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Articles 33181 - 33210 of 291657

Full-Text Articles in Physical Sciences and Mathematics

Unlocking The Potential Of Simulated Hyperspectral Imaging In Agro Environmental Analysis: A Comprehensive Study Of Algorithmic Approaches, Shafaq Khan, Munir Majdalawieh, Boubakeur Boufama, Yajan Sharma, Ashwitha Basani Jan 2024

Unlocking The Potential Of Simulated Hyperspectral Imaging In Agro Environmental Analysis: A Comprehensive Study Of Algorithmic Approaches, Shafaq Khan, Munir Majdalawieh, Boubakeur Boufama, Yajan Sharma, Ashwitha Basani

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This study focuses on identifying and evaluating the severity of powdery mildew disease in tomato plants. The uniqueness of this work lies in combining the imaging and advanced deep learning methods to develop a technique that transforms Red Green Blue (RGB) images into Simulated Hyperspectral Images (SHSI) to perform spectral and spatial analysis for precise detection and assessment of powdery mildew severity, thereby enhancing disease management. Furthermore, this research evaluates three advanced pre-trained VGG16 models, ResNet50 and EfficientNet-B7 algorithms for image preprocessing and feature extraction. Extracted features are passed to a neural network generator model to convert RGB image features …


Fraud Detection In Medical Insurance Claims Using Majority Voting Of Multiple Unsupervised Algorithms, Mohamed Ahmed Abo El-Enen, Dina Tbaishat, Ahmed T. Sahlol, Amril Nazir, Khalid Almaymun, Mustafa Abdulrazek, Reem Muhammad, Fatima Adlan, Ravishankar Sharma Jan 2024

Fraud Detection In Medical Insurance Claims Using Majority Voting Of Multiple Unsupervised Algorithms, Mohamed Ahmed Abo El-Enen, Dina Tbaishat, Ahmed T. Sahlol, Amril Nazir, Khalid Almaymun, Mustafa Abdulrazek, Reem Muhammad, Fatima Adlan, Ravishankar Sharma

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This paper addresses the critical challenge of fraud detection in medical insurance claims, a pervasive issue causing significant financial losses in healthcare. The primary goal is to develop an advanced fraud detection approach by integrating multiple unsupervised machines learning algorithms, leveraging their collective strengths through a majority voting mechanism, where labelling of data is unavailable. Central to this approach is the ensemble of 18 novel unsupervised algorithms, specifically, anomaly detection models. The novelty lies in the majority voting system employed to aggregate the decisions from these diverse algorithms, enhancing the reliability and accuracy of fraud detection. To validate the effectiveness …


Enhancing Medication Adherence With Chronic Diseases Through Iot Technology: A Novel Approach, Nadia Dahmani, Suja A. Alex Jan 2024

Enhancing Medication Adherence With Chronic Diseases Through Iot Technology: A Novel Approach, Nadia Dahmani, Suja A. Alex

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This paper proposes a novel IoT-based Medication Adherence System to combat the pervasive issue of non-adherence among chronic disease patients. This system leverages real-time monitoring and timely reminders to improve medication intake and, consequently, patient outcomes. We delve into the factors behind non-adherence and explore how IoT technology can empower patient education and alleviate medication anxieties. The study emphasizes the significance of proactive interventions in fostering adherence and ultimately improving health for those with chronic conditions. Advocating for a holistic approach that merges patient education, behavioral modifications, and technological advancements, this research proposes a transformative model for chronic disease management. …


Potato Leaf Disease Detection Approach Based On Transfer Learning With Spatial Attention, Rima Grati, Emna Abdallah, Khouloud Boukadi, Ahmed Smaoui Jan 2024

Potato Leaf Disease Detection Approach Based On Transfer Learning With Spatial Attention, Rima Grati, Emna Abdallah, Khouloud Boukadi, Ahmed Smaoui

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No abstract provided.


Complex Shadowed Set Theory And Its Application In Decision-Making Problems, Doaa Alsharo, Eman Abuteen, Abd Ulazeez M.J.S. Alkouri, Mutasem Alkhasawneh, Fadi M.A. Al-Zubi Jan 2024

Complex Shadowed Set Theory And Its Application In Decision-Making Problems, Doaa Alsharo, Eman Abuteen, Abd Ulazeez M.J.S. Alkouri, Mutasem Alkhasawneh, Fadi M.A. Al-Zubi

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Modern technology makes it easier to store datasets, but extracting and isolating useful information with its full meaning from this data is crucial and hard. Recently, several algorithms for clustering data have used complex fuzzy sets (CFS) to improve clustering performance. Thus, adding a second dimension (phase term) to the range of membership avoids the problem of losing the full meaning of complicated information during the decision-making process. In this research, the notion of the complex shadowed set (CSHS) was introduced and considered as an example of the three region approximations method simplifying processing with the support of CFS and …


Mixed Criticality Reward-Based Systems Using Resource Reservation, Amjad Ali, Shah Zeb, Madallah Alruwaili, Asad Masood Khattak, Bashir Hayat, Ki Il Kim Jan 2024

Mixed Criticality Reward-Based Systems Using Resource Reservation, Amjad Ali, Shah Zeb, Madallah Alruwaili, Asad Masood Khattak, Bashir Hayat, Ki Il Kim

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Real-time systems mostly interact with the external world and each input operation must meet predetermined deadlines to be useful. However, in many real-time applications, a partial result is also acceptable. We developed a reward-based mixed criticality system based on the resource reservation approach to address the problem of ensuring the effective execution of low- and high-criticality tasks in both low- and high modes, even under heavy workloads. Using dedicated servers with pessimistic resource allocation for each high criticality task ensured their execution in both modes unaffected by low criticality tasks. The surplus resources are reclaimed and assigned to low critical …


Diagnostic Performance Of Ai-Based Models Versus Physicians Among Patients With Hepatocellular Carcinoma: A Systematic Review And Meta-Analysis, Feras Al-Obeidat, Wael Hafez, Muneir Gador, Nesma Ahmed, Marwa Muhammed Abdeljawad, Antesh Yadav, Asrar Rashed Jan 2024

Diagnostic Performance Of Ai-Based Models Versus Physicians Among Patients With Hepatocellular Carcinoma: A Systematic Review And Meta-Analysis, Feras Al-Obeidat, Wael Hafez, Muneir Gador, Nesma Ahmed, Marwa Muhammed Abdeljawad, Antesh Yadav, Asrar Rashed

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Background: Hepatocellular carcinoma (HCC) is a common primary liver cancer that requires early diagnosis due to its poor prognosis. Recent advances in artificial intelligence (AI) have facilitated hepatocellular carcinoma detection using multiple AI models; however, their performance is still uncertain. Aim: This meta-analysis aimed to compare the diagnostic performance of different AI models with that of clinicians in the detection of hepatocellular carcinoma. Methods: We searched the PubMed, Scopus, Cochrane Library, and Web of Science databases for eligible studies. The R package was used to synthesize the results. The outcomes of various studies were aggregated using fixed-effect and random-effects models. …


Analysis Of Sir Model With Optimal Control Strategy For A Simple Traffic Congestion Process, Ratna Herdiana, Zani Anjani Rafsanjani, R. Heru Tjahjana, Yogi Ahmad Erlangga, Moch Fandi Ansori Jan 2024

Analysis Of Sir Model With Optimal Control Strategy For A Simple Traffic Congestion Process, Ratna Herdiana, Zani Anjani Rafsanjani, R. Heru Tjahjana, Yogi Ahmad Erlangga, Moch Fandi Ansori

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Traffic analysis on highways at the macroscopic level is very similar to the analysis of the spread of infectious diseases, namely the susceptible-infected-recover (SIR) model. We propose the SIR model with a control variable. The dynamics with fixed control and stability of the model are analyzed. Sensitivity analysis was also carried out. Variable control is applied as an effort to regulate or change the duration of the green light at an intersection. We obtain an optimal control strategy when the control is time-dependent. Numerical results show the positive impacts of implementing the control to susceptible vehicles and treatment for congested …


Estimated Glomerular Filtration Rate Slope And Risk Of Primary And Secondary Major Adverse Cardiovascular Events And Heart Failure Hospitalization In People With Type 2 Diabetes: An Analysis Of The Exscel Trial, Abderrahim Oulhaj, Faisal Aziz, Abubaker Suliman, Kathrin Eller, Rachid Bentoumi, John B. Buse, Wael Al Mahmeed, Dirk Von Lewinski, Ruth L. Coleman, Rury R. Holman, Harald Sourij Jan 2024

Estimated Glomerular Filtration Rate Slope And Risk Of Primary And Secondary Major Adverse Cardiovascular Events And Heart Failure Hospitalization In People With Type 2 Diabetes: An Analysis Of The Exscel Trial, Abderrahim Oulhaj, Faisal Aziz, Abubaker Suliman, Kathrin Eller, Rachid Bentoumi, John B. Buse, Wael Al Mahmeed, Dirk Von Lewinski, Ruth L. Coleman, Rury R. Holman, Harald Sourij

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Aim: The decline in estimated glomerular filtration rate (eGFR), a significant predictor of cardiovascular disease (CVD), occurs heterogeneously in people with diabetes because of various risk factors. We investigated the role of eGFR decline in predicting CVD events in people with type 2 diabetes in both primary and secondary CVD prevention settings. Materials and Methods: Bayesian joint modelling of repeated measures of eGFR and time to CVD event was applied to the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial to examine the association between the eGFR slope and the incidence of major adverse CV event/hospitalization for heart failure (MACE/hHF) …


Revised Geologic Map And Structural Interpretation Of The Mineral King Pendant, Southern Sierra Nevada, California (Usa): Evidence For Kilometer-Scale Folding And Structural Imbrication Of A Permian To Mid-Cretaceous Volcanosedimentary Assemblage, David C. Greene, Jade Star Lackey, Erik W. Klemetti Jan 2024

Revised Geologic Map And Structural Interpretation Of The Mineral King Pendant, Southern Sierra Nevada, California (Usa): Evidence For Kilometer-Scale Folding And Structural Imbrication Of A Permian To Mid-Cretaceous Volcanosedimentary Assemblage, David C. Greene, Jade Star Lackey, Erik W. Klemetti

Faculty Publications

No abstract provided.


From Pollution To Resource: Advancing Swine Waste Treatment In The Usa, Viney P. Aneja, Ryke Longest, Matias B. Vanotti, Ariel A. Szogi, Gudigopuram B. Reddy Jan 2024

From Pollution To Resource: Advancing Swine Waste Treatment In The Usa, Viney P. Aneja, Ryke Longest, Matias B. Vanotti, Ariel A. Szogi, Gudigopuram B. Reddy

Faculty Scholarship

Concentrated animal feeding operations (CAFOs) have led to environmental challenges, specifically waste management. Swine CAFOs generate large amounts of waste, requiring proper treatment to avoid air and water pollution. Conventional waste management technologies, such as lagoon and spray field systems, do not prevent air and water pollution impacts. Research for the past few decades led to recommendations for waste treatment technologies superior to lagoons and spray fields. Private environmental sustainability initiatives focused on reducing greenhouse gas emissions in the food supply chain have implemented biogas digester projects for capturing methane in covered swine lagoons to reduce greenhouse gas emissions. However, …


Transdisciplinary Doctoral Training To Address Global Sustainability Challenges, Zoie Taylor Diana, John Virdin, Michelle B. Nowlin, Nishad Jayasundara, Daniel Rittschof Jan 2024

Transdisciplinary Doctoral Training To Address Global Sustainability Challenges, Zoie Taylor Diana, John Virdin, Michelle B. Nowlin, Nishad Jayasundara, Daniel Rittschof

Faculty Scholarship

No abstract provided.


Deepfakes In Court: How Judges Can Proactively Manage Alleged Ai-Generated Material In National Security Cases, Abhishek Dalal, Chongyang Gao, Paul W. Grimm, Maura R. Grossman, Daniel W. Linna Jr., Chiara Pulice, V. S. Subrahmanian, John Tunheim Jan 2024

Deepfakes In Court: How Judges Can Proactively Manage Alleged Ai-Generated Material In National Security Cases, Abhishek Dalal, Chongyang Gao, Paul W. Grimm, Maura R. Grossman, Daniel W. Linna Jr., Chiara Pulice, V. S. Subrahmanian, John Tunheim

Faculty Scholarship

Dall-E. ChatGPT GPT-4. Words that did not exist in the English lexicon just a few years ago are now commonplace. With the widespread availability of Artificial Intelligence (AI) tools, specifically Generative AI, whether in the context of text, audio, video, imagery, or even combinations of these, it is inevitable that trials related to national security will involve evidentiary issues raised by Generative AI. We must confront two possibilities: first, that evidence presented is AI-generated and not real and, second, that other evidence is genuine but alleged to be fabricated. Technologies designed to detect AI-generated content have proven to be unreliable, …


Evaluating Pre-Trial Programs Using Interpretable Machine Learning Matching Algorithms For Causal Inference, Travis Seale-Carlisle, Saksham Jain, Courtney Lee, Caroline Levenson, Swathi Ramprasad, Brandon Garrett, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky Jan 2024

Evaluating Pre-Trial Programs Using Interpretable Machine Learning Matching Algorithms For Causal Inference, Travis Seale-Carlisle, Saksham Jain, Courtney Lee, Caroline Levenson, Swathi Ramprasad, Brandon Garrett, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

Faculty Scholarship

After a person is arrested and charged with a crime, they may be released on bail and required to participate in a community supervision program while awaiting trial. These ‘pretrial programs’ are common throughout the United States, but very little research has demonstrated their effectiveness. Researchers have emphasized the need for more rigorous program evaluation methods, which we introduce in this article. We describe a program evaluation pipeline that uses recent interpretable machine learning techniques for observational causal inference, and demonstrate these techniques in a study of a pre-trial program in Durham, North Carolina. Our findings show no evidence that …


Arsenic And Accountability: A Retrospective On The Arsenic-Life Controversy, Jules Lieberman Jan 2024

Arsenic And Accountability: A Retrospective On The Arsenic-Life Controversy, Jules Lieberman

The Synapse: Intercollegiate science magazine

No abstract provided.


Front Matter Jan 2024

Front Matter

The Synapse: Intercollegiate science magazine

No abstract provided.


Marriage: Penguins And Humans Propose | A Ritual That Transcends Gender And Species, Ann Clark Jan 2024

Marriage: Penguins And Humans Propose | A Ritual That Transcends Gender And Species, Ann Clark

The Synapse: Intercollegiate science magazine

No abstract provided.


From Fictional Pandemics To Potential Reality: The Realities And Myths Of 'The Last Of Us' Cordyceps Fungus, Jaedyn O'Reilly Jan 2024

From Fictional Pandemics To Potential Reality: The Realities And Myths Of 'The Last Of Us' Cordyceps Fungus, Jaedyn O'Reilly

The Synapse: Intercollegiate science magazine

No abstract provided.


Featured Artists: Grace Weber, Anadi Purewal-Legha, Ofek Levy Jan 2024

Featured Artists: Grace Weber, Anadi Purewal-Legha, Ofek Levy

The Synapse: Intercollegiate science magazine

No abstract provided.


In The Age Of Ozempic: Semiglutide's Impact On The Weight-Loss Frontier, Luke Dodson Jan 2024

In The Age Of Ozempic: Semiglutide's Impact On The Weight-Loss Frontier, Luke Dodson

The Synapse: Intercollegiate science magazine

No abstract provided.


Resourceful Remedies: Herbal Medicines In Indigenous Villages Of Northern Thailand, Ava Peyton Jan 2024

Resourceful Remedies: Herbal Medicines In Indigenous Villages Of Northern Thailand, Ava Peyton

The Synapse: Intercollegiate science magazine

No abstract provided.


Navigating Digital Neurodiversity: Understanding The Intersection Of Neurodiversity And Technology For Personal Wellbeing, Haze Doleys Jan 2024

Navigating Digital Neurodiversity: Understanding The Intersection Of Neurodiversity And Technology For Personal Wellbeing, Haze Doleys

The Synapse: Intercollegiate science magazine

No abstract provided.


Learning To Learn: The Neuroscience Of Affective Learning, Emma Barnard Jan 2024

Learning To Learn: The Neuroscience Of Affective Learning, Emma Barnard

The Synapse: Intercollegiate science magazine

No abstract provided.


Materials And Methods In Squid Dissolution, Isabel Hardwig Jan 2024

Materials And Methods In Squid Dissolution, Isabel Hardwig

The Synapse: Intercollegiate science magazine

No abstract provided.


In The Shadow Of The Moon: Reflections On The April 2024 Total Solar, Danielle Oliver Jan 2024

In The Shadow Of The Moon: Reflections On The April 2024 Total Solar, Danielle Oliver

The Synapse: Intercollegiate science magazine

No abstract provided.


Issue 40 Jan 2024

Issue 40

The Synapse: Intercollegiate science magazine

No abstract provided.


Exploring The Neural Jungle: The Positive Effects Of Hallucinogens On The Brain, Izzie Braun Jan 2024

Exploring The Neural Jungle: The Positive Effects Of Hallucinogens On The Brain, Izzie Braun

The Synapse: Intercollegiate science magazine

No abstract provided.


The Synapse Crossword Jan 2024

The Synapse Crossword

The Synapse: Intercollegiate science magazine

No abstract provided.


Honors Research Jan 2024

Honors Research

The Synapse: Intercollegiate science magazine

No abstract provided.


Don’T Touch My Memories! Turning Fake Stories Into Real Memories, Zeynep Kaya Jan 2024

Don’T Touch My Memories! Turning Fake Stories Into Real Memories, Zeynep Kaya

The Synapse: Intercollegiate science magazine

No abstract provided.