scholarly journals Assessment of Machine Learning Classifiers for Malware Detection

2020 ◽  
Vol 8 (5) ◽  
pp. 1840-1844
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Private Information ◽  
Cell Phones ◽  
Machine Learning Techniques ◽  
Accuracy Rate ◽  
Machine Learning Classifiers ◽  
Malware Classification ◽  
Learning Techniques ◽  
Day By Day

In our daily life, cell phones (e.g., cell phones and tablets) have met an expanding business achievement and have turned into an essential component of the regular daily existence for billions of individuals all around the globe. Day by day the advancements in technology is growing like an infinity thing .And the advancements in technology made everyone to use the smart phones and tablets regardless their professions .Everyday a big range of apps coming in to existence which made our lives very comfortable. While installing these apps without knowing we are allowing some malware in to our mobile which may leads to leakage of once private information. So in this paper we are going to analyze some machine learning techniques which will help in malware classification by taking the dataset. In this paper we calculated accuracy rate of malware classifiers such as KNN, Random Forest, SVM, and Gaussian Etc. Where we will be rating all these machine learning techniques according to their rate of accuracy. According to the experiments what we conducted Random forest stood as the best malware classifiers among all the other classifiers. We accept our study will be a reference work for specialists and experts in this examination field.

scholarly journals Learning from Imbalanced Educational Data Using Ensemble Machine Learning Algorithms

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 01) ◽  
pp. 183-195
Author(s):  
Thingbaijam Lenin ◽  
N. Chandrasekaran
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Missing Values ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Adaptive Boosting ◽  
Stochastic Gradient Boosting ◽  
Ensemble Machine Learning ◽  
Learning Techniques ◽  
Student’S Performance

Student’s academic performance is one of the most important parameters for evaluating the standard of any institute. It has become a paramount importance for any institute to identify the student at risk of underperforming or failing or even drop out from the course. Machine Learning techniques may be used to develop a model for predicting student’s performance as early as at the time of admission. The task however is challenging as the educational data required to explore for modelling are usually imbalanced. We explore ensemble machine learning techniques namely bagging algorithm like random forest (rf) and boosting algorithms like adaptive boosting (adaboost), stochastic gradient boosting (gbm), extreme gradient boosting (xgbTree) in an attempt to develop a model for predicting the student’s performance of a private university at Meghalaya using three categories of data namely demographic, prior academic record, personality. The collected data are found to be highly imbalanced and also consists of missing values. We employ k-nearest neighbor (knn) data imputation technique to tackle the missing values. The models are developed on the imputed data with 10 fold cross validation technique and are evaluated using precision, specificity, recall, kappa metrics. As the data are imbalanced, we avoid using accuracy as the metrics of evaluating the model and instead use balanced accuracy and F-score. We compare the ensemble technique with single classifier C4.5. The best result is provided by random forest and adaboost with F-score of 66.67%, balanced accuracy of 75%, and accuracy of 96.94%.

scholarly journals Heart Disease Prediction using Machine Learning Techniques

2021 ◽  
pp. 42-47
Author(s):  
Ramesh Ponnala ◽  
K. Sai Sowjanya
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Linear Model ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Huge Amount ◽  
Healthcare Enterprise ◽  
Learning Techniques ◽  
Accuracy Level

Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by using the healthcare enterprise. on this paper, we advocate a unique technique that pursuits via finding good sized functions by means of applying ML strategies ensuing in improving the accuracy inside the prediction of heart ailment. The severity of the heart disease is classified primarily based on diverse methods like KNN, choice timber and so on. The prediction version is added with special combos of capabilities and several known classification techniques. We produce a stronger performance level with an accuracy level of a 100% through the prediction version for heart ailment with the Hybrid Random forest area with a linear model (HRFLM).

scholarly journals A Heuristic Model for Predicting Human Fall Detection using Machine Learning Techniques

2020 ◽  
Vol 9 (2) ◽  
pp. 119-121
Keyword(s):  
Machine Learning ◽  
Health Problem ◽  
Fall Detection ◽  
Machine Learning Techniques ◽  
Technological Advancement ◽  
Heuristic Model ◽  
Human Beings ◽  
Machine Learning Classifiers ◽  
Learning Techniques ◽  
Smart Health

It is very obvious that human fall due to unconsciousness is a very common health problem in every human being. With the evolution of many smart health devices, we should contribute the technological advancement of machine learning into it. Different techniques are already used in order to detect human fall detection in human beings. In this paper we have studied the patterns of falling of human through the fall detection dataset while this human was performing various motions. By understanding all these we have generated the prediction protocol which estimates the fall of a person using fall detection dataset. Machine Learning classifiers were used to predict the human fall and a comparative study of various algorithms used was developed to find out the best classifier.

Overview of Machine Learning Approaches for Wireless Communication

2021 ◽  
pp. 1579-1597
Author(s):  
Tolga Ensari ◽  
Melike Günay ◽  
Yağız Nalçakan ◽  
Eyyüp Yildiz
Keyword(s):  
Machine Learning ◽  
Wireless Communications ◽  
Smart Phones ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Research Areas ◽  
Learning Techniques ◽  
Recent Developments ◽  
Security And Reliability ◽  
Day By Day

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is continuously changing. Recent developments in smart phones and other devices like drones, wearable devices, machines with sensors need reliable communication within internet of things (IoT) systems. For this purpose, artificial intelligence can increase the security and reliability and manage the data that is generated by the wireless systems. In this chapter, the authors investigate several machine learning techniques for wireless communications including deep learning, which represents a branch of artificial neural networks.

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.

Enhancing alpine glacial lakes detection and mapping using multi-source data and machine learning techniques

2020 ◽  
Author(s):  
Sonam Wangchuk ◽  
Tobias Bolch
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Satellite Images ◽  
Random Forest Classifier ◽  
Machine Learning Techniques ◽  
Glacial Lake ◽  
Glacial Lakes ◽  
Alpine Regions ◽  
Learning Techniques ◽  
Source Data

<p>An accurate detection and mapping of glacial lakes in the Alpine regions such as the Himalayas, the Alps and the Andes are challenged by many factors. These factors include 1) a small size of glacial lakes, 2) cloud cover in optical satellite images, 3) cast shadows from mountains and clouds, 4) seasonal snow in satellite images, 5) varying degree of turbidity amongst glacial lakes, and 6) frozen glacial lake surface. In our study, we propose a fully automated approach, that overcomes most of the above mentioned challenges, to detect and map glacial lakes accurately using multi-source data and machine learning techniques such as the random forest classifier algorithm. The multi-source data are from the Sentinel-1 Synthetic Aperture Radar data (radar backscatter), the Sentinel-2 multispectral instrument data (NDWI), and the SRTM digital elevation model (slope). We use these data as inputs for the rule-based segmentation of potential glacial lakes, where decision rules are implemented from the expert system. The potential glacial lake polygons are then classified either as glacial lakes or non-glacial lakes by the trained and tested random forest classifier algorithm. The performance of the method was assessed in eight test sites located across the Alpine regions (e.g. the Boshula mountain range and Koshi basin in the Himalayas, the Tajiks Pamirs, the Swiss Alps and the Peruvian Andes) of the word. We show that the proposed method performs efficiently irrespective of geographic, geologic, climatic, and glacial lake conditions.</p>

scholarly journals Video spam comment features selection using machine learning techniques

2019 ◽  
Vol 15 (2) ◽  
pp. 1046
Author(s):  
Nabilah Alias ◽  
Cik Feresa Mohd Foozy ◽  
Sofia Najwa Ramli ◽  
Naqliyah Zainuddin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Random Forest ◽  
Feature Detection ◽  
Random Tree ◽  
Machine Learning Techniques ◽  
Decision Table ◽  
Features Selection ◽  
Video Sharing ◽  
Learning Techniques

<p>Nowadays, social media (e.g., YouTube and Facebook) provides connection and interaction between people by posting comments or videos. In fact, comments are a part of contents in a website that can attract spammer to spreading phishing, malware or advertising. Due to existing malicious users that can spread malware or phishing in the comments, this work proposes a technique used for video sharing spam comments feature detection. The first phase of the methodology used in this work is dataset collection. For this experiment, a dataset from UCI Machine Learning repository is used. In the next phase, the development of framework and experimentation. The dataset will be pre-processed using tokenization and lemmatization process. After that, the features to detect spam is selected and the experiments for classification were performed by using six classifiers which are Random Tree, Random Forest, Naïve Bayes, KStar, Decision Table, and Decision Stump. The result shows the highest accuracy is 90.57% and the lowest was 58.86%.</p>

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