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Articles 35101 - 35130 of 294391

Full-Text Articles in Physical Sciences and Mathematics

A Multi-Objective Grey Wolf Optimizer For Energy Planning Problem In Smart Home Using Renewable Energy Systems, Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar, Feras Al-Obeidat, Osama Ahmad Alomari, Ammar Kamal Abasi, Mohammad Tubishat, Zenab Elgamal, Waleed Alomoush Jan 2024

A Multi-Objective Grey Wolf Optimizer For Energy Planning Problem In Smart Home Using Renewable Energy Systems, Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar, Feras Al-Obeidat, Osama Ahmad Alomari, Ammar Kamal Abasi, Mohammad Tubishat, Zenab Elgamal, Waleed Alomoush

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This paper presents the energy planning problem (EPP) as an optimization problem to find the optimal schedules to minimize energy consumption costs and demand and enhance users’ comfort levels. The grey wolf optimizer (GWO), One of the most powerful optimization methods, is adjusted and adapted to address EPP optimally and achieve its objectives efficiently. The GWO is adapted due to its high performance in addressing NP-complex hard problems like the EPP, where it contains efficient and dynamic parameters that enhance its exploration and exploitation capabilities, particularly for large search spaces. In addition, new energy and real-world resources based on solar …

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Assessing The Impact Of Chatbot-Human Personality Congruence On User Behavior: A Chatbot-Based Advising System Case, Mohammad Amin Kuhail, Mohamed Bahja, Ons Al-Shamaileh, Justin Thomas, Amina Alkazemi, Joao Negreiros Jan 2024

Assessing The Impact Of Chatbot-Human Personality Congruence On User Behavior: A Chatbot-Based Advising System Case, Mohammad Amin Kuhail, Mohamed Bahja, Ons Al-Shamaileh, Justin Thomas, Amina Alkazemi, Joao Negreiros

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Chatbot personality has been demonstrated to influence user behavior, such as trust and intended engagement. However, previous research on chatbot-user personality congruence’s influence on user behavior is scant despite its significance in human-human conversations. This study explores the effect of chatbot-human personality trait congruence on user behavior in the context of a chatbot-based advising system. In this study, 54 college students interacted with chatbots with three different personalities (extraversion, agreeableness, and conscientiousness) and rated their trust, usage intention, and intended engagement with the chatbots. Additionally, 18 participants were interviewed to gain further insights into their perceptions and evaluations of the …

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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 …

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Generative Ai And Large Language Models: A New Frontier In Reverse Vaccinology, Kadhim Hayawi, Sakib Shahriar, Hany Alashwal, Mohamed Adel Serhani Jan 2024

Generative Ai And Large Language Models: A New Frontier In Reverse Vaccinology, Kadhim Hayawi, Sakib Shahriar, Hany Alashwal, Mohamed Adel Serhani

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Reverse vaccinology is an emerging concept in the field of vaccine development as it facilitates the identification of potential vaccine candidates. Biomedical research has been revolutionized with the recent innovations in Generative Artificial Intelligence (AI) and Large Language Models (LLMs). The intersection of these two technologies is explored in this study. In this study, the impact of Generative AI and LLMs in the field of vaccinology is explored. Through a comprehensive analysis of existing research, prospective use cases, and an experimental case study, this research highlights that LLMs and Generative AI have the potential to enhance the efficiency and accuracy …

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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) …

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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. …

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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 …

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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. …

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Trace And Rare Earth Elements Analysis Of Oligocene And Miocene Diamictites In The Cape Roberts Project, Ross Sea, Antarctica, Celina Flores Garza Jan 2024

Trace And Rare Earth Elements Analysis Of Oligocene And Miocene Diamictites In The Cape Roberts Project, Ross Sea, Antarctica, Celina Flores Garza

Theses, Dissertations and Culminating Projects

The West Antarctic Ice Sheet is a major contributor to global sea level rise, yet its origin and dynamics are poorly known. The geochemistry of 35 diamictite samples from the CRP-1 and CPR-2A cores recovered by the Cape Roberts Drilling Project in the Ross Sea, Antarctica is evaluated to understand glacial sedimentation and flow paths during the Oligocene and Miocene, a period of warmer than present climate in the past. The major hypothesis to be tested is if the early Miocene ice sheet advance was the first major West Antarctic ice advance in the Ross Sea. The provenance of older …

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Feature Matching Methods Comparison With Limited Computing Power, Xu Du Jan 2024

Feature Matching Methods Comparison With Limited Computing Power, Xu Du

Theses, Dissertations and Culminating Projects

This work presents a comparative analysis of feature-matching techniques implemented on low-end hardware, focusing on their efficiency and performance under various image transformations. The study evaluates several well-established feature matching algorithms, including ORB, AKAZE, BRISK, FAST combined with ORB, and SIFT, for their robustness against rotation, perspective, and scale changes in images. The base image used for experimentation features is the Montclair State University's Red Hawk mascot—a complex, textured subject that presents a substantial challenge for feature matching algorithms. The experiment simulates real-world conditions by applying a series of transformations to the base image and utilizes the default settings of …

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Deterministic And Stochastic Dynamics Of Marine Food Webs, Julian A. Hernandez S. Jan 2024

Deterministic And Stochastic Dynamics Of Marine Food Webs, Julian A. Hernandez S.

Theses, Dissertations and Culminating Projects

Ecologists have long been concerned with understanding the behavior and evolutionary patterns exhibited within complex ecological communities. Under- standing the delicate balance that sustains ecosystems is crucial in determining how these communities evolve over time. Recently, researchers have combined deterministic Lotka-Volterra dynamics with different types of synthetic food webs (cascade, niche and generalized cascade models), and have analyzed the mechanisms behind primary extinction events and the ensuing secondary extinction cascade. These studies also enabled the exploration of the complex interplay of species loss to explain how food web structure influences primary and secondary extinction. We have extended these ideas to …

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Infusing Commonsense Via Knowledge Bases In Multipurpose Robotic Task Organization, Rafael Omar Hidalgo Jan 2024

Infusing Commonsense Via Knowledge Bases In Multipurpose Robotic Task Organization, Rafael Omar Hidalgo

Theses, Dissertations and Culminating Projects

This research explores the innovative integration of commonsense knowledge (CSK) within AI systems, with a particular focus on domestic robotics. At the heart of this study is the Robo- CSK-Organizer, a groundbreaking system that utilizes a classical knowledge base, namely ConceptNet, to enhance robotic decision-making through sophisticated object organization and classification. This system is contrasted with a ChatGPT-based organizer, examining their performance in terms of ambiguity resolution, consistency in object placement, adaptability to task classifications, and crucially, in explainability, a key aspect of XAI (Explainable AI). Through a combination of controlled experiments, quantitative and qualitative analysis, the study demonstrates that …

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Discovery Of Dna Aptamers As Probes For Detecting Ranavirus Mcp, Corbin Hudson Jan 2024

Discovery Of Dna Aptamers As Probes For Detecting Ranavirus Mcp, Corbin Hudson

Theses, Dissertations and Culminating Projects

Global amphibian populations have been declining since the late 20th century; one of the most significant contributors to these declines is a genus of viral pathogens known as Ranavirus (Rv). Frog virus 3 is one of these viral agents and is currently found affecting frog populations throughout NJ, MD, PA, DE, and VA. While Frog virus 3 is most known for afflicting frogs, the Ranavirus genus can also infect bony fish species as well as reptiles, allowing for a wide vector of transmission and making its spread a danger both locally and globally. In the absence of a cure, it …

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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, …

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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 …

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Human Psychology And Robot Evidence In The Courtroom, Alternative Dispute Resolution, And Agency Proceedings, Sara Sun Beale, Hayley Lawrence Jan 2024

Human Psychology And Robot Evidence In The Courtroom, Alternative Dispute Resolution, And Agency Proceedings, Sara Sun Beale, Hayley Lawrence

Faculty Scholarship

In human–robot interactions in legal proceedings, human responses to robot-generated evidence will present unique challenges to the accuracy of litigation as well as ancillary goals such as fairness and transparency, though it may also enhance accuracy in other respects. The most important feature of human–robot interactions is the human tendency to anthropomorphize robots, which can generate misleading impressions and be manipulated by designing robots to make them appear more trustworthy and believable. Although robot-generated evidence may also offer unique advantages, there are concerns about the degree to which the traditional methods of testing the accuracy of evidence, particularly cross-examination, will …

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The Right To A Glass Box: Rethinking The Use Of Artificial Intelligence In Criminal Justice, Brandon L. Garrett, Cynthia Rudin Jan 2024

The Right To A Glass Box: Rethinking The Use Of Artificial Intelligence In Criminal Justice, Brandon L. Garrett, Cynthia Rudin

Faculty Scholarship

Artificial intelligence (“AI”) increasingly is used to make important decisions that affect individuals and society. As governments and corporations use AI more pervasively, one of the most troubling trends is that developers so often design it to be a “black box.” Designers create AI models too complex for people to understand or they conceal how AI functions. Policymakers and the public increasingly sound alarms about black box AI. A particularly pressing area of concern has been criminal cases, in which a person’s life, liberty, and public safety can be at stake. In the United States and globally, despite concerns that …

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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, …

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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.

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Examining Ghana's National Health Insurance Act, 2003 (Act 650) To Improve Accessibility Of Artificial Intelligence Therapies And Address Compensation Issues In Cases Of Medical Negligence, George Benneh Mensah, Maad M. Mijwil, Mostafa Abotaleb Jan 2024

Examining Ghana's National Health Insurance Act, 2003 (Act 650) To Improve Accessibility Of Artificial Intelligence Therapies And Address Compensation Issues In Cases Of Medical Negligence, George Benneh Mensah, Maad M. Mijwil, Mostafa Abotaleb

Mesopotamian Journal of Computer Science

Objective: Examine Ghana’s National Health Insurance Act (Act 650) to identify coverage gaps limiting artificial intelligence (AI) therapy access and address medical negligence liability issues surrounding automated healthcare systems. Methods: Legal and regulatory analysis of Act 650 were conducted, review of academic literature on global uptake of AI interventions and medical negligence principles were elucidated, examination of case studies implementing pilot AI therapy programs under insurance schemes were considered. Results & Conclusions: Act 650 lacks clear provisions for funding innovative AI treatments with proven efficacy and undefined negligence determination guidelines involving AI systems, contributing to accessibility and accountability issues. Proposed …

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New Recruitment Approach Based On Logistic Regression Model, Ishraq Hatif Abd Almajed, Ghalia Nassreddine, Joumana Younis Jan 2024

New Recruitment Approach Based On Logistic Regression Model, Ishraq Hatif Abd Almajed, Ghalia Nassreddine, Joumana Younis

Mesopotamian Journal of Computer Science

Artificial intelligence (AI) is a pivotal technological advancement developed by humans with the aim of enhancing the quality of human existence.  It signifies the capacity of a computerized machine resembling a robot to execute tasks typically performed by humans and replicate human behavior. Machine learning (ML), a subfield of AI, involves the construction of systems that acquire the ability to make predictions about new output values by leveraging existing data, without the need for human interaction. Currently, ML has been incorporated into various fields, including but not limited to medical diagnosis, image processing, prediction, classification, learning association, commerce, finance, and …

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Advanced Image Processing Techniques For Automated Detection Of Healthy And Infected Leaves In Agricultural Systems, E.D. Kanmani Ruby, G. Amirthayogam, G. Sasi, T. Chitra, Abhishek Choubey, S. Gopalakrishnan Jan 2024

Advanced Image Processing Techniques For Automated Detection Of Healthy And Infected Leaves In Agricultural Systems, E.D. Kanmani Ruby, G. Amirthayogam, G. Sasi, T. Chitra, Abhishek Choubey, S. Gopalakrishnan

Mesopotamian Journal of Computer Science

Advances in computer vision and machine learning have transformed leaf disease detection by enabling efficient and accurate identification of subtle disease signs in leaves. Leveraging high-resolution imaging, pattern recognition algorithms, and deep learning models, researchers and farmers can now conduct automated detection across various plant species. The development focuses on sophisticated image processing techniques applied to diverse datasets captured under controlled conditions, ensuring comprehensive coverage of lighting, time, and weather variations. Expert annotation of infection stages and types enhances dataset reliability, while pre-processing stages such as resizing and normalization optimize image consistency for robust model training. Data augmentation techniques enrich …

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A Survey On Artificial Intelligence In Cybersecurity For Smart Agriculture: State-Of-The-Art, Cyber Threats, Artificial Intelligence Applications, And Ethical Concerns, Guma Ali, Maad M. Mijwil, Bosco Apparatus Buruga, Mostafa Abotaleb, Ioannis Adamopoulos Jan 2024

A Survey On Artificial Intelligence In Cybersecurity For Smart Agriculture: State-Of-The-Art, Cyber Threats, Artificial Intelligence Applications, And Ethical Concerns, Guma Ali, Maad M. Mijwil, Bosco Apparatus Buruga, Mostafa Abotaleb, Ioannis Adamopoulos

Mesopotamian Journal of Computer Science

Wireless sensor networks and Internet of Things devices are revolutionizing the smart agriculture industry by increasing production, sustainability, and profitability as connectivity becomes increasingly ubiquitous. However, the industry has become a popular target for cyberattacks. This survey investigates the role of artificial intelligence (AI) in improving cybersecurity in smart agriculture (SA). The relevant literature for the study was gathered from Nature, Wiley Online Library, MDPI, ScienceDirect, Frontiers, IEEE Xplore Digital Library, IGI Global, Springer, Taylor & Francis, and Google Scholar. Of the 320 publications that fit the search criteria, 180 research papers were ultimately chosen for this investigation. The review …

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Segment Anything: A Review, Firas Hazzaa, Innocent Udoidiong, Akram Qashou, Sufian Yousef Jan 2024

Segment Anything: A Review, Firas Hazzaa, Innocent Udoidiong, Akram Qashou, Sufian Yousef

Mesopotamian Journal of Computer Science

Segment Anything (SA) is a state-of-the art method for universal object segmentation, which does not need task-specific training. Herein, we emphasize that SA can overcome the limitations of traditional segmentation frameworks based on requiring extensive manually annotated datasets and predefined architectures, as extensively documented in this review. SB supercharges performance and reduces cost by combining Mutual Information learning with an Efficient Transformer architecture, benefiting from a substantially larger pool of in-the-wild data. In this paper we review SA and its specific key innovations generality, resource boundedness, and scalability to large datasets. We also face obstacles such as data biases, computational …

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Credit Card Fraud Detection Based On Deep Learning Models, El-Sayed M. El-Kenawy, Ahmed Mohamed Zaki, Wei Hong Lim, Abdelhameed Ibrahim, Marwa M. Eid, Ahmed Osman Osman, Ahmed M. Elshewey Jan 2024

Credit Card Fraud Detection Based On Deep Learning Models, El-Sayed M. El-Kenawy, Ahmed Mohamed Zaki, Wei Hong Lim, Abdelhameed Ibrahim, Marwa M. Eid, Ahmed Osman Osman, Ahmed M. Elshewey

Mesopotamian Journal of Computer Science

Credit card fraud detection (FD) protects consumers and financial institutions by identifying suspicious or unauthorized transactions. To improve security and reduce false positives, fraud detection systems can analyze transaction data patterns in real time using advanced machine learning (ML) and deep learning (DL). This paper exploits DL models to detects transactional data which includes anomalies through Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) to verify data and mitigate fraud. The models used precision, recall, F1-score, and AUC on a balanced shared 559856-record Kaggle repository dataset. The RNN model detected anomalies with 99.39% accuracy, 0.9939 …

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An Optimized Model For Liver Disease Classification Based On Bpso Using Machine Learning Models, El-Sayed M. El-Kenawy, Nima Khodadadi, Abdelhameed Ibrahim, Marwa M. Eid, Ahmed M. Osman, Ahmed M. Elshewey Jan 2024

An Optimized Model For Liver Disease Classification Based On Bpso Using Machine Learning Models, El-Sayed M. El-Kenawy, Nima Khodadadi, Abdelhameed Ibrahim, Marwa M. Eid, Ahmed M. Osman, Ahmed M. Elshewey

Mesopotamian Journal of Computer Science

Liver disease (LD) is a world health concern that requires accurate diagnostic methods. This study proposes an optimized machine learning model (ML) based on BPSO for LD classification using a shared public dataset from kaggle Indicates to liver patients from India. The paper used six ML models such as Random Forest (RF), Support Vector Machine (SVM), Dummy Classifier (DC), Extra Trees Classifier (ET), K-Nearest Neighbors (KNN), and Logistic Regression (LT) to evaluate the performance. Through observations we detected that ET achieved an accuracy of 79.82%. The BPSO hyperparameter optimization optimized ET to enhance accuracy to reach 85%. The paper used …

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Kinetic Modelling During Soaking Of Dried Long Grain Paddy Mr297, Jhauharotul Muchlisyiyah, Rosnah Shamsudin, Roseliza Kadir Basha, Radhiah Shukri, Syahmeer How, Masmunira Rambli Jan 2024

Kinetic Modelling During Soaking Of Dried Long Grain Paddy Mr297, Jhauharotul Muchlisyiyah, Rosnah Shamsudin, Roseliza Kadir Basha, Radhiah Shukri, Syahmeer How, Masmunira Rambli

ASEAN Journal on Science and Technology for Development

This study aimed to assess the soaking behaviour of MR 297 dried paddy under two conditions: room temperature and hot water soaking at 60°C. The experiment involved soaking dried MR297 paddy samples in water for 0.5 to 8 hours at room temperature and 0.5 to 5 hours for soaking at 60°C. The water absorption of MR 297 behaved differently at two different soaking conditions. The kinetic behaviour of the soaking process was also simulated using established 25 predictive models and incorporating 5 novel models presented in this work. The comparison of all prediction models involved using three statistical measures: the …

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Word Prediction Using Dynamic Skip Connections Along With Arabert And Lstm In Arabic Language, Ahad Almalki, Faris Kateb, Rayan Mosli Jan 2024

Word Prediction Using Dynamic Skip Connections Along With Arabert And Lstm In Arabic Language, Ahad Almalki, Faris Kateb, Rayan Mosli

ASEAN Journal on Science and Technology for Development

Natural Language Generation (NLG) plays a crucial role in modern digital tools, including chatbots, virtual support, content suggestions, and tailored marketing, making bots more responsive and reducing the need for human staff. While there's much research on NLG for languages like English, languages like Arabic, Urdu, and Chinese still face challenges. This study examines Arabic NLG's unique aspects, dialects, and word variations. With around 420 million Arabic speakers globally, it's crucial to advance NLG for this language. We compared three models: Long Short-Term Memory (LSTM), a mix of Bidirectional Encoder Representations from Transformers (BERT) and LSTM, and a version that …

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Storm-Generated Molluscan Thanatocoenosis Along A Carbonate Paleoshoreline: Southern Eleuthera Island, The Bahamas, Ilya Val Buynevich, Michael Savarese, H. Allen Curran, Tara Ingalsbe Jan 2024

Storm-Generated Molluscan Thanatocoenosis Along A Carbonate Paleoshoreline: Southern Eleuthera Island, The Bahamas, Ilya Val Buynevich, Michael Savarese, H. Allen Curran, Tara Ingalsbe

Geosciences: Faculty Publications

Concentrations of large mollusc shells in coastal deposits provide important information about the local malacofauna and potential transport agents, including extreme events [1-4]. Such accumulations are common in the rock record [5,6], with Quaternary examples serving as good time-averaged examples by combining aspects of both the modern biocoenoses and the fossil record. Death assemblages of local organisms (thanatocoenosis) and their preserved record (taphocoenosis) in carbonate settings, where granulometric spectrum may be very limited (e.g., ooilitic sand), can serve as important paleo-environmental indicators, especially when considered in combination with primary sedimentary structures (in outcrops or geophysical images) and in situ biogenic …

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A New Method To Invert For Interseismic Deep Slip Along Closely Spaced Faults Using Surface Velocities And Subsurface Stressing-Rate Tensors, Hanna Elston, Michele Cooke, John Loveless, Scott Marshall Jan 2024

A New Method To Invert For Interseismic Deep Slip Along Closely Spaced Faults Using Surface Velocities And Subsurface Stressing-Rate Tensors, Hanna Elston, Michele Cooke, John Loveless, Scott Marshall

Geosciences: Faculty Publications

Inversions of interseismic geodetic surface velocities often cannot uniquely resolve the three-dimensional slip-rate distribution along closely spaced faults. Microseismic focal mechanisms reveal stress information at depth and may provide additional constraints for inversions that estimate slip rates. Here, we present a new inverse approach that utilizes both surface velocities and subsurface stressing-rate tensors to constrain interseismic slip rates and activity of closely spaced faults. We assess the ability of the inverse approach to recover slip rate distributions from stressing-rate tensors and surface velocities generated by two forward models: (a) a single strike-slip fault model and (b) a complex southern San …

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