scholarly journals Chronic Kidney Disease using Machine Learning Techniques

2020 ◽  
Vol 9 (7) ◽  
pp. 683-`686
Keyword(s):  
Kidney Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Disease Area ◽  
Course Of Action ◽  
Learning Techniques ◽  
Probabilistic Classifier ◽  
Stage Renal Disease ◽  
End Stage

Interminable Kidney Disease (CKD) proposes the realm of kidney chance which may even crumble by means of time and through implying the factors. If it continues finishing all the more dreadful Dialysis is and most desperate conclusive outcomes believable it'd flash off kidney misery (End-Stage Renal Disease). Area of CKD in a starting period should help in filtering by means of the complexities and harm.In the pastwork portrayal applied are SVM and Naïve Bayes, it happened that the execution time took by methods for Naïve Bayes is irrelevant appeared differently in relation to SVM, confused events are substantially less with SVM that results in less request execution of Naïve Bayes, inferable from gentle exactness distinction. It can be corrected by methods for taking less improvements. Unsuspecting Bayes is a probabilistic classifier a fundamental count by utilizing Bayes Theorem with a prohibitive independence supposition. The artistic creations for the most segment brings around growing symptomatic exactness and decrease commitment time, this is the guideline factor. An undertaking is made to develop a form evaluating CKD data collected from a particular course of action of people. From the model data, recognizing verification should be conceivable. This work has enchanted on developing up a system relying upon gathering procedures: SVM, Naïve Bayes, glomerular filtration rate (GFR) is the best pointer of how well the kidneys are working.CKD has got no cure but it can be treated based on symptoms to reduce complicationsand

scholarly journals Assessment of Chronic Kidney Disease using Classification Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 10385-10389
Keyword(s):  
Kidney Disease ◽  
Naive Bayes ◽  
Early Stage ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Conditional Independence Assumption ◽  
Probabilistic Classifier ◽  
Accuracy Difference ◽  
Stage Renal Disease ◽  
End Stage

Kidney Disease (CKD) implies the condition of kidney risk which may even get worse by time and by referring the factors. If it continues to get worse Dialysis is done and worstcase scenario it may lead to kidney failure (End-Stage Renal Disease). Detection of CKD in an early stage could help in sorting out the complications and damage. In the previous work classification used are SVM and Naïve Bayes, it resulted that the execution time took by Naïve Bayes is minimal compared to SVM, incorrect instances are less for SVM that results in less classification performance of Naïve Bayes, because of slight accuracy difference. It can be rectified by taking a smaller number of attributes. Naïve Bayes is a probabilistic classifier a simple computation by applying Bayes Theorem with a conditional independence assumption. The work mainly results in increasing diagnostic accuracy and decrease diagnosis time, that is the main aim. An attempt is made to develop a model evaluating CKD data gathered from a particular set of people. From the model data, identification can be done. This work has engrossed on developing a system based on classification methods: SVM, Naïve Bayes, KNN.

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 Comparison of Various Classification Techniques for Prediction of the Agriculture Production Based on Different Parameters Rainfall, Temperature

2021 ◽  
Vol 23 (04) ◽  
pp. 356-372
Author(s):  
Manpreet Kaur ◽  
◽  
Dr. Dinesh Kumar ◽  
Keyword(s):  
Naive Bayes ◽  
Confusion Matrix ◽  
Big Data Analysis ◽  
Naïve Bayes ◽  
The Other ◽  
Machine Learning Techniques ◽  
True Positive ◽  
True Negative ◽  
Classification Techniques ◽  
Learning Techniques

The classification techniques based on various machine learning techniques are having use for the Big data analysis. This will be useful in identifying the classification and then finally the prediction which will be useful for the decision managers for having quality decisions. There are various types of supervised and unsupervised learning techniques which are having capabilities in the terms of driving the analysis. This analysis will be useful for having identification of relationship between the various attributes which is required to device the analysis. There are various supervised learning techniques which are useful to drive the analysis. These techniques are SVM, Logistic regression, KNN, Naïve Bayes, Tree, Neural network. The relative comparison of this technique is done in the terms of various parameters for example AUC, CA, F1, Recall and precision. The accuracy in the terms of AUC, CA is highest for the Naïve Bayes. This shows the Naïve Bayes is having higher true positives, true negative ratio. The proposed technique is having higher accuracy of 81% which is far above than all the remaining techniques. The confusion matrix for the Naïve Bayes is having true positive count as 729, true negative at 103. This shows that the true positive and true negative count is far above for this technique compared to the other techniques.

scholarly journals Twitter Sentiment Analysis using Machine Learning Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 4205-4209
Keyword(s):  
Logistic Regression ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Learning Techniques ◽  
Social Media Platforms

Nowadays people share their views and opinions in twitter and other social media platforms, the way of recognizing sentiments and speculation in tweets is Twitter Sentiment Analysis. Determining the contradiction or sentiment of the tweets and then listing them into positive, negative and neutral tweets is the main classifying step in this process. The issue related to sentiment analysis is the naming of the correct congruous sentiment classifier algorithm to list the tweets. The foundation classifier techniques like Logistic regression, Naive Bayes classifier, Random Forest and SVMs are normally used. In this paper, the Naïve Bayes classifier and Logistic Regression has been used to perform sentiment analysis and classify based on the better accuracy of catagorizing Technique. The outcome shows that Naive Bayes classifier works better for this approach. Data pre-processing and feature extraction is realized as a portion of task.

Sentiment Analysis of Tweets on the COVID-19 Pandemic Using Machine Learning Techniques

2021 ◽  
pp. 310-320
Author(s):  
Jothikumar R. ◽  
Vijay Anand R. ◽  
Visu P. ◽  
Kumar R. ◽  
Susi S. ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Respiratory Tract ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Respiratory Tract Diseases ◽  
Thought Processes ◽  
Learning Techniques

Sentiment evaluation alludes to separate the sentiments from the characteristic language and to perceive the mentality about the exact theme. Novel corona infection, a harmful malady ailment, is spreading out of the blue through the quarter, which thought processes respiratory tract diseases that can change from gentle to extraordinary levels. Because of its quick nature of spreading and no conceived cure, it ushered in a vibe of stress and pressure. In this chapter, a framework perusing principally based procedure is utilized to discover the musings of the tweets related to COVID and its effect lockdown. The chapter examines the tweets identified with the hash tags of crown infection and lockdown. The tweets were marked fabulous, negative, or fair, and a posting of classifiers has been utilized to investigate the precision and execution. The classifiers utilized have been under the four models which incorporate decision tree, regression, helpful asset vector framework, and naïve Bayes forms.

scholarly journals Peningkatan Performa Pendeteksian Anomali Menggunakan Ensemble Learning dan Feature Selection

2021 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Ripto Sudiyarno ◽  
Arief Setyanto ◽  
Emha Taufiq Luthfi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Ensemble Learning ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Detection Systems ◽  
Learning Techniques ◽  
Performance Results

Intrusion detection systems (IDS) atau Sistem pendeteksian intrusi dikenal sebagai teknik yang sangat menonjol dan terkemuka untuk menemukan malicious activities pada jaringan komputer, tidak seperti firewall konvensional, IDS berbeda dalam hal pengidentifikasian serangan secara cerdas dengan pendekatan analitik seperti data mining dan teknik machine learning. Dalam beberapa dekade terakhir, ensemble learning sangat memajukan penelitian pada machine learning dan klasifikasi pola, serta menunjukan peningkatan hasil kinerja dibandingkan single classifier. Pada Penelitian ini dilakukan percobaan peningkatan nilai akurasi terhadap sistem pendeteksian anomali, pertama dilakukan klasifikasi menggunakan single classifier untuk didapati hasil nilai akurasi yang nantinya dibandingkan dengan hasil dari ensemble learning dan feature selection. Penggunaan ensemble learning bertujuan untuk mendapatkan nilai akurasi yang terbaik dari single classifier. Hasil didapatkan dari nilai confusion matrix dan akan dilakukan pengujian dengan cara membandingkan nilai kedua metode diatas. Penelitian berhasil mendapatkan nilai akurasi single classifier (naïve bayes) yaitu 77,4% dan nilai ensemble learning 96,8%. Kata Kunci— ensemble learning, nsl-kdd, naïve bayes, anomali, feature selectionIntrusion detection systems (IDS) are known as very prominent and leading techniques for finding malicious activities on computer networks, unlike conventional firewalls, IDS differs in terms of identifying attacks intelligently with analytic approaches such as machine learning techniques. In the last few decades, ensemble learning has greatly advanced research in machine learning and pattern classification it has shown an improve in performance results compared to a single classifier. In this study an attempt was made to increase the accuracy of anomalous detection systems, first by classification using a single classifier to find the results of accuracy which will be compared with the results of ensemble learning and feature selection. The use of ensemble learning aims to get the best accuracy value from a single classifier. The results are obtained from the value of the confusion matrix and will be tested by comparing the values of the two methods above. The research succeeded in getting a single classifier accuracy value of 77,4% and ensemble learning 96,8%. Keywords— ensemble learning, nsl-kdd, naïve bayes, anomali, feature selection

scholarly journals Network Malware Detection using Soft Computing and Machine Learning Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 879-885
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Anomaly Detection ◽  
Soft Computing ◽  
Naive Bayes ◽  
Malware Detection ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Network Anomaly Detection

In today’s world there is rapid increase in the information which makes addressing of security issues more important. Malware detection is an important area for research in effective and secure functioning of computer networks. Research efforts are required to protect the systems from various security attacks. In this paper, we analyze usefulness of Soft Computing and Machine Learning Techniques for network malware detection. Hamamoto et al. [1] used combination of Genetic Algorithm and Fuzzy logic for implementation of network anomaly detection. The research work proposed in this paper extends the concepts discussed in [1]. The proposed work explores use of various Machine Learning algorithms such as K-Nearest Neighbor, Naïve Bayes and Decision Tree for network anomaly detection. The experimental observations are conducted on CIDDS (Coburg Intrusion Detection Data Set) dataset [14]. It is observed that Decision Tree approach gave better results as compared to KNN and Naïve Bayes techniques. Decision Tree technique gives 99% of accuracy and precision of 1 and recall of 1.

scholarly journals Breast Cancer Prediction Using Classification Techniques of Machine Learning

2022 ◽  
Vol 10 (1) ◽  
pp. 51-57
Author(s):  
Angela More
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Decision Tree Classifier ◽  
Learning Techniques ◽  
Tree Classifier ◽  
Abstract Data

Abstract: Data analytics play vital roles in diagnosis and treatment in the health care sector. To enable practitioner decisionmaking, huge volumes of data should be processed with machine learning techniques to produce tools for prediction and classification Breast Cancer reports 1 million cases per year. We have proposed a prediction model, which is specifically designed for prediction of Breast Cancer using Machine learning algorithms Decision tree classifier, Naïve Bayes, SVM and KNearest Neighbour algorithms. The model predicts the type of tumour, the tumour can be benign (noncancerous) or malignant (cancerous) . The model uses supervised learning which is a machine learning concept where we provide dependent and independent columns to machine. It uses classification technique which predicts the type of tumour. Keywords: Cancer, Machine learning, Prediction, Data Visualization, SVM, Naïve Bayes, Classification.

scholarly journals Doc2Vec &Naïve Bayes: Learners’ Cognitive Presence Assessment through Asynchronous Online Discussion TQ Transcripts

2019 ◽  
Vol 14 (08) ◽  
pp. 70 ◽  
Author(s):  
Hind Hayati ◽  
Abdessamad Chanaa ◽  
Mohammed Khalidi Idrissi ◽  
Samir Bennani
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Online Discussions ◽  
Cognitive Presence ◽  
Machine Learning Techniques ◽  
Face To Face ◽  
Learning Techniques ◽  
Bayes Algorithm ◽  
Context Features

Due to the lack of face to face interaction in online learning environment, this article aims essentially to give tutors the opportunity to understand and analyze learners’ cognitive behavior. In this perspective, we propose an automatic system to assess learners’ cognitive presence regarding their social interactions within synchronous online discussions. Combining Natural Language Preprocessing, Doc2Vec document embedding method and machine learning techniques; we first make some transformations and preprocessing to the given transcripts, then we apply Doc2Vec method to represent each message as a vector that will be concatenated with LIWC and context features. The vectors are input data of Naïve Bayes algorithm; a machine learning method; that aims to classify transcripts according to cognitive presence categories.

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