Search Results - (( image classification learning algorithm ) OR ( process optimization problem algorithm ))

Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah by Abu Samah, Abdul Hafiz

“…In general, this thesis introduces an automated machine learning algorithm for detecting diabetic retinopathy (DR) in fundus images. …”
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RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights by Seyed Vahid Moravvej, Roohallah Alizadehsani, Sadia Khanam, Zahra Sobhaninia, Afshin Shoeibi, Fahime Khozeimeh, Zahra Alizadeh Sani, Ru-San Tan, Abbas Khosravi, Saeid Nahavandi, Nahrizul Adib Kadri, N. Arunkumar, U.Rajendra Acharya, Muhammad Mokhzaini Azizan

Detection of corneal arcus using rubber sheet and machine learning methods by Ramlee, Ridza Azri

“…The random dataset are selected by CV in the classification process (i.e. training, testing and validation). The benchmark of the classification algorithm for CA is needed to analyze the optimal output of the algorithm. …”
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Leaf condition analysis using convolutional neural network and vision transformer by Yong, Wai Chun, Ng, Kok Why, Haw, Su Cheng, Naveen, Palanichamy, Ng, Seng Beng

“…Through the use of a hybrid deep learning model that combines vision transformer and convolutional neural networks for classification, the algorithm can be optimized. …”
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A multilayered convolutional sparse coding framework for modeling of pooling operation of convolution neural networks by , Abdul Wahid, Khan, Adnan Umar, , Mukhtarullah, Khan, Sheroz, Shah, Jawad

“…The multilayered version of CSC(ML-CSC) is shown to be connected to forward pass of CNNs and dictionary learning and sparse coding algorithms of this model are analyzed for solving classification and inverse problems in image processing. …”
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Mobile application: coin identification using machine learning / Dania Qistina Mohd Nazly by Mohd Nazly, Dania Qistina

“…With the rise of mobile technology and Internet access, recent developments in machine learning have designed many algorithms to solve diverse human problems. …”
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Phylogenetic tree classification system using machine learning algorithm by Tan, Jia Kae

“…A study is conducted to develop an automated phylogenetic tree image classification system by using machine learning algorithm. …”
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A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification by Madanan M., Gunasekaran S.S., Mahmoud M.A.

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Aerial imagery paddy seedlings inspection using deep learning by Anuar, Mohamed Marzhar, Abdul Halin, Alfian, Perumal, Thinagaran, Kalantar, Bahareh

“…Deep learning algorithms in computer vision for image classification and object detection can facilitate the agriculture industry, especially in paddy cultivation, to alleviate human efforts in laborious, burdensome, and repetitive tasks. …”
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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

“…The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train and test the algorithms. …”
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Real-time oil palm fruit bunch ripeness grading system using image processing techniques by Alfatni, Meftah Salem M.

“…In this research, a real time oil palm grading system was built and an image processing techniques algorithm was developed based on the external features of oil palm fresh fruit bunches (FFB) such as colour, texture, and thorns. …”
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DNA microarray gene expression analysis for diagnosis of oral dysplasia and squamous-cell carcinoma by Zarzar, Mouayad, Razak, Eliza, Htike@Muhammad Yusof, Zaw Zaw, Yusof, Faridah

“…In addition, we combined a RP technique with a support vector machine (SVM) that employs a sequential minimal optimization training algorithm (SMO) in order to efficiently differentiate squamous-cell carcinoma and oral dysplasia. …”
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The use of SOM for fingerprint classification by Turky A.M., Ahmad M.S.

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Classification of brain tumors: using deep transfer learning by Husin, Nor Azura, Husam, Mohamed, Hussin, Masnida

“…Experimenters employed data augmentation and learning algorithms.…”
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Transfer Learning for Lung Nodules Classification with CNN and Random Forest by Abdulrazak, Saleh, Chee, Ka Chin, Ros Ameera, Rosdi

“…This research demonstrates the potential of using machine learning algorithms in the healthcare industry, especially in disease detection and classification.…”
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Gender classification based on asian faces using deep learning by Janahiraman T.V., Subramaniam P.

“…Face recognition; Human computer interaction; Image classification; Learning algorithms; Natural language processing systems; Neural networks; Speech recognition; Systems engineering; Convolutional neural network; Gender classification; Learning architectures; Learning frameworks; NAtural language processing; Recognition accuracy; Recognition process; TensorFlow; Deep learning…”

Classification of Planetary Nebulae through Deep Transfer Learning by Dayang N.F., Awang Iskandar, Zijlstra, Albert A., McDonald, Iain, Rosni, Abdullah, Fuller, Gary A., Ahmad H., Fauzi, Johari, Abdullah

“…It focusses on distinguishing PNe from other types of objects, as well as their morphological classification. We adopted the deep transfer learning approach using three ImageNet pre-trained algorithms. …”
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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad by Ahmad, Khairul Adilah

“…In general, image segmentation isolates an object from the images, feature extraction creates features for classification phase while object classification categorizes objects into the correct groups. …”
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Autism Spectrum Disorder Classification Using Deep Learning by Abdulrazak Yahya, Saleh, Lim Huey, Chern

“…The goal of this paper is to evaluate the deep learning algorithm for people placed in the Autism Spectrum Disorder (ASD) classification. …”
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The formulation of a transfer learning pipeline for the classification of the wafer defects by Lim, Shi Xuen

“…Automated processes have been used commonly in recent years, with the judgement done by using conventional image processing algorithm. However, limitations such as robustness and difficulty in setting up the parameters required for image processing algorithm encourages the investigation in using Deep learning classification in detecting the wafer defects. …”
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