检索结果-南通市图书馆

Flow Injection Chemiluminescence Method for Nalbuphine Hydrochloride in Pharmaceutical Formulations Using Tris(2,2′-bipyridyl)ruthenium(II)Chloride-diperiodatocuprate(III)Reaction

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chemical Research in Chinese Universities 2021年第3期37卷 629-638页

作者： AHMED Khan MUHAMMAD Asghar MOHAMMED Yaqoob MASOOD Ahmed Siddiqui samar ali Department of Chemistry University of BalochistanSariab RoadQuetta87300Pakistan

A sensitive and selective method employing chemiluminescence(CL)coupled with flow injection(FI)is reported for nalbuphine hydrochloride(NAL)assay in pharmaceutical *** enhancement effect of NAL on the CL reaction between tris(2,2′-bipyridyl)ruthenium(II)chloride-diperiodatocuprate(III){Ru[(bpy)_(3)]^(2+)-Cu(III)complex}in acidic medium is used as analytical *** optimal conditions of the CL reaction were sulfuric acid 1.0×10^(−3) mol/L,Ru[(bpy)s]2+7.5×10^(−5) mol/L,Cu(III)/Ag(III)complexes 4.0×10^(−4)/5.0×10^(−4) mol/L,sample loop volume of 120µL and flow rate of 2.5 mL/*** sensitivities of the method in terms of detection(S/N=3)and quantification(S/N=10)limits are 5×10^(−4) and 0.001 ppm(1 ppm=1 mg/L),*** linear response of the instrument in the form of CL intensity with respect to NAL concentration is over the range 0.001-15.0 ppm(R^(2)=0.9999)with relative standard deviation from 0.8%to 3.2%and injection throughput of 120 injection/*** applications of the method include the quantitative analysis of NAL in pharmaceutical injection *** and the average results of the proposed method are not significantly different from the results of a reported method by applying F-and paired student *** most likely CL reaction mechanism is written in accordance with spectrophotometric and CL studies.

关键词： Ru[(bpy)_(3)]^(2+) Cu(III)/Ag(III)complex Chemiluminescence Flow injection analysis Nalbuphine hydrochloride Pharmaceutical

Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第8期140卷 1539-1562页

作者： samar M.Alqhtani Toufique A.Soomro Faisal Bin Ubaid Ahmed ali Muhammad Irfan Abdullah A.Asiri Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Electronic Engineering The University of LarkanoLarkanaSindh75660Pakistan Computer Science Department Sukkur IBA UniversitySukkurSindh65200Pakistan Eletrical Engineering Department Sukkur IBA UniversitySukkurSindh65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various *** resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain *** plays a crucial role in the diagnosis of brain tumors and the examination of other brain ***,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely ***,early diagnosis of brain tumors is intricate,necessitating the use of computerized *** research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain *** proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third *** final step involves classification using the Support Vector Machine(SVM)*** classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)*** proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of ***,this method exhibits a shorter processing time of 0.44 s compared to existing *** performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and *** enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classi

关键词： Brain tumor magnetic resonance imaging principal component analysis fuzzy c-clustering support vector machine

Case Report and Clinical Management of a Case of Osteogenesis Imperfecta Detected in the Prenatal Period

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Open Journal of Obstetrics and Gynecology 2024年第7期14卷 996-1002页

作者： Amina Chaieb Oumayma Ben Rejeb samar Knaz Yasmine Ben ali Syrine Chelly Safia Ernez Mouna Derouiche Department of Gynecology and Obstetrics Farhat Hached University Hospital Sousse Tunisia Faculty of Medicine Ibn Al Jazzar University of Sousse Sousse Tunisia

Osteogenesis imperfecta is a hereditary disease characterized by bone fragility due to a defect in type I collagen synthesis. The diagnosis is typically suspected based on suggestive ultrasound findings and confirmed through genetic studies. We present a case of osteogenesis imperfecta suspected during obstetrical ultrasound at 19 weeks’ gestation, which was later confirmed radiographically through computed tomography. Due to the severity of the condition, therapeutic termination of pregnancy was indicated.

关键词： Osteogenesis Imperfecta Ultrasound Screening Antenatal Diagnosis

UAV Lateral Path Following: Nonlinear Sliding Manifold for Limited Actuation

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

UAV Lateral Path Following: Nonlinear Sliding Manifold for L...

Nonlinear Sliding Manifold for High Performance Lateral Guidance of UAV

第36届中国控制会议

作者： Syed Ussama ali Raza samar M.Zamurad Shah Center of Excellence in Science and Applied Technologies (CESAT)

ISBN: (纸本)9781538629185

The lateral guidance objective is to derive the cross track error towards zero with graceful and stable maneuvers and then to keep it as minimum as possible. A nonlinear sliding surface based lateral guidance scheme is proposed here for Unmanned Aerial Vehicles(UAVs) application with limited actuation. Following the inner(control) and outer(guidance)loop structure, the proposed guidance scheme resides in outer loop and generates reference bank angle commands for the inner control loop. The proposed 1 order SMC algorithm based on nonlinear sliding surface is derived using the kinematic equations for bank-to-turn vehicles. Mathematical proof of existence of sliding mode and actuation boundedness of the closed-loop is also provided. The proposed guidance and control system performance is verified via simulation results for different scenario. A high fidelity nonlinear 6-degrees-offreedom(6-dof) scaled Yak-54 UAV mathematical model is used for flight simulations. Simulation results validated the effectiveness and performance of the proposed lateral guidance scheme.

关键词： Stability analysis Unmanned aerial vehicles Manifolds Actuators Mathematical model Sliding mode control Robustness

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Nonlinear Sliding Manifold for High Performance Lateral Guid...

A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

第36届中国控制会议

作者： Syed Ussama ali Raza samar M.Zamurad Shah Center of Excellence in Science and Applied Technologies （CESAT）

ISBN: (纸本)9781538629185

This paper presents a novel guidance algorithm for UAVs to follow a 2-D ground track by controlling the lateral cross track deviations. The guidance framework based on sliding mode theory is presented to solve the two dimensional path-following problem. To control the lateral track error of the vehicle during flight, the guidance algorithm generates reference bank angle commands for the vehicle control system i.e., by executing roll maneuvers. Novel nonlinear high performance sliding surface is proposed to reduce the lateral cross track error efficiently. Subsequently, 1storder sliding mode guidance logic is then devised using the proposed manifold for generating reference bank angle commands. Stability of the proposed nonlinear manifold is proved using Lyapunov *** boundedness is proved to avoid saturation of control signals. Effectiveness of the proposed algorithm is shown via simulation results for a research scaled YAK-54 UAV. Different scenarios（consisting of both small and large lateral errors）in simulation validates the proposed guidance framework and illustrates the effectiveness of the algorithm.

关键词： Manifolds Unmanned aerial vehicles Stability analysis Sliding mode control Kinematics Switches

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Intelligent Automation & Soft Computing 2023年第2期35卷 1721-1736页

作者： ali Alqahtani Shumaila Akram Muhammad Ramzan Fouzia Nawaz Hikmat Ullah Khan Essa Alhashlan samar MAlqhtani Areeba Waris Zain ali College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan School of Systems and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science COMSATS University IslamabadWah CampusIslamabad47040Pakistan Department of Medical Imaging King Khaled HospitalNajran61441Saudi Arabia Electrical&Electronics Engineering Department Universiti TeknologiPetronas31750Malaysia Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan

Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 *** rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its *** is a signiﬁ-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most *** overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the X-radiation(X-rays)and computed tomography(CT)images for detection of ***-ever,this method burdens the radiologist to examine each ***,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of *** proposed a deep learning approach that uses readily available chest radio-graphs(chest X-rays)to diagnose COVID-19 *** proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest X-rays *** dataset used in this study contains 1504 chest X-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical *** results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.

关键词： COVID-19 transfer learning deep learning artiﬁcial intelligence chest X-rays machine learning

Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第9期72卷 4249-4265页

作者： Muhammad Irfan ali Raza Faisal Althobiani Nasir Ayub Muhammad Idrees Zain ali Kashif Rizwan Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Saifur Rahman Omar Alshorman samar Alqhtani Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadWah Campus22060Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and TechnologyIslamabad44000Pakistan School of Electrical Engineering and Computer Science National University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science and Engineering University of Engineering and TechnologyNarowal CampusLahore54000Pakistan Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial ***,electricity demand and price forecasting play a significant role and can help in terms of reliability and *** to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG *** this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as *** minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant *** this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the ***,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test *** evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark *** proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.

关键词： Smart grid deep neural networks consumer demand big data analytics load forecasting price forecasting

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

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

A U-Net-Based CNN Model for Detection and Segmentation of Brain Tumor

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第1期74卷 1333-1349页

作者： Rehana Ghulam Sammar Fatima Tariq ali Nazir Ahmad Zafar Abdullah A.Asiri Hassan A.Alshamrani samar M.Alqhtani Khlood M.Mehdar Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Radiological Sciences Department College of applied medical sciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajran61441Saudi Arabia

Human brain consists of millions of cells to control the overall structure of the human *** these cells start behaving abnormally,then brain tumors *** and initial stage brain tumor detection has always been an issue in the field of medicines for medical *** handle this issue,various deep learning techniques for brain tumor detection and segmentation techniques have been developed,which worked on different datasets to obtain fruitful results,but the problem still exists for the initial stage of detection of brain tumors to save human *** this purpose,we proposed a novel U-Net-based Convolutional Neural Network(CNN)technique to detect and segmentizes the brain tumor for Magnetic Resonance Imaging(MRI).Moreover,a 2-dimensional publicly available Multimodal Brain Tumor Image Segmentation(BRATS2020)dataset with 1840 MRI images of brain tumors has been used having an image size of 240×240 *** initial dataset preprocessing the proposed model is trained by dividing the dataset into three parts i.e.,testing,training,and validation *** model attained an accuracy value of 0.98%on the BRATS2020 dataset,which is the highest one as compared to the already existing techniques.

关键词： U-net brain tumor magnetic resonance images convolutional neural network segmentation