scholarly journals An Empirical Evaluation of Online Continuous Authentication and Anomaly Detection Using Mouse Clickstream Data Analysis

2021 ◽  
Vol 11 (13) ◽  
pp. 6083
Author(s):  
Sultan Almalki ◽  
Nasser Assery ◽  
Kaushik Roy
Keyword(s):  
Data Analysis ◽  
Anomaly Detection ◽  
Single Point ◽  
Empirical Evaluation ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Clickstream Data ◽  
Continuous Authentication ◽  
Mouse Movement ◽  
Tree Classifier

While the password-based authentication used in social networks, e-mail, e-commerce, and online banking is vulnerable to hackings, biometric-based continuous authentication systems have been used successfully to handle the rise in unauthorized accesses. In this study, an empirical evaluation of online continuous authentication (CA) and anomaly detection (AD) based on mouse clickstream data analysis is presented. This research started by gathering a set of online mouse-dynamics information from 20 participants by using software developed for collecting mouse information, extracting approximately 87 features from the raw dataset. In contrast to previous work, the efficiency of CA and AD was studied using different machine learning (ML) and deep learning (DL) algorithms, namely, decision tree classifier (DT), k-nearest neighbor classifier (KNN), random forest classifier (RF), and convolutional neural network classifier (CNN). User identification was determined by using three scenarios: Scenario A, a single mouse movement action; Scenario B, a single point-and-click action; and Scenario C, a set of mouse movement and point-and-click actions. The results show that each classifier is capable of distinguishing between an authentic user and a fraudulent user with a comparatively high degree of accuracy.

scholarly journals Recommender System for Term Deposit Likelihood Prediction using Cross-validated Neural Network

2021 ◽  
pp. 21-28
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective ◽  
Fold Cross Validation

For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and MSE of 0.1168.

scholarly journals Recommender System for Term Deposit Likelihood Prediction Using Cross-Validated Neural Network

2020 ◽  
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Mean Squared Error ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Squared Error ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective

For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network(NN) along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and Mean Squared Error (MSE) of 0.1168.

scholarly journals Using Decision Tree Classifier for Analyzing Students’ Activities

2013 ◽  
Vol 6 (2) ◽  
Author(s):  
Snježana Milinković ◽  
Mirjana Maksimović
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Decision Tree ◽  
Electrical Engineering ◽  
Decision Tree Classifier ◽  
Final Exam ◽  
Data Mining Tool ◽  
Tree Classifier ◽  
Mining Tool

In this paper students’ activities data analysis in the course Introduction to programming at Faculty of Electrical Engineering in East Sarajevo is performed. Using the data that are stored in the Moodle database combined with manually collected data, the model was developed to predict students’ performance in successfully passing the final exam. The goal was to identify variables that could help teachers in predicting students’ performance and making specific recommendations for improving individual activities that could directly influence final exam successful passing. The model was created using decision tree classifier and experiments were performed using the WEKA data mining tool. The effect of input attributes on the model performances was analyzed and applying appropriate techniques a higher accuracy of the generated model was achieved.

scholarly journals Prediction of Diagnosing Chronic Kidney Disease using Machine Learning: Classification Algorithms

2020 ◽  
Vol 9 (4) ◽  
pp. 1922-1924
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Renal Disorder ◽  
Machine Learning Classification ◽  
Knn Classifier ◽  
Tree Classifier

Chronic Kidney Disease is a very dangerous health problem that has been spreading as well as growing due to diversification in life style such as food habits, changes in the atmosphere, etc. The branch of biosciences has progressive to a bigger extent and has bring out huge amounts of data from Electronic Health Records. The primary aim of this paper is to classify using various Classification techniques like Logistic Regression (LR), K-Nearest Neighbor (KNN) Classifier, Decision Tree Classifier Tree, Random Forest Classifier, Support Vector Machine (SVM), and SGD Classifier. According to the health statistics of India 63538 cases has been registered on chronic renal disorder. Average age of men and women susceptible to renal disorders occurs within the range of 48 to 70 years.

scholarly journals Applying Convolutional-GRU for Term Deposit Likelihood Prediction

2020 ◽  
Author(s):  
Shawni Dutta ◽  
Samir Bandyopadhyay
Keyword(s):  
Predictive Model ◽  
Nearest Neighbor ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Saving Account ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective ◽  
Gated Recurrent Unit

Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits. For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis can influence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludes that proposed model attains an accuracy of 89.59% and MSE of 0.1041 which outperform well other baseline models.

scholarly journals Comparative Analysis of Text Mining Classification Algorithms for English and Indonesian Qur’an Translation

2019 ◽  
Vol 8 (1) ◽  
pp. 47
Author(s):  
Rahmat Hidayat ◽  
Sekar Minati
Keyword(s):  
Text Mining ◽  
Naive Bayes ◽  
Intelligent System ◽  
Naïve Bayes ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Classifier Algorithms

Qur'an, As-Sunnah, and Islamic old book have become the sources for Islam followers as sources of knowledge, wisdom, and law. But in daily life, there are still many Muslims who do not understand the meaning of the sentence in the Qur'an even though they read it every day. It becomes a challenge for Science and Engineering field academicians especially Informatics to explore and represent knowledge through intelligent system computing to answer various questions based on knowledge from the Qur'an. This research is creating an enabling computational environment for text mining the Qur'an, of which purpose is to facilitate people to understand each verse in the Qur'an. The classification experiment uses Support Vector Machine (SVM), Naive Bayes, k-Nearest Neighbor (kNN), and J48 Decision Tree classifier algorithms with Al-Baqarah verses translated to English and Indonesian as the dataset which was labeled by three most fundamental aspects of Islam: 'Iman' (faith), 'Ibadah' (worship), and 'Akhlaq' (virtues). Indonesian translation was processed by using the sastrawi package in Python to do the pre-processing and StringToWord Vector in WEKA with the TF-IDF method to implement the algorithms. The classification experiments are determined to measure accuracy, and f-measure, it tested with a percentage split 66% as the data training and the rest as the data testing. The decision from an experiment that was carried out by the classification results, SVM classifier algorithms have the overall best accuracy performance for the Indonesian translation of 81.443% and the Naïve Bayes classifier has the best accuracy for the English translation, which achieved 78.35%.

scholarly journals A Predictive and Preventive Model for Onset of Alzheimer's Disease

2021 ◽  
Vol 9 ◽  
Author(s):  
Udit Singhania ◽  
Balakrushna Tripathy ◽  
Mohammad Kamrul Hasan ◽  
Noble C. Anumbe ◽  
Dabiah Alboaneen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Thinking Skills ◽  
Lifestyle Changes ◽  
Support Vector ◽  
Amyloid Beta Protein ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Early Stages ◽  
Tree Classifier

Alzheimer's Disease (AD) is a neurodegenerative irreversible brain disorder that gradually wipes out the memory, thinking skills and eventually the ability to carry out day-to-day tasks. The amount of AD patients is rapidly increasing due to several lifestyle changes that affect biological functions. Detection of AD at its early stages helps in the treatment of patients. In this paper, a predictive and preventive model that uses biomarkers such as the amyloid-beta protein is proposed to detect, predict, and prevent AD onset. A Convolution Neural Network (CNN) based model is developed to predict AD at its early stages. The results obtained proved that the proposed model outperforms the traditional Machine Learning (ML) algorithms such as Logistic Regression, Support Vector Machine, Decision Tree Classifier, and K Nearest Neighbor algorithms.

scholarly journals Applying Convolutional-GRU for Term Deposit Likelihood Prediction

2021 ◽  
Vol 11 (3) ◽  
Author(s):  
Shawni Dutta ◽  
Payal Bose ◽  
Vishal Goyal ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Predictive Model ◽  
Nearest Neighbor ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Saving Account ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective ◽  
Gated Recurrent Unit

Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits.For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis caninfluence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludesthat proposed model attainsan accuracy of 89.59% and MSE of 0.1041 which outperform wellother baseline models.

scholarly journals A Framework for AI-Assisted Detection of Patent Ductus Arteriosus from Neonatal Phonocardiogram

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 169
Author(s):  
Sergi Gómez-Quintana ◽  
Christoph E. Schwarz ◽  
Ihor Shelevytsky ◽  
Victoriya Shelevytska ◽  
Oksana Semenova ◽  
...  
Keyword(s):  
Patent Ductus Arteriosus ◽  
Heart Defects ◽  
Ductus Arteriosus ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Heart Sounds ◽  
Decision Tree Classifier ◽  
Support Tool ◽  
Tree Classifier ◽  
Patent Ductus

The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.

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