Computer Sciences Commons^™

Q-Learning Based Routing Protocol For Congestion Avoidance, Daniel Godfrey, Beom Su Kim, Haoran Miao, Babar Shah, Bashir Hayat, Imran Khan, Tae Eung Sung, Ki Il Kim

All Works

The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes. Among lots of feasible approaches to avoid congestion efficiently, congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods. However, these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically. To overcome this drawback, we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources. In a proposed routing protocol, …

An Adaptive Protection Of Flooding Attacks Model For Complex Network Environments, Bashar Ahmad Khalaf, Salama A. Mostafa, Aida Mustapha, Mazin Abed Mohammed, Moamin A. Mahmoud, Bander Ali Saleh Al-Rimy, Shukor Abd Razak, Mohamed Elhoseny, Adam Marks

All Works

Currently, online organizational resources and assets are potential targets of several types of attack, the most common being flooding attacks. We consider the Distributed Denial of Service (DDoS) as the most dangerous type of flooding attack that could target those resources. The DDoS attack consumes network available resources such as bandwidth, processing power, and memory, thereby limiting or withholding accessibility to users. The Flash Crowd (FC) is quite similar to the DDoS attack whereby many legitimate users concurrently access a particular service, the number of which results in the denial of service. Researchers have proposed many different models to eliminate …

A Smart Dynamic Crowd Evacuation System For Exhibition Centers, Faouzi Kamoun, May El Barachi, Fatna Belqasmi, Abderrazak Hachani

All Works

In this paper, we consider the problem of finding the safest evacuation route in a multi-exit exhibition center while the fire hazard spreads. We first propose a system composed of sensor nodes to collect pertinent safety data. We present a real-time dynamic evacuation system that considers the changing conditions in the risks associated with each hallway segment in terms of walking distance, heat, two major asphyxiant fire gases and congestion. Our system activates smart panels placed at major junctions of the hallways to guide evacuees towards the appropriate exit by displaying the proper escape direction. This work can pave the …

Smart Pansharpening Approach Using Kernel-Based Image Filtering, Ahmad A.L. Smadi, Shuyuan Yang, Atif Mehmood, Ahed Abugabah, Min Wang, Muzaffar Bashir

All Works

Remote sensing image fusion plays important roles in numerous applications, including monitoring, metrology, and agriculture. Image fusion gathers essential information from several image sources and consolidates them into a single image called a fused image. The fused image involves relevant data, and it is more informative than any other images extracted from one source. This study proposed a pansharpening technique based on image filtering utilising a bilateral filter to generate high-frequency details from panchromatic image. The various types of side window guided filters are employed to enhance the multispectral band from panchromatic image and then used these filters to adjust …

Early Detection Of Lung Cancer - A Challenge, Fatma Taher, Neema Prakash, Ashraf Alzaabi

All Works

Lung cancer or lung carcinoma, is a common and serious type of cancer caused by rapid cell growth in tissues of the lung. Lung cancer detection at its earlier stage is very difficult because of the structure of the cell alignment which makes it very challenging. Computed tomography (CT) scan is used to detect the presence of cancer and its spread. Visual analysis of CT scan can lead to late treatment of cancer; therefore, different steps of image processing can be used to solve this issue. A comprehensive framework is used for the classification of pulmonary nodules by combining appearance …

Deceptive Opinions Detection Using New Proposed Arabic Semantic Features, Amel Ziani, Nabiha Azizi, Didier Schwab, Djamel Zenakhra, Monther Aldwairi, Nassira Chekkai, Nawel Zemmal, Marwa Hadj Salah

All Works

Some users try to post false reviews to promote or to devalue other’s products and services. This action is known as deceptive opinions spam, where spammers try to gain or to profit from posting untruthful reviews. Therefore, we conducted this work to develop and to implement new semantic features to improve the Arabic deception detection. These features were inspired from the study of discourse parse and the rhetoric relations in Arabic. Looking to the importance of the phrase unit in the Arabic language and the grammatical studies, we have analyzed and selected the most used unit markers and relations to …

Detection Of Freezing Of Gait Using Unsupervised Convolutional Denoising Autoencoder, Mohd Halim Mohd Noor, Amril Nazir, Mohd Nadhir Ab Wahab, Jodene Ooi Yen Ling

All Works

At the advanced stage of Parkinson’s disease, patients may suffer from ‘freezing of gait’ episodes: a debilitating condition wherein a patient’s “feet feel as though they are glued to the floor”. The objective, continuous monitoring of the gait of Parkinson’s disease patients with wearable devices has led to the development of many freezing of gait detection models involving the automatic cueing of a rhythmic auditory stimulus to shorten or prevent episodes. The use of thresholding and manually extracted features or feature engineering returned promising results. However, these approaches are subjective, time-consuming, and prone to error. Furthermore, their performance varied when …

Real-Time Privacy Preserving Framework For Covid-19 Contact Tracing, Akashdeep Bhardwaj, Ahmed A. Mohamed, Manoj Kumar, Mohammed Alshehri, Ahed Abugabah

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The recent unprecedented threat from COVID-19 and past epidemics, such as SARS, AIDS, and Ebola, has affected millions of people in multiple countries. Countries have shut their borders, and their nationals have been advised to self-quarantine. The variety of responses to the pandemic has given rise to data privacy concerns. Infection prevention and control strategies as well as disease control measures, especially real-time contact tracing for COVID-19, require the identification of people exposed to COVID-19. Such tracing frameworks use mobile apps and geolocations to trace individuals. However, while the motive may be well intended, the limitations and security issues associated …

Active Learning Strategy For Covid-19 Annotated Dataset, Amril Nazir, Ricky Maulana Fajri

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The efficient diagnosis of COVID-19 plays a key role in preventing its spread. Recently, many artificial intelligence techniques, such as the deep neural network approach, have been implemented to help efficient diagnosis of COVID-19. However, the accurate performance of deep learning depends on the tuning of many hyperparameters and a large amount of labeled data. This COVID-19 data bottleneck also leads to insufficient human resources for data labeling, which presents a challenging obstacle. In this paper, a novel discriminative batch-mode active learning (DS3) is proposed to allow faster and more effective COVID-19 data annotation. The framework specifically designed to suit …

Multi-Branch Gabor Wavelet Layers For Pedestrian Attribute Recognition, Imran N. Junejo

All Works

CCBYNCND Surveillance cameras are everywhere, keeping an eye on pedestrians as they navigate through a scene. With this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails recognizing attributes such as age-group, clothing style, accessories, footwear style etc. This is a multi-label problem and challenging even for human observers. The problem has rightly attracted attention recently from the computer vision community. In this paper, we adopt trainable Gabor wavelets (TGW) layers and use it with a convolution neural network (CNN). Whereas other researchers are using fixed Gabor filters with the CNN, the proposed layers are …

Convolutional Neural Network Based Vehicle Classification In Adverse Illuminous Conditions For Intelligent Transportation Systems, Muhammad Atif Butt, Asad Masood Khattak, Sarmad Shafique, Bashir Hayat, Saima Abid, Ki Il Kim, Muhammad Waqas Ayub, Ahthasham Sajid, Awais Adnan

All Works

© 2021 Muhammad Atif Butt et al. In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the above-mentioned issues in traditional methods. However, convolutional neural networks require piles of …

An Empirical Investigation Of U.K. Environmental Targets Disclosure: The Role Of Environmental Governance And Performance, Tantawy Moussa, Amr Kotb, Akrum Helfaya

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Although an increasing number of companies have publicly declared environmental targets (ETs), scant research has been conducted in this area. This study, therefore, investigates the extent of corporate environmental targets disclosure (ETD) and empirically examines whether environmental governance and performance influence the ETD of companies in the U.K. during the 2005–2013 period. We find that firms show a large degree of variability and inconsistency in their reporting of ETs. The results indicate that U.K. firms, particularly those with high environmental sensitivity, tend to disclose symbolic soft or semi-hard ETs to manage stakeholder perceptions and legitimize their existence. Moreover, Global Reporting …

A Comprehensive Review On Medical Diagnosis Using Machine Learning, Kaustubh Arun Bhavsar, Ahed Abugabah, Jimmy Singla, Ahmad Ali Alzubi, Ali Kashif Bashir, Nikita

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The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine …

Adversarial Reconstruction Loss For Domain Generalization, Bekkouch Imad Eddine Ibrahim, Dragos Constantin Nicolae, Adil Khan, S. M. Ahsan Kazmi, Asad Masood Khattak, Bulat Ibragimov

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The biggest fear when deploying machine learning models to the real world is their ability to handle the new data. This problem is significant especially in medicine, where models trained on rich high-quality data extracted from large hospitals do not scale to small regional hospitals. One of the clinical challenges addressed in this work is magnetic resonance image generalization for improved visualization and diagnosis of hip abnormalities such as femoroacetabular impingement and dysplasia. Domain Generalization (DG) is a field in machine learning that tries to solve the model’s dependency on the training data by leveraging many related but different data …

Multi-Level Resource Sharing Framework Using Collaborative Fog Environment For Smart Cities, Tariq Qayyum, Zouheir Trabelsi, Asad Waqar Malik, Kadhim Hayawi

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No abstract provided.

Development Of A Real Time Human Face Recognition Software System, Askar Boranbayev, Seilkhan Boranbayev, Mukhamedzhan Amirtaev, Malik Baimukhamedov, Askar Nurbekov

Physics & Astronomy Faculty Publications

In this study, a system for real-time face recognition was built using the Open Face tools of the Open CV library. The article describes the methodology for creating the system and the results of its testing. The Open CV library has various modules that perform many tasks. In this paper, Open CV modules were used for face recognition in images and face identification in real time. In addition, the HOG method was used to detect a person by the front of his face. After performing the HOG method, 128 face measurements were obtained using the image encoding method. A convolutional …

Computing Competencies For Undergraduate Data Science Curricula: Acm Data Science Task Force, Andrea Danyluk, Paul Leidig

College of Computing Peer-Reviewed Articles

At the August 2017 ACM Education Council meeting, a task force was formed to explore a process to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force would seek to define what the computing/computational contributions are to this new field, and provide guidance on computing-specific competencies in data science for departments offering such programs of study at the undergraduate level.

There are many stakeholders in the discussion of data science – these include colleges and universities that (hope to) offer data science …

Learning Accurate And Robust Deep Visual Models, Yandong Li

Electronic Theses and Dissertations, 2020-2023

Over the last decade, we have witnessed the renaissance of deep neural networks (DNNs) and their successful applications in computer vision. There is still a long way to build intelligent and reliable machine vision systems, but DNNs provide a promising direction. The goal of this thesis is to present a few small steps along this road. We mainly focus on two questions: How to design label-efficient learning algorithms for computer vision tasks? How to improve the robustness of DNN based visual models? Concerning label-efficiency, we investigate a reinforced sequential model for video summarization, a background hallucination strategy for high-resolution image …

Evaluating Pmo Sync Implementation For Persistent Memory Object, Faishal Wahiduddin

Electronic Theses and Dissertations, 2020-2023

Persistent Memory, in the form of byte-addressable Non-Volatile Memories (NVMs), provides a low-cost and high-capacity main memory, and provides the ability to store and retain data even when the system is powered off, along with improved performance over traditional storage. Persistent Memory Direct Access (DAX) enables applications to perform byte-addressable operations such as load and store. Filesystem-DAX can store persistent data in NVMs with system call overheads. In order to reduce filesystem overheads, this study utilizes Persistent Memory Object (PMO) as an abstraction for persistent data containers on Non-Volatile Memory (NVM). Persisting data in Persistent Memory Object requires that the …

Joint Carrier Frequency And Phase Offset Estimation Algorithm For Cpm-Dsssbased Secure Point-To-Point Communication, Saima Shehzadi, Farzana Kulsoom, Muhammad Zeeshan, Qasim Umar Khan, Shahzad Amin Sheikh

Turkish Journal of Electrical Engineering and Computer Sciences

A point-to-point (P2P) communication system based on the CPM-DSSS scheme ensures reliability, security, and antijamming capabilities. However, for reliable detection of data carrier synchronization of CPM-DSSS based system is one of the requirements. This paper presents a joint algorithm for carrier frequency offset (CFO) and carrier phase offset (CPO) estimation for CPM-DSSS based P2P system. The results indicate that the proposed CFO estimator is unbiased and can accurately estimate a wide range of offsets. Moreover, the proposed algorithm is compared with another research work. The results show that the proposed CFO and CPO estimation algorithm outperforms its counterpart with a …

A Hybrid Numerical Model For Long-Range Electromagnetic Wave Propagation, Gül Yesa Altun, Özlem Özgün

Turkish Journal of Electrical Engineering and Computer Sciences

A hybrid numerical model is presented for solving long range electromagnetic wave propagation problems involving objects on or above the ground surface by hybridizing the two-way split-step parabolic equation (2W-SSPE) method with the method of moments (MoM). The advantages of the proposed model are twofold: (i) It reduces the staircasing error in irregular terrain modeling, which usually occurs when the standard SSPE method is used alone. This is achieved by employing the MoM to more accurately obtain the scattered fields from slanted/curved surfaces. (ii) It enables the SSPE method to handle the problems involving objects above the Earth's surface, which …

Comparison Of Metaheuristic Optimization Algorithms With A New Modifieddeb Feasibility Constraint Handling Technique, Murat Erhan Çi̇men, Zeynep Gari̇p, Ali̇ Fuat Boz

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, the modification of the Deb feasibility method is considered to solve the constrained optimization problems. In the developed modified Deb feasibility constraint method, the third rule in its procedure was revised in order to increase the performance of the Deb feasibility constraint handling method. The innovation in the method is based on generating a new individual by using both possible solutions that violate the constraints in the method used for solving the problem. In detail, discussions were given about the application and usefulness of six constrained handling techniques. Furthermore, genetic algorithm, particle swarm optimization, Harris hawks optimization, …

Medical Image Fusion With Convolutional Neural Network In Multiscaletransform Domain, Asan Abas, Hasan Erdi̇nç Koçer, Nurdan Baykan

Turkish Journal of Electrical Engineering and Computer Sciences

Multimodal medical image fusion approaches have been commonly used to diagnose diseases and involve merging multiple images of different modes to achieve superior image quality and to reduce uncertainty and redundancy in order to increase the clinical applicability. In this paper, we proposed a new medical image fusion algorithm based on a convolutional neural network (CNN) to obtain a weight map for multiscale transform (curvelet/ non-subsampled shearlet transform) domains that enhance the textual and edge property. The aim of the method is achieving the best visualization and highest details in a single fused image without losing spectral and anatomical details. …

Detection Of Amyotrophic Lateral Sclerosis Disease By Variational Modedecomposition And Convolution Neural Network Methods From Event-Relatedpotential Signals, Fatma Lati̇foğlu, Firat Orhan Bulucu, Rami̇s İleri̇

Turkish Journal of Electrical Engineering and Computer Sciences

Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is a neurological disease that occurs as a result of damage to the nerves in the brain and restriction of muscle movements. Electroencephalography (EEG) is the most common method used in brain imaging to study neurological disorders. Diagnosis of neurological disorders such as ALS, Parkinson's, attention deficit hyperactivity disorder is important in biomedical studies. In recent years, deep learning (DL) models have been started to be applied in the literature for the diagnosis of these diseases. In this study, event-related potentials (ERPs) were obtained from EEG signals obtained as a …

Gene Selection For Cancer Classification: A New Hybrid Filter-C5.0 Approach For Breast Cancer Risk Prediction, Mohammed Hamim, Ismail El Moudden, Hicham Moutachaouik, Mustapha Hain

Department of Medicine Faculty Publications

Despite the significant progress made in data mining technologies in recent years, breast cancer risk prediction and diagnosis at an early stage using DNA microarray technology still a real challenging task. This challenge comes especially from the high-dimensionality in gene expression data, i.e., an enormous number of genes versus a few tens of subjects (samples). To overcome this problem of data imbalance, a gene selection phase becomes a crucial step for gene expression data analysis. This study proposes a new Decision Tree model-based attributes (genes) selection strategy, which incorporates two stages: fisher-score-based filter technique and the gene selection ability of …

Artificial Intelligence As Evidence, Paul W. Grimm, Maura R. Grossman, Gordon V. Cormack

Faculty Scholarship

This article explores issues that govern the admissibility of Artificial Intelligence (“AI”) applications in civil and criminal cases, from the perspective of a federal trial judge and two computer scientists, one of whom also is an experienced attorney. It provides a detailed yet intelligible discussion of what AI is and how it works, a history of its development, and a description of the wide variety of functions that it is designed to accomplish, stressing that AI applications are ubiquitous, both in the private and public sectors. Applications today include: health care, education, employment-related decision-making, finance, law enforcement, and the legal …

Assessment And Learning In Knowledge Spaces (Aleks) Adaptive System Impact On Students' Perception And Self-Regulated Learning Skills, Honda Harati, Laura Sujo-Montes, Chih-Hsiung Tu, Shadow J.W. Armfield, Cherng-Jyh Yen

Educational Leadership & Workforce Development Faculty Publications

Adaptive learning is an educational method that uses computer algorithms and artificial intelligence (AI) to customize learning materials and activities based on each user's model. Adaptive learning has been used for more than 20 years. However, it is still unique, and no other system could bring more or even similar capabilities than the ones adaptive technology offers, including the application of AI, psychology, psychometrics, machine learning, and providing a personalized learning environment. However, there are not many studies on its practicality, usefulness, improving students' learning skills, students' perception, etc., due to the limited number of institutes investing in this new …

The (Digital) Medium Of Mobility Is The Message: Examining The Influence Of E-Scooter Mobile App Perceptions On E-Scooter Use Intent, Rabindra Ratan, Kelsey Earle, Sonny Rosenthal, Vivian Hsueh Hua Chen, Andrew Gambiro, Gerard Goggin, Hallam Stevens, Benjamin Li, Kwan Min Lee

Research Collection College of Integrative Studies

The present research examines how perceptions of e-scooter mobile apps (i.e., a communication technology) influence intent to use e-scooters (i.e., a transportation technology) while considering other perceptions specific to e-scooters (ease of use, usefulness, safety, environmental impact, and enjoyment), context of use (geographic landscape), and demographic factors (age and sex). Results suggest mobile app perceived ease of use is associated with e-scooter use intent and this effect is mediated by e-scooter perceived usefulness, even when controlling for e-scooter perceived ease of use as well as other influential elements of e-scooter use. In addition to illustrating the importance of user experiences …

A Hybrid Gene Selection Strategy Based On Fisher And Ant Colony Optimization Algorithm For Breast Cancer Classification, Mohammed Hamim, Ismail El Moudden, Mohan D. Pant, Hicham Moutachaouik, Mustapha Hain

EVMS School of Health Professions Faculty Publications

Breast cancer poses the greatest threat to human life and especially to women's life. Despite the progress made in data mining technology in recent years, the ability to predict and diagnose such fatal diseases based on gene expression data still reveals a limited prediction performance, which may not be surprising since most of the genes in expression data are believed to be irrelevant or redundant. The dimensionality reduction process may be considered as a crucial step to analyze gene expression data, as it can reduce the high dimensionality of the breast cancer datasets, which may result into a better prediction …

Identification And Classification Of Radio Pulsar Signals Using Machine Learning, Di Pang

Graduate Theses, Dissertations, and Problem Reports (ETD)

Automated single-pulse search approaches are necessary as ever-increasing amount of observed data makes the manual inspection impractical. Detecting radio pulsars using single-pulse searches, however, is a challenging problem for machine learning because pul- sar signals often vary significantly in brightness, width, and shape and are only detected in a small fraction of observed data.

The research work presented in this dissertation is focused on development of ma- chine learning algorithms and approaches for single-pulse searches in the time domain. Specifically, (1) We developed a two-stage single-pulse search approach, named Single- Pulse Event Group IDentification (SPEGID), which automatically identifies and clas- …