Volume 13 | Issue 6 |

 Abstract

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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 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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 Keywords

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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Creative Commons Attribution 4.0 and The Open Definition

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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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 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

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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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 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

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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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 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

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In today's world, the role of language has evolved from merely a communication tool to a powerful asset in the soft power arsenal. Attempts to globalize a language can be beneficial for effectively deploying other soft power instruments, since language is the vehicle of culture, ideas, and vision. It thereby contributes to economic and political influence in an increasingly globalized world. In this paper, based on Joseph Nye's theory of soft power, I will explore the role of language as a constituent of soft power, with a focus on examining the competence of the Hindi language as a soft power tool and its potential influence in China.

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Creative Commons Attribution 4.0 and The Open Definition

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Soft-power, Language, Hindi, China

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Creative Commons Attribution 4.0 and The Open Definition

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This paper examines the integration of Artificial Intelligence (AI) in the retail and supply chain sectors across India and globally. Drawing from 40 research studies, it delves into AI applications in customer experience, inventory and demand forecasting, logistics optimization, and sustainability initiatives. It highlights benefits like improved efficiency and personalization, while also addressing challenges such as data quality, ethics, and costs. The paper includes case studies and concludes with actionable recommendations for leveraging AI in modern retail and logistics.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Retail, Supply Chain, Customer Experience, Demand Forecasting, Sustainability, Automation, Predictive Analytics

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Creative Commons Attribution 4.0 and The Open Definition

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The legalization of cannabis for medical purposes is an increasingly global trend, supported by a growing body of scientific evidence demonstrating its therapeutic efficacy for a variety of conditions. Concurrently, many prescribers have voiced concerns that this increased utilization may lead to the development of cannabis use disorder in patients. While cannabis use disorder has been extensively studied in recreational users, with findings often extrapolated to medical cannabis patients, research specifically addressing dependence on medical cannabis remains limited, and standardized methodologies for assessing this phenomenon are lacking. This article presents a narrative review of existing research, aiming to determine the relevance and applicability of concerns regarding dependence in recreational cannabis users to patients prescribed medical cannabis. The review focuses on key factors related to medical cannabis and dependence, including the influence of dosage, potency, cannabinoid composition, pharmacokinetics, administration route, frequency of use, and the crucial role of set and setting. Significant differences between medical and recreational cannabis use are highlighted, underscoring the difficulties inherent in extrapolating data from recreational use studies. Given the numerous unanswered questions surrounding the potential for dependence arising from medical cannabis use, it is imperative that these issues be addressed to effectively minimize potential harms. This review culminates in seven recommendations designed to enhance the safety of medical cannabis prescribing practices. It is anticipated that this review will contribute to a deeper understanding of the complexities surrounding medical cannabis dependence.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Medical cannabis, Dependence, Recreational cannabis use, Dosage, Potency, Prescribing practices, Cannabinoid composition, Pharmacokinetics, Administration route, Frequency of use, Cannabis use disorder, Harm reduction, Recommendations, Narrative review.

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Creative Commons Attribution 4.0 and The Open Definition