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A Multi-Watermarking Algorithm for Medical Images Using inception v3 and DCT

Computers, Materials & Continua 2023年第1期74卷 1279-1302页

作者： Yu Fan Jingbing Li Uzair Aslam Bhatti Chunyan Shao Cheng Gong Jieren Cheng Yenwei Chen School of Information and Communication Engineering Hainan UniversityHaikou570100China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570100China School of Computer Science and Technology Hainan UniversityHaikou570100China Graduate School of Information Science and Engineering Ritsumeikan UniversityKyoto5258577Japan Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570100China TJ-YZ School of Network Science Haikou University of EconomicsHaikou571127China

Medical images are a critical component of the diagnostic process for *** the quality of medical photographs is essential to the accuracy of a physician’s diagnosis,they must be encrypted due to the characteristics of digital storage and information leakage associated with medical *** watermark embedding algorithm embeds the watermark information into the medical image,which reduces the quality of the medical image and affects the physicians’judgment of patient *** addition,watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are *** paper proposes a novel watermarking algorithm using the convolutional neural networks(CNN)inception v3 and the discrete cosine transform(DCT)to address above mentioned ***,the medical image is input into the inception v3 network,which has been structured by adjusting parameters,such as the size of the convolution kernels and the typical architecture of the convolution ***,the coefficients extracted from the fully connected layer of the network are transformed by DCT to obtain the feature vector of the medical *** last,the watermarks are encrypted using the logistic map system and hash function,and the keys are stored by a third *** encrypted watermarks and the original image features are performed logical operations to realize the embedding of *** the experimental section,multiple watermarking schemes using three different types of watermarks were implemented to verify the effectiveness of the three proposed *** NC values for all the images are more than 90%accurate which shows the robustness of the *** experimental results demonstrate the robustness under both conventional and high-intensity geometric attacks of the proposed algorithm.

关键词： inception v3 multi-watermarking DCT watermark encryption robustness

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基于SE-inception v3与迁移学习的服装袖型识别与分类

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毛纺科技 2022年第10期50卷 99-106页

作者：庹武郭鑫张启泽刘永亮杜聪魏新桥中原工学院服装学院河南郑州451191

为了提高服装袖型的识别与分类效率,提出一种融合SE(Squeeze-and Excitation)注意力机制和inception v3主干网络的分类模型SE-inception v3。针对图片背景等无关信息对识别的干扰问题,添加注意力机制,增强有用特征通道;引入了迁移学习思... 详细信息

为了提高服装袖型的识别与分类效率,提出一种融合SE(Squeeze-and Excitation)注意力机制和inception v3主干网络的分类模型SE-inception v3。针对图片背景等无关信息对识别的干扰问题,添加注意力机制,增强有用特征通道;引入了迁移学习思想,防止因袖型样本数据集较少而产生过拟合问题。将通道注意力和inception模块多尺度卷积二者融合,有效地提升网络的特征提取和表达能力,该模型最终实现了以袖子为例的服装关键部位的识别与分类。通过对泡泡袖、灯笼袖、蝙蝠袖等8类服装袖型数据增强后共计3200个样本进行训练验证,平均准确率达到95.38%。与其它几类主流卷积神经网络模型进行对比实验,结果表明该模型具有较高的识别准确率,可为服装部位的图像分类识别提供有力支撑。

关键词：袖型识别注意力机制 inception v3 迁移学习卷积神经网络

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基于inception v3的高校学生课堂行为识别研究

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电脑知识与技术 2021年第6期17卷 13-15,29页

作者：柯斌杨思林曾睿代飞强振平西南林业大学现代教育技术中心云南昆明650224 西南林业大学大数据与智能工程学院云南昆明650224

随着人工智能和深度学习在教育领域的交叉融合,行为识别技术为学生课堂行为观察提供了一种有别于传统的新方法。以云南省X高校课堂视频为基础,经过预处理,获得六大类行为(听课、看书、书写、拍照、低头玩手机、桌面玩手机)30000张图像样... 详细信息

随着人工智能和深度学习在教育领域的交叉融合,行为识别技术为学生课堂行为观察提供了一种有别于传统的新方法。以云南省X高校课堂视频为基础,经过预处理,获得六大类行为(听课、看书、书写、拍照、低头玩手机、桌面玩手机)30000张图像样本,运用inception v3算法模型进行了研究,实验结果:六大类行为总识别率达到88.10%,但各个行为识别率有所不同,其中“拍照”和“听课”识别率较高。通过进一步的混淆矩阵分析,得到结论:模型对动作姿态单一的行为特征提取效果较好,但模型对手机、笔、课本等重要用具不够重视,不能识别书写动作和眼神角度,导致“看书”“书写”“低头玩手机”和“桌面玩手机”行为因人体动作姿态相似容易混淆。

关键词： inception v3 深度学习学生课堂行为行为识别

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基于改进RegNet网络的玉米叶片病害识别研究

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江苏农业科学 2024年第11期52卷 216-224页

作者：张澳雪崔艳荣李素若陈华锋胡玉荣胡蓉华长江大学计算机科学学院湖北荆州434000 荆楚理工学院计算机工程学院湖北荆门448000

针对目前玉米叶片病害识别模型参数量大、移动端部署难、识别准确率不够高等问题,提出一种基于轻量化网络RegNet和迁移学习的识别方法,首先收集4类常见玉米叶片病害图像样本,通过平移、镜像、旋转等方式对图像进行处理,以增加图片数量,... 详细信息

针对目前玉米叶片病害识别模型参数量大、移动端部署难、识别准确率不够高等问题,提出一种基于轻量化网络RegNet和迁移学习的识别方法,首先收集4类常见玉米叶片病害图像样本,通过平移、镜像、旋转等方式对图像进行处理,以增加图片数量,提升模型识别和泛化能力。接着以轻量化网络RegNet为主体,采用inception A结构对stem中的3×3卷积进行替换,增加模型宽度,以分解卷积的形式对玉米叶片病害进行多尺度特征提取。最后在head中引入金字塔池化模块(pyramid pooling module,PPM),用于减少空间信息丢失,保留病害重要特征和细节。试验结果表明,改进后的模型相比RegNet,Top-1准确率提升1.26百分点,平均精确率提升1.34百分点,平均F1分数提升1.33百分点,平均召回率提升1.34百分点,参数量只增加了0.89×10^(6),改进后的模型具有更好的特征提取能力,该模型为玉米叶片病害类型的识别提供了一种有效的方法。

关键词：玉米叶片病害图像分类 RegNet inception v3 金字塔池化

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Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

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Computers, Materials & Continua 2023年第10期77卷 681-697页

作者： Uzair Aslam Bhatti Sibghat Ullah Bazai Shumaila Hussain Shariqa Fakhar Chin Soon Ku Shah Marjan Por Lip Yee Liu Jing College of Information and Communication Engineering Hainan UniversityHaikou570228China Department of Computer Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Department of Computer Science Sardar Bahadur Khan Women’s UniversityQuettaPakistan Department of Computer Science Universiti Tunku Abdul RahmanKampar31900Malaysia Department of Software Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia

Crop diseases have a significant impact on plant growth and can lead to reduced *** methods of disease detection rely on the expertise of plant protection experts,which can be subjective and dependent on individual experience and *** address this,the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease *** this paper,we propose a novel approach that utilizes a convolutional neural network(CNN)model in conjunction with inception v3 to identify plant leaf *** research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific *** models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct *** rigorous training and evaluation,the proposed system achieved an impressive accuracy rate of 99%.This mobile application serves as a convenient and valuable advisory tool,providing early detection and guidance in real agricultural *** significance of this research lies in its potential to revolutionize plant disease detection and management *** automating the identification process through deep learning algorithms,the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual *** integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.

关键词： Plant disease inception v3 CNN crop diseases

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Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

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Computer Systems Science & Engineering 2023年第8期46卷 1845-1861页

作者： K.Kavin Kumar P.M.Dinesh P.Rayavel L.vijayaraja R.Dhanasekar Rupa Kesavan Kannadasan Raju Arfat Ahmad Khan Chitapong Wechtaisong Mohd Anul Haq Zamil S.Alzamil Ahmed Alhussen Department of Electronics and Communication Engineering Kongu Engineering College638060Tamil NaduIndia Department of Electronics and Communication Engineering Sona College of Technology636005Tamil NaduIndia Department of Computer Science and Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhonKaen40002Thailand School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be *** India,around 15 million cases are diagnosed *** mitigate the seriousness of the tumor it is essential to diagnose at the ***,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor ***,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and ***,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit *** computer models adapted to diagnose the tumor,but the accuracy of the model needs to be *** all the above mentioned things,this work aims to identify the best classification system by considering the prediction accuracy out of Alex-Net,ResNet 50,and inception *** augmentation is performed on the database and fed into the three convolutions neural network(CNN)models.A comparison line is drawn between the three models based on accuracy and *** accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and inception v3 for the 120th *** the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.

关键词： AlexNet brain tumor data augmentation inception v3 ResNet 50

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EfficientNetv2 Model for Plant Disease Classification and Pest Recognition

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Computer Systems Science & Engineering 2023年第5期45卷 2249-2263页

作者： R.S.Sandhya Devi v.Rvijay Kumar P.Sivakumar Department of EEE Kumaraguru College of TechnologyCoimbatore641049India Department of Electronics and Communication Engineering Anna University Regional CampusCoimbatore641046India Department of EEE PSG College of TechnologyCoimbatore641004India

Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of *** perform the investigation on Plant and pest classification,Transfer Learning(TL)approach is used on *** requires limited labelled data and shorter training ***,the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old ***-v2 is a Convolutional Neural Network(CNN)model with significant high speed learning rates across variable sized *** model employs a form of progressive learning mechanism which expands the network topology gradually over the course of training process improving the model’s learning *** provides a better interpretability of the model’s understanding on the test *** these insights,our work investigates the effectiveness of Efficienetv2 model trained on a class imbalanced dataset for plant disease classification and pest recognition by means of combining TL and progressive learning *** Progressive Learning for TL(PL-TL)is used in our work consisting of 38 classes of Plantvillage dataset of crops and fruit species,5 classes of cassava leaf diseases and another dataset with around 102 classes of crop pest images downloaded from popular dataset platforms,though it is not a benchmark *** test the predictability rate of the model in classifying leaf diseases with similar visual symptoms,Mix-up data augmentation technique is used at the ratio of 1:4 on corn and tomato classes which has high probability of misinterpretation of disease ***,the paper compares the TL approach performed on the above mentioned three types of data set using well established CNN based inceptionv3,and vision Transformer a non-CNN *** clearly depicts that EfficientNetv2 has an out

关键词： Image classification transfer learning efficientNetv2 mix-up data augmentation inception v3

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Intelligent Deep Convolutional Neural Network Based Object DetectionModel for visually Challenged People

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Computer Systems Science & Engineering 2023年第9期46卷 3191-3207页

作者： S.Kiruthika Devi Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Mohamed K.Nour Ahmed Ashour Anwer Mustafa Hilal Department of Computing Technology SRM Institute of Science and TechnologyIndia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Engineering Mathematics and Physics Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial Intelligence(AI)and Computer vision(Cv)advancements have led to many useful methodologies in recent years,particularly to help visually-challenged *** detection includes a variety of challenges,for example,handlingmultiple class images,images that get augmented when captured by a camera and so *** test images include all these variants as *** detection models alert them about their surroundings when they want to walk *** study compares four CNN-based pre-trainedmodels:ResidualNetwork(ResNet-50),inception v3,DenseConvolutional Network(DenseNet-121),and SqueezeNet,predominantly used in image recognition *** on the analysis performed on these test images,the study infers that inception v3 outperformed other pre-trained models in terms of accuracy and *** further improve the performance of the inception v3 model,the thermal exchange optimization(TEO)algorithm is applied to tune the hyperparameters(number of epochs,batch size,and learning rate)showing the novelty of the *** accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer,hyperparameter optimizer,and factorization ***,inception v3 can handle images of different *** makes inception v3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things(IoT)-based devices.

关键词： Pre-trained models object detection visually challenged people deep learning inception v3 DenseNet-121

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基于轻量级AlexNet网络的秦简文字识别算法

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中南大学学报（自然科学版） 2023年第9期54卷 3506-3517页

作者：陈炳权汪政阳夏蓉陈明吉首大学通信与电子工程学院湖南吉首416000 湖南大学电气与信息工程学院湖南长沙410082

以样本少且欠均衡的高信噪比秦简文字图像为研究对象,提出一种基于改进的轻量级AlexNet网络模型的秦简文字识别算法。首先,构建秦简单文字图像数据集,对其进行增强降噪、图像归一化等预处理操作;其次,调整AlexNet网络模型结构,构建4层... 详细信息

以样本少且欠均衡的高信噪比秦简文字图像为研究对象,提出一种基于改进的轻量级AlexNet网络模型的秦简文字识别算法。首先,构建秦简单文字图像数据集,对其进行增强降噪、图像归一化等预处理操作;其次,调整AlexNet网络模型结构,构建4层卷积层与池化层的顺序连接,并在前2层卷积层分别融入inceptionA与inceptionC结构,以分解卷积的形式对秦简文字进行局部特征提取,选取ReLU函数作为模型的激活函数,并在全连接层融入Dropout层进一步防止模型出现过拟合现象;最后,调用Softmax分类器完成秦简文字图像识别。研究结果表明:该网络模型在轻量化与识别准确率的表现上具有明显的优势,平均识别耗时为635 ms,识别准确率达到了99.89%,识别效果良好,可为秦简文字识别理论研究提供参考。

关键词：秦简文字图像识别 AlexNet inception v3

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基于深度学习的女衬衫图案样式识别分类

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现代纺织技术 2022年第4期30卷 207-213页

作者：李青冀艳波郭濠奇刘凯旋西安工程大学服装与艺术设计学院西安710048 江西理工大学电气工程与自动化学院江西赣州341000

针对服装图案分类效率低的问题,设计一种基于inception v3算法与迁移学习技术对女衬衫图案进行分类的方法。在inception v3基础上,拓展训练网络架构,对8121张8类女衬衫图片进行训练,并与GoogLeNet等典型算法模型进行准确率与损失值对比... 详细信息

针对服装图案分类效率低的问题,设计一种基于inception v3算法与迁移学习技术对女衬衫图案进行分类的方法。在inception v3基础上,拓展训练网络架构,对8121张8类女衬衫图片进行训练,并与GoogLeNet等典型算法模型进行准确率与损失值对比。结果表明:在相同的识别精度上inception v3具有较好的收敛速率;并且将迁移学习应用到inception v3优化算法中,在保持初始模型识别速度情况下,可使模型识别平均精度提高6%,达到98%,同时参与训练的参数量减少了约91%。研究结果可有效解决服装图案分类困难问题,并为服装图案可视化分类研究提供技术参考。

关键词： inception v3 迁移学习衬衫图案卷积神经网络

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