Search Results - intelligence valid ((svm algorithm) OR (_ algorithm))

Intelligent classification algorithms in enhancing the performance of support vector machine by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…This paper presents two intelligent algorithms that hybridized between ant colony optimization (ACO) and SVM for tuning SVM parameters and selecting feature subset without having to discretize the continuous values. …”
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Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm by Yap K.S., Abidin I.Z., Ahmad A.R., Hussien Z.F., Pok H.L., Ismail F.I., Mohamad A.M.

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Prediction of photovoltaic system output using hybrid Cuckoo Search Least Square Support Vector Machine / Muhammad Aidil Adha Aziz by Aziz, Muhammad Aidil Adha

“…Therefore, Cuckoo Search Algorithm (CS) is hybrid with LS-SVM in order to optimize the RBF parameters for a better prediction performance. …”
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A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation by Mohd Ali, Nursabillilah, Besar, Rosli, Ab Aziz, Nor Azlina

“…Grid search cross validation (CV) is applied for hyperparameter tuning of the algorithms. …”
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A comparative analysis of machine learning algorithms for diabetes prediction by Alansari, Waseem Abdulmahdi, Masnizah Mohd

“…During the DD2019 experiment, the RF and SVM algorithms demonstrated the highest levels of accuracy, achieving 96.65% and 93.93%, respectively. …”
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Dual-tone multifrequency signal detection using support vector machines by Nagi J., Tiong S.K., Yap K.S., Ahmed S.K.

“…A SVM classifier is trained using the estimated fundamental DTMF carrier frequencies, and is validated using the input samples for classification of low and high DTMF frequency groups. …”

Development of colorization of grayscale images using CNN-SVM by Abualola, Abdallah, Gunawan, Teddy Surya, Kartiwi, Mira, Ambikairajah, Eliathamby, Habaebi, Mohamed Hadi

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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

“…Furthermore, sensitivity analyses validated SVM's robustness in capturing the nonlinear relationships governing ion removal efficiency. …”
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Real-time human activity recognition using external and internal spatial features by Htike@Muhammad Yusof, Zaw Zaw, Egerton, Simon, Kuang, Ye Chow

“…Activities are classified by a support vector machine (SVM) with a radial basis kernel. Optimal parameters for the SVM are found through a 10-fold cross-validation. …”
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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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Development of an accurate AI-based dermatology assistant for skin disease recognition using YOLOv8 models by Huzaini, Muhammad Irfan Darwish, Mansor, Hasmah, Gunawan, Teddy Surya, Ahmad, Izanoordina

“…The research compares various algorithms, such as SVM, YOLOv3, YOLOv4, and Dual-Architecture CNN, through a comprehensive review of existing AI applications in dermatology. …”
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Intelligent detection of DTMF tones using a hybrid signal processing technique with support vector machines by Nagi J., Yap K.S., Tiong S.K., Ahmed S.K., Nagi F.

“…Key innovations include the use of a Finite Impulse Response (FIR) bandpass filter for reduction of noise from DTMF input samples, and Support Vector Machines (SVM) for intelligent classification of the detected DTMF carrier frequencies. …”

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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Performance improvement through optimal location and sizing of distributed generation / Zuhaila Mat Yasin by Mat Yasin, Zuhaila

“…Later, a Least-Squares Support Vector Machine (LS-SVM) model was developed using cross-validation technique. …”
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Noise eliminated ensemble empirical mode decomposition scalogram analysis for rotating machinery fault diagnosis by Atik, Faysal

“…The proposed method was validated using bearing and blade datasets. The results show that the machine learning algorithms achieved comparatively lower accuracy than the proposed CNN model. …”
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Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques by Mas Ira Syafila, Mohd Hilmi Tan, Mohd Faizal, Jamlos, Ahmad Fairuz, Omar, Kamarulzaman, Kamarudin, Mohd Aminudin, Jamlos

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Magnetic resonance imaging sense reconstruction system using FPGA / Muhammad Faisal Siddiqui by Muhammad Faisal , Siddiqui

“…This research also proposed an intelligent and robust classification technique to classify the MRI scans as normal or abnormal and also for validation purpose. …”
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Human hearing disorder recognition model using eeg-aep based signal by Md Nahidul, Islam

“…This study investigated two conventional machine learning algorithms: support vector machine (SVM) and k-nearest neighbors (KNN), and two deep learning techniques: convolutional neural network (CNN) and improved-VGG16 model. …”
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Geospatial AI-based approach to assess the spatiotemporal suitability of onshore wind-solar farms in Iraq by Sachit, Mourtadha Sarhan Almushattat

“…In this context, global geospatial data for 13 conditioning factors were collected, and 55,619 inventory samples of wind and solar stations worldwide were prepared to train three machine learning (ML) algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). …”
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Optimisation of Environmental Risk Assessment Architecture using Artificial Intelligence Techniques by Salem S. M. Khalifa

“…The integration of artificial intelligence techniques is becoming necessary for environmental risk assessment systems and decision-making, particularly under the limitations of individual intelligence techniques. …”