scholarly journals Classification of adult autistic spectrum disorder using machine learning approach

2021 ◽  
Vol 10 (3) ◽  
pp. 743
Author(s):  
Nurul Amirah Mashudi ◽  
Norulhusna Ahmad ◽  
Norliza Mohd Noor
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Autism Spectrum ◽  
Naïve Bayes ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Learning Approaches ◽  
Related Disorder ◽  
Machine Learning Approach ◽  
Linear Svm

Autism spectrum disorder (ASD) is a neurological-related disorder. Patients with ASD have poor social interaction and lack of communication that lead to restricted activities. Thus, early diagnosis with a reliable system is crucial as the symptoms may affect the patient’s entire lifetime. Machine learning approaches are an effective and efficient method for the prediction of ASD disease. The study mainly aims to achieve the accuracy of ASD classification using a variety of machine learning approaches. The dataset comprises 16 selected attributes that are inclusive of 703 patients and non-patients. The experiments are performed within the simulation environment and analyzed using the Waikato environment for knowledge analysis (WEKA) platform. Linear support vector machine (SVM), k-nearest neighbours (k-NN), J48, Bagging, Stacking, AdaBoost, and naïve bayes are the methods used to compute the prediction of ASD status on the subject using 3, 5, and 10-folds cross validation. The analysis is then computed to evaluate the accuracy, sensitivity, and specificity of the proposed methods. The comparative result between the machine learning approaches has shown that linear SVM, J48, Bagging, Stacking, and naïve bayes produce the highest accuracy at 100% with the lowest error rate.

scholarly journals Predicting Student’s Performance Using Machine Learning Algorithm

2021 ◽  
pp. 53-58
Author(s):  
Sheela Rani P ◽  
Dhivya S ◽  
Dharshini Priya M ◽  
Dharmila Chowdary A
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

scholarly journals A Deep learning of Autism Spectrum Disorder using Naïve Bayes, IBk and J48 classifiers

2019 ◽  
Vol 8 (2) ◽  
pp. 1428-1432
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Naive Bayes ◽  
Early Stage ◽  
Confusion Matrix ◽  
Autism Spectrum ◽  
Naïve Bayes ◽  
Spectrum Disorder ◽  
The Right ◽  
Fold Cross Validation

Deciding the right classification algorithm to classify and predict the disease is more important in the health care field. The eminence of prediction depends on the accuracy of the dataset and the machine learning method used to classify the dataset. Predicting autism behaviors through laboratory or image tests is very time consuming and expensive. With the advancement of machine learning (ML), autism can be predicted in the early stage. The main objective of the paper is to analyze the three classifiers Naïve Bayes, J48 and IBk (k-NN). An Autism Spectrum Disorder (ASD) diagnosis dataset with 21 attributes is obtained from the UCI machine learning repository. The attributes have experimented with the three classifiers using WEKA tool. 10-fold cross validation is used in all three classifiers. In the analysis, J48 shows the best accuracy compared with the other two classifiers. The architecture diagram is shown to depict the flow of the analysis. The Confusion matrix with other relevant results and figures are shown.

scholarly journals Machine Learning Approaches Applied to GC-FID Fatty Acid Profiles to Discriminate Wild from Farmed Salmon

Foods ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1622
Author(s):  
Liliana Grazina ◽  
P. J. Rodrigues ◽  
Getúlio Igrejas ◽  
Maria A. Nunes ◽  
Isabel Mafra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fatty Acid ◽  
Random Forest ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Learning Approaches ◽  
Machine Learning Classifiers ◽  
Farmed Salmon ◽  
Learning Classifiers

In the last decade, there has been an increasing demand for wild-captured fish, which attains higher prices compared to farmed species, thus being prone to mislabeling practices. In this work, fatty acid composition coupled to advanced chemometrics was used to discriminate wild from farmed salmon. The lipids extracted from salmon muscles of different production methods and origins (26 wild from Canada, 25 farmed from Canada, 24 farmed from Chile and 25 farmed from Norway) were analyzed by gas chromatography with flame ionization detector (GC-FID). All the tested chemometric approaches, namely principal components analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and seven machine learning classifiers, namely k-nearest neighbors (kNN), decision tree, support vector machine (SVM), random forest, artificial neural networks (ANN), naïve Bayes and AdaBoost, allowed for differentiation between farmed and wild salmons using the 17 features obtained from chemical analysis. PCA did not allow clear distinguishing between salmon geographical origin since farmed samples from Canada and Chile overlapped. Nevertheless, using the 17 features in the models, six out of the seven tested machine learning classifiers allowed a classification accuracy of ≥99%, with ANN, naïve Bayes, random forest, SVM and kNN presenting 100% accuracy on the test dataset. The classification models were also assayed using only the best features selected by a reduction algorithm and the best input features mapped by t-SNE. The classifier kNN provided the best discrimination results because it correctly classified all samples according to production method and origin, ultimately using only the three most important features (16:0, 18:2n6c and 20:3n3 + 20:4n6). In general, the classifiers presented good generalization with the herein proposed approach being simple and presenting the advantage of requiring only common equipment existing in most labs.

scholarly journals Automating quranic verses labeling using machine learning approach

2019 ◽  
Vol 16 (2) ◽  
pp. 925
Author(s):  
A. Adeleke ◽  
N. Samsudin ◽  
A. Mustapha ◽  
S. Ahmad Khalid
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Accuracy Score ◽  
Machine Learning Approach ◽  
Class Labels ◽  
Automated Text Classification

Classification of Quranic verses into predefined categories is an essential task in Quranic studies. However, in recent times, with the advancement in information technology and machine learning, several classification algorithms have been developed for the purpose of text classification tasks. Automated text classification (ATC) is a well-known technique in machine learning. It is the task of developing models that could be trained to automatically assign to each text instances a known label from a predefined state. In this paper, four conventional ML classifiers: support vector machine (SVM), naïve bayes (NB), decision trees (J48), nearest neighbor (<em>k</em>-NN), are used in classifying selected Quranic verses into three predefined class labels: faith (<em>iman</em>), worship (<em>ibadah</em>), etiquettes (<em>akhlak</em>). The Quranic data comprises of verses in chapter two (<em>al-Baqara</em>) of the holy scripture. In the results, the classifiers achieved above 80% accuracy score with naïve bayes (NB) algorithm recording the overall highest scores of 93.9% accuracy and 0.964 AUC.

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 Different Machine Learning Classifiers for Music Emotion Recognition

2019 ◽  
Vol 8 (4) ◽  
pp. 2187-2191
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Bayes Classifier ◽  
Promising Alternative ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Statistical Metrics

Music in an essential part of life and the emotion carried by it is key to its perception and usage. Music Emotion Recognition (MER) is the task of identifying the emotion in musical tracks and classifying them accordingly. The objective of this research paper is to check the effectiveness of popular machine learning classifiers like XGboost, Random Forest, Decision Trees, Support Vector Machine (SVM), K-Nearest-Neighbour (KNN) and Gaussian Naive Bayes on the task of MER. Using the MIREX-like dataset [17] to test these classifiers, the effects of oversampling algorithms like Synthetic Minority Oversampling Technique (SMOTE) [22] and Random Oversampling (ROS) were also verified. In all, the Gaussian Naive Bayes classifier gave the maximum accuracy of 40.33%. The other classifiers gave accuracies in between 20.44% and 38.67%. Thus, a limit on the classification accuracy has been reached using these classifiers and also using traditional musical or statistical metrics derived from the music as input features. In view of this, deep learning-based approaches using Convolutional Neural Networks (CNNs) [13] and spectrograms of the music clips for MER is a promising alternative.

scholarly journals Cyber Bullying Detection for Twitter Using ML Classification Algorithms

2021 ◽  
Vol 9 (11) ◽  
pp. 24-29
Author(s):  
Muskan Patidar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Language ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Cyber Bullying ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms

Abstract: Social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. Cyberbullying refers to the use of technology to humiliate and slander other people. It takes form of hate messages sent through social media and emails. With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. We have tried to propose a possible solution for the above problem, our project aims to detect cyberbullying in tweets using ML Classification algorithms like Naïve Bayes, KNN, Decision Tree, Random Forest, Support Vector etc. and also we will apply the NLTK (Natural language toolkit) which consist of bigram, trigram, n-gram and unigram on Naïve Bayes to check its accuracy. Finally, we will compare the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection. Keywords: Cyber bullying, Machine Learning Algorithms, Twitter, Natural Language Toolkit

Preliminary Screening of COVID-19 Infection Employing Machine Learning Techniques From Simple Blood Profile

2021 ◽  
Vol 6 (3) ◽  
pp. 35-47
Author(s):  
Anirudh Reddy Cingireddy ◽  
Robin Ghosh ◽  
Supratik Kar ◽  
Venkata Melapu ◽  
Sravanthi Joginipeli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Naive Bayes ◽  
Albert Einstein ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Blood Profile ◽  
Molecular Tests ◽  
Large Populations

Frequent testing of the entire population would help to identify individuals with active COVID-19 and allow us to identify concealed carriers. Molecular tests, antigen tests, and antibody tests are being widely used to confirm COVID-19 in the population. Molecular tests such as the real-time reverse transcription-polymerase chain reaction (rRT-PCR) test will take a minimum of 3 hours to a maximum of 4 days for the results. The authors suggest using machine learning and data mining tools to filter large populations at a preliminary level to overcome this issue. The ML tools could reduce the testing population size by 20 to 30%. In this study, they have used a subset of features from full blood profile which are drawn from patients at Israelita Albert Einstein hospital located in Brazil. They used classification models, namely KNN, logistic regression, XGBooting, naive Bayes, decision tree, random forest, support vector machine, and multilayer perceptron with k-fold cross-validation, to validate the models. Naïve bayes, KNN, and random forest stand out as the most predictive ones with 88% accuracy each.

scholarly journals Machine Learning Readmission Risk Modeling: A Pediatric Case Study

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Patricio Wolff ◽  
Manuel Graña ◽  
Sebastián A. Ríos ◽  
Maria Begoña Yarza
Keyword(s):  
Machine Learning ◽  
Multilayer Perceptron ◽  
Naive Bayes ◽  
Class Imbalance ◽  
Predictive Performance ◽  
Naïve Bayes ◽  
Distribution Model ◽  
Training Dataset ◽  
Support Vector ◽  
Pediatric Hospital

Background. Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions.Objective. To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile.Materials. An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child’s treatment administrative cost.Methods. Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size.Results. Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms.Conclusions. We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.

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