Search Results - (( data selection method algorithm ) OR ( data normalization learning 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. …”
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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. …”
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A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation by Mohd Ali, Nursabillilah, Besar, Rosli, Ab Aziz, Nor Azlina

“…In this work, simple machine learning methods are used to classify breast cancer microarray data to normal and relapse. …”
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Feature selection in intrusion detection, state of the art: A review by Rais, H.M., Mehmood, T.

“…However, intrusion detection systems highly depend on the features of the input data. These input features give information to the learning algorithms which used in intrusion detection system in the form of the detection method. …”
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The new efficient and accurate attribute-oriented clustering algorithms for categorical data by Qin, Hongwu

“…Four real-life data sets obtained from University of California Irvine (UCI) machine learning repository and ten synthetically generated data sets are used to evaluate MGR and IG-ANMI algorithms, and other four algorithms are used to compare with these two algorithms. …”
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Automated cone cut error detection of bitewing images using convolutional neural network by Mohamed Misbahou Mkouboi, Mohamed Moubarak, Olowolayemo, Akeem, Ghazali, Ahmad Badruddin

“…The deep learning method selected was Convolutional Neural Network (CNN), and the algorithm was used and trained to classify the cone cut error. …”
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Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

“…Genetic algorithm GA had been adopted to perform features selection method; however, this method could not deliver an acceptable detection rate, lower accuracy, and higher false alarm rates. …”
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Forensic language of property theft genre based on mathematical formulae and machine learning algorithms / Hana' Abd Razak by Abd Razak, Hana'

“…Four traits was discovered namely spine, pelvis, knee and ankle. Next, data collection of normal and anomalous behaviour is collected using Kinect sensor. …”
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CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING by ISLAM, BADAR UL ISLAM

“…The proposed model is tested for small and large size grid data, integrated with photovoltaic sources under normal and fluctuating load demand conditions and seasonal variations. …”
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Data‑driven audiogram classifer using data normalization and multi‑stage feature selection by Abeer Elkhouly, Allan MelvinAndrew, HaslizaA Rahim, NidhalAbdulaziz, Mohd FareqAbd Malek, Shafquzzaman Siddique

“…The features used to build the ML model are peculiar and describe the audiograms better. Diferent normalization methods are applied and studied statistically to improve the training data set. …”
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Incremental learning for large-scale stream data and its application to cybersecurity by Ali, Siti Hajar Aminah

“…In our method, we select data that are located in untrained or “not well-learned” region and discard data at trained or “well-learned” region. …”
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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.…”
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Investigation of fault detection and isolation accuracy of different Machine learning techniques with different data processing methods for gas turbine by Molla Salilew, W., Ambri Abdul Karim, Z., Alemu Lemma, T.

“…The faulty and non-faulty data were balanced, smoothed, corrected and normalized. …”
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Investigation of fault detection and isolation accuracy of different Machine learning techniques with different data processing methods for gas turbine by Molla Salilew, W., Ambri Abdul Karim, Z., Alemu Lemma, T.

“…The faulty and non-faulty data were balanced, smoothed, corrected and normalized. …”
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Oil palm maturity classifier using spectrometer and machine learning by Goh, Jia Quan

“…The reflectance data from these five parts was analyzed using statistical method and machine learning algorithm. …”
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Research on the construction of an efficient and lightweight online detection method for tiny surface defects through model compression and knowledge distillation by Chen, Qipeng, Xiong, Qiaoqiao, Huang, Haisong, Tang, Saihong, Liu, Zhenghong

“…The K-means++ clustering algorithm generates candidate bounding boxes, adapting to defects of different sizes and selecting finer features earlier. …”
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Inversion of 2D and 3D DC resistivity imaging data for high contrast geophysical regions using artificial neural networks / Ahmad Neyamadpour by Neyamadpour, Ahmad

“…In the case of 3D study of pole - dipole data, the gradient descent with momentum and an adaptive learning rate algorithm is found to be the most efficient paradigm. …”
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Framework for pedestrian walking behaviour recognition to minimize road accident by Hashim Kareem, Zahraa

“…The results indicate the following: (1) From 262 samples, 66.80% and 48.10% of respondents use mobile phones for calling and chatting, respectively. (2) 263 samples of participants are obtained and analysed, and 90 features are extracted from each sample. (3) 100% classification accuracy are obtained for each class (normal walking, calling, chatting, and running) using the grid optimiser method in machine learning. (4) The precision of classification using Euclidean algorithm for normal walking and calling is 70%. …”
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Framework for pedestrian walking behaviour recognition to minimize road accident by Hashim Kareem, Zahraa

“…Secondly, four different testing scenarios are chosen to acquire pedestrian walking data using the gyroscope sensor, where the essential features were extracted and selected. …”
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An enhanced gated recurrent unit with auto-encoder for solving text classification problems by Zulqarnain, Muhammad

“…Gated Recurrent Unit (GRU) is a type of Recurrent Neural Networks (RNNs), and a deep learning algorithm that contains update gate and reset gate. …”
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