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Sentiment Analysis On Covid-19 Related Social Distancing Across The Globe Using Twitter Data
Covid 19 pandemic has devastated the lives of several people across the globe. Social distancing is considered a major preventive measure to stop the spread of Covid 19. The practice of social distancing has caused a sense of loneliness and mental health problems in society. The aim of this study is to consider global tweet data with social distancing keywords for analyzing the sentiments behind them. Classification of tweets as positive or negative is carried out using Support Vector Machine and Logistic Regression. The Electrochemical Society -
Sentiment Analysis of Stress Among the Students Amidst the Covid Pandemic Using Global Tweets
Covid-19 pandemic has affected the lives of people across the globe. People belonging to all the sectors of the society have faced a lot of challenges. Strict measures like lockdown and social distancing have been imposed several times by governments throughout the world. Universities had to incorporate the online method of teaching instead of the regular offline classes to implement social distancing. Online classes were beneficial to most of the students; at the same time, there were many difficulties faced by the students due to lack of facilities to attend classes online. Students faced a lot of challenges, and a sense of anxiety was prevalent during the uncertain times of the pandemic. This research article analyzes the stress among students considering the tweets across the globe related to students stress. The algorithms considered for classification of tweets as positive or negative are support vector machine (SVM), bidirectional encoder representation from transformers (BERT), and long short-term memory (LSTM). The accuracy of the abovementioned algorithms is compared. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Novel Deep Neural Network Based Stress Detection System
Stress is a state of tension on an emotional or bodily level. Frustration, despair, anxiety, and other mental health problems can all be brought on by Stress. Strain is a side effect of Stress. People can openly share their views and opinions on social media networking sites like Twitter and Facebook, which are highly popular. The COVID 19 pandemic has wreaked havoc on millions of peoples lives all across the world. The public has experienced Stress as a result of the various measures employed to stop the spread of COVID 19, including confinement and social isolation. The current research seeks to develop an unique COVID 19 scenario-based deep neural network-based Stress detection system using tweets related to COVID 19. We use deep learning to create three models. RNN with single LSTM layer, two layers of LSTM with RNN followed by bidirectional LSTM layer is built to detect Stress for the considered dataset. A number of recurrent neural networks are built upon the Keras layers. The optimization algorithm called RMSProp and Sigmoid activation function is used. It is observed that RNN with 2 layers of LSTM outperforms the other deep learning architectures constructed. 2023 American Institute of Physics Inc.. All rights reserved. -
A Novel Auto Encoder- Network- Based Ensemble Technique for Sentiment Analysis Using Tweets on COVID- 19 Data
The advances in digitalization have resulted in social media sites like Twitter and Facebook becoming very popular. People are able to express their opinions on any subject matter freely across the social media networking sites. Sentiment analysis, also termed emotion artificial intelligence or opinion mining, can be considered a technique for analyzing the mood of the general public on any subject matter. Twitter sentiment analysis can be carried out by considering tweets on any subject matter. The objective of this research is to implement a novel algorithm to classify the tweets as positive or negative, based on machine learning, deep learning, the nature inspired algorithm and artificial neural networks. The proposed novel algorithm is an ensemble of the decision tree algorithm, gradient boosting, Logistic Regression and a genetic algorithm based on the auto-encoder technique. The dataset under consideration is tweets on COVID-19 in May 2021. 2024 Taylor & Francis Group, LLC. -
Design of a square-shaped broadband antenna with ground slots for bandwidth improvement
This paper portrays the design of a compact square-shaped microstrip broadband antenna using ground slots. Polygon shaped slots are placed on the ground under the feed line for bandwidth improvement. Similarly, rectangular slots are placed on the square patch for gain enhancement. Effect of these slots on the performance of the antenna in terms of impedance bandwidth, gain and directivity are studied. Results of simulation tests show that a ground slot with proper dimensions placed under the feed line can improve the impedance matching and hence increase the bandwidth without affecting much the performance of the antenna. This compact antenna of size 9.098 x 9.098 mm can be very useful for applications where size is a major constraint. Simple microstrip feed is used to feed the patch. The percentage bandwidth of this antenna is 75.57 %. 2018 Authors. -
Improving Flood Prediction Using Artificial Neural Networks With Optimal Feature Selection on a Benchmark Dataset
Disasters significantly impact people's lives; among them, flooding is the worst common, and it causes sudden and secure damage to both lives and property. Addressing such real-time crisis demands intricate and sophisticated flood prediction models with enhanced capabilities. The development of efficient flood prediction models is often hindered by the lack of available datasets and the need for optimal feature. To address the challenge of data availability, in the proposed research, we have manually prepared a novel dataset by collecting data from NASA's (National Aeronautics and Space Administration) Power Project. The proposed dataset is experimentally evaluated and verified and has been organized into a balanced benchmark dataset with 33 features using the SMOTE algorithm. To enhance the provenance of flood prediction model, we propose a novel feature selection method. This method integrates outcomes from three different feature selection techniques to identify the most prominent features. The proposed feature selection method improves the model's performance and efficiency by identifying optimal predictors. Experimental results demonstrate that the artificial neural network trained with the selected relevant features accurately predicts flood occurrences, showing enhanced accuracy compared to state-of-the-art methods. 2026 John Wiley & Sons Ltd. -
Divergent Synthesis of Azole Tailored Compounds and Their Biological and Photoluminescence Applications
Producing a library of diverse compounds with minor structural differences can provide newlinevaluable information related to the structure-activity relationship (SAR), which would not be possible by studying just one molecule. The main goal of the divergent synthesis approach is to efficiently create a collection of valuable compounds, which is different from the traditional methods of making compounds in a linear or convergent way. This approach, known as divergent synthesis, helps select the best compound from the group for its applications. In the newlinecurrent study, the focus is on synthesizing different types of azoles, such as Thiazole Schiff bases, fused tetrazoles, substituted imidazole, and 1H-tetrazoles, and exploring their potential uses in biological and photoluminescence studies. Several methods were utilized to synthesize the derivatives of azole compounds. The synthesized molecules were examined and identified using techniques like 1HNMR, 13CNMR, Mass spectrometry, and IR spectroscopy. After creating a library of molecules, they were evaluated for their potential applications in biology and photoluminescence. The most promising molecule was selected from the preliminary evaluation for further investigation. newlineThiazole Schiff bases were synthesized, and their photoluminescence properties were newlineinvestigated. Among the synthesized compounds, the bromo derivative showed the most promising results in developing fluorescent organic nanoparticles with versatile applications. The compound delivered exceptional results in aggregation-induced emission (AIE), viscochromism, detection of Al3+ions, pH sensing, latent fingerprint detection, and cell imaging. Synthesis of fused azole-derivatives was accomplished using the organo-catalyst 10- newlinecamphor sulfonic acid. Detailed optimization and mechanistic studies were conducted, along newlinewith evaluating the antifungal activity against Candida tropicalis ATCC 10231 for the newlinesynthesized compounds. -
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Organizational Sustainability:A Study of Corporate Organizations in the Indian Context
Creating and Sustaining an Organization is an all time challenge. The primary research question is mainly of an explorative nature, seeking to comprehend how the Indian companies view and act upon sustainability. The study focused on the Corporate Organization, meaning Multi National Corporations, Public Sector Undertakings and other Private Organizations. The findings of the study facilitate recommendations to the various organizations to improve the managerial practice and guide them to the ways of sustainability. The aim of the study is to examine the different stages of development of various organizations that best describes the organization and strategy of the organization in sustaining the organization. This study is guided to analyze and understand the capacity of the organizations to respond to changing environments (Sustainability). The scope of sustainability are, the Environment and the Social dimension, Institutional / organizational dimension, Profit making / Economic dimension. Sustainability is a contestable concept that can be examined from the dimensions mentioned above. Organizational Sustainability is often guided by vision, mission, policy, planning, financial situation , human resource management, marketing activities, business ethics, organizational culture, organizational climate, business practices, employee treatment, community engagement ( social responsibility practices) etc. The design of the study is based on the Management and Organizational Sustainability Tool (MOST). The first objective of the study is, to investigate if there is a relationship between the vision and mission with strategy, structure and systems in the organizations. newlineIndia, a land of rich culture and heritage, has to an extent made it possible for its firms to have a culture passed to the employees and have them engaged in the organizational sustainability practices, and being socially responsible. The culture of an organization is intertwined with the philosophy, purposes, functions and structures. -
Impact of Lysinibacillus macroides, a potential plant growth promoting rhizobacteria on growth, yield and nutritional value of tomato plant (Solanum lycopersicum L. F1 hybrid Sachriya)
Plant growth promoting bacteria enhance the growth in plants by solubilizing insoluble minerals, producing phytohormones and by secreting enzymes that resist pathogen attack. The present study was aimed at identifying the potential of Lysinibacillus macroides isolated from pea plant possessing rich microbial rhizobiome diversity in promoting the growth of tomato plant (Solanum lycopersicum L.). Potential of L. macroides in the promotion of S. lycopersicum L. growth by increased shoot length, terminal leaf length and breadth was assessed. Anatomical sectioning of stem and root revealed no varied cellular pattern indicating that the supplemented bioculture is not toxic to S. lycopersicum. Plantlets treated with L. macroides along with organic compost showed an increased total phenol content (17.580.4 mg/gm) compared to control samples (12.440.41 mg/g). Carbohydrate content was noticed to be around 1.3 folds higher in the L. macroides plus compost mixture supplemented slots compared to control sample. Significant increase in shoot length was evident in the L. macroides plus compost supplied slots (23.42.7 cm). Plant growth promoting properties might be due to the nitrogen fixing activity of the bacteria which enrich the soil composition along with the nutrients supplied by the organic compost. Rich microbial rhizobiome diversity in pea plant and the usage of L. macroides from a non-conventional source improves the diversity of the available PGPR for agricultural practices. Further research is needed to detect the mechanism of growth promotion and to explore the plant microbe interaction pathway. Jyolsna et al. (2021). -
Pollution forecast of united states using holt-winter exponential method
The United States is the world's most developed country and one of the top ten most air polluted countries in the world. Though the population is not very dense as in India or China, people face immense health problems. The US government is taking a lot of initiatives than any other government globally. However, it still faces issues. This paper mainly focuses on developing a forecasting model of the top four pollutants like SO2, NO2, CO, O3 that will help the country take necessary actions for the near future. This paper involves the secondary data of the daily pollution collected and merged for all states from 2007 to 2017. The forecast will throw the better output at the pollutants for the next four years, until 2021. The findings revealed that despite the increased GDP, the country had controlled the pollution level. NO2 has decreased to a better level. O3 and CO2 are also decreasing but has slight fluctuations. It will take some time to stabilize. SO2 had an increased level till 2017 and has started reducing afterwards. 2021 Ecological Society of India. All rights reserved. -
ANN Based MPPT Using Boost Converter for Solar Water Pumping Using DC Motor
The solar DC pump system is simple to set up and run completely on its own without the need for human intervention. Solar DC pumps require fewer solar panels to operate than AC pumps. Solar PV Arrays, a solar DC regulator, and a DC pump make up the Solar DC Pump system. The nonlinear I-V characteristics of solar cells, PV modules have average efficiency compare to other forms of energy, and output power is affected by solar isolation and ambient temperature. The prominent factor to remember is that there will be a significant power loss owing to a failure to correspond between the source and the load. In order to get the most power to load from the PV panel, MPPT is implemented in the converter circuit using PWM and a microcontroller. In order to give the most power to load from the source, the solar power system should be designed to its full potential. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Synthesis, Characterization and Studies of Hydrazine Based Polyfunctional Ligands and their Metal Chelates
Eight new hydrazine-based zinc (II), copper (II) complexes were synthesized by reacting Zn (OAc)2.2H2O and Cu(CH3COO)2 with N'??(3,5-dibromo-2-hydroxy benzylidene) benzohydrazide (H2L1) and N'??(3,5-dibromo-2-hydroxy benzylidene) nicotinichydrazide (H2L2) respectively. The synthesized complexes were characterized by CHN analyses, IR, UV and 1H NMR. Based on these studies, square planar and octahedral geometries of the metal complexes were revealed. The synthesized metal complexes named [Zn(H2L1)2](OAc)2, [Zn(H2L1)Py](OAc)2, [ZnL2]2, [ZnL2Py], [CuL1]2, [CuL1Py], [CuL2]2 and [CuL2Py]. The formed metal complexes were investigated for DNA binding studies by fluorescence and UV spectroscopy using calf thymus DNA (CT-DNA) and DNA cleavage studies against pBR322 DNA. Both the ligands and their corresponding metal complexes showed the ability for binding to DNA through intercalation/ electrostatic binding. -
Inter-state Disparities in Health Care Facilities During COVID-19: A Study of Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB PM-JAY) in India
Universal Health Coverage (UHC) is one of the primary agendas of the World Health Organization (WHO) for achieving the goal of sustainable development. Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB PM-JAY) is an important landmark in Indias road towards achieving this objective of universal health. The scheme aims to provide access to quality healthcare services through its empanelled public and private hospitals at the secondary and tertiary care levels. The current study attempts to understand the interstate disparities amongst the states using the scheme during COVID-19, and also analyses the interrelationship between the status of COVID-19 and PM-JAY-empanelled health infrastructure. The study has used secondary data from the COVID-19 India dashboard and the PM-JAY website for the analysis. The study found that, despite having an overall positive correlation between the number of COVID cases and the number of beneficiaries treated, there still exist wide disparities among the states in availing treatment. It was also evident that there is no significant relationship between the status of COVID-19 and the empanelled health infrastructure under PM-JAY. The states with a high number of health infrastructure, with high death and fewer recovery cases, had the worst COVID-19 situation. In order to have better utilisation of the scheme, the government can take necessary measures, such as broader coverage of the scheme, sufficient budgetary allocation to the states, and investment in additional private health infrastructure. 2026 Indian Institute of Health Management Research -
Wheat Yield Prediction using Temporal Fusion Transformers
In precision framing, Machine Learning models are an essential decision-making tool for crop yield prediction. They aid farmers with decisions like which crop to grow and when to grow certain crops during the sowing season. Many Machine Learning algorithms have been used to support agriculture yield prediction research, but it is observed that Deep Learning models outperform the benchmark Machine Learning algorithms with a significant difference in accuracy. However, though these Deep Learning models perform better, they are not preferred or widely used in place of Machine Learning models. This is because Deep Learning methods are black box methods and are not interpretable, i.e., they fail to explain the magnitude of the impact of the features on the output, and this is unsuitable for our use case.In this paper, we propose using Temporal Fusion Transformer (TFT), a novel approach published by Google researchers for wheat yield prediction viewed as a Time Series Forecasting problem statement. TFT is the state-of-the-art attention-based Deep Learning architecture, which combines high-performance forecasting with interpretable insights and feature importance. We have used TFT to perform wheat yield prediction and compare its performance with various Machine Learning and Deep Learning algorithms. 2023 IEEE. -
Spoken Language Identification using Deep Learning
A crucial problem in natural language processing is language identification, which has applications in speech recognition, translation services, and multilingual content. The five main Indian languages that are the subject of this study are Hindi, Bengali, Tamil, English, and Gujarati. A Deep Neural Network is introduced in the paper which is specifically made to use Mel-Frequency Cepstral Coefficients (MFCCs) for sophisticated language categorization. The suggested architecture of the model, which includes batch normalisation and tightly linked layers, helps it to be adept at identifying complex linguistic patterns. Comparing the research to the source work [18], promising improvements are shown, highlighting the potential of the model in language detection. 2024 IEEE. -
QUALITY OF WORK LIFE IN RELATION TO PEOPLE CAPABILITY MATURITY MODEL IN IT AND ITES ORGANIZATIONS
The new found concern for Quality of Work Life in corporate life perhaps has been due to the realization that human resource is the most important asset which must be released and developed. Management viewed QWL programs as a way of reducing costs and improving productivity. The success of any organization depends on how it attracts recruits, motivates and retains its workforce. Human capital is clearly emerging as a key engine of economic growth, and it is evident that the skills and competencies of the workforce impact positively on productivity and competitiveness. In this regard investment in human capital would appear to be a prerequisite to economic success .In this new scenario People capability maturity model offers unlimited potential to develop and maximize human capital and organizational competence in the interest of the firm ,the employee ,the consumer ,the shareholder and not least the family. People capability maturity model is a maturity framework developed at the software engineering institute that guides the organizations in improving the ability to attract, develop, motivate, organize and retain talent.. Economies of the world over and companies facing tough domestic and international markets have been posing a serious challenge to all concerned. This coupled with every changing technology and increased access to information has necessitated studying organization with respect to productivity, efficiency and quality of service rendered. All this demands a new work culture, employee motivation, commitment to the job and organizational goals. Some organizations in the service sector have implemented PCMM to address all these organizational issues. However we have very little information at the grass root level to comprehend QWL, and very little research on QWL Life in relation to PCMM hence this study. Based on the objectives of the study a detailed questionnaire was constructed by the researcher. The questionnaire has three parts measuring demographics, implementation of PCMM and six dimensions of QWL. It was measured on a 5 point likert scale 1 indicating strongly disagree to 5 indicating strongly agree. The Cronbachs alpha reliability for the PCMM and the QWL for the present sample was .80 and above. The questionnaire was completed by 230 respondents using judgmental sampling technique from PCMM implemented and non implemented IT and ITES organizations. It was found that Quality of work life was not significantly higher in companies that implemented People capability maturity model as compared to other companies. Amongst all the dimensions of Quality of work life the only dimension influenced and affected by People capability maturity model was self evaluation of performance .It was found that there was a variation of 20.1% in the Quality of work life. In terms of correlation, the study indicated that there was significant intra relationship between the 6 dimensions of Quality of work Life; significant intra relationship between the People Capability Maturity Model related items and significant interrelationship between 6 dimensions of Quality of Work Life and the People capability maturity model related items. Amongst all the 6 dimensions of Quality of Work Life the only dimension that was significantly different across gender was self evaluation of performance. Females had higher self evaluation of performance as compared to the male counterparts. On the basis of the results attained from the current study we can clearly imply that Quality of work life dimensions is definitely positively influenced, affected and correlated with People Capability Maturity Model though there is no difference in Quality of Work Life among People Capability Maturity Model implemented and Non implemented IT and ITES organizations. The results from the study will have significant implications on the companies that have not implemented People Capability Maturity Model to join the group of People capability maturity model implemented companies as this will help the organizations to prepare the employees psychologically to meet the demands and challenges which otherwise may risk a poor Quality of work life program implementation. Key Words: Organizational behavior, Human Resource Management, People Capability Maturity Model, Quality of Work Life, General linear model. -
An iot based wearable device for healthcare monitoring
Nowadays IoT (Internet of Things) devices are popularly used to monitor humans remotely in the healthcare sector. There are many IoT devices that are being introduced to collect data from human beings in a different scenario. These devices are embedded with sensors and controllers in them to collect data. These devices help to support many applications like a simple counting step to an advanced rehabilitation for athletes. In this research work, a mini wearable device is designed with multiple sensors and a controller. The sensors sense the environment and the controller collects data from all the sensors and sends them to the cloud in order to do the analysis related to the application. The implemented wearable device is a pair of footwear, that consists of five force sensors, one gyroscope, and one accelerometer in each leg. This prototype is built using a Wi-Fi enabled controller to send the data remotely to the cloud. The collected data can be downloaded as xlsx file from the cloud and can be used for different analyses related to the applications. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Enhancements to randomized web proxy caching algorithms using data mining classifier model
Web proxy caching system is an intermediary between the users and servers that tries to alleviate the loads on the servers by caching selective web pages, behaves as the proxy for the server, and services the requests that are made to the servers by the users. In this paper, the performance of a proxy system is measured by the number of hits at the proxy. The higher number of hits at the proxy server reflects the effectiveness of the proxy system. The number of hits is determined by the replacement policies chosen by the proxy systems. Traditional replacement policies that are based on time and size are reactive and do not consider the events that will possibly happen in the future. The outcomes of the paper are proactive strategies that augment the traditional replacement policies with data mining techniques. In this work, the performance of the randomized replacement policies such as LRU-C, LRU-S, HARM, and RRGVF are adapted by the data mining classifier based on the weight assignment policy. Experiments were conducted on various data sets. Hit ratio and byte hit ratio were chosen as parameters for performance. Springer Nature Singapore Pte Ltd. 2019. -
Enhancements to Content Caching Using Weighted Greedy Caching Algorithm in Information Centric Networking
Information-Centric Networks (ICN) or Future Internet is the revolutionary concept for the existing infrastructure of the internet that changes the paradigm from host-centric networks to data-centric networks. Caching in Information-Centric Networks (ICN) has become one of the most critical research areas in today's world, especially for the leading in content delivery over Internet companies like Netflix, Facebook, Google, etc. This paper is intended to propose a novel Caching strategy called Weighted Greedy Dual Size Frequency for caching in Information-Centric networks. In this paper, the WGDSF considers multiple critical factors for maintaining the Web Content efficiently in ICN Caching Router. Simulation is done for the various performance metrics like Cache Hit ratio, Link load, Path Stretch, and Latency for WGDSF cache replacement algorithm, and results shown that WGDSF outperforms well compared with LRU, LFU, and RAND Caching Strategies. 2020 The Authors. Published by Elsevier B.V.