Computer Engineering Commons^™

A Brief Review Of Big Data Analytics Based On Machine Learning, Ahmed Hussein Ali, Mahmood Zaki Abdullah, Shams N. Abdul-Wahab, Mohammad Al Sajri

Iraqi Journal for Computer Science and Mathematics

Owing to the exponential expansion in the data size, fast and efficient systems of analysis areextremely needed. The traditional algorithms of machine learning face the challenge of learning bottlenecks suchas; human participation, time, and the accuracy of prediction. But, the efficient and fast methods of dynamiclearning offer considerable advantages like lower human participation, rapid algorithms of learning, and easinessimplementation. This review paper presents the researches with a brief display for recently existing works in big dataanalytics and the effective algorithms of machine learning, furthermore, the issues of resources allocation in big data

2-Visible Submodules And Fully 2-Visible Modules, Mahmood S. Fiadh, Wafaa H. Hanoon

Iraqi Journal for Computer Science and Mathematics

LetXbe aT-module, T is a commutative ring with identity andKbe a proper submodule ofX. In thispaper we introduce the concepts of 2-visible submodules and fully 2-visible modules as a generalizations of visiblesubmodules and fully visible modules resp., whereKis said to be 2-visible wheneverK=I2Kfor every nonzeroidealIofTand AT-moduleXis called fully 2-visible if for any proper submodule of it is 2-visible.Study some ofthe properties of these concepts also discuss the relationship 2-visible submodules and fully 2-visible modules with2-pure submoules and other related submodules and modules resp. are given

Critical Factors Affecting The Adoption Of Open Source Software Inpublic Organizations, Mohanad G. Yaseen, Saad A. Abd, Ibarhim Adeb

Iraqi Journal for Computer Science and Mathematics

Context:The way public organizations produce, acquire, use, and commercialize software ischanging as a result of open source software (OSS). Because of the numerous benefits provided by open sourcesoftware, public organizations began to use it to meet their demands. As a result, governments implement variousmeasures to encourage and facilitate OSS use. A variety of factors continue to influence the choice to implementOSS, which can have a positive or negative impact on the adoption process.Objective:The study’s purpose is to explore the most critical factors that may influence the open source softwareadoption process, which are drawn from chosen case studies from various domains …

Development Of Iot Based Hybrid Autonomous Network Robots (Anr), Chimsom Isidore Chukwuemeka

Theses and Dissertations

The integration of wireless sensor networks (WSNs) and multirobot systems (MRS) represents an active research area supporting a wide range of applications. This is because it enables ubiquitous applications due to the robots' mobility and detection capabilities associated with its deployment. These systems have many benefits, such as perception with extended coverage that facilitate wider exploration and surveillance, efficiency in data routing, effective and reliable task environment management, etc. However, integrating two fields of research means dealing with a range of challenges such as using effective architecture for WSNs and MRS, efficient communication protocols within a network of sensors nodes …

Dual-Axis Solar Tracker, Bryan Kennedy

All Undergraduate Projects

Renewable energies, and fuels that are not fossil fuel-based, are one of the prolific topics of debate in modern society. With climate change now becoming a primary focus for scientists and innovators of today, one of the areas for the largest amount of potential and growth is that of the capturing and utilization of Solar Energy. This method involves using a mechanical system to track the progression of the sun as it traverses the sky throughout the day. A dual-axis solar tracker such as the one designed and built for this project, can follow the sun both azimuthally and in …

How Facets Of Work Illuminate Sociotechnical Challenges Of Industry 5.0, Steven Alter

Business Analytics and Information Systems

This conceptual contribution explains how the idea of “facets of work” can refocus traditional sociotechnical concerns to increase their relevance in increasingly automated and digitalized workplaces far removed from situations studied by early sociotechnical researchers. A background section summarizes how the sociotechnical approach seems pervasive but possibly outdated in some ways. It explains how the idea of “facets of work” emerged from attempting to bring richer, more evocative ide-as to systems analysis and design. Focusing on facets of work during initial discussions of requirements could provide guidance without jumping prematurely to precision and notation needed for producing technical artifacts. Tables …

Exploring Pattern Mining Algorithms For Hashtag Retrieval Problem, Asma Belhadi, Youcef Djenouri, Jerry Chun-Wei Lin, Chongsheng Zhang, Alberto Cano

Computer Science Publications

Hashtag is an iconic feature to retrieve the hot topics of discussion on Twitter or other social networks. This paper incorporates the pattern mining approaches to improve the accuracy of retrieving the relevant information and speeding up the search performance. A novel algorithm called PM-HR (Pattern Mining for Hashtag Retrieval) is designed to first transform the set of tweets into a transactional database by considering two different strategies (trivial and temporal). After that, the set of the relevant patterns is discovered, and then used as a knowledge-based system for finding the relevant tweets based on users' queries under the similarity …

College Of Computing And Engineering Graduate Catalog 2020-2021, Nova Southeastern University

College of Engineering and Computing Course Catalogs

No abstract provided.

Brain Disease Detection From Eegs: Comparing Spiking And Recurrent Neural Networks For Non-Stationary Time Series Classification, Hristo Stoev

Dissertations

Modeling non-stationary time series data is a difficult problem area in AI, due to the fact that the statistical properties of the data change as the time series progresses. This complicates the classification of non-stationary time series, which is a method used in the detection of brain diseases from EEGs. Various techniques have been developed in the field of deep learning for tackling this problem, with recurrent neural networks (RNN) approaches utilising Long short-term memory (LSTM) architectures achieving a high degree of success. This study implements a new, spiking neural network-based approach to time series classification for the purpose of …

Eyecom: An Innovative Approach For Computer Interaction, Anam Mazhar

Theses, Dissertations and Capstones

The world is innovating rapidly, and there is a need for continuous interaction with the technology. Sadly, there do not exist promising options for paralyzed people to interact with the machines i.e., laptops, smartphones, and tabs. A few commercial solutions such as Google Glasses are costly and cannot be afforded by every paralyzed person for such interaction. Towards this end, the thesis proposes a retina-controlled device called EYECOM. The proposed device is constructed from off-the-shelf cost-effective yet robust IoT devices (i.e., Arduino microcontrollers, Xbee wireless sensors, IR diodes, and accelerometer). The device can easily be mounted on to the glasses; …

Load-Balancing Rendezvous Approach For Mobility-Enabled Adaptive Energy-Efficient Data Collection In Wsns, Jian Zhang, Jian Tang, Zhonghui Wang, Feng Wang, Gang Yu

Faculty and Student Publications

Copyright © 2020 KSII The tradeoff between energy conservation and traffic balancing is a dilemma problem in Wireless Sensor Networks (WSNs). By analyzing the intrinsic relationship between cluster properties and long distance transmission energy consumption, we characterize three node sets of the cluster as a theoretical foundation to enhance high performance of WSNs, and propose optimal solutions by introducing rendezvous and Mobile Elements (MEs) to optimize energy consumption for prolonging the lifetime of WSNs. First, we exploit an approximate method based on the transmission distance from the different node to an ME to select suboptimal Rendezvous Point (RP) on the …

Revisiting Lightweight Encryption For Iot Applications: Error Performance And Throughput In Wireless Fading Channels With And Without Coding, Yazid M. Khattabi, Mustafa M. Matalgah, Mohammed M. Olama

Faculty and Student Publications

© 2013 IEEE. Employing heavy conventional encryption algorithms in communications suffers from added overhead and processing time delay; and in wireless communications, in particular, suffers from severe performance deterioration (avalanche effect) due to fading. Consequently, a tremendous reduction in data throughput and increase in complexity and time delay may occur especially when information traverse resource-limited devices as in Internet-of-Things (IoT) applications. To overcome these drawbacks, efficient lightweight encryption algorithms have been recently proposed in literature. One of those, that is of particular interest, requires using conventional encryption only for the first block of data in a given frame being transmitted. …

Cooperative Relay Selection For Load Balancing With Mobility In Hierarchical Wsns: A Multi-Armed Bandit Approach, Jian Zhang, Jian Tang, Feng Wang

Faculty and Student Publications

© 2013 IEEE. Energy efficiency is the major concern in hierarchical wireless sensor networks(WSNs), where the major energy consumption originates from radios for communication. Due to notable energy expenditure of long-range transmission for cluster members and data aggregation for Cluster Head (CH), saving and balancing energy consumption is a tricky challenge in WSNs. In this paper, we design a CH selection mechanism with a mobile sink (MS) while proposing relay selection algorithms with multi-user multi-armed bandit (UM-MAB) to solve the problem of energy efficiency. According to the definition of node density and residual energy, we propose a conception referred to …

Finding Data Races In Software Binaries With Symbolic Execution, Nathan D. Jackson

Browse all Theses and Dissertations

Modern software applications frequently make use of multithreading to utilize hardware resources better and promote application responsiveness. In these applications, threads share the program state, and synchronization mechanisms ensure proper ordering of accesses to the program state. When a developer fails to implement synchronization mechanisms, data races may occur. Finding data races in an automated way is an already challenging problem, but often impractical without source code or understanding how to execute the program under analysis. In this thesis, we propose a solution for finding data races on software binaries and present our prototype implementation BINRELAY. Our solution makes use …

Bimodal Emotion Classification Using Deep Learning, Ashutosh Kumar Singh

Dissertations

Multimodal Emotion Recognition is an emerging associative field in the area of Human Computer Interaction and Sentiment Analysis. It extracts information from each modality to predict the emotions accurately. In this research, Bimodal Emotion Recognition framework is developed with the decision-level fusion of Audio and Video modality using RAVDES dataset. Designing such frameworks are computationally expensive and require more time to train the network. Thus, a relatively small dataset has been used for the scope of this research. The conducted research is inspired by the use of neural networks for emotion classification from multimodal data. The developed framework further confirmed …

Ensemble Lung Segmentation System Using Deep Neural Networks, Redha A. Ali, Russell C. Hardie, Hussin K. Ragb

Electrical and Computer Engineering Faculty Publications

Lung segmentation is a significant step in developing computer-aided diagnosis (CAD) using Chest Radiographs (CRs). CRs are used for diagnosis of the 2019 novel coronavirus disease (COVID-19), lung cancer, tuberculosis, and pneumonia. Hence, developing a Computer-Aided Detection (CAD) system would provide a second opinion to help radiologists in the reading process, increase objectivity, and reduce the workload. In this paper, we present the implementation of our ensemble deep learning model for lung segmentation. This model is based on the original DeepLabV3+, which is the extended model of DeepLabV3. Our model utilizes various architectures as a backbone of DeepLabV3+, such as …

Lm-Based Word Embeddings Improve Biomedical Named Entity Recognition: A Detailed Analysis, Liliya Akhtyamova, John Cardiff

Conference Papers

Recent studies have shown that contextualized word embeddings outperform other types of embeddings on a variety of tasks. However, there is little research done to evaluate their effectiveness in the biomedical domain under multi-task settings. We derive the contextualized word embeddings from the Flair framework and apply them to the task of biomedical NER on 5 benchmark datasets, yielding major improvements over the baseline and achieving competitive results over the current best systems. We analyze the sources of these improvements, reporting model performances over different combinations of word embeddings, and fine-tuning and casing modes.

Recent Developments In The General Atomic And Molecular Electronic Structure System, Guiseppe M.J. Barca, Colleen Bertoni, Laura Carrington, Dipayan Datta, Nuwan De Silva, J. Emillano Deustua, Dmitri G. Fedorov, Jeffrey R. Cour, Anastasia O. Gunina, Emilie Guidez, Taylor Harville, Stephan Irle, Joe Ivanic, Karol Kowalski, Sarom S. Leang, Wei Li, Jesse J. Lutz, Ilias Magoulas, Joani Mato, Vladimir Mironov, Hiroya Nakata, Buu Q. Pham, Piotr Piecuch, David Poole, Spencer R. Pruitt, Alistair P. Rendell, Luke B. Roskop, Klaus Ruedenberg, Tosaporn Sattasathuchana, Michael W. Schmidt, Jun Shen, Lyudmila Slipchenko, Masha Sosonkina, Vaibhav Sundriyal, Ananta Tiwari, Jorge L. Galvez Vallejo, Bryce Westheimer, Marta Włoch, Peng Xu, Federico Zahariev, Mark S. Gordon

Computational Modeling & Simulation Engineering Faculty Publications

A discussion of many of the recently implemented features of GAMESS (General Atomic and Molecular Electronic Structure System) and LibCChem (the C++ CPU/GPU library associated with GAMESS) is presented. These features include fragmentation methods such as the fragment molecular orbital, effective fragment potential and effective fragment molecular orbital methods, hybrid MPI/OpenMP approaches to Hartree-Fock, and resolution of the identity second order perturbation theory. Many new coupled cluster theory methods have been implemented in GAMESS, as have multiple levels of density functional/tight binding theory. The role of accelerators, especially graphical processing units, is discussed in the context of the new features …

Deformable Multisurface Segmentation Of The Spine For Orthopedic Surgery Planning And Simulation, Rabia Haq, Jérôme Schmid, Roderick Borgie, Joshua Cates, Michel Audette

Computational Modeling & Simulation Engineering Faculty Publications

Purpose: We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data.

Approach: This model provides an accurate and robust segmentation scheme for the identification of intervertebral disc pathologies to enable the minimally supervised planning and patient-specific simulation of spine surgery, in a manner that combines multisurface and shape statistics-based variants of the deformable simplex model. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection …

Implementing Asynchronous Linear Solvers Using Non-Uniform Distributions, Erik Jensen, Evan C. Coleman, Masha Sosonkina

Computational Modeling & Simulation Engineering Faculty Publications

Asynchronous iterative methods present a mechanism to improve the performance of algorithms for highly parallel computational platforms by removing the overhead associated with synchronization among computing elements. This paper considers a class of asynchronous iterative linear system solvers that employ randomization to determine the component update orders, specifically focusing on the effects of drawing the order from non-uniform distributions. Results from shared-memory experiments with a two-dimensional finite-difference discrete Laplacian problem show that using distributions favoring the selection of components with a larger contribution to the residual may lead to faster convergence than selecting uniformly. Multiple implementations of the randomized asynchronous …

A Discrimination Aware Model To Predict Childhood Literacy Levels, Kate Byrne

Dissertations

It is illegal in Ireland to discriminate in the provision of education on the basis of multiple characteristics including gender, race and religion. While the increased use of machine learning models can open multiple avenues to identify early intervention strategies in education, caution must be exercised to ensure that any intervention does not discriminate with respect to a protected class. Poor literacy in childhood can have long term effects as the child ages, including on employment and mental health outcomes. Early intervention is key in mitigating this. In this dissertation, a model was created that predicted the outcome of a …

An Evaluation Of Text Representation Techniques For Fake News Detection Using: Tf-Idf, Word Embeddings, Sentence Embeddings With Linear Support Vector Machine., Sangita Sriram

Dissertations

In a world where anybody can share their views, opinions and make it sound like these are facts about the current situation of the world, Fake News poses a huge threat especially to the reputation of people with high stature and to organizations. In the political world, this could lead to opposition parties making use of this opportunity to gain popularity in their elections. In the medical world, a fake scandalous message about a medicine giving side effects, hospital treatment gone wrong or even a false message against a practicing doctor could become a big menace to everyone involved in …

Drug Reviews: Cross-Condition And Cross-Source Analysis By Review Quantification Using Regional Cnn-Lstm Models, Ajith Mathew Thoomkuzhy

Dissertations

Pharmaceutical drugs are usually rated by customers or patients (i.e. in a scale from 1 to 10). Often, they also give reviews or comments on the drug and its side effects. It is desirable to quantify the reviews to help analyze drug favorability in the market, in the absence of ratings. Since these reviews are in the form of text, we should use lexical methods for the analysis. The intent of this study was two-fold: First, to understand how better the efficiency will be if CNN-LSTM models are used to predict ratings or sentiment from reviews. These models are known …

Adapting Microservices In The Cloud With Faas, Mateusz Pietraszewski

Dissertations

This project involves benchmarking, microservices and Function-as-a-service (FaaS) across the dimensions of performance and cost. In order to do a comparison this paper proposes a benchmark framework.

Lightgwas: A Novel Genome-Wide Association Study Procedure, Bruno Ambrozio

Dissertations

This dissertation proposes LightGWAS, a novel machine learning procedure for genome-wide association study (GWAS) based on LightGBM and k-fold cross-validation. The conducted literature review identified that the currently available GWAS implementations rely on massive manual quality control steps to address statistical issues, such as controlling for false-positive inflation and power reduction. It also showed they demand a specific GWAS method for each type of genomic dataset morphology, which consequently increases the human dependency and open margins for misleadings. LightGWAS is a potential single, resilient, autonomous and scalable solution to address such concerns. Through this research, LightGWAS was contrasted against the …

Physical Randomness Can Help In Computations, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Can we use some so-far-unused physical phenomena to compute something that usual computers cannot? Researchers have been proposing many schemes that may lead to such computations. These schemes use different physical phenomena ranging from quantum-related to gravity-related to using hypothetical time machines. In this paper, we show that, in principle, there is no need to look into state-of-the-art physics to develop such a scheme: computability beyond the usual computations naturally appears if we consider such a basic notion as randomness.

Nyku: A Social Robot For Children With Autism Spectrum Disorders, Dan Stephan Stoianovici

Electronic Theses and Dissertations

The continued growth of Autism Spectrum Disorders (ASD) around the world has spurred a growth in new therapeutic methods to increase the positive outcomes of an ASD diagnosis. It has been agreed that the early detection and intervention of ASD disorders leads to greatly increased positive outcomes for individuals living with the disorders. Among these new therapeutic methods, Robot-Assisted Therapy (RAT) has become a hot area of study. Recent works have shown that high functioning ASD children have an affinity for interacting with robots versus humans. It is proposed that this is due to a less complex set of communication …

Microgrid-Enabled Reactive Power Support To Enhance Grid Economics, Sarhan Hasan

Electronic Theses and Dissertations

Reactive power plays an essential role in voltage control and stability in electric power systems. Various Volt/VAR techniques are utilized in electric power systems to maintain the voltage profile within defined acceptable limits and accordingly provide reliability and stability. Reactive power has been commonly generated through large-scale synchronous generators or distributed capacitor banks to provide proper transmission and distribution level system management, however, reactive power can be further used as an effective means to reduce total system operation cost. In this dissertation, an optimal reactive power model is proposed to determine the optimal nodal reactive powers that result in the …

Xylo-Bot: A Therapeutic Robot-Based Music Platform For Children With Autism, Huanghao Feng

Electronic Theses and Dissertations

Children with Autism Spectrum Disorder (ASD) experience deficits in verbal and nonverbal communication skills, including motor control, emotional facial expressions, and eye gaze / joint attention. This Ph.D. dissertation focuses on studying the feasibility and effectiveness of using a social robot, called NAO, and a toy music instrument, xylophone, at modeling and improving the social responses and behaviors of children with ASD. In our investigation, we designed an autonomous social interactive music teaching system to fulfill this mission.

A novel modular robot-music teaching system consisting of three modules is presented. Module 1 provides an autonomous self-awareness positioning system for the …

Deep Siamese Neural Networks For Facial Expression Recognition In The Wild, Wassan Hayale

Electronic Theses and Dissertations

The variation of facial images in the wild conditions due to head pose, face illumination, and occlusion can significantly affect the Facial Expression Recognition (FER) performance. Moreover, between subject variation introduced by age, gender, ethnic backgrounds, and identity can also influence the FER performance. This Ph.D. dissertation presents a novel algorithm for end-to-end facial expression recognition, valence and arousal estimation, and visual object matching based on deep Siamese Neural Networks to handle the extreme variation that exists in a facial dataset. In our main Siamese Neural Networks for facial expression recognition, the first network represents the classification framework, where we …