Browse Items (5511 total)
Sort by:
-
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. -
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. -
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. -
Identification of interstitial lung diseases using deep learning
The advanced medical imaging provides various advantages to both the patients and the healthcare providers. Medical Imaging truly helps the doctor to determine the inconveniences in a human body and empowers them to make better choices. Deep learning has an important role in the medical field especially for medical image analysis today. It is an advanced technique in the machine learning concept which can be used to get efficient output than using any other previous techniques. In the anticipated work deep learning is used to find the presence of interstitial lung diseases (ILD) by analyzing high-resolution computed tomography (HRCT) images and identifying the ILD category. The efficiency of the diagnosis of ILD through clinical history is less than 20%. Currently, an open chest biopsy is the best way of confirming the presence of ILD. HRCT images can be used effectively to avoid open chest biopsy and improve accuracy. In this proposed work multi-label classification is done for 17 different categories of ILD. The average accuracy of 95% is obtained by extracting features with the help of a convolutional neural network (CNN) architecture called SmallerVGGNet. 2020 Institute of Advanced Engineering and Science. All rights reserved. -
An efficient deep learning approach for identifying interstitial lung diseases using HRCT images
Interstitial lung disease (ILD) encompasses over 200 fatal lung disorders affecting the interstitium, leading to significant mortality rates. We propose an AI-driven approach to diagnose and classify ILD from high-resolution computed tomography (HRCT) images. The research utilises a dataset of 3,045 HRCT images and employs a two-tier ensemble method that combines various machine learning (ML) models, convolutional neural networks (CNNs), and transfer learning. Initially, ML models achieve high accuracy, with the J48 model at 93.08% accuracy, mainly highlighting the importance of diagonal-wise standard deviation. Deep learning techniques are then applied, with three CNN models achieving test accuracies of 94.08%, 92.04%, and 93.72%. Transfer learning models also show promise, with InceptionV3 at 92.48% accuracy. Ensembling these models further boosts accuracy, with the ensemble of three CNN models reaching 97.42%. This research has the potential to advance ILD diagnosis, offering a robust computational framework that enhances accuracy and ultimately improves patient outcomes. Copyright 2024 Inderscience Enterprises Ltd. -
A Novel Deep Learning Approach for Identifying Interstitial Lung Diseases from HRCT Images
Interstitial lung diseases (ILDs) are defined as a group of lung diseases that affect the interstitium and cause death among humans worldwide. It is more serious in underdeveloped countries as it is hard to diagnose due to the absence of specialists. Detecting and classifying ILD is a challenging task and many research activities are still ongoing. High-resolution computed tomography (HRCT) images have essentially been utilized in the diagnosis of this disease. Examining HRCT images is a difficult task, even for an experienced doctor. Information Technology, especially Artificial Intelligence, has started contributing to the accurate diagnosis of ILD from HRCT images. Similar patterns of different categories of ILD confuse doctors in making quick decisions. Recent studies have shown that corona patients with ILD also go on to sudden death. Therefore, the diagnosis of ILD is more critical today. Different deep learning approaches have positively impacted various image classification problems recently. The main objective of this proposed research work was to develop a deep learning model to classify the ILD categories from HRCT images. This proposed work aims to perform binary and multi-label classification of ILD using HRCT images on a customized VGG architecture. The proposed model achieved a high test accuracy of 95.18% on untrained data. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
A Novel Artificial Intelligence System for the Prediction of Interstitial Lung Diseases
Interstitial lung disease (ILD) encompasses a spectrum of more than 200 fatal lung disorders affecting the interstitium, contributing to substantial mortality rates. The intricate process of diagnosing ILDs is compounded by their diverse symptomatology and resemblance to other pulmonary conditions. High-resolution computed tomography (HRCT) assumes the role of the primary diagnostic tool for ILD, playing a pivotal role in the medical landscape. In response, this study introduces a computational framework powered by artificial intelligence (AI) to support medical professionals in the identification and classification of ILD from HRCT images. Our dataset comprises 3045 HRCT images sourced from distinct patient cases. The proposed framework presents a novel approach to predicting ILD categories using a two-tier ensemble strategy that integrates outcomes from convolutional neural networks (CNNs), transfer learning, and machine learning (ML) models. This approach outperforms existing methods when evaluated on previously unseen data. Initially, ML models, including Logistic Regression, BayesNet, Stochastic Gradient Descent (SGD), RandomForest, and J48, are deployed to detect ILD based on statistical measures derived from HRCT images. Notably, the J48 model achieves a notable accuracy of 93.08%, with the diagnostic significance of diagonal-wise standard deviation emphasized through feature analysis. Further refinement is achieved through the application of Marker-controlled Watershed Transformation Segmentation and Morphological Masking techniques to HRCT images, elevating accuracy to 95.73% with the J48 model. The computational framework also embraces deep learning techniques, introducing three innovative CNN models that achieve test accuracies of 94.08%, 92.04%, and 93.72%. Additionally, we evaluate five full-training and transfer learning models (InceptionV3, VGG16, MobileNetV2, VGG19, and ResNet50), with the InceptionV3 model achieving peak accuracy at 78.41% for full training and 92.48% for transfer learning. In the concluding phase, a soft-voting ensemble mechanism amplifies training outcomes, yielding ensemble test accuracies of 76.56% for full-training models and 92.81% for transfer learning models. Notably, the ensemble comprising the three newly introduced CNN models attains the pinnacle of test accuracy at 97.42%. This research is poised to drive advancements in ILD diagnosis, presenting a resilient computational framework that enhances accuracy and ultimately betters patient outcomes within the medical domain. 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Perfectionism and self-compassion among emerging adults: The role of disciplining experiences
Although the influence of disciplining experiences on a variety of personality factors has been studied, there is less clarity on how disciplining experiences influence the traits of perfectionism and self-compassion in individuals. The purpose of this study was to examine the relationships between different domains of perfectionism and self-compassion, as well as the influence of specific aspects of disciplining experiences, such as parental warmth and punishment experiences, on perfectionism and self-compassion. In this study, a quantitative cross-sectional correlational design was used. A total of 220 Indian emerging adults from the city of Bangalore were surveyed via convenience sampling. The following scales were administered: Disciplining Experiences Measure, Multidimensional Perfectionism Scale, and Self-Compassion Scale. The results showed that (1) Self Compassion has a significant positive relationship with Perfectionism; (2) Punishment experience has an influence on Other-oriented and Socially Prescribed Perfectionism; (3) Disciplining Helped positively predicted Self-oriented Perfectionism; and (4) Parental Warmth positively predicted Self-compassion in individuals. The findings contribute to the literature emphasizing the influence of disciplining experiences on ones self and personality, as well as the potential benefits of self-compassion-based interventions. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Impact of macroeconomic variables on the stock performance of select companies in manufacturing industry /
International Journal Of Economic Research, Vol.14(8), pp.321-328, ISSN: 0972-9380. -
Asset liability management to control the volatility in net interest income and economic value through gap analysis of selected public and private sector banks /
International Journal Of Academic Research In Business And Social Science, Vol.6, Issue 1, pp.123-142, ISSN: 2222-6990. -
Variations in the l-dopa content, phytochemical constituents and antioxidant activity of different germlines of mucuna pruriens (l.) dc.
In this study a 'wonder plant' Mucuna pruriens (L.) DC., which is commercially important medicinal plant of the Fabaceae family known for its treatment in Central Nervous System disorders like Dementia, Parkinson's, Alzheimer's, etc. have been selected. Different germplasms have been collected to analyze the phytochemical variations between them and quantify the L-DOPA in root, stem, leaves and seeds of all the five germlines using HPLC. Along with the biochemical assays, antioxidant activity by DPPH, phosphomolybdneum method, the metal chelating and reductive potential activity of all the germplasms were studied. All parts of the plant have shown the presence of L-DOPA but, seeds have the highest quantity followed by the roots, stem and leaves. Arka Shubra seeds showed high L-DOPA content (51.9 mg/g) while the other germplasms showed L-DOPA ranging from 43-45 mg/g. Highest content of carbohydrates (258.8 mg/g) and phenolics (157.0 mg/g) was seen in the seeds of Arka Aswini. While the seeds and leaves of Arka Charaka showed high protein (332.2 mg/g) and flavonoid (10.2 mg/g) content, respectively. High proline (1.74 mg/g) was observed in the seeds of Arka Shubra. Antioxidant studies revealed that Arka Charaka and Arka Daksha to be having high reductive power and free radical scavenging activity by phosphomolybdate method while high metal chelating activity was observed in Arka Aswini (88.7%) and high antioxidant activity by DPPH method was seen in Arka Shubra (86.5%). 2021 Chemical Publishing Co.. All rights reserved. -
Biotic elicitation mediated in vitro production of L-DOPA from Mucuna pruriens (L.) DC. cell cultures
With the emerging rise in the need for drugs extracted from various plant sources, there also arises the need for the optimum production of the drugs on a larger scale and conservation of those medicinal plants using different in vitro techniques and biotechnological approaches. Plant tissue culture techniques play a prominent role in mass multiplication of the plant. Whereas, strategies such as precursor feeding, elicitation, increases the metabolite content several-fold. Thus, an attempt of using the biotic elicitors for enhancing L-DOPA production, the anti-Parkinsons drug from Mucuna pruriens (L.) DC. cell cultures, has been reported in the present study. Aqueous extracts of algae [Amphiroa anceps (AA), Gracillaria ferogusonii (GF), Kappaphycus striatum (KS), and Sargassum lanceolatum (SL)], fungus [Aspergillus sps. (AS), Penicillium sps. (PE), and Cordyceps sps (CO)], and polysaccharide [Chitosan (CH)] solution were exposed to the cell cultures for 3, 6, and 9 d, respectively, and their effect on biomass and L-DOPA production was noted. This is the first report demonstrating the enhancement of biomass and L-DOPA from M. pruriens cell cultures with the use of various algal and fungal elicitors. Based on productivity (L-DOPA concentration biomass volume), it was observed that Cordyceps showed the best result and enhanced both biomass and metabolite to a greater scale. The elicitors, which showed a significant increase, are as follows: CO > AS > PE > CH > AA > KS > GF > SL. On the whole, it was noted that fungal extracts showed better results than algae. 2022, The Society for In Vitro Biology. -
Establishment of Mucuna pruriens (L.) DC. callus and optimization of cell suspension culture for the production of anti-Parkinsons drug: L-DOPA
It has become a huge challenge to satisfy the emerging demand for levo-3,4-dihydroxyphenylalanine (L-DOPA), an anti-Parkinsons drug in the international drug market. This is attributed to the conventional methods of extraction from the natural sources of Mucuna spp., which has a low germination rate, less viable seeds, and an irritating, itching trichomes on the pods. The need for an alternative method with continuous supply of L-DOPA without affecting the natural biodiversity has been achieved through in vitro procedures. However, there has not been a systematic approach to optimize the cultural conditions for the maximum productivity. Hence, in this study, we aim at optimizing the cultural conditions for high biomass and L-DOPA production. Various plant growth regulators such as auxins (indole acetic acid, indole butyric acid, picloram [Pic], naphthalene acetic acid, and 2,4-Dichlorophenoxyacetic acid), cytokinins (kinetin, benzylaminopurine, 2-isopentenyl adenine, and thidiazuron), and their combinations have been experimented to figure out the best combination to induce callus. At the same time, various factors such as growth kinetics, different media (MS, Gamborgs-B5, Chus-N6, and Nitsch and Nitsch), media strength (0.5, 1.0, and 2.0X), effect of different macro elements and their strength (0, 0.5,1, 1.5, 2, and 3X), inoculum density, different hydrogen ion concentration (pH), ammonium/nitrate concentration, different sucrose concentrations (010%), and other carbon sources have been investigated in detail for optimizing the cell suspension culture. It was found out that 0.5 mg/L Pic gave the best results for callus induction. With respect to biomass, 6-week growth period (135.7 g/L fresh weight [FW]), 1.0X MS media (126.87 g/L FW), 1.5X magnesium sulfate (266.3 g/L FW), ammonium/nitrate ratio of 21.57/18.8 mM (131.4 g/L FW), pH of 6.0 (129.47 g/L FW), 100 g/L of inoculum (222.2 g/L FW), 3% sucrose concentration (125.6 g/L FW), and 3% glucose (183.4 g/L FW) as other carbon sources were found to give the highest biomass. In terms of L-DOPA production, 3-week growth period (5.90 mg/g dry weight [DW]), 0.5X B5 medium (4.27 mg/g DW), 2.0X calcium chloride (5.06 mg/g DW), ammonium/nitrate ratio of 21.57/18.8 mM (3.44 mg/g DW), pH 6.5 (4.02 mg/g DW), inoculum density of 30 g/L (4.79 mg/g DW), and 2% sucrose (5.17 mg/g DW) resulted in a higher L-DOPA yield. 2022 Rakesh and Praveen. -
Elicitor and precursor-induced approaches to enhance the in vitro production of L-DOPA from cell cultures of Mucuna pruriens
Elicitation and precursor feeding are two important strategies in the in vitro techniques to enhance metabolite production to meet the demand of mankind. The secondary metabolites produced by the plants are extensively used in pharmaceutical, food and agro-chemical industries. One such metabolite is 3,4 dihydroxyphenylalanine (L-DOPA) produced from Mucuna pruriens (L.) DC. is used since ancient times to treat Parkinson's disease. Though all parts produce L-DOPA, the seed has the highest quantity. To overcome the extensive usage of the natural sources whose growth and metabolite production is highly dependent on edaphic and ecological factors, in vitro techniques like establishing cell culture for continuous production of metabolites, precursor feeding and elicitation of cell cultures to enhance the metabolite production has been reported in the present study. Callus was developed from the in vitro leaf explant and cell suspension culture was established in the liquid Murashige and Skoog's medium fortified with 0.5 mg/L picloram. Amino acid precursors like tyrosine, phenylalanine and chemical elicitors like methyl jasmonate, salicylic acid, sodium nitroprusside and silver nitrate were exposed to cell cultures for different periods (3, 6 and 9 days respectively). The precursors showed a better response in enhancing both the biomass and L-DOPA when compared to the elicitors. 500 and 1000 mg/L tyrosine showed a 1.6- and an 8.1-fold increase in biomass and L-DOPA production respectively when supplemented with MS media. However, though all the elicitors enhanced the L-DOPA production by 1.13.3-folds they did not show much significant increase in biomass. Precursor feeding approaches enhanced the metabolite considerably more than the elicitor treatment. Based on the productivity (Biomass L-DOPA conc.) precursors like Tyrosine>Phenylalanine and elicitors like Sodium nitroprusside>Silver nitrate>Methyl jasmonate>Salicylic acid showed better response. 2022 Elsevier B.V.