Hybrid journal (It can contain Open Access articles) ISSN (Print) 1756-7017 - ISSN (Online) 1756-7025 Published by Inderscience Publishers[439 journals]
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Authors:S. Vijaya Bharathi, A. Manikandan Pages: 1 - 31 Abstract: The fundamental goal of this study is to build and use heuristic-based ensemble learning for improved agricultural production prediction. The squirrel tunicate swarm algorithm (STSA), a hybrid squirrel search algorithm (SSA) and tunicate swarm algorithm (TSA), extracts deep features using the optimised convolutional neural network (O-CNN). The datasets for agricultural production prediction are obtained from public sources, and deep features are extracted using an optimised convolutional neural network (O-CNN). Following that, the optimum deep features are exposed to heuristic-based ensemble learning using three distinct classifiers: linear regression (LR), support vector regression (SVR), and long-short-term-memory (LSTM) regression. The suggested STSA is utilised to calibrate the ensemble learning's three classifiers. When comparing the predicted performance of the developed model to that of other procedures, the proposed Heuristic ensemble yield framework beats previous techniques. Keywords: novel crop yield prediction; deep feature extraction; optimised convolutional neural network; heuristic-based ensemble learning; squirrel tunicate swarm algorithm Citation: International Journal of Information and Decision Sciences, Vol. 17, No. 1 (2025) pp. 1 - 31 PubDate: 2025-02-04T23:20:50-05:00 DOI: 10.1504/IJIDS.2025.144259 Issue No:Vol. 17, No. 1 (2025)
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Authors:Mohamed Dif El Idrissi, Abdelkabir Charkaoui, Abdelouahed Echchatbi Pages: 32 - 50 Abstract: Environmental customer collaboration has recently attracted a big attention from researchers and industrial professionals. Many studies show that companies may reach high performance level by considering customer collaboration and environmental regulations. However, literature in the green supply chain management (GSCM) suggests having more structured collaboration and information exchange processes between supply chain partners based on new technologies. For this reason, this work proposes a hybrid solution based on multi-agent systems (MAS) and mixed integer linear programming (MILP) to automate and facilitate the environmental customer collaboration process. The study demonstrates how MAS can be used in the GSCM context to improve communication and reduce complexity. An industrial study case in the automotive spare parts sector is used to demonstrate the applicability of the established MAS model. Keywords: green supply chain management; multi-agent systems; supply chain management; customer collaboration; environmental regulation Citation: International Journal of Information and Decision Sciences, Vol. 17, No. 1 (2025) pp. 32 - 50 PubDate: 2025-02-04T23:20:50-05:00 DOI: 10.1504/IJIDS.2025.144258 Issue No:Vol. 17, No. 1 (2025)
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Authors:Deepika Dadasaheb Patil, T.C. Thanuja, Bhuvaneshwari C. Melinamath Pages: 51 - 75 Abstract: This paper is for air quality prediction. Here, the time-series data is considered for the effective prediction of air quality. Moreover, missing value imputation is applied in this model to perform pre-processing. The technical indicators are extracted as features for the effectual prediction of air quality. The rider deep long short-term memory (LSTM) is also included for predicting air quality, trained by a developed RCSO algorithm. Moreover, the developed rider competitive swarm optimisation (RCSO) approach is newly devised by incorporating rider optimisation algorithm (ROA) and competitive swarm optimiser (CSO). The performance of the developed air quality prediction model is evaluated using several error metrics. The introduced air quality prediction system obtained a minimum mean square error (MSE) of 0.10, a root mean square error (RMSE) of 0.31, a mean absolute percentage error (MAPE) of 8.34%, and mean absolute scaled error (MASE) of 0.30. The results demonstrated that the developed RCSO-based rider deep LSTM model attained better performance than other techniques. Keywords: air quality prediction; competitive swarm optimiser; CSO; rider optimisation algorithm; ROA; rider deep LSTM; triple exponential moving average; TEMA Citation: International Journal of Information and Decision Sciences, Vol. 17, No. 1 (2025) pp. 51 - 75 PubDate: 2025-02-04T23:20:50-05:00 DOI: 10.1504/IJIDS.2025.144261 Issue No:Vol. 17, No. 1 (2025)
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Authors:Mohamed Haykal Ammar, Ezzedine Ben Aissa, Habib Chabchoub Pages: 76 - 109 Abstract: Traceability systems have been the major focus of various works in the literature. The diversity of the studies is explained by the need to propose systems which adapt to the various sectors' constraints, the objectives and the recommendations of the stakeholders. They are also related to the nature and the products 'value or concerned with the services and especially the various activities performed by the various partners and the information to be exchanged among the stakeholders. It is worth noting that the stakeholders insist that this traceability system have two main roles: the alerts' generation to avoid the risks of incidents and the determination of responsibility in the case of an incident. In this work, we proposed the modelling of the different activities related to the crude oil transportation using the UML language aiming at the proposal system. We also introduced and described in detail the proposed prototype. Keywords: traceability; modelling; crude oil; road transport Citation: International Journal of Information and Decision Sciences, Vol. 17, No. 1 (2025) pp. 76 - 109 PubDate: 2025-02-04T23:20:50-05:00 DOI: 10.1504/IJIDS.2025.144262 Issue No:Vol. 17, No. 1 (2025)
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Authors:Arvind Kamble, Virendra S. Malemath Pages: 110 - 131 Abstract: The cyber attacks on cyber physical system leads to actuation and sensing behaviour, safety risks, and rigorous damages to the physical object. Therefore, in this paper, multi-verse rider optimisation (MVRO)-based deep recurrent neural network (DRNN) is devised for identifying intrusions in latency-constrained cyber physical systems. In the latency-constrained cyber physical system, the process is carried out using three layers, end point layer, cloud layer, and fog layer. Here, the feature extraction process is performed using the water wave-based improved rider optimisation algorithm (WWIROA) for the classification process. The MVRO approach is the combination of the rider optimisation algorithm (ROA), and multi-verse optimiser (MVO). The DRNN classifier is utilised for the intrusion detection process. In addition, the DRNN classifier is trained using the introduced MVRO technique for better performance. Furthermore, the MVRO-based DRNN technique achieves low latency of 19.23 s, high specificity, sensitivity, and accuracy of 0.929, 0.974, and 0.956, respectively. Keywords: intrusion detection; cyber physical system; cloud layer; deep recurrent neural network; DRNN; multi-verse optimiser; rider optimisation algorithm; ROA Citation: International Journal of Information and Decision Sciences, Vol. 17, No. 1 (2025) pp. 110 - 131 PubDate: 2025-02-04T23:20:50-05:00 DOI: 10.1504/IJIDS.2025.144260 Issue No:Vol. 17, No. 1 (2025)
Hybrid journal (It can contain Open Access articles)
[439 journals]
