Journal of Scientific Research and Technology

Gesture-Based Air Typing: A Machine Learning Approach for Accessible Text Input

Premala Bhande, Sharanbasava, Pavan, Siddu Patil, Prashanth Panchal — Thu, 02 Jan 2025 00:00:00 +0000

The "Gesture-Based Air Typing: A Machine Learning Approach for Accessible Text Input" project represents a groundbreaking approach to hands-free typing, designed to revolutionize human-computer interaction by leveraging advanced hand gesture recognition technologies. This innovative system employs MediaPipe for precise hand tracking and EasyOCR for accurate text recognition, enabling users to seamlessly input text through freehand gestures captured in realtime using a simple webcam. The project's emphasis on cost-effectiveness and accessibility ensures that it caters to a broad audience, including individuals with physical impairments and those seeking futuristic, touch-free typing solutions. By eliminating the reliance on physical keyboards, this system opens new possibilities for inclusive design and interaction in digital environments.

AI Chat-Bot For Mental Health Support

Rajshekar G, Sachin, Siddu Kolare, Rutuja Patil, Rashmi Patil — Thu, 02 Jan 2025 00:00:00 +0000

One of the most neglected but important parts of our overall health in the modern world is mental health. Because of time, money, and space limitations, and dearth of resources linked with in-person therapy, we suggest a system for a virtual mental health assistant in this work. Problems with mental health can have a domino effect that requires constant monitoring and proactive measures to fix. A virtual mental health chatbot makes this feasible. A conversation function, several language voice input choices, and a mood-boosting suggestion tool will all be part of the suggested chatbot. The project's data was trained using neural networks, and to enhance the findings, Natural Language Processing methods will be used. AI chatbot for mental health assistance is the focus of this article. The increasing demand for accessible mental health resources has led to the emergence of AI-driven solutions. This study examines existing systems, identifies their limitations, and proposes a more effective AI chatbot model. The proposed system aims to enhance user experience through personalized interactions, data privacy, and integration with mental health professionals. By bridging the gap between technology and mental health, this chatbot seeks to provide timely support and resources to individuals in need.

Analysis And Prediction Of Crime Against Women Using Machine Learning

Keerti, Mrs. Neha Deshmukh — Mon, 06 Jan 2025 00:00:00 +0000

A critical worldwide issue, the rising number of crimes perpetrated against women calls for fresh approaches to the problem's prevention and solution. The purpose of this research is to examine crime data from the past, find trends and patterns, and identify possible crime hotspots by using M-L algorithms. Using predictive modeling techniques and complex data analytics, this research aims to provide important information that lawmakers and law enforcement may use to protect women and reduce the likelihood that they will be victims.
Gathering and preparing massive datasets including geographical data, socioeconomic variables, criminal records, and demographic information is the meat and potatoes of this project. Publicly accessible crime reports, police records, and government databases are some of the sources of datasets. For accurate and trustworthy analysis, a comprehensive preparation procedure is carried out to handle missing numbers, normalize the data, and eliminate inconsistencies.

Numerical Modelling Analysis for Design of Optimum Stope and Pillar Geometry for Underground Mining of East Deposit of Kheratarla Wollastonite & Calcite Mine

M. Kulshrestha, Dr. S. C. Jain — Fri, 10 Jan 2025 00:00:00 +0000

This paper presents a comprehensive numerical modeling analysis aimed at designing optimal stope and
pillar geometries for the underground mining of the East Deposit of Kheratarla Wollastonite & Calcite
Mine. Using ITASCA's IMAT software powered by FLAC3D, the study constructs a detailed 3D model
that simulates critical stress distributions within the rock mass, enabling a precise assessment of various
stope geometries' stability. The results provide a robust framework supporting a safe and efficient
transition to underground mining.

Machine Learning Based Night Light Fog Images Enhancement For Underwater Environment

Shridevi Soma, Tamanna Pawar — Sun, 19 Jan 2025 00:00:00 +0000

In the present system, dispersion and immersion of light leads to detraction of images. Enhancement of underwater images at different lighting conditions is the challenging research problem, loss of detail and distorted visual information, underwater imaging in low light and fog is quite difficult. Light and water particles interacting makes picture restoration more difficult in these settings, making traditional dehazing methods ineffective. Using state-of-the-art machine learning methods, proposed system offers a dehazing solution tailored to underwater night light fog images. In order to meet the requirements pre-processing is done on the frame which includes gamma correction for denoising of selected input image and then the fusion method is applied which includes white-balance to decrease the greenish effect of underwater images and for images affected by fog at night, Guided-filter is used to improve the brightness and clarity of underwater photographs, proposed method combines a Guided Filter Transmission with pre- and post-processing approaches. Because it is trained on a wide dataset of both actual and simulated underwater foggy images, the system can adjust to numerous levels of haze and learn complicated patterns. The solution obtained in this paper offers better augmentation of structural features and colour fidelity, a video of 5 minutes 53 seconds is considered in this experiment and converted into 10,578 Frames. The sample images of size 480*360 from generated frame are taken then pre-processed using gamma correction and taken as input to gray world algorithm, the graphical representation of balanced RGB color is obtained as a result. The same image is further processed using guided filter algorithm which includes air-light estimation that increases image brightness which is followed by Dark Channel Prior. This research calculates the computational cost of the single image using guided filter transmission and even though more edge preserved, enhanced image can be obtained and it also surpasses current dehazing algorithms in terms of visual clarity and picture quality metrics with less computational i.e. 1.94 seconds and even more clear underwater image is obtained, according to comparative trials. The experimental results were demonstrated in Python with a 4GB RAM. With its innovative underwater image processing capabilities, the suggested system is sure to revolutionise marine research, undersea exploration, and surveillance.