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Ruralurban financial inclusion: Implications on the cost sustainability of microfinance lenders
Despite the higher demand for credit among the rural poor, many commercial banks and microfinance institutions (MFIs) are averse towards microfinancing activities in rural areas due to their high-cost implication compared to urban areas. Therefore, this study empirically investigates the effect of rural and urban financial inclusion on the cost sustainability of MFIs. To this end, a globally representative sample of 1729 MFIs' data covering the period 20082018 were analyzed. Contrary to the orthodox perception, our overall result revealed that lending in rural areas is more cost-efficient than in urban areas, even after considering various proxies and endogeneity issues. 2021 John Wiley & Sons, Ltd. -
The Content of Heavy Metals in Cigarettes and the Impact of Their Leachates on the Aquatic Ecosystem
Smoked cigarettes and butts are the most common kind of litter around the world. The buildup of these litters has badly polluted local water bodies and their compartments, and the cumulative effect of many cigarette butts scattered in a centralized location may pose a serious hazard to living species. To understand how heavy metals are leached out into the aquatic ecosystem, researchers must analyse the behavior of the materials that make up cigarettes. Using atomic absorption spectrometry, this study evaluated the content of several metals (such as Cd, Cu, Fe, Pb, Sn, Zn, and Hg) leached from various brands of unsmoked and smoked cigarettes and cigarette butts. The findings revealed that heavy metal is more prevalent in butte. These findings indicate that cigarette litter is a major source of metal contamination in the aquatic ecosystem and that apparent leaching may increase the risk of toxicity to aquatic organisms. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
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Currency Exchange Rate Prediction Using Multi-layer Perceptron
Financial forecasting is an estimate of a future financial outcome and this outcome is related to some kind of value. We can measure this outcome for a company to predict its future stock or to detect the viability of a human for the sanction of a loan. In all these cases, we want to estimate the future outcome based on historical data. Various methods have been developed lately, to make time series predictions. In this work, we have used Multi-layer perceptron algorithm to predict the Currency Exchange rate between US dollar and EURO. The training network has been compiled using TensorFlow. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Sign Language Recognizer Using HMMs
In our day to day lives, we come across especially abled people who perform their daily chores with the aid of motivation that they get from self-confidence. There are many with hearing impairment. Sign language is the most expressed and natural way for them to communicate. Some chains of restaurants have, in fact, recruited deaf servers providing them with employment opportunities. Therefore, automatic Sign language recognition has become the crux of vision research. This paper is based on a project that builds a system that can recognize words communicated using the American Sign Language (ASL). Having been provided with a preprocessed dataset of tracked hand and nose positions extracted from the video, the set of Hidden Markov Models are trained. Using a part of this dataset, identification of individual words from test sequences is done. It provides them with the ability to communicate better, opening up a lot of opportunities. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Off balance sheet exposure performance analysis and risk measurment of Indian banks /
Archives of Business Research, Vol.4, Issue 1, pp.112-117, ISSN: 2054-7404. -
Under-pricing of IPOs in Indian capital market and determinants of under-pricing /
Archives of Business Research, Vol.5, Issue 1, ISSN: 2054-7404. -
Electrical transport and magnetoresistance studies on the magnetic moment compensated Mn2V1-xCoxZ (Z=Ga, Al; x=0, 0.25, 0.5, 0.75, 1) Heusler alloys
We report the electrical resistivity and magnetoresistance properties of arc-melted Mn2V1-xCoxZ (Z=Ga, Al; x =0, 0.25, 0.5, 0.75, 1) alloys, which possess compensated ferrimagnetic behaviour with high TC when x=0.5. Apart from metallicity, the alloys in the Ga series with x= 0, 0.75, 1 composition showed a positive to negative crossover in the magnetoresistance versus temperature curves. This crossover was absent for Mn2V0.75Co0.25Ga and the fully compensated ferrimagnet Mn2V0.5Co0.5Ga. In contrast to this, Co-substituted Mn2VAl exhibits distinctly different resistive behaviour. While the alloys Mn2VAl and Mn2CoAl exhibit metallic and semiconducting behaviour respectively, the intermediate compositions show a gradual metallic to semiconducting transition as the Co concentration increases. The compensated ferrimagnet Mn2V0.5Co0.5Al showed a mixed transport behaviour of metallic and semiconducting nature with a resistivity minimum at 140 K. In contrast to this mixed response of the arc-melted bulk sample, the Mn2V0.5Co0.5Al melt-spun ribbon shows a clear semiconducting nature throughout the temperature range, indicating that the sample preparation methods could highly influence the electrical properties of the investigated compensated ferrimagnets. 2024 Elsevier B.V. -
1-Normal DRA for insertion languages
Restarting automaton is a type of regulated rewriting system, introduced as a model for analysis by reduction. It is a linguistically motivated method for checking the correctness of a sentence. In this paper, we introduce a new definition of normal restarting automaton in which only one substring is removed using the DEL operation in a cycle. This DEL operation is applied to reverse the insertion operation in an insertion grammar. We use this 1-normal restarting automaton to solve the membership problem of insertion languages. Further, we introduce some interesting closure properties of 1-normal restarting automata. 2017, Springer International Publishing AG. -
Polynomial time learner for inferring subclasses of internal contextual grammars with local maximum selectors
Natural languages contain regular, context-free, and context-sensitive syntactic constructions, yet none of these classes of formal languages can be identified in the limit from positive examples. Mildly context-sensitive languages are capable to represent some context-sensitive constructions such as multiple agreement, crossed agreement, and duplication. These languages are important for natural language applications due to their expressiveness, and the fact that they are not fully context-sensitive. In this paper, we present a polynomial-time algorithm for inferring subclasses of internal contextual languages using positive examples only, namely strictly and k-uniform internal contextual languages with local maximum selectors which can contain mildly context-sensitive languages. 2017, Springer International Publishing AG. -
Polynomial time algorithm for inferring subclasses of parallel internal column contextual array languages
In [2,16] a new method of description of pictures of digitized rectangular arrays is introduced based on contextual grammars, called parallel internal contextual array grammars. In this paper, we pay our attention on parallel internal column contextual array grammars and observe that the languages generated by these grammars are not inferable from positive data only. We define two subclasses of parallel internal column contextual array languages, namely, k-uniform and strictly parallel internal column contextual languages which are incomparable and not disjoint classes and provide identification algorithms to learn these classes. Springer International Publishing AG 2017. -
Shadowing the image archive: In medias res: Inside Nalini Malani's shadow plays, Mieke Bal (2016) /
Moving Image Review & Art Journal (MIRAJ), Vol.7, Issue 2, pp.325-335, ISSN No: 2045-6298. -
Why Employees Need Both Recognition and Appreciation
We often use the words “recognition” and “appreciation” interchangeably, but there’s a big difference between them. The former is about giving positive feedback based on results or performance. The latter, on the other hand, is about acknowledging a person’s inherent value. This distinction matters because recognition and appreciation are given for different reasons. Even when people succeed, inevitably there will be failures and challenges along the way; depending on the project, there may not even be tangible results to point to. If you focus solely on praising positive outcomes, on recognition, you miss out on lots of opportunities to connect with and support your team members — to appreciate them. Managers should make sure they’re doing both. -
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Prognosis of kidney disease on ultrasound images using machine learning
Kidney diseases can affect the ability to clean the blood, filter extra water out of your blood. The kidneys failure will affect the control over blood pressure and sugar level. It can also affect red blood cell production and vitamin D metabolism which is very important for bone health. When your kidneys are damaged, waste products and fluid can build up in the body. This is harmful to the health. This damages the kidney function, can get worse over time, and when the kidneys stop working completely, this is called kidney failure or end-stage renal disease. Not all patients with kidney disease progress to kidney failure. This disease has emerged as one of the most prominent reasons of death and suffering in this century. Recent studies states that, kidney disease affects most of the population and over two million people require kidney replacement. To help prevent Chronic Kidney Diseases and lower the risk for kidney failure, control risk factors for CKD, get tested yearly, make lifestyle changes, take medicine as needed. The detection of kidney abnormalities at their early stages helps to avoid the impairment of newlinekidney. The US imaging is considered as preliminary diagnostic tool in finding various kidney diseases in the clinical imaging field. This is one of the commonly used imaging modalities due to the inexpensiveness and non-ionization nature. The presence of noise in US images, degrade newlinethe quality and clarity of the images. Also, the heterogeneous structure of kidney, makes it very difficult to detect and measure the size of stones and cysts. Hence, an automatic kidney disease detection system is highly in demand. The proposed model can assist the radiologist in accurate abnormality detection. The proposed model includes different phases such as, pre-processing, features extraction, classification and newlinesegmentation. The pre-processing phase include cropping and noise removal. Further, the GLCM and intensity-based features are extracted for the classification of abnormal kidney images.
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Prognosis of Kidney Disease on Ultrasound Images Using Machine Learning
Kidney diseases can affect the ability to clean the blood, filter extra water out of your blood. The kidneys failure will affect the control over blood pressure and sugar level. It can also affect red blood cell production and vitamin D metabolism which is very important for bone health. When your kidneys are damaged, waste products and fluid can build up in the body. This is harmful to the health. This damages the kidney function, can get worse over time, and when the kidneys stop working completely, this is called kidney failure or end-stage renal disease. Not all patients with kidney disease progress to kidney failure. This disease has emerged as one of the most prominent reasons of death and suffering in this century. Recent studies states that, kidney disease affects most of the population and over two million people require kidney replacement. To help prevent Chronic Kidney Diseases and lower the risk for kidney failure, control risk factors for CKD, get tested yearly, make lifestyle changes, take medicine as needed. The detection of kidney abnormalities at their early stages helps to avoid the impairment of newlinekidney. The US imaging is considered as preliminary diagnostic tool in finding various kidney diseases in the clinical imaging field. This is one of the commonly used imaging modalities due to the inexpensiveness and non-ionization nature. The presence of noise in US images, degrade newlinethe quality and clarity of the images. Also, the heterogeneous structure of kidney, makes it very difficult to detect and measure the size of stones and cysts. Hence, an automatic kidney disease detection system is highly in demand. The proposed model can assist the radiologist in accurate abnormality detection. The proposed model includes different phases such as, pre-processing, features extraction, classification and newlinesegmentation. The pre-processing phase include cropping and noise removal. Further, the GLCM and intensity-based features are extracted for the classification of abnormal kidney images. -
A finger print recognition using CNN Model
The fundamental goal of this research is to improve the new identification accuracy for fingerprint acknowledgment by contrasting Convolutional Neural Networks (CNN) model frameworks for biometric safety in the cloud with Conventional inception models (TIM). Accuracy was computed and compared using a CNN model and standard Inception Models (N=10). The statistical significance was calculated using SPSS. Average and standard deviation for a 95% confidence interval, 0.05% G-power cutoff. The TIM and Convolutional Neural Networks performed an autonomous T-Test on the samples. CNN is more successful (93%) than TIM (61%). Based on a significant value of 0.048 for the comparison ratio (p0.05), there is a statistically significant difference between the CNN and the TIM transformation. According to the findings, the suggested CNN model is 93% accurate on the dataset, with no rejected samples. 2023 IEEE. -
Preparation and Application of Nanoparticles and Core-Shell Nanoparticles of Transition Metals
There is an increasing need for the development of environmentally viable, economically effective, highly active and renewable catalytic systems for the various applications in the industrial field. The demand for the decolorisaion of synthetic dyes using bioremediation methods has been in a decreased mode due to its lower decomposition rates. Hence in recent years the decomposition of these organic colorants considered to be a worldwide need. The term nanocatalysis has gained huge importance in recent years due to its selectivity, higher activity, and productivity compared to their bulk materials. The nanosize, shape, and large surface to volume ratio provide unique properties to the nanomaterials. The principles of green chemistry are mainly relies on the development of catalytic systems that work similar to nature. Nanocatalyst combines both homogeneous and heterogeneous catalysis and provides rapid and selective chemical transformations with high yield and easier separation of catalyst at the end of reaction. In this work we have synthesized various metal doped magnetite nanoparticles (Ferrite nanoparticles-NdMxFe3-xO4 where M (Mn, Co, Cu, Ni)) by precipitation and hydrothermal method. The one objective of this work was to check the photocatalytic application of prepared ferrite nanoparticles for the heterogeneous photo-Fenton degradation of MB and Rh B dyes. Each dye was degraded separately under visible light and dark with the assistance of neutral pH and H2O2, in order to shows the improved activity of catalysts under visible light. The degradation experiments using the photo-Fenton systems (Fe2+/H2O2/Visible light) suggested that, the highest degradation rate was 97% for MB and 81% for Rh B within 4h and the used catalyst was NdFe3-xCuxO4, a good photo-Fenton catalyst. . We have also tried the synthesis of core- shell nanoparticles using NdFe3O4 nanoparticles with the help of polyethylene glycol as dispersing agent. The synthesized samples were characterized by various techniques like, XRD, XPS, SEM and TEM. -
Classification and Retrieval of Research Classification and Retrieval of Research Papers: A Semantic Hierarchical Approach
"Classification and Retrieval of Research papers: A Semantic Hierarchical Approach" demonstrates an effective and efficient technique for classification of Research documents pertaining to Computer Science. The explosion in the number of documents and research publications in electronic form and the need to perform a semantic search for their retrieval has been the incentive for this research. The popularity and the widespread use of electronic documents and publications, has necessitated the development of an efficient document archival and retrieval mechanism. Categorizing journal papers by assigning them relevant and meaningful classes, predicting the latent concept or the topic of research, based on the relevant terms and assigning the appropriate Classification labels is the objective of this thesis. This thesis takes a semantic approach and applies the text mining techniques in a hierarchical manner in order to classify the documents. The use of a lexicon containing domain specific terms (DSL) adds a semantic dimension to classification and document retrieval. The Concept Prediction based on Term Relevance (CPTR) technique demonstrates a semantic model for assigning concepts or topics to papers. This Thesis proposes a conceptual framework for organizing and classifying the research papers pertaining to Computer Science. The efficacy of the proposed concepts is demonstrated with the help of Classification experiments. Classification experiments reveal that the DSL technique of training works efficiently when categorization is based on keywords. The CPTR technique, on the other hand, shows very high accuracy; when the classification is based on the contents of the document. Both these techniques lend a semantic dimension to classification. Narrowing down the scope of search at each level of hierarchy enables time efficient retrieval and access of the goal documents. The hierarchical interface for Document Retrieval enables retrieval of the target documents by gradually restricting the scope of search at each level of hierarchy This work comprises of two main components. 1. The Framework for Hierarchical Classification. 2. The Hierarchical Interface for Document Retrieval. Two distinct techniques for classification are proposed in this thesis. These include 1. The use of Domain Specific Lexicon (DSL) which is comparable to a Domain Specific Ontology. 2. The Concept Prediction Based on Term Relevance (CPTR) technique These techniques lend a semantic dimension to classification. Keywords: Text Mining, Classification, Document Retrieval, Hierarchical, Domain specific lexicon (DSL), Probabilistic Latent Semantic Analysis (PLSA) , Concept Prediction based on Term frequency (CPTR) -
Positive side effects of the Covid-19 pandemic on environmental sustainability: evidence from the quadrilateral security dialogue countries
Purpose: The eruption of coronavirus disease 2019 (COVID-19) has pointedly subdued global economic growth and producing significant impact on environment. As a medicine or a treatment is yet available at mass level, social distancing and lockdown is expected the key way to avert it. Some outcome advocates that lockdown strategies considered to reduce air pollution by curtailing the carbon emission. Current investigation strives to affirm the impact of lockdown and social distancing policy due to covid-19 outbreak on environmental pollution in the QUAD nations. Design/methodology/approach: To calibrate the social movement of public, six indicators such residential mobility, transit mobility, workplace mobility, grocery and pharmacy mobility, retail and recreation mobility and park mobility have been deliberated. The data of human mobility have been gathered from the Google mobility database. To achieve the relevant objectives, current pragmatic analysis exerts a panel autoregressive distributed lag model (ARDL)-based framework using the pooled mean-group (PMG) estimator, proposed by Pesaran and Shin (1999), Pesaran and Smith (1995). Findings: The outcome reveals that in the long-run public mobility change significantly impact the pollutants such as PM2.5 and nitrogen dioxide; however, it does not lead to any changes on ozone level. As per as short run outcome is concerned, the consequence unearths country wise heterogeneous impact of different indicators of public mobility on the air pollution. Research limitations/implications: The ultimate inferences of the above findings have been made merely on the basis of examination of QUAD economies; however, comprehensive studies can be performed by considering modern economies simultaneously. Additionally, finding could be constraint in terms of data; for instance, Google data used may not suitably signify real public mobility changes. Originality/value: A considerable amount of investigation explores the impact of covid-19 on environmental consequences by taking carbon emission as a relevant indicator of environmental pollution. Hence, the present pragmatic investigation attempts to advance the present discernment of the above subject in two inventive ways. Primarily, by investigating other components of environmental pollution such as nitrogen dioxide, PM2.5 and ozone, to reveal the impact of covid-19 outbreak on environmental pollution, as disregarded by the all preceding studies. Additionally, it makes a methodological contribution before integrating supplementary variables accompanying with ecological air pollution. Finally, the current research article provides an alternative and creative approach of modeling the impact of public mobility on environmental sustainability. 2021, Emerald Publishing Limited.