检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Big data, Bigdata 2024

作者： Ziegenbein, Charles B. Ortiz, Bengie L. Gupta, Vibhuti Choi, Sung Won University of Michigan Department of Pediatrics Michigan Medicine Ann ArborMI United States Peraton Labs Autonomous Systems Research Department Basking RidgeNJ United States School of Applied Computational Sciences Meharry Medical College Department of Computer Science and Data Science NashvilleTN United States

ISBN: (纸本)9798350362480

Hematopoietic stem cell transplantation (HCT) is a potentially life-saving treatment that uses healthy blood-forming cells from donors to replace dysfunctional or damaged hematopoietic cells in patients with various blood disorders. This procedure is often employed to treat conditions such as hematological malignancies (e.g., leukemia, lymphoma, myeloma) and other severe blood or immune system diseases. Monitoring post-transplant complications is essential for tracking physiological effects and aiding in clinical decision-making. Biobehavioral aspects of care partners (i.e., unpaid caregivers) can also be influenced during the post-transplant stage of HCT. Wearable devices offer a non-invasive way to continuously track physiological parameters, making them a valuable resource for health monitoring. However, the physiological data collected from wearables is highly unstructured, often containing missing values, outliers, redundant features, and erroneous measurements leading to false conclusions/prediction. Therefore, enhancing data quality is essential for deriving meaningful insights. This paper introduces novel pre-processing methods to build a high quality, comprehensive, standardized, AI/ML ready, and clinically meaningful wearable dataset of HCT patients and caregivers. To test our data cleaning implementation, our cleaned, high-quality dataset is utilized to predict mood and compliance in HCT patients and their caregivers using machine learning algorithms. The paper illustrates our proposed approach and presents experimental results conducted on the data collected from Michigan Medicine for HCT patients and caregivers. Our preliminary experimental results are promising, demonstrating the effectiveness of the proposed methods and the high-quality dataset in predicting mood and compliance for the participants. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Subspace Clustering in High-Dimensional data Streams:A Systematic Literature Review

引用

computers, Materials & Continua 2023年第5期75卷 4649-4668页

作者： Nur Laila Ab Ghani Izzatdin Abdul Aziz Said Jadid AbdulKadir Department of Computer and Information Sciences Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Centre for Research in Data Science(CeRDAS) Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia

Clustering high dimensional data is challenging as data dimensionality increases the distance between data points,resulting in sparse regions that degrade clustering *** clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data *** clustering methods extend traditional clustering to account for the constraints imposed by data *** streams are not only high-dimensional,but also unbounded and *** necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data *** many articles have contributed to the literature review on data stream clustering,there is currently no specific review on subspace clustering algorithms in high-dimensional data ***,this article aims to systematically review the existing literature on subspace clustering of data streams in high-dimensional streaming *** review follows a systematic methodological approach and includes 18 articles for the final *** analysis focused on two research questions related to the general clustering process and dealing with the unbounded and evolving characteristics of data *** main findings relate to six elements:clustering process,cluster search,subspace search,synopsis structure,cluster maintenance,and evaluation *** algorithms use a two-phase clustering approach consisting of an initialization stage,a refinement stage,a cluster maintenance stage,and a final clustering *** density-based top-down subspace clustering approach is more widely used than the others because it is able to distinguish true clusters and outliers using projected *** algorithms implicitly adapt to the evolving nature of the data stream by using a time fading function that is sensitive to *** work can focus on the clustering framework,parameter optimiza

关键词： Clustering subspace clustering projected clustering data stream stream clustering high dimensionality evolving data stream concept drift

来源：评论

学校读者我要写书评

暂无评论

Heart rate variability analysis in controls and epilepsy patients with or without receiving treatment: a clinical review and meta-analysis

引用

Neural Computing and Applications 2024年第29期36卷 18043-18063页

作者： Shahnawaz, Muhammad Bilal Dawooda, Hassan Iqbal, Uzair Department of Information Engineering Technology National Skills University Islamabad Islamabad4400 Pakistan Software Engineering Department University of Engineering and Technology Taxila Pakistan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

The malfunctioning of cardiac autonomic control in epileptic patients develops ventricular tachyarrhythmia and causes sudden unexpected death in epilepsy patients (SUDEP). Various clinical studies investigated the effect of epilepsy on cardiac autonomic control by performing heart rate variability (HRV) analysis;however, results are unclear regarding whether sympathetic, parasympathetic, or both branches of the autonomic nervous system (ANS) are affected in epilepsy and also the impact of anticonvulsant treatment on the ANS. This study follows the systematic protocols to investigate epilepsy and its anticonvulsant treatment on cardiac autonomic control by using linear and nonlinear HRV analysis measures. The electronic databases of PubMed, Embase, and Cochrane Library were used for the collection of studies. Initially, 1475 articles were identified whereas after 2-staged exclusion criteria, 33 studies were selected for execution of the review process and meta-analysis. For meta-analysis, four comparisons were performed (epilepsy patients): (1) controls (healthy subject with no history of epilepsy) versus untreated patients;(2) treated (patients under treatment that have a seizure) versus untreated patients;(3) controls versus treated patients;and (4) refractory versus well-controlled (epilepsy patients that were seizure-free for last 1 year). For treated and untreated patients, there was no significant difference whereas well-controlled patients presented higher values as compared to refractory patients. Meta-analysis was performed for the time-domain, frequency-domain, and nonlinear parameters. Untreated patients in comparison with controls presented significantly lower HF (high-frequency) and LF (low-frequency) values. These LF (g = − 0.9;95% CI − 1.48 to − 0.37) and HF (g = − 0.69;95% confidence interval (CI) − 1.24 to − 0.16) values were affirming suppressed both, vagal and sympathetic activity, respectively. Additionally, LF and HF value was increased in most o

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

WiMA:Towards a Multi-Criterion Association in Software Defined Wi-Fi Networks

引用

computers, Materials & Continua 2023年第5期75卷 2347-2363页

作者： Sohaib Manzoor Hira Manzoor Saddaf Rubab Muhammad Attique Khan Majed Alhaisoni Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Electrical Engineering Mirpur University of Science and Technology(MUST)Mirpur-10250(AJK)Pakistan Faculty of Electronics Telecommunications and InformaticsGdansk University of Technology80-233 GdanskPoland Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates National University of Sciences and Technology(NUST) Islamabad44000Pakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Despite the planned installation and operations of the traditional IEEE 802.11 networks,they still experience degraded performance due to the number of *** of the main reasons is the received signal strength indicator(RSSI)association problem,in which the user remains connected to the access point(AP)unless the RSSI becomes too *** this paper,we propose a multi-criterion association(WiMA)scheme based on software defined networking(SDN)in Wi-Fi *** association solution based on multi-criterion such as AP load,RSSI,and channel occupancy is proposed to satisfy the quality of service(QoS).SDNhaving an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput *** implementWiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network *** performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30%and enhances the throughput by 20%–50%,hence maintaining user fairness and accommodating more wireless devices and traffic load in the network,when compared to traditional client-driven(CD)approach and state of the art Wi-Balance approach.

关键词： Association multi-criterion SDN Wi-Fi

来源：评论

学校读者我要写书评

暂无评论

Cross-Layer Optimization Strategies for Energy-Efficient and Reliable Machine-Type Communication in LPWANs 2

Cross-Layer Optimization Strategies for Energy-Efficient and...

引用

2nd IEEE International Conference on Emerging Research in computational science, ICERCS 2024

作者： Amanullah, M. Manoranjini, J. Sathiya, V. Helen Prabbu Sankar, P. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering Department of Artificial Intelligence and Data Science Chennai India Panimalar Engineering College Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331534950

In the massive Machine-Type Communication (mMTC), the exponential growth of Internet of Things (IoT) devices over Low Power Wide Area Networks (LPWANs) presents substantial issues regarding energy efficiency and stability. The existing frameworks frequently fails in the the intricate relationships among the MAC-layer and physical parameters. To enhanced cross-layer evaluation methodology that integrates adaptive data rate (ADR) optimization, real-time energy consumption monitoring, and dynamic downlink message handling to address the problems. To maintain stability and performance with the network situations, the framework makes use of a Control-Lyapunov function. To enable effective resource allocation uses the Successive Convex Approximation that improve the effects of various transmission systems under quasi-static and dynamic channel conditions. To verify the cross-layer interactions and it impacts the energy efficiency, the proposed method begins with the data collection and it moves to payload size and packet length, are regularly changed to examines the affect throughput and energy consumption. The findings show the high-density node situations, energy consumption that reduced while retaining reliable communication. Furthermore, the results show the interdependencies among network parameters, highlighting the necessity of an integrated strategy. This framework helps to ensure the mMTC scenarios that optimized by improving the long-term viability and dependability of IoT networks. © 2024 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Accelerating VQE Algorithm via Parameters and Measurement Reuse 8

Accelerating VQE Algorithm via Parameters and Measurement Re...

引用

8th IEEE International Conference on Rebooting Computing, ICRC 2023

作者： Li, Xinpeng Liu, Ji Hansen, Ethan H. Xu, Shuai Hovland, Paul Chaudhary, Vipin Case Western Reserve University Department of Computer and Data Science United States Argonne National Laboratory Mathematics and Computational Research Division United States

ISBN: (纸本)9798350382044

The Variational Quantum Eigensolver (VQE), a quantum-classical hybrid algorithm, holds significant potential for advancements in quantum chemistry and optimization tasks. Our study focuses on accelerating the VQE in combinatorial optimization tasks such as Max-Cut, k-Clique, and k-SAT, while addressing the challenges of scalability and measurement overhead in quantum devices. Notably, while solving one problem, it allows us to concurrently compute essential information like expectation values and gradients for multiple other problems. This capability is made feasible because combinatorial optimization problems often share the same measurement basis. Drawing inspiration from this insight, we introduce a method that efficiently solves multiple problems by appropriately selecting their initial points. Subsequently, we propose two selection strategies for the method. Employing these strategies can, to some extent, help avoid the issue of becoming trapped in local optima, thus leading to improved accuracy. Our tests on 5-node and 10-node graphs for Max-Cut and k-Clique show that, in some cases, our method outperforms random initialization in terms of both accuracy and number of iterations. © 2023 IEEE.

关键词： Combinatorial optimization

来源：评论

学校读者我要写书评

暂无评论

Content Based Email Spam Filtering and Detection using Hybrid Supervised Learning Approach 7

Content Based Email Spam Filtering and Detection using Hybri...

引用

7th World Conference on Computing and Communication Technologies, WCCCT 2024

作者： Saquib, Najam U Khalid, Waqar Shaheen, Sahibzadi Annum Asim, Muhammad Ahmad, Naveed Abasyn University Department of Computer Science Islamabad Pakistan Institute of Management Sciences Department of Computer Science Peshawar Pakistan Capital University of Science & Technology Department of Computer Science Islamabad Pakistan Prince Sultan University Eias Data Science and BlockChain Lab College of Computer and Information Science Riyadh11586 Saudi Arabia Prince Sultan University College of Computer and Information Science Riyadh11586 Saudi Arabia

ISBN: (纸本)9798350372328

In today's world, email is used widely for communication purposes globally. Email spam are unwanted emails that are sent to many recipients receivers. It is usually used for commercial purposes More likely spam enail is also used for malicious attempts to gain access to receiver computer/device. This article presents a novel hybrid architecture for spam emails filtering using mails content-based approach. For filtering, Word2Vec (W2V) word embedding procedure is used for feature selection with bio-inspired 'Particle Swarm Optimization (PSO)' and 'Support Vector Machine (SVM)' combined with 'Convolutional Neural Network (CNN)'. Moreover, our proposed hybrid architecture is simulated in open-source tool and the performance is evaluated using standard confusion matrix. Afterw ards, the results are compared with various previous algorithms/models. Experimental results show that our proposed hybrid scheme enhances detection accuracy of spam emails. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Improved optimizer with deep learning model for emotion detection and classification

引用

Mathematical Biosciences and Engineering 2024年第7期21卷 6631-6657页

作者： Joseph, Willson C. Kathrine, G. Jaspher Willsie Shanmuganathan, Vimal Sumathi, S. Pelusi, Danilo Valencia, Xiomara Patricia Blanco Verdú, Elena Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Adi Shankara Institute of Engineering and Technology Kerala India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Sri Eshwar College of Engineering Coimbatore India Communication Sciences University of Teramo Coste Sant’agostino Campus Teramo Italy Universidad Internacional de La Rioja La Rioja Logroño Spain

Facial emotion recognition (FER) is largely utilized to analyze human emotion in order to address the needs of many real-time applications such as computer-human interfaces, emotion detection, forensics, biometrics, and human-robot collaboration. Nonetheless, existing methods are mostly unable to offer correct predictions with a minimum error rate. In this paper, an innovative facial emotion recognition framework, termed extended walrus-based deep learning with Botox feature selection network (EWDL-BFSN), was designed to accurately detect facial emotions. The main goals of the EWDL-BFSN are to identify facial emotions automatically and effectively by choosing the optimal features and adjusting the hyperparameters of the classifier. The gradient wavelet anisotropic filter (GWAF) can be used for image pre-processing in the EWDL-BFSN model. Additionally, SqueezeNet is used to extract significant features. The improved Botox optimization algorithm (IBoA) is then used to choose the best features. Lastly, FER and classification are accomplished through the use of an enhanced optimization-based kernel residual 50 (EK-ResNet50) network. Meanwhile, a nature-inspired metaheuristic, walrus optimization algorithm (WOA) is utilized to pick the hyperparameters of EK-ResNet50 network model. The EWDL-BFSN model was trained and tested with publicly available CK+ and FER-2013 datasets. The Python platform was applied for implementation, and various performance metrics such as accuracy, sensitivity, specificity, and F1-score were analyzed with state-of-the-art methods. The proposed EWDL-BFSN model acquired an overall accuracy of 99.37 and 99.25% for both CK+ and FER-2013 datasets and proved its superiority in predicting facial emotions over state-of-the-art methods. ©2024 the Author(s), licensee AIMS Press.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Re-rank Search Engine Results via Machine Learning 9

Re-rank Search Engine Results via Machine Learning

引用

9th International Conference on Web Research, ICWR 2023

作者： Ataei Monfared, Amir Hussein Avizheh, Amir Hossein Kharazmi University Faculty of Mathematical Sciences and Computer Tehran Iran Shahid Beheshti University Department of Data and Computer Science Tehran Iran

ISBN: (纸本)9798350399691

Research on search engines has demonstrated their significant impact on information search and user behavior and highlighted the need for continued investigation of their effectiveness, efficiency, and ethical implications. Moreover, search engine optimization (SEO) has emerged as a crucial aspect of online content creation and marketing, as it involves optimizing websites and online materials to improve their visibility and ranking on search engine results pages, thereby increasing their reach and impact. SEO metrics provide valuable insight into a website's search engine performance. However, the relative importance of these metrics remains unclear as search engine algorithms and keyword competitiveness can vary widely. Therefore, ongoing research is needed to better understand the relationship between SEO metrics and search engine rankings. Given the challenge of determining the relative importance of SEO metrics using traditional statistical methods, interested in applying machine learning techniques. By training models with custom datasets of three search engine results (Google, Bing, and DuckDuckGo) and their associated SEO metrics, we hope to gain a better understanding of the complex relationships between these variables. The first two pages of each search engine return the most relevant results. In this article, we propose two types of methods to do this. First, classify the page index of web search results. Second, learning ranking methods to rank them. LambdaRank has the best NDCG compared to other methods for Google and DuckDuckGo which are 0.86 and 0.93 respectively. RankNet is the best learning-to-rank method for Bing with 0.85 as the NDCG value. Logistic regression has the highest accuracy compared to other methods in page index classification for Bing and Google, which are 61.29% and 64.71%, respectively. K-nearest neighbor performs best for DuckDuckGo with an accuracy of 66.67%. © 2023 IEEE.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

Deep transformer and few‐shot learning for hyperspectral image classification

引用

CAAI Transactions on Intelligence Technology 2023年第4期8卷 1323-1336页

作者： Qiong Ran Yonghao Zhou Danfeng Hong Meiqiao Bi Li Ni Xuan Li Muhammad Ahmad College of Information Science and Technology Beijing University of Chemical TechnologyBeijingChina Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina China Greatwall Technology Group Co. LtdShenzhenChina Department of Computer Science National University of Computer and Emerging SciencesChiniotPakistan

Recently,deep learning has achieved considerable results in the hyperspectral image(HSI)***,most available deep networks require ample and authentic samples to better train the models,which is expensive and inefficient in practical *** few‐shot learning(FSL)methods generally ignore the potential relationships between non‐local spatial samples that would better represent the underlying features of *** solve the above issues,a novel deep transformer and few‐shot learning(DTFSL)classification framework is proposed,attempting to realize fine‐grained classification of HSI with only a few‐shot ***,the spatial attention and spectral query modules are introduced to overcome the constraint of the convolution kernel and consider the information between long‐distance location(non‐local)samples to reduce the uncertainty of ***,the network is trained with episodes and task‐based learning strategies to learn a metric space,which can continuously enhance its modelling ***,the developed approach combines the advantages of domain adaptation to reduce the variation in inter‐domain distribution and realize distribution *** three publicly available HSI data,extensive experiments have indicated that the proposed DT‐FSL yields better results concerning state‐of‐the‐art algorithms.

关键词： deep learning feature extraction hyperspectral image classification

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：