检索结果-内蒙古大学图书馆

1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Altuwairiqi, Majid College of Computers and Information Technology Department of Computer Science Taif University Saudi Arabia

ISBN: (纸本)9798350345957

The Extreme Learning Machine (ELM) is a revolutionary feed-forward neural network learning technique that functions on a single hidden layer. It is based on the idea that the more information a neural network has, the better it can learn (SLFNs). It uses a random process to set the weights of the hidden neurons and uses the Moore-Penrose (MP) generalised inverse to analytically compute the weights of the output neurons. Both of these computations are done in parallel. ELM does not require iteration for the calculation of the SLFN parameters, in contrast to the sluggish gradient descent- based learning techniques that are typically used for SLFN. This is because the parameters for the hidden layer are initialised in a random manner and are maintained throughout the knowledge procedure. In addition, the output burdens are determined systematically. In most cases, this modification makes the input statistics more identifiable in the ELM topographies space, which makes it easier to solve the underlying or connected problems. In addition to its lightning-fast learning, the ELM algorithm also delivers outstanding results in terms of generalisation. This comes as a bonus to its lightning-fast learning. When classifying and clustering the data for this research article, we make use of ELM in conjunction with random projection (RP). It is conceivable that the choices could be cut in half. The first one is intended for use with enormous datasets, whereas the second one works much better with more limited ones. Building a small network with a strong generalization performance, while utilizing a limited neurons count in the secreted level is the goal of this research paper. © 2023 IEEE.

关键词： Lightning

来源：评论

学校读者我要写书评

暂无评论

A Smart Vehicle Monitoring System Using Real-Time Data Processing and Advanced Sensor Integration 5

A Smart Vehicle Monitoring System Using Real-Time Data Proce...

引用

5th International Conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Sathish, M. Prabha Devi, D. Kanchana, R. Pranesh, R.K. Kumaraguru College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Tamil Nadu Sathyamangalam India Sphoorthy Engineering College Department of Artificial Intelligence and Machine Learning Telangana Hyderabad India Kumaraguru College of Technology Department of Information Science and Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

The 'Smart Vehicle Monitoring System' presents a comprehensive solution for enhancing road safety and user authentication in the realm of modern transportation. This system integrates advanced technologies to monitor crucial aspects of vehicle usage, ensuring both the rider's safety and the vehicle's safety. The core functionalities encompass monitoring the usage of safety gear such as helmets and detecting alcohol consumption. User authentication is achieved through a combination of fingerprint recognition and Radio-frequency identification (RFID). This multi-layered approach ensures that only authorized individuals can operate the vehicle. The proposed system further extends its capabilities to monitor essential vehicle parameters, including tire pressure and fuel levels. In the event of an accident, a vibration sensor is employed to swiftly detect impact, triggering an alert system through GSM communication. This timely notification ensures prompt assistance and emergency response. Wireless communication plays a vital role in the system, with the helmet connected to an Arduino Mega via the ESP protocol. This acilitates real-time data exchange, allowing for seamless integration of various components. To enhance user interface and information dissemination, the system utilizes an LCD to present real-time status updates. Additionally, the integration of a GPS module enables the sharing of live location data, contributing to improved navigation and overall safety. In conclusion, the Smart Vehicle Monitoring System not only prioritizes the safety of the rider but also addresses authentication concerns and provides a holistic approach to vehicle monitoring. Through the amalgamation of cutting-edge technologies, this system aims to redefine the standards of safety and security in contemporary transportation. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

来源：评论

学校读者我要写书评

暂无评论

Wearable Photoplethysmography-based Bipolar Disorder Detection 24

Wearable Photoplethysmography-based Bipolar Disorder Detecti...

引用

8th International Conference on computer science and Artificial Intelligence, CSAI 2024

作者： Lyu, Qiuqi Zhang, Xiao Ding, Xinyi Chen, Jun University of Jinan Shandong Jinan China College of Computer Science Beijing University of Technology Beijing China Beijing University of Technology Beijing China Tsinghua University Beijing China

ISBN: (纸本)9798400718182

Diagnoses of bipolar disorder face a critical challenge in that mood episodes typically occur during distinct long periods, but long-term tracking measurement of emotional states is costly and sometimes infeasible, with remote areas in particular suffering from immense obstacles. The dilemma between validity and efficiency faced by classic approaches, on the other hand, creates demands and opportunities for wearable intelligent techniques to serve for daily monitoring. Based on the coherence between mood episodes and physiological arousal fluctuations, this study proposes an efficient and explainable detection approach of bipolar disorder from photoplethysmography signals, a kind of accessible physiological signal in various wearable devices. The first step is identifying numerous potential biomarkers of bipolar disorder in PPG signals, offering a multi-perspective description of aberrant mood episodes. These biomarkers are later demonstrated as intrinsically the reflection of non-stationary time-frequency attributes of PPG signals commonly used in studies and clinical evaluations. Subsequently, these biomarkers are accurately, efficiently, and uniformly represented using wavelet transforms to characterize bipolar disorder in a biologically and mathematically interpretable manner. Finally, these transformed signals are used in various downstream learning tasks for bipolar disorder recognition using support vector machines. It was found that when aggregating the machine learning (single-case SVM) values for each participant the following median and average accuracy ratings were obtained based on each of the questionnaires (relating to ASRM: Mania, BD: Bipolar, and QIDS: De-pression), in determining the variability of whether or not the target patient has the listed condition. With a final average reading of 86.5%. This confirms that more ergonomically suitable devices such as smartwatches are a viably cheaper and equally competitive option among other contemporary me

关键词： Photoplethysmography

来源：评论

学校读者我要写书评

暂无评论

Integration of data science with the intelligent IoT(IIoT):Current challenges and future perspectives

引用

Digital Communications and Networks 2025年第2期11卷 280-298页

作者： Inam Ullah Deepak Adhikari Xin Su Francesco Palmieri Celimuge Wu Chang Choi Department of Computer Engineering Gachon UniversitySeongnamSujeong-gu 13120Republic of Korea University of Electronic Science and Technology of China Chengdu 611731China College of Information Science and Engineering Hohai University213000China Department of Computer Science University of SalernoItaly Graduate School of Informatics and Engineering The University of Electro-Communications 1-5-1ChofugaokaChofu-shiTokyo182-8585Japan

The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate *** science is the science of dealing with data and its relationships through intelligent *** state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their ***,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various *** paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based *** insights into IoT data security,privacy,and challenges are visualized in the context of data science for *** addition,this study reveals the current opportunities to enhance data science and IoT market *** current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.

关键词： Data science Internet of things(IoT) Big data Communication systems Networks Security Data science analytics

来源：评论

学校读者我要写书评

暂无评论

Unmasking Hypertension Through a Fusion of Human Lifestyle Data using Machine Learning Algorithms 6

Unmasking Hypertension Through a Fusion of Human Lifestyle D...

引用

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

作者： Vishnu Priya, V. Nisanth, G. Nivethaa, R.P. Vijay, Srinidhi Department of Computer Science and Engineering Kongu Engineering College Erode Perundurai India Department of Computer Applications Kongu Engineering College Erode Perundurai India Department of Computer Technology UG Kongu Engineering College Erode Perundurai India

ISBN: (纸本)9798331522667

Hypertension, also referred to as high blood pressure, is a condition arising from the consistently high blood pressure against artery walls. The volume and output of blood from the heart primarily control blood pressure under stiff or tight arterial conditions. High pulse pressure leads to increased blood pressure, since the pumping of more blood makes the arteries inactive. The World Health Organization has a comprehensive guideline in which an evidence-based pharmacological approach has been recommended for managing adult patients with hypertension, not neglecting to mention lifestyle modification in order to support medicinal treatment. Machine learning may revolutionize the way hypertension treatment is projected for the management of personalized treatment routines for patients based on their characteristics. ML provides great promise in seeking ideal medication and predicting responses thereof against massive sets of data for hypertensive patients. The techniques allow for the determination of the individual risk for hypertension and ensure that treatment protocols are optimal. Compared to methods used to predict hypertension in the past, ML algorithms show a marked improvement in performance. © 2025 IEEE.

关键词： Personalized medicine

来源：评论

学校读者我要写书评

暂无评论

A Framework Designing of Routing Model for Path Planning of Vehicles Using IoT

引用

SN computer science 2023年第5期4卷 641页

作者： Bethu, Srikanth Erukala, Suresh Babu Department of Computer Science and Engineering National Institute of Technology Warangal 506004 India Department of Computer Science and Engineering CVR College of Engineering Hyderabad 501510 India

The exponential growth of the metropolitan cities of the country has generated and magnified urban sprawl into the problematic proportions. Lack of the efficient traffic control and management has many a times lead to loss of lives due to ambulances getting stuck in traffic jams. To overcome this problem, network routing model is required with new GPS technology along with optimized path. To improvise it, the need of developing new VANET model with IoT-based Traffic Signal control system is required. In this paper, we discuss on the reliability issues and pitfalls of the existing methods and used ARDUINO setup for experiments. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence Internet of Things (IoT) Machine learning

来源：评论

学校读者我要写书评

暂无评论

Deriving Pipeline for Emergency Services Using Natural Language Processing Techniques 11th

Deriving Pipeline for Emergency Services Using Natural Langu...

引用

11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Anand, Akshat Rajeswari, D. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India

ISBN: (纸本)9789811988240

Communications have a crucial role in times of crisis, particularly in times of emergency. Whenever a region is affected by any disaster, social media sites such as Twitter, etc., are a great way to get information out to those who need it the most. Our paper suggests that in times of crisis, when people are worried about finding the right emergency contacts or are experiencing a transformation in their psychological response, they should manually enter their experience into an app to obtain contact information for the nearest SOS. Alternatively, the proposed application suggests scraping hashtags from around the world with web scraping scripts to retrieve geo-referenced tweets in real time. In order to gather accurate data, Twitter's API or some third-party application can be used in the development of this software. When there is a major crisis, social media user behavior plays a vital role, owing to its efficiency and high reliability with swift data dissemination. Anxiety over finding the right emergency team contact may cause people to lose their cool and become unclear about which authority they should call when an emergency occurs. Eventually, our application bridges the gap between clients in need of help and the approved agency that can really provide that support in different circumstances. In the case of, for example, a lost child or a flooded area, our application helps you get in touch with an organization that can assist you in dealing with the situation. We have used CNN-1-D + LSTM, CNN-1-D + Bi-LSTMs, and CNN-1-D + LSTM + Glove-100-D to get maximum accuracy for the classification model. It is possible to use the prototype solution in a novel way to organize immediate assistance with authorities in comprehensive administration, while integrating Twitter or other social platforms into the application can make it a feasible platform for preventive intervention and catastrophe management. © 2023, The Author(s), under exclusive license to Springer Nature

关键词： Emergency services

来源：评论

学校读者我要写书评

暂无评论

Exercise posture rectifier and tracker using MediaPipe and OpenCV 5

Exercise posture rectifier and tracker using MediaPipe and O...

引用

5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Maitray, Tathagat Singh, Utkarsh Kukreja, Vaidansh Patel, Ashish Sharma, Vishnu Galgotias College Of Engineering And Technology Department Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350330861

This research paper suggests a MediaPipe and OpenCV-based workout posture tracker and rectifier. Various musculoskeletal diseases caused by poor exercise posture can cause discomfort and injury. We created a technology that can instantly identify and fix bad exercise posture in order to solve this problem. When comparing the body's joint angles to the optimal workout posture angles, the system employs MediaPipe for pose estimation and OpenCV for picture processing. When a mismatch is found, the technology gives the user immediate feedback so they can adjust their posture. We ran studies with a wide range of individuals to gauge how well the system performed, and the outcomes showed that the suggested technique is accurate and effective at spotting and correcting bad exercise posture. The proposed system has the potential to improve the effectiveness of exercise programs and reduce the risk of musculoskeletal disorders. © 2023 IEEE.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Time-Series Cryptocurrency Forecasting Using Ensemble Deep Learning

Time-Series Cryptocurrency Forecasting Using Ensemble Deep L...

引用

2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Rao, K Rama Prasad, M Lakshmi Kumar, G Ravi Natchadalingam, R. Hussain, Mohammad Manzoor Reddy, Pundru Chandra Shaker Geethanjali College of Engineering and Technology Electronics and Communication Engineering Telangana Hyderabad India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India CMR College of Engineering & Technology Computer Science and Engineering Hyderabad India SRM Madurai College for Engineering and Technology Computer Science and Engineering Tamilnadu Madurai India B v Raju Institute of Technology Computer Science and Engineering Telangana Narsapur India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350333244

Cryptocurrency is now widely accepted as a payment and exchange method, permeating nearly every aspect of the financial sector. Similar to the non-stationary and very erratic price movements of traditional stocks, cryptocurrency price swings are highly unpredictable. The rising popularity of cryptocurrencies has prompted an increase in the number of studies conducted to predict their future prices. The importance of cryptocurrency forecasting has grown significantly with the advent of deep learning. portfolio optimisation and decision making cannot be achieved without the creation of a smart forecasting model. The primary contribution of this study is the utilize of traditional deep learning (DL) models in combination with the three most popular ensemble learning algorithms (ensemble-averaging, bagging, and stacking) to predict the hourly values of major cryptocurrencies. Traditional DL strategies consisting of combinations of long short-term memory (LSTM), Bi-directional (BiLSTM), and convolutional layers were utilized to assess the suggested ensemble methods. The ensemble techniques were tested on their capacity to forecast the price of a cryptocurrency an hour basis (regression) and to determine whether the price will rise or fall relative to the present (classification). Our in-depth experimental research shows that combining ensemble learning with deep learning can produce robust, stable, and trustworthy forecasting strategies. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

An improved pulse coupled neural networks model for semantic IoT

引用

Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

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：