Artificial Neural Network Assisted Weather Based Plant Disease Forecasting System

An interactive plant disease forecasting system was developed using Artificial Neural Network model with multilayer perceptron architecture having two hidden layers. When data from the same site are used for both training and testing, the prediction accuracy of the model was found to be between 81-87% for rice blast disease. Being a multivariate non-linear non-parametric data driven self adaptive statistical method, it shows significantly higher accuracy then the conventional regression based models. DOI: 10.17762/ijritcc2321-8169.150613

Application and Scope of Data Mining in Agriculture

Making agriculture sustainable and resilient to the ongoing change in climate and social structure is a major challenge for the scientists and researchers across the globe. Agricultural system demands transition and a multidisciplinary approach. Intelligent and precision agricultural approaches were given due importance for increasing production and productivity from the very same limited resources. The approach needs information from various sources and efficient use of them in relevant field. This need lead to growing interest in knowledge discovery from vast piles of data generated out of various research and survey works. The emergence of Data Mining techniques revolutionized the field of information generation and pattern recognition. Though Data Mining is an emerging science, it finds a wide application in agriculture and allied sectors, and has a wide future prospect

The value of the Duke Activity Status Index (DASI) in predicting ischaemia in myocardial perfusion scintigraphy — a prospective study

BACKGROUND: Functional capacity assessment may be a useful tool to stratify patients according to risk of coronary artery disease (CAD). The Duke Activity Status Index (DASI) is a functional assessment based on activities of daily living and cardiovascular fitness, assessed using a self-administered questionnaire. MATERIAL AND METHODS: We assessed the relationship between established clinical risk factors for CAD and the DASI with results of myocardial perfusion scintigraphy (MPS). The MPS results used in the analysis were the presence of reversible ischaemia and the resting left ventricular ejection fraction (LVEF). A DASI self-administered questionnaire was completed by 117 consecutive participants, and a patient history was taken to ascertain established risk factors. All participants underwent a stress test, and myocardial perfusion scintigraphy was performed. Statistical analysis consisted of logistic and linear regression using a statistical software package. RESULTS: The DASI was the only factor that correlated significantly with reversible ischaemia on MPS. None of the previously established risk factors had a significant association with reversible ischaemia within the model. Our study found a potential relationship between the DASI score and the left ventricular ejection fraction (LVEF) although this was not statistically significant. CONCLUSIONS: Our study findings suggest that the DASI may represent a powerful tool for risk stratification prior to investigation of CAD. A further study with a larger sample size will be required to investigate the predictive value of the DASI and the association with LVEF. Nuclear Med Rev 2010; 13, 2: 59–6

Scaling-up of toria (Brassica campestris) productivity using diverse agro-techniques in eastern Himalayan region

Field experimentation on toria (Brassica campestris L.) was carried out with the major objective of utilizing the fallow land after rainy season by following suitable management practices in the region. Results revealed that under conventional tillage, roots were 39.1% longer and 36.8% heavier biomass, contrarily no tillage had 6% more soil organic carbon. The seed yield improved by 44.8% with minimum tillage over no tillage. Crop sown on 15 October obtained 16.9-47.6% additional seed yield over before and after sown crops, but line sowing evidenced 22.1% higher seed yield than the broadcasting. Planting geometry with 30x15 cm noticed 3.1-32.9% more seed yield. Nitrogen application at 75 kg/ha had 5.3-47% improvement of seed yield, whereas nitrogen use efficiency was highest with 50 kg/ha. Phosphorus application at 50 kg/ha added 61.5% more yield, whereas phosphorus-use efficiency was highest at 25 kg/ha. Twice irrigation at 30 and 60 days after sowing (DAS) noticed 40.5% extra seed yield, contrary water-use efficiency was highest with single irrigation at 30 DAS over no irrigation. Hand weeding twice at 25 and 50 DAS supplemented the seed yield by 52.6% with 55.7% weed control efficiency over no weeding. Adoption of better package of practices in newer area under existing cropping system will play a key role in future yield improvement. Therefore, as per the resource availability feasible technologies may harness higher seed yield of toria in eastern Himalayas

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate

Cloning and heterologous expression of a gene encoding lycopene-epsilon-cyclase, a precursor of lutein in tea (Camellia sinensis var assamica)

This report describes the cloning and expression of a gene lycopene epsilon cyclase, (LCYE) from Camellia sinensis var assamica which is a precursor of the carotenoid lutein in tea. The 1982 bp cDNA sequence with 1599 bp open reading frame of LCYE was identified from an SSH library constructed for quality trait in tea. 5’ and 3’ RACE (rapid-amplification of cDNA ends) was done to clone the full length cDNA of LCYE. Homology studies showed that the deduced amino acid sequence of LCYE gene had the highest sequence identity of up to 84% with Vitis vinefera. The cloned gene was successfully expressed in a PET based Escherichia coli expression system. The size of the expressed protein was 59615 Daltons. A suppression subtractive library was constructed using a quality clone H3111 (tester) and a garden series clone T3E3 (driver).Key words: Carotenoid, RACE, heterologous expression, lutein, tea

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals. © 2022 by the authors

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate