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Nexus Between The Carbon Dioxide Emission And Economic Growth: Evidence From India
Increase in economic activities contributes to the economic growth of a country. It is evident that emerging economies have recorded higher economic growth and significant increase in coal consumption, energy consumption and electricity consumption. On the other hand, the emission of greenhouse gases (GHG) generating consequences in the atmosphere. In this context, this study tries to analyse the association between GDP per capita, FDI, population, trade openness and CO2 emissions per capita in India. The study is based on secondary data, which has been collected from the World Bank database. The time period under consideration is from 1960 to 2017. Augmented Dickey Fuller test has been used to test the unit root. VAR lag order criteria have been used for lag selection of the model. Since the variables are integrated at I (1) and I (0), the ARDL model has been used for the purpose of analysis. Furthermore, for checking the stability of the model, the CUSUM test has been used. The results show that in the long run, GDP per capita and FDI has a positive impact on CO2 emission whereas, in the short run coal consumption, FDI, GDP per capita and trade openness appears to have a significant and positive impact towards CO2 emission. 2020 - Kalpana Corporation -
Nexus Between The Carbon Dioxide Emission And Economic Growth: Evidence From India
Increase in economic activities contributes to the economic growth of a country. It is evident that emerging economies have recorded higher economic growth and significant increase in coal consumption, energy consumption and electricity consumption. On the other hand, the emission of greenhouse gases (GHG) generating consequences in the atmosphere. In this context, this study tries to analyse the association between GDP per capita, FDI, population, trade openness and CO2 emissions per capita in India. The study is based on secondary data, which has been collected from the World Bank database. The time period under consideration is from 1960 to 2017. Augmented Dickey Fuller test has been used to test the unit root. VAR lag order criteria have been used for lag selection of the model. Since the variables are integrated at I (1) and I (0), the ARDL model has been used for the purpose of analysis. Furthermore, for checking the stability of the model, the CUSUM test has been used. The results show that in the long run, GDP per capita and FDI has a positive impact on C02 emission whereas, in the short run coal consumption, FDI, GDP per capita and trade openness appears to have a significant and positive impact towards C02 emission. 2020 Kalpana Corporation. All Rights Reserved. -
Unveiling Green Supply Chain Practices: A Bibliometric Analysis and Unfolding Emerging Trends
Supply chain management is a multi-dimensional approach. Growing eco-consciousness has forced businesses to optimize operations and incorporate green practices across all the stages of supply chain in manufacturing and service sectors. Reviewing the past research literature propels us to understand its current and future prospects. Employing a systematic analysis, this research explores the intellectual structure of green supply chain practices and their connection to performance outcomes in various industries. This study covers a systematic literature review, content analysis, and bibliometric analysis on green supply chain management using VosViewer. It utilizes a PRISMA-guided screening method for identification, screening, eligibility and inclusion of literature from the literature available since 1999. The bibliometric analysis reveals key contributors, thematic clusters, prevailing theoretical frameworks, and emerging research trends in the domain of green supply chain management. China, followed by the United States and the United Kingdom, emerged as leading contributors to research in this area, driven by rapid economic growth, heightened environmental concerns, and well-established academic and industrial infrastructures. The study identifies eight thematic clusters within green supply chain management, including the triple bottom line, circular economy, and carbon emissions. The most highly cited papers within these clusters were examined for their methodologies, tools, and key findings, highlighting the prominent theories utilized in this field. Moreover, the research discusses how advanced technologies such as AI, blockchain, and big data analytics are poised to transform supply chains by enhancing decision-making and mitigating risks, thus playing a pivotal role in the future of green supply chain management. Copyright 2024 CA Rajkiran, Shaeril Michel Almeida. -
Polycystic ovary syndrome: An exploration of unmarried women's knowledge and attitudes
Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder among women of reproductive age and a chief cause of subfertility attributed to ovulation. Besides, lack of knowledge about PCOS, its treatment, and lifestyle changes influence the prognosis. The present qualitative inquiry investigates the knowledge and attitudes of unmarried women towards the syndrome, associated treatment, and necessary lifestyle changes in the fight against the same. A total of 15 participants with PCOS were selected using purposive sampling (n from southern parts of India viz. Kerala and Tamil Nadu states. The telephonic interviews were conducted in late November and early December 2020. He conventional content analysis emerged with six major themes. The themes capsulated women's knowledge, causes, complications and risk factors, treatment of PCOS their perceived importance of health promotive behaviours such as physical activity, sleep patterns, and perceived support from society. The importance of diet, exercise and a healthy lifestyle were additional relevant factors stressed by the respondents. Although the medicines helped participants attain regular menstrual cycles, they also had side effects reported in the discussion. Few respondents reported that they lacked the necessary awareness of PCOS when diagnosed at a younger age. The study enhances the understanding of PCOS from a qualitative approach that has cultural relevance apart from pertinent clinical and lifestyle implications. 2022 The Author(s) -
Enhanced Jaya Optimization Algorithm with Deep Learning Assisted Oral Cancer Diagnosis on IoT Healthcare Systems
Recently, healthcare systems integrate the power of deep learning (DL) models with the connectivity and data processing capabilities of the Internet of Things (IoT) to enhance the early recognition and diagnosis of disease. Oral cancer diagnosis comprises the detection of cancerous or pre-cancerous abrasions in the oral cavity. Timely identification is essential for successful treatment and enhanced prognosis. Here is an overview of the key aspects of oral cancer diagnosis. One potential benefit of utilizing DL for oral cancer detection is that it analyses huge counts of data fast and accurately, and it could not need clear programming of the rules for recognizing abnormalities. This can create the procedure of detecting oral cancer more effective and efficient. Thus, the study presents an Enhanced Jaya Optimization Algorithm with Deep Learning Based Oral Cancer Classification (EJOADL-OCC) method. The presented EJOADL-OCC method aims to classify and detect the existence of oral cancer accurately and effectively. To accomplish this, the presented EJOADL-OCC method initially exploits median filtering for the noise elimination. Next, the feature vector generation process is performed by the residual network (ResNetv2) model with EJOA as a hyperparameter optimizer. For accurate classification of oral cancer, a continuously restricted Boltzmann machine with a deep belief network (CRBM-DBN) model. The simulated validation of the EJOADL-OCC algorithm is tested by the series of simulations and the outcome demonstrates its supremacy over present DL approaches. 2024, American Scientific Publishing Group (ASPG). All rights reserved. -
Relation between electricity consumption and economic growth in Karnataka, India: An aggregate and sector-wise analysis
Karnataka is a highly progressive and rapidly growing state in India, with huge potential for industrial growth, however, it grapples with power deficits and other problems in electricity sector, which make it a good case study for Indian electricity sector. Given the importance of electricity in the urbanisation and growth process, the paper analyses the electricity consumption trend in Karnataka, examine its causality with economic growth at aggregate and sectoral levels using Granger causality test, and forecast the future electricity consumption applying Holt-Winters smoothening (no seasonality) technique. The general trend reflects higher consumption by the agricultural consumers, compared to the revenue-generating 'Industries' and 'Commercial' categories, mainly due to the policy of de-metering and providing 'free' power to agricultural consumers since late 1980s. The Granger causality tests reveal that there is no causality relation (neutrality hypothesis) between electricity consumption and economic growth in Karnataka, for total, agricultural and industrial consumption. This basically stems from the inaccurate measurements of agricultural consumption, higher dependence on captive generation, and poor quality grid supply. Finally, electricity consumption is predicted to be around 69,347 GW h by 2019?20. Future policies should focus on universal metering, reducing cross-subsidization, supplying good quality and reliable power to all sectors, and economical planning of resource-mix to achieve adequate, productive and efficient electricity consumption. 2020 Elsevier Inc. -
English to Hindi Translation System Using Hybrid Techniques
Good communication is critical for overcoming cultural and linguistic divides in today's internationalized society. An essential communication component is the Translation of written materials, primarily academic papers, from one language into another. This abstract focuses on the research involved in translating academic publications from Hindi to English. Translating Hindi academic papers into English is naturally hard due to the significant linguistic and cultural differences between the two languages. The proposed work provided an analytical analysis of various models used in language translation, including the seq-to-seq model, MT5, and LSTM, with the help of BLEU score, Learning rate, and average loss. MT5 model outshines others in terms of an average loss of 4.75; meanwhile, LSTM has an average loss of 5.56, and the seq-to-seq model has an average loss of 6.09, implying weaker Translation. 2024 IEEE. -
Investigation of the correlation between optical and ?-ray flux variations in the blazar Ton 599
The correlation between optical and ?-ray flux variations in blazars reveals a complex behaviour. In this study, we present our analysis of the connection between changes in optical and ?-ray emissions in the blazar Ton 599 over a span of approximately 15 yr, from 2008 August to 2023 March. Ton 599 reached its highest flux state across the entire electromagnetic spectrum during the second week of 2023 January. To investigate the connection between changes in optical and ?-ray flux, we have designated five specific time periods, labelled as epochs A, B, C, D, and E. During periods B, C, D, and E, the source exhibited optical flares, while it was in its quiescent state during period A. The ?-ray counterparts to these optical flares are present during periods B, C, and E; however, during period D, the ?-ray counterpart is either weak or absent. We conducted a broad-band spectral energy distribution (SED) fitting by employing a one-zone leptonic emission model for these epochs. The SED analysis unveiled that the optical-ultraviolet emission primarily emanated from the accretion disc in quiescent period A, whereas synchrotron radiation from the jet dominated during periods B, C, D, and E. Diverse correlated patterns in the variations of optical and ?-ray emissions, like correlated optical and ?-ray flares, could be accounted for by changes in factors such as the magnetic field, bulk Lorentz factor, and electron density. On the other hand, an orphan optical flare could result from increased magnetic field and bulk Lorentz factor. 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
Study of correlation between optical flux and polarization variations in BL Lac objects
Polarized radiation from blazars is one key piece of evidence for synchrotron radiation at low energy, which also shows variations. We present here our results on the correlation analysis between optical flux and polarization degree (PD) variations in a sample of 11 BL Lac objects using ?10 yr of data from the Steward Observatory. We carried out the analysis on long-term (?several months) as well as on short-term time-scales (?several days). On long-term time-scales, for about 85 per cent of the observing cycles, we found no correlation between optical flux and PD. On short-term time-scales, we found a total of 58 epochs with a significant correlation between optical flux and PD, where both positive and negative correlation were observed. In addition, we also found a significant correlation between optical flux and ?-ray flux variations on long-term time-scales in 11 per cent of the observing cycles. The observed PD variations in our study cannot be explained by changes in the power-law spectral index of the relativistic electrons in the jets. The shock-in-jet scenario is favoured for the correlation between optical flux and PD, whereas the anticorrelation can be explained by the presence of multizone emission regions. The varying correlated behaviour can also be explained by the enhanced optical flux caused by the newly developed radio knots in the jets and their magnetic field alignment with the large-scale jet magnetic field. 2022 The Author(s). -
Temporal correlation between the optical and ? -ray flux variations in the blazar 3C 454.3
Blazars show optical and ? -ray flux variations that are generally correlated, although there are exceptions. Here we present anomalous behaviour seen in the blazar 3C 454.3 based on an analysis of quasi-simultaneous data at optical, ultraviolet, X-ray, and ? -ray energies, spanning about 9 yr from 2008 August to 2017 February.We have identified four time intervals (epochs), A, B, D, and E, when the source showed large-amplitude optical flares. In epochs A and B the optical and ? -ray flares are correlated, while in D and E corresponding flares in ? -rays are weak or absent. In epoch B the degree of optical polarization strongly correlates with changes in optical flux during a short-duration optical flare superimposed on one of long duration. In epoch E the optical flux and degree of polarization are anticorrelated during both the rising and declining phases of the optical flare. We carried out broad-band spectral energy distribution (SED) modelling of the source for the flaring epochs A,B, D, and E, and a quiescent epoch, C. Our SED modelling indicates that optical flares with absent or weak corresponding ? -ray flares in epochs D and E could arise from changes in a combination of parameters, such as the bulk Lorentz factor, magnetic field, and electron energy density, or be due to changes in the location of the ? -ray-emitting regions. 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. -
Optimized deep maxout for breast cancer detection: consideration of pre-treatment and in-treatment aspect
Breast cancer is one of the deadliest diseases, accounting for the second-highest rate of cancer mortality among females. Breast tissue begins to develop cancerous, malignant lumps as the disease progresses. Self-examinations and routine clinical checks aid in early diagnosis, which considerably increases the likelihood of survival. Because of this, we have created a revolutionary method for finding breast cancer that has the following four steps. Fuzzy filters are used in the initial pre-processing stage to reduce noise and improve outcomes from the incoming data. In the second stage, we have presented an Improved Hierarchical DBSCAN (Density-based clustering algorithm) for the segmentation of anomalous areas. Feature extraction will be carried out following segmentation. We have also developed a better kurtosis-based feature to complement traditional statistical and shape-based features and deliver better results. The Optimized Deep Maxout Neural Network is used for classification in the final step, with the suggested Shark Smell Indulged Shuffled Shepherd Optimization used to optimize the weight parameter (SSISSO). At 90% the learning percentage of the proposed model SSISSO model has achieved 0.984391 accuracy, which is superior to 22.54%, 28.46%, 17.44%, 17%, 15.04%, 13.28%, 29.45%, 28.59%, 21.58%, and 30.72% as compared to other methods like SVM-BS1, CNN-BS7, LSTM, NN, Bi-GRU, RNN, ARCHO, AOA, HGS, CMBO, SSOA, and SSO. Finally, the results of the proposed breast cancer detection technique are compared with conventional techniques. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
State-of-art Techniques for Classification of Breast Cancer: A Review
Cancer is an unexpected and unclear disease that puts many people at risk. Breast cancer has surpassed prostate cancer as the most common cancer in women, as well as the main cause of cancer-related mortality in women. Breast cancer rates have been rising in India for several years, with 100,000 new cases recorded each year. In India, there are up to one million breast cancer patients at any given moment. The survival rate of breast cancer has increased in recent years as a result of advances in technology, effective treatment, and medical care delivery. It extends the lives of the sufferers and improves their quality of life. Breast cancer can be detected using a variety of imaging methods. Radiologists can utilize a computer-aided diagnostic technique to discover and diagnose irregularities earlier and more quickly. Many Computer-Aided Diagnosis methods have been developed to identify breast cancer in its early stages using mammography images. The computer aided diagnostics systems mostly focus on identifying and detecting breast nodules. Staging breast cancer at its detection needs to be focused on, as the treatment is based on the stage of cancer. As a result, this study focuses on producing evaluations on computer aided diagnostics approaches for segmenting nodules and identifying different stages of breast cancer, thereby assisting radiologists in assessing the illness. 2022 IEEE. -
Using machine learning architecture to optimize and model the treatment process for saline water level analysis
Water is a vital resource that makes it possible for human life forms to exist. The need for freshwater consumption has significantly increased in recent years. Seawater treatment facilities are less dependable and efficient. Deep learning systems have the potential to increase the efficiency as well as the accuracy of salt particle analysis in saltwater, which will benefit water treatment plant performance. This research proposed a novel method for optimization and modelling of the treatment process for saline water based on water level data analysis using machine learning (ML) techniques. Here, the optimization and modelling are carried out using molecular separation-based reverse osmosis Bayesian optimization. Then the modelled water saline particle analysis has been carried out using back propagation with Kernelized support swarm machine. Experimental analysis is carried out based on water salinity data in terms of accuracy, precision, recall, and specificity, computational cost, and Kappa coefficient. The proposed technique attained an accuracy of 92%, precision of 83%, recall of 78%, specificity of 81%, computational cost of 59%, and Kappa coefficient of 78%. 2023, IWA Publishing. All rights reserved. -
Demography-Based Hybrid Recommender System for Movie Recommendations
Recommender systems have been explored with different research techniques including content-based filtering and collaborative filtering. The main issue is with the cold start problem of how recommendations have to be suggested to a new user in the platform. There is a need for a system which has the ability to recommend items similar to the users demographic category by considering the collaborative interactions of similar categories of users. The proposed hybrid model solves the cold start problem using collaborative, demography, and content-based approaches. The base algorithm for the hybrid model SVDpp produced a root mean squared error (RMSE) of 0.92 on the test data. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Cross diffusion effects on magnetohydrodynamic slip flow of Carreau liquid over a slendering sheet with non-uniform heat source/sink
Magnetohydrodynamic flow of Carreau fluid over a slendering sheet (variable thickness) has been numerically studied by considering the multiple slips effect. Thermosolutal boundary layer analysis is also accounted in the presence of cross diffusion and non-uniform heat source/sink. The governing nonlinear coupled partial differential equations are transformed to nonlinear coupled ordinary differential equations before being integrated numerically using RungeKutta based Newtons schemes. The effects of various parameters involved in the present problem were elaborately discussed with help of graphs and tables. The present results in a limiting sense are found to accord with the previous study. The present results indicate that the cross diffusion and slip parameters had a tendency to control the flow. The influence of slip is more evident in Carreau fluid case on contrast with the Newtonian fluid case. 2018, The Brazilian Society of Mechanical Sciences and Engineering. -
Advanced Machine Learning Techniques for Detecting Irregularities in Skin Lesion Borders: Enhancing Early Skin Cancer Detection
Dermatograms are pivotal in the early detection of skin cancer, a disease with significant mortality rates. This paper introduces a novel feature extraction method that captures irregularities in the boundaries of abnormal skin regions. Each raw dermatogram is converted into a binary mask image using an effective segmentation algorithm. The boundary of the lesion region is extracted from the mask. The boundary, together with the centroid of the lesion mask, is used to define a set of directional vectors. An Arc is defined using these directional vectors, and a new Arc feature is calculated based on the number of times the lesion boundary crosses the arc. The proposed Arc feature is evaluated using three standard skin lesion datasets: ISBI 2016, HAM10000, and PH2. Additionally, color features and Local Binary Pattern (LBP) features are implemented for comparison. Classical machine learning algorithms are employed to evaluate these features. Results indicate that for the ISBI 2016 and HAM10000 datasets, the Arc feature set demonstrates superior classification accuracy. In contrast, the PH2 dataset benefits more from the LBP feature. Comparative analysis with recent studies highlights the dependency of accuracy on datasets and classifiers, underscoring the necessity for models incorporating feature fusion and ensemble classifiers. The proposed method outperforms traditional color and texture features and shows competitive results against deep learning models, particularly in scenarios with limited computational resources. These findings suggest that the Arc feature is a promising approach for improving skin cancer detection, although further investigation is needed to fine-Tune performance, optimize classifier selection, and explore feature fusion strategies. 2024 World Scientific Publishing Company. -
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. -
Impact of macroeconomic variables on the stock performance of select companies in manufacturing industry
The efficient functioning of a stock market is influenced by different macro economic factors like Inflation, Interest rates, exchange rate etc. The favourable Macro Economic Variables both domestic economy and global economy inspire the organisations to go for strategic investment activities in domestic and global markets and reflect positively on the company financial performance and firms fundamentals like Revenues, Operating margins, Earnings Per Share, the Economic Value , Market value, and the Firms overall Value. These positive indicators in the fundamentals of the firms send positive signals into stock markets and generate positive perceptions about the company's stock prices in the market. Markets become so attractive to domestic and foreign investors which drive the share price of different companies , specially Blue chips upwards and creates value to the shareholders .According to the study organized the impact of macro economic variables is not uniform and the impact varies betweem various macro economic variables on the stock market performance. Serials Publications Pvt. Ltd. -
The behaviour of macro and micro economic variables and the impact on systematic risk of non-banking finance companies
The reforms initiated by the Indian Government during 1990 have brought in drastic changes in the financial sector functioning so as to make competitive with financial markets worldwide. The Indian financial Sector has expanded and acquired greater depth to get itself competitive with new participants in the market. The new liberal business policies introduced by the government due to financial reforms have brought in fundamental changes in the structure and functioning of Banking and Non Banking Institutions, their business models and the products and services offered by them. Global economic developments have altered the macro economic conditions of the respective nations and make the nations and their economies vulnerable to economic shocks in the form of systematic risk associated with their business activities. Macro economic factors at broader level and micro economic factors at the firm level have had effect on the risk level, assessing risk, measuring and managing risk has become paramount for banks and others Non Banking financial Institutions alike. Individual influence of factors on the systematic risk as there is weak relationship with Beta but combined effect of factors is very positive on the systematic risk of the companies. -
Text extraction from video images
Video data contains beneficial textual information such as scene text and caption text. The different types of videos like movies, news videos, and TV programs video etc. are created by various video frames based on its purpose. In a country like India, there are only fewer studies has done on text extraction from video data especially in south Indian languages like Malayalam, Telugu, Kannada, and Tamil. The extracted text has many useful applications in video indexing, video key searching and assisting visually challenged people. Malayalam news channel named Mathrubhumi News videos data are considered for the proposed study. It is very beneficial to Kerala people as it is one of the most media-centric regions in the world. In this proposed paper, a new method for text extraction experiments. The anticipated method extracts 13 different features for classifying the image consists of text or not. Both spatial and frequency domain features are extracted to classify. The different types of classification techniques are used to validate the algorithm. Simple Logistic, J48 and Random Forest classification techniques are giving a good result when compared to other methods. Results are encouraging, the average success rate found to be 98%. Research India Publications.