International Journal of Information and Decision Sciences (36 papers in press)

Regular Issues

• A Novel SMS Spam Dataset and Bi-directional Transformer based Short-Text Representations for SMS Spam Detection  
  by Srishti Maheshwari, Shubhangi Aggarwal, Rishabh Kaushal 
  Abstract: Short Message Service (SMS) is a form of exchanging short messages over mobile phones without the Internet. Unfortunately, the SMS services popularity is exploited to send irrelevant and malicious messages to entrap users into scams and frauds. In this work, we investigate the performance of state-of-the-art Bi-directional encoder representations from transformers for short-text messages in SMS data. For evaluation, we curate a novel augmented SMS spam dataset by extending a classical SMS spam dataset to further categorize spam SMS messages into four fine-grained categories, namely, indecent, malicious, promotional, and updates. We perform experiments on the standard benchmark SMS dataset of spam & non-spam and on our curated multi-class SMS spam dataset. We find that BERT based short-text representations outperform the baseline traditional approach of using handcrafted text-based features by 15-30% for different machine learning algorithms in terms of accuracy on multi-class SMS spam dataset.
  Keywords: Spam Classification;Machine Learning;Word Embedding;Representation Learning;.
  
  
• Multi-Attribute Decision-Making Application Based on Pythagorean Fuzzy Soft Expert Set  
  by Muhammad Ihsan, Muhammad Saeed, Atiqe Ur Rahman 
  Abstract: The Pythagorean fuzzy soft expert set (PFSE-set) is a parameterized family and one of the appropriate extensions of the Pythagorean fuzzy sets. It is also a generalization of intuitionistic fuzzy soft expert set, used to accurately assess deficiencies, uncertainties, and anxiety in evaluation. The most important advantage of over existing sets is that the Pythagorean fuzzy soft expert set is considered a parametric tool. The PFSE-set can accommodate more uncertainty comparative to the intuitionistic fuzzy soft expert set, this is the most important strategy to explain fuzzy information in the decision-making process. The main objective of the present research is to establish the new structure of PFSE-set along with its corresponding fundamental properties in a Pythagorean fuzzy soft expert environment. In this article, we introduce Pythagorean fuzzy soft expert set and discuss their desirable characteristics (i.e. subset, not set and equal set), results (i.e. commutative, associative, distributive and De Morgans Laws) and set-theoretic operations (i.e. complement, union intersection AND, and OR ) are explained. An algorithm is proposed to solve decision-making problem. A comparative analysis with the advantages,effectiveness,flexibility,and numerous existing studies demonstrates the effectiveness of this method.
  Keywords: Soft Expert Set;Pythagorean Fuzzy Soft Set;Pythagorean Fuzzy Soft Expert Set.
  
  
• A game theoretic approach on the investment in economic sectors by multiplier analysis: case study of Irans economy  
  by Atieh Namazi, Mohammad Khodabakhshi, Vahid Reza Salamat 
  Abstract: There is a debate on how the amount of capital should be invested in economic sectors to achieve the most prosperity in the economy. According to the balanced growth theory, some economists believe that large investment in different economic sectors increases productivity and the production size. However, other economists cling to the belief that limiting investment in key economic sectors results in increasing production. In this article, the game theory approach is utilized by using multiplier analysis and the matrix derived from the input-output table. This method is the middle ground between the balanced and unbalanced growth theories and benefits from them. The results obtained from applying the new approach in the economy of Iran indicate that it is more profitable to invest in different economic sectors; however, the investment should be in accordance with the contribution of the economic sectors in the production process.
  Keywords: Game theory; Multi-criteria analysis; Data envelopment analysis; Input-output analysis.
  
  
• Hybrid Multi Agent Framework for Green Supply Chain Management  
  by Mohamed Dif El Idrissi, Abdelkabir Charkaoui, Abdelouahed Echchatbi 
  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.
  
  
• The Importance-Performance Analysis of Lean Human Resource Management Themes  
  by Mohammad Hossein Azadi, Mohammad Hakkak, Reza Sepahvand, Seyed Najmodin Mousavi 
  Abstract: Lean management to human resources (HR) talks about a technique that makes different organization departments, particularly the HR management (HRM) which adhere to a set of executive policies and processes. This descriptive research of applied type was conducted on a statistical population including 23 senior managers working at the Social Security Organization (SSO) in Fars Province, Iran, In the qualitative phase of the study, semi-structured interviews were employed to collect the data, whose validity and reliability were endorsed using content validity ratio (CVR) and Cohens kappa coefficient (?), respectively. To analyze the qualitative findings, thematic analysis was also exercised. Upon examining the existing literature and the expert opinions, seven global themes, as well as 27 and 85 organizing and basic themes were respectively identified. In the quantitative phase, a questionnaire was administered to collect the data, Afterwards, the themes obtained were investigated using the importance-performance analysis (IPA).
  Keywords: Human Resource Management; Importance-Performance Analysis; Fuzzy Numbers; Lean Human Resources; Thematic Analysisrn.
  
  
• Social media and decision making: A data science lifecycle for opinion mining of public reactions to the 2020 international booker prize in twitter  
  by Zhe Chyuan Yeap, Pantea Keikhosrokiani, Moussa Pourya Asl 
  Abstract: The emergence of social media platforms has altered patterns of interaction between individuals and decision-makers. To explore the impact of such changes, this study conducts an opinion mining of public reactions in Twitter to the 2020 International Booker Prize shortlist. Over 13,000 tweets were collected and analysed to examine whether publics emotions and responses to a list of nominees are akin to or influence the judges decisions about the winning novelist. A data science lifecycle for Sentiment Analysis and Topic Modelling is proposed to classify tweet sentiments and identify the dominant topics in relation to the six shortlisted literary works both before and after the announcement of the winner. The findings show a marked discrepancy between readers preference and the judges decision as the prize was granted to one of the least heeded nominees. This difference reinforces the suspicion that the literary prizes are filtered through judges personal views. The proposed digital model in this study can assist critics, book club judges, literary prize-givers, and publishing industries in better decision making.
  Keywords: Decision Making; Opinion Mining; Natural Language Processing; Sentiment Analysis; Topic Modelling; International Booker Prize.
  
  
• A Novel Hybrid Meta-heuristic-enabled Ensemble Learning Model with Deep Feature Extraction for Crop Yield Prediction with Heuristic Ensemble Yield  
  by S. Vijaya Bharathi, Manikandan A 
  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 Optimized Convolutional Neural Network (O-CNN). The datasets for agricultural production prediction are obtained from public sources, and deep features are extracted using an Optimized 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 Regression (LSTM). The suggested STSA is utilized 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; Optimized Convolutional Neural Network; Heuristic-based Ensemble Learning; Squirrel Tunicate Swarm Algorithm.
  DOI: 10.1504/IJIDS.2025.10052900
   
  
• MVRO-based DRNN: Multi-Verse Rider Optimization-based Deep Recurrent Neural Network for intrusion detection in Latency constrained Cyber physical systems  
  by Arvind Kamble, Virendra S. Malemath 
  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 Optimization (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 Optimization Algorithm (WWIROA) for the classification process. The MVRO approach is the combination of the Rider Optimization Algorithm (ROA), and Multi-Verse Optimizer (MVO). The DRNN classifier is utilized 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 19.23s, 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; Multi-Verse Optimizer; Rider Optimization Algorithm.
  DOI: 10.1504/IJIDS.2025.10062381
   
  
• RiCSO-based RiDeep LSTM: Rider Competitive Swarm Optimizer enabled Rider Deep LSTM for air quality prediction  
  by Deepika Dadasaheb Patil, Thanuja T C, Bhuvaneshwari C. Melinamath 
  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 optimization (RCSO) approach is newly devised by incorporating Rider Optimization Algorithm (ROA) and Competitive Swarm optimizer (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 Optimizer; Rider Optimization Algorithm; Rider deep LSTM; Triple Exponential Moving Average.rnrn.
  
  
• A survey on various Alzheimer classification techniques using 3D MRI images: A challenging overview  
  by Neethu M., Roopa Jayasingh J 
  Abstract: This survey presents 50 research papers focussed on various techniques in Alzheimer classification techniques using 3D MRI images, and the categorization of the techniques is made based on the fusion-based, Convolutional Neural Network (CNN)-based, Random forest (RF)-based and Support vector Machine (SVM)-based approaches. Finally, the analysis is to be promoted in the survey based on the research technique, publication year, employed tools, utilized dataset, performance measures and achievement of the research methodologies towards Alzheimer classification techniques using 3D MRI images. At the end, the research gaps and issues of the techniques for Alzheimer classification techniques using 3D MRI images is to be revealed.
  Keywords: Alzheimer classification; Convolutional Neural Network; Random forest; Support vector Machine; Fusion.
  
  
• A Consumer Behaviour Assessment using Dimension Reduction and Deep Learning Classification  
  by Pragya Pandey, Kailash Chandra Bandhu 
  Abstract: Consumer behavior assessment is extremely important for online communities to finding out mindset of customer and change their views about specific products and services. Customers share their experiences with particular goods, and services on channels and social media, empowered by artificial intelligence for consumer knowledge sharing and acquire new information. In this proposed work, a deep learning model has been developed for statistical tests, statistical analysis using correlation and association testing are performed. The ordinary dimension reduction with principal component analysis and module eigenvalues, followed by a second normalization phase that maximizes the coefficient's size using possible values. The keras library was used on the third layer of the deep learning classification hierarchy with the rectified linear unit and sigmoid activation functions. The average F1 score was 98 % accurate and according to the statistics, the proposed strategy had an accuracy of 84% and a recall of 100%.
  Keywords: Consumer Behavior; Artificial Intelligence; Consumer Knowledge-Sharing; Principal Component Analysis; Deep Learning Classification.
  
  
• The Influence of Information Sources on Process and Content Confidence when Making Ill-structured Managerial Decisions  
  by David McLain, Jinpei Wu 
  Abstract: Although information is an important influence on decision confidence, disparate views exist about that influence. Engineering-derived psychological theory associates information non-negatively with confidence whereas the overconfidence literature suggests information has a non-positive influence on confidence. Previous research, however, has used information sources lacking ecological validity and almost exclusively studied well-structured decisions and single facets of confidence. Drawing on research and practice in management, decision making, and the neurosciences, the influences of technology-sourced (web) information and performance feedback information were studied as influences on confidence when making ill-structured decisions. Clear distinctions were made between judgment leading up to a decision, called process confidence, and evaluation of the final decision, called content confidence. Process-integrated web use only weakly increased either confidence while feedback significantly reduced content confidence and left process confidence little changed. This effect was amplified when the feedback clarified others decision expectations. Within subjects, the relationship between quantified feedback and confidence, especially process confidence, was positive and increased with each decision. These findings suggest that an information resource when making ill-structured decisions has little effect on confidence but that credible, post-decision performance information can affect content confidence while process confidence remains resilient.
  Keywords: confidence; information; decision-making; ambiguity; ill-structured decisions; meta-cognition; feedback; web; Internet.
  
  
• Sales Order Booking Process Lead Time Reduction by Deployment of the Lean Principle  
  by Rohit Kenge 
  Abstract: Changing environmental conditions, government imbalances, and COVID 19 disease are resulting in the more product delivery time. We studied the current sales order booking process in the literature survey, found some gaps, and proposed a hypothesis to reduce the lead time of the sales order booking process. We executed a survey of buyers and salespersons with a set of 39 questionnaires for the period of December 2020 to March 2021 on a convenience sampling basis for the 500 samples by circulating the Google form. 479 responses are received and we considered 402 valid responses after rejecting 77 wrongly ?lled answers. Hence, we got 80.33% correct responses. We performed the analysis of survey response data by testing the reliability, validity, correlation matrix, and structural equation modelling and concluded that our proposed model is the best fit and improved the overall lead time for the sales order booking process.
  Keywords: Sales management; Lean; Lead time reduction; Sales Operation excellence; and Sales order booking process.
  
  
• A Comparison of Statistical and Machine Learning Models for Stock Price Prediction  
  by Saurab Iyer, Vraj Patel, Joy Mehta, Jai Prakash Verma, Ankit Sharma 
  Abstract: A huge proportion of money around the world is held by the stock markets. It is one of the most pivotal aspects of the financial institutions and experts. Predicting the movements of stock markets can improve decision making for traders. In this paper, data science techniques are employed to predict the
  Keywords: deep learning; machine learning; stock price prediction; statistical modelling.
  DOI: 10.1504/IJIDS.2025.10061178
   
  
• Digital Traceability System for Road Crude Oil Transport: The Tunisian case  
  by Mohamed Haykal Ammar, Ezzedine Ben Aissa, Chabchoub Habib 
  Abstract: Traceability systems have been the major focus of a large number of 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 of a traceability system. We also introduced and described in details the proposed prototype.
  Keywords: Traceability; Modeling; Crude Oil; Road Transport;.
  
  
• COVID Patients' Severity Level Detection Using Machine Learning Approach  
  by Rishika Anand, Meenakshi Saroha, Pooja Gambhir, Dimple Sethi 
  Abstract: COVID-19 is a contagious disease that is caused by the SARS-CoV-2. This disease originated in Wuhan, China, in 2019, which resulted in a pandemic. This virus is diagnosed using chest computed tomography. Preventive measures like not touching face, maintaining distance, and frequent washing hands are taken care of to reduce disease transmission. There is a vaccine for COVID-19, but it is effective to some extent, whereas fewer hospitals are there for the patients suffering from COVID-19 in India. So, the government needs to admit the patients with the severe infection from COVID-19, and the patients with less severity have to isolate themselves in their homes. In this article, various parameters are considered to detect the severity of the patient suffering from COVID-19. Machine learning techniques are applied to get better accuracy while detecting the severity of the patients.
  Keywords: COVID-19; symptoms of COVID-19; machine learning; the severity of patients.
  DOI: 10.1504/IJIDS.2025.10061179
   
  
• Predictive Data Using Linear Regression in Agricultural Production  
  by Clovis Santos, Carina Dorneles 
  Abstract: In agribusiness some challenges are related to generating information for predictability with an acceptable safety accuracy. In this context, data management systems are usually developed to meet only the operational, legal, and regulatory requirements. The gap in functionalities regarding data science creates the opportunity to develop complementary tools such as business intelligence, data warehousing, online analytics, and others. This paper presents an approach to predict possible scenarios from historical harvested crops datasets. We conducted our proposal using a set of government data on harvests in all regions of Brazil in a historical series of 45 years. We have developed a descriptive application for predictive data analysis and information generation for forecasting scenarios in agriculture, using machine learning with a predictive algorithm implemented with linear regression. Objectively, the results show the use of real datasets to generate possible values in crops according to previous seasons.
  Keywords: agribusiness; database; linear regression data extraction; machine learning.
  DOI: 10.1504/IJIDS.2025.10061180
   
  
• Managerial Practices for Speedy Strategic Decision in Multinational Firms  
  by Amira Khelil 
  Abstract: Making speedy strategic decisions (SSDs) stands as a prerequisite critical for Top management teams (TMTs) to lead their organizations effectively in the international business world. Recently, only a few managers seem to have actually realized how TMTs could reach an efficient strategic decision (SD). For effectively addressing this shortfall, we draw on SD literature to advance a set of relevant enablers, whereby, decisions could be more easily reached. By relying on the SD and RBV theories, we maintain that such factors as centralization, ERP, collaborative culture, and intuition represent key elements likely to help TMTs make prompt decisions and achieve international performance. In this context, using PLS software-based data sources, this study, conducted to deal with Tunisia-based multinational firms, turns out to indicate that both the collaborative culture and ERP factors appear to represent key antecedents of prompt SD a significantly influential key factor necessary for maintaining international performance.
  Keywords: The Strategic Decision; ERP; Collaborative culture; Intuition; International Performance.
  
  
• Lemon Fruit Classification by Transfer Learning Technique: Experimental Investigation of Convolutional Neural Network (CNN)  
  by K. D. Mohana Sundaram, T. Shankar, Sudhakar N 
  Abstract: Before exporting fruits, quality control is extremely important in the fruit industries. The most crucial step in the quality assessment process is to classify the fruit as fresh or spoiled. Convolutional neural network (CNN) is the most recent technology used for classification. Henceforth, in this work, the performance of eight widely used CNNs, namely AlexNet, DenseNet, GoogleNet, Inceptionv-3, MobileNetv-2, ResNet-18, SqueezNet, and VGGNet-19, was evaluated and compared for fruit classification, utilising the Lemon fruit dataset. To classify the lemon fruits into three categories of good-quality, medium-quality, and poor-quality, 1,000 fully connected layers in each CNN were substituted with three fully connected layers. For comparison, all of the CNNs were trained using the Transfer Learning technique with learning rates of 0.1, 0.01, and 0.001. The VGG Net-19 architecture was found to have a validation accuracy of 92.6% for a learning rate of 0.001.
  Keywords: convolutional neural network; CNN; fruit classification; fully connected layers; transfer learning.
  DOI: 10.1504/IJIDS.2025.10061181
   
  
• Pothole Detection and Localization from Images using Deep Learning  
  by Archit Dhiman, Mohit Kumar, Arun Yadav, Divakar Singh Yadav 
  Abstract: The existence of potholes threatens road safety and contributes to a significant portion of accidents worldwide. It takes a lot of work to constantly patch potholes and keep track of when new ones appear. Our goal in this work is to create a pothole detection system that would make it simpler to accurately detect potholes from images. The system can potentially save human lives and assist the government authorities to fix the potholes. In order to achieve this objective, we first make use of a pre-trained deep learning model (VGG-16) and thereafter, propose a novel convolutional neural network (CNN) model. This work employs a publicly available dataset, Nienaber Potholes 2 (Complex), for experiments. The proposed model provides 98.87% accuracy on pothole classification task in images and outperforms recent state-of-the-art approaches in the literature. Further, since no past work has been done on this dataset to detect bounding boxes for potholes, we use YOLO-v3 and YOLO-v5 to generate bounding box predictions on this dataset and evaluate the results. The bounding box task achieves 83.23% mAP and 87.45% precision. Due to the absence of significant existing results, these results for bounding box prediction may be considered as a benchmark.
  Keywords: pothole detection; pothole; convolutional neural network; CNN; bounding box; you only look once; YOLO; Nienaber.
  DOI: 10.1504/IJIDS.2025.10059513
   
  
• The Factors Driving Buyers to Post Their Online Feedback - Ordered Logistic Regression Analysis  
  by Xubo Zhang, Yanbin Tu, Ke Zhong 
  Abstract: Reputation is vital for sellers to survive and grow at online auction marketplaces. Positive feedback ratings from buyers help sellers build such a reputation. In this study, we explore the factors that affect feedback ratings from buyers at online auction marketplaces. We try to identify the factors related to three types of feedback (+, 0, ) posted by buyers. We also study the effects of sellers first move to post their feedback to buyers on the counter-feedback from buyers. We find that characteristics of sellers and products, selling strategies, auction outcomes, and the first mover strategy are significantly associated with feedback posted from buyers. More specifically, sellers first move to post their positive feedback to buyers helps them receive positive counter-feedback from buyers. Our study contributes to the literature by exploring the determinants of online feedback posted from buyers and providing empirical evidence on the effects of sellers first mover strategy.
  Keywords: online auction; buyer's feedback; first mover strategy; marketing analytics.
  DOI: 10.1504/IJIDS.2026.10061463
   
  
• Macroeconomic Variables and Exchange Rates a complex interplay and the role of a central bank  
  by Anupam Mehrotra, Amit Kumar Pandey, Ashok Chopra 
  Abstract: Fluctuations in exchange rates are both the causes and consequences of changes in major macroeconomic variables. One triggers the other in a complex interplay of forces, and there is a chain of actions and reactions leading to a sizeable shift in certain macroeconomic variables at an unacceptable pace or in an undesired direction calling, at times, central banking or government intervention to break the momentum. The paper analyses the nexus between some major macroeconomic variables and the exchange rate. It also examines the situations where it may be required for the central bank to make changes in the policy rates or other components of its monetary policy to curb excessive volatility in exchange rates and/or to influence the macroeconomic variables in the general interest of the economy.
  Keywords: exchange rate; macroeconomic variables; central bank; GDP; inflation; interest rates.
  DOI: 10.1504/IJIDS.2026.10062350
   
  
• A Binomial Simulation Approach to More Consistent AHP Matrices  
  by Phillip W. Witt, Mohsen Hamidi 
  Abstract: Different methods have been proposed for simulating analytical hierarchy process (AHP) matrices. In this paper, we develop a new method assuming responses for AHP matrices follow binomial distributions. In this paper, we exhibit the method with a small data sample which we use to estimate the binomial parameters by transformation, and then backwards solving the logit Newton-Raphson updating algorithm. A parametric bootstrap sample is then used to compare the simulated results against the actual results from the data. In addition to the new simulation method we discover some interesting findings regarding AHP matrix consistency.
  Keywords: analytic hierarchy process; AHP; simulation; decision-making; group decision-making; decision science; managerial decision-making; consistency; Newton-Raphson; binomial.
  DOI: 10.1504/IJIDS.2026.10062775
   
  
• Design of Hybrid SVM Job recommender (HSJR) system for the overlapping target classes  
  by Santhosh Kumar Rajasekar, N. Prakash 
  Abstract: The fresh graduates with no prior experience are struggling to find suitable jobs. The job searching time of the fresh graduate is not reduced. Few researchers used machine learning models for matching the recommended job skill-set with the graduate skill set. If the skill-set of the two jobs is the same, the machine learning algorithms recommend only one job and ignore the other job. To address this problem, we design a hybrid support vector machine job recommendation (HSJR) model. The proposed HSJR model collects the skill set of the graduate and matches it with the current jobs and recommends the most suitable jobs for the graduates. To evaluate the proposed HSJR model, the jobs are recommended for the engineering graduates and the feedback received from the participants. The proposed HSJR model achieves 90% accuracy in the job recommendation. The proposed HSJR model performs better than the traditional job recommender system.
  Keywords: job recommender system; support vector machine; SVM; skill set; career recommendation; recommendation system; recommender system.
  DOI: 10.1504/IJIDS.2025.10063418
   
  
• A Classification Model for Terror Incidents by Affiliation Category of Perpetrators  
  by Donald D. Atsa’am, Benjamin Terzungwe Tough, Doose Atsa'am 
  Abstract: A terror incident could be perpetrated by either a lone wolf who acts on their own or affiliated terrorists who work for a terror group. In this study, the data from the global terrorism database and the artificial neural network algorithm were employed to construct a classification model that could predict the probable affiliation category of the perpetrator(s) of a terror incident. The model uses information such as type of attack, casualty figure, claim of responsibility, and damage to property to distinguish a lone wolf attack from a terror group attack. Various metrics of model diagnostics were employed to test the suitability of the model for predictions, and it yielded a balanced classification accuracy of 85%. The model adds another dimension to the existing criteria for terrorism classification. Further, the model could serve as a useful tool in the study of terrorism and counterterrorism.
  Keywords: lone wolf; affiliated terrorist; affiliation category; classification model; artificial neural network; ANN.
  DOI: 10.1504/IJIDS.2026.10063799
   
  
• Prediction of return on equity using machine learning algorithms: an evidence from India  
  by Amit Hedau, S.V.S. Raja Prasad, Sasikanta Tripathy 
  Abstract: The present study analysed and predicted the return on equity using machine learning algorithms from the historical financial data during April 2018-March 2022 for construction firms operating in India. The study considered sampling bias method to consider the listed 172 companies from construction sector, as this sector generates the second largest contribution to the GDP of India. The machine learning algorithms is used to model the regression equation. The results indicate that market capitalisation, sales, return on asset, current ratio, earning per share, promoter holdings and profit after tax significantly influence the return on equity for construction firm in India during the study period. We conclude that out of six classifiers, XGBoost is more accurate (86%) to predict the return on equity of the construction firms in India. Finally, a financial performance prediction tool is developed to predict the results.
  Keywords: return on equity; ROE; construction; India; machine learning; XGBoost.
  DOI: 10.1504/IJIDS.2025.10064231
   
  
• The Effect of Software-Based Mind Map of Educational Design in Development of the Electrical Engineering Students' Learning Level  
  by Zahra Jamebozorg, Fatemeh Jafarkhani, Khaled Nawaser 
  Abstract: The present study aims to investigate the effect of a software-based mind map for educational design in the electrical engineering students' learning level development. The qualitative phase of the research featured deductive method of content analysis while the quantitative phase was characterised by a quasi-experimental design. The analytical framework employed for the first phase of the research entailed open and axial coding, whereas the second stage involved the utilisation of descriptive and inferential statistical analyses. Results of the first phase of the study including 54 participants revealed the components of instructional design for mind map. The lesson plans for the treatment phase were prepared with respect to the educational design elements and they were conducted by the experimental group including 23 students. Results also showed that applying mind map into the instructional design has improved students' learning level.
  Keywords: instructional design model; mind map; software-based; electrical engineering; learning level.
  DOI: 10.1504/IJIDS.2026.10065142
   
  
• A Bibliometric Analysis of Information Criteria for Forecasting Volatility  
  by W.U. Youyuan, Choo W.E.I. Chong, Matemilola Bolaji Tunde, Jen Sim Ho 
  Abstract: Volatility forecasting model selection is an essential issue when making financial decisions, which increasingly focus on modelling, forecasting, and evaluation. However, this area has not yet undergone a systematic analysis in the relevant literature. This paper takes advantage of the VOSviewer and bibliometric techniques to overview the temporal distribution of articles, the corresponding author's countries, the citation network, the co-occurrence, the thematic evolution, and the top of the journal or authors or articles. Content analysis of 60 pieces of literature, including their data characteristics, theoretical basis, and practical application, as well as suggestions for potential research directions. Through bibliometric techniques and content analysis, this study provides a thorough overview of the research done in the field of volatility forecasting model selection. The research findings indicate that scientific productivity on the subject is expanding rapidly. New methodologies, such as neural networks, have been introduced, necessitating a broad perspective by the researcher in the evaluation of empirical results.
  Keywords: bibliometric analysis; information criteria; volatility forecasting; model selection.
  DOI: 10.1504/IJIDS.2026.10065147
   
  
• Contracted near-shore service part production predictive modelling using BOM-based feature generalisation and deep statistical learning  
  by Donovan Fuqua, Faruk Arslan, Victor Pimentel, Jimho Fatoki, Barry Brewer, Phillip W. Witt, Edward Kennedy 
  Abstract: Intermittent service part production (SPP) is a common manufacturing resource planning (MRP II) problem occurring when a customer requests spare parts from the manufacturer for an item no longer in production but still in widespread use. Manufacturers increasingly turn to contracted SPP (outsourcing) for fabrication of specialised components, including service parts. Despite the ubiquity of contracted SPP in contemporary manufacturing, it is a neglected area of study that relies on dated research, standard operations research models, and simplistic forecasting. Most available literature depends upon the availability of maintenance and part reliability information generally not available to contracted suppliers. In practice, manufacturers tend to add rough service demand estimates into contract costs or negotiate additional production as separate orders. Our model adds to SPP theory by managing irregularly contracted SPP by component item families using out-of-sequence service order histories as a proxy for reliability data.
  Keywords: smart MRP II; lumpy demand; agile manufacturing; explainable machine learning; demand chain management; DCM.
  DOI: 10.1504/IJIDS.2026.10065172
   
  
• AHP Approach for Employee Recruitment with COVID-19 Situation in Thailand  
  by Akan Narabin, Veera Boonjing, Kanognudge Wuttanachamsri 
  Abstract: With COVID-19 circumstance, unlike usually situation, to select an appropriate person for a job position, the criteria and a weight of each criterion are needed to determine and adjust to suit with the crisis. The criteria chosen in this work are emphasized on selecting applicants who, while studying, have been in the midst of the COVID-19 situation. In this research, we employ Analytic Hierarchy Process to assist the committee to have an agreement. In this study, each person in the committee can have his/her own pairwise comparison matrix of the criteria with a three-level hierarchical model. Since initiating hierarchical model may occur some ambiguity in sometime, in this work, the criteria used in the three-level model is properly adjusted to create a four-level hierarchical model. The comparison inspected, which provide some guidance in both theoretical manner and applications.
  Keywords: Analytic Hierarchy Process (AHP); COVID-19; Employee recruitment; Multi-criteria decision-making problem.
  DOI: 10.1504/IJIDS.2026.10065340
   
  
• An Efficient Hybrid Denoising Algorithm for ECG signal using Adaptive Hybrid filtering method and Empirical Model Decomposition method.  
  by Kirubha D, Vinayagam P, Uthayakumar G.S., Usha S 
  Abstract: The majority of the time, the signal from the electrocardiogram (ECG) is obscured by the frequency of the mains, which is either 50 or 60 hertz. A notch filter that is able to selectively filter individual frequencies can be used to cut down on the mains frequency portion of an electrocardiogram (ECG). The adaptive hybrid filter cleans up the ECG signals by removing background noise. The global MIT-BIH database provides the ECG data that is used in the simulation research. Our mathematical analysis makes use of a number of different real ECG recordings in addition to synthetic ECG that has been contaminated by a number of different kinds of noise. The performance of our suggested system is measured by the signal-to-noise and distortion ratio (SINAD), improvement in signal-to-noise ratio (SNR), approximation entropy, and fuzzy entropy. These metrics are used to assess the system’s effectiveness.
  Keywords: ECG signal; empirical mode decomposition; EMD.
  DOI: 10.1504/IJIDS.2026.10065526
   
  
• Dimensions of anti-citizenship behaviours incidence in organisations: a meta-analysis  
  by Fatemeh Gheitarani, Khaled Nawaser, Haniruzila Hanifah, Ali Vafaei-Zadeh 
  Abstract: Research growth in organisational behaviour research, has increased the importance of paying attention to anti-citizenship behaviours. The current research with the aim of quantitative combination, has examined the results of research in effect of underlying factors of organisational anti-citizenship behaviours using meta-analysis method and CMA2 software and 55 articles during the time period of 2000-2020. The results showed a positive significant link between underlying factors of organisational anti-citizenship behaviours and occurrence of these behaviours and this influence was 0.389, 0.338, 0514 and 0.498 (structural, organisational, managerial, employment and professional and socio-economic and cultural factors). The level of connection found relating to each four occurrences is '68 links, 49 links, 93 links and 71 links'. Findings indicate that minute attention has been paid to organisational anti-citizenship behaviours, especially to job and professional factors in research works. Research should be conducted to control and manage these behaviours more purposefully in organisations.
  Keywords: organisational behaviours; meta-analysis; organisational factor; anti-citizenship behaviours; personal factor.
  DOI: 10.1504/IJIDS.2024.10065572
   
  
• A novel approach of psychometric interaction and principal component for analysing factors affecting e-wallet usage  
  by Gurpreet Singh Matharou, Simran Kaur 
  Abstract: The Republic of India has witnessed an enormous leap in financial transactions after a sudden demonetisation in 2016. The study represents an in-depth analysis of the factors influencing e-wallets usage post-COVID situation covering the National Capital Region. The scientifically collected data were subjected to Pearson's correlation to recognise the correlation amongst the selected e-wallets. The usage of e-wallets is observed mainly during recharge, UPI payments, and utility payments. Through psychometric response and interaction analysis, six factors were selected and examined for data distribution and stable observation using standard deviation and variance coefficient. The coefficient of variance for six factors was observed ≤ 1. The weight of the factors noted to be secured way (0.184), to take advantage of cashback (0.182), low risk of theft (0.169), fast service (0.1689), ease to use (0.156), and saves time (0.139) using principal component eigenvectors analysis. Freecharge and Tez wallets reveal a maximum 99.2% correlation.
  Keywords: wallets; correlation; payment; p-value; technology.
  DOI: 10.1504/IJIDS.2024.10065574
   
  
• Hybrid of machine learning-based multiple criteria decision making and mass balance analysis in the new coconut agro-industry product development  
  by Siti Wardah, Mohammad Yani, Taufik Djatna, Marimin Marimin 
  Abstract: Product innovation has become a crucial part of the sustainability of the coconut agro-industry in Indonesia, covering upstream and downstream sides. To overcome this challenge, it is necessary to create several model stages using a hybrid method that combines machine learning based on multiple criteria decision making and mass balance analysis. The research case study was conducted in Tembilahan district, Riau province, Indonesia, one of the primary coconut producers in Indonesia. The analysis results showed that potential products for domestic customers included coconut milk, coconut cooking oil, coconut chips, coconut jelly, coconut sugar, and virgin coconut oil. Furthermore, considering the experts, the most potential product to be developed was coconut sugar with a weight of 0.26. Prediction of coconut sugar demand reached 13,996,607 tons/year, requiring coconut sap as a raw material up to 97,976,249.
  Keywords: coconut agro-industry; hybrid of machine learning; mass balance analysis; multiple criteria.
  DOI: 10.1504/IJIDS.2024.10065573
   
  
• Designing a method to model the socio-technical systems  
  by Mohammad Mirkazemi Mood, Ali Mohaghar, Yaser Nesari 
  Abstract: To capture the complexity and diversity of systems with both technical and social features, modelling methods are needed that similarly provide various tools and concepts. Study of developed methods shows that despite all of their advantages and strengths, there is a need for a method that with a holistic approach integrates perspectives, strengths and tools of the developed methods and models with different aspects of socio-technical systems. The main aim of the current study is to design a method for modelling complex socio-technical systems. To achieve this goal, it is necessary to design a method that is based on creativity and existing knowledge base. Therefore, design science research is used as a research strategy to design proposed method. For the first time, design science research in the field of operations research has been used to design a modelling method. This study also presents new tools and concepts for modelling socio-technical systems.
  Keywords: socio-technical approach; design science research; soft operations research; problem structuring methods; system thinking; meta-synthesis; modelling methods.
  DOI: 10.1504/IJIDS.2024.10065570
   
  
• A data mining model to predict the debts with risk of non-payment in tax administration  
  by José Ordóñez-Placencia, María Hallo, Sergio Luján-Mora 
  Abstract: One of the main tasks in tax administration is debt management. The main goal of this function is tax due collection. Statements are processed in order to select strategies to use in the debt management process to optimise the debt collection process. This work proposes to carry out a data mining process to predict debts of taxpayers with high probability of non-payment. The data mining process identifies high-risk debts using a survival analysis on a dataset from a tax administration. Three groups of tax debtors with similar payment behaviour were identified and a success rate of up to 90% was reached in estimating the payment time of taxpayers. The concordance index (C-index) was used to determine the performance of the constructed model. The highest prediction rate reached was 90.37% corresponding to the third group.
  Keywords: data mining; debt management analysis; machine learning; taxpayer behaviour patterns; survival analysis.
  DOI: 10.1504/IJIDS.2024.10065571