Environmental impact assessment of Flora and its role in offsetting carbon along National Highway-16, India.

The rapid increase in carbon and other greenhouse gases is likely to change the regional and global temperature, and planning of these gases is needed. The present study focused on National Highway-16 of the Srikakulam district, which was upgraded from 4 to 6 lane standards to facilitate traffic. This study intended to compute the carbon sequestration and storage capacity of above and belowground biomass.

Efficient deep reinforcement learning based task scheduler in multi cloud environment.

Task scheduling problem (TSP) is huge challenge in cloud computing paradigm as number of tasks comes to cloud application platform vary from time to time and all the tasks consists of variable length, runtime capacities. All these tasks may generated from various heterogeneous resources which comes onto cloud console directly effects the performance of cloud paradigm with increase in makespan, energy consumption, resource costs. Traditional task scheduling algorithms cannot handle these type of complex workloads in cloud paradigm.

Gd substitution induced incommensurate antiferromagnetism in B20 noncentrosymmetric CoSi.

Rare-earth element Gd doped CoSi, Co1-xGdSi (= 0.01), is investigated using x-ray diffraction and magnetization. It crystallizes in B20 cubic structure with lattice parameter of= 4.

Identification of potential biomarkers for pancreatic ductal adenocarcinoma: a bioinformatics analysis.

PDA is an aggressive cancer with a 5-year survival rate, which is very low. There is no effective prognosis or therapy for PDA because of the lack of target biomarkers. The objective of this article is to identify the target biomarkers for PDA using a bioinformatics approach.

A pilot study on AI-driven approaches for classification of mental health disorders.

The increasing prevalence of mental disorders among youth worldwide is one of society's most pressing issues. The proposed methodology introduces an artificial intelligence-based approach for comprehending and analyzing the prevalence of neurological disorders. This work draws upon the analysis of the Cities Health Initiative dataset.

Detection of COVID-19 using edge devices by a light-weight convolutional neural network from chest X-ray images.

Deep learning is a highly significant technology in clinical treatment and diagnostics nowadays. Convolutional Neural Network (CNN) is a new idea in deep learning that is being used in the area of computer vision. The COVID-19 detection is the subject of our medical study.

An effective correlation-based data modeling framework for automatic diabetes prediction using machine and deep learning techniques.

The rising risk of diabetes, particularly in emerging countries, highlights the importance of early detection. Manual prediction can be a challenging task, leading to the need for automatic approaches. The major challenge with biomedical datasets is data scarcity.

An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets.

The unprecedented rise in the number of COVID-19 cases has drawn global attention, as it has caused an adverse impact on the lives of people all over the world. As of December 31, 2021, more than 2, 86, 901, 222 people have been infected with COVID-19. The rise in the number of COVID-19 cases and deaths across the world has caused fear, anxiety and depression among individuals.

SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19.

Background And Objective: The global population has been heavily impacted by the COVID-19 pandemic of coronavirus. Infections are spreading quickly around the world, and new spikes (Delta, Delta Plus, and Omicron) are still being made. The real-time reverse transcription-polymerase chain reaction (RT-PCR) is the method most often used to find viral RNA in a nasopharyngeal swab.

Multistage classification of oral histopathological images using improved residual network.

Oral cancer is a prevalent disease happening in the head and neck region. Due to the high occurrence rate and serious consequences of oral cancer, an accurate diagnosis of malignant oral tumors is a major priority. Thus, early diagnosis is very effective to give the patient a prompt response to treatment.

Intelligent system for COVID-19 prognosis: a state-of-the-art survey.

This 21st century is notable for experiencing so many disturbances at economic, social, cultural, and political levels in the entire world. The outbreak of novel corona virus 2019 (COVID-19) has been treated as a Public Health crisis of global Concern by the World Health Organization (WHO). Various outbreak models for COVID-19 are being utilized by researchers throughout the world to get well-versed decisions and impose significant control measures.

Smartphone-Based Human Sitting Behaviors Recognition Using Inertial Sensor.

At present, people spend most of their time in passive rather than active mode. Sitting with computers for a long time may lead to unhealthy conditions like shoulder pain, numbness, headache, etc. To overcome this problem, human posture should be changed for particular intervals of time.

Modified pixel level snake using bottom hat transformation for evolution of retinal vasculature map.

Small changes in retinal blood vessels may produce different pathological disorders which may further cause blindness. Therefore, accurate extraction of vasculature map of retinal fundus image has become a challenging task for analysis of different pathologies. The present study offers an unsupervised method for extraction of vasculature map from retinal fundus images.

Multiplicative Holts Winter Model for Trend Analysis and Forecasting of COVID-19 Spread in India.

The surge of the novel COVID-19 caused a tremendous effect on the health and life of the people resulting in more than 4.4 million confirmed cases in 213 countries of the world as of May 14, 2020. In India, the number of cases is constantly increasing since the first case reported on January 30, 2020, resulting in a total of 81,997 cases including 2649 deaths as of May 14, 2020.

An Automated Lightweight Deep Neural Network for Diagnosis of COVID-19 from Chest X-ray Images.

Coronavirus (COVID-19) is an epidemic that is rapidly spreading and causing a severe healthcare crisis resulting in more than 40 million confirmed cases across the globe. There are many intensive studies on AI-based technique, which is time consuming and troublesome by considering heavyweight models in terms of more training parameters and memory cost, which leads to higher time complexity. To improve diagnosis, this paper is aimed to design and establish a unique lightweight deep learning-based approach to perform multi-class classification (normal, COVID-19, and pneumonia) and binary class classification (normal and COVID-19) on X-ray radiographs of chest.

Fusion of intelligent learning for COVID-19: A state-of-the-art review and analysis on real medical data.

The unprecedented surge of a novel coronavirus in the month of December 2019, named as COVID-19 by the World Health organization has caused a serious impact on the health and socioeconomic activities of the public all over the world. Since its origin, the number of infected and deceased cases has been growing exponentially in almost all the affected countries of the world. The rapid spread of the novel coronavirus across the world results in the scarcity of medical resources and overburdened hospitals.

An impact study of COVID-19 on six different industries: Automobile, energy and power, agriculture, education, travel and tourism and consumer electronics.

The recent outbreak of a novel coronavirus, named COVID-19 by the World Health Organization (WHO) has pushed the global economy and humanity into a disaster. In their attempt to control this pandemic, the governments of all the countries have imposed a nationwide lockdown. Although the lockdown may have assisted in limiting the spread of the disease, it has brutally affected the country, unsettling complete value-chains of most important industries.

Evolutionary Algorithm based Ensemble Extractive Summarization for Developing Smart Medical System.

The amount of information in the scientific literature of the bio-medical domain is growing exponentially, which makes it difficult in developing a smart medical system. Summarization techniques help for efficient searching and understanding of relevant information from the medical documents. In the paper, an evolutionary algorithm based ensemble extractive summarization technique is devised as a smart medical application with the idea of hybrid artificial intelligence on natural language processing.

Wavelet Transform and Deep Convolutional Neural Network-Based Smart Healthcare System for Gastrointestinal Disease Detection.

This work presents a smart healthcare system for the detection of various abnormalities present in the gastrointestinal (GI) region with the help of time-frequency analysis and convolutional neural network. In this regard, the KVASIR V2 dataset comprising of eight classes of GI-tract images such as Normal cecum, Normal pylorus, Normal Z-line, Esophagitis, Polyps, Ulcerative Colitis, Dyed and lifted polyp, and Dyed resection margins are used for training and validation. The initial phase of the work involves an image pre-processing step, followed by the extraction of approximate discrete wavelet transform coefficients.

Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges.

Since the past decades, most of the nature inspired optimization algorithms (NIOA) have been developed and become admired due to their effectiveness for resolving a variety of complex problems of dissimilar domain. Firefly algorithm (FA) is well-known, yet efficient nature inspired swarm intelligence (SI) based metaheuristic algorithm. Since from its initiation, FA has become well-liked between the researchers due to its competence and turn out to be an interesting technique for the practitioners as well as researchers for solving the problems of numerous fields of research such as classifications, clustering, neural networks, biomedical engineering, healthcare as well as other research domain.

Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review.

The World Health Organization (WHO) declared novel coronavirus 2019 (COVID-19), an infectious epidemic caused by SARS-CoV-2, as Pandemic in March 2020. It has affected more than 40 million people in 216 countries. Almost in all the affected countries, the number of infected and deceased patients has been enhancing at a distressing rate.

Detailed Review of Chronic Kidney Disease.

Background: Nephropathy problems in the Udhanam region of Andhra Pradesh in India have motivated researchers to investigate the various factors related to chronic kidney disease (CKD). Initially, studies came across the markers of identification of CKD, i.e.

Potentiometric studies of complex equlibria of Ca, Mg and Zn with 5-sulphosalicylic acid in cationic micelles of CTAB.

Formation of metal ligand complex species of 5-sulphosalicylic acid with Ca, Mg and Zn metal ions have been studied potentiometrically in various composition (0.0, 0.5, 1.

Assessment of the cardiac autonomic neuropathy among the known diabetics and age-matched controls using noninvasive cardiovascular reflex tests in a South-Indian population: A case-control study.

Aim: Diabetes mellitus is a chronic condition characterized by hyperglycemia. The objective of the study was to estimate the prevalence of cardiac autonomic neuropathy in a rural area of South India, among the known diabetics after comparing them with the age-matched healthy controls, utilizing noninvasive cardiac autonomic neuropathy reflex tests.

Materials And Methods: A case-control study was conducted for 4 months (October 2014 to January 2015) at an Urban Health and Training Center (UHTC) of a Medical College located in Kancheepuram district, Tamil Nadu.