Volume 14 | Issue 6 |

 Abstract

The increasing adoption of Artificial Intelligence (AI) and machine learning in digital commerce has transformed pricing strategies through algorithmic pricing systems. These systems enable businesses to analyse consumer behaviour, competitor prices, inventory levels, and market trends in real time. While algorithmic pricing enhances efficiency and market responsiveness, it also raises significant competition law concerns. Algorithms may facilitate anti-competitive outcomes such as price-fixing, tacit collusion, discriminatory pricing, and abuse of market dominance without explicit human coordination. This paper examines the legal implications of algorithmic pricing in Indian e-commerce platforms and evaluates the adequacy of the Competition Act, 2002 in addressing AI-driven anti-competitive conduct. The study analyses the risks of algorithmic collusion, reviews relevant statutory provisions and judicial precedents, and compares regulatory approaches adopted in the European Union, United States, and United Kingdom. The research finds that traditional competition law concepts based on agreement and human intent are insufficient to regulate autonomous algorithmic conduct. It concludes that India requires AI-specific competition guidelines, algorithmic transparency measures, audit requirements, and a modernized digital competition framework to safeguard competition and consumer welfare in the digital economy.

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 Keywords

Algorithmic Pricing, Artificial Intelligence, Competition Law, E-Commerce, Digital Markets, Consumer Welfare, Competition Act 2002.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project investigates the relationship between predictive uncertainty and hallucination behavior in Large Language Models (LLMs), with a particular focus on whether token-level uncertainty dynamics contain discriminative signals that are not captured by conventional confidence-based reliability metrics.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Large Language Models (LLMs), Hallucination Detection, Uncertainty Quantification, Predictive Entropy, Calibration Analysis, Reliability Estimation, Token-Level Interpretability, Autoregressive Generation, Statistical Learning, ROC-AUC Evaluation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Phytochemicals derived from medicinal plants exhibit diverse therapeutic activities; however, their clinical application is often limited by poor bioavailability due to high polarity and low lipid solubility. The gastrointestinal membrane, being lipophilic, restricts the absorption of hydrophilic plant constituents such as flavonoids, terpenoids, and phenolic compounds. Novel drug delivery systems have emerged to overcome these limitations by enhancing solubility, stability, and targeted delivery of phytoconstituents. Phytosomes, developed using phosphatidylcholine and related phospholipids, exhibit improved pharmacokinetic and pharmacodynamic profiles compared to conventional herbal extracts. These complexes enhance drug stability, reduce degradation, and facilitate efficient transport across biological membranes. Various preparation methods such as antisolvent precipitation, solvent evaporation, rotary evaporation, and lyophilization are employed to formulate phytosomes. Characterization techniques including particle size analysis, zeta potential measurement, spectroscopic evaluation, and encapsulation efficiency assessment are essential to determine their physicochemical and biological properties Phytosomal formulations demonstrate significant improvements in therapeutic efficacy, including enhanced bioavailability, hepatoprotective, anticancer, antioxidant, transdermal, and antidiabetic activities. These systems also reduce dose requirements and minimize adverse effects. Overall, phytosomes offer a promising strategy for improving the delivery and effectiveness of herbal medicines, making them a valuable approach in modern pharmaceutical research. .

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Phytosomes , Phytochemicals, Bioavailability, Phospholipid complexes, Drug delivery systems, Pharmacokinetics, Targeted delivery

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The study examines the growing importance of fintech innovations in improving access to financial services among rural populations. Financial inclusion plays a vital role in economic development by enabling people to access banking, digital payments, savings, credit, and other financial services. The main purpose of the study is to identify the role of fintech innovations in promoting financial inclusion in rural areas and to analyze the impact of financial literacy on the adoption of fintech services. The study is based on primary data collected from 400 respondents selected through convenience sampling. A descriptive research design was adopted to understand the awareness, accessibility, and usage of fintech services among rural people. Statistical tools such as ANOVA and t-test were applied for data analysis using JAMOVI software. The study highlights how financial literacy and technological accessibility influence fintech adoption in rural Karnataka. The findings of the study contribute to policymakers, financial institutions, fintech companies, and researchers by providing insights into improving digital financial services and enhancing financial inclusion initiatives in rural areas.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fintech Solutions, Financial Inclusion, Rural Development, Digital Financial Services, Financial Literacy

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The pharmacy sector is undergoing a profound digital transformation, driven by a confluence of technological advancements and mounting pressures on traditional healthcare models. This evolution is fundamentally reshaping the roles, responsibilities, and strategic value of pharmacy practice.This paper aims to provide a comprehensive review of this digital metamorphosis by critically examining the impacts of key technologies on pharmacy operations and patient care, delineating the strategic opportunities for professional evolution, and articulating the crucial ethical imperatives required for responsible implementation.A systematic literature survey was conducted across major academic and technical databases, including PubMed, Scopus, Web of Science, and Google Scholar, for the period of 2010 to 2025. The synthesized findings are presented through a structured narrative, conceptual tables, and figures to enhance clarity and understanding.The review identifies a spectrum of core technologies catalyzing this change, including artificial intelligence (AI), robotics and automation, telepharmacy, electronic health records (EHRs), and mobile health (mHealth). These technologies are yielding significant impacts on operational efficiency by automating dispensing, optimizing inventory, and streamlining administrative workflows. In patient care, they are enabling personalized medicine through pharmacogenomics, improving medication adherence, and expanding access to clinical services. This technological shift presents strategic opportunities for pharmacists to transition from a product-centric dispensing role to a knowledge-based, patient-centric service model, assuming new responsibilities as digital health coordinators and data analysts. However, this transformation is accompanied by critical ethical challenges, including the risks of algorithmic bias perpetuating health disparities, significant data privacy and security concerns related to Protected Health Information (PHI), complex questions of legal liability, and the potential for a digital divide to exacerbate health inequities.The future of pharmacy is one of a digitally-integrated, patient-centric practice. For the profession to realize its full potential, it must proactively embrace technological innovation while simultaneously establishing robust ethical frameworks and educational reforms. The digitally-empowered pharmacist is poised to become an indispensable clinical expert in a predictive, preventative, and personalized healthcare ecosystem, ultimately improving patient outcomes on a global scale.

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 Keywords

Digital Pharmacy, Telepharmacy, Artificial Intelligence (AI) in Pharmacy, Pharmaceutical Robotics, Electronic Health Records (EHR), mHealth, Digital Therapeutics (DTx), Pharmacy Informatics, Medication Management Systems, Automation, Data Ethics, Pharmacogenomics, Supply Chain Management, Clinical Decision Support, Digital Divide.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Placing a large language model in front of real patient data in a multi-tenant clinical system creates three problems at the same time. The model can invent clinical numbers, it can expose protected health information, and the volume of text sent to it tends to grow without any natural bound. This paper describes the Fact-Curated Helper (FCH), an architectural pattern we built into Rudhiraksh, a multi-tenant thalassemia and blood-bank management platform that is in live use at a partner blood bank. The central idea is to keep computation out of the model. A deterministic layer, scoped to the caller's role and tenant, computes every clinical fact in SQL and assembles a small JSON object containing only what the caller is permitted to see; the model is then asked to phrase that object in plain language and to do nothing else. We describe three parts of the design. The first is a separation of computation from narration that prevents the model from producing clinical values. The second is a role-aware and tenant-aware grounding step that enforces access control in code before the model is called, so that no prompt can widen the model's view. The third is a three-tier provider stack (Gemini, then Groq, then a deterministic rule engine) that keeps the feature available even when a provider is down. On the deployed system the context stays compact and grows with the caller's authorisation scope rather than with the size of the database: a patient context is roughly 260 to 300 tokens and an administrator's context grows by about 57 tokens for each additional blood bank. In a controlled experiment that calls a hosted model under three prompting conditions, FCH reduces the per-patient prompt by about 55 percent against a naive full- record baseline (measured with the provider's own tokenizer) and removes 23 of 24 identifying fields from anything the model receives, with no loss of accuracy on the questions tested.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Clinical decision support, large language mod- els, data grounding, context compression, multi-tenant security, role-based access control, graceful degradation, healthcare infor- matics

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Forest wildfires pose a serious risk to ecosystems and human safety, making early and accurate detection essential. Machine Vision, which integrates artificial intelligence with digital image processing, provides an effective solution to this challenge. This paper presents a comprehensive experimental study that combines image processing, machine learning, and deep learning techniques for wildfire detection, aiming to develop a system that is both easy to understand for educational purposes and effective for practical applications. A modified Reduce-VGGNet model is proposed for classifying wildfire and non-wildfire images, with a workflow that includes data collection, preprocessing, model training, and performance evaluation. The study also addresses key challenges such as varying lighting conditions, smoke patterns, and background similarities, while demonstrating how deep learning improves the identification of fire and smoke compared to traditional methods. By enhancing detection accuracy and reducing false alarms, this work highlights the potential of Machine Vision in enabling faster and more reliable wildfire detection. Additionally, the model is designed to be computationally efficient, making it suitable for real-time implementation in resource-constrained environments. The experimental results validate the robustness and consistency of the proposed approach across different scenarios. The system can also be integrated with surveillance and monitoring platforms for continuous observation of forest regions. Overall, this paper contributes to the development of intelligent and scalable wildfire detection systems for future applications.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Vision, Wildfire Detection, Deep Learning, Image Processing, Reduce-VGGNet

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project presents a voice-based email system using Generative AI that allows users to compose and manage emails through speech. The system converts spoken input into text using speech recognition and enhances it using AI for grammar correction, clarity, and proper email formatting. It also supports voice commands for reading, sending, replying, and organizing emails efficiently. The system is designed to provide a user-friendly and hands-free experience, reducing the need for manual typing. It is especially beneficial for elderly users, visually impaired individuals, and people with physical disabilities. Additionally, the use of Generative AI enables the system to understand context, suggest appropriate content, and generate professional-quality emails. Overall, this solution improves accessibility, productivity, and ease of communication, making email usage more inclusive and intelligent.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Generative AI, Speech-to-Text, Email Management, Voice Commands, Accessibility, Assistive Computing, Natural Language Generation, Human-Computer Interaction, Intelligent Email Systems, Inclusive Communication.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The ferrite-ferroelectric magnetoelectric (ME) composites with composition (1-x) Ni0.5Cu0.3Zn0.2Fe2O4+ (x) BaTiO3 in which x = 0, 0.2, 0.4, 0.6, 0.8 and 1 mol were prepared by conventional ceramic solid state reaction. The magnetoelectric (ME) composite possess two coupled order parameter in the same material and gives ME effect with variation of magnetic field changes and vice versa. In the present work an attempt is made to prepare ME composite materials consisting ferroelectric phase as a BaTiO3 and ferrite phase as Ni0.5Cu0.3Zn0.2Fe2O4. The presence of constituent phases in the composites was confirmed by x-ray diffraction studies. The dielectric measurements of the composites carried out HP LCR meter/ Impedance analyzer at room temperature in the range 1Hz-1MHz. It is found that as ferroelectric composition in the composite increases the dielectric properties also increases because ferroelectrics posses high dielectric properties than ferrite.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Dielectric, Composites, X-ray diffraction, ferrite, ferroelectric.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Air pollution has emerged as one of the most significant environmental and public health challenges worldwide, particularly in urban and industrial regions. The increasing concentration of harmful gases and pollutants in the atmosphere due to industrial activities, vehicle emissions, and rapid urbanization has led to severe health issues, including respiratory diseases, cardiovascular disorders, and reduced quality of life. Continuous monitoring of air quality is therefore essential to ensure environmental sustainability and public health protection. However, conventional air quality monitoring systems are often expensive, fixed in location, and inaccessible for personal use. This paper presents **BreathX: A Personal Air Quality Checker using Internet of Things (IoT)**, a smart, portable, and cost-effective solution for real-time environmental monitoring. The proposed system is built using the **NodeMCU ESP8266** microcontroller, which provides wireless connectivity and efficient data processing capabilities. The system integrates an **MQ135 gas sensor** to detect harmful air pollutants and a **DHT11 sensor** to measure temperature and humidity levels. The collected environmental data is continuously processed and transmitted to the **ThingSpeak cloud platform**, enabling remote monitoring, data storage, and graphical analysis of air quality parameters. To enhance user awareness and safety, the system incorporates an **OLED display** for local real-time visualization of environmental conditions and an **SG90 servo motor-based alert mechanism** that activates whenever pollutant levels exceed predefined safety thresholds. This feature allows users to receive immediate notifications regarding deteriorating air quality conditions. Experimental results demonstrate that the proposed system effectively monitors environmental parameters and provides reliable real-time air quality information with cloud connectivity. The developed solution offers advantages such as low implementation cost, portability, energy efficiency, and ease of deployment in homes, educational institutions, healthcare facilities, and workplaces. Furthermore, the system contributes to personalized environmental awareness and preventive healthcare by enabling users to make informed decisions based on surrounding air quality conditions. Future enhancements may include the integration of PM2.5 and PM10 particulate matter sensors, GPS-based pollution mapping, mobile application support, and machine learning algorithms for predictive air quality analysis. The proposed BreathX system represents an efficient and scalable approach toward smart environmental monitoring and healthier living environments.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Internet of Things (IoT), Air Quality Monitoring, NodeMCU ESP8266, MQ135 Sensor, DHT11 Sensor, ThingSpeak, Environmental Monitoring, Real-Time Monitoring, Smart Health Systems, Cloud Computing, Air Pollution Detection.

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Creative Commons Attribution 4.0 and The Open Definition