Search Results - (((( pattern means algorithm ) OR ( pattern based algorithm ))) OR ( deep learning algorithm ))

Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia by Latif S.D., Ahmed A.N., Sathiamurthy E., Huang Y.F., El-Shafie A.

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. …”
Get full text
Get full text
Get full text

Cabbage disease detection system using k-NN algorithm by Mohamad Ainuddin Sahimat

“…Identification of plant diseases is key to avoiding losses in agricultural yields and product quantities. Plant disease study means the study of disease patterns that can be visually seen on plants. …”
Get full text
Get full text
Get full text

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.…”
Get full text
Get full text
Get full text
Get full text

Robust tweets classification using arithmetic optimization with deep learning for sustainable urban living by Hamza, Manar Ahmed, Hassan Abdalla Hashim, Aisha, Motwakel, Abdelwahed, Elhameed, Elmouez Samir Abd, Osman, Mohammed, Kumar, Arun, Singla, Chinu, Munjal, Muskaan

“…In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. …”
Get full text
Get full text
Get full text
Get full text

Clustering ensemble learning method based on incremental genetic algorithms by Ghaemi, Reza

“…In the first and second phases, a threshold fuzzy c-means clustering algorithm as a clusterer and a pattern ensemble learning method based on the incremental genetic-based algorithms are proposed respectively. …”
Get full text
Get full text

Anomaly detection of denial-of-service network traffic attacks using autoencoders and isolation forest by Ghulam Hussain, Muhammad Thaqif, Shafeeq Lone, Aman, Maspo, Nur-Adib, Attarbashi, Zainab

“…This paper presents an unsupervised network-based anomaly detection framework that integrates deep autoencoders with the Isolation Forest algorithm. …”
Get full text
Get full text
Get full text

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. …”
Get full text
Get full text

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. …”
Get full text
Get full text
Get full text

Pattern Classification of Human Epithelial Images by Mohd Isa, Mohd Fazlie

“…Last but not least, from the mean of properties, it will classify into the pattern after ranging the value of mean properties of each of the pattern itself that has been done in classification stage.…”
Get full text
Get full text

Analysis of K-Mean and X-Mean Clustering Algorithms Using Ontology-Based Dataset Filtering by Rahmah, Mokhtar, Raza, Muhammad Ahsan, Fauziah, Zainuddin, Nor Azhar, Ahmad, Raza, Muhammad Fahad, Raza, Binish

“…In this paper, we have compared the performance of K-Mean and XMean clustering algorithms using two datasets of student enrollment in higher education institutions. …”
Get full text
Get full text
Get full text

HEP-2 CELL IMAGES CLASSIFICATION BASED ON STATISTICAL TEXTURE ANALYSIS AND FUZZY LOGIC by Jamil, Nur Farahim

“…The algorithm gives a mean accuracy of 84% out of 125 test images.…”
Get full text
Get full text

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. …”
Get full text
Get full text
Get full text

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. …”
Get full text
Get full text
Get full text

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. …”
Get full text
Get full text
Get full text

DC-GAN-based synthetic X-ray images augmentation for increasing the performance of EfficientNet for COVID-19 detection by Pir Masoom Shah, Hamid Ullah, Rahim Ullah, Dilawar Shah, Yulin Wang, Saif ul Islam, Abdullah Gani, Rodrigues, Joel J. P. C.

“…Currently, many deep learning models are being used to classify COVID‐19 and normal cases from chest X‐rays. …”
Get full text
Get full text
Get full text
Get full text
Get full text

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. …”
Get full text
Get full text
Get full text

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. …”
Get full text
Get full text

Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issue... by Abdulsahib A.A., Mahmoud M.A., Mohammed M.A., Rasheed H.H., Mostafa S.A., Maashi M.S.

“…Aldehydes; Blood; Blood vessels; Classification (of information); Deep learning; Eye protection; Image classification; Image segmentation; Learning algorithms; Learning systems; Medical imaging; Blood-vessel segmentations; Deep learning; Fundus image; Machine learning techniques; Retinal blood; Retinal blood vessel segmentation and retinal blood vessel classification; Retinal blood vessels; Retinal vessels; Vessel classification; Ophthalmology; adult; clinical assessment; deep learning; eye fundus; illumination; photography; retina blood vessel; retina image; review…”