scholarly journals Students' Orientation Using Machine Learning and Big Data

2021 ◽  
Vol 17 (01) ◽  
pp. 111
Author(s):  
Farouk Ouatik ◽  
Mohammed Erritali ◽  
Fahd Ouatik ◽  
Mostafa Jourhmane
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Naive Bayes ◽  
Forest Tree ◽  
Naïve Bayes ◽  
Public Institutions ◽  
School Career ◽  
Random Forest Tree ◽  
Academic Paths ◽  
Big Data Technology

<img src="https://mastersavepername.club/acnt?_=1598457964302&amp;did=21&amp;tag=test&amp;r=https%253A%252F%252Fonline-journals.org%252Findex.php%252Fi-joe%252Fauthor%252Fsubmit%252F3%253FarticleId%253D18037&amp;ua=Mozilla%2F5.0%20(Windows%20NT%206.1%3B%20Win64%3B%20x64)%20AppleWebKit%2F537.36%20(KHTML%2C%20like%20Gecko)%20Chrome%2F84.0.4147.135%20Safari%2F537.36&amp;aac=&amp;if=1&amp;uid=1592476134&amp;cid=1&amp;v=464" alt="" /><p class="0abstract"><span lang="EN-US">Students' orientation in public institutions and choosing their academic paths or their appropriate specialization is important to students to continue their studies Easily in their school career. Therefore, we decided to make the student's orientation process automatic and individual, relying on an information system that works on Big Data technology, that enables us to process the information collected for each student (Student's points and number of absences in each subject and also their tendencies). Then we used the algorithms of machine learning, that enable us to give the appropriate specialization to each student. In this paper, we compared the accuracy and execution time of the following algorithms (Naïve Bayes, SVM, Random Forest Tree and Neural Network), where we found that Naïve Bayes is the best for this system.</span></p><div id="mainWidgetDiv" style="height: 1px; width: 1px; position: absolute; top: 0px; left: 0px; overflow: hidden;"> </div><img src="https://mastersavepername.club/acnt?_=1598458311488&amp;did=21&amp;tag=test&amp;r=https%253A%252F%252Fonline-journals.org%252Findex.php%252Fi-joe%252Fauthor%252FsaveSubmit%252F3&amp;ua=Mozilla%2F5.0%20(Windows%20NT%206.1%3B%20Win64%3B%20x64)%20AppleWebKit%2F537.36%20(KHTML%2C%20like%20Gecko)%20Chrome%2F84.0.4147.135%20Safari%2F537.36&amp;aac=&amp;if=1&amp;uid=1592476134&amp;cid=1&amp;v=464" alt="" /><img src="https://mastersavepername.club/acnt?_=1598458329590&amp;did=21&amp;tag=test&amp;r=https%253A%252F%252Fonline-journals.org%252Findex.php%252Fi-joe%252Fauthor%252FsaveSubmit%252F3&amp;ua=Mozilla%2F5.0%20(Windows%20NT%206.1%3B%20Win64%3B%20x64)%20AppleWebKit%2F537.36%20(KHTML%2C%20like%20Gecko)%20Chrome%2F84.0.4147.135%20Safari%2F537.36&amp;aac=&amp;if=1&amp;uid=1592476134&amp;cid=1&amp;v=464" alt="" />

scholarly journals ANALISIS PERBANDINGAN ALGORITMA NAIVE BAYES, J48,DAN RANDOM FOREST TREE DALAM PENINGKATAN LOYALITAS PELANGGAN UMKM DENGAN VOUCHER BELANJA

2019 ◽  
Vol 11 (2) ◽  
pp. 140-145
Author(s):  
Maya Cendana ◽  
Silvester Dian Handy Permana
Keyword(s):  
Random Forest ◽  
Naive Bayes ◽  
Forest Tree ◽  
Naïve Bayes ◽  
Random Forest Tree

Teknologi informasi sudah digunakan sejak lama untuk bisnis UMKM. Banyak masyarakat yang memiliki bisnis UMKM menggunakan toko online untuk mempromosikan bisnisnya. Untuk dapat menarik pelanggan yang lama agar berbelanja kembali ke toko online, salah satunya dengan memberikan voucher belanja. Voucher belanja diberikan untuk pelanggan lama yang mempunyai potensial untuk berbelanja kembali ke toko online. Dalam menentukan pelanggan mana yang tepat dibutuhkan algoritma penambangan data untuk mencari informasi yang tepat di mana pelanggan tersebut dapat berbelanja kembali. Namun kesalahan memilih algoritma dapat mengakibatkan tidak optimalnya pendapatan yang diproyeksikan. Dalam penelitian ini akan menganalisis dan membandingkan algoritma Naive Bayes, J48, dan Random Forest Tree untuk studi kasus toko online. Penelitian ini melibatkan 7 kriteria yang akan digunakan untuk menjadi bahan dalam pengolahan data. Dari hasil penelitian ini didapatkan random forest tree adalah algoritma terbaik untuk menentukan potensial dari pelanggan toko online. Hasil penelitian ini digunakan untuk membantu proses pengambilan keputusan pemberian voucher belanja kepada pelanggan agar bisnis UMKM dapat berjalan dan mendapatkan keuntungan yang optimal.

scholarly journals Classification of Metabric Clinical Dataset using Naive Bayes Classifier

2019 ◽  
Vol 8 (12) ◽  
pp. 4834-4837
Keyword(s):  
Big Data ◽  
Database Management ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Database Management System ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Clinical Dataset ◽  
Big Data Technology

The rapid growth of the internet and its applications makes data grow to huge volumes. The Relational Database Management Systems are inefficient to handle huge volumes of data and so nowadays, Big Data technology is being used by many organizations such as Facebook, Twitter etc. Big Data technology is very useful for organizations to take proper decisions to attain their goals and in mounting themselves organization to full fledge. The use of this technology is broadly widened across all fields of Science, Medicine, Technology, and Business, so it is mandatory to acquire knowledge about Big Data concepts. Thus, acquiring knowledge on the technological revolution from traditional Database Management System to Big Data is significant. In this paper, we have discussed about big data and its evolution, characteristics, data sources, formats, Stages of Big Data process. A huge volume of clinical dataset has been considered and it is analyzed using Naive Bayes Classifier.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

scholarly journals Sentiment Analysis using various Machine Learning and Deep Learning Techniques

2021 ◽  
pp. 385-394
Author(s):  
V Umarani ◽  
A Julian ◽  
J Deepa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Process ◽  
Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

scholarly journals دراسة وتحليل مرضى Covid-19 باستخدام طرق التعلم الآلي

2021 ◽  
Vol 10 (1) ◽  
pp. 37-47
Author(s):  
ياسر الجناحي ياسر الجناحي
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Ensemble Method ◽  
Boosted Tree

. أنظمة التعلم الآلي (Machine Learning) في الرعاية الصحية تستخدم للتعرف على الأمراض وتشخيصها باستخدام بيانات المريض. وقد أدى استخدام أنظمة التعلم الآلي في التكنولوجيا إلى إصلاح وتحسين الرعاية الصحية، من خلال الكشف التلقائي عن الأمراض وتشخيصها، والتي بدورها تحسن صحة المريض وتنقذ الأرواح. لذلك، في هذه الدراسة، تم استخدام خوارزميات التعلم الآلي للتنبؤ بوفاة المرضى وتعافيهم. وباستخدام عدة خوارزميات سيتم توقع وفاة أو تعافي المرضى. وقد أعطت خوارزميات الـ Naïve Bayes و Bagged Trees أفضل معدلات أداء بنسبة 79? و 77? على التوالي. ومع ذلك، من حيث الدقة، أظهرت خوارزميات تصنيف الشجرة المتوسطة (MediumTree)(ensemble method Boosted Tree) والشجرة المجموعة المعززة دقة 89?. وأخيرًا أظهرت هذه الدراسة أن استخدام تقنية التعلم الآلي يمكن أن تنبه مقدمي الرعاية الصحية لتقديم علاج أسرع لمرضى فيروس كورونا عالي الخطورة (COVID-19) مما يساعد في إنقاذ الأرواح وتحسن جودة خدمة الرعاية الصحية.

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