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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 -
Fostering cultural vitality and enhancing sustainable urban tourism through international labelling: anethno-morphological exploration
Purpose Cultural Vitality (CV) nurtures creativity, enhances engagement with the local community and supports the art and culture of the place. In quest of exploring the impact of International Labelling (IL) in enhancing CV and reinforcing Sustainable Development (SD), the study pursues a comprehensive Morphological Analysis (MA) and builds a hypothetical framework bridging culture-driven urban tourism with sustainable growth. Design/methodology/approach An ethno-morphological study based on the Durga Puja Festival (India) was adopted to identify the critical dimensions of culture-driven urban tourism. A series of in-depth interviews and cross-sectional studies were carried out to develop a hypothetical research framework for empirical (quantitative) validation thereon. Findings Critical dimensions such as Cultural Investment, Collaborative Partnership and Embracing Glocal Approach were identified as major constructs towards achieving CV of a destination. IL and Responsible Consumption were found to moderate the effect of antecedents and CV upon sustainable growth. Research limitations/implications The present research limits its scope to the geographical boundary within India, keeping cross-boundary research for future study. This study will aid future researchers and scholars in expanding the domain of culture-driven urban tourism. Practical implications The present study bears significance to the urban policymakers, governing bodies, marketers and tour operators in embracing a culture-driven perspective while undertaking a suitable strategy towards developing CV, promoting urban-tourism attributes and vis-vis ensuring its SD. Originality/value This study makes a novel attempt at adopting an ethno-morphological approach and blending culture-driven tourism with sustainable growth while exploring the impact of IL, all together in a research initiative, making it a single-point reference in urban tourism literature. Emerald Publishing Limited -
Investigation on Electrode/Electrolyte Interfaces through Impedance Spectroscopy
In the present paper, impedance measurements of the battery configuration, Anode?lithium borophosphate glass electrolyte?LiCoO2 cathode, has been carried out to throw some light on the electrochemical interfacial behavior between the chosen electrodes and electrolyte. The cathode material, lithium cobalt oxide (LiCoO2) has been prepared by three different techniques and characterized. Sol-gel synthesized LiCoO2 showed uniformly distributed spherical shape particles with an average size of 500 nm and also exhibited better electrochemical performance. Charging and discharging (23 cycles) of the battery indicated an OCV of 2 V. However, the theoretical OCV of 4 V could not be achieved. The poor performance of the battery could be attributed to the electrochemical processes and SEI film formation at the electrode/electrolyte interfaces. Impedance spectroscopy shows that the major contributions to the impedance of the battery are the electrolyte resistance and the electrode/electrolyte interfacial resistance. With each recharging cycle, the value of electrolyte resistance remains almost constant; however, the interface resistance increases, during the passage of current, due to the interfacial passive layer formation. 2020 Taylor & Francis Group, LLC. -
Secure Decentralization: Examining the Role of Blockchain in Network Security
Blockchain generation has emerged as a novel answer for securing decentralized networks. This technology, which was first created for use in crypto currencies, has received enormous interest in recent years because of its capability for boosting protection in various industries and community protection. The essential precept at the back of block chain technology is the decentralization of statistics garage and control. In a decentralized network, no central authority may control the statistics. Rather, the facts are shipped amongst multiple nodes, making it immune to tampering and single factors of failure. One of the most important advantages of blockchain in community protection is its capacity to offer cozy and transparent communication amongst community customers. Through cryptographic techniques, block chain can affirm the identities of network participants and ensure the authenticity of records trade. This feature is extraordinarily valuable in preventing unauthorized access and facts manipulation. 2024 IEEE. -
Entrepreneurial Attitude and Entrepreneurial Intentions of Female Engineering Students: Mediating Roles of Passion and Creativity
Entrepreneurship holds a crucial function in addressing societal and economic issues like joblessness and inequalities between different regions. Acknowledging its significance, government officials and educational institutions exert considerable energy towards nurturing individuals into entrepreneurs. Multiple elements influence a person's path to becoming an entrepreneur. This research seeks to examine how one's entrepreneurial attitude (EA) impacts one's drive to become an entrepreneur, with passion and creativity serving as an intermediary in this connection. The research is explanatory and employs a survey-based approach. The findings convey that entrepreneurial attitude significantly influences the determination of female engineering students to pursue entrepreneurship. The study highlights the mediating roles of passion and creativity in the relationship between entrepreneurial attitude and intentions. While passion positively mediated the relationship, creativity had a negative mediating effect. 2024, Institute of Economic Sciences. All rights reserved. -
Nexus between Entrepreneurial Education, Entrepreneurial Mindset, and Entrepreneurial Passion on Entrepreneurial Intentions: Mediating Role of Self-efficacy
This study examines the complex dynamics of factors affecting self-efficacy (SE) and entrepreneurial intentions (EIs) among engineering students in India. It investigates the mediating role of SE in the relationships between entrepreneurial education (EE), entrepreneurial mindset (EM), entrepreneurial passion (EP), and EIs. The research reveals that SE remains stable across various personal characteristics, highlighting it as a robust individual trait less influenced by external factors. Gender significantly impacts EIs, underscoring its pivotal role in shaping entrepreneurial intentions, while other personal characteristics show limited influence. Passion and mindset appear to be consistent across demographics, suggesting they are intrinsic qualities. SE serves as a mediator in the connections between entrepreneurial mindset, passion, and intentions, elucidating its pivotal role in the entrepreneurial process. EE indirectly affects EIs and SE through other factors in the research model. Entrepreneurial passion directly influences both EIs and SE, emphasizing its role as a driving force for entrepreneurship. An entrepreneurial mindset doesn't directly affect intentions but significantly influences SE, indicating its importance in shaping self-efficacy, which in turn influences intentions. The findings can guide the development of educational programs and initiatives designed to promote entrepreneurship among engineering students in India while considering the impact of self-efficacy and gender-related factors. 2024, Iquz Galaxy Publisher. All rights reserved. -
Antecedents of Ethical Goods and Services Tax Culture among young adults - Special Reference to Maharashtra and Karnataka
Since the implementation of the Goods and Services Tax (GST) in 2017, it has become clear that this new Indian indirect tax system is here to stay. The Indian GST Council is continuously deliberating and making efforts to improve GST revenue collection at the state and central levels. The focus is now on the young adults in the country who will play a vital role in shaping the future of GST compliance. Their tax mentality and behaviour in contributing to GST revenue as daily consumers will determine the ethical tax culture in India. They need to understand how crucial their role is in discouraging evasive practices by sellers in the unorganised retail sector at the point of sale. The study utilized structural equation modelling to test the acceptability of the model. The process was supported by a structured questionnaire, with 324 respondents between the age group of 17-30 years. Understanding GST significantly influences acceptance of GST as a tax system, however, the acceptance of the GST tax system does not significantly lead to young adults discouraging the evasive behaviour of sellers in the unorganised retail sector at the point of sale. And, finally, the discouragement of evasive behaviour by young adults does influence the possibility of an ethical GST tax culture. The respondents majorly represented young adults between 17-20 years of age. The model has not measured the existence of covariance among the variables, nor has any mediating or moderating factors been identified, as GST tax culture in the Indian context is still unexplored and GST in itself is relatively new in the country. 2024 IEEE. -
Artificial intelligence and service marketing innovation
The integration of artificial intelligence (AI) into service marketing in India is expected to significantly impact marketing strategies and economic dynamics. The emphasis on personalization, automation, predictive analytics, and chatbots will enhance customer engagement and brand loyalty, leading to increased sales and revenue. Automation of marketing workflows will streamline operations, improve efficiency, and foster business growth. AI's predictive analytics capabilities will help businesses make informed decisions about their marketing strategies, particularly in a diverse market like India. AI-driven chatbots will enhance customer satisfaction and engagement, contributing to positive brand perception and loyalty. However, there may be concerns about job displacement, particularly in routine tasks. The growth of AI-driven service marketing can contribute to the development of a technologydriven ecosystem in India, attracting investments, fostering entrepreneurship, and stimulating innovation. 2024 by IGI Global. All rights reserved. -
Face and Emotion Recognition from Real-Time Facial Expressions Using Deep Learning Algorithms
Emotions are faster than words in the field of humancomputer interaction. Identifying human facial expressions can be performed by a multimodal approach that includes body language, gestures, speech, and facial expressions. This paper throws light on emotion recognition via facial expressions, as the face is the basic index of expressing our emotions. Though emotions are universal, they have a slight variation from one person to another. Hence, the proposed model first detects the face using histogram of gradients (HOG) recognized by deep learning algorithms such as linear support vector machine (LSVM), and then, the emotion of that person is detected through deep learning techniques to increase the accuracy percentage. The paper also highlights the data collection and preprocessing techniques. Images were collected using a simple HAAR classifier program, resized, and preprocessed by removing noise using a mean filter. The model resulted in an accuracy percentage for face and emotion being 97% and 92%, respectively. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Hybrid Bidirectional GRU Approach for Crop Yield Prediction and Climate Change Impact Assessment in Agriculture
The impacts of climate change induced by humans will be felt most acutely by the agriculture sector due to its extreme dependence on weather. To ensure a steady supply of food, it is necessary to study and anticipate the effects of climate change on agricultural output. The impact of climate change on agricultural yield predictions is examined in this study using a novel methodology. In the proposed model, preprocessing, feature extraction, and training are the main processes. Data pretreatment guarantees quality by cleaning and normalising the data, while the PCC is utilised for feature selection. The model utilises AM and BiGRU for usage with large datasets. Using word vectors, the word embedding layer improves contextual awareness. Experiment findings show that the model is accurate to within 98.31% and can withstand a wide range of climate conditions. Current state-of-the-art methods are vastly outperformed by it, with performance measures like as R2 = 0.921%, MAE = 0.127%, and RMSE = 0.158%. These findings show that agricultural strategists and lawmakers can use AM-BiGRU to assess the effects of climate change and build a more resilient food system. 2025 IEEE. -
Digital Transaction Cyber-Attack Detection Using Particle Swarm Optimization
The cyber digital world is an essential variant in day-to-day life in advanced technology. There is a better change in the lifestyle as intelligent technology. In larger excite to increase the advanced technology which can be developed to humans in major dependent on network and internet users. Now, in modern times, the internet has changed the primary need in human lifestyle by giving access to everything in the world while sitting in one place knowing and updating the information and usage of online subscribers or Revolution. The world is moving in Rapid and Faster communications within a fraction of a second, at a lesser cost, and it has minimal paper-based processes and relies on the digitization document instead of a paperless environment. The data is handled by finch security practices, which are used in security worldwide to establish protected data management systems like digital lending, credits, mobile Banking, and mobile payment. Cryptocurrency and blockchain, B-trading, and banking as a service are included. At the same time, leveraging the new technologies is to resist hacking cyber-attacks. This article is also involved in artificial intelligence and machine learning (AI&ML) in different cyber-attacks. This article focuses on genetic algorithms to detect the cyber-attack. The main aim of the detection is future to prevent these cyber-attacks. The comparison will take two sample genetic algorithms. The first one is taken for Ant Colony Optimization (ACO), and the proposed model is taken for Particle Swarm Optimization. The average attack detection of ACO algorithm is 45 packets at the same time PSO algorithm will detect 50 packets. 2023 IEEE. -
Structural Health Monitoring Using Machine Learning Techniques
Environmental factors, particularly vibrations and temperature can damage the structural health of the building. To avoid heavy damage to the building and to maintain the building's structural health this paper suggests monitoring of building using machine learning algorithms. Machine learning algorithms are used to predict temperature and vibration damages in buildings. Temperature and vibration values are obtained through the grove vibration sensor and NTC thermistor attached to Raspberry Pi 3B plus. In the Raspberry pi, Machine learning algorithms are executed. The activation functions used are Relu, Sigmoid, and Tanh. The experimental results reveal that the Sigmoid activation function gives the best results in terms of metrics with accuracy 94.25, Precision 0.951, Recall 0.912, and F1 score 0.388. The sigmoid function is used in machine learning algorithms for predicting temperature and vibrations. Predicted temperature and vibrations damages are sent to the server and viewed through the user mobile. K- Nearest Neighbor algorithm produced best results with an accuracy rate of 85.50, Precision of 0.922, Sensitivity of 0.830, Specificity of 0.840 and F1 score of 0.873. 2023 IEEE. -
Regulatory data protection for global economy of biopharmaceuticals: comparative legal analysis with focus on innovative biopharma in India
This provides a new global economy of biopharmaceuticals with an exclusive right over clinical data, meaning that no other person or persons may use them for a specified period. This study, therefore, offers a critical analysis of complementary protection granted to biopharmaceuticals by patents and regulatory data protection (RDP) globally with respect to innovation, competition, and access to medicines. This study probes the effectiveness and the challenge RDP is making using statistical analysis, financial modelling, and comparative analysis of the regulatory framework in Central Drugs Standard Control Organization (CDSCO), Food and Drug Administration (FDA), and Emergency Market Authorization (EMA). The justification for this combination is that RDP fosters innovation due to the protection of clinical trial investments, which provides a drive for the introduction of innovative biologics but does not inhibit the launch of biosimilars. With RDP, though they are very different in what they do, patents have created an enabling environment to make sustainable innovation in biopharmaceuticals accessible. International regulatory hurdles have to emerge so that a balance that advances both innovation and affordability becomes the norm within biopharma. Copyright 2025 Inderscience Enterprises Ltd. -
From Prediction to Action: Counterfactual Explanations and Ensemble Learning for Explainable Maternal Health Risk Modelling
Maternal health is critical to women's well-being, particularly during pregnancy, delivery, and postpartum. Early prediction and prevention of health risks are essential for reducing complications and improving outcomes. This research introduces a stacking ensemble model for maternal health risk prediction, combining the strengths of Random Forest, XGBoost, and Gradient Boosting with XGBoost as the meta-model. The ensemble approach enhances accuracy and reliability, achieving a classification accuracy of 91.13%, with precision, recall, and f1-scores exceeding 85% across all risk categories.Beyond accurate prediction, this study emphasizes model interpretability through Diverse Counterfactual Explanations (DiCE), an Explainable AI (XAI) method that provides actionable insights for risk reduction. Counterfactual analysis identifies the minimal changes needed in the patient features to shift a high- or medium-risk classification to low-risk, offering clinically relevant recommendations. These counterfactuals are generated to ensure feasibility, preserving physiological plausibility and practical applicability for healthcare professionals. This work bridges the gap between black-box machine learning models and actionable decision-making by integrating predictive power with explainability, supporting more transparent and patient-centric maternal health interventions. 2025 IEEE. -
Color image segmentation based on improved sine cosine optimization algorithm
Segmentation refers to the process of dividing an image into multiple regions based on some criteria such as intensity and color. In recent years, color image segmentation has received considerable attention from the researchers. However, it is still a highly complicated task due to the presence of more attributes or components as compared to monochrome images. Numerous meta-heuristics algorithms are developed to determine the optimal threshold value for segmenting color images efficiently. This paper presents an enhanced sine cosine algorithm (ESCA) to seek threshold for segmenting color images. Sine cosine algorithm (SCA) is a population-based optimization algorithm which has the ability of preventing local minima problem. First an input image is transformed to CIE L*a*b* color reduced space. ESCA is applied to determine the optimal threshold values for segmentation. The performance of the proposed method is tested on color images from Berkeley database, and segmentation results are compared with two metaheuristic algorithms, namely particle swarm optimization (PSO) and standard SCA. Experimental results are validated by measuring peak signalnoise ratio (PSNR), structural similarity index and computation time for all the images investigated. Results revealed that the proposed method outperforms the other methods like PSO and SCA by achieving PSNR of 23dB and SSIM of 0.93 and also require less time for finding optimal threshold values than PSO and SCA. 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Transforming Education With Data Science in the AI Era
In this AI era, data science emerges as a transformative tool in education. By using data sets, educators and administrators can make informed decisions that personalize learning and improve resource allocation. As AI technologies become more integrated into educational systems, data science serves as a critical bridge between raw information and actionable strategies, enabling a more adaptive, equitable, and evidence-based approach to teaching and learning. Transforming Education With Data Science in the AI Era explores the intersection of AI and data science in reshaping education. This book offers solutions to key challenges, such as ethical dilemmas, data privacy concerns, and digital inequity, to create a sustainable AI-driven education model. Covering topics such as AI, data science, and education, this book is an excellent resource for academicians, educators, educational leaders, and technology developers. 2026 by IGI Global Scientific Publishing. All rights reserved. -
The use of self-protective measures to prevent COVID-19 spread: an application of the health belief model
This study uses a health belief model to examine the preventive behavioral orientation or self-protective measures adopted by people in the face of the current COVID-19 pandemic. A total of 603 participants were selected from the city of Bangalore, India. The data was collected through an online survey with participants age varying between 17 and 54 and mean as 23 years (SD = 4.32). The findings revealed that perceived barrier has significant negative impact, while perceived threat, perceived consequences, perceived benefits, community and individual self-efficacy, and general health cues have a positive influence on an individuals intention to follow self-protective measures against COVID-19. Based on the constructs of the health belief model, this study proposes multiple health-related interventions to reduce the spread of COVID-19. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Investigating the Impact of Emotional Contagion on Customer Attitude, Trust and Brand Engagement: A Social Commerce Perspective
Social Commerce networks are a powerful platform for spreading positive and negative emotional contagion, which is affecting users from different perspectives, i.e., psychology, attitude, buying decision. Emotional contagion is the phenomenon of having a person's emotions and behaviours directly trigger similar emotions or behaviour in other people. This research proposes a model to analyze the factors influencing emotional contagion that, in turn, impact consumer's attitudes, trust, and brand engagement. This study used a survey approach using a structured questionnaire. Primary data was collected from 174 social media users who shop online. The proposed model was tested using multiple regression analysis. The results demonstrated that effective content, visual or text, triggers customers' emotional contagion, influencing customer attitude and trust leading to brand engagement. The research study's findings can be used for deciding on content strategies of advertisements pertaining to social commerce. 2022 Academy of Taiwan Information Systems Research. All rights reserved. -
An Intelligent Model for Detecting Cervical Cancer Using U-Net Segmentation and YOLO Classification Augmented with Lion Optimization
One of the leading causes of death for women is cervical cancer, and survival rates are significantly increased by early identification using Pap smear analysis. However, manually reviewing Pap smear images takes time and is prone to mistakes. Using an enhanced Convolutional Neural Network (CNN) with Transformer-like classification and U-Net-based nucleus segmen-tation, this study suggests an automated cervical cancer detection system. To increase feature extraction, the image is preprocessed using techniques such as edge detection, contrast enhancement with CLAHE, and greyscale conversion. The PR-processed image is segmented using U Net segmentation. A YoLo-based CNN optimised with the Lion optimiser (Evolved Sign Momentum) is used to classify the segmented nuclei to improve convergence and accuracy. Results from experiments show that our model outperforms con-ventional methods in terms of classification accuracy. By lowering reliance on manual screening and increasing early diagnosis rates, this automated approach can help medical practitioners detect cervical cancer more quickly and accurately. 2025 IEEE. -
Comparing Influence of Depression and Negative Affect on Decision Making
The current study aimed to explore differential value-based decision-making patterns across three groupsindividuals diagnosed with mild-to-moderate depression, a healthy matched control group, and a negative mood induction group. In the current study, drug- and therapy-nae individuals diagnosed with first episode of mild-to-moderate depression (n = 40), healthy individuals matched on age, gender, and education (n = 40), and healthy individuals with no current, past, or family history of any psychiatric conditions in a negative mood-induced state (n = 40) were administered the IOWA Gambling Task (IGT) and the Balloon Analog Risk Task (BART). Results indicated that individuals with depression showed heightened punishment sensitivity on both the IGT and the BART (p < 0.05 on the BART and p < 0.05 on the IGT), andperformed poorly on the IGT indicating poor and slow learning (p < 0.01). A similar, less severe, pattern was observed in the negative mood induction group. Individuals with mild-to-moderate depression performed poorly on tasks of value-based decision making. The significance of process factors in decision making, such as reward and punishment sensitivity, valuation of outcomes and learning, was highlighted in this study. The study also demonstrated how a negative affective state, without the other clusters of depressive symptomatology, can also lead to a less severe, but impaired decision making. 2023, The Author(s) under exclusive licence to National Academy of Psychology (NAOP) India.