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International Journal of Information and Decision Sciences

International Journal of Information and Decision Sciences (IJIDS)

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International Journal of Information and Decision Sciences (51 papers in press)

Regular Issues

  • The Art of Context Classification & Recognition of Text Conversation using CNN   Order a copy of this article
    by Sandeep Rathor, Sanket Agrawal 
    Abstract: This paper proposes a robust model for recognizing the context of a conversation by using CNN. Initially, preprocessing is performed on the input text conversation. It includes lowercase conversion followed by tokenization, padding, and word embedding. The embedding layer gives out a feature matrix. This feature matrix is passed to a multi-level Convolution Neural Network. The proposed model is designed in such a manner that each CNN reduces the input matrix to half of the input size. Thus, the output of the next CNN layer and the pass of the current CNN layer followed by average pooling can be added. This output is named a global pass and passed to another block of the same architecture. The output from the last CNN layer and global outputs are concatenated and finally, passed into two fully connected layers FC(512) & FC(8). The output from FC(8) gives the probability of conversation to belong to eight contexts. Finally, the context with the highest probability is taken.
    Keywords: Context Recognition; Text Conversation; Text Mining; CNN; Machine learning.

  • In Search of Sustainable Electronic Human Resource Management in Public Organizations   Order a copy of this article
    by Reza Sepahvand, Khaled Nawaser, Mohammad Hossein Azadi, Ali Vafaei-Zadeh, Haniruzila Hanifah, Razieh Bagherzadeh Khodashahri 
    Abstract: Over the past two decades, issues such as environmental degradation, continued marginalization of large groups of people, growth of anti-globalization sentiments, and demand for innovation and creativity in public and private sectors have emerged as prominent global organizational problems. These developments have led to a growing interest in the concept of sustainability, which many Human Resource Management (HRM) researchers believe can enhance HRM capabilities and activities, leading to better organizational performance and competitive advantage. Given the significant impact of Information Technology (IT) on HRM, identification of factors affecting the implementation of sustainable Electronic Human Resource Management (SEHRM) in organizations can result in significant cost reduction and more principled organizational decision making. The present study aimed to identify and evaluate the factors affecting the implementation of sustainable EHRM in public organizations using type-2 fuzzy FMEA. After reviewing the research literature and surveying experts, 29 factors in three dimensions of social, environmental and economic were identified. After designing and distributing the questionnaire among experts, type-2 fuzzy AHP was used to determine the weight of risk factors of FMEA (occurrence probability, severity, and detectability). The identified factors were then ranked using type-2 fuzzy TOPSIS. The results showed that the social dimension is the most important dimension for the implementation of sustainable EHRM in the study area. The critical individual factors in order of significance for this implementation were found to be EHRM Reengineering, Green Performance Evaluation, IT Infrastructure, ECRM, Electronic Customer Satisfaction, Employee Safety, and Electronic Services.
    Keywords: Sustainable Electronic Human Resources Management; FMEA; Type 2 Fuzzy TOPSIS; Type 2 Fuzzy AHP.

  • A Multi-Criteria Decision Support System for the Assessment of Cities based on Air Quality Indicators   Order a copy of this article
    by Supriya Raheja, Rakesh Garg, Aakash Gupta, Geetika Munjal 
    Abstract: A deterministic decision support system is developed for the assessment and ranking of various cities based on the air quality indicators in this research. The present study models such assessment of cities as a multi-criteria decision making (MCDM) problem due to the involvement of multiple air quality indicators. Further, to solve the present assessment problem, a hybrid MCDM approach, namely, Entropy- Evaluation based on distance from average solution (EEDAS) is implemented by integrating two well-known approaches such as Shannon entropy and evaluation based on distance from average solution (EDAS). Shannon entropy approach is used to calculate the priority weights of the air quality indicators whereas the EDAS method is used to get the comprehensive ranking of the cities based on the identified air quality indicators.
    Keywords: Air Pollution; Air quality indicators; Multi-Criteria decision making (MCDM); Entropy; EDAS; Decision support system.

  • Analyzing Impact of IT Investments on Banks Performance using Multi-Stage DEA   Order a copy of this article
    by Ankit Mehrotra, Reeti Agarwal 
    Abstract: The paper studies the impact of investments made in information technology (IT) on a firms performance. The paper makes use of multi-stage Data Envelopment Analysis (DEA) to find out the marginal impact of IT investment on firms performance. The paper uses a three-stage DEA approach by introducing a new variable i.e. communication expenses to study what bearing IT has on a firms efficiency attainment. The paper also extends existing literature related to the indirect influence of IT on a firms performance by making use of a non-parametric approach, DEA. To attain the aims of the study, data of a number of banks was considered for a period of five years. The study introduced IT variables at two stages of the analysis to analyze its marginal effect on efficiency outcome.
    Keywords: Data Envelopment Analysis; DEA; performance; efficiency; information technology; bank; marginal effect.

  • Compromise ranking based on superiority, inferiority and Euclidean normalized similarity metrics: The ESIASP method   Order a copy of this article
    by Moufida Hidouri 
    Abstract: Xiaozhan Xu introduced in 2001 the superiority and inferiority ranking (SIR) methods called SIR.TOPSIS and SIR-SAW. The SIR.TOPSIS method has two quite different variants, which we call here SR.TOPSIS and IR.TOPSIS. What is noteworthy is that each variant gives attention only to one type of indexes rather than both types, which may result in questionable ranking results because both variants ignore available relevant indexes. In addition, the SIR.TOPSIS variants (ranking) indexes have been based on the TOPSIS relative closeness coefficient, which is inflexible in the sense of not being affected by the relative importance of separations of each alternative from positive ideal solution and negative ideal solution. The SIR.SAW method ignores the relative significances of superiority flows (overall degrees of support for alternatives) and inferiority flows (overall degrees of support against alternatives). It is therefore worthwhile to introduce a new ranking method to overcome the flaws seen in Xus SIR methods. The crisp method proposed in this paper, called the Evaluation based on Similarity to Ideal Augmented Superiority Profile (ESIASP) method, fully exploits all the available relevant indexes and takes account of their relative importance. Finally, a supplier selection problem is given to demonstrate the proposed method. A comparison of rankings produced by the ESIASP method, SIR.TOPSIS and SIR.SAW variants shows that the suggested method is a relevant and implementable alternative to Xus SIR methods.
    Keywords: crisp method; inferiority; SIR.SAW; SIR.TOPSIS; superiority; supplier selection.

  • Eliciting individual risk attitudes different procedures, different findings   Order a copy of this article
    by Sven Grüner, Norbert Hirschauer, Felix Krüger 
    Abstract: We compare three procedures for eliciting individual risk attitudes: Holt-and-Laury (2002), Eck-el-and-Grossman (2002), and the general willingness-to-take-risks question of the German socio-economic panel. Using a within-subject design, we carry out a classroom experiment with stu-dents who are enrolled in the degree programs Physics, Computer Sciences, Agricultural Scienc-es, Law, and History. We find that the risk attitudes as measured by the three procedures diverge substantially. This poses a serious challenge to the validity of these measurement instruments.
    Keywords: Risk-attitude; Eckel-and-Grossman procedure; Holt-and-Laury procedure; self-reported willingness-to-take-risks; bounded rationality.

  • Evaluation and selection of a casting process using interval type-2 fuzzy analytical hierarchy process   Order a copy of this article
    by Devesh Sahu, Atul Chakrawarti 
    Abstract: Selection of a casting process is a kind of multi criteria decision making process, in which several factors like Cost, material utilization and flexibility, geometrical complexity and flexibility and dimensional accuracy and surface finish. As cost is considered as one of the prime criteria in decision making, so it is further sub categorised into three sub criteria i.e. tooling cost, equipment cost and labour cost. Past researchers have used multi criteria decision making technique like analytical hierarchy process along with type-1 fuzzy in the evaluation and selection of casting process, which is successful in handling the fuzziness of the system, but are unable to address the issue of uncertainty associated with decision making. So, in this paper interval type-2 fuzzy analytical hierarchy process is used to evaluate and select best casting process, which is capable of handling uncertainty associated with decision making. A numerical illustration is solved using the interval type-2 fuzzy analytical hierarchy process and it is found that investment casting is evaluated as best casting process for cam carrier and is ranked number one.
    Keywords: Casting; MCDM; decision support system; type-2 fuzzy; AHP.

  • Combined Application of Condition Based Maintenance and Reliability Centred Maintenance using PFMEA and Lean Concepts A Case Study   Order a copy of this article
    by Claudia Carvalho De Oliveira, José Cristiano Pereira, Nélio Pizzolato 
    Abstract: Productivity is an essential element for competitiveness, helping companies to be better prepared for the future, given all of today's challenges. Such productivity needs to be clearly confirmed by operational results. This paper covers assets maintenance effectiveness, one of the productivity evaluation components increasingly gaining business attention. The proposed methodology helps the achievement of the required effectiveness on asset maintenance, by combining the concepts of Reliability Centered Maintenance and Condition Based Maintenance, also supported by PFMEA, a successful risk management strategy, and Lean Manufacturing practices. A case study was conducted in a high technology enterprise, combining those referred concepts. The implementation process used Minimum Viable Product strategy. The main concept is based on suitable equipment inspection and diagnosis practices which guides interventions and cleverer behaviour towards asset management. As a result, in the first year of implementation, 5 to 10% cost reduction was obtained together with a significant increase in equipment availability which has reached a level of 95%+.
    Keywords: Reliability Centered Maintenance; RCM; Condition Based Maintenance; CBM; PFMEA; Lean Maintenance; asset Management; MVP.
    DOI: 10.1504/IJIDS.2023.10041745
  • Towards Building a Comprehensive Big Data Management Maturity Framework   Order a copy of this article
    by Mervat Helmy, Sherif Mazen, Amal Elgammal, M.Wagdy Youssef 
    Abstract: Profound insights are generated today from exploiting big data. However, organisations are still not recognising how mature their big data management capabilities are, and what improvements are needed. There is still no structured approach to assess the maturity of big data management capabilities. Existing solutions lack a consistent perception of big data management capabilities, a reliable assessment, and a rigid improvement scheme. So, the main contribution of this article is conducting an analytical study on existing key works in assessing and building big data management capabilities, and upon, the main requirements for building a comprehensive big data management maturity framework are proposed. Results of validating the developed framework proved that it enabled organisations to assess, build, and improve their current big data management capabilities.
    Keywords: big data management maturity; big data management; big data management capabilities; big data capabilities; big data analytics capabilities; big data analytics capabilities construct; big data maturity; big data maturity model; big data maturity assessment; big data capabilities assessment.

  • Determining a Model for Eliminating Organizational Lying: A Grounded Theory Approach   Order a copy of this article
    by Mohammad Hakkak, Khaled Nawaser, Mohammad Jalali, Samaneh Ghahremani, Ali Vafaei-Zadeh, Haniruzila Hanifah 
    Abstract: The present study has been implemented to generate a theory in the field of organizational lying for better understanding and explaining the phenomenon in organizations. The research method is qualitative and based on the Grounded Theory. Semi-structured interviews were used for data collection and Strauss and Corbin Method and paradigmatic model were adopted for data analysis. Sampling was done using a theoretical sampling method and targeted (judgmental) and snowball (chain) techniques based on which 21 interviews were conducted with managers, employees and experts of the Imam Khomeini Relief Foundation (a charitable organization founded in 1979 to provide support for poor families and also known as IKRF) of Kurdistan Province in Iran who were familiar with the issue and context. The results of the analysis of the data obtained from the interviews during the open, axial and selective coding processes led to the formation of a grounded theory concerning the organizational lying based on which the purposeful lying, blind lying, and silent lying were introduced as the basic concepts of the Organizational Lying Model.
    Keywords: Organizational Lying; Antecedents of Lying; Consequences of Lying; Grounded Theory.

  • EOQ model for time dependent demand with deterioration, inflation, shortages and trade credits   Order a copy of this article
    by R.P. Tripathi 
    Abstract: The Inflation acts an important role for each area of life in the world. Inflation varies rapidly for high tech commodities with passing over time. This study develops an EOQ model with time sensitive demand rate for deteriorating products and shortages with inflation over a predetermined planning horizon. Mathematical formulations are prepared under two cases (i) time for positive inventory (T1) is greater than credit period M and (ii) T1 is less than or equal to credit period M, to gain optimal number of replenishment and cycle time. An algorithm is presented to find most favorable cycle time so that total annual relevant profit is maximized. We then demonstrate the total profit is concave with respect to number of replenishments. Numerical examples are offered to display the model. Sensitivity investigation for variation of a number of key parameters is also discussed. Mathematica 7.0 software is used to calculate numerical results and optimality conditions.
    Keywords: Cash flow; inflation; non- increasing demand; credit period; shortages.

  • Investigation of the rank reversal problem in some novel objective weight based MADM methods   Order a copy of this article
    by Ravindra Singh Saluja, Varinder Singh 
    Abstract: Objective weight based multi-attribute decision making (OWMADM) methods are applied in decision situations where the weight values are not sought from decision makers and are rather obtained based on the range or spread of attribute data. The present study develops three novel OWMADM methods namely Preference Selection Index Proximity indexed value Method (PSIPIVM), Standard Deviation Proximity indexed value Method (SDPIVM) and Entropy Proximity indexed value Method (EPIVM), by combining the objective weights obtained through three different methods with recently developed proximity indexed value method (PIVM), which is known to promote minimization of the rank reversal problem. Two established criteria are adopted to evaluate the occurrence of rank reversal, one by removing the least preferred alternative from consideration while another involved splitting the considered alternatives into two sets and testing for the transitive property. The paper also attempts to identify suitable normalization techniques for the proposed OWMADM methods to yield robust ranking orders, which may enhance the reliability of decision outcome.
    Keywords: Multi-attribute decision making; Objective weight; PSI; SD; Entropy; PIVM; Rank reversal; Decision analysis.

  • A Bi Objective Optimization Technique for Scheduling Repetitive Projects   Order a copy of this article
    Abstract: Repetitive projects are projects in which the same type of works/activities get repeated in different locations or sites. For each of this type of projects, different crew options are available for each activity and selecting the best option corresponding to each activity is a difficult task. Project managers in these projects are often faced with the task of finding out the best schedule corresponding to the optimum project duration and expenditure which will satisfy different constraints like due time in which each activity to be completed, movement of crew from one location/unit to other, precedence relationship among different activities etc. In this case, the decision maker wants to get a solution that simultaneously optimizes the two conflicting and diverse objectives of duration and expenditure while obtaining an acceptable trade off amongst the objectives. Therefore, development of a suitable solution method, which gives near optimal solutions while considering single objectives like minimization of project duration or project expenditure and also for bi objective optimization considering minimization of the combined effect of duration and cost, is very important. Since the computational complexity is very high in these type of projects, an ABC algorithm based heuristic methodology is developed in this study which can give good solutions for satisfying the above mentioned objectives with respect to different constraints. The proposed methodologys performance is analyzed with exact solutions and the results show that an ABC algorithm based methodology gives significantly good quality solutions.
    Keywords: ABC Algorithm; Repetitive projects; Optimization.

  • Availability and Mean Time to Failure for Repairable k-out-N: G Systems with Identical Components   Order a copy of this article
    by Mohammed Hajeeh 
    Abstract: This study presents Markov models for assessing the availability and the mean time to failure (MTTF) of a k-out-of n: G system with exponentially distributed time between failures and repair times. Generalized analytical expressions for the steady-state availability and MTTF are derived. Furthermore, numerical results are provided to illustrate the performance of several models along with the cost of adopting different configurations.
    Keywords: k-out-of n: G system; Availability; Mean time to failure; Cost.

  • NoSQL based approach in data warehousing and OLAP cube computation   Order a copy of this article
    by Abdelhak Khalil, Mustapha Belaissaoui 
    Abstract: Over the last few years, Not only SQL (NoSQL) databases are gaining increasingly significant ground and are considered as the future of data storage. In this paper we are interested in implementing NoSQL-OLAP systems by defining mapping rules from the multidimensional conceptual level to logical key-value model, and providing a set of online analysis operators. We consider two different approaches in order to implement a big data warehouse within key value stores. The first one uses SQL-like table structure layered on top of the key-value schema, the second one uses a simple key-value pair structure. Then we provide aggregation operators (Map-Reduce Cube and Bit-Encoded Cube) for key value models in order to perform OLAP cube computation. We implemented OLAP operator using Oracle NoSQL database and LevelDB, and we conducted experiments on a fictional data warehouse produced by an existing benchmark that considers NoSQL models. Thus, results showed clearly the performance of OLAP implementations under NoSQL key value stores in terms of efficiency and scalability.
    Keywords: OLAP; Data Warehouse; NoSQL; Big Data; Cube Model.

    by Luciano Ferreira Da Silva, Paulo Oliveira, Gustavo Grander, Renato Penha, Flavio Bizarrias 
    Abstract: This study aims to use fuzzy logic to select a project manager based on soft skills. In the first phase, a focus group interview was applied to establish the weights according to the soft skills list selected. In the second phase, the Fuzzy Topsis logic was applied. According to the concept of the Fuzzy Topsis, a closeness coefficient is defined to determine the ranking order of all alternatives. The results allowed the construction of the framework here called Fuzzy Topsis Ranked Multicriteria for selecting the best candidate according to the profile and criteria adopted. The contribution of this study is to allow the attribution of values to soft skills that, in essence, are subjectivity. This framework is friendly, the investment required is low, and it is adaptable to different contexts.
    Keywords: Fuzzy Topsis; Multicriteria Decision; Project Manager Selection; Soft Skill.

  • A GIS-Based Framework For Flood Hazard Vulnerability Evaluation In Thudawa Area, Sri Lanka   Order a copy of this article
    Abstract: At present flash floods became as one of the most devastating natural disasters all over the world especially in tropical areas including Sri Lanka. Considering the increase of flood events in recent years, accurate flood risk assessment is an essential component of flood mitigation in highly populated urban areas. The objectives of our research were to identifying and classifying flood risk areas into different classes in Thudawa area, Sri Lanka, and developing a Geographical Information System (GIS) model to identify flood vulnerability areas accurately in Thudawa area. Through this, it was expected to proposing preventive guidelines of flood hazard vulnerability using geo-informatics. As located near the Nilvala River mouth Thudawa is having a high risk for flash flood events over the recent period. Therefore, it was very important to conducting a Geo informatics-based risk assessment in the area. The methodological procedure is extremely important in this type of research thus, the spatial Multi-Criteria Decision Analysis (MCDA) procedure was used. Multi-criteria analysis has been widely applied to solve decision-making problems related to the environment, and natural resource management. For this research Analytical Hierarchy Process (AHP) was used for the criterion weighting. To run the GIS model in ARC GIS environment AHP calculations run upon the results of experts' judgment as proposed by the Satty incorporating pair-wise comparison method. The results of this study attempt to analyze the existing flash flood risk levels using the GIS-based multi-criteria analysis technique which allowed ranking of risk areas since it is important in the decision-making process to mitigate the flood risk in the study area. And also, the results of the research will be more useful to identify future risk areas for flood hazards in disaster management and land use planning as well as, on the other hand, it can provide valuable support for a range of decisions such as land use master planning, design of infrastructure, and emergency response preparation in the area. This study can be taken as a reference for researchers who engage with similar studies in the same or other areas using Geo-informatics tools after integrating other methods and tools such as fuzzy AHP method, fuzzy multiple-attribute decision-making method, fuzzy TOPSIS, and Pythagorean fuzzy AHP.
    Keywords: AHP; Flood Hazard; GIS; MCDA; Modelling.

  • D4SP Decision Support System based on the use of the AHP method for Science Park Selection   Order a copy of this article
    by Bruno Moura, Ivo Santos, Nelson Barros, Fernando Luis Almeida 
    Abstract: The literature reveals that science parks offer numerous benefits and support services to the activity of a technological startup. However, the decision of choosing the best science park for the startup tends to be an informal process, technically not very rigorous and planning, arising essentially by affinities with the research center and university. In this study, a decision support system is presented to support entrepreneurs in the process of selecting a science park for the implementation of their startup. The AHP method is used to compare the importance of the criteria for selecting a science park, which includes factors such as location, activity sector, infrastructure, cost, and size. The findings reveal that the use of this decision support system helps entrepreneurs to find a science park that is suitable for the needs of their startup and allows them to comparatively identify the most relevant criteria when choosing a science park.
    Keywords: entrepreneurship; decision science; AHP; startups; new venture; science park.

  • Artificial Neural Networks in the Development of Business Analytics Projects   Order a copy of this article
    by Juan Bernardo Quintero, David Villanueva-Valdés, Bell Manrique-Losada 
    Abstract: The accelerated evolution of information and communication technologies, with an ever-growing increase in their access and availability, has become the foundation for the current big data age. Business Analytics (BA) has helped different organizations leverage the large volumes of information available today. In fact, Artificial Neural Networks (ANNs) provide deep data-mining facilities to organizations for identifying patterns, predict future states, and fully benefit from predictions/forecasts. This article describes three ANNs application scenarios for developing BA projects, by using network learning: i) for executing accounting processes, ii) for time series forecasts, and iii) for regression-based predictions. We validate scenarios by implementing an application-case using actual data, thus demonstrating the full extent of the capabilities of this technique. The main findings exhibit the expressive power of the programming languages used in data analytics, the wide range of tools/techniques available, and the impact these factors may have on the BA development projects.
    Keywords: Artificial Neural Networks; Business Analytics; Data Analytics; Big Data; Deep data mining; Network learning process; Time series forecast; Regression-based prediction; Activity Based Costing; Supervised learning; Decision making.

  • Designing a decision support system for integrated production and distribution planning in shrimp agro-industry   Order a copy of this article
    by Lely Herlina, Machfud Machfud, Elisa Anggraeni, Sukardi Sukardi 
    Abstract: The integration of production and distribution planning is essential for the efficiency and responsiveness of the shrimp agro-industry. Due to the large number of actors involved in the supply chain and intense competition from similar industries, the integration needed. Besides, uncertainties in the supply of perishable raw materials, annual growth and harvest, various sizes and yields, and voluminous are challenges for the shrimp agro-industry. To overcome this, the integration production and distribution planning needed a decision support system (DSS). This study aims to develop a prototype of a decision support system that integrates production and distribution planning in shrimp agro-industry. To test the designed system, a multi-objective Evolutionary Algorithm (MOEA) framework used resulting in that DSS-shrimp can provide a validated mechanism for decision making in an integrated production and distribution planning in shrimp agro-industry.
    Keywords: decision support system; production planning; distribution; supply chain; agro-industry; multi-objective Evolutionary Algorithm;.
    DOI: 10.1504/IJIDS.2024.10042651
  • How young consumers are influenced by the valence of positive and negative frames: a cross-cultural perspective   Order a copy of this article
    by Theodore Tarnanidis, Kofi Osei-Frimpong, Jason Papathanasiou, Nana Owusu-Frimpong 
    Abstract: The specific study examines the use and the impact of three types of framing effects in different mental activities and everyday situations in consumers lives, namely: attribute, goal, and risky choice framing. Although, many studies across the globe have proposed empirical framing models that differ each other due to context discrepancies and other implicitly information. Yet, framing effects in the international cross-cultural perspective remain a pervasive issue. Having that purpose in mind this study aims to contribute to the examination of the literatures on framing effects between two diverse contexts with dissimilar environments, i.e. Greece and Ghana. The first one has a strong democratic culture over the last 2.500 years, whereas the other is characterized by long periods of military rule. Thus, cultural variations make people to make decisions differently. To that extent, data was collected in two stages from 590 young consumers (i.e. students) from Greece and Ghana. The different framing types were examined by using a within subjects design that provided participants the positive and negative conditions of each framing task. The results suggest a partial inter-correlation between the three categories of framing effects. In the attribute framing examinations, gain-framed messages make people to focus on the positive outcomes, whereas loss-framed messages have negative evaluations. Likewise, in the goal framing case, the majority of the subjects from both countries preferred the positive condition. And when decisions involve a risky option, Greeks in the positively framed condition were split between risk avoid and risk seeking behaviour, whereas Ghanaians have only a risk-seeking behaviour. In contrast, in the negatively framed condition all study subjects showed a risk seeking attitude. The findings provide unique technical insights into the consumer framing arena for future evaluations.
    Keywords: decision framing; framing effects; cognition; prospect theory; Greece; Ghana.

  • An Intelligent Decision Support System Modelling for Improving Agroindustrys Supply Chain Performance: A Case Study   Order a copy of this article
    by Muhammad Asrol, Marimin Marimin, Machfud Machfud, Moh. Yani 
    Abstract: Decision-making has an important role to improve agroindustrys business process performance. This research comprises 4 important aspects to determine agroindustrys performance, namely supply chain performance, risk management, green productivity performance and agroindustrys business promising. This paper proposed an Intelligent Decision Support System (IDSS) which was organized of main performance models to improve agroindustrys competitiveness. Supply chain performance modeling was organized by Supply Chain Operation Reference (SCOR) framework, agroindustrys risks assessment using Fuzzy House of Risk, green productivity evaluation using Green Productivity Index (GPI) and Fuzzy Inference System (FIS) while agroindustrys business promising and feasibility assessment which was modelled with FIS. Overall supply chain performance was developed by Single Input Single Output Fuzzy model to realize the final agroindustrys supply chain performance. An IDSS was comprised database, model-base and knowledge base to be periodically simulated the supply chain performance measurement of agroindustry. The proposed IDSS was validated under real condition of a sugarcane agroindustry and found that the supply chain business performance was moderate, it had high risk threat, normal green productivity performance and moderate feasible of business promising performance. The overall supply chain performance validations showed that sugarcane agroindustry -as a case study- performance was moderate. For further research, this paper requires experienced expert verification to formulate the supply chain performance improvement strategy and verify the IDSS model to be implemented for the real world.
    Keywords: Agro-industry; Fuzzy system; Green productivity; Intelligent decision support system; Risk management; Supply chain.

    by Rohit Kenge 
    Abstract: Since December 2019, the world is facing COVID 19 pandemic and its impact on the economy. As the product demand is shrinking, the product supply with the pre-installed capacities is facing major issues like job cuts, high unsold material inventory, and the running of companies at lower capacities. To answer these operational issues, we prepared the research hypothesis framework integrating the twelve operational excellence factors into an operational excellence model consisting of people, process, and flow approach. We evaluated this hypothesis through a set of 36 questionnaires for a survey based on the Likert scale and received 402 complete responses. We performed the analysis of survey response data by testing the reliability, correlation, validity, and structural equation modelling and found out that organizational performance has a significant positive impact on our proposed operational excellence model. Also, organizational performance has a significant positive impact on our proposed operational excellence model.
    Keywords: Operational Excellence; Recession; Organizational Performance; COVID-19; Demand-Supply Cycle.

  • Designing a method to model the socio-technical systems   Order a copy of this article
    by Mohammad Mirkazemi Mood, Ali Mohaghar, Yaser Nesari 
    Abstract: To capture the complexity and diversity of systems with both technical and social features, modeling 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 modeling 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 modeling method. This study also presents new tools and concepts for modeling socio-technical systems.
    Keywords: Design science research; Soft operations research; Problem structuring methods; System thinking; Meta-synthesis; Modeling methods.

  • A data mining model to predict the debts with risk of non-payment in tax administration.   Order a copy of this article
    by Maria Hallo, José Ordoñez Placencia, 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 optimize 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 behavior 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.

  • Dimensions of anti-citizenship behaviors incidence in organizations: A Meta-analysis   Order a copy of this article
    by Fatemeh Gheitarani, Khaled Nawaser, Haniruzila Hanifah, Ali Vafaei-Zadeh 
    Abstract: Research growth in organizational behavior research, has increased the importance of paying attention to anti-citizenship behaviors. The current research with the aim of quantitative combination, has examined the results of research in effect of underlying factors of organizational anti-citizenship behaviors 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 organizational anti-citizenship behaviors and occurrence of these behaviors and this influence was 0.389, 0.338, 0514 and 0.498 (structural, organizational, managerial, employment and professional and socio-economic and cultural factors). The level of connection found relating to each 4 occurrences are 68 links, 49 links, 93 links and 71 links. Findings indicate that minute attention has been paid to organizational anti-citizenship behaviors, especially to job and professional factors in research works. Research should be conducted to control and manage these behaviors more purposefully in organizations.
    Keywords: Organizational Behaviors; Anti-citizenship behaviors; Meta-Analysis; Organizational Factor; Personal Factor.

  • Hybrid of Machine Learning-Based Multiple Criteria Decision Making and Mass Balance Analysis in The New Coconut Agro-industry Product Development   Order a copy of this article
    by Siti Wardah, Moh 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.

  • A Novel approach of psychometric interaction and principal component for analyzing factors affecting e-wallet usage   Order a copy of this article
    by Gurpreet Singh Matharou, Simran Kaur 
    Abstract: The Republic of India has witnessed an enormous leap in financial transactions after a sudden demonetization 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 Pearsons correlation to recognize 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.

  • Discovering Interesting Relationships in the Factors Relating to the Elderly Living Alone in Thailand Using Association Analysis   Order a copy of this article
    by Nichnan Kittiphattanabawon 
    Abstract: Many countries are facing an aging society situation. The problem of the elderly living alone happens often, even in Thailand. This research aimed to discover the factors that reflect the elderly living alone. Association analysis techniques were employed to unearth all possible factor combinations. The measures, called support and confidence, were utilized to determine the strength of the discovered factors. Association rules have also been mined to reveal the relationship between the factors affecting the elderly living alone. The study sample consisted of 36,574 elderly living alone and was received from the National Statistical Office of Thailand. The study showed that the elderly living alone often do not need a caregiver if they have supportive family members, have public healthcare benefits, earn a living from a social security fund, and have amenities in the house. The primary discovery was that they can carry out a routine without assistance. Furthermore, most importantly, they are in good health.
    Keywords: elderly; aloneness; Thailand; association analysis; association rules; interesting relationships.

  • A Novel SMS Spam Dataset and Bi-directional Transformer based Short-Text Representations for SMS Spam Detection   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.

  • Estimating regulatory governance gaps for adoption of augmented reality in automobile sector: the application of analytical hierarchy approach   Order a copy of this article
    by BRAJESH MISHRA, Fateh Bahadur Singh 
    Abstract: This study aims to estimate regulatory gaps that AR applications may encounter in the automobile sector. The theoretical framework of this study is inspired by regulatory commons dynamics approach. The document analysis and thematic analysis of semi-structure interview transcript helped propose an estimation framework of regulatory gaps in the context of AR adoption in Indian automobile sector. Next, the importance of various regulatory gaps domains and sub-domains have been assessed using fuzzy analytical hierarchy process technique. The automobile market regulatory gaps emerged as the most important regulatory gap domains, while the violation liability, market distortion, smart road infrastructure, customer interest, and safety have been globally ranked as the top five important regulatory gap sub-domains. Policymakers and regulators face a big challenge to match market dynamics created by rapidly advancing technologies. Market regulatory gaps and technical regulatory gaps need to be addressed separately for clarity and focus.
    Keywords: Multiple criteria analysis; regulatory gaps; augmented reality; Indian automobile sector; fuzzy AHP; smart road infrastructure.

  • A Novel Hybrid Meta-heuristic-enabled Ensemble Learning Model with Deep Feature Extraction for Crop Yield Prediction with Heuristic Ensemble Yield   Order a copy of this article
    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   Order a copy of this article
    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.

  • RiCSO-based RiDeep LSTM: Rider Competitive Swarm Optimizer enabled Rider Deep LSTM for air quality prediction   Order a copy of this article
    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   Order a copy of this article
    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 Behavior Assessment using Dimension Reduction and Deep Learning Classification   Order a copy of this article
    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   Order a copy of this article
    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.

    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.

  • Digital Traceability System for Road Crude Oil Transport: The Tunisian case   Order a copy of this article
    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.

  • Investigating the determinants of mobile shopping applications continuance usage intention in the post covid-19 pandemic
    by Razib Chandra Chanda, Ali Vafaei-Zadeh, Syafrizal Syafrizal, Haniruzila Hanifah, Karpal Singh Dara Singh 
    Abstract: As COVID-19 evolves and weaken overtime, countries have begun to relax lockdowns, mobility restrictions, and allow businesses and societies to return to normal. Therefore, this study aims to investigate determinants of continuance intention to use mobile applications in the post-pandemic era. Based on the expectation-confirmation theory, the study obtained data from 375 respondents through a quantitative research strategy. The findings of this study show that expectation-confirmation influences satisfaction. Perceived usefulness had a positive influence on both satisfaction and continuance intention to use mobile applications. Customer satisfaction was also found to positively influence continuance intention to use mobile applications. Notably, the study also found that perceived privacy risk negatively moderates the relationship between satisfaction and continuance intention. The study contributes to the continuance intention literature and highlights important factors that can influence customers to continue using mobile applications in the post-pandemic era.
    Keywords: mobile shopping applications; continue use intention; satisfaction; confirmation; perceived privacy risk; perceived usefulness.

  • Global Schema as Local Data Integrator Using Active Learning to Identify Candidates Attributes
    by Clovis Santos, Carina Dorneles 
    Abstract: Data integration represents a challenge in application development. Although there are several alternatives to data integration, such as federated and distributed databases, there are still problems with the standardisation of distinct data sources, and this happens because different companies develop distinct systems with different paradigms and concepts. In this paper, we present a case study, in the agriculture and environment domain, of an essential point in the data integration domain which is to show resources to identify nearby attributes concerning the characteristics of the content foreseen in the requirements presented in the proposed schema. Information technology experts in agribusiness help map the most relevant attributes for the investigated scenario. In our experimental tests, we used a quantitative method data analysis approach to validate the results with quantitative comparisons regarding the percentages of proximity between the attribute contents in the databases. Our proposal presents an alternative to simplify data integration without intermediate application or middleware layers. The results were measured on a scale between 0 and 100% to identify candidate attributes. The results were good in identifying attributes in the databases in almost 67% of the cases.
    Keywords: agribusiness; database; text mining; data extraction; machine learning.

  • 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.

  • Managerial Practices for Speedy Strategic Decision in Multinational Firms   Order a copy of this article
    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.

  • The Multicriteria Group Decision Making FlowSort Method using the output aggregation   Order a copy of this article
    by Daami Fedia, Frikha Moalla Hela 
    Abstract: Real-life problems are multifaceted in nature and can create ambiguity in decision-making. Consequently, it is difficult to make the scoring criteria precise and to ultimate the exact values of attributes in multi-criteria analysis. Usually, an individual DM cannot make a judgment alone. In fact, he was unable to effectively define the opinions and the favorites of the entire team using multiple criteria, as everyone seeks to demonstrate their personal impact on the process in terms of his individual and team interests. This paper examines one of the sorting methods, named FLOWSORT, and extends it to multi-criteria group decision making based on the aggregation of individual sorting result outputs. The evaluation of each alternative is described by lexical terms that can be represented by triangular intuitionistic fuzzy numbers. To validate our extension, an illustrative example and empirical comparison with other multi-criteria decision-making methods is carried out.
    Keywords: Multicriteria Group Decision Makingrn Sorting problematicrn Intuitionistic Fuzzy Setrn FlowSort method.