Sohail Masood

Superior University · Computer Science

Deep learning models, such as convolutional neural network (CNN), are popular now a day to solve various complex problems in medical and other fields, such as image classification, object detection, recommendation of images, processing of natural languages and video and image analysis. So, the idea of studying the architecture of CNNs has gotten a...

In computer vision, the convolutional neural network (CNN) is a very popular model used for emotion recognition. It has been successfully applied to detect various objects in digital images with remarkable accuracy. In this paper, we extracted learned features from a pre-trained CNN and evaluated different machine learning (ML) algorithms to perfor...

Facial expression recognition (FER) is advancing human-computer interaction, especially, today, where facial masks are commonly worn due to the COVID-19 pandemic. Traditional unimodal techniques for facial expression recognition may be ineffective under these circumstances. To address this challenge, multimodal approaches that incorporate data from...

The most lethal and devastating form of cancer, breast cancer, is often first detected when a lump appears in the breast. The cause can be attributed to a typical proliferation of cells in the mammary glands. Early breast cancer detection improves survival. Breast cancer screening and early detection are commonly carried out using imaging technique...

According to the World Health Organization, 31% death rate in the World is because of cardiovascular diseases like heart arrhythmia and heart failure. Early diagnosis of heart problems may help in timely treatment of the patients and hence control death rate. Heart sounds are good signals of heart health if examined by an expert. Moreover, heart so...

In this paper we propose an automatic volcano event detection system based on Hidden Markov Model (HMM) with state and event duration models. Since different volcanic events have different durations, therefore the state and whole event durations learnt from the training data are enforced on the corresponding state and event duration models within t...

In this paper, a new technique for automatically identifying the type of noise in digital images has been proposed. Our statistical features based noise Type identification scheme uses machine learning to distinguish different types of noises. Local features of 3x3 window are used to train the machine learning based classifier. Two types of noise (...

In this paper, a color difference based fuzzy filter is presented for fix and random-valued impulse noise. Noise detection scheme of two stages was applied to detect noise efficiently whereas for noise removal an improved Histogram based Fuzzy Color Filter (HFC) is presented. Pixels detected as noisy by the noise detection scheme are deliberated as...

A probabilistic opposition-based Particle Swarm Optimization algorithm with Velocity Clamping and inertia weights (OvcPSO) is designed for function optimization—to accelerate the convergence speed and to optimize solution’s accuracy on standard benchmark functions. In this work, probabilistic opposition-based learning for particles is incorporated...

In this paper, we propose an image filtering technique based on fuzzy logic control to remove impulse noise for low as well as highly corrupted images. The proposed method is based on noise detection, noise removal and edge preservation modules. The main advantage of the proposed technique over the other filtering techniques is its superior noise r...

In this paper, two-stage machine learning-based noise detection scheme has been proposed for identification of salt-and- pepper impulse noise which gives excellent detection results for highly corrupted images. In the first stage, a window of size $3\times 3$ is taken from image and some other features of this window are used as input to neural n...

In this paper, we propose a neuro-fuzzy based blind image restoration to remove impulse noise from low as well as highly corrupted images. Main components of the proposed technique include noise detection, histogram estimation and noise filtering process. Proposed technique constructs the fuzzy sets using fuzzy number construction algorithm. These...

In this paper, we propose a neuro-fuzzy based blind image restoration method to remove impulse noise. Proposed technique consists of three main components: noise detection, histogram estimation and noise filtering process. Proposed technique constructs the fuzzy sets using fuzzy number construction algorithm. These fuzzy sets are used in noise filt...

In this paper, we present a novel framework -- it uses the information in kernel structures of a process -- to do run-time analysis of the behavior of an executing program. Our analysis shows that classifying a process as malicious or benign -- using the information in kernel structures of a process -- is not only very accurate but also has very lo...

In this paper, a novel image restoration technique based on fuzzy logic and robust order statistics is presented. The proposed method uses the concept of robust order statistics, median and MAD (median of the absolute deviations from the median) along with fuzzy logic. We use the median and MAD to construct trapezoidal shaped fuzzy membership funct...

Segmentation is considered as an essential step in medical image analysis and classification. In this paper we have proposed a method for the automatic segmentation of brain from MR images using Curvelet transform and Fuzzy C-Mean (FCM) with spatial information. Fuzzy entropy has been used for calculating adaptive and optimal threshold to separate...

IEEE recently standardized 802.21-2008 Media Independent Handover (MIH) standard. MIH is a key milestone toward the evolution of integrated heterogeneous 4G wireless networks. MIH provides number of link layer events in a unied way that facilitate upper layer proto- cols in making handover decisions. One such event is Link Going Down (LGD) trigger....

This paper presents an Opposition-based PSO(OVCPSO) which uses Velocity Clamping to accelerate its convergence speed and to avoid premature convergence of algorithm. Probabilistic opposition-based learning for particles has been used in the proposed method which uses velocity clamping to control the speed and direction of particles. Experiments hav...