In this paper, a predictive mathematical model is proposed to identify the best alternatives from the given set of alternatives characterized by multiple criteria. An objective function is developed to find the ranking index of the alternatives. A new Comprehensive-Technique for Order Preference by Similarity to Ideal Solution (C-TOPSIS) method is proposed which combines the comprehensive weights of the criteria with TOPSIS method. The proposed predictive mathematical model generates a ranking of the alternatives. An experimental study has been carried out by taking agricultural data set of rice paddy crop to demonstrate and validate the developed model. The results show significant correlation between the ranks obtained by the proposed model and the ranks obtained from the average yield per hectare. Also the results of the proposed method outperform the results of the other ranking methods, namely VIKOR and ELECTRE, particularly in the real world example. Thus, the developed predictive mathematical model seems to provide better results for the given alternatives and can also be used for other decision-making problems. © 2018, The Natural Computing Applications Forum.

Deepa N

Department of Computer Applications

School of Information Technology and Engineering

Vellore Campus

Sethuramasamyraja B.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

Neural Computing and Applications

One of the crucial elements in decision-making is the calculation of criteria weights. In this paper, a new Modified Integrated Weighting (MIW) method was proposed to combine the weights obtained using different weight calculation methods into a single set of weights. The weights express the relative significance of the criteria and play an essential role in making correct decisions. The proposed method considered both subjective knowledge of the experts and the objectivity of the problem by combining the subjective and objective weight assignment methods. The proposed weight calculation method was applied to the agriculture dataset for the evaluation of groundnut crop sites. A decision-making model was developed via the proposed MIW method and Complex Proportional Assessment (COPRAS) method to rank the given groundnut crop site dataset. The ranking results of the developed decision model were compared with the ranking results of average yield data and other methods for validation purposes. The developed model exhibited better results for the given dataset and could be used to solve various other decision-making problems, thereby realizing sustainable development.

Fulltext

Sustainability

Realizing Sustainable Development via Modified Integrated Weighting MCDM Model for Ranking Agrarian Dataset

Sports events bring in a lot of people to watch the games. In India Especially Cricket. Cricket and now others sports are contributing to the Indian Economy in a high scale. This paper aims in constructing an application that can extract, process and analyse the data and then give the output to other people who plan on setting up any sports events at a given time. The data is collected from sources that supply with climatic data. We shall further analyse the data using R programming to do a comparison of central tendencies by the fitting efficiency in the analysis on the mean and variance of the data (Temperature data). Choosing the right place to conduct a sports event is the most critical assignments for any game especially for cricket in India. We can't have a situation where cricket fans from around the country travel for a special event only to end up watching a rain soaked ground with their favourite players sitting in the stands. Another point to consider is that the climate though it's not really raining will have an impact on the game. For example, taking a bowler who swings a lot, extra wind can help him in swinging ball to greater lengths, as for a batsman a bright day can help him in connecting the ball perfectly but on a shady day that's reduced. So this paper endeavours to anticipate the most appropriate place to choose for a cricket match or any other outdoor sports events which can be manipulated by the weather conditions. A number of algorithms such as Random forest, Naive Bayes, SVM and decision tree classifier and clustering mechanisms are used to predict the expected results. The final decision was supported by the Random Forest. This is the first attempt on predicting the rain interruption which could affect the cricket match and may impact financial losses both for the organizers and the sponsers. Hence predicting rainfall allows you to design the fixtures positvely without any interruption of rain. © 2019 IEEE.

2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence)

Predicting the weather for Uninterrupted Cricket matches and Outdoor Sports events

In this paper, decision-making tool is developed for the identification of the optimal aspirant for the recruitment procedure. It is developed using the IoT-based smart sensor architecture and Visual Studio programming language. The ideal candidates are determined by using the objective function, which is extracted from the sequence of the pairwise comparison performed using the combination of MCDM algorithms. Later, after the pairwise comparison, the process is extended to the next step, that is to allocate ranking for the best suitable candidate. This makes the tool more feasible and accurate. To identify the ranking of the finest aspirant, the combination of two MCDM technologies is used that is TOPSIS and GRA. In TOPSIS, mainly two artificial alternatives are used that is positive ideal alternative and negative ideal alternative. The Grey relational grade reduced by Grey theory (Tsai et al. in Int J 11:45–53, 2003) will be used to make an integrated and authentic evaluation system for identifying who is the best aspirant among all the candidates applied for the job of a professor. © 2018, Springer-Verlag London Ltd., part of Springer Nature.

Service Oriented Computing and Applications

Quick fix for obstacles emerging in management recruitment measure using IOT-based candidate selection

In the present competitive environment, a farmer needs better education, business expertise and good knowledge of technologies and tools to be successful in agriculture. Farmers usually select crop for cultivation according to their traditional knowledge and past experience in farming, but a farmer’s predictions may go wrong due to natural disaster. Thus, decision-making tool need to be developed to help farmers to take decision on crop cultivation. In this paper, decision-making tool was developed for selecting the suitable crop that can be cultivated in a given agricultural land. In the present study, 26 input variables were identified and categorized into six broad heads of main variables such as soil, water, season, input, support and infrastructure. Each main variable has several sub-variables. The priority weights for the variables were determined using the dominance-based rough set approach. In order to convert sub-variable sequences to main variable sequences, evaluation scores of each main variable were calculated by applying the weights of sub-variables and by using simple additive method. Finally, the evaluation scores were applied to Johnson’s reduct algorithm and classification rules were generated. The developed tool predicts each site in the datasets into one of the three crops such as paddy, groundnut and sugarcane. In order to validate the performance of the tool, the same datasets were predicted again by agriculture experts. The results obtained from the tool showed 92% agreement with the results obtained from the experts. Thus, the tool is a feasible tool for cultivating the suitable crops in the agricultural sites. © 2017, The Natural Computing Applications Forum.

Decision-making tool for crop selection for agriculture development

A hybrid soft decision model has been developed in this paper to take decision on agriculture crop that can be cultivated in a given experimental land by integrating few soft computing techniques. The proposed model comprises of three parts, namely weight calculation, classification and prediction. Twenty-seven input criteria were categorized into seven broad criteria, namely soil (11 sub-criteria), water (2 sub-criteria), season (no sub-criterion), input (6 sub-criteria), support (2 sub-criteria), facilities (3 sub-criteria) and risk (2 sub-criteria). In the proposed model, relative weights of main criteria were calculated using Shannon’s Entropy method and relative weights of sub-criteria in each main criterion were calculated using rough set approach. As VIKOR method is effective in sorting the alternatives, it is used to determine the ranking index of main criteria in this study. A soft decision system was constructed from the results of rough set method, VIKOR method and Shannon’s Entropy method. Classification rules were generated for five agriculture crops, namely paddy, groundnut, sugarcane, cumbu and ragi based on the soft decision system using bijective soft set approach. The developed model predicts each site in the validation dataset into one of the five crops. The performance of the proposed model has been sanity checked by agriculture experts. © 2016, The Natural Computing Applications Forum.

Multi-class classification using hybrid soft decision model for agriculture crop selection

Aquaculture International

DMTIOLA: decision making tool for identification of optimal location for aquaculture farming development

Journal of Management and Sustainability

Evaluation of Economics Journals Based on Reduction Algorithm of Rough Set and Grey Correlation

Weighting Methods and their Effects on Multi-Criteria Decision Making Model Outcomes in Water Resources Management SpringerBriefs in Water Science and Technology

Journal	Data powered by TypesetNeural Computing and Applications
Publisher	Data powered by TypesetSpringer Science and Business Media LLC
ISSN	0941-0643
Open Access	No