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Innovation in TeachingExploring the Educators Perspective of AI Functions in Subject Pedagogy
The objective of the research is to gain insight into the viewpoints of educators regarding the role of artificial intelligence (AI) in the teaching of academic subjects. The study endeavours to comprehend the most significant function of AI in attaining a successful teaching experience. Design/Methodology The study is an exploratory research design which utilises quantitative research strategy. Data is collected from students of Autonomous Institutions in Bangalore city. A convenience sampling technique is used to select 56 Educators. The data was analysed using the SPSS and AMOS software. Findings The studys results indicate that educators hold a favourable view of the potential of artificial intelligence (AI) features to enhance subject-specific teaching methods. According to the educators, the provision of instructions beyond the classroom and the facilitation of collaborative functions of artificial intelligence have the greatest impact on the effective delivery of subject matter to students. Originality This study is innovative in its methodology as it considers the viewpoint of educators regarding the role of Artificial Intelligence in the teaching of academic subjects. This study pertains to autonomous institutions located in Bangalore that possess the authority to develop their own subject pedagogies through the integration of artificial intelligence (AI). Implications This research will provide valuable insights for policymakers in the education industry and educational institutions regarding the perceptions of educators on the functions of artificial intelligence. The stakeholders can identify the ineffective functions which can either be substituted or eliminated. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Artificial intelligence-enabled project-based learning to augment subject comprehension among commerce graduates
In project-based learning, students participate in and create knowledge, problem solve and interact with their peers. This method is an active learning program that provides a deep grasp of subject matter as well as teaches critical thinking skills. AI technology in project-based learning creates an environment that is dynamic this allows students to succeed academically. A convenience sample of 100 students will be selected for this experiment-based research. The results using the advanced statistical tools and techniques using the SPSS ver 28 and Independent t test Paired t test were used. The results of the study indicate students in the experiment group had better subject comprehension from project based learning when AI based interventions are given. In contrary, the control group which was not administered with any AI interventions showed moderate increase in subject comprehension. The study's implications emphasise that AI-based projects, real-time feedback, personalized learning resources, and virtual simulations help deepen their understanding of commerce subjects. 2025, IGI Global Scientific Publishing. All rights reserved. -
Modeling of the LiouvilleGreen method to approximate the mechanical waves in functionally graded and piezo material with a comparative study
The present research article studies and compares the surface waves transmission through the functionally graded piezoelectric material (FGPM) club between the piezomagnetic (PM) layer -and half-space, and for a comparative study, lower half-space is assumed to be piezoelectric material. The transmission of mechanical waves in a smart structure is analyzed by following the elastic wave theory of magneto-electro-elasticity. The Liouville-Green (LG) approximation technique is used to solve the differential equation in the FGPM stratum, where exponential variation is assumed in material gradients. It is noticed that the material gradients depend considerably on the angular frequency, which should be a crucial factor in regulating the dispersion characteristics of functionally graded materials (FGM) waveguides. In closed determinant form, the dispersion relation has been obtained for FGPM plate for electrically open and short cases. The profound effect of parameters, such as material gradient, a width of the layer on phase velocity, coupled electromechanical factor, and angular velocity, is observed and delineated graphically. Different parametric plots are sub-plotted into a single figure to increase the readability of the graphs. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Examining the consumption of oil on total factor productivity and CO2 emissions: an analysis of highly oil-consuming countries
Purpose: This study aims to examine the impact of oil consumption on carbon dioxide (CO2) emissions and total factor productivity (TFP) in highly oil-consuming countries of the world from 1995 to 2019. Design/methodology/approach: For this purpose, fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) are applied. Findings: FMOLS and DOLS models reveal that oil consumption, human capital, population, trade openness and nonrenewable energy have a significant positive effect on CO2 emissions. While information and communication technology (ICT), as proxied by mobile and natural resources, has a significant negative effect on CO2 emissions. In the case of TFP, oil consumption, ICT and natural resources have a significant positive effect on the TFP. On the other hand, trade openness, population, human capital and nonrenewable energy have a significant negative effect on TFP. The results of this study can help to provide policy recommendations to reduce CO2 emissions in studied highly oil-consuming countries of the world. Originality/value: Due to the threat to sustainable development, climate change has become a major topic for debate around the world. The influence of oil consumption on CO2 emission and TFP is less known in the available literature. Another significance of this study is that many researchers considered aggregate energy consumption to study this relationship, but the authors have studied the effect of energy consumption, particularly from oil in the top oil-consuming countries, which is a significant shortcoming of the present research. 2023, Emerald Publishing Limited. -
Facial Emotion Detection Using Deep Learning: A Survey
The long history of facial expression analysis has influenced current research and public interest. The scientific study and comprehension of emotion are credited to Charles Darwin's 19th-century publication The Representation of the Sentiment in Man and Animals (originally published in 1872). As Recognition of human emotions from images is one of the utmost important and difficult societal connection study assignments. One advantage of using a deep learning strategy is its independence from human intervention while undertaking feature engineering. This approach involves an algorithm that scans the data for features that connect, then combines them to promote quicker learning without being explicitly told to. Deep learning (DL) based emotion detection outperforms traditional image processing methods in terms of performance. In this analytical study, the creation of an artificial intelligence (AI) system that can recognize emotions from facial expressions is presented. It discusses the various techniques for doing so, which generally involve three steps: face uncovering, feature extraction, and sentiment categorization. This study describes the various existing solutions and methodologies used by the researchers to build facial landmark interpretation. The Significance of this survey paper is to analyze the recent works on facial expression detection and distribute better insights to novice researchers for the upgradation in this domain. 2023 IEEE. -
Resilience and Human-Centric Perspectives for Organizations in Industry 5.0
In the emerging world researchers put forward the idea of industry 5.0, focusing on the concept of human centricity, sustainability and resilience approaches in the organizations. Industry 5.0 includes technological advancement along with giving importance to the human capital. During the past, the industrial revolution emphasized the productivity, production quality and growth of business. There was less importance for the human capital and employees well being in the changing industrial style giving importance to the human capital, by integrating technological advancements. It is recognised for the societal values that are embedded, the whole industry 5.0 revolves around sustainability, human centric and resilience in the organization. In this book chapter we explore the concept and applicability of these topics. The book chapter used literature review method and case study analysis for thorough understanding of the subject. 2025 by IGI Global Scientific Publishing. All rights reserved. -
The Association between Accounting determined and market determined measures of risk: Evidences from Indian Pharmaceutical Industry.
Volume : 3, Issue : 12, pp- 35-44, ISSN: 2249-7307 -
Multi-stage fuzzy swarm intelligence for automatic hepatic lesion segmentation from CT scans
Segmentation of liver and hepatic lesions using computed tomography (CT) is a critical and challenging task for doctors to accurately identify liver abnormalities and to reduce the risk of liver surgery. This study proposed a novel dynamic approach to improve the fuzzy c-means (FCM) clustering algorithm for automatic localization and segmentation of liver and hepatic lesions from CT scans. More specifically, we developed a powerful optimization approach in terms of accuracy, speed, and optimal convergence based on fast-FCM, chaos theory, and bio-inspired ant lion optimizer (ALO), named (CALOFCM), for automatic liver and hepatic lesion segmentation. We employed ALO to guide the FCM to determine the optimal cluster centroids for segmentation processes. We used chaos theory to improve the performance of ALO in terms of convergence speed and local minima avoidance. In addition, chaos theory-based ALO prevented the FCM from getting stuck in local minima and increased computational performance, thus increasing stability, reducing sensitivity in the iterative process, and allowing the best centroids to be used by FCM. We validated the proposed approach on a group of patients with abdominal liver CT images, and the results showed good detection and segmentation performance compared with other popular techniques. This new hybrid approach allowed for the clinical diagnosis of hepatic lesions earlier and more systematically, thereby helping medical experts in their decision-making. 2020 Elsevier B.V. -
Tracing the impact of social media on social cognition: Bibliometric analysis
The words "misinformation, " "fake news, " and "post-truth" have filled social media posts. It is a serious social threat, especially post COVID-19. In this chapter, the authors provide bibliometric analysis of research on social media and its impact on social cognition. This can be useful for identifying gaps for future research in the field. Publication data was obtained from the Web of Science database using a search algorithm. A total of 22,935 articles were extracted, and 22,909 eligible articles were included for analysis. Document co-citation analysis revealed that themes on social engagement, fake news, problematic social media use, and healthcare emerged as trends on shaping the social cognition through social media. Further, India achieved 9th position on the list based on citations and 8th on centrality and did not appear on any of the top-10 lists based on Burst value and Sigma. This indicates that neither sudden trend-setting articles nor scientific novelty-based articles have been published in this domain thus far. There is a considerable research gap in India to counter misinformation. 2024, IGI Global. -
Real-Time Traffic Sign Detection Under Foggy Condition
Traffic congestion becomes high in urban areas and using public and private transportation services. The image of traffic signs gets affected by fog, and the detection of traffic signs has become difficult. To solve this issue, the machine learning technique has been used. Convolution neural network helps to solve real-time problems; hence, it can be used in the study for detecting traffic signs under foggy condition. The study results revealed that the model network has accuracy of 99.8%, and the proposed algorithm detects a traffic sign under foggy conditions in 2s per frame. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Fraud Prevention in Banking: Innovative Techniques for Detecting Payment Fraud
Fraud detection in banking remains one of the most critical challenges, as fraudulent patterns continue changing to avoid detection. Classic rule-based methods provide a basis approach but often lead to high rates of false positives and negatives, which limiting their efficiency. Due to the rapid growth of fraud particularly in Banking Payments, tackling this challenge has become imperative. To this end, we employ the Banksim dataset-a synthetic tool that replicates the various payment behavior of customers- to assess a number of machine learning models, Support Vector Machines, Random Forest, Logistic Regression, AdaBoost and Decision trees. Our model evaluation, using confusion matrices and classification reports, demonstrates the ability of these approaches to provide precision and reliability in detecting fraudulent transactions. This research contributes to enhancing the reliability and integrity of banking services through fraud payment detection improvements. 2025 IEEE. -
A compression system for Unicode files using an enhanced Lzw method
Data compression plays a vital and pivotal role in the process of computing as it helps in space reduction occupied by a file as well as to reduce the time taken to access the file.This work relates to a method for compressing and decompressing a UTF-8 encoded stream of data pertaining to Lempel-Ziv-welch (LZW) method. It is worth to use an exclusive-purpose LZW compression scheme as many applications are utilizing Unicode text. The system of the present work comprises a compression module, configured to compress the Unicode data by creating the dictionary entries in Unicode format. This is accomplished with adaptive characteristic data compression tables built upon the data to be compressed reflecting the characteristics of the most recent input data. The decompression module is configured to decompress the compressed file with the help of unique Unicode character table obtained from the compression module and the encoded output. We can have remarkable gain in compression, wherein the knowledge that we gather from the source is used to explore the decompression process. Universiti Putra Malaysia Press. -
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The intersection of law and wildlife management: A case study on culling of wild boars in Kerala
The inconvenient truth of wildlife co-existence lies in the circumstantial need of species to capitulate to each others will, but how far are we willing to go? Over the decades, the conservative view of eco-centric legislation has legally blanketed the scheduled species from the human acts of hunting, culling, assaulting, and the like. However, as the human population increases, this protected animal population, particularly those undomesticated ones in predator-less areas of the wild, increases exponentially beyond the land-carrying capacity of their habitats. Thus, the soundness of wildlife co-existence has been profoundly disrupted by wild boars (sus scrofa) through incessant encroachments, agricultural and economic damage, human fatalities, etc. Therefore, in light of the concurrent call for the declaration of vermin status of wild boars under the Wild Life (Protection) Act 1972, the paper aims to ascertain the legal and scientific efficacy of culling wild boars in comparison to the preventive strategies used over the years. This is achievable through a jurisprudential and scientific justification facilitated by the theories of anthropocentrism, eco-centrism, utilitarianism, and categorical imperativeness, alongside the capabilities approach. The research methodology entails a doctrinal approach wherein it contains participative observation, statistics, eco-legal analysis, numerical data, etc. Additionally, in the absence of current data on the wild boar population, an exploratory method has been employed in the study area to form a statistical estimation by speculating the reproductive pattern of wild boars. This evidence depicts the preponderance of understanding the interrelation of multiple disciplines by objectifying the currently understudied damage control methods. The Author(s), under exclusive licence to O.P. Jindal Global University (JGU) 2025. -
Microbial Decomposition of Feather Waste
Keratin is generally found as an ?-keratin helix form in hair, nails, horns and ?-keratin sheet form found in feathers, scales, beaks and claws. ?-keratin contains a domain rich in residues favoring to form ?-sheet structures associated with the filament framework. 'N' and 'C' terminal domains are associated with the matrix and forms cross-linking via disulfide bonds. Several million tons of feather waste are generated by poultry industries each year. Since this waste is rich in protein, it contains excellent potential as a protein source for animal feed and other applications.Bacterial and fungal strains used in microbial degradation of feathers are summarized. Various species from the bacterium genus are involved in keratin degradation including Bacillus, Stenotrophomonas, Pseudomonas, Brevibacillus, Fusarium, Geobacillus, Chryseobacterium, Xanthomonas and Serratia which are some keratin degrading bacteria. Actinomycetes and fungi also contribute to feather degradation by the enzyme activity of keratinases. 2022 World Research Association. All rights reserved. -
C-cordial labeling of line signed graphs-I
Let S=(G, ?) be a signed graph. S admits C-cordial labeling if the difference between the number of negative and positive edges (vertices) differ by at most one under canonical marking of S. In this paper, we characterize signed paths and cycles having given number of negative sections where the line signed graphs admit C-cordial labeling. 2020 Author(s). -
Epileptic seizure detection using EEG signals and multilayer perceptron learning algorithm
Purpose: Epileptic is a neurological chronic disorder that causes unprovoked, recurrent seizure. A seizure is a sudden rush of electrical activity in the brain. The central nervous system characterized by the loss of consciousness and convulsions. Epileptic is caused by abnormal electrical discharge that lead to uncountable movements, loss of consciousness and convulsions. 50-80 million people in the world are affected by this disorder. Now a days children and adults are affected the most and it has been medically treated. Sometimes it may lead to death and serious injuries. In this technology world the computerized detection is an enhanced solution to protect epileptic patients from dangers at the time of this seizure. Method: Perceptron learning algorithm is a supervised learning of binary classifiers and also it is a simple prototype of a biological neuron in artificial neural network. EEG is extensively documented for the diagnosing and assessing brain activates and related disorders. In this paper EEG signals are taken as dataset for epilepsy detection. The data is been represented based on three domains namely frequency, time and time-frequency applied by the chebysev filter for processing the signals. Result: Help the patients from dangers at the time of the seizure. Conclusion: The neurological diseases can be divided into two loss of consciousness and convulsions. In this technology world the seizure can be detected by computerized way like EEG and so on. This paper proposes an epileptic seizure detection using EEG (Electroencephalogram) and perceptron learning algorithm. 2020, IJSTR. -
Unveiling the Landscape: A Comparative Study of U-Net Models for Geographical Features Segmentation
Geographical features segmentation is a critical task in remote sensing and earth observation applications, enabling the extraction of valuable information from satellite imagery and aiding in environmental analysis, urban planning, and disaster management. The U-Net model, a deep learning architecture, has proven its efficacy in image segmentation tasks, including geographical feature analysis. In this research paper, a comparative study of various U-Net models customized explicitly for geographical features segmentation is presented. The study aimed to evaluate the performance of these U-Net variants under diverse geographical contexts and datasets. Their strengths and limitations were assessed, considering factors such as accuracy, robustness, and generalization capabilities. The efficacy of integrated components, such as skip connections, attention mechanisms, and multi-scale features, in enhancing the models performance was analyzed. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Heavy metal stress influence the andrographolide content, phytochemicals and antioxidant activity of Andrographis paniculata
Heavy metals (HM) are toxic components present in the earth's crust that can have a negative impact on plants as well as animals. Andrographis paniculata or 'King of bitters' belonging to the family Acanthaceae, is a medicinal herb traditionally used in the treatment of fever, common cold etc. In the present study, the effect of heavy metals (copper, tin and cobalt) on the andrographolide content, biochemical parameters like chlorophyll, carotenoid, protein, Total phenolic content (TPC), Total flavonoid content (TFC) and antioxidant activity in A. paniculata were analysed. Saplings of A. paniculata were treated at 50 and 100 mM concentrations, three different times at a time interval of 7 days. Andrographolide production was found to increase in copper and cobalt treated saplings when compared with the control. From the results, maximum andrographolide concentration was found in the saplings treated with 50 mM copper (8.51 mg/gm of DW) and 50 mM tin (8.10 mg/gm of DW) respectively. 50 mM cobalt treated plants have shown the highest concentration of TPC (17.21 mg/g of extract) and TFC (6.97 mg/gm of extract). Notable variations in other biochemical parameters like total chlorophyll, carotenoid content and antioxidant activities were observed in all treatments compared with the control. Antony & Nagella (2021). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited (https://creativecommons.org/licenses/by/4.0/). -
Effect of heavy metals on the andrographolide content, phytochemicals and antioxidant activity of Andrographis paniculata
Andrographis paniculata is a medicinal plant that has several medicinal properties and has been traditionally used in different medicinal preparations. The present study deals with the influence of heavy metals (lead, mercury and silver) on andrographolide, phytochemicals and antioxidant activity in Andrographis paniculata. Two months old saplings were subjected to heavy metal stress of two different concentrations (0.2 mM and 0.4 mM) for three different times at 3 day time interval. The results showed that the saplings treated with heavy metals showed increased concentration of andrographolide content. The saplings treated with 0.4 mM silver showed the highest increase in the andrographolide content (24.58 2.85 mg/g of DW) compared with control (9.41 1.26 mg/g of DW) and other treatments. Variations in the biochemical parameters like total phenolic content, total flavonoid content, etc. were also prominent with all the treated samples when compared to that of control. 2020 Chemical Publishing Co.. All rights reserved.