Keywords : SVM

EEG based Emotion Recognition and Classification: a Review

Ramprasad Kumawat; Manish Jain

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue ICITCA-2021 5S, Pages 1-10
DOI: 10.47392/irjash.2021.131

Emotion plays a vital role in medical research and interpersonal communication. Essentially feeling can be communicated verbally like discourse or non-verbally like outward appearance and physiological signals. A human emotion is complex physiological state which involves a physiological response, a person’s experience and behavioral change. EEG measures electric current that are generated due to neuronal activities in the human brain. This paper provides an overview of comparative study of various techniques of emotion recognition from EEG signals. Our analysis is based on extracted features and classification methods of emotion recognition. We intended that, this study will be useful for newly researchers those entering in the field of emotion recognition.

An Automatic Segmentation of Lung Structure Using Active Contour Model and Fuzzy Clustering Algorithm

Jalal deen K; Ramesh Kumar R; Vadivel M; Annal Sheeba Rani E

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue ICARD-2021 3S, Pages 82-85
DOI: 10.47392/irjash.2021.070

The aim of this paper was to develop an active contour model based on a region and a Fuzzy C-Means (FCM) technique for lung nodule segmentation. In the end, the mortality rate is increased by detection and assisted diagnosis of nodules at an earlier stage. Computed tomography (CT) is the most sought after among many imaging modalities because of its image sensitivity, high resolution and isotropic acquisition. The suggested technique focuses on CT image acquisition, lung parenchyma reconstruction and segmentation of lung nodules. Using selective binary and Gaussian filtering with a new signed pressure force function (SBGF-new SPF) and clustering methods for nodule segmentation, parenchyma reconstruction can be used. The benefits of the proposed approach in terms of reduced error rate and improved measure of similarity are demonstrated by comparative experiments.

Detection of Brain Tumor Using Unsupervised Enhanced K-Means, PCA and Supervised SVM Machine Learning Algorithms

Hari Prasada Raju Kunadharaju; Sandhya N.; Raghav Mehra

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue Special Issue ICSTM 12S, Pages 62-67
DOI: 10.47392/irjash.2020.262

The brain tumor is an abnormal cell growth in the human body. To know which type of brain tumor it is and where is the exact location of it.  We are using the MR image is a  tomographic imaging technique. MRI is based on Nuclear Magnetic Resonance signals. A brain tumor is of two types 1.  Benignant 2.  malignant. Benignant belongs to I and II grade; this type of tumor is not active cells and have a low-grade tumor.  It has a uniform structure.  Malignant belongs to III and IV grades, this type of tumor are active cells and have a high grade.  It has a non-uniformity structure. The initial phase Input MR image is transformed into a  binary image by the  Otsu threshold technique.  The second step k-means segmentation process is used on binary images.  Third step Discrete Wavelet Transform is used on segmented image for extracting the image and it reduces the large dimensionality by using PCA. It identifies the tumor by using Support Vector Machine classification it gives the final output of a brain tumor that normal or abnormal. The proposed paper experimented on the detection of brain tumors using classification algorithms dataset about BraTS dataset and compared with existing methodologies, and it is then proved that superior to existed.

Digital Assistant for Ventilators Using SVM Algorithm and Speech Recognition

Vishnupriya S

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue 11, Pages 41-49
DOI: 10.47392/irjash.2020.219

Many health care assists had been developed to help the clinicians in treating the patient. A single monitor for managing an instrument seems very expensive. The transferring of data from the single setup also requires high communication costs. The personal assistant developed has many drawbacks due to the changes in prosodic cues according to the people’s language slang and the trouble in analysing the paralinguistic information. The network data and energy consumption required for the transfer of information from the health care devices becomes quite large. The project, involves an easy transmission module and assisting method to avoid these issues. This project is involved in assisting a practitioner, physician or respiratory therapists in proper handling of a ventilator, in accordance with patient’s health state and parameter. On providing ventilation, it is important to notice the ventilator readings such as i-PEEP, Ppeak, Pplat (developed values in the patient’s respiratory system) which are the response of the patient etc.,. On observation of these readings, the parameters such as e-PEEP, VT, RR and FiO2 (values to be set by the clinician) have to be adjusted for better ventilation and for the purpose of weaning of patient in a short period. The preliminary work involved is the data acquisition and logging. The SVM algorithm has been developed with many data points as the parameters obtained from data. The protruding idea is to analyse the patient’s age, gender, weight, disorder, type of surgery and its duration. Thus, the value of the parameter that has to be adjusted can be determined intricately with the protruding idea of digital assist.