A PARALLEL AND DISTRIBUTED FRAMEWORK FOR IMPLEMENTING BACK PROPAGATION ALGORITHM IN BIG DATA by unknown

Get full text
Get full text

Information warfare recovery-fighting back through the matrix. (c2012) by Zbib, Mirna

Get full text
Get full text

A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method by Amit Kumar Balyan (18288964)

“…Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. …”

Predict Student Success and Performance factors by analyzing educational data using data mining techniques by ATIF, MUHAMMAD

“…The research study is performed as experimental analysis and develop models from nine machine learning algorithms including KNN, Naïve Bayes, SVM, Logistic regression, Decision Tree, Random forest, Adaboost, Bagging Classifier, and voting Classifier. …”
Get full text

A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition by Hanif Heidari (22467148)

“…A range of machine learning (ML) methods can be used to recognize facial expressions based on data from small to large datasets. Random Forest (RF) is simpler and more efficient than other ML algorithms. …”

Random Forest Bagging and X‐Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data by Rajesh Kumar Dhanaraj (19646269)

“…<p dir="ltr">In the current ongoing crisis, people mostly rely on mobile phones for all the activities, but query analysis and mobile data security are major issues. Several research works have been made on efficient detection of antipatterns for minimizing the complexity of query analysis. …”

On the Optimization of Band Gaps in Periodic Waveguides by Jamil Renno (14070771)

Subjects:

A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48 by Al-Manaseer, Hitham

“…This was done by studying the performance of three well-known classification algorithms Random Forest Classifier (RFC), Support Vector Machine (SVM), and Decision Tree-J48 (J48), to predict the probability of heart attack. …”
Get full text

Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology by Senyuk, Mihail

“…In this study, the emergency control algorithms based on ensemble machine learning algorithms (XGBoost and Random Forest) were developed for a low-inertia power system. …”
Get full text

A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation by Khaled Dhibi (16891524)

“…In the proposed FDD approach, named interval reduced kernel PCA (IRKPCA)-based Random Forest (IRKPCA-RF), the feature extraction and selection phase is performed using the IRKPCA models while the fault classification is ensured using the RF algorithm. …”

Scalable Nonparametric Supervised Learning for Streaming and Massive Data: Applications in Healthcare Monitoring and Credit Risk by Mohamed Chaouch (17983846)

Subjects:

A combined algorithm based on finite elements and a modified methodof characteristics for the analysis of the corona in wire-ductelectrostatic precipitators by Al-Hamouz, Z.M.

Get full text
Get full text

A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models by Osama Bassam J. Rabie (21323741)

“…Then, the DRF optimization algorithm is applied to optimally tune the features required for accurate intrusion detection and classification. …”

A reduced model for phase-change problems with radiation using simplified PN approximations by Belhamadia, Youssef

“…The integro-differential equation for the full radiative transfer is replaced by a set of differential equations which are independent of the angle variable and easy to solve using conventional computational methods. To solve the coupled equations, we implement a second-order implicit scheme for the time integration and a mixed finite element method for the space discretization. …”
Get full text

Extended Behavioral Modeling of FET and Lattice-Mismatched HEMT Devices by Khawam, Yahya Bader

Subjects: Get full text

Limiting the Collection of Ground Truth Data for Land Use and Land Cover Maps with Machine Learning Algorithms by Usman Ali (6586886)

“…Extracted vegetation indices were evaluated on three ML algorithms, namely, random forest (RF), k-nearest neighbour (K-NN), and k dimensional-tree (KD-Tree). …”

Brain Source Localization in the Presence of Leadfield Perturbations by Momin, Rabiya Nakhat

Get full text

Nested ensemble selection: An effective hybrid feature selection method by Kamalov, Firuz

Get full text

Using Machine Learning Algorithms to Forecast Solar Energy Power Output by Ali Jassim Lari (22597940)

“…We focused on the first 30-min, 3-h, 6-h, 12-h, and 24-h windows to gain an appreciation of the impact of forecasting duration on the accuracy of prediction using the selected machine learning algorithms. The study results show that Random Forest outperformed all other tested algorithms. …”

Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers by Yousef, Hibba

“…In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. …”
Get full text