A Two Layer Machine Learning System for Intrusion Detection Based on Random Forest and Support Vector Machine

2020 ◽  
Author(s):  
Sabrina Afroz ◽  
S.M Ariful Islam ◽  
Samin Nawer Rafa ◽  
Maheen Islam
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Intrusion Detection ◽  
Learning System ◽  
Support Vector

scholarly journals Performance Analysis of Supervised Machine Learning Algorithms on Medical Dataset

2020 ◽  
Vol 8 (6) ◽  
pp. 1637-1642
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Learning System ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Heart Problem

Machine learning (ML) algorithms are designed to perform prediction based on features. With the help of machine learning, system can automatically learn and improve by experience. Machine learning comes under Artificial intelligence. Machine learning is broadly categorized in two types: supervised and unsupervised. Supervised ML performs classification and unsupervised is for clustering. In present scenario, machine learning is used in various areas. It can be used for biometric recognition, hand writing recognition, medical diagnosis etc. In medical field, machine learning plays an important role in identifying diseases based on patient’s features. Presently,doctors use software application based on machine learning algorithm in various disease diagnosis like cancer, cardiac arrest and many more. In this paper we used an ensemble learning method to predict heart problem. Our study described the performance of ML algorithms by comparing various evaluating parameters such as F-measure, Recall, ROC, precision and accuracy. The study done with various combination ML classifiers such as, Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF) algorithm to predict heart problem. The result showed that by combining two ML algorithm, DT with NB, 81.1% accuracy was achieved. Simultaneously, the models like Support Vector machine (SVM), Decision tree, Naïve Bayes, Random Forest models were also trained and tested individually.

scholarly journals An Enhanced Intelligent Intrusion Detection System using Machine Learning

2019 ◽  
Vol 8 (9) ◽  
pp. 2177-2181
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Intrusion Detection ◽  
Performance Metrics ◽  
Recognition Rate ◽  
Reduction Rate ◽  
Training Data ◽  
Support Vector ◽  
Detection Techniques

Numerous Intrusion detection techniques are used to find the anomalies that depends on the accuracy, detection rate etc. The purpose of the system is to detect the anomalies based on the given dataset thereby improving the accuracy. A CWS IDS is proposed to find the anomalies in the network, that combines machine learning techniques autoencoder and support vector machine for feature extraction and classification. This is evaluated on the training and testing datasets of NSL KDD dataset that accomplishes well in terms of reduction rate and precision. By combining autoencoder and support vector machine for finding the anomalies, the performance metrics of the system is improved.The system is related with single SVM and Random forest classifier. The performance measures such as precision, recall, accuracy and F-measure is equated with the SVM, random forest, and CWS IDS for training data and test data. Thereby the recognition rate is enhanced and both false positives, false negatives are lesser

Classification study of solvation free energies of organic molecules using machine learning techniques

RSC Advances ◽  
2014 ◽  
Vol 4 (106) ◽  
pp. 61624-61630 ◽  
Author(s):  
N. S. Hari Narayana Moorthy ◽  
Silvia A. Martins ◽  
Sergio F. Sousa ◽  
Maria J. Ramos ◽  
Pedro A. Fernandes
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Organic Molecules ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Models ◽  
Free Energies ◽  
Learning Techniques ◽  
Solvation Free Energies

Classification models to predict the solvation free energies of organic molecules were developed using decision tree, random forest and support vector machine approaches and with MACCS fingerprints, MOE and PaDEL descriptors.

scholarly journals Execution Assessment of Machine Learning Algorithms for Spam Profile Detection on Instagram

2021 ◽  
Vol 10 (3) ◽  
pp. 1889-1894
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Tools ◽  
Learning Models ◽  
K Nearest Neighbor

Witheverypassingsecondsocialnetworkcommunityisgrowingrapidly,becauseofthat,attackershaveshownkeeninterestinthesekindsofplatformsandwanttodistributemischievouscontentsontheseplatforms.Withthefocus on introducing new set of characteristics and features forcounteractivemeasures,agreatdealofstudieshasresearchedthe possibility of lessening the malicious activities on social medianetworks. This research was to highlight features for identifyingspammers on Instagram and additional features were presentedto improve the performance of different machine learning algorithms. Performance of different machine learning algorithmsnamely, Multilayer Perceptron (MLP), Random Forest (RF), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)were evaluated on machine learning tools named, RapidMinerand WEKA. The results from this research tells us that RandomForest (RF) outperformed all other selected machine learningalgorithmsonbothselectedmachinelearningtools.OverallRandom Forest (RF) provided best results on RapidMiner. Theseresultsareusefulfortheresearcherswhoarekeentobuildmachine learning models to find out the spamming activities onsocialnetworkcommunities.

scholarly journals Application of Natural Language Processing with Supervised Machine Learning Techniques to Predict the Overall Drugs Performance

2020 ◽  
Vol 11 (40) ◽  
pp. 8-23
Author(s):  
Pius MARTHIN ◽  
Duygu İÇEN
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Semantic Analysis ◽  
Classification Tree ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

Online product reviews have become a valuable source of information which facilitate customer decision with respect to a particular product. With the wealthy information regarding user's satisfaction and experiences about a particular drug, pharmaceutical companies make the use of online drug reviews to improve the quality of their products. Machine learning has enabled scientists to train more efficient models which facilitate decision making in various fields. In this manuscript we applied a drug review dataset used by (Gräβer, Kallumadi, Malberg,& Zaunseder, 2018), available freely from machine learning repository website of the University of California Irvine (UCI) to identify best machine learning model which provide a better prediction of the overall drug performance with respect to users' reviews. Apart from several manipulations done to improve model accuracy, all necessary procedures required for text analysis were followed including text cleaning and transformation of texts to numeric format for easy training machine learning models. Prior to modeling, we obtained overall sentiment scores for the reviews. Customer's reviews were summarized and visualized using a bar plot and word cloud to explore the most frequent terms. Due to scalability issues, we were able to use only the sample of the dataset. We randomly sampled 15000 observations from the 161297 training dataset and 10000 observations were randomly sampled from the 53766 testing dataset. Several machine learning models were trained using 10 folds cross-validation performed under stratified random sampling. The trained models include Classification and Regression Trees (CART), classification tree by C5.0, logistic regression (GLM), Multivariate Adaptive Regression Spline (MARS), Support vector machine (SVM) with both radial and linear kernels and a classification tree using random forest (Random Forest). Model selection was done through a comparison of accuracies and computational efficiency. Support vector machine (SVM) with linear kernel was significantly best with an accuracy of 83% compared to the rest. Using only a small portion of the dataset, we managed to attain reasonable accuracy in our models by applying the TF-IDF transformation and Latent Semantic Analysis (LSA) technique to our TDM.

scholarly journals Detecting Real-Time Fall of Elderly People Using Machine Learning

2021 ◽  
Vol 9 (12) ◽  
pp. 1913-1918
Author(s):  
Prathima P
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Elderly People ◽  
Fall Detection ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
False Alarms ◽  
Severe Injuries

Abstract: Fall is a significant national health issue for the elderly people, generally resulting in severe injuries when the person lies down on the floor over an extended period without any aid after experiencing a great fall. Thus, elders need to be cared very attentively. A supervised-machine learning based fall detection approach with accelerometer, gyroscope is devised. The system can detect falls by grouping different actions as fall or non-fall events and the care taker is alerted immediately as soon as the person falls. The public dataset SisFall with efficient class of features is used to identify fall. The Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms are employed to detect falls with lesser false alarms. The SVM algorithm obtain a highest accuracy of 99.23% than RF algorithm. Keywords: Fall detection, Machine learning, Supervised classification, Sisfall, Activities of daily living, Wearable sensors, Random Forest, Support Vector Machine

scholarly journals Aspect Category Classification dengan Pendekatan Machine Learning Menggunakan Dataset Bahasa Indonesia

2021 ◽  
Vol 10 (3) ◽  
pp. 229-235
Author(s):  
Syaifulloh Amien Pandega Perdana ◽  
Teguh Bharata Aji ◽  
Ridi Ferdiana
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Term Weighting ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency ◽  
Bahasa Indonesia

Ulasan pelanggan merupakan opini terhadap kualitas barang atau jasa yang dirasakan konsumen. Ulasan pelanggan mengandung informasi yang berguna bagi konsumen maupun penyedia barang atau jasa. Ketersediaan ulasan pelanggan dalam jumlah besar pada website membutuhkan suatu framework untuk mengekstraksi sentimen secara otomatis. Sebuah ulasan pelanggan sering kali mengandung banyak aspek sehingga Aspect Based Sentiment Analysis (ABSA) harus digunakan untuk mengetahui polaritas masing-masing aspek. Salah satu tugas penting dalam ABSA adalah Aspect Category Detection. Metode machine learning untuk Aspect Category Detection sudah banyak dilakukan pada domain berbahasa Inggris, tetapi pada domain bahasa Indonesia masih sedikit. Makalah ini membandingkan kinerja tiga algoritme machine learning, yaitu Naïve Bayes (NB), Support Vector Machine (SVM), dan Random Forest (RF) pada ulasan pelanggan berbahasa Indonesia menggunakan Term Frequency–Inverse Document Frequency (TF-IDF) sebagai term weighting. Hasil menunjukkan bahwa RF memiliki kinerja paling unggul dibandingkan NB dan SVM pada tiga domain yang berbeda, yaitu restoran, hotel, dan e-commerce, dengan nilai f1-score untuk masing-masing domain adalah 84.3%, 85.7%, dan 89,3%.

scholarly journals Predição de sinistros agrícolas: uma abordagem comparativa utilizando aprendizagem de máquina

2020 ◽  
Vol 24 (4) ◽  
pp. 533-554
Author(s):  
Arthur Lula Mota ◽  
Daniel Lima Miquelluti ◽  
Vitor Augusto Ozaki
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Support Vector ◽  
Nearest Neighbours

O seguro agrícola tem ganho maior atenção no Brasil desde o início da década passada, com a implementação do Programa de Subvenção ao Prêmio do Seguro Rural. O presente estudo testou o desempenho de algoritmos de Machine Learning para as seguradoras anteciparem a ocorrência de sinistro, elaborando previsões por meio de dados de apólices e bases de dados climáticas entre os anos de 2006 e 2017. Foram testados os algoritmos Random Forest, Support Vector Machine e k-Nearest Neighbours. O segundo mostrou melhor performance preditiva de sinistros. No entanto, todos os métodos apresentaram baixa capacidade preditiva para a ocorrência de sinistros.

scholarly journals A Comparative Analysis of Machine Learning Algorithms to Predict Alzheimer’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Morshedul Bari Antor ◽  
A. H. M. Shafayet Jamil ◽  
Maliha Mamtaz ◽  
Mohammad Monirujjaman Khan ◽  
Sultan Aljahdali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Fine Tuning ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

Alzheimer’s disease has been one of the major concerns recently. Around 45 million people are suffering from this disease. Alzheimer’s is a degenerative brain disease with an unspecified cause and pathogenesis which primarily affects older people. The main cause of Alzheimer’s disease is Dementia, which progressively damages the brain cells. People lost their thinking ability, reading ability, and many more from this disease. A machine learning system can reduce this problem by predicting the disease. The main aim is to recognize Dementia among various patients. This paper represents the result and analysis regarding detecting Dementia from various machine learning models. The Open Access Series of Imaging Studies (OASIS) dataset has been used for the development of the system. The dataset is small, but it has some significant values. The dataset has been analyzed and applied in several machine learning models. Support vector machine, logistic regression, decision tree, and random forest have been used for prediction. First, the system has been run without fine-tuning and then with fine-tuning. Comparing the results, it is found that the support vector machine provides the best results among the models. It has the best accuracy in detecting Dementia among numerous patients. The system is simple and can easily help people by detecting Dementia among them.

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