Search results

In recent years, multiuser-multiple-input multiple-output (MU-MIMO)-based wireless communication system has emerged as a prominent 5G technique that has several advantages over conventional MIMO systems such as high data rate and channel capacity. In this paper, the authors introduce a novel low-complexity radix factorization-based fast Fourier transform (FFT) as a multibeamformer and maximal likelihood-MU detection (ML-MUD) techniques as an optimal signal subdetector which results with considerable complexity reduction with intolerable error rate performance.

The proposed radix-factorized FFT-multibeamforming (RF-FFT-MBF) architectures have the potential to reduce both hardware complexity and energy consumptions as compared to its state-of-the-art methods while meeting the throughput requirements of emerging 5G devices. Here through simulation results, the efficiency of the scaled ML subdetector system is compared with the conventional ML detectors.

Here through simulation results, the efficiency of the scaled ML subdetector system is compared with the conventional ML detectors. Through experimental results, it is well proved that the proposed detector offers significant hardware and energy efficiency with the least possible error rate performance overhead.

Here through simulation results, the efficiency of the scaled ML subdetector system is compared with the conventional ML detectors. Through experimental results, it is well proved that the proposed detector offers significant hardware and energy efficiency with the least possible error rate performance overhead.

The problems of Super resolution are broadly discussed in diverse fields. Rather than the progression toward the super resolution models for real-time images, operating hyperspectral images still remains a challenging problem.

This paper aims to develop the enhanced image super-resolution model using “optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT), and Optimized Deep Convolutional Neural Network”. Once after converting the HR images into LR images, the NSSR images are generated by the optimized NSSR. Then the ADWT is used for generating the subbands of both NSSR and HRSB images. The residual image with this information is obtained by the optimized Deep CNN. All the improvements on the algorithms are done by the Opposition-based Barnacles Mating Optimization (O-BMO), with the objective of attaining the multi-objective function concerning the “Peak Signal-to-Noise Ratio (PSNR), and Structural similarity (SSIM) index”. Extensive analysis on benchmark hyperspectral image datasets shows that the proposed model achieves superior performance over typical other existing super-resolution models.

From the analysis, the overall analysis of the suggested and the conventional super resolution models relies that the PSNR of the improved O-BMO-(NSSR+DWT+CNN) was 38.8% better than bicubic, 11% better than NSSR, 16.7% better than DWT+CNN, 1.3% better than NSSR+DWT+CNN, and 0.5% better than NSSR+FF-SHO-(DWT+CNN). Hence, it has been confirmed that the developed O-BMO-(NSSR+DWT+CNN) is performing well in converting LR images to HR images.

This paper adopts a latest optimization algorithm called O-BMO with optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT) and Optimized Deep Convolutional Neural Network for developing the enhanced image super-resolution model. This is the first work that uses O-BMO-based Deep CNN for image super-resolution model enhancement.

The aim of continuous learning is to obtain and fine-tune information gradually without removing the already existing information. Many conventional approaches in streaming data classification assume that all arrived new data is completely labeled. To regularize Neural Networks (NNs) by merging side information like user-provided labels or pair-wise constraints, incremental semi-supervised learning models need to be introduced. However, they are hard to implement, specifically in non-stationary environments because of the efficiency and sensitivity of such algorithms to parameters. The periodic update and maintenance of the decision method is the significant challenge in incremental algorithms whenever the new data arrives.

Hence, this paper plans to develop the meta-learning model for handling continuous or streaming data. Initially, the data pertain to continuous behavior is gathered from diverse benchmark source. Further, the classification of the data is performed by the Recurrent Neural Network (RNN), in which testing weight is adjusted or optimized by the new meta-heuristic algorithm. Here, the weight is updated for reducing the error difference between the target and the measured data when new data is given for testing. The optimized weight updated testing is performed by evaluating the concept-drift and classification accuracy. The new continuous learning by RNN is accomplished by the improved Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO). Finally, the experiments with different datasets show that the proposed learning is improved over the conventional models.

From the analysis, the accuracy of the ONU-SHO based RNN (ONU-SHO-RNN) was 10.1% advanced than Decision Tree (DT), 7.6% advanced than Naive Bayes (NB), 7.4% advanced than k-nearest neighbors (KNN), 2.5% advanced than Support Vector Machine (SVM) 9.3% advanced than NN, and 10.6% advanced than RNN. Hence, it is confirmed that the ONU-SHO algorithm is performing well for acquiring the best data stream classification.

This paper introduces a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data. This is the first work utilizes a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data.

The purpose of this paper is to analyse the social and economic disparities across social groups in rural Uttar Pradesh. The paper demonstrates that the structure of the rural economy in India is characterised by deeply ingrained prejudices and social discrimination. The four-village study undertaken in one of the most populated states in India, Uttar Pradesh, clearly reveals that there is a huge disparity in terms of various social and economic indicators and that the so-called high growth has hardly helped in bettering their lives.

The paper is based on primary data collected from the Census survey of villages exploring socio-economic disparities across social groups by using decomposition models.

The results evidently lend credence to the postulations that a large proportion of the disadvantaged groups are prone to multiple deprivations, both in the society and in labour markets. The inquiry reveals this phenomenon clearly.

From the policy point of view, it is therefore imperative to ensure the direct and focussed provision of basic human requirements in terms of education, employment and income of the state. The implementation of direct policy interventions is an absolute necessity if the state has to guarantee convergence and the inclusive growth process on a sustained basis.

This paper fulfils an identified need to study the inequality among the social groups in terms of education, employment, income and livelihood opportunities in selected villages of four districts of Uttar Pradesh.

A continuous mixed opinion on the relevance of caste-based reservations and caste as a factor of socioeconomic disparity in the recent period demands update of evidence on socioeconomic inequalities among caste groups for effective policy making. The purpose of this paper is to investigate whether the caste inequalities in terms of socioeconomic opportunities and poverty are still persisting in rural Uttar Pradesh based on village census surveys?

This study used data primarily collected from four village census surveys under the project rural transformation in Uttar Pradesh, 2013. Bivariate analyses, human opportunity index (HOI), multidimensional poverty index (MPI) and inequality decomposition analyses used as methods of analyses.

The authors findings suggest that in spite of more than six decades of welfare policies and major political mobilization movements among lower castes in the state, the huge inequalities in terms of critical socioeconomic indicators such as landholding, higher education and wealth distribution and multi-dimensional poverty across the castes are still persisting in the state. Decomposition results suggest that between group inequalities contribute more to the total inequality in landholding whereas within group inequalities contribute maximum to total inequality in education and wealth status of different castes in rural Uttar Pradesh. However, within inequalities much less in general castes compared to SCs/OBCs.

Based on its latest empirical evidence, this study strengthens the argument that caste still matters in socioeconomic achievements of the population in India even after decades of planning and financing of social welfare schemes to uplift the lower castes in India. Thus, provides critical inputs to current debates on the relevance of caste as a determinant of socioeconomic status in India.

The present chapter attempts to highlight the vulnerabilities of female migrants as compared to non-migrants in the unorganized urban labor market. Informal female migrants working in construction, as domestic workers, tailors/boutiques, and garment workers in the urban unorganized sector of Uttar Pradesh’s selected urban locations, are covered in this chapter. Though the fact prevails that workers in the unorganized labor markets are confronted with various livelihood crunches, still those who are migrants and swelling the urban labor markets are more prone to different vulnerabilities. There is scanty literature on the situation and condition of migrants particularly female migrant workers in India, whereas we know more about the condition of international migrants, mainly migrant workers in the Gulf and other regions. The study is based on interviews with 174 female informal workers who have migrated and 222 non-migrants from various regions of the state to the urban locations of selected cities. Our study also attempts to do an in-depth, qualitative exploration of these vulnerable women’s lives and perceptions and tries to capture layered vulnerabilities, risks, and rewards confronted due to both migration and work in the informal sector. Specifically, the findings reflect upon the fact that how strong societal norms may actually prevent women from acknowledging or articulating the true reasons for their migrations.

This paper aims to encapsulate the gendered support and hindering factors along with the role of the state experienced by Delhi-based women entrepreneurs in setting up/operating their enterprises amidst the challenges posed by the coronavirus disease 2019 (COVID-19) pandemic.

The study used mixed methods to explore the challenges faced and recovery mechanisms adopted by women entrepreneurs with special reference to the COVID-19 pandemic. A total of 50 Delhi-based young women entrepreneurs (42 actual entrepreneurs + 8 prospective/struggling entrepreneurs) selected using snowball/purposive sampling were studied through both a semi-structured questionnaire and personal interviews. National Small Industries Corporation (NSIC) and micro, small and medium enterprises (MSME) were consulted for the government’s policy documents and data. The SPSS package was used for quantitative data analysis.

Low-budget/very small-scale women entrepreneurs face common as well as gender-based challenges in the context of the market, finance, social capital, family support and awareness in addition to accessing the state’s resources/policies in both their startups and crisis situations, such as the COVID-19 pandemic. Although gender sensitization, entrepreneurial family background, and equal access to technology and the Internet has enabled women entrepreneurs to initiate, adapt, and scale their enterprises, male domination within the family, society, market, and state apparatuses is omnipresent and has served as a bottleneck for women-owned startups while hindering the recovery of their enterprises amidst the COVID-19 pandemic.

The study was limited to exploring the challenges and prospects of Delhi-based women entrepreneurs at the beginning of their enterprises and amidst the COVID-19 pandemic. On the other hand, the study had access to data and facts announced by the Indian government. No data were available on the implementation of policies and programs, and therefore specific policy analysis was not attempted. However, the reachability and accessibility of government resources and policies were employed.

The study highlights the complexity of patriarchy, which hampers women entrepreneurs in all family, society, market and state domains. Therefore, policy enactment and implementation and further research on women entrepreneurship are suggested to focus more deeply on the gender dimension.

The data used in this work comprised inputs from government sources as well as insights from fieldwork that have not been used by any other publication.

With the growing economic impact of e-commerce and the increasing trend of omnichannel mode, more considerate services can be provided to customers. This paper aims to explore the optimal practice of business strategies and enrich the research content of marine tourism omnichannel.

This paper studies the optimal practice of bundling pricing and service effort strategies between two tourism suppliers (TSs) and a travel agent (TA) who distributes complementary products in marine tourism omnichannel considering joint efforts of both sides. This study develops five models by Stackelberg and Nash game and introduces the revenue-sharing contract. All outcomes/results are analyzed and the corresponding numerical and sensitivity analyses are conducted to derive more managerial implications and business insights.

The main findings show that bundling price is directly proportional to inter-channel integration coefficient and service effort level coefficient, and inversely proportional to the price elasticity coefficient. TA tends to provide a higher level of service effort than TSs when TA plays a dominant role. Improving the service effort level unduly leads to a decline in profits. Moreover, TSs and TA can reach a win-win situation under the coordination mechanism and the marine tourism omnichannel can achieve the best performance.

A novel and useful approach towards joint equilibrium decisions of bundle pricing and service efforts in marine tourism omnichannel with complementary tourism products under different operational strategies is proposed.

The purpose of this study is to explore and evaluate potential nonmonotonicity in the determinants of profit efficiency, specifically IT and R&D investments in the Indian commercial banking sector.

The study employs an alternative stochastic profit efficiency framework and introduces nonmonotonic effects by parameterizing the location and scale parameters of the inefficiency component on an unbalanced panel data set of 72 commercial banks in the 2008–2019 period. Marginal effects across quartiles are calculated using a bias-corrected and accelerated bootstrap procedure of 500 simulations. The study disaggregates across ownership and size for gauging the impact of structure on the associations between determinants of profit efficiency.

The study partially rejects the productivity paradox as it discovers a negative association of IT and R&D with profit inefficiency. However, the observed nonmonotonicity of IT is of significance for bank managers, as the study concludes that overinvestment in IT is detrimental to a bank’s profit-maximizing interests. Further, bank size, loan default and credit risk depict a nonmonotonic relationship across the sample with large banks, high NPAs and high credit risk associated with reducing profit efficiency. In addition, higher margins and greater diversification are related positively to efficiency, and banks with cost-heavy structures or having high liquidity risk associated negatively with efficiency.

To the best knowledge of the authors, the study is perhaps the first to acknowledge and incorporate nonmonotonic associations of IT investments amidst other exogenous determinants under a stochastic profit efficiency framework.

Soy beverage is becoming more and more popular because it is touted as a healthy food containing useful phytochemicals and is free from lactose and cholesterol. The purpose of this paper is to optimize the spray drying process parameters for obtaining soy beverage powder with good reconstitution and handling properties.

Pre-concentrated soy beverage was dried in a laboratory model spray dryer, and the effects of inlet air temperature (180-220°C), feed rate (20-40 ml/min) and feed solid content (15-25 per cent) on some physical parameters and reconstitution properties (wettability and dispersibility) of spray-dried soy beverage powders were investigated. Second order polynomial response surface model was selected for the analysis of data and optimization of the process.

Spray drying of soy beverage at different processing conditions resulted in powders with particle size (volume mean diameter) in the range of 86 to 156 µm. Dispersibility and wetting time of the spray-dried soy beverage powders was found to be in the range of 56 to 78 per cent and 30 to 90 s respectively, under various drying conditions. Inlet air temperature was found to be the main factor affecting most of the quality parameters, followed by solid content of the feed. Temperature significantly affected the wettability, dispersibility, colour parameters, particle size and flowability of the powder at p ≤ 0.01. Lower temperature and higher feed solid content produced bigger-sized powder particles with better handling properties in terms of flowability and cohesiveness. A moderate inlet air temperature (196°C), higher feed solid content (24 per cent) and lower feed rate (27 ml/min) were found suitable for drying of soy beverage.

The study implied the possibility of producing powder from soy beverage using the spray-drying method and optimized drying conditions for obtaining soy beverage powder with good reconstitution properties.

The finding of this study demonstrated for the first time how the inlet air temperature, feed solid content and feed rate during spray-drying influenced different quality parameters of soy beverage powder. Further, an optimized drying condition has been identified.