Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies.

The evolution of the coronavirus (COVID-19) disease took a toll on the social, healthcare, economic, and psychological prosperity of human beings. In the past couple of months, many organizations, individuals, and governments have adopted Twitter to convey their sentiments on COVID-19, the lockdown, the pandemic, and hashtags. This paper aims to analyze the psychological reactions and discourse of Twitter users related to COVID-19.

Design of intelligent diabetes mellitus detection system using hybrid feature selection based XGBoost classifier.

In this work, a non-invasive diabetes mellitus detection system is proposed based on the wristband photoplethysmography (PPG) signal and basic physiological parameters (PhyP) to enable easy detection of diabetes mellitus (DM). A dataset of 217 participants with diabetes, prediabetes and normal conditions is used to develop the system. The Mel frequency cepstral coefficients (MFCC) extracted from 5s PPG signal segments and the PhyP are used as input for the machine learning algorithms.

Optimum multi-drug regime for compartment model of tumour: cell-cycle-specific dynamics in the presence of resistance.

This work is focused on multi-objective optimisation of a multi-drug chemotherapy schedule for cell-cycle-specific cancer treatment under the influence of drug resistance. The acquired drug resistance to chemotherapeutic agents is incorporated into the existing compartmental model of breast cancer. Furthermore, the toxic effect of drugs on healthy cells and overall drug concentration in the patient body are also constrained in the proposed model.

Modified fractional order IMC design based drug scheduling for cancer treatment.

This paper aims to improve chemotherapy drug(s) scheduling of the drug dose injected into a patient. The existing practice has been established on experiments performed during the drug development process. In general, chemotherapeutic drugs are highly toxic in nature and directly affect the probability of a patient's survival.

Prediction of Software Reliability using Bio Inspired Soft Computing Techniques.

A lot of models have been made for predicting software reliability. The reliability models are restricted to using particular types of methodologies and restricted number of parameters. There are a number of techniques and methodologies that may be used for reliability prediction.

Corrigendum: Influence of Landmarks on Wayfinding and Brain Connectivity in Immersive Virtual Reality Environment.

[This corrects the article on p. 1220 in vol. 8, PMID: 28775698.

Influence of Landmarks on Wayfinding and Brain Connectivity in Immersive Virtual Reality Environment.

Spatial navigation is influenced by landmarks, which are prominent visual features in the environment. Although previous research has focused on finding advantages of landmarks on wayfinding via experimentation; however, less attention has been given to identifying the key attributes of landmarks that facilitate wayfinding, including the study of neural correlates (involving electroencephalogram, EEG analyses). In this paper, we combine behavioral measures, virtual environment, and EEG signal-processing to provide a holistic investigation about the influence of landmarks on performance during navigation in a maze-like environment.

Brain connectivity during encoding and retrieval of spatial information: individual differences in navigation skills.

Emerging evidence suggests that the variations in the ability to navigate through any real or virtual environment are accompanied by distinct underlying cortical activations in multiple regions of the brain. These activations may appear due to the use of different frame of reference (FOR) for representing an environment. The present study investigated the brain dynamics in the good and bad navigators using Graph Theoretical analysis applied to low-density electroencephalography (EEG) data.

Two degree of freedom PID based inferential control of continuous bioreactor for ethanol production.

This article presents the development of inferential control scheme based on Adaptive linear neural network (ADALINE) soft sensor for the control of fermentation process. The ethanol concentration of bioreactor is estimated from temperature profile of the process using soft sensor. The prediction accuracy of ADALINE is enhanced by retraining it with immediate past measurements.

Performance Analysis of Fuzzy-PID Controller for Blood Glucose Regulation in Type-1 Diabetic Patients.

This paper presents Fuzzy-PID (FPID) control scheme for a blood glucose control of type 1 diabetic subjects. A new metaheuristic Cuckoo Search Algorithm (CSA) is utilized to optimize the gains of FPID controller. CSA provides fast convergence and is capable of handling global optimization of continuous nonlinear systems.

Performance Evaluation and Implementation of FPGA Based SGSF in Smart Diagnostic Applications.

The main objective of the paper is to implement Savitzky Golay Smoothing Filter (SGSF) so as to apply in pre-processing of real time smart medical diagnostic systems. As very important information of EEG and ECG waveforms lies in the peak of the signal, hence it becomes absolutely necessary to filter noise and artifacts from the signal. The implemented filter should be able to reject the noise efficiently along with the least distortion from the original signal.

Development of soft sensor for neural network based control of distillation column.

The present work is aimed at the design of Levenberg-Marquardt (LM) and adaptive linear network (ADALINE) based soft sensors and their application in inferential control of a multicomponent distillation process. Further the ADALINE sensor is trained online using past measurements, to adapt the changes in the inputs and is termed as dynamic ADALINE (D-ADALINE) sensor. The soft sensors are then used in the control loop to obtain LM based inferential controller (LMIC), ADALINE based inferential controller (ADIC) and D-ADALINE based inferential controller (DADIC) for the process.