检索结果-内蒙古大学图书馆

2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Hasan, Mahadi Islam, Jahirul Ahmed, Minhaz Hasan, Md Maruf The University of Tennessee at Chattanooga Ms in Data Analytics ChattanoogaTN United States New Mexico Institute of Mining and Technology Ms in Cse SocorroNM United States Ms in Cse *** United States The University of South Dakota Department of Comuter Science VermillionSD United States

ISBN: (纸本)9798350327472

Colon cancer is a matter of great importance in the field of global health, as it stands as one of the primary contributors to mortality rates associated with cancer. The timely identification and precise prognostication of colon cancer have the potential to significantly enhance patient outcomes and diminish mortality rates. The global public health systems face a significant challenge in addressing the impact of colon cancer, which underscores the need for efficient screening and prediction techniques. Machine learning techniques present potential solutions by utilizing extensive datasets to detect intricate patterns that may be imperceptible to human observers. This study investigates the viability of employing machine learning techniques to predict colon cancer by utilizing a dataset consisting of 10,000 colonoscopy images that have been classified into two categories: cancerous and non-cancerous cases. The main aim of this study is to assess the efficacy of various machine learning algorithms, namely DenseNet-121, CNN, and ResNet50, in the prediction of colon cancer. The models' performance was comprehensively assessed in terms of their accuracy in predicting colon cancer. Among the models that were evaluated, DenseNet-121 demonstrated exceptional performance, achieving the highest accuracy of 99.6%. © 2023 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Does parental migration affect children’s health in rural India? A fresh look at retrospective data using LASSO Regression Method

引用

SN Social Sciences 2025年第1期5卷 1-24页

作者： Chakraborty, Monalisha Mukherjee, Subrata Department of Data Sciences and Analytics School of Social Sciences MS Ramaiah University of Applied Sciences Gnanagangothri Campus New BEL road MSR Nagar Bangalore 560054 India Institute of Development Studies Kolkata 27/D DD Block Sector I Salt Lake Kolkata 700 064 India

The paper analyses India Human Development Survey-II (2011–12) data to discern the effect of parental out-migration on nutritional health status and morbidity among rural children of 0–14 years age. Adaptive LASSO logistic regression, a machine learning technique, was used to select the predictors, and state cluster fixed effect model was used to find the effect of select variables on children’s nutritional health status and morbidity. Results show that although parental migration lowers underweight among accompanied children of returned migrant parents, left-behind children of current migrant parents are found more likely to be underweight than their peers. Also, accompanied children of returned migrant parents are more likely to be affected by diarrhoea than their peers living in the countryside. ARI is found lower among the children left behind by returned migrant parents as well as by current migrant parents. However, children of remittance-recipient households are found to have performed well in terms of stunting among them. Multi-prolonged strategies are required towards mitigating the negative effects of parental migration on - the children. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Child health Morbidity Nutritional status Parental migration Rural India

来源：评论

学校读者我要写书评

暂无评论

The application and comparison of Deep Learning models for the prediction of chest cancer prognosis

The application and comparison of Deep Learning models for t...

引用

2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Hasan, Mahadi Al Mamun, Miraz Das, Madhab Chandra Hasan, Md Maruf Islam, Asm Mohaimenul The University of Tennessee at Chattanooga Chattanooga Ms in Data Analytics Tennessee United States Christus Health Health Outcomes & Analytics IrvingTX United States University of Information Technology and Sciences Electrical and Electronic Engineering Dhaka Bangladesh University of South Dakota Department of Computer Science VermillionSD United States

ISBN: (纸本)9798350302523

Lung cancers of all varieties, esophageal cancers, and cancers of the mediastinum (the area between the lungs), pleura (the membrane lining the chest cavity and surrounding the lungs), trachea, thymus gland, and heart are all classified as chest cancers, often known as thoracic cancers. Chest cancer can also spread from cancers that start in other places of the body. Chest pain is one of the usual signs of chest cancer, including hemoptysis or a cough that produces blood. Also, Coughing that hurts or a cough that does not go away is a sign of chest cancer. Mesothelioma, a cancer that begins in the lining of the chest or abdomen, frequently affects the lungs and other thoracic organs and tissues, which has prompted us to continue with this disease so that this research will aid in early detection. Chest X-rays and computed tomography (CT) pictures are the two diagnostic techniques that are most frequently utilized for these disorders. This study suggests a multiclassification deep learning model for detecting chest cancer using a dataset of chest CT-Scan pictures. While a chest CT scan is helpful even before symptoms show up and precisely detects the aberrant features that are found in images, a chest X-ray is less effective in the early stages of the ***, employing these kinds of photos will improve classification precision. To the best of our knowledge, no deep learning model in the literature can choose between these disorders. The current work considers the effectiveness of three architectures - CNN, ResNet50, and DenseNet121 -. A thorough assessment of various deep learning architectures is performed using publicly available digital CT datasets with four classifications (Adenocarcinoma, Large Cell Carcinoma, Squamous Cell Carcinoma, and Normal). The study's findings revealed that the DenseNet121 model performs better than the three other suggested models. CNN demonstrated 56.19% accuracy, whereas ResNet50 demonstrated 56.51% accuracy. The DenseNe

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

A Harmful Disorder: Predictive and Comparative Analysis for fetal Anemia Disease by Using Different Machine Learning Approaches 11

A Harmful Disorder: Predictive and Comparative Analysis for ...

引用

11th International Symposium on Digital Forensics and Security, ISDFS 2023

作者： Hasan, Mahadi Tahosin, mst. Sazia Farjana, Afia Sheakh, Md. Alif Hasan, Md Maruf The University of Tennessee Ms in Data Analytics Chattanooga United States Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh University of South Dakota Department of Computer Science VermillionSD United States

ISBN: (纸本)9798350336986

Anemia is a major issue for public health with significant implications for national development, it remains a largely neglected health problem in many developing countries. Iron deficiency is responsible for at least 50% of all cases of anemia and kills nearly 1 million people each year. Africa and Southeast Asia account for three quarters of these deaths. Surprisingly, one of the top ten risk factors that contributes to the global burden of disease is iron deficiency anemia (IDA). This study investigated the use of machine learning models to predict anemia. The study compared the performance of five different machine learning models: K-Nearest Neighbors, Logistic Regression, Support Vector Machines, Gaussian Naive Bayes, and Light Gradient Boosting Machines. These models are combined using a voting classifier method to improve prediction accuracy. The study highlights the importance of accurately predicting diseases in the medical field. The ability to predict anemia at the right time is essential for effective prevention and treatment. This study demonstrates the potential of machine learning models to predict anemia and improve disease prevention and treatment. Using advanced algorithms and data processing techniques can help doctors make accurate predictions and make decisions, leading to better patient outcomes. This research result shows that the voting classifier achieved 99.95% accuracy. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Team UTSA-NLP at SemEval 2024 Task 5: Prompt Ensembling for Argument Reasoning in Civil Procedures with GPT4

arXiv

引用

arXiv 2024年

作者： Schumacher, Dan Rios, Anthony MS Data Analytics College of Business Department of Information Systems and Cyber Security The University of Texas at San Antonio United States

In this paper, we present our system for the SemEval Task 5, The Legal Argument Reasoning Task in Civil Procedure Challenge. Legal argument reasoning is an essential skill that all law students must master. Moreover, it is important to develop natural language processing solutions that can reason about a question given terse domain-specific contextual information. Our system explores a prompt-based solution using GPT4 to reason over legal arguments. We also evaluate an ensemble of prompting strategies, including chain-of-thought reasoning and in-context learning. Overall, our system results in a Macro F1 of .8095 on the validation dataset and .7315 (5th out of 21 teams) on the final test set. Code for this project is available at https://***/danschumac1/ CivilPromptReasoningGPT4. © 2024, CC BY.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Residual minimization formulations for model reduction of steady hypersonic flow

Residual minimization formulations for model reduction of st...

引用

AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2023

作者： Van Heyningen, R. Loek Ching, David S. Blonigan, Patrick J. Parish, Eric J. Rizzi, Francesco Massachusetts Institute of Technology Department of Aeronautics and Astronautics CambridgeMA02139 United States Sandia National Laboratories Thermal/Fluid Science and Engineering MS 9042 LivermoreCA94550 United States Sandia National Laboratories Extreme-Scale Data Science and Analytics MS 9060 LivermoreCA94550 United States Sandia National Laboratories LivermoreCA94550 United States NexGen Analytics SheridanWY82801 United States

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

Computational simulations of high-speed flow play an important role in the design of hypersonic vehicles, for which experimental data are scarce;however, high-fidelity simulations of hypersonic flow are computationally expensive. Reduced order models (ROms) have the potential to make many-query problems, such as design optimization and uncertainty quantification, tractable for this domain. Residual minimization-based ROms, which formulate the projection onto a reduced basis as an optimization problem, are one promising candidate for model reduction of large-scale fluid problems. This work analyzes whether specific choices of norms and objective functions can improve the performance of ROms of hypersonic flow. Specifically, we investigate the use of dimensionally consistent inner products and modifications designed for convective problems, including 1 minimization and constrained optimization statements to enforce conservation laws. Particular attention is paid to accuracy for problems with strong shocks, which are common in hypersonic flow and challenging for projection-based ROms. We demonstrate that these modifications can improve the predictability and efficiency of a ROM, though the impact of such formulations depends on the quantity of interest and problem considered. © 2023, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Accuracy of Chatbots in Financial Literature

arXiv

引用

arXiv 2024年

作者： Erdem, Orhan Hassett, Kristi Egriboyun, Feyzullah University of North Texas Advanced Data Analytics Department 1155 Union Circle #310830 DentonTX76203-5017 United States University of North Texas MS in Advanced Data Analytics Candidate Advanced Data Analytics Department 1155 Union Circle #310830 DentonTX76203-5017 United States Hult International Business School Hult House East 35 Commercial Rd LondonE1 1LD United Kingdom

We evaluate the reliability of two chatbots, ChatGPT (4o and o1-preview versions), and Gemini Advanced, in providing references on financial literature and employing novel methodologies. Alongside the conventional binary approach commonly used in the literature, we developed a nonbinary approach and a recency measure to assess how hallucination rates vary with how recent a topic is. After analyzing 150 citations, ChatGPT-4o had a hallucination rate of 20.0% (95% CI, 13.6%-26.4%), while the o1-preview had a hallucination rate of 21.3% (95% CI, 14.8%-27.9%). In contrast, Gemini Advanced exhibited higher hallucination rates: 76.7% (95% CI, 69.9%-83.4%). While hallucination rates increased for more recent topics, this trend was not statistically significant for Gemini Advanced. These findings emphasize the importance of verifying chatbot-provided references, particularly in rapidly evolving fields. © 2024, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prediction of Colon Cancer using DenseNet121, CNN, and REsNET50 Machine Learning Models and using Image Processing Techniques

Prediction of Colon Cancer using DenseNet121, CNN, and REsNE...

引用

Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Mahadi Hasan Jahirul Islam Minhaz Ahmed Md Maruf Hasan MS in Data Analytics The University of Tennessee at Chattanooga Chattanooga Tennessee USA MS in CSE New Mexico Institute of Mining and Technology Socorro NM MS in CSE Fulfillment Associate_1 (Operations and Supply Chain) Department of Comuter Science The University of South Dakota Vermillion SD USA

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

关键词： Accuracy Image processing Atmospheric modeling Mortality Colorectal cancer Machine learning Predictive models Public healthcare Robots Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

A Harmful Disorder: Predictive and Comparative Analysis for fetal Anemia Disease by Using Different Machine Learning Approaches

A Harmful Disorder: Predictive and Comparative Analysis for ...

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Mahadi Hasan mst. Sazia Tahosin Afia Farjana Md. Alif Sheakh Md Maruf Hasan MS in Data Analytics The University of Tennessee Chattanooga USA Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science University of South Dakota Vermillion SD USA

关键词： Support vector machines Machine learning algorithms Digital forensics Predictive models Developing countries Prediction algorithms Iron

来源：评论

学校读者我要写书评

暂无评论

The application and comparison of Deep Learning models for the prediction of chest cancer prognosis

The application and comparison of Deep Learning models for t...

引用

International Conference on Smart Communications and Networking (SmartNets)

作者： Mahadi Hasan Miraz Al Mamun Madhab Chandra Das Md Maruf Hasan Asm Mohaimenul Islam MS in Data Analytics The University of Tennessee at Chattanooga Chattanooga Tennessee United States Health Outcomes & Analytics CHRISTUS Health Irving Texas USA Electrical and Electronic Engineering University of Information Technology and Sciences Dhaka Bangladesh Department of Computer Science University of South Dakota Vermillion SD USA Department of Computer Science University of South Dakota South Dakota USA

Lung cancers of all varieties, esophageal cancers, and cancers of the mediastinum (the area between the lungs), pleura (the membrane lining the chest cavity and surrounding the lungs), trachea, thymus gland, and heart are all classified as chest cancers, often known as thoracic cancers. Chest cancer can also spread from cancers that start in other places of the body. Chest pain is one of the usual signs of chest cancer, including hemoptysis or a cough that produces blood. Also, Coughing that hurts or a cough that does not go away is a sign of chest cancer. Mesothelioma, a cancer that begins in the lining of the chest or abdomen, frequently affects the lungs and other thoracic organs and tissues, which has prompted us to continue with this disease so that this research will aid in early detection. Chest X-rays and computed tomography (CT) pictures are the two diagnostic techniques that are most frequently utilized for these disorders. This study suggests a multiclassification deep learning model for detecting chest cancer using a dataset of chest CT-Scan pictures. While a chest CT scan is helpful even before symptoms show up and precisely detects the aberrant features that are found in images, a chest X-ray is less effective in the early stages of the ***, employing these kinds of photos will improve classification precision. To the best of our knowledge, no deep learning model in the literature can choose between these disorders. The current work considers the effectiveness of three architectures— CNN, ResNet50, and DenseNet121—. A thorough assessment of various deep learning architectures is performed using publicly available digital CT datasets with four classifications (Adenocarcinoma, Large Cell Carcinoma, Squamous Cell Carcinoma, and Normal). The study’s findings revealed that the DenseNet121 model performs better than the three other suggested models. CNN demonstrated 56.19% accuracy, whereas ResNet50 demonstrated 56.51% accuracy. The DenseNet1

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：