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2022

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Articles 751 - 780 of 3613

Full-Text Articles in Computer Sciences

Study On Invulnerability Of Urban Agglomeration Passenger Traffic Network Considering Time Characteristics, Chengbing Li, Yunfei Li, Peng Wu Sep 2022

Study On Invulnerability Of Urban Agglomeration Passenger Traffic Network Considering Time Characteristics, Chengbing Li, Yunfei Li, Peng Wu

Journal of System Simulation

Abstract: The cascading failure invulnerability study of comprehensive passenger transport network in urban agglomeration is helpful to improve the safety and transportation efficiency of intercity travel. In order to be consistent with the actual situation, a comprehensive passenger transport network model for urban agglomerations is constructed based on multi-layer complex network theory and actual passenger flow. The passenger transport network cascading failure invulnerability model considering time characteristics is established with unit time step. The spatial and temporal evaluation indexes are put forward to analyze the network situation in each period. Taking Hu-Bao-E-Yu urban agglomeration as an example, the results show …

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Research On Ofdm Signal Detection Method Based On Intrawell Stochastic Resonance Of Bistable System, Gaohui Liu, Ying Liang Sep 2022

Research On Ofdm Signal Detection Method Based On Intrawell Stochastic Resonance Of Bistable System, Gaohui Liu, Ying Liang

Journal of System Simulation

Abstract: In order to solve the problem of weak OFDM (orthogonal frequency division multiplexing)signal detection at the receiver in OFDM transmission system, the intrawell stochastic resonance of bistable system is combined with the OFDM signal enhancement and demodulation process. Analytical expression is derived for the time required to change from zero state to potential well state for the intrawell stochastic resonance system under the excitation of multicarrier signals, and the energy loss of multicarrier signals in one symbol caused by the transient response is analyzed. The steady-state output equation of system is derived, and the problem of superimposing the …

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Architecture Design And Prototype Verification Of Railway Vehicle Dynamics Cloud Platform, Junjie Sheng, Zhao Tang, Shaodi Dong, Shuyang Wu, Hao Liang Sep 2022

Architecture Design And Prototype Verification Of Railway Vehicle Dynamics Cloud Platform, Junjie Sheng, Zhao Tang, Shaodi Dong, Shuyang Wu, Hao Liang

Journal of System Simulation

Abstract: Since almost all the software in the railway vehicle field is controlled by foreign capital, it is difficult to catch up with the development of independent vehicle system software based on single machine deployment mode in a short time. In view of this, a set of autonomous and controllable vehicle system dynamics software architecture based on cloud platform is proposed. Based on the railway vehicle system dynamics and cloud services, a cloud platform with automatic process modeling, cloud computing,post-processing analysis is built. A simulation model of a trailer caris applied in the platform, and compared with the SIMPACK …

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A High Resolution Reconstruction Method Of Temperature Distribution In Acoustic Tomography, Lifeng Zhang, Yu Miao Sep 2022

A High Resolution Reconstruction Method Of Temperature Distribution In Acoustic Tomography, Lifeng Zhang, Yu Miao

Journal of System Simulation

Abstract: Accurate measurement temperature distribution is important for industrial production. In order to solve the number of mesh divisions will impact reconstruction accuracy in acoustic tomography, the TR-RBF (Tikhonov regularization-radial basis function) reconstruction algorithm is rebuilt to reconstruct the temperature field with high resolution. The Tikhonov regularization is used to reconstruct the ultrasound time of flight (TOF) to obtain a temperature distribution on coarse grids, and use local weighted regression method to smooth processing; use RBF neural networks to predict the temperature distribution on fine grids. Through numerical simulation with and without noise, compared with ART,SVD and Tikhonov, the proposed …

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Research On Cooperative Adaptive Cruise Control Strategy Based On Improved Mpc, Qiming Wang, Jiangyue Jiang, Zhichao Lü, Hanzu Zhang Sep 2022

Research On Cooperative Adaptive Cruise Control Strategy Based On Improved Mpc, Qiming Wang, Jiangyue Jiang, Zhichao Lü, Hanzu Zhang

Journal of System Simulation

Abstract: To solve the problems of environmental interference, sensor noise and poor tracking stability of time-varying speed, an improved MPC (model predictive control) algorithm based on KF (kalman filtering) is proposed. The longitudinal kinematics model of CACC(cooperative adaptive cruise control)between vehicles is established and the discrete state space equation is created. KF is used to reduce the noise of state variables, and at the same time, the prediction model is designed for robustness. The CACC control objectives are analyzed under different working conditions and the objective optimization functions are created. Verify by building Simulink and CarSim co-simulation model, the simulation …

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Simulation Of The Market Exclusive Competition Between Platforms, Wen Zheng, Zhe Zhang, Jingyi Zhu Sep 2022

Simulation Of The Market Exclusive Competition Between Platforms, Wen Zheng, Zhe Zhang, Jingyi Zhu

Journal of System Simulation

Abstract: As for the problem of an exclusive competition between the platforms, 2 competing PlatformsAgent, 100 ConsumersAgent and 300 SellersAgent are introduced and encapsulated into a closed market environment in BarriersModelSwarm. A two-sided market system is constructed with the cross-network externality. Through BarriersObserverSwarm, the Agents attribute information and behavior strategy are cross-called, and the virtual connection class Orderand ArrayList class in the Virtual Connection Classes are generated to run cyclically. The unilateral dependence degree of Consumers/SellersAgent is triggered, which restores the exclusive of the two-sided market competition in comparison with the platform transaction scale, market concentration, and platform cumulative capital. …

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Turbofan Engine Fault Prediction Based On Evidential Reasoning And Belief Rule Base, Hailong Zhu, Ruxia Jia, Liang Zhang, Wei He Sep 2022

Turbofan Engine Fault Prediction Based On Evidential Reasoning And Belief Rule Base, Hailong Zhu, Ruxia Jia, Liang Zhang, Wei He

Journal of System Simulation

Abstract: Aiming at the fault prediction problem of a turbofan engine, a fault prediction model based on evidential reasoning (ER) and belief rule base (BRB) is proposed. In order to describe the health state of turbofan engine, ER algorithm is adopted to fuse the state information. Combined with prior knowledge, a hybrid driven simulation prediction of BRB model is established. Projection covariance matrix adaptive evolution strategy (P-CMA-ES) is used to optimize the model parameters. The validity of the model is verified by experiments. Experimental results show that the proposed method not only accurately predicts the probability of failure …

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Modulation Recognition Algorithm Based On Truncated Migration And Parallel Resnet, Yecai Guo, Qingwei Wang Sep 2022

Modulation Recognition Algorithm Based On Truncated Migration And Parallel Resnet, Yecai Guo, Qingwei Wang

Journal of System Simulation

Abstract: A truncated migration data preprocessing algorithm is proposed for the problem of limited time series characteristics of the signal extracted by convolutional neural network. The distance unit at one end of the sampling matrix is truncated, migrated to the other end to form a new matrix, allowing the convolutional neural network to extract more sampling points and compare more symbolic information.An improved parallel ResNet is proposed, which focuses on features in both horizontal and vertical directions simultaneously by two parallel branches. The results show that the algorithm has an accuracy rate of about 10% higher than that of ordinary …

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Web Design, Kwame A. Baffour Sep 2022

Web Design, Kwame A. Baffour

Open Educational Resources

No abstract provided.

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A Machine Learning Framework For Automatic Speech Recognition In Air Traffic Control Using Word Level Binary Classification And Transcription, Fowad Shahid Sohail Sep 2022

A Machine Learning Framework For Automatic Speech Recognition In Air Traffic Control Using Word Level Binary Classification And Transcription, Fowad Shahid Sohail

Theses and Dissertations

Advances in Artificial Intelligence and Machine learning have enabled a variety of new technologies. One such technology is Automatic Speech Recognition (ASR), where a machine is given audio and transcribes the words that were spoken. ASR can be applied in a variety of domains to improve general usability and safety. One such domain is Air Traffic Control (ATC). ASR in ATC promises to improve safety in a mission critical environment. ASR models have historically required a large amount of clean training data. ATC environments are noisy and acquiring labeled data is a difficult, expertise dependent task. This thesis attempts to …

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Nft Certificates And Proof Of Delivery For Fine Jewelry And Gemstones, Noura Alnuaimi, Alanoud Almemari, Mohammad Madine, Khaled Salah, Hamda Al Breiki, Raja Jayaraman Sep 2022

Nft Certificates And Proof Of Delivery For Fine Jewelry And Gemstones, Noura Alnuaimi, Alanoud Almemari, Mohammad Madine, Khaled Salah, Hamda Al Breiki, Raja Jayaraman

All Works

Fine jewelry is a unique class of ornaments composed of precious metals and gemstones. Premium-grade metals such as gold, platinum, and sliver, and gemstones such as pearls, diamonds, rubies, and emeralds are used use to make fine jewelry. Paper-based certificates are typically issued by retailers and producers for fine jewelry and gemstones as a proof of origin, sale, ownership, history, and quality. However, paper certificates are subject to counterfeiting, loss, or theft. In this paper, we show how non-fungible tokens (NFTs) and Ethereum blockchain can be used for digital certification, proof of ownership, sale history, and quality, as well as …

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Phishing Detection Using Natural Language Processing And Machine Learning, Apurv Mittal, Dr Daniel Engels, Harsha Kommanapalli, Ravi Sivaraman, Taifur Chowdhury Sep 2022

Phishing Detection Using Natural Language Processing And Machine Learning, Apurv Mittal, Dr Daniel Engels, Harsha Kommanapalli, Ravi Sivaraman, Taifur Chowdhury

SMU Data Science Review

Phishing emails are a primary mode of entry for attackers into an organization. A successful phishing attempt leads to unauthorized access to sensitive information and systems. However, automatically identifying phishing emails is often difficult since many phishing emails have composite features such as body text and metadata that are nearly indistinguishable from valid emails. This paper presents a novel machine learning-based framework, the DARTH framework, that characterizes and combines multiple models, with one model for each composite feature, that enables the accurate identification of phishing emails. The framework analyses each composite feature independently utilizing a multi-faceted approach using Natural Language …

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Using Natural Language Processing To Increase Modularity And Interpretability Of Automated Essay Evaluation And Student Feedback, Chris Roche, Nathan Deinlein, Darryl Dawkins, Faizan Javed Sep 2022

Using Natural Language Processing To Increase Modularity And Interpretability Of Automated Essay Evaluation And Student Feedback, Chris Roche, Nathan Deinlein, Darryl Dawkins, Faizan Javed

SMU Data Science Review

For English teachers and students who are dissatisfied with the one-size-fits-all approach of current Automated Essay Scoring (AES) systems, this research uses Natural Language Processing (NLP) techniques that provide a focus on configurability and interpretability. Unlike traditional AES models which are designed to provide an overall score based on pre-trained criteria, this tool allows teachers to tailor feedback based upon specific focus areas. The tool implements a user-interface that serves as a customizable rubric. Students’ essays are inputted into the tool either by the student or by the teacher via the application’s user-interface. Based on the rubric settings, the tool …

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Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel Sep 2022

Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel

SMU Data Science Review

Since the pandemic started, researchers have been trying to find a way to detect COVID-19 which is a cost-effective, fast, and reliable way to keep the economy viable and running. This research details how chest X-ray radiography can be utilized to detect the infection. This can be for implementation in Airports, Schools, and places of business. Currently, Chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonia. Different pre-trained algorithms were fine-tuned and applied to the images to train the model and the best model obtained was fine-tuned InceptionV3 model …

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Application Of Probabilistic Ranking Systems On Women’S Junior Division Beach Volleyball, Cameron Stewart, Michael Mazel, Bivin Sadler Sep 2022

Application Of Probabilistic Ranking Systems On Women’S Junior Division Beach Volleyball, Cameron Stewart, Michael Mazel, Bivin Sadler

SMU Data Science Review

Women’s beach volleyball is one of the fastest growing collegiate sports today. The increase in popularity has come with an increase in valuable scholarship opportunities across the country. With thousands of athletes to sort through, college scouts depend on websites that aggregate tournament results and rank players nationally. This project partnered with the company Volleyball Life, who is the current market leader in the ranking space of junior beach volleyball players. Utilizing the tournament information provided by Volleyball Life, this study explored replacements to the current ranking systems, which are designed to aggregate player points from recent tournament placements. Three …

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An Enterprise Risk Management Framework To Design Pro-Ethical Ai Solutions, Quintin P. Mcgrath Sep 2022

An Enterprise Risk Management Framework To Design Pro-Ethical Ai Solutions, Quintin P. Mcgrath

USF Tampa Graduate Theses and Dissertations

The effective use of Artificial Intelligence (AI) has immediate business benefits for an organization and its stakeholders through efficiency and quality gains, and the potential to explore and implement new business models. However, there are risks of unintended ethical consequences. Enterprise Risk Management (ERM) focuses on managing risk while maximizing business value from exploiting opportunities. Using applied ethics as a basis and the perspective that ethics includes both enabling human flourishing and not violating accepted norms, I argue that greater business value is achieved when an organization simultaneously targets the maximization of benefits and the minimization of harms for the …

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Learning Hierarchical Metrical Structure Beyond Measures, Junyan Jiang, Daniel Chin, Yixiao Zhang, Gus Xia Sep 2022

Learning Hierarchical Metrical Structure Beyond Measures, Junyan Jiang, Daniel Chin, Yixiao Zhang, Gus Xia

Machine Learning Faculty Publications

Music contains hierarchical structures beyond beats and measures. While hierarchical structure annotations are helpful for music information retrieval and computer musicology, such annotations are scarce in current digital music databases. In this paper, we explore a data-driven approach to automatically extract hierarchical metrical structures from scores. We propose a new model with a Temporal Convolutional Network-Conditional Random Field (TCN-CRF) architecture. Given a symbolic music score, our model takes in an arbitrary number of voices in a beat-quantized form, and predicts a 4-level hierarchical metrical structure from downbeat-level to section-level. We also annotate a dataset using RWC-POP MIDI files to facilitate …

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Deja Vu: Semantics-Aware Recording And Replay Of High-Speed Eye Tracking And Interaction Data To Support Cognitive Studies Of Software Engineering Tasks—Methodology And Analyses, Vlas Zyrianov, Cole S. Peterson, Drew T. Guarnera, Joshua Behler, Praxis Weston, Bonita Sharif Ph.D., Jonathan I. Maletic Sep 2022

Deja Vu: Semantics-Aware Recording And Replay Of High-Speed Eye Tracking And Interaction Data To Support Cognitive Studies Of Software Engineering Tasks—Methodology And Analyses, Vlas Zyrianov, Cole S. Peterson, Drew T. Guarnera, Joshua Behler, Praxis Weston, Bonita Sharif Ph.D., Jonathan I. Maletic

School of Computing: Faculty Publications

The paper introduces a fundamental technological problem with collecting high-speed eye tracking data while studying software engineering tasks in an integrated development environment. The use of eye trackers is quickly becoming an important means to study software developers and how they comprehend source code and locate bugs. High quality eye trackers can record upwards of 120 to 300 gaze points per second. However, it is not always possible to map each of these points to a line and column position in a source code file (in the presence of scrolling and file switching) in real time at data rates over …

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Morton-Ordered Gpu Lattice Boltzmann Cfd Simulations With Application To Blood Flow, Gerald Gallagher, Fergal J. Boyle Sep 2022

Morton-Ordered Gpu Lattice Boltzmann Cfd Simulations With Application To Blood Flow, Gerald Gallagher, Fergal J. Boyle

Conference Papers

Computational fluid dynamics (CFD) is routinely used for numerically predicting cardiovascular-system medical device fluid flows. Most CFD simulations ignore the suspended cellular phases of blood due to computational constraints, which negatively affects simulation accuracy. A graphics processing unit (GPU) lattice Boltzmann-immersed boundary (LB-IB) CFD software package capable of accurately modelling blood flow is in development by the authors, focusing on the behaviour of plasma and stomatocyte, discocyte and echinocyte red blood cells during flow. Optimised memory ordering and layout schemes yield significant efficiency improvements for LB GPU simulations. In this work, comparisons of row-major-ordered Structure of Arrays (SoA) and Collected …

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Led Down The Rabbit Hole: Exploring The Potential Of Global Attention For Biomedical Multi-Document Summarisation, Yulia Otmakhova, Hung Thinh Truong, Timothy Baldwin, Trevor Cohn, Karin Verspoor, Jey Han Lau Sep 2022

Led Down The Rabbit Hole: Exploring The Potential Of Global Attention For Biomedical Multi-Document Summarisation, Yulia Otmakhova, Hung Thinh Truong, Timothy Baldwin, Trevor Cohn, Karin Verspoor, Jey Han Lau

Natural Language Processing Faculty Publications

In this paper we report on our submission to the Multidocument Summarisation for Literature Review (MSLR) shared task. Specifically, we adapt PRIMERA (Xiao et al., 2022) to the biomedical domain by placing global attention on important biomedical entities in several ways. We analyse the outputs of the 23 resulting models, and report patterns in the results related to the presence of additional global attention, number of training steps, and the input configuration. © 2022, CC BY-SA.

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Data Augmentation For Improving Emotion Recognition In Software Engineering Communication, Mia Mohammad Imran, Yashasvi Jain, Preetha Chatterjee, Kostadin Damevski Sep 2022

Data Augmentation For Improving Emotion Recognition In Software Engineering Communication, Mia Mohammad Imran, Yashasvi Jain, Preetha Chatterjee, Kostadin Damevski

Computer Science Faculty Research & Creative Works

Emotions (e.g., Joy, Anger) are prevalent in daily software engineering (SE) activities, and are known to be significant indicators of work productivity (e.g., bug fixing efficiency). Recent studies have shown that directly applying general purpose emotion classification tools to SE corpora is not effective. Even within the SE domain, tool performance degrades significantly when trained on one communication channel and evaluated on another (e.g., Stack Overflow vs. GitHub comments). Retraining a tool with channel-specific data takes significant effort since manually annotating a large dataset of ground truth data is expensive. In this paper, we address this data scarcity problem by …

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Minding The Gap: Computing Ethics And The Political Economy Of Big Tech, Ioannis Stavrakakis, Damian Gordon, Paul John Gibson, Dympna O'Sullivan, Anna Becevel Sep 2022

Minding The Gap: Computing Ethics And The Political Economy Of Big Tech, Ioannis Stavrakakis, Damian Gordon, Paul John Gibson, Dympna O'Sullivan, Anna Becevel

Articles

In 1988 Michael Mahoney wrote that “[w]hat is truly revolutionary about the computer will become clear only when computing acquires a proper history, one that ties it to other technologies and thus uncovers the precedents that make its innovations significant” (Mahoney, 1988). Today, over thirty years after this quote was written, we are living right in the middle of the information age and computing technology is constantly transforming modern living in revolutionary ways and in such a high degree that is giving rise to many ethical considerations, dilemmas, and social disruption. To explore the myriad of issues associated with the …

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“Be A Pattern For The World”: The Development Of A Dark Patterns Detection Tool To Prevent Online User Loss, Jordan Donnelly, Alan Dowley, Yunpeng Liu, Yufei Su, Quanwei Sun, Lan Zeng, Andrea Curley, Damian Gordon, Paul Kelly, Dympna O'Sullivan, Anna Becevel Sep 2022

“Be A Pattern For The World”: The Development Of A Dark Patterns Detection Tool To Prevent Online User Loss, Jordan Donnelly, Alan Dowley, Yunpeng Liu, Yufei Su, Quanwei Sun, Lan Zeng, Andrea Curley, Damian Gordon, Paul Kelly, Dympna O'Sullivan, Anna Becevel

Articles

Dark Patterns are designed to trick users into sharing more information or spending more money than they had intended to do, by configuring online interactions to confuse or add pressure to the users. They are highly varied in their form, and are therefore difficult to classify and detect. Therefore, this research is designed to develop a framework for the automated detection of potential instances of web-based dark patterns, and from there to develop a software tool that will provide a highly useful defensive tool that helps detect and highlight these patterns.

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Uav-Assisted Sensor Data Dissemination In Mmwave Vehicular Networks Based On Network Coding, Shidong Huang, Chuanhe Huang, Dongfang Wu, Yabo Yin, M. Wasim Abbas Ashraf, Bin Fu Sep 2022

Uav-Assisted Sensor Data Dissemination In Mmwave Vehicular Networks Based On Network Coding, Shidong Huang, Chuanhe Huang, Dongfang Wu, Yabo Yin, M. Wasim Abbas Ashraf, Bin Fu

Computer Science Faculty Publications

Due to good maneuverability, UAVs and vehicles are often used for environment perception in smart cities. In order to improve the efficiency of sensor data sharing in UAV-assisted mmWave vehicular network (VN), this paper proposes a sensor data sharing method based on blockage effect identification and network coding. The concurrent sending vehicles selection method is proposed based on the availability of mmWave link, the number of target vehicles of sensor data packet, the distance between a sensor data packet and target vehicle, the number of concurrent sending vehicles, and the waiting time of sensor data packet. The construction method of …

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Technical Debt Is An Ethical Issue, Paul John Gibson, Yannis Stavrakakis, Massamaesso Narouwa, Damian Gordon, Dympna O'Sullivan, Jonathan Turner, Michael Collins Sep 2022

Technical Debt Is An Ethical Issue, Paul John Gibson, Yannis Stavrakakis, Massamaesso Narouwa, Damian Gordon, Dympna O'Sullivan, Jonathan Turner, Michael Collins

Conference Papers

We introduce the problem of technical debt, with particular focus on critical infrastructure, and put forward our view that this is a digital ethics issue. We propose that the software engineering process must adapt its current notion of technical debt – focusing on technical costs – to include the potential cost to society if the technical debt is not addressed, and the cost of analysing, modelling and understanding this ethical debt. Finally, we provide an overview of the development of educational material – based on a collection of technical debt case studies - in order to teach about technical debt …

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A Roller Coaster For The Mind: Virtual Reality Sickness Modes, Metrics, And Mitigation, Dalton C. Sparks Sep 2022

A Roller Coaster For The Mind: Virtual Reality Sickness Modes, Metrics, And Mitigation, Dalton C. Sparks

The Cardinal Edge

Understanding and preventing virtual reality sickness(VRS), or cybersickness, is vital in removing barriers for the technology's adoption. Thus, this article aims to synthesize a variety of academic sources to demonstrate the modes by which VRS occurs, the metrics by which it is judged, and the methods to mitigate it. The predominant theories on the biological origins of VRS are discussed, as well as the individual factors which increase the likelihood of a user developing VRS. Moreover, subjective and physiological measurements of VRS are discussed in addition to the development of a predictive model and conceptual framework. Finally, several methodologies of …

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Unsupervised Lexical Substitution With Decontextualised Embeddings, Takashi Wada, Timothy Baldwin, Yuji Matsumoto, Jey Han Lau Sep 2022

Unsupervised Lexical Substitution With Decontextualised Embeddings, Takashi Wada, Timothy Baldwin, Yuji Matsumoto, Jey Han Lau

Natural Language Processing Faculty Publications

We propose a new unsupervised method for lexical substitution using pre-trained language models. Compared to previous approaches that use the generative capability of language models to predict substitutes, our method retrieves substitutes based on the similarity of contextualised and decontextualised word embeddings, i.e. the average contextual representation of a word in multiple contexts. We conduct experiments in English and Italian, and show that our method substantially outperforms strong baselines and establishes a new state-of-the-art without any explicit supervision or fine-tuning. We further show that our method performs particularly well at predicting low-frequency substitutes, and also generates a diverse list of …

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Artificial Intelligence-Driven Design Of Fuel Mixtures, Nursulu Kuzhagaliyeva, Samuel Horváth, John Williams, Andre Nicolle, S. Mani Sarathy Sep 2022

Artificial Intelligence-Driven Design Of Fuel Mixtures, Nursulu Kuzhagaliyeva, Samuel Horváth, John Williams, Andre Nicolle, S. Mani Sarathy

Machine Learning Faculty Publications

High-performance fuel design is imperative to achieve cleaner burning and high-efficiency engine systems. We introduce a data-driven artificial intelligence (AI) framework to design liquid fuels exhibiting tailor-made properties for combustion engine applications to improve efficiency and lower carbon emissions. The fuel design approach is a constrained optimization task integrating two parts: (i) a deep learning (DL) model to predict the properties of pure components and mixtures and (ii) search algorithms to efficiently navigate in the chemical space. Our approach presents the mixture-hidden vector as a linear combination of each single component’s vectors in each blend and incorporates it into the …

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Triggers And Tweets: Implicit Aspect-Based Sentiment And Emotion Analysis Of Community Chatter Relevant To Education Post-Covid-19, Heba Ismail, Ashraf Khalil, Nada Hussein, Rawan Elabyad Sep 2022

Triggers And Tweets: Implicit Aspect-Based Sentiment And Emotion Analysis Of Community Chatter Relevant To Education Post-Covid-19, Heba Ismail, Ashraf Khalil, Nada Hussein, Rawan Elabyad

All Works

This research proposes a well-being analytical framework using social media chatter data. The proposed framework infers analytics and provides insights into the public's well-being relevant to education throughout and post the COVID-19 pandemic through a comprehensive Emotion and Aspect-based Sentiment Analysis (ABSA). Moreover, this research aims to examine the variability in emotions of students, parents, and faculty toward the e-learning process over time and across different locations. The proposed framework curates Twitter chatter data relevant to the education sector, identifies tweets with the sentiment, and then identifies the exact emotion and emotional triggers associated with those feelings through implicit ABSA. …

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Evaluation Of Geo-Spebh Algorithm Based On Bandwidth For Big Data Retrieval In Cloud Computing, Abubakar Usman Othman, Moses Timothy, Aisha Yahaya Umar, Abdullahi Salihu Audu, Boukari Souley, Abdulsalam Ya’U Gital Sep 2022

Evaluation Of Geo-Spebh Algorithm Based On Bandwidth For Big Data Retrieval In Cloud Computing, Abubakar Usman Othman, Moses Timothy, Aisha Yahaya Umar, Abdullahi Salihu Audu, Boukari Souley, Abdulsalam Ya’U Gital

Al-Bahir

The fast increase in volume and speed of information created by mobile devices, along with the availability of web-based applications, has considerably contributed to the massive collection of data. Approximate Nearest Neighbor (ANN) is essential in big size databases for comparison search to offer the nearest neighbor of a given query in the field of computer vision and pattern recognition. Many hashing algorithms have been developed to improve data management and retrieval accuracy in huge databases. However, none of these algorithms took bandwidth into consideration, which is a significant aspect in information retrieval and pattern recognition. As a result, our …

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