Search Results - (( intelligence pca based algorithm ) OR ( intelligence based svm algorithm ))

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

“…The reconstruction results are compared with the multi-core CPU and Graphical Processing Unit (GPU) based reconstructions of SENSE. 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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Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning by Mohammed, Badiea Abdulkarem, Senan, Ebrahim Mohammed, Al-Mekhlafi, Zeyad Ghaleb, Rassem, Taha Hussein, Makbol, Nasrin M., Alanazi, Adwan Alownie, Almurayziq, Tariq S., Ghaleb, Fuad A., Sallam, Amer A.

“…The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. …”
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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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Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions by Kalantari, Ali, Kamsin, Amirrudin, Shamshirband, Shahaboddin, Gani, Abdullah, Alinejad-Rokny, Hamid, Chronopoulos, Anthony T.

“…On the other hand, the hybridization of SVM with other methods such as SVM-Genetic Algorithm (SVM-GA), SVM-Artificial Immune System (SVM-AIS), SVM-AIRS and fuzzy support vector machine (FSVM) had great performances achieving better results in terms of accuracy, sensitivity and specificity.…”
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Lung cancer medical images classification using hybrid CNN-SVM by Abdulrazak Yahya, Saleh, Chee, Ka Chin, Vanessa, Penshie, Hamada Rasheed Hassan, Al-Absi

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

“…The CNN algorithm produces better results with an accuracy of 97.07%, compared with the SVM algorithm. …”
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Time series data intelligent clustering algorithm for landslide displacement prediction by Han, Liu, Shang, Tao, Shu, Jisen, Khan Chowdhury, Ahmed Jalal

“…To address this problem, an intelligent clustering algorithm for time series data in landslide displacement prediction based on nonlinear dynamic time bending is proposed in this paper. …”
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Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation by Illias, Hazlee Azil, Wee, Zhao Liang

“…In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. …”
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Intrusion Detection Systems, Issues, Challenges, and Needs by Aljanabi, Mohammad, Mohd Arfian, Ismail, Ali, Ahmed Hussein

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Comparative performance of machine learning algorithms for cryptocurrency forecasting by Hitam, Nor Azizah, Ismail, Amelia Ritahani

“…Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. …”
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A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant by Chun T.S., Malek M.A., Ismail A.R.

Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama A

“…Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. …”
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Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama A.

“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40…”
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Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Ahmed Dheyab, Saad, Mohammed Abdulameer, Shaymaa, Mostafa, Salama A

“…Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. …”
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Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama

“…Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. …”
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Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama

“…Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. …”
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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.

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A Hybrid of Functional Networks and Support Vector Machine Models for the Prediction of Petroleum Reservoir Properties by Fatai Adesina, Anifowose, Jane, Labadin, Abdulazeez, Abdulraheem

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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

“…This thesis presents a practical and reliable approach for the prediction of PV power output using an intelligent-based technique namely Cuckoo Search Algorithm - Least Square Support Vector Machine (CS-LSSVM). …”
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Classification of left/right hand movement from EEG signal by intelligent algorithms by Baig, M.Z., Javed, E., Ayaz, Y., Afzal, W., Gillani, S.O., Naveed, M., Jamil, M.

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