检索结果-内蒙古大学图书馆

1st International Visualization, Informatics and Technology conference, IVIT 2022

作者： Chuahan, Ritu Gola, Nidhi Yafi, Eiad Farez, Megat Prasad, Mukesh Amity University Center for Computational Biology Ad Bioinformatics UP India Universiti Kuala Lumpur Malaysian Institute of Information Technology Malaysia Amity University Center for Medical Biotechnology UP Noida India University of Technology Sydney Faculty of Engineering and Information Technology Australia

ISBN: (纸本)9781665486484

The worldwide populace keeps on developing at a consistent speed, and more individuals are moving to urban communities. This led to the generation of idea of smart cities which eventually builds the sustainable environment around the world by advancing technologies which can implacably applied to understand and control various processes of the city which are subjected on water, air and energy. In current study of approach, the focus relies specifically on atmospheric pollutants which arise due to industries, factories, mining, and the combustion of fossil fuels. These activities release air pollutants that are harmful to all living things including Sulphur dioxide, nitrogen dioxide, carbon monoxide, ozone, and various others air pollutants. Additionally, it is a major risk factor for several health conditions, including bronchitis, lung cancer, heart problems, throat and eye disorders, asthma, skin infections, and respiratory system ailments. The aim of the current study was to conduct discrete factor analysis to analyze the factors which are responsible for degradation of the air quality. The proposed study is carried out in two phases, with the first phase measured the variation in the AQI (Air Quality Index) value of different smart cities of India for years 2015-2020, whereas in second phase we analyze the contribution of different gases such as NO2, NO, benzene, toluene, xylene, O3, CO, SO2, NOx towards the AQI value. © 2022 ieee.

关键词： Air quality

来源：评论

学校读者我要写书评

暂无评论

Colored Network Motif Analysis by Dynamic Programming Approach: An Application in Host Pathogen Interaction Network

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第2期18卷 550-561页

作者： Biswas, Sourav Ray, Sumanta Bandyopadhyay, Sanghamitra Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India Aliah Univ Comp Sci Dept Kolkata 700160 India

Network motifs are subgraphs of a network which are found with significantly higher frequency than that expected in similar random networks. Motifs are small building blocks of a network and they have emerged as a way to uncover topological properties of complex networks. A special yet not much explored type of motif is the 'colored motif' where color (type) of each node, and hence the edges, in the motif is distinguishable from each other. A traditional motif is defined as a recurring structure in a network, whereas colored motif introduces detailed information about the color of the nodes. G-trie is a data structure to efficiently store a given set of subgraphs by exploiting the topological overlaps within them. In this article we have implemented a modified g-trie to store colored subgraphs and developed a method to discover colored motifs. Our method uses an approximate enumeration for counting the subgraphs to reduce the runtime. We have applied our method to find colored motifs of size three in a host pathogen protein-protein interaction network having two types of proteins namely HIV-1 and human proteins, and four types of edges. Here, we have discovered eight motifs, six of which contain both HIV-1 and human proteins, while the remaining two contain only human proteins.

关键词： Image color analysis Proteins Data structures Color Pathogens Complex networks Network motif colored network motif G-trie G-table network analysis host pathogen interaction network modified G-trie

来源：评论

学校读者我要写书评

暂无评论

Lesion Segmentation in Ultrasound Using Semi-Pixel-Wise Cycle Generative Adversarial Nets

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2555-2565页

作者： Xing, Jie Li, Zheren Wang, Biyuan Qi, Yuji Yu, Bingbin Zanjani, Farhad Ghazvinian Zheng, Aiwen Duits, Remco Tan, Tao Zhejiang Canc Hosp Hangzhou 310022 Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai 200240 Peoples R China Tokyo Inst Technol Dept Comp Tokyo 1528550 Japan Yale Univ Dept Biomed Engn New Haven CT 06520 USA German Res Ctr Artificial Intelligence Robot Innovat Ctr Bremen Germany Eindhoven Univ Technol Dept Elect Engn NL-5612 AZ Eindhoven Netherlands Eindhoven Univ Technol Dept Math & Comp Sci NL-5612 AZ Eindhoven Netherlands

Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the breast cancer. The area and the shape information of a lesion is very helpful for clinicians to make diagnostic decisions. In this study we propose a new deep-learning scheme, semi-pixel-wise cycle generative adversarial net (SPCGAN) for segmenting the lesion in 2D ultrasound. The method takes the advantage of a fully convolutional neural network (FCN) and a generative adversarial net to segment a lesion by using prior knowledge. We compared the proposed method to a fully connected neural network and the level set segmentation method on a test dataset consisting of 32 malignant lesions and 109 benign lesions. Our proposed method achieved a Dice similarity coefficient (DSC) of 0.92 while FCN and the level set achieved 0.90 and 0.79 respectively. Particularly, for malignant lesions, our method increases the DSC (0.90) of the fully connected neural network to 0.93 significantly (p<0.001). The results show that our SPCGAN can obtain robust segmentation results. The framework of SPCGAN is particularly effective when sufficient training samples are not available compared to FCN. Our proposed method may be used to relieve the radiologists' burden for annotation.

关键词： Lesion segmentation deep learning generative adversarial networks breast cancer ultrasound image analysis

来源：评论

学校读者我要写书评

暂无评论

CCFS: A Confidence-Based Cost-Effective Feature Selection Scheme for Healthcare Data Classification

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第3期18卷 902-911页

作者： Chen, Yiyuan Wang, Yufeng Cao, Liang Jin, Qun Nanjing Univ Posts & Telecommun Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Engn Res Ctr Hlth Serv Syst Based Ubiquitous Wire Jiangsu Key Lab Wireless Commun Nanjing 210003 Jiangsu Peoples R China Alibaba Grp Hangzhou 310052 Zhejiang Peoples R China Waseda Univ Networked Informat Syst Lab Dept Human Informat & Cognit Sci Fac Human Sci Saitama 3591192 Japan

Feature selection (FS) is one of the fundamental data processing techniques in various machine learning algorithms, especially for classification of healthcare data. However, it is a challenging issue due to the large search space. Binary Particle Swarm Optimization (BPSO) is an efficient evolutionary computation technique, and has been widely used in FS. In this paper, we proposed a Confidence-based and Cost-effective feature selection (CCFS) method using BPSO to improve the performance of healthcare data classification. Specifically, first, CCFS improves search effectiveness by developing a new updating mechanism that designs the feature confidence to explicitly take into account the fine-grained impact of each dimension in the particle on the classification performance. The feature confidence is composed of two measurements: the correlation between feature and categories, and historically selected frequency of each feature. Second, considering the fact that the acquisition costs of different features are naturally different, especially for medical data, and should be fully taken into account in practical applications, besides the classification performance, the feature cost and the feature reduction ratio are comprehensively incorporated into the design of fitness function. The proposed method has been verified in various UCI public datasets and compared with various benchmark schemes. The thoroughly experimental results show the effectiveness of the proposed method, in terms of accuracy and feature selection cost.

关键词： Feature extraction Particle swarm optimization Medical services Classification algorithms Genetic algorithms Error analysis Machine learning algorithms Data classification feature selection binary particle swarm optimization swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

CNN-RNN Based Intelligent Recommendation for Online Medical Pre-Diagnosis Support

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第3期18卷 912-921页

作者： Zhou, Xiaokang Li, Yue Liang, Wei Shiga Univ Fac Data Sci Hikone Shiga 5228522 Japan RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Hunan Univ Technol & Business Key Lab Hunan Prov New Retail Virtual Real Techno Changsha 410083 Peoples R China Cent South Univ Business Sch Changsha 410083 Peoples R China

The rapidly developed Health 2.0 technology has provided people with more opportunities to conduct online medical consultation than ever before. Understanding contexts within different online medical communications and activities becomes a significant issue to facilitate patients' medical decision making process. As a subcategory of machine learning, neural networks have drawn increasing attentions in natural language processing applications. In this article, we focus on modeling and analyzing the patient-physician-generated data based on an integrated CNN-RNN framework, in order to deal with the situation that patients' online inquiries are usually not very long. A so-called DP-CRNN algorithm is developed with a newly designed neural network structure, to extract and highlight the combination of semantic and sequential features in terms of patient's inquiries. An intelligent recommendation method is then proposed to provide patients with automatic clinic guidance and pre-diagnosis suggestions, in which a clustering mechanism is utilized to refine the learning process with more precise diagnosis scope and more representative features. Experiments based on the collected real world data demonstrate the effectiveness of our proposed model and method for intelligent pre-diagnosis service in online medical environments.

关键词： Medical diagnostic imaging Recurrent neural networks Feature extraction Convolutional neural networks Machine learning Task analysis Convolutional neural network recurrent neural network natural language processing online medical diagnosis online medical inquiry intelligent recommendation

来源：评论

学校读者我要写书评

暂无评论

Mental Health Diagnostics Using Machine Learning: A Clustering and Predictive Modeling Approach

Mental Health Diagnostics Using Machine Learning: A Clusteri...

引用

International conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Ritu Chauhan Aarushi Mishra Mehak Jena Eiad Yafi Megat F. Zuhairi Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP India Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

The application of machine learning has rapidly advanced as a transformative approach in healthcare, offering significant potential in the analysis of huge amounts of acquired data on Mental health across the world enhancing the treatment of mental health issues. Machine learning algorithms are being utilized to analyse patterns and trends in mental health datasets. This study investigates various machine learning methodologies and descriptive analytics techniques to detect patterns and trends in mental health-related factors, focusing on predicting the likelihood of individuals seeking mental health treatment. We utilized clustering, association rule mining, feature selection, and machine learning classifiers such as Random Forest, Decision Tree, XGBoost, and Logistic Regression. By applying these techniques to a dataset of mental health factors, we identified key predictors influencing mental health outcomes and developed models to enhance predictive accuracy. Our analysis highlighted the effectiveness of machine learning algorithms in extracting meaningful patterns from the dataset, with Random Forest and XGBoost emerging as particularly strong performers with accuracies of 81% and 84%. Other applied algorithms that are Decision Tree, Gradient Boosting and logistic regression have the accuracy of 81%, 77%, and 73% respectively.

关键词： Logistic regression Machine learning algorithms Accuracy Data analysis Mental health Predictive models Market research Decision trees Public healthcare Random forests

来源：评论

学校读者我要写书评

暂无评论

A Context-Guided Attention Method for Integrating Features of Histopathological Patches

A Context-Guided Attention Method for Integrating Features o...

引用

ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Yuqi Chen Juan Liu Peng Jiang Jing Feng Dehua Cao Baochuan Pang Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China AI Center for Pathololgical Diagnosis Landing Intelligence Med. Co. Ltd Wuhan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Lots of researchers have studied for classifying histopathological whole slide images (WSIs). Since a WSI is too large to be processed directly, researchers usually cut it into many small-sized patches and then integrate the discriminative features extracted from the patches to obtain a slide-level feature of the WSI. The integration strategy generating the slide-level features is crucial for the WSI classification model. Lots of attention-based methods have been proposed for such purpose. However, most attention-based methods do not take the patches relationship into consideration, which affects the classification performance of the models. In this work, we propose a novel Context-Guided attention (CGattention) method to integrate the patch-level features, which constructs a context vector to simulate the global context information of the whole WSI and implicitly characterizes the relationship between patches in the WSI. When evaluated on two publicly available datasets, the CGattention based model obtained the better performance than other attention-based models.

关键词： Representation learning Image analysis Histopathology computational modeling Lung cancer Feature extraction computational efficiency

来源：评论

学校读者我要写书评

暂无评论

Diagnosis Neuro-Oncology Diseases via Artificial intelligence and Multi-Sequence MRI Methods

Diagnosis Neuro-Oncology Diseases via Artificial Intelligenc...

引用

Cognitive Sciences, Genomics and bioinformatics (CSGB)

作者： Andrey Letyagin Evgeniya Amelina Bair Tuchinov Vladimir Groza Mikhail Amelin Sergey Golushko Evgeniy Pavlovskiy Branch of IC&G SB RAS Research Institute of Clinical and Experimental Lymphology Novosibirsk Russia SDAML lab. Novosibirsk State University Novosibirsk Russia Radiodiagnosis dept. FSBI “Federal Neurosurgical Center” Novosibirsk Russia Novosibirsk State University Novosibirsk Russia

The study of brain tumor structure and its type-dependent variations is one of the most important research areas in which medical imaging techniques are used. The structural and statistical analysis of these lesions raises various related problems and projects, such as the detection of the neuro oncology diseases, the shape and the segmentation of specific sub-regions (i.e. necrotic part, (non-)enhanced part, edema), the classification of the tumor occurrence and the subsequent treatment up-prognosis. Almost all of these problems are usually solved numerically, particularly with the tendency to use methods related to artificial intelligence (AI), often including deep learning (DL) networks. One of the most complicated, least researched and challenging tasks in this field is the classification of tumor types. This difficulty can be explained by several reasons, the most important of which is the severe limitation of existing open-source datasets that contain clinically confirmed tumor type designations based on radiological examination protocols. Magnetic resonance imaging (MRI) is the most common method for screening, primary detection and non-invasive diagnosis of brain diseases, as well as a source of recommendations for further treatment and observation. In this paper, we extend the previous research works on the robust multi-sequences segmentation and classification methods which allows to consider all available information from MRI scans by the composition of TI, TIC, T2 and T2-FLAIR sequences. It is based on the clinical radiology hypothesis and presents an efficient approach to combining and matching 3D methods to search for areas of comprised the GD-enhancing tumor in order to significantly improve the model's performance of the particular applied numerical problem of brain tumor classification and metastasis segmentation. All investigations performed and results presented are based on the private Siberian brain tumor dataset, including labeled volumetric MR

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prediction of Autism Spectrum Disorder Using AI and Machine Learning

Prediction of Autism Spectrum Disorder Using AI and Machine ...

引用

International conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Ritu Chauhan Khushi Mehta Yafi Eiad Megat Farez Zuhairi Artificial Intelligence and IoT lab Center for Computational Biology and Bioinformatics Amity University UP India Amity Institute of Biotechnology Amity University UP India Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia Malaysian Institute of Information Technology University Kuala Lumpur Kuala Lumpur Malaysia

Knowing the likelihood for a generational shift in the scientific exploration of digital data which has extensively outgrown society with tons of data. Moreover, the scientific community has evolved and outgrown themselves with Artificial intelligence based technology to compute power and detect hidden features and information from outgrown digital databases. These advanced systems have the capability to evaluate enormous amounts of data generated with unprecedent speed, which can detect valuable insights from the data. In addition, the current study of approach focuses on the application of AI with healthcare databases to detect the hidden information from autism spectrum disorder (ASD). In this research we have various algorithmic predictions for Autism Spectrum Disorder (ASD) by combining Machine Learning with the dataset provided. The proposed approach is based on the predictive method where the data preprocessing and data evaluation models are used. The study of approach focuses on ASD with several machine Learning algorithms such as, SVM, KNN, Random Forest, Decision Tree to measure the accuracy and precision among the data and determine the hidden information and patterns. The current research scenario investigated concurrent parameters which are dependable features such as Class\ASD Traits, Sex, Jaundice scores and Q chat- 10 questions by which we have come to a conclusion which corroborated the current analysis with an accuracy Score of 74% for Random Forest, KNN: 71%, SVM: 70.6% and Decision Tree: 69.1% stating whether the individual has the Autism characteristics visible or not using various parametric analysis through data visualization and analyzing results by this approach.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Correspondence Analysis in Pediatric Health by Using Variational Mixture Models

Probabilistic Correspondence Analysis in Pediatric Health by...

引用

Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： Hernan F. Garcia Gloria Liliana Porras-Hurtado Augusto E. Salazar-Jimenez Eliana Vasquez-Osorio Mauricio A. Álvarez Sistemic Research Group University of Antioquia Medellín COL Salud Comfamiliar Caja de Compensación Familiar de Risaralda Pereira COL Cancer Research Centre University of Manchester Manchester UK Centre for AI Fundamentals University of Manchester Manchester UK

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Analyzing brain structures in medical imaging poses challenging problems due to heterogeneity in pediatric diseases. Besides, measuring brain changes quantitatively is crucial to evaluating clinical outcomes related to anatomical factors. From an artificial intelligence perspective, establishing correspondences between nonrigid shapes often requires computing similarity-based measures that, in most cases, are unavailable. This paper proposes an unsupervised probabilistic framework for shape matching on brain structures by using variational unsupervised learning. Our approach comprehensively captures the underlying representation of surface descriptors related to brain structures. Then, we learned group-wise latent space representations of these descriptors using a variational derivation of the Gaussian process latent variable model, which matches the resulting features to establish unsupervised correspondences. The experimental results show how the model captures non-linearities in nonrigid brain structures in real-world neurodevelopmental data. These results demonstrated that the proposed model is suitable for monitoring anatomical changes in healthy and abnormal brain shapes.

关键词： Shape computational modeling Shape measurement Gaussian processes Brain modeling Probabilistic logic Reliability Surface treatment Unsupervised learning Biomedical imaging

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：