Mental Health Chatbot Therapist
AI-Aided Design Studio: Enhancing Graphic Design and User Interface with Machine Learning
Futuristic Smart AI Powered Backpack
Sign Language Recognition with Hand Gestures using Deep Learning
A Review on Early Diagnosis of Glaucoma using Machine Learning Techniques
Retinal Imaging for Diabetic Retinopathy Detection through Deep Learning
Enhanced Mental Health Prediction with Deep Neural Networks for Accurate Diagnosis
Enhanced Mental Health Prediction with Deep Neural Networks for Accurate Diagnosis
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
Mental health illness refers to conditions that affect thinking, feeling, mood, and behavior. These conditions may be occasional or long-lasting. Some examples of mental illness include depression, anxiety disorder, and addictive behaviors, to name a few. Mental health conditions can be caused by various factors, including substance abuse, psychological trauma, unhealthy habits, and genetics. Symptoms of mental illness may include feelings of sadness or hopelessness, extreme mood changes, excessive anger, hostility, and even violence. Individuals with mental health issues often require the assistance of a psychiatrist to undergo therapy, which is typically conducted face-to-face. This in- person approach to therapy can present several challenges, such as high costs, a shortage of psychiatric specialists in hospitals, and the reluctance of some patients to engage in the therapeutic process. Given these drawbacks associated with traditional face-to-face therapy, the concept of creating a mental health chatbot therapist emerged. This chatbot is a computer program designed to simulate conversation with human users, often utilizing Natural Language Processing (NLP) to interpret inputs and generative AI to automate responses. The chatbot aims to provide an interactive conversational experience between the user and the machine, facilitating both written and spoken communication. In this system, there is a user module that requires registration for access. After logging into their accounts, users can begin chatting with the chatbot. The Mental Health Chatbot System operates as a conversational agent, functioning like a real- time therapist that analyzes the user's emotions with each interaction and offers appropriate responses and feedback.
The challenge in a design studio lies in helping graphic designers create dynamic and personalized designs efficiently. To address this, the AI-Aided Design Studio is a web-based application that leverages machine learning algorithms to assist graphic designers in producing more vibrant and customized designs. By integrating artificial intelligence and machine learning techniques, this application seeks to empower designers with tools that can generate design suggestions, such as design elements, color schemes, and layout improvements, automate repetitive tasks, and provide personalized design recommendations based on user preferences and historical data. The development will also involve creating 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 application not only offers practical utility in design but also showcases the potential of artificial intelligence to transform traditional creative processes into more efficient and personalized experiences. The AI-Aided Design Studio will also incorporate 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.
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 utilizes a Raspberry Pi 4 and Arduino UNO to power its array of features. Equipped with 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 individuals who are deaf or hard of hearing, but sign language has become an essential tool. It is an excellent tool that helps individuals with speech and hearing impairments convey their ideas and feelings. This encourages less complicated integration with the outside environment. However 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 technological developments have introduced several methods for automating the recognition of sign movements, providing encouraging alternatives. This invention has the potential to close the long- standing communication gap considerably. This work presents the usage of an exclusive dataset to identify hand motions. The 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.
Glaucoma refers to the accumulated loss of retinal cells within the optic nerve or the gradual visual loss caused by optic neuropathy. It is an illness that affects vision in the eye and is considered an irreversible condition that leads to degradation of eyesight. There are often no early warning signs of glaucoma, making it difficult to notice changes in vision due to subtle effects. To date, a large number of Deep Learning (DL) models have been developed for the accurate diagnosis of glaucoma. This work proposes an architecture for deep learning-based glaucoma detection using Convolutional Neural Networks (CNNs). CNNs can distinguish between patterns associated with glaucoma and non-glaucoma conditions, providing a hierarchical structure for classification. Using the proposed method, the disease is detected based on the optic cup-to-disc ratio. The diagnosis is further enhanced by integrating an image data generator for data augmentation. The results demonstrate that the proposed model achieved 98% accuracy, outperforming many existing algorithms.
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 essential. This study presents an integrated solution comprising two modules: diabetic detection and diabetic retinopathy detection. The diabetic detection module employs machine learning techniques like decision trees, random forests, and KNN for forecasting 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. The trained models are then 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.
This research investigates machine learning models for predicting mental health consequences using survey data. The study employs a two-phase approach first, it utilizes TensorFlow for initial Deep Neural Network (DNN) model building, and then it applies Random Forest (RF), Naive Bayes classifier, and decision tree methods for comparative analysis. The DNN model demonstrates strong performance, achieving high accuracy in mental health prediction. Metrics such as testing time, precision, mean absolute error, and accuracy are 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 clarify the role of machine learning in mental wellness evaluation and intervention, providing guidance for further research and real-world applications. This research enhances the discourse on predictive modeling for mental health outcomes, facilitating advancements in leveraging machine learning to improve mental health assessment and intervention strategies.
