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RETRACTED: Atrial fibrillation classification using deep learning algorithm in Internet of Things-based smart healthcare system (Retracted Article)

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HEALTH INFORMATICS JOURNAL 2020年第3期26卷 1827-1840页

作者： Jeyaraj, Pandia Rajan Nadar, Edward Rajan Samuel Mepco Schlenk Engn Coll Sivakasi India

Detecting the electrocardiogram pattern in Internet of Things-based healthcare system and notifying this to the user is a challenging task. Using advance computing methods for classification of electrocardiogram signal is a notable research topic. In this research work, an intelligent electrocardiogram signal classification, employing deep learning algorithm, developed and tested in Internet of Things-based smart healthcare system was proposed. For classification of acquired electrocardiogram signal, a partitioned deep convolutional neural network was proposed. The electrocardiogram feature continuously in the Internet of Things-based monitoring system was learnt. To make use of learned features in the continuous time series data, it forms a higher order space in the server. We have made quantifiable comparative analysis with other classification algorithm with the same time series data collected from different atrial fibrillation samples in the Internet of Things-based e-health system. Our proposed algorithm learned features were tested in atrial fibrillation classified signal with other conventional classifiers with various performance indices. We obtained an accuracy of 96.3 percent with 93.5-percent sensitivity and 97.5-percent precision. From the obtained result, processing with proposed deep convolutional neural network provides reliable timely assist and accurate classification of electrocardiogram signal in Internet of Things-based smart healthcare system.

关键词： deep learning algorithm electrocardiogram signal processing healthcare system smart medical informatics

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Novel progressive deep learning algorithm for uncovering multiple single nucleotide polymorphism interactions to predict paclitaxel clearance in patients with nonsmall cell lung cancer

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Cancer Innovation 2024年第4期3卷 13-25页

作者： Wei Chen Haiyan Zhou Mingyu Zhang Yafei Shi Taifeng Li Di Qian Jun Yang Feng Yu Guohui Li School of Basic Medicine and Clinical Pharmacy China Pharmaceutical UniversityNanjingChina Pharmacy Department National Cancer Center/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina

Background:The rate at which the anticancer drug paclitaxel is cleared from the body markedly impacts its dosage and chemotherapy ***,paclitaxel clearance varies among individuals,primarily because of genetic *** metabolic variability arises from a nonlinear process that is influenced by multiple single nucleotide polymorphisms(SNPs).Conventional bioinformatics methods struggle to accurately analyze this complex process and,currently,there is no established efficient algorithm for investigating SNP ***:We developed a novel machine‐learning approach called GEP‐CSIs data mining *** algorithm,an advanced version of GEP,uses linear algebra computations to handle discrete *** GEP‐CSI algorithm calculates a fitness function score based on paclitaxel clearance data and genetic polymorphisms in patients with nonsmall cell lung *** data were divided into a primary set and a validation set for the ***:We identified and validated 1184 three‐SNP combinations that had the highest fitness function ***,SERPINA1,ATF3 and EGF were found to indirectly influence paclitaxel clearance by coordinating the activity of genes previously reported to be significant in paclitaxel *** intriguing was the discovery of a combination of three SNPs in genes FLT1,EGF and *** SNPs‐related proteins were confirmed to interact with each other in the protein–protein interaction network,which formed the basis for further exploration of their functional roles and ***:We successfully developed an effective deep‐learning algorithm tailored for the nuanced mining of SNP interactions,leveraging data on paclitaxel clearance and individual genetic polymorphisms.

关键词： deep learning algorithm paclitaxel single nucleotide polymorphisms

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Spherical Aberration Coefficients Identification of Spherical Aberration Interferograms Based on deep learning algorithm 6

Spherical Aberration Coefficients Identification of Spherica...

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6th International Conference on Optical and Photonic Engineering (icOPEN)

作者： Yu, Yingjie Li, Jiaxing Shanghai Univ. (China)

ISBN: (纸本)9781510622579

In the field of optical measurement, the spherical aberration always causes a circular speckle which is symmetrical to the optic axis on interferograms and it can even blur the images. In this paper, a deep learning algorithm is put forward to identify the spherical aberration coefficients of monochromatic spherical aberration interferograms. The deep learning algorithm needs sufficient training samples, but it also takes a lot of time to collect the real interferograms as the training samples. The method of zernike polynomial fitting wave surface is much better relative to the least square method. Therefore, this paper adopts the zernike polynomials fitting method, by means of changing aberration coefficients, to obtain a large number of training samples of spherical aberration interferograms by computer simulation. Different from other traditional methods, using deep learning algorithms to identify the information of the interferograms is more efficient, the spherical aberration coefficients of interferograms can be accurately and quickly identified. What is more, this method also simplify the manual processing.

关键词： deep learning algorithm spherical aberration interferogram spherical aberration coefficient zernike polynomials wavefront fitting

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Image Classification-Based Ransomware Attack Detection Using deep learning algorithm 13

Image Classification-Based Ransomware Attack Detection Using...

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2024 International Conference on Radar Antenna Microwave Electronics and Telecommunications

作者： Abdulhamid, Shafii Muhammad Albalushi, Fawaz Issa Mohammed Al-Kuwari, Mohammed Hamed Al-Sulaiti, Yousef Abdullah Rana, Nadim Al-Ghushami, Abdullah Hussein Community Coll Qatar Dept Informat Technol Lusail Qatar Jazan Univ Dept Comp Sci Jazan Saudi Arabia

ISBN: (纸本)9798350389937;9798350389920

Ransomware represents a significant cybersecurity threat that can potentially damage corporate entities. Conventional antivirus methodologies must be revised to mitigate emerging and complex cyber-attack vectors. This study introduces an architecture for a Convolutional Neural Network (CNN) predicated on deep learning paradigms;it utilizes a dataset composed of both malicious and benign executables. These files were transformed into grayscale images via the Python Imaging Library (PIL) and subsequently processed by the CNN to effectively discriminate between images indicative of ransomware and those classified as benign. The effectiveness of the CNN model was measured using metrics like accuracy, precision, recall, and the F1 score. The training accuracy was 0.9476, while the validation accuracy was 0.9383 percent. Moreover, the precision, F1 score, and recall of training were higher than their respective validation rates. This model can contribute to developing an effective solution for detecting ransomware attacks, complement existing approaches, and improve accuracy.

关键词： Ransomware Cybersecurity deep learning algorithm Convolutional Neural Network Recurrent Neural Networks

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deep learning algorithm Aided E-Commerce Logistics Node Layout Optimization Based on Internet of Things Network

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2023年第4期10卷 1-13页

作者： Li, Lifeng Shenyang Polytechn Coll Shenyang 110045 Liaoning Peoples R China

INTRODUCTION: In recent years, e-commerce has shown a booming trend. Influenced by e-commerce, people's logistics needs have also increased sharply in recent years. OBJECTIVES: Research on the node layout and optimization of e-commerce logistics is conducive to improving the scientificity and rationality of logistics node layout, improving logistics distribution efficiency, reducing logistics distribution costs, and better meeting consumers' logistics needs. However, due to the unreasonable layout of logistics nodes in some areas, it has brought huge logistics cost investment to e-commerce companies, and also laid hidden dangers for the long-term development of e-commerce companies. METHODS: Based on this, this paper studied the node layout and optimization of e-commerce logistics by using IoT big data and deep learning algorithms, and proposed an improved logistics node layout scheme based on IoT big data and deep learning algorithms. The experimental research was carried out from five aspects: the transportation cost of logistics, the efficiency of logistics distribution, the accuracy of logistics information transmission, the location and traffic conditions of logistics nodes, and the evaluation of the plan by e-commerce enterprises. RESULTS: The research results showed that the improved logistics node layout scheme can improve the efficiency of logistics distribution by 3.69% and the accuracy of logistics information transmission by 4.34%, and can reduce the logistics transportation cost of e-commerce enterprises. CONCLUSION: The node locations selected by the improved logistics node layout scheme are more reasonable, and e-commerce companies have higher evaluations of the improved logistics node layout scheme.

关键词： IoT Big Data deep learning algorithm E-commerce Logistics Logistics Node Layout and Optimization Improved Logistics Node Layout Scheme

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Investigation on evaluation of education effect based on deep learning algorithm

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learning AND MOTIVATION 2024年 85卷

作者： Dong, Hao Wang, Guohua Yangzhou Polytech Inst Fac Innovat & Entrepreneurship Yangzhou 225000 Peoples R China Yangzhou Polytech Inst Fac Innovat & Entrepreneurship Yangzhou 225000 Peoples R China

With the rapid development of society today, in the context of the impact of education on personal future development, people have increasingly attached importance to education. Various types of online and offline education institutions have mushroomed. However, the factors that affect the effectiveness of education are numerous and complex. As a very important part of early childhood learning, preschool education should receive more attention. The deep learning algorithm can reasonably classify and plan the factors affecting preschool education through massive data calculations to ensure the reasonable allocation of resources and save the time and energy costs wasted in allocating educational resources. The educational effectiveness of preschool education institutions and organizations can be reasonably evaluated. This article applied deep learning algorithms to the evaluation of preschool education effectiveness. Data related to the effectiveness of preschool education in preschool education institutions using convolutional neural networks and not using algorithms were compared. The experimental results showed that the expert recognition rates of the convolutional neural network algorithm and traditional manual measurement data were 98.8% and 92.55%, respectively. At the time level of teaching quality estimation, five algorithms and 100 sets of relevant experimental data were compared. In terms of the accuracy of feature search, the average accuracy of the convolutional neural network algorithm was 97.96%, while the accuracy of the traditional manual search was 53.9%. Therefore, the application of the convolutional neural network algorithm in deep learning to the evaluation of preschool education effectiveness was more efficient and efficient and could improve the evaluation effect from various aspects.

关键词： Preschool education Convolutional neural network deep learning algorithm Personalized intelligent recommendation

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Microbial Image Data Enhancement Framework with deep learning algorithm 5

Microbial Image Data Enhancement Framework with Deep Learnin...

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

作者： Chen, Lin Yang, Shuhan Qujing Medical College Basic Medical College Yunnan Qujing655000 China

ISBN: (纸本)9798350379945

This study presents a comprehensive framework for enhancing microbial image data using a deep learning algorithm to improve the quality and analysis of microscopic images. The framework includes a deep learning-based image de-noising algorithm, an image anti-blurring algorithm, and an edge enhancement algorithm. The image de-noising process utilizes an iterative learning mapping function to progressively approximate actual noise distribution, resulting in clearer images with reduced noise. The image anti-blurring algorithm employs GAN-based methods to adjust and correct blur information caused by sample movements or lens jitter. The edge enhancement technique uses the Sobel operator to extract and enhance edge features, thereby improving the visual clarity and detail of microbial images. To evaluate the performance of the framework, a diverse database of 1,000,000 microbial images was constructed using the Google image search engine. The effectiveness of the approach was evaluated using three key parameters: Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Model Classification Accuracy (MCA). Experimental results show significant improvements in all metrics, with the proposed methods achieving an average PSNR of 29.0 dB, an average SSIM of 0.923, and an average MCA of 85%. These results highlight the potential of the proposed framework to improve image quality and accuracy in microbial image analysis. © 2024 IEEE.

关键词： Data processing deep learning algorithm Image enhancement Microbial image

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An Intelligent CMOS Image Sensor with a deep learning algorithm for Smart Internet of Things 34

An Intelligent CMOS Image Sensor with a Deep Learning Algori...

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34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC)

作者： Jin, Minhyun Park, Keunyeol Song, Minkyu Dongguk Univ Dept Semicond Sci Seoul South Korea

ISBN: (纸本)9781728132716

Recently, many kinds of tremendous requirements are needed for smart Internet of Things (IoT). Among them, an intelligent and self-working CMOS image sensor (CIS) is a key component to satisfy the specifications of smart IoT. In this paper, therefore, an intelligent CIS with a deep learning algorithm is discussed. With a deep learning and a variable pixel recognition algorithm, an intelligent CIS has been implemented. Even though a low resolution image is obtained, a high resolution image can be taken by the proposed deep learning algorithm. As well as the intelligent CIS chip has been fabricated with a 90nm Samsung CIS technology, the power consumption is extremely low by about 1.0mW with a VGA graphic mode.

关键词： intelligent and self-working CMOS image sensor smart Internet of Things (IoT) deep learning algorithm a variable pixel recognition algorithm

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Growth Height Prediction for the Trees under Overhead Lines based on deep learning algorithm

Growth Height Prediction for the Trees under Overhead Lines ...

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International Conference on Power System Technology (POWERCON)

作者： Su, Chao Wu, Xiaomei Tang, Xiaoliang Hu, Junling Guangdong Power Grid Co Ltd Qingyuan Power Suppy Bur Qingyuan Peoples R China Guangdong Univ Technol Dept Elect Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538664612

The safe operation of transmission overhead lines is often threatened by the fast growing and high growth trees under their line corridor. When the safe distance between the line and the tree is insufficient due to the limited height of transmission network, it is easy to occur tree related fault and tripping. Therefore, in order to effectively prevent the damage to overhead lines caused by the growth of extra high trees, it is necessary to know the growth rule of extra high trees and predict their growth height. In this paper, the deep learning algorithm is used to study the growth rule of extra high trees under overhead transmission lines. Different deep learning and artificial neural network algorithms such as deep Belief Network, AutoEncoder and Long-Short-Term-Memory algorithm are used to predict the tree height, and the validity of these algorithms is verified. Furthermore, these algorithms are combined to verify that the combined algorithm has higher prediction accuracy than the single multilayer perceptron and mathematical statistical model.

关键词： deep learning algorithm Prediction algorithms Transmission lines Tree height

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Research on Building Energy Consumption Prediction and Optimal Control System Based on deep learning algorithm

Research on Building Energy Consumption Prediction and Optim...

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2024 International Conference on Electronics and Devices, Computational Science, ICEDCS 2024

作者： Yang, Jie Hubei Light Industry Technology Institute Wuhan China

ISBN: (纸本)9798331527624

This paper studies the design of building energy consumption prediction and optimization control system using deep learning algorithm. First, this paper uses deep learning algorithms, especially recurrent neural networks (RNN), to capture the cyclic characteristics of building energy consumption data. RNNS and their variants Long Short-Term Memory Networks (LSTMS) perform well in processing time series data and can efficiently extract and utilize useful information from historical energy consumption data. A complete building energy consumption forecasting and control system is designed. In order to further improve the prediction accuracy, this paper also introduces the spectrum analysis method, through the frequency domain characteristics of energy consumption data to extract more hidden patterns and correlation information. In order to verify the effectiveness and reliability of the system, detailed simulation experiments are carried out in this paper. The experimental results show that the system has excellent performance in both prediction accuracy and control effect, and can significantly reduce building energy consumption and improve energy utilization efficiency. The design scheme of building energy consumption prediction and optimization control system based on deep learning algorithm proposed in this paper provides a new technical support and solution for realizing building energy saving and emission reduction. © 2024 IEEE.

关键词： cyclic characteristics deep learning algorithm design of building energy consumption prediction and control system spectrum analysis method system simulation

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