University of Nebraska - Lincoln
Governors State University
Biola University
SUNY Fredonia
University of Pennsylvania
Bryant University
California State University, San Bernardino
Enhanced Content-Based Fake News Detection Methods With Context-Labeled News Sources,
2023
East Tennessee State University
Enhanced Content-Based Fake News Detection Methods With Context-Labeled News Sources, Duncan Arnfield
Electronic Theses and Dissertations
This work examined the relative effectiveness of multilayer perceptron, random forest, and multinomial naïve Bayes classifiers, trained using bag of words and term frequency-inverse dense frequency transformations of documents in the Fake News Corpus and Fake and Real News Dataset. The goal of this work was to help meet the formidable challenges posed by proliferation of fake news to society, including the erosion of public trust, disruption of social harmony, and endangerment of lives. This training included the use of context-categorized fake news in an effort to enhance the tools’ effectiveness. It was found that term frequency-inverse dense frequency provided …
Predicting New Crescent Moon Visibility Applying Machine Learning Algorithms,
2023
Abu Dhabi University
Predicting New Crescent Moon Visibility Applying Machine Learning Algorithms, Murad Al-Rajab, Samia Loucif, Yazan Al Risheh
All Works
The world's population is projected to grow 32% in the coming years, and the number of Muslims is expected to grow by 70%—from 1.8 billion in 2015 to about 3 billion in 2060. Hijri is the Islamic calendar, also known as the lunar Hijri calendar, which consists of 12 lunar months, and it is tied to the Moon phases where a new crescent Moon marks the beginning of each month. Muslims use the Hijri calendar to determine important dates and religious events such as Ramadan, Haj, Muharram, etc. Till today, there is no consensus on deciding on the beginning of …
Software Jimenae Allows Efficient Dynamic Simulations Of Boolean Networks, Centrality And System State Analysis,
2023
Biozentrum der Universität Würzburg
Software Jimenae Allows Efficient Dynamic Simulations Of Boolean Networks, Centrality And System State Analysis, Martin Kaltdorf, Tim Breitenbach, Stefan Karl, Maximilian Fuchs, David Komla Kessie, Eric Psota, Martina Prelog, Edita Sarukhanyan, Regina Ebert, Franz Jakob, Gudrun Dandekar, Muhammad Naseem, Chunguang Liang, Thomas Dandekar
All Works
The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity …
Boosting The Item-Based Collaborative Filtering Model With Novel Similarity Measures,
2023
Zayed University
Boosting The Item-Based Collaborative Filtering Model With Novel Similarity Measures, Hassan I. Abdalla, Ali A. Amer, Yasmeen A. Amer, Loc Nguyen, Basheer Al-Maqaleh
All Works
Collaborative filtering (CF), one of the most widely employed methodologies for recommender systems, has drawn undeniable attention due to its effectiveness and simplicity. Nevertheless, a few papers have been published on the CF-based item-based model using similarity measures than the user-based model due to the model's complexity and the time required to build it. Additionally, the substantial shortcomings in the user-based measurements when the item-based model is taken into account motivated us to create stronger models in this work. Not to mention that the common trickiest challenge is dealing with the cold-start problem, in which users' history of item-buying behavior …
Durability Of Wireless Charging Systems Embedded Into Concrete Pavements For Electric Vehicles,
2023
Utah State University
Durability Of Wireless Charging Systems Embedded Into Concrete Pavements For Electric Vehicles, Pravin Poudel
All Graduate Theses and Dissertations, Fall 2023 to Present
Point clouds are widely used in various applications such as 3D modeling, geospatial analysis, robotics, and more. One of the key advantages of 3D point cloud data is that, unlike other data formats like texture, it is independent of viewing angle, surface type, and parameterization. Since each point in the point cloud is independent of the other, it makes it the most suitable source of data for tasks like object recognition, scene segmentation, and reconstruction. Point clouds are complex and verbose due to the numerous attributes they contain, many of which may not be always necessary for rendering, making retrieving …
On The Effect Of Emotion Identification From Limited Translated Text Samples Using Computational Intelligence,
2023
Edith Cowan University
On The Effect Of Emotion Identification From Limited Translated Text Samples Using Computational Intelligence, Madiha Tahir, Zahid Halim, Muhmmad Waqas, Shanshan Tu
Research outputs 2022 to 2026
Emotion identification from text data has recently gained focus of the research community. This has multiple utilities in an assortment of domains. Many times, the original text is written in a different language and the end-user translates it to her native language using online utilities. Therefore, this paper presents a framework to detect emotions on translated text data in four different languages. The source language is English, whereas the four target languages include Chinese, French, German, and Spanish. Computational intelligence (CI) techniques are applied to extract features, dimensionality reduction, and classification of data into five basic classes of emotions. Results …
Key Communication Technologies, Applications, Protocols And Future Guides For Iot-Assisted Smart Grid Systems: A Review,
2023
Edith Cowan University
Key Communication Technologies, Applications, Protocols And Future Guides For Iot-Assisted Smart Grid Systems: A Review, Md Ohirul Qays, Iftekhar Ahmad, Ahmed Abu-Siada, Md Liton Hossain, Farhana Yasmin
Research outputs 2022 to 2026
Towards addressing the concerns of conventional power systems including reliability and security, establishing modern Smart Grids (SGs) has been given much attention by the global electric utility applications during the last few years. One of the key advantageous of SGs is its ability for two-way communication and bi-directional power flow that facilitates the inclusion of distributed energy resources, real time monitoring and self-healing systems. As such, the SG employs a large number of digital devices that are installed at various locations to enrich the observability and controllability of the system. This calls for the necessity of employing Internet of Things …
Joint Location And Cost Planning In Maximum Capture Facility Location Under Random Utilities,
2023
Singapore Management University
Joint Location And Cost Planning In Maximum Capture Facility Location Under Random Utilities, Ngan H. Duong, Tien Thanh Dam, Thuy Anh Ta, Tien Mai
Research Collection School Of Computing and Information Systems
We study a joint facility location and cost planning problem in a competitive market under random utility maximization (RUM) models. The objective is to locate new facilities and make decisions on the costs (or budgets) to spend on the new facilities, aiming to maximize an expected captured customer demand, assuming that customers choose a facility among all available facilities according to a RUM model. We examine two RUM frameworks in the discrete choice literature, namely, the additive and multiplicative RUM. While the former has been widely used in facility location problems, we are the first to explore the latter in …
Robust Maximum Capture Facility Location Under Random Utility Maximization Models,
2023
Singapore Management University
Robust Maximum Capture Facility Location Under Random Utility Maximization Models, Tien Thanh Dam, Thuy Anh Ta, Tien Mai
Research Collection School Of Computing and Information Systems
We study a robust version of the maximum capture facility location problem in a competitive market, assuming that each customer chooses among all available facilities according to a random utility maximization (RUM) model. We employ the generalized extreme value (GEV) family of models and assume that the parameters of the RUM model are not given exactly but lie in convex uncertainty sets. The problem is to locate new facilities to maximize the worst-case captured user demand. We show that, interestingly, our robust model preserves the monotonicity and submodularity from its deterministic counterpart, implying that a simple greedy heuristic can guarantee …
Your Cursor Reveals: On Analyzing Workers’ Browsing Behavior And Annotation Quality In Crowdsourcing Tasks,
2023
Singapore Management University
Your Cursor Reveals: On Analyzing Workers’ Browsing Behavior And Annotation Quality In Crowdsourcing Tasks, Pei-Chi Lo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
In this work, we investigate the connection between browsing behavior and task quality of crowdsourcing workers performing annotation tasks that require information judgements. Such information judgements are often required to derive ground truth answers to information retrieval queries. We explore the use of workers’ browsing behavior to directly determine their annotation result quality. We hypothesize user attention to be the main factor contributing to a worker’s annotation quality. To predict annotation quality at the task level, we model two aspects of task-specific user attention, also known as general and semantic user attentions . Both aspects of user attention can be …
Towards Safe Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution,
2023
CUNY Hunter College
Towards Safe Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi T. Khatchadourian Ph,D,, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Publications and Research
Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution. We present our ongoing work on automated refactoring that assists developers in specifying whether …
A New Approach To Seasonal Energy Consumption Forecasting Using Temporal Convolutional Networks,
2023
Sultan Qaboos University
A New Approach To Seasonal Energy Consumption Forecasting Using Temporal Convolutional Networks, Abdul Khalique Shaikh, Amril Nazir, Nadia Khalique, Abdul Salam Shah, Naresh Adhikari
All Works
There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature. It is well known that energy forecasting plays a crucial role in several applications in smart grids, including demand-side management, optimum dispatch, and load shedding. A significant challenge in smart grid models is managing forecasts efficiently while ensuring the slightest feasible prediction error. A type of artificial neural networks such as recurrent neural networks, are frequently used to forecast time series data. However, due to certain limitations like vanishing gradients and lack of …
Pymaivar: An Open-Source Python Suit For Audio-Image Representation In Human Action Recognition,
2023
Edith Cowan University
Pymaivar: An Open-Source Python Suit For Audio-Image Representation In Human Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar
Research outputs 2022 to 2026
We present PyMAiVAR, a versatile toolbox that encompasses the generation of image representations for audio data including Wave plots, Spectral Centroids, Spectral Roll Offs, Mel Frequency Cepstral Coefficients (MFCC), MFCC Feature Scaling, and Chromagrams. This wide-ranging toolkit generates rich audio-image representations, playing a pivotal role in reshaping human action recognition. By fully exploiting audio data's latent potential, PyMAiVAR stands as a significant advancement in the field. The package is implemented in Python and can be used across different operating systems.
Predictive Ai For The S&P 500 Index,
2023
Dartmouth College
Predictive Ai For The S&P 500 Index, Jacqueline Rose Perry
Computer Science Senior Theses
Artificial intelligence has powerful applications in virtually every field, and the financial world is no exception. Utilizing various elements of artificial intelligence, this research aims to predict the future value of the S&P 500 index using numerous models, and in doing so, identify relevant features. More specifically, models that include combinations of historical data, public sentiment, and technical indicators were employed to predict the stock price one day and three days forward. To account for public opinion, the sentiment of tweets and news headlines from the beginning of 2015 through the end of 2019 was calculated using FinBERT, a pre-trained …
The Library & Generative Ai,
2023
Minnesota State University, Mankato
The Library & Generative Ai, Nat Gustafson-Sundell, Mark Mccullough
Library Services Publications
A demonstration of several AI tools, including ChatGPT, ChatPDF, Consensus, and more. The focus of the session is on potential student uses of the tools and related library initiatives, so we address the limits of ChatGPT as an information source. Librarians can help students learn how to use these tools responsibly and provide leadership on campus as AI is integrated into assignments.
Online Data Transmission Reduction Scheme For Energy Conservation In Wireless Video Sensor Networks,
2023
Dept. of Software, College of Information Technology, University of Babylon, Babylon, Iraq
Online Data Transmission Reduction Scheme For Energy Conservation In Wireless Video Sensor Networks, Iman Kadhum Abbood, Ali Kadhum Idrees
Karbala International Journal of Modern Science
Wireless Video Sensor Networks (WVSNs) are networks of low-cost, low-power camera sensor nodes. These nodes communicate locally and process information to meet an application's goal. WVSNs are extensively used in diverse monitoring applications, such as security, military, industrial, medical, and environmental monitoring. However, the transmission of large amounts of data collected by video sensor nodes in WVSNs poses challenges in terms of energy consumption, bandwidth usage, and network congestion. Reducing energy for processing and transmitting data in WVSNs is difficult due to the huge amount of sensed data in real-time. To address this issue, this paper proposes an Online Data …
Biogenesis Synthesis Of Zno Nps: Its Adsorption And Photocatalytic Activity For Removal Of Acid Black 210 Dye,
2023
Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
Biogenesis Synthesis Of Zno Nps: Its Adsorption And Photocatalytic Activity For Removal Of Acid Black 210 Dye, Zahraa A. Najm, Mohammed A. Atiya, Ahmed K. Hassan
Karbala International Journal of Modern Science
This study investigated the treatment of textile wastewater contaminated with Acid Black 210 dye (AB210) using zinc oxide nanoparticles (ZnO NPs) through adsorption and photocatalytic techniques. ZnO NPs were synthesized using a green synthesis process involving eucalyptus leaves as reducing and capping agents. The synthesized ZnO NPs were characterized using UV-Vis spectroscopy, SEM, EDAX, XRD, BET, Zeta potential, and FTIR techniques. The BET analysis revealed a specific surface area and total pore volume of 26.318 m2/g. SEM images confirmed the crystalline and spherical nature of the particles, with a particle size of 73.4 nm. A photoreactor was designed …
Ultrasound Assisted Comparative Study Of Fucolam And So-Dium Alginate And Impact On Their Physiochemical Proper-Ties Using Box-Behnken Design,
2023
Department of food and biotechnology, South Ural State University, Russia.
Ultrasound Assisted Comparative Study Of Fucolam And So-Dium Alginate And Impact On Their Physiochemical Proper-Ties Using Box-Behnken Design, Uday Bagale, Ammar Kadi, Artem Malinin, Varisha Anjum, Irina Potoroko
Karbala International Journal of Modern Science
The article discusses about the possibility of comparing the impact of ultrasonic treatment on fucolam and sodium algi-nate. The purpose was to study the effect of micronization on the sulfated heteropolysaccharide fucolam, analyzing its dispersed state and accessibility by reducing its molecular weight and increasing antioxidant activity. The optimization of the micronization process was carried out using the Box-Behnken Design (BBD) method, with a sonication time ranging from 15 to 45 min, power ranging from 50 to 100 W/cm2, and temperature between 30 °C and 40 °C. The fixed lower fucolam concentration was 0.1%. The results illustrated that sonochemical treatment …
Deep Learning-Based Cad System For Predicting The Covid-19 X-Ray Images,
2023
Department of Mathematics, College of Science, University of Basrah, Basrah, Iraq
Deep Learning-Based Cad System For Predicting The Covid-19 X-Ray Images, Aqeel R. Talib, Hana’ M. Ali
Karbala International Journal of Modern Science
According to World Health Organization data, Coronavirus (COVID-19) has infected about 660, 378, 145 patients around the world. It is nonetheless difficult for physicians to detect COVID-19 infections out of CT or X-ray radiographs. Thus, several computer-aided diagnosis (CAD) systems based on deep learning and radiographs were developed to detect COVID-19 infections. However, the majority of approaches considered small datasets, which is ineligible to provide diverse COVID-19 radiographs. This work utilizes a massive number of X-ray radiographs, and compared standard CNN, DenseNet-121, and GoogLeNet for isolating COVID-19 infections out from normal and other pneumonia radiographs. The dataset in this work …
A Neural-Network-Based Landscape Search Engine: Lse Wisconsin,
2023
University of Wisconsin–Eau Claire
A Neural-Network-Based Landscape Search Engine: Lse Wisconsin, Matthew Haffner, Matthew Dewitte, Papia F. Rozario, Gustavo A. Ovando-Montejo
Environment and Society Faculty Publications
The task of image retrieval is common in the world of data science and deep learning, but it has received less attention in the field of remote sensing. The authors seek to fill this gap in research through the presentation of a web-based landscape search engine for the US state of Wisconsin. The application allows users to select a location on the map and to find similar locations based on terrain and vegetation characteristics. It utilizes three neural network models—VGG16, ResNet-50, and NasNet—on digital elevation model data, and uses the NDVI mean and standard deviation for comparing vegetation data. The …
