Search Results - (( using normalization _ algorithm ) OR ( variable learning based algorithm ))

Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
Get full text
Get full text

Weather prediction in Kota Kinabalu using linear regressions with multiple variables by Teong, Khan Vun, Chung, Gwo Chin, Jedol Dayou

“…This study employs machine learning algorithms, a linear regression model using statistics, and two optimization approaches, the normal equation approach, and gradient descent approach to predict the weather based on a few variables. …”
Get full text
Get full text
Get full text
Get full text

One day ahead daily peak hour load forecasting by using invasive weed optimization learning algorithm based Artificial Neural Network by Rahim, Muhammad Fitri

“…Based on result obtained, it shows that IWO learning algorithm is capable to produce accurate prediction load demand. …”
Get full text
Get full text

A study on advanced statistical analysis for network anomaly detection by Ngadi, Md. Asri, Idris, Mohd. Yazid, Abdullah, Abd. Hanan

“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
Get full text

Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud by Shariff, S. Sarifah Radiah, Hud, Hady

“…Secondly, in solving every Machine Learning problem, there is no one algorithm superior to other algorithms. …”
Get full text
Get full text

CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING by ISLAM, BADAR UL ISLAM

“…The solution set (i.e. optimized weight/bias matrix of ANN) provided by the optimized and improved genetic algorithm and modified BP based model is extracted and used in the design and development of a prototype device of the proposed model. …”
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

Predicting wheat yield from 2001 to 2020 in Hebei Province at county and pixel levels based on synthesized time series images of Landsat and MODIS by Zhang, Guanjin, Roslan, Siti Nur Aliaa, Mohd Shafri, Helmi Zulhaidi, Zhao, Yanxi, Wang, Ci, Quan, Ling

“…And the regression algorithm had a more prominent effect on yield prediction, while the yield prediction model using Long Short-Term Memory (LSTM) outperformed the yield prediction model using Light Gradient Boosting Machine (LGBM). …”
Get full text
Get full text
Get full text

Ultrasound-based tissue characterization and classification of fatty liver disease: A screening and diagnostic paradigm by Acharya, U.R., Faust, O., Molinari, F., Sree, S.V., Junnarkar, S.P., Sudarshan, V.

“…These classification algorithms are trained using the features extracted from the patient data in order for them to learn the relationship between the features and the end-result (FLD present or absent). …”
Get full text
Get full text
Get full text

Classification of compressive strength grades for lightweight aggregate concrete with palm oil fuel ash (POFA) using kNearest Neighbour (k-NN) by Mohamad Hushnie, Haron, Nur Azzimah, Zamri, Khairunisa, Muthusamy

“…Next, the grades of concrete were classified using a machine learning algorithm named k-Nearest Neighbour (k-NN). …”
Get full text
Get full text
Get full text

Classification of compressive strength grades for lightweight aggregate concrete with palm oil fuel ash (POFA) using k-Nearest Neighbour (k-NN) by Mohamad Hushnie, Haron, Nur Azzimah, Zamri, Khairunisa, Muthusamy

“…Next, the grades of concrete were classified using a machine learning algorithm named k-Nearest Neighbour (k-NN). …”
Get full text
Get full text
Get full text

Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development by Pande C.B., Egbueri J.C., Costache R., Sidek L.M., Wang Q., Alshehri F., Din N.M., Gautam V.K., Chandra Pal S.

“…The ensemble framework combines three powerful machine learning algorithms: XG-Boost, Bagging-XG-Boost, and AdaBoost, to enhance the accuracy and robustness of LST predictions. …”

Cleansing of inconsistent sample in linear regression model based on rough sets theory by Rasyidah, Rasyidah, Efendi, Riswan, Mohd. Nawi, Nazri, Mat Derisf, Mustafa, Aqil Burney, S.M.

“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
Get full text
Get full text
Get full text

Cleansing of inconsistent sample in linear regression model based on rough sets theory by Rasyidah, Rasyidah, Efendi, Riswan, Mohd. Nawi, Nazri, Mat Deris, Mustafa, Burney, S.M.Aqil

“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
Get full text
Get full text
Get full text

Cleansing of inconsistent sample in linear regression model based on rough sets theory by Rasyidah, Rasyidah, Efendi, Riswan, Mohd. Nawi, Nazri, Mat Deris, Mustafa, Aqil Burney, S.M.

“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
Get full text
Get full text
Get full text

Cleansing of inconsistent sample in linear regression model based on rough sets theory by Rasyidah, Rasyidah, Riswan Efendi, Riswan Efendi, Mohd. Nawi, Nazri, Mat Deris, Mustafa, Aqil Burney, S.M.

“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
Get full text
Get full text
Get full text

Neural network-based prediction models for physical properties of oil palm medium density fiberboard / Faridah Sh. Ismail by Sh. Ismail, Faridah

“…An intelligent predictive model will replace the lengthy procedures by predicting the properties using known fiberboard characteristics. Back-propagation algorithm is a training method widely used in a multilayer perceptron Neural Network model. …”
Get full text
Get full text

Anomaly detection of denial-of-service network traffic attacks using autoencoders and isolation forest by Ghulam Hussain, Muhammad Thaqif, Shafeeq Lone, Aman, Maspo, Nur-Adib, Attarbashi, Zainab

“…This paper presents an unsupervised network-based anomaly detection framework that integrates deep autoencoders with the Isolation Forest algorithm. …”
Get full text
Get full text
Get full text

Cleansing of inconsistent sample in linear regression model based on rough sets theory by Rasyidah, Rasyidah, Riswan Efendi, Riswan Efendi, Nazri Mohd. Nawi, Nazri Mohd. Nawi, Mustafa Mat Deris, Mustafa Mat Deris, S.M.Aqil Burney, S.M.Aqil Burney

“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
Get full text
Get full text
Get full text

Cleansing of inconsistent sample in linear regression model based on rough sets theory by Rasyidah, Rasyidah, Efendi, Riswan, Mohd. Nawi, Nazri, Mat Deris, Mustafa, S.M.Aqil Burney, S.M.Aqil Burney

“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
Get full text
Get full text
Get full text