FUTURISTIC SMART AI POWERED BACKPACK
Sign Language Recognition With Hand Gestures Using Deep Learning
RETINAL IMAGING FOR DIABETIC RETINOPATHY DETECTION THROUGH DEEP LEARNING
A REVIEW ON EARLY DIAGNOSIS OF GLAUCOMA USING MACHINE LEARNING TECHNIQUES
MENTAL HEALTH CHATBOT THERAPIST
AI-Aided Design Studio: Enhancing Graphic Design and User Interface with Machine Learning
Enhanced Mental Health Prediction with Deep Neural Networks for Accurate Diagnosis
Machine Learning Solutions for the Healthcare Industry: A Review
RNN LSTM-Based Emotion Recognition using EEG Signals
Transforming Homestay Guest Experience with AI-Powered Design Tools
Intelligent System-Based Stun Gloves for Women's Protection
AI for the Detection of Neurological Condition: Parkinson's Disease & Emotions
A Novel Approach for Lung Cancer Detection using Deep Belief Networks
Advancing Autism Spectrum Disorder Diagnosis through Ensemble Learning
Enhancing Chronic Kidney Disease Prediction Performance through Algorithm Fusion: A Combined KNN - SVM Approach
A Comparative Exploration of Activation Functions for Image Classification in Convolutional Neural Networks
Machine Learning Solutions for the Healthcare Industry: A Review
The Smartbag revolutionizes independent mobility for the visually impaired by combining cutting-edge technology with intuitive design. Functioning as both a navigational aid and an interactive assistant, it employs a Raspberry Pi 4 and Arduino UNO to power its array of features. By utilizing GPS tracking, ultrasonic sensors, and a facial recognition camera, it detects obstacles, charts safe routes, and captures images upon command. Integrated health monitoring and fall detection systems prioritize user safety, while a built-in Chatbot offers companionship and support during travel. With voice command capabilities and accessible interfaces, the Smartbag ensures seamless interaction and navigation. It represents a comprehensive solution for individuals with visual impairments, empowering them to navigate the world confidently and independently.
There are several obstacles when communicating with those who are deaf or hard of hearing, but sign language has become an essential tool. It is an excellent tool that helps people with speech and hearing impairments convey their ideas and feelings. This encourages less complicated integration with the outside environment. But because sign language has its own set of difficulties, its simple construction is insufficient. It might be difficult to interpret gestures for people who are not familiar with sign language or who speak a different sign language. Thankfully, new developments in technology have brought about a number of methods for automating the recognition of sign movements, which provide encouraging alternatives. This invention has the potential to close the long- standing communication gap considerably. In this work, we presented the usage of our exclusive dataset to identify hand motions. Our research project uses a webcam to allow users to take pictures of their hand gestures. The system's goal is to anticipate and show the name that corresponds to the image that was taken. Convolutional Neural Networks (CNNs) are used for image training and classification, with computer vision aiding in the image collection and capture process.
The prevalence of diabetes is increasing globally, necessitating efficient methods to enhance the timely identification and treatment of diabetes. Focusing on early detection and effective management strategies for complications is critical. In this study, an integrated solution comprising two modules is presented: diabetic detection and diabetic retinopathy detection. The diabetic detection module employs machine learning techniques such as decision trees, random forests, and KNN to forecast the presence of diabetes based on patient data. The diabetic retinopathy detection module utilizes deep learning techniques, specifically the ResNet50 model architecture, to analyze retinal images and identify signs of diabetic retinopathy. A comprehensive implementation of both modules, including data preprocessing, model training, and evaluation, using Python libraries such as TensorFlow, Keras, and scikit-learn is provided. The trained models are integrated into a web application. This web application allows users to input their medical data and retinal images and receive real-time predictions regarding their diabetic status and risk of diabetic retinopathy. The integration of these modules into a web application provides an intuitive interface tailored for both healthcare professionals and patients to assess diabetic risks conveniently. Furthermore, it facilitates early intervention and management of diabetic complications, ultimately improving patient outcomes and reducing healthcare burdens.
Glaucoma is a term used to describe either the progressive loss of retinal cells within the optic nerve or the gradual visual impairment caused by optic neuropathy. Glaucoma is a disease that affects vision in the eye. It is considered an irreversible condition that leads to vision deterioration. There are no early warning signs for glaucoma, and changes in vision may go unnoticed due to their subtle nature. Currently, numerous deep learning (DL) models have been developed for accurate glaucoma diagnosis. Therefore, we propose an architecture for precise glaucoma detection using Convolutional Neural Networks (CNNs). CNNs can distinguish between patterns specific to glaucomatous and non-glaucomatous conditions, providing hierarchical features for distinction in images. In the approach, glaucoma diagnosis hinges on the optic cup-to-disc ratio. The integration of an image data augmentation method enhances diagnostic accuracy. The results indicate that our proposed model, surpassing several existing algorithms, achieved an accuracy of 98%.
Mental illness refers to conditions that affect your thinking, feeling, mood, and behavior, which can vary in duration from occasional to chronic. Examples include depression, anxiety disorders, and addictive behaviors, among others. Such mental health issues may arise from substance abuse, psychological trauma, abuse, unhealthy habits, or genetics. Symptoms of mental illness can include sadness, mood swings, anger, hostility, and violence. Individuals with mental illness often benefit from therapy provided by psychiatrists. Traditional face-to-face therapy sessions, while effective, can be costly and face challenges such as a shortage of psychiatric specialists and patient reluctance to engage in therapy. In response to these challenges, the concept of a mental health chatbot therapist emerged. A mental health chatbot is a computer program that simulates conversations with users, employing Natural Language Processing (NLP) and generative AI to interact and respond. This approach offers an interactive communication channel, both written and spoken, between users and machines. The mental health chatbot system typically includes a user module where users register and log in to their accounts to begin interacting with the chatbot. The chatbot itself acts as a conversational agent, akin to a real-time therapist, analyzing the user's emotions and providing appropriate responses and feedback throughout the interaction.
This project aims to develop a design studio web application that uses machine learning algorithms to assist graphic designers in creating more dynamic and personalized designs and collaborate the fields of graphic design, user interface creation, and artificial intelligence (AI) to advance the design process. By integrating Artificial Intelligence and machine learning techniques, this project seeks to empower designers with tools that can generate design suggestions like design elements, color schemes, or layout improvements, automate repetitive tasks, and provide personalized design recommendations based on user preferences and historical data. The project will also involve the development of an interactive software application that allows users to collaboratively design and customize graphics and User Interface elements, with AI-driven enhancements. Through this innovative approach, the project not only offers practical utility in design but also showcases the potential of Artificial Intelligence in transforming traditional creative processes into more efficient and personalized experiences. The AI-Aided Design Studio also begins with the incorporation of a sophisticated Image-to-Text generator that utilizes state-of-the-art machine learning algorithms. This generator enables designers to analyze and interpret visual elements, automatically extracting relevant textual data from images. This process not only expedites the initial phase of design research but also facilitates a seamless transition between the visual and textual aspects of the creative process.
This research investigates machine learning models for predicting mental health consequences using survey data. The study uses a two-phase approach: employing Tensor Flow for initial deep neural network (DNN) model building, and then using random forest (RF), naive Bayes classifier, and decision tree approaches for comparative analysis. The DNN model demonstrates strong performance, achieving high accuracy in mental health prediction. Metrics like testing time, precision, mean absolute error, and accuracy may all be compared to provide insight into the advantages and disadvantages of each model. While the DNN model excels in accuracy and precision, other models offer trade-offs in computational efficiency. The results help clarify the function of machine learning for mental wellness evaluation and intervention, offering direction for further study and real-world implementations. This research enhances the discourse on predictive modeling for mental health outcomes, facilitating advancements in leveraging machine learning for improving mental health assessment and intervention strategies.
