Search Results - (( using deep method algorithm ) OR ( parameter classification using algorithm ))

Autism Spectrum Disorder Classification Using Deep Learning by Abdulrazak Yahya, Saleh, Lim Huey, Chern

“…In the future, different types of deep learning algorithms need to be applied, and different datasets can be tested with different hyper-parameters to produce more accurate ASD classifications.…”
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Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification by Mohammed, Alhassan Afnan

“…However, finding the most appropriate deep learning algorithm for a medical classification problem along with its optimal parameters becomes a difficult task. …”
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Analysis Of Personal Protective Equipment Classification Method Using Deep Learning by Siti Zahrah Nur Ain, Silopung

“…This classification is performed using Anaconda and Jupyter Notebok Software that use Python as the programming language. …”
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Optimizing in-car-abandoned children’s sounds detection using deep learning algorithms / Nur Atiqah Izzati Md Fisol by Md Fisol, Nur Atiqah Izzati

“…To address this problem, an optimized in-car-abandoned children's sounds detection model using deep learning algorithms is proposed. The objective of this study is to develop an accurate and efficient model capable of recognizing the presence of children in cars based on sound data. …”
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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

“…In addition, the hyperparameter tuning problem is considered in this research to improve the developed hybrid model by using the AOA algorithm. Lastly, a new hybrid technique suggests tackling the current image encryption application problem by using the estimated parameters of chaotic systems with an optimization algorithm, the SKF algorithm. …”
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A Novel Method for Fashion Clothing Image Classification Based on Deep Learning by Yoon Shin, Seong, Jo, Gwanghyun, Wang, Guangxing

“…The methods often used in image classification and recognition tasks are based on deep learning, like Convolutional Neural Networks (CNNs), LeNet, and Long Short-Term Memory networks (LSTM). …”
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A new LoRa based positioning algorithm utilizing sequence based deep learning technique by Suseenthiran, Kavetha

“…Furthermore, an analysis of classification using different sequence length is done. …”
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Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images by Adil Humayun, Khan

“…This proposed classifier achieved 97.9% classification accuracy on the ISIC dataset. In the third classification algorithm, hybrid features are extracted using AlexNet and VGG-16 through a transfer learning approach where parameter manipulation is implemented to simplify the network. …”
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Analysis of multicomponent transient signals using music superresolution technique by Jibia, Abdussamad Umar, Salami, Momoh Jimoh Emiyoka, Khalifa, Othman Omran

“…Many techniques have been suggested by researchers to analyse these signals but they often produce mixed results. A new method of analysis using modified MUSIC (multiple signal classification) subspace algorithm is successfully applied to the analysis of this signal. …”
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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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A stacked ensemble deep learning model for water quality prediction / Wong Wen Yee by Wong , Wen Yee

“…Therefore, this paper aims to explore the use of ensemble and deep learning techniques to simplify the classification process of imbalanced data. …”
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Hybrid of convolutional neural network algorithm and autoregressive integrated moving average model for skin cancer classification among Malaysian by Chin, Chee Ka, Dayang Azra, Awang Mat, Abdulrazak Yahya, Saleh

“…However, these machine learning methods cannot get the deep features from network flow which resulting in low accuracy and the pretrained DNN has the complex network with a huge number of parameters causes the limited classification accuracy. …”
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Optimized techniques for landslide detection and characteristics using LiDAR data by Mezaal, Mustafa Ridha

“…The segmentation process was optimized using Fuzzy-based Segmentation Parameter. Also, six techniques: Ant Colony Optimization (ACO), Gain Ratio (GR), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), Random forest (RF), and Correlation-based Feature Selection (CFS) were used for the feature selection. …”
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A new descriptor for smile classification based on cascade classifier in unconstrained scenarios by Hassen, Oday Ali, Abu, Nur Azman, Zainal Abidin, Zaheera, Saad, Mohamed Darwish

“…The candidate feature set was designed based on the first order histogram probability, and a cascade classifier with a variety of parameters was used at the classification stage. Row transformation is used to exclude any unnecessary coefficients in a vector, thereby enhancing the discriminatory capacity of the extracted features and reducing the sophistication of the calculations. …”
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Skin Cancer Classification using Convolutional Neural Network with Autoregressive Integrated Moving Average by CHEE, KA CHIN, DAYANG AZRA, AWANG MAT, Abdulrazak Yahya, Saleh

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Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

“…Therefore, feature selection using Relief-f with self-adaptive Differential Evolution (rsaDE) algorithm is proposed to select the most significant features. …”
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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

“…This paper presents a novel approach for combining convolutional neural networks (CNN) with OBIA based on joint optimization of segmentation parameters and deep feature extraction. 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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Modeling of road geometry and traffic accidents by hierarchical object-based and deep learning methods using laser scanning data by Sameen, Maher Ibrahim

“…Our method improved the overall accuracy of classification by 7% outperforming the supervised ƙ nearest neighbor method. …”
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Skin Cancer Classification using Convolutional Neural Network with Autoregressive Integrated Moving Average by CHEE, KA CHIN, Dayang Azra, Awang Mat, Abdulrazak Yahya, Saleh

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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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