Titlebook: Computational Intelligence in Information Systems; Proceedings of the C Wida Susanty Haji Suhaili,Nor Zainah Siau,Somnuk P Conference proce

糾纏

Destructive Digital Ecosystem of Cyber Bullying Perfective Within the Information Technology Age whether traditional bullying or cyberbullying, suffer emotionally and psychologically. Support from family, friends, schools, health care providers play an essential role in helping students overcome cyberbullying and negative after-effects of cyberbullying.

咒語(yǔ)

promote

把手

SMAC (Social, Mobile, Analytics, Cloud)-Based Learning Intervention for Introductory Programming – Tbling ubiquitous learning (mobile element), analysing students’ behaviour and predicting their performance (analytics element), and enabling coding on the cloud (cloud element). Most importantly, the SLR results presented a research gap in the use of all four SMAC elements to design a learning inter

推測(cè)

Privacy Policy, Training and Adaption of Employee Monitoring Technology to Curtail Workplace Harassmtion to use the employee monitoring technology by employees by considering various organizational, contextual and individual factors. Data analysis has used Cronbach’s alpha, correlation, and linear regression. Questionnaire consistency has been found to hold. In order to establish the statistically

SUGAR

A Comparative Study of Supervised Machine Learning Techniques for Deceptive Review Identification Uscompare the performance of seven supervised machine learning techniques. It has been demonstrated that the Ensemble Bagged classifier with 3% PCA variance outperforms other six supervised methods resulting in 88% prediction accuracy.

發(fā)怨言

A Robust Ensemble Method for Classification in Imbalanced Datasets in the Presence of Noisef each instance in a training dataset, and ensuring that bootstraps are balanced, and at the same time have instances of varying degrees of hardness (Easy, Normal, and Hard). We evaluate the performance of the proposed method on 30 synthetic imbalanced datasets with different levels of noise and imb

存在主義

A Development Framework for Automated Facial Expression Recognition Systemss unclear which dataset to start with, and 6) no development framework and methodologies to systematically implement and test new models. In this paper, we present a framework with complete source code and algorithms to: 1) detect faces and crop face images in a given dataset for AFER; 2) extract fa

非實(shí)體

誘拐