Integration Of Logistic Regression And Multi-Layer Perceptron For Single And Dual Axis Solar Tracking Systems

Intelligent solar tracking systems to track the trajectory of the sun across the sky has been actively studied and proposed nowadays. However, different solar tracking variables have been employed to build those intelligent solar tracking systems without considering the dominant and optimum ones. In addition, several low performance intelligent solar tracking systems have been designed and implemented due to the inappropriate combination of solar tracking variables and intelligent classifiers to drive the solar trackers. Thus, this research aims to (i) investigate and evaluate the most effective and dominant variables on solar tracking systems, (ii) investigate the appropriate combination of solar variables and intelligent classifier for solar tracking systems, (iii) propose new solar tracking systems by integrating supervised and unsupervised intelligent classifiers. The results revealed that month, day, and time are the most effective variables for single and dual axis solar tracking systems. By using these variables, this study has successfully integrated between multi-layer perceptron (MLP) or cascade multi-layer perceptron (CMLP) and trained logistic regression (LR) models. The proposed MLP-LR system is able to increase the prediction rate of MLP network to 99.13% for single axis tracking systems (i.e. which is 2.35% of improvement). The system is also able to decrease the mean square error (MSE) rate to 0.010 × 10−2 as compared to value of MSE for the conventional MLP. In addition, the proposed CMLP-LR system is able to increase the prediction rate of CMLP network to 99.19% for dual axis tracking system (i.e. 1.23% of improvement), while the MSE rate is decreased to 6.250 × 10−5 as compared to value of MSE for the conventional CMLP. The new developed models achieved less number of overall connections (i.e. which are 77.58% and 86.16% of improvement for MLP and CMLP respectively), less number of neurons (i.e. 63.51% of improvement for both MLP and CMLP), and less time complexity (i.e. which are 70.40% and 99% of improvement for MLP and CMLP respectively). The finding suggests that the proposed intelligent solar tracking systems has a great potential to be applied for real-world applications (i.e. solar heating systems, solar lightening systems, factories, and solar powered ventilation)

Применение метода отбора признаков для долгосрочного прогноза индекса Амманской фондовой биржи

Фондовые биржи — неотъемлемая часть мировой экономики; благодаря отслеживанию ежедневных операций, фондовые индексы отражают изменения показателей деятельности представленных на финансовом рынке фирм. Для построения модели прогнозирования фондового индекса Иордании в данной статье исследованы факторы, напрямую влияющие на индекс фондовой биржи. Чтобы выявить, какие секторы экономики оказывают наибольшее влияние на модель прогнозирования, авторы применили четыре метода отбора признаков для изучения связи между 23 секторами и индексом Амманской фондовой биржи (ASEI100) за период 2008–2018 гг. В каждой модели были выделены 10 наиболее значимых факторов, которые затем они были объединены и внесены в таблицу частот. Для проверки достоверности основных факторов, которые наиболее часто встречались в четы- рех моделях, а также для оценки их влияния на ASEI использовались методы линейной регрессии и обычных наименьших квадратов. Результаты исследования показали, что существует шесть основных секторов, непосредственно влияющих на общий фондовый индекс в Иордании: здравоохранение, горнодобывающая промышленность, производство одежды, текстиля и изделий из кожи, недвижимость, финансовые услуги, транспорт. Показатели этих секторов можно использовать для прогнозирования изменений индекса Амманской фондовой биржи в Иордании. Кроме того, линейная регрессия выявила статистически значимую взаимосвязь между шестью секторами (независимые переменные) и ASEI (зависимая переменная). Полученные результаты, описывающие наиболее важные секторы экономики Иордании, могут быть использованы инвесторами для принятия инвестиционных решений

Применение метода отбора признаков для долгосрочного прогноза индекса Амманской фондовой биржи

Фондовые биржи — неотъемлемая часть мировой экономики; благодаря отслеживанию ежедневных операций, фондовые индексы отражают изменения показателей деятельности представленных на финансовом рынке фирм. Для построения модели прогнозирования фондового индекса Иордании в данной статье исследованы факторы, напрямую влияющие на индекс фондовой биржи. Чтобы выявить, какие секторы экономики оказывают наибольшее влияние на модель прогнозирования, авторы применили четыре метода отбора признаков для изучения связи между 23 секторами и индексом Амманской фондовой биржи (ASEI100) за период 2008–2018 гг. В каждой модели были выделены 10 наиболее значимых факторов, которые затем они были объединены и внесены в таблицу частот. Для проверки достоверности основных факторов, которые наиболее часто встречались в четы- рех моделях, а также для оценки их влияния на ASEI использовались методы линейной регрессии и обычных наименьших квадратов. Результаты исследования показали, что существует шесть основных секторов, непосредственно влияющих на общий фондовый индекс в Иордании: здравоохранение, горнодобывающая промышленность, производство одежды, текстиля и изделий из кожи, недвижимость, финансовые услуги, транспорт. Показатели этих секторов можно использовать для прогнозирования изменений индекса Амманской фондовой биржи в Иордании. Кроме того, линейная регрессия выявила статистически значимую взаимосвязь между шестью секторами (независимые переменные) и ASEI (зависимая переменная). Полученные результаты, описывающие наиболее важные секторы экономики Иордании, могут быть использованы инвесторами для принятия инвестиционных решений

Optimized deep neural network to estimate orientation angles for solar photovoltaics intelligent systems

Using a single hidden layer neural network in estimating orientation angles for solar photovoltaics lacks the complexity required to model nonlinear relationships between input variables and the optimal orientation angles for solar photovoltaics. It struggles to generalize well to new and unseen data. More sophisticated neural network architectures such as deep learning with multi-hidden perceptron (MLP) can solve these issues by changing the architecture by deepening the network. Deepening the network will increase complexity, energy consumption, and time complexity. The study uses a novel approach to outperform traditional MLP models with two, three, four, and five hidden layers. An innovative approach was proposed by enhancing a single hidden layer MLP with a quadratic polynomial function, utilizing two robust methodologies, Least Absolute Residuals (LAR) and Bisquare methods. The results demonstrate that these approaches yield significant improvements in Root Mean Square Error (RMSE) and coefficient of determination (R squared). LAR-based MLP showed superiority over both bisquare-based and conventional MLPs methods in R2 and RMSE, ranging from 1.13 to 1.18 and 2.53 to 3.06, respectively. The study outperformed conventional MLP architectures with five hidden layers regarding accuracy and efficiency. The proposed model offers a more effective and less complex solution for data prediction tasks

Machine Learning to Develop Credit Card Customer Churn Prediction

The credit card customer churn rate is the percentage of a bank’s customers that stop using that bank’s services. Hence, developing a prediction model to predict the expected status for the customers will generate an early alert for banks to change the service for that customer or to offer them new services. This paper aims to develop credit card customer churn prediction by using a feature-selection method and five machine learning models. To select the independent variables, three models were used, including selection of all independent variables, two-step clustering and k-nearest neighbor, and feature selection. In addition, five machine learning prediction models were selected, including the Bayesian network, the C5 tree, the chi-square automatic interaction detection (CHAID) tree, the classification and regression (CR) tree, and a neural network. The analysis showed that all the machine learning models could predict the credit card customer churn model. In addition, the results showed that the C5 tree machine learning model performed the best in comparison with the three developed models. The results indicated that the top three variables needed in the development of the C5 tree customer churn prediction model were the total transaction count, the total revolving balance on the credit card, and the change in the transaction count. Finally, the results revealed that merging the multi-categorical variables into one variable improved the performance of the prediction models

Hybrid grey wolf and whale optimization for enhanced Parkinson's prediction based on machine learning models using biomedical sound

Background: Biomedical voice measurements have been used by many physicians and scientists to distinguish Parkinson's patients from ordinary people. Measurements of biomedical voices involve many variables calculated from signal analysis of the voice. These variables can be used to distinguish Parkinson's patients from non-Parkinson's patients. Unfortunately, using all computed variables may be ineffective and inaccurate due to the complexity of establishing a relationship between all the input variables and Parkinson's states. Methods: This paper describes the development of a hybrid optimizer by combining two optimization algorithms: the Grey Wolf Optimizer and the Whale Optimizer. The hybrid optimizer enhances feature selection to provide a fast and robust Parkinson's prediction model. Additionally, this research incorporates five other feature selection algorithms for comparison purposes: Ranker, Greedy, BestFirst, Hybrid Grey Wolf Optimization, and Whale Optimization. The outputs from these algorithms are fed into six prediction models to determine the most accurate combination. These models include the neural network, Quest, Chi-squared Automatic Interaction Detection, support vector machine, CR-tree, and logistic regression models. Subsequently, the developed models are compared to identify the most accurate model based on various performance metrics. Results: The combination of the hybrid grey wolf and whale models yielded the highest scores in most metrics, achieving a perfect recall and a high F1 score. All models generated similar output, with an accuracy greater than 0.89. Quest, CR-tree, and neural networks are the most reliable and accurate models. Conclusions: Biomedical sound measurements can be used to develop an accurate and cost-effective Parkinson's prediction model

The impact of Russo-Ukrainian war, COVID-19, and oil prices on global food security

Context: Light of recent global upheavals, including volatile oil prices, the Russo-Ukrainian conflict, and the COVID-19 pandemic this study delves into their profound impact on the import and export dynamics of global foodstuffs. With rising staple food prices reminiscent of the 2010–2011 global food crisis, understanding these shifts comprehensively is imperative. Objective: Our objective is to evaluate this impact by examining six independent variables (year, month, Brent crude oil, COVID-19, the Russo-Ukrainian conflict) alongside six food indicators as dependent variables. Employing Pearson's correlation, linear regression, and seasonal autoregressive integrated moving averages (SARIMA), we scrutinize intricate relationships among these variables. Results and conclusions: Our findings reveal varying degrees of association, notably highlighting a robust correlation between Brent crude oil and food indicators. Linear regression analysis suggests a positive influence of the Russo-Ukrainian conflict, Brent oil on food price indices, and COVID-19. Furthermore, integrating SARIMA enhances predictive accuracy, offering insights into future projections. Significance: Finally, this research has a significant role in providing a valuable analysis into the intricate dynamics of global food pricing, informing decision-making amidst global challenges and bridging critical gaps in prior research on forecasting food price indices

Can international market indices estimate tasi's movements? The ARIMA model

This study investigates the effectiveness of six of the key international indices in estimating Saudi financial market (TADAWUL) index (TASI) movement. To investigate the relationship between TASI and other variables, six equations were built using two independent variables of time and international index, while TASI was the dependent variable. Linear, logarithmic, quadratic, cubic, power, and exponential equations were separately used to achieve the targeted results. The results reveal that power equation is the best equation for forecasting the TASI index with a low error rate and high determination coefficient. Additionally, findings of the AutoRegressive Integrated Moving Average (ARIMA) model represent the most important variables to use in order to build a prediction model that can estimate the TASI index. The ARIMA model (with Expert Modeler) coefficients are described as ARIMA (0,1,14). The results show that the SP500, NIKKEI, CAC40, and HSI indices are the most suitable variables for estimating TASI with an R2 and RMSE equal to 0.993 and 113, respectively. This relationship can be used on the previous day to estimate the opening price of TASI based on the closing prices of international indices