Search Results - (( learning classification parallel algorithm ) OR ( evolution classification model algorithm ))

The forecasting of poverty using the ensemble learning classification methods by Zamzuri, Muhammad Haziq Adli, Nadilah, Sofian, Hassan, Raini

“…This research was conducted to forecast poverty using classification methods. Random Forest and Extreme Gradient Boosting (XGBoost) algorithms were applied to forecast poverty since they are supervised learning algorithms that use the ensemble learning approach for classification. …”
Get full text
Get full text
Get full text

Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms by Loo, C.K., Liew, W.S., Seera, M., Lim, Einly

“…This was achieved by mitigating convergence in the genetic algorithms by employing a hierarchical parallel architecture. …”
Get full text
Get full text
Get full text

Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Aljanabi, Mohammad, Mohd Arfian, Ismail, Mezhuyev, Vitaliy

“…The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. …”
Get full text
Get full text
Get full text

Robust tweets classification using arithmetic optimization with deep learning for sustainable urban living by Hamza, Manar Ahmed, Hassan Abdalla Hashim, Aisha, Motwakel, Abdelwahed, Elhameed, Elmouez Samir Abd, Osman, Mohammed, Kumar, Arun, Singla, Chinu, Munjal, Muskaan

“…In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. …”
Get full text
Get full text
Get full text
Get full text

Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam, Sallehuddin, Roselina

“…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
Get full text
Get full text
Get full text

Combining deep and handcrafted image features for MRI brain scan classification by Hasan, Ali M., Jalab, Hamid A., Meziane, Farid, Kahtan, Hasan, Al-Ahmad, Ahmad Salah

“…In this paper, a deep learning feature extraction algorithm is proposed to extract the relevant features from MRI brain scans. …”
Get full text
Get full text
Get full text

Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System by Ali, Mohammed Hasan, Mohamed Fadli, Zolkipli

“…The incorporation of a single parallel hidden layer feed-forward neural network to the Fast Learning Network (FLN) architecture gave rise to the improved Extreme Learning Machine (ELM). …”
Get full text
Get full text

An Improve k-NN Classifier using Similarity Distance Plot-Data Reduction and Dask for Big Datasets by Abdul Muqtasid, Rushdi

“…The k-Nearest Neighbour (k-NN) algorithm is one of the most widely used Instance-Based Learning methods due to its simplicity and ease of implementation. …”
Get full text
Get full text
Get full text
Get full text
Get full text

Classification of Immunosignature Using Random Forests for Cancer Diagnosis by Zarzar, Mouayad, Razak, Eliza, Htike@Muhammad Yusof, Zaw Zaw, Yusof, Faridah

“…In this work, we will develop a robust classification model that can be utilized in cancer diagnosis using immunofingerprint data. …”
Get full text
Get full text
Get full text

Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
Get full text
Get full text

Finding an effective classification technique to develop a software team composition model by Gilal, Abdul Rehman, Jaafar, Jafreezal, Capretz, Luiz Fernando, Omar, Mazni, Basri, Shuib, Abdul Aziz, Izzatdin

Get full text
Get full text
Get full text

Finding an effective classification technique to develop a software team composition model by Gilal, A.R., Jaafar, J., Capretz, L.F., Omar, M., Basri, S., Aziz, I.A.

Get full text
Get full text

Finding an effective classification technique to develop a software team composition model by Gilal, A.R., Jaafar, J., Capretz, L.F., Omar, M., Basri, S., Aziz, I.A.

Get full text
Get full text

Deep learning detector for pests and plant disease recognition by Ileladewa, Oluwatimilehin Adekunle

“…Meanwhile, evolution in deep convolutional neural networks for image classification has rapidly improved the accuracy of object detection, classification and system recognition. …”
Get full text
Get full text

Y-type Random 2-satisfiability In Discrete Hopfield Neural Network by Guo, Yueling

“…Finally, a new logic mining model namely Y-Type Random 2-Satisfiability Reverse Analysis was proposed, which showed optimal performances in terms of several metrics as compared to the existing classification models. …”
Get full text
Get full text

Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

“…However, the learning complexity of classification is increased due to the expansion number of learning model. …”
Get full text
Get full text

Digital economy tax compliance model in Malaysia using machine learning approach by Raja Azhan Syah Raja Wahab, Azuraliza Abu Bakar

“…The experimental results show that the ensemble method can improve the single classification model’s accuracy with the highest classification accuracy of 87.94% compared to the best single classification model. …”
Get full text
Get full text
Get full text

Stock market turning points rule-based prediction / Lersak Photong … [et al.] by Photong, Lersak, Sukprasert, Anupong, Boonlua, Sutana, Ampant, Pravi

“…Feature selection was used to sort out key features for further classification. News classification into factors affecting stock market turning point was done using Naïve Bayes, Deep Learning, Generalized Linear Model (GLM) and Support Vector Machine (SVM). …”
Get full text
Get full text

Classification with degree of importance of attributes for stock market data mining by Khokhar, Rashid Hafeez, Md. Sap, Mohd. Noor

“…The SVM is a training algorithm for learning classification and regression rules from data [7]. …”
Get full text
Get full text

ios Mobile Malware Deep Analysis And Classification For Social Media And Online Banking Exploitation by Muhammad ‘Afif Bin Husainiamer