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The application of artificial intelligence in education: Opportunities and challenges
The chapter investigates the challenges of implementing AI in education, including ethical and privacy concerns, the impact on teacher and student roles, bias and fairness in AI-based systems, and technical challenges. Furthermore, it discusses AI's potential policy and practice implications in education and outlines future research directions. The chapter concludes by emphasizing the transformative nature of AI in education and its capacity to enhance teaching and learning experiences, improve educational outcomes, and foster equitable access to quality education. 2024, IGI Global. All rights reserved. -
THE ANALYSIS OF THE FLOW OF BLOOD IN A STENOSED ARTERY THROUGH SIMULATION: A COMPARISON AMONG VARIOUS NON-NEWTONIAN MODELS
This paper focuses on the dual quality of blood, Newtonian and non-Newtonian, in particular by exploring the energy curves. Careful investigation of the dual property of blood has been made by considering two different geometries to represent a stenosed arterial segment. We present a cautious assessment of non-Newtonian blood rheology impacts in arterial stream simulations by coupling the Newtonian and non-Newtonian models. The flow of energy through the two flow dimensions is meticulously investigated using velocity (kinetic energy), pressure, and wall shear stress (pressure energy). Besides, the proper implementation of an interface boundary condition (IBC) was emphasized to ensure consistency with the flow conditions downstream of a backward-facing step. The integration of the Newtonian and non-Newtonian models adjoins the novelty of the current research. The energy curves are obtained by implementing five different non-Newtonian models to designate a suitable non-Newtonian model for blood flow investigations. The combination of the non-Newtonian models enforced in this research is novel and particular attention is paid to the energy curves obtained. The conclusion was to elect the Carreau model as a suitable non-Newtonian rheological model for the blood flow study. This study was able to finalize the fact that the coupling of Newtonian and non-Newtonian models is necessary to obtain accurate results. For the sinusoidal waveform considered for the velocity, Carreau and the Power law models yield better results, eliminating the other non-Newtonian models from the list. With a better inlet condition imposed in the form of the Fourier series for pressure and velocity, the Carreau model yields the best results. 2024 World Scientific Publishing Company. -
The analogy of nanoparticle shapes on the theory of convective heat transfer of AuFe3O4 Casson hybrid nanofluid
In recent times, Au nanoparticles have beencommonly used for delivering the drug especially in the case of hypothermia of tumors, but low absorption of IR light does not solve destruction of tumor cells. However, nanoparticles such as Fe3O4 coated with Au could be used to deliver the drug to a specific spot due to applied external magnetic field. Due to these applications, boundary layer approximation is invoked to simplify the mathematical model. This paper presents the nanoparticle shape analysis and heat transfer features of the AuFe3O4blood hybrid nanofluid flowing past a stretching surface on a magnetohydrodynamic medium. Numerical solutions of nonlinear differential equations are obtained by RKF-45 method with the help of shooting technique. The behavior of emerging parameters is described graphically for velocity and temperature profiles. It is found that the blade-shaped Au and Fe3O4 nanoparticles have better thermal conductance than brick, sphere, cylinder, needle, and platelet shapes. It is also observed that the Lorentz force generated due to magnetic field helps in controlling the flow and enhance the thermal conductivity of hybrid nanofluid. 2021 Wiley Periodicals LLC. -
The aesthetics of corpses in popular culture
[No abstract available] -
The accumulation, antioxidant defences, and secondary metabolite production in common sage (Salvia officinalis L.) under lead toxicity
The growing levels of lead (Pb) in agricultural soil and water bodies as a result of industrialization and human activity present serious challenges for medicinal plants. The present study investigates the complex responses of Salvia officinalis to toxicity caused by Pb including metal accumulation, translocation dynamics, antioxidant defenses, and the production of secondary metabolites. Pb showed preferential accumulation in the roots, peaking at (1000 ppm Pb) (2484.2 mg kg-1). The reduced levels of total chlorophyll (1.29-fold), protein (1.9-fold), and carbohydrate (2.5-fold) under prolonged exposure to stress demonstrate the toxic impacts of lead. Proline and total phenolic content (TPC) increased concentration-dependently under lead stress, while flavonoids were found to be decreased with the enhancement of lead toxicity. Enzymatic antioxidants (catalase, APX, and SOD) showed notable increase, especially in the 30 days of treatment, demonstrating the plants strong defenses. S. officinaliss adaptive responses were highlighted by concentration-dependent increases in non-enzymatic antioxidants such as total antioxidant capacity (TAC) and DPPH radical scavenging activity. Crucially, under lead stress, S. officinalis showed 1.7-fold increased rosmarinic acid (RA) production, in plants exposed to 200 ppm for 30 days treatment. However, further exposure to lead significantly caused the reduction of RA production. The results add to our knowledge of how sage plants respond to environmental stress and offer important insights for future uses in phytoremediation and the breeding of stress-tolerant plant cultivars. Furthermore, the research highlights about the S. officinaliss potential as a source of bioactive compounds possessing antioxidant qualities, under low levels of lead stress. 2024, Indian journals. All rights reserved. -
The Accountability of Stakeholders in Combating Domestic Violence with Women in India
As per the Hindu tradition, women are considered as ardhangini and the western civilisation considers them as better half. Since ancient times, women have been considered as the epitome of love, kindness, care and above all the mother of mankind. But on the contrary, the most terrible and horrifying cruelties are imposed upon her. The predominant type of violence which is inflicted upon a woman is domestic violence also known as intimate partner violence. According to the World Health Organisation, almost one third, i.e., 27% of the women aged between 15 and 49 years, who have been in a relationship report that they have been subjected to some form of physical or sexual violence by their intimate partner. The issue of domestic violence has been addressed at both international and national levels, but still there exists a persistent gap in enforcing and implementing them. And this can be easily proved from the continuous rise in the cases of domestic violence worldwide, especially during the COVID times. The problem that needs to be addressed at hand is the role of several accountable stakeholders involved in the process of providing access to justice to the women affected by domestic violence. According to the Protection of Women from Domestic Violence Act (2005), in India, the several stakeholders include the protection officers, service providers, lawyers, police officers, shelter homes and judges. These are the people with whom the power to protect and help the women are vested, but instead the affected women fall a prey to this system due to various reasons like lack of resources, fewer power and many more which will be further discussed in the article. 2024 selection and editorial matter Dr. Shilpi Sharma and Baidya nath Mukherjee; individual chapters, the contributors. -
THAYIL, JEET (1959-)
[No abstract available] -
Textual and media-based self-learning modules: Support for achievement in algebra and geometry
Owing to the importance of a subject like mathematics in the teaching and learning of science, self-learning often poses a challenge to the educator. The objective of this study is to analyse the enhancement of the textual and the media form of self-learning modules to teach algebra and geometry to eighth graders considering their retention levels. A pre-Test post-Test single-group quasi experimental design was tested and tried out on 49 participants of a school. The 20 modules of self-learning material covering content in the topics of algebra and geometry in the textual and media-Assisted forms of self-learning were administered over three months. The findings of the study revealed the ability of media-Assisted self-learning modules to enhance achievement in the post-Test when compared to the pre-Test. The textual-Assisted learning modules were able to enhance significant difference in the achievements in geometry, but not of algebra. The delayed post-Test results were found to indicate an improved achievement in mathematics. 2022 IGI Global. All rights reserved. -
Textile tourism and the challenges of the indigenous handloom sector in Northern Kerala
This study investigates the functioning of indigenous handloom enterprises and their relationship with textile tourism. It also explores the regional textile industry and the challenges weavers encounter in promoting their goods to visitors and exporters. Data was collected using a purposive sampling method, and a structured questionnaire was administered to 120 weavers from four textile weaving centers in Kozhikode, Kerala. The most significant obstacle for weavers and independent producers is the lack of direct communication with customers and the limited access to information provided by manufacturers, corporations, and gallery owners. Firstly, handcrafted items are becoming more accessible and affordable; secondly, the interest of the younger generation is gradually fading, which reduces the number of skilled professionals. The result of this study provides insight into how Khadi Textiles has the potential to contribute positively to socioeconomic achievements and enhance weaver's and destination image. 2024, IGI Global. All rights reserved. -
Text-Based Sentimental Analysis to Understand User Experience Using Machine Learning Approaches
Data Analysis is turning into a driving force in every industry. It is a process in which data is analyzed in multiple ways to come to certain conclusions for the given situation. Sentiment analysis can be said to be a sub-section of data analysis where analysis is carried out on the emotions and opinions of the text. Social media has a plethora of sentiment data in various forms such as tweets, updates on the status, and so forth. Sentiment analysis on the huge volume of data can help in identifying the opinions of the general mass.The primary goal is to find the opinion of customers on the services of the Bangalore airport and to enhance the nature of these services according to the feedback provided. In this paper, we aim to measure customer opinion on services provided by Bangalore Airport through sentiment. Data is collected by a python-based scraper. The tweets are processed to determine whether they are of positive or negative opinion. These opinions are then analyzed to determine the factors which cause the negative opinions and the airport staff are alerted about the same. Various algorithms were used as part of the experimental analysis. LSTM produces more accuracy compared with existing approaches. 2023 IEEE. -
Text summarization using residual-based temporal attention convolutional neural network
To address the computational complexity and limited to large data Enhanced Residual based Temporal Attention Convolutional Neural Network (ERTACNN) with Improved Initialization strategy-based Aquila Optimization Algorithm (IIAOA) is proposed. Initially the document is pre-processed to get structured data and given to feature extraction. Then the features are selected with Aquila Optimization Algorithm to remove redundant or unrelated features from high-dimensional data, from which the entropy values are calculated and given to proposed classifier. In this classification, the temporal attention mechanism is combined with classifier to compute attention weight and accompanied with important time points for classifying the documents. Finally, the proposed method is implemented in python and evaluated against existing works which achieves 70.34, 55.6 and 72.4 Recall Oriented Understudy for Gisting Evaluation (ROUGE) score than existing approaches. 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management. -
Text Summarization Using Combination of Sequence-To-Sequence Model with Attention Approach
In daily life, we come across tons and tons of information which can be related to news articles or any kind of social media posts or customer reviews related to product. It is difficult to read all the content due to time constraint. Being able to develop the software that can identify and automatically extract the important information. There are two types of summarization methods. Extractive text summarization is the method where it picks the important content from the source text and gives same in the form of short summary, and on the other hand, abstractive summarization is the technique where it gets the context of the source text, and based on that context, it regenerates small and crisps summary. In this paper, we use the concept of neural network with attention layer to deal with abstractive text summarization that generates short summary of a long piece of text using review dataset. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Text Summarization Techniques for Kannada Language
Text Summarization is summarizing the original text document into a shorter description. This short version should retain the meaning and information content of the original text document. A concise summary can help humans quickly understand a large original document better in a short time. Summarization can be used in many text documents, such as reviews of books, movies, newspaper articles, content, and huge documents. Text summarization is broadly classified into extractive Text Summarization (ETS) and Abstractive Text Summarization (ATS). Even though more research works are carried out using extractive methods, meaningful summaries can be attained using abstractive summary techniques, which are more complex. In Indian languages, very few works are carried out in abstract summarization, and there is a high need for research in this area. The paper aims to generate extractive and abstractive summaries of the text by using deep learning and extractive summaries and comparisons between them in the Kannada language. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Text Mining-A Comparative Review of Twitter Sentiments Analysis
Background: Text mining derives information and patterns from textual data. Online social media platforms, which have recently acquired great interest, generate vast text data about human behaviors based on their interactions. This data is generally ambiguous and unstructured. The data includes typing errors and errors in grammar that cause lexical, syntactic, and semantic uncertainties. This results in incorrect pattern detection and analysis. Researchers are employing various text mining techniques that can aid in Topic Modeling, the detection of Trending Topics, the identification of Hate Speeches, and the growth of communities in online social media net-works. Objective: This review paper compares the performance of ten machine learning classification techniques on a Twitter data set for analyzing users' sentiments on posts related to airline usage. Methods: Review and comparative analysis of Gaussian Naive Bayes, Random Forest, Multinomial Naive Bayes, Multinomial Naive Bayes with Bagging, Adaptive Boosting (AdaBoost), Optimized AdaBoost, Support Vector Machine (SVM), Optimized SVM, Logistic Regression, and Long-Short Term Memory (LSTM) for sentiment analysis. Results: The results of the experimental study showed that the Optimized SVM performed better than the other classifiers, with a training accuracy of 99.73% and testing accuracy of 89.74% compared to other models. Conclusion: Optimized SVM uses the RBF kernel function and nonlinear hyperplanes to split the dataset into classes, correctly classifying the dataset into distinct polarity. This, together with Feature Engineering utilizing Forward Trigrams and Weighted TF-IDF, has improved Optimized SVM classifier performance regarding train and test accuracy. Therefore, the train and test accuracy of Optimized SVM are 99.73% and 89.74% respectively. When compared to Random Forest, a mar-ginal of 0.09% and 1.73% performance enhancement is observed in terms of train and test accuracy and 1.29% (train accuracy) and 3.63% (test accuracy) of improved performance when compared with LSTM. Likewise, Optimized SVM, gave more than 10% of enhanced performance in terms of train accuracy when compared with Gaussian Nae Bayes, Multinomial Nae Bayes, Multinomial Nae Bayes with Bagging, Logistic Regression and a similar enhancement is observed with Ada-Boost and Optimized AdaBoost which are ensemble models during the experimental process. Optimized SVM also has outperformed all the classification models in terms of AUC-ROC train and test scores.. 2024 Bentham Science Publishers. -
Text extraction from video images
Video data contains beneficial textual information such as scene text and caption text. The different types of videos like movies, news videos, and TV programs video etc. are created by various video frames based on its purpose. In a country like India, there are only fewer studies has done on text extraction from video data especially in south Indian languages like Malayalam, Telugu, Kannada, and Tamil. The extracted text has many useful applications in video indexing, video key searching and assisting visually challenged people. Malayalam news channel named Mathrubhumi News videos data are considered for the proposed study. It is very beneficial to Kerala people as it is one of the most media-centric regions in the world. In this proposed paper, a new method for text extraction experiments. The anticipated method extracts 13 different features for classifying the image consists of text or not. Both spatial and frequency domain features are extracted to classify. The different types of classification techniques are used to validate the algorithm. Simple Logistic, J48 and Random Forest classification techniques are giving a good result when compared to other methods. Results are encouraging, the average success rate found to be 98%. Research India Publications. -
Testing the Diversifying Asset Hypothesis between Clean Energy Stock Indices and Oil Price
In theory, geopolitical risk and political uncertainty can directly affect energy markets. Fluctuations lead to the cost of clean energy sources as they compete with traditional energy. The purpose of this study is to analyse financial integration and test the diversifying asset hypothesis between clean energy indices, specifically the Clean Energy Fuels (CLNE), Nasdaq Clean Edge Green Energy (CELS), S&P Global Clean Energy (SPGTCLEN), TISDALE Clean Energy (TCEC.CN), Wilderhill (ECO) and West Texas Intermediate (WTI) stock indices, over the period from 1 January 2018 to 23 November 2023. Analysing the results reveals a scenario where most of the clean energy indices show cointegration with each other, indicating long-term relationships that reflect common trends in the clean energy sector. However, the relative independence of the WTI suggests that Oil still acts as an important and potentially diversifying external factor for investors focused on sustainable energy. Structural breaks in 2021 and 2022 in several indices point to significant events that have altered market dynamics, possibly including changes in environmental policies, technological innovations and the impacts of the COVID-19 pandemic. The cointegration evidence and structural breaks provide valuable information for building investment portfolios. Investors can consider the WTI to diversify portfolios dominated by clean energy assets, taking advantage of Oils relative independence. On the other hand, the high correlation between clean energy indices suggests that, within this sector, diversification options are more limited, requiring careful analysis of the specific characteristics of each index and the macroeconomic forces affecting them. 2024, Econjournals. All rights reserved. -
Testing The Causal Link Between Perceived Fee Fairness and Student Loyalty-Empirical Assessment in the Context of Service Marketing in Higher Education
Journal of Global Management Outlook, Vol-1 (5), pp. 43-55. ISSN-2277-3789 -
Testing of long run association between crude oil and gold commodities: An empirical study in India /
Test engineering & Management, Vol.82, pp.2902-2906, ISSN No: 0193-4120. -
Testing for the Bidirectional Relationship Between FDI in Services and Trade in Services: Evidence from Emerging Economies
We examine the two-way links between foreign direct investments (FDI) in services and trade in services for 26 emerging economies from 2003 to 2015 using sectoral and sectoral disaggregated FDI data. Within a multivariate framework, we use panel unit root tests, recently developed heterogeneous panel cointegration and panel vector error correction model (VECM). Our results confirmed the cointegrating relationship between trade in services, FDI in services, financial services FDI and nonfinancial services FDI. We find the existence of long-run unidirectional causality from trade in services to FDI in services. However, the disaggregated analysis shows a bidirectional link between nonfinancial services FDI and trade in services in the short run. Still, there is no causality between financial services FDI and trade in services both in the short run and long run. The result also shows the evidence of unidirectional causality running from trade in services to nonfinancial services FDI in the long run. It implies that sectoral decomposition matters in the FDItrade nexus in emerging economies. JEL Codes: G20, F14, G20, F23 2022 Indian Institute of Foreign Trade.