检索结果-内蒙古大学图书馆

Integrated medical resource consumption stratification in hospitalized patients:an Auto Triage Management model based on accurate risk,cost and length of stay prediction

引用

science China(Life sciences) 2022年第5期65卷 988-999页

作者： Qin Zhong Zongren Li Wenjun Wang Lei Zhang Kunlun He Medical Big Data Research Center Chinese PLA General HospitalBeijing 100853China Medical Artificial Intelligence Research Center Chinese PLA General HospitalBeijing 100853China Bio-engineering Research Center Chinese PLA General HospitalBeijing 100039China Department of Cardiology Fourth Medical Center of Chinese PLA General HospitalBeijing 100048China Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine Chinese PLA General HospitalBeijing 100048China China-Australia Joint Research Center for Infectious Diseases School of Public HealthXi’an Jiaotong University Health Science CenterXi’an 710061China

Triage management plays important roles in hospitalized patients for disease severity stratification and medical burden *** progression risks have been extensively researched for numbers of diseases,other crucial indicators that reflect patients’economic and time costs have not been systematically *** address the problems,we developed an automatic deep learning based Auto Triage Management(ATM)Framework capable of accurately modelling patients’disease progression risk and health economic *** on them,we can first discover the relationship between disease progression and medical system cost,find potential features that can more precisely aid patient triage in resource allocation,and allow treatment plan searching that has cured *** ATM in COVID-19,we built a joint model to predict patients’risk,the total length of stay(Lo S)and cost when at-admission,and remaining Lo S and cost at a given hospitalized time point,with C-index0.930 and 0.869 for risk prediction,mean absolute error(MAE)of 5.61 and 5.90 days for total Lo S prediction in internal and external validation data.

关键词： patient triage AutoML electronic medical records

来源：评论

学校读者我要写书评

暂无评论

Development of Recommender Systems for Better Services and Products using data science

Development of Recommender Systems for Better Services and P...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： S. Padmapriya Thamizhamuthu R S. Jagadeesh D. M. Kalai Selvi M. Ahsan Shariff Department of Artificial Intelligence and Data Science Velammal Institute of Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus-Chengalpattu Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sridevi Women’s Engineering College Hyderabad Telangana India Department of Computer Science and Engineering R.M.D. Engineering College Chennai Tamil Nadu India M. Tech - Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

Online social networking and e-commerce are becoming increasingly popular. Recommender Systems (RS) let users find relevant information from several possibilities. Internet applications currently need RS. This technology uses huge data to provide customized suggestions to improve customer happiness. Concerns and ideas help customers choose items. Sentiment Analysis (SA) may increase RS recommendation accuracy by improving user behaviour, views, and responses. RS solves information overload in information retrieval, but data sparsity remains a big problem. SA is notable for reading text and expressing user preferences. It helps E-Commerce to monitor product feedback and to understand what client wants and their preferences. This research presents a hybrid recommendation approach to increase RS accuracy and correctness. The hybrid approach beats standard models in several assessment criteria. Modern retailing businesses’ e-commerce operations are impossible without RSs. The content-based and context-aware techniques are hybridized for providing promising results. Content-based approaches connect consumers to new things based on prior ratings and activities. Create user profiles and classify it. Knowledge-based algorithms propose customized items with minimal use history. These systems use case-based recommendations or limitations to make recommendations. Finally, ensemble recommender systems combine data source prediction power.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Approach to Predict the Autism Spectrum Disorders using Deep Learning Mechanism

A Comprehensive Approach to Predict the Autism Spectrum Diso...

引用

International Conference on Smart Structures and Systems (ICSSS)

作者： Gayathri S Deepa A K.B. Aruna P. Vijayaraghavan V. Samuthira Pandi K. Pavithra Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu India Department of Computer Science and Engineering Jeppiaar Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering S.A.Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamil Nadu India

Children often suffer from Autism Spectrum Disorder (ASD), a neurological disease. With stereotypical behavior and difficulties in social communication, ASD cannot be defined by chronological age alone. It is speculated that this condition results from malfunction in the brain caused by the interplay of several ecological, inherited and regulatory variables. More than one percent of the world's population has ASD, prompting policymakers, educators, and health care professionals to develop plans to combat the condition. Maximizing the potential of patients with autism demands a multidisciplinary approach. Therefore, it is crucial that various care models complement one another and do not cause the patient any unnecessary anxiety. Natural therapies have the potential to significantly enhance the long-term success of conventional treatments including psychotherapy, Occupational-Therapy (OT) and PT. According to the progression of symptoms, ASD can take on a number of different forms. However, there is still no agreed-upon gold standard therapy for ASD. If a systematic method is adopted, assessing a kid with suspected ASD is straightforward. There are substantial gains in interpersonal interaction, replication, as well as intellectual and adaptive performance following initial treatment. In this paper, a novel deep learning based prediction approach is designed called as Intelligent Learning Scheme to Predict Autism (ILSPA). This algorithm is cross-validated with the conventional learning approach called Deep Neural Network (DNN) to evaluate the performance and efficiency of the proposed scheme. The resulting scenario provides the proper proof to the efficiency of the proposed algorithm called ILSPA.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Early Classification of Lung Cancer Based on Cell Morphology Features 1st

Early Classification of Lung Cancer Based on Cell Morphology...

引用

1st International Conference on Advanced Network Technologies and Computational intelligence, ICANTCI 2024

作者： Byeon, Haewon Soni, Mukesh Deb, Nabamita Rivera, Richard Sharma, Nilesh Vijay Kaushal, Chetna Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of Dr. D. Y. Patil School of Science and Technology Tathawade Pune India Department of Information Technology Gauhati University Guwahati India Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador Artificial Intelligence and Data Science SNJB′s Late Sau. K. B. Jain College of Engineering Nashik Chandwad India Institute of Engineering and Technology Chitkara University Chitkara University Punjab India

ISBN: (纸本)9783031860683

In response to the challenges of limited annotated lung cancer pathology images with complex cell morphology, we propose a lung cancer pathology image classification method based on contrastive learning of cell morphology features. This approach integrates highly reliable unlabelled data into the training data through contrastive learning to address the issue of insufficient labelled data. Building upon the foundation of k-nearest neighbour contrastive learning, we introduce farthest and nearest neighbour contrastive learning, simultaneously using the farthest and nearest neighbour images for contrastive learning. This enhances the performance of contrastive learning by increasing the difficulty of positive sample learning and diversifying the dataset. We employ a ResNet50 encoder with deformable convolutions and dynamic convolutions to improve the extraction of cell morphology features. Experimental results demonstrate that, especially when labelled data is scarce, our classification method effectively leverages cell feature information from both labelled and unlabelled cancer pathology images, resulting in superior classification performance compared to existing methods. When applied to 80%, 60%, and 40% labeled data, respectively, CCCM outperforms other approaches with superior performance metrics, reaching F1 Score of 0.9584, 0.9488, and 0.9386. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Energy Efficient Clustering for Equating the Load in Wireless Sensor Network

Energy Efficient Clustering for Equating the Load in Wireles...

引用

Augmented intelligence and Sustainable Systems (ICAISS), International Conference on

作者： D. K. Karthika P. D. Selvam R. Hemalatha A. Sathiya Department of Information Technology Sri Sai Ranganathan Engineering College Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Chennai Tamil Nadu India Department of Electronics and Communication Engineering Shree Sathyam College of Engineering and Technology Salem Tamil Nadu India Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India

Wireless Sensor Network (WSN) contains numerous sensor nodes and energy management is a major significant problem in WSNs. To solve this issue, Energy Efficient Clustering for equating the Load (EECL) is proposed. The objective of the EECL strategy is to improve the Quality of Service and enhance energy efficiency. The sleep and awake scheduling is employed to minimize the unwanted sensing the energy utilization. The multi-level clustering strategy is discussed in this work provides single-hop communication inside the cluster utilizing link correlation and a Time Division Multiple Access (TDMA) time slot. By using network coding, Cluster Head (CH) multi-hop communication is effectively managed. Furthermore, Queue Priority Indicator (QPI) as well as Geographic Priority Indicator (GPI) are also utilized to choose the optimal forwarder node as well as discover the competent route. The QPI is applied for equating the load as well as energy utilization. The EECL approach has been simulated and examined by applying the network simulator-3, and the outcomes have improved the effectiveness of the CH round and node lifetime.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Natural Language Processing in Virtual Assistants Current Approaches and Challenges

Natural Language Processing in Virtual Assistants Current Ap...

引用

science, technology, engineering and Management (ICSTEM), International Conference on

作者： Gautam Solaimalai J. Maria Shanthi Sampath Kumar S Priyanka Khabiya K. Geetha Gokul Talele US. Bank Software Engineering Manager Georgia United States of America Department of Artificial Intelligence and Machine Learning J B. Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science Engineering Mandsaur University Mandsaur Madhya Pradesh India Department of Computer Science G. T.N Arts College Dindigul Tamil Nadu India Department of Data Science Organization name Accenture Pune Maharashtra India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The proliferation of virtual sidekicks in colorful disciplines has prodded a swell of interest in advancing Natural Language Processing(NLP) ways to enhance their effectiveness. This paper provides a comprehensive review of the current approaches and challenges encountered in integrating NLP into virtual sidekicks. It begins by outlining the foundational generalities of NLP and its vital part in enabling mortal- suchlike relations with virtual sidekicks. latterly, it delves into a discussion of the different methodologies employed in NLP for understanding and generating mortal language, ranging from rule-grounded systems to deep literacy models. likewise, the paper highlights the crucial challenges such as nebulosity resolution, environment understanding, and handling different verbal variations that stymie the flawless functioning of virtual sidekicks. By synthesizing perceptivity from recent exploration trials, this paper offers precious perspectives on the state-of-the-art NLP ways and directions for unborn exploration to overcome the challenges and propel the development of further intelligent and intuitive virtual sidekicks.

关键词： Reviews Virtual assistants Propulsion Natural language processing Distance measurement

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Impact of Special Characters on Password Security: a Comparative Analysis

引用

IEEE Network 2025年

作者： He, Daojing Liu, Zhiyong Chan, Sammy Zhou, Beibei Guizani, Mohsen Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Guizhou University State Key Laboratory of Public Big Data Guizhou China Research and Development Center Guangzhou Jingyuan Security Technology Co. Ltd Guangzhou China City University of Hong Kong Kowloon Department of Electrical Engineering Hong Kong Hong Kong Shanghai Urban Construction Vocational College School of Artificial Intelligence Application Shanghai China Machine Learning Department Abu Dhabi United Arab Emirates

Text-based password authentication remains essential for identity verification on internet platforms and blockchain systems. However, as computational power advances, security threats to user passwords have intensified, raising concerns about their effectiveness. While special characters (i.e., non-alphanumeric symbols, which include punctuation marks, mathematical symbols, and other non-letter/number characters like '.', '@', '#', '$', and similar) are often recommended to enhance password strength, the National Institute of Standards and technology (NIST) prioritizes length over complexity, challenging their necessity. This study fills a critical gap in understanding user behavior regarding special character usage. By analyzing 15 datasets from Chinese, English, and German languages, we examined patterns of special character incorporation and their impact on password strength. Our findings indicate that users frequently employ predictable methods for including special characters, potentially compromising security, especially under stringent policies. We present three key contributions: (1) an analysis of special character usage patterns, (2) a quantitative assessment of strength differences using the PCFGv4.1 model, and (3) actionable recommendations for stakeholders to enhance password security practices. This research further advocates for aligning password policies with user behavior for more effective security. © 1986-2012 IEEE.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

A 1D Plug-and-Play Synthetic data Deep Learning For Undersampled Magnetic Resonance Image Reconstruction

A 1D Plug-and-Play Synthetic Data Deep Learning For Undersam...

引用

IEEE International Conference on Image Processing

作者： Min Xiao Zi Wang Jiefeng Guo Di Guo Xiaobo Qu Institute of Artificial Intelligence Xiamen University Xiamen China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen China Department of Microelectronics and Integrated Circuit Xiamen University Xiamen China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Magnetic resonance imaging (MRI) plays a pivotal role in modern medical diagnosis yet is often hindered by the long imaging time. MRI imaging can be accelerated through undersampling, but the introduced aliasing artifacts should be removed during image reconstruction. While deep learning reconstruction methods excel at image de-aliasing, they may yield suboptimal results when training sampling settings differ from those at the time of reconstruction. To decouple from specific sampling settings, we propose using synthetic data to generate a substantial training dataset and pre-train a 1D deep denoiser. We then integrate the trained deep denoiser into the iterative reconstruction process as a replacement for the approximation operator within the deep plug-and-play framework. In vivo results indicate that the proposed method exhibits robust and visually appealing image reconstruction when there is a mismatch between the training and reconstruction undersampling settings, such as different undersampling patterns and sampling rates.

关键词： Training Deep learning In vivo Magnetic resonance imaging Magnetic resonance Training data Trajectory

来源：评论

学校读者我要写书评

暂无评论

Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicles

SSRN

引用

SSRN 2022年

作者： Chebil, Khalil Htiouech, Skander Khemakhem, Mahdi Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Data Engineering and Semantics Research Unit Faculty of Sciences of Sfax University of Sfax Sfax Tunisia Department of Mathematics and Business Intelligence College of Electronics and Telecommunications Engineering of Sfax University of Sfax Sfax Tunisia University of Carthage Tunisia

The crowd management and analysis (CMA) systems gained a lot of interest by the vulgarization of unmanned aerial vehicles (UAVs) use. Crowd tracking using UAVs is among the most important services provided by a CMA. In this paper, we consider the periodic crowd tracking (PCT) problem. It consists to follow up crowds, with UAVs, during the life-cycle of an open crowded area (OCA). Two criteria are considered for this purpose. The first is related to the CMA initial investment, and the second is to guarantee quality of service (QoS). The existing works are either focused on a very specified hypotheses or highly committed to CMAs applications context. Motivated by a real-world applications, but with a consideration of a high level of abstraction, we outline a new binary linear programming (BLP) model to optimally solve the PCT. To be closest to many real-world contexts, we carefully define and study a set of parameters related to the OCA characteristics, behaviors and the CMA initial infrastructure investment (e.g. UAVs, Charging Stations (CSs)). In order to periodically update the UAVs/crowds and UAVs/CSs assignments, we integrate the proposed BLP into a linear algorithm called PCTs *** main goal in this research is to study the PCT problem from both theoretical and numerical viewpoints. To investigate the PCTs solver effectiveness, we generate a diversified set of PCTs instances with different scenarios for simulation purpose. The empirical analysis of the results brought us to validate the BLP model and the PCTs solver, then to release a set of new challenges for future research directions. © 2022, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Rice quality Prediction using Convolution Neural Network

Rice quality Prediction using Convolution Neural Network

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： V. Rekha R. M. Shiny J. Ramya B. Sakthivel V. G. Pratheep Ashok Kumar Department of Bachelors's in Computer Application AgurchandManmull Jain College Chennai India Department of Artificial Intelligence and Data Science Velammal Institute of Technology Thiruvallur India Department of Computer Science Engineering St. Joseph's College of Engineering Chennai India Department of Electronics and Communication Engineering Vivekanandha College of Engineering for Women Tamakkal India Department of Mechatronics Engineering Kongu Engineering College Perundurai India Department of Computer Science Banasthali Vidyapith Banasthali India

Rice is the most widely grown crop in Asia. In Asia, 60% of the population relies on rice, China, Indonesia especially India consumes the majority of it. Food is the single most direct way for an individual to become infected with any ailment. Predicting the quality of rice is a crucial aspect as it is one of the most consumed foods in Asia. In this study, the quality of the rice is predicted using a Deep Learning (DL) model to predict the quality more precisely. The dataset required for this study is collected from the Kaggle website composed of a total of 5000 images of 5 different rice varieties. The collected raw dataset is then pre-processed using several methods to make them compatible with the DL model. The pre-proceed images are then subjected to the prediction of the quality of the rice using the Convolutional Neural Network (CNN), a DL model. Performance metrics such as accuracy, loss, and time are calculated to validate the precision of the model. The experimental result showed that the model has the highest accuracy of about 98.6% when compared to the other existing models. The model demonstrated a very minimal loss percentage of only about 1.24% and the model predicts the result in about 25 microseconds. The proposed DL model can be utilized for better and more objectively evaluate the quality of the rice at trading points inside the rice marketing chain due to its fairly high precision.

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：