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Feature selection/dimensionality reduction
In today's world, medical image analysis is a critical component of research, and it has been extensively explored over the last few decades. Machine learning in healthcare is a fantastic advancement that will improve disease detection efficiency and accuracy. In many circumstances, it will also allow for early detection and treatment in remote or developing areas. The amount of medical data created by various applications is growing all the time, creating a bottleneck for analysis and necessitating the use of a machine learning method for feature selection and dimensionality reduction techniques. Feature selection is an important concept of machine learning since it affects the model's performance and the data parameters you utilize to train your machine learning models to have a big influence on the performance. The approach of minimizing the number of inputs in training data by reducing the dimension of your feature set is known as dimensionality reduction. Reduced dimensionality aids in the overall performance of the machine learning algorithms. 2023 River Publishers. -
Predicting heart ailment in patients with varying number of features using data mining techniques
Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Nae Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Nae Bayes and Random Forest algorithms comparatively outperforms with these sets of data. 2019 Institute of Advanced Engineering and Science. -
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Keratin as a sustainable biopolymer for waste water treatment
Keratin is one of the most abundant natural polymers with potential application in various fields but is usually seen discarded as waste generated from poultry farms along roadsides and landfills. These are indeed the cheapest source of keratin protein which could be used for various applications. Owing to the structural properties, keratinous materials are now being exploited in wastewater treatment systems as adsorbents. The rich amino acid content having hydroxyl, carboxyl and amino groups has been found to be beneficial in removing contaminants from waste waters like heavy metals and dyes. Research based on this idea has received peak attention to a point where formulations of different adsorbent materials like nanofibre, biofilms and biocomposite from keratinous raw materials are now available for commercial use. This review summarises the application of keratin as an efficient adsorbent for waste water treatment providing an insight into its structure, forms of keratin used for treatments and mechanism of adsorption of different components in waste water. 2022 World Research Association. All rights reserved. -
Effect of Chicken Feather Hydrolysate on Growth of Spinach through Soil Amendment Method: Unraveling A Potential Liquid Biofertilizer
The study aims to investigate the effectiveness of chicken feather hydrolysate for promoting the growth of Spinacia oleracea L., a commonly consumed leafy green vegetable. An earlier isolated and identified keratinolytic bacterial species Bacillus tropicus was utilized for the preparation of chicken feather hydrolysate through submerged fermentation. Minimal media which was supplemented with chicken feather was used for the preparation of hydrolysate. The bacterial strain degraded chicken feather within 4 days of incubation after which the feather hydrolysate was collected and tested to check plant growth promoting activity through the seed germination trials and greenhouse study. Upon characterization of feather hydrolysate, it was found that the hydrolysate was a cocktail of Nitrogen, Phosphorus and Potassium (NPK) as well as other micro elements needed for plant growth. Four different concentrations of feather hydrolysate were employed for both the seed germination and greenhouse study which ranged from 25% (v/v), 30% (v/v), 35% (v/v) and 40% (v/v) including a control group (CN) which was not supplemented with feather hydrolysate. The hydrolysate supplementation brought about plant growth in all the four test concentrations with 35% (v/v) giving the highest result of 14 cm and 27.6 mg/g for tested parameters like plumule length and total chlorophyll content, respectively. The same concentration supported maximum seed germination and highest radicle extension for the germination studies as well. This study investigates the efficacy of chicken feather hydrolysate in promoting spinach growth, elucidating its potential as a fertilizer. The Author(s) 2024. Open Access. -
Stress and resilience in British Indian parents with an autistic child: a comparative study with white British and Indian parents
Purpose: This study aims to examine the levels of stress and resilience in a sample of British Indian parents bringing up a child with autism. Design/methodology/approach: A total of 52 British Indian parents took part in a survey that included measures of stress, resilience, support and child adaptive functioning. Results were compared to a sample of white British (n = 120) and Indian parents (n = 120). Findings: The British Indian parents recorded higher levels of stress and less perceived social support than their white British counterparts. British Indian parents took longer to register concern about their childs development and sought a diagnosis at a later age than the white British group. The delay in concern and diagnosis was similar to that found in the India group. Originality/value: The research suggests that British Indian parents are disadvantaged in social support and mental well-being compared to white British parents and may face similar community pressures to parents bringing up a child in India. 2023, Emerald Publishing Limited. -
Extended virtual reality based memory enhancement model for autistic children using linear regression
Extended Virtual Reality has expanded its wings to almost each and every sector enabling immersive experience in various fields and has found applications in gamification, learning, healthcare, etc. This technology has aided in providing solutions to various problems in different fields, and healthcare is the most prominent one among them. Children suffering from ASD which is a developmental disorder affecting the brain that impacts how a person perceives external responses, are finding it increasingly difficult to get treated as the treatment methods are tedious. There are very few methods which are regarded as standardized means of treating autistic children but there are a few common traits that can be found in children affected by ASD which can be grouped under three common categories. They are lack of communication skills, lack of basic mathematical knowledge and low levels of remembrance. With the help of Gamification, which provides therapy by means of games to those affected, the kids affected by ASD can be treated, powered by the concept of Extended Virtual Reality. In this paper, we have developed a model to provide autistic children a real world experience of playing games which will help them in enhancing their skills without any external interferences. Children who play these Extended Virtual Reality based games show gradual improvement, for which the results can be facilitated with the help of a Linear Regression model, helping us predict future response times. The proposed model results in enhancement of memory levels of the kids as a result of the game and classifies kids based on their enhancement in memory into high, medium and low. The mean absolute error of the linear regression model is found to be 0.0394. 2024, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden. -
Impact of pharmacy industries growth on India economy during covid 19 /
Patent Number: 202241050891, Applicant: Deepha V.
Impact of Pharmacy Industries growth on India Economy during COVID 19 Abstract Pharmacy is an industry that can continue to function without being affected by economic fluctuations. This industry is socially respected by people. Whether people have food to eat or not, everyone wants to preserve the health of the body. In particular, the demand for medicines is more than ever in today's era. -
Research Competence of University Teachers in Relation to Organisational Ethos and Research Culture
The standards of research depend on the maintenance and coordination of research activities that are conducted by the universities. The flexibility in ordinance and statutes empowers the universities to frame the guidelines that empower the research competence of the teachers. However, newlinethe existing framework is not adjusted to modern approaches to research competence which creates issues in developing a framework for evaluation for research competence. The objective of the present newlinestudy is to develop and validate a framework for research competence for university teachers. The study measures the relationship between research competence, organisational ethos, and research culture of university teachers. The study used the existing measuring instruments to evaluate newlineorganisational ethos and research culture. Researcher has developed the measurement scale for research competence. The validity and reliability have been done for all the measuring instruments research competence, organisational ethos and research culture. The factor analysis has conducted for newlinethe measuring instrument of research competence. The quantitative data for the study has been taken from the self-reported experience of 451 university teachers. The study found that there was a significant difference in demographic variables such as gender, age, work experience, educational newlinequalification, and subject background with organisational ethos, research culture, and research competence of university teachers. The structural equation model showed the relationship between the components of research competence, organisational ethos, and research culture. The present study can newlineassist policymakers to evaluate the research competence, organisational ethos, and research culture of the university teachers. The study indicates the practical and academic importance of university newlineteachers to enhance research performance. -
Unveiling the Dynamics: A Performance Analysis of RPL under Congestion in IoT Network
The Routing Protocol for Low Power and Lossy Network (RPL) is a standardized routing protocol for resource constraint devices deployed in diverse applications in Internet of Things (IoT). RPL is the most efficient protocol which is carefully designed to meet energy efficiency of sensor nodes. However, this protocol is prone to network congestion which is one of most crucial bottlenecks of this protocol. In the current study a thorough analysis of effect of congestion on RPL routing metrics are analyzed. We have designed a congestion scenario using Cooja simulator and analyzed its effects on ETX, Power, Duty Cycle through graphs. The results of the experiments finally outline the critical parameters affected due to congestion in RPL. Grenze Scientific Society, 2024. -
MMOF: A Multi-Metric Objective Function for Congestion Detection Under Varying Transmission Ranges in RPL-Based WSN
The Routing Protocol for Low Power Lossy Networks (RPL) is prone to congestion under high traffic. The single-path routing strategy and single-parent selection make RPL energy and resource-efficient only when the traffic is low and uniform. Two Objective Functions (OFs) are defined for RPL, which use single routing metrics-Expected Transmission Count (ETX) and hop count, to select the best parent and path toward the root. However, considering a single metric for OFs is unsuitable for detecting congestion in Lossy Networks (LLNs) applications as each metric has limitations. The current study proposes a novel Multi-Metric Objective Function (MMOF) that combines these two metrics and removes the weakness of the existing OFs. The proposed MMOF works under the nodes' varying transmission ranges (Tx ranges) to reduce the congestion. By changing Tx ranges, we show that the congestion in a fixed topology RPL network reduces, and MMOF can detect this congestion state more accurately than the existing OFs. The research introduces a successful transmission probability metric that makes MMOF more efficient in detecting congestion than ETX and Hop-Count. We prove that considering these two parameters individually is misleading and cannot contribute 100% to detect congestion state. Increasing transmission range can decrease congestion, and MMOF can detect this state transition with 100% accuracy. Simulation results in Cooja show that MMOF outperforms these two metrics and that the robust metric shows a linear relationship with the Tx range. Finally, two quality of service (QoS) parameters are derived to prove the method's efficiency and novelty. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. -
An analysis of the ethical challenges of blockchain-enabled E-healthcare applications in 6G networks
Developments in blockchain technology coupled with rapid developments in network technologies have disrupted traditional business and service models. One such application is in the domain of healthcare. However, the domain's sensitive nature and complexity require blockchain-enabled e-healthcare to ensure utilitarianism while suitably addressing the associated ethical challenges. In this milieu, the paper attempts to identify and evaluate the parameters of ethical challenges associated with blockchain adoption in e-healthcare. This paper contributes to the extant body of knowledge by presenting a critical review of the ethical considerations at the meso level of blockchains in e-healthcare. Based on findings from the literature, the study identified nine parameters of blockchain ethics. Of these, Accuracy and Right to be Forgotten were found to be most critical in terms of ethical dilemmas in healthcare applications. No evidence of ethical dilemma could be found with respect to Accountability and Data Ownership. As these services are deployed over networks, all these challenges are further evaluated in the context of 6G network-based models. This will not only provide the stakeholders with a holistic view of the ethical challenges in various blockchain-enabled healthcare applications but also enable a meticulous transition to the 6G network. 2021 -
Impact of corporate governance attributes on cost of equity: Evidence from an emerging economy
Purpose: The purpose of this study is to construct a comprehensive Indian corporate governance index in light of the recently introduced Companies Act, 2013, which is further validated by analyzing its impact on the cost of equity of a firm. Design/methodology/approach: Based on the hand-collected data from firms listed on S&P BSE 500 from 2001 to 2016, this index comprises seven equally weighted sub-indices, comprising a total of 43 corporate governance attributes. This index and the sub-indices have further been regressed with the cost of equity of a firm. Findings: The results suggest a negative significant relationship between the overall corporate governance and the cost of equity. The study also suggests that among all the sub-indices, board composition predicts the cost of equity to a greater extent. Other than this, the audit committee sub-index has a negative significant association with the cost of equity. The findings imply that a well-governed firm enjoys ease of access to equity finance from the market. Originality/value: The corporate governance index is based on the recent regulatory reforms introduced in India. The index, with certain changes suitable to the local context, can be applied to similar emerging economies as well. The causal relationship tested using this method is the first one done in India. This study adds to the domain of corporate governance literature with special focus on the construction of an index for an emerging economy. 2019, Emerald Publishing Limited. -
Analysis of Cardiovascular Diseases Prediction Using Machine Learning Classification Algorithms
Worldwide healthcare systems have faced enormous hurdles because of the COVID-19 pandemic, especially when it comes to treating individuals who already have pre-existing disorders such as cardiovascular diseases (CVDs). Prioritizing medical therapies and resources for COVID-19 patients who are at increased risk of mortality from underlying CVDs requires early identification. In this work, we investigate how well three machine learning algorithms-, Random Forest, XGBoost, and Logistic Regression-predict death in COVID-19 patients who already have cardiovascular disease. We performed grid search and cross-validation using a dataset of clinical and demographic features of COVID-19 patients with and without CVDs to reduce overfitting and maximize model performance. Our findings show that among patients with CVDs, Logistic Regression had the best accuracy in predicting COVID-19 fatality, followed by Random Forest and Decision Tree coming in a close second. These results highlight how machine learning algorithms can help clinical professionals detect high-risk COVID-19 patients who have underlying cardiovascular diseases (CVDs), enable prompt interventions, and enhance patient outcomes. 2024 IEEE. -
Listen to the heart or mind first? Examining sequential coping mechanisms among Indians during the COVID-19 pandemic
The present study examines the mediating role of emotion-focused and problem-focused coping between stress and psychological well-being during the COVID-19 pandemic. The sample comprised 501 (312 women and 184 men aged between 18 and 42) Indians who experienced the first-ever continued lockdown in India during the COVID-19 pandemic. The results of this study confirmed the presence of perceived stress due to the lockdown and pandemic among participants. Furthermore, perceived stress, coping including emotion-focused and problem-focused, and psychological well-being were found to be interrelated. The serial mediation analysis revealed that participants dealt with stress by choosing emotion-focused coping first as an immediate resort. After a reappraisal of stress-inducing situations, they used problem-focused coping, and this sequence of constant coping mechanisms helped maintain their psychological well-being. The findings of this study can be applied to develop strategies for peoples mental health by public health organizations and health professionals. Copyright 2023 Srivastava, Upadhaya and Jain. -
Aging, sexual intimacy, and challenges in contemporary India: A qualitative study
An individual's life is shaped by age norms practiced in a particular society. In most societies, there is a deadline for every life event. Sexual intimacy is an essential part of every individual. However, sexual intimacy seems appropriate for young individuals, and middle-aged and older are considered asexual. Those who share sexual intimacy at a later age have to face the consequences for this age-inappropriate behavior in society. This study analyses Badhaai Ho film to explore the consequences of sharing sexual intimacy by middle-aged heterosexual couples in their 50s as it is forbidden by prevalent social norms. This study also explores the role of family in dealing with the repercussions of actions against the prescribed social norms. Thematic analysis suggests that society has a predefined age-bound box for individuals with different age categories. The middle-aged couple suffers various consequences for breaking the prescribed age-bound box. The role of the family is found to be crucial in mending the box by replacing it with an updated version. There are also gender differences in attitude toward sexual intimacy. Implications of this study can be utilized to explore the pathway of social change in existing social (age) norms in any society. Copyright 2022 Srivastava and Upadhaya. -
Multi-criteria decision making (MCDM) in diverse domains of education: a comprehensive bibliometric analysis for research directions
Multiple Criteria Decision Making has been one of the powerful and structured approach in solving real world problems in the past. The aim is to determine the best alternative based on multiple criteria. It has shown a remarkable performance in the field of education. In order to gain insights into the existing body of research in this area, a bibliometric analysis was conducted. The study is conducted to provide a comprehensive analysis since 2000 in the field of application of MCDM in the various domains of education. The publication information was accessed from Scopus Database on 1 December 2023 and the bibliometric analysis has been done through Vosviewer, R package bibliometrics and Tableau. Initially 5185 documents were found which were reduced to 1706 after multi layered screening criteria. The analysis is performed to find the relevant documents, most valuable researchers, the major countries where the research in this area is exhaustively conducted. After extensive research it is observed that researchers belonging to China are highly involved in the domain taken for study. Also, research conducted in China is highly cited which shows its quality of work. Further, it is observed that mostly fuzzy analysis techniques are widely used for MCDM. The collaborative work done by Arunodaya Raj Mishra and Rani Pratibha research work is remarkable and highly recommended to conduct the research in the considered domain in the research paper. The conducted bibliometric analysis provides an overview of the scope and global trends of MCDM in shaping the education sector. This would help the researchers to explore the most relevant study, analysis and finding the research gaps as per their research needs. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024. -
Analyzing the Prospects of Blockchain in Healthcare Industry
Deployment of secured healthcare information is a major challenge in a web-based environment. eHealth services are subjected to same security threats as other services. The purpose of blockchain is to provide a structure and security to the organization data. Healthcare data deals with confidential information. The medical records can be well organized and empower their propagation in a secured manner through the usage of blockchain technology. The study throws light on providing security of health services through blockchain technology. The authors have analyzed the various aspects of role of blockchain in healthcare through an extensive literature review. The application of blockchain in COVID-19 has also been analyzed and discussed in the study. Further application of blockchain in Indian healthcare has been highlighted in the paper. The study provides suggestions for strengthening the healthcare system by blending machine learning, artificial intelligence, big data, and IoT with blockchain. 2022 Shilpa Srivastava et al. -
Analysis and prediction of Indian stock market: a machine-learning approach
Prediction of financial stock market is a challenging task because of its volatile and non- linear nature. The presence of different factors like psychological, sentimental state, rational or irrational behaviour of investors make the stock market more dynamic. With the inculcation of algorithms based on artificial intelligence, deep learning algorithms, the prediction of movement of financial stock market is revolutionized in the recent years. The purpose of using these algorithms is to help the investors for taking decisions related to the Stock Pricing. A model has been proposed to predict the direction of movement of Indian stock market in the near future. This model makes use of historical Indian stock data of companies in nifty 50 since they came existence along with some financial and social indicators like financial news and tweets related to stocks. After pre-processing and normalization various machine learning algorithms like LSTM, support vector machines, KNearest neighbour, random forest, gradient boosting regressor are applied on this time series data to produce better accuracy and to minimize the RMSE error. This model has the ability to reduce major losses to the investors who invest in stock market. The social indicators will give an insight for predicting the direction of stock market. The LSTM network will make use of historical closing prices, tweets and trading volume. 2023, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden. -
Introduction: Sexuality and sexualities
Introduction The most significant recent development, a break with the past, in the study of sexual cultures has to do with the term culture itself: that we think of sexuality (and sexualities) as having cultures. Historically, both in academic and popular thinking, the term sexuality most frequently elicited responses that have to do with biology. That is, whether as an area of study or as a set of ideas people have about their intimate lives, sexuality was too easily detached from the social contexts where it belongs and presented as something of itself. There is a strong tendency to view our sexual lives as dictated by their own peculiar rules that (a) are biologically derived, (b) have been historically stable (that is, the same since the dawn of time), (c) are essentially about our private lives, and (d) are basically the same across different cultures. Ironically, while, on the one hand, we think of sexuality as a world-untoitself - such that it is regarded as a very narrowly confined domain that has nothing to do with, say, politics and economics, we also simultaneously think of it as something of very general significance that is absolutely fundamental to our being. We tend to both downplay its meanings as well as inflate its significance. So, for example, if one is a bad cook, its a minor blemish, but being bad at sex is seen as a major crisis which requires intervention (through seeking the help of sexologists, for example). The sexuality-as-a-drive perspective which was, most famously, both problematized but also institutionalized by Sigmund Freud presents itself in the Indian context in peculiarly Indian ways. It was, for example, at the heart of many of the arguments that were made - and continue to be made - about the difference between Hindus and Muslims, those between tribal and ?ontribal populations, and between the middle-class and poorer populations. So, with respect to the last point, the rise of sexology and the family planning movements are directly linked to the early-twentieth-century perception of the different sexual drives that supposedly characterized the educated and the uneducated (Ahluwalia 2013; Srivastava 2007). Sexology was intended to cater to the more evolved sexual desires of the middle classes, whereas family planning was directed towards controlling the uncontrollable drives of the poor, one that threatened nation-building. Indian Institute of Advanced Studies, Shimla 2020.