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The full potential of data analysis is crippled by imbalanced and high-dimensional data, which makes these topics significantly important. Consequently, substantial research efforts have been directed to obtain dimension reduction and resolve data imbalance, especially in the context of fraud detection analysis. This work aims to investigate the effectiveness of hybrid learning methods for alleviating the class imbalance and integrating dimensionality reduction techniques. In this regard, the current study examines different classification combinations to achieve optimal savings and improve classification performance. Against this background, several well-known machine learning models are selected such as logistic regression, random forest, CatBoost (CB), and XGBoost. These models are constructed and optimized based on Bayes minimum risk (BMR) associated with the oversampling method synthetic minority oversampling technique (SMOTE) and different feature selection (FS) techniques, both univariate and multivariate. To investigate the performance of the proposed approach, different possible scenarios are analyzed both with and without balancing, with and without FS, and optimization using BMR. With a major insight about the best method to use, BMR shows a good optimization when used with SMOTE, symmetrical uncertainty for FS, and CB as a boosted classifier, principally in terms of F1 score and savings metrics.

Because of the growing popularity of smartphones and tablets, the use of mobile applications has exploded recently. However, the variety of mobile platforms compels developers to create an app for each, making the process more complex and expensive. Thus, open-source cross-platform mobile frameworks have been developed to address this problem, allowing the same code to be imported across various operating systems. This paper comes to propose a new framework for the selection of the appropriate platform for the implementation of a cross-platform mobile application. This framework is based on the most used multi-criteria decision-making (MCDM) methods, namely analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) methods are used. A demonstrative example is proposed to illustrate the suggested methodology.

Aim: For a Wireless Sensor Network (WSN), the sensor node deployment is a critical issue since it reflects the coverage quality. It is the most fundamental issue in WSNs, and it has a great effect on the overall WSN application performance. When it is necessary to deploy randomly sensor nodes in a field of interest to form a WSN, ensuring a high coverage quality becomes difficult. In the random deployment scenario, the most difficult aspect of sensing coverage is to find how well the sensor nodes cover the field of interest. In this paper, we analyze the intrusion detection in a WSN which is defined as a mechanism for monitoring and detecting any intruder in a field of interest, with the objective of enhancing the detection quality in a random WSN. It is required to establish more specific measurements of node density and sensor range that impact the overall system performance especially in the intrusion detection application. To enhance the quality of intrusion detection, several probabilistic models are adopted for heterogeneous WSN in the random deployment scenario. Methods: Multi-intrusion detection model probability in a heterogeneous wireless sensor network for random deployment was used. Results and Discussion: In both homogeneous and heterogeneous WSNs, we analyzed our probabilistic model for multi-intrusion detection in single and multi-sensing detection. Conclusion: Our probabilistic models are useful in selecting the critical parameters of WSN in order to meet the detection quality requirement.

The growth of structured, semi-structured, and unstructured data produced by the new applications is a result of the development and expansion of social networks, the Internet of Things, web technology, mobile devices, and other technologies. However, as traditional databases became less suitable to manage rapidly growing data quantity and the variety of data structures, a new class of database management systems named NoSQL is required to satisfy the new requirements. Although NoSQL databases are generally schemaless, significant research has been conducted on NoSQL design. A literature review presented in this paper let us claim the need to create modeling techniques to describe how to structure data in NoSQL databases. Key-value is one of the NoSQL families, which has received too little attention, especially about its design methodology. Most studies have focused on the other families like column-oriented and document-oriented. This paper aims to present a design approach named KVMod (key-value modeling) specific to key-value databases. The purpose is to provide to the scientific community and engineers a methodology for the design of key-value stores using the maximum automation and therefore the minimum human intervention, which equals the minimum of errors. A software tool called KVDesign has been implemented to automate the proposed methodology and thus the most time-consuming database modeling tasks. The complexity is also discussed to assess the efficiency of our proposed algorithms.

It gets harder and harder to guarantee the quality of software systems due to their increasing complexity and fast development. Because it helps spot errors and gaps during the first phases of software development, software testing is one of the most crucial stages of software engineering. Software testing used to be done manually, which is a time-consuming, imprecise procedure that comes with errors and gaps and costs money, time, and effort. Currently, testing professionals routinely automate testing to obtain trustworthy results while saving time, cost, and labor. We’ve also moved the starting point of the software cycle to the developer, and made write tests before even writing code, or what’s known as TDD (Test Driven Development). The use of new artificial intelligence techniques will enable the generation of smart test cases to improve test quality and provide better coverage and accurate results. In this study, we used the Web of Science database to acquire bibliometric data about intelligent software testing papers which were conducted between 2012 and 2022, and we used Biblioshiny from the R bibliomerix package, alongside with VOSViewer in order to analyze the data and extract insights and answer research questions about the authors, articles, journals, organizations, and countries publishing in the field of intelligent software testing. The focus of this study is on scientific progress and collaborative trends in scholarly research, providing a blueprint for showcasing worldwide developments in the realm of intelligent software testing. By gaining a comprehensive understanding of the present state of research on the application of artificial intelligence in software testing, this study can offer valuable insights to software engineers, architects, and researchers in the field.

Blockchain technology has received wide attention during recent years, and has huge potential to transform and improve supply chain management. However, its implementation in the SSCM (Sustainable Supply Chain Management) strategy is sophisticated, and the challenges are not explored very well, especially in the Moroccan context. To this end, the chief objective of the current endeavor is to investigate the barriers that hinder the adoption of blockchain technology in SSCM from the Moroccan industry and service sectors’ perspective. Based on a comprehensive literature search and the use of experts’ viewpoints, the barriers affecting the successful implementation of blockchain are classified into three categories called TEO: technological and system, environmental, and intra-organizational dimensions. In this context, a fuzzy group decision-making framework is organized by combining DEMATEL (Decision-Making Trial and Evaluation Laboratory) and IFAHP (Intuitionistic Fuzzy Analytic Hierarchy Process). The IFAHP technique helps to determine the importance/priorities of barriers affecting blockchain adoption, while the DEMATEL technique forms the cause–effect interconnections between these barriers and classifies them concerning the degree of importance and relationships. The results reveal that ‘government policy and support’ and ‘challenges in integrating sustainable practices and blockchain technology through SCM’ are significant adoption barriers of blockchain in Moroccan SSCM. The proposed solution can support industrial decision makers to form flexible short- and long-term decision-making strategies to efficiently manage a sustainable supply chain.

Currently, the emerging countries like Morocco seeks to benefit from the potential of blockchain technology to meet its various growing demands, especially in sustainable supply chain management (SSCM). This explains the need for more effort to understand blockchain implementation and identify the barriers influencing the blockchain adoption decision in SSCM, especially, from Moroccan industry and service sectors perspective. In this context, this research paper proposes a group decision-making approach to identify the barriers from a comprehensive literature search, then evaluate them based on intuitionistic fuzzy analytic hierarchy process (IFAHP). Due to the varied importance of the selected barriers, IFAHP is utilized to allocate priority weights for each barrier according to its importance level. The evaluation results reveal that “Government policy and support” and “Challenges in integrating sustainable practices and blockchain technology through sustainable supply chain management (SCM)” are the best ranked barriers that impact the implementation of blockchain technology in Moroccan context. The main objective is to inquire the barriers preventing the blockchain implementation, and assist industry decision-makers in developing supple short- and long-term decision-making strategies for better sustainable supply chain management.

Business Intelligence (BI) is a collection of tools, technologies, and practices that include the entire process of collecting, processing, and analyzing qualitative information, to help entrepreneurs better understand their business and marketplace. Every day, social networks expand at a faster rate and pace, which sees them as a source of Big Data. Therefore, BI is developed in the same way on VoC (Voice of Customer) expressed in social media as qualitative data for company decision-makers, who desire to have a clear perception of customers’ behaviour. In this article, we present a comparative study between traditional BI and social BI, then examine an approach to social business intelligence. Next, we are going to demonstrate the power of Big Data that can be integrated into BI so that we can finally describe in detail how Big Data technologies, like Apache Flume, help to collect unstructured data from various sources such as social media networks and store it in Hadoop storage.

Both Social Network Analysis (SNA) and Association Rules Learning (ARL) enriched our daily-lives through various applications, by improving axial roles in several domains. In particular, the community detection in online social networks (OSN) has interested researchers, for its valuable contribution in understanding systems complexity, as either for academic, commercial or further purposes. The aim of this paper is the identification of communities in OSN using knowledge extraction based on association rules methods. Furthermore, we propose a new approach, namely ARL Clustering, using association rules learning for SNA. Particularly, we base our detection on user’s friendships of OSN by processing a four level technique to extract meaningful rules, converted later to communities. The conducted experimentation was applied on two synthetic real-world networks, and improved important results in identifying potential communities in comparison with existing approaches.

A data warehouse (DW) is a vast repository of data that facilitates decision-making for businesses and companies. This concept dates back to the 1980s and it has been widely accepted. One of the key points for the success of the process of data warehousing lies in the definition of the warehouse model depending on data sources and analysis needs. Once the data warehouse is designed, the content and structure of the data sources, as well as the requirements analysis are required to evolve, therefore, an evolution of the model must take place (diagram and data). In this context, several approaches have been developed to design and implement data warehouses. Nevertheless, there is no standard process that deals with designing all of the data warehouse layers, also, there is no software that encompasses this type of problem. In general, the majority of these approaches focus on a particular aspect of data warehouse such as data storage, ETL process, OLAP, reporting, etc, and does not cover its entire lifecycle. A Model-Driven Architecture (MDA) is a standard approach, its aims to support all phases of software manufacturing by promoting the use of models and the transformations between them. Moreover, this approach aims to automate the process of software engineering, thereby decreasing the cost of software development and enhancing its productivity. In this study, we present a systematic review of various works on the data warehouse design methods. We compare and discuss these works according to the criteria that seem relevant for this issue. We present a new design approach for multidimensional schemas construction from relational models using MDA techniques, we also develop the resulting research perspectives.

The objective of this paper is to propose a hybrid decision-making methodology based on affinity diagram, fuzzy analytic hierarchy process (FAHP) and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) to evaluate, rank and select the most appropriate cloud solutions to accommodate and manage big data projects. In fact, the strategic priority of many corporations consists in the creation of competitive advantages by using new available technologies, processes and governance mechanisms, such as big data and cloud computing. Since the technology is permanently subject to advances and developments, the question for many businesses is how to benefit from big data using the power of technical flexibility that cloud computing can provide. In this context, selecting the most adequate cloud solution to host big data projects is a complex issue that requires an extensive evaluation process. Thus, to assist users to efficiently select their most preferred cloud solution, we propose a hybrid decision-making methodology that meets these requirements in four stages. In the first stage, the identification of evaluation criteria is performed by a decision-making committee using Affinity Diagram. Due to the varied importance of the selected criteria, a FAHP process is used in the second stage to assign the importance weights for each criterion, while FTOPSIS process, in the third stage, employs these weighted criteria as inputs to evaluate and measure the performance of each alternative. In the last step, a sensitivity analysis is performed to evaluate the impact of criteria weights on the final rankings of alternatives.

The location selection for Landfill of Industrial Wastes (LIW) is a very significant task in waste management studies, which has significant impacts on sustainable development of the region. Furthermore, the selection of the appropriate and efficient sites for LIWs is an important multi-criteria decision making problem. The present document suggests an OLAP/GIS-Fuzzy AHP-TOPSIS based methodology for evaluation and selection of best sites for LIWs. In this respect, the candidate locations are specified based on the combination of On-Line Analytical Processing and Geographic Information System (OLAP/GIS). The Fuzzy Analytical Hierarchy Process (Fuzzy-AHP), a multi-criteria decision-making method is applied to analyze the structure of the problem and obtain the weights of the qualitative and quantitative criteria, by incorporating the uncertainty values in decision-making. Then, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is taken into account to assess and rank the alternative locations. Finally, a hypothetical application of the proposed approach is illustrated by a case study of location selection for LIWs in Morocco. The results show that the proposed methodology can successfully achieve the aim of this work.

Geospatial Business Intelligence (Geospatial BI) is a system that combines multidimensional analysis and cartographic visualization. It plays an important role in decision making process for enterprises. Adopting such a comprehensive solution may result in the great investment decision for them, so great deal of attention should be given in the selection of the optimal system. As there are many impacting factors in the selection of Geospatial BI system, the same process is considered as a complex multi-criteria decision making problem. In this paper, we explore the application of an integrated methodology for the evaluation of various Geospatial BI alternatives. The proposed methodology integrates the three well-known decision-making techniques, namely Modified Delphi, fuzzy analytic hierarchical process (fuzzy-AHP), and preference ranking organization method for enrichment evaluations (PROMETHEE). In this respect, the modified Delphi is used to select the most impacting factors by a few decision-makers. The fuzzy-AHP is employed to analyze the structure of the problem and to obtain the weights of the qualitative and quantitative criteria, by incorporating the uncertainty values. Then, the PROMETHEE technique is used for optimal ranking of the alternative system choices. A step-by-step, numerical study is illustrated by using the proposed methodology on the decision making problem of a company that is faced to five Geospatial BI solutions. The results demonstrate that the proposed methodology can successfully accomplish our goal of this study.

The location selection for landfill municipal solid waste is an important issue in waste management. Selection of the optimal location requires consideration of multiple alternative solutions and evaluation factors because of system complexity. Further, ranking of the alternative locations and selection of the most optimal and efficient locations for landfill waste are an important multi-criteria decision-making problem. In this study, the candidate locations for landfill are determined based on Political, Economical, Environmental and Social factors are assessed through decision-makers’ opinion and by the methodology that incorporates fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS). Specifically, the fuzzy AHP technique is applied to determine the weights of selected criteria impacting the location selection process, and the fuzzy AHP is adapted to model the linguistic vagueness and ambiguity, which can also be expressed as triangular fuzzy numbers. Furthermore, fuzzy TOPSIS technique is employed to rank the alternative locations. The applicability of this methodology is demonstrated by a case study of landfill waste location selection in the region of Casablanca, Morocco, and the results are compared with other techniques. Finally, to complete the treatment, a sensitivity analysis is performed to examine the impact of the preferences given by decision-makers to choose the best location.

Sustainable economic growth is nowadays driving firms to extend toward the adoption of many green supply chain management (GSCM) solutions. However, the evaluation and selection of these solutions is a matter of concern that needs very serious decisions, involving complexity owing to the presence of various associated factors. To resolve this problem, a comparative analysis approach based on multi-criteria decision-making methods is proposed for adequate evaluation of sustainable supply chain management solutions. In the present paper, we propose an integrated decision-making model based on FAHP (Fuzzy Analytic Hierarchy Process), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluations) to contribute to a better understanding and development of new sustainable strategies for industrial organizations. Due to the varied importance of the selected criteria, FAHP is used to identify the evaluation criteria and assign the importance weights for each criterion, while TOPSIS and PROMETHEE methods employ these weighted criteria as inputs to evaluate and rank the alternatives. The main objective is to provide a comparative analysis based on TOPSIS and PROMETHEE processes to help make sound and reasoned decisions related to the selection problem of GSCM solution.

Recently, Cloud computing has known a fast growth in term of applications and the end users. In addition to the growth and evolution of the Cloud environment, many challenges that impact the performances of the Cloud applications emerged. One of these challenges is the Load Balancing between the virtual machines of a Datacenter, which is needed to balance the workload of each virtual machine while hoping to get a better Quality of services (QoS). Many approaches were proposed in hope of offering a good QoS. But due to the fact that the Cloud environment is exponentially evolving, these approaches became outdated. In this axis of research, we are proposing a new approach based on the Simulated Annealing and different parameters that affect the distribution of the tasks between the virtual machines. A simulation is also done to compare our approach with other existing algorithms using Cloudsim.

This study aims to propose a decision-making approach combining multi-criteria analysis and fuzzy logic within the online analytical processing data cube model (OLAP). Indeed, most decision-making systems are based on models of operational research. These models are often composed of quantitative data and postulate the existence of a single objective function (criterion) representing the preferences of decision-makers. However, in reality, we are faced with a more complex situation where several criteria (quantitative and/or qualitative) should be taken into account. It is therefore natural to consider different types of data (more criteria) in the design of OLAP cubes and decision-making systems. Multi-criteria decision analysis (MCDA) combined with fuzzy sets theory offers an efficient approach to solve complex decision problems. So we believe it is useful and necessary to envisage, for OLAP cubes, an optimized data model taking into account several criteria, on which we can apply new methods of MCDA. We end our contribution by applying the decision support process of this paper to propose a scheme of green logistics for large industrial zones in the city of Casablanca, Morocco.

OLAP (Online Analytical Processing) system appears as a revolutionary technology that provides adequate analytic solutions for decision support. Using OLAP, analysts and policymakers are able to process and analyze data in an interactive, fast, and effective way according to several axes. This provides a clear vision of their business at any time and in real time. However, these systems suffer from certain limitations related to the consideration of both the multicriteria and fuzzy aspects of multidimensional data when making decisions. To overcome these limitations, we propose an integrated decision-making prototype based on OLAP system and multicriteria analysis (MCA) to generate a hybrid analysis process dealing with complex multicriteria decision-making situations. This proposed solution, based on our previous contributions, allows the analyst to extract data from OLAP data cube model and analyze them using OLAP operators and MCA methods.

Fuzzy multi-criteria group decision making (FMCGDM) process is usually used when a group of decision-makers faces imprecise data or linguistic variables to solve the problems. However, this process contains many methods that require many time-consuming calculations depending on the number of criteria, alternatives and decision-makers in order to reach the optimal solution. In this study, a web-based FMCGDM framework that offers decision-makers a fast and reliable response service is proposed. The proposed framework includes commonly used tools for multi-criteria decision-making problems such as fuzzy Delphi, fuzzy AHP and fuzzy TOPSIS methods. The integration of these methods enables taking advantages of the strengths and complements each method’s weakness. Finally, a case study of location selection for landfill waste in Morocco is performed to demonstrate how this framework can facilitate decision-making process. The results demonstrate that the proposed framework can successfully accomplish the goal of this study.

This paper discusses the intake of combining multi-criteria decision analysis (MCDA) with OLAP systems, to generate an integrated analysis process dealing with complex multi-criteria decision-making situations. In this context, a multi-agent modeling is presented for decision support systems by combining multi-criteria decision analysis (MCDA) with OLAP systems. The proposed modeling which consists in performing the multi-agent system (MAS) architecture, procedure and protocol of the negotiation model is elaborated as a decision support tool for complex decision-making environments. Our objective is to take advantage from the multi-agent system which distributes resources and computational capabilities across interconnected agents, and provide a problem modeling in terms of autonomous interacting component-agents. Thus, the identification and evaluation of criteria as well as the evaluation and ranking of alternatives in a decision support situation will be performed by organizing tasks and user preferences between different agents in order to reach the right decision. At the end, an illustrative example is conducted to demonstrate the function and effectiveness of our MAS modeling.

In this paper, we examine the issue of strategic industrial location selection in uncertain decision making environments for implanting new industrial corporation. In fact, the industrial location issue is typically considered as a crucial factor in business research field which is related to many calculations about natural resources, distributors, suppliers, customers, and most other things. Based on the integration of environmental, economic and social decisive elements of sustainable development, this paper presents a hybrid decision making model combining fuzzy multi-criteria analysis with analytical capabilities that OLAP systems can provide for successful and optimal industrial location selection. The proposed model mainly consists in three stages. In the first stage, a decision-making committee has been established to identify the evaluation criteria impacting the location selection process. In the second stage, we develop fuzzy AHP software based on the extent analysis method to assign the importance weights to the selected criteria, which allows us to model the linguistic vagueness, ambiguity, and incomplete knowledge. In the last stage, OLAP analysis integrated with multi-criteria analysis employs these weighted criteria as inputs to evaluate, rank and select the strategic industrial location for implanting new business corporation in the region of Casablanca, Morocco. Finally, a sensitivity analysis is performed to evaluate the impact of criteria weights and the preferences given by decision makers on the final rankings of strategic industrial locations. Electronic supplementary material The online version of this article (doi:10.1186/s40064-015-1404-x) contains supplementary material, which is available to authorized users.

Actually, a set of ETL software (Extract, Transform and Load) is available to constitute a major investment market. Each ETL uses its own techniques for extracting, transforming and loading data into data warehouse, which makes the task of evaluating ETL software very difficult. However, choosing the right software of ETL is critical to the success or failure of any Business Intelligence project. As there are many impacting factors in the selection of ETL software, the same process is considered as a complex multi-criteria decision making (MCDM) problem. In this study, an application of decision-making methodology that employs the two well-known MCDM techniques, namely Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods is designed. In this respect, the aim of using AHP is to analyze the structure of the ETL software selection problem and obtain weights of the selected criteria. Then, TOPSIS technique is used to calculate the alternatives’ ratings. An example is given to illustrate the proposed methodology. Finally, a software prototype for demonstrating both methods is implemented.

The decisions support Systems are based on decision technologies enabling organizations to make better use of their data flow analysis by simplifying them. Indeed, OLAP (On Line Analytical Processing) tools such as decisional technology, offer the possibility of archiving, management, analysis and multidimensional modeling. However, they are limited in the consideration of the multicriteria and quality aspect of the decision problem. To overcome these limitations, we proposed in this research, a methodological approach for integrating multicriteria analysis in OLAP systems. Our aim of this approach is to create a hybrid model of data (OLAP / MCA), which will expand the capabilities of OLAP through the contribution of MCA as a tool for multi-criteria analysis. In order to validate our contribution, we present the results of experiments showing the efficiency and performance of our approach.

Landfill location selection is a multi-criteria decision problem and has a strategic importance for many regions. The conventional methods for landfill location selection are insufficient in dealing with the vague or imprecise nature of linguistic assessment. To resolve this problem, fuzzy multi-criteria decision-making methods are proposed. The aim of this paper is to use fuzzy TODIM (the acronym for Interactive and Multi-criteria Decision Making in Portuguese) and the fuzzy analytic hierarchy process (AHP) methods for the selection of landfill location. The proposed methods have been applied to a landfill location selection problem in the region of Casablanca, Morocco. After determining the criteria affecting the landfill location decisions, fuzzy TODIM and fuzzy AHP methods are applied to the problem and results are presented. The comparisons of these two methods are also discussed.