Search Results - (( using deep method algorithm ) OR ( pattern classification using algorithm ))
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Classification of chest radiographs using novel anomalous saliency map and deep convolutional neural network
Published 2021“…The rapid advancement in pattern recognition via the deep learning method has made it possible to develop an autonomous medical image classification system. …”
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An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA
Published 2025“…This integration optimizes feature extraction by capturing both spatial and temporal relationships, enhancing the detection of complex network behaviors. Using the NSL-KDD dataset for evaluation, the proposed method demonstrates superior performance compared to conventional algorithms and related deep learning techniques, achieving higher precision, recall, F1 scores and overall accuracy in both binary and multi-class classification tasks. …”
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Identifying melanoma characteristics using directional imaging algorithm and convolutional neural network on dermoscopic images / Mohammad Asaduzzaman Rasel
Published 2024“…Several imaging, computer vision, and pattern recognition algorithms are employed to describe five dermoscopic features. …”
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Deep plant: A deep learning approach for plant classification / Lee Sue Han
Published 2018“…Besides using solely a single leaf organ to recognize plant species, numerous studies have employed DL methods to solve multi-organ plant classification problem. …”
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VGG16-based deep learning architectures for classification of lung sounds into normal, crackles, and wheezes using Gammatonegrams
Published 2023“…In this study, we conducted a comparison of two versions of the VGG16-based deep learning model for breathing sound classification using Gammatonegrams as input. …”
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Conference or Workshop Item -
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Systematic review for phonocardiography classification based on machine learning
Published 2023“…This systematic review aims to examine the existing literature on phonocardiography classification based on machine learning, focusing on algorithms, datasets, feature extraction methods, and classification models utilized. …”
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Random Undersampling on Imbalance Time Series Data for Anomaly Detection
Published 2023Conference Paper -
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Enhancement of text representation for Indonesian document summarization with deep sequential pattern mining
Published 2023“…Therefore, the present study aims: (1) to improve Indonesian text summary by enhancing the Sequence of Word (SoW) as text representation using Sequential Pattern Mining (SPM) with PrefixSpan algorithm since the effectiveness of SPM in Indonesian is proven useful for text classification and clustering; (2) to combine SPM and Deep Learning (DeepSPM) in text summarization with Indonesian text, as a result of its superior accuracy when trained with large amounts of data; and (3) to evaluate the readability of Indonesian text summary with several evaluation scenarios. …”
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A hybrid interpretable deep structure based on adaptive neuro‑fuzzy inference system, decision tree, and K‑means for intrusion detection
Published 2022“…For generating an interpretable deep architecture for identifying deep intrusion patterns, this study proposes an approach that combines ANFIS (Adaptive Network-based Fuzzy Inference System) and DT (Decision Tree) for interpreting the deep pattern of intrusion detection. …”
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Enhancing land cover classification in remote sensing imagery using an optimal deep learning model
Published 2023“…hyperparameter selection is accomplished with the help of the Reptile Search Algorithm (RSA). The simulation analysis was conducted upon the ISCSODL-LCC model using two benchmark datasets and the results established the superior performance of the proposed model. …”
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Systematic Review for Phonocardiography Classification Based on Machine Learning
Published 2023“…This systematic review aims to examine the existing literature on phonocardiography classification based on machine learning, focusing on algorithms, datasets, feature extraction methods, and classification models utilized. …”
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Empirical Analysis of Intra vs. Inter-Subject Variability in VR EEG-Based Emotion Modelling
Published 2018“…These methods showed highly promising results and will be further enhanced using other machine learning approaches such as deep learning in VR stimulus.…”
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Diabetic retinopathy detection using fusion of textural and optimized convolutional neural network features / Uzair Ishtiaq
Published 2024“…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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15
Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions
Published 2019“…These sensors are pre-processed and different feature sets such as time domain, frequency domain, wavelet transform are extracted and transform using machine learning algorithm for human activity classification and monitoring. …”
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Condition monitoring of deep drilling process for cooling channel making in hot press die
Published 2016“…Deep drilling operation is one of the major process that is widely used in the manufacturing industry. …”
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Undergraduates Project Papers -
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Adaptive Feature Selection and Image Classification Using Manifold Learning Techniques
Published 2024“…Dimension reduction is the field of interest and demand of many data analysts and is widely used in computer vision, image processing, pattern recognition, neural networks, and machine learning. …”
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Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…In pattern recognition system, achieving high accuracy in pattern classification is crucial. …”
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Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier
Published 2018“…There are a lot of feature extraction methods and classification methods for iris classification. Classic local binary pattern (LBP) is one of the most useful feature extraction methods. …”
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