|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Total knee arthroplasty:Effect of obesity and other patients' characteristics on operative duration and outcome显示文摘AIM: To examine the effects of patients' characteristics mainly obesity on operative duration and other outcomemeasures of knee arthroplasty. METHODS: This is a retrospective chart review of 204 patients who had knee arthroplasty within the past five years(2007-2011) at King Abdulaziz Medical City in Riyadh, Kingdom of Saudi Arabia. The data collection form was developed utilizing the literature review to gather all the needed variables. Data were gathered from admission notes, nursing notes, operative reports and discharge summaries.RESULTS: A feasible sample of 204 patients were included in the study. Of those patients, 155(76%) were females. The mean age was 70.1 years for males(SD ± 9.4) and 62.7 years(SD ± 8) for females. Regarding the type of total knee replacement(TKR), 163(79.9%) patients had unilateral TKR and 41(20.1%) had bilateral TKR. Nine patients(4.4%) had a normal body mass index(BMI)(18.5 to < 25). Overweight patients(BMI 25 to < 30) represented 18.1%. Obesity class Ⅰ(BMI 30 to < 35) and obesity class Ⅱ(BMI from 35 to < 40) were present in 23% and 29.9% of the patients, respectively. Morbid obesity(BMI greater than 40) was present in 24.5%. The mean duration of surgery was 126.3 min(SD ± 30.8) for unilateral TKR and 216.6 min(SD ± 55.4) for bilateral TKR.The mean length of stay in the hospital was 12 d(SD ± 4.9). The complications that patients had after the operation included 2 patients(1%) who developed deep venous thrombosis, 2 patients(1%) developed surgical wound infections and none had pulmonary embolism. Patients' characteristics(including age, gender, BMI and co-morbidities) did not have an effect on the operative duration of knee replacement nor the length of hospital stay. CONCLUSION: Our study shows that obesity and other patients' characteristics do not have effect on the operative duration nor the length of hospital stay following TKR. | Abdulaziz Saud Al Turki Yazeed Al Dakhil Abdulah Al Turki Mazen Saleh Ferwana | 2015 | World Journal of Orthopedics2015，6，2： | 3 |
| 2 | Plate Girders with Corrugated Steel Webs 显示文摘 | AHMED S YAZEED E | 2005 | Engineering Journal2005，42，1： | 1 |
| 3 | Plate Girders with Corrugated Steel Webs显示文摘 | Ahmed S Yazeed E | | 0，，01： | 1 |
| 4 | Human Pose Estimation and Object Interaction for Sports Behaviour显示文摘In the new era of technology,daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds.To understand the scenes and activities from human life logs,human-object interaction(HOI)is important in terms of visual relationship detection and human pose estimation.Activities understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been explained.Some existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures,occluded regions,and unsatisfactory detection of objects,especially small-sized objects.The existing HOI detection techniques are instancecentric(object-based)where interaction is predicted between all the pairs.Such estimation depends on appearance features and spatial information.Therefore,we propose a novel approach to demonstrate that the appearance features alone are not sufficient to predict the HOI.Furthermore,we detect the human body parts by using the Gaussian Matric Model(GMM)followed by object detection using YOLO.We predict the interaction points which directly classify the interaction and pair them with densely predicted HOI vectors by using the interaction algorithm.The interactions are linked with the human and object to predict the actions.The experiments have been performed on two benchmark HOI datasets demonstrating the proposed approach. | Ayesha Arif Yazeed Yasin Ghadi Mohammed Alarfaj Ahmad Jalal Shaharyar Kamal Dong-Seong Kim | 2022 | Computers, Materials & Continua2022，，7： | 1 |
| 5 | CNN Based Multi-Object Segmentation and Feature Fusion for Scene Recognition显示文摘Latest advancements in vision technology offer an evident impact on multi-object recognition and scene understanding.Such sceneunderstanding task is a demanding part of several technologies,like augmented reality-based scene integration,robotic navigation,autonomous driving,and tourist guide.Incorporating visual information in contextually unified segments,convolution neural networks-based approaches will significantly mitigate the clutter,which is usual in classical frameworks during scene understanding.In this paper,we propose a convolutional neural network(CNN)based segmentation method for the recognition of multiple objects in an image.Initially,after acquisition and preprocessing,the image is segmented by using CNN.Then,CNN features are extracted from these segmented objects,and discrete cosine transform(DCT)and discrete wavelet transform(DWT)features are computed.After the extraction of CNN features and computation of classical machine learning features,fusion is performed using a fusion technique.Then,to select theminimal set of features,genetic algorithm-based feature selection is used.In order to recognize and understand the multi-objects in the scene,a neuro-fuzzy approach is applied.Once objects in the scene are recognized,the relationship between these objects is examined by employing the object-to-object relation approach.Finally,a decision tree is incorporated to assign the relevant labels to the scenes based on recognized objects in the image.The experimental results over complex scene datasets including SUN Red Green Blue-Depth(RGB-D)and Cityscapes’demonstrated a remarkable performance. | Adnan Ahmed Rafique Yazeed Yasin Ghadi Suliman AAlsuhibany Samia Allaoua Chelloug Ahmad Jalal Jeongmin Park | 2022 | Computers, Materials & Continua2022，，12： | 1 |
| 6 | Behavior of Steel and Composite Girders with Corrugated Steel Webs显示文摘 | YAZEED E | 2001 | Canadian Journal of Civil Engineering2001，28，4： | 1 |
| 7 | A novel 5.8<ce:hsp sp='0.25'/>GHz quasi-lumped element resonator antenna显示文摘 | Mohd F. Ain Seyi S. Olokede Yazeed M. Qasaymeh Arjuna Marzuki Julie J. Mohammed Srimala Sreekantan Sabar D. Hutagalung Zainal A. Ahmad Mohd Z. Abdulla | 2013 | AEUE - International Journal of Electronics and Communications2013，，7： | 1 |
| 8 | Behaviour of steel and (or) composite girders with corrugated steel webs显示文摘 | Ezzeldin Yazeed Sayed-Ahmed | 2001 | Canadian Journal of Civil Engineerng2001，28，4： | 1 |
| 9 | Evaluation of antibacterial activity of crude protein extracts from seeds of six different medical plants against standard bacterial strains显示文摘 | Raid Al Akeel Yazeed Al-Sheikh Ayesha Mateen Rabbani Syed K. Janardhan V.C. Gupta | 2013 | Saudi Journal of Biological Sciences2013，，： | 1 |
| 10 | Automated Facial Expression Recognition and Age Estimation Using Deep Learning显示文摘With the advancement of computer vision techniques in surveillance systems,the need for more proficient,intelligent,and sustainable facial expressions and age recognition is necessary.The main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor environments.The proposed system first takes an input image pre-process it and then detects faces in the entire image.After that landmarks localization helps in the formation of synthetic face mask prediction.A novel set of features are extracted and passed to a classifier for the accurate classification of expressions and age group.The proposed system is tested over two benchmark datasets,namely,the Gallagher collection person dataset and the Images of Groups dataset.The system achieved remarkable results over these benchmark datasets about recognition accuracy and computational time.The proposed system would also be applicable in different consumer application domains such as online business negotiations,consumer behavior analysis,E-learning environments,and emotion robotics. | Syeda Amna Rizwan Yazeed Yasin Ghadi Ahmad Jalal Kibum Kim | 2022 | Computers, Materials & Continua2022，，6： | 1 |
| 11 | Behavior of Steel and Composite Girders with Corrugated Steel Webs显示文摘 | Ezzeldin Yazeed Sayed-Ahmed | | 0，，04： | 1 |
| 12 | Research for design information and design rationale integrated CAD sup-porting conceptual design by using descriptions of influ-ential relations between parts显示文摘 | Tsumaya A Yaze H Morinaga E | 2010 | Transactions of the Ja-pan Society of Mechanical Engineers2010，76，771： | 1 |
| 13 | Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors显示文摘Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to improveHGRaccuracy.We have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors. | Hammad Rustam Muhammad Muneeb Suliman A.Alsuhibany Yazeed Yasin Ghadi Tamara Al Shloul Ahmad Jalal Jeongmin Park | 2023 | Computers, Materials & Continua2023，，4： | 0 |
| 14 | A Highly Secured Image Encryption Scheme using Quantum Walk and Chaos显示文摘The use of multimedia data sharing has drastically increased in the past few decades due to the revolutionary improvements in communication technologies such as the 4th generation(4G)and 5th generation(5G)etc.Researchers have proposed many image encryption algorithms based on the classical random walk and chaos theory for sharing an image in a secure way.Instead of the classical random walk,this paper proposes the quantum walk to achieve high image security.Classical random walk exhibits randomness due to the stochastic transitions between states,on the other hand,the quantum walk is more random and achieve randomness due to the superposition,and the interference of the wave functions.The proposed image encryption scheme is evaluated using extensive security metrics such as correlation coefficient,entropy,histogram,time complexity,number of pixels change rate and unified average intensity etc.All experimental results validate the proposed scheme,and it is concluded that the proposed scheme is highly secured,lightweight and computationally efficient.In the proposed scheme,the values of the correlation coefficient,entropy,mean square error(MSE),number of pixels change rate(NPCR),unified average change intensity(UACI)and contrast are 0.0069,7.9970,40.39,99.60%,33.47 and 10.4542 respectively. | Muhammad Islam Kamran Muazzam A.Khan Suliman A.Alsuhibany Yazeed Yasin Ghadi Arshad Jameel Arif Jawad Ahmad | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 15 | A Novel Parallel Computing Confidentiality Scheme Based on Hindmarsh-Rose Model显示文摘Due to the inherent insecure nature of the Internet,it is crucial to ensure the secure transmission of image data over this network.Additionally,given the limitations of computers,it becomes evenmore important to employ efficient and fast image encryption techniques.While 1D chaotic maps offer a practical approach to real-time image encryption,their limited flexibility and increased vulnerability restrict their practical application.In this research,we have utilized a 3DHindmarsh-Rosemodel to construct a secure cryptosystem.The randomness of the chaotic map is assessed through standard analysis.The proposed system enhances security by incorporating an increased number of system parameters and a wide range of chaotic parameters,as well as ensuring a uniformdistribution of chaotic signals across the entire value space.Additionally,a fast image encryption technique utilizing the new chaotic system is proposed.The novelty of the approach is confirmed through time complexity analysis.To further strengthen the resistance against cryptanalysis attacks and differential attacks,the SHA-256 algorithm is employed for secure key generation.Experimental results through a number of parameters demonstrate the strong cryptographic performance of the proposed image encryption approach,highlighting its exceptional suitability for secure communication.Moreover,the security of the proposed scheme has been compared with stateof-the-art image encryption schemes,and all comparison metrics indicate the superior performance of the proposed scheme. | Jawad Ahmad Mimonah Al Qathrady Mohammed SAlshehri Yazeed Yasin Ghadi Mujeeb Ur Rehman Syed Aziz Shah | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
| 16 | Active Learning Strategies for Textual Dataset-Automatic Labelling显示文摘The Internet revolution has resulted in abundant data from various sources,including social media,traditional media,etcetera.Although the availability of data is no longer an issue,data labelling for exploiting it in supervised machine learning is still an expensive process and involves tedious human efforts.The overall purpose of this study is to propose a strategy to automatically label the unlabeled textual data with the support of active learning in combination with deep learning.More specifically,this study assesses the performance of different active learning strategies in automatic labelling of the textual dataset at sentence and document levels.To achieve this objective,different experiments have been performed on the publicly available dataset.In first set of experiments,we randomly choose a subset of instances from training dataset and train a deep neural network to assess performance on test set.In the second set of experiments,we replace the random selection with different active learning strategies to choose a subset of the training dataset to train the same model and reassess its performance on test set.The experimental results suggest that different active learning strategies yield performance improvement of 7% on document level datasets and 3%on sentence level datasets for auto labelling. | Sher Muhammad Daudpota Saif Hassan Yazeed Alkhurayyif Abdullah Saleh Alqahtani Muhammad Haris Aziz | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
| 17 | Improving the Ambient Intelligence Living Using Deep Learning Classifier显示文摘Over the last decade,there is a surge of attention in establishing ambient assisted living(AAL)solutions to assist individuals live independently.With a social and economic perspective,the demographic shift toward an elderly population has brought new challenges to today’s society.AAL can offer a variety of solutions for increasing people’s quality of life,allowing them to live healthier and more independently for longer.In this paper,we have proposed a novel AAL solution using a hybrid bidirectional long-term and short-term memory networks(BiLSTM)and convolutional neural network(CNN)classifier.We first pre-processed the signal data,then used timefrequency features such as signal energy,signal variance,signal frequency,empirical mode,and empirical mode decomposition.The convolutional neural network-bidirectional long-term and short-term memory(CNN-biLSTM)classifier with dimensional reduction isomap algorithm was then used to select ideal features.We assessed the performance of our proposed system on the publicly accessible human gait database(HuGaDB)benchmark dataset and achieved an accuracy rates of 93.95 percent,respectively.Experiments reveal that hybrid method gives more accuracy than single classifier in AAL model.The suggested system can assists persons with impairments,assisting carers and medical personnel. | Yazeed Yasin Ghadi Mouazma Batool Munkhjargal Gochoo Suliman AAlsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 18 | An Intelligent Framework for Recognizing Social Human-Object Interactions显示文摘Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public areas.It involves localizing the human and object targets and then identifying the interactions between them.However,it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient classifiers.Hence,the proposed system offers an automated body-parts-based solution for HOI recognition.This system uses RGB(red,green,blue)images as input and segments the desired parts of the images through a segmentation technique based on the watershed algorithm.Furthermore,a convex hullbased approach for extracting key body parts has also been introduced.After identifying the key body parts,two types of features are extracted.Moreover,the entire feature vector is reduced using a dimensionality reduction technique called t-SNE(t-distributed stochastic neighbor embedding).Finally,a multinomial logistic regression classifier is utilized for identifying class labels.A large publicly available dataset,MPII(Max Planck Institute Informatics)Human Pose,has been used for system evaluation.The results prove the validity of the proposed system as it achieved 87.5%class recognition accuracy. | Mohammed Alarfaj Manahil Waheed Yazeed Yasin Ghadi Tamara al Shloul Suliman A.Alsuhibany Ahmad Jalal Jeongmin Park | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 19 | Reducing Dataset Specificity for Deepfakes Using Ensemble Learning显示文摘The emergence of deep fake videos in recent years has made image falsification a real danger.A person’s face and emotions are deep-faked in a video or speech and are substituted with a different face or voice employing deep learning to analyze speech or emotional content.Because of how clever these videos are frequently,Manipulation is challenging to spot.Social media are the most frequent and dangerous targets since they are weak outlets that are open to extortion or slander a human.In earlier times,it was not so easy to alter the videos,which required expertise in the domain and time.Nowadays,the generation of fake videos has become easier and with a high level of realism in the video.Deepfakes are forgeries and altered visual data that appear in still photos or video footage.Numerous automatic identification systems have been developed to solve this issue,however they are constrained to certain datasets and performpoorly when applied to different datasets.This study aims to develop an ensemble learning model utilizing a convolutional neural network(CNN)to handle deepfakes or Face2Face.We employed ensemble learning,a technique combining many classifiers to achieve higher prediction performance than a single classifier,boosting themodel’s accuracy.The performance of the generated model is evaluated on Face Forensics.This work is about building a new powerful model for automatically identifying deep fake videos with the DeepFake-Detection-Challenges(DFDC)dataset.We test our model using the DFDC,one of the most difficult datasets and get an accuracy of 96%. | Qaiser Abbas Turki Alghamdi Yazed Alsaawy Tahir Alyas Ali Alzahrani Khawar Iqbal Malik Saira Bibi | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 20 | A compact and reconfigurable low noise amplifier employing combinational active inductors and composite resistors feedback techniques显示文摘A compact and reconfigurable low noise amplifier(LNA)is proposed by combining an input transistor,composite transistors with Darlington configuration as the amplification and output transistor,T-type structure composite resistors instead of a simplex structure resistor,a shunt inductor feedback realized by a tunable active inductor(AI),a shunt inductor peaking technique realized by another tunable AI.The division and collaboration among different resistances in the T-type structure composite resistor realize simultaneously input impedance matching,output impedance matching and good noise performance;the shunt feedback and peaking technique using two tunable AIs not only extend frequency bandwidth and improve gain flatness,but also make the gain and frequency band can be tuned simultaneously by the external bias of tunable AIs;the Darlington configuration of composite transistors provides high gain;furthermore,the adoption of the small size AIs instead of large size passive spiral inductor,and the use of composite resistors make the LNA have a small size.The LNA is fabricated and verified by GaAs/InGaP hetero-junction bipolar transistor(HBT)process.The results show that at the frequency of 7 GHz,the gain S_(21)is maximum and up to 19 dB;the S_(21)can be tuned from 17 dB to 19 dB by tuning external bias of tunable AIs,that is,the tunable amount of S_(21)is 2 dB,and similarly at 8 GHz;the tunable range of 3 dB bandwidth is 1 GHz.In addition,the gain S_(21)flatness is better than 0.4 dB under frequency from 3.1 GHz to 10.6 GHz;the size of the LNA only has 760μm×1260μm(including PADs).Therefore,the proposed strategies in the paper provide a new solution to the design of small size and reconfigurable ultra-wideband(UWB)LNA and can be used further to adjust the variations of gain and bandwidth of radio frequency integrated circuits(RFICs)due to package,parasitic and the variation of fabrication process and temperature. | 张正 Zhang Yanhua Yang Ruizhe Shen Pei Ding Chunbao Liu Yaze Huang Xin Chen Jitian | 2021 | High Technology Letters2021，27，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员