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Lumbar Spine Vertebral Compression Fracture Case Diagnosis Using Machine Learning Methods on CT images 4

Lumbar Spine Vertebral Compression Fracture Case Diagnosis U...

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4th International Conference on Pattern Recognition and Image Analysis, IPRIA 2019

作者： Yousefi, Hamid Salehi, Elham Sheyjani, Omid Sojoodi Ghanaatti, Hossein Dept. Faculty of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Dept. Medical Imaging Center Imam Khomeini University Hospital Tehran Iran

ISBN: (纸本)9781728116211

Fracture with partial collapse of vertebral bodies is generally known as vertebral compression fracture (VCF) which is vary considerably in types and causes and usually results from severity of trauma condition such as osteoporosis. Due to the medical error and the need to have sufficient experience to diagnose this disease, many researchers are thinking of using intelligent diagnostic methods by modeling the knowledge of radiologists using machine learning algorithms. The location and the severity of abnormalities on the vertebral body and its height are determined by the radiologist. In this auto-diagnostic system, we use morphometric features and measure three parts of vertebral body to diagnosis the vertebral compression and the location of the anomaly by segmentation and finding vertebral edges. We use support vector machine and k nearest neighbor as classifier and gained 88.3% accuracy and 92.5% sensitivity in diagnosis vertebral compression fracture and also achieved an accurate diagnosis of 86.2% to detect an abnormal location on 25 CT scans clinical images from the spinal column. Finally, we also compared our method with some other studies by implementing them on our used dataset. © 2019 IEEE.

关键词： Support vector machines

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A New Model Based on Graph Neural Network Feedback for Crystal Property Prediction 5

A New Model Based on Graph Neural Network Feedback for Cryst...

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5th International Conference on Big Data and Artificial Intelligence and Software engineering, ICBASE 2024

作者： Zhu, Jian Ren, Yongshen Zang, Huaijuan Zhan, Shu Hefei University of Technology School of Computer and Information Engineering Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Kunming University of Science and Technology Faculty of Metallurgical and Energy Engineering Kunming China Kunming University of Science and Technology National Engineering Research Center of Vacuum Metallurgy Kunming China

ISBN: (纸本)9798331506612

The prediction of crystal properties is crucial in crystal design. Currently, most methods employ graph neural networks to model crystal structures and have achieved satisfactory prediction accuracy. However, accurately simulating the complex relationships between atoms and molecules in crystal structures remains challenging. The prediction performance of crystal graph networks also depends on the effective extraction of crystal features, typically requiring a close correlation between the properties to be predicted and the extracted features. In this study, a model retraining method is utilized, where the loss function in the retraining phase is constructed using feedback from a pre-trained crystal graph network model to adjust model parameters and enhance the accuracy of predicting crystal properties. This method enhances the predictive performance of multiple crystal graph neural networks and accelerates the development of high-performance crystals without the need to add various features or incorporate additional neural network layers. The research findings demonstrate the significant effectiveness of the proposed method in enhancing the prediction performance of four models: CGCNN, OGCNN, GATGNN, and ALIGNN. Optimization metrics support this claim. © 2024 IEEE.

关键词： Prediction models

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Comparison analysis of video game purchase factors between Japanese and American consumers

Comparison analysis of video game purchase factors between J...

引用

Lecture Notes in computer Science

作者： Kitami, Kodai Saga, Ryosuke Matsumoto, Kazunori Kanagawa Institute of Technology Graduate School of Engineering 1030 Shimo-ogino Atsugi Kanagawa 243-0292 Japan Kanagawa Institute of Technology Faculty of Information and Computer Science 1030 Shimo-ogino Atsugi Kanagawa 243-0292 Japan

ISBN: (纸本)9783642238536

This paper describes a comparison analysis of video game purchase factors between Japanese and American consumers. The Japanese game industry's growth has been sluggish recently, and Japanese video game publishers have been focusing on foreign markets. To develop a best-seller, the publishers must understand consumers' psychological purchase factors such as ideas and preference. However, traditional studies have not analyzed consumers' latent psychological purchase factors. Therefore, we extract efficient information for developing best-selling video games for domestic and foreign consumers by comparing purchase factor models of Japanese and American consumers. In this experiment, we construct six purchase factor models of Japanese and American consumers by using a creativity technique, and we analyze the purchase factor differences between consumer groups. The results indicate that Japanese consumers tend to pay attention to publisher brand, whereas American consumers attach importance to game platform and expansion of games. © 2011 Springer-Verlag.

关键词： Human computer interaction

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Estimating the Distribution of Oral Presentation Skills in an Educational Institution: A Novel Methodology 15

Estimating the Distribution of Oral Presentation Skills in a...

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15th International Conference on computer Supported Education, CSEDU 2023

作者： Domínguez, Federico Eras, Leonardo Tomalá, Josué Collaguazo, Adriana Information Technology Center Escuela Superior Politécnica Del Litoral ESPOL Guayaquil Ecuador Faculty of Electrical and Computer Engineering Escuela Superior Politécnica Del Litoral ESPOL Guayaquil Ecuador

ISBN: (纸本)9789897586415

Mastering oral presentation skills is of paramount importance for new graduates as they navigate the competitive job market of the 21st century. Consequently, procuring the effective development of these skills in students is an essential task for higher education institutions (HEIs). We developed a technological solution that facilitates oral presentation skills learning by providing automatic and immediate feedback using machine learning algorithms on audiovisual recordings of oral presentations. We have been using this tool to record novice students’ presentations since 2017 and, by using the resulting data corpus, developed a methodology to accurately detect and evaluate posture and gaze in oral presentations. This article presents this methodology and its application on more than 3,000 recordings from more than 2,000 different students across all study programs at our university. Preliminary results provide a glimpse of the prevalence and distribution of oral presentation skills across several demographic variables. Statistically significant patterns point to possible oral communication deficiencies in engineering programs at our HEI, highlighting the potential of our methodology to serve as a diagnostic tool for communication skills learning strategies. Copyright © 2023 by SCITEPRESS – Science and technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Students

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Models for dependable heterogenous multi- and many-core system software design revisited 29th

Models for dependable heterogenous multi- and many-core syst...

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29th International Conference on Architecture of Computing Systems, ARCS 2016

作者： Wägemann, Tobias Langer, Tobias Mottok, Jürgen Osinski, Lukas Stappert, Friedhelm Kolagari, Ramin Tavakoli Department of Computer Science Technische Hochschule Nürnberg Georg Simon Ohm Nuremberg90489 Germany Faculty of Electrical Engineering and Information Technology Ostbayerische Technische Hochschule Regensburg Regensburg93053 Germany

ISBN: (纸本)9783800741571

—Modelling approaches have to satisfy certain criteria in order to sufficiently encompass the characteristics of dependable heterogenous multi- and many-core system architectures. This work-in-progress paper gives an overview of modern modelling approaches and their related research projects, particularly those regarding domain specific architecture description languages, as well as of the specific challenges of dependable systems and heterogenous multi- and many-core designs, i.e. scheduling techniques for real-time requirements and concerns regarding functional safety. Furthermore, an ongoing research effort in order to identify a set of criteria for evaluating the eligibility of modelling approaches for the task of adequately representing these systems and their specific characteristics is presented. © VDE VERLAG GMBH, Berlin, Offenbach.

关键词： Software design

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American Sign Language Recognition Based on Machine Learning and Neural Network 2

American Sign Language Recognition Based on Machine Learning...

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2nd International Conference on Machine Learning and Intelligent Systems engineering, MLISE 2022

作者： Li, Lanxi Liu, Da Shen, Chenlin Sun, Jing The Ohio State University Computer Science And Engineering Columbus43201 United States Sichuan University Department Of Computer Science Chengdu610207 China Jilin University Faculty Of Software Engineering Changchun130012 China Institute Of Information And Software Engineering University Of Electronic Science And Technology Of China Chengdu610066 China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

Numerous disabilities such as deaf and mute are suffered from not being capable of communicating with normal people, it is necessary to find a way to solve this problem. A feasible method is Sign Language Recognition (SLR) which is a sort of pattern recognition technique. In this paper, machine learning and deep learning methods are applied to recognize and classify American Sign Language (ASL), and only 24 English letters are classified because letter J and Z require fingers to move. First, Principal Component Analysis (PCA) and manifold algorithms are used to do dimension reduction to accelerate the training of machine learning and visualize it. Second, various machine learning methods such as Random Forest Classification (RFC), K-Nearest Neighbor (KNN), Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD) are applied to classify the pattern. Since the SVM algorithm has several hyperparameters, this study uses the Grid Search method to find the best combination of hyperparameter which lead to predicting more accurately. It is found that different dimensionality reduction algorithms have unequal effects on the accuracy of each prediction model, and it can be concluded that the manifold algorithm is the best dimension reduction algorithm only for KNN but not for other prediction models, and PCA is much more feasible than KNN applied in such machine learning algorithms except KNN. Two deep learning algorithms such as Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) are also used in classification and their accuracy is highest among such algorithms mentioned above. © 2022 IEEE.

关键词： Principal component analysis

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Distributed Sensing and Intelligent Systems 1

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丛书名： Studies in Distributed Intelligence

1000年

作者： Mohamed Elhoseny Xiaohui Yuan Salah-ddine Krit

ISBN: (数字)9783030642587

ISBN: (纸本)9783030642570;9783030642600

This book is the proceeding of the 1st International Conference on Distributed Sensing and Intelligent Systems (ICDSIS2020) which will be held in The National School of Applied Sciences of Agadir, Ibn Zohr University, Agadir, Morocco on February 01-03, 2020. ICDSIS2020 is co-organized by computer Vision and Intelligent Systems Lab, University of North Texas, USA as a scientific collaboration event with The National School of Applied Sciences of Agadir, Ibn Zohr University. ICDSIS2020 aims to foster students, researchers, academicians and industry persons in the field of computer and information Science, Intelligent Systems, and Electronics and Communication engineering in general. The volume collects contributions from leading experts around the globe with the latest insights on emerging topics, and includes reviews, surveys, and research chapters covering all aspects of distributed sensing and intelligent systems. The volume is divided into 5 key sections: Distributed Sensing Applications; Intelligent Systems; Advanced theories and algorithms in machine learning and data mining; Artificial intelligence and optimization, and application to Internet of Things (IoT); and Cybersecurity and Secure Distributed Systems.;This conference proceeding is an academic book which can be read by students, analysts, policymakers, and regulators interested in Distributed Sensing, Smart Network approaches, Smart Cities, IoT Applications, and Intelligent Applications. It is written in plain and easy language, and describes new concepts when they appear first so that a reader without prior background of the field finds it readable. The book is primarily intended for research students in sensor networks and IoT applications (including intelligent information systems, and smart sensors applications), academics in higher education institutions including universities and vocational colleges, policy makers and legislators.

关键词： Remote Sensing/Photogrammetry Data Mining and Knowledge Discovery Cyber-physical systems, IoT Machine Learning Computational Intelligence

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Comorbidity Diseases Diagnosis Using Machine Learning Methods and Chi-Square Feature Selection Technique 16

Comorbidity Diseases Diagnosis Using Machine Learning Method...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Mukhlif, Dheyauldeen M. Abd, Dhafar Hamed Ejbali, Ridha Alimi, Adel M. Sfax Tunisia University of Anbar College of Computer Science and Information Technology Ramadi Iraq Gabes Tunisia Sfax Tunisia University of Johannesburg Faculty of Engineering and the Built Environment Department of Electrical and Electronic Engineering Science Johannesburg South Africa

ISBN: (纸本)9798350381344

The diagnosis of common diseases, in which people suffer from several bad health conditions, is a complex medical challenge. This study investigated the use of machine learning methods combined with the Chi-square feature selection technique to improve the accuracy and efficiency of comorbidity diseases diagnosis. Using various decision trees of machine learning algorithms, random forest, Gradient Boost, AdaBoost, Bagging, and extra trees, this research aimed to improve the identification and prediction of comorbid conditions and ultimately advance early detection and treatment strategies. Using the selection of Chi-Square features helped give priority to the most relevant attributes, reduce noise, and refine the models. The results showed that both the AdaBoost and Gradient Boost algorithms obtained an accuracy rate of 91.33%, confirming their efficacy in comorbidity diseases diagnosis. However, it is essential to ensure their reliability and efficacy in the real clinical environment, including the practical implementation and validation of these methods, to improve patient care and medical decisions. © 2023 IEEE.

关键词： Adaptive boosting

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Assessment of profitability of latvian commercial banks using OLS evaluation method 19

Assessment of profitability of latvian commercial banks usin...

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19th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2015

作者： Erina, Jana Erins, Ingars Faculty of Engineering Economics and Management Riga Technical University 6 Kanciema Str. RigaLV-1048 Latvia Faculty of Computer Science and Information Technology Institute of Applied Computer Systems Riga Technical University 1/4 Meža Str. RigaLV-1048 Latvia

ISBN: (纸本)9781941763292

The aim of the present article is to assess the Latvian commercial banks profitability using OLS evaluation method. In order to achieve the results of the present research, the qualitative overview of the scientific literature on Ordinary Least Squares (OLS) method has been performed. To gain the required data, the authors of the present article have analyzed the work of Latvian commercial banks and the branches/agencies of the foreign banks, as well as the work of the credit institutions registered in the member states of the European Economic Area (EEA) and their affiliations operating in Latvia in the reporting period of 2006-2012. The authors have concluded that HI, H4, H5 and H6 hypotheses have been completely proven;however, H2 and H3 hypotheses have been rejected as a result of the Pearson correlation since they demonstrated very weak correlation, as well as due to a correlation between bank indicators, which is also confirmed by the results of studies conducted by other scientists.

关键词： Profitability

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Data Augmentation and Feature Fusion for Melanoma Detection with Content Based Image Classification 4th

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4th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2019

作者： Das, Rik De, Sourav Bhattacharyya, Siddhartha Platos, Jan Snasel, Vaclav Hassanien, Aboul Ella Department of Information Technology Xavier Institute of Social Service RanchiJharkhand India Department of Computer Science and Engineering Cooch Behar Government Engineering College Cooch Behar India Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava Ostrava Czech Republic Cairo University Giza Egypt

ISBN: (纸本)9783030141172

computer aided diagnosis has leveraged a new horizon for accurate diagnosis of numerous fatal diseases. Melanoma is considered as one of the most lethal form of skin cancer which is increasingly affecting the population in recent times. The disease can be completely healed if diagnosed and addressed at an early stage. However, in most of the cases patients receive delayed care which results in fatal consequences. The authors have attempted to design an automated melanoma detection system in this work by means of content based image classification. Extraction of content based descriptors can nullify the requirement for manual annotation of the dermoscopic images which consumes considerable time and effort. The work has also undertaken a fusion based approach for feature combination for evaluating classification performances of hybrid architecture. The results have outclassed the state-of-the-art outcomes and have established significant performance improvement. © 2020, Springer Nature Switzerland AG.

关键词： computer aided diagnosis

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