Similarity measures for Fermatean fuzzy sets and its applications in group decision-making

The intention of this paper is to propose some similarity measures between Fermatean fuzzy sets (FFSs). Firstly, we propose some score based similarity measures for finding similarity measures of FFSs and also propose score based cosine similarity measures between FFSs. Furthermore, we introduce three newly scored functions for effective uses of Fermatean fuzzy sets and discuss some relevant properties of cosine similarity measure. Fermatean fuzzy sets introduced by Senapati and Yager can manipulate uncertain information more easily in the process of multi-criteria decision making (MCDM) and group decision making. Here, we investigate score based similarity measures of Fermatean fuzzy sets and scout the uses of FFSs in pattern recognition. Based on different types of similarity measures a pattern recognition problem viz. personnel appointment is presented to describe the use of FFSs and its similarity measure as well as scores. The counterfeit results show that the proposed method is more malleable than the existing method(s). Finally, concluding remarks and the scope of future research of the proposed approach are given.

Parametric analysis of dry machining process using a novel integrated multi-attribute decision making approach

In most of the machining processes, the complexity arises in the selection of the right process parameters, which influence the machining process and output responses such as machinability and surface roughness. In such situations, it is important to estimate the inter-relationships among the output responses. One such method, Decision-Making Trial and Evaluation Laboratory (DEMATEL) is applied to study the inter-relationships of the output responses. Estimation of proper weights is also crucial where the output responses are conflicting in nature. In the current study, DEMATEL technique is used for estimating the inter-relationships for output responses in machining of EN 24 alloy under dry conditions. CRiteria Importance Through Inter-criteria Correlation (CRITIC) method is used to estimate the weights and finally the optimal selection of machining parameters is carried out using Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The model developed guides the decision maker in selection of precise weights, estimation of the inter relationships among the responses and selection of optimal process parameters.

Decision making of maritime development scenario on the impact of naval base for supporting navy ships operations

The Indonesian Navy is the spearhead in maintaining maritime security in Indonesian waters. In carrying out its main tasks, the Indonesian Navy has components of an Integrated Fleet Weapon System in which there are elements of Ships and Naval Bases. To ensure the effectiveness of carrying out operations by ship elements, ship operations are supported by the Naval Base as the organizer of the support function. Naval Base's carrying capacity consists of 5 (five) support functions, including: (1) support for anchoring facilities; (2) support for supply facilities; (3) support for maintenance and repair facilities; (4) support facility maintenance personnel; and (5) support for base development facilities. Naval Base does not yet have its dock to support anchoring facilities for ship operations. In addition to cooperation in the use of the Naval Base anchorage facility, there is also cooperation in port security, both in terms of land and port water aspects. As the number of ship visits at Naval Base Harbor increases, the dock utility increases. The increase in dock utility resulted in a decrease in port services which also resulted in a decrease in the Naval Base Carrying Capacity. To improve port services, Pelindo III implements the port development program contained in the Naval Base Port Master Plan in Permen KP number 792 of 2017. In this study, an analysis of the impact of the Naval Base Port development policy on the carrying capacity of the Naval Base was carried out. The data analysis uses System Dynamics modeling with a simulation period of 30 years in 3 development scenarios, namely short-term scenarios, medium-term scenarios, and long-term scenarios. From the simulation results, it is found that the construction of the Naval Base port affects the Naval Base Carrying Capacity with an average increase of 1.8% in each policy scenario. The increase in Naval Base Carrying Capacity has an effect on increasing Ship Operations by an average of 1.8% and also increasing the Security of Naval Base Harbor by an average of 0.14%. The results of the analysis of this study can be used as consideration for policymaking by the Navy.

Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network

COVID-19 has spread to more than a hundred countries worldwide since the first case reported in late 2019 in Wuhan, China. As one of the countries affected by the spread of COVID-19 cases, the local government of Malaysia has issued several policies to reduce the spread of this outbreak. One of the measures taken by the Malaysian government, namely the Movement Control Order, has been carried out since March 18, 2020. In order to provide precise information to the government so that it can take the appropriate measures, many researchers have attempted to predict and create the model for these cases to identify the number of cases each day and the peak of this pandemic. Therefore, hospitals and health workers can anticipate a surge in COVID-19 patients. In this research, confirmed, recovered, and death cases prediction was performed using the neural network as one of the machine learning methods with high accuracy. The neural network model used is the Multi-Layer Perceptron, Neural Network Auto-Regressive, and Extreme Learning Machine. The three models calculated the average percentage error (APE) values for 7 days and obtained APE values for most cases less than 10%; only 1 case in the last day of one method had an APE value of approximately 11%. Furthermore, based on the best model, then the forecast is made for the next 7 days. In conclusion, this study identified that the MLP model is the best model for 7-step ahead forecasting for confirmed, recovered, and death cases in Malaysia. However, according to the result of testing data, the ELM performs better than the MLP model.

Optimal selection of an electric power wheelchair using an integrated COPRAS and EDAS approach based on Entropy weighting technique

The decision to purchase the best available electric power wheelchair (EPWC) for a person with a disability in a low-resource context is very stressful, whether it is based on financial circumstances or the availability of medical solutions. The study's objective is to assess the EPWC options available on the market, focused on a set of conflicting criteria. In this research, three multi-criteria decision-making (MCDM) approaches are used to make decisions. ENTROPY method for weightage calculation of various parameters, COPRAS and EDAS methods for evaluating and ranking alternatives are applied. Both COPRAS and EDAS are applied separately for ranking of selected wheelchair models, and to check the robustness of the applied method, sensitivity analysis on cost criterion is carried out. The result shows that for both methods, EPWC-1 is the top priority model to buy, whereas EPWC-7 is the worst model for COPRAS, and EPWC-10 is the worst model for EDAS among the ten alternatives.

Binary social group optimization algorithm for solving 0-1 knapsack problem

In this paper, we propose the binary version of the Social Group Optimization (BSGO) algorithm for solving the 0-1 knapsack problem. The standard Social Group Optimization (SGO) is used for continuous optimization problems. So a transformation function is used to convert the continuous values generated from SGO into binary ones. The experiments are carried out using both low-dimensional and high-dimensional knapsack problems. The results obtained by the BSGO algorithm are compared with other binary optimization algorithms. Experimental results reveal the superiority of the BSGO algorithm in achieving a high quality of solutions over different algorithms and prove that it is one of the best finding algorithms especially in high-dimensional cases.

Assessing the forecasting model ability in measuring the prevention transmission of COVID-19 pandemic: An application of visibility analysis using Inductive logic

Forecasting is an integral approach due to its ability to make informed act decisions and develop data-driven strategies. It's also used to make decisions related to current circumstances and predictions on future conditions. An integral part has been developed using visibility analysis for COVID-19 Outbreak, a lesson from Indonesia. The author identified that its topic has limited attention, especially in assessing the forecasting models. The issue comes from predicted results that are questionable or cannot be trusted without applying the visibility analysis in the forecasting model. The visibility analysis is required to assess the model's ability to forecast future events. In conjunction with the issue, this paper introduces the analysis of visibility error with the different concepts during model development for the transmission prevention measures in making the decision. This study applied a statistical approach to assess the visibility error of forecasting performance in determining how long periods of forecasting and deciding for transmission prevention measures COVID-19 pandemics. Also, we developed the visibility error of time-variant using inductive logic. The result indicated that the number of data required to perform forecasting work on the basis of forecasting model specifications. In conclusion, this study has been completed to develop the statistical formula for identifying the largest time horizon in forecasting model N = V + 2. Also, this developed model can assist the stakeholder in forecasting the number of transmission prevention and making the decision in case of COVID-19 pandemic.

Comparing best-worst method and full consistency method in a fuzzy environment

Multicriteria Decision Making (MCDM) is one the most important branches of decision theory. Due to the fact that MCDM methods have the utmost significance in management, scholars try to develop more MCDM methods. Since calculating the weights of criteria is an important step in any MCDM method, increasing the accuracy of weight calculating methods can highly affect these methods. This accuracy can be improved by less pairwise comparison between criteria. To this end, the present study seeks to make a comparison between two new weight calculating techniques, namely BWM and FUCOM in a fuzzy environment using a real-world case study Results of this study shows that FUCOM-F provides more reliable results compared to FBWM since its consistency is less than FBWM by a great amount.

An optimal Islamic investment decision in two-region economy: The case of Indonesia and Malaysia

In this work, the possibility of cross-border activities between two regions in the framework of the investment contract is viewed as optimal allocation problems. The problems of determining the optimal proportion of funds to be invested in liquidity and technology are analyzed in two different environments. In the first case, we consider a two-region and two-technology economy in which both regions possess the same productive technology or project, but a different stream of return. While in the second case, we examine an economy where two regions (i.e., Indonesia and Malaysia) hold different Islamic productive projects with identical returns. Allocation models are formulated in terms of investors’ expected utility maximization problem under budget constraints with respect to regional and sectoral shocks. It is revealed that optimal parameters for liquidity ratio, technological investment profile, and bank repayment are analytically characterized by the return of a more productive project and the proportion of impatient and patient investors in the region. Even though both cases employ different assumptions, they provide the same expressions of optimal parameters. The model suggests that cross-border Islamic investment activities between two regions might be realized, provided both regions hold productive projects with an identical stream of return. This paper also shows that by increasing the lower return of the project approaching the higher return, a room for inter-region investment can be created. An analytical framework of an investment contract in terms of optimal allocation model is provided.

Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms

There is an acute need to evaluate the energy consumption of buildings in response to climate change. The “occupant” factor has been largely overlooked in building energy analysis. This research aims at investigating occupancy existence in the office environment using a hybrid artificial neural network with metaheuristic algorithms for improved energy management. It proposes and compares three classification models, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and hybrid PSO-GSA in combination with the feedforward neural network (FFNN). The inputs to these models are data related to temperature, humidity, light, and carbon dioxide emissions. Two data sets are used for testing the models while the office door is open and closed. The capabilities of the optimized models are evaluated using best, average, median, and standard deviation of the mean squared error. Most of the performance metrics indicate that the FFNN-PSO-GSA model exhibits better performance compared to the other models using the two datasets. The proposed model yields a classification accuracy ranging between 98.47-98.73% using one predictor (i.e., temperature). Besides, it yields an accuracy ranging between 85.45-94.03% using temperature and CO2 predictors. It can be concluded that the FFNN combined with PSO and GSA algorithms can be a useful tool for occupancy detection modeling.