检索结果-内蒙古大学图书馆

42nd IEEE/ACM International Conference on computer-Aided Design, ICCAD 2023

作者： Feng, Yu Ma, Tianrui Boloor, Adith Zhu, Yuhao Zhang, Xuan University of Rochester Department of Computer Science United States Washington University Department of Electrical and Systems Engineering St. Louis United States

ISBN: (纸本)9798350315592

Visual computing is vital for numerous applications. In conventional visual computing systems, CMOS image sensors (CIS) act as pure imaging devices for capturing images, however, recent CIS designs increasingly integrate processing capabilities such as Deep Neural Networks (DNN), which give rise to a notion of in-sensor computing. In this paper, we propose a new concept, learned in-sensor visual computing, which exploits end-to-end optimization of in-sensor processing and downstream vision tasks to achieve better overall algorithm accuracy and adopts hardware/algorithm co-design to achieve ultra-low sensor energy consumption. Two examples of the learned in-sensor visual computing, Leca and EDGAzE, are demonstrated. © 2023 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A Robust Development of an Efficient Industrial Monitoring and Fault Identification Model using Internet of Things

A Robust Development of an Efficient Industrial Monitoring a...

引用

2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Al Belushi, Yahya Yaqoob Obaid Jasmin Dennis, P. Deepa, S. Arulkumar, V. Kanchana, D. Ragini, Y.P. Military Technical College Department of Systems Engineering Oman Vi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India RMD Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavarapettai India Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Department of Information Technology Tamil Nadu Chennai India SRM Institute of Science and Technology Ramapuram Department of Computer Applications Tamil Nadu Chennai India SIMATS Department of Biosciences Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350374100

Internet of Things (IoT) is rapidly developing technologies and the Internet of Things (IoT) is a network of physical items or effects embedded with computer software, detectors, and network connectivity. The Internet of Things also addresses the issue of centralizing physical bias management online. We are leveraging the Internet of Things (IoT) idea to construct a system that will automatically monitor all artificial processes and issue warnings or expert comments when necessary. nowadays, gas leakage is a serious concern in the house and as well as vigilance. If the gas sense is indeed low, it's because we've been too careless, intolerant, or both to look for it. If the gas position is raised, however, calamity may ensue. In this research, the crucial concept is introduced to prevent this catastrophe from occurring. In this study, we propose an alternate approach to this impending calamity and design a system with integrated sensors, controllers, and some IoT-assisted software. The suggested method, known as the Internet of Things based Industrial Safety System (IoTISS), is compared against the more established Industrial Safety System (ISS) to see how well it performs. In this system, we are monitoring the detection of gas leakages with various warning characteristics as well as other devices are used for monitoring various aspects like humidity and temperature using DHT22 sensor, MQ2 gas monitoring sensor, fire sensor and NodeMCU ESP8266 WiFi module. NodeMCU ESP8266, which doubles as a WiFi module, will receive data from all of these types of sensors. © 2024 IEEE.

关键词： Accident prevention

来源：评论

学校读者我要写书评

暂无评论

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

引用

computer systems science & engineering 2023年第6期45卷 2377-2389页

作者： Mohamed Esmail Karar Omar Reyad Hazem Ibrahim Shehata College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Computer and Systems Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest *** cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural ***,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated *** suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a *** includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and *** results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural *** considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

关键词： Elderly population fall detection wireless sensor networks internet of medical things deep forest

来源：评论

学校读者我要写书评

暂无评论

Human Gait Recognition:A Deep Learning and Best Feature Selection Framework

引用

computers, Materials & Continua 2022年第1期70卷 343-360页

作者： Asif Mehmood Muhammad Attique Khan Usman Tariq Chang-Won Jeong Yunyoung Nam Reham R.Mostafa Amira ElZeiny Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Information Systems Faculty of Computers and Information SciencesMansoura UniversityMansoura35516Egypt Department of Information Systems Faculty of Computers and InformationDamietta UniversityDamiettaEgypt

Background—Human Gait Recognition(HGR)is an approach based on biometric and is being widely used for *** is adopted by researchers for the past several *** factors are there that affect the system performance such as the walking variation due to clothes,a person carrying some luggage,variations in the view ***—In this work,a new method is introduced to overcome different problems of HGR.A hybrid method is proposed or efficient HGR using deep learning and selection of best *** major steps are involved in this work-preprocessing of the video frames,manipulation of the pre-trained CNN model VGG-16 for the computation of the features,removing redundant features extracted from the CNN model,and *** the reduction of irrelevant features Principal Score and Kurtosis based approach is proposed named *** that,the features of PSbK are fused in one ***,this fused vector is fed to the One against All Multi Support Vector Machine(OAMSVM)classifier for the final ***—The system is evaluated by utilizing the CASIA B database and six angles 00◦,18◦,36◦,54◦,72◦,and 90◦are used and attained the accuracy of 95.80%,96.0%,95.90%,96.20%,95.60%,and 95.50%,***—The comparison with recent methods show the proposed method work better.

关键词： Human gait recognition deep features extraction features fusion features selection

来源：评论

学校读者我要写书评

暂无评论

Autonomous 3D Reconstruction with Small Unmanned Aerial Vehicle (UAV) using Structure from Motion: Practical Aspects

Autonomous 3D Reconstruction with Small Unmanned Aerial Vehi...

引用

AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Rayon, Nathan Stevens, Raymond Sevil, Hakki Erhan Department of Computer Science University of West Florida FL32514 United States Department of Electrical and Computer Engineering University of West Florida FL32514 United States Department of Intelligent Systems & Robotics University of West Florida FL32514 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper explores the practical considerations and challenges involved in achieving autonomous 3D reconstruction utilizing small Unmanned Aerial Vehicles (UAVs) through the framework of Structure from Motion (SFM). The utilization of small UAVs equipped with lightweight cameras presents a promising resource for cost-effective and rapid 3D mapping in various domains such as disaster response, infrastructure inspection, and search and rescue. In this study, the critical practical aspects of this process, including height level for taking images, UAV selection, number of images, image acquisition strategies, and computational time. The effectiveness and limitations of autonomous 3D reconstruction with small UAVs through practical insights are summarized, with discussions on potential advancements and areas for future research. This paper serves as a guide for researchers interested in utilizing small UAVs for autonomous 3D reconstruction tasks applications. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. 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

Investigation of Polycystic Ovary Syndrome (PCOS) Diagnosis Using Machine Learning Approaches 5

Investigation of Polycystic Ovary Syndrome (PCOS) Diagnosis ...

引用

5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Sultan Bin Habib, Al Zadid Bin Syed, Md Asif Islam, Md. Ekramul Tasnim, Tanpia West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States West Virginia University Department of Industrial and Management Systems Engineering MorgantownWV26506 United States Stamford University Bangladesh Department of Computer Science & Engineering Dhaka1217 Bangladesh Green University of Bangladesh Department of Computer Science and Engineering Narayanganj Dhaka1461 Bangladesh

ISBN: (纸本)9798350394290

Polycystic Ovarian Syndrome (PCOS) is a widespread hormone problem for women of childbearing age. Women with PCOS may not ovulate;they might have high levels of androgens and have many small cysts on the ovaries. It can cause missed or irregular menstrual periods, excess hair growth, acne, infertility, and weight gain. Machine Learning (ML) can effectively diagnose this disease at an earlier stage as tons of medical data are available now. Traditional approaches to detect PCOS encompass a combination of clinical evaluation, medical history assessment, physical examination, and laboratory tests. These approaches aim to identify the characteristic symptoms and hormonal imbalances associated with PCOS. Physical examination requires good resources and costs time and money. In recent times, data-driven techniques have substantially advanced disease prediction within the medical field. We aim to utilize ML approaches, incorporating unique feature selection algorithms, to predict PCOS. This paper introduces a data-driven approach to PCOS diagnosis, combining Feature engineering and ML. Several feature selection approaches have been considered to select sets of features for training the ML model, including CatBoost, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), AdaBoost, Random Forest (RF). Results demonstrate that AdaBoost, with ten features selected by RF Feature Importance and Highest Correlation (HC), provides the highest test accuracy. © 2023 IEEE.

关键词： Adaptive boosting

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Eagle's Web Swarm Optimization (EWSO) technique for effective cloud resource management 12

A Hybrid Eagle's Web Swarm Optimization (EWSO) technique for...

引用

12th IEEE International Conference on Advanced Computing, ICoAC 2023

作者： Jagadish Kumar, N. Balasubramanian, C. Kumar, A. Naresh Kumar, G.Praveen Sree, S. Jindhiya Sri Sai Ram Engineering College Department of Computer Science and Business Systems West Tambaram Chennai India P.S.R Engineering College Department of Computer Science and Engineering Sivakasi India Velammal Institute of Technology Pancheti Department of Information Technology Chennai India

ISBN: (纸本)9798350318210

encompasses a range of tasks focused on the efficient allocation and administration of resources within a cloud computing environment, all in order to meet the requirements of cloud users. The key components of cloud computing resource management consist of resource allocation, task scheduling, load balancing, and data storage optimization. Cloud resource management poses several challenges, including the optimization of resource allocation, efficient task scheduling, and effective data storage management in distributed file systems like HDFS. These challenges require careful consideration and strategic approaches to ensure optimal utilization of resources, timely execution of tasks, and efficient handling of large-scale data. This paper introduces a novel approach for optimizing cloud resources using Eagle's Web Swarm Optimization (EWSO). The EWSO technique integrates three distinct algorithms, namely the Black Widow optimizer, Golden Eagle optimizer, and Chaotic Leader & Salp optimizer, to address specific aspects of resource optimization in the cloud. By combining these algorithms, the proposed approach aims to enhance the efficiency and the reliability of resource allocation, task scheduling, and HDFS optimization in the cloud environment. To evaluate the effectiveness of the proposed EWSO technique, its efficiency is compared to several other bio-inspired optimization techniques commonly used in cloud resource management. These include WARMS, AARP, ILPEPS and BFDHP. Through this comparison, the performance, and capabilities of the EWSO technique can be assessed in the context of cloud resource management. The findings from experiments prove the effectiveness of the proposed EWSO technique compared to other bio-inspired mechanisms in terms of resource allocation, task scheduling, and HDFS optimization. These findings provide empirical evidence that the EWSO technique outperforms alternative approaches in managing and optimizing cloud resources, highlighting its

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS BY DEEP SUBSPACE ENCODING 11

CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Beintema, Gerben I. Schoukens, Maarten Tóth, Roland Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to be solved in full, considering common experimental aspects such as the presence of external inputs, measurement noise, latent states, and general robustness. This paper presents a novel estimation method that addresses all these aspects and that can obtain state-of-the-art results on multiple benchmarks with compact fully connected neural networks capturing the CT dynamics. The proposed estimation method called the subspace encoder approach (SUBNET) ascertains these results by efficiently approximating the complete simulation loss by evaluating short simulations on subsections of the data, by using an encoder function to estimate the initial state for each subsection and a novel state-derivative normalization to ensure stability and good numerical conditioning of the training process. We prove that the use of subsections increases cost function smoothness together with the necessary requirements for the existence of the encoder function and we show that the proposed state-derivative normalization is essential for reliable estimation of CT NL-SS models. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Cost functions

来源：评论

学校读者我要写书评

暂无评论

FinSenticNet: A Concept-Level Lexicon for Financial Sentiment Analysis

FinSenticNet: A Concept-Level Lexicon for Financial Sentimen...

引用

2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Du, Kelvin Xing, Frank Mao, Rui Cambria, Erik School of Computer Science and Engineering Nanyang Technological University Singapore National University of Singapore Department of Information Systems and Analytics Singapore

ISBN: (纸本)9781665430654

Sentiment lexicons are important tools for research involving opinion mining and sentiment analysis. They are highly inter-operable, and address critical limitations of learning-based or large language model-based sentiment analysis, providing better reproducibility and explainability. Existing financial sentiment lexicons, manually crafted or automatically constructed, primarily comprise single-word entries despite the fact that jargon, terminologies, and collocations in finance are often multi-word expressions. To address this gap, we present FinSenticNet, a concept-level domain-specific lexicon specifically designed for financial sentiment analysis, where over 65% entries are multi-word expressions. Our construction approach is semi-supervised: the framework consists of a concept parser, a sentiment seeds generation module, and a semantic graph construction module. Each concept (graph node) is subsequently classified in terms of its polarity using the Label Propagation Algorithm and Graph Convolutional Network. Compared to other financial sentiment lexicons, FinSenticNet captures domain-specific language features and has a broader coverage. We demonstrate this with superior evaluation results, i.e., sentiment analysis accuracy and F-scores, on multiple well-received benchmark datasets. © 2023 IEEE.

关键词： financial sentiment analysis graph convolutional network label propagation opinion mining sentiment lexicon

来源：评论

学校读者我要写书评

暂无评论

Method for preserving privacy in wearable IoT devices by extending security using fog computing

Method for preserving privacy in wearable IoT devices by ext...

引用

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Sathyaraj, P. Singh, Mukesh Umaeswari, P. Mahesh Kumar, A.S. Vekariya, Vipul Maranan, Ramya R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Puduvoyal601206 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India R.M.K Engineering College Department of Computer Science and Business Systems Thiruvallur Tamil Nadu Kavaraipettai India Pes College of Engineering Department of Electronics and Communication Engineering Karnataka Mandya571401 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat India Simats Saveetha School of Engineering Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350373783

The overarching objective has been achieved through the development of a method enabling the extension of Fog Computing (FC) to facilitate the transport and encryption of sensitive data from wearable devices. The study presents a method leveraging Fog Computing (FC) to transport and encrypt sensitive data from wearable devices, effectively mitigating privacy risks. Through comprehensive tests, NFC emerges as the optimal technology for data transport, while the DHEC algorithm is identified as the best encryption method for securing data between FC and the cloud. Real-world simulations validate the proposed approach, demonstrating its feasibility and effectiveness. The MyNFC app facilitates secure data transmission to AWS, with ServerNFC successfully decrypting data. The AES algorithm exhibited an average CPU usage of 31.8375, with low energy consumption and an average execution time of 26.404 seconds. For the RSA algorithm, the average CPU usage was 33.5875, also with low energy consumption, and an average execution time of 27.8 seconds. The DHEC algorithm showed an average CPU usage of 33.25, with low energy consumption, and an average execution time of 26.711 seconds. Lastly, the HJP algorithm displayed an average CPU usage of 32.375, with low energy consumption, and an average execution time of 30.163 seconds. Overall, the study highlights the ability of FC to safeguard information against interception, endorsing the proposed method's security and scalability. © 2024 IEEE.

关键词： Fog computing

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：