Almost all of the today’s modern daily life conditions of humankind depend on the electricity. The countries either by only themselves or sometimes with some international intuitions and/or organizations have been trying to find the best methods, ways, and projects to supply the electricity to their societies. One of the important tools for the countries to increase the amount and quality of the electricity generation is to activate/ignite/initiate the private investment capabilities/opportunities. The electricity generation market in Turkey is a free/open market for both the foreign and domestic private investors. Hence, both the foreign and domestic private investors have been looking for the most suitable electricity generation plant projects. Small hydropower plant (SHPP) investments (SHPPIs) are one of the alternatives in the Turkish electricity generation market especially for the private investors searching for the renewable energy investments. This experimental research study investigates the possibility of using the ELECTRE III/IV, Shannon’s Entropy, and Saaty’s Analytic Hierarchy Process (AHP) subjective weighting (for criteria) methods for the solution of this problem. In the experimental case study, the most appropriate SHPPIs amongst five alternative SHPPIs at the SHPPIs’ predevelopment investment stages in Turkey were evaluated and ranked in order.

https://downloads.hindawi.com/archive/2015/548460.pdf

An Experimental Research Study on the Solution of a Private Small Hydropower Plant Investments Selection Problem by ELECTRE III/IV, Shannon’s Entropy, and Saaty’s Subjective Criteria Weighting

Cancer is one of the most common diseases worldwide and its treatment is a complex and time-consuming process. Specifically, prostate cancer as the most common cancer among male population has received the attentions of many researchers. Oncologists and medical physicists usually rely on their past experience and expertise to prescribe the dose plan for cancer treatment. The main objective of dose planning process is to deliver high dose to the cancerous cells and simultaneously minimize the side effects of the treatment. In this article, a novel TOPSIS case based reasoning goal-programming approach has been proposed to optimize the dose plan for prostate cancer treatment. Firstly, a hybrid retrieval process TOPSIS–CBR [technique for order preference by similarity to ideal solution (TOPSIS) and case based reasoning (CBR)] is used to capture the expertise and experience of oncologists. Thereafter, the dose plans of retrieved cases are adjusted using goal-programming mathematical model. This approach will not only help oncologists to make a better trade-off between different conflicting decision making criteria but will also deliver a high dose to the cancerous cells with minimal and necessary effect on surrounding organs at risk. The efficacy of proposed method is tested on a real data set collected from Nottingham City Hospital using leave-one-out strategy. In most of the cases treatment plans generated by the proposed method is coherent with the dose plan prescribed by an experienced oncologist or even better. Developed decision support system can assist both new and experienced oncologists in the treatment planning process.

/pdf/a-novel-topsis-cbr-goal-programming-approach-to-sustainable-1r5u9ha58x.pdf

A novel TOPSIS–CBR goal programming approach to sustainable healthcare treatment

Dose planning of prostate cancer is a complex and time-consuming process. Usually, oncologists use past experience and spend a large amount of time to determine the optimal combination of dose in phase I and II of treatment. In this article, a novel TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) case-based reasoning (CBR) approach is proposed to capture the past experience and expertise of oncologists. Initially, cases that resemble new case are extracted from database. Thereafter, inferred cases are evaluated using TOPSIS, a multi-criteria decision-making approach to prescribe an optimal dose plan. Robustness of the proposed method is validated on data sets collected from the City Hospital Campus, Nottingham University Hospitals, NHS, UK, using leave-one-out strategy. In experiment, the proposed methodology outperformed CBR approach. It also endorses the suitability of multi-criteria decision-making approach. This method will help oncologists to make a better trade-off be...

An efficient approach to radiotherapy dose planning problem: a TOPSIS case-based reasoning approach

In order to define and implement the most effective measures to overcome the difficulties of the post-crisis period, the policy-makers of ECB must identify not just main weaknesses of each banking system, but their strong points also. This requires the application of multi-criteria analysis, considering that policy-makers need to take into account a number of different aspects that, on the whole, indicate the quality of the banking system. Our aim is a comparative analysis of European banking systems right after the Brexit moment and within the framework of the tight new Basel III regulations. In this paper, we have ranked the banking systems of the 28 EU member states using multi-criteria analysis, specifically the PROMETHEE II method. The use of the PROMETHEE II method in combination with the entropy method offers a comprehensive insight into the banking system of each member state, given that the observed countries are ranked according to 9 conflicting criteria that are mostly used in banking system analysis. Our analysis shows that the banking systems in Central and Eastern Europe are the best performers, while the EMU’s developed banking systems such as the German, Italian, British, and French one are positioned among the last ranked. The Portuguese and Greek banking systems are, as expected, ranked in the last positions in our list. The obtained results also pointed out that the ECB should change its approach to the management and further development of a European Banking Union.

/pdf/ranking-of-eu-national-banking-systems-using-multi-criteria-2xno9f70hw.pdf

Ranking of EU National Banking Systems Using Multi-Criteria Analysis in the Light of Brexit

In this work we propose an approach of multi-criteria decision making (MCDM) using Elimination Et Choice Transiting Reality (ELECTRE) methods, interval-valued intuitionistic fuzzy (IVIF) sets and possibility theory. The proposition concerns the computation of concordance sets and discordance sets using possibility measures. The proposed approach is applied to select the best investment projects decision problem from literature. Therefore results are compared and concluding remarks are given.

ELECTRE method using interval-valued intuitionistic fuzzy sets and possibility theory for multi-criteria decision making problem resolution

Application of Group Decision Support System (GDSS) can assist for delivering the decision of various opinions (preference) cancer detection based on the preferences of various expertise. In this paper we propose ELECTRE-Entropy for GDSS Modeling. We propose entropy weighting for each criteria under ELECTRE Method.ELECTRE is one method in Multi-Attribute Decision Making (MADM). Modeling of Group Decision Support Sytemapplyfor multi-criteria which the simulation data mutated genes that can cause cancer and solution recommended.

/pdf/electre-entropy-method-in-group-decision-support-system-41ax2bbu28.pdf

ELECTRE-Entropy method in Group Decision Support System Modelto Gene Mutation Detection

• A new sales forecasting model that integrates Best-Worst and k-Means methods. • Best-Worst Method is utilized to extract significant forecasting criteria. • K-means method is applied to improve the quality of product clustering results. • The RFM model is used to assess the product. Inaccurate sales forecasting (SF) leads to over-stock or stock-out, in which can increase inventory costs, thereby reducing profits and return on investment. Furthermore, stock-out can eliminate customer loyalty, as well as opportunities to acquire new customers, and maximize sales or competitive advantage. This study proposed a retail SF model called the SalesKBR, which integrates the decision-making (Best-Worst Method/BWM) and the data mining methods (k-Means) into the Recency-Frequency-Monetary (RFM) model. The BWM was used to extract criteria that have a significant influence on retail SF. The k-Means method and six validity indices were applied to improve the quality of the product clustering results, while the RFM model was used for assessment. The extraction results from BWM that correlate with the retail company databases showed the criteria that significantly affect SF are frequency, quantity, and monetary. The results also showed that SalesKBR is a retail SF model with a reasonable level of accuracy. Therefore, it can be utilized to forecast retail sales, and it has the potential to be an alternative solution for making scientific and valuable management decisions. 

An approach for sales forecasting

Detection of gene mutation is an activity that can provide contribution in the medical field. Detection of mutated gene is needed to avoid the diseases caused by them such as cancer. The detection of gene mutations can be performed by utilizing computer-based system. Group Decision Support System (GDSS) is a computer-based system that can be utilized in detecting human gene mutations that cause disease. The ELECTRE method, which is a Multi-Attribute Decision Making, is a method in modeling multi-criteria GDSS. In this paper we propose a model for multi-criteria GDSS in which the simulation data is the mutated genes that can cause cancer.

MADM methods in solving group decision support system on gene mutations detection simulation

Detection of gene mutation is an activity that can provide contribution in the medical field Detection of mutated gene is needed to avoid the diseases caused by them such as cancer The detection of gene mutations can be performed by utilizing computer-based system Group Decision Support System (GDSS) is a computer-based system that can be utilized in detecting human gene mutations that cause disease The ELECTRE method, which is a Multi-Attribute Decision Making, is a method in modeling multi-criteria GDSS In this paper we propose a model for multi-criteria GDSS in which the simulation data is the mutated genes that can cause cancer

/pdf/madm-methods-in-solving-group-decision-support-system-on-5fl7esgr4r.pdf

Identifying student learning outcomes data in a subject to predict student performance is interesting research. This study was conducted to identify the factors that affect student performance by applying feature selection techniques based on Particle Swarm Optimization (FSPSO) and feature selection techniques based on Genetic Algorithm (FSGA) on the Machine Learning algorithm, namely Support Vector Regression (SVR). The data used are students' personal data and data on the academic value of Mathematics in Portugal. Experiments were carried out by applying the PSO feature selection (FSPSO) and GA (FSGA) feature selection techniques to obtain the selected features. Then the prediction modeling of student performance was carried out using SVR. The experiment was carried out using the pycharm 2021.1 application with the python programming language. This study uses a 10-fold cross-validation method. Evaluation of student performance prediction modeling is done by comparing the error values of regression performance metrics and processing time with feature selection and without feature selection. The experimental results show that FSPSO-SVR modeling compared to SVR modeling, the error reduction of the regression performance metric on RMSE is 34.36%, while for MAE it is 45.09% in 500 iterations. In FSGA-SVR modeling compared to SVR modeling, the error reduction of the regression performance metric in RMSE is 1.59% %, while the MAE is 3.04% at iteration 50. The improvement of the time process value in the FSPSO-SVR modeling compared to the SVR modeling, resulted in a decrease in process time of 44.68%. while the FSGA-SVR modeling compared to SVR modeling, resulted in a decrease in process time of 33.33%. From the comparison of experimental results, it can be concluded that the FSPSO-SVR modeling is the best predictive modeling of student performance with the lowest error value and the fastest time process in 500 iterations, so it can be used as a reference in making decisions to improve student performance in the future.

Implementation of Feature Selection Based on Particle Swarm Optimization and Genetic Algorithm on Support Vector Regression Algorithm to Predict Student Performance

Ermatita

Papers

ELECTRE-Entropy method in Group Decision Support System Modelto Gene Mutation Detection

An approach for sales forecasting

MADM methods in solving group decision support system on gene mutations detection simulation

MADM methods in solving group decision support system on gene mutations detection simulation

Implementation of Feature Selection Based on Particle Swarm Optimization and Genetic Algorithm on Support Vector Regression Algorithm to Predict Student Performance