Samir Anter
University of Hassan II Casablanca
Human–computer interactionAlgorithmMachine learningSelection (linguistics)Baum–Welch algorithmArtificial intelligenceInformation systemPersonalizationInformation retrievalHidden Markov modelData scienceProduction (economics)Context (language use)User profileTask (project management)On demandInformation integrationComputer scienceSpark (mathematics)SpeedupCluster analysisBig dataDatabaseCloud computingAdaptation (computer science)
17Publications
5H-index
21Citations
Publications 18
Newest
#1Imad SassiH-Index: 4
#2Samir AnterH-Index: 5
Last. Abdelkrim BekkhouchaH-Index: 2
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To address the challenges of big data analytics, several works have focused on big data optimization using metaheuristics. The constraint satisfaction problem (CSP) is a fundamental concept of metaheuristics that has shown great efficiency in several fields. Hidden Markov models (HMMs) are powerful machine learning algorithms that are applied especially frequently in time series analysis. However, one issue in forecasting time series using HMMs is how to reduce the search space (state and observ...
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Hidden null M null arkov models (HMMs) are one of machine learning algorithms which have been widely used and demonstrated their efficiency in many conventional applications. This paper proposes a modified posterior decoding algorithm to solve hidden Markov models decoding problem based on MapReduce paradigm and spark’s resilient distributed dataset (RDDs) concept, for large-scale data processing. The objective of this work is to improve the performances of HMM to deal with big data challenges. ...
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#1Imad SassiH-Index: 4
#2Samir AnterH-Index: 5
Last. Abdelkrim BekkhouchaH-Index: 2
view all 3 authors...
A new fast parallel constrained Viterbi algorithm for big data is proposed in this paper. We provide a detailed analysis of its performance on big data frameworks. This performance analysis includes the evaluation of execution time, speedup, and prediction accuracy. Additionally, we compare the impact of the proposed approach on the performance of our parallel constrained algorithm with other benchmark versions. We use synthetic data and real-world data in our experiments to describe the behavio...
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#1Oumaima Reda (Mohammed V University)
#2Imad SassiH-Index: 4
Last. Samir AnterH-Index: 5
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In recent years, as more and more data sources have become available and the volumes of data potentially accessible have increased, the assessment of data quality has taken a central role whether at the academic, professional or any other sector. Given that users are often concerned with the need to filter a large amount of data to better satisfy their requirements and needs, and that data analysis can be based on inaccurate, incomplete, ambiguous, duplicated and of poor quality, it makes everyo...
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5 CitationsSource
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#1Imad SassiH-Index: 4
#2Sara Ouaftouh (Mohammed V University)H-Index: 3
Last. Samir AnterH-Index: 5
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Big Data Analytics presents a great opportunity for scientists and businesses. It changed the methods of managing and analyzing the huge amount of data. To make big data valuable, we often use Machine Learning algorithms. Indeed, these algorithms have shown, in the past, their processing speed, efficiency and accuracy. But today, with the complex characteristics of big data, new problems have emerged and we are facing new challenges when developing and designing a new Machine Learning algorithm ...
4 CitationsSource
#1Sara Ouaftouh (Mohammed V University)H-Index: 3
#2Imad SassiH-Index: 4
Last. Samir AnterH-Index: 5
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Recommender systems are more and more used in different domains of computer science. The collaborative filtering remains a highly prized recommendation technique used by the e-services on the internet. This technique is mainly based on deducing a part of the user interests from the preferences of other users with similar profiles. Among the different approaches, the clustering technique is used to implement collaborative filtering. We propose in this work a comparison between hierarchical and fl...
1 CitationsSource
#1Imad SassiH-Index: 4
#2Samir AnterH-Index: 5
5 CitationsSource