Author: Joshi, G., Srivastava, A., Yagnik, B., Hasan, M., Saiyed, Z., Gabralla, L.A., Abraham,A., Walambe, R., Kotecha, K.
Explainable Misinformation Detection Across Multiple Social Media Platforms
Publisher: IEEE Access, 2023
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Web Information Processing (W.I.P.) has enormously impacted modern society since a huge percentage of the population relies on the internet to acquire information. Social Media platforms provide a channel for disseminating information and a breeding ground for spreading misinformation, creating confusion and fear among the population. One of the techniques for the detection of misinformation is machine learning-based models. However, due to the availability of multiple social media platforms, developing and training AI-based models has become a tedious job. Despite multiple efforts to develop machine learning-based methods for identifying misinformation, more work must be done on developing an explainable generalized detector capable of robust detection and generating explanations beyond black-box outcomes. Knowing the reasoning behind the outcomes is essential to make the detector trustworthy. Hence employing explainable A.I. techniques is of utmost importance. In this work, the integration of two machine learning approaches, namely domain adaptation and explainable A.I., is proposed to address these two issues of generalized detection and explainability. Firstly the Domain Adversarial Neural Network (DANN) develops a generalized misinformation detector across multiple social media platforms. DANN generates the classification results for test domains with relevant but unseen data. The DANN-based, traditional black-box model cannot justify and explain its outcome, i.e., the labels for the target domain. Hence a Local Interpretable Model-Agnostic Explanations (LIME) explainable A.I. model is applied to explain the outcome of the DANN model. To demonstrate these two approaches and their integration for effective explainable generalized detection, COVID-19 misinformation is considered a case study. We experimented with two datasets and compared results with and without DANN implementation.
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Author: Gawde, S., Patil, S., Kumar, S., Kamat, P., Kotecha, K., Abraham, A.
Multi-fault diagnosis of Industrial Rotating Machines using Data-driven approach : A review of two decades of research
Publisher: Engineering Applications of Artificial Intelligence, 2023
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Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of machinery. The majority of these machines comprise rotating components and are called rotating machines. The engineers’ top priority is to maintain these critical machines to reduce the unplanned shutdown and increase the useful life of machinery. Predictive maintenance is the current trend of smart maintenance, followed by most maintenance engineers. The challenging task in predictive maintenance is to diagnose the type of fault. With Artificial Intelligence (AI) advancement, a data-driven approach for predictive maintenance is taking a new flight towards smart manufacturing. Several researchers have published work related to fault diagnosis in rotating machines, mainly exploring a single type of fault. However, a consolidated review of literature that focuses more on the “multi-fault diagnosis” aspect of rotating machines is lacking. There is a need for a study that would systematically cover all the aspects right from sensor selection, data acquisition, feature extraction, multi-sensor data fusion to the systematic review of AI techniques employed in multiple fault diagnosis. In this regard, this paper attempts to achieve the same by implementing a systematic literature review on a Data-driven approach for multi-fault diagnosis of Industrial Rotating Machines using the “Preferred Reporting Items for Systematic Reviews and Meta-Analysis” (PRISMA) method. The PRISMA method is a collection of guidelines for the composition and structure of systematic reviews and other meta-analyses. This paper identifies the foundational work done in the field and gives a comparative study of different aspects related to multi-fault diagnosis of industrial rotating machines. The paper also identifies the major challenges, research gap. It gives solutions using recent advancements in AI in implementing multi-fault diagnosis, giving a strong base for future research in this field.
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Author: Saini, N., Chattopadhyay, C., Das, D.
E2AlertNet: An explainable, efficient, and lightweight model for emergency alert from aerial imagery
Publisher: Remote Sensing Applications: Society and Environment, 2023
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Deep learning-based algorithms have shown significant state-of-the-art accuracy in aerial image classification. Besides, the nature of these algorithms is a black box, which puts the question of why a particular output is produced. Therefore explainability is one kind of solution to improve the transparency of DNN network's decision. In this paper, we have designed a lightweight and explainable convolutional neural network (CNN) architecture for emergency monitoring from aerial imagery. We interpret the outcomes of the proposed architecture with a newly designed explainable algorithm which is the improved version of the model-agnostic methods such as Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME). We have used a dedicated dataset named as Aerial Image Database for Emergency Response (AIDER) for the experiments and explained the decisions of the proposed CNN classifier to ensure reliability. The proposed classifier achieves 96% accuracy with minimal memory requirements on a benchmark set with known ground truth and explains their outcomes with the newly proposed explainable algorithm.
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Author: Chouthoy, S., Kazi, R., Dass, R., Mishra, M.
A consumer perception study on CSR reputation shaping brand image in India
Publisher: International Journal of Management and Enterprise Development, 2023
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The purpose of this study is to reveal that a brand incorporating corporate social responsibility (CSR) initiatives helps strategically shape its brand image and enables it to stand apart in a competitive market. Data for this research was gathered from a primary data collection exercise done across a sample of respondents in India. The paper succeeds in establishing the hypothesis (H1) of 'Whether CSR reputation (CSRR) and brand image (BI) are statistically related? ' through canonical correlation. The study established that there exists a positive correlation between CSRR and BI; consumers are willing to praise CSR performing brands publicly and easily distinguish such brands from other competing brands. Consumers' respect for such brands grows every time they learn of their CSR efforts. This study extends the dynamic relationship between the consumer and the brand. It concludes by suggesting that strengthening CSR makes business sense.
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Author: Upadhyay, N., Kamble, A.
Examining Indian consumer pro-environment purchase intention of electric vehicles: Perspective of stimulus-organism-response
Publisher: Technological Forecasting and Social Change, 2023
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This paper examines the antecedents of Indian consumers' pro-environment purchase intention of electric vehicles utilizing the Stimulus-Organism-Response theory. The conceptualized model is analyzed by collecting 1143 responses and validated using structural equation modelling. The results show that pro-environment responsibility significantly impacts the pro-environment value and pro-environment attitude; the pro-environment value has a significant impact on the pro-environment attitude; pro-environment value and pro-environment attitude have a significant impact on the pro-environment purchase intention of electric vehicles. Additionally, the results depict that a pro-environment attitude mediates the relationship between pro-environment value and pro-environment purchase intention of electric vehicles. Finally, this paper contributes to theory in the adoption literature by proposing an alternate perspective considering “value-attitude-behavior” linkages, particularly pro-environment behavior. The study contributes to practitioners' space.
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Author: Rakesh, V.T., Menon, P., Raman, R.
Attribute-based choice model and willingness to pay for industrial services
Publisher: Benchmarking: An International Journal, 2023
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Pricing is widely acknowledged as a market entry challenge for servitising companies. The purpose of this research is to ascertain the attributes that contribute to willingness to pay (WTP) for industrial services and suggest incorporating those attributes to a pricing model.
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Author: Khade, R., Jariwala, K., Chattopadhyay, C.
An Interactive Floor Plan Image Retrieval Framework Based on Structural Features
Publisher: Arabian Journal for Science and Engineering, 2023
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To build a house, buyers meet with an architect, who analyses the land’s built-up area and creates a development blueprint. Customers may have different perspectives on their residence. As a result, in order to save architects’ time and provide an appropriate floor plan to the customer, we presented an interactive framework that allows the client to change existing designs and search for similar ones in a repository. To do this, we created a vocabulary of annotations for inserting new items, deleting existing objects, shifting the position of objects, and moving the locations of objects. The proposed framework accepts a digital form of a printed floor plan image with or without hand-drawn annotations as a query, then performs suggested editing operations and converts the image to standard form by identifying annotations. The retrieval model then analyses the processed image and provides further recommendations similar to the query floor plan image. Our contributions include (1) an interactive platform for updating an existing floor plan based on predefined vocabulary of annotations, and (2) a unique structure-based features for retrieving similar floor plans with a 10% improvement in mAP score. The suggested framework’s performance was examined using publicly available datasets, and it obtained 0.85 mAP which edge over existing state-of-the-art approaches.
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Author: Kalhan, A., Singh, S., Moghe, K.
Consequences of Blindsiding the Informally Employed
Publisher: Economic and Political Weekly, 2023
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Employment, incomes, and the bargaining power of the informally employed, who form 86% of workers in the Indian economy, have been repeatedly impoverished due to disruptions like demonetisation and the lockdown. This article presents additional evidence from a survey of the stranded interstate migrant workers, informally employed in the city of Pune. The lockdown worsened the already depressed labour markets for the informal workers, as it affected their employment, incomes, savings, and well-being. The majority of the workers being from the socially marginalised castes faced multiple vulnerabilities, along with their dependents.
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Author: Mazumdar, K., Gupta, S.P., Sen. I
Mothering load: Underlying realities of professionally engaged Indian mothers during a global crisis
Publisher: Gender, Work & Organization, 2023
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The current global crisis has had a significant impact on professionally engaged mothers and the proliferation of the invisible nature of the work that they are engaged in on a daily basis. Several research studies have indicated how mothers seem to have been particularly affected. Mothers experienced an exacerbation in their domestic household and child-care responsibilities due to the absence of househelp and other child care arrangements while balancing their professional careers. These challenges crystallized the existing gender inequalities and the gendered nature of parenting. Using a feminist lens, this study explores the experiences of mothering load during COVID-19 against the backdrop of urban India. A total of two themes and six sub-themes were identified through the process of thematic analysis—Triad of work included increased care work, increased formal work, and increased worry work and Mothering experiences: Burdens and Biases included the lack of support, parental role overload, and gendered nature of parenting. This study adds to the limited empirical evidence of working mothers in India while straddling the worlds of feminism and mental health activism. Findings indicate the need to explicitly highlight the invisibilized phenomena of unpaid care work, worry work, and the gendered nature of parenting that contribute to the larger experience of mothering load. The findings also point toward acknowledging the importance of maternal mental health and the United Nations Sustainable Development Goal 5 to establish and enforce stronger policies around recognizing and appreciating unpaid care and domestic work to promote gender equality and empowering women at all levels. This may be enacted through the encouragement of shared responsibilities within the household and family units as contextually feasible and through the development of appropriate infrastructure, social protection policies, and the delivery of public services.
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Author: Sarkar, A., Blaney, A.
Material Metaphor and Reflexivity in Contemporary Painting:A Practice-based Investigation
Publisher: The Journal of Aesthetic Education, 2023
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Contemporary painting is a complex practice, and artists regularly incorporate elements from different media such as photography, textile, and performance. Despite its status being diminished by different conceptual art movements, painting still has a critically important place in the artworld. This importance is largely due to painting’s ability to stretch across media and make a direct appeal to the senses. In this article, an attempt is made to theorize the facility of painting to incorporate different media and its resulting reflexivity. Maurice Merleau-Ponty’s theory of embodiment and phenomenological theory of metaphor provide the theoretical means to articulate reflexivity in painting. Curatorial writing about contemporary painting and case studies of young painters from India form a backdrop for the analysis of examples from the painting practice of one of the authors to demonstrate the material dynamism of painting. It is shown that this reflexivity is intimately related to the sensuous texture of materials used in the creation of the paintings under discussion. The first-person narrative of artistic process and production provides insight into the embodied process of making and manipulating media and evidence that painters can create new insights about the ontology of painting.
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Author: Rajalakshmi, E., Elakkiya, R., Subramaniyaswamy, V., Alexey, L.P., Makhail, G.,Bakaev, M., Kotecha, K., Gabralla, L.A., Abraham, A.
Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition Using Hybrid Deep Neural Architecture
Publisher: IEEE Access, 2023
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The speech and hearing-impaired community use sign language as the primary means of communication. It is quite challenging for the general population to interpret or learn sign language completely. A sign language recognition system must be designed and developed to address this communication barrier. Most current sign language recognition systems rely on wearable sensors, keeping the recognition system unaffordable for most individuals. Moreover, the existing vision-based sign recognition frameworks do not consider all of the spatial and temporal information required for accurate recognition. A novel vison-based hybrid deep neural net methodology is proposed in this study for recognizing Indian and Russian sign gestures. The proposed framework is aimed to establish a single framework for tracking and extracting multi-semantic properties, such as non-manual components and manual co- articulations. Furthermore, spatial feature extraction from the sign gestures is deployed using a 3D deep neural net with atrous convolutions. The temporal and sequential feature extraction is carried out by employing attention-based Bi-LSTM. In addition, the distinguished abstract feature extraction is done using the modified autoencoders. The discriminative feature extraction for differentiating the sign gestures from unwanted transition gestures is done by leveraging the hybrid attention module. The experimentation of the proposed model has been carried out on the novel multi-signer Indo-Russian sign language dataset. The proposed sign language recognition framework with hybrid neural net yields better results than other state-of-the-art frameworks.
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Author: Yazdani-Asrami, M., Song, W., Morandi, A.,De Carne, G., Murta-Pina, J., Pronto, A., Oliveira, R., Shen, B., Coombs, T., Salmi, T., Wu, D., GroBe, V., Mauro, D., Senatore, C., S., Yoon, T.L., Wang, Y., Camata, R.P., Chen, C.C., Madureira, A.M., Abraham, A.
Roadmap on artificial intelligence and big data techniques for superconductivity
Publisher: Superconductor Science and Technology, 2023
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This paper presents a roadmap to the application of AI techniques and big data (BD) for different modelling, design, monitoring, manufacturing and operation purposes of different superconducting applications. To help superconductivity researchers, engineers, and manufacturers understand the viability of using AI and BD techniques as future solutions for challenges in superconductivity, a series of short articles are presented to outline some of the potential applications and solutions. These potential futuristic routes and their materials/technologies are considered for a 10–20 yr time-frame.
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Author: Guleria, H.V., Luqmani, A.M., Kothari, H.D., Phukan, P., Patil, S., Pareek, P., Kotecha, K., Abraham, A., Gabralla, L.A.
Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder
Publisher: International Journal of Environmental Research and Public Health, 2023
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A breast tissue biopsy is performed to identify the nature of a tumour, as it can be either cancerous or benign. The first implementations involved the use of machine learning algorithms. Random Forest and Support Vector Machine (SVM) were used to classify the input histopathological images into whether they were cancerous or non-cancerous. The implementations continued to provide promising results, and then Artificial Neural Networks (ANNs) were applied for this purpose. We propose an approach for reconstructing the images using a Variational Autoencoder (VAE) and the Denoising Variational Autoencoder (DVAE) and then use a Convolutional Neural Network (CNN) model. Afterwards, we predicted whether the input image was cancerous or non-cancerous. Our implementation provides predictions with 73% accuracy, which is greater than the results produced by our custom-built CNN on our dataset. The proposed architecture will prove to be a new field of research and a new area to be explored in the field of computer vision using CNN and Generative Modelling since it incorporates reconstructions of the original input images and provides predictions on them thereafter.
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Author: Rivi, V., Batabyal, A., Benatti, C., Blom, J.M., Tascedda, F., Lukowiak, K.
Novel taste, sickness, and memory: Lipopolysaccharide to induce a Garcia-like effect in inbred and wild strains of Lymnaea stagnalis
Publisher: Physiology & Behavior, 2023
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Food is not only necessary for our survival but also elicits pleasure. However, when a novel food is followed sometime later by nausea or sickness animals form a long-lasting association to avoid that food. This phenomenon is called the ‘Garcia effect’. We hypothesized that lipopolysaccharide (LPS) could be used as the sickness-inducing stimulus to produce a Garcia-like effect in inbred and wild populations of Lymnaea stagnalis. We first demonstrated that the injection of 25 μg (6.25 µg/mL) of Escherichia coli-derived LPS serotype O127:B8 did not by itself alter snails’ feeding behavior. Then we showed that the presentation of a novel appetitive stimulus (i.e., carrot slurry) and LPS resulted in a taste-specific and long-lasting feeding suppression (i.e., the Garcia-like effect). We also found strain-specific variations in the duration of the long-term memory (LTM). That is, while the LTM for the Garcia-like effect in W-strain snails persisted for 24h, LTM persisted for 48h in freshly collected Margo snails and their F1 offspring. Finally, we demonstrated that the exposure to a non-steroidal anti-inflammatory drug, aspirin (acetylsalicylic acid) before the LPS injection prevented both the LPS-induced sickness state and the Garcia-like effect from occurring. The results of this study may pave the way for new research that aims at (1) uncovering the conserved molecular mechanisms underlying the Garcia-like effect, (2) understanding how cognitive traits vary within and between species, and (3) creating a holistic picture of the complex dialogue between the immune and central nervous systems.
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Author: Kumar, P., Singh, V.K., Rao, S.
Does the substitution effect lead to feedback effect linkage between ethanol, crude oil, and soft agricultural commodities?
Publisher: Energy Economics, 2023
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Despite increased demand for cleaner fuel alternatives such as ethanol in recent decades, portfolio weight allocation has become challenging due to the complex interlinkage amongst crude, ethanol and soft agricultural commodities that form part of the value chain. As a result, portfolio returns face three trade-offs in terms of risk: dispersion across mean, risk arising due to market interconnectedness, and risk arising due to global shocks for assets sharing common macroeconomic fundamentals. This study proposes an optimal weight allocation portfolio strategy, encapsulating the three risk measures and returns, estimated using state-of-the-art multi-objective elitist Non-Dominated Sorting Algorithm II (NSGA-II). Our proposed strategy performs well for newly constituted objectives against the Markowitz Mean-Variance approach and Global Minimum Variance. A balanced diversification escapes the feedback spillover loop trap at the same time. Our results indicate that soybean oil, sugar, and rice offer a better reward to risk, aiding portfolio immunisation to extreme market movements. Furthermore, using GJR-GARCH volatility to capture the volatility asymmetry effect, the Generalized Forecast Error Variance Decomposition (GFEVD) shows the existence of a strong triplet pair Crude-Ethanol-Soybean as a breeding ground for the feedback effect to occur. Moreover, replacing crude weight with ethanol depicts a fall in spillover risk up to a threshold of 30% Ethanol weight, after which the feedback effect kicks in.
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ABDC : A*
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SCOPUS®
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Q1
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Author: Bodhanwala, S., Bodhanwala, R.
Environmental, social and governance performance: influence on market value in the COVID-19 crisis
Publisher: Management Decision, 2023
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"The aim of this paper is to study whether adoption of sustainability policies by firms makes their stock market performance resilient to the downside risk during the crisis period.
Design/methodology/approach
The paper empirically examines the relationship between environmental, social and governance (ESG) and stock market performance for Indian companies that have consistently been a part of Refinitiv Eikon ESG database. Further, the study examines whether there exist significant differences in stock market performance of high ESG and low ESG-compliant firms during crisis period. The sample was made up of 70 Indian firms studied over the period 2016–2019 defined as “normal period” as well as for the declared COVID-19 crisis period, i.e. January–March 2020, and full year 2020. The authors used multivariate panel data regression, robust least square multivariate regression, pooled OLS model and two-stage least square regression method."
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Author: Krishna, Y.
Film and Caste Anxieties in Early Twentieth-Century India
Publisher: South Asia: Journal of South Asia Studies, 2023
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Film was one of the new technological inventions of the ‘long nineteenth century’ that often instilled a fear of loss of the existing way of life and of disturbing existing social structures. In India, this manifested in a variety of ways, such as through the censorship of films by the government, but also by social codes which created major barriers for many to work in the film industry. In this article, I discuss film and its negotiations with caste in the twentieth century through close reading of the Indian Cinematograph Committee Report, film magazines, advertisements, audience responses and the speeches of nationalist leaders. I argue that rather than democratising desire by producing the universally desirable object of the star body, popular films in India produced desire that conformed to existing caste structures. The paper contributes to debates about early cinema and caste beyond the question of representation.
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Author: Kudtarkar, S.K., Dhadwal, R.
Noise induced bistability in a fluctuating environment
Publisher: Physica A: Statistical Mechanics and its Applications, 2023
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Bistability in some systems does not occur at the mean-field level but is induced by noise. In this work, we consider a system which exhibits bistability induced by noise but in the presence of environmental fluctuations. The system can transition between two environmental states at a specified rate. It has absorbing boundaries in one state and can switch between two metastable states in the other. At small values of a system parameter (), the model exhibits noise-induced switching behaviour and the probability distribution is bimodal which becomes unimodal at large values of . We calculate the exact eigenvalues and steady-state solutions of the system. We further extend the investigation by calculating the mean switching time between the two metastable states in the small parameter limit and compare its accuracy with simulation results.
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Author: Narayanan, S., Singh, G.A.
Consumers' willingness to pay for corporate social responsibility: Theory and evidence
Publisher: International Journal of Consumer Studies, 2023
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In an era when consumers believe that businesses should engage in corporate social responsibility (CSR), it is vital to understand how it affects consumers' willingness to pay (WTP) for the goods and services offered by such businesses. There is a need for an in-depth study into the relationship between CSR and WTP, and to identify the mediators and the moderators affecting this relationship. To investigate this, we conducted a systematic literature review based on a preliminary search result of 116 unique articles indexed on this topic in four bibliographic databases—Scopus, Google Scholar, Dimensions and Web of Science—published over the previous seven decades. The findings confirm the overall positive effect of CSR on WTP. This study also reveals the indirect effect between CSR and WTP, mediated by variables like Brand Trust, Brand Loyalty, Brand Love, Customer Satisfaction, Brand Attitude, Purchase Intention and Brand Equity. The relationship is impacted by moderators, including demographics, cause-based aspects, company characteristics, personal aspects and types of products. The theory explaining the evidence of each of these aspects provides a deeper understanding of the relationship between CSR and WTP, and the intervening variables. Based on these, a conceptual framework of this relationship involving all the variables is developed. The Theory, Context and Method (TCM) framework is employed to identify gaps and systematically make recommendations for future research. The findings of this study will aid marketers in developing pricing strategies based on a thorough understanding of consumer behaviour in terms of CSR perceptions. Scholars can use this study's conceptual framework to examine previously unexplored relationships. As the literature on CSR and its influence on consumers' purchase behaviour grows, this comprehensive systematic literature review on the effects of CSR on WTP fills an important gap.
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Author: Thakur, B.K., Bal, D.P., Nurujjaman, Md., Debnath, K.
Developing a model for residential water demand in the Indian Himalayan Region of Ravangla, South Sikkim, India
Publisher: Groundwater for Sustainable Development, 2023
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Climate change and increased demand for human needs have led to drinking water scarcity in the cities and towns located in the Indian Himalayan region. This paper aims to address the challenge of drinking water security by proposing a model of water demand among households in Ravangla town of South Sikkim, India. Our field survey results indicate that the water demand increases significantly due to the number of household members between the age of 18 and 60. Further, we find that the number of taps in the household is positively and significantly correlated with water demand. Number of water taps, storage capacity and the water consumption are positively related. However, we did not find any such strong association with the number of toilets in the household. The GIS maps of South Sikkim shows the areas with households that will soon face acute water stress and drinking water insecurity. In order to address the scarcity of water in these regions, policymakers may develop plans like rainwater harvesting to ameliorate the situation through a GIS based decision support system.
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