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Hybrid Active Contour Mammographic Mass Segmentation and Classification

K. Yuvaraj, U. S. Ragupathy
2022 Computer systems science and engineering  
By using adaptive neuro fuzzy inference system, classification is done and results in a sensitivity of 94.73%, accuracy of 93.93%, and Mathew's correlation coefficient (MCC) of 0.876.  ...  Three methods are proposed, namely, segmentation of mass based on iterative active contour, automatic region growing, and fully automatic mask selectionbased active contour techniques.  ...  This research proposes a new hybrid active contour mass segmentation.  ... 
doi:10.32604/csse.2022.018837 fatcat:csxtlf54mfeddmxok543b3imsq

Hybrid Scoring and Classification Approaches to Predict Human Pregnane X Receptor Activators

Sandhya Kortagere, Dmitriy Chekmarev, William J. Welsh, Sean Ekins
2008 Pharmaceutical Research  
This indicates that molecular shape descriptors are useful in classification of compounds binding to this receptor.  ...  and non-activators.  ...  In addition we will assess the broader applicability of this hybrid docking and classification approach to other proteins.  ... 
doi:10.1007/s11095-008-9809-7 pmid:19115096 pmcid:PMC2836910 fatcat:cpddl4rx4zfxvadgpel24pxj5i

Discrete event based hybrid framework for petroleum products pipeline activities classification

S. S. Udoh, O. C. Akinyokun, U. G. Inyang, O. Olabode, G. B. Iwasokun
2017 Artificial intelligence research  
This paper develops an intelligent hybrid system, driven by discrete event system specification (DEVS) and adaptive neuro-fuzzy inference system (ANFIS) for detection and classification of activities on  ...  Hybrid learning algorithm was observed to converge faster than the back propagation algorithm in the detection of pipeline activities.  ...  This research, indeed develops an intelligent hybrid system driven by DEVS and ANFIS for classification of activities on PPP.  ... 
doi:10.5430/air.v6n2p39 fatcat:5vy2qb7nkvh7vjasfscymdtfke

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments

Wenjing Han, Eduardo Coutinho, Huabin Ruan, Haifeng Li, Björn Schuller, Xiaojie Yu, Xuan Zhu, Friedhelm Schwenker
2016 PLoS ONE  
In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training.  ...  Coping with scarcity of labeled data is a common problem in sound classification tasks.  ...  The major contribution of this work is the application of a hybrid method combining AL and SSL in the field of sound classification, which is of extreme importance to the field given the scarcity of labeled  ... 
doi:10.1371/journal.pone.0162075 pmid:27627768 pmcid:PMC5023122 fatcat:a3cerzf6s5exlbfktgqore7xue

Hybrid Scoring and Classification Using Shape-Based Approaches to Predict Human PXR Activators

Sandhya Kortagere, Dmitriy Chekmarev, William J. Welsh, Sean Ekins
2009 Biophysical Journal  
Mutations disrupting ClC-5 lead to proteinuria reflecting a severe impairment of renal receptor endocytosis and Board B265 Hybrid Scoring and Classification Using Shape-Based Approaches to Predict Human  ...  PXR Activators Sandhya Kortagere 1 , Dmitriy Chekmarev 2 , William J.  ... 
doi:10.1016/j.bpj.2008.12.2285 fatcat:ghtztyaow5csvemfpvmnrcjjge

A Hybrid CNN–LSTM Network for the Classification of Human Activities Based on Micro-Doppler Radar

JianPing Zhu, HaiQuan Chen, Wenbin Ye
2020 IEEE Access  
CONCLUSION In this paper, we introduced a deep learning model composed of one-dimensional CNNs and RNNs for human activity classification.  ...  The classification performance of the selected model is evaluated in fold 1 by precision, recall and F1-score.  ... 
doi:10.1109/access.2020.2971064 fatcat:xevslcdiyjf2tfjmrbf3yhxxii

A Novel Hybrid Framework for Optimal Feature Selection and Classification of Human Activity Recognition

Nilam Dhatrak, Anil Kumar Dudyala
2018 International Journal of Engineering & Technology  
Hence, in this paper, we propose a two hybrid frameworks which gives us optimal number of features that can be used with different classifiers to recognize the Human Activity accurately.  ...  When this data is used for classification, the classifier may be over trained or will definitely give high error rate.  ...  Using thus obtained dataset, a classification algorithm is trained and a model is built.  ... 
doi:10.14419/ijet.v7i3.8.15221 fatcat:nirgyd73unatrimq7zgifacuv4

Counting and size classification of active soil bacteria by fluorescence in situ hybridization with an rRNA oligonucleotide probe

H Christensen, M Hansen, J Sorensen
1999 Applied and Environmental Microbiology  
Counting and size classification into groups of small, medium, and large bacteria were performed by fluorescence microscopy.  ...  In both soils, the majority (68 to 77%) of actively growing bacteria were members of the smallest size class (cell width, 0.25 to 0.5 micrometer), but the active (and growing) bacteria still represented  ...  Binnerup for their support and advice in developing the in situ hybridization method.  ... 
pmid:10103277 pmcid:PMC91247 fatcat:tcif534jb5baxjhb2hkwhsipm4

A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State

Jaeyoung Shin, Jinuk Kwon, Chang-Hwan Im
2018 Frontiers in Neuroinformatics  
The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI.  ...  However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary  ...  using the hybrid approach.  ... 
doi:10.3389/fninf.2018.00005 pmid:29527160 pmcid:PMC5829061 fatcat:uwwbo5l52rfafkemon22z5dghu

Hybrid wavelet packet-support vector classification of atrial activation patterns

D.J. Strauss, J. Jung
Computers in Cardiology  
In this study, we propose hybrid wavelet packet-support vector classifiers for the recognition of atrial activation patters as rate independent approach.  ...  Consecutive beats representing antegrade (AA) and retrograde atrial (RA) activation patterns within data segments of 10s duration were supplied to a binary hybrid wavelet packet-support vector classifier  ...  In this paper, we present a new hybrid wavelet packetsupport classification the antegrade atrial (AA) activation and retrograde atrial (RA) activation.  ... 
doi:10.1109/cic.2002.1166812 fatcat:idylvybnh5b4ljmjiqqggiqmce

A Hybrid Data Mining Classification Technique for the Detection of Suspicious Criminal Activities on Emails Using Neuro-Genetic Approach

Hitesh Mahanand, Deepak Ku
Xaxa Internati onal Journal of Innovations & Advancement in Computer Science IJ IACS   unpublished
The paper presents a hybrid data mining classification technique for detection of suspicious criminal activities on e-mails.  ...  The presented hybrid model focuses on the evaluation of knowledge based learning algorithms to detecting the suspicious criminal activities on e-mails.  ...  This paper proposed a hybrid classification data mining to detect the criminal activities on E-mails with using a Neuro-Genetic approach.  ... 

Deep-Learning-Based Active Hyperspectral Imaging Classification Method Illuminated by the Supercontinuum Laser

Yu Liu, Zilong Tao, Jun Zhang, Hao Hao, Yuanxi Peng, Jing Hou, Tian Jiang
2020 Applied Sciences  
Besides, a deep-learning-based classifier, hybrid DenseNet, is created to learn the feature representations of spectral and spatial information parallelly from active HSI data and is used for the active  ...  HSI classification.  ...  Figure 9 . 9 Hybrid DenseNet framework for active HSI classification. Figure 9 . 9 Hybrid DenseNet framework for active HSI classification.  ... 
doi:10.3390/app10093088 fatcat:bhb62jr5szajzf775dlyeywr2y

Near Perfect Neural Critic from Motor Cortical Activity Toward an Autonomously Updating Brain Machine Interface [article]

Junmo An, Taruna Yadav, Mohammad Badri Ahmadi, Venkata Aditya Tarigoppula, Joseph Thachil Francis
2018 bioRxiv   pre-print
We conclude that hybrid features of PSD and SFC show higher classification performance than PSD or SFC alone (accuracy was 92% for manual tasks, and 97% for observational).  ...  In the future, we will employ these hybrid features toward our autonomously updating BMI.  ...  Such an RL-BMI's performance is directly dependent on the fidelity of the neural critic, and therefore accurate classification of neural activity by the critic is helpful.  ... 
doi:10.1101/250316 fatcat:bn6rpjhrprgghkbqcqbczj3s3u

Mental Tasks Classification for a Noninvasive BCI Application [chapter]

Alexandre Ormiga G. Barbosa, David Ronald A. Diaz, Marley Maria B. R. Vellasco, Marco Antonio Meggiolaro, Ricardo Tanscheit
2009 Lecture Notes in Computer Science  
Thus, the purpose of this study is to assess the performance of different pattern recognition methods on the classification of mental activities present in electroencephalograph signals.  ...  Mapping brain activity patterns in external actions has been studied in recent decades and is the base of a brain-computer interface.  ...  By analyzing the difficulty in separating some of the brain activities, a hierarchical hybrid model was proposed, which led to a better overall classification accuracy, as well as better classification  ... 
doi:10.1007/978-3-642-04277-5_50 fatcat:udjziuuafbchzbijrsyxsacx34

Hybrid flexible neural tree approach for leukemia cancer classification

Lei Zhang, Yuehui Chen, Ajith Abraham, Zhenxiang Chen
2011 2011 World Congress on Information and Communication Technologies  
The experimental results indicate that the proposed method illustrates feasible and efficient for the classifications of microarray data.  ...  The hybrid flexible neural tree with pre-defined instruction sets can be created and evolved.  ...  The experiments result of our method demonstrate that the hybrid flexible neural tree model may provide better classification result and better efficiency than other classification models.  ... 
doi:10.1109/wict.2011.6141213 fatcat:lfcwvm3yojhgjngasnlntn3r54
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