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Rain of Life, Rain of Music: Music as Life Power in Indian Thought and Contemporary Musical Traditions
Conceived as a life force, rain has a significant place in Indian thought. Sanskrit and vernacular literary and religious texts, as well as visual arts, emphasise its auspiciousness and importance in human life. Additionally, through the use of poetical images and metaphors, these texts and images associate rain with music and identify thunder with drums. Through the analysis of compositions from the repertoire of different drums such as the dhrupad pakh?vaj, the mi??vu of K?tiy???a? Sanskrit theatre, and the ritual music of Brahmanical temples of Kerala, this article studies the association of drumming with rain as a symbol of life force, consciousness and enlightenment. 2022 South Asian Studies Association of Australia. -
A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing
Congenital heart disease (CHD) represents a multifaceted medical condition that requires early detection and diagnosis for effective management, given its diverse presentations and subtle symptoms that manifest from birth. This research article introduces a groundbreaking healthcare application, the Machine Learning-based Congenital Heart Disease Prediction Method (ML-CHDPM), tailored to address these challenges and expedite the timely identification and classification of CHD in pregnant women. The ML-CHDPM model leverages state-of-the-art machine learning techniques to categorize CHD cases, taking into account pertinent clinical and demographic factors. Trained on a comprehensive dataset, the model captures intricate patterns and relationships, resulting in precise predictions and classifications. The evaluation of the models performance encompasses sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Remarkably, the findings underscore the ML-CHDPMs superiority across six pivotal metrics: accuracy, precision, recall, specificity, false positive rate (FPR), and false negative rate (FNR). The method achieves an average accuracy rate of 94.28%, precision of 87.54%, recall rate of 96.25%, specificity rate of 91.74%, FPR of 8.26%, and FNR of 3.75%. These outcomes distinctly demonstrate the ML-CHDPMs effectiveness in reliably predicting and classifying CHD cases. This research marks a significant stride toward early detection and diagnosis, harnessing advanced machine learning techniques within the realm of ECG signal processing, specifically tailored to pregnant women. 2024 by the authors. -
Assessing the role of trade openness, FDI, and political stability on sustainable development: Evidence from developed and developing economies
The study tries to investigate the long run and short run relationship between trade openness (TO), political stability (PO), and FDI on sustainable development of select developed and developing nations. Time series data from 1995 to 2021 of about 25 economies-10 developed economies and 15 developing economies-was collected and analyzed using Phillips Perron Fisher panel unit root test, panel auto regressive distributed lag (PARDL) model, and panel fully modified least squares/fully modified OLS. From the result, it found that FDI and TO are positively contributing to sustainability development index (SDI) in developing countries rather than the developed countries in the long run. In addition to this, changes in the SDI score is significantly influenced by the present and past import and export activities in developed as well as developing economies in the short run. 2023, IGI Global. All rights reserved. -
Exploring the Influence Dynamism of Economic Factors on Fluctuation of Exchange Rate-An Empirical Investigation for India Using ARDL Model
The Indian Foreign Exchange Market has experienced significant changes over the past decade, due to high degree of instability of the Indian Rupee leading to its devaluation against major global currencies. Exchange rate is considered as one of crucial indicators to determine the economic growth. Volatility of exchange rate of each day is influenced by various factors such as demand and supply, Gross Domestic Product, Interest rate, employment rate, public debt, balance of payments, inflation etc. Though there are multiple causes to determine the movement of exchange rate, but still the accurate level of causation is unpredictable. Keeping this in mind, this paper tries to attempt the relationship that exists between the exchange rate and select macroeconomic factors. To analyse the extent of influence of the selected variables on the exchange rate, the research paper uses 10 years of data spanning from Jan 2013 to Nov 2022. Further, the study uses monthly data of above-mentioned variables to bring out the analysis to meet the objectives. Descriptive statistics is used to find the characteristics of the data, correlation analysis and Ordinary Least Square method is used to find the relationship and impact level select macroeconomic factors on exchange rate. Autoregressive Distributed Lag (ARDL) model is used to find if any short run and long run association exists between the variables and the exchange rate. 2023, ASERS Publishing House. All rights reserved. -
Measurement Model of CO-PO Attainment in Higher Education: A Simplified Approach
The educational system in most countries are moving toward Outcome-Based Education (OBE) which is a student-centric teaching and learning methodology. The basic idea behind the adoption of OBE model is that the graduates should possess a sound knowledge in their respective disciplines and also have global mobility and acceptance. The Outcome-Based Education (OBE) should be based on the vision and mission of the institution. The institutions should clearly spell out the learning objectives of the program and course. The Course Outcome (CO), Program Outcome (PO), Program Specific Outcome (PSO) and Program Educational Objectives (PEO) determine clearly what the students are expected to accomplish, post their course or program respectively. This study aims to provide the simplified approach on assessment, evaluation and calculating the attainment levels of students through COs and POs in a management program. To assess the CO attainment for management courses, the authors have identified the subject Entrepreneurship Development offered in the first semester from the 2018-2020 batch of 60 students from the MBA program of an autonomous institute. The Course Outcome (CO) and Program Outcome (PO) are mapped with the Continuous Internal Assessments (CIA) and Semester Exam End (SEE) and thus the attainment levels of each CO are measured. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Attention to Economic Factors and Its Response to Foreign Portfolio Investment: An Evidence from Indian Capital Market
Stock market consists of a variety of investors. Among these, Foreign Portfolio Investors (FPIs) is a key investment influx. These investments can change or fluctuate due to several macroeconomic factors which can cause a shift in the dynamics of the markets in India. This paper examines the factors influencing for foreign portfolio investment in long run as well as short run. The sample comprises of 120 monthly observations on Foreign Portfolio Investment (FPIs) and Macro economic variables such as Oil prices (OP), Gross Domestic Product (GDP), Interest Rate (IR), Exchange rate of Indian Rupee with USD (ER), Inflation (CPI), Nifty Index (NSEI), 10year Bond Prices (BP) and Index of Industrial production (IIP) over a period of 10years, spanning from January 2013 to November 2022. The study employed Autoregressive Distributed Lag model (ARDL) to establish the long run association with error correction models. The result indicates that there is long run association between the Foreign Portfolio Investment and macro-economic variables. Among this, NSEI, IIP and ER played a significant role to determine FPI investments in the long run, whereas in the short run, FPI was impacted by ER and NSEI significantly. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Autoregressive Distributed Lag Approach for Estimating the Nexus between Net Asset Value of Mutual Fund and Economic Determinants in India
India has seen a phenomenal growth in cumulative mutual fund investment from Rs 7.93 trillion in 2012 to Rs 40.38 trillion in 2022, which is more than a five-fold increase since last 10 years. Retail investors are now realizing the power of savings and Systematic Investment Plans (SIP) to build long term wealth. A financial literacy wave which is sweeping across India has projected mutual funds as a significant contributor and beneficiary of this phenomenon. The evolving economic landscape of India provides investors with excellent opportunities to capitalize on these fluctuations through systematic investment in safe investment vehicles like mutual funds. The market associated with mutual funds is always subjected to economic risks. The erratic fluctuations in macroeconomic variables can largely explain the Volatility in Net Asset Value (NAV) of equity oriented mutual fund schemes. With this background, this paper examines the impact of select macroeconomic variables on mutual funds performance in India. To analyse this, monthly observations of select macroeconomic variables, average NAV of large cap, mid cap, and small cap funds collected for a period of 10 years starting from January 2013 to November 2022. Descriptive statistics is used to probe the characteristics of the variable. In addition, correlation and ordinary least square method is applied to check the existing relationship and impact level of macroeconomic factors on NAV of select schemes. Lastly, short and long run relationship is analysed using Autoregressive Distributed Lag Model (ARDL). 2023, ASERS Publishing House. All rights reserved. -
Driving profitable business growth through economical optimization, energy management, and industrial 5.0 innovations
The chapter emphasizes the significance of economic optimization, energy efficiency, and Industrial 5.0 innovations in driving sustainable growth and profitability in today's business landscape. It highlights the strategic allocation of resources to maximize efficiency and minimize costs, using lean management principles, automation, and data analytics. Energy management is crucial for reducing operational costs and mitigating environmental impact, using renewable energy sources and smart technologies. Industrial 5.0, a new era of industrial transformation, combines automation, connectivity, and data exchange, with technologies like artificial intelligence, IoT, and blockchain. 2024, IGI Global. -
Scripts influence on reading processes and cognition: a preamble
[No abstract available] -
Editorial: Methods and applications in cognitive science
[No abstract available] -
A Multifaceted Approach at Discerning Redditors Feelings Towards ChatGPT
Generative AI platforms like ChatGPT have leapfrogged in terms of technological advancements. Traditional methods of scrutiny are not enough for assessing their technological efficacy. Understanding public sentiment and feelings towards ChatGPT is crucial for pre-empting the technologys longevity and impact while also providing a silhouette of human psychology. Social media platforms have seen tremendous growth in recent years, resulting in a surge of user-generated content. Among these platforms, Reddit stands out as a forum for users to engage in discussions on various topics, including Generative Artificial Intelligence (GAI) and chatbots. Traditional pedagogy for social media sentiment analysis and opinion mining are time consuming and resource heavy, while lacking representation. This paper provides a novice multifrontal approach that utilises and integrates various techniques for better results. The data collection and preparation are done through the Reddit API in tandem with multi-stage weighted and stratified sampling. NLP (Natural Language processing) techniques encompassing LDA (Latent Dirichlet Allocation), Topic modelling, STM (Structured Topic Modelling), sentiment analysis and emotional analysis using RoBERTa are deployed for opinion mining. To verify, substantiate and scrutinise all variables in the dataset, multiple hypothesises are tested using ANOVA, T-tests, KruskalWallis test, Chi-Square Test and MannWhitney U test. The study provides a novel contribution to the growing literature on social media sentiment analysis and has significant new implications for discerning user experience and engagement with AI chatbots like ChatGPT. 2024 Padarha et al., licensed to EAI. -
Leveraging ensemble learning for enhanced security in credit card transaction fraudulent within smart cities for cybersecurity challenges
In the age of digital transactions, credit cards have emerged as a prevalent form of payment in smart cities. However, the surge in online transactions has heightened the challenge of accurately discerning legitimate from fraudulent activities. This paper addresses this crucial concern by introducing a pioneering system for detecting fraudulent credit card transactions, particularly within highly imbalanced datasets, in the realm of cybersecurity. This paper proposes a hybrid model to effectively manage imbalanced data and enhance the detection of fraudulent transactions. This paper emphasizes the efficacy of the hybrid approach in proficiently identifying and mitigating fraudulent activities within highly imbalanced datasets, thereby contributing to the reduction of financial losses for both merchants and customers in smart cities. As cybersecurity in smart cities evolves, this paper underscores the significance of ensemble learning and cross-validation techniques in optimizing credit card transaction analysis and fortifying the security of digital payment systems. 2024, Taru Publications. All rights reserved. -
Legal conundrums of space tourism
Private commercial space tourism carrying passengers to outer space is no longer a distant or far-fetched fantasy, rather it is at verge of becoming an affordable reality with exponential development in space technology including development of Reusable Launch Vehicle (RLV), increasing involvement of private companies like Virgin Galactic, SpaceX, Blue Origin etc. into research and funding of space tourism explorations and applications. It is also receiving huge attention from the public. These developments reflect the infinite possibilities and inevitability of space tourism in near future. However, space tourism may also pose many critical legal issues which must be addressed to ensure the consistent and sustainable development of space tourism, and to secure the rights of all stakeholders involved including operators, passengers, launching State etc. The research paper would highlight the crucial legal issues associated with the space tourism. The paper would critically analyze the efficiency of the present international space treaties in dealing with these issues. At the end, the paper would also attempt to provide few suggestions and solutions to these legal conundrums relating to space tourism. 2021 IAA -
Humour as a Moderator Between Hassles and Well-Being
Humour is a universal phenomenon that offers several physiological and psychological benefits across cultures. The objectives of this study were to examine the relationships between daily hassles, humour and well-being; and to investigate the moderating effect of humour on the relationship between hassles and well-being. A correlational design was adopted to collect data from 644 participants (men = 300, women = 344), aged between 18 and 58years using purposive and snowballing sampling techniques. The Daily Hassles Scale, Sense of Humour Questionnaire (SHQ-R) and the Personal Well-Being IndexAdult (PWI-A) were administered to the sample. The self-report measures were appropriately scored and the collective data were analyzed. Statistical analyses revealed a positive relationship between sense of humour and well-being. A negative relationship was observed between sense of humour and hassles; and between well-being and hassles. Further, sense of humour was found to be moderating the relationship between daily hassles and well-being. This study highlights the role of humour in softening the impact of hassles on the well-being of the Indian population. This strengthens the construct of humour in the context of positive psychology. The Author(s) under exclusive licence to National Academy of Psychology (NAOP) India 2024. -
A hybrid crypto-compression model for secure brain mri image transmission
Medical image encryption is a major issue in healthcare applications where memory, energy, and computational resources are constrained. The modern technological architecture of digital healthcare systems is, in fact, insufficient to handle both the current and future requirements for data. Security has been raised to the highest priority. By meeting these conditions, the hybrid crypto-compression technique introduced in this study can be used for securing the transfer of healthcare images. The approach consists of two components. In order to construct a cutting-edge generative lossy compression system, we first combine generative adversarial networks (GANs) with oearned compression. As a result, the second phase might address this problem by using highly effective picture cryptography techniques. A randomly generated public key is subjected to the DNA technique. In this application, pseudo-random bits are produced by using a logistic chaotic map algorithm. During the substitution process, an additional layer of security is provided to boost the techniques fault resilience. Our proposed system and security investigations show that the method provides trustworthy and long-lasting encryption and several multidimensional aspects that have been discovered in various public health and healthcare issues. As a result, the recommended hybrid crypto-compression technique may significantly reduce a photos size and remain safe enough to be used for medical image encryption. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Optical design studies for national large optical-IR telescope
A 1012 m class national large optical-IR telescope (NLOT) is envisaged to meet the growing scientific requirements in astronomy and astrophysics. Telescopes of such dimensions can only be made by segmenting the primary mirror, as it eases a more prominent primary mirrors fabrication, transportation, operation, and maintenance process. This paper presents the various optical designs analyzed for NLOT that can be fabricated using the India TMT Optics Fabrication Facility (ITOFF) at the Centre for Research and Education in Science and Technology (CREST) campus. We present the primary mirror segmentation details, its ideal optical performance, and study each designs advantages and technical complexities. Based on the above analysis, we have narrowed it down to an optimal design, and its performance analysis is also discussed. Indian Academy of Sciences 2024. -
The role of religious and financial factors in eudaimonic well-being among Indian adults
Purpose: The available literature has explored the various psychosocial determinants of well-being to some extent. The earlier works have focused primarily on hedonic well-being with little focus on eudaimonic aspects. Therefore, this study aims to understand the role of parentchild religious attendance during childhood, religious connectedness, and financial-material stability on eudaimonic well-being among adults in India. Design/methodology/approach: The authors used the India data from Global Flourishing Study Wave 1 that addressed flourishing among adults above 18 years. The authors considered 9,076 Indian adults and used descriptive and correlation statistics. In addition, the authors conducted path analysis and t-test. Findings: The likelihood of eudaimonic well-being increased with parentchild religious attendance during childhood (= ?0.044, p < 0.01) along with religious connectedness (= ?0.112, p < 0.01) and financial-material stability (= 0.145, p < 0.01) as an adult. In addition, a significant difference existed in terms of religious connectedness and eudaimonic well-being with income and perceived feelings about income. Originality/value: This study emphasizes financial stabilitys relevance in well-being and suggests the importance of considering religious factors during childhood and adulthood. Emphasizing factors influencing eudaimonic well-being is relevant due to its influence on mental health and quality of life. 2024, Emerald Publishing Limited. -
A Model for Detecting Type 2 Diabetes Using Mixed Single-Cell RNA Sequencing with Optimized Data
Diabetes is a critical disease and is crucial to personage agility. Type 2 Diabetes (T2D) accounts for 92% of epithetical cases. This paper proposes an optimized type 2 diabetes detection model using mixed single-cell RNA sequencing (scRNA-seq) technology. Diabetes is a chronic metabolic disorder affecting millions of people worldwide. Early detection of the disease can greatly improve treatment outcomes, but current diagnostic methods have limitations. Our proposed model integrates scRNA-seq data from both human pancreatic beta cells to identify gene expression patterns associated with diabetes. Our study shows that the proposed model is highly accurate in identifying diabetes, achieving an area under the curve (AUC) of 0.98. We employed an optimized model to improve the detection of diabetes at an early stage, leading to better treatment outcomes and an improved quality of life for patients. We initially incorporated optimal features from the dataset using the Monte Carlo (MC) feature selection method. This method helped us to estimate the relative importance (RI) score of each gene or feature, which is then used to rank the features. Further, we proposed an optimized deep belief network (ODBN) as a classification model to classify T2D and non-diabetes. To improve the performance of ODBN, an adaptive chimp optimization algorithm (AChOA) is introduced to optimize the weight parameters and achieved a performance accuracy of 96.57%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
An Investigation of Complex Interactions Between Genetically Determined Protein Expression and the Metabolic Phenotype of Human Islet Cells Using Deep Learning
The relationship between gene modules and several genome-scale metrics was examined, including heterozygosity that caused type 2 diabetes due to insulin deuteration, differential expression, genotyping association, methylation, and copy number changes. This work investigates the complex relationships between protein expression, genetic polymorphisms, and metabolic properties of human islet cells using expression quantitative trait loci (eQTL) detection. We looked at the genomic, transcriptomic, and proteomic information from islet cells in persons with type 2 diabetes. From the information from different levels, we noticed novel eQTLs that regulate crucial metabolic and signaling pathways in islet cells. Our study highlights the importance of a systems-level approach in understanding the complicated biological processes by highlighting the complexity of the link between genetic variants, protein expression, and metabolic abnormalities using the PIMA Indian dataset. Our findings provide novel insights into the molecular mechanisms behind islet cell failure in type 2 diabetes, potential targets for emerging treatment strategies, and the genomic implications of variations in gene expression, mutations, and other factors. To accomplish this purpose, we proposed a novel BLB model and obtained 99.89%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
An Human Islet Cell RNA-Seq for Genome-Wide Genotype Deepsec Framework Using Deep Learning Based Diabetes Prediction
Evaluating the tissues responsible for complicated human illnesses is important to rank significance of genetic revision connected to features. In order to make predictions about the regulatory functions of geneticsvariations athwart wide range of epigenetic changes, this article introduces a Convolutional neural network (CNN) model upgraded filters and Deepsec framework incorporated with comprehensive ENCODE and Roadmap consortia have compiled a human epigenetic map that indicates specificity to certain tissues or cell types. Deepsec framework integrates transcription factors, histone modification markers, and RNA accessibility maps to comprehensively evaluate the consequences of non-coding alterations on the most important components, even for uncommon variations or novel mutations. By using trait-associated loci and more than 30 different human pancreatic islets and their subsets of cells sorted using fluorescence-activated cell sorting, annotations of epigenetic profiling were obtained (FACS) on a genome-wide scale. The proposed model, used '1492' publicly available GWAS datasets. My team presented that deepsec framework does epigenetic annotations found important GWAS associations and uncover regulatory loci from background signals when exposed to CNN-based analysis, offering fresh intuition underlying nadir causes of type 2diabetes. The suggested approaches are anticipated to be extensively used in downstream GWAS analysis, making it possible to assess non-coding variations and conduct downstream GWAS analysis 2023 IEEE.