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Book part
Publication date: 1 September 2023

Ishu Chadda

Abstract

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Social Sector Development and Inclusive Growth in India
Type: Book
ISBN: 978-1-83753-187-5

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Article
Publication date: 11 January 2013

R.R. Biradar

The aim of the study is to attempt to analyze the trends and patterns of institutional credit flow, deployed by the CBs, SCBs and RRBs, for production and investment purposes in…

697

Abstract

Purpose

The aim of the study is to attempt to analyze the trends and patterns of institutional credit flow, deployed by the CBs, SCBs and RRBs, for production and investment purposes in agriculture and allied activities in India in the light of banking sector reforms initiated in the early 1990s.

Design/methodology/approach

The study is based on secondary data collected from the Handbook of Statistics of Indian Economy, 2009‐2010 published by the Reserve Bank of India, Agricultural Statistics at a Glance, Economic Survey of India, etc. The data relating to institutional credit at the all India level were collected for 1971‐1972 to 2007‐2008. The period from 1971‐1972 to 1980‐1981 is considered as the beginning of multi‐agency approach and bank branch expansion, from 1981‐1982 to 1990‐1991 is regarded as the pre‐reform period, from 1991‐1992 to 2007‐2008, as the post‐reform period. In order to examine the extent of institutional credit flow for development of agriculture and allied activities, the indictors such as the average institutional credit per hectare cultivated area and as percentage of agricultural GDP were estimated, besides the CAGR during different periods.

Findings

The study found that the annual growth rate of total institutional credit for agriculture and allied activities was much higher during the reform period as compared to that of pre‐reform period. The average institutional credit per hectare and as a percentage of agricultural GDP has gone up significantly during the reform regime. The RRBs followed by the SCBs registered highest growth rates of production credit as compared to that of CBs during the entire period; it was higher during the reform than the pre‐reform period. The growth rate of investment credit was highest for SCBs followed by the RRBs as against the CBs during the reform period. It has been observed that the CBs have lost their historical prime position in provision of agricultural credit. The growth pattern of production as well as investment credit constituted what can be described as the “U‐shaped” curve. This implies that the bulk of the increase in institutional credit for agriculture and allied activities during the reform period was attributed to the banking sector reforms initiated in the early 1990s.

Research limitations/implications

The data on institutional credit provided by the SCARDBs and PCARDBs were not included under the co‐operative sector prior to 1999‐2000, and it covered credit by only PACs. Hence, the temporal comparability of data on institutional credit under the co‐operative sector for the period 1998‐1999 to 2007‐2008 with that of earlier periods may be erroneous.

Practical implications

Adequate and timely inflow of both production and investment credit for development of agriculture and allied activities through further reforms in the banking sector would go a long way in sustained growth of agriculture and food security for a great majority of the rural masses in India.

Originality/value

The study establishes the “U‐shaped” curve for the growth pattern of institutional credit for development of agriculture and allied activities in India. This follows that the increase in the growth rates of institutional credit during 1991‐1992 to 2007‐2008 was largely due to the banking sector reforms.

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Article
Publication date: 17 July 2019

Prasad A.Y. and Balakrishna Rayanki

In the present networking scenarios, MANETs mainly focus on reducing the consumed power of battery-operated devices. The transmission of huge data in MANETs is responsible for…

80

Abstract

Purpose

In the present networking scenarios, MANETs mainly focus on reducing the consumed power of battery-operated devices. The transmission of huge data in MANETs is responsible for greater energy usage, thereby affecting the parameter metrics network performance, throughput, packet overhead, energy consumption in addition to end-to-end delay. The effective parameter metric measures are implemented and made to enhance the network lifetime and energy efficiency. The transmission of data for at any node should be more efficient and also the battery of sensor node battery usage should be proficiently applied to increase the network lifetime. The paper aims to discuss these issues.

Design/methodology/approach

In this research work for the MANETs, the improvement of energy-efficient algorithms in MANETs is necessary. The main aim of this research is to develop an efficient and accurate routing protocol for MANET that consumes less energy, with an increased network lifetime.

Findings

In this paper, the author has made an attempt to improve the genetic algorithm with simulated annealing (GASA) for MANET to minimize the energy consumption of 0.851 percent and to enhance the network lifetime of 61.35 percent.

Originality/value

In this paper, the author has made an attempt to improve the GASA for MANET to minimize the energy consumption of 0.851 percent and to enhance the network lifetime of 61.35 percent.

Details

International Journal of Intelligent Unmanned Systems, vol. 8 no. 1
Type: Research Article
ISSN: 2049-6427

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Article
Publication date: 15 October 2021

Rangayya, Virupakshappa and Nagabhushan Patil

One of the challenging issues in computer vision and pattern recognition is face image recognition. Several studies based on face recognition were introduced in the past decades…

142

Abstract

Purpose

One of the challenging issues in computer vision and pattern recognition is face image recognition. Several studies based on face recognition were introduced in the past decades, but it has few classification issues in terms of poor performances. Hence, the authors proposed a novel model for face recognition.

Design/methodology/approach

The proposed method consists of four major sections such as data acquisition, segmentation, feature extraction and recognition. Initially, the images are transferred into grayscale images, and they pose issues that are eliminated by resizing the input images. The contrast limited adaptive histogram equalization (CLAHE) utilizes the image preprocessing step, thereby eliminating unwanted noise and improving the image contrast level. Second, the active contour and level set-based segmentation (ALS) with neural network (NN) or ALS with NN algorithm is used for facial image segmentation. Next, the four major kinds of feature descriptors are dominant color structure descriptors, scale-invariant feature transform descriptors, improved center-symmetric local binary patterns (ICSLBP) and histograms of gradients (HOG) are based on clour and texture features. Finally, the support vector machine (SVM) with modified random forest (MRF) model for facial image recognition.

Findings

Experimentally, the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy, similarity index, dice similarity coefficient, precision, recall and F-score results. However, the proposed method offers superior recognition performances than other state-of-art methods. Further face recognition was analyzed with the metrics such as accuracy, precision, recall and F-score and attained 99.2, 96, 98 and 96%, respectively.

Originality/value

The good facial recognition method is proposed in this research work to overcome threat to privacy, violation of rights and provide better security of data.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 2
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 13 October 2021

Sharanabasappa and Suvarna Nandyal

In order to prevent accidents during driving, driver drowsiness detection systems have become a hot topic for researchers. There are various types of features that can be used to…

103

Abstract

Purpose

In order to prevent accidents during driving, driver drowsiness detection systems have become a hot topic for researchers. There are various types of features that can be used to detect drowsiness. Detection can be done by utilizing behavioral data, physiological measurements and vehicle-based data. The existing deep convolutional neural network (CNN) models-based ensemble approach analyzed the behavioral data comprises eye or face or head movement captured by using a camera images or videos. However, the developed model suffered from the limitation of high computational cost because of the application of approximately 140 million parameters.

Design/methodology/approach

The proposed model uses significant feature parameters from the feature extraction process such as ReliefF, Infinite, Correlation, Term Variance are used for feature selection. The features that are selected are undergone for classification using ensemble classifier.

Findings

The output of these models is classified into non-drowsiness or drowsiness categories.

Research limitations/implications

In this research work higher end camera are required to collect videos as it is cost-effective. Therefore, researches are encouraged to use the existing datasets.

Practical implications

This paper overcomes the earlier approach. The developed model used complex deep learning models on small dataset which would also extract additional features, thereby provided a more satisfying result.

Originality/value

Drowsiness can be detected at the earliest using ensemble model which restricts the number of accidents.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 2
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 31 December 2021

Jyothi N. and Rekha Patil

This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection.

152

Abstract

Purpose

This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection.

Design/methodology/approach

The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance.

Findings

A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead.

Practical implications

The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks.

Originality/value

This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.

Details

International Journal of Pervasive Computing and Communications, vol. 18 no. 3
Type: Research Article
ISSN: 1742-7371

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Article
Publication date: 26 May 2020

Anupama Sharma, Abhay Bansal and Vinay Rishiwal

Quality communication is a big challenge in mobile ad hoc networks because of a restricted environment for mobile devices, bandwidth-constrained radio connections, random mobility…

150

Abstract

Purpose

Quality communication is a big challenge in mobile ad hoc networks because of a restricted environment for mobile devices, bandwidth-constrained radio connections, random mobility of connected devices, etc. High-quality communication through wireless links mainly depends on available bandwidth, link stability, energy of nodes, etc. Many researchers proposed stability and link quality methods to improve these issues, but they still require optimization. This study aims to contribute towards better quality communication in temporarily formed networks. The authors propose the stable and bandwidth aware dynamic routing (SBADR) protocol with the aim to provide an efficient, stable path with sufficient bandwidth and enough energy hold nodes for all types of quality of service (QoS) data communication.

Design/methodology/approach

The proposal made in this work used received signal strength from the media access control (MAC) layer to estimate the stability of the radio connection. The proposed path stability model combines the stability of the individual link to compute path stability. The amount of bandwidth available for communication at a specific time on a link is defined as the available link bandwidth that is understood as the maximum throughput of that link. Bandwidth as a QoS parameter ensures high-quality communication for every application in such a network. One other improvement, towards quality data transmission, is made by incorporating residual energies of communicating and receiving nodes in the calculation of available link bandwidth.

Findings

Communication quality in mobile ad hoc network (MANET) does not depend on a single parameter such as bandwidth, energy, path stability, etc. To address and enhance quality communication, this paper focused on high impact factors, such as path stability, available link bandwidth and energy of nodes. The performance of SBADR is evaluated on the network simulator and compared with that of other routing protocols, i.e. route stability based QoS routing (RSQR), route stability based ad-hoc on-demand distance vector (RSAODV) and Ad-hoc on-demand distance vector (AODV). Experimental outcomes show that SBADR significantly enhanced network performance in terms of throughput, packet delivery ratio (PDR) and normalized control overhead (NCO). Performance shows that SBADR is suitable for any application of MANET having random and high mobility.

Research limitations/implications

QoS in MANET is a challenging task. To achieve high-quality communication, the authors worked on multiple network parameters, i.e. path stability, available link bandwidth and energy of mobile nodes. The performance of the proposed routing protocol named SBADR is evaluated by a network simulator and compared with that of other routing protocols. Statistical analysis done on results proves significant enhancement in network performance. SBADR is suitable for applications of MANET having random and high mobility. It is also efficient for applications having a requirement of high throughput.

Practical implications

SBADR shows a significant enhancement in received data bytes, which are 1,709, 788 and 326 more in comparison of AODV, RSAODV and RSQR, respectively. PDR increased by 21.27%, 12.1%, 4.15%, and NCO decreased by 9.67%, 5.93%, 2.8% in comparison of AODV, RSAODV and RSQR, respectively.

Social implications

Outcomes show SBADR will perform better with applications of MANET such as disaster recovery, city tours, university or hospital networks, etc. SBADR is suitable for every application of MANET having random and high mobility.

Originality/value

This is to certify that the reported work in the paper entitled “SBADR: stable and bandwidth aware dynamic routing protocol for mobile ad hoc network” is an original one and has not been submitted for publication elsewhere. The authors further certify that proper citations to the previously reported work have been given and no data/tables/figures have been quoted verbatim from the other publications without giving due acknowledgment and without permission of the author(s).

Details

International Journal of Pervasive Computing and Communications, vol. 16 no. 3
Type: Research Article
ISSN: 1742-7371

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Article
Publication date: 29 September 2020

G. Sreeram, S. Pradeep, K. Sreenivasa Rao, B. Deevana Raju and Parveen Nikhat

The paper aims to precise and fast categorization on to transaction evolves into indispensible. The effective capacity difficulty of all the IDS simulates today at below discovery…

52

Abstract

Purpose

The paper aims to precise and fast categorization on to transaction evolves into indispensible. The effective capacity difficulty of all the IDS simulates today at below discovery amount of fewer regular barrage associations and therefore the next warning rate.

Design/methodology/approach

The reticulum perception is that the methods which examine and determine the scheme of contact on unearths toward number of dangerous and perchance fateful interchanges occurring toward the system. Within character of guaran-teeing the slumberous, opening and uprightness count of to socialize for professional. The precise and fast categorization on to transaction evolves into indispensible. The effective capacity difficulty of all the intrusion detection simulation (IDS) simulates today at below discovery amount of fewer regular barrage associations and therefore the next warning rate. The container with systems of connections are reproduction everything beacon subject to the series of actions to achieve results accepts exists a contemporary well-known method. At the indicated motivation a hybrid methodology supported pairing distinct ripple transformation and human intelligence artificial neural network (ANN) for IDS is projected. The lack of balance of the situation traversing the space beyond information range was eliminated through synthetic minority oversampling technique-based oversampling have low regular object and irregular below examine of the dominant object. We are binding with three layer ANN is being used for classification, and thus the experimental results on knowledge discovery databases are being used for the facts in occurrence of accuracy rate and disclosure estimation toward identical period. True and false made up accepted.

Findings

At the indicated motivation a hybrid methodology supported pairing distinct ripple transformation and human intelligence ANN for IDS is projected. The lack of balance of the situation traversing the space beyond information range was eliminated through synthetic minority oversampling technique-based oversampling have low regular object and irregular below examine of the dominant object.

Originality/value

Chain interruption discovery is the series of actions for the results knowing the familiarity opening and honor number associate order, the scientific categorization undertaking become necessary. The capacity issues of invasion discovery is the order to determine and examine. The arrangement of simulations at the occasion under discovery estimation for low regular aggression associations and above made up feeling sudden panic amount.

Details

International Journal of Pervasive Computing and Communications, vol. 17 no. 1
Type: Research Article
ISSN: 1742-7371

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Article
Publication date: 24 March 2022

Elavaar Kuzhali S. and Pushpa M.K.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150…

137

Abstract

Purpose

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

Design/methodology/approach

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

Findings

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

Originality/value

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Details

Journal of Engineering, Design and Technology , vol. 22 no. 3
Type: Research Article
ISSN: 1726-0531

Keywords

Available. Open Access. Open Access
Article
Publication date: 23 September 2024

Prabhugouda Mallanagouda Patil, Bharath Goudar and Ebrahim Momoniat

Many industries use non-Newtonian ternary hybrid nanofluids (THNF) because of how well they control rheological and heat transport. This being the case, this paper aims to…

376

Abstract

Purpose

Many industries use non-Newtonian ternary hybrid nanofluids (THNF) because of how well they control rheological and heat transport. This being the case, this paper aims to numerically study the Casson-Williamson THNF flow over a yawed cylinder, considering the effects of several slips and an inclined magnetic field. The THNF comprises Al2O3-TiO2-SiO2 nanoparticles because they improve heat transmission due to large thermal conductivity.

Design/methodology/approach

Applying suitable nonsimilarity variables transforms the coupled highly dimensional nonlinear partial differential equations (PDEs) into a system of nondimensional PDEs. To accomplish the goal of achieving the solution, an implicit finite difference approach is used in conjunction with Quasilinearization. With the assistance of a script written in MATLAB, the numerical results and the graphical representation of those solutions were ascertained.

Findings

As the Casson parameter β increases, there is an improvement in the velocity profiles in both chord and span orientations, while the gradients Re1/2Cf,Re1/2C¯f reduce for the same variations of β. The velocities of Casson THNF are greater than those of Casson-Williamson THNF. Approximately, a 202% and a 32% ascension are remarked in the magnitudes of Re1/2Cf and Re1/2C¯f for Casson-Williamson THNF than the Casson THNF only. When velocity slip attribute S1 jumps to 1 from 0.5, magnitude of both F(ξ,η) and Re1/2Cf fell down and it is reflected to be 396% at ξ=1, Wi=1 and β=1. An augmentation in thermal jump results in advanced fluid temperature and lower Re1/2Nu. In particular, about 159% of down drift is detected when S2 taking 1.

Originality/value

There is no existing research on the effects of Casson-Williamson THNF flow over a yawed cylinder with multiple slips and an angled magnetic field, according to the literature.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 34 no. 12
Type: Research Article
ISSN: 0961-5539

Keywords

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