Prabhakaran N. and Sudhakar M.S.
The purpose of this paper is to propose a novel curvilinear path estimation model employing multivariate adaptive regression splines (MARS) for mid vehicle collision avoidance…
Abstract
Purpose
The purpose of this paper is to propose a novel curvilinear path estimation model employing multivariate adaptive regression splines (MARS) for mid vehicle collision avoidance. The two-phase path estimation scheme initially uses the offset (position) value of the front and the mid (host) vehicle to build the crisp model. The resulting crisp model is MARS regressed to deliver a closely aligned actual model in the second phase. This arrangement significantly narrows the gap between the estimated and the true path analyzed using the mean square error (MSE) for different offsets on Next Generation Simulation Interstate 80 (NGSIM I-80) data set. The presented model also covers parallel parking by encompassing the reverse motion of the host vehicle in the path estimation, thereby, making it amicable for real-road scenarios.
Design/methodology/approach
The two-phase path estimation scheme initially uses the offset (position) value of the front and the mid (host) vehicle to build the crisp model. The resulting crisp model is MARS regressed to deliver a closely aligned actual model in the second phase.
Findings
This arrangement significantly narrows the gap between the estimated and the true path studied using MSE for different offsets on real (Next Generation Simulation-NGSIM) data. The presented model also covers parallel parking by encompassing the reverse motion of the host vehicle in the path estimation. Thereby, making it amicable for real-road scenarios.
Originality/value
This paper builds a mathematical model that considers the offset and host (mid) vehicles for appropriate path fitting.
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Shafeeq Ahmed Ali, Mohammad H. Allaymoun, Ahmad Yahia Mustafa Al Astal and Rehab Saleh
This chapter focuses on a case study of Kareem Exchange Company and its use of big data analysis to detect and prevent fraud and suspicious financial transactions. The chapter…
Abstract
This chapter focuses on a case study of Kareem Exchange Company and its use of big data analysis to detect and prevent fraud and suspicious financial transactions. The chapter describes the various phases of the big data analysis cycle, including discovery, data preparation, model planning, model building, operationalization, and communicating results, and how the Kareem Exchange Company team implemented each phase. This chapter emphasizes the importance of identifying the business problem, understanding the resources and stakeholders involved, and developing an initial hypothesis to guide the analysis. The case study results demonstrate the potential of big data analysis to improve fraud detection capabilities in financial institutions, leading to informed decision making and action.
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Neha Chhabra Roy and Sreeleakha Prabhakaran
The study aims to overview the different types of internal-led cyber fraud that have gained mainstream attention in recent major-value fraud events involving prominent Indian…
Abstract
Purpose
The study aims to overview the different types of internal-led cyber fraud that have gained mainstream attention in recent major-value fraud events involving prominent Indian banks. The authors attempted to identify and classify cyber frauds and its drivers and correlate them for optimal mitigation planning.
Design/methodology/approach
The methodology opted for the identification and classification is through a detailed literature review and focus group discussion with risk and vigilance officers and cyber cell experts. The authors assessed the future of cyber fraud in the Indian banking business through the machine learning–based k-nearest neighbor (K-NN) approach and prioritized and predicted the future of cyber fraud. The predicted future revealing dominance of a few specific cyber frauds will help to get an appropriate fraud prevention model, using an associated parties centric (victim and offender) root-cause approach. The study uses correlation analysis and maps frauds with their respective drivers to determine the resource specific effective mitigation plan.
Findings
Finally, the paper concludes with a conceptual framework for preventing internal-led cyber fraud within the scope of the study. A cyber fraud mitigation ecosystem will be helpful for policymakers and fraud investigation officers to create a more robust environment for banks through timely and quick detection of cyber frauds and prevention of them.
Research limitations/implications
Additionally, the study supports the Reserve Bank of India and the Government of India's launched cyber security initiates and schemes which ensure protection for the banking ecosystem i.e. RBI direct scheme, integrated ombudsman scheme, cyber swachhta kendra (botnet cleaning and malware analysis centre), National Cyber Coordination Centre (NCCC) and Security Monitoring Centre (SMC).
Practical implications
Structured and effective internal-led plans for cyber fraud mitigation proposed in this study will conserve banks, employees, regulatory authorities, customers and economic resources, save bank authorities’ and policymakers’ time and money, and conserve resources. Additionally, this will enhance the reputation of the Indian banking industry and extend its lifespan.
Originality/value
The innovative insider-led cyber fraud mitigation approach quickly identifies cyber fraud, prioritizes it, identifies its prominent root causes, map frauds with respective root causes and then suggests strategies to ensure a cost-effective and time-saving bank ecosystem.
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Neha Chhabra Roy and Sreeleakha Prabhakaran
This study addresses the growing cyber risks of banks by proposing an innovative, end-to-end dual-layer blockchain-based cyber fraud (CF) response system that integrates Safeguard…
Abstract
Purpose
This study addresses the growing cyber risks of banks by proposing an innovative, end-to-end dual-layer blockchain-based cyber fraud (CF) response system that integrates Safeguard (SG) and Block guard (BG) mechanisms. The comprehensive solution offers an actionable framework for bank managers to mitigate CFs by prioritizing fraud detection, leveraging early warning signals (EWS), and implementing tailored, need-based control measures before, during, and after a fraud event.
Design/methodology/approach
The study uses a multi-method approach, beginning with an extensive literature review on fraud identification, assessment, and prevention strategies. A theoretical framework is constructed to support the proposed SG and BG measures. Machine learning-based data analysis, using Artificial Neural Networks, is employed to dynamically assess the severity of CFs in real time. A managerial action plan for each phase of the fraud lifecycle is presented.
Findings
The research underscores the necessity for an adaptable, dual-layered response system that transitions from reactive to proactive and predictive mitigation strategies. The study introduces a novel approach incorporating SG and BG mitigation measures, enabling managers to detect early warning signals and implement robust post-fraud interventions.
Practical implications
The dual-layer approach enhances the sector's resilience to CFs by providing a robust, adaptive framework for fraud prevention and mitigation. This approach helps maintain stability, SG the bank's reputation, and improve overall risk management practices.
Originality/value
This study is unique in its development of an integrated SG and BG response system, combining machine learning, blockchain technology, early warning signals, and a structured before-during-after fraud control model. The research also highlights the critical role of bank managers in implementing and overseeing this innovative response system.
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Bhoomika N. Jadhav, P. Padma Sri Lekha, E.P. Abdul Azeez, Jyoti Sharma, Archana Yadav and Mufina Begam J.
Gender discrimination exists in various settings globally and harms women’s mental health. This study aims to understand the impact of gender discrimination on hopelessness and…
Abstract
Purpose
Gender discrimination exists in various settings globally and harms women’s mental health. This study aims to understand the impact of gender discrimination on hopelessness and emotional vulnerability. Further, we attempted to determine whether benevolent childhood experiences (BCEs) moderate the relationships of gender discrimination with hopelessness and emotional vulnerability.
Design/methodology/approach
Data from 445 young women from India was gathered from a cross-sectional survey. Measures included gender discrimination inventory, Beck’s hopelessness inventory, emotional vulnerability scale and BCE scale.
Findings
Results yielded a significant positive association of gender discrimination with hopelessness and emotional vulnerability. BCEs were negatively related to hopelessness, emotional vulnerability and gender discrimination. Further, gender discrimination predicted increased feelings of hopelessness and emotional vulnerability. However, BCEs do not neutralize the effect of gender discrimination.
Social implications
It is evident from this study that gender discrimination exists independent of socioeconomic class, domicile and educational qualification, taking a toll on women’s well-being and mental health. Incorporating attitudinal changes at the community and societal level in reducing gender norms responsible for negative outcomes will allow women to function to their full capacity and experience improved mental health.
Originality/value
The research on gender discrimination and its impact on women’s mental health is limited, especially exploring the role of BCEs. Previous studies have indicated that BCEs have protective roles in neutralizing adversities. However, the present study uniquely contributes to establishing the limited role of BCEs in the context of gender discrimination, though it contributes to mental health. The policy and psychosocial implications of the study are discussed.
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Neha Chhabra Roy and Sreeleakha P.
This study addresses the ever-increasing cyber risks confronting the global banking sector, particularly in India, amid rapid technological advancements. The purpose of this study…
Abstract
Purpose
This study addresses the ever-increasing cyber risks confronting the global banking sector, particularly in India, amid rapid technological advancements. The purpose of this study is to de velop an innovative cyber fraud (CF) response system that effectively controls cyber threats, prioritizes fraud, detects early warning signs (EWS) and suggests mitigation measures.
Design/methodology/approach
The methodology involves a detailed literature review on fraud identification, assessment methods, prevention techniques and a theoretical model for fraud prevention. Machine learning-based data analysis, using self-organizing maps, is used to assess the severity of CF dynamically and in real-time.
Findings
Findings reveal the multifaceted nature of CF, emphasizing the need for tailored control measures and a shift from reactive to proactive mitigation. The study introduces a paradigm shift by viewing each CF as a unique “fraud event,” incorporating EWS as a proactive intervention. This innovative approach distinguishes the study, allowing for the efficient prioritization of CFs.
Practical implications
The practical implications of such a study lie in its potential to enhance the banking sector’s resilience to cyber threats, safeguarding stability, reputation and overall risk management.
Originality/value
The originality stems from proposing a comprehensive framework that combines machine learning, EWS and a proactive mitigation model, addressing critical gaps in existing cyber security systems.
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Abdurrahim Dal, Mahir Sahin and Mustafa Kilic
Bearing performance characteristics, such as stiffness and load capacity, are related to the viscosity of the fluid circulating through the gap. Nanoparticle additives in…
Abstract
Purpose
Bearing performance characteristics, such as stiffness and load capacity, are related to the viscosity of the fluid circulating through the gap. Nanoparticle additives in lubricant are one way to enhance of the viscosity. This study aims to investigate the effect of nanoparticle additives on the thermohydrodynamic performance of journal bearing with different bearing parameters.
Design/methodology/approach
The temperature distribution is modeled using a three-dimensional energy equation. The velocity components are calculated on the pressure distribution governed by Dowson’s equation. Moreover, the heat transfer between the journal and lubricant is modeled with Fourier heat conduction equation. On the other hand, the viscosity equation is derived for Al2O3 nanoparticles as a function of the volume ratio and the temperature. An algorithm based on the finite difference method is developed, and a serial simulation is performed for different parameters and different volume ratio of nanoparticle.
Findings
With the increase in the nanoparticle volume ratio, the maximum temperature decreases for the lower clearance values, but the addition of the nanoparticle influence on the maximum temperature reverses when the clearance grows up. The nanoparticle additives increase further the maximum temperature for higher values of L/D ratios. Moreover, the effects of the nanoparticle additives on the pressure are stronger at high eccentricity ratios for all bearing parameters.
Originality/value
This paper provides valuable design parameters for journal bearing with lubricant containing the nanoparticle additives.
Details
Keywords
Sreedhar Babu Kalakada, Prabhakaran Nair Nair Kumarapillai and Rajendra Kumar P K
The purpose of this work is to investigate the static performance characteristics of thermohydrodynamic journal bearing operating under nanolubricants (lubricants containing per…
Abstract
Purpose
The purpose of this work is to investigate the static performance characteristics of thermohydrodynamic journal bearing operating under nanolubricants (lubricants containing per cent weight concentration of nanoparticles).
Design/methodology/approach
Addition of nanoparticles in the lubricant increases lubricant viscosity. To study the effect of this variation on journal bearing, analytical models are developed for the relationship between viscosity, 0-0.5 per cent weight concentration of nanoparticles and temperature range of 300-900°C. To obtain pressure and temperature distribution, modified Reynolds and energy equations are solved by using the finite element method. The viscosity field (varies with temperature and per cent weight concentration of nanoparticles) is updated in these two equations by using the developed analytical model. The steady-state performance characteristics are computed for various values of eccentricity ratios for non-thermoviscous (viscosity of lubricant varies with per cent weight concentration of nanoparticles) and thermoviscous (viscosity of lubricant varies with per cent weight concentration of nanoparticles and temperature) cases. The lubricant and the nanoparticles used for the present work are SAE15W40, copper oxide (CuO), cerium oxide (CeO2) and aluminum oxide (Al2O3).
Findings
The pressure and temperature distribution across the lubricant film in the clearance space of journal bearing and static performance characteristics are calculated.
Originality/value
The computed results show that addition of nanoparticles in the lubricant influences the performance characteristics considerable in thermoviscous case than non-thermoviscous case.
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Arun Bangotra and Sanjay Sharma
This study aims to investigate the impact of surface waviness on the static performance parameters of hydrodynamic journal bearings operating with lubricants containing copper…
Abstract
Purpose
This study aims to investigate the impact of surface waviness on the static performance parameters of hydrodynamic journal bearings operating with lubricants containing copper oxide (CuO) and cerium oxide (CeO2) nanoparticles.
Design/methodology/approach
The static performance parameters of bearings with surface waviness and the addition of nanoparticles in lubricants were calculated using the nondimensional form of Reynolds equation and finite element method. Static performance parameters are calculated at different waviness numbers in the circumferential, axial and both directions at various wave amplitudes with variable viscosities of lubricants with nanoparticles using the viscosity equation forming a relationship between the relative viscosity, temperature and weight fraction of nanoparticles in lubricant developed from the experimental results.
Findings
The computed results indicate that the impact of waviness on the bearing surface enhances the load capacity, reduces friction coefficient, and is more effective in the circumferential direction than in the axial direction or in both directions. The addition of CuO and CeO2 to the lubricant enhanced its viscosity which further improved the steady-state parameters of the wave bearing.
Research limitations/implications
This study is based on a numerical technique, which has significant limitations, and the simulated results must be tested experimentally.
Practical implications
The current findings will be beneficial for designers to improve the performance of hydrodynamic journal bearings.
Originality/value
The calculated results demonstrate that the combined effect of the surface waviness on bearings and the addition of nanoparticles to lubricants can greatly increase the performance of hydrodynamic journal bearings.
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Kapil Bansal, Aseem Chandra Paliwal and Arun Kumar Singh
Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased…
Abstract
Purpose
Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation.
Design/methodology/approach
The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model.
Findings
There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2).
Originality/value
This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.