Search Results - (( data application testing algorithm ) OR ( parameter detection learning algorithm ))

Evaluation of the Transfer Learning Models in Wafer Defects Classification by Jessnor Arif, Mat Jizat, Anwar, P. P. Abdul Majeed, Ahmad Fakhri, Ab. Nasir, Zahari, Taha, Yuen, Edmund, Lim, Shi Xuen

“…In this paper, an evaluation for these transfer learning to be applied in wafer defect detection. The objective is to establish the best transfer learning algorithms with a known baseline parameter for Wafer Defect Detection. …”
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Study of machine learning in computer vision using Raspberry Pi by Chong, Kah Luo

Modeling, Testing and Experimental Validation of Laser Machining Micro Quality Response by Artificial Neural Network by Sivarao, Subramonian

“…One such method is machine learning, which involves computer algorithm to capture hidden knowledge from data. …”
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ECG peak recognition using Artificial Neural Network / Sharifah Saliha Syed Bahrom and Leong Jenn Hwai. by Syed Bahrom, Sharifah Saliha, Leong, Jenn Hwai

“…ECG peak recognition is fundamental for parameter detection or pattern recognition of an ECG signal. …”
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Image Splicing Detection With Constrained Convolutional Neural Network by Lee, Yang Yang

“…It is shown that CNN with constrained convolution algorithm can be used as a general image splicing detection task.…”
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Effective use of artificial intelligence by Malaysian manufacturing firms to enable sustainability 4.0 by Chong, Agnes Wen Lin

“…In this project, Fornell-Larker parameters are used to test the measurement algorithm for the research's discriminant validity. 380 valid questionnaires returnedback and conducted the calculation of the algorithm's assessment to constructs' validity and realibility assessment, discriminant validity using HTMTand Fornell-Larker approaches. …”
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Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models by Bakar M.A.A.A., Ker P.J., Tang S.G.H., Baharuddin M.Z., Lee H.J., Omar A.R.

“…These works have shown that, despite using only the optical chromaticity of the chicken comb as the input data, the developed models (95% accuracy) have performed exceptionally well, compared to other reported results (99.469% accuracy) which utilize more sophisticated input data such as morphological and mobility features. …”

Malware Detection using Deep Learning (DL) by Chowdhury Sajadul Islam, Madihah Mohd Saudi, Nur Hafiza Zakaria, Muji Setiyo

Water quality monitoring using machine learning and IoT: a review by Hasan, Tahsin Fuad, Kabbashi, Nassereldeen Ahmed, Saleh, Tanveer, Alam, Md. Zahangir, Abd Wahab, Mohd Firdaus, Nour, Abdurahman Hamid

“…The paper explores various ML algorithms, including supervised and unsupervised learning and deep learning, along with their applications, and discusses the use of IoT sensors for real-time monitoring of water quality parameters such as pH, dissolved oxygen, temperature, and turbidity.…”
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Detection of sweetness level for fruits (watermelon) with machine learning by Wan Nazulan, Wan Nurul Suraya, Asnawi, Ani Liza, Mohd Ramli, Huda Adibah, Jusoh, Ahmad Zamani, Ibrahim, Siti Noorjannah, Mohamed Azmin, Nor Fadhillah

“…The objective of this work is to investigate the sweetness parameter for the fruit’s detection and classification algorithm in machine learnings. …”
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Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal by Adam, A., Ibrahim, Z., Mokhtar, N., Shapiai, M.I., Cumming, P., Mubin, M.

“…In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. …”
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Evaluation of Different Time Domain Peak Models using Extreme Learning Machine‐Based Peak Detection for EEG Signal by Asrul, Adam, Zuwairie, Ibrahim, Norrima, Mokhtar, Mohd Ibrahim, Shapiai, Cumming, Paul, Marizan, Mubin

“…In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. …”
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Operational structural damage identification using de-noised modal feature in machine learning / Chen Shilei by Chen , Shilei

“…By integrating ISMA, both supervised and unsupervised machine learning algorithms were investigated to develop real-time damage identification schemes. …”
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Compact Convolutional neural network (CNN) based on SincNet for end-to-end motor imagery decoding and analysis by Ahmad Izzuddin, Tarmizi, Mat Safri, Norlaili, Othman, Mohd Afzan

“…Recently, due to the popularity of end-to-end deep learning, the applicability of algorithms such as convolutional neural networks (CNN) has been explored to achieve the mentioned tasks. …”
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Emotion Modelling Using Neural Network by Lam, Choong Kee

“…The dataset was tested on Multipayer Perceptron with backpropagation learning algorithm. The emotion model obtained in this study uses parameters such as; learning rate 0.1, momentum rate 0.1, Sigmoid activation function, 200 epoch learning stopping criteria, with its architecture, 82 input units, 10 hidden units and 6 output layer units. …”
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Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems by Mohammad Khamees Khaleel, Alsajri

“…Most of the currently existing intrusion detection systems (IDS) use machine learning algorithms to detect network intrusion. …”
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Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models by Bakar, Mohd Anif A. A., Ker, Pin Jern, Tang, Shirley G. H., Baharuddin, Mohd Zafri, Lee, Hui Jing, Omar, Abdul Rahman

“…These works have shown that, despite using only the optical chromaticity of the chicken comb as the input data, the developed models (95 accuracy) have performed exceptionally well, compared to other reported results (99.469 accuracy) which utilize more sophisticated input data such as morphological and mobility features. …”
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Incremental learning for large-scale stream data and its application to cybersecurity by Ali, Siti Hajar Aminah

“…To process large-scale data sequences, it is important to choose a suitable learning algorithm that is capable to learn in real time. …”
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Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Aljanabi, Mohammad, Mohd Arfian, Ismail, Mezhuyev, Vitaliy

“…The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. …”
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Monitoring water quality in Pusu river using Internet of Things (IoT) and Machine Learning (ML) by Kabbashi, Nassereldeen Ahmed, Hasan, Tahsin Fuad, Alam, Md Zahangir, Saleh, Tanveer, Hassan Abdalla Hashim, Aisha

“…In this dissertation, we propose the use of an IoT device to monitor the performance of a water treatment system and collect data on key water quality indicators. Machine learning (ML) tools will be employed to analyze and simulate these data, enabling the prediction of future water quality parameters. …”
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