Search Results - (( using optimization methods algorithm ) OR ( time optimization svm algorithm ))*

Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…This paper presents two algorithms that integrate new Ant Colony Optimization (ACO) variants which are Incremental Continuous Ant Colony Optimization (IACOR) and Incremental Mixed Variable Ant Colony Optimization (IACOMV) with Support Vector Machine (SVM) to enhance the performance of SVM.The first algorithm aims to solve SVM model selection problem. …”
Get full text
Get full text
Get full text

Rao-SVM machine learning algorithm for intrusion detection system by Abd, Shamis N., Alsajri, Mohammad, Ibraheem, Hind Raad

“…Most of the intrusion detection systems are developed based on optimization algorithms as a result of the increase in audit data features; optimization algorithms are also considered for IDS due to the decline in the performance of the human-based methods in terms of their training time and classification accuracy. …”
Get full text
Get full text
Get full text

Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems by Mohammad Khamees Khaleel, Alsajri

“…To achieve this goal, an improved Teaching Learning-Based Optimization (ITLBO) algorithm was proposed in dealing with subset feature selection. …”
Get full text
Get full text

Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves by Lia, Kamelia

“…With SSA optimization, the Sauvola method combined with SVM reaches an accuracy of 99.58%, surpassing other methods that use image processing and ANN classification. …”
Get full text
Get full text
Get full text
Get full text

Improved intrusion detection algorithm based on TLBO and GA algorithms by Aljanabi, Mohammad, Mohd Arfian, Ismail

“…Optimization algorithms are widely used for the identification of intrusion. …”
Get full text
Get full text
Get full text

Logistic regression methods for classification of imbalanced data sets by Santi Puteri Rahayu, -

“…These results can be seen as further explanation on the success of Truncated Newton method in TR-KLR and TR Iteratively Re-weighted Least Square (TR-IRLS) algorithm respectively, because of the equivalence of iterative method used by these algorithms. …”
Get full text
Get full text

A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
Get full text
Get full text
Get full text
Get full text

A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
Get full text
Get full text
Get full text
Get full text

Optimization of feature selection in Support Vector Machines (SVM) using recursive feature elimination (RFE) and particle swarm optimization (PSO) for heart disease detection by Bayuaji, Luhur, Kusnadi, Kusnadi, Amzah, Mochamad Yamin, Pebrianti, Dwi

“…One effective approach to detect heart disease is to use Support Vector Machine (SVM) as a machine learning algorithm. …”
Get full text
Get full text
Get full text
Get full text

Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill by Abunama, Taher, Othman, Faridah, Ansari, Mozafar, El-Shafie, Ahmed

“…These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model’s accuracy. …”
Get full text
Get full text

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

“…Also, six techniques: Ant Colony Optimization (ACO), Gain Ratio (GR), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), Random forest (RF), and Correlation-based Feature Selection (CFS) were used for the feature selection. …”
Get full text
Get full text
Get full text

Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms by Lai V., Ahmed A.N., Malek M.A., Afan H.A., Ibrahim R.K., El-Shafie A., El-Shafie A.

Hybrid feature selection of microarray prostate cancer diagnostic system by Mohd Ali, Nursabillilah, Hanafi, Ainain Nur, Karis, Mohd Safirin, Shamsudin, Nur Hazahsha, Shair, Ezreen Farina, Abdul Aziz, Nor Hidayati

“…The outcome of this work is a performance comparison table for the proposed methods on the dataset. The performance of GA, particle swarm optimization (PSO), and whale optimization algorithm (WOA) is compared in terms of accuracy, computation time, and the number of selected features. …”
Get full text
Get full text
Get full text
Get full text

P300 detection of brain signals using a combination of wavelet transform techniques by Motlagh, Farid Esmaeili

“…Meanwhile the new approaches in channels selection methods help the algorithms for convenient online usage.…”
Get full text
Get full text

An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…Feature subset selection and classifier ensemble learning are familiar techniques with high ability to optimize above problems. Recently, various techniques based on different algorithms have been developed. …”
Get full text
Get full text

Mental stress classification based on selected electroencephalography channels using correlation coefficient of Hjorth parameters by Hag, Ala, Al-Shargie, Fares, Handayani, Dini Oktarina Dwi, Asadi, Houshyar

“…Leveraging features from the time, frequency, and time–frequency domains of these channels, and employing machine learning algorithms, notably RLDA, SVM, and KNN, our approach achieved a remarkable accuracy of 81.56% with the SVM algorithm outperforming existing methodologies. …”
Get full text
Get full text
Get full text
Get full text
Get full text

Enhanced extreme learning machine for general regression and classification tasks by Mahmood, Saif F

“…To address this problem, a new approach referred to SVM-ELM is proposed, which utilizes 1-norm support vector machine (SVM) to the hidden layer matrix of ELM in order to automatically discover the optimal number of hidden nodes. …”
Get full text
Get full text

Enhancing time series prediction with Hybrid AFSA-TCN: A unified approach to temporal data and optimization by Nur Alia Shahira, Mohd Zaidi, Zuriani, Mustaffa, Muhammad Arif, Mohamad

“…The study introduces a hybrid model that integrates TCN with Artificial Fish Swarm Algorithm (AFSA), a bio-inspired optimization technique designed to fine-tune TCN parameters. …”
Get full text
Get full text
Get full text

SVM, ANN, and PSF modelling approaches for prediction of iron dust minimum ignition temperature (MIT) based on the synergistic effect of dispersion pressure and concentration by Arshad, U., Taqvi, S.A.A., Buang, A., Awad, A.

“…Data-driven models for predicting fire and explosion-related properties have been improved greatly in recent years using machine-learning algorithms. However, choosing the best machine learning approach is still a challenging task. …”
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 experimental results show that predictive FDT algorithm can generate a relatively optimal tree without much computation effort (comprehensibility), and WFPRs have a better predictive accuracy of stock market time series data. …”
Get full text
Get full text