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                <text>Societal Approaches to AI, Psychology, and Digital Culture;pp.375-394</text>
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                <text>Kumar R., Christ University, India</text>
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                <text>In this Chapter we will discuss the addiction of smartphones and Social Media Addiction. In this generation accessing the internet and getting connected online with people is very easy now people carry their smart devices with them everywhere and today people use mobile data more than calling someone because each and every individual with all age categories from a kid to senior citizen everyone is being Addicted. While the smartphone and social media platforms have played a crucial role in connecting people in a very easy manner. When people meet social gathering or in a parties they should interact with the people and have should socialise with the people but instead of that a notification from social media apps on smartphones pop's up and within a sec person get engaged with their phones or checking the notification. This is a major issue that is happening in today's time.  2026 by IGI Global Scientific Publishing. All rights reserved.</text>
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                <text>&lt;a href="https://doi.org/10.1504/IJMEI.2025.148638" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1504/IJMEI.2025.148638&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105017018666?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105017018666?origin=resultslist&lt;/a&gt;</text>
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                <text>Kumar S.B., Department of AI, ML &amp;amp; DS, CHRIST University, Karnataka, Bangalore, India; Kumar M.V., Department of Information Science and Engineering, RV Institute of Technology and Management, Karnataka, Bangalore, India, Visvesvaraya Technological University, Karnataka, Belagavi, 590018, India</text>
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                <text>Lung cancers hold a critical spot among the reasons for most cancer deaths among humans. The better way to maximise the survival rate is the detection of cancer at the earliest. But existing traditional techniques are time-consuming and error-prone. This study is a significant and efficient method for the detection and classification of lung cancer into large cell carcinomas, small cell, adenocarcinoma, squamous cell carcinomas, or benign respectively. In the proposed technique, a novel algorithm is implemented to generate the Image patches from whole slide histopathological images. Then, histogram normalisation is carried out to remove noise and enhance the image. Then a novel extended Mobius transformation technique is used for image augmentation. Finally, Dense EfficientNetB7 is trained to extract the features for the detection and classification of lung cancer. The performance of the proposed technique is proved more efficient and par with histologists attaining an accuracy of 98.87%. Copyright  2025 Inderscience Enterprises Ltd.</text>
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                <text>ISSN: 17550653;</text>
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                <text>Environmentally conscious synthesis of novel pyrano[2,3-d]pyrimidines via ternary deep eutectic solvents</text>
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                <text>Journal of Molecular Liquids;Volume;417;Issue;;Article No.;126651;</text>
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                <text>Kumar S.L., Department of Chemistry, Christ University, Bengaluru, 560029, India; Tabassum S., Department of Chemistry, Surana College, Bengaluru, 560074, India; Govindaraju S., Department of Sciences and Humanities, Christ University, Bengaluru, 560074, India</text>
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                <text>Pyrano[2,3-d]pyrimidine and its analogues have gained considerable courtesy because of their diverse biological functions and wide-ranging applications, from pharmaceutical agents to essential natural pigments. However, synthesising pyrano[2,3-d]pyrimidine with multiple reactants is challenging and requires advanced green chemistry solutions. This study investigates the generation of thirteen new pyrano[2,3-d]pyrimidine analogues through a single-step, open-flask, multicomponent reaction (MCR) strategy involving aldehydes, phenylhydrazine, ethyl acetoacetate, and barbituric acid via deep eutectic solvents (DES). These DESs serve as environmentally friendly alternatives to traditional solvents. A ternary deep eutectic solvent (TDES) was evaluated for its catalytic solvent activity among ten different formulations. TDES-7 (5 mL) demonstrated the best performance, achieving 95 % product formation within 30 min at room temperature. Its remarkable catalytic activity and ability to produce high yields across multiple reaction cycles make it a standout choice for this application. The collaboration between MCR and TDES underscores an important blend of two significant green aspects, demonstrating their potential to achieve a green and productive sustainable synthesis method with an noble E-factor of 0.1236.  2024 Elsevier B.V.</text>
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                <text>Kumar S., KLEF College of Engineering, Department of Computer Science and Information Technology, KLEF, K L Deemed to be University, Green Fields, Vaddeswaram, Guntur District, 522302, India; Ranjan A., Department of Electronics and Communication Engineering, Faculty of Engineering andTechnology, University of Lucknow, Uttar Pradesh, Lucknow, 226021, India; Saxena S., Department of Computer Science, CHRIST University, 560 029, Karnataka, Bengaluru, India; Ashish, KLEF College of Engineering, Department of Computer Science and Information Technology, KLEF, K L Deemed to be University, Green Fields, Vaddeswaram, Guntur District, 522302, India</text>
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                <text>Kumar S., Sharda University, India; Arora A.K., School of Business and Management, Christ University, Bengaluru, India; Sharma P., Dewan Institute of Management Studies, Meerut, India; Bag A., School of Law and Justice, Adamas University, Barasat, India</text>
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                <text>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.</text>
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