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Impact of Smart Phones and Social Media Consumer Addiction Affecting Interpersonal Relationship
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. -
Effect of Impulse Buying on Socio-economic factors and Retail Categories
Indian Journal of Marketing, Vol. 46, Issue 9, pp. 24-34, ISSN No. 0973-8704 -
Factors Affecting the Performance of Private Label Brands in Indian Online Market: An Assessment of Reliability and Validity
Asian Journal of Management, Vol. 7, Issue 3, pp. 223-230, ISSN No. 0973-8705 -
Detection and classification of lung cancer using deep neural network
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. -
Environmentally conscious synthesis of novel pyrano[2,3-d]pyrimidines via ternary deep eutectic solvents
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. -
Emergency response to natural disaster victim identification: Blockchain to the rescue
Rapid, coordinated action by various stakeholders is required to respond effectively to a natural disaster. Suffice it; such efficiency has been missing in many previous rescue attempts. Is blockchain capable of making this happen? The Disaster Victim Identification (DVI) process is a sophisticated operation in which post-mortem (PM) identifying data, including fingerprints, DNA, and dental records, are acquired and matched with antemortem (AM) data from the missing people list. Although there are solutions to human identification, they must provide the tools required to achieve human identification promptly. Blockchain technology is one of the technologies that has gained much attention recently and is undergoing heavy media operations. It creates trustworthy, secure, and comprehensive ecosystems by disseminating siloed AM and PM data across systems, preventing breaches, redundancies, inconsistencies, and errors. Using real-world scenarios, the authors present several good use cases in this chapter to gain a holistic understanding of the challenges and how blockchain technology addresses such challenges and facilitates multi-jurisdictional data information sharing in conjunction with the upcoming distribution of patients electronic medical and dental records. 2025 Elsevier Inc. All rights reserved. -
ANFIS-Based Multi-Sensor Data Fusion Model for Optimized Autonomous Vehicle Navigation Using Big Data and Filtering Techniques
The navigation of an autonomous vehicle depends mostly on the integration of multi-sensor data from sources such as LiDAR, GPS, radar, and cameras. Issues like sensor noise, data asynchrony, and fusion inaccuracies hamper reliable real-time decision-making. This paper proposes an optimized multi-sensor data fusion framework integrating big data analytics with modern filtering techniques to increase navigation accuracy and system robustness. The proposed model integrates Kalman Filter (KF), Extended Kalman Filter (EKF), and Adaptive Neuro-Fuzzy Inference System (ANFIS) for dynamic state estimation and adaptive noise accommodation. In addition, sensor reliability and position tracking are enhanced via Bayesian data fusion and Particle Filter. Simulation results show that the proposed technique is evidently superior to existing models in accuracy (1.5 RMSE), convergence time (0.98s), and latency (50 ms). The fusion system enhances stability and responsiveness in autonomous navigation and offers an intelligent transportation framework that can be deployed efficiently at a real-time scale. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
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.