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| 1 | Evaluation of management of tibial non - union defect with ilizarov fixator 显示文摘 | Farmanullah Muhammad Shoaib Khan Syed Muhammad Awais | 2007 | Ayub Med Coil Abbottabad2007，19，3： | 1 |
| 2 | 2D Carbon Fiber Reinforced High Density Polyethylene Multi-Layered Laminated Composite Panels:Structural,Mechanical,Thermal,and Morphological Profile显示文摘Carbon fiber reinforced high density polyethylene multi-layered laminated composite panels(HDPE/CF MLCP) with excellent in-plane properties along transverse direction have been formulated. Composite architectures with carbon fiber(CF) designed in 2D layout in conventional composites can alleviate their properties in thickness direction, but all attempts so far developed have achieved restrained success. Here,we have exposed an approach to the high strength composite challenge, without altering the 2D stack design on the basis of concept of fiber reinforced laminated composites that would provide enhanced mechanical and thermal properties along transverse direction. CF sheets allowed the buckling of adjoining plies in 2D MLCP. We fabricated 2D MLCP by stacking the alternative CF and HDPE layers under different loading conditions, which resulted in high strength composites. These plies of CF and HDPE served as unit cells for MLCP, with CF offering much-needed fracture toughness and hardness to these materials.For 2D HDPE/CF MLCP, we demonstrated noteworthy improvement in physical and chemical interaction between CF and HDPE, in-plane fracture strain, flexural strength(30.684 MPa), bending modulus(7436.254 MPa), thermal stability(40.94%), and surface morphology, upon increasing the CF layers up to twenty, enabling these composites truly for high temperature and high strength applications. | Shahzad Maqsood Khan Nafisa Gull Muhammad Azeem Munawar Atif Islam Saba Zia Muhammad Shafiq Aneela Sabir Syed Muhammad Awais Muhammad Arif Butt Muhammad Taqi Zahid Butt Tahir Jamil | 2016 | Journal of Materials Science & Technology2016，32，10： | 1 |
| 3 | Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic显示文摘Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively.Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment.The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand.Elasticity in cloud computing is one of the fundamental properties,and elastic load balancing automatically distributes incoming load to multiple virtual machines.This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing.In this article,a model is proposed in which the fuzzy logic approach is used for load balancing to avoid underload and overload of resources.A Simulator in Matlab is used to test the effectiveness and correctness of the proposed model.The simulation results have shown that our proposed intelligent cloud-based load balancing system empowered with fuzzy logic is better than previously published approaches. | Atif Ishaq Khan Syed Asad Raza Kazmi Ayesha Atta Muhammad Faheem Mushtaq Muhammad Idrees Ilyas Fakir Muhammad Safyan Muhammad Adnan Khan Awais Qasim | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 4 | Signet Ring Cell Detection from Histological Images Using Deep Learning显示文摘Signet Ring Cell(SRC)Carcinoma is among the dangerous types of cancers,and has a major contribution towards the death ratio caused by cancerous diseases.Detection and diagnosis of SRC carcinoma at earlier stages is a challenging,laborious,and costly task.Automatic detection of SRCs in a patient’s body through medical imaging by incorporating computing technologies is a hot topic of research.In the presented framework,we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning(DL)technique named Mask Region-based Convolutional Neural Network(Mask-RCNN).In the first step,the input image is fed to Resnet-101 for feature extraction.The extracted feature maps are conveyed to Region Proposal Network(RPN)for the generation of the region of interest(RoI)proposals as well as they are directly conveyed to RoiAlign.Secondly,RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected(FC)network and performs classification along with Bounding Box(bb)generation by using FC layers.The annotations are developed from ground truth(GT)images to perform experimentation on our developed dataset.Our introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical trials.We aim to release the employed database soon to assist the improvement in the SRC recognition research area. | Muhammad Faheem Saleem Syed Muhammad Adnan Shah Tahira Nazir Awais Mehmood Marriam Nawaz Muhammad Attique Khan Seifedine Kadry Arnab Majumdar Orawit Thinnukool | 2022 | Computers, Materials & Continua2022，，9： | 0 |
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