Company bankruptcy prediction framework based on the most influential features using XGBoost and stacking ensemble learning

Company bankruptcy is often a very big problem for companies. The impact of bankruptcy can cause losses to elements of the company such as owners, investors, employees, and consumers. One way to prevent bankruptcy is to predict the possibility of bankruptcy based on the company's financial data. Therefore, this study aims to find the best predictive model or method to predict company bankruptcy using the dataset from Polish companies bankruptcy. The prediction analysis process uses the best feature selection and ensemble learning. The best feature selection is selected using feature importance to XGBoost with a weight value filter of 10. The ensemble learning method used is stacking. Stacking is composed of the base model and meta learner. The base model consists of K-nearest neighbor, decision tree, SVM, and random forest, while the meta learner used is LightGBM. The stacking model accuracy results can outperform the base model accuracy with an accuracy rate of 97%

Algoritma Berkebarangkalian dalam Pengoptimuman (Author from Kolej Universiti Sains dan Teknologi Malaysia)

This article discusses the efficiency of the probabilistic algorithm for determining the optimal point of global optimization problems. Randomness and normality tests were conducted to verify that the optimization problem constitutes a Wiener process. Furthermore, two numerical examples are given with different gamma values to illustrate the efficiency of the algorithm

Application of fuzzy AHP method based on eigenvalues for decision making in transportation problems

There is extensive evidence that current environmental systems are at alarming level of degradations which can cause unfavourable impacts on human life and other interacting ecosystems. Consequently, firms were driven to review their current practices and identify any opportunity of introducing green initiatives in their operations to support the ability of current generations to meet their needs without compromising the ability of meeting needs of future generations. The strategic incorporation of environmental concerns in firm’s innovation efforts has been recognized to have great potential for supporting sustainable development (Xavier, 2015). Ecological innovation (eco-innovation) is one of the indispensable strategies for firms to overcome the increasing environmental challenges and contribute to achieve improved and sustainable environment. According to Tarnawska (2013), eco-innovations are basic sources of ecological growth and efficiency which resulted in technological improvements and greater sustainability. Thus, over the past two decades, firms were actively searching for the secret recipe on how to integrate the principles of eco-innovation into their long and short-term strategies

An Improvement of Numerical Result of Crashing CPM/PERT Network

The research introduces and develops a mathematical modeling technique with linearized Taylor’s first order expansion and solve by using the simplex method. The main objective is to minimize the pessimistic time of the activity which is lie on the critical path by investing additional amounts of money to the project. 7 different amounts of money which is $5000,$10000, $15000,$20000, $25000,$30000 and $35000 will be invest to the project, to show the increase amount of money invest in the project will tend to minimize the pessimistic time to decrease the expected time and project duration. Then at the same time, it is also reduces its variance and standard deviation. As the result of the research, it will bring to the increase of the probability or percentage of completing the project on or before the completion time. The PERT and normal distribution will display the differences between of the amounts of money that will invest to the project

An Improvement of Computing Newton’s Direction for Finding Unconstrained Minimizer for Large-Scale Problems with an Arrowhead Hessian Matrix

In large-scale problems, classical Newton’s method requires solving a large linear system of equations resulting from determining the Newton direction. This process often related as a very complicated process, and it requires a lot of computation (either in time calculation or memory requirement per iteration). Thus to avoid this problem, we proposed an improved way to calculate the Newton direction using an Accelerated Overrelaxation (AOR) point iterative method with two different parameters. To check the performance of our proposed Newton’s direction, we used the Newton method with AOR iteration for solving unconstrained optimization problems with its Hessian is in arrowhead form and compared it with a combination of the Newton method with Gauss-Seidel (GS) iteration and the Newton method with Successive Over Relaxation (SOR) iteration. Finally, comparison results show that our proposed technique is significantly more efficient and more reliable than reference methods

Solving one-dimensional unconstrained global optimization problem using parameter free filled function method

It is generally known that almost all filled function methods for one-dimensional unconstrained global optimization problems have computational weaknesses. This paper introduces a relatively new parameter free filled function, which creates a non-ascending bridge from any local isolated minimizer to other first local isolated minimizer with lower or equal function value. The algorithm’s unprecedented function can be used to determine all extreme and inflection points between the two considered consecutive local isolated minimizers. The proposed method never fails to carry out its job. The results of the several testing examples have shown the capability and efficiency of this algorithm while at the same time, proving that the computational weaknesses of the filled function methods can be overcomed

Islamic Economic and Banking : A Scientrometric Analysis of Publications During 1989-July 2023

This Scientometric analysis examines the current status of broad research papers on Islamic Economic and Banking, as published in the Scopus database. The analysis primarily centers on providing descriptions pertaining to the characteristics and trends observed within keywords, authors, and journals. A total of 48 research studies were analyzed in this study. The search utilized to ascertain the research dataset was last updated in July 2023. Descriptive statistical methods were employed, and bibliometric analysis was conducted using the R Biblioshiny tool to ascertain the bibliometric map. In recent years, there has been a substantial increase in the quantity of scholarly articles addressing the topic of Islamic Economic and Banking. Several academic journals publish research on this theme, with Humanomics being one of the most prominent. Demiralp S. is widely regarded as an exceptionally productive writer with local impact by H index. The prevalent keywords observed over the timeframe including 2020 to July 2023 are Communism, Christianity, Capitalism, AAOIFI (The Accounting and Auditing Organization for Islamic Financial Institutions), Moral Economics, and Islamic socialism. This study offers a comprehensive analysis of prevailing patterns in subject matter, keywords, scholarly publications, and authors within the realm of Islamic Economic and Banking. Consequently, it furnishes valuable insights for researchers specializing in this area of study. This particular theme shows potential for further development

Financial performance of public listed private healthcare in Malaysia using data envelopment analysis (DEA)

The healthcare system in Malaysia has a dual-tiered which are a government-led, also known as public hospital and private hospital that funded by organization or company (Quek, 2014). The public sector wholly subsidized by the government with patients paying only nominal fees for admission to both outpatients and hospitalizations. Thus, the mass of patients led to longer waiting time, heavy workload (Pillay et al., 2011) and overworked (Ren, 2007)

An alternative approach for finding Newton's direction in solving large-scale unconstrained optimization for problems with an arrowhead Hessian matrix

In this paper, we proposed an alternative way to find the Newton direction in solving large-scale unconstrained optimization problems where the Hessian of the Newton direction is an arrowhead matrix. The alternative approach is a two-point Explicit Group GaussSeidel (2EGGS) block iterative method. To check the validity of our proposed Newton’s direction, we combined the Newton method with 2EGGS iteration for solving unconstrained optimization problems and compared it with a combination of the Newton method with Gauss-Seidel (GS) point iteration and the Newton method with Jacobi point iteration. The numerical experiments are carried out using three different artificial test problems with its Hessian in the form of an arrowhead matrix. In conclusion, the numerical results showed that our proposed method is more superior than the reference method in term of the number of inner iterations and the execution time