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In this paper, we present applications of Markov rough approximation framework (MRAF). The concept of MRAF is defined based on rough sets and Markov chains. MRAF is used to obtain the probability distribution function of various reference points in a rough approximation framework. We consider a set to be approximated together with its dynamacity an...
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The concept of linear Diophantine fuzzy sets (LDFSs) is a new approach for modeling uncertainties in decision analysis. Due to the addition of reference or control parameters with membership and non-membership grades, LDFS is more flexible and reliable than existing concepts of intuitionistic fuzzy sets (IFSs), Pythagorean fuzzy sets (PFSs), and q-...
Citations
... Kedukodi et al. (2019) introduced interval valued equiprime, 3-prime and c-prime L-fuzzy ideals of a nearring N by using interval valued t-norms and interval valued t-conorms and characterized them. Subsequently, Koppula et al. (2020) related these ideas with Markov frameworks and gave their application in decision-making problems. Aishwarya et al. (2022) investigated permutation identities satisfied by weak semigroup left ideals in prime nearrings and obtained results on commutativity of addition and multiplication using these identities. ...
We define row path norm and column path norm of a matrix and relate path norms with other standard matrix norms. A row (resp. column) path norm gives a path that maximizes relative row (resp. column) distances starting from the first row (resp. column). The comparison takes place from the last row (resp. column) to the first row (resp. column), tracing the path. We categorize different versions of path norms and provide algorithms to compute them. We show that brute-force methods to compute path norms have exponential running time. We give dynamic programming algorithms, which, in contrast, take quadratic running time for computing the path norms. We define path norms on Church numerals and Church pairs. Finally, we present applications of path norms in computing condition number, and ordering the solutions of magic squares and Latin squares
... It, therefore, satisfies the Markov property (memorylessness). It can, thus, be used to describe processes that follow a sequence of linked events, where the current state depends only on the previous state of the system (Corrêa et al., 2020;Koppula et al., 2020). Markov chains are commonly used in fields ranging from music composition to search engine algorithms, voice recognition to mapping populations of animal life (Tang et al., 2020). ...
Severe deterioration of urban air quality in Asian cities is the cause of a large number of deaths every year. A Markov chain–based IoT system is developed in this study to monitor, analyze, and predict urban air quality. The proposed sensing setup is integrated with an automobile and is used for collecting air quality information. An Android application is used to transfer and store the sensed data in the data cloud. The data stored is used to generate the transition matrix of the AQI states and calculate return periods for each AQI state. The estimated time interval after which an AQI event recurs or is repeated is known as return period. The actual return periods for each AQI state at the test locations in Delhi-NCR are compared with those predicted using discrete time Markov chain (DTMC) models. Average absolute forecast error using our model was found to be 3.38% and 4.06%, respectively, at the selected locations.
... For the examples on decision-making problems, we refer (Koppula et al. 2019(Koppula et al. , 2020Riaz et al. 2020;Chen and Huang 2021). ...
We define $$2n+1$$ 2 n + 1 and 2 n fuzzy numbers, which generalize triangular and trapezoidal fuzzy numbers, respectively. Then, we extend the fuzzy preference relation and relative preference relation to rank $$2n+1$$ 2 n + 1 and 2 n fuzzy numbers. When the data is representable in terms of $$2n+1$$ 2 n + 1 fuzzy number, we generalize the FMCDM (fuzzy multi-criteria decision making) model constructed with TOPSIS and relative preference relation. Lastly, we give an example from telecommunications to present the proposed FMCDM model and validate the results obtained.
... Stock trend or price prediction has long been a popular research topic. Recently, numerous new techniques such as machine learning [6,7], deep learning frameworks [8][9][10][11][12], fuzzy/ rough sets [13,14], sentiment analysis [15,16], genetic algorithms [17], Markov decision processes [18], and entropy [19] have been implemented. [20] explores the impact factors of virtual online stock message boards in terms of past performance. ...
In this study, we propose algorithms to predict future stock market trends based on 8 different input features, including financial technology indicators, gold prices, a gold price volatility index, crude oil price, a crude oil price volatility index, and other characteristic data using two different labeling methods with separate classification algorithms of two and three output categories, respectively including predicted stock price changes (up and down) and recommended trading actions (buy, sell, and hold), and analyze the validity of these characteristic data in terms of their ability to predict future trends. The S&P 500 (GSPC) is the target of these forecasts. Sample data from 2010 to 2018 are divided 8:2, between training and verification data, while data from 2019 are used to test the proposed approach. CNN and LSTM models are used for comparison of classification accuracy and investment returns, respectively. Bayesian optimization (BO) hyperparameters are used to improve the accuracy of the model and increase the return on investment (ROI) of the output predictions.
The purpose of this study is to verify whether using gold prices, a gold volatility index, crude oil price, and a crude oil price volatility indices as input features can enable a deep learning model accurately to predict future stock price trends, and to discuss separately the applicability of CNN and LSTM models to the abovementioned characteristics and financial indicators. We also present the results of experiments conducted to evaluate the proposed method in terms of classification accuracy and confusion matrix. In the case of three-category classification, the model takes feature data as input to outputs a predicted trading order on whether to buy, sell, or hold a given set of stocks tomorrow as well as the timing of entry and exit from each position, and also backtests the data outside the sample to find the combination of characteristics and indicators best maximizing ROI. Using this three-category method, we obtain a comprehensive ROI for a given set of individual stocks and assess whether each type of stock is suitable for the prediction model based on input features such as gold and crude oil or the fields that are suitable for the given feature.
Experimental results show that the proposed approach as able to predict whether stock price will rise or fall in the next 10 days, and the model accuracy rate can reach 67%. The results of experiments on the proposed combined CNN model with eight features, referred to asCNN8, achieved an ROI on 2019 data outside the sample period of up to 13.23%, which was superior to the 12.08% and 11.06% obtained by the models designed CNN4 (CNN with four input features) and LSTM8(LSTM with eight input features), respectively. The F1 score increased from 0.75 0.79 as a result of applying BO. The results indicate that considering the price of gold, the gold volatility index, crude oil price, and crude oil price volatility index can help obtain better ROI for companies in certain fields, such as the semiconductor, petroleum, and automotive industries, rather than merely considering financial indicators. However, for companies related to apparel, fast food, and copy processing, the input characteristics of purely financial technical indicators were found to be suitable.
