Volume 14 | Issue 4 |

 Abstract

Monitoring vegetation canopy cover is vital for ecological balance, biodiversity preservation, and sustainable land management in the Marathwada region of Maharashtra, India, where agricultural growth, deforestation, and climate variability have driven significant environmental shifts. This study employs MODIS satellite imagery (2013-2023) to assess vegetation dynamics through integrated remote sensing and machine learning approaches. Pre-processing involved Dark Object Subtraction (DOS) for atmospheric correction, pixel normalization, and geometric alignment to ensure data accuracy. The Normalized Difference Vegetation Index (NDVI) served as the primary metric for vegetation density, with classification performed using supervised methods (Random Forest, XGBoost, Maximum Likelihood Classification) and unsupervised techniques (PCA + K-Means clustering). Random Forest achieved perfect classification accuracy (100%), followed closely by XGBoost (99.29%), surpassing Maximum Likelihood Classification (98.31%) and K-Means clustering. Temporal analysis revealed canopy cover fluctuations, with losses linked to urbanization and deforestation, alongside localized regrowth. PCA-based feature reduction preserved 94% of data variance, enhancing classification efficiency. The study achieved over 85% overall accuracy, highlighting the efficacy of ensemble classifiers (Random Forest and XGBoost) for precise land cover mapping and the utility of PCA + K-Means for initial vegetation analysis. These insights support environmental monitoring, climate impact evaluation, and sustainable land-use strategies in ecologically critical regions.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

NDVI, Vegetation Canopy Cover (VCC), Remote Sensing, Maximum Likelihood Classification, MODIS Imagery, PCA + K-Means

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cinema has long served as a powerful medium for reflecting and shaping societal perceptions of marginalized communities. The representation of transgender individuals in Indian cinema has historically been limited to stereotypes, ridicule, or marginal roles. However, contemporary films have begun to challenge these conventions by offering more nuanced portrayals. This research paper examines the portrayal of transgender identity in the Tamil film Super Deluxe, directed by Thiagarajan Kumararaja. Through a qualitative case study approach, the research analyzes the character of Shilpa, portrayed by Vijay Sethupathi, focusing on themes of identity, societal acceptance, stigma, and agency. The study evaluates how the film navigates between progressive representation and problematic stereotyping. While Super Deluxe presents a layered and humanized depiction of a transgender woman, it has also faced criticism from sections of the transgender community for reinforcing certain misconceptions. The research concludes that the film marks a significant transition in Tamil cinema toward inclusive storytelling, though it still reflects the complexities and contradictions inherent in representing marginalized identities.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Representation of Transgender, Stereotyping, Marginalized communities, Qualitative case study

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Organic Trace blockchain framework presents a decentralized solution to enhance transparency, traceability, and reliability in agricultural supply chains. Built using Ethereum smart contracts and a Python Flask backend, the system securely tracks products from harvesting to distribution by storing immutable records on the blockchain. It enables real-time monitoring for stakeholders through role-based access and allows customers to verify product origin using QR codes. The system improves data integrity, operational efficiency, and trust among participants in the agricultural ecosystem.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blockchain, Agriculture, Supply Chain Management, Smart Contracts, Traceability, Transparency, Web3, Ethereum, Data Integrity, Flask.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Student Interest; Academic Performance; Rural Education; Core Subjects; Government Schools

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

As therapeutic applications that are easily integrated and collaborated with wearable medical equipment, biosensors are essential in the diagnosis and monitoring of diseases. For this new medical device industry, several regulatory markets are putting development and management concepts into practice. We anticipate that wearable technology will become more integrated into people's daily lives and play a bigger role in the field of health care as science and technology advance and personalised health concepts gain popularity. To investigate more wearable device uses in the medical field, more research is necessary. We anticipate that this evaluation will serve as a helpful resource for the creation of wearable medical technology. In India, medical devices classified as medications under Section 3(b)(iv) of the medications and Cosmetics Act are sold, imported, and manufactured under the principal regulatory authority of the Central Drugs Standard Control Organization (CDSCO). The insecurity of wearable mobile devices is explained in this article. Examples are used to analyse their vulnerabilities. It is feasible to show wearable gadgets' weakness using concrete examples of hacking.Wearable technology and telemonitoring are revolutionising healthcare by facilitating ongoing patient monitoring and tailored therapies

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Wearable, Biosensors, Regulatory Framework, Medical Devices, Regulatory Challenges

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Principal component analysis, K-means clustering, Hierarchical clustering, Therapeutic growth factor, Walk-and-talk therapy, Himalayan psychology, Latent structure, Regression Modeling, Nature based intervention and Unsupervised learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Hypertension, a major health issue in developing countries, contributes significantly to cardiovascular disease. This study explores the potential of drumstick seed powder incorporated sprouted millet malt in managing high blood pressure. Drumstick seed powder showed moderate toxicity in the brine shrimp assay, while heavy metal levels showed within safe limits and did not contribute toxicity. Quantitative analysis using UV-spectrophotometry. Nutrient analysis showed that drumstick seed powder contained high levels of calcium, magnesium and potassium, along with vitamin C, all of these nutrients help for antihypertensive properties. The Sorghum Millet was germinated and malted and also drumstick seed powder was incorporated in 5g, 10g and 15g variations. Sensory evaluation using a 9-point hedonic scale, indicated that 10 g of drumstick seed incorporation variation in sprouted sorghum millet malt had the highest acceptability. Phytochemical analysis of SM2 showed the presence of coumarins, flavanoids, phenols and polyphenols. In Quantitative analysis using UV- Spectrophotometery results showed that coumarins were present at 0.12 mg/g, flavonoids at 37.8 mg/g, phenols at 44.3 mg/g, and polyphenols at 25 mg/g. The moisture content, pH, bulk density, tapped density, color and water absorption capacity are confirmed in physicochemical properties.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Hypertension, drumstick seed powder, sprouted sorghum millet, phytochemical analysis, nutrient analysis, physicochemical properties and shelf life.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper presents an Intelligent Anomaly Detection and Cyber Defence System -- a real-time Network Intrusion Detection System (NIDS) capable of classifying network traffic into five attack superclasses: Normal, DOS, PROBE, R2L, and U2R. The system implements a dual prediction pipeline architecture: a Legacy Path utilizing full-session feature classification based on the 41-feature NSL-KDD dataset, and an Early-Stage Path enabling fast probabilistic risk scoring derived from 2-second packet aggregation windows. The Early-Stage Path extracts six lightweight behavioral features -- packet count, SYN rate, average packet size, payload entropy, connection attempts, and window duration -- and maps them through either a trained RandomForest classifier or a heuristic risk estimator when the trained model is unavailable. The system is deployed as a Flask-based web application integrated with a Scapy packet capture daemon, a thread-safe producer-consumer architecture, and a real-time browser dashboard with live polling. A training pipeline converts NSL-KDD connection records into synthetic 2-second windows using sliding window aggregation, and a RandomForestClassifier with balanced class weighting is trained on the resulting dataset. Experimental results demonstrate effective detection of DOS and PROBE attacks in real-time, with graceful degradation to heuristic estimation under model-unavailable conditions. The system bridges the gap between offline model training and live network defense, providing a deployable, scalable intrusion detection framework.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Network Intrusion Detection System, Machine Learning, Random Forest, NSL-KDD, Anomaly Detection, Real-Time Traffic Analysis, Shannon Entropy, Cyber Security, DoS Detection, Packet Analysis

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Mental health among students has become a key societal health issue in every corner of the world, and the barriers to access, social stigma, and lack of resources have been impeding the timely access of the significant portion of the student population. The paper describes Sanctuary, an anonymous mental wellness web platform giving solutions to these challenges, which uses AI. The platform offers a non-stigmatized safe setting where students can evaluate their wellness in four clinical domains (mood, sleep, stress, and behaviour), receive an AI-generated personalized wellness reports, follow a gamified suite of wellness activities, chat with an anonymous AI therapist (LLM), and find mental health services nearby with an integrated geolocation module. The identity architecture is built using pseudonymous identities, each user is assigned a unique Wellness ID and display alias, which makes the identity absolutely anonymous. A population-level risk stratification and engagement monitoring dashboard is available to the institutional stakeholders on a real-time basis, without revealing individual identities. Pilot deployment with six users confirmed platform functionality in each of the modules. The system is deployed with React.js, Node.js/Express, and the Anthropic Claude API using geolocation features with the OpenStreetMap and Leaflet.js. The paper will include the system architecture in its entirety, four data tables that are detailed, pilot outcome, ethical discourse, and next-generation research directions

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Mental health platform, student wellness, gamification, AI therapist, self-screening, anonymity, mood tracking, mindfulness, geolocation, large language models

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The growing awareness among consumers about environmentally friendly products has led to the increasing importance of green marketing. Many organisations have revised or developed their products and processes to position themselves as environmentally responsible. Advertising has become a key communication tool for marketers to convey their environmental commitment and shape consumer perceptions by creating a green image. Consequently, consumer perception plays a critical role in influencing green purchase behaviour and guiding companies' strategic decisions. The present study examines the perception of consumers among Calicut district, Kerala towards green advertisement its influence on green purchase intention. A sample size of 250 consumers was surveyed and the data was analysed using descriptive statistics and Chi-square tests. The findings reveal that a positive perception of green advertisement among consumers with significant variations based on demographic factors such as age, income and educational qualification.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Green Advertising, Green Purchase Intention and Perception Analysis.

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Creative Commons Attribution 4.0 and The Open Definition