Browsing by Author "Anand, A."

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A green human resource management approach of participation in decision-making and behavioural outcomes – a moderated mediated model
(Emerald Publishing, 2023) Kumar, S.P.; Saha, S.; Anand, A.
Purpose: This study aims to assess the moderating and mediating role of supportive culture (SC) in the relationship between participation in decision-making (PDM) and job satisfaction (JS) and the dimensions of commitment, such as affective commitment (AC), normative commitment (NC) and continuance commitment (CC). Design/methodology/approach: Data were collected from 712 employees working in different public sector undertakings (PSUs) across India. Necessary condition analysis and partial least square analysis were used to test the proposed hypotheses. Findings: The findings of the present study indicated that SC is partially mediating the relationship between PDM and JS; PDM and AC. However, SC did not mediate the relationship between PDM and NC; PDM and CC. PDM was positively and significantly related to SC, JS, AC, NC and CC. JS had a significant impact on AC, NC and CC. It is highly desirable for organizations to retain their employees ranging from line managers to top management levels and provide opportunities for everyone to actively use their experience and expertise. Originality/value: The findings have implications for managers, as well as employees in PSUs, as they demonstrate how several work-related factors can be emphasized to maintain employees' commitment and motivation. Until now, India has paid scant attention to the role of SC as a mediator and moderator between PDM, JS and multiple commitments. This study cautiously collected responses from unbiased employees working in a variety of organizational functional units. © 2021, Emerald Publishing Limited.
A Survey on Threat Intelligence Techniques for Constructing, Detecting, and Reacting to Advanced Intrusion Campaigns
(Springer, 2023) Anand, A.; Singhal, M.; Guduru, S.; Chandavarkar, B.R.
The rise of intrusion has increased the need for cybersecurity in various organizations. A set of these intrusions by an adversary against a particular organization are called intrusion campaigns. Threat intelligence techniques help detect and respond to intrusion attempts and help organizations set up a framework that can secure their services and interests. This chapter surveys different parameters and resources required to construct such a threat intelligence technique for an organization. Furthermore, the chapter discusses the various cases and models of an Intrusion Detection System (IDS) and Intrusion Response System (IRS) along with their comparison using the security resources collected during the construction of a Threat Intelligence model. All of this combined forms the threat intelligence technique. Â© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
A Three-Dimensional Investigation on the Efficacy of Different Configuration Settings of Micropiles in Enhancement of Seismic Slope Stability
(Springer Science and Business Media Deutschland GmbH, 2025) Kumar, S.; Anand, A.; Sarkar, R.; Nainegali, L.
Micropiles have emerged as an effective measure to strengthen the stability of slopes. However, its efficacy in improving the stability of slopes under seismic loading conditions has not been fully established. This paper intends to investigate the performance of micropiles with different configurations to improve the stability of a slope under static and seismic loading conditions. A clayey slope of height 10 m underlain by a sandy soil layer was adopted for the investigation. Three-dimensional nonlinear finite element models were developed for the slope-micropile systems. Five different configurations of micropiles, considering a single micropile on two faces of the slope, were adopted for investigation. Further, a study was carried out with eight different combinations of these configurations of micropiles for strengthening the slope. Initially, static analyses were carried out for the different configurations of micropiles. Next, for seismic loading, pseudo-static analyses were carried out for all the configurations. The efficacy of different configurations of micropiles was compared through the factor of safety obtained. Analyses were also carried out considering the water table, and the efficacy of micropiles was established in the same way. Finally, nonlinear dynamic analyses were carried out for different configurations of micropiles with real earthquake time history, and the improvement in seismic performance of the micropile-strengthened slope was reported. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.
Basics on Categorizing Travel-Time-Based Degrees of Satisfaction Using Triangular Fuzzy-Membership Functions
(Elsevier B.V., 2020) Anand, A.; George, V.; Kanthi, R.; Tagore, M.; Padmashree, M.S.
The travel desires of trip-makers in urban activity centres depend mainly on the location of residential areas, proximity to various activity centres, household characteristics, and socio-economic factors that influence the choice of travel modes. Decision-making with regard to the choice of a particular mode of travel is fuzzy in nature, and seldom follows a rigid rule-based approach. In this context, the fuzzy-logic approach was considered since it could handle inherent randomness in decision-making related to mode-choice. The present study focuses on the application of this technique making use of revealed preference survey data collected through CES and MVA Systra, later compiled and corrected in various stages at NITK. The difference between the actual travel time by a particular mode, and the theoretical travel time based on average vehicular speeds was used as an important indicator in determining the degrees of satisfaction of the trip-maker. This indicator was computed, and fitted using a normal distribution. It was assumed that indicator values between Î¼-3Ïƒ and Î¼ could be considered for the category of satisfied trip-makers according to the three sigma rule where Î¼ is the mean indicator value, and Ïƒ represents the standard deviation. The computed values of the indicators were used in classifying the data into 6 categories of degrees of satisfaction that formed the basic framework for modelling using fuzzy-logic technique. This paper aims at understanding the basic mathematical computations involved in defuzzification using the centroid method for triangular membership functions, and provides a comparison with results obtained using MATLAB. Â© 2020 The Authors. Published by Elsevier B.V.
CL-NERIL: A Cross-Lingual Model for NER in Indian Languages (Student Abstract)
(Association for the Advancement of Artificial Intelligence, 2022) Prabhakar, A.; Majumder, G.S.; Anand, A.
Developing Named Entity Recognition (NER) systems for Indian languages has been a long-standing challenge, mainly owing to the requirement of a large amount of annotated clean training instances. This paper proposes an end-to-end framework for NER for Indian languages in a low-resource setting by exploiting parallel corpora of English and Indian languages and an English NER dataset. The proposed framework includes an annotation projection method that combines word alignment score and NER tag prediction confidence score on source language (English) data to generate weakly labeled data in a target Indian language. We employ a variant of the Teacher-Student model and optimize it jointly on the pseudo labels of the Teacher model and predictions on the generated weakly labeled data. We also present manually annotated test sets for three Indian languages: Hindi, Bengali, and Gujarati. We evaluate the performance of the proposed framework on the test sets of the three Indian languages. Empirical results show a minimum 10% performance improvement compared to the zero-shot transfer learning model on all languages. This indicates that weakly labeled data generated using the proposed annotation projection method in target Indian languages can complement well-annotated source language data to enhance performance. Our code is publicly available at https://github.com/aksh555/CL-NERIL. Â© Â© 2022, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Modal Split and Cost-Sensitivity Analysis for Various Travel Modes Using Calibrated Parameters in NL Modeling
(Elsevier B.V., 2025) George, V.; Anand, A.
The present study demonstrates the use of the nested-logit (NL) approach in modal-split modeling with input related to the total cost of travel determined based on the cost of travel per km, and the cost of travel time per minute incurred by trip-makers. The calibrated best value of the scaling parameter that simulated the actual travel pattern as in Amritsar city in India was identified based on a trial-and-error approach, followed by cost-based sensitivity analyses. The study revealed that an increase in the combined costs of travel by private modes including intermediate public transport (IPT) modes resulted in a higher estimated ridership by public modes of travel such as mini-buses. Similar analyses were performed to estimate the ridership for private modes Vs IPT modes. One of the key findings of the cost-sensitivity analysis is that even when the total cost of travel by private modes including IPT was increased by up to 35%, the ridership by these modes remained more dominant than that of public modes. In Amritsar, trip-makers rely on independently operated minibuses and IPT alternatives that provide shared rides in place of buses. The insights provided can help formulate policies for promoting public transport modes. Â© 2024 The Authors.
Modeling Trip-generation and Distribution using Census, Partially Correct Household Data, and GIS
(Salehan Institute of Higher Education, 2022) Anand, A.; George, V.
The efficiencies of urban transport systems in several cities are drastically affected due to difficulties imposed by rapid urbanization and the proliferation of private modes of transport. The conventional four-stage travel demand modeling approach provides an ideal platform to formulate strategies to rectify problems in urban transport. Trip generation is the first stage in this exercise (where trip production and trip attractions are modelled), followed by trip distribution in the second stage. The present work related to the development of models for trip generation and trip distribution necessitated the use of census data related to the number of households in each zone since the available revealed preference (RP) data compiled based on household interview surveys was partially incorrect. A review of the literature indicated that studies on the use of sparsely available and partially inaccurate data such as revealed preference and zone-specific secondary data on trip generation and trip distribution were limited. In the present study, the use of the initial trip generation regression models developed based on existing household survey data resulted in prediction errors ranging between 26% and 32%. Modeling efforts after applying corrections to zone-specific characteristics based on secondary data and the use of trip rate per household later resulted in prediction errors of less than ±5%. In the latter phase of work related to trip distribution modeling, a log-linear regression model was developed based on a smaller refined set of the revealed preference data obtained by eliminating erroneous data in a stage-wise manner. The use of the calibrated and validated model ensured that the errors in predicted trip frequencies were less than 0.6%. Here, the information on the inter-zonal aerial distances that formed part of the trip distribution model was obtained using GIS approaches that employed the moment area method, which considered the intensity of land use at the sub-zone level. The combined strategy incorporates the use of GIS-based approaches to determine inter-zonal aerial distances, and the use of the refined relationship between trip interchanges and the inter-zonal aerial distances in the development of a reliable log-linear regression model for trip distribution contributed towards attaining higher accuracies in travel demand estimation. The modeling approaches described herein do not rely on the use of sophisticated technology, and time-consuming data processing. The study will provide the basic framework for transport planners to formulate better strategies for travel demand modeling where available data is noisy and less reliable. © 2022 by the authors. Licensee C.E.J, Tehran, Iran.
PUF-Based Ownership Transfer Using Blockchain
(Springer Science and Business Media Deutschland GmbH, 2025) Cunha, T.B.D.; Manjappa, M.; Singh, V.; Anand, A.
Counterfeiting of electronic components in the branded products is one of the most important and difficult issues to deal with in national/international markets along with the trusted ownership transfer of the product. Today we have to trust an individual while buying a product believing that the product is not tampered. But, we do not have any trusted source which can back this claim. This creates a lot of speculation in the market. For a long time RFID tags were used to find the anti counterfeits in the supply chain, but the problem with the RFID tag is that they can be cloned and hence the authenticity of the tags over the network is questionable. Hence, in order to counter this, we are leveraging blockchain technology to build a novel ownership transfer protocol where the ownership transfer mechanism is secured and authenticated using Physically Unclonable Functions (PUF). The genuinity of the product is checked by PUF by using Challenge Response check during the ownership transfer. Further, the ownership transfer history of the particular product is also maintained in the blockchain which helps the buyer to get more details on the product. The proposed blockchain architecture also provides a temporary ownership transfer option for the owners during servicing or leasing. The proposed architecture is implemented in ethereum blockchain platform and tested for its efficiency. The architecture is found to be promising. Â© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Quantum Machine Learning: A Review and Current Status
(Springer Science and Business Media Deutschland GmbH, 2021) Mishra, N.; Kapil, M.; Rakesh, H.; Anand, A.; Mishra, N.; Warke, A.; Sarkar, S.; Dutta, S.; Gupta, S.; Prasad Dash, A.; Gharat, R.; Chatterjee, Y.; Roy, S.; Raj, S.; Kumar Jain, V.; Bagaria, S.; Chaudhary, S.; Singh, V.; Maji, R.; Dalei, P.; Behera, B.K.; Mukhopadhyay, S.; Panigrahi, P.K.
Quantum machine learning is at the intersection of two of the most sought after research areasâ€”quantum computing and classical machine learning. Quantum machine learning investigates how results from the quantum world can be used to solve problems from machine learning. The amount of data needed to reliably train a classical computation model is evergrowing and reaching the limits which normal computing devices can handle. In such a scenario, quantum computation can aid in continuing training with huge data. Quantum machine learning looks to devise learning algorithms faster than their classical counterparts. Classical machine learning is about trying to find patterns in data and using those patterns to predict further events. Quantum systems, on the other hand, produce atypical patterns which are not producible by classical systems, thereby postulating that quantum computers may overtake classical computers on machine learning tasks. Here, we review the previous literature on quantum machine learning and provide the current status of it. Â© 2021, Springer Nature Singapore Pte Ltd.
Supportive culture and job involvement in public sector: the mediating role of participation in decision making and organizational learning
(Emerald Group Holdings Ltd., 2022) Kumar, P.K.; Saha, S.; Anand, A.
Purpose: The purpose of this study is to determine whether participation in decision-making (PDM) and organizational learning (OL) act as mediating factors in the relationship between supportive culture and job involvement. Design/methodology/approach: Data were collected from 712 employees working in different public sector undertakings (PSUs) across India. Necessary condition analysis (NCA) analysis and partial least square (PLS) analysis were used to test the proposed hypotheses. Findings: The findings of the present study indicated that PDM and OL act as a full mediator respectively in the relationship between supportive culture (SC) and job involvement (JI). The SC was positively and significantly related to PDM and OL. However, SL did not have a significant impact on JI. In addition, higher PDM and OL were found to be significantly impacting JI. Practical implications: The results suggest that PDM and OL facilitate the impact of SC on JI and may help organizations to retain their employees. The implications of these findings for all hierarchical levels in PSUs are discussed. Originality/value: OL and PDM as mediators between SC and JI have received very little attention from the context of India. The results add to the growing literature of culture from a non-western context as this study is based on Indian samples. This study has taken care to provide unbiased responses by utilizing data from employees working in various functional units of the organizations. © 2022, Emerald Publishing Limited.
Trusted Federated Learning Framework for Attack Detection in Edge Industrial Internet of Things
(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, M.P.; Anand, A.; Prateek Janaswamy, L.A.; Sundarrajan, S.; Gupta, M.
The edge Industrial Internet of Things (IIoT) is highly vulnerable to attacks due to the vast number of connected devices and the lack of security features. Attacks in edge IIoT can lead to significant damage, including data theft, malfunctioning, and privacy breaches. Federated Learning (FL) is a promising approach to detecting attacks by utilizing edge devicesâ€™ collective intelligence. FL allows devices to collaboratively learn from multiple devicesâ€™ data without centralized sharing, which preserves data privacy and reduces communication costs. However, FL has vulnerabilities that can compromise model accuracy, privacy, and security. Trusted FL is essential for collaboration among multiple edge IIoT devices while preserving data privacy and security. Trust plays a critical role in the success of FL, as edge IIoT devices must trust that the models are accurately learning and that their data is protected. To address this, we propose an FL framework that uses Federated Averaging (FedAvg) and Convolutional Neural Network (CNN) to detect attacks in edge IIoT. We also propose a mechanism to calculate trust for appropriate edge IIoT device selection by measuring each deviceâ€™s (a.k.a clientâ€™s) performance during model training. The proposed edge IIoT device selection method, client selection, can fairly select clients for model training and improve trust in the entire system. Although the proposed FL approach does not outperform the centralized ResNet-18 CNN model on experimental analysis, improving its performance can be a promising solution for detecting attacks in edge IIoT. Â© 2023 IEEE.