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Exploring AI-Driven Economic Decision Making and Role in Promoting Green Investment
Artificial Intelligence has assumed a disruptive role in the sphere of economic decision-making, specifically in the field of capital allocation towards green investments that would meet global sustainability requirements. Using machine-learning algorithms, neural networks, and big-data analytics, AI can offer greater accuracy in predicting economic patterns and risk assessment of the environment, and using AI can diversify portfolios with low-carbon assets, commercializing the old dichotomy between the financial value of profit and the eco-friendliness. This study discusses the transformations that AI-based tools are ready to make to the traditional economic paradigms, including the predictive analytics in terms of renewable-energy valuation, natural-language processing that would analyze sustainability reporting, or both in combination, a means of creating a paradigm shift where green investments would no longer be considered an act of charity, but rather a data-driven necessity of constructing long-term values. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Exploring Applications, Datasets, Algorithms, and Technologies in Satellite Image Processing
Amidst an era filled with complex local and global problems, satellite data presents itself as a revolutionary tool with unmatched potential to tackle practical problems in a variety of fields. This article investigates how satellite imagery, which is available through open data programs and repositories, is a valuable tool for applications including wildlife conservation, urban planning, precision agriculture, and disaster management. It highlights the unique perspective that satellite data offers. Various sources for data acquisition, the applications that are suitable for a chosen satellite data and commonly used algorithms and techniques are discussed. Through case studies, the paper demonstrates how quick and reliable data provided by satellites can be used to solve complex real-world problems. The benefits of satellite data are emphasized, including its affordability, ability to monitor in real-time, and ability to support sustainable behaviours and policy-making. The study explores cutting-edge technologies, highlighting cloud computing and GIS integration as well as machine learning algorithms to build robust solutions using satellite data. The immense potential of satellite data is accompanied by challenges, including data integration, computational complexity, and ethical considerations. These challenges underscore the need for standardization and continuous efforts to fully realize the potential of satellite data in sustainable development and informed decision-making. 2025 Bijeesh TV, Bejoy BJ, Michael Moses Thiruthuvanthan and Raju G. -
Exploring ARIMA Models with Interacted Lagged Variables for Forecasting
Including interactions among the explanatory variables in regression models is a common phenomenon. However, including interactions existing among lagged variables in autoregressive models has not been explored so far. In this paper, Autoregressive Integrated Moving Average (ARIMA) model with interactions among the lagged variables is proposed for improving forecast accuracy. The methodology for identifying the interacted lagged variables and including them in the ARIMA model is suggested. Using five different data sets of different types, the paper explores the effect of interacted lagged variables in ARIMA model. The experimental results exhibit that when interactions do actually exist, ARIMA model with interactions improves the forecast accuracy as compared to ARIMA model without interactions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Exploring artificial intelligence techniques for diabetic retinopathy detection: A case study
There is a notable increase in the prevalence of Diabetic Retinopathy (DR) globally. This increase is caused due to type2 diabetes, diabetes mellitus (DM). Among people, diabetes leads to vision loss or Diabetic Retinopathy. Early detection is very much necessary for timely intervention and appropriate treatment on vision loss among diabetic patients. This chapter explores how Artificial Intelligence (AI) methods are helpful in automated detection of diabetic retinopathy. In this chapter deep learning algorithm is proposed that is used to extract important features from retinal images and classify the images to identify the presence of DR. The model is evaluated using various metrics like specificity, sensitivity etc. The results of the case study provide an AI driven solution to existing methods used to identify DR and this can improve the early detection and appropriate treatment at the right time. 2024, IGI Global. All rights reserved. -
Exploring BERT and Bi-LSTM for Toxic Comment Classification: A Comparative Analysis
This study analyzes on the classification of toxic comments in online conversations using advanced natural language processing (NLP) techniques. Leveraging advanced natural language processing (NLP) techniques and classification models, including BERT and Bi-LSTM models to classify comments into 6 types of toxicity: toxic, obscene, threat, insult, severe toxic and identity hate. The study achieves competitive performance. Specifically, fine-tuning BERT using TensorFlow and Hugging Face Transformers resulted in an AUC ROC rate of 98.23%, while LSTM yielded a binary accuracy of 96.07%. The results demonstrate the effectiveness of using transformer-based models like BERT for toxicity classification in text data. The study discusses the methodology, model architectures, and evaluation metrics, highlighting the effectiveness of each approach in identifying and classifying toxic language. Additionally, the paper discusses the implementation of a userfriendly interface for real-time toxic comment detection, leveraging the trained models for efficient moderation of online content. 2024 IEEE. -
Exploring best practices in mobile app design patterns and tools: A user-centered approach
Design patterns are reusable solutions to common design problems that provide a consistent user experience across different apps. This article explores the best practices in mobile app design patterns and tools with a focus on the user-centered approach to design. Design patterns such as navigation bars, tab bars, list views, and card views are discussed, along with design tools such as Sketch, Figma, Adobe XD, and InVision. The problem is to ensure that mobile app design is centered around the needs and preferences of the user, rather than the designer or the technology, and that the right design patterns and tools are used to create interfaces that are familiar and easy to use. The chapter emphasizes the importance of conducting user research to understand the needs and preferences of the target audience and using design patterns and tools to create interfaces that are familiar and easy to use. Mobile apps have become an integral part of our lives, and designing a successful mobile app is a challenging task that requires a thorough understanding of user needs and preferences. 2023, IGI Global. All rights reserved. -
Exploring Bio Signals for Smart Systems: An Investigation into the Acquisition and Processing Techniques
Bio signals play a vital role in terms of communication in the absence of normal communication. Bio signals were automatically evolved from the body whenever any actions took place. There are lots of different types of bio signal based research going on currently from several researchers. Signal acquisition, processing the signals and segmenting the signal were totally different from one technique to another. Placing electrodes and its standard measurements were varied. The signals gathered from each subject may be varied due to their involvement. Each and every trial of signals can generate different patterns. Each and every pattern generated from the activities also has a different meaning. In this study we planned to analyze the basic measurement techniques handled to record the bio signals like Electrooculogram. 2023 IEEE. -
Exploring boronate-appended hyperbranched amino-functionalized dendrimer-empowered sensors for the potential recognition of FSH in age-categorized human plasma samples
Boronic acids can act as ideal saccharide receptors as they possess a high affinity for diols and readily form cyclic-boronate esters when reacting in an aqueous medium. Here, we present hydrophilic amino-functionalized boronic acid dendrimer (Af-BAD) for the first time, with significantly enhanced sensitivity towards Follicle Stimulating Hormone (FSH) detection. In this study, newly synthesized Af-BAD was dip-coated on a gold substrate to create an impedance-type sensing working electrode. The effects of Af-BAD coating on the gold chip, the sensing properties for FSH recognition, sensitivity, and stability were measured by the charge transfer resistance across the electrochemical setup. The impedimetric measurements were conducted in the presence of [Fe(CN)6]3-/[Fe(CN)6]4- redox reporter at pH 7.4. The increments in the charge-transfer resistance were monitored upon increasing the FSH concentrations from 25 fg/mL to 100 pg/mL. The device achieved good sensitivity with a calculated detection limit of 4.01 fg/mL and acceptable linearity. The observed behavior was linear concerning the tested concentrations. An attempt at a real application to serum samples was also successfully conducted. Meanwhile, the level of tolerance of boronic acid dendrimer with other competing glycoproteins and monosaccharides was also tested. In this study, we also compared human plasma FSH levels in female oral cancer patients and normal controls using the Af-BAD modified device and the clinically used ELISA method. With a sound understanding of boronate materials and their affinity, amino functionalized multi-boronic acid dendrimer was developed as a highly selective conjugate toward glycoprotein FSH detection. Copyright 2025. Published by Elsevier Ltd. -
Exploring challenges in online higher education for AI integration using MICMAC analysis
The consequence of Covid-19 has affected the traditional higher education system. Acknowledging the significant role of online education in national development for accessibility and quality education, countries around the world have understood its importance in current digital era. Indian policymakers have been giving due importance to enhancing the education quality, however the progress made by the country in higher education is not adequate. Amidst all the inadequacies of traditional education system, artificial intelligence (AI) technologies are bringing new ray of hope to democratize education system. This chapter is subjected to identify the challenges in online education and suggest specific ways to address each of them. The challenges are categorized into internal and external challenges/barriers. These challenges have been modeled with the expertise of educationalist's opinions and interpretive structural modeling to create a hierarchy of the barriers using MICMAC analysis and categorize these barriers into four clusters. 2024, IGI Global. All rights reserved. -
Exploring chatbot trust: Antecedents and behavioural outcomes
An awareness about the antecedents and behavioural outcomes of trust in chatbots can enable service providers to design suitable marketing strategies. An online questionnaire was administered to users of four major banking chatbots (SBI Intelligent Assistant, HDFC Bank's Electronic Virtual Assistant, ICICI bank's iPal, and Axis Aha) in India. A total of 507 samples were received of which 435 were complete and subject to analysis to test the hypotheses. Based on the results, it is found that the hypothesised antecedents, except interface, design, and technology fear factors, could explain 38.6% of the variance in the banking chatbot trust. Further, in terms of behavioural outcomes chatbot trust could explain, 9.9% of the variance in customer attitude, 11.4% of the variance in behavioural intention, and 13.6% of the variance in user satisfaction. The study provides valuable insights for managers on how they can leverage chatbot trust to increase customer interaction with their brand. By proposing and testing a novel conceptual model and examining the factors that impact chatbot trust and its key outcomes, this study significantly contributes to the AI marketing literature. 2023 The Authors -
Exploring Communication Authenticity Anxiety: A Data-DrivenPsychological Analysis of Al-Generated Content on StudentSelf-Perception and Expression
Generative artificial intelligence (AI) tools such as ChatGPT and Gemini are becoming more common in student communication, owing to the improvement that they offer in fluency and efficiency, but at the same time raise concerns about authenticity. Students struggle to put their authentic voice forward in the quest to enhance their work using these writing assistants. Many surveys have been conducted, which indicate widespread use of AI tools for education-related chores, yet these studies ignore the emotional effects related to this. The psychological discomfort related to authenticity in text-based communication is still not well examined and to address this gap, this study introduces a term called Communication Authenticity Anxiety and successfully examines its relationship with self-perception, academic stress, resilience, and AI dependence. Data were collected via a structured student survey and analyzed using exploratory factor analysis, regression modeling and machine learning techniques. Results show that self-perception and academic stress are the strongest predictors of authenticity anxiety, while resilience and AI dependence have weaker effects. These findings were further validated by Machine Learning models, with Random Forest achieving 75% accuracy and XGBoost achieving {9 2%}. This study, thus, successfully contributes to understanding the various psychological consequences of AI-generated content on student identity and expression, thereby providing valuable insights for crafting responsible educational policies. 2025 IEEE. -
Exploring Communication Authenticity Anxiety: A Data-DrivenPsychological Analysis of Al-Generated Content on StudentSelf-Perception and Expression
Generative artificial intelligence (AI) tools such as ChatGPT and Gemini are becoming more common in student communication, owing to the improvement that they offer in fluency and efficiency, but at the same time raise concerns about authenticity. Students struggle to put their authentic voice forward in the quest to enhance their work using these writing assistants. Many surveys have been conducted, which indicate widespread use of AI tools for education-related chores, yet these studies ignore the emotional effects related to this. The psychological discomfort related to authenticity in text-based communication is still not well examined and to address this gap, this study introduces a term called Communication Authenticity Anxiety and successfully examines its relationship with self-perception, academic stress, resilience, and AI dependence. Data were collected via a structured student survey and analyzed using exploratory factor analysis, regression modeling and machine learning techniques. Results show that self-perception and academic stress are the strongest predictors of authenticity anxiety, while resilience and AI dependence have weaker effects. These findings were further validated by Machine Learning models, with Random Forest achieving 75% accuracy and XGBoost achieving {9 2%}. This study, thus, successfully contributes to understanding the various psychological consequences of AI-generated content on student identity and expression, thereby providing valuable insights for crafting responsible educational policies. 2025 IEEE. -
Exploring Conditional Generative Models for Sketch-to-Image Translation: cGAN, cVAE, and Conditional Diffusion Models
Creating realistic facial pictures from hand-drawn sketches is of significant utility in forensic investigations because eyewitness drawings are frequently the only visual leads for suspect identification. Turning a hand-drawn sketch into a realistic image is a difficult task. This is because sketches lack detailed information, they are abstracted, and ambiguous. Most of the conventional image creation and generation techniques tend to lose facial structure, identity, and realism. This makes it a great area for generative AI. This paper is a comparative analysis of three generative models: Conditional GANs, Conditional VAEs, and Conditional Diffusion Models. We evaluate these models on the sketch-to-image synthesis problem using the CUHK Face Sketch Dataset. We recognize and compare how every model handles the challenge of generating images from sketches of faces, with an emphasis on producing realistic images, maintaining identity and diversity. The paper demonstrates the advantages and disadvantages of each approach. It also offers insights into their usefulness for forensic applications and suggests directions for future improvements through combined or specialized generative structures. 2025 IEEE. -
Exploring Conditional Generative Models for Sketch-to-Image Translation: cGAN, cVAE, and Conditional Diffusion Models
Creating realistic facial pictures from hand-drawn sketches is of significant utility in forensic investigations because eyewitness drawings are frequently the only visual leads for suspect identification. Turning a hand-drawn sketch into a realistic image is a difficult task. This is because sketches lack detailed information, they are abstracted, and ambiguous. Most of the conventional image creation and generation techniques tend to lose facial structure, identity, and realism. This makes it a great area for generative AI. This paper is a comparative analysis of three generative models: Conditional GANs, Conditional VAEs, and Conditional Diffusion Models. We evaluate these models on the sketch-to-image synthesis problem using the CUHK Face Sketch Dataset. We recognize and compare how every model handles the challenge of generating images from sketches of faces, with an emphasis on producing realistic images, maintaining identity and diversity. The paper demonstrates the advantages and disadvantages of each approach. It also offers insights into their usefulness for forensic applications and suggests directions for future improvements through combined or specialized generative structures. 2025 IEEE. -
Exploring Consumer Choices and Shopping Patterns: Examining Influences on Consumer Choices
This chapter explores the dynamic world of consumer behaviour and buying patterns, focusing on the psychological, social, cultural, and economic factors that shape decisions. It examines how consumers manage their preferences and choices in various market situations, highlighting trends like sustainable consumption, loyalty-driven purchases, and impulsive buying. The chapter also investigates the impact of the digital revolution, including social media and e-commerce, on consumer engagement and purchasing habits. By addressing elements such as peer influence, brand perception, and decision-making processes, it emphasises the importance of understanding consumer diversity in demographics, culture, and lifestyle. Combining theoretical frameworks, real-world examples, and data-driven insights, this chapter provides businesses and researchers with a foundation for predicting demands and creating effective marketing strategies. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Exploring Cross-cultural Comfort Food Narratives in Beryl Shereshewskys YouTube Videos
This article explores how certain food and the stories linked to the same are capable of evoking feelings of comfort and security. Food binds people together. The rituals and practices surrounding food inspire and sustain the association of various memories, experiences and emotions. The area of food studies is especially interested in how these linkages translate into the practice of nourishment. The narratives surrounding comfort food take on a cross-cultural flavour in the videos from Beryl Shereshewskys YouTube channel. This article analyses these narratives through the lens of Symbolic Interactionism to explicate how these food narratives bring people together from across the world by evoking the universal needs of food and comfort. Consequently, it is seen that even though it is true that the experience of consuming comfort food is extremely personal, it is also rendered as a universal phenomenon through the narratives that are created and shared. 2023 MICA-The School of Ideas. -
Exploring cultural contexts of dog ownership Mental health and satisfaction with life among university students in India
The rising social value of pet ownership is infuenced by social media and evidence of positive efects on well-being, leading to a rise in dog ownership in younger generations. However, the mental health outcomes of this broader shif, especially in India, have not been studied. The study explored the association between dog owners relationships, mental health, and satisfaction with life among university students. A cross-sectional correlational design was used with 250 students aged 1825 who were either dog owners or without pets. The dog owners responded to the Monash Dog Owner Relationship Scale, apart from the Mental Health Continuum and Satisfaction with Life Scale. Results showed a non-signifcant diference between mental health and satisfaction with life between dog owners and non-pet respondents. A positive relationship could not be established between dog ownership, mental health, and satisfaction with life. The dogs gender and breed infuenced the owners emotional bonding and interactions. Low perceived costs were related to a strong emotional bond with the dog, highlighting the complex nature of the pet ownership experience. Dog ownerships efect on students well-being is not universal and might depend on various individual, cultural, and contextual factors. Exploration of these human-animal interactions is warranted. 2025 John Benjamins Publishing Company. -
Exploring cybernetic approaches to sustainable co-working spaces in emerging economies: a sentiment analysis
Purpose In quest of achieving long-term sustainability of co-working spaces (CWSs) and drawing on the cybernetic principles, this study aims to develop a resilient business model promoting economic viability, encouraging environmental responsibility and reinforcing its social impact. Furthermore, to address the transformative shift in way people work in emerging economies, this study probed respondents from India and United Arab Emirates (UAE) and finally identified critical challenges and opportunities bringing in maximum customer satisfaction and achieving long-term business profitability. Design/methodology/approach Using a multi-method qualitative triangulation approach (sentiment analysis), the study collected primary data from India and UAE, analysed through the grounded theory approach. Whereas secondary data in form of tweets was tested using text-mining approach using NVivo. The findings from the dual study were corroborated to identify common dimensions, leading to the development of a hypothetical framework. Findings In CWSs business, dynamic organisation culture holds key in fostering future sustainability, and the study has explored its important antecedents like adaptive management, continuous innovation and technological integration. The impact of these antecedents was found to be moderated by two critical dimensions of regulatory challenges and competitive landscape. Furthermore, the study delved into connecting with the principles of circular economy moderating the impact of dynamic organisation culture towards long-term sustainability of CWSs. Practical implications This study applies cybernetic principles alongside shared and circular economy frameworks to assess consumer perceptions of CWSs. The insights generated can guide researchers, entrepreneurs, urban planners and policymakers in designing flexible business models, strengthening community networks and exploring diverse revenue streams to enhance resilience and long-term growth. Originality/value This research provides empirical evidence on the sustainability dynamics of CWSs, offering a balanced perspective on overcoming challenges and leveraging growth opportunities. Additionally, it bridges the concepts of cybernetics, shared economy and circular economy, presenting a novel framework for ensuring the sustainable development of CWS businesses. 2025 Emerald Publishing Limited -
Exploring digital age influences on undergraduate students mental health through social media, academic pressure and digital literacy
The research aims to measure the impact of usage of social media, academic pressure, and digital literacy, on mental health. It also aims to measure the mediating role of perceived stress on mental health of undergraduate students. Survey method was used for collecting data from a sample of 565 undergraduate students from state and private universities of Tamil Nadu, Karnataka, Andhra Pradesh, Telangana, and Kerala. EFA and Path analysis was used for testing and validating the conceptual model. The results showed that Social Media Usage increases Perceived Stress and negatively impacts Mental Health Outcomes both directly and indirectly through Perceived Stress. Academic Pressure increases Perceived Stress, which negatively impacts Mental Health Outcomes indirectly. Digital Literacy reduces Perceived Stress and has a positive effect on Mental Health Outcomes both directly and indirectly through reduced stress. Perceived Stress was found to have a significantly negative impact on the Mental Health Outcomes. The demographic variables namely; age, gender, living status, family type, and course type were found to have a significant impact on the usage of social media, academic pressure, digital literacy, perceived stress, and mental health scores of undergraduate students. The study also came up with interventions for managing mental health of under graduate students. The Author(s) 2025. -
Exploring digital twins: Attributes, challenges and risks
The recent approach to digitalization and digital transformation is based on the focus of every industry to develop systems and practices for optimizing the operational phase of the product lifecycle and beyond. Digital twins have become the buzzword in the domain of digital transformation. These Digital twins, which are a virtual representation of real-world occurrences such as processes, services, or products offer a new perspective to digitalization. It has emerged from Industry 4.0 and involves a mapping of the real physical world and the virtual world through Digital Twinning. Artificial Intelligence, Cryptography, Blockchain, Big Data technologies, and IoT act as technology enablers for Digital Twins. The capability of Digital Twin is its ability to cater to diverse applications. Within a decade, it has penetrated deeply into every functional aspect of business right from Patient Health Information Systems to remote control and maintenance of satellites/ space stations and to agriculture. This chapter has a focus on the key attributes, challenges, and risk factors that pertain to digital twin technologies and provides adequate examples from diverse sectors. The key challenges of digital twin technologies include Modeling the unknown, Transparency, Interpretability, Interactions with physical assets, Large-scale computation, Physical realism, Future projections, Data management, Privacy, Security and Quality. The four facets of risks related to Digital Twins include restrictions in access to system resources, theft of intellectual property, lack of compliance, and integrity issues in data/information. Hence, additional efforts and a holistic approach towards privacy and security are required to manage these risks. The holistic approach should cover hardware, software, and firmware together with the information that passes between them. Further, it is required to ensure that system, assets and data are adequately protected. Digital Twin technologies provide enormous competitive advantage for an organization, and a more pragmatic approach for mitigation of risks associated with digital twins is required. This would involve co-creation of Digital Twins with clients along with combined extensive knowledge of physical assets, disruptive technologies and appropriate security measures. 2023 Nova Science Publishers, Inc. All rights reserved.