Search Results - (( features mobile learning algorithm ) OR ( java implication based algorithm ))

Automated feature selection using boruta algorithm to detect mobile malware by Che Akmal, Che Yahaya, Ahmad Firdaus, Zainal Abidin, Salwana, Mohamad, Ernawan, Ferda, Mohd Faizal, Ab Razak

“…Boruta algorithm is used to select features automatically for assisting the machine learning. …”
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Designing algorithm visualization on mobile platform: The proposed guidelines by Supli, Ahmad Affandi, Shiratuddin, Norshuhada

“…In fact, mobile learning has been proved to enhance engagement in learning circumstances, and thus effect student’s performance.In addition, the researchers highly recommend including UI design and Interactivity in designing effective AV system.However, the discussions of these two aspects in previous AV design guidelines are not comprehensive.The UI design in this paper describes the arrangement of AV features in mobile environment, whereas interactivity is about the active learning strategy features based on learning experiences (how to engage learners). …”
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Algorithm visualization design guidelines for mobile leaning by Supli, Ahmad Affandi, Shiratuddin, Norshuhada

“…The guidelines are useful for AV designers in constructing AV mobile learning.…”
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Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques by Farhan, Farhan Abdel-Fattah Ahmad

“…In machine learning algorithm, choosing the most relevant features for each attack is a very important requirement, especially in mobile ad hoc networks where the network topology dynamically changes. …”
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Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid by Wahid, Muhammad Hazim

“…This study aims to propose the best machine learning algorithm for predicting mobile network performance. …”
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Android mobile malware detection model based on permission features using machine learning approach by Sharfah Ratibah, Tuan Mat

“…Chi-square and information gain algorithms were used for features selection. The aim is to learn the behaviour of permission features that react to the accuracy according to the number of features. …”
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A New Mobile Botnet Classification based on Permission and API Calls by Yusof, M, Saudi, MM, Ridzuan, F

“…As a result, 16 permissions and 31 API calls that are most related with mobile botnet have been extracted using feature selection and later classified and tested using machine learning algorithms. …”

Enhancement Of Static Code Analysis Malware Detection Framework For Android Category-Based Application by Aminordin, Azmi

“…Static analysis is where the static features are examined. Too many features used, features extraction time consuming and the reliability of accuracy result by various machine learning algorithm are the main issues spotted in static analysis approach. …”
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Behavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications by Aun, Yichiet

“…This thesis identified domain-specific features that are effective for accurate, large-scale and scalable mobile applications classification using machine learning techniques. …”
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Age And Gender Recognition Mobile App by Wee, Quo Lung

“…Therefore, this study aimed to develop age and gender recognition mobile application using deep learning algorithm. …”
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A new mobile botnet classification based on permission and API calls by Yusof M., Madihah Mohd Saudi, Farida Hazwani Mohd Ridzuan

Bio-inspired for Features Optimization and Malware Detection by Mohd Faizal, Ab Razak, Nor Badrul, Anuar, Fazidah, Othman, Ahmad, Firdaus, Firdaus, Afifi, Rosli, Salleh

“…A static analysis technique with machine learning classifier is developed from the permission features noted in the Android mobile device for detecting the malware applications. …”
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Bio-inspired for Features Optimization and Malware Detection by Razak, Mohd Faizal Ab, Anuar, Nor Badrul, Othman, Fazidah, Firdaus, Ahmad, Afifi, Firdaus, Salleh, Rosli

“…A static analysis technique with machine learning classifier is developed from the permission features noted in the Android mobile device for detecting the malware applications. …”
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A new SMS spam detection method using both Content-Based and non Content-Based features by Sulaiman N.F., Jali M.Z.

BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning by Chimeleze C., Jamil N., Ismail R., Lam K.-Y., Teh J.S., Samual J., Akachukwu Okeke C.

“…Android (operating system); Android malware; Classification (of information); Feature Selection; Learning systems; Mobile security; Android apps; Classification models; Feature weight; Features selection; Machine learning algorithms; Machine-learning; Malware detection; Malwares; Memory usage; Selection techniques; Learning algorithms…”

“myHerbs”: A mobile based application for herbal leaf recognition using sift / Nur Nabilah Abu Mangshor …[et al.] by Abu Mangshor, Nur Nabilah, Abdul Rahman, Mohamed Al Arabee, Sabri, Nurbaity, Ibrahim, Shafaf, Ibrahim, Zaidah, Shari, Anis Amilah

“…In addition, it also contributes to the exploration and implementation of learning algorithm in mobile-based application.…”
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Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA) by Nur Khairani, Kamarudin, Ahmad Firdaus, Zainal Abidin, Azlee, Zabidi, Ferda, Ernawan, Syifak, Izhar Hisham, Mohd Faizal, Ab Razak

“…For machine learning classification algorithms, we used a type of fuzzy logic called lattice reasoning. …”
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Quantifying usability prioritization using K-means clustering algorithm on hybrid metric features for MAR learning by Lim K.C., Selamat A., Mohamed Zabil M.H., Selamat M.H., Alias R.A., Puteh F., Mohamed F., Krejcar O.

Optimising acoustic features for source mobile device identification using spectral analysis techniques / Mehdi Jahanirad by Mehdi , Jahanirad

“…Both models optimize acoustic features for source mobile device identification based on near-silent segments. …”
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Mobile Learning: An Application Prototype for AVL Tree Learning Object by Ibrahim, Mohammad Noor, Mahamad, Saipunidzam, Chua, Edrea Ning Wei

“…Our approach is to incorporate video clips in presenting the algorithm systematically. With this mobile learning application, student could learn at his or her own pace, anywhere anytime. …”
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