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-a-probabilistic-object-detection-in-computer-vision-using-deep-learning-

-a-probabilistic-object-detection-in-computer-vision-using-deep-learning-approach

-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.

-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.

-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%).

-fake-news-detection-

Fake news is misinformation or manipulated news that is spread across the social media with an intention to damage a person, agency and organisation. Due to the dissemination of fake news, there is a need for computational methods to detect them. Fake news detection aims to help users to expose varieties of fabricated news. To achieve this goal, first we have taken the datasets which contains both fake and real news and conducted various experiments to organize fake news detector. We used natural processing, machine learning and deep learning techniques to classify the datasets. We yielded a comprehensive audit of detecting fake news by including fake news categorization, existing algorithms from machine learning techniques. In this project, we explored different machine learning models like Naïve Bayes, K nearest neighbors, decision tree, random forest and deep learning networks like Shallow Convolutional Neural Networks (CNN), Deep Convolutional Neural Network (VDCNN), Long Short-Term Memory Network (LSTM), Gated Recurrent Unit Network (GRU), Combination of Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network with Gated Recurrent Unit (CNN-LSTM).

-image-to-speech-convertor-

The aim of the project was to convert an image to speech. An image is processed and segmented to identify the text in the image. Then the characters are combined to form words and save it as a text file. This text file is converted to speech. We use two tools for the completion of image to text to speech conversion. They are OCR (Optical Character Recognition) and TTS (Text to Speech) engines. Using OCR, we can optically recognize the characters in an image. TTS is used to convert the text file to speech. The audio output can be heard by using a python library Pygame for playing the audio at runtime

-real-time-vehicle-classification-and-localization-using-edge-computing-

The project aims to develop a traffic monitoring system using convolution neural network. We had modified existing tiny YOLO model for Indian vehicle such as auto rickshaw, bicycle, motorbikes etc. To do this, first we developed a data set of these vehicles then we retrained the existing tiny YOLO model. Moreover, with raspberry Pi we have developed a prototype for edge device which can count incoming and outgoing traffic from a particular point. There are various applications of such devices for example, it can be used as traffic monitoring system, surveillance, traffic load prediction. This is a fully functional independent device. All the decision has been done locally, this make this device highly useful for IoTs.

1-multiple-aggregation-on-ensembles-for-feature-selection-a-heterogeneous-approach-

Ensemble learning is a creative field in Machine Learning. Since it is based on the assumption that combining the output of multiple models is better than using a single model. So, the main Purpose of this project is to apply the various Feature Selection Algorithm in a single data set to find the Ranks of the particular data set while go through with different feature selection algorithms . Feature selection consists of selecting the relevant features for a problem and discard those irrelevant or redundant, with the main goal of improving classification accuracy.

1-symmetric-image-encryption-technique-for-iot-applications-using-cellular-automata

1footprint-recognition-using-one-shot-learning

1optimized-implementation-of-cnn-

1topic-modelling-to-group-reviews-

2-optimized-implementation-of-cnn-

3d-point-cloud-classification-using-deep-neural-network

a-decentralized-voting-application-using-blockchain-technology

a-faster-cnn-for-identifying-the-help-non-help-situations

a-light-weight-cnn-for-bird-species-classification

a-structured-filter-pruning-approach-for-efficient-inference-of-deep-neural-networks

abnormal-human-behaviour-detection-in-restricted-environment

abstractive-text-summarization

acapellapp-separating-music-and-voice-from-vocal-rap-songs-for-generating-engaging-visualizer

accapellapp

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.

affect-in-tweets-using-deep-learning-techniques

age-detection-of-indian-actors

age-detection-of-indian-actors1

Computers can be trained to detect human faces, and even predict their age. This may seem like an easy job for humans. Digital face sensing and processing in human brains is generally regarded as an effective and quick multistage process.

ai-based-prediction-of-rainfall-from-satellite-observation-for-disaster-management

anomaly-detection-in-network-activities

anomaly-detection-in-network-activities1