Indian Sugar Industry: Towards Self-reliance for Sustainability.

The South-Asian region including India is a major hub of sugar producing countries with ample presence in the global sugar scenario. India has a rich history of sugarcane and sugar production since time immemorial, and the industry has gradually evolved to find a place among the top sugar producing countries of the world. The innovative technological interventions for sugarcane improvement, production and management have helped the industry to progress towards a diversified and bio-based productive, sustainable and profitable one, thereby gradually becoming self-reliant. This self-reliant industry with the right mix of linkages and collaborations, has been successful in tackling the various unforeseen challenges including those that cropped up during COVID-19 pandemic. The industry also fulfils its Corporate Social Responsibilities leading to the overall betterment of its stakeholders. This has enabled the Indian sugar industry to align itself with the 2030 Agenda for Sustainable Development Goals.

AI Enabled Chat Bot for COVID'19

The coronavirus outbreak has far-reaching ramifications for civilizations all around the world. People are worried and have a lot of requests. A research department from Covid19 Awareness was our recommendation. We supplemented it with AI-based chatbot models to aid hospitals, patients, medical facilities, and congested areas such as airports. We propose to develop this chatbot to support current scenarios and enable hospitals or governments to achieve more to solve the objective, given the two primary factors that inexpensive and fast production is now necessary. It is an immediate necessity in this epidemic circumstance. We built this bot from the ground up to be open source, so that anybody or any institution can use it to fight Corona, and commercialization is strictly prohibited. This bot isn't for sale;instead, we'd like to devote it to the country to help with current pandemic situations. The design of advanced artificial intelligence is presented in this paper (AI). If patients are exposed to COVID-19, the chatbot assesses the severity of the illness and consults with registered clinicians if the symptoms are severe, evaluating the diagnosis and recommending prompt action. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Bioacoustic signal analysis through complex network features.

The paper proposes a graph-theoretical approach to auscultation, bringing out the potential of graph features in classifying the bioacoustics signals. The complex network analysis of the bioacoustics signals - vesicular (VE) and bronchial (BR) breath sound - of 48 healthy persons are carried out for understanding the airflow dynamics during respiration. The VE and BR are classified by the machine learning techniques extracting the graph features - the number of edges (E), graph density (D), transitivity (T), degree centrality (Dcg) and eigenvector centrality (Ecg). The higher value of E, D, and T in BR indicates the temporally correlated airflow through the wider tracheobronchial tract resulting in sustained high-intense low-frequencies. The frequency spread and high-frequencies in VE, arising due to the less correlated airflow through the narrow segmental bronchi and lobar, appears as a lower value for E, D, and T. The lower values of Dcg and Ecg justify the inferences from the spectral and other graph parameters. The study proposes a methodology in remote auscultation that can be employed in the current scenario of COVID-19.

A Statistical Review on Covid-19 Pandemic and Outbreak

Throughout the history, various pandemics invaded the human race deducing their existence by half or more. A brief study on the past pandemics holds the above statement true. A similar situation is being experienced in the present day, a war against an invisible enemy;the novel COVID-19 coronavirus. A statistical study of the current case helps to analyze the impact of the pandemic and take required measures and is preserved for future use in case of a similar situation. The world data of covid-19 is compared and studied graphically from the very start of the pandemic till date. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Complex network-based pertussis and croup cough analysis: A machine learning approach

The paper proposes a novel approach to bring out the potential of complex networks based on graph theory to unwrap the hidden characteristics of cough signals, croup (BC), and pertussis (PS). The spectral and complex network analyses of 48 cough sounds are utilized for understanding the airflow through the infected respiratory tract. Among the different phases of the cough sound time-domain signals of BC and PS – expulsive (X), intermediate (I), and voiced (V) - the phase ‘I’ is noisy in BC due to improper glottal functioning. The spectral analyses reveal high-frequency components in both cough signals with an additional high-intense low-frequency spread in BC. The complex network features created by the correlation mapping approach, like number of edges (E), graph density (G), transitivity (Tr), degree centrality (D), average path length (L), and number of components (Nc) distinguishes BC and PS. The higher values of E, G, and Tr for BC indicate its musical nature through the strong correlation between the signal segments and the presence of high-intense low-frequency components in BC, unlike that in PS. The values of D, L, and Nc discriminate BC and PS in terms of the strength of the correlation between the nodes within them. The linear discriminant analysis (LDA) and quadratic support vector machine (QSVM) classifies BC and PS, with greater accuracy of 94.11% for LDA. The proposed work opens up the potentiality of employing complex networks for cough sound analysis, which is vital in the current scenario of COVID-19.

Graph-based feature extraction and classification of wet and dry cough signals: a machine learning approach

This article proposes a unique approach to bring out the potential of graph-based features to reveal the hidden signatures of wet (WE) and dry (DE) cough signals, which are the suggestive symptoms of various respiratory ailments like COVID 19. The spectral and complex network analyses of 115 cough signals are employed for perceiving the airflow dynamics through the infected respiratory tract while coughing. The different phases of WE and DE are observed from their time-domain signals, indicating the operation of the glottis. The wavelet analysis of WE shows a frequency spread due to the turbulence in the respiratory tract. The complex network features namely degree centrality, eigenvector centrality, transitivity, graph density and graph entropy not only distinguish WE and DE but also reveal the associated airflow dynamics. A better distinguishability between WE and DE is obtained through the supervised machine learning techniques (MLTs)—quadratic support vector machine and neural net pattern recognition (NN), when compared to the unsupervised MLT, principal component analysis. The 93.90% classification accuracy with a precision of 97.00% suggests NN as a better classifier using complex network features. The study opens up the possibility of complex network analysis in remote auscultation.

Neural net pattern recognition based auscultation of croup cough and pertussis using phase portrait features

Cough signal analysis for understanding the pathological condition has become important from the outset of the exigency posed by the epidemic COVID-19. The present work suggests a surrogate approach for the classification of cough signals - croup cough (CC) and pertussis (PT) – based on spectral, fractal, and nonlinear time-series techniques. The spectral analysis of CC reveals the presence of more frequency components in the short duration cough sound compared to PT. The musical nature of CC is unveiled not only through the spectral analysis but also through the phase portrait features – sample entropy (S), maximal Lyapunov exponent (L), and Hurst exponent (Hb). The modifications in the internal morphology of the respiratory tract, giving rise to more frequency components associated with the complex airflow dynamics, get staged through the higher fractal dimension of CC. Among the two supervised classification tools, cubic KNN (CKNN) and neural net pattern recognition (NNPR), used for classifying the CC and PT signals based on nonlinear time series parameters, NNPR is found better. Thus, the study opens the possibility of identification of pulmonary pathological conditions through cough sound signal analysis. © 2021

Is SARS CoV-2 a Multifractal?-Unveiling the Fractality and Fractal Structure

A first report of unveiling the fractality and fractal nature of severe acute respiratory syndrome coronavirus (SARS CoV-2) responsible for the pandemic disease widely known as coronavirus disease 2019 (COVID 19) is presented. The fractal analysis of the electron microscopic and atomic force microscopic images of 40 coronaviruses (CoV), by the normal and differential box-counting method, reveals its fractal structure. The generalised dimension indicates the multifractal nature of the CoV. The higher value of fractal dimension and lower value of Hurst exponent (H) suggest higher complexity and greater roughness. The statistical analysis of generalised dimension and H is understood through the notched box plot. The study on CoV clusters also confirms its fractal nature. The scale-invariant value of the box-counting fractal dimension of CoV yields a value of 1.820. The study opens the possibility of exploring the potential of fractal analysis in the medical diagnosis of SARS CoV-2.

Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation.

The development of novel digital auscultation techniques has become highly significant in the context of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series, fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried out with 37 breath sound signals. The spectral analysis brings out the signatures of VB and BB through the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal component analysis helps in classifying VB and BB sound signals through the feature extraction from the power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through lung auscultation.

Time series and fractal analyses of wheezing: a novel approach.

Since the outbreak of the pandemic Coronavirus Disease 2019, the world is in search of novel non-invasive methods for safer and early detection of lung diseases. The pulmonary pathological symptoms reflected through the lung sound opens a possibility of detection through auscultation and of employing spectral, fractal, nonlinear time series and principal component analyses. Thirty-five signals of vesicular and expiratory wheezing breath sound, subjected to spectral analyses shows a clear distinction in terms of time duration, intensity, and the number of frequency components. An investigation of the dynamics of air molecules during respiration using phase portrait, Lyapunov exponent, sample entropy, fractal dimension, and Hurst exponent helps in understanding the degree of complexity arising due to the presence of mucus secretions and constrictions in the respiratory airways. The feature extraction of the power spectral density data and the application of principal component analysis helps in distinguishing vesicular and expiratory wheezing and thereby, giving a ray of hope in accomplishing an early detection of pulmonary diseases through sound signal analysis.

Impact of COVID-19 on Indian Sugar Industry.

The Indian sugar industry, a significant player in the national economy, has faced many challenges in the course of its journey. The threat posed by the growing pandemic novel corona virus (COVID-19), has been the most recent one and it is impacting sugar industry stakeholders and its integrated industries, not only in India, but all over the world. The entire value chain of the Indian sugar industry, viz., sugarcane, sugar, molasses, ethanol and their subsequent marketing and export, has been adversely affected from the spillover impacts. The major impacts of COVID-19 on Indian sugar industry are discussed.