@article {Boudad2020243,
	title = {Exploring the Use of Word Embedding and Deep Learning in Arabic Sentiment Analysis},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1105 AISC},
	year = {2020},
	note = {cited By 3},
	pages = {243-253},
	abstract = {In the past couple of years, improving Arabic sentiment Analysis systems has been one of the important fields of research. There are several challenges and issues facing existing systems, especially, handling multiple dialects and feature extraction. Most of those systems are generated using linear classification models and traditional bag-of-word features. In this work, we explore the use of word embedding as a modern feature representation, and Convolutional Neural Networks as a Deep Neural Network in a sentiment classification of Arabic texts. The application of our model on five benchmark datasets has yielded results that outperform previous works on 4 out of 5 datasets. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Arabic texts, Bag of words, Benchmark datasets, Benchmarking, Convolution, Convolutional neural networks, Deep learning, Deep neural networks, Embeddings, Existing systems, Feature representation, Intelligent systems, Linear classification, planning, Sentiment analysis, Sentiment classification, Sustainable development, Word embedding},
	doi = {10.1007/978-3-030-36674-2_26},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080913982\&doi=10.1007\%2f978-3-030-36674-2_26\&partnerID=40\&md5=816d3952c972aa5ce92cb7318fe768aa},
	author = {Boudad, N. and Ezzahid, S. and Faizi, R. and Thami, R.O.H.}
}
@article {ElFazziki2020136,
	title = {Improving Collaborative Filtering Approach by Leveraging Opposite Users},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1102 AISC},
	year = {2020},
	note = {cited By 0},
	pages = {136-145},
	abstract = {Collaborative filtering is a widely used recommendation approach that aims to predict for a target user the most appropriate items. This approach uses the ratings given by users who share similar tastes and preferences to predict ratings for items that haven{\textquoteright}t been rated yet. Despite its simplicity and justifiability, CF approach stills suffering from several drawbacks and problems, including sparsity, gray sheep and scalability. These problems affect the accuracy of the obtained results. In this work, we present a novel collaborative filtering approach based on the opposite preferences of users. We focus on enhancing the accuracy of predictions and dealing with gray sheep problem by inferring new similar neighbors based on users who have dissimilar tastes and preferences. For instance, if a user X is dissimilar to a user Y then the user +X is similar to the user Y. The Experimental results performed on two datasets including MovieLens and FilmTrust show that our approach outperforms several baseline recommendation techniques. {\textcopyright} Springer Nature Switzerland AG 2020.},
	keywords = {Collaborative filtering, Forecasting, Gray sheep, Intelligent systems, Learning systems, Movielens, Opposite neighbors, planning, Recommendation techniques, Recommender Systems, Similar neighbors, Similarity measure, Sustainable development},
	doi = {10.1007/978-3-030-36653-7_14},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078457465\&doi=10.1007\%2f978-3-030-36653-7_14\&partnerID=40\&md5=fdeb5a86480517f6637d45ebc49b0849},
	author = {El Fazziki, A. and El Madani El Alami, Y. and El Aissaoui, O. and El Allioui, Y. and Benbrahim, M.}
}
@article {Kamri2020298,
	title = {Machine Learning Approach for Smart Self-diagnosing Autonomic Computing Systems},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1105 AISC},
	year = {2020},
	note = {cited By 0},
	pages = {298-307},
	abstract = {While modern systems and networks are continuously growing in size, complexity and diversity, the monitoring and diagnosing of such systems is becoming a real challenge. Technically and economically, more automation of the classical diagnosing tasks is needed. This has triggered a considerable research initiative, grouped under the terms self-management and Autonomic Computing. In this paper we propose a new model for smart self-diagnosing systems based on Autonomic Computing principles and Machine Learning techniques. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Autonomic Computing, Autonomic computing system, Diagnosing system, Intelligent systems, Learning systems, Machine learning, Machine learning approaches, Machine learning techniques, planning, Research initiatives, Self management, Self-diagnosing, Sustainable development, wireless networks},
	doi = {10.1007/978-3-030-36674-2_31},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080896883\&doi=10.1007\%2f978-3-030-36674-2_31\&partnerID=40\&md5=3dce8bc657338047166fb3ded16c10c8},
	author = {Kamri, H. and Bounabat, B.}
}
@article {ElAissaoui20209,
	title = {A Multiple Linear Regression-Based Approach to Predict Student Performance},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1102 AISC},
	year = {2020},
	note = {cited By 15},
	pages = {9-23},
	abstract = {Predicting students{\textquoteright} academic outcome is useful for any educational institution that aims to ameliorate students{\textquoteright} performance. Based on the resulted predictions, educators can provide support to students at risk of failure. Data mining and machine learning techniques were widely used to predict students{\textquoteright} performance. This process called Educational data mining. In this work, we have proposed a methodology to build a student{\textquoteright} performance prediction model using a supervised machine learning technique which is the multiple linear regression (MLR). Our methodology consists of three major steps, the first step aims to analyze and preprocess the students{\textquoteright} attributes/variables using a set of statistical analysis methods, and then the second step consists in selecting the most important variables using different methods. The third step aims to construct different MLR models based on the selected variables and compare their performance using the k-fold cross-validation technique. The obtained results show that the model built using the variables selected from the Multivariate Adaptive Regression Splines method (MARS), outperforms the other constructed models. {\textcopyright} Springer Nature Switzerland AG 2020.},
	keywords = {Data mining, Education computing, Educational data mining, Forecasting, Intelligent systems, K fold cross validations, Learning systems, Linear regression, Machine learning techniques, Multiple linear regressions, Multivariate adaptive regression splines, Performance prediction models, planning, Predictive analytics, Statistical analysis methods, Students, Supervised learning, Supervised machine learning, Sustainable development},
	doi = {10.1007/978-3-030-36653-7_2},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078413089\&doi=10.1007\%2f978-3-030-36653-7_2\&partnerID=40\&md5=893772562739d5d20e6b6ae21ebae325},
	author = {El Aissaoui, O. and El Alami El Madani, Y. and Oughdir, L. and Dakkak, A. and El Allioui, Y.}
}
@article {Korachi2020456,
	title = {Towards a Maturity Model for Digital Strategy Assessment},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1105 AISC},
	year = {2020},
	note = {cited By 7},
	pages = {456-470},
	abstract = {Information Technologies (ITs) lead to creativity and innovation in education, business, and management. Creativity and innovation improve the growth and profitability of organizations and the quality of their products and services. Many of today{\textquoteright}s social, business, economic and political problems can be solved using ITs. To take advantage of the digital revolution, there is a need for having a digital strategy and evaluation system of the digital transformation. Many works show that the strategic alignment between business and ITs is the key element for successful digital projects. This work presents a Maturity Model for Digital Strategy Assessment (MMDSA). MMDSA has been introduced as a guide and reference framework for digital transformation management within different industries and sectors. Its objective is to master and improve digital transformation processes and consequently to control the quality of the products and services resulting from these processes. The proposed model allows assessing IT-businesses alignment, helps organizations to determine where they stand in terms of their digital transformation and provides strategic components and guidelines to help organizations to move up from the current maturity level to the next one. The paper illustrates a software solution allowing to create customized maturity models to assess digital strategy progress and evaluate the business value of the digital transformation. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Digital strategies, Digital transformation, Growth and profitability, Innovation in educations, Intelligent systems, maturity model, planning, Products and services, Quality control, Reference frameworks, Strategic alignment, Sustainable development},
	doi = {10.1007/978-3-030-36674-2_47},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080925319\&doi=10.1007\%2f978-3-030-36674-2_47\&partnerID=40\&md5=7980842dc0e1d8c42164d36d653172cd},
	author = {Korachi, Z. and Bounabat, B.}
}
@article {9954595520141215,
	title = {Solving a mixed integer linear program with times setup for the steel-continuous casting planning and scheduling problem.},
	journal = {International Journal of Production Research},
	volume = {52},
	number = {24},
	year = {2014},
	pages = {7276 - 7296},
	abstract = {In this paper, we present a novel model that generalise the steelmaking continuous casting planning and scheduling problem. The model includes three stages: converters namely CV, refining stands namely RS and continuous castings namely CC. The obtained problem can be formulated as a mathematical program with typical constraints, namely the times setup constraints. We develop a mixed integer linear program (MILP) to schedule several sequences of charges for more than two continuous castings devices. The aim is: (i) on one hand, to schedule a list of several sequences that are already ordered for each continuous casting with times setup between two successive sequences, (ii) on the other hand, to determine the optimal order of the successive sequences. We use a benchmark and randomly generated instances to test the model and solved by mean of the commercial software solverCplex v12.5. These problems span a variety of instances varying from small to large sized instances with different i},
	keywords = {Continuous casting, Linear programming, Mathematical models, Mathematical optimization, MILP, Mixed integer linear programming, optimisation, planning, Scheduling, setup, steel},
	issn = {00207543},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=bth\&AN=99545955\&site=ehost-live},
	author = {Sbihi, Abdelkader and Bellabdaoui, Adil and Teghem, Jacques}
}
