检索结果-南通市图书馆

Solid phase microextraction chemical biopsy tool for monitoring of doxorubicin residue during in vivo lung chemo-perfusion

引用

Journal of Pharmaceutical Analysis 2021年第1期11卷 37-47页

作者： Barbara Bojko Nikita Looby Mariola Olkowicz Anna Roszkowska Bogumiła Kupcewicz Pedro Reck dos Santos Khaled Ramadan shaf keshavjee Thomas K.Waddell German Goomez-Rios Marcos Tascon Krzysztof Gorynski Marcelo Cypel Janusz Pawliszyn Department of Chemistry University of WaterlooWaterlooON M1B 6G3Canada Department of Pharmacodynamics and Molecular Pharmacology Faculty of PharmacyCollegium Medicum in BydgoszczNicolaus Copernicus University in Torun85-089BydgoszczPoland Jagiellonian Centre for Experimental Therapeutics(JCET) Jagiellonian University30-348 KrakowPoland Department of Pharmaceutical Chemistry Medical University of Gdansk80-416GdanskPoland Department of Inorganic and Analytical Chemistry Faculty of PharmacyCollegium Medicum in BydgoszczNicolaus Copernicus University in Torun85-089BydgoszczPoland University Health Network-TGH TorontoON M5G 2C4Canada

Development of a novel in vivo lung perfusion(IVLP)procedure allows localized delivery of high-dose doxorubicin(DOX)for targeting residual micrometastatic disease in the ***,DOX delivery via IVLP requires careful monitoring of drug level to ensure tissue concentrations of this agent remain in the therapeutic window.A small dimension nitinol wire coated with a sorbent of biocompatible morphology(Bio-SPME)has been clinically evaluated for in vivo lung tissue extraction and determination of DOX and its key *** in vivo Bio-SPME-IVLP experiments were performed on pig model over various(150 and 225 mg/m^(2))drug doses,and during human clinical *** patients with metastatic osteosarcoma were treated with a single 5 and 7 μg/mL(respectively)dose of DOX during a 3-h *** both pig and human cases,DOX tissue levels presented similar trends during *** lung tissue concentrations of drug ranged between 15 and 293 μg/g over the course of the IVLP *** addition to DOX levels,Bio-SPME followed by liquid chromatography-mass spectrometry analysis generated 64 metabolic features during endogenous metabolite screening,providing information about lung status during drug ***-time monitoring of DOX levels in the lungs can be performed effectively throughout the IVLP procedure by in vivo Bio-SPME chemical biopsy ***-SPME also extracted various endogenous molecules,thus providing a real-time snapshot of the physiology of the cells,which might assist in the tailoring of personalized treatment strategy.

关键词： In vivo solid phase microextraction Metabolite profiling Spatial resolution Therapeutic drug monitoring Tissue analysis

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Optimizing Bearing Fault Detection:CNN-LSTM with Attentive TabNet for Electric Motor Systems

引用

Computer Modeling in Engineering & Sciences 2024年第12期141卷 2399-2420页

作者： Alaa U.Khawaja Ahmad shaf Faisal Al Thobiani Tariq Ali Muhammad Irfan Aqib Rehman Pirzada Unza Shahkeel Nautical Science Department Faculty of MaritimeKing Abdulaziz UniversityJeddah22230Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Marine Engineering Department Faculty of MaritimeKing Abdulaziz UniversityJeddah22230Saudi Arabia Artificial Intelligence and Sensing Technologies(AIST)Research Center University of TabukTabuk71491Saudi Arabia Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia

Electric motor-driven systems are core components across industries,yet they’re susceptible to bearing *** fault diagnosis poses safety risks and economic instability,necessitating an automated *** study proposes FTCNNLSTM(Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory),an algorithm combining Convolutional Neural Networks,Long Short-Term Memory Networks,and Attentive Interpretable Tabular *** model preprocesses the CWRU(Case Western Reserve University)bearing dataset using segmentation,normalization,feature scaling,and label *** architecture comprises multiple 1D Convolutional layers,batch normalization,max-pooling,and LSTM blocks with dropout,followed by batch normalization,dense layers,and appropriate activation and loss ***-tuning techniques prevent *** were conducted on 10 fault classes from the CWRU *** was benchmarked against four approaches:CNN,LSTM,CNN-LSTM with random forest,and CNN-LSTM with gradient boosting,all using 460 *** FTCNNLSTM model,augmented with TabNet,achieved 96%accuracy,outperforming other *** establishes it as a reliable and effective approach for automating bearing fault detection in electric motor-driven systems.

关键词： Electric motor-driven systems bearing faults automation fine tunned convolutional neural network long short-term memory fault detection

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

引用

Computers, Materials & Continua 2023年第7期76卷 711-729页

作者： Muhammad Irfan Ahmad shaf Tariq Ali Umar Farooq Saifur Rahman Salim Nasar Faraj Mursal Mohammed Jalalah Samar M.Alqhtani Omar AlShorman Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia College of Engineering Najran UniversityNajran61441Saudi Arabia

A brain tumor is a mass or growth of abnormal cells in the *** children and adults,brain tumor is considered one of the leading causes of *** are several types of brain tumors,including benign(non-cancerous)and malignant(cancerous)*** brain tumors as early as possible is essential,as this can improve the chances of successful treatment and *** this problem,we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models(Resnet50,Vgg16,Vgg19,U-Net)and their integration for computer-aided detection and localization systems in brain *** pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome *** dataset consists of 120 *** pre-trained models have been used to classify tumor or no tumor images,while integrated models are applied to segment the tumor region *** have evaluated their performance in terms of loss,accuracy,intersection over union,Jaccard distance,dice coefficient,and dice coefficient *** pre-trained models,the U-Net model achieves higher performance than other models by obtaining 95%*** contrast,U-Net with ResNet-50 out-performs all other models from integrated pre-trained models and correctly classified and segmented the tumor region.

关键词： Brain tumor deep learning ensemble detection healthcare

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Novel Inherited Modeling Structure of Automatic Brain Tumor Segmentation from MRI

引用

Computers, Materials & Continua 2022年第11期73卷 3983-4002页

作者： Abdullah AAsiri Tariq Ali Ahmad shaf Muhammad Aamir Muhammad Shoaib Muhammad Irfan Hassan A.Alshamrani Fawaz F.Alqahtani Osama M.Alshehri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Informatics and Systems University of Management and TechnologyLahore54000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia

Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to *** resonance imaging methods capture skull images that contain healthy and affected *** checked the affected tissue in the slice-by-slice manner,which was timeconsuming and hectic ***,auto segmentation of the affected part is needed to facilitate ***,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)for the auto-segmented brain tumor *** CNN model is initially used to detect brain tumors,while SVM is integrated to segment the tumor region *** proposed method was evaluated on a publicly available BraTS2020 *** statistical parameters used in this work for the mathematical measures are precision,accuracy,specificity,sensitivity,and dice ***,our method achieved an accuracy value of 0.98,which is most prominent than existing ***,the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.

关键词： Brain tumor support vector machine convolutional neural network BraTS classification

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

引用

Computer Systems Science & Engineering 2023年第8期46卷 1355-1369页

作者： Ali Usman Javed Ferzund Ahmad shaf Muhammad Aamir Samar Alqhtani Khlood M.Mehdar Hanan Talal Halawani Hassan A.Alshamrani Abdullah A.Asiri Muhammad Irfan Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical Sciences and Information SystemsNajran UniversityNajran61441Kingdom of Saudi Arabia Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia

Assistive devices for disabled people with the help of Brain-Computer Interaction(BCI)technology are becoming vital bio-medical *** with physical disabilities need some assistive devices to perform their daily *** these devices,higher latency factors need to be addressed ***,the main goal of this research is to implement a real-time BCI architecture with minimum latency for command *** proposed architecture is capable to communicate between different modules of the system by adopting an automotive,intelligent data processing and classification ***-sky mind wave device has been used to transfer the data to our implemented server for command ***-Net Convolutional Neural Network(TN-CNN)architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data *** collection and processing are the responsibility of the central integrated server for system load *** of implemented architecture and deep learning model shows excellent *** proposed system integrity level was the minimum data loss and the accurate commands processing *** training and testing results are 99%and 93%for custom model implementation based on *** proposed real-time architecture is capable of intelligent data processing unit with fewer errors,and it will benefit assistive devices working on the local server and cloud server.

关键词： Disable person electroencephalogram convolutional neural network brain signal classification

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

交换数学与非交换数学

引用

数学译林 1992年第2期11卷 94-104,93页

作者： shaf.,IR 潘建中

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Block-Wise Neural Network for Brain Tumor Identification in Magnetic Resonance Images

引用

Computers, Materials & Continua 2022年第12期73卷 5735-5753页

作者： Abdullah A.Asiri Muhammad Aamir Ahmad shaf Tariq Ali Muhammad Zeeshan Muhammad Irfan Khalaf A.Alshamrani Hassan A.Alshamrani Fawaz F.Alqahtani Ali H.D.Alshehri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran 61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Computer Science Bahauddin Zakariya UniversityMultan 66000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia

The precise brain tumor diagnosis is critical and shows a vital role in the medical support for treating tumor *** brain tumor segmentation for cancer analysis from many Magnetic Resonance Images(MRIs)created in medical practice is a problematic and timewasting task for *** a result,there is a critical necessity for more accurate computeraided methods for early tumor *** remove this gap,we enhanced the computational power of a computer-aided system by proposing a finetuned Block-Wise Visual Geometry Group19(BW-VGG19)*** this method,a pre-trained VGG19 is fine-tuned with CNN architecture in the block-wise mechanism to enhance the system`s *** publicly accessible Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)dataset collected from 2005 to 2020 from different hospitals in China has been used in this *** proposed method is simple and achieved an accuracy of 0.98%.We compare our technique results with the existing Convolutional Neural network(CNN),VGG16,and VGG19 *** results indicate that our proposed technique outperforms the best results associated with the existing methods.

关键词： CNN brain tumor block-wise structure VGG19 VGG16

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Breast Cancer Detection in Saudi Arabian Women Using Hybrid Machine Learning on Mammographic Images

引用

Computers, Materials & Continua 2022年第9期72卷 4833-4851页

作者： Yassir Edrees Almalki Ahmad shaf Tariq Ali Muhammad Aamir Sharifa Khalid Alduraibi Shoayea Mohessen Almutiri Muhammad Irfan Mohammad Abd Alkhalik Basha Alaa Khalid Alduraibi Abdulrahman Manaa Alamri Muhammad Zeeshan Azam Khalaf Alshamrani Hassan A.Alshamrani Division of Radiology Department of MedicineMedical CollegeNajran UniversityNajran61441Kingdom of Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Radiology College of MedicineQassim UniversityBuraidah52571Kingdom of Saudi Arabia Department of Radiology King Fahad Specialist HospitalBuraydah52571Kingdom of Saudi Arabia Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Kingdom of Saudi Arabia Radiology Department Human Medicine CollegeZagazig UniversityZagazig44631Egypt Department of Surgery College of MedicineNajran UniversityNajran61441Kingdom of Saudi Arabia Department of Computer Science Bahauddin Zakariya UniversityMultan66000Pakistan Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Kingdom of Saudi Arabia

Breast cancer(BC)is the most common cause of women’s deaths *** mammography technique is the most important modality for the detection of *** detect abnormalities in mammographic images,the Breast Imaging Reporting and Data System(BI-RADs)is used as a *** correct allocation of BI-RADs categories for mammographic images is always an interesting task,even for *** this work,to detect and classify the mammogram images in BI-RADs,a novel hybrid model is presented using a convolutional neural network(CNN)with the integration of a support vector machine(SVM).The dataset used in this research was collected from different hospitals in the Qassim health cluster of Saudi *** collection of all categories of BI-RADs is one of the major contributions of this *** significant contribution is the development of a hybrid approach through the integration of CNN and *** proposed hybrid approach uses three CNN models to obtain ensemble CNN model *** ensemble model saves the values to integrate them with *** proposed system achieved a classification accuracy,sensitivity,specificity,precision,and F1-score of 93.6%,94.8%,96.9%,96.6%,and 95.7%,*** proposed model achieved better performance compared to previously available methods.

关键词： Breast cancer CNN SVM BIRADS classification

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

引用

Intelligent Automation & Soft Computing 2023年第4期36卷 127-143页

作者： Abdullah A.Asiri Ahmad shaf Tariq Ali Muhammad Aamir Ali Usman Muhammad Irfan Hassan A.Alshamrani Khlood M.Mehdar Osama M.Alshehri Samar M.Alqhtani Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients *** in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original *** artiﬁcially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented *** over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artiﬁcially realistic data for MRI *** GAN network contains mainly two parts known as generator and ***,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural *** this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been *** proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.

关键词： GAN network CE-MRI images convolutional neural network brain tumor classiﬁcation

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

ULSI材料性能鉴定战略

引用

电子材料（机电部） 1994年第3期 3-5页

作者： shaf.,TJ 文汉

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：