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Robust Malicious Executable Detection Using Host-Based Machine Learning Classifier
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作者 Khaled Soliman Mohamed Sobh Ayman M.Bahaa-Eldin 《Computers, Materials & Continua》 SCIE EI 2024年第4期1419-1439,共21页
The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are ins... The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach.The most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks. 展开更多
关键词 Portable executable MALWARE intrusion detection CYBERSECURITY zero-day threats Host IntrusionDetection system(HIDS) machine learning Anomaly-based Intrusion Detection system(AIDS) deep learning
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CADGen:Computer-aided design sequence construction with a guided codebook learning
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作者 Shengdi Zhou Xiaoqiang Zan +1 位作者 Zhuqing Li Bin Zhou 《Digital Twins and Applications》 2024年第1期75-87,共13页
Computer-aided design(CAD)software continues to be a crucial tool in digital twin application and manufacturing,facilitating the design of various products.We present a novel CAD generation method,an agent that constr... Computer-aided design(CAD)software continues to be a crucial tool in digital twin application and manufacturing,facilitating the design of various products.We present a novel CAD generation method,an agent that constructs the CAD sequences containing the sketch-and-extrude modelling operations efficiently and with high quality.Starting from the sketch and extrusion operation sequences,we utilise the transformer encoder to encode them into different disentangled codebooks to represent their distribution properties while considering their correlations.Then,a combination of auto-regressive and non-autoregressive samplers is trained to sample the code for CAD sequence con-struction.Extensive experiments demonstrate that our model generates diverse and high-quality CAD models.We also show some cases of real digital twin applications and indicate that our generated model can be used as the data source for the digital twin platform,exhibiting designers'potential. 展开更多
关键词 CAD sequence construction code sample computer‐aided design digital twins hierarchical code learning
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Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review 被引量:11
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作者 Samy A Azer 《World Journal of Gastrointestinal Oncology》 SCIE CAS 2019年第12期1218-1230,共13页
BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algor... BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algorithm similar to deep learning,has demonstrated its capability to recognise specific features that can detect pathological lesions.AIM To assess the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer and evaluating the accuracy level of CNNs and their performance.METHODS The databases PubMed,EMBASE,and the Web of Science and research books were systematically searched using related keywords.Studies analysing pathological anatomy,cellular,and radiological images on HCC or liver masses using CNNs were identified according to the study protocol to detect cancer,differentiating cancer from other lesions,or staging the lesion.The data were extracted as per a predefined extraction.The accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were analysed.The primary outcomes of the study were analysing the type of cancer or liver mass and identifying the type of images that showed optimum accuracy in cancer detection.RESULTS A total of 11 studies that met the selection criteria and were consistent with the aims of the study were identified.The studies demonstrated the ability to differentiate liver masses or differentiate HCC from other lesions(n=6),HCC from cirrhosis or development of new tumours(n=3),and HCC nuclei grading or segmentation(n=2).The CNNs showed satisfactory levels of accuracy.The studies aimed at detecting lesions(n=4),classification(n=5),and segmentation(n=2).Several methods were used to assess the accuracy of CNN models used.CONCLUSION The role of CNNs in analysing images and as tools in early detection of HCC or liver masses has been demonstrated in these studies.While a few limitations have been identified in these studies,overall there was an optimal level of accuracy of the CNNs used in segmentation and classification of liver cancers images. 展开更多
关键词 Deep learning Convolutional neural network HEPATOCELLULAR CARCINOMA LIVER MASSES LIVER cancer Medical imaging Classification Segmentation Artificial INTELLIGENCE COMPUTER-aided diagnosis
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Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification
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作者 K.Kalyani Sara A Althubiti +4 位作者 Mohammed Altaf Ahmed ELaxmi Lydia Seifedine Kadry Neunggyu Han Yunyoung Nam 《Computers, Materials & Continua》 SCIE EI 2023年第4期149-164,共16页
Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. ... Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. Computer-Aided Diagnostic (CAD) tool saves time and effort indiagnosing melanoma compared to existing medical approaches. In this background,there is a need exists to design an automated classification modelfor melanoma that can utilize deep and rich feature datasets of an imagefor disease classification. The current study develops an Intelligent ArithmeticOptimization with Ensemble Deep Transfer Learning Based MelanomaClassification (IAOEDTT-MC) model. The proposed IAOEDTT-MC modelfocuses on identification and classification of melanoma from dermoscopicimages. To accomplish this, IAOEDTT-MC model applies image preprocessingat the initial stage in which Gabor Filtering (GF) technique is utilized.In addition, U-Net segmentation approach is employed to segment the lesionregions in dermoscopic images. Besides, an ensemble of DL models includingResNet50 and ElasticNet models is applied in this study. Moreover, AOalgorithm with Gated Recurrent Unit (GRU) method is utilized for identificationand classification of melanoma. The proposed IAOEDTT-MC methodwas experimentally validated with the help of benchmark datasets and theproposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset. 展开更多
关键词 Skin cancer deep learning melanoma classification DERMOSCOPY computer aided diagnosis
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Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework
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作者 Thavavel Vaiyapuri S.Srinivasan +4 位作者 Mohamed Yacin Sikkandar T.S.Balaji Seifedine Kadry Maytham N.Meqdad Yunyoung Nam 《Computers, Materials & Continua》 SCIE EI 2022年第12期5543-5557,共15页
In past decades,retinal diseases have become more common and affect people of all age grounds over the globe.For examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is nee... In past decades,retinal diseases have become more common and affect people of all age grounds over the globe.For examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated diagnosis.To analyze the retinal malady,the system proposes a multiclass and multi-label arrangement method.Therefore,the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge,which tends to be time-consuming,vulnerable generalization ability,and unfeasible in massive datasets.Therefore,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)models.With this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus images.The proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological Myopia.Besides,the artificial flora algorithm with Shannon’s function(AFA-SF)basedmulti-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly detected.In addition,SqueezeNet based feature extractor is employed to generate a collection of feature vectors.Finally,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal diseases.The efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different classes.The experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963. 展开更多
关键词 Multi-retinal disease computer aided diagnosis fundus images deep learning SEGMENTATION intelligent models
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Symbiotic Organisms Search with Deep Learning Driven Biomedical Osteosarcoma Detection and Classification
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作者 Abdullah M.Basahel Mohammad Yamin +3 位作者 Sulafah M.Basahel Mona M.Abusurrah K.Vijaya Kumar E.Laxmi Lydia 《Computers, Materials & Continua》 SCIE EI 2023年第4期133-148,共16页
Osteosarcoma is one of the rare bone cancers that affect the individualsaged between 10 and 30 and it incurs high death rate. Early diagnosisof osteosarcoma is essential to improve the survivability rate and treatment... Osteosarcoma is one of the rare bone cancers that affect the individualsaged between 10 and 30 and it incurs high death rate. Early diagnosisof osteosarcoma is essential to improve the survivability rate and treatmentprotocols. Traditional physical examination procedure is not only a timeconsumingprocess, but it also primarily relies upon the expert’s knowledge.In this background, the recently developed Deep Learning (DL) models canbe applied to perform decision making. At the same time, hyperparameteroptimization of DL models also plays an important role in influencing overallclassification performance. The current study introduces a novel SymbioticOrganisms Search with Deep Learning-driven Osteosarcoma Detection andClassification (SOSDL-ODC) model. The presented SOSDL-ODC techniqueprimarily focuses on recognition and classification of osteosarcoma usinghistopathological images. In order to achieve this, the presented SOSDL-ODCtechnique initially applies image pre-processing approach to enhance the qualityof image. Also, MobileNetv2 model is applied to generate a suitable groupof feature vectors whereas hyperparameter tuning of MobileNetv2 modelis performed using SOS algorithm. At last, Gated Recurrent Unit (GRU)technique is applied as a classification model to determine proper class labels.In order to validate the enhanced osteosarcoma classification performance ofthe proposed SOSDL-ODC technique, a comprehensive comparative analysiswas conducted. The obtained outcomes confirmed the betterment of SOSDLODCapproach than the existing approaches as the former achieved a maximumaccuracy of 97.73%. 展开更多
关键词 OSTEOSARCOMA medical imaging deep learning feature vectors computer aided diagnosis image classification
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Deer Hunting Optimization with Deep Learning Model for Lung Cancer Classification
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作者 Mahmoud Ragab Hesham A.Abdushkour +1 位作者 Alaa F.Nahhas Wajdi H.Aljedaibi 《Computers, Materials & Continua》 SCIE EI 2022年第10期533-546,共14页
Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages.Early recognition of lung cancer is essential to increase the survival rate of persons a... Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages.Early recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare sector.Computer aided diagnosis(CAD)models can be designed to effectually identify and classify the existence of lung cancer using medical images.The recently developed deep learning(DL)models find a way for accurate lung nodule classification process.Therefore,this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and classification(DHODCNNLCC)model.The proposed DHODCNN-LCC technique initially undergoes pre-processing in two stages namely contrast enhancement and noise removal.Besides,the features extraction process on the pre-processed images takes place using the Nadam optimizer with RefineDet model.In addition,denoising stacked autoencoder(DSAE)model is employed for lung nodule classification.Finally,the deer hunting optimization algorithm(DHOA)is utilized for optimal hyper parameter tuning of the DSAE model and thereby results in improved classification performance.The experimental validation of the DHODCNN-LCC technique was implemented against benchmark dataset and the outcomes are assessed under various aspects.The experimental outcomes reported the superior outcomes of the DHODCNN-LCC technique over the recent approaches with respect to distinct measures. 展开更多
关键词 Lung cancer image classification computer aided diagnosis deep learning medical imaging parameter optimization
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Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model
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作者 Mesfer Al Duhayyim Areej A.Malibari +4 位作者 Sami Dhahbi Mohamed K.Nour Isra Al-Turaiki Marwa Obayya Abdullah Mohamed 《Computer Systems Science & Engineering》 SCIE EI 2023年第4期753-767,共15页
Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare sector.The advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective d... Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare sector.The advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective design of CAD models,which enables to detection of the existence of diseases using various imaging modalities.Oral cancer(OC)has commonly occurred in head and neck globally.Earlier identification of OC enables to improve survival rate and reduce mortality rate.Therefore,the design of CAD model for OC detection and classification becomes essential.Therefore,this study introduces a novel Computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification(CADOC-SFOFC)model.The proposed CADOC-SFOFC model determines the existence of OC on the medical images.To accomplish this,a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network(ResNet)model.Besides,feature vectors are fused and passed into the extreme learning machine(ELM)model for classification process.Moreover,SFO algorithm is utilized for effective parameter selection of the ELM model,consequently resulting in enhanced performance.The experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%.Therefore,the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency. 展开更多
关键词 Oral cancer computer aided diagnosis deep learning fusion model seagull optimization ClasSIFICATION
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Intelligent Beetle Antenna Search with Deep Transfer Learning Enabled Medical Image Classification Model
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作者 Mohamed Ibrahim Waly 《Computer Systems Science & Engineering》 SCIE EI 2023年第9期3159-3174,共16页
Recently,computer assisted diagnosis(CAD)model creation has become more dependent on medical picture categorization.It is often used to identify several conditions,including brain disorders,diabetic retinopathy,and sk... Recently,computer assisted diagnosis(CAD)model creation has become more dependent on medical picture categorization.It is often used to identify several conditions,including brain disorders,diabetic retinopathy,and skin cancer.Most traditional CAD methods relied on textures,colours,and forms.Because many models are issue-oriented,they need a more substantial capacity to generalize and cannot capture high-level problem domain notions.Recent deep learning(DL)models have been published,providing a practical way to develop models specifically for classifying input medical pictures.This paper offers an intelligent beetle antenna search(IBAS-DTL)method for classifying medical images facilitated by deep transfer learning.The IBAS-DTL model aims to recognize and classify medical pictures into various groups.In order to segment medical pictures,the current IBASDTLM model first develops an entropy based weighting and first-order cumulative moment(EWFCM)approach.Additionally,the DenseNet-121 techniquewas used as a module for extracting features.ABASwith an extreme learning machine(ELM)model is used to classify the medical photos.A wide variety of tests were carried out using a benchmark medical imaging dataset to demonstrate the IBAS-DTL model’s noteworthy performance.The results gained indicated the IBAS-DTL model’s superiority over its pre-existing techniques. 展开更多
关键词 Medical image segmentation image classification decision making computer aided diagnosis deep learning
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Automated Skin Lesion Diagnosis and Classification Using Learning Algorithms
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作者 A.Soujanya N.Nandhagopal 《Intelligent Automation & Soft Computing》 SCIE 2023年第1期675-687,共13页
Due to the rising occurrence of skin cancer and inadequate clinical expertise,it is needed to design Artificial Intelligence(AI)based tools to diagnose skin cancer at an earlier stage.Since massive skin lesion dataset... Due to the rising occurrence of skin cancer and inadequate clinical expertise,it is needed to design Artificial Intelligence(AI)based tools to diagnose skin cancer at an earlier stage.Since massive skin lesion datasets have existed in the literature,the AI-based Deep Learning(DL)modelsfind useful to differentiate benign and malignant skin lesions using dermoscopic images.This study develops an Automated Seeded Growing Segmentation with Optimal EfficientNet(ARGS-OEN)technique for skin lesion segmentation and classification.The proposed ASRGS-OEN technique involves the design of an optimal EfficientNet model in which the hyper-parameter tuning process takes place using the Flower Pollination Algorithm(FPA).In addition,Multiwheel Attention Memory Network Encoder(MWAMNE)based classification technique is employed for identifying the appropriate class labels of the dermoscopic images.A comprehensive simulation analysis of the ASRGS-OEN technique takes place and the results are inspected under several dimensions.The simulation results highlighted the supremacy of the ASRGS-OEN technique on the applied dermoscopic images compared to the recently developed approaches. 展开更多
关键词 Computer aided diagnosis deep learning image segmentation skin lesion diagnosis dermoscopic images medical image processing
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Deep Learning with Optimal Hierarchical Spiking Neural Network for Medical Image Classification
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作者 P.Immaculate Rexi Jenifer S.Kannan 《Computer Systems Science & Engineering》 SCIE EI 2023年第2期1081-1097,共17页
Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented... Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented and exhibited complementary in medical images.The recently developed deep learning(DL)approaches pave an efficient method of constructing dedicated models for classification problems.But the maximum resolution of medical images and small datasets,DL models are facing the issues of increased computation cost.In this aspect,this paper presents a deep convolutional neural network with hierarchical spiking neural network(DCNN-HSNN)for medical image classification.The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images.In addition,region growing segmentation technique is involved to determine the infected regions in the medical image.Moreover,NADAM optimizer with DCNN based Capsule Network(CapsNet)approach is used for feature extraction and derived a collection of feature vectors.Furthermore,the shark smell optimization algorithm(SSA)based HSNN approach is utilized for classification process.In order to validate the better performance of the DCNN-HSNN technique,a wide range of simulations take place against HIS2828 and ISIC2017 datasets.The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures.Please type your abstract here. 展开更多
关键词 Medical image classification spiking neural networks computer aided diagnosis medical imaging parameter optimization deep learning
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Analysis of the Role of Problem-Based Independent Learning Model in Teaching Cerebral Ischemic Stroke First Aid in Emergency Medicine
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作者 Hua Liu 《Journal of Contemporary Educational Research》 2024年第6期16-21,共6页
Objective:To analyze the effect of using a problem-based(PBL)independent learning model in teaching cerebral ischemic stroke(CIS)first aid in emergency medicine.Methods:90 interns in the emergency department of our ho... Objective:To analyze the effect of using a problem-based(PBL)independent learning model in teaching cerebral ischemic stroke(CIS)first aid in emergency medicine.Methods:90 interns in the emergency department of our hospital from May 2022 to May 2023 were selected for the study.They were divided into Group A(45,conventional teaching method)and Group B(45 cases,PBL independent learning model)by randomized numerical table method to compare the effects of the two groups.Results:The teaching effect indicators and student satisfaction scores in Group B were higher than those in Group A(P<0.05).Conclusion:The use of the PBL independent learning model in the teaching of CIS first aid can significantly improve the teaching effect and student satisfaction. 展开更多
关键词 Problem-based independent learning model Emergency medicine Ischemic stroke First aid teaching SATISFACTION
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改进YOLOv7的结直肠息肉检测算法
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作者 薛钦原 胡珊珊 +1 位作者 胡新军 严松才 《计算机工程与应用》 北大核心 2025年第1期243-251,共9页
计算机辅助诊断对提高息肉诊断准确率和降低结直肠癌死亡率至关重要,但息肉形态各异,息肉类似物和肠内的复杂环境导致目前的方法存在较多的误诊和漏诊。因此提出了一种改进的YOLOv7结直肠息肉检测算法(YOLOv7-IDH),使用含隐式知识的高... 计算机辅助诊断对提高息肉诊断准确率和降低结直肠癌死亡率至关重要,但息肉形态各异,息肉类似物和肠内的复杂环境导致目前的方法存在较多的误诊和漏诊。因此提出了一种改进的YOLOv7结直肠息肉检测算法(YOLOv7-IDH),使用含隐式知识的高效解耦头,充分利用隐含信息并防止分类和回归任务之间相互干扰;引入全局注意力机制,增强模型对浅层特征的提取能力;对SPPCSPC模块进行优化,减少模型参数和提高收敛速度。实验结果表明,改进模型在组合数据集上的F1分数和mAP@0.5分别达到了94.8%和97.1%,可以满足息肉自动检测的要求。 展开更多
关键词 息肉检测 深度学习 计算机辅助诊断 解耦头 注意力机制
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机械类E-Learning系统设计与实现
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作者 郗涛 王学彬 王莉静 《天津工业大学学报》 CAS 2008年第1期74-77,共4页
针对机械及相关专业以图形为主的协作式学习,利用现有计算机辅助设计技术及协同设计技术,开发设计了本E-Learning系统.这一系统主要包括三大功能模块:交互学习模块、教师管理模块和系统管理模块.在交互学习模块的设计中根据机械专业以... 针对机械及相关专业以图形为主的协作式学习,利用现有计算机辅助设计技术及协同设计技术,开发设计了本E-Learning系统.这一系统主要包括三大功能模块:交互学习模块、教师管理模块和系统管理模块.在交互学习模块的设计中根据机械专业以图形为主的特点实现了机械图形在线交互功能和远程协助功能.经过一段时间的应用,证实该系统效果良好. 展开更多
关键词 E-learning 远程协助 机械图形交互
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多媒体技术在英语音素教学中的实践探索——FLASH在创作“学习英语音素”音像课件中的设计与应用
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作者 张鸽 刘琳 《现代教育技术》 CSSCI 2010年第9期80-84,共5页
利用多媒体与计算机技术,设计集"看、听、模仿、练习"四位一体的人机互动式英语音素教与学模式。展现FLASH技术创作"学习英语音素"课件的探索,把英语48个音素发音的口形、舌位、声带的运动过程与教师的发音要领讲... 利用多媒体与计算机技术,设计集"看、听、模仿、练习"四位一体的人机互动式英语音素教与学模式。展现FLASH技术创作"学习英语音素"课件的探索,把英语48个音素发音的口形、舌位、声带的运动过程与教师的发音要领讲解、发音示范通过动画手段同步展示给学生。课件还设置了"跟读演示"和"单词练习"键,人机可互动交流。另外对在FLASH制作中遇到的问题及解决方法与注意事项加以说明,为使用FLASH软件丰富多媒体教学与网络自主学习资源,提供帮助。 展开更多
关键词 英语音素 FlasH软件 “学习英语音素”音像课件 多媒体教学
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Role of artificial intelligence in the diagnosis of oesophageal neoplasia:2020 an endoscopic odyssey 被引量:1
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作者 Mohamed Hussein Juana González-Bueno Puyal +2 位作者 Peter Mountney Laurence B Lovat Rehan Haidry 《World Journal of Gastroenterology》 SCIE CAS 2020年第38期5784-5796,共13页
The past decade has seen significant advances in endoscopic imaging and optical enhancements to aid early diagnosis.There is still a treatment gap due to the underdiagnosis of lesions of the oesophagus.Computer aided ... The past decade has seen significant advances in endoscopic imaging and optical enhancements to aid early diagnosis.There is still a treatment gap due to the underdiagnosis of lesions of the oesophagus.Computer aided diagnosis may play an important role in the coming years in providing an adjunct to endoscopists in the early detection and diagnosis of early oesophageal cancers,therefore curative endoscopic therapy can be offered.Research in this area of artificial intelligence is expanding and the future looks promising.In this review article we will review current advances in artificial intelligence in the oesophagus and future directions for development. 展开更多
关键词 Artificial intelligence Oesophageal neoplasia Barrett's oesophagus Squamous dysplasia Computer aided diagnosis Deep learning
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Hyperparameter Tuning Bidirectional Gated Recurrent Unit Model for Oral Cancer Classification
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作者 K.Shankar E.Laxmi Lydia +4 位作者 Sachin Kumar Ali S.Abosinne Ahmed alkhayyat A.H.Abbas Sarmad Nozad Mahmood 《Computers, Materials & Continua》 SCIE EI 2022年第12期4541-4557,共17页
Oral Squamous Cell Carcinoma(OSCC)is a type of Head and Neck Squamous Cell Carcinoma(HNSCC)and it should be diagnosed at early stages to accomplish efficient treatment,increase the survival rate,and reduce death rate.... Oral Squamous Cell Carcinoma(OSCC)is a type of Head and Neck Squamous Cell Carcinoma(HNSCC)and it should be diagnosed at early stages to accomplish efficient treatment,increase the survival rate,and reduce death rate.Histopathological imaging is a wide-spread standard used for OSCC detection.However,it is a cumbersome process and demands expert’s knowledge.So,there is a need exists for automated detection ofOSCC using Artificial Intelligence(AI)and Computer Vision(CV)technologies.In this background,the current research article introduces Improved Slime Mould Algorithm with Artificial Intelligence Driven Oral Cancer Classification(ISMA-AIOCC)model on Histopathological images(HIs).The presented ISMA-AIOCC model is aimed at identification and categorization of oral cancer using HIs.At the initial stage,linear smoothing filter is applied to eradicate the noise from images.Besides,MobileNet model is employed to generate a useful set of feature vectors.Then,Bidirectional Gated Recurrent Unit(BGRU)model is exploited for classification process.At the end,ISMA algorithm is utilized to fine tune the parameters involved in BGRU model.Moreover,ISMA algorithm is created by integrating traditional SMA and ChaoticOppositional Based Learning(COBL).The proposed ISMA-AIOCC model was validated for performance using benchmark dataset and the results pointed out the supremacy of ISMA-AIOCC model over other recent approaches. 展开更多
关键词 Computer aided diagnosis deep learning BGRU biomedical imaging oral cancer histopathological images
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Differences Between American and Chinese Classroom Teaching
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作者 郭凉燕 《海外英语》 2011年第15期8-9,共2页
Different teaching philosophies derive from different cultural background.There are many differences between American and Chinese teaching philosophies because of their different cultural background.Under the guidance... Different teaching philosophies derive from different cultural background.There are many differences between American and Chinese teaching philosophies because of their different cultural background.Under the guidance of different philosophies,there are different teaching styles between American and Chinese Classroom teaching. 展开更多
关键词 TEACHING PHILOSOPHY TEACHING style teachers’ TALKING time(TTT) PEER learning TEACHING aids ROTE memorization
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基于Meta-face Learning的工件定位算法
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作者 朱丽敏 丁伯慧 俞冠珉 《机械科学与技术》 CSCD 北大核心 2015年第10期1543-1546,共4页
提出了一种包含自由曲面特征的工件定位的Meta-face Learning(MFL)算法。利用基于字典学习的图像稀疏表示方法,在交替迭代优化的基础上,通过逐次修正Euclidean变换矩阵的列向量更新测量点到名义工件模型的位姿变换,确定工件坐标系相对... 提出了一种包含自由曲面特征的工件定位的Meta-face Learning(MFL)算法。利用基于字典学习的图像稀疏表示方法,在交替迭代优化的基础上,通过逐次修正Euclidean变换矩阵的列向量更新测量点到名义工件模型的位姿变换,确定工件坐标系相对于测量坐标系的位姿。设计了两个自由曲面验证了本文算法,并通过与现有算法的比较说明了其具有较高的计算效率和定位精度。 展开更多
关键词 工件定位 Meta-face learning算法 迭代优化 Euclidean变换
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浅谈LAS/ARC/AIDS某些免疫病理进展
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作者 郭子舟 刘畅 《石河子科技》 1990年第4期33-35,共3页
关键词 免疫病理学 las ARC AIDS
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