Manar Hosny

Followers

295

Following

Co-authors

Public Views

Christine Mumford

Pankaj Basnal

Ferdinando Pezzella

Università Politecnica delle Marche, Italy

Roskilde University

Cardiff University

WBUT

Université Blaise-Pascal, F- Clermont-Ferrand

Rong Qu

University of Nottingham

Gabriela Ochoa

InterestsView All (28)

Uploads

Papers by Manar Hosny

Using Pupil Size Variation during Visual Emotional Stimulation in Measuring Affective States of Non Communicative Individuals

Lecture Notes in Computer Science, 2013

Download

An Adaptive Large Neighborhood Search Heuristic for the Green Dial‐a‐Ride Problem

This chapter presents a new extension of the Dial‐a‐Ride Problem (DARP), in which a fleet of Alte... more This chapter presents a new extension of the Dial‐a‐Ride Problem (DARP), in which a fleet of Alternative Fuel Vehicles (AFVs) is considered. Due to the limited driving range, the AFVs may visit some Alternative Fuel Stations to be refueled with a partial refueling quantity during its journey to serve all users' demands. The proposed variant is called the Green DARP (G‐DARP). The chapter introduces a linear mixed‐integer mathematical formulation of the G‐DARP, and proposes an efficient Adaptive Large Neighborhood Search heuristic algorithm to solve the G‐DARP. The algorithm is supported by efficient local search operators to enhance the search and improve the quality of solutions, as well as a flexible acceptance function to more explore the search space. The chapter also presents numerical experiments to demonstrate that the solution approach provides high‐quality solutions for newly generated instances.

An Efficient Greedy Randomized Heuristic for the Maximum Coverage Facility Location Problem with Drones in Healthcare

Applied Sciences

Recently, drones, have been utilized in many real-life applications including healthcare services... more Recently, drones, have been utilized in many real-life applications including healthcare services. For example, providing medical supplies, blood samples, and vaccines to people in remote areas or during emergencies. In this study, the maximum coverage facility location problem with drones (MCFLPD) was studied. The problem is the application of drones in the context of the facility location and routing. It involves selecting the locations of drone launching centers, which maximizes patient service coverage within certain drone range constraints. In this study, a heuristic named the maximum coverage greedy randomized heuristic (MCGRH) is developed. The idea of the algorithm is to first choose some facilities to open at random from among those that can handle the most weight of the patient demands. After that, patients are assigned to the closest opened facility with the capacity to serve them. Finally, drones are assigned to patients based on the least amount of battery consumed betw...

Download

A Genetic-Based Nurse Rostering Tool : A Riyadh Hospital Case

Recently, many healthcare institutions became interested in automating the process of personnel s... more Recently, many healthcare institutions became interested in automating the process of personnel scheduli ng. Nurse Rostering is a difficult scheduling problem, which is defined as generating rosters by assigning working shifts to a number of nurses over a certain planning period, such that a predefined set of constraints is satisfied. There is a high demand to automate this process, since many healthcare institutions still make nurse rosters manually . In this research we design a scheduling tool that is based on a Genetic Algorithm (GA) approach to solve the nurse rostering problem for the surgical unit in a Riyadh hospital. Our GA-based tool will automatically create a schedule while taking into account a number of difficult constraints. Some constraints are specified by the hospital, and mainly relate to maximizing the coverage of shifts and adhering to workload requirements and certain shift succession patterns. Other constraints relate to preferences of the nurses regarding t h...

Download

Investigating Female Students ’ Attitude towards Cheating and Plagiarism : A Study in King Saud University

During their studies, many students commit some form of academic dishonesty, such as cheating and... more During their studies, many students commit some form of academic dishonesty, such as cheating and plagiarism, often to obtain higher grades than they are capable of. The current widespread use of the Internet, mobile and wireless devices has made it easier for students to illegally access information, and at the same time it has become difficult for academic institutions to control and discover such instances. Hence, it is essential that students become aware of the seriousness of these offences, and be encouraged to avoid them. In this paper, we study the attitude towards cheating and plagiarism among female students in

Download

BeamGA Median: A Hybrid Heuristic Search Approach

World Academy of Science, Engineering and Technology, International Journal of Biological, Biomolecular, Agricultural, Food and Biotechnological Engineering, 2017

The median problem is significantly applied to derive the most reasonable rearrangement phylogene... more The median problem is significantly applied to derive the most reasonable rearrangement phylogenetic tree for many species. More specifically, the problem is concerned with finding a permutation that minimizes the sum of distances between itself and a set of three signed permutations. Genomes with equal number of genes but different order can be represented as permutations. In this paper, an algorithm, namely BeamGA median, is proposed that combines a heuristic search approach (local beam) as an initialization step to generate a number of solutions, and then a Genetic Algorithm (GA) is applied in order to refine the solutions, aiming to achieve a better median with the smallest possible reversal distance from the three original permutations. In this approach, any genome rearrangement distance can be applied. In this paper, we use the reversal distance. To the best of our knowledge, the proposed approach was not applied before for solving the median problem. Our approach considers tr...

Download

A Genetic-Frog Leaping Algorithm for Large Dataset Document Clustering

2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), 2019

This study describes the development of a text document clustering optimization model using a nov... more This study describes the development of a text document clustering optimization model using a novel Genetic Algorithm-Shuffled Frog Leaping Algorithm (GA-SFLA), which clusters text documents based on selected features in an efficient way. In the approach proposed in this study, a Genetic Algorithm (GA) handles the feature selection task, while a Shuffled Frog-Leaping Algorithm (SFLA) handles the clustering task. The effectiveness of the proposed approach was evaluated by testing it on the popular "20Newsgroup" text document dataset. After multiple experiments, it was found that using GA-SFLA on the 20Newsgroup dataset considerably helped to enhance the text document clustering task, compared to classical K-means clustering. In addition, the feature selection stage greatly contributed to improving the results of clustering. Nevertheless, this improvement comes at the expense of longer computational time.

Vehicle Routing with Pickup and Delivery

Solving the Vehicle Routing Problem (VRP) and its related variants is at the heart of scientific ... more Solving the Vehicle Routing Problem (VRP) and its related variants is at the heart of scientific research for optimizing logistics planning. One important variant of the VRP is the Pickup and Delivery Problem (PDP). In the PDP, it is generally required to find one or more minimum cost routes to serve a number of customers, where two types of services may be performed at a customer location, pickup or a delivery. We considered two variants of the PDP, the Pickup and Delivery Problem with Time Windows (PDPTW), and the One-commodity Pickup and Delivery Problem (1-PDP). We investigated heuristic and meta-heuristic approaches for solving the selected PDP variants. Our research focuses on handling the difficult problem constraints in a simple and effective way. Two main aspects of the solution algorithm are directed to achieve this goal, the solution representation and the neighborhood moves. In general, the findings of the research indicate the success of our approach in devising simple ...

An Adaptive Genetic Algorithm Approach for Optimizing Feature Weights in Multimodal Clustering

Social Media is a popular channel of communication, where people exchange different types of high... more Social Media is a popular channel of communication, where people exchange different types of high volume and multimodal data. Cluster analysis is used to categorize this data to extract useful information. However, the variation of features that can be used in clustering makes the clustering process difficult, since some features may be more important than others, and some may be irrelevant or redundant. An alternative to traditional feature selection techniques, especially with the absence of domain knowledge, is to assign feature weights that depend on their importance in the clustering process. In this paper, we introduce a multimodal adaptive genetic clustering (MAGC) algorithm that clusters data according to multiple features. This is done by adding feature weights as an extension to the clustering solution. In other words, feature weights evolve and improve alongside the original clustering solution. In addition, the number of clusters is not determined a priori, but it is ada...

A Two Level Hybrid Bees Algorithm for Operating Room Scheduling Problem

In patients’ healthcare, manual planning of operating rooms is very difficult due to having a lar... more In patients’ healthcare, manual planning of operating rooms is very difficult due to having a large number of constraints that the planner should take into consideration. The aim of this paper is to solve the operating room scheduling problem using a hybrid Bees Algorithm. Our focus is to solve a two-level variant of the problem, the Master Surgery Scheduling Problem and the Surgical Case Assignment Problem, where both hospital cost and patient cost are considered. We use a hybrid population based meta-heuristic, namely, a Hybrid BA with Simulated Annealing. The performance of our algorithm is compared against a Tabu Search single solution based method from the literature using the same test data. The experimental results demonstrate the advantages of using our proposed approach.

What does BERT Learn from Arabic Machine Reading Comprehension Datasets?

In machine reading comprehension tasks, a model must extract an answer from the available context... more In machine reading comprehension tasks, a model must extract an answer from the available context given a question and a passage. Recently, transformer-based pre-trained language models have achieved state-of-the-art performance in several natural language processing tasks. However, it is unclear whether such performance reflects true language understanding. In this paper, we propose adversarial examples to probe an Arabic pre-trained language model (AraBERT), leading to a significant performance drop over four Arabic machine reading comprehension datasets. We present a layer-wise analysis for the transformer’s hidden states to offer insights into how AraBERT reasons to derive an answer. The experiments indicate that AraBERT relies on superficial cues and keyword matching rather than text understanding. Furthermore, hidden state visualization demonstrates that prediction errors can be recognized from vector representations in earlier layers.

Download

Metaheuristic Approaches for Solving University Timetabling Problems: A Review and Case Studies from Middle Eastern Universities

Information Systems and Technologies to Support Learning, 2018

University timetabling problems are concerned with the assignment of events and tasks that occur ... more University timetabling problems are concerned with the assignment of events and tasks that occur frequently in universities, like exams, courses, projects, and faculty load. These problems are difficult and consume a lot of time and effort if done manually. Automating such tasks will save time and cost, and increase the satisfaction of the stakeholders. Since university timetabling problems are mostly NP-hard, heuristics and metaheuristics are often used for solving them. In this survey, we review different university timetabling problems, such as: Examination Timetabling, Course Timetabling, and Staff Timetabling. We also propose a new problem, which is Project Timetabling. In addition, we discuss some case studies that successfully tackled these problems using metaheuristic algorithms. However, due to the huge number of papers published worldwide in this research area, we focus in this survey on papers published in the Middle Eastern region. The findings of this survey indicate th...

Recognition of Affective States via Electroencephalogram Analysis and Classification

Intelligent Human Systems Integration

Understanding and reacting to the affective state of users is increasingly becoming important in ... more Understanding and reacting to the affective state of users is increasingly becoming important in the field of human–computer interaction (HCI) research and practice. Recent developments in brain–computer interface (BCI) technology has facilitated improved accuracy in human emotion detection and classification. In this paper, we investigate the possibility of using electroencephalogram (EEG) for the detection of four affective states based on a dimensional model (valence and arousal) of emotions. We conduct rigorous offline analysis for investigating the deep neural network (DNN) classification method in emotion detection. We also compare our classification performance with a random forest (RF) classifier and support vector machine (SVM). The data analysis results revealed that the proposed DNN-based classifier method outperformed the methods based on the SVM and RF classifiers.

An adaptive hybrid genetic-annealing approach for solving the map problem on belief networks

Download

An Intelligent Bio-Inspired Algorithm for the Faculty Scheduling Problem

International Journal of Advanced Computer Science and Applications, 2018

All universities have faculty members who need to be assigned to teach courses. Those members hav... more All universities have faculty members who need to be assigned to teach courses. Those members have various specialties, preferences and different levels of experience. The manual assignment of courses is a very tedious and timeconsuming task that the scheduling committee frequently faces in every department. To solve this timetabling problem, we proposed a novel approach using the Bees Algorithm (BA), which is inspired from bees' foraging behavior, hybridized with Demon algorithm and Hill Climbing for more extensive search. The scheduling process took into consideration all constraints and variables associated with scheduling courses, according to the requirements of the Computer Science department in our college. The results showed that the schedules produced from the algorithm outperformed the manual schedules in terms of achieving the objective function and satisfying the constraints. In addition, the hybridized version produced better results than the standard BA version without hybridization. The hybridized algorithm is designed for faculty scheduling, but can be further generalized to solve various timetabling problems.

Download

Investigating the use of metaheuristics for solving single vehicle routing problems with time-varying traversal costs

by Christine Mumford and Manar Hosny

Journal of the Operational Research Society, 2013

Download

The fleet size and mix vehicle routing problem with synchronized visits

Transportation Letters

Container truck transportation routing as a Mixed Fleet Heterogeneous Dial-a-Ride Problem

MATEC Web of Conferences

In this paper, a new variant of the Heterogeneous Dial-a-Ride Problem (HDARP) is innovatively app... more In this paper, a new variant of the Heterogeneous Dial-a-Ride Problem (HDARP) is innovatively applied to a routing problem of trucks picking up and delivering full and empty containers in the logistic chain, including container depots, importers demanding full containers and exporters requesting empty containers. On the route different types of trucks pick-up and deliver full and empty 20- and 40-foot containers. We consider trucks that are operated by alternative fuel, as well as the need for refuelling of trucks in any alternative fuel station to eliminate the risk of running out of fuel during its route. The objective of the paper is to contribute to the optimization of container truck transport routing with multiple pick¬ups and deliveries by providing a mathematical programming model aiming at minimizing the transportation cost as well as the negative environmental impacts.

Download

Genetic–Frog-leaping Algorithm for Text Document Clustering

Computers, Materials & Continua

In recent years, the volume of information in digital form has increased tremendously owing to th... more In recent years, the volume of information in digital form has increased tremendously owing to the increased popularity of the World Wide Web. As a result, the use of techniques for extracting useful information from large collections of data, and particularly documents, has become more necessary and challenging. Text clustering is such a technique; it consists in dividing a set of text documents into clusters (groups), so that documents within the same cluster are closely related, whereas documents in different clusters are as different as possible. Clustering depends on measuring the content (i.e., words) of a document in terms of relevance. Nevertheless, as documents usually contain a large number of words, some of them may be irrelevant to the topic under consideration or redundant. This can confuse and complicate the clustering process and make it less accurate. Accordingly, feature selection methods have been employed to reduce data dimensionality by selecting the most relevant features. In this study, we developed a text document clustering optimization model using a novel genetic frogleaping algorithm that efficiently clusters text documents based on selected features. The proposed approach is based on two metaheuristic algorithms: a genetic algorithm (GA) and a shuffled frog-leaping algorithm (SFLA). The GA performs feature selection, and the SFLA performs clustering. To evaluate its effectiveness, the proposed approach was tested on a well-known text document dataset: the "20Newsgroup" dataset from the University of California Irvine Machine Learning Repository. Overall, after multiple experiments were compared and analyzed, it was demonstrated that using the proposed algorithm on the 20Newsgroup dataset greatly facilitated text document clustering, compared with classical K-means clustering. Nevertheless, this improvement requires longer computational time.

Download

Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network

International Journal of Advanced Computer Science and Applications

Estimation of human emotions from Electroencephalogram (EEG) signals plays a vital role in develo... more Estimation of human emotions from Electroencephalogram (EEG) signals plays a vital role in developing robust Brain-Computer Interface (BCI) systems. In our research, we used Deep Neural Network (DNN) to address EEG-based emotion recognition. This was motivated by the recent advances in accuracy and efficiency from applying deep learning techniques in pattern recognition and classification applications. We adapted DNN to identify human emotions of a given EEG signal (DEAP dataset) from power spectral density (PSD) and frontal asymmetry features. The proposed approach is compared to state-of-the-art emotion detection systems on the same dataset. Results show how EEG based emotion recognition can greatly benefit from using DNNs, especially when a large amount of training data is available.

Download

Using Pupil Size Variation during Visual Emotional Stimulation in Measuring Affective States of Non Communicative Individuals

Lecture Notes in Computer Science, 2013

Download

An Adaptive Large Neighborhood Search Heuristic for the Green Dial‐a‐Ride Problem

An Efficient Greedy Randomized Heuristic for the Maximum Coverage Facility Location Problem with Drones in Healthcare

Applied Sciences

Download

A Genetic-Based Nurse Rostering Tool : A Riyadh Hospital Case

Download

Investigating Female Students ’ Attitude towards Cheating and Plagiarism : A Study in King Saud University

Download

BeamGA Median: A Hybrid Heuristic Search Approach

World Academy of Science, Engineering and Technology, International Journal of Biological, Biomolecular, Agricultural, Food and Biotechnological Engineering, 2017

Download

A Genetic-Frog Leaping Algorithm for Large Dataset Document Clustering

2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), 2019