scholarly journals A Framework of New Hybrid Features for Intelligent Detection of Zero Hour Phishing Websites

2019 ◽  
pp. 36-46
Author(s):  
Thomas Nagunwa ◽  
Syed Naqvi ◽  
Shereen Fouad ◽  
Hanifa Shah
Keyword(s):  
Hybrid Features ◽  
Intelligent Detection

Using WPNNA Classifier in Ubiquitination Site Prediction Based on Hybrid Features

2013 ◽  
Vol 20 (3) ◽  
pp. 318-323 ◽  
Author(s):  
Kai-Yan Feng ◽  
Tao Huang ◽  
Kai-Rui Feng ◽  
Xiao-Jun Liu
Keyword(s):  
Hybrid Features ◽  
Site Prediction ◽  
Ubiquitination Site

Hybrid features prediction model of movie quality using Multi-machine learning techniques for effective business resource planning

2021 ◽  
Vol 40 (5) ◽  
pp. 9361-9382 ◽  
Author(s):  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Faisal Jamil ◽  
Do-Hyeun Kim
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Resource Planning ◽  
Experimental Results ◽  
Quality Prediction ◽  
Classification Models ◽  
Hybrid Features ◽  
Social Media Data ◽  
Media Data

Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.

scholarly journals A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 786
Author(s):  
Yenny Villuendas-Rey ◽  
Eley Barroso-Cubas ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Swarm Intelligence ◽  
Data Clustering ◽  
Incomplete Data ◽  
Missing Values ◽  
Clustering Algorithms ◽  
Bat Algorithm ◽  
Hybrid Features ◽  
Bee Colony ◽  
Learning Tasks ◽  
Clustering Data

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.

scholarly journals Structure Design of Cable-Stayed Bridge Intelligent Detection Robot

2021 ◽  
Vol 632 ◽  
pp. 022061
Author(s):  
Hongyu He ◽  
Guichuan Hu ◽  
Jing Ren ◽  
Jing Hu ◽  
Hang Zhao ◽  
...  
Keyword(s):  
Structure Design ◽  
Cable Stayed Bridge ◽  
Intelligent Detection

Digital Image Forensic Analysis using Hybrid Features

2021 ◽  
Author(s):  
Tahira Nazir ◽  
Aun Irtaza ◽  
Ali Javed ◽  
Hafiz Malik ◽  
Awais Mehmood ◽  
...  
Keyword(s):  
Digital Image ◽  
Forensic Analysis ◽  
Hybrid Features ◽  
Image Forensic
Sign in / Sign up

Export Citation Format

Share Document