检索结果-内蒙古大学图书馆

5th International Conference on Sustainable Communication Networks and application, ICSCNA 2024

作者： Verma, Shanti Modi, Hetal Desai, Jaynesh H Rami, Khyati Lokmanya Institute of Management and Computer Application Gujarat Technological University Gujarat Ahmedabad India Bhagwan Mahavir College of Computer Application Bhagwan Mahavir University Gujarat Surat India Vidyadeep University Gujarat Surat India SAL Institute of Technology & Engineering Research Department of Computer Science Gujarat Ahmedabad India

ISBN: (纸本)9798331530013

Thyroid disease has recently adopted the related classification which has been worthy in the contemporary system of healthcare delivery because of the advances in diagnostics. This work proposes an automated diagnostic system for thyroid disease using machine learning approach to improve the diagnostic accuracy of the disease. The presented model also uses the features selection and classifiers like SVM and Random Forest for the classification of different thyroid disorders including hypothyroidism, hyperthyroidism, and euthyroidism. Missing values in the dataset are treated to provide actual worth in the results while imbalance is also dealt with. The process involves feeding its input a well-constructed data set after which it is checked for its performance through a validation process. The performance of the model is evaluated based on thyroid disease related parameters and the findings show a better performance compared to conventional diagnosis techniques. In this study, the authors also present their future work on how to possibly incorporate this machine learning framework into clinical practice although the study shows how this framework will help the healthcare workers to make better decisions in relation to thyroid diseases. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A Multimodal Deep Learning Approach for Advancing Liver Disease Diagnosis and Prognosis Prediction 2

A Multimodal Deep Learning Approach for Advancing Liver Dise...

引用

2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Kumar, Mukesh Shelke, Nitin Arvind Singh, Jagendra Sharma, Kamal Nain Sharma, Rajat Kumar, Rakesh The Technological Institute of Textile & Sciences Electronics and Communication Engineering Bhiwani India Bennett University School of Computer Science Engineering & Technology Greater Noida India Lovely Professional University School of Computer Application Jalandhar India G D Goenka University Department of Computer Science and Engineering - SOES Gurugram India Magadh University Department of Computer Science and I.T. Bodh Gaya India

ISBN: (纸本)9798350368109

Using a multimodal deep learning framework, we have demonstrated the possibility to predict liver disease subtyping and prognosis with high accuracy. The framework includes over 1600 patients, for which medical imaging and clinical data have been collected. Convolutional Neural Networks (CNNs), Recurrent Neural Network (RNNs), and Long short-term memory (LSTMs) have been combined in the work to extract features from the input data and demonstrate correlation between input and output and model complexity. Such an approach allowed analyzing images and predicting liver diseases based on structural abnormality analysis with the use of CNNs. RNNs and LSTMs, in their turn, allowed combining features for multimodal data source analysis and sequential clinical data analysis providing information about patient history. The obtained results were accurate for each feature extraction and modeling, with the best performance demonstrated by CNN with RNN where the accuracy was 97.8% and another CNN with LSTM where the accuracy was around 94.5%. The model proved to be feasible and accurate in the context of its application to hepatology since multiple data sources were combined in the work. This approach allows accounting for all modes of data source and offers a comprehensive assessment including disease diagnosis and prognosis. The obtained results can be also applied to clinical practice where doctors can add data about their patients to receive more information on diagnosis or prognosis for better decision-making. Finally, the idea to use a multilayered feature from multimodal data sources can be transferable to other types of sequential and structural data and applied to other diseases for their diagnosis and prognosis. However, it is essential to test the framework in other settings to refine it or find limitations to its application. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

PROMO: PROactive MObility-support model for task scheduling in fog computing

引用

International Journal of computers and applications 2022年第11期44卷 1092-1101页

作者： Kaur, Navjeet Kumar, Ashok Kumar, Rajesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Application Lovely Professional University Phagwara India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

In fog computing, closer proximity of fog devices to the end-user provides several benefits, such as reduced End-to-End (E2E) delay and better Quality of Service (QoS). Although, fog computing gives promising benefits of uninterrupted services to mobile users. The critical challenge is to decide when and where to re-schedule tasks according to the user's mobility. This paper targets this issue and proposes a Proactive Mobility-support (PROMO) model for re-scheduling tasks to mitigate the issues that influence QoS in mobility. The PROMO model is implemented and validated on MobFogSim, which significantly improves delay with 37% and around 17% downtime during task re-scheduling. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Research on central arterial pressure estimation algorithm based on clustering 2022

Research on central arterial pressure estimation algorithm b...

引用

2nd International Conference on Bioinformatics and Intelligent Computing, BIC 2022

作者： Zhang, Jiahao Ge, Hui Computer Application China Aerospace Academy of Systems Science and Engineering Beijing China

ISBN: (纸本)9781450395755

Central arterial pressure (CAP) plays an important role in the detection and diagnosis of cardiovascular diseases. Although the traditional universal transfer function method has good accuracy in measuring central arterial pressure, it does not take into account the characteristic information contained in pulse wave shape, and the accuracy needs to be further ***, a clustering based estimation algorithm for central arterial pressure is proposed. First of all, 5 layer Symlets wavelet and hard-soft compromised threshold was used to remove high-frequency noise of the data set. Secondly, in order to extract the characteristics of pulse waves themselves, k-means++ clustering was carried out for pulse waves in the training set. In order to reduce the over-fitting phenomenon, the initial number of cluster centers is selected as 1000. Finally, in each cluster, discrete Fourier transform is performed on pulse wave and central artery wave to obtain amplitude and phase data and train the transfer function. The test set was used for verification. Firstly, the corresponding clustering category of pulse wave was calculated, and then CAP was calculated by transfer function. The results showed that the absolute errors of systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP) were 2.50±2.22mmHg, 4.47±2.72mmHg and 4.60±3.73mmHg respectively. Compared with other algorithms, our method has better accuracy. © 2022 ACM.

关键词： Transfer functions

来源：评论

学校读者我要写书评

暂无评论

Oddity Based Botnet Recognition Utilizing AI Model in the Internet of Things Framework 4

Oddity Based Botnet Recognition Utilizing AI Model in the In...

引用

4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Rani, Rupa Chakraborty, Sudeshna Singh, Pooja Varshney, Neha Abes Institute of Technology Computer Science & Engineering U.P Ghaziabad India Banasthali Vidyapith Department of Computer Science & Engineering Rajasthan India School of Computer Application and Technology Galgotias University UP India Abes Institute of Technology Department of Computer Science & Engineering U.P Ghaziabad India

ISBN: (纸本)9798350393248

Advanced hooligans are grabbing the opportunity to mull over related contraptions with their IoT adware. The majority of digital attacks target these devices due to the rapid evolution of IoT advancements. With the diversity of modern advancements such as Industry 4.0, IoT, and Modern IoT, the linked gadgets are gradually expanding. The malware is zeroing in on frail devices. In this particular circumstance, we suggested an abnormality-based simulated intelligence model that can capably recognize the threatening practices in the association using change hyper-limit Light GBM computation. The proposed framework can be reached to identify new sorts of impending assaults and also, accomplish a lightweight nature. With the most recent Bot-IoT dataset, our model is prepared. Furthermore, by utilizing the Destroyed technique, the model similarly monitored the dataset's lopsidedness condition. With only one helping cycle, our suggested model demonstrates 99.99% accuracy with © 2023 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Key-based data augmentation with curriculum learning for few-shot code search

引用

Neural Computing and applications 2025年第3期37卷 1475-1490页

作者： Zhang, Fan Peng, Manman Wu, Qiang Shen, Yuanyuan College of Computer Science and Electronic Engineering Hunan University Lushan South Road Hunan Province Changsha410082 China Hunan Provincial Key Laboratory of Blockchain Infrastructure and Application Hunan University Lushan South Road Hunan Province Changsha410082 China

Given a natural language query, code search aims to find matching code snippets from a codebase. Recent works are mainly designed for mainstream programming languages with large amounts of training data. However, code search is also needed for domain-specific programming languages, which have fewer training data, and it is a heavy burden to label a large amount of training data for each domain-specific language. To this end, we propose DAFCS, a data augmentation framework with curriculum learning for few-shot code search tasks. Specifically, we first collect unlabeled codes in the same programming language as the original codes, which can provide additional semantic signals to the original codes. Second, we employ an occlusion-based method to identify key statements in code fragments. Third, we design a set of new key-based augmentation operations for the original codes. Finally, we use curriculum learning to reasonably schedule augmented samples for training well-performing models. We conduct retrieval experiments on a public dataset and find that DAFCS surpasses state-of-the-art methods by 5.42% and 5.05% in the Solidity and SQL domain-specific languages, respectively. Our study shows that DAFCS, which adopts data augmentation and curriculum learning strategies, can achieve promising performance in few-shot code search tasks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Code search Curriculum learning Data augmentation Few shot learning

来源：评论

学校读者我要写书评

暂无评论

Cross-Guided Attention and Combined Loss for Optimizing Gastrointestinal Visual Question Answering

Cross-Guided Attention and Combined Loss for Optimizing Gast...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Qian Liu, Lijun Yang, Xiaobing Liu, Li Peng, Wei Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Yunnan Key Laboratory of Computer Technology Application Kunming650500 China

ISBN: (纸本)9798350386226

Medical Visual Question Answering (Med-VQA) aims at answering clinical questions related to medical imaging, providing important reference for medical imaging diagnosis. Gastrointestinal visual question answering can deeply understand and analyze gastrointestinal medical images, provides supporting information to answer questions about the diagnostic process, and improves diagnostic accuracy and efficiency. However, most existing Med-VQA can only handle simple and general questions, struggling to tackle visual question answering related to complex diseases. Therefore, this paper proposes a Med-VQA model called CACL, which uses cross-guidance and improved combined loss. The model focuses on gastrointestinal image analysis and achieves efficient and accurate feature extraction for gastrointestinal images. A cross-guided attention module is also designed to enhance the model's reasoning ability when dealing with complex cross-modal tasks. In addition, a multi-task composite loss function is designed to balance the loss of segmentation task and classification task, and improve the overall performance. Experimental results show that the proposed method can effectively improve the accuracy of gastrointestinal visual question answering. © 2024 IEEE.

关键词： Multi-task learning

来源：评论

学校读者我要写书评

暂无评论

Predicting the Spread of Covid-19 Using Machine as a Service (MaaS) Through Modified Apriori Algorithm

Predicting the Spread of Covid-19 Using Machine as a Service...

引用

International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Sett, Samir Kumar Sett, Aishik Ghosh, Anupam Department of Computer Application Techno India Kolkata India ITC Infotech Kolkata India Department of Computer Science and Engineering and Department of Artificial Intelligence & Machine Learning Netaji Subhash Engineering College Kolkata India

ISBN: (纸本)9789819613472

The cloud computing and machine learning is one of growing technology in the field of computer science. In recent years, the word "Cloud" is like a cup of tea in our daily lives. Every day, a significant volume of data is produced by different IT sectors, banking sectors, e-commerce websites like flipkart. It provides the organization with consistency, scalability, and strong enumerating power. It also delivers eminence service by generating proper outcomes in a brief amount of time. The objective of this paper is to predict the growth of Covid-19 virus in near future using Machine as a Service (MaaS) through modified Apriori algorithm. In this paper, we will obtain the working graph of the algorithm. The result is also analyzed by obtaining the graphs for confidence against lift, support against confidence. At last, a running time comparative graph for modified apriori and apriori is shown for a certain duration of time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Apriori Cloud computing Confidence Covid-19 MaaS Support

来源：评论

学校读者我要写书评

暂无评论

Construction of PCOS Prediction Model based on BP Neural Network 6

Construction of PCOS Prediction Model based on BP Neural Net...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Hu, Xianqing Yadav, Amit Khan, Asif Sah, Abhishek Pratap Azam, Sami School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP Neural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients. © 2025 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

SQAT-LD: SPeech Quality Assessment Transformer Utilizing Listener Dependent Modeling for Zero-Shot Out-of-Domain MOS Prediction

SQAT-LD: SPeech Quality Assessment Transformer Utilizing Lis...

引用

2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Shen, Kailai Yan, Diqun Dong, Li Ren, Ying Wu, Xiaoxun Hu, Jing Ningbo University Faculty of Electrical Engineering and Computer Science China Zhejiang Key Laboratory of Mobile Network Application Technology China

ISBN: (纸本)9798350306897

In this paper, we propose the speech quality assessment transformer utilizing listener dependent modeling (SQAT-LD) mean opinion score (MOS) prediction system, which was submitted to the 2023 VoiceMOS Challenge. The system is based on a combination of self-supervised learning (SSL) models and listener-dependent modeling. Due to this challenge's emphasis on real-world and challenging zero-shot out-of-domain MOS prediction in three different voice evaluation scenarios, we specifically designed a two-branch module to predict scores and weights for each frame, aiming to achieve better generalization. In the challenge, our system achieved fourth place in Track 1a, second place in Track 1b and first place in Track 2. Additionally, we conducted an ablation study to investigate the effectiveness of our proposed method. © 2023 IEEE.

关键词： Forecasting

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：