Search results

1 – 10 of 149
Per page
102050
Citations:
Loading...
Access Restricted. View access options
Article
Publication date: 16 February 2022

Krishna Mohan A., Reddy P.V.N. and Satya Prasad K.

In the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best…

59

Abstract

Purpose

In the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best performance. The purpose of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG and Harris are used for the process of feature extraction. The authors’ proposed method will give the best results when compared with other existing methods.

Design/methodology/approach

The visual tracking of many real-world applications such as robotics, smart monitoring systems, independent driving and human-computer interactions are a major and current research problem in the field of computer vision. This refers to the automated trajectory prediction of an arbitrary target object, often given in the first frame in a bounding box while moving about in successive video frames. In the community of visual tracking or object tracking, DCF has gained more importance. Discriminative trackers strive to train a classifier that differentiates the target item from the background. The fundamental concept is to train a correlation filter that creates high responses around the target and low responses elsewhere. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG and Harris are used for the process of feature extraction. Through experimental analysis, the authors have evaluated several performance assessment metrics such as accuracy, precision, F-measure and specificity. The authors’ proposed method will give the best results when compared with other existing methods.

Findings

This process involved DCF which gained more importance. When it comes to speed, DCF gives the best performance. The main objective of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique for tracking the objects and these results will be used for identifying the action of the object.

Originality/value

The main theme exists in the process is to identify the tracking motion of the object by using convolution regression with varied features. This method proves that it will provide better results when compared to state of art methods.

Details

International Journal of Pervasive Computing and Communications, vol. 18 no. 5
Type: Research Article
ISSN: 1742-7371

Keywords

Access Restricted. View access options
Article
Publication date: 20 December 2021

Krishna Mohan A, Reddy PVN and Satya Prasad K

In the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best…

109

Abstract

Purpose

In the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best performance. The main objective of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG & Harris are used for the process of feature extraction. The proposed method will give the best results when compared to other existing methods.

Design/methodology/approach

This paper introduces the concept and research status of tracks; later the authors focus on the representative applications of deep learning in visual tracking.

Findings

Better tracking algorithms are not mentioned in the existing method.

Research limitations/implications

Visual tracking is the ability to control eye movements using the oculomotor system (vision and eye muscles working together). Visual tracking plays an important role when it comes to identifying an object and matching it with the database images. In visual tracking, deep learning has achieved great success.

Practical implications

The authors implement the multiple tracking methods, for better tracking purpose.

Originality/value

The main theme of this paper is to review the state-of-the-art tracking methods depending on deep learning. First, we introduce the visual tracking that is carried out manually, and secondly, we studied different existing methods of visual tracking based on deep learning. For every paper, we explained the analysis and drawbacks of that tracking method. This paper introduces the concept and research status of tracks, later we focus on the representative applications of deep learning in visual tracking.

Details

International Journal of Intelligent Unmanned Systems, vol. 11 no. 1
Type: Research Article
ISSN: 2049-6427

Keywords

Available. Content available

Abstract

Details

International Journal of Intelligent Unmanned Systems, vol. 11 no. 1
Type: Research Article
ISSN: 2049-6427

Access Restricted. View access options
Book part
Publication date: 17 June 2024

Akansha Mer, Kanchan Singhal and Amarpreet Singh Virdi

In today's advanced economy, there is a broader presence of information revolution, such as artificial intelligence (AI). AI primarily drives modern banking, leading to innovative…

Abstract

Purpose

In today's advanced economy, there is a broader presence of information revolution, such as artificial intelligence (AI). AI primarily drives modern banking, leading to innovative banking channels, services and solutions disruptions. Thus, this chapter intends to determine AI's place in contemporary banking and stock market trading.

Need for the Study

Stock market forecasting is hampered by the inherently noisy environments and significant volatility surrounding market trends. There needs to be more research on the mantle of AI in revolutionising banking and stock market trading. Attempting to bridge this gap, the present research study looks at the function of AI in banking and stock market trading.

Methodology

The researchers have synthesised the literature pool. They undertook a systematic review and meta-synthesis method by identifying the major themes and a systematic literature review aided in the critical analysis, synthesis and mapping of the body of existing material.

Findings

The study's conclusions demonstrated the efficacy of AI, which has played a robust role in banking and finance by reducing risk and operational costs, enabling better customer experience, improving regulatory complaints and fraud detection and improving credit and loan decisions. AI has revolutionised stock market trading by forecasting future prices or trends in financial assets, optimising financial portfolios and analysing news or social media comments on the assets or firms.

Practical Implications

AI's debut in banking and finance has brought sea changes in banking and stock market trading. AI in the banking industry and capital market can provide timely and apt information to its customers and customise the products as per their requirements.

Access Restricted. View access options
Article
Publication date: 12 July 2024

Siva Rama Krishna Uppuluri, Yatin Chaudhary, Mohan H. Badiger, Vijaya Gowri Turumella, Krishna Rao S. and Keerthana E.

Designing a sustainable bituminous concrete with long-term performance is a challenging problem. In addition, strength of the subgrade has a crucial impact on pavement design…

27

Abstract

Purpose

Designing a sustainable bituminous concrete with long-term performance is a challenging problem. In addition, strength of the subgrade has a crucial impact on pavement design. This paper aims to concentrate on subgrade soil stabilization with granite dust powder (GDP) and crumb rubber powder (CRP) to improve the engineering properties of the soil. Further design of bituminous concrete pavement with cement-treated layers in base and subbase course layers was carried out with life cycle cost analysis and life cycle assessment for 1 km of a four-lane national highway.

Design/methodology/approach

Subgrade soil stabilized with GDP and CRP is characterized as per Indian Standards (IS)-2720 to determine the optimum dosage. Further, the mechanistic-empirical pavement design was carried out using Indian Road Congress-37 (2018), analyzed using IITPAVE software and validated with ANSYS software. The life cycle cost analysis is carried out using the net present value method, and the life cycle assessment is performed according to the cradle-to-grave approach.

Findings

A soil mix comprising 10% GDP and 2.5% CRP yielded a soaked California bearing ratio value of 6.58%. In addition, the design of bituminous concrete pavement with cement-treated granular layers showed a 26.9% reduction in life cycle cost and 59.4% reduction in total carbon footprint per kilometer compared to the pavement with traditional aggregate layers.

Originality/value

The research on subgrade stabilization with sustainable materials like GDP and CRP incorporating mechanistic empirical pavement design, life cycle cost analysis and life cycle assessment is limited. Overall, the study recommends the use of GDP and CRP to stabilize soil for subgrade application and incorporate cement-treated granular layers, which offer economic and environmental benefits compared to traditional pavement construction.

Details

World Journal of Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1708-5284

Keywords

Access Restricted. View access options
Article
Publication date: 5 April 2024

Abhishek Kumar Singh and Krishna Mohan Singh

In the present work, we focus on developing an in-house parallel meshless local Petrov-Galerkin (MLPG) code for the analysis of heat conduction in two-dimensional and…

83

Abstract

Purpose

In the present work, we focus on developing an in-house parallel meshless local Petrov-Galerkin (MLPG) code for the analysis of heat conduction in two-dimensional and three-dimensional regular as well as complex geometries.

Design/methodology/approach

The parallel MLPG code has been implemented using open multi-processing (OpenMP) application programming interface (API) on the shared memory multicore CPU architecture. Numerical simulations have been performed to find the critical regions of the serial code, and an OpenMP-based parallel MLPG code is developed, considering the critical regions of the sequential code.

Findings

Based on performance parameters such as speed-up and parallel efficiency, the credibility of the parallelization procedure has been established. Maximum speed-up and parallel efficiency are 10.94 and 0.92 for regular three-dimensional geometry (343,000 nodes). Results demonstrate the suitability of parallelization for larger nodes as parallel efficiency and speed-up are more for the larger nodes.

Originality/value

Few attempts have been made in parallel implementation of the MLPG method for solving large-scale industrial problems. Although the literature suggests that message-passing interface (MPI) based parallel MLPG codes have been developed, the OpenMP model has rarely been touched. This work is an attempt at the development of OpenMP-based parallel MLPG code for the very first time.

Details

Engineering Computations, vol. 41 no. 2
Type: Research Article
ISSN: 0264-4401

Keywords

Access Restricted. View access options
Book part
Publication date: 2 August 2023

Shampa Roy

While popular genre fictions like detective novels are often centred around formulaic plots and stereotypical characters, they also undergo several exciting changes when adapted…

Abstract

While popular genre fictions like detective novels are often centred around formulaic plots and stereotypical characters, they also undergo several exciting changes when adapted in a diverse array of cultural and linguistic contexts. My chapter examines the first female detective of a Bangla crime writing series, Detective (Goyenda) Krishna as a figure that challenges patriarchal stereotypes related to violent women and dismantles the illusory neatness of binaries associated with ‘good’ and ‘bad’ femininity. The gun-toting, vengeance-seeking literary detective is also examined as mediating shifts and transitions in gendered practices and norms in Bengal – its socio-political as well as literary contexts – as it negotiated ideas of decoloniality from the first decade of the twentieth century and emerged as part of a new, partitioned nation in 1947. She is seen as a creative response to the changes related to gender that had been gradually taking shape in colonised Bengal and as articulating radically re-imagined possibilities and opportunities related to female subjectivities in a newly decolonised nation.

Details

The Emerald International Handbook of Feminist Perspectives on Women’s Acts of Violence
Type: Book
ISBN: 978-1-80382-255-6

Keywords

Access Restricted. View access options
Article
Publication date: 5 July 2021

Abhishek Kumar Singh and Krishna Mohan Singh

The work presents a novel implementation of the generalized minimum residual (GMRES) solver in conjunction with the interpolating meshless local Petrov–Galerkin (MLPG) method to…

215

Abstract

Purpose

The work presents a novel implementation of the generalized minimum residual (GMRES) solver in conjunction with the interpolating meshless local Petrov–Galerkin (MLPG) method to solve steady-state heat conduction in 2-D as well as in 3-D domains.

Design/methodology/approach

The restarted version of the GMRES solver (with and without preconditioner) is applied to solve an asymmetric system of equations, arising due to the interpolating MLPG formulation. Its performance is compared with the biconjugate gradient stabilized (BiCGSTAB) solver on the basis of computation time and convergence behaviour. Jacobi and successive over-relaxation (SOR) methods are used as the preconditioners in both the solvers.

Findings

The results show that the GMRES solver outperforms the BiCGSTAB solver in terms of smoothness of convergence behaviour, while performs slightly better than the BiCGSTAB method in terms of Central processing Unit (CPU) time.

Originality/value

MLPG formulation leads to a non-symmetric system of algebraic equations. Iterative methods such as GMRES and BiCGSTAB methods are required for its solution for large-scale problems. This work presents the use of GMRES solver with the MLPG method for the very first time.

Details

Engineering Computations, vol. 39 no. 2
Type: Research Article
ISSN: 0264-4401

Keywords

Access Restricted. View access options
Article
Publication date: 17 December 2024

Sudheer Reddy, Aditya Mohan Jadhav and Krishna Prasad

This paper explores the relationship between gender diversity on corporate boards and the accuracy of analysts’ earnings forecasts. The study focuses on gender-diverse boards as…

48

Abstract

Purpose

This paper explores the relationship between gender diversity on corporate boards and the accuracy of analysts’ earnings forecasts. The study focuses on gender-diverse boards as effective monitors, which are expected to influence corporate disclosures, reducing information asymmetry positively and improving forecast accuracy. The unique context of India’s gender quota policy on corporate boards and its relatively weak corporate governance structure offers an ideal setting to investigate this relationship.

Design/methodology/approach

The study utilises the generalised method of moments dynamic panel regression to address this research objective, analysing data from 217 Indian firms listed on the National Stock Exchange from 2014 to 2019.

Findings

The findings reveal that greater gender diversity on corporate boards positively impacts forecast accuracy. Specifically, having more women directors on the board enhances forecast accuracy, with a critical mass of women directors (more than one woman) further amplifying this effect. The study also shows that independent women directors significantly improve forecast accuracy, whereas grey women directors (those with family connections or non-independent roles) negatively affect it.

Originality/value

This study contributes significantly in two key aspects. Firstly, it sheds light on the value of women directors on boards in a country where women’s representation is mandated. Secondly, the research highlights the crucial role of independent women directors in ensuring robust financial oversight, particularly in an emerging economy.

Details

Journal of Accounting in Emerging Economies, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2042-1168

Keywords

Access Restricted. View access options
Article
Publication date: 10 May 2019

Rituraj Singh and Krishna Mohan Singh

The purpose of this paper is to assess the performance of the stabilised moving least squares (MLS) scheme in the meshless local Petrov–Galerkin (MLPG) method for heat conduction…

133

Abstract

Purpose

The purpose of this paper is to assess the performance of the stabilised moving least squares (MLS) scheme in the meshless local Petrov–Galerkin (MLPG) method for heat conduction method.

Design/methodology/approach

In the current work, the authors extend the stabilised MLS approach to the MLPG method for heat conduction problem. Its performance has been compared with the MLPG method based on the standard MLS and local coordinate MLS. The patch tests of MLS and modified MLS schemes have been presented along with the one- and two-dimensional examples for MLPG method of the heat conduction problem.

Findings

In the stabilised MLS, the condition number of moment matrix is independent of the nodal spacing and it is nearly constant in the global domain for all grid sizes. The shifted polynomials based MLS and stabilised MLS approaches are more robust than the standard MLS scheme in the MLPG method analysis of heat conduction problems.

Originality/value

The MLPG method based on the stabilised MLS scheme.

Details

Engineering Computations, vol. 36 no. 4
Type: Research Article
ISSN: 0264-4401

Keywords

1 – 10 of 149
Per page
102050