Volume 13 | Issue 6 |

 Abstract

This study investigates the crucial relationship between brand awareness and its subsequent impact on sales performance. The research explores how varying levels of brand recognition familiarity and recall influence consumer purchasing decisions. A quantitative approach was employed utilizing surveys and sales data analysis to assess the correlation between brand awareness metrics and actual sales figures. The investigation focuses on examining consumer perceptions and brand associations. The research seeks to determine the extent to which increased brand awareness translates into tangible sales growth. Furthermore the study analyzes the effectiveness of different marketing strategies in building and sustaining brand awareness. The impact of digital marketing social media presence and traditional advertising methods are considered. The analysis identifies key drivers of brand awareness and their relative contribution to sales uplift. The findings reveal a significant positive correlation between heightened brand awareness and increased sales revenue. The results demonstrate that brands with greater visibility and positive consumer perception tend to achieve higher sales volumes. The research suggests that strategic investments in brand building activities are essential for achieving sustainable business growth. The study contributes to the understanding of brand equity and its financial implications. The research offers practical insights for marketers seeking to optimize their brand strategies and improve sales performance. The implications of the research extend to businesses of all sizes seeking to leverage brand awareness for competitive advantage. The study acknowledges limitations such as the specific industry focus and the reliance on survey data. Future research directions include exploring the long-term effects of brand awareness campaigns and the influence of brand reputation on consumer loyalty.

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 Keywords

Brand awareness, Sales performance, Brand reputation, competitive advantage

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Water is requisite for chow of life. It is a bounded resource. Human utilize both forms of water, Surface water and Groundwater. But groundwater resources are foremost for drinking water needs and agriculture in Bihar. The state government has carried out a multipurpose scheme JAL-JEEVAN-HARIYALI MISSION (JJHM) to nurture groundwater conservation in all districts of Bihar. This program has inspired schools to harvest rainwater within their outskirts in Kaimur district. Not only the new creation of water bodies but also the identification and rejuvenation of conventional water bodies; Wells, Ponds, Tanks and Ahar Pyne is going on and some water bodies have been completed in the study area. This mission has not been completed yet so work is going on. This approach will enhance the potential for groundwater recharge. This study is based on secondary data. These are sources; National Compilation on Dynamic Ground Water Resources of India, 2022 and JJHM Annual Report FY 2022-23. This scheme plays a remarkable role in assuring sustainable water management in Kaimur. by elevating rainwater harvesting and reviving conventional water sources, the Bihar government is working to conserve invaluable groundwater resources for future generations.

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Groundwater Conservation, Groundwater Extraction, Groundwater Resource, JAL-JEEVAN-HARIYALI MISSION(JJHM), Rainwater Harvesting

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Nanotechnology is revolutionizing the field of medicine, offering innovative solutions for drug delivery, diagnostics, and treatment. By leveraging nanoscale materials and structures, researchers are developing highly targeted and efficient therapies that minimize side effects and enhance patient outcomes. Nanoparticles are being used to deliver drugs directly to diseased cells, improving the precision of treatments for conditions such as cancer, cardiovascular diseases, and neurodegenerative disorders. In diagnostics, nanotechnology enables early detection of diseases through advanced imaging techniques and biosensors with unparalleled sensitivity. This seminar explores the applications of nanotechnology in medicine, focusing on its role in personalized medicine, nanotheranostics (a combination of therapy and diagnostics), and the development of biocompatible nanomaterials. Additionally, it addresses the challenges of clinical translation, including regulatory hurdles, safety concerns, and scalability. By understanding these advancements, healthcare professionals, researchers, and policymakers can harness nanotechnology to shape the future of medicine and improve global health outcomes.

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Nanomedicine, Targeted Drug Delivery, Diagnostic Nanoparticles, Biosensors, Personalized Medicine, Biomedical Nanotechnology ________________________

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 Abstract

Uttarkhand ke sanskrutik lokachar ki prushbhumi me Dr. Ramesh Pokhriyal "nishank" ke gadhya sahitya mein mahilao ka pratinidhitva, netrutva evam sangharsh

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Uttarkhand ke sanskrutik lokachar ki prushbhumi me Dr. Ramesh Pokhriyal "nishank" ke gadhya sahitya mein mahilao ka pratinidhitva, netrutva evam sangharsh

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Addressing visual disorders--such as cataracts, retinal degeneration from diabetes, and elevated intraocular pressure--at their onset is key to avoiding irreversible sight damage in aging and high-risk populations. Deep learning, as an advanced subset of modern computational intelligence, has reshaped the landscape of automated medical diagnostics, particularly in ophthalmology. This report investigates its use in recognizing three prominent vision-related disorders--cataract, diabetic retinal complications, and glaucoma--by highlighting crucial factors such as algorithm design, data variability, and real-world clinical integration. Contemporary neural systems, including convolution-driven architectures and attention-based visual models, are employed to extract both structural and contextual details from retinal imagery like fundus scans, OCT outputs, and slit-lamp visuals. Despite their promise, these systems often struggle with the limited availability of high-quality, annotated data--commonly affected by class disparities or visual inconsistencies due to equipment differences. To enhance detection accuracy and generalization, practitioners utilize methods like domain-adapted transfer learning, synthetic augmentation, and precision-tuning based on ocular features. Furthermore, clinical implementation demands interpretable models, regulatory validation, and seamless integration with electronic health records. Real-world deployments in telemedicine platforms and mobile eye-care units have demonstrated the scalability and cost-effectiveness of AI-driven diagnostics, especially in resource-limited settings. By addressing both technical and clinical challenges, deep learning offers a promising pathway toward timely and accurate detection of vision-threatening conditions.

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Redefining Eye Disease Detection: Deep Learning-Driven Identification of Cataract, Diabetic Retinopathy, and Glaucoma

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The early identification of ocular diseases--namely cataract, diabetic retinopathy (DR), and glaucoma--is vital for preventing permanent vision loss, especially among elderly individuals and patients with diabetes. With the rising global prevalence of these conditions, there is an urgent need for scalable and accurate screening solutions. Over the past few years, deep learning has become a reliable approach for recognizing diseases by processing and interpreting medical images automatically. This report investigates the role of deep learning in the early diagnosis of cataract, DR, and glaucoma, focusing on critical aspects such as image acquisition, data preprocessing, model architecture, and clinical applicability. Modern AI architectures, like convolutional neural networks and vision transformers, have proven highly effective in examining intricate visual data from retinal and ocular scans. Moreover, the report discusses the challenges related to dataset variability, imbalance, and annotation, as well as the importance of explainability and validation in clinical environments. As the field progresses, the integration of deep learning-based tools into routine ophthalmic care holds the potential to enhance diagnostic accuracy, reduce workload for healthcare professionals, and improve outcomes for patients worldwide.

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Enhancing Solar Energy Forecasting Accuracy through Machine Learning and Deep Learning Techniques

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The integration of Artificial Intelligence (AI) into corporate operations is rapidly transforming the landscape of corporate governance and accountability in India. As companies increasingly adopt AI-driven tools for decision-making, compliance, risk management, and internal audits, significant legal and ethical implications emerge. This paper explores how AI challenges traditional models of corporate governance and necessitates a rethinking of regulatory frameworks to ensure accountability, transparency, and fairness. In India, the Companies Act, 2013 and the evolving jurisprudence around corporate responsibility do not yet fully address the complexities introduced by autonomous and semi-autonomous AI systems. Key concerns include the delegation of decision-making to AI without clear accountability, biases in algorithmic processes, data privacy issues, and the risk of regulatory arbitrage. Furthermore, questions arise regarding liability attribution when AI errors lead to financial misreporting, discrimination, or regulatory non-compliance. This paper argues that while AI can enhance governance efficiency, it also complicates the assignment of responsibility, thereby demanding a more robust legal framework. It calls for the introduction of AI governance norms tailored to the Indian corporate context, including mandatory algorithmic audits, board-level tech literacy, and legal recognition of AI-assisted decision-making protocols. Additionally, the role of regulators such as SEBI and the Ministry of Corporate Affairs must evolve to address AI-specific challenges. Through case studies and comparative analysis with global practices, the paper highlights both the opportunities and regulatory gaps in India's current corporate governance regime. Ultimately, it seeks to propose a balanced approach that enables innovation while safeguarding accountability and public trust.

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Artificial Intelligence, Corporate Governance, Legal Accountability, Indian Companies Act, Algorithmic Regulation

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The absorption process is developed in biological systems to deliver necessary organic and inorganic substances into systemic circulation while maintaining bioavailability. Bioavailability issues might be caused by insufficient solubility or permeability. Most chemicals have solubility difficulties. As a result, as chemical science advances, so does the necessity for the creation of pharmaceutical technologies, which vary depending on the medicine. The medicine has relatively low water solubility, which means that it dissolves slowly in the gastrointestinal tract. The oral route is the most desirable and preferred method of giving medicinal medicines because of their systemic effect. Drugs are categorized into four classes according on their solubility under the BCS classification. Various strategies are employed to increase the solubility of poorly soluble medications, including physical and chemical alterations of the drug, as well as additional methods such as particle size reduction, crystal engineering, salt creation, solid dispersion, surfactant application, and complexation. The choice of solubility-improving technology is determined by the drug's properties, absorption site, and dose form requirements.

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KEY WORDS: Bioavailability, Novel methods, Solubility, BCS Class.

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Railway infrastructure is a fundamental pillar of modern transportation networks, playing a critical role in facilitating the movement of goods and passengers across vast geographical regions. Its reliability, cost-efficiency, and ability to handle large volumes make it indispensable for both urban and rural connectivity. However, the continuous exposure to dynamic loads, environmental stressors, and operational wear renders rail tracks susceptible to a wide range of structural defects, such as cracks, surface wear, and misalignments. These defects, if not identified and addressed promptly, can escalate into severe safety hazards, potentially leading to derailments, delays, or costly repairs. Traditionally, rail track inspection has relied heavily on manual monitoring by field personnel or basic mechanical systems. While effective to a degree, these methods are inherently limited by human fatigue, subjective judgment, and the inability to conduct continuous or large-scale inspections efficiently. As a result, there has been a growing emphasis on adopting intelligent, automated systems that can offer real-time, high-precision defect detection.

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Advanced Rail Track Defect Detection Using Deep Learning

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Credit card fraud has become a significant threat in the digital age, necessitating the development of robust and intelligent detection systems. This paper presents a comprehensive review of machine learning techniques applied to credit card fraud detection, analyzing their strengths, limitations, and real-world applicability. Various supervised, unsupervised, and hybrid approaches are critically examined, with a focus on performance metrics, data imbalance handling, and adaptability to evolving fraud patterns. The review also explores current challenges such as data privacy, scalability, and interpretability, while proposing future research directions to enhance detection accuracy and efficiency. This study aims to provide researchers and practitioners with valuable insights for developing more effective and resilient fraud detection frameworks.

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 Keywords

Credit Card Fraud Detection, Machine Learning, Supervised Learning, Unsupervised Learning, Data Imbalance, Fraud Analytics, Anomaly Detection, Model Interpretability, Cybersecurity, Financial.

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