Volume 14 | Issue 5 |

 Abstract

Neuromorphic sensors are bio-inspired sensing devices that emulate the functioning of the human nervous system through event-driven processing, adaptive learning, and ultra-low-power computation. These sensors are increasingly integrated into smart wearable systems for healthcare, fitness, yoga, and meditation applications. Smart wearable devices equipped with neuromorphic sensing capabilities can continuously monitor physiological and behavioral signals such as electroencephalography (EEG), electromyography (EMG), heart rate variability (HRV), respiration rate, skin temperature, and body posture in real time. Unlike conventional wearable systems, neuromorphic wearables provide energy-efficient edge intelligence, reduced latency, adaptive learning, and personalized feedback mechanisms. This paper presents a comprehensive study on neuromorphic sensors for yoga and meditation wearables, including system architecture, sensing mechanisms, machine learning integration, applications, advantages, limitations, and future research directions. The proposed framework demonstrates how neuromorphic wearable systems can improve mental wellness, posture correction, stress reduction, mindfulness tracking, and adaptive meditation guidance. The research also highlights the role of low-power edge AI and brain-inspired computing in next-generation intelligent healthcare systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Neuromorphic Sensors, Smart Wearables, Yoga Monitoring, Meditation Assistance, Edge AI, EEG Sensors, Human Activity Recognition, Brain-Inspired Computing, IoT Healthcare.

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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

FOUR score, Glasgow Coma Scale, pediatric non-traumatic coma, PICU, outcome prediction, mortality

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The proposed system presents an embedded vision-based solution for automated validation of resume photographs using Raspberry Pi and OpenCV. In modern recruitment processes, verifying whether candidate photographs meet professional standards is typically performed manually, leading to inconsistencies, time consumption, and human error. To address this issue, the system captures real-time images using a camera module and performs image processing techniques such as face detection, facial landmark identification, pose estimation, and upper-body framing validation. It evaluates key parameters including face alignment, ear visibility, image clarity, and lighting conditions to classify images as valid or invalid. The system provides immediate feedback through an LCD display and buzzer alert, and also sends email notifications to inform users about the validation results. By integrating hardware and software components into a compact and cost-effective embedded platform, the proposed system ensures standardized, accurate, and efficient resume photo validation, significantly reducing manual effort while improving reliability in recruitment

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Embedded Vision System, Raspberry Pi, Resume Photo Validation, OpenCV, Facial Feature Detection, Image Processing, Automation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The increasing number of campus events in universities has introduced challenges in efficient event discovery, registration, and management. Traditional approaches relying on social media and manual coordination often result in fragmented communication, missed opportunities, and inefficient participation. This paper presents RevaShow, a full-stack, role-based web platform developed for REVA University that consolidates event discovery, registration, and administration into a single system. The platform supports three user roles -- Student, Organizer, and Administrator -- enforced through JWT-based Role-Based Access Control (RBAC). Built on React.js, Node.js, Express.js, PostgreSQL, and Prisma ORM, the system features real-time seat availability management, an organizer analytics dashboard, and a comprehensive admin panel. Functional evaluation across 101 test cases yielded a 98% pass rate. The system eliminates overbooking, reduces average registration time from over five minutes to under sixty seconds, and provides organizers with instant registration analytics. RevaShow demonstrates the viability of applying enterprise-grade web engineering principles to solve practical academic institution challenges.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Campus Event Management, Role-Based Access Control, Web Application, Node.js, React, PostgreSQL, Prisma ORM, Event Registration, Attendance Management, Centralized Platform, Student Portal, Express.js, RESTful API, Authentication

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The present study discusses the effects of colonialism and globalisation on the indigenous amaXhosa people of South Africa and their environment. it discussses how erasure of their space have occurred and how they have resisted the complete disruption.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Colonialism, Globalisation, Space, Indigenous people

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

India's new data protection law, the Digital Personal Data Protection Act of 2023, and its 2025 Rules, create a new kind of intermediary called the Consent Manager. The idea is that instead of every individual having to deal separately with hundreds of companies that collect their personal data, a single registered platform will let the user give, review, manage, and withdraw consent across all of them. This article examines whether the design of the Consent Manager will actually work for ordinary Indians, particularly those who are poor, less digitally literate, do not read English or Hindi well, share their phones with family members, or live in remote areas. It traces the legal architecture of the Consent Manager through the DPDP Act and the 2025 Rules, compares it with the existing Account Aggregator framework in the financial sector, and tests it against the constitutional standards of equality and proportionality laid down by the Supreme Court of India. The argument is that the present design assumes a data principal who is literate, English-speaking, smartphone-owning, and individually situated. Most data principals in India are not all of these things. The article concludes with five concrete reforms that can be brought in through delegated rules and regulatory guidance, without amending the Act itself, to make the Consent Manager genuinely accessible to the populations it most needs to protect.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The ability to effectively ensure the safety of individuals within crowded public venues is one of the most challenging tasks in urban planning today. This area of concern rests at the intersection of urban infrastructure, public health, and emergency preparedness. Monitoring human security via video camera often results in staff losing concentration over time, and the ability to monitor multiple simultaneous video feeds becomes severely limited. We present a real-time crowd detection and social distance monitoring system built on YOLOv5x, the extra-large variant of Ultralytics' YOLOv5 family, running fully on GPU via PyTorch Hub. The system incorporates confidence-filtered COCO person detection, pairwise Euclidean proximity analysis with configurable thresholds, an OpenCV-based bird's-eye view perspective correction module to reduce foreshortening-induced false positives, and automated SMS alerts via the Twilio API when crowd count exceeds a defined threshold. At a confidence threshold of 0.5, the system achieves Precision 0.912, Recall 0.887, and F1-score 0.899. The bird's-eye transform improves violation detection accuracy by 10.3% and reduces false alerts by approximately 60%. The system sustains 31.4 FPS on an NVIDIA T4 GPU, with end-to-end SMS alert latency of 1.84 seconds over a 4G connection.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Crowd Detection YOLOv5 Social Distance Monitoring Bird's-Eye View Perspective Transform Homography Twilio SMS Alert OpenCV PyTorch People Counting Public Safety Real-Time Video Analysis

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Nanoemulsions have emerged as advanced colloidal carriers capable of enhancing topical drug delivery by improving solubility, permeation, and therapeutic efficacy. Their nanoscale droplet size, high kinetic stability, and compatibility with diverse pharmaceutical actives make them ideal for dermatological and cosmeceutical applications. This review provides a comprehensive analysis of nanoemulsion formulation strategies, mechanistic insights into skin permeation, characterization techniques, and translational progress toward clinical use. Special emphasis is placed on nanoemulgels, surfactant/co-surfactant systems, droplet engineering, and therapeutic applications in inflammation, infection control, wound healing, and chronic skin diseases. Challenges related to scale-up, regulatory pathways, and long-term safety are critically evaluated. Future directions highlight personalized nanoemulsion therapy, multifunctional systems, and AI-driven formulation optimization.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Nanoemulsion, Nanoemulgel, Topical drug delivery, Skin permeation, Dermatology, Clinical translation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract-- Finding reliable local skilled workers such as electricians, plumbers, carpenters, and appliance repair technicians remains a major challenge in urban and semi-urban regions. Existing methods primarily depend on informal referrals, which lack transparency, worker verification, and service quality assurance. This research proposes a secure digital labour marketplace that connects customers with verified local skilled workers through an AHP-weighted trust-aware recommendation system.

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Creative Commons Attribution 4.0 and The Open Definition

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Keywords: Digital Marketplace, Labour Platform, AHP Weight Derivation, Composite Trust Ranking, OTP Authentication, Worker Verification, XGBoost Validation, Location-Based Services

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

As the popularity of digital devices and digital interactive systems has increased over the previous few years, the understanding of human emotions has become an important area of study. Emotion detection is important in various application domains, such as healthcare, education, and human-computer interaction. Accurately identifying emotions is a complex process, since various factors contribute to emotion detection, including facial expressions, behavior patterns, and context. This research project investigates the ability of deep learning techniques to detect emotion based on visual representations of people (e.g. images). This study explores various types of features used to categorize emotions (e.g. facial expressions, image-based cues) and assesses performance when different types of deep learning models are employed. In contrast to developing one type of deep learning model specifically to detect emotions, this research compares different approaches in order to establish models that perform well under similar conditions. The final goal is to generate an effective framework that can be applied and implemented in real-world practices with minimal effort and/or modification to the models.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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Creative Commons Attribution 4.0 and The Open Definition