Measuring Sustainability Performance Indicators Using FUCOM-MARCOS Methods

Badi, Ibrahim; Muhammad, L. J.; Abubakar, Mansir; Bakır, Mahmut

doi:10.31181/oresta040722060b

Cited by 44 publications

(30 citation statements)

References 39 publications

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“…Convolutional Neural Network (CNN) is one of the most discriminatory algorithms using layers consisting of convolutional layers and pooling layers [19,20]. Layers are Fig.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana

et al. 2022

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Background COVID-19 pandemic has indeed plunged the global community especially African countries into an alarming difficult situation culminating into a great deal amounts of catastrophes such as economic recession, political instability and loss of jobs. The pandemic spreads exponentially and causes loss of lives. Following the outbreak of the omicron new variant of concern, forecasting and identification of the COVID-19 infection cases is very vital for government at various levels. Hence, having knowledge of the spread at a particular point in time, swift actions can be taken by government at various levels with a view to accordingly formulate new policies and modalities towards minimizing the trajectory of the consequences of COVID-19 pandemic to both public health and economic sectors. Methods Here, a potent combination of Convolutional Neural Network (CNN) learning algorithm along with Long Short Term Memory (LSTM) learning algorithm has been proposed in this work in order to produce a hybrid of a deep learning algorithm Convolutional Neural Network -Long Short Term Memory (CNN-LSTM) for forecasting COVID-19 infection cases particularly in Nigeria, South Africa and Botswana. Forecasting models for COVID-19 infection cases in Nigeria, South Africa and Botswana, were developed for 10 days using deep learning-based approaches namely CNN, LSTM and CNN-LSTM deep learning algorithm respectively. Results The models were evaluated on the basis of four standard performance evaluation metrics which include accuracy, MSE, MAE and RMSE respectively. However, the CNN-LSTM deep learning-based forecasting model achieved the best accuracy of 98.30%, 97.60%, and 97.74% for Nigeria, South Africa and Botswana respectively; and in the same manner, achieved lesser MSE, MAE and RMSE values compared to models developed with CNN and LSTM respectively. Conclusions Taken together, the CNN-LSTM deep learning-based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana dramatically surpasses the two other DL based forecasting models (CNN and LSTM) for COVID-19 infection cases in Nigeria, South Africa and Botswana in terms of not only the best accuracy of with 98.30%, 97.60%, and 97.74% but also in terms of lesser MSE, MAE and RMSE.

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“…Convolutional Neural Network (CNN) is one of the most discriminatory algorithms using layers consisting of convolutional layers and pooling layers [19,20]. Layers are Fig.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana

et al. 2022

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“…The available portable SSD from SSD-1 to SSD-26 are shown in Table 1. In addition, the information obtained on the SSD with the seven features/attributes such as S-1, S-2, S-3, S-4, S-5, S-6 and S-7 of eleven brands with twenty-six models are presented in Table 1, also referred to as decision matrix as per Equation (1).…”

Section: Case Study: Selection Of Portable Solid-state Drivementioning

confidence: 99%

“…So, the sensitivity scrutiny was completed by altering the three most vital criteria, S-4, S-1 and S-7, on the rank results of SSDs using Equation ( 17) [1], [42], and a total of 18 scenarios were formed.…”

Section: Sensitivity Analysismentioning

confidence: 99%

A New Joint Strategy for Multi-Criteria Decision-Making: A Case Study for Prioritizing Solid-State Drive

Kumar

Goel²,

Zavadskas

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Solid-state data storage is becoming a widely accepted technology and is looking for new ways to provide cost-effective solutions across various information systems. Solid-state drives (SSDs), existing in different types and models, have several sustainable features: storage, dimensions, volume, etc. Due to the wide range of attributes, designing a robust method can easily select from the purchaser/retailer/wholesaler point of view. This work offers a joint multi-criteria decision-making (MCDM) to rank SSD alternatives, and a newly developed approach, namely Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) technique, is utilised, and a comparative investigation has also been achieved with other MCDM methods. Data of separate SSDs have been collected from the Indian market with twenty-six different models of eleven brands. The Bonferroni operator (BFO) allocates and compiles the objective weights using the Entropy weights technique (EWT), the Criteria Importance through Inter criteria Correlation (CRITIC) and the Method based on the Removal Effects of Criteria (MEREC). The sensitivity analysis using objective weights considering 18 scenarios was performed, and analysis with the Standard deviation shows that the joint MCDM possesses high accuracy and robustness. The results achieved have been tested with Spearman’s rank and Wojciech-Salabun (WS) coefficient, and the first rank goes to SSD-7. The presented results benefit the manufacturers to understand the market requirement better and for the consumer to make a wise decision while purchasing SSD. It also offers future scope for applying the proposed methodology in similar areas, social sciences and engineering, to make complex decisions.

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“…We preferred to apply this approach, as it is impossible to collect accurate and reliable crisp data; in addition, crisp values for some data may not exist in some situations. COmbinative Distance-based ASsessment (CODAS) technique is a very novel and robust method, and it has been applied in some studies in the literature (Ghorabaee et al 2016 ; Badi et al 2022 ; Tuş and Adalı 2018 ; Panchal et al 2017 ). According to this method, two types of distances, which are called Euclidean distance (ED) and Taxicab distance (TD), are used to evaluate the desirability of alternatives (Deveci et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the route selection in international freight transportation by using the CODAS technique based on interval-valued Atanassov intuitionistic sets

2022

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The selection of a proper international freight transport route is one of the crucial tasks for decision-makers since it can affect costs, efficiency, and transportation performance. Besides, the selection of suitable and appropriate freight routes can also reduce external costs of transportation such as emissions, noise, traffic congestions, accidents, and so on. Route selection in international transportation is a complicated decision-making problem as many conflicting factors and criteria affect the assessment process. It has been observed that there is no mathematical model and methodological frame used for solving these selection problems, and decision-makers make decisions on this issue based on their own experiences and verbal judgments in the research process. Therefore, a methodological frame is required to make rational, realistic, and optimal decisions on route selection. From this perspective, the current paper proposes using the IVAIF CODAS, an extended version of the traditional CODAS techniques, and using the Atanassov interval-valued intuitionistic fuzzy sets (IVAIFS) for processing better the existing uncertainties. The proposed model is applied to solve the route selection, a real-life decision-making problem encountered in international transportation between EU countries and Turkey. According to the results of the analysis, option A6 (i.e., Route-6 (Bursa–Istanbul–Pendik–Trieste (Ro-Ro)–Austria–Frankfurt/Germany) has been determined as the best alternative. These obtained results have been approved by a comprehensive sensitivity analysis performed by using different MCDM techniques based on interval-valued intuitionistic fuzzy sets. Hence, it can be accepted that the proposed model is an applicable, robust, and powerful mathematical tool; also, it can provide very reliable, accurate, and reasonable results. As a result, the proposed model can provide a more flexible and effective decision-making environment as well as it can provide valuable advantages to the logistics and transport companies for carrying out practical, productive, and lower cost logistics operations.

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Measuring Sustainability Performance Indicators Using FUCOM-MARCOS Methods

Cited by 44 publications

References 39 publications

CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana

CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana

A New Joint Strategy for Multi-Criteria Decision-Making: A Case Study for Prioritizing Solid-State Drive

Evaluation of the route selection in international freight transportation by using the CODAS technique based on interval-valued Atanassov intuitionistic sets

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