Search Results - (( developing interactive svm algorithm ) OR ( java application kano algorithm ))

Forest mapping in Peninsular Malaysia using Random Forest and Support Vector Machine Classifiers on Google Earth Engine by Farah Nuralissa Muhammad, Lam, Kuok Choy

“…The accuracy assessment test using the Kappa coefficient resulted in a value of 0.7893 for the RF algorithm and 0.6328 for the SVM algorithm for the year 2010. …”
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Development of interactive application for classification of Artocarpus Species by Abdul Ghapar, Nadia

“…The combination of Prewitt algorithm, Canny alogorithm, Gray-Level co-occurrence matrix will be used in SVM. …”
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Interaction effect of process parameters and Pd-electrocatalyst in formic acid electro-oxidation for fuel cell applications: Implementing supervised machine learning algorithms by Hossain S.K.S., Ali S.S., Rushd S., Ayodele B.V., Cheng C.K.

Support vector machine in precision agriculture: a review by Kok, Zhi Hong, Mohamed Shariff, Abdul Rashid, M. Alfatni, Meftah Salem, Bejo, Siti Khairunniza

“…The Support Vector Machine (SVM) is a Machine Learning (ML) algorithm which may be used for acquiring solutions towards better crop management. …”
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Classification of hand gestures from EMG signals / Diaa Albitar by Albitar, Diaa

“…This study is to develop classification model to classify six hand gestures using Artificial Intelligent algorithm. …”
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Sentiment analysis of customer review for Tina Arena Beauty by Amri, Nur Najwa Shahirah

“…Three machine learning algorithms Naive Bayes, Random Forest, and Support Vector Machine (SVM) were evaluated, and SVM achieved the highest accuracy and was selected as the final classifier. …”
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Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir by Mahadhir, Khairul Azmi

“…In this work, an agricultural robot is embedded with machine learning algorithm based on Support Vector Machine (SVM). The aim is to evaluate the effectiveness of the Support Vector Machine in recognizing different terrain conditions in an agriculture field. …”
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Gesture recognition system for Nigerian tribal greeting postures using support vector machine / Segun Aina …[et al.] by Aina, Segun, V. Sholesi, Kofoworola, R. Lawal, Aderonke, D. Okegbile, Samuel, I. Oluwaranti, Adeniran

“…This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. …”
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Fine-scale predictive modeling of Aedes mosquito abundance and dengue risk indicators using machine learning algorithms with microclimatic variables by Che Dom, Nazri, Mohd Hardy Abdullah, Nur Athen, Dapari, Rahmat, Salleh, Siti Aekbal

“…Variations in model performance were likely due to species-specific responses to environmental conditions and the nonlinear interactions captured by the algorithms. Compared to benchmarks in related tropical settings, the reported error metrics demonstrate improved prediction accuracy. …”
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A study on component-based technology for development of complex bioinformatics software by Ali Shah, Zuraini, Deris, Safaai, Othman, Muhamad Razib, Zakaria, Zalmiyah, Saad, Puteh, Hassan, Rohayanti, Muda, Mohd. Hilmi, Kasim, Shahreen, Roslan, Rosfuzah

“…SOM and K-Means are integrated as a clustering algorithm to produce a granular input, while SVM is then used as a classifier. …”
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Sentiment analysis of customer reviews for Konda Kondi Cafe & Bistro by Abd Azizul Rahman, Munirah Syafiqah

“…The SVM model achieved the highest accuracy of 89% with an 80:20 data split. …”
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DNA enhancer prediction using machine learning techniques with novel feature representation by Fong, Pui Kwan

“…Technical contributions of this study are: 1) complex tree-feature modelling using genetic algorithm (CTreeGA): Automated feature generation framework to capture patterns of interactions among short DNA segments in histone sequences.…”
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Improved building roof type classification using correlation-based feature selection and gain ratio algorithms by Norman, M., Mohd Shafri, Helmi Zulhaidi, Pradhan, Biswajeet, Yusuf, B.

“…The classification results using SVM classifier produced an overall accuracy of 83.16%. …”
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Intent-IQ: customer’s reviews intent recognition using random forest algorithm by Mazlan, Nur Farahnisrin, Ibrahim Teo, Noor Hasimah

“…Two machine learning model is chosen to build the classification models which are Random Forest (RF) algorithm and Multinomial Naïve Bayes (MNB) algorithm. …”
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EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter by Woon, W.C., Yahya, N., Badruddin, N.

“…Hence, this work aims to develop an algorithm using statistical-CSP feature for eye state classification from EEG signal. …”
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EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter by Woon, W.C., Yahya, N., Badruddin, N.

“…Hence, this work aims to develop an algorithm using statistical-CSP feature for eye state classification from EEG signal. …”
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Internet of Things-based Home Automation with Network Mapper and MQTT Protocol by Alam T., Rokonuzzaman M., Sarker S., Abadin A.F.M.Z., Debnath T., Hossain M.I.

“…The proposed system is developed using a Raspberry Pi 3 Home Server (RHS) driven by the Support Vector Machine (SVM) algorithm. …”

CNN architectures for road surface wetness classification from acoustic signals by Bahrami, Siavash, Doraisamy, Shyamala, Azman, Azreen, Nasharuddin, Nurul Amelina, Yue, Shigang

“…The classification of road surface wetness is important for both the development of future driverless vehicles and the development of existing vehicle active safety systems. …”
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Analyzing UiTMCTKKT vehicle utilization and travel pattern using predictive analytics by Syed Mohamad, Sharifah Masyitah

“…The Experiment 1 focused on classifying vehicle types based on utilization and Experiment 2 involved predicting no of trips per day using classifiers such as Random Forest, Decision Tree, and Support Vector Machine (SVM). Data from 2023 to 2024 was cleaned and analyzed, and visualizations were developed through an interactive dashboard using Power BI. …”
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