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Integration of sustainability in business through finance
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Integration of technology initiatives with educational neuroscience and its impact on technology readiness to technology adoption by HSS Teachers, Kerala
The technology-enabled education process remoulded the modern education systems. The facelift of education 4.0 process harmonized the education systems with industrial demands and technology advancements. The education reforms of the State of Kerala with the tools of technology and neuroscience could achieve remarkable milestones in the education sector. This case study analyses the digital initiatives of KITE and its role on providing uninterrupted-effective education during the Covid-19 pandemic in Kerala. This study is affirmed with quantitative study on how these integrated technology initiatives impact on Technology Adoption of the HSS teachers with respect to their Technology Readiness. Responses of 857 teachers from six education districts of Kerala were used for this study. This study is relevant as it could connect the pre-Covid digital initiatives which could successfully empower the teachers to face the Covid-19 pandemic situation without interrupting the education process amidst the Covid-19 restrictions in Kerala. The study identified that the technology learning initiatives with tools of educational neurosciences have partially mediated teachers' Technology Readiness to Technology Adoption. The multiple learning initiatives integrated with the tools of technology and educational neuroscience could fully support the virtual learning throughout the State of Kerala during the Covid-19 pandemic situations. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Integrity assured multi-functional multi-application secure data aggregation in wireless sensor networks (IAMFMA-SDA)
Industrial revolutions and demand of novel applications drive the development of sensors which offer continuous monitoring of remote hostile areas by collecting accurate measurement of physical phenomena. Data aggregation is considered as one of the significant energy-saving mechanism of resource constraint Wireless Sensor Networks (WSNs) which reduces bandwidth consumption by eliminating redundant data. Novel applications demand WSN to provide information about the monitoring region in multiple aspects in large scale. To meet this requirement, different kinds of sensors of different parameters are deployed in the same region which in turn demands the aggregator node to integrate diverse data in a smooth and secure manner. Novelty in applications also requires Base station (BS) to apply multiple statistical functions. Hence, we propose to develop a novel secure cost-efficient data aggregation scheme based on asymmetric privacy homomorphism to aggregate data of multiple parameters and facilitate the BS to compute multiple functions in one round of data collection by providing elaborated view of monitoring region. To meet the claim of large scale WSN which requires dynamic change in size, vector-based data collection method is adopted in our proposed scheme. The security aspect is strengthened by allowing BS to verify the authenticity of source node and validity of data received. The performance of the system is analyzed in terms of computation and communication overhead using the mathematical model and simulation results. 2023 - IOS Press. All rights reserved. -
Intellectual capital independent directors and leverage as determinants of sustainable growth in Indian pharmaceutical companies listed in the NSE NIFTY pharma index
This research investigates how Intellectual Capital (IC) influences the Sustainable Growth Rate (SGR) of Indian pharmaceutical firms that are part of the NSE NIFTY Pharma index. This study delves deeper into the moderating influence of Independent Directors and examines the control effect of Leverage (Debt-Equity Ratio) on this relationship. A descriptive research design was utilized, employing panel data from FY 2015 to FY 2024. The dataset was obtained from the Prowess database (CMIE), and the Two-Step System GMM method was utilized with STATA 18 to guarantee a thorough econometric analysis. The findings indicate that Intellectual Capital (IC) plays a crucial role in enhancing SGR, thereby reinforcing the Resource-Based View (RBV). Independent Directors effectively moderate this relationship, strengthening Agency Theory. Nonetheless, leverage has a detrimental effect on SGR, consistent with Pecking Order Theory. Pharmaceutical companies ought to allocate resources towards Intellectual Capital, enhance corporate governance, and uphold appropriate debt levels to ensure sustained long-term growth. This study effectively combines IC, corporate governance, and financial leverage in the Indian pharmaceutical sector, providing valuable concrete insights for policymakers, academics, and industry experts. The Author(s) 2026. -
Intellectual Capital, FinTech Innovation, and Sustainable Performance: Moderating Role of Financial Literacy
In a fast-paced digital world of growing sustainability demands, organizations must utilize knowledge-based and financial resources in order to stay competitive and accountable. Even though there is increasing academic interest, the empirical evidence on how and when the major dimensions of intellectual capital (IC), human, structural, and relational capital, combine to contribute to sustainable performance (SP) remains fragmented and inconclusive. Given this, the present study empirically investigates the impact of IC, including human, structural, and relational capital, on SP. It also aims to examine the potential impact of FinTech innovation (FI) and financial literacy on IC and SP. Based on the Technology innovation theory and Resource based view, this study develops and empirically tests the proposed model that integrates intangible knowledge resources and technological capabilities to explain SP. Data were collected from 413 managers of the Indian banking industry. Structural equation modeling and Hayes Process were used to test the hypotheses. The findings exhibit that IChuman, structural, relational capital, and financial literacyplay a significant and positive role in FI and SP. The path analysis also confirmed a significant role of FI on IC and SP. This study suggests that managers and policymakers can improve the banking performance in an eco-friendly manner by actively resorting to investments in knowledge resources and digital transformation initiatives. Accordingly, the study model offers an integrative framework showing how financial institutions can ensure SP by converging IC, FI, and FL. 2026 ERP Environment and John Wiley & Sons Ltd. -
Intellectual property and human rights of farmers: Striking a balance in the era of globalization
Globalization has fueled a knowledge- based economy, and intellectual property rights (IPR) are a key driver of innovation. Agricultural businesses have traditionally not been linked to IPRs since farming and related processes were deemed common and traditional knowledge. Globalization of agriculture presents both threats and opportunities for innovative ideas and inventions. It creates a complex tension between IPRs and human rights of farmers, particularly in developing countries. IPRs restrict farmers' access to traditional seeds, limit ability to save and exchange seeds, and force them into dependence on patented technology. This dependence led to indebtedness, loss of biodiversity, and decline in food security. Providing farmers with access to information, training, and financial resources can help them to make informed decisions about their farming practices and to negotiate with corporations on equitable terms. Keeping in view the above, this chapter provides a comprehensive roadmap to explore the intersection of globalization, intellectual property, and farmers' human rights. 2025, IGI Global Scientific Publishing. All rights reserved. -
Intellectual Property Right - Copyright
The power of cognition of human beings is beyond the imagination of any cognitive person. As gifted and nurtured property, the intellect of human beings has the potential to be original, creative, and innovative. Has the human being got absolute control over her/his intellect? Can human beings possess absolute rights over any product of her/his intellect? How far is a human being indebted to society? If human beings are not given due credit to the product of her/his intellect, the enthusiasm to be more creative and productive may take a coarser path. Human beings have the fundamental right to use her/his intellect to live a life of their choice enjoying economic and non-economic benefits. The right to intellectual property is fundamental to human beings. Hence, any infringement of intellectual property has to be dealt with appropriately. At the same time, human beings should be indebted to society for nurturing their intellect. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Intellectual property rights vis-a-vis food security: A critical analysis
The Right to Food is undoubtedly a human right since it is one of the basic necessities without which it is impossible to sustain life. Food Security refers to the availability as well as accessibility to sufficient and quality food by all individuals. However, there persists a problem of food insecurity which is a major problem especially in the underdeveloped and, to a considerable extent, the developing countries. At an individual level, food security is limited to one's access to food but on a broader sense food security cannot be isolated from agricultural policies, crop technologies, economic and trade conditions. The intersection of crop technologies with economic factors is what links food security with Intellectual Property Rights (IPR). Over the recent past, IPR has gained immense importance in a number of fields including agriculture. It provides the incentive for the private sector development in advancement of plant science and crop technologies which helps in ensuring food security in the long term. This study aims at discussing the issues of food security with a specific focus on the developing nations, IPR regime, and its introduction into the agriculture sector. It intends to explore the connections and linkages between IPR and food security, especially how intellectual property can act as a medium to cover the path toward achieving global food security. The author aims to put forth the ability of IPR as a means to achieve food security by incentivising human creativity through a detailed study from an international as well as region-specific perspective. 2023 Apple Academic Press, Inc. All rights reserved. -
Intelligence in Children Whose Either Parent Is Treated For Schizophrenia.
G.J.B.A.H.S.,Vol.2(4):119-123- October- December ISSN: 2319-5584 -
Intelligence-Software Cost Estimation Model for Optimizing Project Management
With the evolution of pervasive and ubiquitous application, the rise of web-based application as well as its components is quite rising as such applications are used both for development and analysis of the web component by developers. The estimation of software cost is controlled by multiple factors right from human-driven to process driven. Most importantly, some of the factors are never even can be guessed. At present, there are no records of literature to offer a robust cost estimation model to address this problem. Therefore, the proposed system introduces an intellectual model of software cost model that is mainly targets to perform optimization of entire cost estimation modeling by incorporating predictive approach. Powered by deep learning approach, the outcome of the proposed model is found to be cost effective in comparison to existing cost estimation modeling. 2019, Springer Nature Switzerland AG. -
Intelligent Agents System for Vegetable Plant Disease Detection Using MDTW-LSTM Model
When it comes to agricultural output, nation, India, ranks first in the world, and agriculture is unparalleled. The need to categorize and trade agricultural goods is paramount. Manual organization, which is tedious and laborious, is not a choice. When agricultural products are graded automatically, a lot of time is saved. The application of image processing techniques facilitates the examination and evaluation of the products. A technique for identifying diseased vegetables is the focus of this effort. Feature extraction, preprocessing, segmentation, and training the model are all heavily dependent on sequence. Among the preprocessing technologies at disposal are image segmentation and filtering. Using Kapur's thresholding based segmentation method, the image's sick areas can be located during the segmentation process. Use k-means clustering for feature extraction to identify vegetable plant diseases. The training of an MDTW-LSTM model relies heavily on feature selection. In terms of performance, the proposed method surpasses two cutting-edge algorithms: LSTM and DTW. The results showed an accuracy of 97.35 percent, indicating a remarkable improvement. 2024 IEEE. -
Intelligent agriculture - Smart IOT system to assist farmers in effective decision making using data science /
Patent Number: 202141046585, Applicant: Dr.S.Balamurugan.
Research studies shows that the current world population of 7.3 billion is expected to increase to 9.3 billion by the ear 2050. In order to feed the increasing population, Food and Agricultural Organization (FAO), plans to increase the crop cultivation by 70%. Recent days have seen a steep rise in the adoption of IoT to various factors affecting agriculture like climate change monitoring, greenhouse automation, crop cultivation and management, cattle monitoring and management, precision farming, agricultural drones, predictive analysis for smart farming and many more. This invention discloses a Data-driven smart IoT system to help farmers for effective decision making on the choice of the crop to be cultivated in the given time. IoT sensors are capable to predict the humidity in the soil, nature of chemical resources that are apt for cultivation and weather forecasting. -
Intelligent Analytical Framework to Improve Customer Retention in the SaaS Industry
In the software as a service (SaaS) sector, churn is a crucial indicator as it directly affects a businesss earnings, prospects for expansion, and viability over time. Because SaaS companies mostly rely on recurring income from subscriptions, high churn rates can be detrimental to their operations. Customer retention is crucial for SaaS companies as it is frequently more profitable and cost-effective than bringing on new customers. Retention expenses and efforts can be decreased by focusing on an appropriate set of customers. This study focuses on an intelligent analytical framework that uses machine learning and artificial intelligence techniques to find the ideal group of customers for a SaaS-based organization to retain. The previous papers concentrated on either classification or survival analysis to determine the probabilities of churn. A few studies used explainable AI models to improve the predictability of the model predictions. Not having a holistic prediction model and retention strategies provides the research gap for this study. The proposed methodology used feature selection models to identify the most significant drivers of churn, and the most popular predictive models, like logistic regression, random forest, support vector machine, and neural networks, are applied to the training set. The likelihood of churn is calculated by using classification models. The Kaplan-Meier estimate is used for survival analysis to determine the odds of survival based on the tenure of each account. Lastly, the prediction models interpretability is enhanced by using explainable AI models like SHapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME). The neural networks model gave the best accuracy of 71% for the classification model, which provided the probability of churn and the likelihood of survival, has been predicted by Survival Analysis. Explainable AI models have identified the most important features that the model considers when arriving at the probability. This enabled the company to segment the data based on the probabilities of churn and survival, and the feature importance and respective retention strategies have been planned for each segment. By implementing the suggested analytical methodology, the business may determine which customers are most important to target with customer retention strategies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Intelligent approach to automate a system for simulation of nanomaterials
Nanomaterial composites are generally found to have great thermal properties and hence have witnessed an increasing demand in the recent years for manufacturing of efficient miniature electronic devices. The process of finding the right composites that exhibit the desired properties is a rather tedious task involving a lot of trial and error in the current scenario. This paper proposes a methodology to digitize and automate this entire process by administering certain efficient practices of assessing the properties of nanomaterial like Coarse Grained Molecular Dynamics thus resulting in faster simulations. 2023 Author(s). -
Intelligent Approaches of Clinical and Nonclinical Type-1 Diabetes Data Clustering and Analysis
Every year in India, there are nearly 15,600 fresh cases being reported among these age groups. In 2011, in the United States, 18,000 children under 15 were newly reported for T1DM. Over 13years, the Karnataka state government has a list of records showing that out of 100,000, 37% of boys and 40% of girls are affected by T1DM Disease. This paper investigates two methodologies to identify significant details about Type-1 diabetes. The first methodology is applicable to clinical data. The second methodology is demonstrated for the NDA T1D dataset. The dataset is utilized further to apply machine learning techniques to group similar patient traits. Exploratory data analysis on the dataset has revealed significant information answering a few research questions. This analysis can be useful for India, China, and other countries with high populations. In this paper, a unique methodology based on Artificial Intelligence Technique is proposed for both clinical and non-clinical data. The Autoimmune Disease, Diabetes Type 1-T1D, is focused. Non Clinical data based on 2021 reports are collected to identify patterns. Substantial unique issues are addressed in this work which were never reported before. The knowledge generated can be helpful for creating new clinical datasets, methodology and new insights related to Type-1 diabetes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Intelligent Course Recommendation for Higher Education based on Learner Proficiency
A course recommendation provides valuable guidance and support to learners navigating their educational and career journeys. Artificial Intelligence paves the way for recommending higher education courses. In this article, a framework is proposed that uses different features like learners' interest, their past performance and mainly their family talent history. This framework emphasizes the Intelligence Robotic Course Recommendation System. The system is very helpful for the learner who don't have that much of an explorer of the current trends happening in the world. When the learners similarity knowledge interest is known with respect to real-world needs, the perfect higher education is suggested for them. This paper shows that the framework gives better results when using with artificial intelligence algorithms. 2023 IEEE. -
Intelligent deep-well rescue system using ultrasonic sensors /
Patent Number: 201941048191, Applicant: Dr. Debabrata Samanta.
The present invention is related to an intelligent deep-well rescue system using ultrasonic sensors. The system for rescue a human in a narrow diameter deep-well (bore-well) comprises an ultrasonic sensor module, a grappling module, a central computing unit. The objective of the present invention is to solve the problems of the prior arts in solving issues of rescue of human being from the deep-well or bore well. -
Intelligent deep-well rescue system using ultrasound sensors /
"Patent Number: 201941048191, Applicant: Debabrata Samanta.
The present invention is related to an intelligent deep-well rescue system using ultrasonic sensors. The system for rescue a human in a narrow diameter deep-well (bore-well) comprises an ultrasonic sensor module, a grappling module, a central computing unit. The objective of the present invention is to solve the problems of the prior arts in solving issues of rescue of human being from the deep-well or bore well." -
Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease
Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by the kidneys through urine. In the early stages of the condition, the patient has no problems, but recovery is difficult in the later stages. Doctors must be able to recognize this condition early in order to save the lives of their patients. To detect this illness early on, researchers have used a variety of methods. Prediction analysis based on machine learning has been shown to be more accurate than other methodologies. This research can help us to better understand global disparities in kidney disease, as well as what we can do to address them and coordinate our efforts to achieve global kidney health equity. This study provides an excellent feature-based prediction model for detecting kidney disease. Various machine learning algorithms, including k-nearest neighbors algorithm (KNN), artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and others, as well as Re-cursive Feature Elimination (RFE) and Chi-Square test feature-selection techniques, were used to build and analyze various prediction models on a publicly available dataset of healthy and kidney disease patients. The studies found that a logistic regression-based prediction model with optimal features chosen using the Chi-Square technique had the highest accuracy of 98.75 percent. White Blood Cell Count (Wbcc), Blood Glucose Random (bgr), Blood Urea (Bu), Serum Creatinine (Sc), Packed Cell Volume (Pcv), Albumin (Al), Hemoglobin (Hemo), Age, Sugar (Su), Hypertension (Htn), Diabetes Mellitus (Dm), and Blood Pressure (Bp) are examples of these traits. 2022 by the authors.