arXiv subjects

Faisal Ghaffar

Publications and source records attributed to Faisal Ghaffar.

6 recordsLinked to original sources

arXiv2022

Facial Emotions Recognition using Convolutional Neural Net

Facial expressions vary from person to person, and the brightness, contrast, and resolution of every random image are different. This is why recognizing facial expressions is very difficult. This article proposes an efficient system for facial emotion recognition for the seven basic human emotions (angry, disgust, fear, happy, sad, surprise, and neutral), using a convolution neural network (CNN), which predicts and assigns probabilities to each emotion. Since deep learning models learn from data, thus, our proposed system processes each image with various pre-processing steps for better prediction. Every image was first passed through the face detection algorithm to include in the training dataset. As CNN requires a large amount of data, we duplicated our data using various filters on each image. Pre-processed images of size 80*100 are passed as input to the first layer of CNN. Three convolutional layers were used, followed by a pooling layer and three dense layers. The dropout rate for the dense layer was 20%. The model was trained by combining two publicly available datasets, JAFFE and KDEF. 90% of the data was used for training, while 10% was used for testing. We achieved maximum accuracy of 78.1 % using the combined dataset. Moreover, we designed an application of the proposed system with a graphical user interface that classifies emotions in real-time.

cs.CV↗

arXiv2022

Macromolecule Classification Based on the Amino-acid Sequence

Deep learning is playing a vital role in every field which involves data. It has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using traditional machine learning techniques in the past. In this study we focused on classification of protein sequences with deep learning techniques. The study of amino acid sequence is vital in life sciences. We used different word embedding techniques from Natural Language processing to represent the amino acid sequence as vectors. Our main goal was to classify sequences to four group of classes, that are DNA, RNA, Protein and hybrid. After several tests we have achieved almost 99% of train and test accuracy. We have experimented on CNN, LSTM, Bidirectional LSTM, and GRU.

q-bio.BM↗

arXiv2022

Classification of Macromolecule Type Based on Sequences of Amino Acids Using Deep Learning

The classification of amino acids and their sequence analysis plays a vital role in life sciences and is a challenging task. This article uses and compares state-of-the-art deep learning models like convolution neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU) to solve macromolecule classification problems using amino acids. These models have efficient frameworks for solving a broad spectrum of complex learning problems compared to traditional machine learning techniques. We use word embedding to represent the amino acid sequences as vectors. The CNN extracts features from amino acid sequences, which are treated as vectors, then fed to the models mentioned above to train a robust classifier. Our results show that word2vec as embedding combined with VGG-16 performs better than LSTM and GRU. The proposed approach gets an error rate of 1.5%.

q-bio.BM↗

arXiv2022

Controlling Traffic with Humanoid Social Robot

The advancement of technology such as artificial intelligence, machine learning and internet of things it became easy to develop more humanoid robots and automate different processes. An interactive robot must have high social behavior so that it can be easily accepted by the people using it. In this study we designed a traffic police robot (TRAPROB) to automate the traffic control at intersection. The human police officer experiences high stress because of long duty hours as well as pose the risk of accidents. The digital electronic signals are automatic but we want to create a system which is more human like and looks like an officer controlling the traffic at intersection. We used Thiago++ robot in this study and modified its look to like a police officer, and then programmed it to imitate and make gestures just like traffic police officer makes gestures for controlling traffic. We evaluated the looks, gestures, functionality, and social behavior of the robot. We asked a limited sample of two participants to identify the TRAPBOT, rate its look, the social behaviors and gestures in comparison to a real life police officer. we found that people can identify the robot as traffic police robot. Our analysis also shows that TRAPBOT has appearance like a traffic robot and can make similar signal gestures as a traffic police officer.

cs.RO↗

arXiv2022

The Rise and Fall of Robotic World (A case study of WALL-E)

The current trend in technology shows that the robots will soon be seen interacting with humans and handling different tasks more efficiently in future. With advancement in artificial intelligence it can be foreseen that robots will definitely take over all of the major and minor jobs, will socially interact with humans and will minimize the burden of human significantly. With the passage of time humans will forget about their reality and the labour work they currently do by themselves. That age will be exactly the peak age of robots and we can call it age of the rise of the robots. With the rise of robots, they will start taking decision by themselves regarding the human life and the planet. Human will again start to minimize the influence of robots and take control of their life with the help of some robots. The WALL-E narrates such a story of human life dependency on robots in spaceship when earth is destroyed because of some catastrophe. The clash between human and robots occurs when humans find the earth is survivable and should go back but robots do not allow them. With the help of some social robots humans again take control and take the ship back to earth. The major part of the movie is about the a lonely robot living on planet earth, the love of two robots, their interaction. Those two robots help humans to move back to earth.

cs.RO↗

arXiv2020

Detection of Diabetic Anomalies in Retinal Images using Morphological Cascading Decision Tree

This research aims to develop an efficient system for screening of diabetic retinopathy. Diabetic retinopathy is the major cause of blindness. Severity of diabetic retinopathy is recognized by some features, such as blood vessel area, exudates, haemorrhages and microaneurysms. To grade the disease the screening system must efficiently detect these features. In this paper we are proposing a simple and fast method for detection of diabetic retinopathy. We do pre-processing of grey-scale image and find all labelled connected components (blobs) in an image regardless of whether it is haemorrhages, exudates, vessels, optic disc or anything else. Then we apply some constraints such as compactness, area of blob, intensity and contrast for screening of candidate connectedcomponent responsible for diabetic retinopathy. We obtain our final results by doing some post processing. The results are compared with ground truths. Performance is measured by finding the recall (sensitivity). We took 10 images of dimension 500 * 752. The mean recall is 90.03%.

eess.IV↗