Vinai George Biju

Technical University of Applied Sciences Würzburg-Schweinfurt

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Blessy Baby Mathew

Dr. Hari Singh Gour University

P. Mythili

Cochin University of Science and Technology

C. M. Prashanth

Koneru Lakshmaiah Education Foundation

C.M. Prashanth

Lakshmi Priya C V

A P J Abdul Kalam Technological University

C. M. Prashanth

Mangalore Institute of Oncology

B. Vinod

PSG INSTITUTE OF TECHNOLOGY AND APPLIED RESEARCH

Krishnamurthy Nideghatta Beeregowda

Santanu Kumar Rath

National Institute of Technology Rourkela

Harshita Katoch

Christ University

Cooperative Institutions

Christ University

M S Ramaiah University of Applied Sciences

A P J Abdul Kalam Technological University

Dr. Hari Singh Gour University

University of Kerala

Rajiv Gandhi Technical University

Kyungpook National University

Vellore Institute of Technology University

Indian Institute of Technology Madras

Technical University of Applied Sciences Würzburg-Schweinfurt

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Fuzzy Clustering Algorithms for cDNA Microarray Image Spots Segmentation

Procedia Computer Science (2015)

Vinai George Biju P. Mythili

cDNA microarray image provides useful information about thousands of gene expressions simultaneously. Hence microarray image segmentation is an important task. In this paper, existing fuzzy clustering image segmentation methods in the literature have been tested for its suitability to perform segmentation of noisy cDNA microarray images. The algorithms considered for this purpose include fuzzy clustering based methods like, Fuzzy c-means (FCM), Possibilistic c means (PCM), Possibilistic fuzzy c means (PFCM) and Fuzzy local information c means (FLICM). The results of segmentation shows that FLICM is better in segmenting microarray spots compared to the other under the presence of noise.

10.1016/j.procs.2015.02.039

Cite

Citations (12)

A Predictive Stock Data Analysis with SVM-PCA Model

International Journal of Computer Science and business informatics/International journal of computer science and business informatics (2014)

Divya Joseph Vinai George Biju

In this paper the properties of Support Vector Machines on the financial time series data has been analyzed. The high dimensional stock data consists of many features or attributes. Most of the attributes of features are uninformative for classification. Detecting trends of stock market data is a difficult task as they have complex, nonlinear, dynamic and chaotic behaviour. To improve the forecasting of stock data performance different models can be combined to increase the capture of different data patterns. The uninformative attributes from the stock data are eliminated using the dimensionality reduction technique: Principal Component Analysis. The classification accuracy measure is compared when all the attributes are being considered and only when informative attributes are being considered.

Stock (firearms)

Source

Cite

Citations (0)

Analysis of Differentially Expressed Genes in Prunus persica for the Treatment of Hypoxia Using Generalized Linear Models

Journal of Computational and Theoretical Nanoscience (2020)

Nisha Baid Preethi Meghadri Vinai George Biju Blessy Baby Mathew C. M. Prashanth

The gene structure of organisms gets altered when exposed to an abnormal condition which could adversely affect the growth of the target. RNA sequencing can be deployed to identify diseasecausing mutations in the genes of patients for whom genetic analysis failed to return a diagnosis. One such disease is Hypoxia, a condition in which there is a deficiency in the availability of oxygen in the tissues. RNA sequencing helps in analyzing the global expression patterns of hypoxia and in understanding the cellular alterations of those suffering from it. It gives an understanding of the comprehensive regulation of the gene expression by environmental spur or specific factors which can be used to diagnose and treat hypoxia before it gets fatal. Prunus persica is a plant which has a high capacity for anoxic tolerance, and analyzing the gene expression changes which are associated to hypoxia treatments in the root tissues of two genotypes of the peach plant (Flooding tolerant and Flooding sensitive) can prevent physiological disorders. Further, gene ontology is used to cover three domains-cellular component, molecular function and biological processes related to the differentially expressed genes. We use Generalized Linear Models here, to find the differentially expressed genes in Prunus persica when exposed to the conditions of Hypoxia (Absence of Oxygen) and Normoxia (Excess of Oxygen) and find their Ontologies and genomic pathways to understand and diagnose the Processes that are most affected.

Hypoxia

10.1166/jctn.2020.9042

Cite

Citations (0)

Kappa and accuracy evaluations of machine learning classifiers

B S Sasikala Vinai George Biju C. M. Prashanth

Machine learning is a method in which computers are given the competence to acquire without being unambiguously programmed. Machine learning discovers the learning and structuring of algorithms that can learn from the past data and make predictions on the same. Methods for relating two or more algorithms on a single dataset have been inspected in the current scenario, comparison of algorithms on multiple datasets is even more crucial for a typical machine learning studies. In this paper I have discussed about the Kappa and Accuracy Evaluations of Machine Learning Classifiers on multiple datasets. The objective of this paper is to compare and analyze the execution of these algorithms based on the efficiency of machine learning algorithms such as Classification and Regression Tree (CART), Linear Discriminant Analysis (LDA), k-Nearest Neighbor (kNN), Support Vector Machine (SVM) and Random Forest.

Online machine learning

Learning classifier system

Instance-based learning

Kappa

Statistical classification

Cohen's kappa

10.1109/rteict.2017.8256551

Cite

Citations (31)

ChIPSeq Analysis with Bayesian Machine Learning

Anthony Suresh Aniketh Issac John Jamie Mathew Harshita Katoch Aashish Suresh Siddarth

ChIP-sequencing, otherwise called ChIP-seq, is a technique used to identify protein co-operations with DNA. A crucial advancement to the field of bio-informatics, ChIP sequencing is conducted in research labs around the world to get a better understanding of the way transcription factors and other associated proteins influence the gene in many biological processes and in tackling disease states. ChIP-seq is predominantly a field under the domain of biotechnology, however recent advancements and development of tools to process ChIP data have turned the study into one involving bio-informatics, allowing computer scientists and lab technicians to work on an otherwise scholarly field of biochemistry, molecular biology, microbiology and biomedicine. This report illustrates the predominant work-flow undertaken to sequence chromatin from a cell and to gain insights on the gene/protein of interest. Another aspect added is to use Machine Learning with Bayesian statistical techniques for Peak Calling. The different stages enumerated in this paper have been completed either with the R language or on a Web Server titled Galaxy.org.

Biomedicine

10.1109/icondsc.2019.8816879

Cite

Citations (0)

A Review on Feature Selection Algorithms

Advances in intelligent systems and computing (2019)

Savina Jassica Colaco Sujit Kumar Amrita Tamang Vinai George Biju

Feature (linguistics)

10.1007/978-981-13-6001-5_11

Cite

Citations (24)

MICROARRAY IMAGE GRIDDING USING GRID LINE REFINEMENT TECHNIQUE

DOAJ (DOAJ: Directory of Open Access Journals) (2015)

Vinai George Biju P. Mythili

Line (geometry)

Source

Cite

Citations (0)

Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

Journal of Applied Computer Science Methods (2017)

Vinai George Biju CM Prashanth

Abstract This paper describes a number of experiments to compare and validate the performance of machine learning classifiers. Creating machine learning models for data with wide varieties has huge applications in predictive modelling across multiple domain of science. This work reviews state of the art techniques in machine learning classifiers methods with several extent of magnitude in statistics and key findings that will be helpful in establishing best methodological practices for class predictions. Comprehensive comparative review analysis with statistical validations for various machine learning algorithm for SVM, Bagging, Boosting, Decision Trees and Nearest Neighborhood algorithm on multiple data sets is carried out. Focus on the statistical analysis of the results using Friedman-Test and Wilcoxon Test as well as other interpretative metrics like classification rate, ROC, F-measure are evaluated to benchmark results.

Boosting

Benchmark (surveying)

Friedman test

Gradient boosting

10.1515/jacsm-2017-0002

Cite

Citations (14)

Deep learning approaches for breast cancer detection in histopathology images: A review

Cancer Biomarkers (2024)

Lakshmi Priya C V Vinai George Biju B. Vinod Sivakumar Ramachandran

BACKGROUND: Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. In recent years, there has been a significant increase in research exploring the use of deep-learning approaches for breast cancer detection from histopathology images. OBJECTIVE: To provide an overview of the current state-of-the-art technologies in automated breast cancer detection in histopathology images using deep learning techniques. METHODS: This review focuses on the use of deep learning algorithms for the detection and classification of breast cancer from histopathology images. We provide an overview of publicly available histopathology image datasets for breast cancer detection. We also highlight the strengths and weaknesses of these architectures and their performance on different histopathology image datasets. Finally, we discuss the challenges associated with using deep learning techniques for breast cancer detection, including the need for large and diverse datasets and the interpretability of deep learning models. RESULTS: Deep learning techniques have shown great promise in accurately detecting and classifying breast cancer from histopathology images. Although the accuracy levels vary depending on the specific data set, image pre-processing techniques, and deep learning architecture used, these results highlight the potential of deep learning algorithms in improving the accuracy and efficiency of breast cancer detection from histopathology images. CONCLUSION: This review has presented a thorough account of the current state-of-the-art techniques for detecting breast cancer using histopathology images. The integration of machine learning and deep learning algorithms has demonstrated promising results in accurately identifying breast cancer from histopathology images. The insights gathered from this review can act as a valuable reference for researchers in this field who are developing diagnostic strategies using histopathology images. Overall, the objective of this review is to spark interest among scholars in this complex field and acquaint them with cutting-edge technologies in breast cancer detection using histopathology images.

Histopathology

10.3233/cbm-230251

Cite

Citations (6)

Genetic Algorithm Based Indicator Sequence Method for Exon Prediction

International Conference on Advances in Computing, Control, and Telecommunication Technologies (2009)

Vinai George Biju P. Mydhili

Considerable research effort has been devoted in predicting the exon regions of genes. The binary indicator (BI), Electron ion interaction pseudo potential (EIIP), Filter method are some of the methods. All these methods make use of the period three behavior of the exon region. Even though the method suggested in this paper is similar to above mentioned methods , it introduces a set of sequences for mapping the nucleotides selected by applying genetic algorithm and found to be more promising .

Sequence (biology)

10.1109/act.2009.217

Cite

Citations (1)