Search Results - (( using optimization method algorithm ) OR ( image classifications learning algorithm ))

Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves by Lia, Kamelia

“…The proposed method integrates colour and texture feature-based image analysis with machine learning algorithms for classification. …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN 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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Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images by Adil Humayun, Khan

“…This proposed classifier achieved 98.2% classification accuracy on the ISIC dataset. These algorithms are proposed while implying modifications to existing statistical, machine, and deep learning methods.…”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

“…The combination of gist, MTH and SIFT features increased the performance of image identification and showed 49% accuracy. Moreover, instead of concatenating feature vectors together and send to classifier, sparse coding and dictionary learning methods are used and instead of considering all features as one view (visual feature), K-SVD algorithm that is one of the famous algorithms for sparse representation is optimized and developed to multi-view model.The experimental results prove that the proposed methods has improved accuracy by 53.77% compared to concatenating features and classic K-SVD dictionary learning model as well.…”
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Transfer Learning for Lung Nodules Classification with CNN and Random Forest by Abdulrazak Yahya, 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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Detection of corneal arcus using rubber sheet and machine learning methods by Ramlee, Ridza Azri

“…The benchmark of the classification algorithm for CA is needed to analyze the optimal output of the algorithm. …”
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Analysis Of Personal Protective Equipment Classification Method Using Deep Learning by Siti Zahrah Nur Ain, Silopung

“…Based on the result, CNN algorithm is a good algorithm as the binary classification of PPE achieved high accuracy result.…”
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Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm by Yap K.S., Abidin I.Z., Ahmad A.R., Hussien Z.F., Pok H.L., Ismail F.I., Mohamad A.M.

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A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

“…The main keys of the new classifier are based on the new kernel method, new learning metric and a new optimization algorithm in order to optimize the SVM decision function. …”
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Breast cancer histological images nuclei segmentation and optimized classification with deep learning by Abbasi, Muhammad Inam, Khan, Fawad Salam, Khurram, Muhammad, Mohd, Mohd Norzali, Khan, Muhammad Danial

“…A breast cancer multi-classification technique based on a suggested deep learning algorithm was examined to achieve the accuracy of 99.2% using a huge database of ICIAR 2018, demonstrating the method’s efficacy in offering an important weapon for breast cancer multi-classification in a medical setting. …”
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Diabetic retinopathy detection using fusion of textural and optimized convolutional neural network features / Uzair Ishtiaq by Uzair , Ishtiaq

“…Combining Local Binary Patterns (LBP) based texture features and deep learning features resulted in the creation of the fused features vector which was then optimized using Binary Dragonfly Algorithm (BDA) and Sine Cosine Algorithm (SCA). …”
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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption by Nurnajmin Qasrina Ann, Ayop Azmi

“…The fitness function used is the correlation function in the SKF algorithm to optimize the cipher image produced using the Lorenz system. …”
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Automated plant classification system using a hybrid of shape and color features of the leaf by Hamid, Laith Emad

“…Automated plant leaf classification is a computerized approach that employs computer vision and machine learning algorithms to identify a plant based on the features of its leaf. …”
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A hybrid deep CNN model for fast class-incremental food classification / Aymen Taher Ahmed al-Ashwal by Aymen Taher , Ahmed al-Ashwal

“…Lastly, the incremental learning algorithm ABACOC is used to classify each feature of food classes. …”
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Deep learning-based item classification for retail automation by Ling, Ji Xiang

“…This project focuses on developing a deep learning-based system for retail item classification. …”
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A joint Bayesian optimization for the classification of fine spatial resolution remotely sensed imagery using object-based convolutional neural networks by Azeez, Omer Saud, M. Shafri, Helmi Z., Alias, Aidi Hizami, Haron, Nuzul Azam

“…A Bayesian technique was used to find the best parameters for the multiresolution segmentation (MRS) algorithm while the CNN model learns the image features at different layers, achieving joint optimization. …”
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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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Radiomics analysis and supervised machine learning model for classification of cervical cancer images using diffusion weighted imaging-MRI by Ramli, Zarina

“…This study investigates the efficacy of staging classification using diffusion-weighted imaging magnetic resonance imaging (DWIMRI) through radiomic analysis and machine learning. …”
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Image Splicing Detection With Constrained Convolutional Neural Network by Lee, Yang Yang

“…It is able to discriminate the authentic and splicing border in a wide range of images in the cross-database test. It is shown that CNN with constrained convolution algorithm can be used as a general image splicing detection task.…”
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