Search Results - (( model evaluation between algorithm ) OR ( level classification based algorithm ))*

Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms by Al-Rawashdeh, Mohammad, Al Nawaiseh, Moh’d, Yousef, Isam, Bisharah, Majdi, Alkhadrawi, Sajeda, Al-Bdour, Hamza

“…This study compares Bayesian Optimization-based machine learning systems that anticipate earthquake-damaged buildings and to evaluates building damage classification models. …”
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Software defect prediction framework based on hybrid metaheuristic optimization methods by Wahono, Romi Satria

“…For the purpose of this study, ten classification algorithms have been selected. The selection aims at achieving a balance between established classification algorithms used in software defect prediction. …”
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Combining cluster quality index and supervised learning to predict students’ academic performance by Suhaila Zainudin, Rapi’ah Ibrahim, Hafiz Mohd Sarim

“…Three classification algorithms have been selected: Logistic Regression (LR), Support Vector Machine (SVM) and Decision Tree (DT). …”
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An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding by Safa, Soodabeh, Khalid, Fatimah

“…Classic bag of visual words algorithm is based on k-means clustering and every SIFT features belongs to one cluster and it leads to decreasing classification results. …”
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Multitasking deep neural network models for Arabic dialect sentiment analysis by Alali, Muath Mohammad Oqlah

“…The existing approaches are based on traditional machine learning algorithms, such as support vector machine (SVM). …”
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Muscle tone level classification based on upper-limb impedance model by Zaw, Lay Htoon, Sidek, Shahrul Na'im, Fatai, Sado

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Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning by Solihin M.I., Yanto, Hayder G., Maarif H.A.-Q.

“…One of the prominent methods to improve machine learning accuracy is by using ensemble method which basically employs multiple base models. In this paper, the stacking ensemble method is used to increase the accuracy of the machine learning model for LSM where the base (first-level) learners use five ML algorithms namely decision tree (DT), k-nearest neighbor (KNN), AdaBoost, extreme gradient boosting (XGB) and random forest (RF). …”

Optimized techniques for landslide detection and characteristics using LiDAR data by Mezaal, Mustafa Ridha

“…Furthermore, RNN and MLP-NN models in the test area showed 81.11%, and 74.56%, accuracy level, respectively. …”
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Improving multi-resident activity recognition in smart home using multi label classification with adaptive profiling by Mohamed, Raihani

“…When the data are induced with the lower quality model, the performance is also truncated. Furthermore, there is tendency that multi label classifications used instead of traditional single label classification technique. …”
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Classification and visualization of e-commerce product reviews comparison using support vector machine / Nuwairah Aimi Ahmad Kushairi by Ahmad Kushairi, Nuwairah Aimi

“…It means a significant difference exists between the time taken for manual evaluation and the evaluation using the web-based system. …”
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Assessment of near-infrared and mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm plantation by Liaghat, Shohreh

“…The results indicated that LDA-based model resulted in high average overall classification accuracies of 92% (leaf samples) and 94% (trunk samples) when mid-infrared absorbance spectra were analyzed. …”
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Improving automatic forced alignment for phoneme segmentation in Quranic recitation by Alqadasi, Ammar Mohammed Ali, Khedher, Akram M Z M, Sunar, Mohd Shahrizal, Hj Salam, Md. Sah, Abdulghafor, Rawad, Khaled, Nashwan Abdo

“…These enhancements encompass the adaptation of an acoustic model tailored for Qur’anic recitation as preprocessing and culminate in the development of an algorithm aimed at refining forced alignment based on the phonetic nuances of the Qur’an. …”
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Multi-Class Multi-Level Classification of Mental Health Disorders Based on Textual Data from Social Media by Sutranggono, Abi Nizar, Sarno, Riyanarto, Ghozali, Imam

“…The results of the experiments show that the MCML classification algorithm successfully performs detailed classification and produces promising results for each classification level. …”
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Real-time oil palm fruit bunch ripeness grading system using image processing techniques by Alfatni, Meftah Salem M.

“…These results are optimal based on the thorns model. A new approach was developed using expert rules-based system. …”
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Symmetric Key Size for Different Level of Information Classification by Ibrahim, Subariah, Maarof, Mohd. Aizaini

“…Confidential information can be categorized into various levels of classification. The classification depends on the level of damage to an organization or to national security when the information is disclosed. …”
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A speech enhancement framework using discrete Krawtchouk-Tchebichef Transform by Mahmmod, Basheera M.

“…The analytical solution is derived from the assumption that speech and noise components can be modeled based on a combination between Gamma and Laplacian distributions. …”
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Performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves by Mat Lazim, Siti Saripa Rabiah, Sulaiman, Zulkefly, Mat Nawi, Nazmi, Mohd Mustafah, Anas

“…This work shows that the spectroscopic measurement combined with classification techniques are promising strategy to classify severity level of WRD based on the spectral data of the rubber leaves.…”
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Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study by Sim, Doreen Ying Ying, Teh, Chee Siong, Ahmad Izuanuddin, Ismail

“…Prediction and/or classification accuracies of cfsw-SVM algorithms are significantly improved.…”
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Evaluation of multiple In Situ and remote sensing system for early detection of Ganoderma boninense infected oil palm by Ahmadi, Seyedeh Parisa

“…Even though at first glance the classification accuracy was moderate, the level of details provided by the imageries suggests that the accuracies were acceptable. …”
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Automatic classification of medical x-ray images by Zare, M.R., Seng, W.C., Mueen, A.

“…These features have been exploited in different algorithms for automatic classification of medical X-ray images. …”
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