JournalTOCs

International Journal of Information and Decision Sciences
Journal Prestige (SJR): 0.233

Citation Impact (citeScore): 1
Number of Followers: 10

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1756-7017 - ISSN (Online) 1756-7025
Published by Inderscience Publishers

[451 journals]

An adaptive neuro-fuzzy inference for blockchain-based smart job
recommendation system
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  Authors: E.P. Ephzibah, R. Sujatha, Jyotir Moy Chatterjee
  Pages: 1 - 14
  Abstract: Blockchain is a technology that supports secured transaction in a public distributed database. It maintains a peer-to-peer network where a transaction cannot be modified or tampered by unauthenticated users. It provides a safe message transfer from a sender to a receiver. Job recommendation is an online system that provides a mapping between the job seeker and the employer. This paper proposes a public blockchain of job recommendations based on incremental hashing. The examinations show that this blockchain job recommendation provides process integrity, traceability, security, high levels of transparency, drastic reduction in operational cost and high standard and systematic. The system has two stages. Firstly, using blockchain technology, the authenticated data is fetched. Secondly, a classification model using adaptive neuro-fuzzy inference system is built for mapping the job seeker to the recruiter. This approach proves to be authenticated as well as a smart job recommendation system.
  Keywords: blockchain; distributed database; peer to peer network; job recommendation system; unsecured message transmission; unauthenticated data; time-consuming search; incremental hashing; classification model; adaptive neuro-fuzzy inference system;
  Citation: International Journal of Information and Decision Sciences, Vol. 14, No. 1 (2022) pp. 1 - 14
  PubDate: 2022-05-09T23:20:50-05:00
  DOI: 10.1504/IJIDS.2022.122719
  Issue No: Vol. 14, No. 1 (2022)
Human decision making modelling for gambling task under uncertainty and
risk
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  Authors: Nimisha Gupta, Mitul Kumar Ahirwal, Mithilesh Atulkar
  Pages: 15 - 38
  Abstract: In this paper, modelling of human decision making process and comparison among various reinforcement learning (RL) techniques with utility functions has been performed. Iowa gambling task (IGT) is used to collect real time data to understand and model the decision making (DM) process involving uncertainty, risk or ambiguity. Performance of models is evaluated based on their mean square deviation (MSD) value. This helps to predict the probability of the next choice that lead to the selection of the advantageous deck as compared to disadvantageous one. Along with that, the deck selection pattern between male and female with the learning process of the participants were also analysed. By comparing the MSD value of various RL models, it is found that the MSD value of DM model consists of prospect utility (PU)-decay reinforcement learning (DRI) with trial dependent choice (TDC) rule is best.
  Keywords: human decision making; Iowa gambling task; IGT; reinforcement learning model; utility functions
  Citation: International Journal of Information and Decision Sciences, Vol. 14, No. 1 (2022) pp. 15 - 38
  PubDate: 2022-05-09T23:20:50-05:00
  DOI: 10.1504/IJIDS.2022.122723
  Issue No: Vol. 14, No. 1 (2022)
Stock price forecasting using hidden Markov model
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  Authors: Ernest Oseghale Amiens, Ifuero Osad Osamwonyi
  Pages: 39 - 59
  Abstract: We used hidden Markov model (HMM) with single observation to estimate stock prices of selected manufacturing companies from the Nigerian Stock Exchange. Data from 22 November 2013 to 6 July 2018 were partitioned into two datasets for training and testing. Subsequently, the data were differenced, trained, tested and used to forecast closing prices for 60 days for each equity. The HMM was implemented with Matlab. The research revealed closing price prediction accuracy ranging from 3.33% to 96.67% and trade signal precision ranging from 31.67% to 97.67%. Also, the MAE values range from 0.0013 to 34.2867 while the MAPE values are between 0.1498% and 6.0034%. The hypothesis tested revealed that the model is efficient. Similarly, the comparison test conducted revealed the performance of HMM is better than ARIMA and neural network (NN). The research proposes that hidden Markov model be adopted in the exercise of stock price forecasting.
  Keywords: stock forecasting; hidden Markov model; HMM; stock price; manufacturing firms; neural network; auto-regressive integrated moving average; ARIMA; mean absolute percentage error; MAPE; Nigerian Stock Exchange; NSE; forecast accuracy
  Citation: International Journal of Information and Decision Sciences, Vol. 14, No. 1 (2022) pp. 39 - 59
  PubDate: 2022-05-09T23:20:50-05:00
  DOI: 10.1504/IJIDS.2022.122721
  Issue No: Vol. 14, No. 1 (2022)
What determines the household decision to borrow for investment or
repayment of old debt? The Indian story
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  Authors: Moumita Poddar, Tanmoyee Banerjee, Ajitava Raychaudhuri
  Pages: 60 - 84
  Abstract: Borrowing for investment in either physical or human capital promotes growth while that for consumption or debt repayment may lead to so called 'debt-trap' for the households. The present paper probes deeper into the decision-making process of the households regarding choice between these alternative borrowing. The data comes from All India Debt and Investment Survey (NSS 70th round). These methods used are Cragg's Box-Cox double hurdle model and instrumental variable (IV) probit model. Our study shows the decision to borrow for investment purposes depends on such factors as gender, religion, location, education, asset position as well as on the status of financial inclusion of households. The decision to borrow for repayment of existing debt is most prevalent among urban educated households in addition to land-owning rural borrowers.
  Keywords: institutional borrowing; capital formation; financial inclusion; inequality; potential debt-trap
  Citation: International Journal of Information and Decision Sciences, Vol. 14, No. 1 (2022) pp. 60 - 84
  PubDate: 2022-05-09T23:20:50-05:00
  DOI: 10.1504/IJIDS.2022.122720
  Issue No: Vol. 14, No. 1 (2022)
Testing the validity of Cauchy model based on the informational energy
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  Authors: Moumita Poddar, Tanmoyee Banerjee, Ajitava Raychaudhuri
  Pages: 85 - 96
  Abstract: In this article, a test statistic for testing the validity of the Cauchy model based on the informational energy is introduced. Consistency of our test is shown. Also, we show that the distribution of the test statistic does not depend on the location and scale parameters. Through a simulation study, we obtain the critical values of the test statistic and then the power of the test is computed by Monte Carlo method against different alternatives. The performance of the proposed test with some competing tests is compared. Our results show that our test is superior to the classical non-parametric tests and can apply to a testing problem in practice.
  Keywords: goodness of fit test; Cauchy distribution; informational energy; power of test; Monte Carlo simulation
  Citation: International Journal of Information and Decision Sciences, Vol. 14, No. 1 (2022) pp. 85 - 96
  PubDate: 2022-05-09T23:20:50-05:00
  DOI: 10.1504/IJIDS.2022.122722
  Issue No: Vol. 14, No. 1 (2022)