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The youth's way of personal branding as bookstagrammers
The Bookstagrammers use Instagram to post about their life as avid readers and regard themselves as social media influencers (SMIs) for books. Creating a personal brand helps influencers differentiate themselves from other users. Similarly, Bookstagrammers have made a place for themselves as book influencers in this growing SMI market. Since Bookstagrammers have the potential to influence the publishing industry's sales, creating a personal brand plays a significant role in their career as an influencer. Some Bookstagrammers have successfully created their personal brand by posting book-related and reader-centric content on Instagram and are followed by a niche audience of readers. This study conducted content analysis on India's two most popular Bookstagrammers, and discusses their personal branding strategies. The results showcase 8 broad categories of content shared by the Bookstagrammer that are mapped to three elements of personal branding: brand identity, brand positioning, and brand image, showing how social media fuels the youth's creativity to as SMIs. 2023, IGI Global. All rights reserved. -
Drivers of Mandatory and Non-Mandatory Internet Corporate Reporting in Public and Private Sector Indian Companies
The papers objective was to measure the drivers of mandatory and non-mandatory internet corporate reporting by public and private sector companies following the internet disclosure compliance of listing and obligation requirement of SEBI under Clause 46. Several drivers, namely firm size, profitability, leverage, liquidity, board size, independence of board, and CEO duality, were used to measure the effectiveness of mandatory and non-mandatory disclosure. A multiple regression model was applied to test the present papers hypotheses. The results of multiple regression revealed that the firms size was exceptionally important for both sectors. In contrast, public sector disclosure was largely impacted by leverage, liquidity, board size, and board independence. In comparison, the private sector disclosure scores were mainly impacted by leverage and board size, although there is no relationship between ICR and firm profitability and CEO duality. In performing separate multivariate regression between the two sectors, many disparities emerged. This disparity showed that public and private sector corporations had quite different firm and governance characteristics of the disclosure. As the first exploratory research to assess the mandate internet disclosure of public and private sector companies in India, it is very informational, specifically for those working on Indian companies regulation, compliance, and research. 2022, Associated Management Consultants Pvt. Ltd. All rights reserved. -
Is corporate reputation associated with voluntary cybersecurity risk reporting?
Purpose: This study investigated the effect of voluntary cybersecurity risk reporting (VCRR) on corporate reputation. By examining the association between VCRR and corporate reputation, this study aims to provide exploratory evidence of how cybersecurity risk is sensitive to a companys image and reputation. Design/methodology/approach: An automated content analysis of VCRR by 95 Bombay Stock Exchange-listed companies was undertaken using Python code. Signaling and legitimacy theories were adopted to interpret the findings, establishing whether VCRR was related to corporate reputation. Findings: The results confirm that VCRR improves the corporate reputation in the financial market. The results also confirm the signalling and legitimacy theory that a company can manage reputational risks through higher voluntary risk disclosure. Practical implications: The corporations managers can gain insights from the studys findings and proactively address cybersecurity risks through strategic disclosure and management practices. In addition, organizations can recognize that investors value transparency and establish a positive reputation for those who communicate openly. Social implications: A significant association between VCRR and corporate reputation implies that such disclosures enhance trust and transparency in the business sector and induce security and accountability among investors engaging with the company. Originality/value: To the best of the authors knowledge, this study is the first that empirically investigates this issue and adds to the international literature a new explanatory variable, corporate reputation, to explain VCRR practices. 2024, Emerald Publishing Limited. -
Voluntary cybersecurity risk disclosures and firms characteristics: the moderating role of the knowledge-intensive industry
Purpose: This study examines voluntary cybersecurity risk disclosures (VCRD) by listed Indian companies. It also investigates how it relates to firm-specific characteristics such as size, leverage, profitability, liquidity, beta, market growth and industry. Design/methodology/approach: The extent of VCRD was measured by assessing the cumulative occurrence of cybersecurity risk keywords in the annual report of 100 listed Indian non-financial companies. Keyword extraction and occurrence counts were performed using Python software. A multiple regression analysis was applied to predict the characteristics of VCRD. Findings: The results showed that the theoretical frameworks underpinned by agency and signalling theories continued to provide a valid explanation of VCRD by Indian companies. Specifically, the findings emphasized the importance of firm size, leverage, and beta as significant VCRD determinants. Additionally, the study found that knowledge-intensive industries had a favourable impact on the extent of VCRD. Research limitations/implications: This study is relevant because it informs company management, regulators and investors about the nature and characteristics of companies that satisfy stakeholder demands to prevent cyber breaches. Originality/value: Understanding disclosure characteristics is crucial from policy and regulatory perspectives. Studies on cybersecurity disclosures are related to developed economies such as the United States of America and Canada. This is the first study to explore this issue in a developing nation, in general, and in India, in particular, where cybersecurity risk disclosure has yet to be recognized. 2025, Harmandeep Singh. -
Randomized response model to alter the nuisance effect of non-response due to stigmatized issues in survey sampling
The present study deals with the estimation procedures of the mean number of persons bearing a rare sensitive attribute in the clustered population under two-stage sampling scheme. The resultant estimators have been suggested using two-stage randomized response model when a rare unrelated attribute is assumed to be known as well as unknown. The properties of resultant estimators are studied where the first-stage samples are drawn using the probability proportional to the size with replacement sampling scheme. The estimation procedures have been further extended for the stratified clustered population. The empirical studies are performed for the validation of the suggested estimation procedures. Recommendations have been made to survey practitioners for their real-life applications. 2020 Informa UK Limited, trading as Taylor & Francis Group. -
An Improved Alternative Method of Imputation for Missing Data in Survey Sampling
In the present paper, a new and improved method of ratio type imputation and corresponding point estimator to estimate the finite population mean is proposed in case of missing data problem. It has been shown that this estimator utilizes the readily available auxiliary information efficiently and gives better results than the ratio and mean methods of imputation; furthermore, its efficiency is also compared with the regression method of imputation and some other imputation methods, discussed in this article, using four real data sets. A simulation study is carried out to verify theoretical outcomes, and suitable recommendations are made. 2022 NSP Natural Sciences Publishing Cor. -
Proficient randomized response model based on blank card strategy to estimate the sensitive parameter under negative binomial distribution
This paper has great potential for estimating population proportion who possess stigmatized character by using Negative binomial distribution as a randomization device. The properties of the proposed estimation procedure have been examined. Measure of privacy protection for the proposed randomization device has been also quantified. Empirical studies are performed to support the theoretical results, which show the dominance of the proposed estimator over its competitors. Results are analysed and suitable recommendations are put forward for survey practitioners whenever they deal with sensitive characteristics. 2021 -
CNN-RNN based Hybrid Machine Learning Model to Predict the Currency Exchange Rate: USD to INR
Foreign currency exchange plays an imperative part in the global business and in monetary market. It is also an opportunity for many traders as an investment option and the advance knowledge of fluctuation helps the investors making right decision on time. However, due to its volatile nature, prediction of foreign currency exchange is a challenging task. This paper implements two models based on machine learning, namely Recurrent Neural Networks (RNN) and a Hybrid model of Convolutional Neural Networks (CNN) with RNN known as CNN-RNN to assess the accuracy in predicting the conversion rate of US Dollar (USD) to Indian Rupees (INR). The data set used to verify and validate the models is the daily currency exchange rate (USD to INR) available in public domain. The experimental results show that the simple RNN model performs slightly better than the hybrid model in this particular case. Though the accuracy of the hybrid model is very high in terms of error calculation still the single RNN model is the better performer. This does not straight away reject the hybrid model rather needs more experimental analysis with changing architecture and data set. 2022 IEEE. -
Impact of Using Partial Gait Energy Images for Human Recognition by Gait Analysis
Gait analysis is a behavioral biometric that classifies human, based on how they walk and other variables involved in the forward movement. In this study, we have attempted to comprehend the significance of the upper portion of the body in gait analysis for human recognition. The data for this study came from the CASIA dataset, which was donated by the Chinese Academy of Sciences Institute of Automation. We began by extracting the gait energy image (GEI) from the dataset and employing principal component analysis to minimize the dimensionality (PCA). For classification, random forest, support vector machine (SVM), and convolution neural network (CNN) algorithms are implemented to recognize the human subjects. This paper provides experimental results to show the accuracy attained when classification is done on GEI of full-body images is higher than the accuracy attained when classification is done on GEI of the lower portion of the body only. It also shows the significance of the GEI of the upper portion of the body. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Applicability of Search Engine Optimization for WordPress (WP) Website
91 percent of online experiences begin with a search, according to the Content Marketing Institute. That is the hunt for an explanation. As a result, search marketing is a critical practice for any businesses looking to grow and improve. Marketers and clients that paid for adverts began researching SEO and SEM at that time. This pursuit plans to give knowledge into the paid and unpaid procedures of search engine marketing (SEM) and what falls under its umbrella including search engine optimization (SEO) and pay per click (PPC). So in this exploration work, we feel the most ideal approach to utilize a web search tool SEM, is such a method of Internet showcasing that incorporates the utilization of web crawler result pages to advance business sites. SEM was earlier used as a protective gadget for anything to be done with the online search marketing field and it was girdled along with SEO. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Ear Recognition Using ResNet50
Deep learning techniques have become increasingly common in biometrics over the last decade. However, due to a lack of large ear datasets, deep learning models in ear biometrics are limited. To address this drawback, researchers use transfer learning based on various pre-trained models. Conventional machine learning algorithms using traditional feature extraction techniques produce low recognition results for the unconstrained ear dataset AWE. In this paper, an ear recognition model based on the ResNet-50 pretrained architecture outperforms traditional methods in terms of recognition accuracy in AWE dataset. A new feature level fusion of ResNet50 and GLBP feature is also experimented to improve the recognition accuracy compared to traditional features. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Search Engine Optimization for Digital Marketing to Raise the Rank, Traffic, and Usability of the Website
According to the Content Marketing Institute, 93% of online experiences start with search. That is the explanation search. Thats why search promoting is a crucial procedure for all organizations to improve and develop their organizations. At that time the marketers and the clients who paid for advertisements started analyzing SEO and SEM. Web crawler promoting expands the perceivability of sites through SEO or through paid publicizing with the plan of expanding traffic to the site. SEM eludes to all advertising exercises that utilization web index innovation for promoting purposes. These incorporate SEO, paid postings and advertisements, and other web crawler related administrations and capacities that will expand reach and introduction of the site, bringing about more prominent traffic. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Early diagnosis of COVID-19 patients using deep learning-based deep forest model
Coronavirus disease-19 (COVID-19) has rapidly spread all over the world. It is found that the low sensitivity of reverse transcription-polymerase chain reaction (RT-PCR) examinations during the early stage of COVID-19 disease. Thus, efficient models are desirable for early-stage testing of COVID-19 infected patients. Chest X-ray (CXR) images of COVID-19 infected patients have shown some bilateral changes. In this paper, deep transfer learning and a deep forest-based model are proposed to diagnose COVID-19 infection from CXR images. Initially, features of X-ray images are extracted using the well-known deep transfer learning model (i.e., ResNet101), which does not require tuning many parameters compared to the deep convolutional neural network (CNN). After that, the deep forest model is utilised to predict COVID-19 infected patients. The deep forest is based upon ensemble learning and requires a small number of hyper-parameters. Additionally, the proposed model is trained on a multi-class dataset that contains four different classes as COVID-19 (+), pneumonia, tuberculosis, and healthy patients. The comparisons are drawn among the proposed deep transfer learning and deep forest-based models, the competitive models. The obtained results show that the proposed model effectively diagnoses COVID-19 infection with an accuracy of 99.4%. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Juice Jacking: Security Issues and Improvements in USB Technology
For a reliable and convenient system, it is essential to build a secure system that will be protected from outer attacks and also serve the purpose of keeping the inner data safe from intruders. A juice jacking is a popular and spreading cyber-attack that allows intruders to get inside the system through the web and theive potential data from the system. For peripheral communications, Universal Serial Bus (USB) is the most commonly used standard in 5G generation computer systems. USB is not only used for communication, but also to charge gadgets. However, the transferal of data between devices using USB is prone to various security threats. It is necessary to maintain the confidentiality and sensitivity of data on the bus line to maintain integrity. Therefore, in this paper, a juice jacking attack is analyzed, using the maximum possible means through which a system can be affected using USB. Ten different malware attacks are used for experimental purposes. Various machine learning and deep learning models are used to predict malware attacks. An extensive experimental analysis reveals that the deep learning model can efficiently recognize the juice jacking attack. Finally, various techniques are discussed that can either prevent or avoid juice jacking attacks. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Approach for Collision Minimization and Enhancement of Power Allocation in WSNs
Wireless sensor networks (WSNs) have attracted much more attention in recent years. Hence, nowadays, WSN is considered one of the most popular technologies in the networking field. The reason behind its increasing rate is only for its adaptability as it works through batteries which are energy efficient, and for these characteristics, it has covered a wide market worldwide. Transmission collision is one of the key reasons for the decrease in performance in WSNs which results in excessive delay and packet loss. The collision range should be minimized in order to mitigate the risk of these packet collisions. The WSNs that contribute to minimize the collision area and the statistics show that the collision area which exceeds equivalents transmission power has been significantly reduced by this technique. This proposed paper optimally reduced the power consumption and data loss through proper routing of packets and the method of congestion detection. WSNs typically require high data reliability to preserve identification and responsiveness capacity while also improving data reliability, transmission, and redundancy. Retransmission is determined by the probability of packet arrival as well as the average energy consumption. 2021 Debabrata Singh et al. -
Deep Learning Inspired Nonlinear Classification Methodology for Handwritten Digits Recognition Using DSR Encoder
The overlapped handwritten digit classification is a global challenge and a significant measure to assess the network recognition ability ratio. Most efficient models have been designed based on convolutional neural networks (CNN) for effective image classification and digit identification. Subsequently, multiple CNN models have inadequate accuracy because of high degree parameter dimensions that lead to abnormal digit detection error rates and computation complexity. We propose a Deep Digit Recognition Network (DDRNet) based on Deep ConvNets to minimize the number of parameters and features to keep the model light while maximizing the accuracy with an adaptive voting (AV) scheme for digit recognition. The individual digit is identified by CNN, and uncertain digits or strings are identified by Deep Convolutional Network (DCN) with AV scheme through Voting-Weight Conditional Random Field (VWCRF) strategy. These methods originated with the YOLO algorithm. The simulations show that our DDRNet approach achieves an accuracy of 99.4% without error fluctuations, in a stable state with less than 15 epochs contrast with state-of-art approaches. Additionally, specific convolution techniques (SqueezeNet, batch normalization) and image augmentation techniques (dropout, back-propagation, and an optimum learning rate) were examined to assess the system performance based on MNIST dataset (available at: http://yann.lecun.com/exdb/mnist/). 2022, King Fahd University of Petroleum & Minerals. -
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A high-efficiency poly-input boost DCDC converter for energy storage and electric vehicle applications
This research paper introduces an avant-garde poly-input DCDC converter (PIDC) meticulously engineered for cutting-edge energy storage and electric vehicle (EV) applications. The pioneering converter synergizes two primary power sourcessolar energy and fuel cellswith an auxiliary backup source, an energy storage device battery (ESDB). The PIDC showcases a remarkable enhancement in conversion efficiency, achieving up to 96% compared to the conventional 8590% efficiency of traditional converters. This substantial improvement is attained through an advanced control strategy, rigorously validated via MATLAB/Simulink simulations and real-time experimentation on a 100 W test bench model. Simulation results reveal that the PIDC sustains stable operation and superior efficiency across diverse load conditions, with a peak efficiency of 96% when the ESDB is disengaged and an efficiency spectrum of 9195% during battery charging and discharging phases. Additionally, the integration of solar power curtails dependence on fuel cells by up to 40%, thereby augmenting overall system efficiency and sustainability. The PIDCs adaptability and enhanced performance render it highly suitable for a wide array of applications, including poly-input DCDC conversion, energy storage management, and EV power systems. This innovative paradigm in power conversion and management is poised to significantly elevate the efficiency and reliability of energy storage and utilization in contemporary electric vehicles and renewable energy infrastructures. The Author(s) 2024. -
Design and performance evaluation of a multi-load and multi-source DC-DC converter for efficient electric vehicle power systems
This paper introduces the design and comprehensive performance evaluation of a novel Multi-Load and Multi-Source DC-DC converter tailored for electric vehicle (EV) power systems. The proposed converter integrates a primary battery power source with a secondary renewable energy sourcespecifically, solar energyto enhance overall energy efficiency and reliability in EV applications. Unlike conventional multi-port converters that often suffer from cross-regulation issues and limited scalability, this converter ensures stable power distribution to various EV subsystems, including the motor, air conditioning unit, audio systems, and lighting. A key feature of the design is its ability to independently manage multiple power loads while maintaining isolated outputs, thus eliminating the inductor current imbalance that is common in traditional systems. Experimental validation using a 100W prototype demonstrated the converters ability to deliver stable 24V and 48V outputs from a 12V input, with output voltage deviations kept within 1%, significantly improving upon the 5% deviations typically seen in existing converters. Furthermore, the system achieved an impressive 93% efficiency under variable load conditions. The modular nature of the converter makes it not only suitable for EV applications but also for a broader range of industries, including renewable energy systems and industrial power supplies. This paper concludes by discussing optimization strategies for future improvements and potential scaling of the technology for commercial use in sustainable energy applications. The Author(s) 2024.