Search Results - (( model validation from algorithm ) OR ( identification _ learning algorithm ))

System identification using Extended Kalman Filter by Alias, Ahmad Hafizi

“…In order to evaluate the performance of the EKF learning algorithm, the proposed algorithm validation were analyzed using model validation methods as a checker such as One Step Ahead (OSA) and correlation coefficient (R2). …”
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Approach For Optimizing Course Recommendation Based On Integrating Modified Felder-Silverman Learning Style Model With Meta-Heuristics Algorithm by Ashraf, Erum

“…It involves identifying potential courses learning style, validated through genetic and surrogate meta-heuristics optimization algorithms, and employing Felder-Silverman model for learning style identification. …”
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Musical instrument identification using Convolutional Neural Network (CNN) algorithm / Muhammad Nur Azri Irfan Abdul Rahman by Abdul Rahman, Muhammad Nur Azri Irfan

“…The motivation behind the project was to help automate the cumbersome task of validating instruments from images using Convolutional Neural Network (CNNs) algorithm to identify the musical instrument so that this task could be completed with higher accuracy. …”
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Opposition- based simulated kalman filters and their application in system identification by Kamil Zakwan, Mohd Azmi

“…According to the experimental results, COOBSKF provides average of maximum model validation up to 90%. This technique can be an alternative approach to solve system identification problem, apart from using conventional method.…”
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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm by Reza M.S., Hannan M.A., Mansor M., Ker P.J., Rahman S.A., Jang G., Mahlia T.M.I.

“…In addition, to validate the prediction performance of the proposed LSA + LSTM model, extensive comparisons are performed with other popular optimization-based deep learning methods including artificial bee colony (ABC) based LSTM (ABC + LSTM), gravitational search algorithm (GSA) based LSTM (GSA + LSTM), and particle swarm optimization (PSO) based LSTM (PSO + LSTM) model using different error matrices. …”

Identification Of Flow Blockage Levels In Centrifugal Pump By Machine Learning by Ng, Woon Li

“…Besides, the result of the Linear SVM model is validated by using the classification packages in Google Colaboratory. …”
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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

“…In order to further validate the position of the tagging in the pallet box of the Random Forest model developed, a different predefined location was used to validate the model. …”
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Hyperparameter tuned deep learning enabled intrusion detection on internet of everything environment by Ahmed Hamza, Manar, Hassan Abdalla Hashim, Aisha, G. Mohamed, Heba, S. Alotaibi, Saud, Mahgoub, Hany, S. Mehanna, Amal, Motwakel, Abdelwahed

“…Finally, MVO algorithm is exploited with Bidirectional Gated Recurrent Unit (BiGRU) model for classification. …”
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Prediction of novel doping agent through the integration of chemical and biological data using in silico method by Mohd Rosman, Nurul Ain

“…The in silica method, also known as in silica target prediction, first analyse patterns of protein-ligand binding from chemical and biological data through the use of machine learning algorithm. …”
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Malaysian license plate recognition system using Convolutional Neural Network (CNN) on web application / Nur Farahana Mahmud by Mahmud, Nur Farahana

“…Nowadays, there are numerous license plate recognition systems that have been developed and analysed effectively by previous researchers using different machine learning algorithms. However, according to a recent study, ANN algorithms require a huge amount of training data while BPFFNN algorithms only have an average success rate of 70% in recognizing all the characters. …”
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Spotted hyena optimizer with deep learning driven cybersecurity for social networks by Mustafa Hilal, Anwer, Hassan Abdalla Hashim, Aisha, G. Mohamed, Heba, A. Alharbi, Lubna, K. Nour, Mohamed, Mohamed, Abdullah, S. Almasoud, Ahmed, Motwakel, Abdelwahed

“…The presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the OSN. …”
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Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region by Anees, Shoaib Ahmad, Mehmood, Kaleem, Khan, Waseem Razzaq, Sajjad, Muhammad, Alahmadi, Tahani Awad, Alharbi, Sulaiman Ali, Luo, Mi

“…Through the utilization of openly accessible fine-resolution data and employing the RF algorithm, the research demonstrated promising outcomes in the identification of optimal predictor-algorithm combinations for forest AGB mapping. …”
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Artificial neural network implementation on firearm recognition system with respect to ring firing pin impression image by Ahmad Kamaruddin, Saadi, Md Ghani, Nor Azura, Liong, Choong-Yeun, Jemain, Abdul Aziz

“…Firearms identification is a vital aim of firearm analysis. The firing pin impression image on a cartridge case from a fired bullet is one of the most significant clues in firearms identification. …”
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Feature extraction and supervised learning for volatile organic compounds gas recognition by Mohd Tombel, Nor Syahira, Mohd Zaki, Hasan Firdaus, Mohd Fadglullah, Hanna Farihin

“…The performance of each model was evaluated and compared using k-Fold cross-validation (k=10) and metrics derived from the confusion matrix. …”
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Hospital site suitability assessment using three machine learning approaches: evidence from the Gaza strip in Palestine by Almansi, Khaled Yousef, Mohamed Shariff, Abdul Rashid, Abdullah, Ahmad Fikri, Syed Ismail, Sharifah Norkhadijah

“…To find the most significant parameters that reduce the error rate and increase the efficiency for the suitability analysis, this study utilized machine learning methods. Identification of the most significant parameters (conditioning factors) that influence a suitable hospital location was achieved by employing correlation-based feature selection (CFS) with the search algorithm (greedy stepwise). …”
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Firearm recognition based on whole firing pin impression image via backpropagation neural network by Ahmad Kamaruddin, Saadi, Md Ghani, Nor Azura, Liong, Choong-Yeun, Jemain, Abdul Aziz

“…Firearms identification is a vital aim of firearm analysis. The firing pin impression image on a cartridge case from a fired bullet is one of the most significant clues in firearms identification. …”
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Firearm recognition based on whole firing pin impression image via backpropagation neural network by Ahmad Kamaruddin, Saadi, Md Ghani, Nor Azura, Liong, Choong-Yeun, Jemain, Abdul Aziz

“…Firearms identification is a vital aim of firearm analysis. The firing pin impression image on a cartridge case from a fired bullet is one of the most significant clues in firearms identification. …”
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ScannerVision: Scanner-based image acquisition of medically important arthropods for the development of computer vision and deep learning models by Ong, Song Quan, Nathan Pinoy, Lim, Min Hui, Kim Bjerge, Francisco Javier Peris-Felipo, Rob Lind, Jordan P. Cuff, Samantha M. Cook, Toke Thomas Høye

“…In this paper, we present a high-throughput scanner-based method for capturing images of arthropods that can be used to generate large datasets suitable for training machine learning algorithms for identification. We demonstrate the ability of this approach to image arthropod samples collected with different sampling methods, such as sticky traps (unbaited, in different colors), baited mosquito traps as used by the US Centers for Disease Control and Prevention (CDC) and BioGents-Sentinel (BGS), and UV light traps with a sticky pad. …”
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Context enrichment framework for sentiment analysis in handling word ambiguity resolution by Yusof, Nor Nadiah

“…Machine learning algorithms are deployed to perform sentiment classification. …”
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Oil palm female inflorescences anthesis stages identification using selected emissivities through thermal imaging and Machine Learning by Yousefidashliboroun, Mamehgol

“…These models’ performance is validated statistically and empirically considering the average accuracy, recall, precision, and F-measure in classifying the present datasets. …”
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