scholarly journals Tackling pandemics in smart cities using machine learning architecture

2021 ◽  
Vol 18 (6) ◽  
pp. 8444-8461
Author(s):  
Desire Ngabo ◽  
◽  
Wang Dong ◽  
Ebuka Ibeke ◽  
Celestine Iwendi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Logistic Regression ◽  
Decision Tree ◽  
Naive Bayes ◽  
Smart Cities ◽  
Naïve Bayes ◽  
Healthcare Data ◽  
Runny Nose ◽  
Rate Analysis ◽  
Nearest Neighbours

<abstract><p>With the recent advancement in analytic techniques and the increasing generation of healthcare data, artificial intelligence (AI) is reinventing the healthcare system for tackling pandemics securely in smart cities. AI tools continue register numerous successes in major disease areas such as cancer, neurology and now in new coronavirus SARS-CoV-2 (COVID-19) detection. COVID-19 patients often experience several symptoms which include breathlessness, fever, cough, nausea, sore throat, blocked nose, runny nose, headache, muscle aches, and joint pains. This paper proposes an artificial intelligence (AI) algorithm that predicts the rate of likely survivals of COVID-19 suspected patients based on good immune system, exercises and age quantiles securely. Four algorithms (Naïve Bayes, Logistic Regression, Decision Tree and k-Nearest Neighbours (kNN)) were compared. We performed True Positive (TP) rate and False Positive (FP) rate analysis on both positive and negative covid patients data. The experimental results show that kNN, and Decision Tree both obtained a score of 99.30% while Naïve Bayes and Logistic Regression obtained 91.70% and 99.20%, respectively on TP rate for negative patients. For positive covid patients, Naïve Bayes outperformed other models with a score of 10.90%. On the other hand, Naïve Bayes obtained a score of 89.10% for FP rate for negative patients while Logistic Regression, kNN, and Decision Tree obtained scores of 93.90%, 93.90%, and 94.50%, respectively.</p></abstract>

scholarly journals Komparasi Tujuh Algoritma Identifikasi Fraud ATM Pada PT. Bank Central Asia Tbk

2020 ◽  
Vol 7 (3) ◽  
pp. 441-450
Author(s):  
Haliem Sunata
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Central Asia ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Random Tree

Tingginya penggunaan mesin ATM, sehingga menimbulkan celah fraud yang dapat dilakukan oleh pihak ketiga dalam membantu PT. Bank Central Asia Tbk untuk menjaga mesin ATM agar selalu siap digunakan oleh nasabah. Lambat dan sulitnya mengidentifikasi fraud mesin ATM menjadi salah satu kendala yang dihadapi PT. Bank Central Asia Tbk. Dengan adanya permasalahan tersebut maka peneliti mengumpulkan 5 dataset dan melakukan pre-processing dataset sehingga dapat digunakan untuk pemodelan dan pengujian algoritma, guna menjawab permasalahan yang terjadi. Dilakukan 7 perbandingan algoritma diantaranya decision tree, gradient boosted trees, logistic regression, naive bayes ( kernel ), naive bayes, random forest dan random tree. Setelah dilakukan pemodelan dan pengujian didapatkan hasil bahwa algoritma gradient boosted trees merupakan algoritma terbaik dengan hasil akurasi sebesar 99.85% dan nilai AUC sebesar 1, tingginya hasil algoritma ini disebabkan karena kecocokan setiap attribut yang diuji dengan karakter gradient boosted trees dimana algoritma ini menyimpan dan mengevaluasi hasil yang ada. Maka algoritma gradient boosted trees merupakan penyelesaian dari permasalahan yang dihadapi oleh PT. Bank Central Asia Tbk.

scholarly journals Prediction on Cardiovascular disease using Decision tree and Naïve Bayes classifiers

2022 ◽  
Vol 2161 (1) ◽  
pp. 012015
Author(s):  
V Sai Krishna Reddy ◽  
P Meghana ◽  
N V Subba Reddy ◽  
B Ashwath Rao
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Factors ◽  
Heart Failure ◽  
Cardiovascular Disease ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Correct Decision ◽  
Medical Field

Abstract Machine Learning is an application of Artificial Intelligence where the method begins with observations on data. In the medical field, it is very important to make a correct decision within less time while treating a patient. Here ML techniques play a major role in predicting the disease by considering the vast amount of data that is produced by the healthcare field. In India, heart disease is the major cause of death. According to WHO, it can predict and prevent stroke by timely actions. In this paper, the study is useful to predict cardiovascular disease with better accuracy by applying ML techniques like Decision Tree and Naïve Bayes and also with the help of risk factors. The dataset that we considered is the Heart Failure Dataset which consists of 13 attributes. In the process of analyzing the performance of techniques, the collected data should be pre-processed. Later, it should follow by feature selection and reduction.

Predicting Bug Priority Using Topic Modelling in Imbalanced Learning Environments

2021 ◽  
Vol 11 (1) ◽  
pp. 31-42
Author(s):  
Jayalath Bandara Ekanayake
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Prediction Models ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Potential Candidate ◽  
Priority Level ◽  
Bug Reports ◽  
Manual Classification

Manual classification of bug reports is time-consuming as the reports are received in large quantities. Alternatively, this project proposed automatic bug prediction models to classify the bug reports. The topics or the candidate keywords are mined from the developer description in bug reports using RAKE algorithm and converted into attributes. These attributes together with the target attribute—priority level—construct the training datasets. Naïve Bayes, logistic regression, and decision tree learner algorithms are trained, and the prediction quality was measured using area under recursive operative characteristics curves (AUC) as AUC does not consider the biasness in datasets. The logistics regression model outperforms the other two models providing the accuracy of 0.86 AUC whereas the naïve Bayes and the decision tree learner recorded 0.79 AUC and 0.81 AUC, respectively. The bugs can be classified without developer involvement and logistic regression is also a potential candidate as naïve Bayes for bug classification.

scholarly journals IProCAD: Intelligent Prognosis of Coronary Artery Disease Excluding Angiogram in Patient with Stable Angina

2020 ◽  
Vol 9 (5) ◽  
pp. 2032-2040
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Heart Disease ◽  
Random Forest ◽  
Decision Tree ◽  
Stable Angina ◽  
Naive Bayes ◽  
Feature Vector ◽  
Naïve Bayes ◽  
The Other

Cardiovascular diseases are one of the main causes of mortality in the world. A proper prediction mechanism system with reasonable cost can significantly reduce this death toll in the low-income countries like Bangladesh. For those countries we propose machine learning backed embedded system that can predict possible cardiac attack effectively by excluding the high cost angiogram and incorporating only twelve (12) low cost features which are age, sex, chest pain, blood pressure, cholesterol, blood sugar, ECG results, heart rate, exercise induced angina, old peak, slope, and history of heart disease. Here, two heart disease datasets of own built NICVD (National Institute of Cardiovascular Disease, Bangladesh) patients’, and UCI (University of California Irvin) are used. The overall process comprises into four phases: Comprehensive literature review, collection of stable angina patients’ data through survey questionnaires from NICVD, feature vector dimensionality is reduced manually (from 14 to 12 dimensions), and the reduced feature vector is fed to machine learning based classifiers to obtain a prediction model for the heart disease. From the experiments, it is observed that the proposed investigation using NICVD patient’s data with 12 features without incorporating angiographic disease status to Artificial Neural Network (ANN) shows better classification accuracy of 92.80% compared to the other classifiers Decision Tree (82.50%), Naïve Bayes (85%), Support Vector Machine (SVM) (75%), Logistic Regression (77.50%), and Random Forest (75%) using the 10-fold cross validation. To accommodate small scale training and test data in our experimental environment we have observed the accuracy of ANN, Decision Tree, Naïve Bayes, SVM, Logistic Regression and Random Forest using Jackknife method, which are 84.80%, 71%, 75.10%, 75%, 75.33% and 71.42% respectively. On the other hand, the classification accuracies of the corresponding classifiers are 91.7%, 76.90%, 86.50%, 76.3%, 67.0% and 67.3%, respectively for the UCI dataset with 12 attributes. Whereas the same dataset with 14 attributes including angiographic status shows the accuracies 93.5%, 76.7%, 86.50%, 76.8%, 67.7% and 69.6% for the respective classifiers

scholarly journals Automation of gender determination in human canines using artificial intelligence

2018 ◽  
Vol 50 (3) ◽  
pp. 116
Author(s):  
F. Fidya ◽  
Bayu Priyambadha
Keyword(s):  
Artificial Intelligence ◽  
Sexual Dimorphism ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Model ◽  
Accuracy Rate ◽  
Identification Process ◽  
Gender Determination ◽  
Lower Canine

Background: Gender determination is an important aspect of the identification process. The tooth represents a part of the human body that indicates the nature of sexual dimorphism. Artificial intelligence enables computers to perform to the same standard the same tasks as those carried out by humans. Several methods of classification exist within an artificial intelligence approach to identifying sexual dimorphism in canines. Purpose: This study aimed to quantify the respective accuracy of the Naive Bayes, decision tree, and multi-layer perceptron (MLP) methods in identifying sexual dimorphism in canines. Methods: A sample of results derived from 100 measurements of the diameter of mesiodistal, buccolingual, and diagonal upper and lower canine jaw models of both genders were entered into an application computer program that implements the algorithm (MLP). The analytical process was conducted by the program to obtain a classification model with testing being subsequently carried out in order to obtain 50 new measurement results, 25 each for males and females. A comparative analysis was conducted on the program-generated information. Results: The accuracy rate of the Naive Bayes method was 82%, while that of the decision tree and MLP amounted to 84%. The MLP method had an absolute error value lower than that of its decision tree counterpart. Conclusion: The use of artificial intelligence methods produced a highly accurate identification process relating to the gender determination of canine teeth. The most appropriate method was the MLP with an accuracy rate of 84%.

scholarly journals A COMPARATIVE STUDY OF THE DIFFERENT CLASSIFICATION ALGORITHMS ON FOOTBALL ANALYTICS

2021 ◽  
Vol 9 (08) ◽  
pp. 392-407
Author(s):  
Karan Bhowmick ◽  
Vivek Sarvaiya
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Evaluation Metrics ◽  
Support Vector ◽  
Decision Tree Classification ◽  
Nearest Neighbours ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification

Sports analytics is on the rise, with many teams looking to use data science and machine learning algorithms to augment their teams research and boost team performance. This is especially true in the case of Football Clubs. In this work, we have taken the statistics of matches for each team from five major football leagues. These include the English Premier League, La Liga, Serie A, Bundesliga, and Ligue 1. We use this data for two kinds of classification to predict a teams win, loss, or draw. First, we implement Multiclass Classification using Naive Bayes classification, Decision Tree classification, and K-Nearest Neighbours classification. We use f1-score, recall, and precision to evaluate the model. Next, we use Binary Classification to predict if a team wins or does not win, i.e., a loss or a draw. We achieve this by using Support Vector Machines, Logistics Regression, K-Nearest Neighbours classification, Decision Tree classification, and Naive Bayes classification. We evaluate the results using the evaluation metrics mentioned above. Now, we compare the accuracy and efficacy of these algorithms based on the evaluation metrics. This will help standardize the means of classification in sports and football analytics in the future.

scholarly journals Implementing Classification Algorithms for Predicting Chronic Diabetes Diseases

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1748-1751
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Decision Tree ◽  
Naive Bayes ◽  
Early Stage ◽  
Food Habit ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Pima Indians ◽  
Sugar Levels

Now a day Chronic Diabetes Disease is increasing due to many reasons like changes in life style, food habit. It causes an increase in blood sugar levels. If Diabetes Disease remains untreated or unidentified, many different types of complications may be occurred. The doctors have the problem to identify these kinds of diseases easily. The machine learning algorithms helps the doctor to solve these types of problems. In this paper, we implemented three algorithms namely logistic regression, Naive Bayes and Decision tree algorithms to predict diabetes at an early stage. Experiments are performed on Pima Indians Diabetes Dataset, which is from UCI machine learning repository. The performance of all the three algorithms is evaluated using measures on Accuracy. Results obtained showed logistic regression displays 75.3%, Decision tree displays 77.9% and Naive Bayes classifier displays the accuracy value is 76.6%.

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