32,403 research outputs found

    Predictive Data Mining: Promising Future and Applications

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    Predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends. The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behavior. For example, an insurance company is likely to take into account potential driving safety predictors such as age, gender, and driving record when issuing car insurance policies. Multiple predictors are combined into a predictive model, which, when subjected to analysis, can be used to forecast future probabilities with an acceptable level of reliability. In predictive modeling, data is collected, a statistical model is formulated, predictions are made and the model is validated (or revised) as additional data becomes available. Predictive analytics are applied to many research areas, including meteorology, security, genetics, economics, and marketing. In this paper, we have done an extensive study on various predictive techniques with all its future directions and applications in various areas are being explaine

    Implementing e-Services in Lagos State, Nigeria: the interplay of Cultural Perceptions and Working Practices during an automation initiative : Nigeria e-government culture and working practices

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    Accepted for publication in a forthcoming issue of Government Information Quarterly.The public sector’s adoption of Information and Communication Technologies is often seen as a way of increasing efficiency. However, developing public e-Services involves a series of organisational and social complexities. In this paper, we examine the organisational issues of implementing an ERP system, which was designed and developed within the context of Lagos State’s e-Services project. By doing so, we showcase the impact of organisational cultural perceptions and working practices of individuals. Our findings illustrate the strong role of cultural dimensions, particularly those pertaining to religion and multi-ethnicity. Our study provides insights to international organisations and governments alike toward project policy formulation within the context of ICT-based initiatives and reforms that aim to bring forward developmental progress.Peer reviewedFinal Accepted Versio

    A Review of Rule Learning Based Intrusion Detection Systems and Their Prospects in Smart Grids

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    LITIGATIONS, DAMAGES AND SOLUTIONS IN RESIDENTIAL MORTGAGE-BACKED SECURITIES

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    Mortgage-backed securities (MBS) are debt obligations whose cash flows are backed by the principal and interest payments of pools of mortgage loans, most commonly on residential property (Riddiough, 2001). Lenders establish underwriting guidelines, evaluate prospective homeowners’ credit, and make loans. Having done so, lenders generally hold only a fraction of the loans they make in their own portfolios. Most are sold to the secondary market, where they are pooled and become the underlying assets for residential mortgage-backed securities. Individuals with strong credit qualify for traditional mortgages, whereas those with weak credit histories that include payment delinquencies, and possibly more severe problems such as charge-offs, judgments, and bankruptcies qualify for subprime loans (Hayre, 2001). Securitization is the financial technology that integrates the market for residential mortgages with the capital markets. Investment banks take pools of home loans, carve up the cash flows from those receivables, and convert the cash flows into bonds that are secured by the mortgages. The bonds are variously known as residential mortgage-backed securities (R-MBS) or asset-backed securities (ABS).Mortgage-backed securities, residential property, mortgage loans, residential mortgage-backed securities, subprime loans, asset-backed securities, Securitization.

    Artificial Intelligence & Machine Learning in Finance: A literature review

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    In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical Researc

    Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

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    The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface

    Competency Maturing grounded theory

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    Becoming a competent IS/IT graduate is not a once-off event because rapid technological changes require that IS/IT graduates continually strive to be up-to-date and relevant. Continuous updating of knowledge, acquiring a diverse set of IS/IT/ICT competencies, and being competent is a problematic task globally, which requires building competencies comprising knowledge, skills, abilities, and values. This study employs Classic Grounded Theory Methodology to identify the main concern of senior IS undergraduates during their learning process, and how they resolve the concern. The students’ main concern emerged as a perceived lack of IS Competency. Maturing competency is a substantive theory which explains how these students attempt to resolve their concern. Three phases of the basic social process of Maturing Competency are student engagement, self-awareness of competency, and self-development. The findings suggest that creating an organic learning environment can be a useful approach to developing more competent IS graduates
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