Search Results - (( java application mining algorithm ) OR ( encoder segmentation using algorithm ))

Dual u-net with resnet encoder for segmentation of medical images by Syed, Qamrun Nisa, Ismail, Amelia Ritahani

“…The algorithms used in this paper for segmentation of gastrointestinal polyp images depend on profound deep convolutional neural network architectures: FCN, Dual U-net with Resnet Encoder, U-net, and Unet_Resnet. …”
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Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

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Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

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Extremal region detection and selection with fuzzy encoding for food recognition by Razali @ Ghazali, Mohd Norhisham

“…In the third algorithm, a soft assignment technique using fuzzy encoding is used to transform low-level features into a higher-level feature representation. …”
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Deep learning segmentation of brain ischemic lesion from magnetic resonance images for three-dimensional modelling by Muhammad Ismaill, Mat Lizah, Nasrul Hadi, Johari, Mohd Jamil, Mohamed Mokhtarudin

“…Then, the U-Net architecture is generated using encoder depth of 7. Certain hyperparameters including the number of epochs, encoder depth, and optimizers are varied. …”
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Iris Segmentation Analysis using Integro-Differential Operator and Hough Transform in Biometric System by Zainal Abidin, Zaheera, -, M.Manaf, Shibghatullah, Abdul Samad, -, S.H.A.Mohd Yunos, -, S.Anawar, -, Z.Ayop

“…There are four steps in iris recognition: segmentation,normalization, encoding and matching. Here, iris segmentation has been implemented using Hough Transform and Integro-Differential Operator techniques. …”
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Iris Segmentation Analysis Using Integro-Differential Operator And Hough Transform In Biometric System by Zainal Abidin, Zaheera, Anawar, Syarulnaziah, Ayop, Zakiah, Manaf, Mazani, Shibghatullah, A.S., Mohd Yunos, S.H.A.

“…Iris segmentation is foremost part of iris recognition system.There are four steps in iris recognition: segmentation,normalization,encoding and matching.Here, iris segmentation has been implemented using Hough Transform and IntegroDifferential Operator techniques.The performance of iris recognition system depends on segmentation and normalization technique.Iris recognition systems capture an image from individual eye.Then the image captured is segmented and normalized for encoding process.The matching technique,Hamming Distance,is used to match the iris codes of iris in the database weather it is same with the newly enrolled for verification stage.These processes produce values of average circle pupil,average circle iris,error rate and edge points.The values provide acceptable measures of accuracy False Accept Rate (FAR) or False Reject Rate (FRR).Hough Transform algorithm,provide better performance,at the expense of higher computational complexity.It is used to evolve a contour that can fit to a non-circular iris boundary.However,edge information is required to control the evolution and stopping the contour.The performance of Hough Transform for CASIA database was 80.88% due to the lack of edge information.The GAR value using Hough Transform is 98.9% genuine while 98.6% through Integro-Differential Operator.…”
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Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Codebook excited linear predictive coding by Shah, Asadullah, Shaikh, Muniba

“…In standard Codebook Exited Linear Coding (CELP) the encoding method uses random values for excitation vectors. …”
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Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images by Adil Humayun, Khan

“…The third proposed segmentation algorithm involves two pipelines for feature extraction: split & merge methods and Contextual Encoding Network (EncNet) with an attention mechanism. …”
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Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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A web-based implementation of k-means algorithms by Lee, Quan

“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Conflict-free automated guided vehicles routing using multi-objective genetic algorithm by Umar, Ali Umar, Mohd Ariffin, Mohd Khairol Anuar, Ismail, Napsiah, Tang, Sai Hong

“…Conflict is detected and avoided using the route occupation time of each segment. …”
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Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

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The automatic focus segmentation of multi-focus image fusion by Kamarul Hawari, Ghazali, Ismail, .

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Fractal coding of bio-metric image for face authentication by Ahadullah, Md

“…We assess the study results in three essential areas: encoding time complexity in second, fractal object (image) quality in PSNR, SSIM, FSIM (seventeen quality features have been used), dimensionality reduction (Compression Ratio). …”
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Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform by E. GUMAH, MOHAMED

“…In the segmentation stage, Hough transform approach was used for line extraction. …”
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Ensemble learning of deep learning and traditional machine learning approaches for skin lesion segmentation and classification by Adil H., Khan, Dayang Nurfatimah, Awang Iskandar, Jawad F., Al-Asad, Hiren, Mewada, Muhammad Abid, Sherazi

“…After that segmented region is classified into three types of skin lesion using hybrid features of Alex-Net and VGG-16 through the transfer learning approach. …”
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Heartbeat disease diagnosis using text-based approaches by Khorasani, Ehsan Safar

“…The Longest Common Subsequence (LCS) matching algorithm was used for identifying similarities from the database. …”
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A Reinforcement Learning Based Adaptive ROI Generation for Video Object Segmentation by Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., Roy, A.

“…The proposed method has been validated using four commonly used video entity segmentation datasets: SegTrack V2, DAVIS 2016, CdNet 2014, and the Youtube-Object dataset. …”
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