Search Results - (( mobile application svm algorithm ) OR ( some application testing algorithm ))*

A Proposed False Report Identification Algorithm for a Mobile Application in the IoT Environment by Rajoo, S, Magalingam, P, Idris, NB, Samy, GN, Maarop, N, Shanmugam, B, Perumal, S

Sentiment Analysis on Users' Satisfaction for Mobile Banking Apps in Malaysia by Misinem, ., Tri Basuki, Kurniawan, Mohd Zaki, Zakaria, Muhammad Aqil Azfar, Uzailee

“…The dataset was compared with five algorithms: Linear Regression, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machine (SVM). …”
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Android Mobile Malware Classification Based on System Call and Permission Using Tokenization by Intan Nurfarahin Binti Ahmad

Identification Of Flow Blockage Levels In Centrifugal Pump By Machine Learning by Ng, Woon Li

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Color Image Segmentation Based on Bayesian Theorem for Mobile Robot Navigation by Rahimizadeh, Hamid

“…The experimental results show the proposed algorithm is simple and robust, for real time application on vision based mobile robot for navigation, in spite of presence of other shapes and colors in the environment …”
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Human activity and posture classification using smartphone sensors and Matlab mobile by Jamian, Syahirah, Gunawan, Teddy Surya, Kartiwi, Mira, Ahmad, Robiah, Kadir, Kushairy, Nordin, Muhammad Noor

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Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models by Bakar M.A.A.A., Ker P.J., Tang S.G.H., Baharuddin M.Z., Lee H.J., Omar A.R.

“…The development of the algorithms shows that Logistic Regression, SVM with Linear and Polynomial kernels performed the best with 95% accuracy, followed by SVM-RBF kernel, and KNN with 93% accuracy, Decision Tree with 90% accuracy, and lastly, SVM-Sigmoidal kernel with 83% accuracy. …”

Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models by Bakar, Mohd Anif A. A., Ker, Pin Jern, Tang, Shirley G. H., Baharuddin, Mohd Zafri, Lee, Hui Jing, Omar, Abdul Rahman

“…The development of the algorithms shows that Logistic Regression, SVM with Linear and Polynomial kernels performed the best with 95 accuracy, followed by SVM-RBF kernel, and KNN with 93 accuracy, Decision Tree with 90 accuracy, and lastly, SVM-Sigmoidal kernel with 83 accuracy. …”
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Design of Predictive Model for TCM Tongue Diagnosis In Malaysia Using Machine Learning by Koe, Jia Chi

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Test Data Generation for Event Driven System Using Bees Algorithm by Mohammed Zabil, Mohd H., Kamal Z., Zamli

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Hotel recommendation system using machine learning by Wong, Wai On

“…The future work includes expanding the dataset, refining the recommendation algorithm, using natural language processing techniques with the addition of multilingual reviews, and deploying the system as a user-friendly application or mobile application.…”
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A wheelchair sitting posture detection system using pressure sensors by Mohamad Yusoff, Muhammad Annuar Alhadi, Azmi, Nur Liyana, Nordin, Nor Hidayati Diyana

“…Three classification algorithms based on the supervised learning of machine learning, such as support vector machine (SVM), random forest (RF),and decision tree (DT) were used to classify the postures which produced an accuracy of 95.44%, 98.72%,and 98.80%, respectively. …”
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Diabetes prediction system using clonal selection algorithm / Nor Aishah Mustapa by Mustapa, Nor Aishah

“…Diabetes Prediction system using clonal selection algorithm is available to test whether patients suffering from diabetes or not. …”
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Optimization of k-Nearest Neighbour to categorize Indonesian’s news articles by Ihsan, Afdhalul, Rainarli, Ednawati

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Empirical study on intelligent android malware detection based on supervised machine learning by Abdullah, Talal A.A., Ali, Waleed, Abdulghafor, Rawad Abdulkhaleq Abdulmolla

“…The increasing number of mobile devices using the Android operating system in the market makes these devices the first target for malicious applications. …”
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Malware visualizer: A web apps malware family classification with machine learning by Mohd Zamri, Osman, Ahmad Firdaus, Zainal Abidin, Rahiwan Nazar, Romli

“…This project uses three classification algorithm which are Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). …”
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Place recognition using semantic concepts of visual words by Rostami, Vahid, Ramli, Abdul Rahman, Samsudin, Khairulmizam, Saripan, M. Iqbal

“…Due to varying views and dynamic objects, this problem is more complicated in the mobile robot applications such as global localization and place recognition systems. …”
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Artificial Bee Colony Algorithm for Pairwise Test Generation by Alazzawi, Ammar K., Homaid, Ameen A. Ba, Alomoush, Alaa A., Alsewari, Abdulrahman A.

“…In this paper, we evaluated and proposed a pairwise strategy named Pairwise Artificial Bee Colony algorithm (PABC). According to the benchmarking results, the PABC strategies outdo some existing strategies to generate a test case in many of the system configurations taken into consideration. …”
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Fsm-Based Enhanced March C- Algorithm For Memory Built-In Self-Test by CH’NG , MING ZONG

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1D Multigrid Solver For Finite Element Method by Azhar, Mohamad Amiruddin

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