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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4179 条记录，以下是911-920 订阅

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Malaysian Sign Language Mobile application Using Deep learning 4

Malaysian Sign Language Mobile Application Using Deep Learni...

引用

4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Azahari, Abang Asyraaf Aiman Abang Nordin, Sharifalillah School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi Mara Selangor Malaysia

ISBN: (纸本)9798350318432

this research paper presents the development of a Malaysian Sign Language (MSL) application. the project aims to bridge the communication gap between the deaf community and other Malaysians. the application utilizes the Yolov5 algorithm, chosen for its real-time object detection capabilities and low memory requirements. the waterfall model has been implemented and has been divided into 8 phases. the American Sign Language (ASL) dataset from Roboflow was adapted for MSL due to similar hand gestures. the model achieved a high mAp score of 87.23% after 500 iterations, successfully classifying MSL alphabets with the exception of 'J' and 'Z'. © 2023 IEEE.

关键词： Object detection

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AutismSense: Early Prediction of Autism in Children using machine learning 4

AutismSense: Early Prediction of Autism in Children using Ma...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Albert Mayan, J. Reddy, Vasantha Sunil Varma, Buddaraju Tarun Mohamed, thameem Vishal, P. Sathyabama Institute of Science and Technology School of Computing Chennai India Sathyabama Institute of Science and Technology Department of CSE Chennai India

ISBN: (纸本)9798350300093

In this research, a ML-based strategy is presented for early diagnosis of Autism Spectrum Disorder (ASD). Autism is a neurological condition with complicated symptoms. In order to create a reliable ASD prediction model, the suggested method combines RF-CART & RFID3. An easy-to-use smartphone app for autism spectrum disorder prediction was also created using the suggested model. the evaluation findings shown that the proposed model outperforms the state-of-the-art methods on both the AQ10 and real-world datasets in regards to reliability, sensitivity, susceptibility, clarity, as well as false positive rate. By identifying those who may benefit from early intervention and assistance, this method has the potential to cut down on the time and money spent on screening examinations © 2023 IEEE.

关键词： Diagnosis

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AquaAE: A Lightweight Deep learning Network for Underwater Image Restoration 4

AquaAE: A Lightweight Deep Learning Network for Underwater I...

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4th international conference on machine learning and Intelligent Systems Engineering (MLISE)

作者： Yang, Chun Xie, Haijun Wang, Jiahang Liang, Haohua Zhang, Yuting Deng, Yi Beijing Inst Technol Zhuhai Guangdong Peoples R China

ISBN: (纸本)9798350375084;9798350375077

Due to the physical properties of water and the presence of suspended particles, underwater images often exhibit a bluish-green tint, reduced contrast, and uneven light distribution. Many researchers strive for better image restoration techniques, but they often overlook the high computational demands of these models, limiting their application in resource-constrained scenarios. To address this, we have introduced a model named AquaAE for image restoration. this model adopts a simple autoencoder structure, utilizing skip connections to merge features from the encoder and decoder, and incorporates a red channel enhancement to improve image restoration quality. Compared to advanced deep learning networks like U-Transformer, Twin-UIE, and Semi-UIR, our model is more straightforward, employing only simple convolution and upsampling. Combined with our specially calculated red channel enhancement coefficients tailored for different water conditions, AquaAE efficiently captures local features and spatial relationships, thereby better restoring image *** excels in classical evaluation metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). More critically, our model demonstrates outstanding computational efficiency, with its FLOPs being only 2.6% of Twin-UIE's (1.32G) and its parameter count merely 2.8% of U-Transformer's (0.88M), highlighting the lightweight nature of the model. this lightweight design is crucial not only for improving image restoration effectiveness but also for underwater mobile devices with limited computational resources and battery life. We trained AquaAE on the underwater scenes subset of the EUVP dataset and tested it on the underwater ImageNet subset of the EUVP dataset. the results show that AquaAE performs exceptionally well in underwater image restoration.

关键词： AquaAE U-shaped net Image restoration

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Research on Image Classification Based on Extreme learning machine and Convolutional Neural Network 4

Research on Image Classification Based on Extreme Learning M...

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4th international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Sun, ChangXu Zhang, Rui Shenyang Jianzhu University China

ISBN: (纸本)9781510666412

In image classification and recognition, in order to obtain higher classification accuracy, it is necessary to extract more accurate and more expressive features of image semantic information[1]. this paper proposes an improved image classification model based on convolutional neural network. By improving and optimizing AlexNet from both the network architecture and the internal structure, we can further improve the expression ability of features. By introducing the extreme learning machine in the full connection layer, not only the classification ability and classification time of the network are improved, but also the structure has better data processing ability. © 2023 SPIE.

关键词： Image classification

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Phishing Perception and Prediction 4

Phishing Perception and Prediction

引用

4th international conference on Innovative Trends in Information Technology, ICITIIT 2023

作者： Ashwitha Noble, P. Hubert Veyannie, V. Summia Parveen, H. Sri Eshwar College of Engineering Dept. Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665494144

A practice known as URL phishing involves cybercriminals creating fake websites in order to lure victims and steal sensitive information. the attacker disguises themselves in an email, instant message, or text message, pose as a reliable source to persuade the recipient to open it. Once the recipient clicks the link, they realize they were deceived into clicking a potentially dangerous link. In this case, ransomware can be set up on the recipient's PC using a malware that can be used to lock it down or even worse the private data can be released. In spite of the fact that fraudulent websites often resemble the real thing, checking for warning signals alone is not enough to prevent URL phishing. A study found that around 90% of the data breach is due to phishing. the variety of phishing schemes has expanded over the years and they may now be more dangerous than ever before. © 2023 IEEE.

关键词： machine learning

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Emergency Care Patient Prediction using Electronic Health Records (EHR) Data: An End-to-End machine learning Pipeline 10

Emergency Care Patient Prediction using Electronic Health Re...

引用

10th international conference on Future Internet of things and Cloud, FiCloud 2023

作者： Sumon, Maruful Abedin Kwemebe, Tor A. Melapu, Venkata K. Hasan, Md Mohiuddin Jackson State University Computational Data-Enabled Science and Engineering Jackson United States Jackson State University Department of Mathematics and Computer Science Jackson United States Jackson State University Computer Science and Engineering Jackson United States

ISBN: (纸本)9798350316353

Using electronic health records (EHR) data for predicting the condition of patients who are in need of emergency care is a promising application of machine learning. With the help of machine learning, complex problems of identification of patterns can be solved with ease and accuracy when proper machine learning model is implemented. this paper uses EHR data to build an end-to-end machine learning pipeline for predicting the outcome of emergency care patients. As a machine learning process, the pipeline includes data preprocessing, feature engineering, model selection, data set training and testing, evaluation, and deployment. By using EHR data in this pipeline, the model has been trained and tested on a large dataset of many patients information with different types of machine learning algorithms to improve and get the best prediction accuracy. this pipeline can aid in managing primary care patients efficiently and effectively with the implementation of best suitable machine learning algorithm at the time of urgency. By implementing the model on the EHR dataset, an accuracy of more than 75\% is obtained which indicates the efficiency of the machine learning process in the process of identifying patients who need the emergency care more than the general patients when taken to the primary care or emergency. © 2023 IEEE.

关键词： Forecasting

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Integration of Vision IoT, AI-based OCR and Blockchain Ledger for Immutable Tracking of Vehicle's Departure and Arrival Times 23

Integration of Vision IoT, AI-based OCR and Blockchain Ledge...

引用

8th international conference on machine learning Technologies (ICMLT)

作者： Sichinga, Marumbo Nizeyimana, Eric Gatare, Ignace Nsenga, Jimmy Univ Rwanda African Ctr Excellence Internet Things ACEIoT Kigali Rwanda

ISBN: (纸本)9781450398329

Vehicle based logistics are hinged on their ability to timely deliver goods, services, and people. the classical expression of "time is money" comes alive in the logistics industry yielding potentially huge financial and health consequences in case of missing deadlines. this is especially the case for time sensitive pharmaceuticals, delivery of perishable goods, delivery of people travelling, delivery of services in fault fixing/recovery sector. All these use cases motivate the need for an immutable, secure, and immortalized process of tracking time. To solve this challenge, this paper presents prototype-based research that integrates the 4th industrial revolution technologies of vision Internet of things (IoT), Artificial Intelligence (AI)-based Optical Character Recognition (OCR) and blockchain. the developed prototype features a Raspberry-PI board embedding a camera, an Artificial Intelligence (AI) model to recognize plate letters from the image and a crypto wallet to sign the logging of plate number and time events on the NEAR blockchain, an emerging sharded, proof-of-stake, layer-one blockchain that is simple to use, secure and scalable. the effective operation of the developed prototype has been validated inside a campus parking and shows an accuracy of 80%. the benefits of transparency, security, and immutability of the blockchain combined with the intelligence, data capture, and processing of IoT will enable to develop accountability solutions trusted by all different logistic stakeholders.

关键词： Web3 Blockchain Dapp NEAR IoT YoloV4 OCR

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Polarization Insensitive Programmable Metasurface RCS Controlling Based on Deep learning 4

Polarization Insensitive Programmable Metasurface RCS Contro...

引用

4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Bian, Xiaobei Yong, Shaowei Xu, Hantao Gu, Ziyang Chen, Guanchao Fu, Yufei Zhang, Qingqing National University of Defense Technology College of Electronic Science and Technology Changsha China

ISBN: (纸本)9798350373233

Radar Cross Section (RCS) control based on programmable metasurfaces has significant research prospects in radar stealth and deception. However, traditional optimization algorithms cannot accomplish the metasurface encoding design for given RCS control indicators. therefore, this paper proposes an RCS control inverse design encoding scheme based on a tandem neural network. the training set is based on a programmable metasurface composed of 1-bit polarization-insensitive metasurface units with good phase and amplitude characteristics in the wide frequency band of 7.87-12.44GHz. By combining forward prediction and inverse design in a tandem network architecture, real-time encoding design can be achieved. Simulation results show that by performing spatial encoding throughout the X-band, dynamic RCS control of the normally incident plane wave can be achieved, and the maximum RCS attenuation value of a single frequency point exceeds 20dB. © 2023 IEEE.

关键词： Deep learning

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Analyzing the Sentiments of Filipino Commuters: An application of Data Science Techniques through the Use of machine learning Algorithms 13

Analyzing the Sentiments of Filipino Commuters: An Applicati...

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13th international conference on Software Technology and Engineering, ICSTE 2023

作者： De Goma, Joel C. Quiambao, Alfred Alexander M. Pascua, Justin Gillian M. Bravo, Paul Bryan V. Mapua University School of Information Technology Makati Philippines Mapua University School of Information Technology Manila Philippines

ISBN: (纸本)9798350371475

Living in the 21st century, the rise of science and technology has brought upon various developments that help improve the quality of life for all humans. Diverse innovations are slowly being developed into better ideas, thus causing people to think outside the box. Various computer-related techniques are being applied in order to make life easier. this study is an application of data science techniques with regard to analyzing the Twitter sentiments of Filipino commuters. Moreover, this study also determines and compares which data science model obtained the most accurate result with regard to the prediction and analysis of Twitter sentiments. the algorithms used in this study are the Naive-Bayes, Support Vector machine, and Deep learning (Artificial Neural Network) containing English and Filipino language datasets. © 2023 IEEE.

关键词： Sentiment analysis

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Research on new recognition method of web component based on machine learning 5

Research on new recognition method of web component based on...

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5th international conference on computer Information Science and Artificial Intelligence, CISAI 2022

作者： Yang, Kaiming Zhou, Tianyang Wang, JunYi Institute of Cyberspace Security Information Engineering University Zhengzhou450001 China National Digital Switching System Engineering & Technology Research Center Zhengzhou450001 China

ISBN: (纸本)9781510662520

the security of web components is increasingly concerned by the industry, and identifying web components is of great significance to both network attack and defense. Aiming at this problem, we propose a novel identification method of web components based on machine learning. We transform the Web component identification problem into a classification problem. On this basis, we further construct the framework for realizing this method, and implement the framework. the experimental results show that the application of this method can have a good recognition effect on Web components. © 2023 SPIE.

关键词： Websites

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