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DeepBSA: A deep-learning algorithm improves bulked segregant analysis for dissecting complex traits

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Molecular Plant 2022年第9期15卷 1418-1427页

作者： Zhao Li Xiaoxuan Chen Shaoqiang Shi Hongwei Zhang Xi Wang Hong Chen Weifu Li Lin Li National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070China Hainan Yazhou Bay Seed Lab HainanChina College of Science Huazhong Agricultural UniversityWuhan 430070China Institute of Crop Science Chinese Academy of Agricultural SciencesBeijing 100081China

Bulked segregant analysis(BSA)is a rapid,cost-effective method for mapping mutations and quantitative trait loci(QTLs)in animals and plants based on high-throughput sequencing.However,the algorithms currently used for BSA have not been systematically evaluated and are complex and fallible to operate.We developed a BSA method driven by deep learning,DeepBSA,for QTL mapping and functional gene cloning.DeepBSA is compatible with a variable number of bulked pools and performed well with various simulated and real datasets in both animals and plants.DeepBSA outperformed all other algorithms when comparing absolute bias and signal-to-noise ratio.Moreover,we applied DeepBSA to an F2 segregating maize population of 7160 individuals and uncovered five candidate QTLs,including three well-known plant-height genes.Finally,we developed a user-frienDLy graphical user interface for DeepBSA,by integrating five widely used BSA algorithms and our two newly developed algorithms,that is easy to operate and can quickly map QTLs and functional genes.The DeepBSA software is freely available to noncommercial users at http://***/tools.html and https://***/lizhao007/DeepBSA.

关键词： QTL mapping bulked segregant analysis BSA deep learning DL functional genomics plant height

Multi Criteria Decision Making System for Parking System

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Computer Systems Science & Engineering 2021年第1期36卷 101-116页

作者： Manjur Kolhar Abdalla Alameen College of Arts and Science Prince Sattam bin Abdulaziz University11990Saudi Arabia

System supported smart parking can reduce traffic by making it stress free to locate empty parking spaces,hence lowering the risk of unfocussed driving.In this study,we propose a smart parking system using deep learning and an application-based approach.This system has two modules,one module detects and recognizes a license plate(LP),and the other selects a parking space;both modules use deep learning techniques.We used two modules that work independently to detect and recognize an LP by using an image of the vehicle.To detect parking space,only deep learning techniques were used.The two modules were compared with other state-of-the-art solutions.We utilized the You Only Look Once(YOLO)architecture to detect and recognize an LP because its performance in the context of Saudi Arabian LP numbers was superior to that of other solutions.Compared with existing state-of-the-art solutions,the performance of the proposed solution was more effective.The solution can be further improved for use in the city and large organizations that have priority parking spaces.A dataset of LP-annotated images of vehicles was used.The results of this study have considerable implications for smart parking,particularly in universities;in addition,they can be utilized for smart cities.

关键词： Parking LBP DL CNN license plate

Efficient Deep CNN Model for COVID-19 Classification

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Computers, Materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in China.World Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death rate.Chest X-Ray(CXR)and Computerized Tomography(CT)screening of infected persons are essential in diagnosis applications.There are numerous ways to identify positive COVID-19 cases.One of the fundamental ways is radiology imaging through CXR,or CT images.The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality.Hence,automated classification techniques are required to facilitate the diagnosis process.Deep Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical images.The deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image datasets.In this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is offered.Three activation functions as well as three optimizers are tested and compared for this task.The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it.The performance is tested and investigated on the CT and CXR datasets.Three activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch sizes.Different optimizers are studied with different batch sizes and a constant learning rate.Finally,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is determined.Hence,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage.The proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR and CT datasets,respectively.The best results are

关键词： COVID-19 image classification CNN DL activation functions optimizers

Deep Learning-Assisted OFDM Detection with Hardware Impairments

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Journal of Communications and Information Networks 2023年第4期8卷 378-388页

作者： Amit Singh Sanjeev Sharma Kuntal Deka Vimal Bhatia Indian Institute of Technology(IIT)BHU Varanasi Varanasi 221005India Indian Institute of Technology(IIT)Guwahati Guwahati 781039India Indian Institute of Technology(IIT)Indore Indore 452020India

This paper introduces a deep learning(DL)algorithm for estimating doubly-selective fading channel and detecting signals in orthogonal frequency division multiplexing(OFDM)communication systems affected by hardware impairments(HIs).In practice,hardware imperfections are present at the transceivers,which are modeled as direct current(DC)offset,carrier frequency offset(CFO),and in-phase and quadrature-phase(IQ)imbalance at the transmitter and the receiver in OFDM system.In HIs,the explicit system model could not be mathematically derived,which limits the performance of conventional least square(LS)or minimum mean square error(MMSE)estimators.Thus,we consider time-frequency response of a channel as a 2D image,and unknown values of the channel response are derived using known values at the pilot locations with DL-based image super-resolution,and image restoration techniques.Further,a deep neural network(DNN)is designed to fit the mapping between the received signal and transmit symbols,where the number of outputs equals to the size of the modulation order.Results show that there are no significant effects of HIs on channel estimation and signal detection in the proposed DL-assisted algorithm.The proposed DL-assisted detection improves the OFDM performance as compared to the conventional LS/MMSE under severe Hls.

关键词： OFDM DL HIs channel estimation signal detection

番薯胡萝卜苷亚油酸酯抑制乳腺癌转移机制研究

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番薯胡萝卜苷亚油酸酯抑制乳腺癌转移机制研究

作者：黄璐西南大学

学位级别：硕士

近10年来,癌症一直是全球主要的公共卫生问题,且已经成为我国第三大、美国第二大死亡原因。近年来,我国乳腺癌的发病率呈现上升趋势,在55岁左右达到发病高峰期,其中城市地区的发病率和死亡率均高于农村地区,其中后期乳腺癌发生转移才是... 详细信息

近10年来,癌症一直是全球主要的公共卫生问题,且已经成为我国第三大、美国第二大死亡原因。近年来,我国乳腺癌的发病率呈现上升趋势,在55岁左右达到发病高峰期,其中城市地区的发病率和死亡率均高于农村地区,其中后期乳腺癌发生转移才是导致乳腺癌患者死亡的主要原因。但是到目前止,我们对转移性乳腺癌的基础理论研究尚不充分,临床上也缺乏有效的检测靶标和治疗药物。转移性乳腺癌是一个世界性难题。目前针对乳腺癌转移的治疗有放疗化疗,但易出现耐药性和毒副作用,因此开发安全有效且毒副作用小的乳腺癌辅助药物很有必要。番薯作为世界上第七大经济作物,其块根、茎和叶都可食用,并且含有多种对人体健康有益的成分,如糖蛋白、胡萝卜素等。课题组前期研究发现番薯提取物中的胡萝卜苷亚油酸酯(Daucosterol Linoleate,DL)对乳腺癌肺转移有显著抑制作用。本文旨在探究DL对乳腺癌转移的抑制作用机制。本研究主要从以下几方面展开实验:(1)研究DL体外对乳腺癌细胞的转移的抑制作用。运用MTT实验分析DL对乳腺癌细胞4T1、MCF-7、MDA-MB-231细胞活力的影响,结果发现,在三种乳腺癌细胞中,MCF-7细胞对DL处理较敏感,在三个细胞中,DL抑制4T1细胞活力较弱(IC50:96.45μM),其次是MDA-MB-231(IC50:93.00μM),MCF-7(IC50:79.84μM)。运用细胞划痕实验和Transwell迁移实验检测DL对三个细胞系迁移能力的抑制作用,分析发现DL对4T1抑制迁移能力最强,其次是MCF-7和MDA-MB-231。(2)研究DL体内抗乳腺癌转移作用。选用高转移性MDA-MB-231乳腺癌细胞建立裸鼠尾静脉注射肺转移模型,通过灌胃DL的方式观察体内DL抗MDA-MB-231乳腺癌转移的效果。结果显示DL灌胃剂量87.8 mg/kg时对裸鼠的体重无影响。肺组织切片H&E染色结果显示,DL对MDA-MB-231乳腺癌裸鼠尾静脉注射肺转移模型的抑制率可达到63.16%。(3)探索DL抑制乳腺癌转移的潜在靶标。利用前期课题组荷瘤裸鼠肺转移蛋白质组学进行GSEA基因集富集分析,结果显示上皮间质-转化和雌激素早期应答与DL组显著相关。经筛选找出VAMP3、TAP2、CHK1、SCD1和LOXL2,这些DL潜在作用靶标,经q PCR验证、分子对接模拟DL与之的结合模型,最终筛选到SCD1为潜在靶标。(4)研究DL与SCD1在乳腺癌转移抑制过程中的关系。运用q PCR和WB检测4T1、MCF-7、MDA-MB-231三个细胞系中SD1的m RNA和蛋白表达水平,选择表达水平最高的MDA-MB-231细胞开展后续实验。用MTT法测定SCD1蛋白抑制剂PluriSIn1处理MDA-MB-231细胞24h的IC50。通过细胞划痕实验和Transwell迁移实验检测过表达SCD1后给予DL对MDA-MB-231迁移能力的影响,结果显示,过表达SCD1后细胞迁移能力显著提高,DL处理可以有效降低细胞的迁移率,使迁移率仅达到普通MDA-MB-231细胞的49.4%-67.5%,表明DL可以逆转SCD1过表达导致的MDA-MB-231的迁移率能力增强,SCD1蛋白抑制剂PluriSIn1对MDA-MB-231的迁移抑制作用显著。(5)DL调节EMT途径标志蛋白的表达。用1/2IC50和IC50浓度的DL和PluriSIn1处理MDA-MB-231,WB检测EMT途径标志蛋白N-Cadherin、MMP2、Vimentin、Snail和ER的蛋白表达,均出现剂量依赖性下降,SCD1同样表达下降。WB检测过表达SCD1的MDA-MB-231中的EMT途径标志蛋白的表达,发现过表达SCD1后这些蛋白均出现表达上调,但给予DL或PluriSIn1处理后,EMT途径标志蛋白的表达水平下降至与Victor相近。(6)验证DL在体内通过降低SCD1的表达来抑制乳腺癌的转移。通过慢病毒包装系统构建稳定过表达SCD1的oe-SCD-MDA-MB-231细胞系,建立乳腺癌裸鼠尾静脉注射肺转移模型,接种oe-SCD-MDA-MB-231一周后开始灌胃DL组,PluriSIn1作为靶向SCD1的阳性药通过腹腔注射给药,3周后取裸鼠肺组织染色后发现,与oe-SCD组比较,DL和PluriSIn1可显著抑制oe-SCD-MDA-MB-231在裸鼠体内的肺转移,减少肺转移结节数量,抑制转移灶大小。综上所述,本研究证实DL具有显著的抗乳腺癌转移的作用,DL通过降低SCD1的表达参与抑制乳腺癌细胞迁移。通过oe-SCD-MDA-MB-231的系列实验证明,SCD1的表达与EMT途径标志蛋白的表达呈正相关性,DL可通过降低SCD1的表达来抑制EMT和乳腺癌转移的发生。

关键词：乳腺癌转移 DL SCD1

为专业而来——海力欧DL-200大功率太阳灯

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人像摄影 2022年第11期 162-165页

作者：冯靖宇(摄影) 不详

今天为大家推荐的是海力欧DL-200大功率太阳灯,它以高显色性、高色彩还原为摄影师带来专业的LED微电影光源。从性能和光质来说,比较迎合现在主流的微电影摄影演播室要求,DL-200提供了稳定的显色性,解决专业拍摄中的一些问题和困扰,在影... 详细信息

今天为大家推荐的是海力欧DL-200大功率太阳灯,它以高显色性、高色彩还原为摄影师带来专业的LED微电影光源。从性能和光质来说,比较迎合现在主流的微电影摄影演播室要求,DL-200提供了稳定的显色性,解决专业拍摄中的一些问题和困扰,在影室的布光和后期环节为摄影师带来了更好的体验。

关键词：演播室布光色彩还原电影摄影显色性 LED DL 大功率

Intrusion Detection System Through Deep Learning in Routing MANET Networks

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Intelligent Automation & Soft Computing 2023年第7期37卷 269-281页

作者： Zainab Ali Abbood DoguÇagdaşAtilla Çagatay Aydin Department Electrical and Computer Engineering Altinbas UniversityIstanbulTurkey Department Computer Technology Engineering Al-Esraa UniversityBaghdadIraq Electric Autonomous and Unmanned Vehicles Application and Research CentreAltinbas UniversityIstanbulTurkey Faculty of Engineering and Architecture Altinbas UniversityIstanbulTurkey Department Electrical and Electronics Engineering Ege UniversityIzmirTurkey

Deep learning(DL)is a subdivision of machine learning(ML)that employs numerous algorithms,each of which provides various explanations of the data it consumes;mobile ad-hoc networks(MANET)are growing in promi-nence.For reasons including node mobility,due to MANET’s potential to provide small-cost solutions for real-world contact challenges,decentralized management,and restricted bandwidth,MANETs are more vulnerable to security threats.When protecting MANETs from attack,encryption and authentication schemes have their limits.However,deep learning(DL)approaches in intrusion detection sys-tems(IDS)can adapt to the changing environment of MANETs and allow a sys-tem to make intrusion decisions while learning about its mobility in the environment.IDSs are a secondary deﬁance system for mobile ad-hoc networks vs.attacks since they monitor network trafﬁc and report anything unusual.Recently,many scientists have employed deep neural networks(DNNs)to address intrusion detection concerns.This paper used MANET to recognize com-plex patterns by focusing on security standards through efﬁciency determination and identifying malicious nodes,and mitigating network attacks using the three algorithms presented Cascading Back Propagation Neural Network(CBPNN),Feedforward-Neural-Network(FNN),and Cascading-Back-Propagation-Neural-Network(CBPNN)(FFNN).In addition to Convolutional-Neural-Network(CNN),these primary forms of deep neural network(DNN)building designs are widely used to improve the performance of intrusion detection systems(IDS)and the use of IDS in conjunction with machine learning(ML).Further-more,machine learning(ML)techniques than their statistical and logical methods provide MANET network learning capabilities and encourage adaptation to differ-ent environments.Compared with another current model,The proposed model has better average receiving packet(ARP)and end-to-end(E2E)performance.The results have been obtained from CBP,FFNN and CNN 74%,82%and 85%,respectively,by the time(27,18,and 17 s).

关键词： ARP CBPNN CNN DNN DL E2E FFNN IDS ML MANET security

A New Method for Scene Classification from the Remote Sensing Images

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Computers, Materials & Continua 2022年第7期72卷 1339-1355页

作者： Purnachand Kollapudi Saleh Alghamdi Neenavath Veeraiah Youseef Alotaibi Sushma Thotakura Abdulmajeed Alsufyani Department of CSE B V Raju Institute of TechnologyNarsapurMedakTelanganaIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications DVR&DHS MIC Engineering CollegeKanchikacharlaVijayawadaA.P.India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of ECE P.V.P Siddhartha Institute of TechnologyVijayawadaIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The mission of classifying remote sensing pictures based on their contents has a range of applications in a variety of areas.In recent years,a lot of interest has been generated in researching remote sensing image scene classification.Remote sensing image scene retrieval,and scene-driven remote sensing image object identification are included in the Remote sensing image scene understanding(RSISU)research.In the last several years,the number of deep learning(DL)methods that have emerged has caused the creation of new approaches to remote sensing image classification to gain major breakthroughs,providing new research and development possibilities for RS image classification.A new network called Pass Over(POEP)is proposed that utilizes both feature learning and end-to-end learning to solve the problem of picture scene comprehension using remote sensing imagery(RSISU).This article presents a method that combines feature fusion and extraction methods with classification algorithms for remote sensing for scene categorization.The benefits(POEP)include two advantages.The multi-resolution feature mapping is done first,using the POEP connections,and combines the several resolution-specific feature maps generated by the CNN,resulting in critical advantages for addressing the variation in RSISU data sets.SeconDLy,we are able to use Enhanced pooling tomake the most use of themulti-resolution feature maps that include second-order information.This enablesCNNs to better cope with(RSISU)issues by providing more representative feature learning.The data for this paper is stored in a UCI dataset with 21 types of pictures.In the beginning,the picture was pre-processed,then the features were retrieved using RESNET-50,Alexnet,and VGG-16 integration of architectures.After characteristics have been amalgamated and sent to the attention layer,after this characteristic has been fused,the process of classifying the data will take place.We utilize an ensemble classifier in our classification algorithm that utilizes the architecture of a Dec

关键词： Remote sensing RSISU DL RESNET-50 VGG-16

An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications

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Computers, Materials & Continua 2022年第8期72卷 3529-3548页

作者： Ghalib H.Alshammri Amani K.Samha Ezz El-Din Hemdan Mohammed Amoon Walid El-Shafai Department of Computer Science Community CollegeKing Saud UniversityRiyadh28095Saudi Arabia Deanship of Scientific Research Saudi Electronic UniversityRiyadhSaudi Arabia Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh28095Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Electronics and Electrical Communications Eng.Depart. Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process.In recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable controller.Therefore,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment.Consequently,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five algorithms.Furthermore,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)dataset.First,the utilized dataset is clustered into normal and four major attack categories via the clustering process.Then,a deep learning method is projected for building an efficient SDN-based intrusion detection system.The results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD dataset.Similarly,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing techniques.For example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.

关键词： Deep neural network DL WEKA network traffic intrusion and anomaly detection SDN clustering and classification KDD dataset