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| 1 | 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 |
| 2 | An Intelligent Graph Edit Distance-Based Approach for Finding Business Process Similarities显示文摘There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models. | Abid Sohail Ammar Haseeb Mobashar Rehman Dhanapal Durai Dominic Muhammad Arif Butt | 2021 | Computers, Materials & Continua2021，，12： | 1 |
| 3 | Feature Enhanced Stacked Auto Encoder for Diseases Detection in Brain MRI显示文摘The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)images.These techniques involve training neural networks on large datasets of MRI images,allowing the networks to learn patterns and features indicative of different brain diseases.However,several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques.This paper implements a Feature Enhanced Stacked Auto Encoder(FESAE)model to detect brain diseases.The standard stack auto encoder’s results are trivial and not robust enough to boost the system’s accuracy.Therefore,the standard Stack Auto Encoder(SAE)is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy froman image.The proposed model consists of four stages.First,pre-processing is performed to remove noise,and the greyscale image is converted to Red,Green,and Blue(RGB)to enhance feature details for discriminative feature extraction.Second,feature Extraction is performed to extract significant features for classification using DiscreteWavelet Transform(DWT)and Channelization.Third,classification is performed to classify MRI images into four major classes:Normal,Tumor,Brain Stroke,and Alzheimer’s.Finally,the FESAE model outperforms the state-of-theart,machine learning,and deep learning methods such as Artificial Neural Network(ANN),SAE,Random Forest(RF),and Logistic Regression(LR)by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images.The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes. | Umair Muneer Butt Rimsha Arif Sukumar Letchmunan Babur Hayat Malik Muhammad Adil Butt | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
| 4 | Numerical Treatment for Stochastic Computer Virus Model显示文摘This writing is an attempt to explain a reliable numerical treatment for stochastic computer virus model.We are comparing the solutions of stochastic and deterministic computer virus models.This paper reveals that a stochastic computer virus paradigm is pragmatic in contrast to the deterministic computer virus model.Outcomes of threshold number C^?hold in stochastic computer virus model.If C^?<1 then in such a condition virus controlled in the computer population while C^?>1 shows virus persists in the computer population.Unfortunately,stochastic numerical methods fail to cope with large step sizes of time.The suggested structure of the stochastic non-standard finite difference scheme(SNSFD)maintains all diverse characteristics such as dynamical consistency,boundedness and positivity as defined by Mickens.The numerical treatment for the stochastic computer virus model manifested that increasing the antivirus ability ultimates small virus dominance in a computer community. | Ali Raza Muhammad Shoaib Arif Muhammad Rafiq Mairaj Bibi Muhammad Naveed Muhammad Usman Iqbal Zubair Butt Hafiza Anum Naseem Javeria Nawaz Abbasi | 2019 | Computer Modeling in Engineering & Sciences2019，，8： | 0 |
| 5 | On Computing the Suitability of Non-Human Resources for Business Process Analysis显示文摘Business process improvement is a systematic approach used by several organizations to continuously improve their quality of service.Integral to that is analyzing the current performance of each task of the process and assigning the most appropriate resources to each task.In continuation of our previous work,we categorize resources into human and non-human resources.For instance,in the healthcare domain,human resources include doctors,nurses,and other associated staff responsible for the execution of healthcare activities;whereas the non-human resources include surgical and other equipment needed for execution.In this study,we contend that the two types of resources(human and non-human)have a different impact on the process performance,so their suitability should be measured differently.However,no work has been done to evaluate the suitability of non-human resources for the tasks of a process.Consequently,it becomes difficult to identify and subsequently overcome the inefficiencies caused by the non-human resources to the task.To address this problem,we present a three-step method to compute a suitability score of non-human resources for the task.As an evaluation of the proposed method,a healthcare case study is used to illustrate the applicability of the proposed method.Furthermore,we performed a controlled experiment to evaluate the usability of the proposed method.The encouraging response shows the usefulness of the proposed method. | Abid Sohail Khurram Shahzad P.D.D.Dominic Muhammad Arif Butt Muhammad Arif Muhammad Imran Tariq | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 6 | Competency Driven Resource Evaluation Method for Business Process Intelligence显示文摘Enterprises are continuously aiming at improving the execution of processes to achieve a competitive edge.One of the established ways of improving process performance is to assign the most appropriate resources to each task of the process.However,evaluations of business process improvement approaches have established that a method that can guide decision-makers to identify the most appropriate resources for a task of process improvement in a structured way,is missing.It is because the relationship between resources and tasks is less understood and advancement in business process intelligence is also ignored.To address this problem an integrated resource classification framework is presenting that identifies competence,suitability,and preference as the relationship of task with resources.But,only the competence relationship of human resources with a task is presented in this research as a resource competence model.Furthermore,the competency calculation method is presented as a user guider layer for business process intelligencebased resource competence evaluation.The computed capabilities serve as a basic input for choosing the most appropriate resources for each task of the process.Applicability of method is illustrated through a heathcare case study. | Abid Sohail Dhanapal Durai Dominic Mohammad Hijji Muhammad Arif Butt | 2021 | Computers, Materials & Continua2021，，10： | 0 |
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