
Mudassar RazaHITEC University · Computer Science
Mudassar Raza
PhD
About
157
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Introduction
Mudassar Raza is an Associate professor at HITEC University Taxila, Pakistan. He completed his PhD from the University of Science and Technology of China, Anhui, Hefei, China (USTC).
Additional affiliations
September 2014 - December 2017
October 2006 - present
Publications
Publications (157)
Code related to "Anomaly Recognition in Surveillance Based on Feature Optimizer Using Deep Learning
Shaista Khanam, Muhammad Sharif, Mudassar Raza, Waqar Ishaq, Muhammad Fayyaz, Seifedine Kadry"
Detecting lung diseases in medical images can be quite challenging for radiologists. In some cases, even experienced experts may struggle with accurately diagnosing chest diseases, leading to potential inaccuracies due to complex or unseen biomarkers. This review paper delves into various datasets and machine learning techniques employed in recent...
Knee Osteoarthritis (KOA), the most prevalent joint disease, significantly impacts elderly mobility due to progressive cartilage degeneration. Early prediction is crucial for preventing disease progression and guiding effective treatment plans. This paper proposes an EnsembleTL-ACO, fully automated, computer-aided diagnosis (CAD) system for accurat...
Corn diseases significantly impact crop yields, posing a major challenge to agricultural productivity. Early and accurate detection of these diseases is crucial for effective management and mitigation. Existing methods, mostly relying on analyzing corn leaves, often lack the precision to identify and classify a wide range of diseases under varying...
The classification of medical images has had a significant influence on the diagnostic techniques and therapeutic interventions. Conventional disease diagnosis procedures require a substantial amount of time and effort to accurately diagnose. Based on global statistics, gastrointestinal cancer has been recognized as a major contributor to cancer‐re...
Accurate detection and classification of artifacts within the gastrointestinal (GI) tract frames remain a significant challenge in medical image processing. Medical science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous diseases. Key to this is the development of robust algorithms for image cl...
[This corrects the article DOI: 10.1371/journal.pone.0292601.].
Colorectal cancer has become a significant global health challenge and causes millions of fatalities every year. One of the major causes for its high fatality rate is the lack of timely screening and diagnosis. Rapid detection of cancer precursors like polyps through artificial intelligence based automated systems is vital for mitigating its global...
The increasing demand for food production due to the growing population is raising the need for more food-productive environments for plants. The genetic behavior of plant traits remains different in different growing environments. However, it is tedious and impossible to look after the individual plant component traits manually. Plant breeders nee...
Breast cancer (BC) is a widely diagnosed deadly disease commonly present in middle-aged women around the globe. Ultrasound (U/S) imaging is widely used for the early prediction and segmentation of BC due to low radiation and cheapness. Manual BC segmentation from ultrasound imaging is a complex and laborious task due to inherited noise. Many deep l...
White blood cells (WBCs) are an indispensable constituent of the immune system. Efficient and accurate categorization of WBC is a critical task for disease diagnosis by medical experts. This categorization helps in the correct identification of medical problems. In this research work, WBC classes are categorized with the help of a transform learnin...
Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial intelligence have helped medical experts find GIT diseases through endoscopic procedures. Wired endoscopy is a controlled procedure that helps the medical expert in disease diagnosis. Manual screening o...
Human Action Recognition (HAR) in uncontrolled environments targets to recognition of different actions from a video. An effective HAR model can be employed for an application like human-computer interaction, health care, person tracking, and video surveillance. Machine Learning (ML) approaches, specifically, Convolutional Neural Network (CNN) mode...
Anomalous situations in surveillance videos or images that may result in security issues, such as disasters, accidents, crime, violence, or terrorism , can be identified through video anomaly detection. However, differentiating anomalous situations from normal can be challenging due to variations in human activity in complex environments such as tr...
Video surveillance and activity monitoring are the practical real-time applications of Human Action Recognition (HAR). A fusion of several Convolutional Neural Network (CNN) architectures has been widely used for effective HAR and achieved impressive results. Feature fusion of multiple pre-trained models also extracts redundant features due to the...
Fruit is an essential element of human life and a significant gain for the agriculture sector. Guava is a common fruit found in different countries. It is considered the fourth primary fruit in Pakistan. Several bacterial and fungal diseases found in guava fruit decrease production daily. Leaf Blight is a common disease found in guava fruit that af...
COVID-19 is a challenging worldwide pandemic disease nowadays that spreads from person to person in a very fast manner. It is necessary to develop an automated technique for COVID-19 identification. This work investigates a new framework that predicts COVID-19 based on X-ray images. The suggested methodology contains core phases as preprocessing, f...
In this manuscript, imbalanced and small sample space (IB-SSS) dataset problems for pedestrian gender classification using fusion of selected deep and traditional features (PGC-FSDTF) are considered. In this regard, data preparation is first done through data augmentation and preprocessing steps to handle imbalanced classification problem and envir...
An effective communication application necessitates the cancellation of Impulsive Noise (IN) from Orthogonal Frequency Division Multiplexing (OFDM), which is widely used for wireless applications due to its higher data rate and greater spectral efficiency. The OFDM system is typically corrupted by Impulsive Noise, which is an unwanted short-duratio...
Optical coherence tomography (OCT) is one of the principal imaging modalities for retinal eye disease detection and classification. Different retinal eye diseases are the leading cause of blindness that can be overcome by early detection. However, ophthalmologists are currently carrying out retinal eye disease detection manually with the help of OC...
Automated formulation of sketches from face photos has seen successive growth since the work of Wang and Tang in recent years. Each new methodology is, however, able to partially achieve its objective of sketch synthesis while using pairs of photos and viewed sketches as a training medium. The viewed sketches are also used as a testing medium to de...
p>Classical blockchain cryptographic primitives are susceptible to quantum computing technology due to its unprecedented growth because it has the potential to make current blockchain encryption techniques obsolete against quantum attacks. Shor algorithm can perform transactional hijacking and forge digital signatures to impersonate blockchain user...
p>Classical blockchain cryptographic primitives are susceptible to quantum computing technology due to its unprecedented growth because it has the potential to make current blockchain encryption techniques obsolete against quantum attacks. Shor algorithm can perform transactional hijacking and forge digital signatures to impersonate blockchain user...
Computer-aided polyp segmentation is a crucial task that supports gastroenterologists in examining and resecting anomalous tissue in the gastrointestinal tract. The disease polyps grow mainly in the colorectal area of the gastrointestinal tract and in the mucous membrane, which has protrusions of micro-abnormal tissue that increase the risk of incu...
Recent advancements with deep generative models have proven significant potential in the task of image synthesis, detection, segmentation, and classification. Segmenting the medical images is considered a primary challenge in the biomedical imaging field. There have been various GANs-based models proposed in the literature to resolve medical segmen...
White blood cells (WBCs) are the important constituent of a blood cell. These blood cells are responsible for defending the body against infections. Abnormalities identified in WBC smears lead to the diagnosis of disease types such as leukocytosis, hepatitis, and immune system disorders. Digital image analysis for infection detection at an early st...
Internet of Things (IoT) vision has astoundingly transcended the environmental sensing with integrated computing systems and smart devices, providing seamless connectivity among humans, machines and their environment to cooperate for convenience and economical benefits. Apart from all the tremendous benefits of IoT, this paradigm still suffers from...
Internet of Things (IoT) vision has astoundingly transcended the environmental sensing with integrated computing systems and smart devices, providing seamless connectivity among humans, machines and their environment to cooperate for convenience and economical benefits. Apart from all the tremendous benefits of IoT, this paradigm still suffers from...
Human Action Recognition (HAR) is still considered as a significant research area due to its emerging real-time applications like video surveillance, automated surveillance, real-time tracking and resecue missions. HAR domain still have gaps to cover, i.e., random changes in human variations, clothes, illumination, and backgrounds. Different camera...
Traditional methods for behavior detection of distracted drivers are not capable of capturing driver behavior features related to complex temporal features. With the goal to improve transportation safety and to reduce fatal accidents on roads, this research article presents a Hybrid Scheme for the Detection of Distracted Driving called HSDDD. This...
The recent development in the area of IoT technologies is likely to be implemented extensively in the next decade. There is a great increase in the crime rate, and the handling officers are responsible for dealing with a broad range of cyber and Internet issues during investigation. IoT technologies are helpful in the identification of suspects, an...
Malaria is a severe illness triggered by parasites that spreads via mosquito bites. In underdeveloped nations, malaria is one of the top causes of mortality, and it is mainly diagnosed through microscopy. Computer-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infecte...
Crops are very important to the financial needs of a country. Due to various diseases caused by different pathogens, a large number of crops have been destroyed. As humanoids, our basic need is food for survival, and the most basic foundation of our food is agriculture. For many developing countries, it is mainly an important source of income. Bact...
White blood cells, WBCs for short, are an essential component of the human immune system. These cells are our body's first line of defense against infections and diseases caused by bacteria, viruses, and fungi, as well as abnormal and external substances that may enter the bloodstream. A wrong WBC count can signify dangerous viral infections, autoi...
Automatic gastrointestinal (GI) tract disease recognition is an important application of biomedical image processing. Conventionally, microscopic analysis of pathological tissue is used to detect abnormal areas of the GI tract. The procedure is subjective and results in significant inter-/intra-observer variations in disease detection. Moreover, a...
In medical imaging, automated detection of stomach and gastrointestinal diseases using WCE (wireless capsule endoscopy) images is an emerging research domain. It includes numerous limitations and challenges such as variation in the contrast, texture variation, color and complexity in the background etc. To overcome these challenges, several compute...
Intelligent visual surveillance systems are attracting much attention from research and industry. The invention of smart surveillance cameras with greater processing power has now been the leading stakeholder, making it conceivable to design intelligent visual surveillance systems. It is possible to assure the safety of people in both homes and pub...
White blood cells (WBCs) are a vital part of the immune system that protect the body from different types of bacteria and viruses. Abnormal cell growth destroys the body’s immune system, and computerized methods play a vital role in detecting abnormalities at the initial stage. In this research, a deep learning technique is proposed for the detecti...
Exam proctoring is a hectic task i.e., the monitoring of students’ activities becomes difficult for supervisors in the examination rooms. It is a costly approach that requires much labor. Also, it is a difficult task for supervisors to keep an eye on all students at a time. Automatic exam activities recognition is therefore necessitating and a dema...
Multiclass classification of brain tumors is an important area of research in the field of medical imaging. Since accuracy is crucial in the classification, a number of techniques are introduced by computer vision researchers; however, they still face the issue of low accuracy. In this article, a new automated deep learning method is proposed for t...
Provocative heart disease is related to ventricular arrhythmias (VA). Ventricular tachyarrhythmia is an irregular and fast heart rhythm that emerges from inappropriate electrical impulses in the ventricles of the heart. Different types of arrhythmias are associated with different patterns, which can be identified. An electrocardiogram (ECG) is the...
Appearance-based gender classification is one of the key areas in pedestrian analysis, and it has many useful applications such as visual surveillance, predict demographics statistics, population prediction, and human–computer interaction. For pedestrian gender classification, traditional and deep convolutional neural network (CNN) approaches are e...
To overcome the problems of automated brain tumor classification, a novel approach is proposed based on long short-term memory (LSTM) model using magnetic resonance images (MRI). First, N4ITK and Gaussian filters having size 5 × 5 are used to boost the of multi-sequence MRI quality. The presented deep LSTM model having four layers is utilized for c...
Gliomas are dreadful and common type of brain tumor. Therefore, treatment planning is significant to increase the survival rate of gliomas patients. The large structural and spatial variation between tumors makes an automated detection more challenging. Brain magnetic resonance imaging is utilized for tumor evaluation on the basis of automated segm...
Background: Traditional endoscopy is an invasive and painful method of examining the gastrointestinal tract (GIT) not supported by the physicians and patients. To handle this issue, video endoscopy (VE) or wireless capsule endoscopy (WCE) is recommended and utilized for GIT examination. Furthermore, manual assessment of captured images is not possi...
As the number of internet users increases so does the number of malicious attacks using malware. The detection of malicious code is becoming critical, and the existing approaches need to be improved. Here, we propose a feature fusion method to combine the features extracted from pre-trained AlexNet and Inception-v3 deep neural networks with feature...
Person re-identification (ReID) is an imperative area of pedestrian analysis and has practical applications in visual surveillance. In the person ReID, the robust feature representation is a key issue because of inconsistent visual appearances of a person. Also, an exhaustive gallery search is required to find the target image against each probe im...
In the agriculture farming business, weeds, pests, and other plant diseases are the major reason for monetary misfortunes around the globe. It is an imperative factor, as it causes a significant diminution in both quality and capacity of crop growing. Therefore, detection and taxonomy of various plants diseases are crucial, and it demands utmost at...
The physical appearance of a brain tumor in human beings may be an indication of problems in psychological (cognitive) functions. Such functions include learning, understanding, problem solving, decision making, and planning. Early brain tumor detection can be done by using the proper procedure of screening. MRI is used for the detection of disease...
Automated skin lesion diagnosis from dermoscopic images is a difficult process due to several notable problems such as artefacts (hairs), irregularity, lesion shape, and irrelevant features extraction. These problems make the segmentation and classification process difficult. In this research, we proposed an optimized colour feature (OCF) of lesion...
Brain tumor detection depicts a tough job because of its shape, size and appearance variations. In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image to prominent the inhomogeneities field effect of the MR slices and fused with the i...
Human action recognition (HAR) has gained much attention in the last few years due to its enormous applications including human activity monitoring, robotics, visual surveillance, to name but a few. Most of the previously proposed HAR systems have focused on using hand-crafted images features. However, these features cover limited aspects of the pr...
Tumor in brain is a major cause of death in human beings. If not treated properly and timely, there is a high chance of it to become malignant. Therefore, brain tumor detection at an initial stage is a significant requirement. In this work, initially the skull is removed through brain surface extraction (BSE) method. The skull removed image is then...
Accurate glioma detection using magnetic resonance imaging (MRI) is a complicated job. In this research, deep learning model is presented for glioma and stroke lesion detection. The proposed architecture consists of 14 layers. The first input layer is followed by three convolutional layers while 5th, 6th and 7th layers correspond to batch normaliza...
Biometrics is becoming an important technology in automated person recognition. With the help of biometrics, the individuals are recognized through their unique characteristics and behaviors of various body parts. Some most famous biometrics techniques include the recognition of face, finger prints, iris, gate and signature. This chapter encompasse...
In the area of machine learning and pattern recognition, object classification is getting an attraction due to its range of applications such as visual surveillance. In recent times, numerous deep learning-based methods are presented for object classification but still, set of problems/concerns exists which reduce the overall classification accurac...
Background and objective:
Brain tumor occurs because of anomalous development of cells. It is one of the major reasons of death in adults around the globe. Millions of deaths can be prevented through early detection of brain tumor. Earlier brain tumor detection using Magnetic Resonance Imaging (MRI) may increase patient's survival rate. In MRI, tu...
Automated detection of brain tumor is a more challenging work due to the variability and complexity of shape, size, texture and location of lesions. The non-invasive MRI methods appear as a front line brain tumor detection tools (without ionization radiation). In this manuscript, an unsupervised clustering approach for tumor segmentation is propose...