Computer Sciences Commons^™

Classification Of Skin Disease Using Deep Learning Neural Networks With Mobilenet V2 And Lstm, Parvathaneni N. Srinivasu, Jalluri G. Siva Sai, Muhammad F. Ijaz, Akash K. Bhoi, Wonjoon Kim, James J. Kang

Research outputs 2014 to 2021

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning-based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), …

Infrequent Pattern Detection For Reliable Network Traffic Analysis Using Robust Evolutionary Computation, A. N. M. Bazlur Rashid, Mohiuddin Ahmed, Al-Sakib K. Pathan

Research outputs 2014 to 2021

While anomaly detection is very important in many domains, such as in cybersecurity, there are many rare anomalies or infrequent patterns in cybersecurity datasets. Detection of infrequent patterns is computationally expensive. Cybersecurity datasets consist of many features, mostly irrelevant, resulting in lower classification performance by machine learning algorithms. Hence, a feature selection (FS) approach, i.e., selecting relevant features only, is an essential preprocessing step in cybersecurity data analysis. Despite many FS approaches proposed in the literature, cooperative co-evolution (CC)-based FS approaches can be more suitable for cybersecurity data preprocessing considering the Big Data scenario. Accordingly, in this paper, we have …

A Novel Augmented Deep Transfer Learning For Classification Of Covid-19 And Other Thoracic Diseases From X-Rays, Fouzia Atlaf, Syed M. S. Islam, Naeem K. Janjua

Research outputs 2014 to 2021

Deep learning has provided numerous breakthroughs in natural imaging tasks. However, its successful application to medical images is severely handicapped with the limited amount of annotated training data. Transfer learning is commonly adopted for the medical imaging tasks. However, a large covariant shift between the source domain of natural images and target domain of medical images results in poor transfer learning. Moreover, scarcity of annotated data for the medical imaging tasks causes further problems for effective transfer learning. To address these problems, we develop an augmented ensemble transfer learning technique that leads to significant performance gain over the conventional transfer …

Digital Forensic Readiness In Operational Cloud Leveraging Iso/Iec 27043 Guidelines On Security Monitoring, Sheunesu Makura, H. S. Venter, Victor R. Kebande, Nickson M. Karie, Richard A. Ikuesan, Sadi Alawadi

Research outputs 2014 to 2021

An increase in the use of cloud computing technologies by organizations has led to cybercriminals targeting cloud environments to orchestrate malicious attacks. Conversely, this has led to the need for proactive approaches through the use of digital forensic readiness (DFR). Existing studies have attempted to develop proactive prototypes using diverse agent-based solutions that are capable of extracting a forensically sound potential digital evidence. As a way to address this limitation and further evaluate the degree of PDE relevance in an operational platform, this study sought to develop a prototype in an operational cloud environment to achieve DFR in the cloud. …

Digital Forensic Readiness Intelligence Crime Repository, Victor R. Kebande, Nickson M. Karie, Kim-Kwang R. Choo, Sadi Alawadi

Research outputs 2014 to 2021

It may not always be possible to conduct a digital (forensic) investigation post-event if there is no process in place to preserve potential digital evidence. This study posits the importance of digital forensic readiness, or forensic-by-design, and presents an approach that can be used to construct a Digital Forensic Readiness Intelligence Repository (DFRIR). Based on the concept of knowledge sharing, the authors leverage this premise to suggest an intelligence repository. Such a repository can be used to cross-reference potential digital evidence (PDE) sources that may help digital investigators during the process. This approach employs a technique of capturing PDE from …

Virtual Tutor Personality In Computer Assisted Language Learning, Johanna Dobbriner, Cathy Ennis, Robert J. Ross

Conference papers

The use of intelligent virtual agents in language learning has increased in recent years. Studies into several aspects of personalisation aiming to increase user engagement are an ongoing research topic with avatar personality being one such aspect. As a step towards our development of intelligent virtual avatars, we present two of our initial experiments to explore differences in user interaction with two contrasting avatar personalities -- P1: open-minded, friendly and sociable and P2: closed-off, curt and distant. Each user interacted with a single personality in a video-call setting and gave feedback on the interaction. Our expectations, that P1 would be …

Cybersecurity Leaders: Knowledge Driving Human Capital Development, Sharon L. Burton

Publications

Cybersecurity leaders must be able to use critical reading and thinking skills, exercise judgment when policies are not distinct and precise, and have the knowledge, skills, and abilities to tailor technical and planning data to diverse customers’ levels of understanding. Ninety-three percent of cybersecurity leaders do not report directly to the chief operating officer. While status differences influence interactions amid groups, attackers are smarter. With the aim of protecting organizations and reducing risk, knowledge about security must increase. Understanding voids are costly and increased breach chances are imminent. Burning questions exist. What are needed technological learnings for cybersecurity leaders to …

Security Against Data Falsification Attacks In Smart City Applications, Venkata Praveen Kumar Madhavarapu

Doctoral Dissertations

Smart city applications like smart grid, smart transportation, healthcare deal with very important data collected from IoT devices. False reporting of data consumption from device failures or by organized adversaries may have drastic consequences on the quality of operations. To deal with this, we propose a coarse grained and a fine grained anomaly based security event detection technique that uses indicators such as deviation and directional change in the time series of the proposed anomaly detection metrics to detect different attacks. We also built a trust scoring metric to filter out the malicious devices. Another challenging problem is injection of …

Robustness Against Attacks And Uncertainties In Smart Cyber-Physical Systems, Prithwiraj Roy

Doctoral Dissertations

Cyber-Physical Systems (CPS) are sensing, processing, and communicating platforms, embedded with physical devices that provide real-time monitoring and control. Security challenges in CPS necessitate solutions that are robust against attacks and uncertainties and provide a seamless operation, especially when used in real-time applications to monitor and secure critical infrastructures. CPS mainly consists of a physical component for sensing or monitoring and a cyber component for processing and communicating. The quality of interactions between physical and cyber systems has direct impacts on the system’s performance and reliability.

CPS plays a major role in smart services and applications within a smart living …

Secure Data Sharing In Cloud And Iot By Leveraging Attribute-Based Encryption And Blockchain, Md Azharul Islam

Doctoral Dissertations

“Data sharing is very important to enable different types of cloud and IoT-based services. For example, organizations migrate their data to the cloud and share it with employees and customers in order to enjoy better fault-tolerance, high-availability, and scalability offered by the cloud. Wearable devices such as smart watch share user’s activity, location, and health data (e.g., heart rate, ECG) with the service provider for smart analytic. However, data can be sensitive, and the cloud and IoT service providers cannot be fully trusted with maintaining the security, privacy, and confidentiality of the data. Hence, new schemes and protocols are required …

Spatio-Temporal Representation For Reasoning With Action Genome, Kesar Tumkur Narasimhamurthy

Electronic Theses and Dissertations, 2020-2023

Representing Spatio-temporal information in videos has proven to be a difficult task compared to action recognition in videos involving multiple actions. A single activity consists many smaller actions that can provide a better understanding of the activity. This paper tries to represent the varying information in a scene-graph format in order to answer temporal questions to obtain improved insights for the video, resulting in a directed temporal information graph. This project will use the Action Genome dataset, which is a variation of the charades dataset, to capture pairwise relationships in a graph. The model performs significantly better than the benchmark …

Algorithms And Lower Bounds For Ordering Problems On Strings, Daniel Gibney

Electronic Theses and Dissertations, 2020-2023

This dissertation presents novel algorithms and conditional lower bounds for a collection of string and text-compression-related problems. These results are unified under the theme of ordering constraint satisfaction. Utilizing the connections to ordering constraint satisfaction, we provide hardness results and algorithms for the following: recognizing a type of labeled graph amenable to text-indexing known as Wheeler graphs, minimizing the number of maximal unary substrings occurring in the Burrows-Wheeler Transformation of a text, minimizing the number of factors occurring in the Lyndon factorization of a text, and finding an optimal reference string for relative Lempel-Ziv encoding.

Analyzing The Blockchain Attack Surface: A Top-Down Approach, Muhammad Saad

Electronic Theses and Dissertations, 2020-2023

Blockchains enable secure asset exchange in a distributed system, thereby facilitating innovative applications such as cryptocurrencies and smart contracts. Although the cryptographic constructs of blockchains are highly secure, however, their practical deployments are vulnerable to various attacks due to their application-specific policies, and their peer-to-peer (P2P) network intricacies. In this work, we take a top-down approach towards exploring those attacks, starting with the application-specific abuse of blockchain-based cryptocurrencies and concluding with the network conditions that violate the blockchain consistency. In the top-down approach, we first analyze the application-specific abuse of blockchain-based cryptocurrencies by uncovering (1) covert cryptocurrency mining in the …

Detecting Incentivized Review Groups With Co-Review Graph, Yubao Zhang, Shuai Hao, Haining Wang

Computer Science Faculty Publications

Online reviews play a crucial role in the ecosystem of nowadays business (especially e-commerce platforms), and have become the primary source of consumer opinions. To manipulate consumers’ opinions, some sellers of e-commerce platforms outsource opinion spamming with incentives (e.g., free products) in exchange for incentivized reviews. As incentives, by nature, are likely to drive more biased reviews or even fake reviews. Despite e-commerce platforms such as Amazon have taken initiatives to squash the incentivized review practice, sellers turn to various social networking platforms (e.g., Facebook) to outsource the incentivized reviews. The aggregation of sellers who …

Implication Of Manifold Assumption In Deep Learning Models For Computer Vision Applications, Marzieh Edraki

Electronic Theses and Dissertations, 2020-2023

The Deep Neural Networks (DNN) have become the main contributor in the field of machine learning (ML). Specifically in the computer vision (CV), there are applications like image and video classification, object detection and tracking, instance segmentation and visual question answering, image and video generation are some of the applications from many that DNNs have demonstrated magnificent progress. To achieve the best performance, the DNNs usually require a large number of labeled samples, and finding the optimal solution for such complex models with millions of parameters is a challenging task. It is known that, the data are not uniformly distributed …

A Novel Efficient Quantum Random Access Memory, Mohammed Zidan, Abdel-Haleem Abdel-Aty, Ashraf Khalil, Mahmoud Abdel-Aty, Hichem Eleuch

All Works

Owing to the significant progress in manufacturing desktop quantum computers, the quest to achieve efficient quantum random access memory (QRAM) became inevitable. In this paper, we propose a novel efficient random access memory for quantum computers. The proposed QRAM has a fixed structure and can be used efficiently to store both known and unknown classical/quantum data. The storage capacity of the proposed QRAM is more efficient than that of the classical RAMs and can be used to store both classical and quantum information. Furthermore, the proposed model can access an arbitrary location in O(1) compared with other state-of-the-art models.

Synergygrids: Blockchain-Supported Distributed Microgrid Energy Trading, Moayad Aloqaily, Ouns Bouachir, Öznur Özkasap, Faizan Safdar Ali

All Works

Growing intelligent cities is witnessing an increasing amount of local energy generation through renewable energy resources. Energy trade among the local energy generators (aka prosumers) and consumers can reduce the energy consumption cost and also reduce the dependency on conventional energy resources, not to mention the environmental, economic, and societal benefits. However, these local energy sources might not be enough to fulfill energy consumption demands. A hybrid approach, where consumers can buy energy from both prosumers (that generate energy) and also from prosumer of other locations, is essential. A centralized system can be used to manage this energy trading that …

A Data-Based Guiding Framework For Digital Transformation, Zakaria Maamar, Saoussen Cheikhrouhou, Said Elnaffar

All Works

This paper presents a framework for guiding organizations initiate and sustain digital transformation initiatives. Digital transformation is a long-term journey that an organization embarks on when it decides to question its practices in light of management, operation, and technology challenges. The guiding framework stresses out the importance of data in any digital transformation initiative by suggesting 4 stages referred to as collection, processing, storage, and dissemination. Because digital transformation could impact different areas of an organization for instance, business processes and business models, each stage suggests techniques to expose data. 2 case studies are adopted in the paper to illustrate …

Autobiographical Meaning Making Protects The Sense Of Self-Continuity Past Forced Migration, Christin Camia, Rida Zafar

All Works

Forced migration changes people’s lives and their sense of self-continuity fundamentally. One memory-based mechanism to protect the sense of self-continuity and psychological well-being is autobiographical meaning making, enabling individuals to explain change in personality and life by connecting personal experiences and other distant parts of life to the self and its development. Aiming to replicate and extend prior research, the current study investigated whether autobiographical meaning making has the potential to support the sense of self-continuity in refugees. We therefore collected life narratives from 31 refugees that were coded for autobiographical reasoning, selfevent connections, and global narrative coherence. In line …

Data-Fusion For Epidemiological Analysis Of Covid-19 Variants In Uae, Anoud Bani-Hani, Anaïs Lavorel, Newel Bessadet

All Works

Since December 2019, a new pandemic has appeared causing a considerable negative global impact. The SARS-CoV-2 first emerged from China and transformed to a global pandemic within a short time. The virus was further observed to be spreading rapidly and mutating at a fast pace, with over 5,775 distinct variations of the virus observed globally (at the time of submitting this paper). Extensive research has been ongoing worldwide in order to get a better understanding of its behaviour, influence and more importantly, ways for reducing its impact. Data analytics has been playing a pivotal role in this research to obtain …

A Deep Learning Approach For Real-Time Analysis Of Attendees’ Engagement In Public Events, Sujith Samuel Mathew, Manar Alkhatib, May El Barachi

All Works

Smart city analytics requires the harnessing and analysis of emotions and sentiments conveyed by images and video footage. In recent years, facial sentiment analysis attracted significant attention for different application areas, including marketing, gaming, political analytics, healthcare, and human computer interaction. Aiming at contributing to this area, we propose a deep learning model enabling the accurate emotion analysis of crowded scenes containing complete and partially occluded faces, with different angles, various distances from the camera, and varying resolutions. Our model consists of a sophisticated convolutional neural network (CNN) that is combined with pooling, densifying, flattening, and Softmax layers to achieve …

The Impact Of Arabic Part Of Speech Tagging On Sentiment Analysis: A New Corpus And Deep Learning Approach, Abdul Munem Nerabie, Manar Alkhatib, Sujith Samuel Mathew, May El Barachi, Farhad Oroumchian

All Works

Sentiment Analysis is achieved by using Natural Language Processing (NLP) techniques and finds wide applications in analyzing social media content to determine people’s opinions, attitudes, and emotions toward entities, individuals, issues, events, or topics. The accuracy of sentiment analysis depends on automatic Part-of-Speech (PoS) tagging which is required to label words according to grammatical categories. The challenge of analyzing the Arabic language has found considerable research interest, but now the challenge is amplified with the addition of social media dialects. While numerous morphological analyzers and PoS taggers were proposed for Modern Standard Arabic (MSA), we are now witnessing an increased …

Opposition-Based Quantum Bat Algorithm To Eliminate Lower-Order Harmonics Of Multilevel Inverters, Jahedul Islam, Sheikh Tanzim Meraj, Ammar Masaoud, Md Apel Mahmud, Amril Nazir, Muhammad Ashad Kabir, Md Moinul Hossain, Farhan Mumtaz

All Works

Selective harmonic elimination (SHE) technique is used in power inverters to eliminate specific lower-order harmonics by determining optimum switching angles that are used to generate Pulse Width Modulation (PWM) signals for multilevel inverter (MLI) switches. Various optimization algorithms have been developed to determine the optimum switching angles. However, these techniques are still trapped in local optima. This study proposes an opposition-based quantum bat algorithm (OQBA) to determine these optimum switching angles. This algorithm is formulated by utilizing habitual characteristics of bats. It has advanced learning ability that can effectively remove lower-order harmonics from the output voltage of MLI. It can …

Integration And Development Of Learning Management Features Into The Colums Platform, Samer Qahtan Hameed, Tam Sakirin, Yahya Hakami

Mesopotamian Journal of Computer Science

Due to the increasing use of computerized information systems in higher and further education for both administrative (Human Resources, Finance, Student Records) and instructional (Teaching, Learning, and Research) purposes, the challenge of systems integration has emerged like Virtual Learning Environments, electronic resource discovery tools, etc. COLUMS is a comprehensive and deeply integrated software product for large, medium, and small educational institutions that automates data stream and can be thought of as an Organization Wide Computing Package.  The concept of COLUMS is to interconnect Students, Teachers, Parents and Management in effective manner. To meet all the requirements of the customer, the …

Character Recognition By Implementing Fpga-Based Artificial Neural Network, Ahmed Hussein Ali, Mostafa Abdulghfoor Mohammed, Munef Abdullah Ahmed

Mesopotamian Journal of Computer Science

A non-linear applied math knowledge modelling tool, Artificial Neural Networks (ANN) are predominantly used to model complicated interactions between inputs and outputs or to look for patterns within the data. Using VHDL coding, we developed a generic hardware-based ANN. This classifier has been trained to recognize letters on a 4x4 binary grid that a user fills out using 16 toggle switches. An LCD shows the most likely classification that the ANN proposed. The ANN was taught to recognize 20 English character patterns and 9 Arabic character patterns on a 4x4 grid to showcase the viability of the FPGA execution of …

Character Recognition Techniques And Approaches: A Literature Review, Mostafa Abdulghfoor Mohammed, Shuyuan Yang

Mesopotamian Journal of Computer Science

Researchers have carried out many approaches to recognizing the OCR through software-based and FPGA-based and its relationship with the ANNs and training these NNs by using the BP algorithm. The FPGA supports high speed to recognize the character because it works in parallel. It involves many logic circuits and can process at the same time (clock cycle). There have been problems with the established system because the size, shape, and style of supposedly identical characters might differ from person to person and even within the same person on rare occasions. The photograph is vulnerable to noise and can lose some …

Learning Management System Developments And Challenges: A Literature Review, Samer Qahtan Hameed, Hind Salman Hasan

Mesopotamian Journal of Computer Science

Learning Management Systems (LMS) are web-based software that is used to create and deliver educational content in a controlled and measurable way. They are designed for an LMS-specific teaching process. These systems are available in different forms and can be purchased as open source or closed source solutions. This paper provides an explanation for the LMS and the approach used to implement it. We introduce and describe background study of LMS and discusses the advantages and disadvantages of LMS. Then the study on other LMS existing systems that have the same purpose of this project and explains the advantages and …

Characterizing Visual Programming Approaches For End-User Developers: A Systematic Review, Mohammad Amin Kuhail, Shahbano Farooq, Rawad Hammad, Mohammed Bahja

All Works

Recently many researches have explored the potential of visual programming in robotics, the Internet of Things (IoT), and education. However, there is a lack of studies that analyze the recent evidence-based visual programming approaches that are applied in several domains. This study presents a systematic review to understand, compare, and reflect on recent visual programming approaches using twelve dimensions: visual programming classification, interaction style, target users, domain, platform, empirical evaluation type, test participants' type, number of test participants, test participants' programming skills, evaluation methods, evaluation measures, and accessibility of visual programming tools. The results show that most of the selected …

Enhanced Concept-Level Sentiment Analysis System With Expanded Ontological Relations For Efficient Classification Of User Reviews, Asad Khattak, Muhammad Zubair Asghar, Zain Ishaq, Waqas Haider Bangyal, Ibrahim A. Hameed

All Works

Background/introduction: Concept-level sentiment analysis deals with the extraction and classification of concepts and features from user reviews expressed online about products and other entities like political leaders, government policies, and others. The prior studies on concept-level sentiment analysis have used a limited set of linguistic rules for extracting concepts and their associated features. Furthermore, the ontological relations used in the early works for performing concept-level sentiment analysis need enhancement in terms of the extended set of features concepts and ontological relations. Methods: This work aims at addressing the aforementioned issues and tries to bridge the literature gap by proposing an …

A Parallelized Database Damage Assessment Approach After Cyberattack For Healthcare Systems, Sanaa Kaddoura, Ramzi A. Haraty, Karam Al Kontar, Omar Alfandi

All Works

In the current Internet of things era, all companies shifted from paper-based data to the electronic format. Although this shift increased the efficiency of data processing, it has security drawbacks. Healthcare databases are a precious target for attackers because they facilitate identity theft and cybercrime. This paper presents an approach for database damage assessment for healthcare systems. Inspired by the current behavior of COVID-19 infections, our approach views the damage assessment problem the same way. The malicious transactions will be viewed as if they are COVID-19 viruses, taken from infection onward. The challenge of this research is to discover the …