Search Results - (( pattern based algorithm ) OR ((( between mining algorithm ) OR ( deep learning algorithm ))))

Enhancement of text representation for Indonesian document summarization with deep sequential pattern mining by Dian Sa’adillah Maylawati

“…Second, the proposed SPM approach is combined with Deep Belief Network (DBN), called DeepSPM, based on the unsupervised Deep Learning method. …”
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Modifying iEclat algorithm for infrequent patterns mining by Julaily Aida, J., Mustafa, M.

“…This paper proposes an enhancement algorithm based on iEclat algorithms for mining infrequent pattern.…”
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Modifying iEclat algo ithm for infrequent patterns mining by Julaily Aida, Jusoh, Mustafa, Man

“…This paper proposes an enhancement algorithm based on iEclat algorithms for mining infrequent pattern.…”
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Web user navigation pattern mining approach based on graph partitioning algorithm. by Jalali, Mehrdad, Mustapha, Norwati, Mamat, Ali, Sulaiman, Md. Nasir

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Efficient prime-based method for interactive mining of frequent patterns. by Mohammad Hossein, Nadimi Shahraki, Mustapha, Norwati, Sulaiman, Md. Nasir, Mamat, Ali

“…Since rerunning mining algorithms from scratch is very costly and time-consuming, researchers have introduced interactive mining of frequent patterns. …”
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Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm by Jaber M.M., Yussof S., Elameer A.S., Weng L.Y., Abd S.K., Nayyar A.

“…Automation; Complex networks; Computational complexity; Deep learning; Image analysis; Medical imaging; Pattern matching; Pixels; Distribution pattern-matching rule; Distribution patterns; Gray wolf-optimized deep convolution network; Gray wolves; Learning patterns; Matching rules; Medical fields; Medical image analysis; Pattern matching algorithms; Pattern-matching; Convolution…”

Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system by Maseer, Ziadoon Kamil

“…In future, the HW-DBN algorithm can be proposed as an integrated deep Learning for the classification performance of attack detection models.…”
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A Web-Based Recommendation System To Predict User Movements Through Web Usage Mining by Jalali, Mehrdad

“…The approach in the offline phase is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining. …”
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Toxic Gas Dispersion Model Based On Neural Pattern Recognition Networks by Roslan, Nurfarah Arina

“…Prairie Grass experiment database is used as a data to develop toxic gas dispersion prediction model based on deep learning networks. Thus, in this study, development of deep neural network is carried out using MATLAB. …”
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Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification by Chong, Hou Ming, Yin Yap, Xien, Seng Chia, Kim

“…This study proposes a system that can classify tomato plant health into five categories of healthy, early blight, late blight, bacterial spot, and yellow leaf curl virus based on their leaves using deep learning algorithms as feature extractors. Five different pre-trained deep learning algorithms (i.e. …”
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Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification by Hou Ming Chong, Hou Ming Chong, Xien Yin Yap, Xien Yin Yap, Kim Seng Chia, Kim Seng Chia

“…This study proposes a system that can classify tomato plant health into five categories of healthy, early blight, late blight, bacterial spot, and yellow leaf curl virus based on their leaves using deep learning algorithms as feature extractors. Five different pre-trained deep learning algorithms (i.e. …”
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Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification by Hou Ming Chong, Hou Ming Chong, Xien Yin Yap, Xien Yin Yap, Kim Seng Chia, Kim Seng Chia

“…This study proposes a system that can classify tomato plant health into five categories of healthy, early blight, late blight, bacterial spot, and yellow leaf curl virus based on their leaves using deep learning algorithms as feature extractors. Five different pre-trained deep learning algorithms (i.e. …”
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Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification by Hou Ming Chong, Hou Ming Chong, Xien Yin Yap, Xien Yin Yap, Kim Seng Chia, Kim Seng Chia

“…This study proposes a system that can classify tomato plant health into five categories of healthy, early blight, late blight, bacterial spot, and yellow leaf curl virus based on their leaves using deep learning algorithms as feature extractors. Five different pre-trained deep learning algorithms (i.e. …”
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Deep learning in face recognition for attendance system: an exploratory study / Mochamad Azkal Azkiya Aziz, Shahrinaz Ismail and Noormadinah Allias by Aziz, Mochamad Azkal Azkiya, Ismail, Shahrinaz, Allias, Noormadinah

“…An interview was conducted with an expert in the field, to understand the concept, trend, and use of deep learning in face recognition, as well as to determine the suitable algorithm for the attendance system. …”
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Optimize and deploy machine learning algorithms on embedded devices for manufacturing applications by Teoh, Ming Xue

“…In recent studies, we seen developers and researchers proposing solutions on deep learning algorithms like YOLO, EfficientNet, CNN, MobileNet etc. …”
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Mining dense data: Association rule discovery on benchmark case study by Bakar, W.A.W.A., Saman, M.D.M., Abdullah, Z., Jalil, M.A., Herawan, T.

“…In this article, we present comparison result between Apriori and FP-Growth algorithm in generating association rules based on a benchmark data from frequent itemset mining data repository. …”
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Glass break detection system using deep auto encoders with fuzzy rules induction algorithm by Nyein Naing, Wai Yan, Htike, Zaw Zaw

“…This paper proposes a new design of a glass break detection algorithm based on Fuzzy Deep Auto-encoder Neural Network. …”
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Underwater Image Recognition using Machine Learning by Divya, N.K., Manjula, Sanjay Koti, Priyadarshini, S

“…A Convolutional Neural Network (CNN) is a type of a deep learned an algorithm that has been created for image processing when using convolutional layers to automatically and in a hierarchical way learn features from the input images. …”
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An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA by Bian, Hui

“…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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Social media mining: a genetic based multiobjective clustering approach to topic modelling by Rayner Alfred, Loo Yew Jie, Joe Henry Obit, Yuto Lim, Haviluddin Haviluddin, Azreen Azman

“…Then, the mapping percentages between the predefined and produced clusters are used to assess the performance of the proposed algorithm. …”
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