Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Singapore Management University (6713)
- China Simulation Federation (3285)
- TÜBİTAK (3006)
- Selected Works (2682)
- Wright State University (2602)
-
- Purdue University (2055)
- Old Dominion University (1215)
- Edith Cowan University (1159)
- Missouri University of Science and Technology (1106)
- University of Texas at El Paso (1069)
- San Jose State University (993)
- Air Force Institute of Technology (981)
- Dartmouth College (979)
- University of Nebraska - Lincoln (948)
- Embry-Riddle Aeronautical University (850)
- City University of New York (CUNY) (802)
- Technological University Dublin (782)
- Washington University in St. Louis (779)
- University of Massachusetts Amherst (728)
- Brigham Young University (724)
- California Polytechnic State University, San Luis Obispo (708)
- Kennesaw State University (706)
- University for Business and Technology in Kosovo (626)
- Nova Southeastern University (532)
- SelectedWorks (517)
- Portland State University (511)
- Western University (510)
- New Jersey Institute of Technology (499)
- Syracuse University (497)
- University of Nebraska at Omaha (480)
- Keyword
-
- Machine learning (1241)
- Deep learning (720)
- Machine Learning (621)
- Security (603)
- Computer Science (595)
-
- Artificial intelligence (577)
- Computer science (433)
- Privacy (394)
- Cybersecurity (384)
- Simulation (374)
- Classification (350)
- Technical Reports (338)
- UTEP Computer Science Department (336)
- Data mining (333)
- Algorithms (327)
- Deep Learning (323)
- Optimization (318)
- Neural networks (274)
- Artificial Intelligence (262)
- Computer vision (261)
- College for Professional Studies (253)
- Education (248)
- Department of Computer Science and Engineering (243)
- Clustering (239)
- Applied sciences (237)
- Cloud computing (237)
- School of Computer & Information Science (236)
- Software engineering (230)
- Visualization (230)
- Natural language processing (220)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (6402)
- Journal of System Simulation (3285)
- Turkish Journal of Electrical Engineering and Computer Sciences (3006)
- Theses and Dissertations (2346)
- Department of Computer Science Technical Reports (1721)
-
- Computer Science & Engineering Syllabi (1312)
- Departmental Technical Reports (CS) (861)
- Master's Projects (844)
- Electronic Theses and Dissertations (718)
- Computer Science Faculty Publications (703)
- All Computer Science and Engineering Research (683)
- Computer Science Technical Reports (673)
- Faculty Publications (574)
- Kno.e.sis Publications (543)
- Journal of Digital Forensics, Security and Law (536)
- Computer Science Faculty Publications and Presentations (499)
- Doctoral Dissertations (493)
- Dissertations (491)
- CCE Theses and Dissertations (485)
- Walden Dissertations and Doctoral Studies (467)
- All Works (443)
- Computer Science Faculty Research & Creative Works (422)
- Chulalongkorn University Theses and Dissertations (Chula ETD) (412)
- Masters Theses (398)
- Computer Science and Software Engineering (332)
- Electronic Thesis and Dissertation Repository (322)
- Mathematics, Statistics and Computer Science Faculty Research and Publications (315)
- Theses (315)
- Computer Science: Faculty Publications (313)
- USF Tampa Graduate Theses and Dissertations (312)
- Publication Type
Articles 1 - 30 of 55777
Full-Text Articles in Physical Sciences and Mathematics
Phoneme Recognition For Pronunciation Improvement, Matthew Heywood
Phoneme Recognition For Pronunciation Improvement, Matthew Heywood
Theses/Capstones/Creative Projects
This project aims to improve English pronunciation by investigating speech errors and developing a tool to provide precise feedback. The study focuses on creating a new pronunciation tool that offers localized feedback, identifies specific errors, and suggests corrective measures. By addressing the shortcomings of current methods, this research seeks to enhance pronunciation refinement.
Utilizing cutting-edge technology, the tool leverages speech-to-phoneme AI models and modified lazy string matching algorithms to compare the user's spoken input with the intended pronunciation. This allows for a detailed analysis of discrepancies, providing users actionable insights into their phonetic errors. The speech-to-phoneme AI models mark a …
Jamming Precoding In Af Relay-Aided Plc Systems With Multiple Eavessdroppers, Zhengmin Kong, Jiaxing Cui, Li Ding, Tao Huang, Shihao Yan
Jamming Precoding In Af Relay-Aided Plc Systems With Multiple Eavessdroppers, Zhengmin Kong, Jiaxing Cui, Li Ding, Tao Huang, Shihao Yan
Research outputs 2022 to 2026
Enhancing information security has become increasingly significant in the digital age. This paper investigates the concept of physical layer security (PLS) within a relay-aided power line communication (PLC) system operating over a multiple-input multiple-output (MIMO) channel based on MK model. Specifically, we examine the transmission of confidential signals between a source and a distant destination while accounting for the presence of multiple eavesdroppers, both colluding and non-colluding. We propose a two-phase jamming scheme that leverages a full-duplex (FD) amplify-and-forward (AF) relay to address this challenge. Our primary objective is to maximize the secrecy rate, which necessitates the optimization of the …
Exploring Post-Covid-19 Health Effects And Features With Advanced Machine Learning Techniques, Muhammad N. Islam, Md S. Islam, Nahid H. Shourav, Iftiaqur Rahman, Faiz A. Faisal, Md M. Islam, Iqbal H. Sarker
Exploring Post-Covid-19 Health Effects And Features With Advanced Machine Learning Techniques, Muhammad N. Islam, Md S. Islam, Nahid H. Shourav, Iftiaqur Rahman, Faiz A. Faisal, Md M. Islam, Iqbal H. Sarker
Research outputs 2022 to 2026
COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and …
Sustainable Energysense: A Predictive Machine Learning Framework For Optimizing Residential Electricity Consumption, Murad Al-Rajab, Samia Loucif
Sustainable Energysense: A Predictive Machine Learning Framework For Optimizing Residential Electricity Consumption, Murad Al-Rajab, Samia Loucif
All Works
In a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate and computing the total electricity consumption based on the number of hours the appliance was used. The application …
Asthma Prevalence Among United States Population Insights From Nhanes Data Analysis, Sarya Swed, Bisher Sawaf, Feras Al-Obeidat, Wael Hafez, Amine Rakab, Hidar Alibrahim, Mohamad Nour Nasif, Baraa Alghalyini, Abdul Rehman Zia Zaidi, Lamees Alshareef, Fadel Alqatati, Fathima Zamrath Zahir, Ashraf I. Ahmed, Mulham Alom, Anas Sultan, Abdullah Almahmoud, Agyad Bakkour, Ivan Cherrez-Ojeda
Asthma Prevalence Among United States Population Insights From Nhanes Data Analysis, Sarya Swed, Bisher Sawaf, Feras Al-Obeidat, Wael Hafez, Amine Rakab, Hidar Alibrahim, Mohamad Nour Nasif, Baraa Alghalyini, Abdul Rehman Zia Zaidi, Lamees Alshareef, Fadel Alqatati, Fathima Zamrath Zahir, Ashraf I. Ahmed, Mulham Alom, Anas Sultan, Abdullah Almahmoud, Agyad Bakkour, Ivan Cherrez-Ojeda
All Works
Asthma is a prevalent respiratory condition that poses a substantial burden on public health in the United States. Understanding its prevalence and associated risk factors is vital for informed policymaking and public health interventions. This study aims to examine asthma prevalence and identify major risk factors in the U.S. population. Our study utilized NHANES data between 1999 and 2020 to investigate asthma prevalence and associated risk factors within the U.S. population. We analyzed a dataset of 64,222 participants, excluding those under 20 years old. We performed binary regression analysis to examine the relationship of demographic and health related covariates with …
Granular3d: Delving Into Multi-Granularity 3d Scene Graph Prediction, Kaixiang Huang, Jingru Yang, Jin Wang, Shengfeng He, Zhan Wang, Haiyan He, Qifeng Zhang, Guodong Lu
Granular3d: Delving Into Multi-Granularity 3d Scene Graph Prediction, Kaixiang Huang, Jingru Yang, Jin Wang, Shengfeng He, Zhan Wang, Haiyan He, Qifeng Zhang, Guodong Lu
Research Collection School Of Computing and Information Systems
This paper addresses the significant challenges in 3D Semantic Scene Graph (3DSSG) prediction, essential for understanding complex 3D environments. Traditional approaches, primarily using PointNet and Graph Convolutional Networks, struggle with effectively extracting multi-grained features from intricate 3D scenes, largely due to a focus on global scene processing and single-scale feature extraction. To overcome these limitations, we introduce Granular3D, a novel approach that shifts the focus towards multi-granularity analysis by predicting relation triplets from specific sub-scenes. One key is the Adaptive Instance Enveloping Method (AIEM), which establishes an approximate envelope structure around irregular instances, providing shape-adaptive local point cloud sampling, thereby …
Enabling Iov Communication Through Secure Decentralized Clustering Using Federated Deep Reinforcement Learning, Chandler Scott
Enabling Iov Communication Through Secure Decentralized Clustering Using Federated Deep Reinforcement Learning, Chandler Scott
Electronic Theses and Dissertations
The Internet of Vehicles (IoV) holds immense potential for revolutionizing transporta- tion systems by facilitating seamless vehicle-to-vehicle and vehicle-to-infrastructure communication. However, challenges such as congestion, pollution, and security per- sist, particularly in rural areas with limited infrastructure. Existing centralized solu- tions are impractical in such environments due to latency and privacy concerns. To address these challenges, we propose a decentralized clustering algorithm enhanced with Federated Deep Reinforcement Learning (FDRL). Our approach enables low- latency communication, competitive packet delivery ratios, and cluster stability while preserving data privacy. Additionally, we introduce a trust-based security framework for IoV environments, integrating a central authority …
Creating A Virtual Hierarchy From A Relational Database, Yucong Mo
Creating A Virtual Hierarchy From A Relational Database, Yucong Mo
All Graduate Theses and Dissertations, Fall 2023 to Present
In data management and modeling, the value of the hierarchical model is that it does not require expensive JOIN operations at runtime; once the hierarchy is built, the relationships among data are embedded in the tree-like hierarchical structure, and thus querying data could be much faster than using a relational database. Today most data is stored in relational databases, but if the data were stored in hierarchies, what would these hierarchies look like? And more importantly, would this transition lead to a more efficient database? This thesis explores these questions by introducing a set of algorithms to convert a relational …
Data Collector Selection Ranking-Based Method For Collaborative Multi-Tasks In Ubiquitous Environments, Belal Z. Hassan, Ahmed. A. A. Gad-Elrab, Mohamed S. Farag, S. E. Abu-Youssef
Data Collector Selection Ranking-Based Method For Collaborative Multi-Tasks In Ubiquitous Environments, Belal Z. Hassan, Ahmed. A. A. Gad-Elrab, Mohamed S. Farag, S. E. Abu-Youssef
Al-Azhar Bulletin of Science
In Ubiquitous Computing and the Internet of Things, the sensing and control of objects involve numerous devices collecting and transmitting data. However, connecting these devices without fostering collaboration leads to suboptimal system performance. As the number of connected sensing devices in Internet of Things increases, efficient task accomplishment through collaboration becomes imperative. This paper proposes a Data Collector Selection Method for Collaborative Multi-Tasks to address this challenge, considering task preferences and uncertainty in data collectors' contributions. The proposed method incorporates three key aspects: (1) Using Fuzzy Analytical Hierarchy Process to determine optimal weights for task preferences; (2) Ranking data collectors …
Unveiling The Dynamics Of Crisis Events: Sentiment And Emotion Analysis Via Multi-Task Learning With Attention Mechanism And Subject-Based Intent Prediction, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang
Unveiling The Dynamics Of Crisis Events: Sentiment And Emotion Analysis Via Multi-Task Learning With Attention Mechanism And Subject-Based Intent Prediction, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang
Research Collection School Of Computing and Information Systems
In the age of rapid internet expansion, social media platforms like Twitter have become crucial for sharing information, expressing emotions, and revealing intentions during crisis situations. They offer crisis responders a means to assess public sentiment, attitudes, intentions, and emotional shifts by monitoring crisis-related tweets. To enhance sentiment and emotion classification, we adopt a transformer-based multi-task learning (MTL) approach with attention mechanism, enabling simultaneous handling of both tasks, and capitalizing on task interdependencies. Incorporating attention mechanism allows the model to concentrate on important words that strongly convey sentiment and emotion. We compare three baseline models, and our findings show that …
Hierarchical Damage Correlations For Old Photo Restoration, Weiwei Cai, Xuemiao Xu, Jiajia Xu, Huaidong Zhang, Haoxin Yang, Kun Zhang, Shengfeng He
Hierarchical Damage Correlations For Old Photo Restoration, Weiwei Cai, Xuemiao Xu, Jiajia Xu, Huaidong Zhang, Haoxin Yang, Kun Zhang, Shengfeng He
Research Collection School Of Computing and Information Systems
Restoring old photographs can preserve cherished memories. Previous methods handled diverse damages within the same network structure, which proved impractical. In addition, these methods cannot exploit correlations among artifacts, especially in scratches versus patch-misses issues. Hence, a tailored network is particularly crucial. In light of this, we propose a unified framework consisting of two key components: ScratchNet and PatchNet. In detail, ScratchNet employs the parallel Multi-scale Partial Convolution Module to effectively repair scratches, learning from multi-scale local receptive fields. In contrast, the patch-misses necessitate the network to emphasize global information. To this end, we incorporate a transformer-based encoder and decoder …
Context In Computer Vision: A Taxonomy, Multi-Stage Integration, And A General Framework, Xuan Wang
Context In Computer Vision: A Taxonomy, Multi-Stage Integration, And A General Framework, Xuan Wang
Dissertations, Theses, and Capstone Projects
Contextual information has been widely used in many computer vision tasks, such as object detection, video action detection, image classification, etc. Recognizing a single object or action out of context could be sometimes very challenging, and context information may help improve the understanding of a scene or an event greatly. However, existing approaches design specific contextual information mechanisms for different detection tasks.
In this research, we first present a comprehensive survey of context understanding in computer vision, with a taxonomy to describe context in different types and levels. Then we proposed MultiCLU, a new multi-stage context learning and utilization framework, …
To Protect Or To Hide: An Investigation On Corporate Redacted Disclosure Motives Under New Fast Act Regulation, Yan Ma, Qian Mao, Nan Hu
To Protect Or To Hide: An Investigation On Corporate Redacted Disclosure Motives Under New Fast Act Regulation, Yan Ma, Qian Mao, Nan Hu
Research Collection School Of Computing and Information Systems
China adopted amendments allowing companies to redact filings without prior approval in 2016. Leveraging this change as a quasi-nature experiment, we explore whether managers utilize redacted information to withhold bad information in the more lenient regulatory environment. Our investigation uncovers a significant shift in managerial behavior: Since 2016, managers incline to employ redactions to obscure negative news rather than safeguarding proprietary data. Furthermore, we find that the poorer firm performance and a higher cost of equity are associated with the redacted disclosures after 2016, suggesting that investors perceive an increase in firm-specific risk attributed to withholding bad news through redactions.
On Coresets For Fair Clustering In Metric And Euclidean Spaces And Their Applications, Sayan Bandyapadhyay, Fedor V. Fomin, Kirill Simonov
On Coresets For Fair Clustering In Metric And Euclidean Spaces And Their Applications, Sayan Bandyapadhyay, Fedor V. Fomin, Kirill Simonov
Computer Science Faculty Publications and Presentations
Fair clustering is a constrained clustering problem where we need to partition a set of colored points. The fraction of points of each color in every cluster should be more or less equal to the fraction of points of this color in the dataset. The problem was recently introduced by Chierichetti et al. (2017) [1]. We propose a new construction of coresets for fair clustering for Euclidean and general metrics based on random sampling. For the Euclidean space Rd, we provide the first coreset whose size does not depend exponentially on the dimension d. The question of whether such constructions …
A Sentiment Analysis Approach For Understanding Users’ Perception Of Metaverse Marketplace, Ahmed Al-Adaileh, Mousa Al-Kfairy, Mohammad Tubishat, Omar Alfandi
A Sentiment Analysis Approach For Understanding Users’ Perception Of Metaverse Marketplace, Ahmed Al-Adaileh, Mousa Al-Kfairy, Mohammad Tubishat, Omar Alfandi
All Works
This research explores the user perceptions of the Metaverse Marketplace, analyzing a substantial dataset of over 860,000 Twitter posts through sentiment analysis and topic modeling techniques. The study aims to uncover the driving factors behind user engagement and sentiment in this novel digital trading space. Key findings highlight a predominantly positive user sentiment, with significant enthusiasm for the marketplace's revenue generation and entertainment potential, particularly within the gaming sector. Users express appreciation for the innovative opportunities the Metaverse Marketplace offers for artists, designers, and traders in handling and trading digital assets. This positive outlook is tempered by notable concerns regarding …
Ethical Considerations Toward Protestware, Marc Cheong, Raula Kula, Christoph Treude
Ethical Considerations Toward Protestware, Marc Cheong, Raula Kula, Christoph Treude
Research Collection School Of Computing and Information Systems
This article looks into possible scenarios where developers might consider turning their free and open source software into protestware. Using different frameworks commonly used in artificial intelligence (AI) ethics, we extend the applications of AI ethics to the study of protestware.
Synthesis Of Dyes Sulfamidazole: Characterization, Evaluation, Molecular Docking And Global Descriptors By Density Functional Theory (Dft)., Athra G. Sager, Jawad Kadhim Abaies, Zeena R. Katoof
Synthesis Of Dyes Sulfamidazole: Characterization, Evaluation, Molecular Docking And Global Descriptors By Density Functional Theory (Dft)., Athra G. Sager, Jawad Kadhim Abaies, Zeena R. Katoof
Karbala International Journal of Modern Science
In the present work, novel azo compounds of sulfamidazole were created via the reaction of diazonium salt of sulfamidazole with several aromatic molecules including (resorcinol, 2-nitro phenol, 3-nitro phenol, and 4-nitro phenol)) (Z1–Z4). The new compounds (Z1-Z4) were identified using FTIR, 1HNMR techniques, in addition to melting point measurements. The biological activity of compounds (Z1-Z4) was studied against four kinds of bacteria including E. coli, Klebsiella pneumonia, Salmonella, and Staphylococcus aureus. The findings showed that all compounds (Z1-Z4) were active against the examined bacteria. Theoretical studies of the antibacterial ability of the prepared compound against DNA gyrase enzyme …
Unveiling The Metaverse: A Survey Of User Perceptions And The Impact Of Usability, Social Influence And Interoperability, Mousa Al-Kfairy, Ayham Alomari, Mahmood Al-Bashayreh, Omar Alfandi, Mohammad Tubishat
Unveiling The Metaverse: A Survey Of User Perceptions And The Impact Of Usability, Social Influence And Interoperability, Mousa Al-Kfairy, Ayham Alomari, Mahmood Al-Bashayreh, Omar Alfandi, Mohammad Tubishat
All Works
This review explores the Metaverse, focusing on user perceptions and emphasizing the critical aspects of usability, social influence, and interoperability within this emerging digital ecosystem. By integrating various academic perspectives, this analysis highlights the Metaverse's significant impact across various sectors, emphasizing its potential to reshape digital interaction paradigms. The investigation reveals usability as a cornerstone for user engagement, demonstrating how social dynamics profoundly influence user behaviors and choices within virtual environments. Furthermore, the study outlines interoperability as a paramount challenge, advocating for establishing unified protocols and technologies to facilitate seamless experiences across disparate Metaverse platforms. It advocates for the adoption …
Intelligent Solutions For Retroactive Anomaly Detection And Resolution With Log File Systems, Derek G. Rogers, Chanvo Nguyen, Abhay Sharma
Intelligent Solutions For Retroactive Anomaly Detection And Resolution With Log File Systems, Derek G. Rogers, Chanvo Nguyen, Abhay Sharma
SMU Data Science Review
This paper explores the intricate challenges log files pose from data science and machine learning perspectives. Drawing inspiration from existing methods, LAnoBERT, PULL, LLMs, and the breadth of recent research, this paper aims to push the boundaries of machine learning for log file systems. Our study comprehensively examines the unique challenges presented in our problem setup, delineates the limitations of existing methods, and introduces innovative solutions. These contributions are organized to offer valuable insights, predictions, and actionable recommendations tailored for Microsoft's engineers working on log data analysis.
Leveraging Transformer Models For Genre Classification, Andreea C. Craus, Ben Berger, Yves Hughes, Hayley Horn
Leveraging Transformer Models For Genre Classification, Andreea C. Craus, Ben Berger, Yves Hughes, Hayley Horn
SMU Data Science Review
As the digital music landscape continues to expand, the need for effective methods to understand and contextualize the diverse genres of lyrical content becomes increasingly critical. This research focuses on the application of transformer models in the domain of music analysis, specifically in the task of lyric genre classification. By leveraging the advanced capabilities of transformer architectures, this project aims to capture intricate linguistic nuances within song lyrics, thereby enhancing the accuracy and efficiency of genre classification. The relevance of this project lies in its potential to contribute to the development of automated systems for music recommendation and genre-based playlist …
Crisis Management: Unveiling Information And Communication Technologies’ Revamped Role Through The Lens Of Sub-Saharan African Countries During Covid-19, Ruthbetha Kateule, Egidius Kamanyi, Mahadia Tunga
Crisis Management: Unveiling Information And Communication Technologies’ Revamped Role Through The Lens Of Sub-Saharan African Countries During Covid-19, Ruthbetha Kateule, Egidius Kamanyi, Mahadia Tunga
The African Journal of Information Systems
The management of COVID-19 pandemic has revealed inefficiencies in coordinating global response, particularly in African countries. Therefore, creating an urgent need to examine the literature on Information and Communication Technologies (ICT) in crisis management to appreciate its contextual role. Employing a systematic review, using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), this paper critically assessed the extent of the use of ICT in crisis management in Africa’s response to COVID-19 to reconstruct its resilience against future crises. Findings indicate that while countries with limited ICT infrastructure faced considerable challenges in utilizing ICT solutions in COVID-19 management, countries …
Novel In Situ Synthesis Of Copper Oxide Nanoparticles In Epoxy Network: Kinetics, Composite Mechanical And Dielectric Properties, Elena Bobina, Maxim Danilaev, Safaa.M.R.H. Hussein, Sergey Karandashov, Vladimir Kuklin, Ivan Lounev, Konstantin Faizullin
Novel In Situ Synthesis Of Copper Oxide Nanoparticles In Epoxy Network: Kinetics, Composite Mechanical And Dielectric Properties, Elena Bobina, Maxim Danilaev, Safaa.M.R.H. Hussein, Sergey Karandashov, Vladimir Kuklin, Ivan Lounev, Konstantin Faizullin
Karbala International Journal of Modern Science
>Mechanical properties of polymer composites with dispersed nanoparticles (CDNP) depend on interaction between the nanoparticles and the polymer matrix. Strength of polymer composites significantly decreases when there is no interaction between dispersed nanoparticles and the polymer. This limits the application of functional polymer composites with dispersed nanoparticles. In this study, CDNP based on ED-20 epoxy resin with dispersed copper oxide nanoparticles was obtained.These nanoparticles were synthesized in epoxy resin before curing: the nanoparticles were obtained by decomposition of copper hydroxide by heating its solution in ED-20 resin.The kinetics of copper oxide nanoparticles formation in CDNP samples were studied using two …
Elm And Lightgbm: A Hybrid Machine Learning Technique With Intelligent Iot To Predict The Cardiovascular Disease, Gorapalli Srinivasa Rao, G Muneeswari
Elm And Lightgbm: A Hybrid Machine Learning Technique With Intelligent Iot To Predict The Cardiovascular Disease, Gorapalli Srinivasa Rao, G Muneeswari
Karbala International Journal of Modern Science
Cardiologists can more accurately classify a patient's condition by performing an accurate diagnostic and prognosis of cardiovascular disease (CVD). The clinical diagnosis, and therapies processes within the medical field have been substantially accelerated by ML-based approaches enabled by IoT-based systems. This structure is based on IoT-based system with enabled ML approach. This study investigates an approach known as ensemble categorization, which enhances the precision of weak algorithms by integrating multiple classifiers. For effective CVD classification, we utilized Ensemble learning machine (ELM) and Light GBM. The appropriate traits are chosen to speed up the categorization process using the Gorilla Troops Optimizer …
Context Aware Music Recommendation And Playlist Generation, Elias Mann
Context Aware Music Recommendation And Playlist Generation, Elias Mann
SMU Journal of Undergraduate Research
There are many reasons people listen to music, and the type of music is largely determined by what the listener may be doing while they listen. For example, one may listen to one type of music while commuting, another while exercising, and yet another while relaxing. Without access to the physiological state of the user, current music recommendation methods rely on collaborative filtering - recommending music based on what other similar users listen to - and content based filtering - recommending songs based on their similarities to songs the user already prefers. With the rise in popularity of smart devices …
Classification Of Major Solar Flares From Extremely Imbalanced Multivariate Time Series Data Using Minimally Random Convolutional Kernel Transform, Kartik Saini, Khaznah Alshammari, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Classification Of Major Solar Flares From Extremely Imbalanced Multivariate Time Series Data Using Minimally Random Convolutional Kernel Transform, Kartik Saini, Khaznah Alshammari, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Computer Science Faculty and Staff Publications
Solar flares are characterized by sudden bursts of electromagnetic radiation from the Sun’s surface, and are caused by the changes in magnetic field states in active solar regions. Earth and its surrounding space environment can suffer from various negative impacts caused by solar flares, ranging from electronic communication disruption to radiation exposure-based health risks to astronauts. In this paper, we address the solar flare prediction problem from magnetic field parameter-based multivariate time series (MVTS) data using multiple state-of-the-art machine learning classifiers that include MINImally RandOm Convolutional KErnel Transform (MiniRocket), Support Vector Machine (SVM), Canonical Interval Forest (CIF), Multiple Representations Sequence …
Neighboring-Aware Hierarchical Clustering, Ali A. Amer, Muna Al-Razgan, Hassan I. Abdalla, Mahfoudh Al-Asaly, Taha Alfakih, Muneer Al-Hammadi
Neighboring-Aware Hierarchical Clustering, Ali A. Amer, Muna Al-Razgan, Hassan I. Abdalla, Mahfoudh Al-Asaly, Taha Alfakih, Muneer Al-Hammadi
All Works
In this work, a simple yet robust neighboring-aware hierarchical-based clustering approach (NHC) is developed. NHC employs its dynamic technique to take into account the surroundings of each point when clustering, making it extremely competitive. NHC offers a straightforward design and reliable clustering. It comprises two key techniques, namely, neighboring- aware and filtering and merging. While the proposed neighboring-aware technique helps find the most coherent clusters, filtering and merging help reach the desired number of clusters during the clustering process. The NHC's performance, which includes all evaluation metrics and run time, has been thoroughly tested against nine clustering rivals using four …
Signer-Independent Sign Language Recognition With Feature Disentanglement, İnci̇ Meli̇ha Baytaş, İpek Erdoğan
Signer-Independent Sign Language Recognition With Feature Disentanglement, İnci̇ Meli̇ha Baytaş, İpek Erdoğan
Turkish Journal of Electrical Engineering and Computer Sciences
Learning a robust and invariant representation of various unwanted factors in sign language recognition (SLR) applications is essential. One of the factors that might degrade the sign recognition performance is the lack of signer diversity in the training datasets, causing a dependence on the singer’s identity during representation learning. Consequently, capturing signer-specific features hinders the generalizability of SLR systems. This study proposes a feature disentanglement framework comprising a convolutional neural network (CNN) and a long short-term memory (LSTM) network based on adversarial training to learn a signer-independent sign language representation that might enhance the recognition of signs. We aim to …
Joint Control Of A Flying Robot And A Ground Vehicle Using Leader-Follower Paradigm, Ayşen Süheyla Bağbaşi, Ali Emre Turgut, Kutluk Bilge Arikan
Joint Control Of A Flying Robot And A Ground Vehicle Using Leader-Follower Paradigm, Ayşen Süheyla Bağbaşi, Ali Emre Turgut, Kutluk Bilge Arikan
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, a novel control framework for the collaboration of an aerial robot and a ground vehicle that is connected via a taut tether is proposed. The framework is based on a leader-follower paradigm. The leader follows a desired trajectory while the motion of the follower is controlled by an admittance controller using an extended state observer to estimate the tether force. Additionally, a velocity estimator is also incorporated to accurately assess the leader’s velocity. An essential feature of our system is its adaptability, enabling role switching between the robots when needed. Furthermore, the synchronization performance of the robots …
Stereo-Image-Based Ground-Line Prediction And Obstacle Detection, Emre Güngör, Ahmet Özmen
Stereo-Image-Based Ground-Line Prediction And Obstacle Detection, Emre Güngör, Ahmet Özmen
Turkish Journal of Electrical Engineering and Computer Sciences
In recent years, vision systems have become essential in the development of advanced driver assistance systems or autonomous vehicles. Although deep learning methods have been the center of focus in recent years to develop fast and reliable obstacle detection solutions, they face difficulties in complex and unknown environments where objects of varying types and shapes are present. In this study, a novel non-AI approach is presented for finding the ground-line and detecting the obstacles in roads using v-disparity data. The main motivation behind the study is that the ground-line estimation errors cause greater deviations at the output. Hence, a novel …
Unveiling Anomalies: A Survey On Xai-Based Anomaly Detection For Iot, Esin Eren, Feyza Yildirim Okay, Suat Özdemi̇r
Unveiling Anomalies: A Survey On Xai-Based Anomaly Detection For Iot, Esin Eren, Feyza Yildirim Okay, Suat Özdemi̇r
Turkish Journal of Electrical Engineering and Computer Sciences
In recent years, the rapid growth of the Internet of Things (IoT) has raised concerns about the security and reliability of IoT systems. Anomaly detection is vital for recognizing potential risks and ensuring the optimal functionality of IoT networks. However, traditional anomaly detection methods often lack transparency and interpretability, hindering the understanding of their decisions. As a solution, Explainable Artificial Intelligence (XAI) techniques have emerged to provide human-understandable explanations for the decisions made by anomaly detection models. In this study, we present a comprehensive survey of XAI-based anomaly detection methods for IoT. We review and analyze various XAI techniques, including …