Mobile Platform with Dynamic Optimization of the Pattern in Education in Colleges Through the Perspective of Network Informatization

The combination of mobile learning platforms and network informatization offers numerous benefits to learners, educators, and institutions. Learners can take control of their learning journey, accessing educational materials at their convenience and engaging in collaborative learning activities with peers from diverse backgrounds. This paper aims to explore the integration of mobile learning platforms and network informatization, examining their impact on educational practices, learner engagement, and the overall learning experience. The network informatization is assessed and monitored with Dynamic Programming Optimization (DPO) to compute the feature in reverse osmosis in English education. The attributes and features in the English language are computed and estimated for the periodic information update within the system. The DPO process is implemented along with the mandhani fuzzy set for the estimation of features in English education in colleges and universities. The information processed is updated in the mobile learning platform for the computation of the features in the English language and classification is performed with the deep learning model. Simulation analysis stated that constructed model is effective for the estimation and computation of the features and patterns in English language teaching in colleges and universities

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

Gene expression programming for Efficient Time-series Financial Forecasting

Stock market prediction is of immense interest to trading companies and buyers due to high profit margins. The majority of successful buying or selling activities occur close to stock price turning trends. This makes the prediction of stock indices and analysis a crucial factor in the determination that whether the stocks will increase or decrease the next day. Additionally, precise prediction of the measure of increase or decrease of stock prices also plays an important role in buying/selling activities. This research presents two core aspects of stock-market prediction. Firstly, it presents a Networkbased Fuzzy Inference System (ANFIS) methodology to integrate the capabilities of neural networks with that of fuzzy logic. A specialised extension to this technique is known as the genetic programming (GP) and gene expression programming (GEP) to explore and investigate the outcome of the GEP criteria on the stock market price prediction. The research presented in this thesis aims at the modelling and prediction of short-tomedium term stock value fluctuations in the market via genetically tuned stock market parameters. The technique uses hierarchically defined GP and gene-expressionprogramming (GEP) techniques to tune algebraic functions representing the fittest equation for stock market activities. The technology achieves novelty by proposing a fractional adaptive mutation rate Elitism (GEP-FAMR) technique to initiate a balance between varied mutation rates between varied-fitness chromosomes thereby improving prediction accuracy and fitness improvement rate. The methodology is evaluated against five stock market companies with each having its own trading circumstances during the past 20+ years. The proposed GEP/GP methodologies were evaluated based on variable window/population sizes, selection methods, and Elitism, Rank and Roulette selection methods. The Elitism-based approach showed promising results with a low error-rate in the resultant pattern matching with an overall accuracy of 95.96% for short-term 5-day and 95.35% for medium-term 56-day trading periods. The contribution of this research to theory is that it presented a novel evolutionary methodology with modified selection operators for the prediction of stock exchange data via Gene expression programming. The methodology dynamically adapts the mutation rate of different fitness groups in each generation to ensure a diversification II balance between high and low fitness solutions. The GEP-FAMR approach was preferred to Neural and Fuzzy approaches because it can address well-reported problems of over-fitting, algorithmic black-boxing, and data-snooping issues via GP and GEP algorithmsSaudi Cultural Burea

Review on recent advances in information mining from big consumer opinion data for product design

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design

The foundation of capability modelling: A study of the impact and utilisation of human resources

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research aims at finding a foundation for assessment of capabilities and applying the concept in a human resource selection. The research identifies a common ground for assessing individuals’ applied capability in a given job based on literature review of various disciplines in engineering, human sciences and economics. A set of criteria is found to be common and appropriate to be used as the basis of this assessment. Applied Capability is then described in this research as the impact of the person in fulfilling job requirements and also their level of usage from their resources with regards to the identified criteria. In other words how their available resources (abilities, skills, value sets, personal attributes and previous performance records) can be used in completing a job. Translation of the person’s resources and task requirements using the proposed criteria is done through a novel algorithm and two prevalent statistical inference techniques (OLS regression and Fuzzy) are used to estimate quantitative levels of impact and utilisation. A survey on post graduate students is conducted to estimate their applied capabilities in a given job. Moreover, expert academics are surveyed on their views on key applied capability assessment criteria, and how different levels of match between job requirement and person’s resources in those criteria might affect the impact levels. The results from both surveys were mathematically modelled and the predictive ability of the conceptual and mathematical developments were compared and further contrasted with the observed data. The models were tested for robustness using experimental data and the results for both estimation methods in both surveys are close to one another with the regression models being closer to observations. It is believed that this research has provided sound conceptual and mathematical platforms which can satisfactorily predict individuals’ applied capability in a given job. This research has contributed to the current knowledge and practice by a) providing a comparison of capability definitions and uses in different disciplines, b) defining criteria for applied capability assessment, c) developing an algorithm to capture applied capabilities, d) quantification of an existing parallel model and finally e) estimating impact and utilisation indices using mathematical methods

Modelling Credit Risk for SMEs in Saudi Arabia

The Saudi Government’s 2030 Vision directs local banks to increase and improve credit for the Small and Medium Enterprises (SMEs) of the economy (Jadwa, 2017). Banks are, however, still finding it difficult to provide credit for small businesses that meet Basel’s capital requirements. Most of the current credit-risk models only apply to large corporations with little constructed for SMEs applications (Altman and Sabato, 2007). This study fills this gap by focusing on the Saudi SMEs perspective. My empirical work constructs a bankruptcy prediction model based on logistic regressions that cover 14,727 firm-year observations for an 11-year period between 2001 and 2011. I use the first eight years data (2001-2008) to build the model and use it to predict the last three years (2009-2011) of the sample, i.e. conducting an out-of-sample test. This approach yields a highly accurate model with great prediction power, though the results are partially influenced by the external economic and geopolitical volatilities that took place during the period of 2009-2010 (the world financial crisis). To avoid making predictions in such a volatile period, I rebuild the model based on 2003-2010 data, and use it to predict the default events for 2011. The new model is highly consistent and accurate. My model suggests that, from an academic perspective, some key quantitative variables, such as gross profit margin, days inventory, revenues, days payable and age of the entity, have a significant power in predicting the default probability of an entity. I further price the risks of the SMEs by using a credit-risk pricing model similar to Bauer and Agarwal (2014), which enables us to determine the risk-return tradeoffs on Saudi’s SMEs

Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: a mini review

Abstract:Site suitability problems in renewable energy studies have taken a new turn since the advent of geographical information system (GIS). GIS has been used for site suitability analysis for renewable energy due to its prowess in processing and analyzing attributes with geospatial components. Multi-criteria decision making (MCDM) tools are further used for criteria ranking in the order of influence on the study. Upon location of most appropriate sites, the need for intelligent resource forecast to aid in strategic and operational planning becomes necessary if viability of the investment will be enhanced and resource variability will be better understood. One of such intelligent models is the adaptive neuro-fuzzy inference system (ANFIS) and its variants. This study presents a mini-review of GIS-based MCDM facility location problems in wind and solar resource site suitability analysis and resource forecast using ANFIS-based models. We further present a framework for the integration of the two concepts in wind and solar energy studies. Various MCDM techniques for decision making with their strengths and weaknesses were presented. Country specific studies which apply GIS-based method in site suitability were presented with criteria considered. Similarly, country-specific studies in ANFIS-based resource forecasts for wind and solar energy were also presented. From our findings, there has been no technically valid range of values for spatial criteria and the analytical hierarchical process (AHP) has been commonly used for criteria ranking leaving other techniques less explored. Also, hybrid ANFIS models are more effective compared to standalone ANFIS models in resource forecast, and ANFIS optimized with population-based models has been mostly used. Finally, we present a roadmap for integrating GIS-MCDM site suitability studies with ANFIS-based modeling for improved strategic and operational planning

Distance to default for Turkish banking sector

100 pagesThis thesis examines the riskiness of the Turkish Banking system analyzing 16 banks traded at Borsa Istanbul(BIST) Banking Index between 1996 and 2018 by using a structural approach known as the Merton Model. Also, whether the model is a good predictor of the financial crisis and financial failure is investigated. Since the literature is heavily dependent on accounting-based models and artificial intelligence models, the alternative measurement for riskiness for Turkish Banks is suggested. In this context, the distance to default based on Merton’s structural approach is measured and via suitable logit and probit model is converted to the default probability. Using these results, whether the model can be used as an early warning indicator for the crisis and bank failure is examined. According to the results, the logit and probit model is statistically significant at 1% level of significance up to 12 months. The results also show that DD, in the case of Turkish Banking Sector, can be useful as an early warning indicator for banking failure but, there is no evidence that it can be helpful to detect economic crisis.Bu tezde, 1996 ve 2018 yılları arasında BIST’te işlem görmüş 16 banka Merton Modeli olarak bilinen yapısal yaklaşım analiz edilerek Türk Bankacılık Sisteminin riskleri incelenmiştir. Ayrıca modelin finansal krizlerin ve finansal başarısızlıkların iyi bir tahmincisi olup olmadığı araştırılmıştır. Literatür, ağırlıklı olarak muhasebeye ve yapay zeka modellerine dayalı olduğundan, Türk Bankaları için alternatif risklilik ölçüm yöntemi önerilmiştir. Bu kapsamda Merton’un yapısal yaklaşımı kullanılarak, temerrüde olan uzaklık ölçülmeye çalışılmış ve probit ve logit regresyon aracılığıyla temerrüde olan uzaklık temerrüt olasılığına dönülmüştür. Bu sonuçlara göre temerrüde olan uzaklık ölçüsünün krizleri ve banka başarısızlıklarını açıklamada erken uyarı göstergesi olarak kullanılıp kullanılmayacağı analiz edilmiştir. Bulgulara göre, logit ve probit regresyonlar 12 aya kadar 1% önem düzeyinde istatistiksel olarak anlamlı çıkmıştır. Ayrıca sonuçlar, temerrüde olan uzaklığın finansal başarısızlıkları tahmin etmede anlamlı olduğunu krizlerin tahmin edilmesi için erken uyarı göstergesi olarak kullanılmasına yönelik yeterince kanıt olmadığını göstermiştir

Decision Support Systems for Risk Assessment in Credit Operations Against Collateral

With the global economic crisis, which reached its peak in the second half of 2008, and before a market shaken by economic instability, financial institutions have taken steps to protect the banks’ default risks, which had an impact directly in the form of analysis in credit institutions to individuals and to corporate entities. To mitigate the risk of banks in credit operations, most banks use a graded scale of customer risk, which determines the provision that banks must do according to the default risk levels in each credit transaction. The credit analysis involves the ability to make a credit decision inside a scenario of uncertainty and constant changes and incomplete transformations. This ability depends on the capacity to logically analyze situations, often complex and reach a clear conclusion, practical and practicable to implement. Credit Scoring models are used to predict the probability of a customer proposing to credit to become in default at any given time, based on his personal and financial information that may influence the ability of the client to pay the debt. This estimated probability, called the score, is an estimate of the risk of default of a customer in a given period. This increased concern has been in no small part caused by the weaknesses of existing risk management techniques that have been revealed by the recent financial crisis and the growing demand for consumer credit.The constant change affects several banking sections because it prevents the ability to investigate the data that is produced and stored in computers that are too often dependent on manual techniques. Among the many alternatives used in the world to balance this risk, the provision of guarantees stands out of guarantees in the formalization of credit agreements. In theory, the collateral does not ensure the credit return, as it is not computed as payment of the obligation within the project. There is also the fact that it will only be successful if triggered, which involves the legal area of the banking institution. The truth is, collateral is a mitigating element of credit risk. Collaterals are divided into two types, an individual guarantee (sponsor) and the asset guarantee (fiduciary). Both aim to increase security in credit operations, as an payment alternative to the holder of credit provided to the lender, if possible, unable to meet its obligations on time. For the creditor, it generates liquidity security from the receiving operation. The measurement of credit recoverability is a system that evaluates the efficiency of the collateral invested return mechanism. In an attempt to identify the sufficiency of collateral in credit operations, this thesis presents an assessment of smart classifiers that uses contextual information to assess whether collaterals provide for the recovery of credit granted in the decision-making process before the credit transaction become insolvent. The results observed when compared with other approaches in the literature and the comparative analysis of the most relevant artificial intelligence solutions, considering the classifiers that use guarantees as a parameter to calculate the risk contribute to the advance of the state of the art advance, increasing the commitment to the financial institutions.Com a crise econômica global, que atingiu seu auge no segundo semestre de 2008, e diante de um mercado abalado pela instabilidade econômica, as instituições financeiras tomaram medidas para proteger os riscos de inadimplência dos bancos, medidas que impactavam diretamente na forma de análise nas instituições de crédito para pessoas físicas e jurídicas. Para mitigar o risco dos bancos nas operações de crédito, a maioria destas instituições utiliza uma escala graduada de risco do cliente, que determina a provisão que os bancos devem fazer de acordo com os níveis de risco padrão em cada transação de crédito. A análise de crédito envolve a capacidade de tomar uma decisão de crédito dentro de um cenário de incerteza e mudanças constantes e transformações incompletas. Essa aptidão depende da capacidade de analisar situações lógicas, geralmente complexas e de chegar a uma conclusão clara, prática e praticável de implementar. Os modelos de Credit Score são usados para prever a probabilidade de um cliente propor crédito e tornar-se inadimplente a qualquer momento, com base em suas informações pessoais e financeiras que podem influenciar a capacidade do cliente de pagar a dívida. Essa probabilidade estimada, denominada pontuação, é uma estimativa do risco de inadimplência de um cliente em um determinado período. A mudança constante afeta várias seções bancárias, pois impede a capacidade de investigar os dados que são produzidos e armazenados em computadores que frequentemente dependem de técnicas manuais. Entre as inúmeras alternativas utilizadas no mundo para equilibrar esse risco, destacase o aporte de garantias na formalização dos contratos de crédito. Em tese, a garantia não “garante” o retorno do crédito, já que não é computada como pagamento da obrigação dentro do projeto. Tem-se ainda, o fato de que esta só terá algum êxito se acionada, o que envolve a área jurídica da instituição bancária. A verdade é que, a garantia é um elemento mitigador do risco de crédito. As garantias são divididas em dois tipos, uma garantia individual (patrocinadora) e a garantia do ativo (fiduciário). Ambos visam aumentar a segurança nas operações de crédito, como uma alternativa de pagamento ao titular do crédito fornecido ao credor, se possível, não puder cumprir suas obrigações no prazo. Para o credor, gera segurança de liquidez a partir da operação de recebimento. A mensuração da recuperabilidade do crédito é uma sistemática que avalia a eficiência do mecanismo de retorno do capital investido em garantias. Para tentar identificar a suficiência das garantias nas operações de crédito, esta tese apresenta uma avaliação dos classificadores inteligentes que utiliza informações contextuais para avaliar se as garantias permitem prever a recuperação de crédito concedido no processo de tomada de decisão antes que a operação de crédito entre em default. Os resultados observados quando comparados com outras abordagens existentes na literatura e a análise comparativa das soluções de inteligência artificial mais relevantes, mostram que os classificadores que usam garantias como parâmetro para calcular o risco contribuem para o avanço do estado da arte, aumentando o comprometimento com as instituições financeiras