Volume 13 | Issue 6 |

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

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This paper presents an Android-based healthcare application designed to enhance accessibility to medical services for patients and healthcare providers. The application allows users to book appointments, maintain digital health records, receive medication reminders, and consult doctors remotely. It aims to simplify the interaction between patients and healthcare professionals, especially in remote or underserved areas. The system leverages mobile technology to provide a user-friendly interface, real-time updates, and secure data handling. This solution promotes efficiency, reduces paperwork, and supports digital transformation in the healthcare sector.

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Android Application, Healthcare, Firebase, Patient Management, Telemedicine.

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: Personalized media experiences are rapidly evolving from static, preference-based models to dynamic, context-aware systems that respond in real-time to users' emotional states and activities. In this paper, we present a novel, integrated pipeline that fuses real-time facial emotion detection (captured via webcam) and offline activity recognition (analyzing uploaded video files) to drive a contextual song recommendation engine. The system comprises three tightly coupled modules: a Kivy-based GUI application leveraging OpenCV and DeepFace for low-latency facial affect analysis; a Flask web service for user management, video ingestion, and recommendation logic; and an offline video processor employing an Ultralytics YOLOv5 model fine-tuned for "running" and "sleeping" activities. We detail data collection and annotation procedures, model architectures and training regimes, algorithmic pseudocode, deployment via container orchestration, and front-end integration. Quantitative evaluation demonstrates 87-90% accuracy in seven-class emotion classification, 90.1% mAP in two-class activity detection, and round-trip latencies under 100 ms for emotion feedback. A user study with thirty participants reports 92% satisfaction with recommendation relevance and 4.6/5 mean perceived utility. Compared to standalone emotion- or activity-based recommenders, our unified approach yields a 25% uplift in personalization metrics. We conclude by mapping future research avenues: expanding affective and activity taxonomies, reinforcement-learning driven playlist adaptation, multimodal sensor fusion, and on-device inference for privacy.

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: Convolutional Neural Networks, Facial Expression Recognition, Activity-Based Learning, Machine Learning, Emotion Identification, Mood-Based Music Recommendation, Personalized Audio Experience.

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Oral diseases have a significant impact on the quality of life of children. Early exposure to irritants in the infant's environment (e.g., bacteria or sugars) can cause oral problems. Many synthetic compounds have strong antimicrobial activity and consequently are widely utilized in pediatric medicine, they may have side effects such as the disruption of the natural micro-flora, leading to microbial resistance. These aspects thus suggest the need for studies and the development of alternative antimicrobials. Potable plant extracts have been widely used as therapeutic agents in oral health, with an important number of active components. The antimicrobial activities of these agents have been tested side by side with conventional antibiotic treatments. Furthermore, the introduction of plant-derived antimicrobials is receiving a growing interest from the pharmaceutical industry because of their effectiveness and increased safety margin as compared to their synthetic analogues. Plant-based antimicrobials hold promise for improving pediatric oral health by providing safe and effective alternatives to synthetic agents. However, further research and development are necessary to fully realize their potential.

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Antimicrobial agents, alternative antimicrobials, plant extracts, Microbial Ecology, Antimicrobial Resistance, Flavonoids, Terpenoids, Alkaloids

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