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Exploring the challenges and prospects of healthcare reporting in Indias Hinterland
Differential access and utilisation of healthcare services are caused by multiple Social Determinants of Health (SDHs), which requires adequate and informed policy intervention. The mass media, mainly the news media, have been seen as a practical approach in communicating the health anomalies at the policy level. The comprehensive coverage of factors associated with the healthcare system can further lead to addressing inequalities in health. The study was aimed to identify the factors that act against effective reporting of healthcare news from peripheral regions of India. The qualitative method was employed to examine media professionals' persistent challenges and experiences in covering health-related disparities. Sixteen media professionals were interviewed during October 2019 employed in vernacular news agencies all across the north-eastern state of Assam, India. Three themes have emerged from the analysis covering health-related news and barriers to healthcare in the peripheral region and identifying health-related disparities. It is found that an effective reporting mechanism of the health news will positively influence the policymakers and undertake efforts to address the health-related disparities. Copyright 2022 (Jyoti Nath, Tamuli). -
Exploring Drivers of Healthcare Utilization amongthe Working and Non-Working Elderly Population: Insights from LASI
Background: The elderly population of India has been growing exponentially over the past few decades, caused by a decline in fertility and an increase in life expectancy. The growth eventually has transcended the disease burden on the public healthcare system. This calls for a need to evaluate the healthcare utilization pattern of the elderly based on their socioeconomic and working condition. Methods: Study used access to public and private healthcare services to measure healthcare utilization. Descriptive analysis and multivariable logistic regression were used to understand utilization patterns by working status and some selected sociodemographic parameters. All the results were reported at a 95% confidence interval. Results: Using the data from the first wave of Longitudinal Ageing Study in India (LASI) with a sample of 22,680 older persons 60 years and above. The study identified that 50% of the working elderly access private services; however, 26% access public healthcare services. It was found that the working status of the elderly alone did not influence access to healthcare services, but education is also an essential indicator for utilizing healthcare services. Further, factors such as gender, marital status, religion, wealth, tobacco usage, self-rated health, ADL and IADL were significant predictors of healthcare services utilization for the elderly. Conclusion: This study suggests that there are not many differences found among working and non-working status with healthcare utilization, although some sociodemographic indicators are associated with the utilization of healthcare services, highlighting that increasing health needs among the elderly requires strengthening the quality and appropriate public investment in health. 2024 Taylor & Francis Group, LLC. -
An Efficient Deep Learning-Based Hybrid Architecture for Hate Speech Detection in Social Media
Social media has become an integral part of life as users are spending a significant amount of time networking online. Two primary reasons for its increasing popularity are ease of access and freedom of speech. People can express themselves without worrying about consequences. Due to lack of restriction, however, cases of cyberbullying and hate speeches are increasing on social media. Twitter and Facebook receive over a million posts daily, and manual filtration of this enormous number is a tedious task. This paper proposes a deep learning-based hybrid architecture (CNN + LSTM) to identify hate speeches by using Stanfords GloVe, which is a pre-trained word embedding. The model has been tested under different parameters and compared with several state-of-the-art models. The proposed framework has outperformed existing models and has also achieved the best accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Resveratrol as a therapeutic choice for traumatic brain injury: An insight into its molecular mechanism of action
Traumatic brain injury (TBI) is a global health challenge owing to its high incidence rate, long-term sequelae, and complex pathophysiology with limited available treatment options. Food supplement rich in polyphenols has shown promising health benefits in TBI. Resveratrol, a phytoalexin stilbenoid commonly found in many plants, including grapes, nuts, and berries, is endowed with several health-promoting effects. In this review, the pathophysiology of TBI and the underlying mechanism for Resveratrol-induced neuroprotection in TBI has been discussed. The spectrum of injuries in TBI, including the acute primary injury, and delayed secondary injury often leading to other forms of neurodegenerative disorders, indicates the dysregulation of multiple pathways following TBI and its complex pathophysiology. Despite poor bioavailability and solubility, Resveratrol as a therapeutic in neurological illnesses, including TBI, is attributable to its ability to cross the blood-brain barrier, detectable level in the brain, and the lack of unfavorable toxicity. Substantial preclinical evidence has associated Resveratrol with protection against TBI and subsequent secondary brain injury as it has significant anti-oxidant, anti-inflammatory, and anti-apoptotic properties, emphasizing its promising scope in the treatment of TBI. 2022 -
Enhancing the stability of electrochemical asymmetric supercapacitor by incorporating thiophene-pyrrole copolymer with nickel sulfide/nickel hydroxide composite
The practical application of a supercapacitor predominantly relies on its sustained cyclic stability. Hence it is essential to develop materials with high stability for the efficient supercapacitor applications. Herein, we demonstrate the integration of a copolymer of poly thiophene-pyrrole (cPPyTh) to surpass the limited cyclic stability of the nickel sulfide/nickel hydroxide (NSH) composite. Though the lower electronegativity of sulfur in coexistence with hydroxide achieves a superior capacity for NSH, it lacks extended cyclic stability. By incorporating cPPyTh into the layers of NSH, the stability of the resultant composite (NCP) could be enhanced by preventing the aggregation of layered NSH during longer runs. NCP electrode provides a specific capacity of 87 C/g at a current density of 1 A/g in a three-electrode system. An energy density of 25.47 Wh/kg and power density of 8.65 kW/kg is obtained for the asymmetric supercapacitor fabricated with NCP as positive and modified activated carbon (MAC) as negative electrode. The NCP demonstrates a superior cyclic stability of over 94% for 10,000 cycles in comparison to NSH with stability ? 73% over 5,000 cycles for the asymmetric supercapacitor. 2021 -
Is gold price volatility in India leveraged?
This paper examined the presence of leverage effect on the gold price volatility in six major Indian cities using PGARCH model. This study also examined the impact of US gold price return on the volatility of gold price in India. For this study, daily time series data of gold price in six major Indian cities and gold price in the United States over a period of seven years (January 2011 to August 2017) were collected. The results suggest that conditional volatility of gold price in all the six cities in India carries volatility clustering feature. Leverage effect was also found in the gold price volatility of five out of six Indian cities studied. The United States gold returns had a significant influence on the gold price volatility of five out of six Indian cities studied. Hence, the gold price volatility in India is indeed leveraged. -
Social environment based on sentiments using globalized user review analysis /
Patent Number: 202141007727, Applicant: Dr.G Muneeswari.
A simple yet efficient model, called Globalized User Sentiment Analysis (GURA) by using the property that sentiment classification has two opposite class labels (i.e., positive and negative), we first propose a data expansion technique by creating sentiment toggled reviews. The original and switched reviews are constructed in a one-to-one correspondence. Thereafter, we enhance the dual training (DT) algorithm and a dual forecasting (DF) algorithm separately, to make use of the original and switched samples in pairs for training a statistical classifier and make predictions. -
Does integrated store service quality determine omnichannel customer lifetime value? Role of commitment, relationship proneness, and relationship program receptiveness
Purpose: Building on the relationship marketing and stimulus-organism-response (SOR) theory, the purpose of this paper is to study the impact of the integrated store service quality (ISSQ) on the omnichannel customer lifetime value (CLV). The mediating role of customer commitment (affective, normative and continuance) and relationship program receptiveness with the moderating role of customer relationship proneness were relied upon to better understand the omnichannel customer profitability metric (CLV). Design/methodology/approach: The study is descriptive and relies upon the cross-sectional data collected using the self-administered structured questionnaires from 785 omnichannel shoppers. A purposive sampling technique was performed in the study. Structural equation modeling was performed using the SMART-PLS 4.0 software to analyze the data. Findings: The results indicate that omnichannel customer commitment (affective, normative and continuance) differentially mediates the relationship between ISSQ and relationship program receptiveness, subsequently impacting the omnichannel CLV. The customer relationship proneness significantly and positively moderated the relationships between different dimensions of customer commitment and relationship program receptiveness. Research limitations/implications: The study relied upon the cross-sectional data from the Indian population aged above 18years for testing the proposed model. Further studies could test the model across different populations to generalize the study results. Originality/value: This study addresses the need to investigate the omnichannel retail store customer profitability and their relationship performance with the store. By testing the customer relationship management model in the omnichannel retail store context, this study is the first to show that ISSQ will impact the customer profitability and relationship performance metric (CLV) through omnichannel customer commitment and relationship program receptiveness. The moderating effect of customer relationship proneness on a few proposed hypotheses was also tested to give managerial recommendations. 2024, Emerald Publishing Limited. -
Unveiling metaverse sentiments using machine learning approaches
Purpose: The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers ones intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience. Design/methodology/approach: The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently. Findings: The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models. Research limitations/implications: Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverses experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverses economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust. Social implications: In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators. Originality/value: The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models. 2024, Emerald Publishing Limited. -
Exploring tourists metaverse experience using destination spatial presence quality & perceived augmentation: metaverse exploration, physical expedition (MEPE)
A recent surge of interest surrounds metaverse tourism, with researchers highlighting its potential to revolutionize the tourism industry and attract new travellers. This article delves into the key features of a tourist's metaverse experience that influence their desire to visit a destination in the real world using systems theory. In addition, the current study also explores the moderating role of FOMO (Fear of missing out) in few of the proposed relationships. The study is a cross-sectional descriptive investigation carried out among Indian tourists chosen based on the simple random sampling technique and is analyzed using the Smart PLS software. The findings of the study reveal that several attributes of a tourist's metaverse experience, including entertainment, interaction, trendiness, novelty, and intimacy, significantly enhance both the perceived quality of spatial presence within the destination and the level of perceived augmentation experienced by tourists. Notably, both these factors then exert a significant positive influence on a destination's brand equity, ultimately explaining tourists' intentions to visit the physical location. Interestingly, the moderating role of Fear of Missing Out (FOMO) suggests that the relationship between brand equity and the likelihood of tourists undertaking a physical visit is strengthened as their perceived FOMO increases. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Growth of some urinary crystals and studies on inhibitors and promoters. II. X-ray studies and inhibitory or promotery role of some substances
Best conditions were established for the gel growth of three urinary crystals viz., calcium oxalate monohydrate, calcium hydrogen phosphate dihydrate and ammonium magnesium phosphate hexahydrate. The crystals grown were characterized using single crystal X-ray diffraction techniques and density measurements. Crystal growth experiments were carried out by incorporating the extracts or juices of some natural products in the gel media. By carefully observing the changes in the growth of crystals (compared to control experiments carried out at the same conditions), results about the inhibitory or promotery role of the substance incorporated were obtained. It was found that the extracts or juices of many of the naturally occurring substances have interesting inhibitory or promotery effects. These results may have useful applications in the treatment of recurrent stone patients. -
Ensemble Model of Machine Learning for Integrating Risk in Software Effort Estimation
The development of software involves expending a significant quantum of time, effort, cost, and other resources, and effort estimation is an important aspect. Though there are many software estimation models, risks are not adequately considered in the estimation process leading to wide gap between the estimated and actual efforts. Higher the level of accuracy of estimated effort, better would be the compliance of the software project in terms of completion within the budget and schedule. This study has been undertaken to integrate risk in effort estimation process so as to minimize the gap between the estimated and the actual efforts. This is achieved through consideration of risk score as an effort driver in the computation of effort estimates and formulating a machine learning model. It has been identified that risk score reveals feature importance and the predictive model with integration of risk score in the effort estimates indicated an enhanced fit. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Transparent Data Encryption: Comparative Analysis and Performance Evaluation of Oracle Databases
This Transparent Data Encryption (TDE) can provide enormous benefits to the Relational Databases in the aspects of Data Security, Cryptographic Encryption, and Compliances. For every transaction, the stored data must be decrypted before applying the updates as well as should be encrypted before permanently storing back at the storage level. By adding this extra functionality to the database, the general thinking denotes that the Database (DB) going to hit some performance overhead at the CPU and storage level. However, The Oracle Corporation has adversely claimed that their latest Oracle DB version 19c TDE feature can provide significant improvement in the optimization of CPU and no overhead at the storage level for data processing. Impressively, it is true. the results of this paper prove too. Most interestingly the results also revealed about highly impacted components in the servers which are not yet disclosed in any of the previous research work. This paper completely concentrates on CPU, IO, and RAM performance analysis and identifying the bottlenecks along with possible solutions. 2020 IEEE. -
Cyber secure man-in-the- middle attack intrusion detection using machine learning algorithms
The main objective of this chapter is to enhance security system in network communication by using machine learning algorithm. Cyber security network attack issues and possible machine learning solutions are also elaborated. The basic network communication component and working principle are also addressed. Cyber security and data analytics are two major pillars in modern technology. Data attackers try to attack network data in the name of man-in-the-middle attack. Machine learning algorithm is providing numerous solutions for this cyber-attack. Application of machine learning algorithm is also discussed in this chapter. The proposed method is to solve man-in-the-middle attack problem by using reinforcement machine learning algorithm. The reinforcement learning is to create virtual agent that should predict cyber-attack based on previous history. This proposed solution is to avoid future cyber middle man attack in network transmission. 2020, IGI Global. -
Cyber secure man-in-the-middle attack intrusion detection using machine learning algorithms
The main objective of this chapter is to enhance security system in network communication by using machine learning algorithm. Cyber security network attack issues and possible machine learning solutions are also elaborated. The basic network communication component and working principle are also addressed. Cyber security and data analytics are two major pillars in modern technology. Data attackers try to attack network data in the name of man-in-the-middle attack. Machine learning algorithm is providing numerous solutions for this cyber-attack. Application of machine learning algorithm is also discussed in this chapter. The proposed method is to solve man-in-the-middle attack problem by using reinforcement machine learning algorithm. The reinforcement learning is to create virtual agent that should predict cyber-attack based on previous history. This proposed solution is to avoid future cyber middle man attack in network transmission. 2022 by IGI Global. All rights reserved. -
Intersection of AI and business intelligence in data-driven decision-making
In today's rapidly evolving business landscape, organizations are inundated with vast amounts of data, making it increasingly challenging to extract meaningful insights and make informed decisions. The traditional business intelligence (BI) approach must often address the complexity and speed required for effective decision-making in this data-rich environment. As a result, many businesses need help to leverage their data to drive sustainable growth and remain competitive. Intersection of AI and Business Intelligence in Data-Driven Decision-Making presents a transformative solution to this pressing challenge. By exploring the convergence of artificial intelligence (AI) and BI, our book provides a comprehensive framework for leveraging AI-powered BI to revolutionize data analysis, predictive modeling, and decision-making processes. Readers will gain valuable insights into practical applications, emerging trends, and ethical considerations, inspiring and exciting them about the potential of AI in driving business success. Through in-depth discussions, case studies, and best practices, this book equips professionals, researchers, and students with the knowledge and tools needed to navigate the complexities of AI-powered business intelligence. Whether you're looking to predict trends, analyze consumer behavior, or optimize supply chains, this book offers actionable strategies and techniques for implementing AI-powered BI solutions in your organization. 2024 by IGI Global. All rights reserved. -
A comprehensive LR model for predicting banks stock performance in Indian stock market
The study focusses on developing a Logistic Regression model to distinguish between Good and Poor Performance of Bank-stocks which are traded in Indian stock market with regard to the financial ratios. The study- sample comprises of financialratios of 40 nationalised and private banks, for a period of six years. The study ascertains and scrutinizes eleven financial ratios that can categorize the Banksbroadly into two categories as good or poor, up to the accuracy level of 78 percent, based on their rate of return. First, the study predicts the performance of banks by using financial ratios and tries to build the goodness of fit by using Logistic Regression approach. The study also emphasizes that this model can enrich an investors ability to forecast the price of various stocks. However, the paper confers the real-world implications of Logistic Regression model to envisage the performance of Banks in the stock market. The study reveals that the model could be useful to potential investors, fund managers, and investment companies to improve their strategies and to select the out-performing Bank-stocks. Serials Publications Pvt. Ltd. -
Literary Cartography of Performance Ecologies in Sheela Tomys Valli
The shift towards posthumanism is characterized by blurring boundaries between humans and other species alongside emerging narratives centred on climate catastrophes and ecological crises. Sheela Tomys Valli (2022) is one of the most recent works of Indian fiction that actively promotes ecological consciousness. Set against the picturesque landscape of Wayanad, Valli intricately captures the essence of the indigenous community, weaving their stories into its narrative. The paper suggests that reading Valli through a cartographic lens transforms the narrative into an intelligent discourse on spatial politics. The performances in Valli are understood through the lens of performance ecology (Jeff Grygny), reflecting ongoing contemporary ecological debates. Their interrelation is explored by mapping spatial memory and schema of the characters, based on Robert T. Tallys theory of literary cartography (2013). Additionally, the paper will provide an overview of the ecopolitics of Wayanad, with a specific focus on the socio-political conditions of the Paniyar and Kuruchiyar scheduled tribes from which the characters are drawn. The study will underscore the triad of space, performance, and ecology in Valli, invoking a sense of ecoprecarity essential for rethinking and potentially expanding our notion of sustainability. 2024, University of Malaya. All rights reserved. -
Implementation of Movie Recommendation System Using Hybrid Filtering Methods and Sentiment Analysis of Movie Reviews
In present era of digitization of entertainment, immense volume of movies are produced, which results in the necessity of sophisticated recommendation systems. In the streaming platform these systems empower users to discover new and relevant movies, benefiting both viewers and the entertainment industry. This research paper offers a comprehensive method for incorporating movie review sentiment analysis into a hybrid recommendation system. The study focuses on 4890 movies using a broad dataset containing the detailed descriptions of the movies along with the reviews. To employ the demographic filtering, the popularity score of the movies were calculated, then to apply the collaborative filtering, the textual movie descriptions were vectorized using the countvectorizer method. To predict the sentiment of the movie reviews, the high accuracy model "ControX/Sen1"was used. This hybrid recommendation system ranked the movies based on the user's preferences by employing cosine similarity, the sorted list was further filtered with the positive sentiment reviews. By including sentiment analysis, this research advances sophisticated movie recommendation systems by providing a comprehensive method for addressing user preferences and emotional resonance in film selections. 2024 IEEE.