Sajid Iqbal

Bahauddin Zakariya University, Computer Sciecne, Faculty Member

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I am an academician and researcher in the field of Computer Science. For last 10 years i am associated with higher education and teaching different courses at graduate and undergraduate level. I am passionate to learn new things and do their implementations wherever possible.
Address: Multan, Punjab, Pakistan

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A Data Driven Approach for Automated Risk Assessment and Survival Prediction of Coronavirus Using Artificial Neural Networks

Journal of Computing & Biomedical Informatics

Coronavirus is proved to be a severe epidemic diseasethroughout the world. Despite of endeavoring... more Coronavirus is proved to be a severe epidemic diseasethroughout the world. Despite of endeavoring lot of medicalfacilities for mitigating with this pandemic, still the number ofinfected cases increased rapidly, which leads to lack of healthcareresources (i.e. hospitals, doctors and other healthcare amenities). Early stage risk prediction by analyzing several clinical andbehavioral risk factors is considered to be a promising solutionfor prescribing appropriate triage to patients and to reduce themortality rate due to this fetal disease. To cope up with thisproblem, in our study we have proposed a deep learning basedapproach for the early stage prediction of risk of infection andrisk of mortality in individuals possessing certain risk factors. Wehave utilized a publically available covid-19 dataset incorporatingseveral risk factors that may cause this infection. For the selectionof most significant risk factors i.e. with respect to their level ofimportance in risk prediction, we have...

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A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks

Journal of Medical Systems, 2019

Alzheimer's disease (AD) is an incurable neurodegenerative disorder accounting for 70%-80% dement... more Alzheimer's disease (AD) is an incurable neurodegenerative disorder accounting for 70%-80% dementia cases worldwide. Although, research on AD has increased in recent years, however, the complexity associated with brain structure and functions makes the early diagnosis of this disease a challenging task. Resting-state functional magnetic resonance imaging (rs-fMRI) is a neuroimaging technology that has been widely used to study the pathogenesis of neurodegenerative diseases. In literature, the computer-aided diagnosis of AD is limited to binary classification or diagnosis of AD and MCI stages. However, its applicability to diagnose multiple progressive stages of AD is relatively under-studied. This study explores the effectiveness of rs-fMRI for multi-class classification of AD and its associated stages including CN, SMC, EMCI, MCI, LMCI, and AD. A longitudinal cohort of resting-state fMRI of 138 subjects (25 CN, 25 SMC, 25 EMCI, 25 LMCI, 13 MCI, and 25 AD) from Alzheimer's Disease Neuroimaging Initiative (ADNI) is studied. To provide a better insight into deep learning approaches and their applications to AD classification, we investigate ResNet-18 architecture in detail. We consider the training of the network from scratch by using single-channel input as well as performed transfer learning with and without fine-tuning using an extended network architecture. We experimented with residual neural networks to perform AD classification task and compared it with former research in this domain. The performance of the models is evaluated using precision, recall, f1-measure, AUC and ROC curves. We found that our networks were able to significantly classify the subjects. We achieved improved results with our fine-tuned model for all the AD stages with an accuracy of 100%, 96.85%, 97.38%, 97.43%, 97.40% and 98.01% for CN, SMC, EMCI, LMCI, MCI, and AD respectively. However, in terms of overall performance, we achieved state-of-the-art results with an average accuracy of 97.92% and 97.88% for off-the-shelf and fine-tuned models respectively. The Analysis of results indicate that classification and prediction of neurodegenerative brain disorders such as AD using functional magnetic resonance imaging and advanced deep learning methods is promising for clinical decision making and have the potential to assist in early diagnosis of AD and its associated stages.

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High performance stemming algorithm to handle multi-level inflections in Urdu Language

Stemming is an essential step in varied Natural Language Processing (NLP) applications. It is use... more Stemming is an essential step in varied Natural Language Processing (NLP) applications. It is used to reduce different variants of the query words to a standard form to avoid the vocabulary mismatch issue in Information Retrieval (IR) systems. There are a lot of stemming algorithms in English language, but Urdu NLP still in its infancy. A stemmer develops and evaluates in a language cannot be efficiently used for other languages. Urdu has the highly inflected and complex morphological structure, and most of the current Urdu stemmers remove a few numbers of affixes and leads to inefficient results. In this context, according to the best of our knowledge, this is a first comprehensive effort towards the proposition and evaluation of a novel Urdu Text Stemmer (UTS) that cope with multi-level inflection and derivation forms in Urdu text. The experimental evaluation of the proposed scheme has been conducted on the text-based and words-based custom developed corpus. The proposed stemming ...

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PWMStem: A Corpus-Based Suffix Identification and Stripping Algorithm for Multi-lingual Stemming

Journal of Advances in Information Technology

Stemming is a common preprocessing method aggregating all word variants to a standard stem to aid... more Stemming is a common preprocessing method aggregating all word variants to a standard stem to aid various Natural Language Processing (NLP) tasks. This work proposes a new unsupervised corpus-based stemmer that identifies the candidate suffixes using pivot word matching. Then candidate suffix statistics are used to remove the potential suffixes. After this, lexical similarity is measured to cluster the morphological related words. Finally, the smallest word in each cluster is designated as a stem. To quantify the performance of proposed method, two corpus-based and two linguistic knowledge-based stemmers for Urdu and English languages are used. The performance of each stemmer is evaluated on two different datasets for each language. The results show that the proposed PWMStem method outperforms the selected stemmers, achieving an accuracy of 0.876 for Urdu and 0.877 for English. To assess the performance of PWMStem through different aspects multiple evaluation metrics are used. The evaluation scores of other metrics are Index Compression Factor (ICF) = 73, Mean Number of Words per Conflation Class (MWC) = 3.7 for Urdu, and ICF = 71 and MWC = 3.5 for English. In the Urdu dataset, PWMStem achieved the lowest Understemming Index (UI) of 0.026479, Over-stemming Index (OI) of 0.000021, and an Error Rate Relative to Truncation (ERRT) of 0.610. In the English dataset, the values for UI, OI, and ERRT were measured as 0.102089, 0.000015, and 0.498, respectively.

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Automatic Diagnosis of Glaucoma from Retinal Images Using Deep Learning Approach

Diagnostics

Glaucoma is characterized by increased intraocular pressure and damage to the optic nerve, which ... more Glaucoma is characterized by increased intraocular pressure and damage to the optic nerve, which may result in irreversible blindness. The drastic effects of this disease can be avoided if it is detected at an early stage. However, the condition is frequently detected at an advanced stage in the elderly population. Therefore, early-stage detection may save patients from irreversible vision loss. The manual assessment of glaucoma by ophthalmologists includes various skill-oriented, costly, and time-consuming methods. Several techniques are in experimental stages to detect early-stage glaucoma, but a definite diagnostic technique remains elusive. We present an automatic method based on deep learning that can detect early-stage glaucoma with very high accuracy. The detection technique involves the identification of patterns from the retinal images that are often overlooked by clinicians. The proposed approach uses the gray channels of fundus images and applies the data augmentation tec...

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Volumetric Segmentation of Brain Regions From MRI Scans Using 3D Convolutional Neural Networks

IEEE Access, 2020

Automated brain segmentation is an active research domain due to the association of various neuro... more Automated brain segmentation is an active research domain due to the association of various neurological disorders with different regions of the brain, to help medical professionals in prognostics and diagnostics. Traditional techniques like atlas-based and pattern recognition-based methods led to the development of various tools for automated brain segmentation. Recently, deep learning techniques are outperforming classical state-of-the-art methods and gradually becoming more mature. Consequently, deep learning has been extensively employed as a tool for precise segmentation of brain regions because of its capability to learn the intricate features of the high-dimensional data. In this work, a network for the segmentation of multiple brain regions has been proposed that is based on 3D convolutional neural networks and utilizes residual learning and dilated convolution operations to efficiently learn the end-to-end mapping from MRI volumes to the voxel-level brain segments. This research is focused on the segmentation of up to nine brain regions including cerebrospinal fluid, white matter and gray matter as well as their sub-regions. Mean dice scores of 0.879 and 0.914 have been achieved for three and nine brain regions, respectively by using the data from three different sources. Comparative analysis shows that our network gives better dice scores for most of the brain regions than state-of-the-artwork. Moreover, the mean dice score of 0.903, obtained for eight brain regions segmentation with MRBrains18 dataset, is better than 0.876 which was achieved in the previous work.

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THREAT MODELLING METHODOLOGIES: A SURVEY

The security of software systems can be broadly divided into two categories namely external secur... more The security of software systems can be broadly divided into two categories namely external security and internal security. The internal security of software systems is the main issue for any secure system. This issue of software security depends on the design of software systems and integration of security features into the design. This process involves the identification of security threats to software systems, identification of relevant mitigation measures and their integration into the design of software systems. To identify security requirements of software systems, many techniques have been developed. Threat modelling is one of the approaches to identify security requirements and helps to add them into the design of the software systems. Threat modelling makes it possible to identify all potential threats to the software systems and hence helps software designers to add mitigations to make their software design more secure and reliable. Many threat modelling approaches had been developed over the time since its emergence. This paper presents a survey on various threat modelling techniques. In this paper, we have focussed on the techniques in the context of secure web applications.

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Datamining based decision support system for students

Students have to make many decisions in their educational tenure. The study program offered at un... more Students have to make many decisions in their
educational tenure. The study program offered at university level
generally consists of two type of courses: Core and Electives. The
core courses are compulsory for students to enroll whereas in
elective subjects they are provided with options to choose
according to their interest or specialization. Sometime it becomes
bit difficult for students to formulate right decision in course
selection according to their strengths and weakness. The
proposed system help the students, at this course selection stage,
with list of courses that are preferable for them based on their
previous course performances. This recommendation system is
based on ID3 decision tree algorithm and simply bases its
decisions on probability calculations

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Introduction to Programming Using C#

A book for basic programming course

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A Comparative Study of Garbage Collection Techniques in Java Virtual Machines

Garbage collection mechanisms implemented in Java Virtual Machines (JVMs) are used to reclaim mem... more Garbage collection mechanisms implemented in Java Virtual Machines (JVMs) are used to reclaim memory allocated to the
objects that become inaccessible during program execution. An efficient garbage collection mechanism certainly facilitates in smooth running of an application.
This paper performs a comparative analysis of garbage collection techniques implemented in Sun HotSpot, Oracle JRockit and IBM J9 virtual machines. We perform experiments with several benchmarks containing dynamic creation for objects of TreeMap, ArrayList and String array data types. Due to the creation of a large number of big sized memory objects and loss of references, the garbage is created that is collected by the garbage collectors implemented in the virtual machines.
The experiments are carried out on different hardware architectures including Intel Core 2 Quad and Intel Xeon based systems. The performance of each garbage collector is computed in terms of the rate of garbage collection that mainly depends upon the strategy implemented for garbage collection. Our results show that the Sun HotSpot garbage collector performs 4.66 times better than the Oracle JRockit and 144.06 times better than the IBM J9 garbage collectors.

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Sajid Iqbal

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