@article {Derrouz20225561,
	title = {End-to-end quantum-inspired method for vehicle classification based on video stream},
	journal = {Neural Computing and Applications},
	volume = {34},
	number = {7},
	year = {2022},
	note = {cited By 1},
	pages = {5561-5576},
	abstract = {Intelligent Transportation Systems (ITS) are the most widely used systems for road traffic management. The vehicle type classification (VTC) is a crucial ITS task due to its capability to gather valuable traffic information. However, designing a performant VTC method is challenging due to the considerable intra-class variation of vehicles. This paper presents a new quantum decision-based method for VTC applied to video streaming. This method allows for earlier decision-making by considering a few stream{\textquoteright}s images. Our method is threefold. First, the video stream is acquired and preprocessed following a specific pipeline. Second, we aim to detect and track vehicles. Therefore, we apply a deep learning-based model to detect vehicles, and then a vehicle tracking algorithm is used to track each detected vehicle. Third, we seek to classify the tracked vehicle according to six defined classes. Furthermore, we transform the tracked vehicles according to a pipeline, consisting of the histogram of oriented gradients (HOG), and principal component analysis (PCA) methods. Then, we estimate the vehicles{\textquoteright} probabilities of belonging to each class by training multilayer perceptron (MLP) classifier with the resulting features. To assign a class to a vehicle, we apply a quantum-inspired probability integrator that handles each frame{\textquoteright}s information flow. The unique characteristics of the work we propose, compared to the existing ones, are expressed in the decision-making process, since the former requires a sequence of frames of different sizes, compared to the image-based-decision made by the other methods. Our method outperformed the baseline methods with an accuracy up to 96\%. {\textcopyright} 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.},
	keywords = {Classification (of information), Decision making, Decisions makings, Deep learning, Early decision, Early decision making, End to end, Intelligent systems, Intelligent transportation systems, Intelligent vehicle highway systems, Pipelines, Principal component analysis, Quantum inspired algorithm, Tracked vehicles, Type classifications, Vehicle classification, Vehicle type classification, Vehicle types, Video streaming},
	doi = {10.1007/s00521-021-06718-9},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122251939\&doi=10.1007\%2fs00521-021-06718-9\&partnerID=40\&md5=8e1d19359f8245b2f82d8558670d4ba1},
	author = {Derrouz, H. and Cabri, A. and Ait Abdelali, H. and Oulad Haj Thami, R. and Bourzeix, F. and Rovetta, S. and Masulli, F.}
}
@article {Saadaoui2022106436,
	title = {A Local PSO-Based Algorithm for Cooperative Multi-UAV Pollution Source Localization},
	journal = {IEEE Access},
	volume = {10},
	year = {2022},
	note = {cited By 0},
	pages = {106436-106450},
	abstract = {Recently, air pollution has grown significantly, and it can frequently be challenging to identify the sources of contaminants. This article studies the deployment of multi-cooperative unmanned aerial vehicles (UAVs) to look for sources of pollution in an unknown region. Specifically, a probabilistic search strategy based on the Local Particle Swarm Optimization (LoPSO) method is suggested to design an optimal strategy that enhances the cooperative search of drones while cutting down on overall search time and improving source detection efficiency. The entire strategy is divided into two phases: exploration and exploitation. In the first phase, the detection and tracing of the plume is the favored task, in which each UAV operates in either the Greedy or LoPSO mode and chooses its path based on plane coordinates generated according to the active mode. By utilizing the areas with a high probability of discovering the source on flight mode LoPSO, the search is focused during the exploitation phase on the precise search of the exact location of the pollutant source. This is done under the direction of a probabilistic computation that uses the Bayesian process model to create and update the probability map of the pollutant source location as new sensor data becomes available. The simulation data of the proposed technique demonstrates promising results in terms of the complexity and accuracy attained in identifying pollution sources. {\textcopyright} 2013 IEEE.},
	keywords = {Aerial vehicle, Air pollution, Aircraft detection, Antennas, Cooperative search, Decision making, Decisions makings, Drones, Fleet operations, Greedy, Job analysis, Location, Location awareness, Particle swarm, particle swarm optimization, Particle swarm optimization (PSO), Pollution sources, Robot kinematics, Swarm optimization, Task analysis, Unmanned aerial vehicle},
	doi = {10.1109/ACCESS.2022.3212079},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140203997\&doi=10.1109\%2fACCESS.2022.3212079\&partnerID=40\&md5=8b9e22a8dd7a3ee80fc14fb00819a50f},
	author = {Saadaoui, H. and El Bouanani, F.}
}
@conference {Najmani2022,
	title = {Offline and Online Evaluation for Recommender Systems},
	booktitle = {2022 International Conference on Intelligent Systems and Computer Vision, ISCV 2022},
	year = {2022},
	note = {cited By 0},
	abstract = {Recommender systems aim to facilitate decision making for users by offering them information according to their preferences, they are now popular in several application domains. The evaluation of recommender systems is very important to have an effective application in practice. In addition, it focuses to find better algorithms and evaluate their performance. However, researchers did not give much attention to it in this field. There are various ways to evaluate a recommender system. In this paper, we will discuss the main types of evaluations in this domain, which are offline and online evaluation, we will start with an overview of recommender systems, then we will present each type of evaluation, and we will compare the offline and the online evaluation for recommender systems. We will base on ten factors which are, the reproducibility, the reliability of the results of each type of evaluation, the preparation cost, the evaluation, the stability, the possibility of extensibility that{\textquoteright}s mean if we can add new metrics or not, the scalability, the passed time, deep analysis, and the sparsity metric. Finally, we will discuss the factors which are presented in the comparison. {\textcopyright} 2022 IEEE.},
	keywords = {Applications domains, Decision making, Decisions makings, It focus, Offline, Offline evaluation, On-line evaluation, Online systems, Performance, Recommender Systems, Reliability analysis, Reproducibilities},
	doi = {10.1109/ISCV54655.2022.9806059},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134293509\&doi=10.1109\%2fISCV54655.2022.9806059\&partnerID=40\&md5=31b11e740c58602df2b12bf3260a1ec9},
	author = {Najmani, K. and Ajallouda, L. and Benlahmar, E.H. and Sael, N. and Zellou, A.}
}
@article {ElHaddouti2022575,
	title = {A Secure and Trusted Fog Computing Approach based on Blockchain and Identity Federation for a Granular Access Control in IoT Environments},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {13},
	number = {3},
	year = {2022},
	note = {cited By 0},
	pages = {575-584},
	abstract = {Fog computing is a new computing paradigm that is an extension of the standard cloud computing model, which can be adopted as a cost effective strategy for managing connected objects, by enabling real-time computing and communication for analytical and decision making. Nonetheless, even though Fog-based Internet of Things networks optimize the standard architecture by moving computing, storage, communication, and control decision closer to the edge network, the technology becomes open to malicious attackers and remains many business risks that are not yet resolved. In fact, access control, privacy as well as trust risks present major challenges in Internet of Things environments based on Fog computing due to the large scale distributed nature of devices at the Fog layer. In addition, the traditional authentication methods are not adequate in Fog-based Internet of Things contexts since they consume significantly more computation power and incur high latency. To deal with these gaps, we present in this paper a secure and trusted Fog Computing approach based on Blockchain and Identity Federation technologies for a granular access control in IoT environments. The proposed scheme uses Smart Contract concept and Attribute-Based Access Control model to ensure the level of security and scalability required for data integrity without resorting to a central authority to make an access decision {\textcopyright} 2022. International Journal of Advanced Computer Science and Applications.All Rights Reserved.},
	keywords = {Access control, Block-chain, Cloud-computing, Computing model, Computing paradigm, Cost effective strategies, Cost effectiveness, Decision making, Decisions makings, Digital storage, Fog, Fog computing, Identity federation, Internet of things, IOT, Real-time communication, Real-time computing, Smart contract, Trusted computing},
	doi = {10.14569/IJACSA.2022.0130368},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129902962\&doi=10.14569\%2fIJACSA.2022.0130368\&partnerID=40\&md5=65966293c50d57a061513c913c7b5bb3},
	author = {El Haddouti, S. and El Kettani, M.D.E.-C.}
}
@article {BenRabia2022,
	title = {Simulation-based analytics: A systematic literature review},
	journal = {Simulation Modelling Practice and Theory},
	volume = {117},
	year = {2022},
	note = {cited By 1},
	abstract = {Over time, Decision Support Systems have helped decision makers solve complex problems through Operational Research and Simulation. Nowadays, data explosion is having a profound effect on the ways in which many sectors operate. This advent of massive data gives rise to new concepts and requires new methods and analysis tools. In this paper, we highlight the role of simulation in Business Analytics. In a framework-based analytics, simulation is a technique that can be incorporated into predictive or prescriptive stage. For that, we have posed research questions to limit results to what give a comprehensive description of models, techniques and architectures used in the hybridization between simulation and business analytics. The presented analyses confirm that simulation remains an indispensable mechanism for adding value to analytics project and the coupling between the two techniques is in its embryonic phase. A conclusion presented prospects and future improvements found during the writing of the research. {\textcopyright} 2022 Elsevier B.V.},
	keywords = {Artificial intelligence, Big data, Business analytics, Competitive intelligence, Data Analytics, Data driven decision, Data-driven decision making, Decision making, Decision support systems, Decisions makings, Model-driven, Model-driven decision making, Predictive analytics, Simulation \& analytic ops, Systematic literature review, What-if Analysis},
	doi = {10.1016/j.simpat.2022.102511},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125647406\&doi=10.1016\%2fj.simpat.2022.102511\&partnerID=40\&md5=b8a2d6f53d7e2a53c6c1e7675770c74b},
	author = {Ben Rabia, M.A. and Bellabdaoui, A.}
}
@article {Lamrhari2022,
	title = {A social CRM analytic framework for improving customer retention, acquisition, and conversion},
	journal = {Technological Forecasting and Social Change},
	volume = {174},
	year = {2022},
	note = {cited By 4},
	abstract = {Social Customer Relationship Management (social CRM) has become one of the central points for many companies seeking to improve their customer experience. It comprises a set of processes that allows decision-makers to analyze customer data, in order to launch an efficient, customer-centric, and cost-effective marketing strategy. Nonetheless, the inclusion of social media data in CRM introduces new challenges, as it requires advanced analytical approaches to extract actionable insight from such a huge amount of data. Thus, in this paper, we propose a social CRM analytic framework, which includes various analytical approaches, aiming at improving customer retention, acquisition, and conversion. This framework has been tested on various datasets and extensively evaluated based on several performance metrics. The obtained results suggest that the proposed framework can effectively extract relevant information and support decision-making processes. From an academics perspective, the study contributes to an understanding of customers{\textquoteright} experiences throughout their engagement on social media and focuses on long-term relationships with customers. From a managerial perspective, companies should leverage the insight generated through every customer engagement on social media to drive effective marketing strategies. {\textcopyright} 2021},
	keywords = {Acquisition, Conversion, Cost effectiveness, Customer acquisition, Customer conversion, Customer relationship management, Customer retention, Customer satisfaction, Customers{\textquoteright} satisfaction, Decision making, Digital storage, Machine learning, management, marketing, Public relations, Sales, Sentiment analysis, Social CRM, social media, Social networking (online), Strategic planning},
	doi = {10.1016/j.techfore.2021.121275},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118116967\&doi=10.1016\%2fj.techfore.2021.121275\&partnerID=40\&md5=10ffdb7f551bf1e135c01fb362d1a559},
	author = {Lamrhari, S. and Ghazi, H.E. and Oubrich, M. and Faker, A.E.}
}
@article {AitTemghart2021335,
	title = {Game Theoretic Approaches to Mitigate Cloud Security Risks: An Initial Insight},
	journal = {Lecture Notes in Business Information Processing},
	volume = {416 LNBIP},
	year = {2021},
	note = {cited By 1},
	pages = {335-347},
	abstract = {Cloud computing is one of the most promising innovations impacting data storage and processing. And with it, clients rely on the IT solutions offered by an external provider instead of on-premise applications. Despite its enormous impacts, customers are still reluctant to outsource their business processes because of security concerns. As data is typically stored and governed by cloud vendors, users need to deal with security issues linked to the loss of control over their sensitive data. Cloud providers need to implement the appropriate security measures that might attract more clients while making the minimum investment. While used in various disciplines, game theory has recently expanded to investigate the effect of the defenders{\textquoteright} and attackers{\textquoteright} behaviors on strategic decision-making. This study aims to develop insights into how game theory can develop better security policies in cloud computing. First, we perform threat modeling to identify the potential threats facing cloud. Second, we identify the limitations of existing game based solutions and then suggest an improved model define an adequate strategy that would figure out the right balance between the required security level and the profit margins. Besides, we present future directions that can be explored to build highly reliable and optimal strategies for cloud services. {\textcopyright} 2021, Springer Nature Switzerland AG.},
	keywords = {Business Process, Cloud computing, Cloud securities, Computation theory, Computer games, Data privacy, Decision making, Decision theory, Digital storage, Game theory, Information analysis, Optimal strategies, Potential threats, Security issues, Security measure, Security policy, Strategic decision making},
	doi = {10.1007/978-3-030-76508-8_24},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111082922\&doi=10.1007\%2f978-3-030-76508-8_24\&partnerID=40\&md5=1619b858093bfb251eb24a3209534772},
	author = {Ait Temghart, A. and Ait Omar, D. and Outanoute, M. and Marwan, M.}
}
@article {Saadaoui2021134998,
	title = {Information sharing based on local pso for uavs cooperative search of moved targets},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	note = {cited By 3},
	pages = {134998-135011},
	abstract = {This paper proposes an optimization strategy for searching moving targets{\textquoteright} locations using cooperative unmanned aerial vehicles (UAVs) in an unknown environment. Such a strategy aims at reducing the overall search time and impact of uncertainties caused by the motion of targets, as well as improving the detection efficiency of UAVs. Specifically, we report, based on the UAV{\textquoteright}s scan of a location and taking into account (i) the detection and communication coverage limitations, and (ii) either a false alarm or inaccurate detection of the target, either the existence or the absence of the target. Moreover, leveraging a cooperative and competitive particle swarm optimization (PSO) algorithm, a decentralized target search model, relying on a real-time dynamic construction of cooperative UAV local sub-swarms (LoPSO), is proposed. Each sub-swarm strives to validate quickly the target location, updated based on the Bayesian theory. In such a strategy, each UAV operates in two flight modes, namely, either in swarm mode or in Greedy mode, and takes into consideration the received data from other UAVs to improve the overall environmental information. The simulation results revealed that the LoPSO outperforms other well-known searching methods of target methods for target search in unknown environments in terms of both performance and computational complexity. {\textcopyright} 2013 IEEE.},
	keywords = {Aircraft detection, Antennas, Cooperative search, Decision making, Decisions makings, Information sharing, Location, Moved target, Optimization strategy, Particle swarm optimization (PSO), Sub-swarms, Target location, Target search, Unknown environments, Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)},
	doi = {10.1109/ACCESS.2021.3116919},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117048719\&doi=10.1109\%2fACCESS.2021.3116919\&partnerID=40\&md5=9c295c1ff03ee777bc468333f6bc6f4e},
	author = {Saadaoui, H. and Bouanani, F.E. and Illi, E.}
}
@conference {Marhraoui2021133,
	title = {An integrated human-AI Framework towards organizational agility and sustainable performance},
	booktitle = {Proceedings - 2021 International Conference on Digital Age and Technological Advances for Sustainable Development, ICDATA 2021},
	year = {2021},
	note = {cited By 0},
	pages = {133-139},
	abstract = {Companies are facing important challenges related to markets{\textquoteright} internationalization, regulatory restrictions and fierce competition especially during the COVID19 crisis. They should embrace change and be agile in order to prevent risks and seize opportunities quickly and efficiently. In this article, we examine how artificial intelligence (AI) can help companies to enhance their organizational agility. Based on two systematic literature reviews on the subject, we identified the weaknesses on relying only on a digital enabler or human resource (HR) practices. Thus, we propose a Framework integrating artificial intelligence and human practices in order to help companies in their efforts towards agility. This latter allows companies to adapt to new regulations in the society, to customers{\textquoteright} expectations and to environmental changes. Ultimately, agile companies can ensure a sustainable performance. {\textcopyright} 2021 IEEE.},
	keywords = {Artificial intelligence, Customer expectation, Decision making, Decisions makings, Environmental change, Environmental regulations, Human practices, Human resource practices, internationalisation, Organizational agility, Seizing, Sustainable development, Sustainable performance, Systematic literature review},
	doi = {10.1109/ICDATA52997.2021.00035},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119507801\&doi=10.1109\%2fICDATA52997.2021.00035\&partnerID=40\&md5=af6ef5c548c44726a28be19a747ef11f},
	author = {Marhraoui, M.A. and Idrissi, M.A.J. and El Manouar, A.}
}
@article {Zerouaoui2021,
	title = {Reviewing Machine Learning and Image Processing Based Decision-Making Systems for Breast Cancer Imaging},
	journal = {Journal of Medical Systems},
	volume = {45},
	number = {1},
	year = {2021},
	note = {cited By 19},
	abstract = {Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40~years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten criteria: year and publication channel, empirical type, research type, medical task, machine learning techniques, datasets used, validation methods, performance measures and image processing techniques which include image pre-processing, segmentation, feature extraction and feature selection. Results showed that diagnosis was the most used medical task and that Deep Learning techniques (DL) were largely used to perform classification. Furthermore, we found out that classification was the most ML objective investigated followed by prediction and clustering. Most of the selected studies used Mammograms as imaging modalities rather than Ultrasound or Magnetic Resonance Imaging with the use of public or private datasets with MIAS as the most frequently investigated public dataset. As for image processing techniques, the majority of the selected studies pre-process their input images by reducing the noise and normalizing the colors, and some of them use segmentation to extract the region of interest with the thresholding method. For feature extraction, we note that researchers extracted the relevant features using classical feature extraction techniques (e.g. Texture features, Shape features, etc.) or DL techniques (e. g. VGG16, VGG19, ResNet, etc.), and finally few papers used feature selection techniques in particular the filter methods. {\textcopyright} 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.},
	keywords = {Article, breast, Breast Cancer, cancer diagnosis, classifier, Computer-Assisted, Convolutional neural network, Decision making, Deep learning, deep neural network, diagnostic accuracy, diagnostic imaging, echography, Feature extraction, feature selection, female, human, Humans, image processing, image segmentation, Machine learning, Magnetic Resonance Imaging, mammography, multilayer perceptron, nuclear magnetic resonance imaging},
	doi = {10.1007/s10916-020-01689-1},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100125842\&doi=10.1007\%2fs10916-020-01689-1\&partnerID=40\&md5=3074b3d443373f75f5a3c1c6134512ee},
	author = {Zerouaoui, H. and Idri, A.}
}
@conference {Mouncif2020,
	title = {Blood collection supply chain management: A critical review and future perspective},
	booktitle = {6th International Conference on Optimization and Applications, ICOA 2020 - Proceedings},
	year = {2020},
	note = {cited By 0},
	abstract = {Blood supply chain is an essential part of modern healthcare system, and an appropriate feeding of blood units and blood products is necessary for several care treatments and surgeries. Any improvement in the management of these chains will guarantee an adequate blood availability to meet the demand and save lives. The applications of operational research and management science techniques enable decision makers to come up with better and more efficient decisions for a wide range of problems in blood transfusion. Various papers on blood collection management, dealing with different problems and constraints, have been proposed in the literature. The purpose of this paper is to integrate the topic in recent research on blood collection management and try to create awareness for a deeper knowledge on blood collection and donation issues. In this paper, we systematically analyze and classify existing studies, addressing various cases, in order to highlight opportunities and to open practical future research directions for operational research and management science about optimization of blood collection. {\textcopyright} 2020 IEEE.},
	keywords = {Blood, Blood collection, Blood transfusion, Decision makers, Decision making, Future perspectives, Future research directions, Health-care system, Operational research, Recent researches, Supply chain management},
	doi = {10.1109/ICOA49421.2020.9094514},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085745137\&doi=10.1109\%2fICOA49421.2020.9094514\&partnerID=40\&md5=1d84b86d8dc5c94a6ed1a1437e4f86c3},
	author = {Mouncif, C. and Bellabdaoui, A.}
}
@conference {Maqboul2020,
	title = {Contribution of Artificial Neural Network in Predicting Completeness Through the Impact and Complexity of its Improvement},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	abstract = {The technological evolution and the immensity of the data produced, circulated into company makes these data, the real capital of the companies to the detriment of the customers. The erroneous data put the knockout to relationships with customers, the company must address this problem and identify the quality projects on which it must make an effort. In this article, we will present an approach based on qualitative and quantitative analysis to help the decision-makers to target data by its impacts and complexities of process improvement. The Qualitative study will be a survey and a quantitative to learn from survey data to decide the prediction and the completeness of data. {\textcopyright} 2020 ACM.},
	keywords = {Decision makers, Decision making, Erroneous datum, Information systems, Information use, Neural networks, Process Improvement, Qualitative and quantitative analysis, Qualitative study, Survey data, Surveys, Technological evolution},
	doi = {10.1145/3386723.3387850},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123040935\&doi=10.1145\%2f3386723.3387850\&partnerID=40\&md5=222e383506f6fed754588124c4a4be60},
	author = {Maqboul, J. and Jaouad, B.B.}
}
@conference {Boumahdi2020603,
	title = {Crisis management systems: Big data and machine learning approach},
	booktitle = {ENASE 2020 - Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering},
	year = {2020},
	note = {cited By 0},
	pages = {603-610},
	abstract = {A crisis is defined as an event that, by its nature or its consequences, poses a threat to the vital national interests or the basic needs of the population, encourages rapid decision making, and requires coordination between the various departments and agencies. Hence the need and importance of crisis and disaster management systems. These crisis and disaster management systems have several phases and techniques, and require many resources and tactics and needs. Among the needs of these systems are useful and necessary information that can be used to improve the making of good decisions, such as data on past and current crises. The application of machine learning and big data technologies in data processing of crises and disasters can yield important results in this area. In this document, we address in the first part the crisis management systems, and the tools of big data and machine learning that can be used. Then in the second part, we have established a literature review that includes a state of the art, and a discussion. Then we established a machine learning and big data approach for crisis management systems, with a description and experimentation, as well as a discussion of results and future work. {\textcopyright} Copyright 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Big data, Crisis management systems, Data handling, Data technologies, Decision making, Disaster management, Disaster prevention, Disasters, Information management, Literature reviews, Machine learning, Machine learning approaches, Software engineering, State of the art},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088381343\&partnerID=40\&md5=b90b38f94e34455a12781472d1aba3ab},
	author = {Boumahdi, A. and El Hamlaoui, M. and Nassar, M.}
}
@conference {Gogolou20201857,
	title = {Data Series Progressive Similarity Search with Probabilistic Quality Guarantees},
	booktitle = {Proceedings of the ACM SIGMOD International Conference on Management of Data},
	year = {2020},
	note = {cited By 18},
	pages = {1857-1873},
	abstract = {Existing systems dealing with the increasing volume of data series cannot guarantee interactive response times, even for fundamental tasks such as similarity search. Therefore, it is necessary to develop analytic approaches that support exploration and decision making by providing progressive results, before the final and exact ones have been computed. Prior works lack both efficiency and accuracy when applied to large-scale data series collections. We present and experimentally evaluate a new probabilistic learning-based method that provides quality guarantees for progressive Nearest Neighbor (NN) query answering. We provide both initial and progressive estimates of the final answer that are getting better during the similarity search, as well suitable stopping criteria for the progressive queries. Experiments with synthetic and diverse real datasets demonstrate that our prediction methods constitute the first practical solution to the problem, significantly outperforming competing approaches. {\textcopyright} 2020 Association for Computing Machinery.},
	keywords = {Analytic approach, Decision making, Nearest neighbors, Practical solutions, Prediction methods, Probabilistic Learning, Progressive queries, Query processing, Search engines, Similarity search, Stopping criteria},
	doi = {10.1145/3318464.3389751},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084509188\&doi=10.1145\%2f3318464.3389751\&partnerID=40\&md5=71d788a1cdf706d6ae4f9dfa38089a7f},
	author = {Gogolou, A. and Tsandilas, T. and Echihabi, K. and Bezerianos, A. and Palpanas, T.}
}
@article {Lhazmir2020,
	title = {A decision-making analysis in UAV-enabled wireless power transfer for IoT networks},
	journal = {Simulation Modelling Practice and Theory},
	volume = {103},
	year = {2020},
	note = {cited By 8},
	abstract = {We consider an IoT network with energy-harvesting capabilities. To extend the network lifetime, we propose a novel unmanned aerial vehicle (UAV)- enabled wireless power transfer (WPT) system, where UAVs move among IoT devices and act as data aggregators and wireless power providers. This paper addresses the decision-making problem since the limited buffer and energy resources constrain all nodes. Each IoT node must decide on whether to request a data transmission, to ask for a wireless energy transfer or to abstain and not take any action. When a UAV receives a request from an IoT device, either for data reception or wireless energy transmission, it has to accept or decline. In this paper, we aim to find a proper packet delivery and energy transfer policy according to the system state that maximizes the data transmission efficiency of the system. We first formulate the problem as a Markov Decision Process (MDP) to tackle the successive decision issues, to optimize a utility for each node upon a casual environment. As the MDP formalism achieves its limits when the interactions between different nodes are considered, we formulate the problem as a Graph-based MDP (GMDP). The transition functions and rewards are then decomposed into local functions, and a graph illustrates the dependency{\textquoteright} relations among the nodes. To obtain the optimal policy despite the system{\textquoteright}s variations, Mean-Field Approximation (MFA) and Approximate linear-programming (ALP) algorithms were proposed to solve the GMDP problem. {\textcopyright} 2020 Elsevier B.V.},
	keywords = {Antennas, Approximate linear programming, Approximation algorithms, Behavioral research, Data communication systems, Data transfer, Data transmission efficiency, Decision making, Decision making analysis, Decision-making problem, Energy harvesting, Energy resources, Energy transfer, Graph theory, Graphic methods, Inductive power transmission, Internet of things, Linear programming, Markov Decision Processes, Markov processes, Mean field approximation, Unmanned aerial vehicles (UAV), Wireless energy transfers, Wireless power transfer (WPT)},
	doi = {10.1016/j.simpat.2020.102102},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084066274\&doi=10.1016\%2fj.simpat.2020.102102\&partnerID=40\&md5=6a1c9e107244a170a782e61d0f9755cc},
	author = {Lhazmir, S. and Oualhaj, O.A. and Kobbane, A. and Mokdad, L.}
}
@conference {Boukhayma202037,
	title = {Evaluating Ongoing Decision Support System: A Case Study},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {37-42},
	abstract = {The aim of this paper is to put into practice a framework we proposed in an earlier work for evaluating on-going decision support systems. The framework serves as a base for a method to compute evaluation criteria scores using an AHP model. The paper also illustrates the automation of this method into a tool to enable GASCO company to choose between two already implemented DSSs in order to reduce IT operating cost. The evaluation covers three axes: Process, outcome and decision maker metrics. The results highlight the importance of using users{\textquoteright} learning as a measure to determine DSS success. {\textcopyright} 2020 ACM.},
	keywords = {Decision makers, Decision making, Decision support systems, Evaluation criteria, Intelligent systems, Three axes},
	doi = {10.1145/3419604.3419802},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096414260\&doi=10.1145\%2f3419604.3419802\&partnerID=40\&md5=d07ab659252fea5554cc22b2500955cb},
	author = {Boukhayma, K. and Idrissi, M.A.J. and Benhiba, L.}
}
@article {Rachad2020,
	title = {Intelligent Mobile Applications: A Systematic Mapping Study},
	journal = {Mobile Information Systems},
	volume = {2020},
	year = {2020},
	note = {cited By 3},
	abstract = {Smart mobiles as the most affordable and practical ubiquitous devices participate heavily in the enhancement of our daily life by the use of many convenient applications. However, the significant number of mobile users in addition to their heterogeneity (different profiles and contexts) obligates developers to enhance the quality of their apps by making them more intelligent and more flexible. This is realized mainly by analyzing mobile user{\textquoteright}s data. Machine learning (ML) technology provides the methodology and techniques needed to extract knowledge from data to facilitate decision-making. Therefore, both developers and researchers affirm the benefits of combining ML techniques and mobile technology in several application fields as e-health, e-learning, e-commerce, and e-coaching. Thus, the purpose of this paper is to have an overview of the use of ML techniques in the design and development of mobile applications. Therefore, we performed a systematic mapping study of papers published on this subject in the period between 1 January 2007 and 31 December 2019. A total number of 71 papers were selected, studied, and analyzed according to the following criteria, year, sources and channel of publication, research type, and methods, kind of collected data, and finally adopted ML models, tasks, and techniques. {\textcopyright} 2020 Taoufik Rachad and Ali Idri.},
	keywords = {Application fields, Decision making, Design and Development, Mapping, Methodology and techniques, Mobile applications, Mobile commerce, Mobile computing, Mobile Technology, Mobile users, Systematic mapping studies, Ubiquitous devices},
	doi = {10.1155/2020/6715363},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083095554\&doi=10.1155\%2f2020\%2f6715363\&partnerID=40\&md5=d474ab3cece2179d34f54fa7087a4cfb},
	author = {Rachad, T. and Idri, A.}
}
@conference {Berradi2020,
	title = {Optimize the QoS Metrics in Smart Mobile Communication using Multi-Objective Function},
	booktitle = {3rd International Conference on Advanced Communication Technologies and Networking, CommNet 2020},
	year = {2020},
	note = {cited By 1},
	abstract = {The impact of mobility is a major factor that can have an influence on the stability of network behaviour in mobile ad-hoc networks (MANET). The selection of paths having stability, security and an extended network lifetime is one of the crucial issues in the design of routing protocols. In this paper, the authors propose an enhanced algorithm relying on a multipurpose function according to three metrics, mobility, energy and reputation levels to improve the routing decision-making process. The main goal is to identify better performing Multipoint relays (MPRs) which are used to relay messages between nodes. They are also necessary for routing and selecting the appropriate route from any source to any destination node. The identification of the most efficient MPRs can upgrade the network performing even in the most challenging conditions of the dynamic environment. The authors implement the advanced algorithm in the OLSR and get the performance result by using a network simulator (NS3) running under the mobility model of the Manhattan network. The achieved results show important performance compared to the default version. In addition, the proposed model can be employed to improve network efficiency for proactive protocols in ad-hoc networks. {\textcopyright} 2020 IEEE.},
	keywords = {Decision making, Destination nodes, Dynamic environments, Internet protocols, Manhattan networks, Mobile ad hoc networks, Mobile communications, Mobile telecommunication systems, Multi-objective functions, Network efficiency, Network security, Network simulators, Proactive protocol},
	doi = {10.1109/CommNet49926.2020.9199632},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093653757\&doi=10.1109\%2fCommNet49926.2020.9199632\&partnerID=40\&md5=83e6a0c98ac8386acb6e6aad76a531ec},
	author = {Berradi, H. and Habbani, A. and Souidi, M. and Elmahdi, F. and Mouchfiq, N.}
}
@conference {Lamrhari2020,
	title = {Random Forest-based Approach for Classifying Customers in Social CRM},
	booktitle = {2020 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2020},
	year = {2020},
	note = {cited By 0},
	abstract = {Social Customer Relationship Management (social CRM) has become one of the central points for many companies seeking to improve their customer experience. It comprises a set of processes that allows decision-makers to analyze customer data in order to launch an efficient customer-centric and cost-effective marketing strategy. However, targeting all potential customers with one general marketing strategy seems to be inefficient. While targeting each potential customer with a specific strategy can be cost demanding. Thus, it is essential to group customers into specific classes and target each class according to its respective customer needs. In this paper, we develop a Random Forest- based approach to classify potential customers into three main categories namely, prospects, satisfied and unsatisfied customers. The proposed model has been trained, tested, and compared to some state-of-the-art classifiers viz., Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) based on several metrics including accuracy, sensitivity, specificity, false positive rate, and false negative rate. The reported results were satisfactory with an accuracy of 98.46\%, a sensitivity of 97.69\%, and a specificity of 98.84\%. {\textcopyright} 2020 IEEE.},
	keywords = {Commerce, Cost effectiveness, Customer experience, Customer relationship management, Decision making, Decision trees, False negative rate, False positive rates, Industrial management, K nearest neighbor (KNN), Marketing strategy, Nearest neighbor search, Neural networks, Potential customers, Public relations, Random forests, Sales, State of the art, Strategic planning, Support vector machines},
	doi = {10.1109/ICTMOD49425.2020.9380602},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103738578\&doi=10.1109\%2fICTMOD49425.2020.9380602\&partnerID=40\&md5=c672980200a36b34b0ae6df0842ef948},
	author = {Lamrhari, S. and Elghazi, H. and El Faker, A.}
}
@conference {Rabia2020,
	title = {Simulation as a decision-making tool in a business analytics environment},
	booktitle = {Proceedings - 2020 5th International Conference on Logistics Operations Management, GOL 2020},
	year = {2020},
	note = {cited By 0},
	abstract = {Nowadays, Business Intelligence (BI) plays a key role in the decision-making chain because of what it offers in terms of data availability, structuring, analysis and presentation in the form of dashboards or Key Performance Indicators (KPI) to assist decision-making. Despite its advantages in terms of historicizing the company{\textquoteright}s activity, traditional BI tools do not allow us to project our business into the future and evaluate the impact of a decision on the entire activity of a company. Given this consideration, What-if analysis positions itself as a data-intensive simulation to examine the impact of a decision. In this article, we intend to highlight the role that simulation techniques can play in prescriptive analysis. Then, we present the different architectures that combine the use of simulation in an analytical environment. Finally, we also provide an idea of the future perspectives and challenges in simulation-based analytics. {\textcopyright} 2020 IEEE.},
	keywords = {Advanced Analytics, Benchmarking, Business analytics, Data availability, Data intensive, Decision making, Decision making tool, Future perspectives, Key performance indicators, Simulation technique, What-if Analysis},
	doi = {10.1109/GOL49479.2020.9314725},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100164050\&doi=10.1109\%2fGOL49479.2020.9314725\&partnerID=40\&md5=03afaf905e87539e719dc12b27acd09d},
	author = {Rabia, M.A.B. and Bellabdaoui, A.}
}
@conference {Maha2020,
	title = {The Two Phases Method for operating rooms planning and scheduling},
	booktitle = {2020 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2020},
	year = {2020},
	note = {cited By 2},
	abstract = {Over the last three or four decades, there have been numerous optimization problems in Healthcare which have been approached by researchers. Hospital logistics which must be organized and structured in order to secure patient satisfaction in terms of quality, quantity, time, security and least cost, forms part of the quest for global performance.According to the literature review, the problem of operating rooms planning and scheduling involves different conflicting objectives while considering constraints on availability of rooms, patients and doctors. In this paper, we proposed the Two Phases Method (TPM), which is a general technique that is likely to solve multi-objective combinatorial optimization (MOCO) problems.As it is known, TPM has never been applied to solve operating room planning and scheduling problem. In this paper, we developed the TPM to resolve the cited issue, while focusing on optimizing both total completion time and patients{\textquoteright} waiting time. {\textcopyright} 2020 IEEE.},
	keywords = {Combinatorial Optimization, Conflicting objectives, Constraint satisfaction problems, Decision making, Hospital logistics, Industrial management, Literature reviews, Multiobjective combinatorial optimization, Operating rooms, Optimization problems, Patient satisfaction, Planning and scheduling, Scheduling, Total completion time},
	doi = {10.1109/ICTMOD49425.2020.9380584},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103741668\&doi=10.1109\%2fICTMOD49425.2020.9380584\&partnerID=40\&md5=a5086170077f1fe352eb34de4e820aac},
	author = {Maha, T. and Achchab, S. and Omar, S.}
}
@conference {Lhazmir2020,
	title = {UAV for Wireless Power Transfer in IoT Networks: A GMDP approach},
	booktitle = {IEEE International Conference on Communications},
	volume = {2020-June},
	year = {2020},
	note = {cited By 3},
	abstract = {Unmanned aerial vehicles (UAVs) are a promising technology employed as moving aggregators and wireless power transmitters for IoT networks. In this paper, we consider an UAV-IoT wireless energy and data transmission system and the decision-making problem is investigated. We aim at optimizing the nodes{\textquoteright} utilities by defining a good packet delivery and energy transfer policy according to the system state. We formulate the problem as a Markov Decision Process (MDP) to tackle the successive decision issues. As the MDP formalism achieves its limits when the neighbors{\textquoteright} interactions are considered, we formulate the problem as a Graph-based MDP (GMDP). We then propose a Mean-Field Approximation (MFA) algorithm to find a solution. The simulation results show that our framework achieves a good analysis of the system behavior. {\textcopyright} 2020 IEEE.},
	keywords = {Antennas, Approximation algorithms, Behavioral research, Decision making, Decision-making problem, Energy transfer, Graphic methods, Inductive power transmission, Internet of things, Markov Decision Processes, Markov processes, Mean field approximation, Packet Delivery, System behaviors, System state, Unmanned aerial vehicles (UAV), Wireless energy, Wireless power},
	doi = {10.1109/ICC40277.2020.9148956},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089418365\&doi=10.1109\%2fICC40277.2020.9148956\&partnerID=40\&md5=a3d1ed58b9a7436f2191be8372102f51},
	author = {Lhazmir, S. and Oualhaj, O.A. and Kobbane, A. and Amlioud, E.M. and Ben-Othman, J.}
}
@article {7391589120120201,
	title = {Software Productivity: Harmonization in ISO/IEEE Software Engineering Standards.},
	journal = {Journal of Software (1796217X)},
	volume = {7},
	number = {2},
	year = {2012},
	pages = {462 - 470},
	abstract = {The software productivity is an important key of software quality factors. The productivity measure has become a tool for managers since it is used to compare the performance between different companies (benchmarking) and to compare the efficiency of different developers in the same company. Therefore, it allows doing strategic planning and decision making based on such measurement. A variety of international standardization bodies such as IEEE and ISO as well as software engineering researchers have proposed a set of factors which influence the software productivity attribute, and also a set of measures to evaluate it. However, there is no unique model that integrates all the software productivity best practices. The aim of this paper is to survey the available international standards and research work on software productivity and figure out the key differences in order to propose a standards-based model. Such model will include the set of quality attributes that could be used to ref},
	keywords = {Benchmark problems (Computer science), Computer programmers, Decision making, IEEE 802.11 (Standard), IEEE Std. 1045, International Organization for Standardization, ISO 9126, Measurement, Measurements, Productivity Drivers, Quality Attributes, Quality Models, Software Developers Productivity, Standards},
	issn = {1796217X},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=73915891\&site=ehost-live},
	author = {Cheikhi, Laila and Al-Qutaish, Rafa E. and Idri, Ali}
}
@article {6016824720110101,
	title = {Toward a Novel Methodology for IT Strategic Planning.},
	journal = {Proceedings of the European Conference on Information Management \& Evaluation},
	year = {2011},
	pages = {263 - 273},
	abstract = {Defining IT target state and the specific roadmap to attain it, are main focuses of IT strategic planning which is considered increasingly as the basis for IT decision-making and governance. Unfortunately, it hasn{\textquoteright}t evolved with the same speed as other fields in the IT sphere. Most of the techniques, approaches and methods related to IT Strategic Planning date back to the 80s or 90s and are most often oriented business rather than IT. Enterprise Architecture is a really promising discipline aimed at capturing the as-is architecture of an enterprise, defining the target and the roadmap to get from existing to desired state. In that way, it is tightly related to IT strategic planning and it can provide a framework to fill the gap and contribute in structuring and formalizing IT Strategic Planning field. Enterprise Architecture benefits from a standardization effort as well as from tool support. Deliverables and artifacts are generally well defined and structured in the existing framewor},
	keywords = {Business enterprises, content framework, Decision making, enterprise architecture, Information technology, IS strategic planning, metamodel, Methodology, Standardization, Strategic planning},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=bth\&AN=60168247\&site=ehost-live},
	author = {Lakhdissi, Mouhsine and Bounabat, Bouchaib}
}
