Saira Gilani

Corvinus University of Budapest, Department of Computer Science, Department Member

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Saira Gillani received her PhD degree in Information Sciences from Corvinus University of Budapest, Hungary. She joined the COMSATS Institute of Information Technology, Islamabad, Pakistan in 2016. She is currently serving as an assistant professor in Saudi Electronic University, Jeddah, Saudi Arabia. Previously, she was worked as research scholar in Corvinno, Technology Transfer Center of Information Technology and Services in Budapest, Hungary and also worked as research associate in CoReNet (Center of Research in Networks and Telecom), CUST, Pakistan. Her areas of interest include Text Mining, Data Mining, Machine Learning, Vehicular Ad hoc Networks, Mobile Edge Computing and Internet of Things.
Address: Budapest, Budapest fovaros, Hungary

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Papers by Saira Gilani

The Role of Edge Computing in Internet of Things

IEEE Communications Magazine, Nov 1, 2018

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Aspect-Based Sentiment Analysis Using a Hybridized Approach Based on CNN and GA

IEEE Access, 2020

Sentiment analysis is a computational analysis of unstructured textual data, used to assess the p... more Sentiment analysis is a computational analysis of unstructured textual data, used to assess the person's attitude from a piece of text. Aspect-based sentimental analysis defines the relationship among opinion targets of a document and the polarity values corresponding to them. Since aspects are often implicit, it is an extremely challenging task to spot them and calculate their respective polarity. In recent years, several methods, strategies and improvements have been suggested to address these problems at various levels, including corpus or lexicon-based approaches, term frequency and reverse document frequency approaches. These strategies are quite effective when aspects are correlated with predefined groups and may struggle when low-frequency aspects are involved. In terms of accuracy, heuristic approaches are stronger than frequency and lexicon based approaches, however, they consume time due to different combinations of features. This article presents an effective method to analyze the sentiments by integrating three operations: (a) Mining semantic features (b) Transformation of extracted corpus using Word2vec (c) Implementation of CNN for the mining of opinion. The hyperparameters of CNN are tuned with Genetic Algorithm (GA). Experimental results revealed that the proposed technique gave better results than the state-of-the-art techniques with 95.5% accuracy rate, 94.3% precision rate, 91.1% recall and 96.0% f-measure rate. INDEX TERMS Aspect-based sentiment analysis, convolutional neural network, genetic algorithm.

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Vehicular Ad Hoc Network (VANET): A Survey, Challenges, and Applications

Advances in intelligent systems and computing, 2017

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Secure Gait Recognition-Based Smart Surveillance Systems Against Universal Adversarial Attacks

Journal of Database Management, Feb 16, 2023

Currently, the internet of everything (IoE) enabled smart surveillance systems are widely used in... more Currently, the internet of everything (IoE) enabled smart surveillance systems are widely used in various fields to prevent various forms of abnormal behaviors. The authors assess the vulnerability of surveillance systems based on human gait and suggest a defense strategy to secure them. Human gait recognition is a promising biometric technology, but one significantly hindered because of universal adversarial perturbation (UAP) that may trigger system failure. More specifically, in this research study, the authors emphasize on sample convolutional neural network (CNN) model design for gait recognition and assess its susceptibility to UAPs. The authors compute the perturbation as non-targeted UAPs, which trigger a model failure and lead to an inaccurate label to the input sample of a given subject. The findings show that a smart surveillance system based on human gait analysis is susceptible to UAPs, even if the norm of the generated noise is substantially less than the average norm of the images. Later, in the next stage, the authors illustrate a defense mechanism to design a secure surveillance system based on human gait.

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Empirical Analysis for Crime Prediction and Forecasting Using Machine Learning and Deep Learning Techniques

IEEE Access, 2021

Crime and violation are the threat to justice and meant to be controlled. Accurate crime predicti... more Crime and violation are the threat to justice and meant to be controlled. Accurate crime prediction and future forecasting trends can assist to enhance metropolitan safety computationally. The limited ability of humans to process complex information from big data hinders the early and accurate prediction and forecasting of crime. The accurate estimation of the crime rate, types and hot spots from past patterns creates many computational challenges and opportunities. Despite considerable research efforts, yet there is a need to have a better predictive algorithm, which direct police patrols toward criminal activities. Previous studies are lacking to achieve crime forecasting and prediction accuracy based on learning models. Therefore, this study applied different machine learning algorithms, namely, the logistic regression, support vector machine (SVM), Naïve Bayes, k-nearest neighbors (KNN), decision tree, multilayer perceptron (MLP), random forest, and eXtreme Gradient Boosting (XGBoost), and time series analysis by long-short term memory (LSTM) and autoregressive integrated moving average (ARIMA) model to better fit the crime data. The performance of LSTM for time series analysis was reasonably adequate in order of magnitude of root mean square error (RMSE) and mean absolute error (MAE), on both data sets. Exploratory data analysis predicts more than 35 crime types and suggests a yearly decline in Chicago crime rate, and a slight increase in Los Angeles crime rate; with fewer crimes occurred in February as compared to other months. The overall crime rate in Chicago will continue to increase moderately in the future, with a probable decline in future years. The Los Angeles crime rate and crimes sharply declined, as suggested by the ARIMA model. Moreover, crime forecasting results were further identified in the main regions for both cities. Overall, these results provide early identification of crime, hot spots with higher crime rate, and future trends with improved predictive accuracy than with other methods and are useful for directing police practice and strategies. INDEX TERMS LSTM and ARIMA based crime prediction, analysis and forecast.

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An efficient deep learning-assisted person re-identification solution for intelligent video surveillance in smart cities

Frontiers of Computer Science, Dec 12, 2022

A Hybrid Model to Detect Phishing-Sites Using Supervised Learning Algorithms

Since last decades, online technologies have revolutionized the modern computing world. However, ... more Since last decades, online technologies have revolutionized the modern computing world. However, as a result, security threats are increasing rapidly. A huge community is using the online services even from chatting to banking is done via online transactions. Customers of web technologies face various security threats and phishing is one of the most important threat that needs to be address. Therefore, the security mechanism must be enhanced. The attacker uses phishing attack to get victims credential information like bank account number, passwords or any other information by mimicking a website of an enterprise, and the victim is unaware of phishing website. In literature, several approaches have been proposed for detection and filtering phishing attack. However, researchers are still searching for such a solution that can provide better results to secure users from phishing attack. Phishing websites have certain characteristics and patterns and to identify those features can help us to detect phishing. To identify such features is a classification task and can be solved using data mining techniques. In this paper, we are presenting a hybrid model for classification to overcome phishing-sites problem. To evaluate this model, we have used the dataset from UCI repository which contains 30 attributes and 11055 instances. The experimental results showed that our proposed hybrid model outperforms in terms of high accuracy and less error rate.

A Supervised Learning Model for AGV Perception in Unstructured Environment

Advances in data mining and database management book series, 2015

This chapter focuses on an Autonomous Ground Vehicle (AGV), also known as intelligent vehicle, wh... more This chapter focuses on an Autonomous Ground Vehicle (AGV), also known as intelligent vehicle, which is a vehicle that can navigate without human supervision. AGV navigation over an unstructured road is a challenging task and is known research problem. This chapter is to detect road area from an unstructured environment by applying a proposed classification model. The Proposed model is sub divided into three stages: (1) -preprocessing has been performed in the initial stage; (2) -road area clustering has been done in the second stage; (3) -Finally, road pixel classification has been achieved. Furthermore, combination of classification as well as clustering is used in achieving our goals. K-means clustering algorithm is used to discover biggest cluster from road scene, second big cluster area has been classified as road or non road by using the well-known technique support vector machine. The Proposed approach is validated from extensive experiments carried out on RGB dataset, which shows that the successful detection of road area and is robust against diverse road conditions such as unstructured nature, different weather and lightening variations.

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An autonomous decision-making framework for gait recognition systems against adversarial attack using reinforcement learning

Isa Transactions, 2023

POSNet: a hybrid deep learning model for efficient person re-identification

The Journal of Supercomputing

Deepfake attack prevention using steganography GANs

PeerJ Computer Science

Background Deepfakes are fake images or videos generated by deep learning algorithms. Ongoing pro... more Background Deepfakes are fake images or videos generated by deep learning algorithms. Ongoing progress in deep learning techniques like auto-encoders and generative adversarial networks (GANs) is approaching a level that makes deepfake detection ideally impossible. A deepfake is created by swapping videos, images, or audio with the target, consequently raising digital media threats over the internet. Much work has been done to detect deepfake videos through feature detection using a convolutional neural network (CNN), recurrent neural network (RNN), and spatiotemporal CNN. However, these techniques are not effective in the future due to continuous improvements in GANs. Style GANs can create fake videos with high accuracy that cannot be easily detected. Hence, deepfake prevention is the need of the hour rather than just mere detection. Methods Recently, blockchain-based ownership methods, image tags, and watermarks in video frames have been used to prevent deepfake. However, this pro...

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A deep learning-based framework for accurate identification and crop estimation of olive trees

The Journal of Supercomputing

Exploiting vulnerability of convolutional neural network-based gait recognition system

The Journal of Supercomputing

Academic Excellence in E Learning Based Virtual Assistance

2021 Sustainable Leadership and Academic Excellence International Conference (SLAE)

Small obstacles detection on roads scenes using semantic segmentation for the safe navigation of autonomous vehicles

Journal of Electronic Imaging

An efficient recommender system algorithm using trust data

The Journal of Supercomputing, 2021

Ontology Maintenance Through Semantic Text Mining

Innovations, Developments, and Applications of Semantic Web and Information Systems, 2018

Manual ontology population and enrichment is a complex task that require professional experience ... more Manual ontology population and enrichment is a complex task that require professional experience involving a lot of efforts. The authors' paper deals with the challenges and possible solutions for semi-automatic ontology enrichment and population. ProMine has two main contributions; one is the semantic-based text mining approach for automatically identifying domain-specific knowledge elements; the other is the automatic categorization of these extracted knowledge elements by using Wiktionary. ProMine ontology enrichment solution was applied in IT audit domain of an e-learning system. After seven cycles of the application ProMine, the number of automatically identified new concepts are significantly increased and ProMine categorized new concepts with high precision and recall.

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Transfer learning-assisted multi-resolution breast cancer histopathological images classification

The Visual Computer, 2021

Breast cancer is one of the leading death cause among women nowadays. Several methods have been p... more Breast cancer is one of the leading death cause among women nowadays. Several methods have been proposed for the detection of breast cancer. Various machine learning-based automatic diagnosis systems have been developed known as Computer Aided Diagnostics (CAD) systems. Initial CAD systems were based on machine learning algorithms however due to the automatic feature extraction ability of convolutional neural networks (CNN)-based deep learning models are widely adopted. Deep learning is widely used in various fields. Healthcare is one of the essential field that deep learning has transformed. Another common issue faced by the patients is difference of opinion among different pathologists and medical practitioners. Such human errors often lead to misleading or delayed judgment, which may be fatal to human life. To improve decision consistency, efficiency, and error reduction, researchers in the field of healthcare are using deep learning-based approaches and achieved state of art results. In this study, a deep learning (DL) and transfer learning-based approach is proposed to classify histopathological images for breast cancer diagnosis. In this study, we have adopted patch selection approach to classify breast histopathological images on small number of training images using transfer learning without losing the performance. Initially, patches are extracted from Whole Slide Images and fed into the CNN for features extraction. Based on these features, the discriminative patches are selected and then fed to Efficient-Net architecture pre-trained on ImageNet dataset. Features extracted from Efficient-Net architecture are also used to train a SVM classifier. The proposed model outperforms the baseline methods in terms of multiple performance measures.

An Efficient Gait Recognition Method for Known and Unknown Covariate Conditions

IEEE Access, 2021

Gait is a unique non-invasive biometric form that can be utilized to effectively recognize person... more Gait is a unique non-invasive biometric form that can be utilized to effectively recognize persons, even when they prove to be uncooperative. Computer-aided gait recognition systems usually use image sequences without considering covariates like clothing and possessions of carrier bags whilst on the move. Similarly, in gait recognition, there may exist unknown covariate conditions that may affect the training and testing conditions for a given individual. Consequently, common techniques for gait recognition and measurement require a degree of intervention leading to the introduction of unknown covariate conditions, and hence this significantly limits the practical use of the present gait recognition and analysis systems. To overcome these key issues, we propose a method of gait analysis accounting for both known and unknown covariate conditions. For this purpose, we propose two methods, i.e., a Convolutional Neural Network (CNN) based gait recognition and a discriminative features-based classification method for unknown covariate conditions. The first method can handle known covariate conditions efficiently while the second method focuses on identifying and selecting unique covariate invariant features from the gallery and probe sequences. The feature set utilized here includes Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and Haralick texture features. Furthermore, we utilize the Fisher Linear Discriminant Analysis for dimensionality reduction and selecting the most discriminant features. Three classifiers, namely Random Forest, Support Vector Machine (SVM), and Multilayer Perceptron are used for gait recognition under strict unknown covariate conditions. We evaluated our results using CASIA and OUR-ISIR datasets for both clothing and speed variations. As a result, we report that on average we obtain an accuracy of 90.32% for the CASIA dataset with unknown covariates and similarly performed excellently on the ISIR dataset. Therefore, our proposed method outperforms existing methods for gait recognition under known and unknown covariate conditions. INDEX TERMS Gait recognition, covariate conditions, discriminative feature learning, FLDA.

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A Residual-Learning-Based Multi-Scale Parallel-Convolutions- Assisted Efficient CAD System for Liver Tumor Detection

Mathematics, 2021

Smart multimedia-based medical analytics and decision-making systems are of prime importance in t... more Smart multimedia-based medical analytics and decision-making systems are of prime importance in the healthcare sector. Liver cancer is commonly stated to be the sixth most widely diagnosed cancer and requires an early diagnosis to help with treatment planning. Liver tumors have similar intensity levels and contrast as compared to neighboring tissues. Similarly, irregular tumor shapes are another major issue that depends on the cancer stage and tumor type. Generally, liver tumor segmentation comprises two steps: the first one involves liver identification, and the second stage involves tumor segmentation. This research work performed tumor segmentation directly from a CT scan, which tends to be more difficult and important. We propose an efficient algorithm that employs multi-scale parallel convolution blocks (MPCs) and Res blocks based on residual learning. The fundamental idea of utilizing multi-scale parallel convolutions of varying filter sizes in MPCs is to extract multi-scale f...

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