Prediction of Banks Financial Distress

In this research we conduct a comprehensive review on the existing literature of prediction techniques that have been used to assist on prediction of the bank distress. We categorized the review results on the groups depending on the prediction techniques method, our categorization started by firstly using time factors of the founded literature, so we mark the literature founded in the period (1990-2010) as history of prediction techniques, and after this period until 2013 as recent prediction techniques and then presented the strengths and weaknesses of both. We came out by the fact that there was no specific type fit with all bank distress issue although we found that intelligent hybrid techniques considered the most candidates methods in term of accuracy and reputatio

Vcluster: A Portable Virtual Computing Library For Cluster Computing

Message passing has been the dominant parallel programming model in cluster computing, and libraries like Message Passing Interface (MPI) and Portable Virtual Machine (PVM) have proven their novelty and efficiency through numerous applications in diverse areas. However, as clusters of Symmetric Multi-Processor (SMP) and heterogeneous machines become popular, conventional message passing models must be adapted accordingly to support this new kind of clusters efficiently. In addition, Java programming language, with its features like object oriented architecture, platform independent bytecode, and native support for multithreading, makes it an alternative language for cluster computing. This research presents a new parallel programming model and a library called VCluster that implements this model on top of a Java Virtual Machine (JVM). The programming model is based on virtual migrating threads to support clusters of heterogeneous SMP machines efficiently. VCluster is implemented in 100% Java, utilizing the portability of Java to address the problems of heterogeneous machines. VCluster virtualizes computational and communication resources such as threads, computation states, and communication channels across multiple separate JVMs, which makes a mobile thread possible. Equipped with virtual migrating thread, it is feasible to balance the load of computing resources dynamically. Several large scale parallel applications have been developed using VCluster to compare the performance and usage of VCluster with other libraries. The results of the experiments show that VCluster makes it easier to develop multithreading parallel applications compared to conventional libraries like MPI. At the same time, the performance of VCluster is comparable to MPICH, a widely used MPI library, combined with popular threading libraries like POSIX Thread and OpenMP. In the next phase of our work, we implemented thread group and thread migration to demonstrate the feasibility of dynamic load balancing in VCluster. We carried out experiments to show that the load can be dynamically balanced in VCluster, resulting in a better performance. Thread group also makes it possible to implement collective communication functions between threads, which have been proved to be useful in process based libraries

HOME ENERGY MANAGEMENT SYSTEM FOR DEMAND RESPONSE PURPOSES

The growing demand for electricity has led to increasing efforts to generate and satisfy the rising demand. This led to suppliers attempting to reduce consumption with the help of the users. Requests to shift unnecessary loads off the peak hours, using other sources of generators to supply the grid while offering incentives to the users have made a significant effect. Furthermore, automated solutions were implemented with the help of Home Energy Management Systems (HEMS) where the user can remotely manage household loads to reduce consumption or cost. Demand Response (DR) is the process of reducing power consumption in a response to demand signals generated by the utility based on many factors such as the Time of Use (ToU) prices. Automated HEMS use load scheduling techniques to control house appliances in response to DR signals. Scheduling can be purely user-dependent or fully automated with minimum effort from the user. This thesis presents a HEMS which automatically schedules appliances around the house to reduce the cost to the minimum. The main contributions in this thesis are the house controller model which models a variety of thermal loads in addition to two shiftable loads, and the optimizer which schedules the loads to reduce the cost depending on the DR signals. The controllers focus on the thermal loads since they have the biggest effect on the electricity bill, they also consider many factors ignored in similar models such as the physical properties of the room/medium, the outer temperatures, the comfort levels of the users, and the occupancy of the house during scheduling. The DR signal was the hourly electricity price; normally higher during the peak hours. Another main part of the thesis was studying multiple optimization algorithms and utilizing them to get the optimum scheduling. Results showed a maximum of 44% cost reduction using different metaheuristic optimization algorithms and different price and occupancy schemes

The Designing Web Based Media “Active, Creative, Innovative, and Fun” Learning Process

In Government Regulation No.19 year 2005, it is stated that “ Learning process in an educational institution has to be conducted in interactive, inspirational, fun, challenging way, able to motivate the learners to participate actively, and give enough space for initiative, creativity, and independence based on the learners’ aptitude, interest, and physical development”. This development research tried to implement a learning process which fits the Government Regulation No.19 year 2005 web based media. This application is designed by using Joomla and has been visited by more than 19,000 visitors

Techniques for Stock Market Prediction: A Review

Stock market forecasting has long been viewed as a vital real-life topic in economics world. There are many challenges in stock market prediction systems such as the Efficient Market Hypothesis (EMH), Nonlinearity, complex, diverse datasets, and parameter optimization. A stock's value on the stock market fluctuates due to many factors like previous trends of the stock, the current news, twitter feeds, any online customer feedbacks etc. In this paper, the literature is critically analysed on approaches used for stock market prediction in terms of stock datasets, features used, evaluation metrics used, statistical, machine learning and deep learning techniques along with the directions for the future. The focus of this review is on trend and value prediction for stocks. Overall, 68 research papers have been considered for review from years 1998-2023. From the review, Indian stock market datasets are found to be most frequently used datasets. Evaluation metrics used commonly are accuracy and Mean Absolute Percentage Error. ARIMA is reported as the most used frequently statistical technique for stick market prediction. Long-Short Term Memory and Support Vector Machine are the commonly used algorithms in stock market prediction. The advantages and disadvantages of frequently used evaluation metrics, machine learning, deep learning and statistical approaches are also included in this survey

Investigating the Efficacy of Algorithmic Student Modelling in Predicting Students at Risk of Failing in the Early Stages of Tertiary Education: Case study of experience based on first year students at an Institute of Technology in Ireland.

The application of data analytics to educational settings is an emerging and growing research area. Much of the published works to-date are based on ever-increasing volumes of log data that are systematically gathered in virtual learning environments as part of module delivery. This thesis took a unique approach to modelling academic performance; it is a first study to model indicators of students at risk of failing in first year of tertiary education, based on data gathered prior to commencement of first year, facilitating early engagement with at-risk students. The study was conducted over three years, in 2010 through 2012, and was based on a sample student population (n=1,207) aged between 18 and 60 from a range of academic disciplines. Data was extracted from both student enrolment data maintained by college administration, and an online, self-reporting, learner profiling tool developed specifically for this study. The profiling tool was administered during induction sessions for students enrolling into the first year of study. Twenty-four factors relating to prior academic performance, personality, motivation, self-regulation, learning approaches, learner modality, age and gender were considered. Eight classification algorithms were evaluated. Cross validation model accuracies based on all participants were compared with models trained on the 2010 and 2011 student cohorts, and tested on the 2012 student cohort. Best cross validation model accuracies were a Support Vector Machine (82%) and Neural Network (75%). The k-Nearest Neighbour model, which has received little attention in educational data mining studies, achieved highest model accuracy when applied to the 2012 student cohort (72%). The performance was similar to its cross validation model accuracy (72%). Model accuracies for other algorithms applied to the 2012 student cohort also compared favourably; for example Ensembles (71%), Support Vector Machine (70%) and a Decision Tree (70%). Models of subgroups by age and by academic discipline achieved higher accuracy than models of all participants, however, a larger sample size is needed to confirm results. Progressive sampling showed a sample size \u3e 900 was required to achieve convergence of model accuracy. Results showed that factors most predictive of academic performance in first year of study at tertiary education included age, prior academic performance and self-efficacy. Kinaesthetic modality was also indicative of students at risk of failing, a factor that has not been cited previously as a significant predictor of academic performance. Models reported in this study show that learner profiling completed prior to commencement of first year of study yielded informative and generalisable results that identified students at risk of failing. Additionally, model accuracies were comparable to models reported elsewhere that included data collected from student activity in semester one, confirming the validity of early student profiling

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things