LeadingIndiaAI/Multimodal-Emotion-Recognition-in-Polish LeadingIndiaAI/Multimodal-Emotion-Recognition-in-Polish

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LeadingIndiaAI/Multimodal-Emotion-Recognition-in-Polish
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Multimodal emotion recognition is a challenging task because emotions can be expressed through various modalities. It can be applied in various fields, for example, human-computer interaction, crime, healthcare, multimedia retrieval, etc. In recent times, neural networks have achieved overwhelming success in determining emotional states. Motivated by these advancements, we present a multimodal emotion recognition system which is based on body language, facial expression and speech. This paper presents the techniques used in the Multimodal Emotion Recognition in Polish challenge. To detect the emotional state for various videos, data preprocessing operations are performed and robust features are extracted. For this purpose, we have used facial landmark detection for facial expressions and MFCC for speech.

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49

-Face-recognition-technique-in-bank-locker-systems-for-security-purpose-using-deep-learning-

Face Recognition is turning into another pattern in the security validation frameworks. Present day FR frameworks can even identify, if the individual is real (live) or not, while doing face acknowledgment, keeping the frameworks being hacked by demonstrating the photo of a genuine individual. I am certain, everybody pondered when Facebook executed the auto-labeling method. It recognizes the individual and label him/her at whatever point you transfer a photo. It is efficient to the point that, notwithstanding when the individual's face is blocked or the photo is taken in obscurity, it labels precisely. All these effective face acknowledgment frameworks are the after effects of ongoing progressions in the field of PC vision, which is upheld by intense profound learning calculations. In the present current world, security assumes an imperative part. For that reason, we proposed propel security frameworks for saving money locker framework and the bank clients. This specific security is proposed through two unique modules in mix i.e. confront identification procedure and password verification. All these means are followed in the grouping on the off chance, that if anything turns out badly he or she can't get to the framework. Presently clients don't need to stress over the illicit access to their locker frameworks. These propelled procedures in this day and age influence individuals to feel anchor. This likewise prompts aversion of burglary. We have developed a Web Application to showcase our project. It has been observed by comparing all the models that CNN provides high accuracy (98.3%).
JavaScript
3
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50

Image-Inpainting

Python
2
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51

HUMAN-WITH-MASK-OR-WITHOUT-MASK

Jupyter Notebook
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52

STRAY-ANIMAL-DETECTION

Python
2
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53

Using-DCGAN-To-Generate-Faces-Of-Animated-Characters

Jupyter Notebook
2
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54

Sentimental-Analysis-Of-Memes

When someone sends you a meme can you tell the sender actually happy, angry, or neutral? This makes sentiment analysis more important than ever.
Jupyter Notebook
2
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55

Leaf-Segmentation-Challenge-Using-UNET

In this paper, we are concentrating on the problem of segmenting tobacco and arbidopsis leaves from an RGB image, an important task in plant phenotyping.To complete this project this task, we use state-of-the-art deep learning architectures: UNET, a convolutional neural network for initial segmentation.
Python
2
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56

Deep-Learning-Based-Water-Feature-Mapping-Using-Sentinel--2-Satellite-Image

Jupyter Notebook
2
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57

-CLASSIFICATION-OF-IMAGES-INTO-NATURAL-DIBR-RETARGETED-OR-SCREEN-CONTENT-

The project deals with Image Classification. Image classification is an important topic in computer vision systems and has drawn a significant amount of interest over the last decade. This field aims to classify an input image based on visual content. The main objective of this project is to make a deep learning model for categorisation of images into DIBR, Natural, Retargeted or Screenshot, so that user can check to which category a test image belongs to and to improve the accuracy value or the prediction score of classifying the test image into one of these four categories. In this project, 3 different choices of models (building from scratch, VGG16, InceptionV3) were tested. Building own network from scratch did not yield great results when compared to the other two because the dataset was not extensive enough to train the model with good accuracy from scratch. Best results were obtained on InceptionV3 model followed by VGG16. Top performing model achieved an accuracy of 98.3 %. The reason why team could arrive at such great results is probably the similarity between the ImageNet dataset and our prediction classes.
Python
2
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58

Face-Recognition-and-Tracking

The human-face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a very important task and has wide variety of application in security systems, authentication etc. Tracking the individuals can give us the valuable insights. In this project, we have developed Computer Vision based face recognition and tracking system with OpenCV and dlib. Our model is able to recognize and differentiate between known and unknown faces. For the tracing part, time when a person enters and leaves the premises is captured and the difference is calculated. The model was trained on dataset of 50 different individuals with 10 different images of each. We achieved state-of-the-art accuracy.
Python
2
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59

-DESIGN-AND-IMPLEMENTATION-OF-PROCESSING-MODULE-FOR-OBJECT-DETECTION-AND-WEAPON-CLASSIFICATION-WITH-

Deep Learning has emerged as a new area in machine learning and is applied to a number of image applications. The main purpose of the this work is to apply the concept of a Deep Learning algorithm namely, Convolutional neural networks (CNN) in image classification. The algorithm is tested on standard COCO datasets. The performance of the algorithm is evaluated based on the quality metric known as Mean Squared Error (MSE) and classification accuracy. The experimental result analysis based on the quality metrics and the graphical representation proves that the algorithm (CNN) gives fairly good classification accuracy for all the tested datasets. Then we used visualization technique on the particular image for understanding which part of a given image led to convert to its final classification decision. For this we used CAM Visualisation technique. We also tried in doing object detection by using Pytorch.
Python
2
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60

WIDER-Face-and-Persons-Challenge-2019-Track-2-Pedestrian-Detection

Pedestrian Detection is an application of computer vision which is close to object detection, which has a wide range of applications. It can be used in surveillance monitoring, autonomous vehicles, face recognition, etc. This project is based on pedestrian detection that detects pedestrians and cyclists equally. The detector used must have high accuracy and precision. The model used is Faster R-CNN, which has more accurate than YOLOv3 but is comparatively slower.
Python
2
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61

Characterization-of-Binary-Machine-Learning-Classifier-for-Robust-Heart-Disease-Prediction

Large number of patients related data is stored and maintained in the health industry. Heart disease is the most common one nowadays. The different ways of predicting it are Electrocardiogram (ECG), stress test, and Heart MRI. Here, the proposed model uses 13 parameters for the prediction of heart disease that includes heart rate, chest pain, cholesterol level, blood pressure, Age etc. The aim of this model is to predict whether heart disease is present or not using the various machine learning models such as Decision Tree, Random Forest, Logistic Regression, Naïve Bayes. We have achieved 0.3312 log loss using the Logistic Regression.
Jupyter Notebook
2
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62

Image-Classification-Using-CNN

The identification and classification of items in this world surrounding us has been a part of machine learning and a primary element of image recognition. Computers can be taught to identify the visual factors on images given so that it could use the trained neural network to extract features for this demanding responsibility. In our project, we trained multinomial classifiers and used Convolutional neural networks for learning the features of the images and analysed the output to improve the accuracy. We built database by collecting the universally used databases available. Our initial attempt for training using VGG16 model wasn’t providing satisfactory results. So we assessed the performance of the CNN architectures chosen namely VGG16, InceptionV3 & MobileNet to have knowledge on the outputs obtained from different models and then to select the model with highest performance comparatively by analysing the output. We got an accuracy of around 78% for VGG16, around 87% for MobileNet, around 90% for InceptionV3. We selected InceptionV3 since it had better performance comparatively, for further processing, we used the model to train on NVIDIA DGX and acquired an accuracy of 97.88. The output acquired over the dataset consisting of four classifiers shows that this is the maximum accuracy that can be obtained using the currently available dataset and techniques. For learning purpose, we tried increasing the amount of dataset and observed that the accuracy is improved. The initial dataset consisted of images around 3300 and we further increased it to 5100. The results point out that the performance and accuracy for the model can be enhanced using advanced architectures and providing large amount of images as input in the dataset.
Python
2
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63

DOG-BREED-IDENTIFICATION

Dog breed identification is a very specific application of convolutional neural networks. It falls under the category of finedgrained image classification problem, where inter -class variations are small and often one small part of the image considered makes the difference in the classification and identification. The various classes of ImageNet can have large inter-class variations, making it easier to categorize correctly.
Jupyter Notebook
1
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64

MODULE-ON-ENGLISH-TO-HINDI-NEURAL-MACHINE-TRANSLATION

Jupyter Notebook
1
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65

-A-Probabilistic-Object-Detection-in-Computer-Vision-Using-Deep-Learning-Approach

Jupyter Notebook
1
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66

Identification-of-Aortic-Valve-Opening-Points-using-Seismocardiogram-Signals

1
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67

Technical-Analysis-Based-Stock-Prediction

Jupyter Notebook
1
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68

Evaluation-of-Object-Detection-Approaches-for-Real-Time-Face-Mask-Detection

Jupyter Notebook
1
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69

Chest-X-Ray-Images-Pneumonia-Detection

Jupyter Notebook
1
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70

Stock-Market-prediction

Python
1
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71

FLU-SHOT-LEARNING-PREDICT-H1N1-AND-SEASONAL-FLU-VACCINES

Jupyter Notebook
1
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72

Multi-Person-Pose-Estimation

Jupyter Notebook
1
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73

Mortality-Prediction-using-Machine-Learning-Techniques

The prediction of mortality of a human is a foremost challenging task in today’s era. We are evaluating the prediction model on 79999 patients with 342 features. Here we have predicted mortality of patient i.e. (DEAD or ALIVE) who is admitted in the hospital using Deep Neural Networks and various Machine Learning methods where Linear SVM showed the best accuracy.
Jupyter Notebook
1
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74

Classification-of-COVID-19-chest-X-ray-images-using-Convolutional-Neural-Networks-CNN-

Jupyter Notebook
1
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75

Disease-Identification-in-Kharif-Crop

Jupyter Notebook
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76

Video-Object-Segmentation-from-One-Frame-Annotation

Python
1
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77

Surveillance-of-Identifying-Vehicles-Parked-In-No-Parking

Jupyter Notebook
1
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78

SDN-Traffic-Classification-using-Deep-Learning

Jupyter Notebook
1
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79

Native-and-Non-Native-English-Speech-Classification--A-premise-to-Accent-Conversion

Jupyter Notebook
1
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80

Aerial-Cactus-Identification-using-Deep-Learning-

This paper focusses on various convolutional neural network architectures for the aerial cactus identification task. Our main effort is a thorough experimentation and evaluation of performance of different networks to identify columnar cactus in aerial imagery using deep learning.
Jupyter Notebook
1
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81

Crop-Yield-Prediction-through-Different-Machine-Learning

Jupyter Notebook
1
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82

AI-based-Prediction-of-Rainfall-from-Satellite-Observation-for-Disaster-Management

Jupyter Notebook
1
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83

Recommendation-Engine-Personalized-Approach

With Personalized and Efficient performance taken as primary goals, this paper focuses on building a recommendation engine. We have proposed a method which recommends product based on user’s like and dislike.It focuses on building a Restricted Boltzmann Machine which suggest product to buyer and helps in providing ease while shopping.Reader gets an insight on how RBM helps in having a good recommending engine as compared to other long running traditional methods.In this paper we have explored the use of RBM with two layers by converting a tabular data into user item matrix.
Jupyter Notebook
1
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84

Human-activity-Detection

Human activity detection plays a significant role in human-to-human interaction and interpersonal relations. Because it provides information about the identity of a person, their personality, and psychological state, it is difficult to extract. The human ability to recognize another person’s activities is one of the main subjects of study of the scientific areas of computer vision and machine learning. As a result of this research, many applications, including video surveillance systems, human-computer interaction, and robotics for human behavior characterization, require a multiple activity detection system.
Jupyter Notebook
1
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85

Multi-Person-Pose-Tracking

Accessible structures for a video based pose estimation and tracking struggle to perform well on practical accounts with numerous people , every now and again fail to yield body-pose directions unsurprising over some time. So, in this work, we present the troublesome issue of joint multi-singular pose estimation and tracking of many number of individuals in unconstrained recordings.
Python
1
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86

LUNG-DISEASE-CLASSIFICATION-AND-QUANTIFICATION-WITH-EXPLAINABLE-AI

The project undertaken aims at harnessing the power of Computer Vision, Image Processing, and Deep Learning Techniques to develop a Smart Radiology System that correctly distinguishes between three classes - COVID-19, PNEUMONIA, and NORMAL Lungs using Chest X-RAY images. The system aims at segmenting the infected part of the lungs and provides an Activation map to highlight the regions of interest.
1
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87

Heart-Disease-Prediction-Using-Machine-Learning-Techniques

Heart diseases are the number one cause of deaths of people around the world. They refer to the disorders related to the heart and blood vessels. The health sector maintains an enormous quantity of patient-related information. This stored information may be helpful for future disease prediction. In this research paper, we attempt to concentrate on different algorithms for machine learning that effectively predict heart diseases.
Jupyter Notebook
1
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88

Swedish-Leaf-Dataset-Classification

This paper introduces a specific approach for leaf classification based on Machine Learning (ML), Transfer Learning (TL), and Convolutional Neural Network (CNN). The proposed method consists of three stages, pre-processing, feature extraction, and classification.
Jupyter Notebook
1
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89

Review-On-Machine-Learning-Algorithms-For-Dengue-Disease-Spread-Prediction

Dengue is a disease caused by four types of related viruses transmitted by a mosquito, most commonly Aedes aegypti. In its less severe form infected patients will experience flu like symptoms that vary from mild to intense, but severe dengue or, Dengue Hemorrhagic Fever, can be fatal without proper medical care. The disease is considered an alarming threat to the health of populations spanning millions of people living in tropical and subtropical areas of the globe where the mosquito thrives. A large number of studies have confirmed that the incidence of dengue is positively correlated with climatic conditions, specifically, temperature, humidity and precipitation levels.
Jupyter Notebook
1
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90

Traffic-Flow-Prediction-

Traffic flow predicting has long been regarded as a critical problem for the intelligent transportation system. It aims at estimating traffic flow of a road in next several time intervals to the future. Time intervals are usually defined as short-term intervals varying from 5 minutes to 15 minutes. Two types of data are usually used in traffic flow prediction.
1
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91

RICHTER-PREDICTION-PREDICTING-DAMAGE-CAUSED-BY-EARTHQUAKES

In this we are going to do the Earthquake magnitude damage prediction using region and it has been carried out using the temporal sequence of historic seismic activities in combination with machine learning classifier. These parameters are based on geophysical fact distribution of characteristic earthquake magnitude and seismic quiescence.
Jupyter Notebook
1
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92

COCO-DATASET-STUFF-SEGMENTATION-CHALLENGE

There are three levels in image analysis, Classification, Detection and Segmentation. Image segmentation is the division of an image into regions or categories. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.
Python
1
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93

Video-Object-Segmentation

Video object segmentation allows computer vision to identify objects as they move through the space in a given video. Here we presents One-Shot Video Object Segmentation (OSVOS), a CNN architecture to tackle the problem of semisupervised video object segmentation, that is, the classification of all pixels of a video sequence into background and foreground,given the manual annotation of one of its frames.
Python
1
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94

SIMAH-SocIaL-Media-And-Harassment-

The social networking platform has made millions of users to share their opinions and thoughts concerning totally different aspects and events on the micro-blogging platforms. However, the utilization of social media and lack in protection of private information has lead to broadcasting of bully and harassment messages and has conjointly brought with it a risk of online harassment.
Jupyter Notebook
1
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95

ImageNet-Object-Localization-Challenge

Cutting edge object finding systems rely upon district scheme cunnings to speculate object areas. Advances Faster RCNN has diminished the running time of the dis-covery systems, uncovering area proposition calculation of jam. In present work, we present a RPN that offers whole-images convolutional highlights with location or-ganize, along these lines empowering almost sans cost locale recommendations.
Python
1
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96

DESIGN-AND-IMPLEMENTATION-OF-PROCESSING-MODULE-FOR-OBJECT-DETECTION-AND-WEAPON-CLASSIFICATION...

Deep Learning has emerged as a new area in machine learning and is applied to a number of image applications. The main purpose of the this work is to apply the concept of a Deep Learning algorithm namely, Convolutional neural networks (CNN) in image classification. The algorithm is tested on standard COCO datasets. The performance of the algorithm is evaluated based on the quality metric known as Mean Squared Error (MSE) and classification accuracy. The experimental result analysis based on the quality metrics and the graphical representation proves that the algorithm (CNN) gives fairly good classification accuracy for all the tested datasets. Then we used visualization technique on the particular image for understanding which part of a given image led to convert to its final classification decision. For this we used CAM Visualisation technique. We also tried in doing object detection by using Pytorch.
Python
1
star
97

Human-Identification-Using-Autonomous-Drone

Detecting a specific person from the crowd using drone along with some resource constraint device is a major concern which we are discussing in the paper. Combining the advanced algorithms and some smart hardware material, we will be finding a way to search for a missing individual in a crowd or at some location. We can also search for a person at a particular location by setting our aerial vehicle to fly autonomously and search for the required person. This will help us to cover areas which cannot be reached by humans easily.
Python
1
star
98

Video-Segmentation

Video Segmentation -which means segmentation of all objects of the scene and classifying them based on certain classes. • Video segmentation will have a major role in day to day activities like traffic counting ,movie editing ,individual tracking etc.
Python
1
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99

WIDER-FACE-DETECTION

Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations and occlusions. We propose a deep cascaded multi-task framework which boost up the detection performance. In particular, our framework leverages a cascaded architecture with three stages of carefully designed deep convolutional networks to predict face and landmark location in a coarse-to-fine manner. Our method achieves superior accuracy over the state-of-the-art techniques on the challenging WIDER FACE benchmarks for face detection while keeps real time performance.
Python
1
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100

Emotion-Detection-using-Video

Facial expressions play a key role for detecting emotion and understanding humans. The term “interface” suggests importance of face plays in communication between two entities. Studies have shown that reading of facial expressions can significantly determine the personality of the person. Regardless of cultural barriers and language and cultural there will always be a set of fundamental facial expressions that people assess and communicate with. Humans ability to interpret emotions is very important to effective communication, accounting for up to 93% of communication used in a normal conversation depends on emotion of an entity. The seven basic emotions that are considered universal to human beings. Neutral, angry, disgust, fear, happy, sad, and surprise are the basic emotions can be recognized from human’s facial expression.
Python
1
star