Vipin Khattri, Sandeep Kumar Nayak and Deepak Kumar Singh
Currency usage either in the physical or electronic marketplace through chip-based or magnetic strip-based plastic card becoming the vulnerable point for the handlers. Proper…
Abstract
Purpose
Currency usage either in the physical or electronic marketplace through chip-based or magnetic strip-based plastic card becoming the vulnerable point for the handlers. Proper education and awareness can only thrive when concrete fraud detection techniques are being suggested together with potential mitigation possibilities. The purpose of this research study is tendering in the same direction with a suitable plan of action in developing the authentication strength metric to give weightage marks for authentication techniques.
Design/methodology/approach
In this research study, a qualitative in-depth exploration approach is being adapted for a better description, interpretation, conceptualization for attaining exhaustive insights into specific notions. A concrete method of observation is being adopted to study various time boxed reports on plastic card fraud and its possible impacts. Content and narrative analysis are being followed to interpret more qualitative and less quantitative story about existing fraud detection techniques. Moreover, an authentication strength metric is being developed on the basis of time, cost and human interactions.
Findings
The archived data narrated in various published research articles represent the local and global environment and the need for plastic card money. It gives the breathing sense and capabilities in the marketplace. The authentication strength metric gives a supporting hand for more solidification of the authentication technique with respect to the time, cost and human ease.
Practical implications
The research study is well controlled and sufficient interpretive. The empirical representation of authentication technique and fraud detection technique identification and suggestive mitigation gives this research study an implication view for the imbibing research youths. An application and metric based pathway of this research study provides a smoother way to tackle futuristic issues and challenges.
Originality/value
This research study represents comprehensive knowledge about the causes of the notion of plastic card fraud. The authentication strength metric represents the novelty of a research study which produced on the basis of rigorous documentary and classified research analysis. The creativity of the research study is rendering the profound and thoughtful reflection of the novel dimension in the same domain.
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Sandeep Kumar and Amandeep Verma
The current study immerses in the realm of bank mergers among prominent PSBs in India, focusing on the financial performance of six recently merged PSBs entities. Amidst the…
Abstract
Purpose
The current study immerses in the realm of bank mergers among prominent PSBs in India, focusing on the financial performance of six recently merged PSBs entities. Amidst the global impact of the COVID-19 pandemic on economies, the study aims to uncover the efficiency of these PSBs in navigating this unprecedented crisis.
Design/methodology/approach
The evaluation encompasses panel data on an annual basis spanning from 2020 to 2023. To assess the overall efficiency of merged PSBs, the advanced statistical technique like one-step system generalized method of moments has been applied to estimate its efficiency.
Findings
The study findings affirm that PSB mergers have bolstered financial metrics and efficiency. Enhanced return on equity (ROA) and net profit margin (NPM) signify improved profitability and efficiency. The consolidation also facilitates better asset management and utilization. Moreover, merged entities benefit from economies of scale, cost efficiencies, risk diversification, technological investments, and overall performance improvements.
Practical implications
The study's policy suggestions stress ongoing consolidation efforts to boost banking sector resilience, advocating for improved efficiency, governance, and asset quality management. These steps are crucial for successful bank mergers and fostering a robust, competitive banking landscape in India.
Originality/value
This study is a novel attempt to analyze Indian bank profitability and efficiency post PSB mergers amid COVID-19 pandemic. In a developing country like India, especially in PSBs has experienced significant structural changes over the previous 7 years just before pandemic, such a study necessitates a prompt empirical investigation.
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Indian families are undergoing a transition due to a combination of factors such as rapid urbanization, economic development, educational advancements and major global connect…
Abstract
Indian families are undergoing a transition due to a combination of factors such as rapid urbanization, economic development, educational advancements and major global connect. The shift from traditional joint families to urban nuclear families, changing pattern of the size of families, transformed gender roles in the domestic sphere, rising educational and career aspirations and increased occupational choices, new technological and economic contributions collectively contribute to a new landscape for Indian families. While these changes bring about new forms and structures of modern Indian families, they also reflect the resilience of the conventional value system of Indian families in adapting to the demands of a rapidly changing world. This chapter outlines the changing dynamics of Indian families in the 21st century. Changes in marriages and their influence on family making have been discussed with a special focus on inter-caste and intra-caste marriages and modern families. In urban India, age at marriage is also discussed in the background of formation of families. This chapter provides a discussion on changed gender roles and hierarchies within families. This chapter also highlights separation and divorce which led to single-parent families and broken families in Indian society.
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Ved Prabha Toshniwal, Rakesh Jain, Gunjan Soni, Sachin Kumar Mangla and Sandeep Narula
This study is centered on the identification of the most appropriate Technology Adoption (TA) model for investigating the adoption of Industry 4.0 technologies within…
Abstract
Purpose
This study is centered on the identification of the most appropriate Technology Adoption (TA) model for investigating the adoption of Industry 4.0 technologies within pharmaceutical and related enterprises. The aim is to facilitate a smooth transition to advanced technologies while concurrently achieving environmental sustainability.
Design/methodology/approach
Selection of a suitable TA theory is carried out using a hybrid multi-criteria decision-making (MCDM) approach incorporating PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) and Fuzzy Measurement of alternatives and ranking according to Compromise solution (F-MARCOS) methods. A group of three experts is formulated for the ranking of criteria and alternatives based on those criteria.
Findings
The results indicate that out of all six TA models considered unified theory of acceptance and use of technology (UTAUT) model gets the highest utility function value, followed by the technical adoption model (TAM). Further, sensitivity analysis is conducted to confirm the validity of the MCDM model employed.
Research limitations/implications
Challenging times like COVID-19 pointed out the importance of technology in the pharmaceutical and healthcare sectors. TA studies in this area can help in the identification of critical factors that can assist pharmaceutical firms in their efforts to embrace emerging technologies, enhance their outputs and increase their efficiency.
Originality/value
The novelty of this research lies in the fact that the utilization of a TA theory prior to its implementation has not been witnessed in existing scholarly literature. The utilization of a TA theory, specifically within the pharmaceutical industry, can assist enterprises in directing their attention toward pertinent factors when contemplating the implementation of emerging technologies and achieving sustainable development.
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Sandeep Kumar Hegde and Monica R. Mundada
Chronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio…
Abstract
Purpose
Chronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio vasculardisease (CVD) and chronic kidney disease (CKD) are major chronic diseases responsible for millions of death. Each of these diseases is considered as a risk factor for the other two diseases. Therefore, noteworthy attention is being paid to reduce the risk of these diseases. A gigantic amount of medical data is generated in digital form from smart healthcare appliances in the current era. Although numerous machine learning (ML) algorithms are proposed for the early prediction of chronic diseases, these algorithmic models are neither generalized nor adaptive when the model is imposed on new disease datasets. Hence, these algorithms have to process a huge amount of disease data iteratively until the model converges. This limitation may make it difficult for ML models to fit and produce imprecise results. A single algorithm may not yield accurate results. Nonetheless, an ensemble of classifiers built from multiple models, that works based on a voting principle has been successfully applied to solve many classification tasks. The purpose of this paper is to make early prediction of chronic diseases using hybrid generative regression based deep intelligence network (HGRDIN) model.
Design/methodology/approach
In the proposed paper generative regression (GR) model is used in combination with deep neural network (DNN) for the early prediction of chronic disease. The GR model will obtain prior knowledge about the labelled data by analyzing the correlation between features and class labels. Hence, the weight assignment process of DNN is influenced by the relationship between attributes rather than random assignment. The knowledge obtained through these processes is passed as input to the DNN network for further prediction. Since the inference about the input data instances is drawn at the DNN through the GR model, the model is named as hybrid generative regression-based deep intelligence network (HGRDIN).
Findings
The credibility of the implemented approach is rigorously validated using various parameters such as accuracy, precision, recall, F score and area under the curve (AUC) score. During the training phase, the proposed algorithm is constantly regularized using the elastic net regularization technique and also hyper-tuned using the various parameters such as momentum and learning rate to minimize the misprediction rate. The experimental results illustrate that the proposed approach predicted the chronic disease with a minimal error by avoiding the possible overfitting and local minima problems. The result obtained with the proposed approach is also compared with the various traditional approaches.
Research limitations/implications
Usually, the diagnostic data are multi-dimension in nature where the performance of the ML algorithm will degrade due to the data overfitting, curse of dimensionality issues. The result obtained through the experiment has achieved an average accuracy of 95%. Hence, analysis can be made further to improve predictive accuracy by overcoming the curse of dimensionality issues.
Practical implications
The proposed ML model can mimic the behavior of the doctor's brain. These algorithms have the capability to replace clinical tasks. The accurate result obtained through the innovative algorithms can free the physician from the mundane care and practices so that the physician can focus more on the complex issues.
Social implications
Utilizing the proposed predictive model at the decision-making level for the early prediction of the disease is considered as a promising change towards the healthcare sector. The global burden of chronic disease can be reduced at an exceptional level through these approaches.
Originality/value
In the proposed HGRDIN model, the concept of transfer learning approach is used where the knowledge acquired through the GR process is applied on DNN that identified the possible relationship between the dependent and independent feature variables by mapping the chronic data instances to its corresponding target class before it is being passed as input to the DNN network. Hence, the result of the experiments illustrated that the proposed approach obtained superior performance in terms of various validation parameters than the existing conventional techniques.
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Sandeep Singh, Shashi Kant Sharma and M. Abdul Akbar
The purpose of this work is to improve the air entrainment capacity of a concrete by using fine mineral admixtures such as fly ash (FA) and silica fume (SF) as cement substitute…
Abstract
Purpose
The purpose of this work is to improve the air entrainment capacity of a concrete by using fine mineral admixtures such as fly ash (FA) and silica fume (SF) as cement substitute, and coal bottom ash (CBA) as fine aggregate substitute. Air entrainment capacity has been studied indirectly as a measure of heat resistance of concrete. Literature has suggested that mineral admixtures improve the air absorption in the paste component of the concrete, on the one hand, whereas they perform pore and grain size refinement, on the other, thereby reducing the air entrainment. CBA, which being porous, creates the possibility of air adsorption by the aggregate component. Therefore, the study finds out whether a double benefit of adding both of these materials will be achieved, or CBA will try to improve the deficiency in the air entrainment created by the mineral admixtures.
Design/methodology/approach
Air-entrained concrete (AEC) mixes were constituted in three groups. First group represents mixes with natural fine aggregates only, and second with 25% fine aggregates substituted by CBA. Progressively, the third group has 50% fine aggregates substituted with CBA. In all the three groups, cement was substituted with FA and SF @ 0%, 20% and 40%, and 0%, 5% and 10%, respectively, thereby creating four binary and four ternary mixes corresponding to each group. Compressive and flexural strength tests were conducted at 28 days on the concrete mixes pre and post high-temperature heat treatment, i.e. 100°C, 200°C and 400°C, respectively. This study also examines the microstructure characteristics of AEC after 14 days of curing via X-ray diffraction. Sorptivity test was also conducted to estimate the capillary and air-entrained voids in concrete.
Findings
It was found that a concrete mix containing 20% FA and 10% SF along with 50% CBA could give similar post-heated strength to a normal (without mineral admixtures) AEC. In AECs where only CBA is present and cement paste is not substituted, both of the pre- and post-heated strengths of concrete reduce. Also, some mixtures containing large amounts of mineral admixtures in concrete with nil CBA show a high reduction in post-heated strength though they show good pre-heated strength. Therefore, mineral admixtures and CBA complement each other in improving the post-heated strength. Air pore structure found from sorptivity test also verifies these results.
Originality/value
AEC is very helpful for insulation of buildings during summer season by absorbing heat waves. AEC containing FA and CBA reduces carbon footprint because of substitution of cement and it also helps to conserve natural resources by the use of CBA in place of fine aggregates.
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Kurukulasuriya Dinesh Udana Devindra Fernando and Nawalage Seneviratne Cooray
Introduction: In the context of Sri Lanka, this study compares how institutions and financial development (FD) affect economic growth (EG) and inclusive growth (IG).Purpose: The…
Abstract
Introduction: In the context of Sri Lanka, this study compares how institutions and financial development (FD) affect economic growth (EG) and inclusive growth (IG).
Purpose: The well-structured administration and judicial system at the provincial level have been established against the socioeconomic vulnerabilities in the country for an extended period. Still, the country as a whole and provincial level is experiencing huge income and social inequality, though there are required provisions for enhancing the well-being of the people.
Methodology: The study consists of data from the nine provinces from 2013 to 2019. The analysis used the Dynamic Spatial Durbin Model (D-SDM) to explore the spatial dependencies between the provinces. Two models were developed: the interaction of the financial service activities (FSA) and insurance, reinsurance, and pension (INPEN), representing the FD with the EG and IG with and without. The IG index was estimated by principal component analysis (PCA) using indicators of the four dimensions. The results indicated spatial dependency among FD’s interaction with EG when provincial tax (PROTAX) and provincial expenses (PROEXP) are the provincial institutions.
Findings: The IG model results showed the IG’s spatial dependency moderated by the FD and only the IG model between the provinces. PROEXP showed a significant positive spillover impact among provinces towards the IG.
Practical Implications: The finding inform economic policy making while identifying weaknesses in existing local governments. Attention must be given to how poverty can be reduced, enhancing the well-being of the people with the proper channelling of finance and government institutional mechanisms.
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Santosh Bopche and Sandeep Lamba
This paper aims to present experimental work examining the effect of opening size on the collection efficiency of cavity-type receiver geometries, e.g. modified cavity and…
Abstract
Purpose
This paper aims to present experimental work examining the effect of opening size on the collection efficiency of cavity-type receiver geometries, e.g. modified cavity and spherical cavity with single- as well as dual-stage water heating. The correlations, obtained using the experimentally obtained data, are helpful in designing of cavity receivers (modified and spherical geometry type) to be used in solar-power harnessing assignments/projects, for yielding better system performance.
Design/methodology/approach
The parameters of study encompass receiver opening or aperture ratios (d/D, ratio of diameter of opening to the maximum diameter of spherical cavity) of 0.4, 0.47, 0.533 and 0.6; flow Reynolds numbers of 938, 1,175, 1,525 and 1,880 with water as a coolant; and receiver inclination angles of 90, 60, 45 and 30° (with 90° as receiver-opening facing downward and 30° as receiver-aperture facing closer to sideway). A modified cavity receiver was examined for opening ratios of 0.46, 0.6, 0.7 and 0.93. The glass covers, with thickness 2, 4 and 6 mm, were positioned at the opening of cavity to mitigate the energy losses.
Findings
The experiments have been conducted at a lesser incoming radiative heat flux, for receiver cavity wall surface temperatures ranging from 90°C to 180°C. The collection efficiency values of both the receivers, modified cavity and spherical cavity types, are seen increasing with coolant flow rate and receiver tilt (inclination) angles, i.e. 30° → 90°. The collection efficiency exhibits maxima at an opening ratio of 0.533 in case of both single- and double-stage spherical cavity receiver. This value was observed as 0.6 for modified cavity receiver. The mathematical correlations developed for obtaining the collection efficiency values of modified cavity-type receiver, spherical cavity receiver with single stage and spherical cavity receiver with dual-stage water heating are given as
Social implications
The findings of the paper may be helpful in erecting concentrating solar collector systems for household water heating, concentrating solar-based power generation as well as for various agricultural applications.
Originality/value
The experimental investigations are fewer in the literature examining the combined geometrical influence on the efficiency of cavity receivers with single- and double-stage water heating provisions.
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Syed Modassir Hussain, Rohit Sharma, Manoj Kumar Mishra and Jitendra Kumar Singh
Nanosized honeycomb-configured materials are used in modern technology, thermal science and chemical engineering due to their high ultra thermic relevance. This study aims to…
Abstract
Purpose
Nanosized honeycomb-configured materials are used in modern technology, thermal science and chemical engineering due to their high ultra thermic relevance. This study aims to scrutinize the heat transmission features of magnetohydrodynamic (MHD) honeycomb-structured graphene nanofluid flow within two squeezed parallel plates under Joule dissipation and solar thermal radiation impacts.
Design/methodology/approach
Mass, energy and momentum preservation laws are assumed to find the mathematical model. A set of unified ordinary differential equations with nonlinear behavior is used to express the correlated partial differential equations of the established models, adopting a reasonable similarity adjustment. An approximate convergent numerical solution to these equations is evaluated by the shooting scheme with the Runge–Kutta–Fehlberg (RKF45) technique.
Findings
The impression of pertinent evolving parameters on the temperature, fluid velocity, entropy generation, skin friction coefficients and the heat transference rate is explored. Further, the significance of the irreversibility nature of heat transfer due to evolving flow parameters are evaluated. It is noted that the heat transference rate performance is improved due to the imposition of the allied magnetic field, Joule dissipation, heat absorption, squeezing and thermal buoyancy parameters. The entropy generation upsurges due to rising magnetic field strength while its intensification is declined by enhancing the porosity parameter.
Originality/value
The uniqueness of this research work is the numerical evaluation of MHD honeycomb-structured graphene nanofluid flow within two squeezed parallel plates under Joule dissipation and solar thermal radiation impacts. Furthermore, regression models are devised to forecast the correlation between the rate of thermal heat transmission and persistent flow parameters.
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Sudhakar T., Hannah Inbarani H. and Senthil Kumar S.
The purpose of this paper is to obtain correctly classified routes based on their parameters.
Abstract
Purpose
The purpose of this paper is to obtain correctly classified routes based on their parameters.
Design/methodology/approach
In this paper, a covering rough set (CRS) approach is proposed for route classification in wireless ad hoc networks. In a wireless network, mobile nodes are deployed randomly in a simulation region. This work addresses the problem of route classification.
Findings
The network parameters such as bandwidth, delay, packet byte rate and packet loss rate changes due to the frequent mobility of nodes lead to uncertainty in wireless networks. This type of uncertainty can be very well handled using a rough set concept. An ultimate aim of classification is to correctly predict the decision class for each instance in the data.
Originality/value
The traditional classification algorithms, named K-nearest neighbor, J48, general rough set theory, naive Bayes, JRIP and multilayer perceptron, are used in this work for comparison and for the proposed CRS based on route classification approach revealing better accuracy than traditional classification algorithms.