Machine Learning Based Scholarship and Credit Pre-Assessment System

The researcher explained the implementation process of finding the scholarship for the students by using machine learning supervised learning algorithm i.e. Naïve Bayes algorithm. Addition to this it includes a small description of naïve bayes classifier which used to be used through the authors. It explains the significance of training facts set and trying out information set in Machine mastering techniques. Machine learning nowadays becomes plenty used technique in the field of IT industry. It is a very effective instrument and technique for many quite a number fields such as education, IT and even in enterprise industry. In this paper, the researcher attempt to find computerized end result reputation of scholarships of college students by way of using naïve bayes classifier algorithm primarily based on the scholar educational performance, conversation skills, greedy power, IHS, income, time management, regularity etc. A scholarship offers a strength and self assurance to a student. It also boosts the performance of students indirectly. Usually scholarships are furnished by governments or authorities organizations. It is very essential for students to recognize their personal potentiality early in their educational profession so that they faster its growth, receiving attention from an employer or corporation helps college students take this step. Students can apply for scholarships primarily based on the eligibility criteria (such as caste category, annual income, etc). The scholarship will be issued based on merit, student performance and career specific. Different schemes of scholarships are provided for the students based on distinct eligibility criteria. By the use of a naïve bayes classifier, the researcher acquired a end result with accuracy of 96.7% and error of 3.3%. The repute of scholarship students was once displayed in the form of yes or no.

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Detecting Malicious Defects in 3D Printing Process Using Machine Learning and Image Classification

Volume 14: Emerging Technologies; Materials: Genetics to Structures; Safety Engineering and Risk Analysis ◽

10.1115/imece2016-67641 ◽

2016 ◽

Cited By ~ 13

Author(s):

Mingtao Wu ◽

Vir V. Phoha ◽

Young B. Moon ◽

Amith K. Belman

Keyword(s):

Machine Learning ◽

3D Printing ◽

Image Classification ◽

Decision Trees ◽

Naive Bayes ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Printing Process

3D printing, or additive manufacturing, is a key technology for future manufacturing systems. However, 3D printing systems have unique vulnerabilities presented by the ability to affect the infill without affecting the exterior. In order to detect malicious infill defects in 3D printing process, this paper proposes the following: 1) investigate malicious defects in the 3D printing process, 2) extract features based on simulated 3D printing process images, and 3) an experiment of image classification with one group of non-defect infill image and the other group of defect infill training image from 3D printing process. The images are captured layer by layer from the top view of software simulation preview. The data extracted from images is input to two machine learning algorithms, Naive Bayes Classifier and J48 Decision Trees. The result shows Naive Bayes Classifier has an accuracy of 85.26% and J48 Decision Trees has an accuracy of 95.51% for classification.

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Count Vectorized Spam and Ham Discernment of Short Message Service using Machine Learning Classification

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d7287.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 557-561

Keyword(s):

Machine Learning ◽

Naive Bayes ◽

Naïve Bayes ◽

Gradient Boosting ◽

Svm Classifier ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Machine Learning Classification ◽

Tree Classifier

With the growing volume and the amount of spam message, the demand for identifying the effective method for spam detection is in claim. The growth of mobile phone and Smartphone has led to the drastic increase in the SMS spam messages. The advancement and the clean process of mobile message servicing channel have attracted the hackers to perform their hacking through SMS messages. This leads to the fraud usage of other accounts and transaction that result in the loss of service and profit to the owners. With this background, this paper focuses on predicting the Spam SMS messages. The SMS Spam Message Detection dataset from KAGGLE machine learning Repository is used for prediction analysis. The analysis of Spam message detection is achieved in four ways. Firstly, the distribution of the target variable Spam Type the dataset is identified and represented by the graphical notations. Secondly, the top word features for the Spam and Ham messages in the SMS messages is extracted using Count Vectorizer and it is displayed using spam and Ham word cloud. Thirdly, the extracted Counter vectorized feature importance SMS Spam Message detection dataset is fitted to various classifiers like KNN classifier, Random Forest classifier, Linear SVM classifier, Ada Boost classifier, Kernel SVM classifier, Logistic Regression classifier, Gaussian Naive Bayes classifier, Decision Tree classifier, Extra Tree classifier, Gradient Boosting classifier and Multinomial Naive Bayes classifier. Performance analysis is done by analyzing the performance metrics like Accuracy, FScore, Precision and Recall. The implementation is done by python in Anaconda Spyder Navigator. Experimental Results shows that the Multinomial Naive Bayes classifier have achieved the effective prediction with the precision of 0.98, recall of 0.98, FScore of 0.98 , and Accuracy of 98.20%..

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A comparison of three discrete methods for classification of heart disease data

Bangladesh Journal of Scientific and Industrial Research ◽

10.3329/bjsir.v50i4.25839 ◽

2015 ◽

Vol 50 (4) ◽

pp. 293-296 ◽

Cited By ~ 4

Author(s):

D Chaki ◽

A Das ◽

MI Zaber

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Naive Bayes ◽

Naïve Bayes ◽

Supervised Machine Learning ◽

Support Vector ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier

The classification of heart disease patients is of great importance in cardiovascular disease diagnosis. Numerous data mining techniques have been used so far by the researchers to aid health care professionals in the diagnosis of heart disease. For this task, many algorithms have been proposed in the previous few years. In this paper, we have studied different supervised machine learning techniques for classification of heart disease data and have performed a procedural comparison of these. We have used the C4.5 decision tree classifier, a naïve Bayes classifier, and a Support Vector Machine (SVM) classifier over a large set of heart disease data. The data used in this study is the Cleveland Clinic Foundation Heart Disease Data Set available at UCI Machine Learning Repository. We have found that SVM outperformed both naïve Bayes and C4.5 classifier, giving the best accuracy rate of correctly classifying highest number of instances. We have also found naïve Bayes classifier achieved a competitive performance though the assumption of normality of the data is strongly violated.Bangladesh J. Sci. Ind. Res. 50(4), 293-296, 2015

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Biomarker Selection and Classification of “-Omics” Data Using a Two-Step Bayes Classification Framework

BioMed Research International ◽

10.1155/2013/148014 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Anunchai Assawamakin ◽

Supakit Prueksaaroon ◽

Supasak Kulawonganunchai ◽

Philip James Shaw ◽

Vara Varavithya ◽

...

Keyword(s):

Machine Learning ◽

Naive Bayes ◽

Naïve Bayes ◽

Computational Time ◽

Learning Approaches ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Classification Framework ◽

Hidden Naive Bayes

Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes. The top-ranked features are then used in a Hidden Naïve Bayes classifier to construct a classification prediction model from these filtered attributes. In order to obtain the minimum set of the most informative biomarkers, the bottom-ranked features are successively removed from the Naïve Bayes-filtered feature list one at a time, and the classification accuracy of the Hidden Naïve Bayes classifier is checked for each pruned feature set. The performance of the proposed two-step Bayes classification framework was tested on different types of -omicsdatasets including gene expression microarray, single nucleotide polymorphism microarray (SNParray), and surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) proteomic data. The proposed two-step Bayes classification framework was equal to and, in some cases, outperformed other classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time.

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Classification and Optimization Scheme for Text Data using Machine Learning Naïve Bayes Classifier

2018 IEEE World Symposium on Communication Engineering (WSCE) ◽

10.1109/wsce.2018.8690536 ◽

2018 ◽

Cited By ~ 1

Author(s):

Venkatesh ◽

K. V. Ranjitha

Keyword(s):

Machine Learning ◽

Naive Bayes ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Optimization Scheme ◽

Text Data

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NAIVE BAYES CLASSIFIER, DECISION TREE AND ADABOOST ENSEMBLE ALGORITHM – ADVANTAGES AND DISADVANTAGES

6th ERAZ Conference Proceedings (part of ERAZ conference collection) ◽

10.31410/eraz.2020.153 ◽

2020 ◽

Author(s):

Neli Kalcheva ◽

◽

Maya Todorova ◽

Ginka Marinova ◽

◽

...

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Universal Method ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Advantages And Disadvantages ◽

Ensemble Algorithm

The purpose of the publication is to analyse popular classification algorithms in machine learning. The following classifiers were studied: Naive Bayes Classifier, Decision Tree and AdaBoost Ensemble Algorithm. Their advantages and disadvantages are discussed. Research shows that there is no comprehensive universal method or algorithm for classification in machine learning. Each method or algorithm works well depending on the specifics of the task and the data used.

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Implementation of Naïve Bayes Classifier-based Machine Learning to Predict and Classify New Students at Matana University

Journal of Physics Conference Series ◽

10.1088/1742-6596/1842/1/012008 ◽

2021 ◽

Vol 1842 (1) ◽

pp. 012008

Author(s):

Simon Prananta Barus

Keyword(s):

Machine Learning ◽

Naive Bayes ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

New Students

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Analisis Sentimen Masyarakat Berdasarkan Opini dari Sosial Media Menggunakan Metode Naive Bayes Classifier (Study Kasus : Universitas Sjakhyakirti)

Jurnal Ilmiah Informatika Global ◽

10.36982/jig.v11i1.1071 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Paisal Paisal

Keyword(s):

Machine Learning ◽

Data Mining ◽

Social Media ◽

Naive Bayes ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Mining Operations ◽

Use Of Social Media

AbstractThe use of social media today is not only to communicate between friends, but also is needed to make facilities to convey the aspirations of certain people in Indonesia about legal issues relating to government and other issues. One of the aspirations conveyed through social media is a hash that is widely seen by one of the Sjakhyakirti University from the use of social media. Then there arises a lot of sentiment from every community, there are those that give positive sentiments and also negative sentiments that can have a good or bad impact on daily life. days in the community. Some reasons for positive and negative sentiments sourced from this social media, will use social media. From this debate the researchers found a solution where this hashtag can provide good results for the general public or vice versa. In analyzing this, the researcher uses the Naïve Bayes Classifier method which is one of the machine learning methods that uses calculations, the classification of automated hashes can help minimize personal misclassification by obtaining positive or negative sentiment information by using data mining that is carried out by using tools that execute the tools that execute data mining operations that have been determined based on the analysis of models of hidden data on big data thus outlining the discovery of knowledge about Sjakhyakirti University.Keywords : Social Media, Sjakhyakirti, Naïve Bayes ClassifieAbstrakPemanfaatan sosial media saat ini tidak hanya untuk berkomunikasi antara teman saja, akan tetapi sering juga dijadikan sebuah sarana untuk menyampaikan suatu aspirasi bagi masyarakat khususnya masyarakat indonesia mengenai masalah hukum ataupun masalah yang berhubungan dengan pemerintahan serta masalah lainnnya. Salah satu aspirasi yang disampaikan melalui sosial media ini adalah sebuah hastag yang banyak dilihat setiap harinya salah satunya mengenai Universitas Sjakhyakirti dari pemanfaaat sosial media ini maka munculah banyak sentimen dari setiap masyarakat, ada yang memberikan sentimen positif dan juga sentimen negatif mengenai tanggapan terhadap hastag tersebut yang dapat berdampak baik atau buruk bagi kehidupan sehari-hari dimasyarakat. Beberapa alasan sentimen positif dan negatif yang bersumber dari sosial media ini, akan memanfaatkan sosial media. Dari permasalahan ini peneliti menghasilkan sebuah solusi dimana hastag tersebut apakah dapat memberikan dampak yang baik bagi masyarakat umumumnya ataupun sebaliknya. Dalam menganalisa ini, peneliti menggunakan metode Naïve Bayes Classifier yang merupakan salah satu metode machine learning yang menggunakan perhitungan probabilitas, pengklasifikasian hastag otomatis ini dapat disesuaikan sehingga meminimalisasi aksi salah pengklasifikasian secara personal dengan memproleh informasi sentimen positif atau negative dengan menggunakan data mining yang dilakukan dengan tool weka yang mengeksekusi operasi data mining yang telah didefinisikan berdasarkan model analisis dari data tersembunyi pada sejumlah data besar sehingga menguraikan penemuan pengetahuan mengenai Universitas Sjakhyakirti.Kata kunci : Sosial Media, Sjakhyakirti, Naïve Bayes Classifie

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Analisa Kepuasan Mahasiswa Terhadap E-Learning Menggunakan Teknik Machine Learning

INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics ◽

10.51211/itbi.v5i2.1521 ◽

2021 ◽

Vol 5 (2) ◽

pp. 137

Author(s):

Isse Liana Septiani ◽

Abdul Rasyid Faiq Hadinata ◽

Agus Bahtiar ◽

Nana Suarna ◽

Nining R

Keyword(s):

Machine Learning ◽

Data Mining ◽

Naive Bayes ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

E Learning

: E-Learning merupakan salah satu media pembelajaan yang didukung oleh teknologi komputer dan jaringan internet yang didalamnya terdapat konten pembelajaran serta dapat diakses kapanpun dan dimanapun tanpa adanya keterbatasan jarak dan waktu. Kepuasan mahasiswa pada pembelajaran machine learning memiliki keterkaitan yang kuat. Semakin berkualitas penerapan pembelajaran di machine learning, maka semakin tinggi pula pencapaian kepuasan mahasiswa. Penelitian ini menggunakan metode algoritma naïve bayes classifier dengan menggunakan aplikasi rapidminer. Menggunakan teknik pengumpulan data kuantitatif dalam mengumpulkan data yang akan dijadikan sebagai sampel. Sumber data yang diperoleh dengan cara wawancara kepada pihak Biro Administrasi Akademik dan Kemahasiswaan (BAAK) STMIK IKMI Cirebon dan menyebarkan link kuesioner kepada responden yaitu mahasiswa kelas reguler sore secara online dengan menggunakan Google Form. Atribut yang digunakan pada data mining sistem pembelajaran mahasiswa kelas reguler sore antara lain: Ketersediaan Indigoes (A1), Penggunaan Indigoes (A2), Pengujian Indigoes (A3), Aktifitas Indigoes (A4), Kemudahan Indigoes (A5). Dari hasil pengolahan akan didapat hasil (Hasil Kepuasan) dan memperoleh klasifikasi tingkat kepuasan mahasiswa terhadap e-learning dimasa pandemic covid-19. Tujuan penelitian ini adalah ingin mengklasifikasikan tingkat kepuasan mahaiswa dengan penerapan data mining menggunakan algoritma naïve bayes classifier dalam mengetahui klasifikasi kepuasan mahaiswa dalam pembelajaran menggunakan e-learning dimasa pandemic covid-19. Pada penelitian ini diperoleh hasil tingkat akurasi sebesar 100%, recall 100% dan precision 100% dan hasil kepuasan mahasiswa terhadap e-learning dikategorikan “PUAS. Hasil penelitian ini diharapkan dapat dimanfaatkan sebagai tolok ukur dalam mengetahui tingkat kepuasan mahasiswa pada pembelajaran melalui e-learning dimasa pandemic yang sangat berpengaruh terhadap sistem pembelajaran mahasiswa.

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Contagious disease detection in cereals crops and classification as 'solid' or 'undesirable': an application of pattern recognition, image processing and machine learning algorithms

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.2.9043 ◽

2017 ◽

Vol 7 (1.2) ◽

pp. 160

Author(s):

Priyanka Thakur ◽

Preeti Aggarwal ◽

Mamta Juneja

Keyword(s):

Machine Learning ◽

Naive Bayes ◽

Parasitic Infection ◽

Naïve Bayes ◽

Machine Learning Algorithms ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Grouping Strategy ◽

Different Sources

Illnesses in plants diminish the profitability and economy of a nation. Building up a robotization framework for location and arrangement of illnesses in tainted plants is a thriving exploration territory in the field of exactness farming. Oats crops are generally developed temperate product on the planet. Observing of these yields, particularly amid development, empowers us to lessen the harm at the soonest and exact conclusion of these maladies can diminish the sickness spread which will bring about ecological assurance and better return. By utilizing design acknowledgment and picture preparing calculations, the advancement of choice emotionally supportive network for plant security turns out to be more proficient. This paper shows a way to deal with recognize parasitic maladies in three oats trims in particular Maize, Rice and Wheat, utilizing design acknowledgment, machine-learning and picture handling strategies and arrange them as 'Solid' or 'Unfortunate'. It is finished by separating distinctive highlights like shading, shape and surface from the tainted areas of these plant pictures. 227 parasitic infection pictures of three oat crops i.e. Maize (71), Rice (92) and Wheat (64) were downloaded from different sources and considered in this exploration. Some solid pictures of same harvests were additionally downloaded for characterization reason. According to the calculation took after, after the pre-handling step, K-implies grouping strategy was utilized to section the unhealthy zone from the plant and in view of that three bunches of pictures (K=3) were created. Highlight extraction was performed trailed by include decrease utilizing diverse techniques lastly seven diminished highlights for maize, three highlights for rice and five highlights for wheat were chosen which brought about most extreme grouping precision of 87.60% for maize utilizing Naive Bayes classifier, 92.30% for rice utilizing both Naive Bayes and LibSVM classifiers, and 94.18% for wheat utilizing Multilayer Perceptron. On a huge scale, it can be finished up from the outcomes that Naive Bayes classifier gave best characterization exactness of 90.97% for all the three grain crops consolidated.

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