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Feminism in popular culture: A case study on justice league /
With popular culture stemming largely from the superhero comics it is difficult to ignore them nowadays. Marvel and DC have also been going at great lengths to ensure that for the next ten years, there exists a whole stock of such movies, comics and merchandise. This in turn means that there is a growing need to document and study the patterns of this growing culture. Within this, the evolution of female characters and feminist ideologies must be mapped. -
Feminism in belly dance and its portrayal in the media /
Bellydance is an art form that has gained tremendous popularity in the world today. There are different kinds of bellydance such as folk, tribal fusion, cabaret etc. In a world and time where women‟s rights and respect has been the foremost of most people‟s concerns, bellydance with its barrier breaking techniques and methods have created controversies yet have also resulted in a form of dance that is beautiful and helps a woman celebrate herself. -
Female masculinities and women of third nature: Analyzing the gender and sexual politics of identity and visibility of alternative masculinities through indian mythologies and literary narratives
Alternative sexualities have been a part of the Indian past since time immemorial, and mention of them is often visible in Sanskrit mythological texts. As much as the presence of hijras and other gendered cultural identities is in Indian and Western public discourses, there is a narrow space occupied by women of the third kind with female masculinities, with scant attention leading to the higher invisibility of women of the third kind. Female masculinity is often considered a "rejected shred, " while male masculinity is seen as real and heroic. This chapter focuses on "masculinity without men" to explore alternative masculinities-the concept popularized by Judith Halberstam (Judith Halberstam, Female Masculinity. Zubaan Books, New Delhi, 1998). We delve into the politics of alternative modes of enactment and production where male masculinity is embedded. This chapter centers on female masculinity and alternative forms of masculinities performed, enacted, and embodied by female individuals as reflected in the Indian past and mythology. This chapter further delves into identifying histories and representations of female masculinities in Indian literature to bring female masculinities and women of the third kind into academic discourse. Springer Nature Switzerland AG 2022. All rights reserved. -
Female entrepreneurship: Challenges faced in a global perspective
Women being employees is a very appreciative aspect. But women being employers is a bold and massive decision that they make, considering their busy life schedule, which traditionally includes looking after their families and themselves. This book chapter aims to identify the diverse challenges female entrepreneurs face, which could be in the context of society, structure, or finance. Identifying and vocalizing these challenges faced by these emerging entrepreneurs is inadequate, but tackling them is equally called for. This study provides a framework and scope for further research to look into more opportunities and measures to tackle these roadblocks. Last but not least, this book chapter anticipates inspiring all women and driving them to explore the essence of entrepreneurship. 2023, IGI Global. All rights reserved. -
Female Director and Agency Cost: Does board gender diversity at Indian corporate board reduce agency conflict?
We examined the presence of women directors in top-level management and their effect on principal-principal conflict (PP) and principal-agent conflict (PA) on the firms listed on Indian stock exchange using a panel model approach. For analysis purpose, this study covers the sample of 75 companies belonging to various industries and listed in Bombay Stock Exchange Index, has been studied over thirteen financial years, i.e. from year 2006 to year 2019. This study uses panel data analysis, i.e. fixed effect model and random effect model. The proportion and presence (dichotomous) of women directors on top level management board is taken as the independent variable. Principalprincipal conflict measured by assets utilization ratio (AUR), and principal-agent conflict is been measured by dividend payout ratio (DPR), are taken as dependent variable in this study. The prime results of this study using panel data analysis, i.e. fixed effect (FE) and random effects (RE) estimation models point towards no significant impact of the female director (proportion and presence) on the firm's agency cost (PP and PA). 2021. Transnational Press London. All Rights Reserved. -
Federated Learning and Blockchain: A Cross-Domain Convergence
Gaining significant attention within decentralized contexts, Federated Learning (FL) has been positioned as a highly desirable method for machine learning. By enabling multiple entities to train a shared model cooperatively, data privacy and security are preserved by Federated Learning. Harnessing inherent transparency and accountability of blockchain technology to trace and authenticate updates effectively in federated learning has transpired as an up-and-coming avenue to tackle data challenges related to confidentiality, protection, and reliability. This study examines the viability of federated learning and blockchain integration across multiple dimensions. The technological components of this integration., including incentive systems, consensus mechanisms, data validation, and smart contracts, are delved into. In the study, a novel proposed model for federated learning integrated with blockchain is designed and implemented. It is observed that the mean cypher size is 100 bytes for varying values of gradients. The average throughput recorded is 1.7 bytes per second, while the mean accuracy is 87.1% for 50 epochs. 2023 IEEE. -
FEC & BCH: Study and implementation on VHDL
Channel encoding and Forward Error Correction is a crucial element of any communication system. This paper gives a brief overview of the fundamentals, mechanism and importance of Forward Error Correction. The design and implementation of a (63,36,5) BCH Codec is also projected in the later sections. All simulations are made on MATLAB R2018b and the VHDL implementations have been carried out using Xilinx Vivado 2018.2. 2019 IEEE -
Features of Vitamin Model Affecting Psychological Empowerment: Serial Mediation Role of Job Crafting and Work Engagement
The current research aimed to investigate the association between the variables under the study, that is, the vitamin model features of a job: job crafting, work engagement, and psychological empowerment. It also attempted to analyze the serial mediational role of the two causally linked mediators, that is, job crafting and work engagement with the job features of the vitamin model and psychological empowerment. By investigating these variables, we tried to explore how the employees redesigned the well-defined jobs to match their capabilities, which enhanced commitment to work and led to positive behavioral outcomes, such as empowerment, work meaningfulness, and improved performance. Primary data were collected from 453 knowledge workers in the information technology (IT) and information technology-enabled services (ITES) industry. Using SPSS software, the correlation method revealed significant positive correlations between the variables under study. PROCESS macro (Haynes, 2012) was applied in SPSS AMOS regression-based path coefficients and bootstrap confidence intervals at a 95% confidence level. As the bootstrap confidence intervals did not include zero, a significant mediational role of the serial mediators was observed between the relationship of features in the vitamin model and psychological empowerment [Estimate =.0761, 95% CI (.0257,.1902)]. So, it could be concluded that job crafting made the employees the mechanic of their vehicle (work), leading to work engagement, increased performance, and psychological well-being at the workplace. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Feature Subset Selection Techniques with Machine Learning
Scientists and analysts of machine learning and data mining have a problem when it comes to high-dimensional data processing. Variable selection is an excellent method to address this issue. It removes unnecessary and repetitive data, reduces computation time, improves learning accuracy, and makes the learning strategy or data easier to comprehend. This chapterdescribes various commonly used variable selection evaluation metrics before surveying supervised, unsupervised and semi-supervised variable selection techniques that tend to be often employed in machine learningtasks including classification and clustering. Finally, ensuing variable selection difficulties are addressed. Variant selection is an essential topic in machine learning and pattern recognition, and numerous methods have been suggested. This chapter scrutinizesthe performance of various variable selection techniques utilizing public domain datasets. We assessed the quantity of decreased variants and the increase in learning assessment with the selected variable selection techniques and then evaluated and compared each approach based on these measures. The evaluation criteria for the filter model are critical. Meanwhile, the embedded model selects variations during the learning model's training process, and the variable selection result is automatically outputted when the training process is concluded. While the sum of squares of residuals in regression coefficients is less than a constant, Lasso minimizes the sum of squares of residuals, resulting in rigorous regression coefficients. The variables are then trimmed using the AIC and BIC criteria, resulting in a dimension reduction. Lasso-dependent variable selection strategies, such as the Lasso in the regression model and others, provide a high level of stability. Lasso techniques are prone to high computing costs or overfitting difficulties when dealing with high-dimensional data. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
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. -
Feature selection based on the classifier models: Performance issues in the prediagnosis of lung cancer
Dimensionality reduction is generally carried out to reduce the complexity of the computations in the large data set environment by removing redundant or de-pendent attributes. For the Lung cancer disease prediction, in the pre-diagnosis stage, symptoms and risk factors are the main information carriers. Large number of symptoms and risk attributes poses major challenge in the computation. Here in this study an attempt is made to compare the performance of the attribute selection models prior and after applying the classifier models. A total of 16 classifier models are preferred based on relevancy of the models with respect to the data types chosen, which are based on statistical, rule based, logic based and artificial neural network approaches. Feature set selection and ranking of attributes are done based on individual models. Based on the confusion matrix parameters the models prediction outcomes are found out in the supervisory training mode. The Confusion matrix of the models before and after dimensionality reduction is computed. Models are compared based on weighted Reader Operator Characteristics. Normalized weights are assigned based for the result of individual models and predictive model is developed. Predictive models performance is studied with target under supervised classifier model and it is observed that it is tallying with the expected outcome. 2005 - 2014 JATIT & LLS. All rights reserved. -
Feature selection based on the classifier models: Performance issues in the pre-diagnosis of lung cancer /
Journal of Theoretical and Applied Information Technology, Vol-59(3), pp.549-555. ISSN-1992-8645. -
Feature films as pedagogy in higher education: A case study of Christ University, Bengaluru
Contemporary education system in India was initiated by the British for the maintenance of their imperial administration. After India became an independent country, conscious efforts were made to overhaul the educational system to produce proper administrators and contributors for Indian polity, economy and culture. To assess dynamics of Indian education, various committees and commissions were formed. It also meant change in education programs, curricula and syllabi to meet national needs and global challenges. Most universities in India have limited infrastructure, thus the role of private or deemed to be university becomes crucial. Christ University attending to the social structure, internationalization and employability demands, offers a number of quality educational programs to ensure employable graduates. This has led the way in devising pedagogy and curricula to align with the global higher education practices. Here we discuss the use of commercial feature film as a pedagogical tool in the classrooms within the Deanery of Humanities and Social Sciences and its implication. 2018, IGI Global. -
Feature extraction of optical character recognition: Survey
Optical Character Recognition is still prevailing even after many decades of implementation. The challenges faced here are increasing day by day so as its applications. From Punched cards to Handwritten Text, from images to video, from uniform font to universal font, from English text to Global language, from researchers to visually handicapped are the transformations obtained from an era of the 1980s to 2010. This paper has covered the advancement of acknowledging the characters, how are features are extracted, various methodologies used and more importantly what is the use of OCR. Research India Publications. -
Feature extraction of clothing texture patterns for classification
Different features are extracted for Pattern Recognition using an efficient algorithms like Scale Invariant Feature Transform, Rotation invariant Radon Transform and extracting statistical features of a texture image. Support vector machine with RBF kernel in Weka is used in this paper for classification. This paper shows method to classify the clothing texture patterns like strips, plaid, pattern less and irregular pattern. This paper also proposes a method which can be efficient method to apply for the real time natural texture patterns and colors recognition systems. This paper gives the experiments results and the proposed method to enhance the experiments accuracy in future scope. 2015 IEEE. -
Feature Extraction for Collaborative Filtering: A Genetic Programming Approach
International Journal of Computer Science Issues, Vol. 9, Issue, 5, No. 1, pp. 348-354, ISSN No. 1694-0814 -
Feature extraction and diagnosis of dementia using magnetic resonance imaging
Dementia is a state of mind in which the sufferer tends to forget important data like memories, language, etc.. This is caused due to the brain cells that are damaged. The damaged brain cells and the intensity of the damage can be detected by using Magnetic Resonance Imaging. In this process, two extraction techniques, Gray Level Co-Occurrence Matrix (GLCM) and the Gray Level Run-Length matrix (GLRM), are used for the clear extraction of data from the image of the brain. Then the data obtained from the extraction techniques are further analyzed using four machine learning classifiers named Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and the combination of two classifiers (SVM+KNN). The results are further analyzed using a confusion matrix to find accuracy, precision, TPR/FPR - True and False Positive Rate, and TNR/FNR - True and False Negative Rate. The maximum accuracy of 93.53% is obtained using the GLRM Feature Extraction (FE) technique with the combination of the SVM and KNN algorithm. 2023, Bentham Books imprint. All rights reserved. -
Feature extraction and classification techniques of modi script character recognition
Machine simulation of human reading has caught the attention of computer science researchers since the introduction of digital computers. Character recognition is the process of recognizing either printed or handwritten text from document images and converting it into machine-readable form. Character recognition is successfully implemented for various foreign language scripts like English, Chinese and Latin. In the case of Indian language scripts, the character recognition process is comparatively difficult due to the complex nature of scripts. MODI script-an ancient Indian script, is the shorthand form for the Devanagari script in which Marathi was written. Though at present, the script is not used officially, it has historical importance. MODI character recognition is a very complex task due to its variations in the writing style of individuals, shape similarity of characters and the absence of word stopping symbol in documents. The advances in various machine learning techniques have greatly contributed to the success of various character recognition processes. The proposed work provides an overview of various feature extraction and classification techniques used in the recognition of MODI script till date and also provides evaluation and comparison of these techniques. 2019, Universiti Putra Malaysia Press. All rights reserved. -
Feature Based Fuzzy Framework for Sentimental Analysis of Web Data
Social mass media has emerged as a projectile platform for the evolution of web data. The sentimental Analysis where the huge textual online reviews are analyzed to extract the actual sentiment or emotions hidden in the reviews. In this paper an effective approach for sentimental analysis of web data is proposed which deploys the fuzzy based machine learning algorithm to accomplish fine-level sentiment analysis of huge online opinions by assimilating the fuzzy linguistic hedges influence on opinion descriptors. The seven layered categories are designed that uses SentiWordNet which has three stages: Pre-processing phase, Feature Selection Phase and Fuzzy based Sentiment Analysis phase. Various machine learning algorithms like AdaBoost, (IBK) K-Nearest Neighbour, (NB) Nae Bayes and (SVM)/SMO Support Vector Machine are used for classification. Jsoup is implemented for gathering web opinions which are subjected to initial processing task later applied with stemming and tagging. This fuzzy based methodology is investigated for Mobile, Laptops dataset, also compared with state-of-the-art approaches which demonstrate upper indication of 94.37% accurateness through Kappa indicators showcasing lesser error rates. The investigational outcomes are tested on training data using Ten-Fold cross validation which concludes that this approach can be efficaciously used in Sentimental analysis as an aid for online decision. 2019 IEEE. -
Fear of COVID-19, workplace phobia, workplace deviance and perceived organizational support: A moderated mediation model
This paper aims to test a moderated-mediation model examining therelationships between Fear of COVID-19, workplace phobia, work deviance behaviourand perceived organizational support among hotel employees. An online questionnaire was administered to collect data, to which 481 responded. Data was collected from full-time frontline employees working in the Maldivian hospitality industry. The moderated-mediation model explained 44% of the variance in workplace deviance behaviourscan be predicted bythe fear of COVID-19, perceived organisational support and workplace phobia. The findingsshowthat perceived organizational support reduces the negative impact of COVID-19 fear on workplace phobia and deviance. Results suggest that to reduce the negative effect of the pandemic, organisations should adopt support measures across different managerial levels at different scales rather than providing one-size-fits-all solutions. 2023 The Authors. Stress and Health published by John Wiley & Sons Ltd.