@article {Wadghiri2022,
	title = {Ensemble blood glucose prediction in diabetes mellitus: A review},
	journal = {Computers in Biology and Medicine},
	volume = {147},
	year = {2022},
	note = {cited By 0},
	abstract = {Considering the complexity of blood glucose dynamics, the adoption of a single model to predict blood glucose level does not always capture the inter- and intra-patients{\textquoteright} context changes. Ensembles are a set of machine learning techniques combining multiple single learners to find a better variance/bias trade-off and hence improve the prediction accuracy. The present paper aims to review the state of the art in predicting blood glucose using ensemble methods with regard to 8 criteria: publication year and sources, datasets used to train/evaluate the models, types of ensembles used, single learners involved to construct ensembles, combination schemes used to aggregate the base learners, metrics and validation methods adopted to assess the performance of ensembles, reported overall performance of the predictors and accuracy comparison of ensemble techniques with single models. A systematic literature review has been conducted in order to analyze and synthetize primary studies published between 2000 and 2020 in six digital libraries. A total of 32 primary papers were selected and reviewed with regard to eight review questions. The results show that ensembles have gained wider interest during the last years and improved in general the performance compared with other single models. However, multiple gaps have been identified concerning the ensembles construction process and the performance metrics used. Several recommendations have been made in this regard to design accurate ensembles for blood glucose level prediction. {\textcopyright} 2022 Elsevier Ltd},
	keywords = {algorithm, Algorithms, Blood, Blood glucose, Blood glucose level, Data mining, Diabetes mellitus, Digital libraries, Economic and social effects, Ensemble methods, Forecasting, Glucose, glucose blood level, Glucose dynamics, human, Humans, Machine learning, Machine learning techniques, Machine-learning, Performance, Single models, Trade off},
	doi = {10.1016/j.compbiomed.2022.105674},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132341147\&doi=10.1016\%2fj.compbiomed.2022.105674\&partnerID=40\&md5=05bd59c3726654494a4be346e4e6a682},
	author = {Wadghiri, M.Z. and Idri, A. and El Idrissi, T. and Hakkoum, H.}
}
@article {ElKaid2022,
	title = {Top-Down System for Multi-Person 3D Absolute Pose Estimation from Monocular Videos},
	journal = {Sensors},
	volume = {22},
	number = {11},
	year = {2022},
	note = {cited By 2},
	abstract = {Two-dimensional (2D) multi-person pose estimation and three-dimensional (3D) root-relative pose estimation from a monocular RGB camera have made significant progress recently. Yet, real-world applications require depth estimations and the ability to determine the distances between people in a scene. Therefore, it is necessary to recover the 3D absolute poses of several people. However, this is still a challenge when using cameras from single points of view. Furthermore, the previously proposed systems typically required a significant amount of resources and memory. To overcome these restrictions, we herein propose a real-time framework for multi-person 3D absolute pose estimation from a monocular camera, which integrates a human detector, a 2D pose estimator, a 3D root-relative pose reconstructor, and a root depth estimator in a top-down manner. The proposed system, called Root-GAST-Net, is based on modified versions of GAST-Net and RootNet networks. The efficiency of the proposed Root-GAST-Net system is demonstrated through quantitative and qualitative evaluations on two benchmark datasets, Human3.6M and MuPoTS-3D. On all evaluated metrics, our experimental results on the MuPoTS-3D dataset outperform the current state-of-the-art by a significant margin, and can run in real-time at 15 fps on the Nvidia GeForce GTX 1080. {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.},
	keywords = {3d multi-person pose estimation, Absolute pose, algorithm, Algorithms, Camera-centric coordinate, Cameras, Computer vision, Deep learning, Deep-learning, human, Humans, Imaging, Monocular video, Pose-estimation, procedures, Real- time, Relative pose, Three-Dimensional, Three-dimensional imaging, Topdown, Two-dimensional},
	doi = {10.3390/s22114109},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130790166\&doi=10.3390\%2fs22114109\&partnerID=40\&md5=7009c9053fd9d12efcd40ade931932f7},
	author = {El Kaid, A. and Brazey, D. and Barra, V. and Baina, K.}
}
@article {11128948620160201,
	title = {Accuracy Comparison of Analogy-Based Software Development Effort Estimation Techniques.},
	journal = {International Journal of Intelligent Systems},
	volume = {31},
	number = {2},
	year = {2016},
	pages = {128 - 152},
	abstract = {Estimation by analogy is a commonly used software effort estimation technique and a suitable alternative to other conventional estimation techniques: It predicts the effort of the target project using information from former similar projects. While it is relatively easy to handle numerical attributes, dealing with categorical attributes is one of the most difficult issues for analogy-based estimation techniques. Therefore, we propose, in this paper, a novel analogy-based approach, called 2FA-kprototypes, to predict effort when software projects are described by a mix of numerical and categorical attributes. To this aim, the well-known fuzzy k-prototypes algorithm is integrated into the process of estimation by analogy. The estimation accuracy of 2FA-kprototypes was evaluated and compared with that of two techniques: (1) classical analogy-based technique and (2) 2FA-kmodes, which is a technique that we have developed recently. The comparison was performed using four data sets that are},
	keywords = {Algorithms, Artificial intelligence, Computer software development, Fuzzy control systems, Numerical analysis, Prototypes},
	issn = {08848173},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=111289486\&site=ehost-live},
	author = {Idri, Ali and Amazal, Fatima Azzahra and Abran, Alain}
}
@article {10115338520131101,
	title = {A New Feedback-Analysis based Reputation Algorithm for E-Commerce Communities.},
	journal = {E-Ti: E-Review in Technologies Information},
	number = {7},
	year = {2013},
	pages = {46 - 58},
	abstract = {Dealing with the ever-growing content generated by users in the e-commerce applications, Trust Reputation Systems (TRS) are widely used online to provide the trust reputation of each product using the customers{\textquoteright} ratings. However, there is also a good number of online customer reviews and feedback that must be used by the TRS. As a result, we propose in this work a new architecture for TRS in e-commerce application which includes feedback{\textquoteright} mining in order to calculate reputation scores. This architecture is based on an intelligent layer that proposes to each user (i.e. {\guillemotleft}feedback provider{\guillemotright}) who has already given his recommendation, a collection of prefabricated feedback to like or dislike. Then the proposed reputation algorithm calculates the trust degree of the user, the feedback{\textquoteright}s trustworthiness and generates the global reputation score of the product according to his {\guillemotleft}likes{\guillemotright} and {\guillemotleft}dislikes{\guillemotright}. In this work, we present also a state of the art of text mining tools and algorithms that can},
	keywords = {Algorithms, analyse de sentiment, e-commerce, Electronic commerce, La confiance, le e-commerce, le textmining, les feedback textuels, les syst{\`e}mes de r{\'e}putation, Reliability (Personality trait), Sentiment analysis, text mining, Text mining (Information retrieval), textual feedback, Trust, Trust Reputation Systems, Virtual communities},
	issn = {11148802},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=101153385\&site=ehost-live},
	author = {Rahimi, Hasnae and El Bakkali, Hanan}
}
@article {2489083820070426,
	title = {Fast L4 template matching using frequency domain.},
	journal = {Electronics Letters},
	volume = {43},
	number = {9},
	year = {2007},
	pages = {507 - 508},
	abstract = {Fast and simple L4 norm computation for translational template matching is presented. The proposed method is based on expressing the L4 norm in terms of two cross-correlations. Speed is obtained from computing the correlations in the frequency domain using FFT algorithms. [ABSTRACT FROM AUTHOR]},
	keywords = {Algebra, Algorithms, Correlation (Statistics), Mathematical statistics, Pattern recognition systems},
	issn = {00135194},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=24890838\&site=ehost-live},
	author = {Essannouni, F. and Oulad Haj Thami, R. and Aboutajdine, D. and Salam, A.}
}
@article {1344754220010801,
	title = {Integrating WordNet knowledge to supplement training data in semi-supervised agglomerative hierarchical clustering for text categorization.},
	journal = {International Journal of Intelligent Systems},
	volume = {16},
	number = {8},
	year = {2001},
	pages = {929 - 947},
	abstract = {The text categorization (TC) is the automated assignment of text documents to predefined categories based on document contents. TC has been an application for many learning approaches, which proved effective. Nevertheless, TC provides many challenges to machine learning. In this paper, we suggest, for text categorization, the integration of external WordNet lexical information to supplement training data for a semi-supervised clustering algorithm which (i) uses a finite design set of labeled data to (ii) help agglomerative hierarchical clustering algorithms (AHC) partition a finite set of unlabeled data and then (iii) terminates without the capacity to classify other objects. This algorithm is the {\textquotedblleft}semi-supervised agglomerative hierarchical clustering algorithm{\textquotedblright} (ssAHC). Our experiments use Reuters 21578 database and consist of binary classifications for categories selected from the 89 TOPICS classes of the Reuters collection. Using the vector space model (VSM), each document is repre},
	keywords = {Algorithms, Artificial intelligence, Hierarchy (Linguistics), John Wiley \& Sons Inc., Linguistic analysis (Linguistics), Machine learning},
	issn = {08848173},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=13447542\&site=ehost-live},
	author = {Benkhalifa, Mohammed and Mouradi, Abdelhak and Bouyakhf, Houssaine}
}
