Volume 14 | Issue 4 |

 Abstract

In recent times, the security of women and children has gained importance among other societal concerns owing to rising crime rates and non-existence of instantaneous aid mechanisms. In this context, this paper proposes an IoT-based safety solution through integration of Raspberry Pi Pico along with ESP32-CAM, GPS, and GSM modules. The proposed IoT solution will be capable of locating the user, sending emergency message notifications and calls to specified contacts, and even capturing the live image of the concerned individual. The activation of the alarm will occur through pressing of a panic button and indication of system activity will take place with LEDs. Once activated, the system will send location information of the user through SMS and along with capturing of the live image using ESP32- CAM.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Raspberry Pi Pico, GPS, GSM, Panic Button and ESP32- CAM module

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Personal safety, particularly for women, remains a paramount concern in today's society. This project report outlines the development of a compact and efficient women's safety device designed to enhance personal security through the integration of several critical technologies. The device employs an Arduino Nano, GSM SIM800L, NEO-6M GPS Module, DC Buck Converter, and a push button for activation, ensuring a practical and user-friendly safety solution. At the heart of the device is the Arduino Nano, a small yet versatile microcontroller that manages the system's operations. The GSM SIM800L module is used for reliable communication, enabling the device to send emergency messages and make phone calls to pre-defined contacts. This ensures that, in the event of a distress situation, immediate assistance can be requested. The NEO-6M GPS module provides accurate location tracking, transmitting real-time GPS coordinates to emergency contacts. This feature is crucial for ensuring that the user's exact location is known, allowing for swift and precise intervention. The DC Buck Converter is incorporated to stabilize the power supply, ensuring that the device operates efficiently even with variations in input voltage. This component helps in maintaining a consistent power output, thereby extending the device's operational life and enhancing its reliability. A push button is included to allow for straightforward activation of the emergency alert system. When pressed, the button triggers the device to send a distress signal along with the GPS coorinates to designated contacts, ensuring that help can be dispatched promptly.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

A push button is included to allow for straightforward activation of the emergency alert system. When pressed, the button triggers the device to send a distress signal along with the GPS coorinates to designated contacts, ensuring that help can be dispatched promptly.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Financial markets provide high return potential but also expose traders, particularly beginners, to significant risk due to the absence of structured platforms for validating trading strategies before live deployment. Many novice traders depend on unverified tips and emotional decision-making, often leading to substantial losses. This paper proposes a web-based back-tested technical strategy simulator that enables risk-free, systematic evaluation of trading strategies using historical market data. The system integrates financial analytics with full-stack web technologies to support strategy testing across multiple asset classes, including equities, cryptocurrencies, forex, and commodities. Trend-following approaches based on Exponential Moving Average (EMA) crossovers and the Supertrend indicator are implemented to generate trading signals over the charts. Multi-timeframe analysis is incorporated to capture broader market trends while accounting for short-term price fluctuations and pullbacks. A structured backtesting framework simulates trade execution and evaluates performance using metrics such as win rate, total trades, net returns, and drawdown. Additionally, sentiment analysis is incorporated to complement technical indicators by providing fundamental market insights. Experimental results demonstrate that systematic technical backtesting along with sentiment analysis improves strategy reliability, reduces emotional bias, and supports disciplined, data-driven decision-making for traders.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Algorithmic Trading, Backtesting, Supertrend Indicator, Exponential Moving Average (EMA), Technical Analysis, Trading Simulator

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Chronic pain remains one of the most challenging health conditions to manage, often affecting quality of life, mobility, and mental well-being. Conventional treatments--primarily pharmaceutical--may offer temporary relief but are frequently associated with side effects and long-term dependency. In contrast, naturopathic medicine offers a holistic and non-invasive approach that addresses the root causes of pain and supports the body's natural healing mechanisms.This review examines the effects of naturopathic therapies on chronic pain, with a particular focus on conditions such as slipped disc (lumbar disc herniation), cervical spondylosis, and arthritis (including rheumatoid and osteoarthritis). Drawing on findings from clinical studies, traditional knowledge, and modern integrative health research, the review examines a range of naturopathic interventions including hydrotherapy, herbal medicine, yoga and therapeutic movement, massage therapy, dietary modifications, and detoxification practices.Evidence suggests that these therapies help reduce inflammation, alleviate muscular tension, promote spinal alignment, and improve joint function. Specifically, yoga and physical therapy-based practices have shown promise in managing cervical and lumbar pain, while anti-inflammatory herbs and an alkaline diet may offer relief in arthritis. Hydrotherapy and massage have been beneficial in reducing stiffness and improving circulation in affected areas.In summary, naturopathic medicine provides a comprehensive, patient-centred approach to managing chronic pain. When personalised and used under professional guidance, these therapies can enhance functional outcomes, reduce reliance on medication, and improve overall well-being in individuals suffering from long-term musculoskeletal pain. This review advocates for further clinical studies and greater integration of naturopathic strategies into mainstream pain care protocols.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Chronic Pain, Naturopathy, Slipped Disc, Cervical, Arthritis, Holistic Healing, Yoga Therapy, Integrative Medicine, Natural Pain Management.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Vision-threatening ocular conditions represent one of the most pressing yet underaddressed challenges in modern global healthcare. The progressive and often silent nature of diseases such as glaucoma, cataracts, diabetic retinopathy, and conjunctivitis means that millions of patients worldwide experience significant deterioration in visual function before receiving an accurate diagnosis. The consequences of delayed or missed detection are frequently irreversible, making the development of robust, accessible, and transparent automated diagnostic tools a matter of clinical urgency. While the last decade has witnessed remarkable advances in artificial intelligence, particularly in the domain of computer vision through convolutional neural networks (CNNs), the translation of these technologies into genuine clinical practice has remained disappointing. The fundamental obstacle is not classification accuracy -- modern deep learning architectures routinely achieve performance metrics comparable to trained specialists on benchmark datasets -- but rather the opacity, inflexibility, and clinical narrowness of existing systems. Current AI-based retinal diagnostic tools operate almost exclusively as black-box classifiers: they accept an image as input and produce a disease label as output, with little regard for how that decision was reached, what symptoms the patient is reporting, or what should happen next in the patient's care journey. This paper introduces a novel, end-to-end intelligent eye disease detection and management framework that fundamentally reimagines the role of AI in ophthalmological diagnosis. The proposed system moves well beyond single-modality image classification by integrating four tightly coupled components: a multi-stage CNN pipeline for hierarchical feature extraction from retinal fundus images, a voice-enabled symptom capture interface for multimodal patient input, an AI-driven clinical reasoning engine that cross-validates imaging findings against reported symptoms, and a comprehensive post-diagnosis care coordination module. The result is a system that does not merely classify retinal images but reasons about them in a manner that is transparent, patient-centered, and actionable. Clinicians receive not a binary label but a structured diagnostic narrative accompanied by confidence estimates, symptom consistency scores, risk stratification, specialist referral guidance, and automated scheduling of follow-up appointments.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

CNN(Convolutional neural network),Eye disease prediction, Deep Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid growth of artificial intelligence has significantly transformed the healthcare and fitness domain by enabling intelligent and personalized solutions. This paper presents "House of Fitness", a hybrid AI-based fitness assistant that integrates multiple modules including real-time posture detection, personalized diet recommendation, exercise suggestion, nutrition tracking, and an AI-powered chatbot. The proposed system aims to provide a comprehensive and cost-effective digital fitness solution for users with minimal dependency on professional trainers. The posture detection module utilizes computer vision techniques with MediaPipe and OpenCV to analyze human body landmarks and detect incorrect exercise postures in real time, while a voice feedback mechanism provides immediate corrective guidance to improve exercise accuracy and reduce injury risk. The diet recommendation system generates personalized meal plans based on user parameters such as age, weight, height, and fitness goals using dataset-driven analysis, and the nutrition tracking module evaluates daily intake of calories and macronutrients to ensure balanced dietary habits. Additionally, the system incorporates an AI chatbot powered by a hybrid conversational model to assist users with real-time fitness queries, enhancing user interaction and engagement. Experimental results demonstrate that the proposed system achieves high accuracy in posture detection (90-95%) and provides efficient response times across modules, making the system scalable, user-friendly, and effective for personalized fitness management.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Artificial Intelligence, Fitness Assistant, Computer Vision, Posture Detection, Diet Recommendation, Nutrition Tracking, AI Chatbot, Machine Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rise in proportion of mental health disorders has increased the need for effective solutions and monitoring systems. The traditional assessment methods often fail to detect the symptoms early, which can lead to delay in intervention. Artificial intelligence (AI) , gives us a promising approach by making us use continuous monitoring systems and analysis of all the behavioral data. This paper reviews AI-driven mental health monitoring systems, which focuses on predictive analysis and early intervention. It helps us to understand how we can use the data from mobile applications, wearable devices, and digital interactions to identify patterns associated with mental health conditions. The study also discusses AI-based interventions such as chatbots and automated alerts. Other key challenges, including privacy, data security, and ethical concerns are also highlighted in this paper. While we cannot use AI to replace professional care, we can use it to detect symptoms early and improve accessibility to mental health resources.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Mental Health, Predictive Analysis, Chatbots, Monitoring Systems

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

AI tools are now widely used by students for studying and exam preparation. However, most of these tools are general-purpose and do not follow university-specific answer formats, syllabus structure, or marking schemes. Because of this, students may understand concepts but still fail to score well in exams. To solve this, we propose ELEVATE AI, an AI-based system designed specifically for exam-oriented learning. The system generates structured answers using syllabus data, textbooks, previous year questions, and predefined diagrams. It also introduces a practice-based approach where students write answers and get feedback by comparing with ideal model answers. In addition, voice interaction is supported using speech-to-text and text-to-speech, allowing the system to act like a tutor. The backend uses a multi-agent architecture along with Retrieval-Augmented Generation and high-speed inference through Groq with LLaMA 3.3. The system focuses not just on understanding but also on answer writing and presentation, helping students improve both learning and scoring.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Large Language Models, Retrieval-Augmented Generation, Exam-Oriented Learning, Intelligent Tutoring System, Previous Year Question Analysis, Educational Chatbot

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Large Language Models have demonstrated exceptional capabilities in natural language understanding and text generation. However, these systems often generate responses that appear convincing but lack factual accuracy due to their dependence on static training data. Retrieval-Augmented Generation introduces a hybrid framework that combines external knowledge retrieval with generative modeling to improve reliability and contextual grounding. Instead of relying solely on internal parameters, RAG systems dynamically retrieve relevant information from structured knowledge sources before generating responses. This paper presents a comprehensive study of the RAG architecture, its operational workflow, practical applications, advantages, and limitations. The study highlights how Retrieval-Augmented Generation reduces hallucination, enhances transparency, and strengthens trust in AI-driven systems deployed in knowledge-intensive domains such as healthcare, enterprise automation, and education.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Retrieval-Augmented Generation, Large Language Models, Knowledge Retrieval, Generative Artificial Intelligence, Intelligent Information Systems

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Heart failure continues to present significant challenges in global healthcare despite advances in modern therapeutics. The ancient Ayurvedic system offers a distinct perspective on cardiac disorders through the concept of Hridroga, which merits scientific exploration alongside contemporary cardiology.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Hridroga, Heart failure, Ayurveda, Terminalia arjuna, Integrative cardiology, Panchakarma

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Creative Commons Attribution 4.0 and The Open Definition