Volume 14 | Issue 3 |

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords - Financial Literacy, Risk Tolerance, Behavioural Biases, Investment Behaviour, Herd Behaviour, Loss Aversion, Overconfidence, Locus of Control, Individual Investors, Valsad District

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A rare localized variation of Darier's disease, linear Darier's disease is characterized by aberrant keratinization and epidermal acantholysis. Clinically, it might resemble other linear dermatoses like verrucous epidermal nevus and appear with hyperkeratotic papules grouped along the lines of Blaschko, making diagnosis challenging. We describe a young woman who had several verrucous, hyperkeratotic plaques arranged in a straight line. Initially, verrucous epidermal nevus was suspected based on the clinical presentation. However, the diagnosis of linear Darier's illness was confirmed by histological analysis of the skin biopsy, which showed supranasal acantholysis and dyskeratosis with distinctive corps ronds and grains. In order to distinguish between clinically similar dermatological disorders and make an appropriate diagnosis, this case emphasizes the significance of histological investigation and clinicopathological correlation.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Linear Darier's Disease, Verrucous Epidermal Nevus, Histopathology, Skin biopsy, Hyperkeratotic plaques, Blaschko's lines

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rising problem of stress now affects mental health, work efficiency, and total human health. The process of detecting stress at an early stage works to establish both prompt treatment and the avoidance of extended health issues. The research presents a dual system which detects stress in real time through two different approaches that track body functions and assess emotional states to achieve better stress measurement outcomes. The system uses multiple methods to assess how body functions and human emotional signals interact which helps to measure a person's stress level more accurately. The research team applied machine learning and deep learning methods for their study of stress pattern detection using data that they collected. The system uses multiple models that work together to evaluate incoming data and determine whether a person experiences stress through a final stress assessment. The proposed framework uses information from multiple sources to solve the problems that single-modality systems face when detecting stress. The created method offers an effective method which can monitor people continuously while it supports mental health monitoring and workplace wellness programs and human- computer interaction needs.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Stress detection, multimodal analysis, electro- cardiogram (ECG), affective computing, deep learning, sentiment analysis.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Brain Computer Interface (BCI) technology enables direct communication between the human brain and external devices by translating neural activity into control commands. This paper presents the design and development of a low cost BCI-based smart wheelchair intended to assist individuals with severe motor disabilities. The proposed system utilizes electroencephalography (EEG) signals acquired from the scalp to detect user intent and convert it into real-time wheelchairmotion commands. An embedded control unit based on an Arduino microcontroller processes the classified EEG signals and controls the wheelchair motors through an L298N motor driver. To enhance user safety, ultrasonic sensors are integrated for obstacle detection and emergency stopping. The developed prototype demonstrates reliable hands-free navigation with a cost-effective and scalable architecture, making it suitable for assistive mobility and rehabilitation applications.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Brain-Computer Interface (BCI), Electroencephalography (EEG), Smart Wheelchair, Assistive Technology

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The problem of navigation assistance for visually impaired people is still a challenge due to the lack of environmental awareness and the high probability of encountering obstacles during navigation. Existing navigation aids like white canes are only limited to providing basic assistance and cannot detect obstacles at varying heights and distances. This paper proposes the design and implementation of a Smart Blind Stick, which is based on IoT technology and aims to help visually impaired people by enhancing their safe navigation and communication during emergencies. In this proposed system, an ESP32 module, ultrasonic sensors, and a servo motor are used to detect obstacles in different directions, i.e., left, front, and right, whereas another ultrasonic sensor is used at the bottom of the stick to detect obstacles at low levels. The system constantly monitors the environment and provides instant feedback using a buzzer alert system. This helps the user recognize the location of the obstacles. In addition to this, the system communicates with a mobile application using Bluetooth technology. This mobile application provides voice-based alerts for the direction of the obstacle. The mobile application also includes a GPS-based emergency alert system, which helps the user send their location to caregivers using Telegram and SMS by pressing a button. This provides double assurance of emergency alerting even when the internet connection is limited. The proposed solution incorporates low-cost hardware components, wireless communication, and mobile application support to design a practical and user-friendly assistive system. Experimental evaluation of the proposed system has shown that obstacles within a predetermined range can be successfully identified and alerted to prevent accidents. The proposed Smart Blind Stick is portable, cost-effective, and easy to use, making it a viable solution to improve the safety and mobility of visually impaired people in real-world environments.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Smart Blind Stick, Internet of Things (IoT), Assistive Technology, ESP32 Microcontroller, Ultrasonic Sensors, Obstacle Detection, Servo Motor,Bluetooth Communication, GPS Location Tracking, Emergency Alert System, Mobile Application.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The development of sustainable nanomaterials through green chemistry approaches has gained significant attention in recent years. In the present study, silver nanoparticles (AgNPs) were synthesized using aqueous bark extract of Spondias pinnata, a medicinal plant rich in bioactive phytochemicals. The green synthesis approach eliminates the need for toxic chemical reducing agents and provides a simple, cost-effective, and environmentally benign route for nanoparticle production. The formation of AgNPs was initially confirmed by a visible color change from pale yellow to dark brown due to surface plasmon resonance. The synthesized nanoparticles were characterized using UV-Visible spectroscopy, Field Emission Scanning Electron Microscopy (FESEM), Energy-Dispersive X-ray Spectroscopy (EDX), and zeta potential analysis. UV-Vis spectral analysis exhibited a characteristic absorption peak at 411 nm, confirming the formation of silver nanoparticles. FESEM images revealed predominantly spherical nanoparticles with an average particle size of 33.96 nm. EDX analysis confirmed the presence of elemental silver as the major component, along with minor signals corresponding to organic residues from plant biomolecules. Zeta potential analysis demonstrated a negative surface charge (-26.2 mV), indicating excellent colloidal stability and minimal aggregation. The study highlights the efficiency of Spondias pinnata bark extract as a natural reducing and stabilizing agent and supports its potential application in sustainable nanotechnology and biomedical research.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Green synthesis, Silver nanoparticles, Spondias pinnata, Bark extract, UV-Vis spectroscopy, FESEM, Zeta potential, EDX

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Application of Computational tools in Biodiversity Assessment is a multidisciplinary research approach that integrates biodiversity data with information technology and computational tools through remote sensing, satellite imaging, sensor systems, signal processing, data modelling, and statistical analysis. Technological applications enable large-scale monitoring of ecosystems, climate variables, and species distribution. The application of computational tools lies in advancements in sensor technology, enhanced AI algorithms, improved scalability and network connectivity. The utilization of big data and cloud computing will further help in revolutionizing the assessment of various levels of biodiversity (Species, Genetic and Ecosystem). This research article explores the advancements in computational tools applicable in assessment of biodiversity highlighting the data acquisition systems, imaging techniques, and computational modelling. The study emphasizes principles involved in various computational tools that support accurate biodiversity assessment and contribute to sustainable environmental management.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Biodiversity Assessment, Computational tools, Remote Sensing, Data Modelling, eDNA tools.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper explores how digital media, new platforms and emerging technologies change advertising in India's Direct to Consumer(D2C) market. The research uses a qualitative case-based approach, focusing on an in-depth interview with Aunip Jain, the founder of Curious Apes, a consulting firm based in Jaipur, which works with D2C brands in paid media, creative production, website optimization and artificial intelligence marketing workflows. In particular, India's context is relevant, because digital access has expanded rapidly: at the beginning of 2025, India had 806billion Internet users and 491 billion social media user identities (Kemp, 2025), while TRAI reported more than 1.028 billion internet subscribers by the quarter ending December 2025 (TRAI, 2026). Analysis shows that D2C advertising is moving from broad-ranging advertising to a system defined by native targeting of platforms, continuous creative tests, data-driven personalization and AI-assisted production. The interview also revealed a significant internal differentiation between product categories, with AI proven to be more effective in static-heavy advertising sectors than in aesthetically dependent sectors such as clothing. At the same time, the interview exposes tensions about the fatigue of influencers, privacy, skill gaps, and the limits of complete automation. The paper claims that the future of D2C advertising in India lies in the integration of human judgment, emotional intelligence, platform functionality and AI-enabled operating speed.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

D2C Advertising, Digital Media, Artificial Intelligence, Social Commerce, Personalization, Performance Marketing

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In today's digital era, organizations handle a large number of business documents such as invoices, receipts, purchase orders, and reports. Traditionally, these documents are processed manually by employees. Manual document processing is time consuming, inefficient, and often leads to human errors. It also becomes difficult to manage and organize a large number of documents properly. Therefore, there is a need for an automated system that can process documents quickly and accurately. The AI Powered Business Document Automation System is developed to automate the processing of business documents, especially invoices. The system provides a web-based interface where users can securely log in and upload invoice documents in image or PDF format. After the document is uploaded, the system uses Optical Character Recognition (OCR) technology to extract text from the document. In this project, the Tesseract OCR engine is used for text extraction. Once the text is extracted, the system analyzes the text and identifies important invoice information such as invoice number, invoice date, client email, and total amount. The extracted data is then stored in a MySQL database for future reference and management. The system also provides a dashboard interface where users can view previously processed invoices. Additionally, the system can automatically send extracted invoice information to the client through email notifications using SMTP service. This automation significantly reduces manual work and improves efficiency in document processing. The system is developed using Spring Boot for backend development, HTML and CSS with Thymeleaf for frontend interface, MySQL for database management, and Tesseract OCR for text extraction. The proposed system demonstrates how artificial intelligence and automation technologies can simplify business document management and improve overall productivity.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Document Automation, Optical Character Recognition (OCR), Invoice Processing, Tesseract OCR, Spring Boot, Web Application, MySQL Database, Email Automation, Data Extraction.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study presents an innovative urban stormwater management approach, combining detention basins, bioswales, and hanging gardens to address urbanization and climate change challenges. Detention basins, with efficient controls, store and release stormwater gradually, incorporating sustainable materials and biodiversity. Bioswales, integrated into the landscape, filter stormwater with native vegetation and permeable surfaces. Hanging gardens on vertical surfaces capture rainwater and enhance the system's ecological benefits. Smart technologies enable real-time monitoring for adaptive decision-making. Permeable pavements, integrated with basins, bioswales, and gardens, create a multifaceted approach, optimizing water management, biodiversity, and sustainable urban development. This research contributes valuable insights into resilient and sustainable urban infrastructure, offering a holistic solution to stormwater management for environmentally conscious urban spaces.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Stormwater Bioswales Hanging Gardens Permeable Pavements

  License

Creative Commons Attribution 4.0 and The Open Definition