检索结果-内蒙古大学图书馆

14th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2022

作者： Vicerra, Ryan Rhay P. Gamara, Rex Paolo C. San Juan, Jayne Lois G. Bandala, Argel A. Concepcion, Ronnie Dadios, Elmer P. Loresco, Pocholo James M. Espanola, Jason E. Culaba, Alvin Mayol, Andres Philip De la Salle University Department of Manufacturing Engineering and Management Manila Philippines De la Salle University Department of Electronics and Computer Engineering Manila Philippines De la Salle University Department of Industrial and Systems Engineering Manila Philippines FEU Institute of Technology Electrical and Electronics Engineering Department Manila Philippines De la Salle University Department of Mechanical Engineering Manila Philippines

ISBN: (纸本)9781665464932

The COVID-19 pandemic has caused disruption to the economy due to the increasing infection that affects the workforce in different sectors. The Philippine government has imposed lockdowns to control the spread of infection. This urged the different sectors to implement flexible work schedules or work from home setup. A work-from-home (WFH) setup burdens both the employee and employer by installing different equipment set-ups such as WiFi-equipped laptops, computers, tablets, or smartphones. However, the internet stability in some of the areas in the Philippines is not yet reliable. In this study, an application is used collect survey information and provide an estimate of the telework internet cost requirement of a given government employee or a given government employee implementing a work-from-home set up in their respective household. This involves survey results from different respondents who are currently on a work-from-home setup and significant factors from the survey have been analyzed using machine learning (ML) algorithms. Among the machine learning algorithms used, the ensemble bagged trees model outperformed the other ML models. This work can be extended by incorporating a wider scope of datasets from different industry doing work from home set-up. In addition, in terms of education, it is also recommended to determine the WFH set up not just with the government employee and employer but to also extend this into the education side. © 2022 IEEE.

关键词： Cost benefit analysis

来源：评论

学校读者我要写书评

暂无评论

Development of a Web Application for Telecommuting Capability Assessment Embedded with Fuzzy Model 14

Development of a Web Application for Telecommuting Capabilit...

引用

14th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2022

ISBN: (纸本)9781665464932

By early 2020, COVID-19 has caused a global pandemic which led to an enormous number of challenges worldwide in various sectors. The Philippine government has implemented multiple quarantine guidelines and travel restrictions to ensure the people's health and safety. However, the International Labour Organization projected an initial economic and labor market disruption affecting 11 million workers, or about 25% of the Philippine workforce, due to the pandemic. Therefore, the government, thru the concerned agencies continues to encourage employers to implement alternative work plans such as a work-from-home (WFH) operation in compliance with the established regulations in line with existing laws and policies. In line with the telecommuting concept, various research has already been performed, however, some were regarded inconclusive and require further study. Hence, in this study, a Web application was developed along with an embedded fuzzy model to evaluate the telecommuting capability assessment of employees. The proposed web application with embedded fuzzy model is capable of providing capability assessment using the four main input variables which are also relatively characterized for possible telecommuting cost assessment. © 2022 IEEE.

关键词： Fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

MRI-Based Deep Learning Models for Precise Detection of Alzheimer’s Disease

MRI-Based Deep Learning Models for Precise Detection of Alzh...

引用

International Conference on computer and Information Technology (ICCIT)

作者： Md Younus Ahamed Md Asif Bin Syed Mafuza Nasrin Tani Ahmed Shoyeb Raihan Al Zadid Sultan Bin Habib Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown WV USA Department of Industrial and Management Systems Engineering West Virginia University Morgantown WV USA Army Medical College Rangpur Rangpur Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

As a progressive neurodegenerative disease, Alzheimer’s poses a significant challenge in determining an accurate diagnosis and classification. The medical interpretation of its symptoms primarily relies on the expertise of trained professionals, which can lead to variations in diagnosis and potential errors. A promising solution is to leverage machine learning and deep learning techniques. This study evaluates various deep-learning approaches for classifying Alzheimer’s disease using MRI images. We explored traditional CNNs, ResNet50, Inception v3, Swin Transformer, and Vision Transformer (ViT), comparing their performance with and without data augmentation. Our results demonstrate that the deep learning models, particularly ViT and Swin Transformer, significantly improved classification metrics when data augmentation was applied. Specifically, ViT achieved an accuracy of 95.67%, precision of 96.21%, recall of 95.67%, and an AUC of 99.75% after augmentation. This study highlights the effectiveness of deep learning in enhancing diagnostic accuracy for Alzheimer’s disease, underscoring the potential of these techniques in clinical settings to reduce diagnostic variability and improve patient outcomes.

关键词： Deep learning computer vision Accuracy Magnetic resonance imaging Transfer learning Transformers Data augmentation Alzheimer's disease Medical diagnostic imaging Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

Internet of Things (IoT) in solar energy: A Bibliometrics analysis and global publications trends 13

Internet of Things (IoT) in solar energy: A Bibliometrics an...

引用

Energy Harvesting and Storage: Materials, Devices, and Applications XIII 2023

作者： AlMallahi, Maryam Nooman Nassan, Wafaa Al Obaideen, Khaled Yousef, Bashria A. A. AlJaghoub, Haya Rami Ramadan, Omar H. Bonny, Talal AlShabi, Mohammad Department of Mechanical and Aerospace Engineering United Arab Emirates University Al Ain City United Arab Emirates Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates Sustainable Energy & Power Systems Research Centre RISE University of Sharjah Sharjah United Arab Emirates Department of Sustainable and Renewable Energy Engineering University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering and Engineering Management University of Sharjah Sharjah United Arab Emirates Department of Electrical and Electronic Engineering University of Sharjah United Arab Emirates Department of Mechanical and Nuclear Engineering University of Sharjah United Arab Emirates

ISBN: (数字)9781510661417

ISBN: (纸本)9781510661400

With the modernization of cities, the concept of the Internet of Things (IoT) is gaining popularity and becoming a vital source of smart developments. An added advantage of solar energy systems, IoT applications enable automatic and remote sensing, processing, and execution. IoT ensures that information is easily available and accessible from any location around the world. The IoT applications improve the visibility, scalability, and cost-effectiveness of solar energy generation and service. A bibliometric analysis of scientific publications in the field of solar PV and IoT applications was conducted using the Scopus database between the years 2011 and 2023. Many studies of technological development have been discovered, and some insights can still be approached in such a way that the practical implementation of photovoltaic solar systems is improved. Since 2013, there has been an increase in the rate of publications. The majority of these studies were conducted in India, and the most common IoT applications reported were in the fields of computer science and engineering. This article identifies knowledge gaps to inform the community, industry, and government officials about IoT research directions in the solar energy field. © 2023 SPIE.

关键词： Solar energy

来源：评论

学校读者我要写书评

暂无评论

Automated Bi-Fold Weighted Ensemble Algorithms and its Application to Brain Tumor Detection and Classification

arXiv

引用

arXiv 2024年

作者： Huang, PoTsang B. Rizwan, Muhammad Ali, Mehboob Department of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan Department of Industrial and Systems Engineering Chung Yuan Christian University Zhongli Taiwan Department of Applied Mathematics Chung Yuan Christian University Zhongli Taiwan

The uncontrolled and unstructured growth of brain cells is known as brain tumor, which has one of the highest mortality rates among diseases from all types of cancers. Due to limited diagnostic and treatment capabilities, they pose significant challenges, especially in third-world countries. Early diagnosis plays a vital role in effectively managing brain tumors and reducing mortality rates. However, the availability of diagnostic methods is hindered by various limitations, including high costs and lengthy result acquisition times, impeding early detection of the disease. In this study, we present two cutting-edge bi-fold weighted voting ensemble models that aim to boost the effectiveness of weighted ensemble methods. These two proposed methods combine the classification outcomes from multiple classifiers and determine the optimal result by selecting the one with the highest probability in the first approach, and the highest weighted prediction in the second technique. These approaches significantly improve the overall performance of weighted ensemble techniques. In the first proposed method, we improve the soft voting technique (SVT) by introducing a novel unsupervised weight calculating schema (UWCS) to enhance its weight assigning capability, known as the extended soft voting technique (ESVT). Secondly, we propose a novel weighted method (NWM) by using the proposed UWCS. Both of our approaches incorporate three distinct models: a custom-built CNN, VGG-16, and InceptionResNetV2 which has been trained on publicly available datasets. The effectiveness of our proposed systems is evaluated through blind testing, where exceptional results are achieved. We then establish a comparative analysis of the performance of our proposed methods with that of SVT to show their superiority and effectiveness. © 2024, CC BY.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Endurance-Limited Memories: Capacity and Codes

Endurance-Limited Memories: Capacity and Codes

引用

作者： Chee, Yeow Meng Horovitz, Michal Vardy, Alexander Vu, Van Khu Yaakobi, Eitan Department of Industrial Systems Engineering and Management National University of Singapore Singapore119077 Singapore Department of Computer Science Tel-Hai College Galilee Research Institute-Migal Upper Galilee Kiryat Shmona11016 Israel Department of Electrical and Computer Engineering The Department of Computer Science and Engineering University of California at San Diego La Jolla CA92093 United States Computer Science Department Technion-Israel Institute of Technology Haifa3200003 Israel

Resistive memories, such as phase change memories and resistive random access memories have attracted significant attention in recent years due to their better scalability, speed, rewritability, and yet non-volatility. However, their limited endurance is still a major drawback that has to be improved before they can be widely adapted in large-scale systems. In this work, in order to reduce the wear out of the cells, we propose a new coding scheme, called endurance-limited memories (ELM) codes, that increases the endurance of these memories by limiting the number of cell programming operations. Namely, an -change t-write ELM code is a coding scheme that allows to write t messages into some n binary cells while guaranteeing that each cell is programmed at most ×. In case =1, these codes coincide with the well-studied write-once memory (WOM) codes. We study some models of these codes which depend upon whether the encoder knows on each write the number of times each cell was programmed, knows only the memory state, or even does not know anything. For the decoder, we consider these similar three cases. We fully characterize the capacity regions and the maximum sum-rates of three models where the encoder knows on each write the number of times each cell was programmed. In particular, it is shown that in these models the maximum sum-rate is\log\sum i=0 t i. We also study and expose the capacity regions of the models where the decoder is informed with the number of times each cell was programmed. Finally we present the most practical model where the encoder read the memory before encoding new data and the decoder has no information about the previous states of the memory. © 1963-2012 IEEE.

关键词： Phase change memory

来源：评论

学校读者我要写书评

暂无评论

Optimization for Robustness Evaluation Beyond ℓp Metrics

Optimization for Robustness Evaluation Beyond ℓp Metrics

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hengyue Liang Buyun Liang Ying Cui Tim Mitchell Ju Sun Electrical & Computer Engineering University of Minnesota Minneapolis USA Computer Science & Engineering University of Minnesota Minneapolis USA Industrial & Systems Engineering University of Minnesota Minneapolis USA Queens College City University of New York New York City USA

Empirical evaluation of the adversarial robustness of deep learning models involves solving non-trivial constrained optimization problems. Popular numerical algorithms to solve these constrained problems rely predominantly on projected gradient descent (PGD) and mostly handle adversarial perturbations modeled by the ℓ 1 , ℓ 2 , and ℓ ∞ metrics. In this paper, we introduce a novel algorithmic framework that blends a general-purpose constrained-optimization solver PyGRANSO, With Constraint-Folding (PWCF), to add reliability and generality to robustness evaluation. PWCF 1) finds good-quality solutions without the need of delicate hyperparameter tuning and 2) can handle more general perturbation types, e.g., modeled by general ℓ p (where p > 0) and perceptual (nonℓ p ) distances, which are inaccessible to existing PGD-based algorithms.

关键词： Measurement Deep learning Perturbation methods Signal processing algorithms Signal processing Robustness Numerical models

来源：评论

学校读者我要写书评

暂无评论

Multi-modal Motion Prediction using Temporal Ensembling with Learning-based Aggregation

arXiv

引用

arXiv 2024年

作者： Hong, Kai-Yin Wang, Chieh-Chih Lin, Wen-Chieh The Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan The College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan The Institute of Multimedia Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

Recent years have seen a shift towards learning-based methods for trajectory prediction, with challenges remaining in addressing uncertainty and capturing multi-modal distributions. This paper introduces Temporal Ensembling with Learning-based Aggregation, a meta-algorithm designed to mitigate the issue of missing behaviors in trajectory prediction, which leads to inconsistent predictions across consecutive frames. Unlike conventional model ensembling, temporal ensembling leverages predictions from nearby frames to enhance spatial coverage and prediction diversity. By confirming predictions from multiple frames, temporal ensembling compensates for occasional errors in individual frame predictions. Furthermore, trajectory-level aggregation, often utilized in model ensembling, is insufficient for temporal ensembling due to a lack of consideration of traffic context and its tendency to assign candidate trajectories with incorrect driving behaviors to final predictions. We further emphasize the necessity of learning-based aggregation by utilizing mode queries within a DETR-like architecture for our temporal ensembling, leveraging the characteristics of predictions from nearby frames. Our method, validated on the Argoverse 2 dataset, shows notable improvements: a 4% reduction in minADE, a 5% decrease in minFDE, and a 1.16% reduction in the miss rate compared to the strongest baseline, QCNet, highlighting its efficacy and potential in autonomous driving. © 2024, CC BY-SA.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Boolean matrix compressed sensing

Boolean matrix compressed sensing

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qiang Soleymani, Mahdi Mahdavifar, Hessam National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Halıcıoğlu Data Science Institute University of California La Jolla San DiegoCA92093 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9798350368741

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements. © 2025 IEEE.

关键词： Belief Propagation Boolean Matrix Compressed Sensing Matrix Completion

来源：评论

学校读者我要写书评

暂无评论

Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories

arXiv

引用

arXiv 2023年

作者： Zhang, Zixuan Chen, Minshuo Wang, Mengdi Liao, Wenjing Zhao, Tuo Affiliated With School of Industrial and Systems Engineering at Georgia Tech United States Affiliated With Electrical and Computer Engineering at Princeton University United States Affiliated With School of Mathematics at Georgia Tech United States

Existing theories on deep nonparametric regression have shown that when the input data lie on a low-dimensional manifold, deep neural networks can adapt to the intrinsic data structures. In real world applications, such an assumption of data lying exactly on a low dimensional manifold is stringent. This paper introduces a relaxed assumption that the input data are concentrated around a subset of Rd denoted by S, and the intrinsic dimension of S can be characterized by a new complexity notation – effective Minkowski dimension. We prove that, the sample complexity of deep nonparametric regression only depends on the effective Minkowski dimension of S denoted by p. We further illustrate our theoretical findings by considering nonparametric regression with an anisotropic Gaussian random design N(0,Σ), where Σ is full rank. When the eigenvalues of Σ have an exponential or polynomial decay, the effective Minkowski dimension of such an Gaussian random design is p = O(plogn) or p = O(nγ), respectively, where n is the sample size and γ ∈ (0,1) is a small constant depending on the polynomial decay rate. Our theory shows that, when the manifold assumption does not hold, deep neural networks can still adapt to the effective Minkowski dimension of the data, and circumvent the curse of the ambient dimensionality for moderate sample sizes. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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：