检索结果-内蒙古大学图书馆

Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： P Varun T. Sunitha Mythili Nagalingam P. Nithiya S Selvakumaran T A Mohanaprakash Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Department of Computer Science and Engineering St. Joseph's Institute of Technology OMR Chennai Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Department of Computer Science and Engineering P.B college of engineering Chennai India Department of Artificial Intelligence and Data Science Velammal Engineering College Surapet Chennai Department of Computer Science and Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities.

关键词： Training Photovoltaic systems Cloud computing Predictive models Inverters data models Smart grids

来源：评论

学校读者我要写书评

暂无评论

Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement 41

Zero-Shot ECG Classification with Multimodal Learning and Te...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Liu, Che Wan, Zhongwei Ouyang, Cheng Shah, Anand Bai, Wenjia Arcucci, Rossella Data Science Institute Imperial College London United Kingdom Department of Earth Science and Engineering Imperial College London United Kingdom Ohio State University Columbus United States Department of Engineering Science University of Oxford Oxford United Kingdom Institute of Clinical Sciences Imperial College London United Kingdom Department of Infectious Disease Epidemiology Imperial College London United Kingdom Royal Brompton and Harefield Hospitals United Kingdom Department of Computing Imperial College London United Kingdom Department of Brain Sciences Imperial College London United Kingdom

Electrocardiograms (ECGs) are non-invasive diagnostic tools crucial for detecting cardiac arrhythmic diseases in clinical practice. While ECG Self-supervised Learning (eSSL) methods show promise in representation learning from unannotated ECG data, they often overlook the clinical knowledge that can be found in reports. This oversight and the requirement for annotated samples for downstream tasks limit eSSL's versatility. In this work, we address these issues with the Multimodal ECG Representation Learning (MERL) framework. Through multimodal learning on ECG records and associated reports, MERL is capable of performing zero-shot ECG classification with text prompts, eliminating the need for training data in downstream tasks. At test time, we propose the Clinical Knowledge Enhanced Prompt Engineering (CKEPE) approach, which uses Large Language Models (LLMs) to exploit external expert-verified clinical knowledge databases, generating more descriptive prompts and reducing hallucinations in LLM-generated content to boost zero-shot classification. Based on MERL, we perform the first benchmark across six public ECG datasets, showing the superior performance of MERL compared against eSSL methods. Notably, MERL achieves an average AUC score of 75.2% in zero-shot classification (without training data), 3.2% higher than linear probed eSSL methods with 10% annotated training data, averaged across all six datasets. Copyright 2024 by the author(s)

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Hybrid Machine Learning Approach: SVM with ANN Kernel for HSI Medical Image Enhancement 2

Hybrid Machine Learning Approach: SVM with ANN Kernel for HS...

引用

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

作者： Aravindhan, K. Revathi, K. Priyanka, S. Priyadharshini, S. Keerthana, P.K. Muthulakshmi, K. REVA University School of Computing and Information Technology Karnataka Bengaluru India SNS College of Engineering Department of Information Technology Tamil Nadu Coimbatore India SNS College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Manipal Institute of Technology Department of Information Technology Karnataka Bengaluru India

ISBN: (纸本)9798331534950

In medicine image processing provides a perceptive study on disease diagnosis and identification as well as treatment preparation. Since it can acquire complete spectral information over a wide spectrum of wavelengths, especially hyperspectral imaging (HSI) has shown great potential for the non-invasive characterization of tissue. On the other hand, due to their high-dimensional character as well as the presence of noise HSI medical images present a great challenge for processing and enhancement. Even if traditional methods are effective since they sometimes struggle to manage the complexity of these datasets, more sophisticated approaches are needed and demanded. The proposed a hybrid machine learning model including a support vector machine (SVM) classifier and an artificial neural network (ANN) kernel inside the scope of this work to improve and classify HSI images. The ANN kernel is designed to control the inherently complex and non-linear elements in HSI data. Acting as a mapping tool, the artificial neural network kernel transforms the input space into a feature space easily managed in which the SVM can operate with higher accuracy. Main goals are to lower image noise and increase contrast to improve diagnosis correctness. The method was evaluated with a readily available general public HSI medical image collection. In the experimental results, image quality and classification accuracy show appreciable rise. Comparatively to the traditional SVM methods, the signal-to-noise ratio (SNR) increased by 15%;hence, the general classification accuracy was found to be 94.8%. Moreover, the better images show more clear tissue limits and spectral features, which helps clinical interpretation of the results. The results imply that the SVM with ANN kernel model provides a strong solution for the enhancement and classification of HSI medical images, so opening a new path for the improvement of medical diagnostics. © 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

An Experimental Study of Passive UAV Tracking with Digital Arrays and Cellular Downlink Signals

arXiv

引用

arXiv 2024年

作者： Sun, Yifei Yu, Chao Luo, Yan Han, Tony Xiao Tan, Haisheng Wang, Rui Lau, Francis C.M. The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China The Department of Computer Science School of Computing and Data Science The University of Hong Kong Hong Kong Huawei Technologies Company Ltd. Shenzhen518063 China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230027 China

Given the prospects of the low-altitude economy (LAE) and the popularity of unmanned aerial vehicles (UAVs), there are increasing demands on monitoring flying objects at low altitude in wide urban areas. In this work, the widely deployed long-term evolution (LTE) base station (BS) is exploited to illuminate UAVs in bistatic trajectory tracking. Specifically, a passive sensing receiver with two digital antenna arrays is proposed and developed to capture both the line-of-sight (LoS) signal and the scattered signal off a target UAV. From their cross ambiguity function, the bistatic range, Doppler shift and angle-of-arrival (AoA) of the target UAV can be detected in a sequence of time slots. In order to address missed detections and false alarms of passive sensing, a multi-target tracking framework is adopted to track the trajectory of the target UAV. It is demonstrated by experiments that the proposed UAV tracking system can achieve a meter-level accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Long Term Evolution (LTE)

来源：评论

学校读者我要写书评

暂无评论

Apptainer-Based Containers for Legacy and Platform Dependent Machine Learning Applications on HPC Systems

Apptainer-Based Containers for Legacy and Platform Dependent...

引用

IEEE International Conference on Big data

作者： Rajesh Pasupuleti Ravi Vadapalli Christopher Mader Victor J. Milenkovic Frost Institute for Data Science and Computing(IDSC) University of Miami Coral Gables Miami FL USA Department of Computer Science University of Miami Coral Gables Miami FL USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Apptainer (Formerly known as Singularity) is a secure, portable, and easy-to-use container system that provides absolute trust and security. It is widely used across industry and academia and suitable for filling the gaps in integration between running applications on new software technologies and legacy hardware using the optimized resource utilization of CPU and memory. It runs complex applications on HPC clusters in a simple, reproducible way. In this paper we are discussing about various implementations of Artificial Intelligence and Machine learning container-based applications running on Pegasus Supercomputing Nodes using Singularity, Nextflow. It reduces configuration setup work manually by singularity applications and it increases current workflows of High-Performance computing (HPC), High Throughput computing (HTC) and run time performance by 3X. we also incorporated comparative based evaluation analytical results of running an application through normal LSF job with singularity container CPU, GPU utilization and its tradeoffs.

关键词： Industries High performance computing Graphics processing units Machine learning Containers Throughput Software Hardware Security Resource management

来源：评论

学校读者我要写书评

暂无评论

Analyzing Land Submergence Impact on Temperature Using Random Forest Regression and Explainable AI Techniques

Analyzing Land Submergence Impact on Temperature Using Rando...

引用

Intelligent Technologies (ACOIT), Asian Conference on

作者： Ujan Pradhan M.N. Aditya R. Raj Kumar Department of Computing Technologies SRM Institute of Science & Technology Kattankulathur Chennai India Department of Electronics & Communication Engineering SRM Institute of Science & Technology Kattankulathur Chennai India Department of Data Science & Business Systems SRM Institute of Science & Technology Kattankulathur Chennai India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Socio-economic impacts of coastal erosion are serious, more so in Asia. Indeed, there is a greater need for advanced predictive models to be able to manage and reduce risks from increased coastal hazards due to climate change. Most studies provide predictions for shoreline changes using historical data or simple statistical models, which are less accurate and interpretable for proper decision-making. Results show that combining machine learning with explainable AI techniques gives an improvement in predictive accuracy and transparent insight into coastal erosion for informed management decisions. Other methods, such as ensemble techniques, like Random Forest, have been used to improve accuracy and avoid overfitting. In our paper, “Analyzing Land Submergence Impact on Temperature Using Random Forest Regression and Explainable AI Techniques,” we have applied Random Forest Regression for the prediction of the effect of land submergence on temperature change by using the strength of the model. Explainable AI techniques like LIME and SHAP enhance insight into the decision process of the model, but very few studies apply these to coastal erosion. This research fills this gap by using ML models that have explainable AI for more accurate and more interpretable forecasts of temperature variations affecting coastal erosion. A number of explainable AI models are presented in this paper for improving interpretability. Traditionally, black-box ML models are very good at prediction, though often lack transparency, making it hard for stakeholders to understand and build trust in. Explainable models present how each feature contributes to the prediction This transparency enhances trust among coastal managers and policymakers. It aids better decision-making by explaining the drivers of erosion to users. Interpretable models thus help the stakeholders to focus mitigation efforts with directed attention on their part, which helps in ensuring sustainable coastal management.

关键词： Accuracy Temperature Explainable AI Prevention and mitigation Sea measurements Predictive models Land surface temperature Stakeholders Random forests Erosion

来源：评论

学校读者我要写书评

暂无评论

3DMambaIPF: A State Space Model for Iterative Point Cloud Filtering via Differentiable Rendering

arXiv

引用

arXiv 2024年

作者： Zhou, Qingyuan Yang, Weidong Fei, Ben Xu, Jingyi Zhang, Rui Liu, Keyi Luo, Yeqi He, Ying School of Computer Science Fudan University China Department of Information Engineering The Chinese University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore

Noise is an inevitable aspect of point cloud acquisition, necessitating filtering as a fundamental task within the realm of 3D vision. Existing learning-based filtering methods have shown promising capabilities on commonly used datasets. Nonetheless, the effectiveness of these methods is constrained when dealing with a substantial quantity of point clouds. This limitation primarily stems from their limited denoising capabilities for dense and large-scale point clouds and their inclination to generate noisy outliers after denoising. To deal with this challenge, we introduce 3DMambaIPF, for the first time, exploiting Selective State Space Models (SSMs) architecture to handle highly-dense and large-scale point clouds, capitalizing on its strengths in selective input processing and large context modeling capabilities. Additionally, we present a robust and fast differentiable rendering loss to constrain the noisy points around the surface. In contrast to previous methodologies, this differentiable rendering loss enhances the visual realism of denoised geometric structures and aligns point cloud boundaries more closely with those observed in real-world objects. Extensive evaluations on commonly used datasets (typically with up to 50K points) demonstrate that 3DMambaIPF achieves state-of-the-art results. Moreover, we showcase the superior scalability and efficiency of 3DMam-baIPF on highly dense and large-scale point clouds with up to 500K points compared to off-the-shelf methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Wiener filtering

来源：评论

学校读者我要写书评

暂无评论

Bayesian Mixture Generalized Extreme Value Regression with Double-Exponential CAR Frailty for Dengue Haemorrhagic Fever in Pamekasan, East Java, Indonesia 3

Bayesian Mixture Generalized Extreme Value Regression with D...

引用

3rd International Conference on Statistics, Mathematics, Teaching, and Research 2019, ICSMTR 2019

作者： Rantini, D. Iriawan, N. Irhamah Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

There is a lot of research on infectious diseases. One of the dangers is caused by dengue hemorrhagic fever (DHF) viruses. Almost all the world there are dengue viruses that spread. In health science, this research continues to increase over time. It has been widely studied by medical circles, but from a statistical point of view, patient survival can be investigated probabilistically. The survival time of DHF patients could refer to the time of arrival of DHF patients to the hospital with all the health conditions of the patient to be discharged from the hospital. It is because the patient has been declared medically cured or further improved from the initial hospital admission. In this case, survival analysis will be appropriate to be implemented, where the event of the study is DHF patients who are discharged from the hospital due to recovery. DHF patient survival data is found following the multimodal Generalized Extreme Value distribution so that a mixture model would be employed. There is a lot of transmission media for diseases transmitted by the DHF virus, but the fastest transmission medium occurs in a short time, called the Aedes Aegypti Mosquito. This transmission of the DHF virus by mosquitoes can occur between adjacent neighboring regions. This article brings together two fields of research, namely survival models with spatial random effects. The Bayesian analysis will be used as a model parameter estimation approach, whereby unmeasured autocorrelations between intersecting regions will be captured through the conditional autoregressive (CAR). Two different distributions approach for these unmeasured autocorrelations will be given, i.e. the Normal CAR and the Double Exponential CAR. Four predictor variables, namely sex, age, hematocrit level, and platelet count in each DHF patient are used to explain the variability of the survival. The Cox Proportional Hazard regression is employed to find out the significant influence of those variables on the surviva

关键词： Hospitals

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach for Driver Drowsiness Detection using Deep Neural Networks 2

A Novel Approach for Driver Drowsiness Detection using Deep ...

引用

2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Sharon, K.V. Natarajan, B. Madhumitha, P. Dhanamathi, A. Prabu, S. Saranya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Dept. of Artificial Intell. and Data Science Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India K. Ramakrishnan College of Engineering Dept. of Cse Tamil Nadu Tiruchirappalli621112 India

ISBN: (纸本)9798350383287

One major problem that contributes to many traffic accidents that cause injuries and fatalities all over the world is drowsy driving. This research introduces the novel approach for detecting driver drowsiness using deep networks and computer vision techniques aims to solve this issue and improve road safety. The Proposed system employs an InceptionV3-based Convolutional Neural Network (CNN) model to precisely determine whether the driver's eyes are open or closed. A sizable dataset of eye images taken in actual driving situations is used to train the CNN model. Whenever the indicators of drowsiness are detected, the proposed system will identify them in real time and promptly notify the driver. The proposed model not only has strong real-time capabilities but also exhibits superior accuracy based on stringent evaluation metrics, which makes a significant contribution to efforts aimed at promoting road safety. The performance and adaptability of the system are improved and further integrates alarm activation for drowsiness detection in a variety of driving conditions. © 2024 IEEE.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Cloud-Based Foot Pressure Analysis for Diabetic Care Using ANN 5

Cloud-Based Foot Pressure Analysis for Diabetic Care Using A...

引用

5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Pradeepa, H. Amarnath, Raveendra N. Sivakumar, V.G. Thamizhamuthu, R. Manikandan, G. Bhuvaneswari, G. Thiagarajar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurai India Mphasis Campbell Dr MelissaTX United States Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science RSM Nagar Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

It introduces a new method for caring for diabetic feet combines smart pressure monitoring for the feet with wearable gear that is cloud-based and uses artificial neural networks (ANNs). It is essential to recognize diabetic foot ulcers early and monitor them continuously to avoid complications. Wearable pressure sensors record data on the user's foot pressure in real time make up the proposed system. ANNs trained to detect patterns recommend a possible risk of ulceration by analyzing this data once it is transferred to a cloud-based platform. By using cloud-based architecture, healthcare practitioners can remotely monitor patients and intervene as necessary. Clinical trials show the technology improves diabetic foot care by detecting irregularities in pressure distribution and lowering the risk of ulcers and their sequel. Improved patient outcomes and decreased healthcare expenses are achieved via the preventive treatment of diabetic feet made possible by this system, which makes use of breakthroughs in wearable technology and artificial intelligence (AI). © 2024 IEEE.

关键词： Remote patient monitoring

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：