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| 1 | Utilization of rice husk ash as viscosity modifying agent in self-compacting concrete 显示文摘 | Shazim Ali Memon Muhammad Ali Shaikh Hassan Akbar | 2011 | Construction and Building Materials2011，25，2： | 1 |
| 2 | Association of XRCC1, XRCC3, and XPD genetic polymorphism with an increased risk of hepatocellular carcinoma because of the hepatitis B and C virus显示文摘 | Asma Gulnaz Ali H. Sayyed Farah Amin Abrar ul Haq Khan Muhammad A. Aslam Rehan S. Shaikh Muhammad Ali | 2013 | European Journal of Gastroenterology & Hepatology2013，，2： | 1 |
| 3 | Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges显示文摘Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed. | Samina Amin Muhammad Ali Zeb Hani Alshahrani Mohammed Hamdi Mohammad Alsulami Asadullah Shaikh | 2024 | Computer Modeling in Engineering & Sciences2024，139，5： | 0 |
| 4 | Improved-Equalized Cluster Head Election Routing Protocol for Wireless Sensor Networks显示文摘Throughout the use of the small battery-operated sensor nodes encou-rage us to develop an energy-efficient routing protocol for wireless sensor networks(WSNs).The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of WSN.Many routing protocols are available,but the issue is still alive.Clustering is one of the most important techniques in the existing routing protocols.In the clustering-based model,the important thing is the selection of the cluster heads.In this paper,we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each cluster.Initially,the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small distance.The proposed scheme performs hierarchal routing and direct routing with some energy thresholds.The simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its performance.Moreover,the simulations will be performed in two scenarios,gate-way-based and without gateway to achieve more energy-efficient results. | Muhammad Shahzeb Ali Ali Alqahtani Ansar Munir Shah Adel Rajab Mahmood Ul Hassan Asadullah Shaikh Khairan Rajab Basit Shahzad | 2023 | Computer Systems Science & Engineering2023，44，1： | 0 |
| 5 | Application and comparison of kernel functions for linear parameter varying model approximation of nonlinear systems显示文摘In this paper,a comparative study for kernel-PCA based linear parameter varying(LPV)model approximation of sufficiently nonlinear and reasonably practical systems is carried out.Linear matrix inequalities(LMIs)to be solved in LPV controller design process increase exponentially with the increase in a number of scheduling variables.Fifteen kernel functions are used to obtain the approximate LPV model of highly coupled nonlinear systems.An error to norm ratio of original and approximate LPV models is introduced as a measure of accuracy of the approximate LPV model.Simulation examples conclude the effectiveness of kernel-PCA for LPV model approximation as with the identification of accurate approximate LPV model,computation complexity involved in LPV controller design is decreased exponentially. | Faisal Saleem Ahsan Ali Inam-ul-hassan Shaikh Muhammad Wasim | 2023 | Applied Mathematics(A Journal of Chinese Universities)2023，38，1： | 0 |
| 6 | Decision Support System for Diagnosis of Irregular Fovea显示文摘Detection of abnormalities in human eye is one of the wellestablished research areas of Machine Learning.Deep Learning techniques are widely used for the diagnosis of RetinalDiseases(RD).Fovea is one of the significant parts of retina which would be prevented before the involvement of Perforated Blood Vessels(PBV).Retinopathy Images(RI)contains sufficient information to classify structural changes incurred upon PBV but Macular Features(MF)and Fovea Features(FF)are very difficult to detect because features ofMFand FF could be found with Similar Color Movements(SCM)with minor variations.This paper presents novel method for the diagnosis of Irregular Fovea(IF)to assist the doctors in diagnosis of irregular fovea.By considering all above problems this paper proposes a three-layer decision support system to explore the hindsight knowledge of RI and to solve the classification problem of IF.The first layer involves data preparation,the second layer builds the decision model to extract the hidden patterns of fundus images by using Deep Belief Neural Network(DBN)and the third layer visualizes the results by using confusion matrix.This paper contributes a data preparation algorithm for irregular fovea and a highest estimated classification accuracy measured about 96.90%. | Ghulam Ali Mallah Jamil Ahmed Muhammad Irshad Nazeer Mazhar Ali Dootio Hidayatullah Shaikh Aadil Jameel | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 7 | Giardiasis in patients with dyspeptic symptoms显示文摘AIM: To investigate the prevalence of giardiasis in patients with dyspeptic symptoms.METHODS: Clinical records of consecutive patients who attended Gastroenterology Department at Aga Khan University Hospital from January 2000 to June 2003 and had esophagogastroduodenoscopy (EGD) with duodenal biopsies and international classification of diseases 9th revision with clinical modifications (ICD-9-CM) coded with giardiasis were studied.RESULTS: Two hundred and twenty patients fulfilled the above criteria. There were 44% (96/220) patients who were giardiasis positive, 72% (69/96) of them were males and 28% (27/96) of them were females.There were 65% (81/124) males and 35% (43/124)females who were giardiasis negative. The mean age of patients with giardiasis was 28±17 years, while that of giardiasis negative patients was 40±18 years (P<0.001).In patients with giardiasis, abdominal pain was present in 71% (68/96) of patients (P = 0.02) and diarrhea in 29% (28/96) (P = 0.005); duodenitis in 25% (24/96) on EGD (P = 0.006) and in 68% (65/96) on histopathology (P = 0.002).CONCLUSION: Giardiasis occurs significantly in young people with abdominal pain, while endoscopic duodenitis is seen in only 25% of giardiasis positive cases, which supports routine duodenal biopsy. | Javed Yakoob Wasim Jafri Shahab Abid Nadim Jafri Saeed Hamid Hasnain Ali Shah Lubna Rizvi Muhammad Islam Hizbullah Shaikh | 2005 | World Journal of Gastroenterology2005，11，42： | 0 |
| 8 | Robust design of sliding mode control for airship trajectory tracking with uncertainty and disturbance estimation显示文摘The robotic airship can provide a promising aerostatic platform for many potential applications.These applications require a precise autonomous trajectory tracking control for airship.Airship has a nonlinear and uncertain dynamics.It is prone to wind disturbances that offer a challenge for a trajectory tracking control design.This paper addresses the airship trajectory tracking problem having time varying reference path.A lumped parameter estimation approach under model uncertainties and wind disturbances is opted against distributed parameters.It uses extended Kalman filter(EKF)for uncertainty and disturbance estimation.The estimated parameters are used by sliding mode controller(SMC)for ultimate control of airship trajectory tracking.This comprehensive algorithm,EKF based SMC(ESMC),is used as a robust solution to track airship trajectory.The proposed estimator provides the estimates of wind disturbances as well as model uncertainty due to the mass matrix variations and aerodynamic model inaccuracies.The stability and convergence of the proposed method are investigated using the Lyapunov stability analysis.The simulation results show that the proposed method efficiently tracks the desired trajectory.The method solves the stability,convergence,and chattering problem of SMC under model uncertainties and wind disturbances. | WASIM Muhammad ALI Ahsan CHOUDHRY Mohammad Ahmad SHAIKH Inam Ul Hasan SALEEM Faisal | 2024 | Journal of Systems Engineering and Electronics2024，35，1： | 0 |
| 9 | MDEV Model:A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images显示文摘Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful;thus,catching it early is crucial.Medical physicians’time is limited in outdoor situations due to many patients;therefore,automated systems can be a rescue.The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’experience.Therefore,radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays.In medical classifications,deep convolution neural networks are commonly used.This research aims to use deep pretrained transfer learning models to accurately categorize CXR images into binary classes,i.e.,Normal and Pneumonia.The MDEV is a proposed novel ensemble approach that concatenates four heterogeneous transfer learning models:Mobile-Net,DenseNet-201,EfficientNet-B0,and VGG-16,which have been finetuned and trained on 5,856 CXR images.The evaluation matrices used in this research to contrast different deep transfer learning architectures include precision,accuracy,recall,AUC-roc,and f1-score.The model effectively decreases training loss while increasing accuracy.The findings conclude that the proposed MDEV model outperformed cutting-edge deep transfer learning models and obtains an overall precision of 92.26%,an accuracy of 92.15%,a recall of 90.90%,an auc-roc score of 90.9%,and f-score of 91.49%with minimal data pre-processing,data augmentation,finetuning and hyperparameter adjustment in classifying Normal and Pneumonia chests. | Mehwish Shaikh Isma Farah Siddiqui Qasim Arain Jahwan Koo Mukhtiar Ali Unar Nawab Muhammad Faseeh Qureshi | 2023 | Computer Systems Science & Engineering2023，46，7： | 0 |
| 10 | Time and Quantity Based Hybrid Consolidation Algorithms for Reduced Cost Products Delivery显示文摘In today’s competitive business environment,the cost of a product is one of the most important considerations for its sale.Businesses are heavily involved in research strategies to minimize the cost of elements that can impact on the final price of the product.Logistics is one such factor.Numerous products arrive from diverse locations to consumers in today’s digital era of online businesses.Clearly,the logistics sector faces several dilemmas from order attributes to environmental changes in this regard.This has specially been noted during the ongoing Covid-19 pandemic where the demands on online businesses have increased several fold.Consequently,the methodology to optimise delivery cost and its impact on environmental focus by reducing CO_(2) emissions has gained relevance.The resultant strategy of Shipment Consolidation that has evolved is an approach that combines one or more transport orders in the same vehicle for delivery.Shipment Consolidation has been categorized in three order scheduling approaches:Time based consolidation,Quantity based consolidation,and a Hybrid(Time-Quantity)based consolidation.In this paper,a new Hybrid Consolidation approach is presented.Using the Hybrid approach,it has been shown that order delivery can be facilitated by taking into account not only the order pick up time,but also the total order quantity.These results have shown that if a time window is available in respect of the order delivery time,then the order can be delayed from pickup to consolidate it with other orders for cost optimization.This hybrid approach is based on four consolidation principles,two of which work on fixed departure and two,on demand departure.Three of these rules have been implemented and tested here with an application case study.Statistical analysis of the results is illustrated with different planning evaluation indicators.The Result analyses indicate that consolidation of orders is increased with each implemented rule hence motivating us towards the implementation of the fourth rule.Testing with bigger data sets is required. | Muhammad Ali Memon Asadullah Shaikh Adel Sulaiman Abdullah Alghamdi Mesfer Alrizq Bernard Archimède | 2021 | Computers, Materials & Continua2021，，10： | 0 |
| 11 | Optimality of Solution with Numerical Investigation for Coronavirus Epidemic Model显示文摘The novel coronavirus disease,coined as COVID-19,is a murderous and infectious disease initiated from Wuhan,China.This killer disease has taken a large number of lives around the world and its dynamics could not be controlled so far.In this article,the spatio-temporal compartmental epidemic model of the novel disease with advection and diffusion process is projected and analyzed.To counteract these types of diseases or restrict their spread,mankind depends upon mathematical modeling and medicine to reduce,alleviate,and anticipate the behavior of disease dynamics.The existence and uniqueness of the solution for the proposed system are investigated.Also,the solution to the considered system is made possible in a well-known functions space.For this purpose,a Banach space of function is chosen and the solutions are optimized in the closed and convex subset of the space.The essential explicit estimates for the solutions are investigated for the associated auxiliary data.The numerical solution and its analysis are the crux of this study.Moreover,the consistency,stability,and positivity are the indispensable and core properties of the compartmental models that a numerical design must possess.To this end,a nonstandard finite difference numerical scheme is developed to find the numerical solutions which preserve the structural properties of the continuous system.The M-matrix theory is applied to prove the positivity of the design.The results for the consistency and stability of the design are also presented in this study.The plausibility of the projected scheme is indicated by an appropriate example.Computer simulations are also exhibited to conclude the results. | Naveed Shahid Dumitru Baleanu Nauman Ahmed Tahira Sumbal Shaikh Ali Raza Muhammad Sajid Iqbal Muhammad Rafiq Muhammad Aziz-ur Rehman | 2021 | Computers, Materials & Continua2021，，5： | 0 |
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