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1
Detection of acute leukaemia cells using variety of features and neural networks
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Working Paper -
2
Immune Multiagent System for Network Intrusion Detection using Non-linear Classification Algorithm
Published 2010“…A new non classification algorithm was developed based on the danger theory model of human immune system (HIS).The abstract model of system algorithm is inspired from HIS cell mechanism mainly, the Dendritic cell behavior and T-cell mechanisms. …”
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Citation Index Journal -
3
HEP-2 CELL IMAGES CLASSIFICATION BASED ON STATISTICAL TEXTURE ANALYSIS AND FUZZY LOGIC
Published 2014“…A working classification algorithm is developed by using MATLAB and the Fuzzy Logic Toolbox to differentiate and classify the staining pattern of HEp-2 cell images. …”
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Final Year Project -
4
FLUORESCENCE INTENSITY POSITIVITY CLASSIFICATION OF HEP-2 CELLS IMAGES USING FUZZY LOGIC
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Final Year Project -
5
Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition
Published 2021“…This paper proposed the fuzzy-ID3 (FID3) algorithm, a fuzzy decision tree as the classification method in breast cancer detection. …”
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Article -
6
Geometric Feature Extraction for Identification and Classification of Overlapping Cells for Leukaemia
Published 2022“…This paper describes the study of overlapping leukaemia cells based on geometric features for identification and classification. …”
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Article -
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HEp-2 cell images classification based on statistical texture analysis and fuzzy logic
Published 2014“…This paper proposes a pattern recognition algorithm consisting of statistical methods to extract seven textural features from the HEp-2 cell images followed by classification of staining patterns by using fuzzy logic. …”
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Conference or Workshop Item -
8
Classification of acute leukemia using image processing and machine learning techniques / Hayan Tareq Abdul Wahhab
Published 2015“…In total, 601 features were extracted from each blast cell, and its nucleus: 31of these were shape-based features, while 534 were texture-based features and 36 were color-based features. …”
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Thesis -
9
Geometric Feature Extraction for Identification and Classification of the Overlapping Cells for Leukaemia
Published 2018“…Overlapping cell identification and classification of microscopic blood cell image is proposed to increase the accuracy of the number of automated counting and the percentage of the overall accuracy. …”
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Thesis -
10
New Algorithm of Location Model based on Robust Estimators and Smoothing Approach
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Conference or Workshop Item -
11
RSA Encryption & Decryption using JAVA
Published 2006“…References and theories to support the research of 'RSA Encryption/Decryption using Java' have been disclosed in Literature Review section. …”
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Final Year Project -
12
Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms
Published 2021“…The objective of this study is to perform DM classification using various machine learning algorithms using Weka as a tool. …”
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Final Year Project -
13
Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System
Published 2011“…Artificial Immune Recognition System is one of the several immune inspired algorithms that can be used to perform classification, a data mining task. …”
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Thesis -
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Provider independent cryptographic tools
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Monograph -
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Classification of Diabetes Mellitus using Ensemble Algorithms
Published 2021“…The objective of this study is to perform DM classification using various machine learning algorithms. …”
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Conference or Workshop Item -
17
An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving
Published 2014“…In addition, the experiments using cervical cell images and HEp-2 cell images showed great potential of the AFCEDP technique as a technique for enhancing medical microscopic images.…”
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Article -
18
Validation on an enhanced dendrite cell algorithm using statistical analysis
Published 2017“…In practice, most of studies apply a straightforward approach for evaluation where appropriate performance metrics such as classification accuracy is selected, computes the mean and its variance over several repetitive experiments, and then compares it with the base algorithm or other comparative approach. …”
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Application Of Neural Network In Malaria Parasites Classification
Published 2006“…The purpose of this project is to classify malaria parasites into Plasmodium falciparum, Plasmodium vivax and Plasmodium malariae based on ratio of infected red blood cell’s (RBC) size to normal RBC’s size, shape of parasite, location of chromatin, number of chromatin, texture of infected RBC, and number of parasite per RBC using different types of neural network. …”
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Monograph
