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.
https://www.epw.in/journal/2023/10/commentary/consequences-blindsiding-informally-employed.html
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: 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
Abstract
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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.
https://doi.org/10.1007/s13369-023-07672-5
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: Rakesh, V.T., Menon, P., Raman, R.
Attribute-based choice model and willingness to pay for industrial services
Publisher: Benchmarking: An International Journal, 2023
Abstract
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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.
https://doi.org/10.1108/BIJ-01-2023-0012
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: 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.
https://doi.org/10.1016/j.techfore.2023.122344
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: 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
Abstract
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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.
https://dx.doi.org/10.1504/IJMED.2023.10053196
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: 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
Abstract
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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.
https://doi.org/10.1016/j.rsase.2022.100896
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: 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
Abstract
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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.
https://doi.org/10.1016/j.engappai.2023.106139
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: 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
Abstract
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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.
https://doi.org/10.1109/ACCESS.2023.3251892
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: Devi Priya, R., Karthikeyan, S., Indra, J., Kirubashankar, S., Abraham, A., Gabralla, L., Sivaraj, R., Nandhagopal, S.M.
Self-Adaptive Hybridized Lion Optimization Algorithm with Transfer Learning for Ancient Tamil Character Recognition in Stone Inscriptions
Publisher: IEEE Access, 2023
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Tamil character recognition serves as a vital research problem in pattern recognition since there are many serious technical difficulties due to similarity and complexity of characters when compared with other languages. Stone inscriptions reveal details of luxury, lifestyle, economic status, cultural practices, administrative tasks followed by various rulers and dynasties of Tamil Nadu. Since ancient stone inscriptions are in existence for a longer period, there are possibilities of natural erosion and no early protection measures are available. The ancient stone inscriptions are always not complete which creates many difficulties in reading and understanding them and their aesthetic appreciation. There is a difficulty in recognizing Tamil characters mainly because of the characters with a number of holes, loops and curves. The number of letters in Tamil language is higher when compared to other languages. Even though there are various approaches provided by the researchers, challenges and issues still prevail in recognition of tamil text in stone inscriptions. In the existing systems, detection algorithms fail to produce desired accuracy and hence stone inscription recognition using transfer learning, a promising method is proposed here. Lion Optimization Algorithm (LOA) is applied to optimize brightness and contrast and then stone inscription images are pre-processed for noise removal and then each character is separated by identifying contours. Characters are recognized using Transfer Learning (TL), a Deep Convolution Neural Network-based multi classification approach. The proposed hybrid model Self-Adaptive Lion Optimization Algorithm with Transfer Learning (SLOA-TL) when implemented in images of stone inscriptions achieves better accuracy and speed than other existing methods. It serves as an efficient design for recognition of tamil characters in stone inscriptions and preserving tamil traditional knowledge.
https://doi.org/10.1109/ACCESS.2023.3268545
Tamil character recognition serves as a vital research problem in pattern recognition since there are many serious technical difficulties due to similarity and complexity of characters when compared with other languages. Stone inscriptions reveal details of luxury, lifestyle, economic status, cultural practices, administrative tasks followed by various rulers and dynasties of Tamil Nadu. Since ancient stone inscriptions are in existence for a longer period, there are possibilities of natural erosion and no early protection measures are available. The ancient stone inscriptions are always not complete which creates many difficulties in reading and understanding them and their aesthetic appreciation. There is a difficulty in recognizing Tamil characters mainly because of the characters with a number of holes, loops and curves. The number of letters in Tamil language is higher when compared to other languages. Even though there are various approaches provided by the researchers, challenges and issues still prevail in recognition of tamil text in stone inscriptions. In the existing systems, detection algorithms fail to produce desired accuracy and hence stone inscription recognition using transfer learning, a promising method is proposed here. Lion Optimization Algorithm (LOA) is applied to optimize brightness and contrast and then stone inscription images are pre-processed for noise removal and then each character is separated by identifying contours. Characters are recognized using Transfer Learning (TL), a Deep Convolution Neural Network-based multi classification approach. The proposed hybrid model Self-Adaptive Lion Optimization Algorithm with Transfer Learning (SLOA-TL) when implemented in images of stone inscriptions achieves better accuracy and speed than other existing methods. It serves as an efficient design for recognition of tamil characters in stone inscriptions and preserving tamil traditional knowledge.
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Author: Passah, A., Sur, S.N., Abraham, A., Kandar, D.
Synthetic Aperture Radar image analysis based on deep learning: A review of a decade of research
Publisher: Engineering Applications of Artificial Intelligence, 2023
Abstract
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Artificial intelligence research in the area of computer vision teaches machines to comprehend and interpret visual data. Machines can properly recognize and classify items using digital images captured by cameras and videos, deep learning models, and then respond to what they observe. Similarly, artificial intelligence has also been able to learn complex images captured by Synthetic Aperture Radar (SAR) that are widely used for various purposes but still leave room for improvements. Researchers have proposed numerous approaches in this field, from SAR target detection to SAR target recognition. This paper presents a survey on the different techniques and architectures proposed in the literature for various SAR image applications. The paper covers a survey on target detection models and target recognition models and their respective workflow to analyze the techniques involved and the performances of these models. This paper makes novel discussions, comparisons, and observations. It highlights the advantages and disadvantages of different approaches to give researchers the idea of how each technique can influence the performance for adoption in the future. The potential future directions along with hybrid models on each processing method are also highlighted based on the study.
https://doi.org/10.1016/j.engappai.2023.106305
Artificial intelligence research in the area of computer vision teaches machines to comprehend and interpret visual data. Machines can properly recognize and classify items using digital images captured by cameras and videos, deep learning models, and then respond to what they observe. Similarly, artificial intelligence has also been able to learn complex images captured by Synthetic Aperture Radar (SAR) that are widely used for various purposes but still leave room for improvements. Researchers have proposed numerous approaches in this field, from SAR target detection to SAR target recognition. This paper presents a survey on the different techniques and architectures proposed in the literature for various SAR image applications. The paper covers a survey on target detection models and target recognition models and their respective workflow to analyze the techniques involved and the performances of these models. This paper makes novel discussions, comparisons, and observations. It highlights the advantages and disadvantages of different approaches to give researchers the idea of how each technique can influence the performance for adoption in the future. The potential future directions along with hybrid models on each processing method are also highlighted based on the study.
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Author: Rajpal, S., Rajpal, A., Agarwal, M., Kumar, V., Abraham, A., Khanna, D., Kumar, N.
XAI-CNVMarker: Explainable AI-based copy number variant biomarker discovery for breast cancer subtypes
Publisher: Biomedical Signal Processing and Control, 2023
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"Breast cancer is a leading cause of cancer-related deaths among women. The multi-omic data has revolutionized the methodology to unravel molecular heterogeneity in breast cancer. As genetic variations captured from Copy Number Variation (CNV) data are considered the most stable amongst the multi-omic data, it leads to robust biomarkers. Thus, this paper targets the discovery of a set of CNV biomarkers for dissecting this heterogeneity. The existing algorithms yield biomarkers, too huge to be interpreted clinically. So, in this paper, we have proposed XAI-CNVMarker—an explainable AI-based post-hoc biomarker discovery framework to discover a small set of interpretable biomarkers. We exploit the power of deep learning to build DLmodel—a deep learning model for breast cancer classification. Subsequently, the trained model is analyzed using different explainable AI methods to arrive at a set of 44 CNV biomarkers. Using 5-fold cross-validation, we obtained a classification accuracy of 0.712 (
0.048) at a 95% confidence interval. Gene set analysis revealed 37 subtype-specific enriched Reactome and Kegg pathways, 21 druggable genes, and 13 biomarkers linked with the prognostic outcome. Finally, we validated the efficacy of the identified biomarkers on METABRIC. Thus, the proposed framework demonstrates the role of explainable AI in discovering clinically reliable biomarkers."
https://doi.org/10.1016/j.bspc.2023.104979
"Breast cancer is a leading cause of cancer-related deaths among women. The multi-omic data has revolutionized the methodology to unravel molecular heterogeneity in breast cancer. As genetic variations captured from Copy Number Variation (CNV) data are considered the most stable amongst the multi-omic data, it leads to robust biomarkers. Thus, this paper targets the discovery of a set of CNV biomarkers for dissecting this heterogeneity. The existing algorithms yield biomarkers, too huge to be interpreted clinically. So, in this paper, we have proposed XAI-CNVMarker—an explainable AI-based post-hoc biomarker discovery framework to discover a small set of interpretable biomarkers. We exploit the power of deep learning to build DLmodel—a deep learning model for breast cancer classification. Subsequently, the trained model is analyzed using different explainable AI methods to arrive at a set of 44 CNV biomarkers. Using 5-fold cross-validation, we obtained a classification accuracy of 0.712 (
0.048) at a 95% confidence interval. Gene set analysis revealed 37 subtype-specific enriched Reactome and Kegg pathways, 21 druggable genes, and 13 biomarkers linked with the prognostic outcome. Finally, we validated the efficacy of the identified biomarkers on METABRIC. Thus, the proposed framework demonstrates the role of explainable AI in discovering clinically reliable biomarkers."
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Author: Bhosale, H., Pandya, M.A., Chatterjee, I., Mukunth, A., Sureshkumar, B., Rajagopalan, R.S., Parlikkad, N.R., Valadi, J.
Development of Gradient Boosting Machines for Estimation of Total and Dynamic Liquid Holdup in Trickle Bed Reactor
Publisher: Industrial & Engineering Chemistry Research, 2023
Abstract
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Prediction of liquid holdup is of significance in designing and in evaluating the performance of trickle bed contactors. The present work focuses on the development of Gradient Boosting Machines (GBM) for the prediction of total and dynamic liquid holdup in trickle bed reactors. A comprehensive data set of 394 data points of total liquid holdup and 416 data points of dynamic liquid holdup curated from open literature is used in this study. We built GBM models with the input data sets containing 11 governing variables. GBM provided excellent predictions for both data sets. We have also compared the GBM predictions with that of the Random Forest (RF) and Artificial Neural Networks (ANN) predictions. As GBM provided the best performance, we further employed SHAP (SHapley Additive exPlanations) with GBM black box models to get local and global interpretability. Also, we have used SHAP to identify informative subsets of governing variables. The work shall pave the way for use of GBM in prediction of hydrodynamic parameters in multiphase systems.
https://doi.org/10.1021/acs.iecr.3c00231
Prediction of liquid holdup is of significance in designing and in evaluating the performance of trickle bed contactors. The present work focuses on the development of Gradient Boosting Machines (GBM) for the prediction of total and dynamic liquid holdup in trickle bed reactors. A comprehensive data set of 394 data points of total liquid holdup and 416 data points of dynamic liquid holdup curated from open literature is used in this study. We built GBM models with the input data sets containing 11 governing variables. GBM provided excellent predictions for both data sets. We have also compared the GBM predictions with that of the Random Forest (RF) and Artificial Neural Networks (ANN) predictions. As GBM provided the best performance, we further employed SHAP (SHapley Additive exPlanations) with GBM black box models to get local and global interpretability. Also, we have used SHAP to identify informative subsets of governing variables. The work shall pave the way for use of GBM in prediction of hydrodynamic parameters in multiphase systems.
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Author: Jain, P., Sharma, S.
“Children of the Soil” to “Dark Wind”: Nature, Environment and Climate in Indian Films
Publisher: Visual Anthropology, 2023
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India is widely known as the biggest producer of films, now globally known with the portmanteau “Bollywood.” India also grabs the media attention for another reason—climate change. In 2015, The New York Times published an op-ed with a cartoon showing India as the proverbial “elephant” blocking the progress at the Paris Climate Change Conference. With the staggering number of films India produces and the steady increase in climate change-related disasters that India faces, the critics embraced the film Kadvi Hawa (literally, Dark Wind or Bitter Wind, 2017) as the “pioneering” film raising the critical issue of climate change. However, the issues raised in the movie were amply dealt with in several other Indian films in the last several decades. This article is a survey of Indian films that have shown or dealt with nature, environment, or climate starting from the 1940s till the present time.
https://doi.org/10.1080/08949468.2022.2129258
India is widely known as the biggest producer of films, now globally known with the portmanteau “Bollywood.” India also grabs the media attention for another reason—climate change. In 2015, The New York Times published an op-ed with a cartoon showing India as the proverbial “elephant” blocking the progress at the Paris Climate Change Conference. With the staggering number of films India produces and the steady increase in climate change-related disasters that India faces, the critics embraced the film Kadvi Hawa (literally, Dark Wind or Bitter Wind, 2017) as the “pioneering” film raising the critical issue of climate change. However, the issues raised in the movie were amply dealt with in several other Indian films in the last several decades. This article is a survey of Indian films that have shown or dealt with nature, environment, or climate starting from the 1940s till the present time.
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Author: Chaudhry, S., Ghura, A.S.
Primes and Zooms: A Need for Growth Strategy
Publisher: Ivey Publishing, 2023
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Author: Bodhanwala, S., Bodhanwala, R.
Tata Motors: The Dividend Dilemma
Publisher: Ivey Publishing, 2023
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Author: Karim, U., Kapur, S.
Regional Security in South Asia and the Gulf
Publisher: Routledge, 2023
Links
https://www.routledge.com/Regional-Security-in-South-Asia-and-the-Gulf/Karim-Kapur/p/book/9781032254142#
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Author: Kapur, S.
India, the Persian Gulf, and the Emergence of a Supercomplex
Publisher: Routledge, 2023
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Author: Walia, R.
“A Great Republic of Hurt Sentiments”: Counter Histories, Nationalism, and the Controversy of the Historical. In: Gopinath, S., Deshmukh, R. (eds.)Historicizing Myths in Contemporary India: Cinematic Representations and Nationalist Agendas in Hindi Cinema
Publisher: Routledge, 2023
Links
https://www.taylorfrancis.com/chapters/edit/10.4324/9781003363149-11/great-republic-hurt-sentiments-ramna-walia?context=ubx&refId=b92c36e6-814c-4dd9-b731-40af341ef02b
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Author: Latif, R., Sohoni, P.
Sultanate Ahmadabad And Its Monuments: The City Of Muzaffarids (Ahmad Shahis)
Publisher: Primus Books, 2023
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Author: Kushik, R.
The Indian National Innovation System: historical perspective and key characteristics
Publisher: Bite-Sized Books Ltd., 2023
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