|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Retinal light toxicity 显示文摘 | Youseef PN Sheibani N Albert DM | 2011 | Eye (Lord)2011，25，1： | 1 |
| 2 | Radar cross section of complex targets 显示文摘 | Youseef Nazih N | 1989 | IEEE Transactions on Antennas and Propagation1989，37，5： | 1 |
| 3 | Infulence of Additives and Pulse Electrodeposition Parameters on Production of Nanaocrystalline Zinc from Zinc Chloride Electrolytes显示文摘 | Youseef K Koch C C Fedkiw P S | 2004 | Electrochem Soc2004，151，： | 1 |
| 4 | Brain Tumor:Hybrid Feature Extraction Based on UNet and 3DCNN显示文摘Automated segmentation of brain tumors using Magnetic Resonance Imaging(MRI)data is critical in the analysis and monitoring of disease development.As a result,gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation methods.It is intended to extract characteristics from an image using the Gray Level Co-occurrence(GLC)matrix feature extraction method described in the proposed work.Using Convolutional Neural Networks(CNNs),which are commonly used in biomedical image segmentation,CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain tumor.Using two segmentation networks,a U-Net and a 3D CNN,we present a major yet easy combinative technique that results in improved and more precise estimates.The U-Net and 3D CNN are used together in this study to get better and more accurate estimates of what is going on.Using the dataset,two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour sub-region.Then,the estimates was made by two separate models that were put together to produce the final prediction.In comparison to current state-of-the-art designs,the precision(percentage)was 98.35,98.5,and 99.4 on the validation set for tumor core,enhanced tumor,and whole tumor,respectively. | Sureshkumar Rajagopal Tamilvizhi Thanarajan Youseef Alotaibi Saleh Alghamdi | 2023 | Computer Systems Science & Engineering2023，45，5： | 1 |
| 5 | Fruit yield and quality of strawberry plants transformed with a fruit specific strawberry pectate lyase gene显示文摘 | YOUSEEF S M JIMENEZ-BERMUDEZ S BELLIDO M L MARTIN-PIZARRO C BARCELO M ABDAL-AZIZ S A CABALLERO J L LOPEZ-ARANDA J M PLIEGO-ALFARO F MUNOZ J QUESADA M A MERCADO J A | 2009 | Scientia Horticuhurae2009，119，2： | 1 |
| 6 | Increase renal expression of vascular endothelial growth factor(VEGF) and its receptor VEGF-2 in experimental diabetes显示文摘 | Cooper ME Veanes D Youseefs | 1999 | Diabetes1999，48，： | 1 |
| 7 | Web Attack Detection Using the Input Validation Method:DPDA Theory显示文摘A major issue while building web applications is proper input validation and sanitization.Attackers can quickly exploit errors and vulnerabilities that lead to malicious behavior in web application validation operations.Attackers are rapidly improving their capabilities and technologies and now focus on exploiting vulnerabilities in web applications and compromising confidentiality.Cross-site scripting(XSS)and SQL injection attack(SQLIA)are attacks in which a hacker sends malicious inputs(cheat codes)to confuse a web application,to access or disable the application’s back-end without user awareness.In this paper,we explore the problem of detecting and removing bugs from both client-side and server-side code.A new idea that allows assault detection and prevention using the input validation mechanism is introduced.In addition,the project supports web security tests by providing easy-to-use and accurate models of vulnerability prediction and methods for validation.If these attributes imply a program statement that is vulnerable in an SQLIA,this can be evaluated and checked for a set of static code attributes.Additionally,we provide a script whitelisting interception layer built into the browser’s JavaScript engine,where the SQLIA is eventually detected and the XSS attack resolved using the method of input validation and script whitelisting under pushdown automatons.This framework was tested under a scenario of an SQL attack and XSS.It is demonstrated to offer an extensive improvement over the current framework.The framework’s main ability lies in the decrease of bogus positives.It has been demonstrated utilizing new methodologies,nevertheless giving unique access to sites dependent on the peculiarity score related to web demands.Our proposed input validation framework is shown to identify all anomalies and delivers better execution in contrast with the current program. | Osamah Ibrahim Khalaf Munsif Sokiyna Youseef Alotaibi Abdulmajeed Alsufyani Saleh Alghamdi | 2021 | Computers, Materials & Continua2021，，9： | 1 |
| 8 | 显示文摘 | Vranes D YouseefS | 1999 | Diabetes1999，48，： | 1 |
| 9 | Acute percutaneous lactic acid poisoning in a child显示文摘 | Ramirez ME Youseef WF Romero RG | 2006 | Pediatric Dermatology2006，23，3： | 1 |
| 10 | A Novel Convolutional Neural Networks Based Spinach Classification and Recognition System显示文摘In the present scenario,Deep Learning(DL)is one of the most popular research algorithms to increase the accuracy of data analysis.Due to intra-class differences and inter-class variation,image classification is one of the most difficult jobs in image processing.Plant or spinach recognition or classification is one of the deep learning applications through its leaf.Spinach is more critical for human skin,bone,and hair,etc.It provides vitamins,iron,minerals,and protein.It is beneficial for diet and is readily available in people’s surroundings.Many researchers have proposed various machine learning and deep learning algorithms to classify plant images more accurately in recent years.This paper presents a novel Convolutional Neural Network(CNN)to recognize spinach more accurately.The proposed CNN architecture classifies the spinach category,namely Amaranth leaves,Black nightshade,Curry leaves,and Drumstick leaves.The dataset contains 400 images with four classes,and each type has 100 images.The images were captured from the agricultural land located at Thirumanur,Salem district,Tamil Nadu.The proposed CNN achieves 97.5%classification accuracy.In addition,the performance of the proposed CNN is compared with Support Vector Machine(SVM),Random Forest,Visual Geometry Group 16(VGG16),Visual Geometry Group 19(VGG19)and Residual Network 50(ResNet50).The proposed provides superior performance than other models,namely SVM,Random Forest,VGG16,VGG19 and ResNet50. | Sankar Sennan Digvijay Pandey Youseef Alotaibi Saleh Alghamdi | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 11 | Suggestion Mining from Opinionated Text of Big Social Media Data显示文摘:Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and services.The increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making process.To overcome this challenge,extracting suggestions from opinionated text is a possible solution.In this study,the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’reviews.A classification using a word-embedding approach is used via the XGBoost classifier.The two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews.F1,precision,recall,and accuracy scores are calculated.The results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%.Moreover,the results revealed that suggestion keywords and phrases are the predominant features for suggestion extraction.Thus,this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews. | Youseef Alotaibi Muhammad Noman Malik Huma Hayat Khan Anab Batool Saif ul Islam Abdulmajeed Alsufyani Saleh Alghamdi | 2021 | Computers, Materials & Continua2021，，9： | 0 |
| 12 | Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning显示文摘Sentiment Analysis(SA)is often referred to as opinion mining.It is defined as the extraction,identification,or characterization of the sentiment from text.Generally,the sentiment of a textual document is classified into binary classes i.e.,positive and negative.However,fine-grained classification provides a better insight into the sentiments.The downside is that fine-grained classification is more challenging as compared to binary.On the contrary,performance deteriorates significantly in the case of multi-class classification.In this study,pre-processing techniques and machine learning models for the multi-class classification of sentiments were explored.To augment the performance,a multi-layer classification model has been proposed.Owing to similitude with social media text,the movie reviews dataset has been used for the implementation.Supervised machine learning models namely Decision Tree,Support Vector Machine,and Naive Bayes models have been implemented for the task of sentiment classification.We have compared the models of single-layer architecture with multi-tier model.The results of Multi-tier model have slight improvement over the single-layer architecture.Moreover,multi-tier models have better recall which allow our proposed model to learn more context.We have discussed certain shortcomings of the model that will help researchers to design multi-tier models with more contextual information. | Hameedur Rahman Junaid Tariq M.Ali Masood Ahmad F.Subahi Osamah Ibrahim Khalaf Youseef Alotaibi | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 13 | Enhancement of UAV Data Security and Privacy via Ethereum Blockchain Technology显示文摘Unmanned aerial vehicles(UAVs),or drones,have revolutionized a wide range of industries,including monitoring,agriculture,surveillance,and supply chain.However,their widespread use also poses significant challenges,such as public safety,privacy,and cybersecurity.Cyberattacks,targetingUAVs have become more frequent,which highlights the need for robust security solutions.Blockchain technology,the foundation of cryptocurrencies has the potential to address these challenges.This study suggests a platform that utilizes blockchain technology tomanage drone operations securely and confidentially.By incorporating blockchain technology,the proposed method aims to increase the security and privacy of drone data.The suggested platform stores information on a public blockchain located on Ethereum and leverages the Ganache platform to ensure secure and private blockchain transactions.TheMetaMask wallet for Ethbalance is necessary for BCT transactions.The present research finding shows that the proposed approach’s efficiency and security features are superior to existing methods.This study contributes to the development of a secure and efficient system for managing drone operations that could have significant applications in various industries.The proposed platform’s security measures could mitigate privacy concerns,minimize cyber security risk,and enhance public safety,ultimately promoting the widespread adoption of UAVs.The results of the study demonstrate that the blockchain can ensure the fulfillment of core security needs such as authentication,privacy preservation,confidentiality,integrity,and access control. | Sur Singh Rawat Youseef Alotaibi Nitima Malsa Vimal Gupta | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
| 14 | Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model显示文摘Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with others.Eye tracking(ET)has become a useful method to detect ASD.One vital aspect of moral erudition is the aptitude to have common visual attention.The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection.Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD detection.It operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with autism.The purpose of this research is to use deep learning to identify autistic disorders based on eye tracking.The Chaotic Butterfly Optimization technique is used to identify this specific disturbance.Therefore,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)technique.The presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL models.To accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested AREASS.In addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter optimization.Finally,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of ASD.To assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data repository.The experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better performance in the ETASD-CBODL technique over recent approaches. | Tamilvizhi Thanarajan Youseef Alotaibi Surendran Rajendran Krishnaraj Nagappan | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
| 15 | EsECC_SDN:Attack Detection and Classification Model for MANET显示文摘Mobile Ad Hoc Networks(MANET)is the framework for social networking with a realistic framework.In theMANETenvironment,based on the query,information is transmitted between the sender and receiver.In the MANET network,the nodes within the communication range are involved in data transmission.Even the nodes that lie outside of the communication range are involved in the transmission of relay messages.However,due to the openness and frequent mobility of nodes,they are subjected to the vast range of security threats inMANET.Hence,it is necessary to develop an appropriate security mechanism for the dataMANET environment for data transmission.This paper proposed a security framework for the MANET network signature escrow scheme.The proposed framework uses the centralised Software Defined Network(SDN)with an ECC cryptographic technique.The developed security framework is stated as Escrow Elliptical Curve Cryptography SDN(EsECC_SDN)for attack detection and classification.The developed EsECC-SDN was adopted in two stages for attack classification and detection:(1)to perform secure data transmission between nodes SDN performs encryption and decryption of the data;and(2)to detect and classifies the attack in theMANET hyper alert based HiddenMarkovModel Transductive Deep Learning.Furthermore,the EsECC_SDN is involved in the assignment of labels in the transmitted data in the database(DB).The escrow handles these processes,and attacks are evaluated using the hyper alert.The labels are assigned based on the k-medoids attack clustering through label assignment through a transductive deep learning model.The proposed model uses the CICIDS dataset for attack detection and classification.The developed framework EsECC_SDN’s performance is compared to that of other classifiers such as AdaBoost,Regression,and Decision Tree.The performance of the proposed EsECC_SDN exhibits∼3%improved performance compared with conventional techniques. | Veera Ankalu Vuyyuru Youseef Alotaibi Neenavath Veeraiah Saleh Alghamdi Korimilli Sirisha | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 16 | Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization显示文摘Software needs modifications and requires revisions regularly.Owing to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free functioning.The time and cost incurred in this process,need to be reduced by the method of test case selection and prioritization.It is observed that many nature-inspired techniques are applied in this area.African Buffalo Optimization is one such approach,applied to regression test selection and prioritization.In this paper,the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization.The proposed algorithm converges in polynomial time(O(n^(2))).In this paper,the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple iterations.An astounding 62.5%drop in size and a 48.57%drop in the runtime of the original test suite were recorded.The obtained results are compared with Ant Colony Optimization.The comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities(80%),and a reduction in the overall execution time and size of the resultant test suite.The results and analysis,hence,advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization. | Shweta Singhal Nishtha Jatana Ahmad F Subahi Charu Gupta Osamah Ibrahim Khalaf Youseef Alotaibi | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 17 | EACR-LEACH:Energy-Aware Cluster-based Routing Protocol for WSN Based IoT显示文摘Internet of Things(IoT)is a recent paradigm to improve human lifestyle.Nowadays,number devices are connected to the Internet drastically.Thus,the people can control and monitor the physical things in real-time without delay.The IoT plays a vital role in all kind of fields in our world such as agriculture,livestock,transport,and healthcare,grid system,connected home,elderly people carrying system,cypher physical system,retail,and intelligent systems.In IoT energy conservation is a challenging task,as the devices are made up of low-cost and low-power sensing devices and local processing.IoT networks have significant challenges in two areas:network lifespan and energy usage.Therefore,the clustering is a right choice to prolong the energy in the network.In LEACH clustering protocol,sometimes the same node acts as CH again and again probabilistically.To overcome these issues,this paper proposes the Energy-Aware Cluster-based Routing(EACRLEACH)protocol in WSN based IoT.The Cluster Head(CH)selection is a crucial task in clustering protocol inWSN based IoT.In EACR-LEACH,the CH is selected by using the routing metrics,Residual Energy(RER),Number of Neighbors(NoN),Distance between Sensor Node and Sink(Distance)and Number of Time Node Act as CH(NTNACH).An extensive simulation is conducted on MATLAB 2019a.The accomplishment of EACR-LEACH is compared to LEACH and SE-LEACH.The proposed EACR-LEACH protocol extends the network’s lifetime by 4%-8%and boosts throughput by 16%–24%. | Sankar Sennan Kirubasri Youseef Alotaibi Digvijay Pandey Saleh Alghamdi | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 18 | Development of Mobile App to Support the Mobility of Visually Impaired People显示文摘In 2017,it was estimated that the number of persons of all ages visually affected would be two hundred and eighty-five million,of which thirty-nine million are blind.There are several innovative technical solutions available to facilitate the movement of these people.The next big challenge for technical people is to give cost-effective solutions.One of the challenges for people with visual impairments is navigating safely,recognizing obstacles,and moving freely between locations in unfamiliar environments.A new mobile application solution is developed,and the application can be installed in android mobile.The application will visualize the environment with portable cameras and persons with visual impairment directly to the environment.The designed system mainly uses the YOLO3 program to identify and locate the distance between the objects and the camera.Furthermore,it determines the direction of the object.Finally,the system will give the voice command to teach/inform the visually impaired people to navigate the environment.It is a novel work at the global level.The proposed approach is cost-effective and affordable to all strata of society. | R.Meenakshi R.Ponnusamy Saleh Alghamdi Osama Ibrahim Khalaf Youseef Alotaibi | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 19 | Modified Bat Algorithm for Optimal VM’s in Cloud Computing显示文摘All task scheduling applications need to ensure that resources are optimally used,performance is enhanced,and costs are minimized.The purpose of this paper is to discuss how to Fitness Calculate Values(FCVs)to provide application software with a reliable solution during the initial stages of load balancing.The cloud computing environment is the subject of this study.It consists of both physical and logical components(most notably cloud infrastructure and cloud storage)(in particular cloud services and cloud platforms).This intricate structure is interconnected to provide services to users and improve the overall system’s performance.This case study is one of the most important segments of cloud computing,i.e.,Load Balancing.This paper aims to introduce a new approach to balance the load among Virtual Machines(VM’s)of the cloud computing environment.The proposed method led to the proposal and implementation of an algorithm inspired by the Bat Algorithm(BA).This proposed Modified Bat Algorithm(MBA)allows balancing the load among virtual machines.The proposed algorithm works in two variants:MBA with Overloaded Optimal Virtual Machine(MBAOOVM)and Modified Bat Algorithm with Balanced Virtual Machine(MBABVM).MBA generates cost-effective solutions and the strengths of MBA are finally validated by comparing it with Bat Algorithm. | Amit Sundas Sumit Badotra Youseef Alotaibi Saleh Alghamdi Osamah Ibrahim Khalaf | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 20 | Fruit Image Classification Using Deep Learning显示文摘Fruit classification is found to be one of the rising fields in computer and machine vision.Many deep learning-based procedures worked out so far to classify images may have some ill-posed issues.The performance of the classification scheme depends on the range of captured images,the volume of features,types of characters,choice of features from extracted features,and type of classifiers used.This paper aims to propose a novel deep learning approach consisting of Convolution Neural Network(CNN),Recurrent Neural Network(RNN),and Long Short-TermMemory(LSTM)application to classify the fruit images.Classification accuracy depends on the extracted and selected optimal features.Deep learning applications CNN,RNN,and LSTM were collectively involved to classify the fruits.CNN is used to extract the image features.RNN is used to select the extracted optimal features and LSTM is used to classify the fruits based on extracted and selected images features by CNN and RNN.Empirical study shows the supremacy of proposed over existing Support Vector Machine(SVM),Feed-forwardNeural Network(FFNN),and Adaptive Neuro-Fuzzy Inference System(ANFIS)competitive techniques for fruit images classification.The accuracy rate of the proposed approach is quite better than the SVM,FFNN,and ANFIS schemes.It has been concluded that the proposed technique outperforms existing schemes. | Harmandeep Singh Gill Osamah Ibrahim Khalaf Youseef Alotaibi Saleh Alghamdi Fawaz Alassery | 2022 | Computers, Materials & Continua2022，，6： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员