Research work
- VirLeafNet: Automatic analysis and viral disease diagnosis using deep-learning in Vigna mungo plant
- SLINet: Dysphasia detection in children using deep neural network
- Cytokine gene variants and socio-demographic characteristics as predictors of cervical cancer: A machine learning approach
-
A Deep Learning Approach for Epilepsy Seizure Detection using EEG Signals
-
Vocalist Identification in Audio Songs using Convolutional Neural Network
Journal Papers:
A machine learning-based approach is proposed in this paper for the risk prediction of cervical cancer.
Different risk factors adopting demographic details and cytokine genes were analysed on collected dataset.
Various statistical parameters are used for the evaluation on different machine-learning approaches.
The proposed approach performs well on 5-fold cross-validation and testing in unseen data records.
The risk factor analysed in this study can be taken as a biomarker in developing a cervical cancer diagnosis system.
A child has specific language impairment (SLI) or developmental dysphasia (DD) when
the speech is delayed or has disordered language development for no apparent reason.
As it may be related to loss of hearing, speech abnormality should be diagnosed at an
early stage. The existing methods are mainly based on the utterance of vowels and have
a high misclassification rate. This article proposes an automatic deep learning model
that can be an effective tool to diagnose SLI at the early stage. In the proposed work,
raw audio data is processed using Short-time Fourier transform and converted to decibel
(dB) scaled spectrograms which are classified using the proposed convolutional neural
network (CNN). This approach consists of utterances that contained seven types of
vocabulary (vowels, consonant and different syllable Isolated words).
Cervical cancer is still one of the most prevalent cancers in women and a significant
cause of mortality. Cytokine gene variants and socio-demographic characteristics have
been reported as biomarkers for determining the cervical cancer risk in the Indian
population. This study was designed to apply a machine learning-based model using
these risk factors for better prognosis and prediction of cervical cancer. This study
includes the dataset of cytokine gene variants, clinical and socio-demographic
characteristics of normal healthy control subjects, and cervical cancer cases.
Different risk factors, including demographic details and cytokine gene variants,
were analysed using different machine learning approaches. Various statistical
parameters were used for evaluating the proposed method. After multi-step data
processing and random splitting of the dataset, machine learning methods were applied
\and evaluated with 5-fold cross-validation and also tested on the unseen data records
of a collected dataset for proper evaluation and analysis. The proposed approaches were
verified after analysing various performance metrics.