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Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Top 50 Deep Learning Projects is extremely beneficial to data scientists who are tasked with i.e.; collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier.
The effective aid is to identify children under the age of five suffering from malnutrition to target with a portion of proper food. It is as physical development and also determines how well malnutrition affects children.
Banana is affected by numerous diseases and pests. It is rapid methods for the timely detection of pests and diseases will allow to surveil and develop control measures with greater efficiency.
The image segmentation technique with RCNN is to detect license plates and read their number using OCR. It is a tested on two datasets that contain images under various conditions poor picture quality, image perspective distortion, bright day, night, and complex environment.
A model of quality testing and identification is constructed i.e.; predicated on geometric and color options with the technology of neural network. It is determining the unknown grain varieties and its quality. This technique offers smart leads to an analysis of rice quality.
A deep learning technique is used to detect the curved path in autonomous vehicles. It is customized lane detection algorithm was implemented to detect the curvature of the lane. Deep learning is used to detect the curved path in autonomous vehicle.
Semantic segmentation is one of the high-level tasks that paves towards complete scene understanding. It is understanding as a core computer vision problem is highlighted by an increasing number of applications nourish from inferring knowledge to imagery.
It is to create an intelligent model using image processing techniques in order to categorize the internal fault i.e.; which are low, intermediate, medium and high.
The optimized model is evaluated and validated through analysis of performance indicators frequently used in any classification model. It is captured using infrared thermography camera in which the color image is stored and processed using MATLAB.
This is to investigates the possibility of diagnosing brain hemorrhage using an image segmentation of CT scan images the appropriate inputs extracted to an artificial neural network for classification. It is used to verify diagnosis to minimize errors in current methods.
This system is the base for many different types of applications in various fields, to use in daily lives. It is to recognize the characters using a back propagation algorithm and the effect of variations of error percentage with number of hidden layers in a neural network.
It focuses on methods with applicability to automated diagnosis of images obtained from gastrointestinal biopsies. These deep learning techniques for biopsy images may help detect distinguishing features in tissues affected by enteropathies.
The special kind of multi-layer neural networks, designed to recognize visual patterns directly from pixel images with minimal preprocessing.
This code uses pretrained Alex net deep learning model i.e.; by retraining the model to many users face images to do face recognition and face verification.
Pneumonia is a disease which occurs in the lungs caused by a bacterial infection. Early diagnosis is an important factor in terms of the successful treatment process.
However, the exception network a more successful result in detecting pneumonia cases.
Palmprint recognition requires extraction of features before classification which will affect the recognition rate. It uses the convolutional neural network (CNN) structure dense net to realize palmprint recognition.
The ROI area after processing is taken as input of convolutional neural network.
This investigates methods and procedures to construct an efficient system to assist blinds in everyday life. It provides the instructions to blinds for efficient navigation and safe guidance by incorporating object in real-time.
It is implemented Glaucoma detection system for medical applications. This system was implemented to detect the various stages of glaucoma.
The data augmentation strategies are adopted to further boost the performance of glaucoma diagnosis. It is proposed algorithm in terms of accuracy.
The analysis of comparison results illustrated the superior capability of Cuckoo search algorithm in optimizing the enhancement functions for digital image processing.
Cuckoo Search (CS) algorithm is one such algorithm which is efficient in solving optimization problems in varied fields. It discovers how it is efficient in detecting tumors and compare the results with the other used optimization algorithms.
Segmentation refers to the process of partitioning a digital image into multiple segments. The segmented primary and secondary regions are compressed with hybrid techniques for telemedicine application.
It proposes to spot the tumor from MRI scanned medical images using morphological process.
License Plate Recognition is an image-processing technology used to identify vehicles by license plates. This technology is used in various security and traffic applications.
The input image is taken and converted into grayscale image and the processed image is filtered through bilateral filter to remove unwanted characters.
The leaf disease diagnosis using image processing techniques for automated vision system used at agricultural field. It is essential one in monitoring large fields of crops, and thus it detects symptoms of disease as soon as it appears on plant leaves.
It is a computer vision technology that helps to visualize human faces in digital images. This technique is a specific use of that deals with detecting instances of semantic objects of a certain class in digital images and videos.
A deep learning algorithm i.e.; used to analyze static food image. By analyzing the composition of the food, the algorithm can calculate how much calories the dish has. It provides a more efficient way of estimating calories.
Pedestrian detection is an essential and significant task in surveillance system, as it provides the fundamental information for semantic understanding of the video footages. It indicates the presence of pedestrians i.e.; at various scales and locations in the images.
It identifies the blur type from a mixed input of image patches corrupted by various blurs with different parameters. The effectiveness of the proposed method in several tasks with better or competitive results compared with the state of the art on two standard image data sets.
The key elements of face are considered for detection of face and prediction of expressions or emotions of face. To determine the different facial expressions, the variations in each facial feature are used.
The device will be trained to identify test images in order to assess object characteristics. With simulated results show i.e.; used network classifier has a low error rate during higher classification accuracy.
Speech emotion recognition have been utilized to extract emotions from signals, including many well-established speech analysis and classification techniques.
It enhances the speech interaction and adaption of computer systems according to the mood and emotion of an individual.
It is an open-source computer vision and image processing. Using this library for processing each frame from a video captured by a device. This function takes an image, then identifies the QR code and barcode from the image, and decodes the value of it.
It is the capability of the computer to identify and understand handwritten digits automatically. The progress in the field of science and technology is being digitalized to reduce human effort.
It performs Digit Recognition and the analysis of accuracy of algorithms Neural Network.
Optical Character Recognition is vital and a key aspect and python programming language. The application of such concepts in real-world scenarios is numerous.
It extracts image segments the detector has identified as containing text and enhance using various image filters from the OpenCV module.
Deep learning became a major tool for Ad Tech. A Logo detection system is used to identify the targets from i.e.; brands, products, and logos on publicly posted images. The easiest way to identify brand from images is by its logo.
Social distancing aims to decrease in a population i.e.; by minimizing contact between potentially infected individuals and healthy individuals with high rates of transmission or low levels of transmission.
It develops the traffic control framework by detecting system, which gives an input to the current system, that can adjust the changing traffic density patterns and provides a vital sign to the controller in a continuous activity.
Using this method, improvement of the traffic signal switching expands i.e.; street limit, saves time, and prevents traffic congestion.
Face Mask detection model is built using the Deep Learning technique. It is built using OpenCV library which is highly used for real-time applications.
The system uses OpenCV to detect the person with or without a mask. It connected with any surveillance system installed at premises.