business could be studied with greater interest for a specific organisation

A geographic knowledge discovery approach to property valuation

This thesis involves an investigation of how knowledge discovery can be applied in the area Geographic Information Science. In particular, its application in the area of property valuation in order to reveal how different spatial entities and their interactions affect the price of the properties is explored. This approach is entirely data driven and does not require previous knowledge of the area applied. To demonstrate this process, a prototype system has been designed and implemented. It employs association rule mining and associative classification algorithms to uncover any existing inter-relationships and perform the valuation. Various algorithms that perform the above tasks have been proposed in the literature. The algorithm developed in this work is based on the Apriori algorithm. It has been however, extended with an implementation of a ‘Best Rule’ classification scheme based on the Classification Based on Associations (CBA) algorithm. For the modelling of geographic relationships a graph-theoretic approach has been employed. Graphs have been widely used as modelling tools within the geography domain, primarily for the investigation of network-type systems. In the current context, the graph reflects topological and metric relationships between the spatial entities depicting general spatial arrangements. An efficient graph search algorithm has been developed, based on the Djikstra shortest path algorithm that enables the investigation of relationships between spatial entities beyond first degree connectivity. A case study with data from three central London boroughs has been performed to validate the methodology and algorithms, and demonstrate its effectiveness for computer aided property valuation. In addition, through the case study, the influence of location in the value of properties in those boroughs has been examined. The results are encouraging as they demonstrate the effectiveness of the proposed methodology and algorithms, provided that the data is appropriately pre processed and is of high quality

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

Contextual impacts on industrial processes brought by the digital transformation of manufacturing: a systematic review

The digital transformation of manufacturing (a phenomenon also known as "Industry 4.0" or "Smart Manufacturing") is finding a growing interest both at practitioner and academic levels, but is still in its infancy and needs deeper investigation. Even though current and potential advantages of digital manufacturing are remarkable, in terms of improved efficiency, sustainability, customization, and flexibility, only a limited number of companies has already developed ad hoc strategies necessary to achieve a superior performance. Through a systematic review, this study aims at assessing the current state of the art of the academic literature regarding the paradigm shift occurring in the manufacturing settings, in order to provide definitions as well as point out recurring patterns and gaps to be addressed by future research. For the literature search, the most representative keywords, strict criteria, and classification schemes based on authoritative reference studies were used. The final sample of 156 primary publications was analyzed through a systematic coding process to identify theoretical and methodological approaches, together with other significant elements. This analysis allowed a mapping of the literature based on clusters of critical themes to synthesize the developments of different research streams and provide the most representative picture of its current state. Research areas, insights, and gaps resulting from this analysis contributed to create a schematic research agenda, which clearly indicates the space for future evolutions of the state of knowledge in this field

Look at Me Now: What Attracts U.S. Shareholders?

This paper investigates the underlying determinants of home bias using a comprehensive data set on U.S. investors' aggregate holdings of every foreign stock. Among those foreign stocks that are not listed on U.S. exchanges, which account for more than 96 percent of our usable data sample, we find that U.S. investors prefer firms with characteristics associated with greater information transparency, such as stronger home-country accounting standards. We document that a U.S. cross-listing is economically important, as U.S. ownership of a foreign firm roughly doubles upon cross-listing in the United States. We explore the cross-sectional variation in this "cross-listing effect" and find that the increase in U.S. investment is greatest for firms that are from weak accounting backgrounds and are otherwise informationally opaque, suggesting that the key effect of cross-listing is improvements in disclosure that are valued by U.S. investors. By contrast, cross-listing does not increase the appeal of stocks from countries with weak shareholder rights, suggesting that U.S. cross-listing cannot substitute for legal protections in the home country. Nor does the cross-listing effect appear to be driven simply by increased "familiarity"� with the stock or lowered cross-border transactions costs.