549 skills found · Page 13 of 19
pranjalGupta1555 / Super Resolution Image GANThis project aims to recover or restore high resolution image from low resolution image by applying deep neural network with adversarial network (Generative Adversarial Networks). The main target is to reconstruct super resolution image or high resolution image by up-scaling low resolution image such that texture detail in the reconstructed super resolution image is not lost. This has numerous applications like satellite and aerial image analysis, medical image processing, compressed image/video enhancement etc. The implementation revolves around GAN which is a deep neural network architectures comprised of two networks (Generator and Discriminator) pitting one against the other (thus the “adversarial”) whose main focus is to generate data from scratch.
MehradAria / FFR EstimationNon-Invasive Fractional Flow Reserve Estimation using Deep Learning on Intermediate Left Anterior Descending Coronary Artery Lesion Angiography Images
abhi-abhi86 / Disease PredictorAI disease detection and prediction for humans, plants, and animals. Complete ML project with custom training, offline operation, no API keys. Detect diseases from images using deep learning and computer vision. Open-source disease detection system for healthcare, agriculture, and veterinary applications. Full code and deployment guides.
Aryia-Behroziuan / Robot LearningIn developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation. Association rules Main article: Association rule learning See also: Inductive logic programming Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[60] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[61] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[62] For example, the rule {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}}\{{\mathrm {onions,potatoes}}\}\Rightarrow \{{\mathrm {burger}}\} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[63] Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[64][65][66] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[67] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. Models Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Artificial neural networks Main article: Artificial neural network See also: Deep learning An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68]
zakaria-29-dev / Vuejs Vuetify UI Design Medical App System Dashboard JS HTML CSS Vue Chart Vuejs - Vuetify UI Design, Medical Application System - Dashboard - Javascript | HTML - CSS | Vue-Chart, Line Chart - Polararea Chart
raunakpandey1 / Medstore MernMedical E-commerce Web Application. It can be used by both customers and sellers for purchasing and selling medical essentials.
Vivirinter / Medicine AppMedical Application
MeazaEyakem1 / Application Of AI In Medical Imaging High resolution (HR) is desirable in many areas, in particular, in medical imaging. Since such images are an important method to find certain diseases, the high resolution should improve the success rate of correct diagnosis. There are many super-resolution techniques which show their potential in achieving super-resolution images which attempt to reach the quality of HR images. This report provides the detailed overview of most important research articles starting from such traditional methods as interpolation to deep neural networks which have achieved great success in single image super-resolution (SISR). As the idea of skip connections promotes better performance, UNet was implemented and trained with pixel-wise losses (\textit{l1} loss, \textit{l2} loss, and \textit{Smooth L1-loss} ) on SISR dataset of ultrasound medical images collected for this research as a baseline solution to SISR. In order to overcome some limitations as simple, one-step fusion in UNet, as well as to improve visual SISR results, we propose some modifications to this baseline as a further direction of our research - SISR with the Generative Adversarial Network, and Deep Layer Aggregation.
Sachinn-64 / CureConnectExperience seamless healthcare with our telemedicine platform—video consultations, real-time chat, AI-driven ECG & X-ray analysis, personalized health tips, emergency hospital locator, and an intuitive appointment dashboard—all in one place.
NicoNex / TheBigBrotherPrevents you from touching your face while you think, work or relax.
XingJinming-real / Weighted Fuzzy Rough Sets Based Tri Training And Its Application To Medical DiagnosisThe python implementation of paper "Weighted fuzzy rough sets-based tri-training and its application to medical diagnosis"
Nikhilkohli1 / Heart Disease Diagnosis AssistantA Streamlit Application to assist doctors in prediction of Heart Diseases based on their medical readings & characteristics.
imeka / Mi BrainImeka medical imaging applications based on MITK 2018.04.
ArpitGupta4957 / SOS Frontend FileThis is the user-side mobile application for the SOS Emergency Response System. It allows users to instantly report fire, medical, or crime emergencies by capturing images and location data.
mbiparva / MitorchThis package implements deep learning modules for medical imaging application in PyTorch (miTorch).
radinshayanfar / HabcoWeb-API for Habco, a medical application for connecting patients, doctors, nurses, and pharmacies.
gitKeshav11 / Health Care Portal"A comprehensive healthcare management application designed to bridge the gap between patients and medical services, featuring doctor discovery, health tracking, and user-friendly medical assistance."
aaronpk / Geoloqi Emergency Beacon MapThis application is a resource for citizens, medical teams and governments before, during and after disasters.
menoc61 / Gestion Des Soin LaravelThe patient management system in Laravel and MySQL for Sai I Lama enterprise in Cameroon is a specialized application designed to streamline and automate the management of patient-related information. It provides a comprehensive set of features to efficiently handle tasks such as patient registration, appointment scheduling, medical record etc...»
jonathanlawhh / Posture Detection BackendSitting posture identifier that identifies your sitting posture, giving insights and serving as a base model for extended use in the medical field. Application: https://posture.jonathanlawhh.com/
