Comparative study of MapReduce classification algorithms for students orientation

Ouatik, Fahd; Erritali, Mohammed; Jourhmane, Mostafa

doi:10.1016/j.procs.2020.03.030

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…The impact of big data on healthcare services and various tools available in the Hadoop ecosystem are examined, and the conceptual architecture of data analytics is explored. In the research, attention was paid to big data, Hadoop, and MapReduce tools [22].…”

Section: Resultsmentioning

confidence: 99%

Big Data in Health and the Importance of Data Visualization Tools

Skender

Ali

2022

J Intell Syst Appl

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Big data concepts are increasing with their spatial speed, from personal information to extensive volume data. Since the human brain perceives visual data faster, the data must be processed and displayed appropriately. As in all areas of life, the size of the data obtained in the health sector has increased rapidly. Data storage and security have gained importance with the excessive increase in data. Big data, data mining, and visualization tools have become increasingly important to process and use data for valuation purposes. Therefore, the visualization of data and the use of analysis tools play a significant role in data processing and decision-making in the development of the health sector. The importance of data visualization tools in the health sector will become increasingly indispensable. There are many software tools developed for these purposes. This study's literature review explained the basic concepts of big data and data visualization. Research in the health sector around the world was summarized. In addition to this literature review, analyses with comparison and deduction research methods were also carried out. As a result, suggestions were made by making predictions for future studies in the health sector.

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Section: Resultsmentioning

confidence: 99%

Big Data in Health and the Importance of Data Visualization Tools

Skender

Ali

2022

J Intell Syst Appl

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“…Students will assemble the electrical diagram by wiring between devices of the circuit, and read value of tension from measurement device, all via the web Figure 2 The most important technologies used in [19,20] and the hardware part is an embedded system. To have animation on the user interface for student can drag and drop devices and wiring them, we choice to use Canvas of html5 for to have compatibility with all web browser.…”

Section: User Interface For Remote and Wiringmentioning

confidence: 99%

Online instrument systems in reality for remote wiring and measurement of electronic in e-learning from LabVIEW+NI ELVIS II vs embedded system+web services

Ouatik

Raoufi

Ouatik

et al. 2021

IJECE

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Recently The prestigious universities of the world strive and aim to computerize their distance education service and more specifically the remote practical work, which allows students to manipulate electronic experiments via the web, for to solve a set of problems: pedagogical, management, security, restriction by time and place and the problems the overcrowding of students in universities. This paper will describe the E@SLab system developed by the university Cadi Ayyad member of the e-live project funded by EU in the frame of ERASMUS+E@SLab is based on the latest technologies of development and respects educational and pedagogical standards. E@SLab offers 2 versions different of remote laboratory: first version (s1) is an embedded system its part software is node js+Ubuntu and the part hardware a card pcduino or raspebery. The second version (s2) is based on LABview and its hardware part is the NI ELVIS II pedagogical map. In this paper, we will compare and discuss the architecture, performance of the 2 versions of E@SLAB and review other famous approaches NetLab, VISIR, for comparing with E@SLab with the intention show its singularity.

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“…And also [20] used the classification algorithms, K-Nearest Neighbor, Neural Network, Naive Bayes and Big Data technology for student orientation. Other classification algorithms used by [21], Naïve Bayes, SVM, Random Forest Tree and Neural Network using the MOA tool for the selection of the specialty suitable for the student. While other research has focused on the factors that influence the choice of specialty, namely [22], [23], [24], [25], [26] and [27].…”

Section: Introductionmentioning

confidence: 99%

Student Orientation Recommender System using TOPSIS and AHP

Ouatik,

Erritali,

Ouatik

et al. 2022

JIOS

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The process of traditional school guidance is carried out by specialists. They make astudy of the student files based on the marks of the first year and the second year ofthe baccalaureate. Considering the progressive number of students and also the lack oftime to make the decision, as the selection of the specialty has a great effect on theacademic course of the students, we have realized a system of specialtyrecommendation, to computerize the orientation process and save time. But the majorproblem is that the students do not care about this process and pay no attention to itdespite its importance. As well as the software which makes the orientation ischargeable.To order these specialties to take the best specialty. We have arrived at aproblem of multi-criteria which makes it impossible to make a decision with thesecriteria. Because these criteria do not have the same importance and also are notcompatible, as there are criteria that must be maximum and other criteria must beminimal. To solve this problem, two systems of orientation and academicreorientation of students have been implemented. In both systems, the SMOTEmethod has been used to balance the learning data in the preprocessing phase. Then inthe treatment phase, we sorted the specialties using in the first system, a hybridizationof TOPSIS method and the information gain to find the weights of the criteria used,and in the second system, we used a hybridization of the AHP method andinformation gain. The results obtained indicate that before balancing data using the SMOTE method, the total accuracy of TOPSIS (84.20%) is higher than the total accuracy of AHP (83.71%). After applying balancing data using the SMOTE method,the total accuracy has increased. The total accuracy of TOPSIS (91.35%) is alsohigher than the total accuracy of AHP (90.83%). For the complexity of the twomethods, it is related to the number of criteria and the number of alternatives. If thenumber of criteria is more than 10 criteria, the complexity of TOPSIS is less than thecomplexity of AHP, and vice versa. The complexity of the two methods also dependson the number of alternatives, if the number of alternatives exceeds 10, the TOPSISmethod becomes more complex than the AHP method. In general, the system basedon TOPSIS method and the information gain is more precise than the system based onthe AHP method and the information gain. But the complexity of the AHP method isless than the complexity of the TOPSIS method.

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Comparative study of MapReduce classification algorithms for students orientation

Cited by 7 publications

References 10 publications

Big Data in Health and the Importance of Data Visualization Tools

Big Data in Health and the Importance of Data Visualization Tools

Online instrument systems in reality for remote wiring and measurement of electronic in e-learning from LabVIEW+NI ELVIS II vs embedded system+web services

Student Orientation Recommender System using TOPSIS and AHP

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