Habib Shah

Habib Shah
King Khalid University | KKU · College of Computer Science

PhD
Working on three research Projects including Numerical Optimization, Smart Cities, and Deep Learning with time Series

About

70
Publications
28,535
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
668
Citations
Citations since 2017
47 Research Items
554 Citations
2017201820192020202120222023050100150
2017201820192020202120222023050100150
2017201820192020202120222023050100150
2017201820192020202120222023050100150
Additional affiliations
October 2016 - present
King Khalid University
Position
  • Professor (Assistant)
June 2014 - present
University of Malakand
Position
  • Professor (Assistant)
June 2014 - March 2015
University of Malakand
Position
  • Professor (Assistant)

Publications

Publications (70)
Article
Full-text available
This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where multiple UAVs are used to serve mobile users. We aim to minimize the overall energy consumption of the system by planning the trajectories of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UA...
Article
Full-text available
To solve constrained optimization problems (COPs), teaching learning-based optimization (TLBO) has been used in this study as a baseline algorithm. Different constraint handling techniques (CHTs) are incorporated in the framework of TLBO. The superiority of feasibility (SF) is one of the most commonly used and much effective CHTs with various decis...
Article
Full-text available
Large-scale global optimization problems are ambitious and quite difficult to handle with deterministic methods. The use of stochastic optimization techniques is a good choice for dealing with these problems. Nature-inspired algorithms (NIAs) are stochastic in nature, computer-based, and quite easy to implement due to their population-based nature....
Article
Full-text available
Novel Pandemic COVID-19 led globally to severe health barriers and financial issues in different parts of the world. The forecast on COVID-19 infections is significant. Demeanor vital data will help in executing policies to reduce the number of cases efficiently. Filtering techniques are appropriate for dynamic model structures as it provide reason...
Article
Full-text available
Motivation Many real applications such as businesses and health generate large categorical datasets with uncertainty. A fundamental task is to efficiently discover hidden and non-trivial patterns from such large uncertain categorical datasets. Since the exact value of an attribute is often unknown in uncertain categorical datasets, conventional clu...
Article
Full-text available
Document Image Analysis (DIA) is one of the research areas of Artificial Intelligence (AI) that converts document images into machine-readable codes. In DIA systems, Optical Character Recognition (OCR) plays a key role in digitizing document images. The output of an OCR system is further used in many applications including, Natural Language Process...
Article
Full-text available
Localization of multiple targets is a challenging task due to immense complexity regarding data fusion received at the sensors. In this context, we propose an algorithm to solve the problem for an unknown number of emitters without prior knowledge to address the data fusion problem. The proposed technique combines the time difference of arrival (TD...
Article
Full-text available
Unmanned aerial vehicles/drones are considered an essential ingredient of traffic motoring systems in smart cities. Interconnected drones, also called the Internet of Drones (IoD), gather critical data from the environmental area of interest and transmit the data to a server located at the control room for further processing. This transmission occu...
Article
Full-text available
Internet of Things-enabled smart grid (SG) technology provides ample advantages to traditional power grids. In an SG system, the smart meter (SM) is the critical component that collects the power usage information related to users and delivers the accumulated vital information to the central service provider (CSP) via the Internet, imperiled to num...
Article
Full-text available
A Complex q-rung orthopair fuzzy set (CQROFS) is one of the useful tools to handle the uncertainties in the data. The main characteristic of the CQROFS is that it handles the imprecise information in the data using the membership degrees such that the sum of the q-powers of the real parts (also for imaginary parts) of the membership and non-members...
Preprint
Full-text available
Teaching learning based optimization (TLBO) is a stochastic algorithm which was first proposed for unconstrained optimization problems. It is population based, nature-inspired, and meta-heuristic that imitates teaching learning process. It has two phases, teacher and learner. In teacher phase, the teacher who is well-learned person transfers his/he...
Article
Full-text available
Brain Hemorrhage is the eruption of the brain arteries due to high blood pressure or blood clotting that could be a cause of traumatic injury or death. It is the medical emergency in which a doctor also need years of experience to immediately diagnose the region of the internal bleeding before starting the treatment. In this study, the deep learnin...
Article
Purpose Breast cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications. Design/methodology/approach The new artificial bee colony (ABC) implementation has been applied to probabilistic neural network (PNN) for training and testing purpose to classify the breast canc...
Article
Full-text available
Evolutionary computing is an exciting sub-field of soft computing. Many evolutionary algorithm based on the Darwinian principles of natural selection are developed under the umbrella of EC in the last two decades. EAs provide a set of optimal solutions in single simulation unlike traditional optimization techniques for dealing with large-scale glob...
Article
Full-text available
Internet of Drones (IoD) is a decentralized networking architecture that makes use of the internet for uniting drones to enter controlled airspace in a coordinated manner. On the one hand, this new clan of interconnected drones has ushered in a new era of real-world applications; Small drones, on the other hand, are generally not designed with secu...
Article
Full-text available
Recent years have witnessed the use of metaheuristic algorithms to solve the optimization problems that usually require extensive computations and time. Among others, scatter search is the widely used evolutionary metaheuristic algorithm. It uses the information of global optima, which is stored in a different and unique set of solutions. In this p...
Article
Full-text available
In this paper, a new method is proposed to expand the family of lifetime distributions. The suggested method is named as Khalil new generalized family (KNGF) of distributions. A special submodel, termed as Khalil new generalized Pareto (KNGP) distribution, is investigated from the family with one shape and two scale parameters. A number of mathemat...
Article
Full-text available
The purpose of smart city is to enhance the optimal utilization of scarce resources and improve the resident’s quality of live. The smart cities employed Internet of Things (IoT) to create a sustainable urban life. The IoT devices such as sensors, actuators, and smartphones in the smart cities generate data. The data generated from the smart cities...
Article
Full-text available
In this research study, we investigated and performed coating analysis of wire by using MHD convective third-order fluid in the presence of a permeable matrix taking into account the Hall current. The equations that control the motion of fluid in the chamber are first modeled and then numerically solved by using 4th order Runge–Kutta–Fehlberg techn...
Article
Full-text available
The purpose of this research is to demonstrate the ability of machine-learning (ML) methods for liver cancer classification using a fused dataset of two-dimensional (2D) computed tomography (CT) scans and magnetic resonance imaging (MRI). Datasets of benign (hepatocellular adenoma, hemangioma, cyst) and malignant (hepatocellular carcinoma, hepatobl...
Conference Paper
Full-text available
This paper presents a comparative study between statistical and machine learning methods in forecasting Bitcoin's closing prices. Thirteen forecasting methods namely average, naive, drift, auto-regressive integrated moving-average, simple exponential smoothing (SES), Holt, and damped exponential smoothing, the average of SES, Holt and damped method...
Conference Paper
Full-text available
Neural networks (NNs) have been used extensively for forecasting problems. NN with error feedbacks is a type of NNs that showed more accurate forecasts compared to feedforward NNs and NNs with output feedbacks with some forecasting problems. The main issue with NNs with error feedbacks appears when there is a need for recursive multi-step forecast...
Article
Full-text available
The present paper related to thin film flows of two immiscible third grade fluids past a vertical moving belt with slip conditions in the presence of uniform magnetic field. Immiscible fluids we mean superposed fluids of different densities and viscosities. The basic governing equations of continuity, momentum and energy are incorporated. The model...
Conference Paper
Full-text available
Social networks are of utmost popularity in current era. People from various age groups are part of them as it attracts the user and totally engage them. As most of the social networks are publically available for everyone, digital security is being compromised due to careless use of it. There arises number of vulnerabilities of threats which mostl...
Article
Full-text available
Differential Evolution (DE) is one of the prevailing search techniques in the present era to solve global optimization problems. However, it shows weakness in performing a localized search, since it is based on mutation strategies that take large steps while searching a local area. Thus, DE is not a good option for solving local optimization proble...
Conference Paper
Full-text available
In this paper, a one-step forecasting comparison using a simulated nonlinear autoregressive moving-average time series (NARMA) was conducted between two groups of neural networks. Group I is neural networks that use only autoregressive inputs, while Group II is neural networks that use autoregressive and moving-average (i.e., error feedback) inputs...
Article
Full-text available
Energy is considered the most costly and scarce resource, and its demand is increasing day-by-day. Globally, a significant amount of energy is consumed in residential buildings, i.e., 30-40% of total energy consumption. An active energy prediction system is highly desirable for efficient energy production and utilization. In this paper, we have pro...
Article
Full-text available
Data clustering is a widespread data compression, vector quantization, data analysis, and data mining technique. In this work, a modified form of ABC, i.e. global artificial bee colony search algorithm (GABCS) is applied to data clustering. In GABCS the modification is due to the fact that experienced bees can use past information of quantity of fo...
Article
Influence maximization is the main source of virality of any social media post/marketing activity. In recent trends, influence maximization has moved towards analytic approach instead of just being a suggestive metaphor for various social media paradigm. In this article, ego-centric approach and a bio-inspired algorithm is applied on social coding...
Article
Full-text available
Accurate and efficient image registration, based on interested common sub-regions is still a challenging task in medical image analysis. This paper presents an automatic features based approach for the rigid and deformable registration of medical images using interested common sub-regions. In the proposed approach, interested common sub-regions in...
Article
Full-text available
Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hyb...
Article
Differential evolution (DE) is a versatile and fast evolutionary algorithm (EA) for real world global optimization problems. It has been widely applied to diverse areas as a remarkable search technique. However, because of large step sizes in mutation, DE is known to be incapable of exploiting the existing population than exploring the search regio...
Article
Full-text available
The objective of this work is to present a Quick Gbest Guided artificial bee colony (ABC) learning algorithm to train the feedforward neural network (QGGABC-FFNN) model for the prediction of the trends in the stock markets. As it is quite important to know that nowadays, stock market prediction of trends is a significant financial global issue. The...
Chapter
Full-text available
Some bio-inspired methods are cuckoo search, fish schooling, artificial bee colony (ABC) algorithms. Sometimes, these algorithms cannot reach to global optima due to randomization and poor exploration and exploitation process. Here, the global artificial bee colony and Levenberq-Marquardt hybrid called GABC-LM algorithm is proposed. The proposed GA...
Article
Full-text available
Background: Hypospadias is the most common congenital malformation of the urethra with the prevalence of 1 in 200-300 live male births. The objective of this trial was to compare the frequency of urethrocutaneous fistula between Snodgrass and two staged Aivar Bracka repair of distal penile hypospadias in male children. Material & Methods: This RCT...
Chapter
Full-text available
The standard firefly algorithm is suffered from three major drawbacks. Firstly, imbalanced exploration and exploitation due to random initial solution generation. Secondly, the local convergence rate is low when the randomization factor is large. Thirdly, low quality local and global search capability at termination stage that result in failing to...
Chapter
Full-text available
The artificial neural network has been proved among the best tools in data mining for classification tasks. The concept of obtaining more accurate classifier with less computational complexity has been gaining importance, because of day by day increase in the data. Several numbers of models have been developed for classification problems. This pape...
Article
Various binary similarity measures have been employed in clustering approaches to make homogeneous groups of similar entities in the data. These similarity measures are mostly based only on the presence or absence of features. Binary similarity measures have also been explored with different clustering approaches (e.g., agglomerative hierarchical c...
Conference Paper
Full-text available
Using typical algorithms for training multilayer perceptron (MLP) creates some difficulties like slow convergence speed and local minima trapping in the solution space. Bio-inspired learning algorithms are famous for solving linear and nonlinear combinatorial problems. Artificial Bee Colony (ABC) algorithm is one among the famous bio-inspired algor...
Article
Full-text available
Daily large number of bug reports are received in large open and close source bug tracking systems. Dealing with these reports manually utilizes time and resources which leads to delaying the resolution of important bugs. As an important process in software maintenance, bug triaging process carefully analyze these bug reports to determine, for exam...
Article
Full-text available
The vital role of medical imaging in the automatic and efficient diagnosis and treatment in a short frame of time cannot be ignored. There are many imaging techniques for the diagnostic purpose of the human brain with each technique having its own advantages and disadvantages. One of the most important imaging modalities for diagnosis and treatment...
Article
Full-text available
Objective: Test cases tend to be large in number as redundant test cases are generated due to the presence of code smells, hence the need to reduce these smells. Methods/Statistical Analysis: This research adopts a proactive approach of reducing test cases by detecting the lazy class code smells based on the cohesion and dependency of the code and...
Chapter
Full-text available
Breast cancer has increased mortality rate, one out of eight women have breast cancer. The breast cancer is viewed as the second most-common type of cancer. This is a big threat to women health and survival. One of the popular methods to predict breast cancer is the bio-inspired computing. Bio-inspired computing approaches are global optimization a...
Research
Full-text available
Cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications. The accurate prediction in patients with early-stage cancer is of significant importance to reduce the mortality rate of those patients. Therefore, biologically inspired approaches that are motivated by the nat...
Conference Paper
Full-text available
Artificial Neural Networks (ANN) performance depends on network topology, activation function, behaviors of data, suitable synapse’s values and learning algorithms. Many existing works used different learning algorithms to train ANN for getting high performance. Artificial Bee Colony (ABC) algorithm is one of the latest successfully Swarm Intellige...
Conference Paper
Full-text available
Many different earning algorithms used for getting high performance in mathematics and statistical tasks. Recently, an Artificial Bee Colony (ABC) developed by Karaboga is a nature inspired algorithm, which has been shown excellent performance with some standard algorithms. The hybridization and improvement strategy made ABC more attractive to rese...
Conference Paper
Full-text available
Artificial bee colony (ABC) algorithm which used the honey bee intelligence behaviors, is a new learning technique comparatively attractive for solving optimization problems. Artificial Neural Network (ANN) trained with the ABC algorithm normally has poor exploration and exploitation processes due to the random and similar strategies for finding be...
Conference Paper
Full-text available
In this chapter, we build an intelligent model based on soft computing technologies to improve the prediction accuracy of Energy Consumption in Greece. The model is developed based on Genetic Algorithm and Co-Active Neuro Fuzzy Inference System (GACANFIS) for the prediction of Energy Consumption. For verification of the performance accuracy, the re...
Conference Paper
A social insect’s techniques become more focus by researchers because of its nature behavior processing and by training neural networks through agents. Chief among them are Swarm Intelligence (SI), Ant Colony Optimization (ACO), and recently Artificial Bee Colony algorithm, which produced easy way for solving combinatorial problems and for training...
Chapter
Artificial Neural Networks (ANN) performance depends on network topology, activation function, behaviors of data, suitable synapse's values and learning algorithms. Many existing works used different learning algorithms to train ANN for getting high performance. Artificial Bee Colony (ABC) algorithm is one of the latest successfully Swarm Intellige...
Conference Paper
Full-text available
Abstract. Backpropagation is a well-known learning algorithm used to train Multilayer Perceptron (MLP) with the iterative process. However, one of the critical shortcomings with the BP learning strategy is that it can sometimes trapped in the local minima with suboptimal weights due to the existence of many local optima in the solution space. To re...
Article
Full-text available
The performance of Neural Networks (NN) depends on network structure, activation function and suitable weight values. For finding optimal weight values, freshly, computer scientists show the interest in the study of social insect’s behavior learning algorithms. Chief among these are, Ant Colony Optimzation (ACO), Artificial Bee Colony (ABC) algorit...