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The Influence of Cartoons Soundscape Irrelevant Sound Effects on young children's Auditory Processing and Working memory skills
Background: Irrelevant sound or speech effect (ISE) affects an individual's serial recall task of visual and auditory presentations. Cartoon soundscape mimics irrelevant sound effect hypothesis. A constant and repeated exposure to cartoons in early childhood should influence children's auditory learning or recall performance. Purpose: To investigate the effects of cartoons' soundscape irrelevant sound effects on young children's auditory processing and working memory skills. Research Design: A cross-sectional study was used to observe the influence of the cartoon soundscape irrelevant sound effects on children. Study sample: Sixty young children with normal hearing in the age range 5-6years were exposed to cartoons (Indian plus Non-Indian) considered for the study. Data Collection and analysis: Pitch Pattern Test (PPT), Duration Pattern Test (DPT), and Corsi-Block working memory apparatus were applied to the participants exposed to cartoons. The data obtained were compared statistically in terms of the groups' performances. Results: There was a significant difference in PPT (p=.023) and DPT (p=.001) between the cartoon exposed and non-exposed groups. In contrast, there was no significant difference between the two groups in Corsi-Block working memory(p>0.05). Conclusion: Cartoon soundscape irrelevant sound or speech affects young children's auditory processing skills. The visual-spatial recall follows a different developmental pattern in young children without recoding to phonological aspects. It is predicted that our study findings might help determine the ill effects of cartoons on the auditory and language development process. 2022 American Academy of Audiology. All rights reserved. -
Non-Fungible Token (NFT): Bubble or Future in the World of Block Chain Technology
The introduction of blockchain technology entering into human existence, which is a reinforcement of the cryptocurrency space, is both a concern and an opportunity. The main motivation underlying such an invention is conditional transparency and the unmatched ability to protect people against data destruction. The collecting drive of NFTs is profitable and also has sparked curiosity, with everyone vying for the first piece of the package, increasing the future Value of an NFT, as it is a very new topic about NFT using block-chain technology. It is something quite about a flurry of blockchain technological stories that leave us wondering. In this research paper, we explained the new emerging Non-Fungible Token (NFT), its uses, and implications. 2023 American Institute of Physics Inc.. All rights reserved. -
Leveraging Ensemble Methods for Accurate Prediction of Customer Spending Scores in Retail
This study primarily aims to estimate consumer spending trends in a retail context. The goal is to identify the best model for predicting Purchasing Scores, which indicate customer loyalty and potential income, using demographic and financial data. The dataset included information about customers' age, gender, and annual income, and the objective was to analyze their Spending Scores. Several regression models were tested, including Linear Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors (KNN), and Lasso Regression. To improve the models, we engineered features like Age Squared, Income per Age, and Spending Score per Income. Each model was trained and tested using 3fold cross-validation. We evaluated their performance with Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. The results showed significant differences in model performance. The Random Forest model stood out, with the lowest Mean Absolute Error (MAE) of 0.33, Root Mean Square Error (RMSE) of 0.52, and the highest R-squared (R22) score of 0.9997. Gradient Boosting also performed well, achieving a Mean Absolute Error (MAE) of 1.77, Root Mean Square Error (RMSE) of 2.41, and an Rsquared (R2) score of 0.9930. While Linear Regression showed moderate accuracy, KNN and Lasso Regression had higher errors and lower R2 values, indicating less reliable predictions. The findings suggest that ensemble methods, particularly Random Forest, excel at predicting customer Spending Scores. The high accuracy and reliability of this model point to its potential for customer segmentation and targeted marketing strategies, ultimately enhancing customer relationship management and boosting business value. Further refinement and exploration of additional features could further improve these prediction capabilities. 2024 IEEE. -
Fintech implications on ESG practices: Evidence from the Indian banking industry
This chapter aims to provide evidence-based insights into how Fintech is influencing ESG practices of Indian banks. The discussions focus on key areas such as Green fintech, Governance and risk management approach, financing Agri business, promoting carbon net zero mission, and also the fintech intervention towards financial inclusion. The chapter also exhibits the role played by public sector banks and private sector banks in promoting ESG practices. Methodology used is secondary data based on case studies, industry reports and empirical data sources. Insights from higher officials of the banking sector are also included to examine the challenges faced by the banking sector in this regard. Based on findings, the chapter provides a suggestive model on the potentialities of fintech to support ESG agenda which can be an insight for policy makers, financial institutions and other stakeholders. 2025, IGI Global Scientific Publishing. All rights reserved. -
Imposter detection with canvas and WebGL using Machine learning.
Authentication offers a way to confirm the legitimacy of a user attempting to access any protected information that is hosted on the web as organizations are moving their applications online. It has long been believed that IP addresses and Cookies are the most reliable digital fingerprints used to authenticate and track people online. But after a while, things got out of hand when modern web technologies allowed interested organizations to use new ways to identify and track users. There are many new reliable digital fingerprints that can be used such as canvas and WebGL. The canvas and WebGL render the image which is dependent on the software and hardware of the system. In our work with the generated hash value value from canvas and WebGL we create a model using KNN to identify the imposters. The model has proved to be accurate in authentication of user with an accuracy of 89%. 2023 IEEE. -
Employee Challenges and its Solutions in Virtual Information Technology Industry
This study aimed at identifying the challenges faced by the employee working in virtual environment, to further propose a conceptual model and to explore the enabling factors required to provide sustainable solutions to these challenges. An organizations precursors are a must to mitigate the identified challenges by adopting the suggested solutions. In this era of IT and ICT, it is inevitable to understand what are those challenges, issues or problems employee of a virtual team faces and how do they resolve them or behave in that particular scenario. Radically changing work environment impacts the workforce productivity. In this ICT environment, it is unavoidable to expect challenges emerged out of such working conditions. Further, to study challenges becomes crucial for a better work environment. The qualitative grounded theory method approach has been used to identify challenges of 20 cases through in-depth interview techniques. The interviews have been then transcribed, coded and categorized. The conceptual model is the final outcome of this research work that depicts the challenges, the precursors ?? a company must have and last but not the least the recommended solutions to mitigate challenges. Keywords ?? IT (Information Technology), ICT (Information and Communication Technology), Challenges (A challenge is a general term referring to things that are imbued with a sense of difficulty and victory). -
Era of Education 5.0: Disruptive Technologies in a Learner- Cantered Educational Landscape
The chapter focuses on concepts of Education 5.0 and its competence in shaping future learning environments. It emphasises on learners social and personal growth by improving quality of life standards with the help of current technologies and digitalisation. (Shabir Ahmad, 2023) To deliver humanised approach by the application of new technologies is the primary use of Education 5.0. However, usage of new age technology in education doesnt mean giving laptop and tablet to each and every child and the usage of digital mediums for teaching and learning. After covid- 19, digitisation becomes the integral part of our life, education is no exception for that. (Shabir Ahmad, 2023) Beyond digitalisation pandemic also remained us the importance of human hardships to social transformation with emotional intelligence driving technology as a tool. In short education 5.0. (SYDLE.com, 2023) referring to the significance of human, social and emotional abilities to enhance wellbeing of an individual by using technology advancement as a tool. 2025 by IGI Global Scientific Publishing. -
A learner-cantered educational landscape: Era of education 5.0 and disruptive technologies
The chapter focuses on concepts of Education 5.0 and its competence in shaping future learning environments. It emphasises on learners social and personal growth by improving quality of life standards with the help of current technologies and digitalisation. (Shabir Ahmad, 2023) To deliver humanised approach by the application of new technologies is the primary use of Education 5.0. However, usage of new age technology in education doesn't mean giving laptop and tablet to each and every child and the usage of digital mediums for teaching and learning. After covid-19, digitisation becomes the integral part of our life, education is no exception for that. (Shabir Ahmad, 2023) Beyond digitalisation pandemic also remained us the importance of human hardships to social transformation with emotional intelligence driving technology as a tool. In short education 5.0. (SYDLE. com, 2023) referring to the significance of human, social and emotional abilities to enhance wellbeing of an individual by using technology advancement as a tool. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Protecting Medical Research Data Using Next Gen Steganography Approach
In this paper our main aim is to protect medical research information, when data either images or information shared via internet or stored on hard drive 3rd person cant access without authentication. As needs be, there has been an expanded enthusiasm for ongoing years to upgrade the secrecy of patients data. For this we combined different techniques to provide more security. Our approach is a combination of cryptography, Steganography & digital watermarking we named this technique as Next Gen. We used cryptography for encrypting the patients information even if they find image is stegonized and digital watermarking for authenticity and for Steganography we used most popular least significant bit algorithm (LSB). The experimental outcomes with various inputs show that the proposed technique gives a decent tradeoff between security, implanting limit and visual nature of the stego pictures. 2020, Springer Nature Switzerland AG. -
Research asset creation (RAC) model for PhD awarding universities in India
Importance of higher education and research is becoming more prominent and admission for PhD is increasing year by year in India. Most of the time Research done as part of PhD ends with the submission and acceptance of thesis in Universities. This research future work or extension work might be picked by other PhD candidate in the same or from the different Universities but not sure when it will happen. Also not sure the research completed has achieved its end objective and University is fine to stop that research after spending so much of time and resources of University. This paper insists on continuous research with PhD candidates every year in Universities until the completion of research and scope for reducing the waste of resources and time. This paper considers the importance and effectiveness of Indexing parameters, Indexing agencies, review methods, Journals and relevant concepts. Considering the usefulness and facts of the same to research community building a standard process with procedures to control quality, performance and original research in place, I have built a Research Asset Creation (RAC) model for universities offering PhDs in India by making use of University level Indexing, Indexing parameters, Inter and Intra University peer review methods and University Journal as magazine and as well as Indexing agency. Research cannot be considered just for the award of degree. There is a need for making use of research, resources of University and time spent on research till the objective of the research is accomplished. This can be accomplished by adopting Research Asset Creation model. Complete details of the model, components of the model, implementation, its benefits and usage is discussed in this paper. This is a generalized Model and fit for all Countries which are looking for effective use of research and resources in a progressive manner. BEIESP. -
Predicting Stock Market Price Movement Using Machine Learning Technique: Evidence from India
The stock market is uncertain, volatile, and multidimensional. Stock prices have been difficult to predict since they are influenced by a variety of factors. In order to make critical investment and financial decisions, investors and analysts are interested in predicting stock prices. Predicting a stock's price entails developing price pathways that a stock might take in the future. ANN and mathematical Geometric Brownian movement technique were employed in this study to forecast a stock market closing price of Indian companies. The comparative analysis indicates that the Geometric Brownian Method is better than ANN in giving better MAPE and RMSE Values. 2022 IEEE. -
A Bibliometric Analysis of Industry 4.0 and Health-Care Services
A key moment in health care is marked by the Fourth Industrial Revolution, commonly referred to as Industry 4.0. This transformation, driven by the convergence of digital technologies with automation and data driving processes, has led to a paradigm shift in how health care is provided. The integration of the emerging technologies in Industry 4.0, such as Internet of Things, Artificial Intelligence, Big Data Analytics and Advanced Robots, are revolutionizing patient care, improving resource allocation and shaping research's landscape. To learn more about the ever-evolving relationship between Industry 4.0 and health care, this research paper begins with a bibliographic analysis. In this interdisciplinary convergence, our bibliometric analyses serve as a lens through which we can see the key trends, research areas and influential players. The review of literature highlights the profound impact of Industry 4.0 on health care, revealing that Internet of Things technologies for real-time patient tracking, proliferation of artificial intelligence in medical diagnosis and transforming power of big data Analytics are changing health care decision making. Methodologically, we leverage bibliometrics as a quantitative analytical tool, drawing on citation counts, bibliographic coupling, and keyword co-occurrence analysis. The data for this analysis, which covered the period 20152023, was carefully collected from Scopus database. The analysis of the information reveals that, particularly from 2018 onwards, there has been a significant increase in publications concerning Industry 4.0 and health care. In this research landscape India has emerged as a strong contributor, with countries such as the United States and Italy making significant progress. Publication trends and bibliographic coupling among countries and sources shed light on collaborative networks and research focus. The emergence of machine learning, artificial intelligence and data analysis as important themes is illustrated by a co-occurrence analysis of keywords that elucidates evolving research interests. In the complicated terrain of health care converging with Industry 4.0, this research paper serves as a compass. The report highlights this convergence's transformative potential, highlighting the pivotal role that bibliometrics analysis must play in determining future research areas in adopting Industry 4.0 in the health-care sector. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
AI-Based Real-Time Class Engagement Emotions Monitoring System
This chapter introduces an AI-based system for real-time monitoring of student engagement by analyzing emotional responses and attention levels during classroom sessions. Using a camera placed in front of students, the system applies computer vision algorithms to detect focus and distraction through eye movements and facial expressions. The core of the system is the Emo-Engage Analysis framework, which classifies engagement into three levels based on eye retention, duration of attentiveness, and indicators of positive emotional response. Drawing on recent research in emotional and attentional regulation, this method offers a fine-grained analysis of student engagement. Aggregated engagement data allow instructors to assess both individual and group dynamics throughout a lesson. These insights support the evaluation of teaching strategies and provide meaningful feedback on instructional impact, helping educators adapt their methods to foster more effective and emotionally supportive learning environments. 2026, IGI Global Scientific Publishing. All rights reserved. -
A Bibliometric Analysis of Industry 4.0 and Health-Care Services
A key moment in health care is marked by the Fourth Industrial Revolution, commonly referred to as Industry 4.0. This transformation, driven by the convergence of digital technologies with automation and data driving processes, has led to a paradigm shift in how health care is provided. The integration of the emerging technologies in Industry 4.0, such as Internet of Things, Artificial Intelligence, Big Data Analytics and Advanced Robots, are revolutionizing patient care, improving resource allocation and shaping research's landscape. To learn more about the ever-evolving relationship between Industry 4.0 and health care, this research paper begins with a bibliographic analysis. In this interdisciplinary convergence, our bibliometric analyses serve as a lens through which we can see the key trends, research areas and influential players. The review of literature highlights the profound impact of Industry 4.0 on health care, revealing that Internet of Things technologies for real-time patient tracking, proliferation of artificial intelligence in medical diagnosis and transforming power of big data Analytics are changing health care decision making. Methodologically, we leverage bibliometrics as a quantitative analytical tool, drawing on citation counts, bibliographic coupling, and keyword co-occurrence analysis. The data for this analysis, which covered the period 20152023, was carefully collected from Scopus database. The analysis of the information reveals that, particularly from 2018 onwards, there has been a significant increase in publications concerning Industry 4.0 and health care. In this research landscape India has emerged as a strong contributor, with countries such as the United States and Italy making significant progress. Publication trends and bibliographic coupling among countries and sources shed light on collaborative networks and research focus. The emergence of machine learning, artificial intelligence and data analysis as important themes is illustrated by a co-occurrence analysis of keywords that elucidates evolving research interests. In the complicated terrain of health care converging with Industry 4.0, this research paper serves as a compass. The report highlights this convergence's transformative potential, highlighting the pivotal role that bibliometrics analysis must play in determining future research areas in adopting Industry 4.0 in the health-care sector. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Polyphenol composition and antioxidant activity of andrographis paniculata L. nees /
Mapana Journal of Sciences, Vol.13, Issue 4, pp.481-494, ISSN No: 0975-3303 (Print) -
Bacteriocins as Biotechnological Tools in Food and Pharmaceuticals: Applications and Future Prospects
The World Health Organization (WHO) and FAO have defined probiotics as non- pathogenic living organisms that greatly benefit host cells and have several positive outcomes at the level of gut. The intake of probiotics at an adequate amount confers good health and many times is used for several treatments (Hill et al. 2014; Gibson et al. 2017). Not only the microorganisms as a whole, but the proteins or peptides secreted by these species have tremendous applications in food spoilage, pharmaceuticals, antibiotic development, and much more. Thus, antimicrobial peptides from bacteria have drawn more attention for their wide range of applications. 2023 selection and editorial matter, Arti Gupta and Ram Prasad; individual chapters, the contributors. -
Plant- based Metabolites as Source of Antimicrobial Therapeutics: Prospects and Challenges
Plants are used as traditional medicines from ancient times to today as they are the largest living storehouses of bio- chemicals and pharmaceuticals known on Earth (Abdallah, 2011). The World Checklist of Vascular Plants (WCVP) database reported in April 2021 that there are 1,383,297 plant names with 996,093 plants identified at species level, constituting 342,953 accepted vascular plant species (Govaerts et al., 2021). Around 10% of the reported vascular plants are used as medicines (Salmer- Manzano et al., 2020). According to the MPNS, 33,443 species are recorded as being used for medicinal purpose (MNPS, 2021). Medicinal plants are those that have therapeutic properties which can pose pharmacological effect on the human or animal body (Namdeo, 2018). About 80% of the world's population depends on plant- based medicine for treatment of diseases (Okoye et al., 2014). The medicinal property of a plant is attributed to rich and diverse secondary metabolites (Allemailem, 2021). Secondary metabolites are intermediates or products of primary metabolism that are not involves directly in the growth and development of the plant (Jain et al., 2019). Plants generate secondary metabolites in response to stresses posed by biotic factors (bacteria, fungi, viruses, parasites, pests, weeds, and herbivore animals) and abiotic environmental factors (temperature, salinity, drought, UV radiation etc.) so as to adapt and survive in response to environmental stimuli during their life time (Yang et al., 2018). 2023 selection and editorial matter, Arti Gupta and Ram Prasad; individual chapters, the contributors. -
Experimental Investigation on Density and Volume Fraction of Void, and Mechanical Characteristics of Areca Nut Leaf Sheath Fiber-Reinforced Polymer Composites
Natural fiber-reinforced polymer composite is a rapidly growing topic of research due to the simplicity of obtaining composites that is biodegradable and environmentally friendly. The resulting composites have mechanical properties comparable to synthetic fiber-reinforced composites. In this regard, the present work is formulated with the objectives related to the development, characterization, and optimization of the wt% of reinforcements and the process parameters. The novelty of this work is related to the identification and standardization of the appropriate wt% of reinforcements and parameters for the processing of the areca nut leaf sheath fiber-based polymer composites for enhanced performance attributes. With this basic purview and scope, the composites are synthesized using the hand layup process, and the composite samples of various fiber compositions (20%, 30%, 40%, and 50%) are fabricated. The mechanical characteristics of biodegradable polymer composites reinforced with areca nut leaf sheath fibers are investigated in the present work, with a focus on the effect of fiber composition (tensile properties, flexural strength, and impact strength). The properties of composites are enhanced by combining the areca nut leaf sheath fiber and epoxy resin, with a fiber content of 50% being the optimal wt%. The Scanning electron microscopy (SEM) investigations also ascertain this by depicting the good interfacial adhesion between the areca nut leaf sheath fiber and the epoxy resin. The tensile strength of the composite specimen reinforced with 50% areca nut fiber increases to 44.6 MPa, while the young's modulus increases to 1900 MPa, flexural strength increases to 64.8 MPa, the flexural modulus increases to 37.9 GPa, and impact strength increases to 34.1 k J/m2. As a result, the combination of areca nut leaf sheath fiber reinforced epoxy resin shows considerable potential as a renewable and biodegradable polymer composite. Furthermore, areca nut leaf sheath fiber-reinforced epoxy resin composites are likely to replace petroleum-based polymers in the future. The ecosustainability and biodegradability of the composite specimen alongside the improved mechanical characteristics serve as the major highlight of the present work, and can help the polymer composite industry to further augment the synthetic matrix and fiber-based composites with the natural fiber-reinforced composites. 2022 B. A. Praveena et al. -
Balancing module in evolutionary optimization and Deep Reinforcement Learning for multi-path selection in Software Defined Networks
Software Defined Network (SDN) has been used in many organizations due to its efficiency in transmission. Machine learning techniques have been applied in SDN to improve its efficiency in resource scheduling. The existing models in SDN have limitations of overfitting, local optima trap and lower efficiency in path selection. This study applied Balancing Module (BM)-Spider Monkey Optimization (SMO)-Crow Search Algorithm (CSA) for multi path selection in SDN to improve its efficiency. The balancing module applies Gaussian distribution to balance between exploration and exploitation in the multi-path selection process. The Balancing module helps to escape local optima trap and increases the convergence rate. Deep Reinforcement learning is applied for resource scheduling in SDN. The Deep reinforcement learning technique uses the reward function to improve the learning performance, and the BM-SMO-CSA technique has 30 J energy consumption, where the existing models: DRL has 40 J energy consumption, and Graph-ACO has 62 J energy consumption. 2022 -
CNN-based Indian medicinal leaf type identification and medical use recommendation
Medicinal leaves are playing a vital role in our everyday life. There are an enormous amount of species present in the world. Identification of each type would be a tedious task. Using image processing technology, we can overcome this problem by providing computer vision with the help of a convolution neural network (CNN). The objective of this research is to find out the best CNN model that helps in classifying the plant leaf species and identifying its category. In this research work, the proposed basic CNN model consisting of four convolution layers uses ten different medicinal leaf species each belonging to two categories providing an accuracy of 96.88%. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.