Search Results - (( intelligence based svm algorithm ) OR ( intelligence _ learning algorithm ))

Autism Spectrum Disorder Classification Using Deep Learning by Abdulrazak Yahya, Saleh, Lim Huey, Chern

“…Recently, deep learning methods have significantly sharpened the cutting edge of learning algorithms in a wide range of artificial intelligence tasks. …”
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Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study by Lestari, Widya, Abdullah, Adilah Syahirah, Ahmad Amin, Afifah Munirah, faridah, Nur, Sukotjo, Cortino, Ismail, Azlini, mohamad ibrahim, mohamad shafiq, Insani, Nashuha, Utomo, Chandra Prasetyo

“…RF was the most precise algorithm for predicting grades A, B, and C, followed by LR, DT, and SVM. …”
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Comparative performance of machine learning algorithms for cryptocurrency forecasting by Hitam, Nor Azizah, Ismail, Amelia Ritahani

“…Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. …”
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Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review by Yafouz A., Ahmed A.N., Zaini N., El-Shafie A.

“…Decision trees; Forecasting; Multilayer neural networks; Ozone; Predictive analytics; Support vector machines; Artificial intelligence techniques; Machine learning techniques; Multi layer perceptron; Optimization approach; Ozone concentration forecasting; Prediction accuracy; Stand-alone algorithm; Tropospheric ozone concentration; Learning systems; ozone; air quality; algorithm; concentration (composition); machine learning; optimization; ozone; prediction; theoretical study; air pollutant; air quality; artificial intelligence; artificial neural network; concentration (parameter); decision tree; feed forward neural network; forecasting; fuzzy system; human; measurement accuracy; multilayer perceptron; prediction; random forest; recurrent neural network; Review; support vector machine; systematic review…”

Intrusion Detection Systems, Issues, Challenges, and Needs by Aljanabi, Mohammad, Mohd Arfian, Ismail, Ali, Ahmed Hussein

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A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani by Mahmoud Reza, Saybani

“…The increasing size of data being stored have created the need for computer-based methods for automatic data analysis. Many researchers, who have developed methods and algorithms within the field of artificial intelligence, machine learning and data mining, have addressed extracting useful information from the data. …”
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Artificial Intelligence (AI) to predict dental student academic performance based on pre-university results by Ahmad Amin, Afifah Munirah, Abdullah, Adilah Syahirah, Lestari, Widya, Sukotjo, Cortino, Utomo, Chandra Prasetyo, Ismail, Azlini

“…Objective: This study aims to predict the academic performance of dental students based on their admission results using Artificial Intelligence. …”
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Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms by Masrom, S., Rahman, R.A., Mohamad, M., Rahman, A.S.A., Baharun, N.

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Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms by Masrom, S., Rahman, R.A., Mohamad, M., Rahman, A.S.A., Baharun, N.

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Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan by Meisam , Gordan

“…In the modeling phase, amongst all DM algorithms, the applicability of machine learning, artificial intelligence and statistical data mining techniques were examined using Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Tree (CART) to detect the hidden patterns in vibration data. …”
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A preliminary lightweight random forest approach-based image classification for plant disease detection by Mashitah Ibrahim, Muzaffar Hamzah, Mohammad Fadhli Asli

“…Random Forest is a special kind of ensemble learning technique and it turns out to perform very well compared to other classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN). …”
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Improved whale optimization algorithm for feature selection in Arabic sentiment analysis by Tubishat, Mohammad, Abushariah, Mohammad A.M., Idris, Norisma, Aljarah, Ibrahim

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Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination by Nallakukkala, Sirisha, Tackie-Otoo, Bennet Nii, Aliyu, Ruwaida, Lal, Bhajan, Nallakukkala, Jagadish Ram Deepak, Devi, Gayathri

“…The integration of machine learning (ML) with gas hydrate-based desalination (GHBD) presents a significant advancement in the produced water treatment with special focus on efficient prediction of removal efficiency. …”
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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

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Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market by Hazirah Halul

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Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review by Aliyu, R., Mokhtar, A.A., Hussin, H.

“…To further understand its potential, various algorithms and related theories are examined. Based on the examined studies, machine learning shows potential for prognostics and defect diagnosis. …”
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Evaluation of rehearsal effects of multimedia content based on EEG using machine learning algorithms by Mazher, M., Aziz, A.A., Malik, A.S.

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Evaluating JA-ABC5 hyperparameter optimisation with classifiers by Ravindran, Nadarajan, Noorazliza, Sulaiman, Junita, Mohamad-Saleh

“…Because of its simplicity, flexibility, and robustness, the Artificial Bee Colony (ABC) algorithm, a swarm intelligence-based optimisation method, has been widely applied in a variety of fields. …”
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Feature extraction and supervised learning for volatile organic compounds gas recognition by Mohd Tombel, Nor Syahira, Mohd Zaki, Hasan Firdaus, Mohd Fadglullah, Hanna Farihin

“…This research project aims to investigate effective feature extraction techniques that can be employed as discriminative features for machine learning algorithms. A preliminary dataset was used to predict VOC classification through the application of five supervised machine learning algorithms: k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), and Artificial Neural Networks (ANN). …”
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New techniques incorporating computational intelligence based for voltage stability evaluation and improvement in power system / Nur Fadilah Ab. Aziz by Ab. Aziz, Nur Fadilah

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