Sustainable Energysense: A Predictive Machine Learning Framework For Optimizing Residential Electricity Consumption, 2024 Abu Dhabi University
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, 2024 Aleppo Faculty of Medicine
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 …
Enabling Iov Communication Through Secure Decentralized Clustering Using Federated Deep Reinforcement Learning, 2024 East Tennessee State University
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 …
Data Collector Selection Ranking-Based Method For Collaborative Multi-Tasks In Ubiquitous Environments, 2024 Faculty of Science, Al-Azhar University Cairo, Egypt
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 …
Creating A Virtual Hierarchy From A Relational Database, 2024 Utah State University
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 …
Hierarchical Damage Correlations For Old Photo Restoration, 2024 Singapore Management University
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 …
Unveiling The Dynamics Of Crisis Events: Sentiment And Emotion Analysis Via Multi-Task Learning With Attention Mechanism And Subject-Based Intent Prediction, 2024 Singapore Management University
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 …
A Sentiment Analysis Approach For Understanding Users’ Perception Of Metaverse Marketplace, 2024 Kingston University
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 …
Context In Computer Vision: A Taxonomy, Multi-Stage Integration, And A General Framework, 2024 The Graduate Center, City University of New York
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, …
On Coresets For Fair Clustering In Metric And Euclidean Spaces And Their Applications, 2024 Portland State University
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 …
Analysing An Imbalanced Stroke Prediction Dataset Using Machine Learning Techniques, 2024 Department of Data Science and Business Systems, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai-603203, Tamilnadu, India
Analysing An Imbalanced Stroke Prediction Dataset Using Machine Learning Techniques, Viswapriya Subramaniyam Elangovan, Rajeswari Devarajan, Osamah I. Khalaf, Mhd Saeed Sharif, Wael Elmedany
Karbala International Journal of Modern Science
A stroke is a medical condition characterized by the rupture of blood vessels within the brain which can lead to brain damage. various symptoms may be exhibited when the brain's supply of blood and essential nutrients is disrupted. To forecast the possibility of brain stroke occurring at an early stage using Machine Learning and Deep Learning is the main objective of this study. Timely detection of the various warning signs of a stroke can significantly reduce its severity. This paper performed a comprehensive analysis of features to enhance stroke prediction effectiveness. A reliable dataset for stroke prediction is taken from …
Potential Enhancement Of Microbial Disinfection Using Oxygen Enriched Cold Atmospheric-Pressure Argon (Ar/O2) Plasma Jet, 2024 Vice Presidency for Scientific Research and innovation, Imam Abdulrahman Bin Faisal University, P.O.Box 1982, Dammam 31441,Saudi Arabia
Potential Enhancement Of Microbial Disinfection Using Oxygen Enriched Cold Atmospheric-Pressure Argon (Ar/O2) Plasma Jet, Waleed O. Younis, Mahmoud M. Berekaa, Mostafa A. Ellbban, Abdel-Sattar S. Gadallah, Jamal Q. Almarashi, Abdel-Aleam H. Mohamed
Karbala International Journal of Modern Science
Oxygen activated cold-atmospheric-pressure-argon plasma jet (APPJ) has gained prominence over the regular argon plasma especially in disinfection and decontamination. As an objective of the current research, an oxygen-enriched argon system was built, where plasma produced through a vessel metallic tube that is introduced into alumina one. A sinusoidal high voltage signal of 25 kHz was used to generate plasma jet. Potential impact of oxygen enriched APP jet (Ar/O2) in decontamination of different microbial cells was observed. For examination, suspension of each tested microbe was placed in contact with plasma jet nearly 10 mm away from the jet nozzle …
A Potential Of Watercress Nasturtium Officinale Bioactive Compounds In Inhibiting Infectious Myonecrosis Virus (Imnv) By Targeting Rna-Dependent Rna Polymerase (Rdrp) Virus From Several Countries: In Silico Approach, 2024 Biology Department, Faculty of Mathematics and Natural Sciences, Brawijaya University, Malang, Indonesia
A Potential Of Watercress Nasturtium Officinale Bioactive Compounds In Inhibiting Infectious Myonecrosis Virus (Imnv) By Targeting Rna-Dependent Rna Polymerase (Rdrp) Virus From Several Countries: In Silico Approach, Qurrota A’Yunin, Fatchiyah Fatchiyah, Maftuch Maftuch, Feri Eko Hermanto, Muhammad Hermawan Widyananda, Narendra Santika Hartana, Muhaimin Rifa’I, Yoga Dwi Jatmiko
Karbala International Journal of Modern Science
Infectious myonecrosis virus (IMNV) disease causes mass mortality and decreased shrimp production. The RdRp region projects to the interior, where it may function in transcription. The focus of this study was to determine the effect of amino acid polymorphisms from several countries on the structure of RdRp and identify the potential of watercress in inhibiting IMNV by targeting the RdRp protein of IMNV through an in silico approach. The results showed that the structure of the IMNV RdRp protein from Indonesia was similar to Mexico, and the protein structure from India_QDN was identical to India_QIL. Ligand binding affinity values showed …
Extraction Of Morphometric Features The Shape Of Mangrove Leaves Based On Digital Images And Classification Using The Support Vector Machine, 2024 Marine Information System, Universitas Pendidikan Indonesia, West Java 40154, Indonesia
Extraction Of Morphometric Features The Shape Of Mangrove Leaves Based On Digital Images And Classification Using The Support Vector Machine, Ishak Ariawan, Della Ayu Lestari, Luthfi Anzani, Tri Yanti, Cakra Rahardjo, M. Saleh, Sahril Angga Permana, Dea Aisyah Rusmawati
Karbala International Journal of Modern Science
At present, several botanists still rely on the use of manual estimating methods to assess the carbon content in mangrove. However, these methods have been reported to be extremely time-consuming, showing the need to develop a system for prediction. An effective solution lies in the creation of an artificial intelligence application, which can provide rapid and cost-effective results. In constructing this application, careful consideration must be given to the selection of parameters or attributes. Species is an essential parameter for the assessment of carbon content, but its determination has proven to be challenging due to the similarities of mangrove. The …
A Comparative Analysis Of Source Identification Algorithms, 2024 Virginia Commonwealth University
A Comparative Analysis Of Source Identification Algorithms, Pablo A. Curiel
Biology and Medicine Through Mathematics Conference
No abstract provided.
The Classification Of Internet Memes Through Supervised And Unsupervised Machine Learning Algorithms, 2024 Kennesaw State University, Marietta, GA 30060
The Classification Of Internet Memes Through Supervised And Unsupervised Machine Learning Algorithms, William H. Little
Symposium of Student Scholars
Memes, those captivating internet phenomena, effortlessly deliver online entertainment. By leveraging time-series data from Google Trends, we can vividly illustrate and dissect the dynamic trends in meme popularity. Previous studies have discerned four distinct post-peak popularity patterns— "smoothly decaying," "spikey decaying," "leveling off," and "long-term growth"—and elegantly modeled these using ordinary differential equations.
This research introduces a programmatic approach that harnesses both supervised and unsupervised machine learning algorithms. The dataset, now expanded to over 2000 elements, becomes the canvas for exploration. The K-means algorithm identifies clusters, which then serve as labels for the supervised SVC algorithm. The overarching goal is …
Exploring Neural Networks For Breast Cancer Tissue Classification, 2024 Kennesaw State University
Exploring Neural Networks For Breast Cancer Tissue Classification, Stephen Jacobs, Md Abdullah Al Hafiz Khan
Symposium of Student Scholars
Last year, more than 240 thousand women in the United States were diagnosed with breast cancer. These patients are benefitting from decades of data that have been collected by cancer research institutions around the world. Tissue samples are analyzed and cataloged by these institutions, and several facilities like the University of Wisconsin are sharing this historical data to promote the advancement of new cancer treatments. Deep learning and neural network models are being built for this data to help doctors diagnose faster and design treatment options for patients by comparing their tissue samples with these historical datasets. We will use …
Mending Trust In Ai: Trust Repair Policy Interventions For Large Language Models In Visual Data Journalism, 2024 Washington University in St. Louis
Mending Trust In Ai: Trust Repair Policy Interventions For Large Language Models In Visual Data Journalism, Hangxiao Zhu
McKelvey School of Engineering Theses & Dissertations
Trust in Large Language Models (LLMs) emerged as a pivotal concern. This is because, despite the transformative potential of LLMs in enhancing the interpretability and interactivity of complex datasets, the opacity of these models and instances of inaccuracies or biases have led to a significant trust deficit among end-users. Moreover, there is a tendency for people to personify AI tools that utilize these LLMs, attributing abilities and sensibilities that they do not truly possess. This thesis exploits this personification and proposes a comprehensive framework of trust repair policies tailored to address the challenges inherent in LLM annotations within data journalism …
Fea Simulations For Thermal Distributions Of Large Scale 3dic Packages, 2024 Henan Vocational College of Light Industry, Zhengzhou, Henan, China
Fea Simulations For Thermal Distributions Of Large Scale 3dic Packages, Suxia Chen, Qiang Wu, Wayne Xun, Jiachen Zhang, Jianping Xun
Computer Science Faculty Publications and Presentations
As the market increases for Artificial Intelligence and High-Performance Computing applications, the geometry of 3-Dimensional Integrated Circuit packages becomes more complicated; therefore, predicting the thermal distributions of the structures becomes not only more important but also more challenging. The physics governing the thermal distribution is a 3-dimensional partial differential equation. In order to predict the thermal distributions, various approaches such as the layer modeling method have been invented. While practical, these approaches solve a simplified version of the differential equation placing an inherent limitation on their capabilities which may be improved upon. In this research we solve the actual differential …
Companionship, Romance, And Self-Perception With Conversational Chatbots, 2024 University of Mary Washington
Companionship, Romance, And Self-Perception With Conversational Chatbots, Jonathan Windsor
Student Research Submissions
Serving as a metaphorical gateway transcending the communicative barriers of physical relationships in interpersonal dialogues, artificial imators of human behavior and speech, also known as conversational chatbots; a simulation of human knowledge and existence in a bi-directional conversation, functions as a rhetor of expression. Spanning from contexts of professional to romantic, I serve to dissect and critically analyze the nuances of human-machine relationships based on pre-established literature, inviting ethical considerations and biases in their design and marketing. Corporate influences spark pre-established servitude-esque relationships with conversational agents. Professional applications, both task-oriented and emotionally based alike, paint a mixed picture of …