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Detection of toxic comments over the internet using deep learning methods
People now share their ideas on a wide range of topics on social media, which has become an integral part of contemporary culture. The majority of people are increasingly turning to social media as a necessity, and there are numerous incidents of social media addiction that have been reported. Socialmedia channels. Socialmedia platforms have established their worth over time by bringing individuals from different backgrounds together, but they have also shown harmful side effects that could have serious consequences. One such unfavourable result is how extremely poisonous many discussions on social media are. Online abuse, hate speech, and occasionally outrage culture are now all considered to be toxic. In this study, we leverage the Transformers Bidirectional Encoder Representations to build an efficient model to detect and classify toxicity in user-generated content on social media. The Kaggle dataset with labelled toxic comments, was used to refine the BERT pre-trained model. Other Deep learning models, including Bidirectional LSTM, Bidirectional-LSTM with attention, and a few other models, were also tested to see which performed best in this classification task. We further evaluate the proposed models utilising dataset obtained from Twitter in order to find harmful content (tweets) using relevant hashtags. The findings showed how well the suggested methodology classified and analysed toxic comments. 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors. -
Detection of toxic comments over the internet using deep learning methods
People now share their ideas on a wide range of topics on social media, which has become an integral part of contemporary culture. The majority of people are increasingly turning to social media as a necessity, and there are numerous incidents of social media addiction that have been reported. Socialmedia channels. Socialmedia platforms have established their worth over time by bringing individuals from different backgrounds together, but they have also shown harmful side effects that could have serious consequences. One such unfavourable result is how extremely poisonous many discussions on social media are. Online abuse, hate speech, and occasionally outrage culture are now all considered to be toxic. In this study, we leverage the Transformers Bidirectional Encoder Representations to build an efficient model to detect and classify toxicity in user-generated content on social media. The Kaggle dataset with labelled toxic comments, was used to refine the BERT pre-trained model. Other Deep learning models, including Bidirectional LSTM, Bidirectional-LSTM with attention, and a few other models, were also tested to see which performed best in this classification task. We further evaluate the proposed models utilising dataset obtained from Twitter in order to find harmful content (tweets) using relevant hashtags. The findings showed how well the suggested methodology classified and analysed toxic comments. 2024 The Author(s). -
Detection of tuberculosis using convolutional neural network with transfer learning
Tuberculosis is sighted as the one of the life causing disease in the recent time. The current research work focus on detection of Tuberculosis using Convolutional Neural Network with Transfer Learning for chest X-ray images. The proposed research work uses two different datasets for detecting Tuberculosis from Chest X-ray images, which is taken from National Institutes of Heaths. During the experimental work, the total sample size used for detecting Tuberculosis is 800 instances. Initially, the image processing techniques were applied to increase the quality of Chest X-ray images. The proposed model uses Convolution Neural Network with transfer learning for the detection of Tuberculosis with 98.7% as accuracy. The proposed model is checked with convolutional neural network without transfer learning. From the experimental evaluation, it is found that the proposed model works better than the Convolution Neural Network without using the transfer learning. 2017, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Detection of Various Security Threats in IoT and Cloud Computing using Machine Learning
Due to the growth of internet technology, there is a sharp rise in the growth of IoT enabled devices. IoT (Internet of Things) refers to the connection of various embedded devices with limited processing and memory. With the heavy adoption of IoT applications, cloud computing is gaining traction with the ever-increasing demand to process and compute a massive amount of data coming from various devices. Hence, cloud computing and IoT are often related to each other. However, there are two challenges in deploying the IoT and cloud computing frameworks: security and Privacy. This article discusses various types of security threats affecting IoT and cloud computing, and threats are classified using machine learning (ML). ML has gained much momentum in recent years and is applied in various domains. One of the main subdomains of machine learning is used in IoT and cloud security. A machine learning model can be trained with data based on which the model can predict the impending security threats. Popular security techniques to protect IoT devices from hackers are IoT authentication, access control, malware detection, and secure overloading. Supervised learning algorithms can be used to detect malware in the runtime behavior of applications. The malware is detected from network traffic and is labeled based on its suspicious behavior. Post identification of malware, the application data is stored in a database trained via an ML classifier algorithm (KNN or Random Forest). With increased training, the model can identify malware applications with higher accuracy. 2022 IEEE. -
Detection of X-ray polarization in the high synchrotron peaked blazar 1ES 1959+650
We report the measurement of X-ray polarization in the high synchrotron peaked blazar 1ES 1959+650. Of the four epochs of observations from the Imaging X-ray Polarimetry Explorer, we detected polarization in the 28 keV band on two epochs. From the model-independent analysis of the observations on 28 October 2022, in the 28 keV band, we found the degree of polarization of ?X=9.01.6% and an electric vector position angle of ?X=535 deg. Similarly, from the observations on 14 August 2023, we found ?X and ?X values as 12.50.7% and 202 deg, respectively. These values are also in agreement with the values obtained from spectro-polarimetric analysis of the I, Q, and U spectra. The measured X-ray polarization is larger than the reported optical values, ranging between 2.5% and 9% when observed from 2008 to 2018. Broadband spectral energy distribution constructed for the two epochs is well described by the one-zone leptonic emission model with the bulk Lorentz factor (?) of the jet larger on 14 August 2023 compared to 28 October 2022. Our results favor the shock acceleration of the particles in the jet, with the difference in ?X between the two epochs being influenced by a change in the ? of the jet. Indian Academy of Sciences 2024. -
Determinant of Capital Structure in Indian Manufacturing Sector
Asia-Pacific Journal of Management Research and Innovation Vol. 8, No. 3. pp 265-269, ISSN No. 2319-510X -
Determinants and Impacts of Mergers and Acquisitions in the Drugs and Pharmaceutical Industry in India
Mergers and Acquisitions (MandAs) are inorganic growth strategies adopted by firms for achieving the objective of long-term growth maximization. Compared to other inorganic growth strategies like joint ventures and strategic alliances, MandAs offer deeper restructuring opportunities and better control over business over a long-term newlinebasis. During the third wave of globalization which started in early 1990s, MandAs became a popular strategy for firms to expand their businesses beyond the national boundaries. newlineIndian economy has been witnessing buzzling activity in the MandA landscape. A sectoral analysis of MandA trends identifies pharmaceutical sector as one of the top 5 newlinesectors with the highest MandA deal values during the period 2013-2016. Though Pharma sector has witnessed a decline in deal values during few years in the recent past, the resilience of the sector is visible through its ability to bounce back with record newlinebreaking deal values. Due to the continuous regulatory changes occurring in the domestic and foreign markets, pharma companies have to constantly change their strategies to survive and grow in the industry. MandAs enable pharma companies to adapt to these changes quickly. This study explores how the firms in the pharmaceutical sector use MandA as a strategy to navigate through the dynamic competitive landscape. The objectives of this research are threefold developing an understanding of the motives behind MandA decisions of the pharma firms, identification of the firm level determinants of acquisition probability and assessment of impact of MandAs. This study newlineuses qualitative content analysis for identification of MandA motives. The firm level newlinedeterminants of acquisition probability have been explored using Random Effect Logistic (REL) regression using panel data. Case study approach has been employed to assess the MandA impact by comparing the MandA motives with the post MandA outcomes. -
Determinants of adoption of digital payment services among small fixed retail stores in Bangalore, India
India is well on its way to becoming a trillion-dollar digital economy and the government is actively working towards it. Digital payment is taking up and gaining momentum in India. Digital payments have penetrated in all parts of life in India. But it is reported that digital payments are less penetrated among small vendors across the country. This study intends to identify and analyse the factors that determine the adoption of digital payment technologies among small fixed retail stores in tier 1 cities such as Bangalore. The study is based on primary data which is collected through well-structured questionnaires from small fixed retail merchants. The collected data are analysed to determine the factors affecting the adoption of digital payment services among small fixed retail merchants using appropriate statistical tools. The study has found that habit, pervasiveness, and operating costs are the factors that significantly affect the adoption of digital payment services among small fixed retail merchants. Copyright 2022 Inderscience Enterprises Ltd. -
Determinants of audit fee-evidence from indian companies
Several studies have examined the factors influencing audit fees across the world, especially on the possible conflict of interest of auditor and client that may be affecting the quality of audit. The paper is about the trend in audit fee in the Indian setting, with the backdrop of two regulatory changes: mandatory auditor rotation and the implementation of Ind AS, the converged version of IFRS. Examining the determinants of audit fees, the paper categorized the explanatory variables into three attributes; auditee (size, risk, and complexity), auditor (auditor size, tenure, joint audit, and auditor rotation), and regulatory (mandatory auditor rotation and IFRS). The sample consisted of all non-financial companies listed on the National Stock Exchange for a period of 10 years from 20092018 resulting in 12,419 firm years. The paper deployed panel data regression with fixed effects with audit fee as the dependent variable. The key findings suggested that audit fee was positively associated with the size of the auditor and the auditee and the ratio of accounts receivable. The paper also indicated that with the tenure of the auditor, the fee tended to increase, and auditor rotation had a significant impact on the auditor's fee. The findings of the study will help the policymakers on the regulation around auditor engagements. 2021, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Determinants of balance of payments- Evidence from Indian and US /
International Journal of Management Studies, Vol.5, Issue 5, Part , pp.116-120, ISSN No: 2249-0302 -
Determinants of bank profitability in India: Applications of count data models
This paper employs count data models, namely Poisson and negative binomial regression to investigate whether macroeconomic factors increase or decrease the count of number of 18 Indian public sector banks in losses. The analysis is based on quarterly data from Q3 2009 to Q4 2019. This paper also considers one and two lagged macroeconomic factors. The results provide a new perspective for understanding the determinants of bank profitability. The contemporary, one and two lagged gross domestic product (GDP) growth rate and inflation increase the count of number of banks in losses. Further, the count of number of banks in losses surges with increase in contemporary and one lagged index of industrial production (IIP). However, one and two lagged exchange rates are significant to shrink the count of number of banks in losses. This study enables banks and policy makers to deliberate on the macroeconomic determinants considered for this study. 2020 Inderscience Enterprises Ltd. -
Determinants of Banks' Profitability: An Empirical Study on Select Indian Public and Private Sector Banks
In this study the determinants of banking profitability has been studied based on the secondary data. The entire study is classified into two parts (i) Public Sector Banks and (ii) Private Sector Banks. Various variables such as NPA, Operating Profit, Credit Size, ROA, Operating Expense, Total Income, Capital etc. and their interrelationship is studied through correlation coefficients, regression analysis, anova etc. The research observes that a large number of independent factors are responsible in determining banking profitability and that in those determinants some create a significant effect on profitability but some factors do not create any significant effect. It is observed that though macroeconomic variables are not so important to determine the profitability of a bank but the GDP growth rate creates a significant effect on determining the profitability of a bank. According to the study based on facts and figures collected, private sector banks performance is better than public sector banks. Indian Institute of Finance. -
Determinants of Book Built IPO underpricingdifferential issue size and market momentum approach revisited
Pricing of an Initial public offering (IPO) is a complex phenomenon. Price anomalies are commonly observed in IPO markets, especially in emerging markets. Investors perceived underpricing creates undue market momentum during the offer period with an asymmetric effect across different issue sizes. This study examines the determinants of Book Built IPOs underpricing by considering a sample of 180 Book Built IPOs that went public in India between 2011 and 2020. The determinants were verified for differential issue size public offers. Listing day performance was measured using Listing Day-Absolute Return (LD-AR) and Listing Day-Market Adjusted Return (LD-MAR) models. Further, the data obtained was tested for the explanatory capabilities of firm-specific and market momentum factors for underpricing using OLS models. Concerning the differential issue size, the study found a direct relationship between the issue size and underpricing. Dominant underpricing was observed in the case of moderate to large issue size with a linear progressive return, confirming that there was over-optimism on the part of investors. The studys results also revealed that momentum-specific factors have a significant influence along with firm-specific factors such as firm size, cash flows, a subscription rate of QIBs and RIIs in the listing day return, and underpricing. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Determinants of Charitable Giving of Employees in the Organized Sector: A Case Study in Bengaluru Urban District.
The study aims at identifying the determinants of charitable giving among working professionals who are into white collar jobs in the organized sector in Bengaluru urban district. The study also tries to understand the effect of Income and Non-Income factors that could affect decisions made by individuals towards charitable donations. The study captures charity donations in terms of money, time and gifts, based on its objectives; the focus is on monetary donations. Thus, based on the various available models based on demographic variables and attitudinal factors, the study has developed a comprehensive function that that includes both demographic and attitude related factors that could predict the charity behavior of an individual and in this case it is the working professional. The population includes all the white collar jobs and the sample size was 132 respondents. This includes both charity givers and non-givers. The sampling technique used was purposive random sampling and data was collected through questionnaire method and the questionnaire begins with an introductory question seeking if the respondent have made any donations in the last 12 months and based on their answer they are directed towards the three sections such as section A, B and C for those who said yes to the question and B and c for those who said no to the question. Section A is about the charity activities of the respondents, B is about the demographic details and C measures altruism, prestige, care and other attitude related factors using a 5 point Liker scale. Factor analysis was made used for the purpose of model testing. However, prior to the model testing, bi-variate and multi-variate exploratory analysis was done using cross tabulation in SPSS and python to understand the association between variables used in the study. To further clarify and conclude the relationship and strength of association between variables, Pearson???s Chi square was conducted. Based on these results most of the demographic variables seem to have positive relationship with charitable giving and few had partially negative relation with the incidence of giving. For example Gender, where being male have less chance of making charitable donations. Religion has no impact on the likelihood of making donations. viii Certain other variables such as age, education level and income have a positive relationship with charity giving. In other words as age, income, level of education are higher, the chances of making monetary donations increases. From the results of exploratory bi -variate analysis, certain variables were removed and were not part of factor analysis towards testing the model. The results of factor analysis shows that charitable giving (monetary) is the function of three factors namely benevolence, socioeconomic status and warm glow giving and thus it proves the model developed by the study. Thus the major determinants or predictors of charitable giving (monetary) are benevolent behaviour, socioeconomic status and warm glow giving. -
Determinants of consumer product return behavior with respect to online shopping of apparels
This research aims at finding the determinants of consumer product return newlinebehavior with respect to online shopping of apparel in Bangalore city. The study was administered to 600 respondents, and the response received was from 465 respondents. The convenience sampling method was used to collect samples across Bangalore city. Product return behavior was measured using a newlinefive-point Likert scale for 34 items. The literature review was conducted extensively, covering both Indian and international context. This research is designed to address the literature gaps. Many hypotheses were proposed in the thesis and were examined using structural equation modeling. The hypotheses were tested with the software newlineAMOS 25 and SPSS 25 to fulfill the research objectives. Confirmatory factor analysis was done on the data to confirm the instrument reliability and validity. Confirmatory factor analysis was used to verify the constructs developed from the detailed literature review. ANOVA post hoc test was done to check the relationship among the demographic variables. Descriptive statistics were used newlineto interpret the data. With the help of structural equation modeling, the causal newlinerelationship between the dependent variable and the independent variables were identified. The study on the determinants of product return behavior has provided a lot of newlineinsights. Customer attitude has a significant and negative impact on product return behavior. The customers with a positive attitude towards online apparel purchases will be less likely to return products. The previous customer experience and their consumption pattern have a significant and negative impact on product return behavior. The customers with a bad experience with newlinebuying online apparel products, tend to return their products more. The perceived risk of online apparel purchases has a significant and positive impact on product return behavior. The customers with a high perceived risk of online apparel purchases will be more likely to return their products. -
Determinants of consumer product return behaviour with respect to online shopping of apparels
This research aims at finding the determinants of consumer product return behavior with respect to online shopping of apparel in Bangalore city. The study was administered to 600 respondents, and the response received was from 465respondents. The convenience sampling method was used to collect samples across Bangalore city. Product return behavior was measured using a five-point Likert scale for 34 items. -
Determinants of consumer retention strategies for telecom service industry in Central India
The telecommunication industry has witnessed a tremendous growth in recent times in India. It has not only been limited to voice calls, but also integrated into every aspect of human life. This has resulted in the rapid rise of market players, offering innovative products and services. In this changing scenario, we have tried to design and check a model of various factors such as loyalty, satisfaction and switching barriers (customer relationship management, alternative attractiveness and switching cost) influencing consumer retention strategies in Indian telecom service industry. A structured and undisguised questionnaire and a convenient sampling method have been used to collect the data from respondents from three most populous cities (Indore, Bhopal, and Ujjain) of Central India. Around 450 questionnaires were distributed, out of which 318 usable responses were received for final analysis. The instrument was checked for validity and reliability before the data was analyzed. The hypotheses were tested through Structure Equation Modelling (SEM) for direct effect, and Multiple Moderating Regression Analysis (MMRA) for moderating effect. The results suggested that loyalty, satisfaction, switching barriers and customer relationship management are positively related and have a direct influence on consumer retention, but the relationship with alternative attractiveness has been found weak. Switching cost, as moderating variable, was found to be very effective and showed significant deviation in the relationship between independent and dependent variables. Vinod Sharma, Sunny Joseph, Jeanne Poulose, 2018 -
Determinants of corporate dividend policy in India: A dynamic panel data analysis
The present study empirically examines the determinants of dividend policy of National Stock Exchange (NSE) listed firms in India, using dynamic panel data model for the sample of 95 NSE listed firms with continuous dividend payments from 2012/2013 to 2017/2018. The empirical results reveal that profitability, liquidity, leverage, risk, size of the firm and inflation are the major determinants of dividend policy of selected NSE listed firms in India. Findings deduced from empirical evidence bears testimony to the fact that profitability, liquidity, size of the firm and inflation have significant negative impact on dividend policy of the selected NSE firms covered by the study. These findings seem contradictory to the expected outcome contained in the existing literature on the Indian context. The risk variable tends to have negative and significant impact, which is line with the existing literature. Besides, the lagged dividend, investment opportunities, taxation and yield curve do not play significant role in determining the dividend policy. 2020 Allied Business Academies. -
DETERMINANTS OF CREDIT RISK: EMPIRICAL EVIDENCE FROM INDIAN COMMERCIAL BANKS
Credit risk is a significant factor affecting the financial stability of banks. Keeping the credit risk under control is essential to maintain a banks cash flow. This paper examines the various profitability, microeconomic and macroeconomic indicators that affect a banks credit risk. The study uses the dataset of 31 banks from 2012 to 2021 and employs a panel data modelling approach to account for any variations in risk-taking behavior. The results revealed a statistically significant negative relationship between return on equity and credit risk when nonperforming loans proxy credit risk. This finding was consistent across fixed effect, random effect, and pooled OLS methods, at 1 percent significance (P value < 0.00), indicating that the extent of credit risk decreases as profitability increases. It was further found that bank age and ownership type positively affect a banks credit risk, while factors such as bank size and operational efficiency negatively affect credit risk when nonperforming loans proxy credit risk. Further, macroeconomic variables showed that gross domestic product is positively associated with credit risk, while inflation negatively affects credit risk. Overall, the findings of this paper demonstrated that credit risk is affected by both micro and macroeconomic factors. The paper also addresses significant policy implications as it helps various stakeholders to examine the determinants of credit risk, make credit decisions, and ultimately lower their credit risk. Tisa Maria Antony, Suresh G., 2023. -
Determinants of customer loyalty and retention : A Study of supermarket customers in Bangalore
Considering the ever dynamic lifestyle of the customers in Bangalore,the proposed study tried to find out the expectations and aspirations of supermarket customers in Bangalore.Most of the currently available studies are based on conceptual understanding and don t have an empirical backup. The proposed study is designed to determine the customer loyalty and retention of Supermarket Customers in Bangalore. newlineThe literature initiates an exhaustive discussion of various constructs leading to customer retention. Based on references from the literature constructs identified for customer retention are customer satisfaction, switching costs and customer loyalty and for customer loyalty the constructs identified are trust,commitment and customer satisfaction.For determining satisfaction the constructs identified in the proposed study are convenience of location, store atmosphere,promotion, customer relationship management practices and merchandise. Through extensive literature review,hypotheses were derived and the proposed conceptual model is developed. newlineObjectives of the proposed study are to empirically validate a model linking customer satisfaction, customer loyalty and customer retention with select antecedents. Research Methodology explains about the population spread from which the samples are collected, the justification for using the particular sampling technique and also about the tool employed for data collection. The techniques employed for checking the reliability and validity of the tool and pilot data analysis are also explained. Data collection was conducted using a structured questionnaire designed using Likert scale measurement. The pilot sample data consisted of 250 respondents.The questionnaires were analyzed using SPSS(v.20,software using Cronbach Alpha, Intra Class Correlation and Confirmatory factor analysis. newlineThe data collected from 600 respondents in Bengaluru city was used for the full fledged study.