Search Results - (( developing mobile svm algorithm ) OR ( java implication based 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

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

“…SVM model with cubic kernel is preferable as the training time taken is relatively lower than Ensemble Bagged Tree due to the ensemble algorithms are more complex. …”
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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.

“…Based on the X and Z chromaticity data from the chromaticity analysis, the color of the infected chicken�s comb converged from red to green and yellow to blue. 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

“…Based on the X and Z chromaticity data from the chromaticity analysis, the color of the infected chickens comb converged from red to green and yellow to blue. 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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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

“…This work aims to evaluate the accuracy of the triaxial accelerometer in the Matlab Mobile and examine the development and performance of the algorithms in identifying human motions on individuals of similar ages and physical appearances. …”
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Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak by Dak, Ahmad Yusri

“…The second stage is detecting jammers by integrating both lower layers by developing Integrated Combined Layer Algorithm (ICLA). …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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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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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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Design of Predictive Model for TCM Tongue Diagnosis In Malaysia Using Machine Learning by Koe, Jia Chi

“…The lack of objectivity and standard evaluation criteria in tongue diagnosis have restricted its development. In this project, machine learning algorithm will be applied to design a predictive model for TCM tongue diagnosis. …”
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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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Empirical study on intelligent android malware detection based on supervised machine learning by Abdullah, Talal A.A., Ali, Waleed, Abdulghafor, Rawad Abdulkhaleq Abdulmolla

“…In recent years, several Android malware applications were developed to perform certain illegitimate activities and harmful actions on mobile devices. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Simultaneous measurement of multiple soil properties through proximal sensor data fusion: a case study by Wenjun, Ji, Adamchuk, Viacheslav I., Song, Chao Chen, Mat Su, Ahmad S., Ismail, Ashraf, Qianjun, Gan, Zhou, Shi, Biswas, Asim

“…After choosing the optimal sensor combination for each soil property, the predictive capability was compared using different data mining algorithms, including support vector machines (SVM), random forest (RF), multivariate adaptive regression splines (MARS), and regression trees (CART). …”
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