University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

The essence of this paper is to design a mechanism for detecting emotional state of person using different bio-potential signals like electroencephalogram (EEG) signals of frontal lobe, pulse rate and SpO2. We record EEG signals of Fp1, Fp2, F3 and F4 electrodes, pulse rate and SpO2 of thirty subjects and extract twenty-two features from the recorded bio-potential signals. We design a k-nearest neighbor (KNN) classifier model for predicting the emotional state of a person. The designed KNN classifier model is trained with the extracted feature values of the thirty subjects. We again record the same bio-potential signals of ten new subjects and extract features. These extracted feature values are used for validating the performance of the trained KNN classifier model. The obtained overall efficiency of our designed emotion detection mechanism is 95.4%. © 2021, Springer Nature Singapore Pte Ltd.

World University root node

Radio Physics and Electronics

Lecture Notes in Networks and Systems

Detecting Different Emotional States of Human Brain Using Bio-potential Signals

The main challenges of 5G cellular network are capacity increase of the existing cellular system, providing seamless and ubiquitous communication with very less delay. In future, femtocells and mobile femtocells will be deployed with macrocell base stations. The handoff algorithms which are available cannot be implemented efficiently in femtocell/mobile femtocell/macrocell network scenario. The essence of this paper is to propose a new handoff algorithm for 5G cellular network and evaluate its performance in 5G network scenarios. The performance of the proposed handoff algorithm is compared with a conventional handoff algorithm in terms of number of handoff, number of disconnection and efficiency for 10,000, 20,000, 30,000 randomly generated instances of 5G network scenarios. © 2021, Springer Nature Singapore Pte Ltd.

A Novel Handoff Algorithm for 5G

One of the major causes of human death worldwide is cardiac arrhythmia. Atrial Fibrillation, Atrial Flutter and Ventricular Fibrillation are considered the most prevalent among the various arrhythmia types. Hence, computer-aided detection (CAD) system has been developed to help the clinicians to interpret the electrocardiogram signal reliably within short period of time. The proposed ranking based scheme and weight factor analysis for the construction of fused feature set improve the classification accuracy compared to the latest available methods in the literature. The diagnostic ability of the CAD system has been shown in seven performance measure parameter analysis. The best classification result has been obtained using Adaptive Neuro-Fuzzy Interface System (ANFIS) classifier with the fused feature set. The proposed system of using ANFIS classifier has achieved average classification accuracy of 99.88%, precision of 99.25% and recall of 99.98%. The proposed system using ANFIS algorithm has high recognition rate compared with that of Support Vector Machine and Random Forest classifiers. Our proposed model is significantly efficient to discriminate the normal electrocardiogram signal and three cardiac arrhythmia types. © 2021 Elsevier Ltd

Biomedical Signal Processing and Control

Automated detection of fibrillations and flutters based on fused feature set and ANFIS classifier

The essence of the paper is to develop a stress detection mechanism and a stress level indicator circuit for measuring the stress level of human brain using the Electro-encephalogram (EEG) Signal. Signals coming from the frontal lobe of human brain have been used for the measurement of stress. The brain signals of the thirty subjects are recorded while they are solving five mathematical question sets with increasing complexity. We assume that the subjects undergo through five different stress levels i.e. 'Relaxed', 'Less stressed', 'Moderately Stressed', 'High Stressed' and 'Alarmingly Stressed' while solving these question sets. After that recorded EEG data is processed and features are extracted. We design a feed forward neural network for classifying the stress level in human brain. We prepare a new question set consisting of easy as well as complex numerical questions for testing purpose. We record the EEG data of a subject while solving this question set. We extract six feature values from the processed EEG data of the subject. These data is fed to the designed feed forward neural network. The neural network predicts the stress level and the predicted stress level is indicated in the 'Stress Indicating' circuit. © 2019 IEEE.

2019 2nd International Conference on Advanced Computational and Communication Paradigms, ICACCP 2019

Detection of stress in human brain

In this paper, we propose an image classification technique which uses a simple autoencoder with a regularizer. Nowadays, Convolutional Neural Networks (CNN) are primarily used for image classification. Our method can be used for image classification with much reduced requirement of computational capability than a complex CNN which has a huge number of degrees of freedom. Here, the terms simple and complex, respectively, correspond to the simplicity and the complexity of a network in terms of the number of learnable parameters (degrees of freedom) and the number of hidden layers. This technique uses features extracted from a pretrained CNN, trained on a completely different dataset. Genetic algorithm solves for the optimal hyperparameters of the pretrained CNN. It is observed that these features serve as important and robust parameters for the training of the autoencoder, as a final average image classification accuracy improvement of about 17.45% is observed with the inclusion of these features. We use a pretrained CNN on MNIST dataset and classify images of several other benchmark datasets. We utilize different classifiers for image classification based on features extracted from the autoencoder and repeat each of the experiments a number of times with different random initialization of the classifier and the weight matrix of the autoencoder. We also perform experiments by pretraining the CNN with different datasets. Our results show a notable image classification accuracy and a significant reduction of training time with respect to a complex CNN. © 2019 IEEE.