Search Results - (( data normalization based algorithm ) OR ( data validation learning algorithm ))

Classification of fault and stray gassing in transformer by using duval pentagon and machine learning algorithms by Haw, Jia Yong, Mohd Yousof, Mohd Fairouz, Abd Rahman, Rahisham, Talib, Mohd Aizam, Azis, Norhafiz

“…However, there are times where the produce of stray gassing event might lead to fault indication in the transformer. Machine learning algorithms are used to classify the DGA data into normal condition and corresponding faults based on IEEE limits and Duval pentagon method. …”
Get full text
Get full text

Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly by Zulkifly, Ahmad Zuladzlan

“…Even a normal people using clustering to grouping their data. …”
Get full text
Get full text

Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions by Shamshad, H., Ullah, F., Ullah, A., Kebande, V.R., Ullah, S., Al-Dhaqm, A.

“…Thus, this proposed system employs a data science-based framework and six highly advanced data-driven Machine learning and Deep learning algorithms: Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. …”
Get full text
Get full text

The Impact of Normalization Techniques on Performance Backpropagation Networks by Norlida, Hassan

“…To explore the impact of normalization technique on the performance on NN, medical datasets with Boolean target were preprocessed, trained, validated and tested using backpropagation learning algorithm. …”
Get full text
Get full text
Get full text

Characterization of oil palm fruitlets using artificial neural network by Olukayode, Ojo Adedayo

“…To further validate the generalization accuracy of the LSB_ANN, its performance was compared with that of a Multi-ANFIS network as well as those of three different ANN training algorithms: Levenberg Marquardt (LM) algorithm, Resilient Backpropagation (RP) algorithm and Gradient Descent with Adaptive learning rate (GDA). …”
Get full text
Get full text

An improved diabetes risk prediction framework : An Indonesian case study by Sutanto, Daniel Hartono

“…Pre-processing resolves the issue of missing data and hence normalizes the data.Outlier treatment employs k-mean clustering to validate the class.Suitable components were selected through comparison of classifier algorithms and feature selection.Attribute weighting based feature selection was selected for assigning weightage.Weighted risk factor was used on training dataset in order to improve accuracy and computation time of the prediction. …”
Get full text
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

“…The proposed method integrates colour and texture feature-based image analysis with machine learning algorithms for classification. …”
Get full text
Get full text
Get full text
Get full text

Automated cone cut error detection of bitewing images using convolutional neural network by Mohamed Misbahou Mkouboi, Mohamed Moubarak, Olowolayemo, Akeem, Ghazali, Ahmad Badruddin

“…Data augmentation was used to increase the amount of data for training, validation, and testing. …”
Get full text
Get full text

Visual analysis to investigate the capability of ANFIS in modelling hydrological relationship using synthetic dataset by Ngahzaifa, Ab. Ghani, Zuriani, Mustaffa, Zafril Rizal, M Azmi

“…In using most of the machine learning algorithms including ANFIS, to obtain the best model, the common and normal approach is always by comparing models of different parameter settings based on the goodness-offit statistical measures. …”
Get full text
Get full text
Get full text

Predicting energy consumption pattern based on top trending videos YouTube 2021 using machine learning techniques by Ng, Jiun Shen

“…Furthermore, the data will be clustered in this project utilising threshold-based approaches. …”
Get full text
Get full text

Hybrid neural network in medicolegal degree of injury determination based on Visum et Repertum by Wardhana, Mohammad Hadyan

“…Pre-processing phase overcomes the issue of incomplete data by performing data cleansing and data normalization. …”
Get full text
Get full text
Get full text

IoT-Enabled Waste Tracking and Recycling Optimization : Enhancing Sustainable Waste Management by Eugine Teh, Yin Jie, Chee Soon, Chong, Rozaimi, Ghazali, Hazriq Izzuan, Jaafar, Muhamad Fadli, Ghani, Howe Cheng, Teng, Nur Farhanah, Zulkipli, Siaw Hong, Liew

“…Advanced data preprocessing, such as augmentation and normalization, ensures robust model training, while optimized algorithms guide waste sorting based on classification results. …”
Get full text
Get full text
Get full text

Validation assessments on resampling method in imbalanced binary classification for linear discriminant analysis by Jamaluddin, Ahmad Hakiim, Mahat, Nor Idayu

“…This manuscript attempted to shed more light on the effect of a resampling method (ROS or RUS) on the performance of LDA based on true positive rate and true negative rate through five validation strategies, i.e. leave-one-out cross-validation, k-fold cross-validation, repeated k-fold cross-validation, naive bootstrap, and .632+ bootstrap. 100 twogroup bivariate normally distributed simulated and four real data sets with severe class imbalance ratio were utilised. …”
Get full text
Get full text
Get full text
Get full text

Validation assessments on Resampling Method in Imbalanced Binary Classification for Linear Discriminant Analysis by Jamaluddin, Ahmad Hakiim, Mahat, Nor Idayu

“…This manuscript attempted to shed more light on the effect of a resampling method (ROS or RUS) on the performance of LDA based on true positive rate and true negative rate through five validation strategies, i.e. leave-one-out cross-validation, k-fold cross-validation, repeated k-fold cross-validation, naive bootstrap, and .632+ bootstrap. 100 two-group bivariate normally distributed simulated and four real data sets with severe class imbalance ratio were utilised. …”
Get full text
Get full text
Get full text
Get full text

Addressing imbalanced EEG data for improved microsleep detection: An ADASYN, FFT and LDA-based approach by Hasan, Md Mahmudul, Khandaker, Sayma, Norizam, Sulaiman, Hossain, Mirza Mahfuj, Islam, Ashraful

“…This paper introduces a novel approach to detecting driver microsleep by leveraging EEG signals and advanced machine learning techniques. The methodology begins with preprocessing raw EEG data to improve quality and balance, utilizing the ADASYN algorithm to address dataset imbalances. …”
Get full text
Get full text
Get full text

DC-GAN-based synthetic X-ray images augmentation for increasing the performance of EfficientNet for COVID-19 detection by Pir Masoom Shah, Hamid Ullah, Rahim Ullah, Dilawar Shah, Yulin Wang, Saif ul Islam, Abdullah Gani, Rodrigues, Joel J. P. C.

“…However, the available data (X‐rays) for COVID‐19 is limited to train a robust deep‐learning model. …”
Get full text
Get full text
Get full text
Get full text
Get full text

End-to-end DVB-S2X system design with deep learning-based channel estimation over satellite fading channels by Mfarej, Sumaya Dhari Awad

“…In the fourth part a deep learning (DL) algorithm of channel estimation for two fad�ing channel models, Tropical and Temperate in the satellite communication system is presented. …”
Get full text
Get full text

Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data by Ting, Tin Tin, Khiew, Jie Xin, Ali Aitizaz, Lee, Kuok Tiung, Teoh, Chong Keat, Hasan Sarwar

“…The algorithms are used to construct, test, and validate three categories of cybercrime threat (Malware, Social Engineering, and Password Attack) predictive models. …”
Get full text
Get full text
Get full text
Get full text

Classification of SNPs for obesity analysis using FARNeM modelling by Ong, Phaik Ling, Choo, Yun Huoy, Emran, Nurul Akmar

“…But, FARNeM did not achieve good reduction rate when applied to the experimental data set. However, the overall analysis showed that, it is encouraging to include feature selection process before the learning algorithms.…”
Get full text
Get full text

Risk prediction analysis for classifying type 2 diabetes occurrence using local dataset by Abd Rahman, M. Hafiz Fazren, Wan Salim, Wan Wardatul Amani, Abd-Wahab, Firdaus

“…The steep rise of cases pertaining to Diabetes Mellitus (DM) condition among global population has encouraged extensive researches on DM, which led to exhaustive accumulation of data related to DM. In this case, data mining and machine learning applications prove to be a powerful tool in transforming data into a meaningful knowledge. …”
Get full text
Get full text
Get full text