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Detection and identification of un-uniformed shape text from blurred video frames
The identification and recognition of text from video frames have received a lot of attention recently, that makes many computer vision-based applications conceivable. In this study, we modify the picture mask and the original identification of the mask region convolution neural network and permit detection in three levels, including holistic, sequence, and at the level of pixels. To identify the texts and determine the text forms, semantics at the pixel and holistic levels can be used. With masking and detection, existences of the character and the word are separated and recognised. In addition, text detection using the results of 2-D feature space instance segmentation is done. Moreover, we explore text recognition using an attention-based optical character recognition (OCR) method with mask region convolution neural networks (R-CNN) to address and detect the problem of smaller and blurrier texts at the sequential level. Using attribute maps of the word occurrences in sequence to seq, the OCR method calculates the character sequence. At last, a fine-grained learning strategy is proposed to constructs models at word level using the annotated datasets, resulting in the training of a more precise and reliable model. The well-known benchmark datasets ICDAR 2013 and ICDAR 2015 are used to test our suggested methodology. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Detection and localization for watermarking technique using LSB encryption for DICOM Image
Watermarking is an effective way of transferring hidden data from one place to another, or proving ownership of digital content. The hidden data can be text, audio, images GIF etc., the data is embedded in a cover object usually an image or a video sequence. Usually the watermarking system(s) rely on their hidden aspect, as their primary security measure, once this is established that the cover object is counting some hidden data, then it is generally possible to recover the hidden information. The author proposed an in-genuine technique for DICOM color image water marking by combining Multi Quadrant LSB with truly random mixed key cryptography. This system provides a high level of security by just the water marking technique, as it breaks the cover image into up to four quadrants, & does LSB replacement of two bytes each quadrant. The bit sequence as the quadrant sequence can be randomized to increase the randomness, use of truly random mixed key cryptography, by using a pre shared, variable length, truly random, private key, turns hidden data into noise, which can only be recovered by having the private key. Thus, the proposed technique truly diminishes the probability of recovering hidden data, even if it is detected that something is hidden in cover object. 2022 Taru Publications. -
Detection of a new sample of Galactic white dwarfs in the direction of the Small Magellanic Cloud
Aims. In this study, we demonstrate the efficacy of the Ultraviolet Imaging Telescope (UVIT) in identifying and characterizing white dwarfs (WDs) within the Milky Way Galaxy. Methods. Leveraging the UVIT point-source catalogue towards the Small Magellanic Cloud and cross-matching it with Gaia DR3 data, we identified 43 single WDs (37 new detections), 13 new WD+main-sequence candidates, and 161 UV bright main-sequence stars by analysing their spectral energy distributions. Using the WD evolutionary models, we determined the masses, effective temperatures, and cooling ages of these identified WDs. Results. The masses of these WDs range from 0.2 to 1.3 M? and the effective temperatures (Teff) lie between 10 000 K to 15 000 K, with cooling ages spanning 0.1-2 Gyr. Notably, we detect WDs that are hotter than reported in the literature, which we attribute to the sensitivity of UVIT. Furthermore, we report the detection of 20 new extremely low-mass candidates from our analysis. Future spectroscopic studies of the extremely low-mass candidates will help us understand the formation scenarios of these exotic objects. Despite limitations in Gaia DR3 distance measurements for optically faint WDs, we provide a crude estimate of the WD space density within 1kpc of 1.3 10-3 pc-3, which is higher than previous estimates in the literature. Conclusions. Our results underscore the instrumental capabilities of UVIT and anticipate forthcoming UV missions such as INSIST for systematic WD discovery. Our method sets a precedent for future analyses in other UVIT fields to find more WDs and perform spectroscopic studies to verify their candidacy. The Authors 2024. -
Detection of carbapenem resistance genes and cephalosporin, and quinolone resistance genes along with oqxAB gene in Escherichia coli in hospital wastewater: A matter of concern
Aims: This study was performed to detect the presence of Escherichia coli resistant to cephalosporins, carbapenems and quinolones in hospital wastewater. Methods and Results: Wastewaters from a rural (H1) and an urban (H2) hospital were tested for E.coli resistant to cephalosporins, carbapenem and quinolones. Genes coding for chromosomal and plasmid-mediated resistance and phylogenetic grouping was detected by multiplex polymerase chain reaction (PCR) and for genetic relatedness by rep-PCR. Of 190 (H1=94; H2=96) E.coli examined, 44% were resistant to both cephalosporins and quinolones and 3% to imipenem. ESBLs were detected phenotypically in 96% of the isolates, the gene blaCTX-M coding for 87% and blaTEM for 63%. Quinolone resistance was due to mutations in gyrA and parC genes in 97% and plasmid-coded aac-(6?)-Ib-cr in 89% of isolates. Only in one carbapenem-resistant E.coli, NDM-1 was detected. Nearly 67% of the isolates belonged to phylogenetic group B2. There was no genetic relatedness among the isolates. Conclusions: Hospital wastewater contains genetically diverse multidrug-resistant E.coli. Significance and Impact of the Study: This study stresses the need for efficient water treatment plants in healthcare settings as a public health measure to minimize spread of multidrug-resistant bacteria into the environment. 2014 The Society for Applied Microbiology. -
Detection of Forest Fire Using Modified LSTM Based Feature Extraction with Waterwheel Plant Optimisation Algorithm Based VAE-GAN Model
A crucial natural resource that directly affects the ecology is forests. Forest fires have become a noteworthy problem recently as a result of both natural and man-made climatic changes. A smart city application that uses a forest fire discovery technology based on artificial intelligence is provided in order to prevent significant catastrophes. A major danger to the environment, animals, and human lives is posed by forest fires. The early detection and suppression of these fires is crucial. This work offers a thorough method for detecting forest fires using advanced deep learning (DL) algorithms. Preprocessing the forest fire dataset is the initial step in order to improve its relevance and quality. Then, to enable the model to capture the dynamic character of forest fire data, long short-term memory (LSTM) networks are used to extract useful feature from the dataset. In this work, weight optimisation in LSTM is performed using a Modified Firefly Algorithm (MFFA), which enhances the model's performance and convergence. The Variational Autoencoder Generative Adversarial Networks (VAEGAN) model is used to classify the retrieved features. Furthermore, every DL model's success depends heavily on hyperparameter optimisation. The hyperparameters of an VAEGAN model are tuned in this research using the Waterwheel Plant Optimisation Algorithm (WWPA), an optimisation technique inspired by nature. WPPA uses the idea of plant growth to properly tune the VAEGAN's parameters, assuring the network's peak fire detection performance. The outstanding accuracy (ACC) of 97.8%, precision (PR) of 97.7%, recall (RC) of 96.26%, F1-score (F1) of 97.3%, and specificity (SPEC) of 97.5% of the suggested model beats all other existing models, which is probably owing to its improved architecture and training techniques. Copyright: 2024 The authors. This piece is published by IIETA and is approved under the CC BY 4.0 license. -
Detection of high-frequency pulsation in WR135: Investigation of stellar wind dynamics
We report the detection of high-frequency pulsations in WR 135 from short-cadence (10 minute) optical photometric and spectroscopic time series surveys. The harmonics up to the sixth order are detected from the integrated photometric flux variations, while the comparatively weaker eighth harmonic is detected from the strengths of the emission lines. We investigate the driving source of the stratified winds of WR 135 using the radiative transfer modeling code, CMFGEN, and find the physical conditions that can explain the propagation of such pulsations. From our study, we find that the optically thick subsonic layers of the atmosphere are close to the Eddington limit and are launched by the Fe opacity. The outer optically thin supersonic winds (Tross = 0.1 0.01) are launched by the He II and C IV opacities. The stratified winds above the sonic point undergo velocity perturbation that can lead to clumps. In the optically thin supersonic winds, dense clumps of smaller size (fVFF = 0.27 0.3, where fVFF is the volume filling factor) pulsate with higher-order harmonics. The larger clumps (fVFF = 0.2) oscillate with lower-order harmonics of the pulsation and affect the overall wind variability. 2024. The Author(s). -
Detection of picric acid in industrial effluents using multifunctional green fluorescent B/N-carbon quantum dots
Carbon quantum dots have recently gained widespread attention due to their excellent physicochemical features. The rapid escalation in the dumping of hazardous chemicals into water, spurred demand for developing efficient and selective sensors for toxic chemicals. Herein, we have developed a novel fluorescence sensor for picric acid which is a major pollutant in industrial effluents. The new strategy exploits the development of a fluorescence sensor based on N-doped carbon quantum dots (N-CQDs) followed by boron functionalization. The N-CQDs were synthesized in a rapid single-step microwave technique by employing L-serine and citric acid. Subsequent boron functionalization of N-CQDs was carried out using boric acid for the synthesis of Boron-nitrogen carbon quantum dots (B/N-CQDs). The B/N-CQDs were found to exhibit high quantum yield (24%), good water solubility, outstanding photostability features, and bright green fluorescence under UV light. The morphology of B/N-CQDs is spherical, with scattered particle sizes ranging from 2 to 8 nanometers. Furthermore, B/N-CQDs were found to be an effective fluorescence probe for the selective and sensitive detection of picric acid, with a good linear range of 37 nM-30 M and a detection limit of 1.8 nM. The Photoluminescence (PL) intensity of B/N-CQDs was selectively quenched by picric acid. The quenching mechanism was conclusively established using fluorescence lifetime decay studies. Moreover, the synthesized B/N-CQDs was successfully employed for the analysis of picric acid from industrial effluents and cell imaging with Hela cells to showcase the utility of the developed fluorescent probe. 2022 Elsevier Ltd -
Detection of picric acid in industrial effluents using multifunctional green fluorescent B/N-carbon quantum dots /
Journal of Environmental Chemical Engineering, Vol.10, Issue 2, ISSN No: 2213-3437.
Carbon quantum dots have recently gained widespread attention due to their excellent physicochemical features. The rapid escalation in the dumping of hazardous chemicals into water, spurred demand for developing efficient and selective sensors for toxic chemicals. Herein, we have developed a novel fluorescence sensor for picric acid which is a major pollutant in industrial effluents. The new strategy exploits the development of a fluorescence sensor based on N-doped carbon quantum dots (N-CQDs) followed by boron functionalization. The N-CQDs were synthesized in a rapid single-step microwave technique by employing L-serine and citric acid. -
Detection of strangers based on dogs sound
Nowadays, people having a pet at home are increasing. Usually, dog is the favorite pet animal for most of the people in the world. Dogs are more capable of identifying strangers in the surroundings than humans. The proposed work identifies the strangers based on the barking sound of the dog. In this anticipated work, multiple features are extracted from the dogs barking sound using Fast Fourier Transform and Statistical based methods. The classification is done using Nae Bayes classifier. The dataset contains 770 barking audio files of 8 dogs. Whenever known and unknown person comes home, the sounds of the dogs are recorded. The classification result for identifying the stranger is 79.1094%. BEIESP. -
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 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 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 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.