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| 1 | 角区三维分离流附着鞍点拓扑结构及其演化显示文摘实验研究角区层流边界层三维定常分离附着鞍点拓扑结构的存在性及其演化规律.二维粒子图像测速(PIV,Particle Image Velocimetry)激光片光源设置于给定平板和不同模型组成的角区对称面,采用微距镜头捕捉对称面奇点附近的局部流动拓扑结构.实验证实在一定流动参数条件下多种形状模型与平板形成的角区均存在有别于经典分离的附着鞍点拓扑结构;随着雷诺数增加或者随着模型钝度的变化,附着鞍点拓扑结构与经典分离鞍点拓扑结构之间存在着一定的演化规律;对附着鞍点拓扑结构与Lighthill经典三维分离模式的关系进行了分析,表明Lighthill经典三维分离模式也适用于分析附着鞍点拓扑结构. | 张华 胡波 Muhammad Yamin Younis 王宏 | 2012 | 北京航空航天大学学报2012，38，7： | 4 |
| 2 | Simulation, Modeling, and Optimization of Intelligent Kidney Disease Predication Empowered with Computational Intelligence Approaches显示文摘Artificial intelligence(AI)is expanding its roots in medical diagnostics.Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications.Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure.High blood pressure,diabetes mellitus,and glomerulonephritis are the root causes of kidney disease.Therefore,the present study is proposed a set of multiple techniques such as simulation,modeling,and optimization of intelligent kidney disease prediction(SMOIKD)which is based on computational intelligence approaches.Initially,seven parameters were used for the fuzzy logic system(FLS),and then twenty-five different attributes of the kidney dataset were used for the artificial neural network(ANN)and deep extreme machine learning(DEML).The expert system was proposed with the assistance of medical experts.For the quick and accurate evaluation of the proposed system,Matlab version 2019 was used.The proposed SMOIKD-FLSANN-DEML expert system has shown 94.16%accuracy.Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels. | Abdul Hannan Khan Muhammad Adnan Khan Sagheer Abbas Shahan Yamin Siddiqui Muhammad Aanwar Saeed Majed Alfayad Nouh Sabri Elmitwally | 2021 | Computers, Materials & Continua2021，，5： | 2 |
| 3 | Serious games in science education:a systematic literature review显示文摘Teaching science through computer games,simulations,and artificial intelligence(AI)is an increasingly active research field.To this end,we conducted a systematic literature review on serious games for science education to reveal research trends and patterns.We discussed the role of virtual reality(VR),AI,and augmented reality(AR)games in teaching science subjects like physics.Specifically,we covered the research spanning between 2011 and 2021,investigated country-wise concentration and most common evaluation methods,and discussed the positive and negative aspects of serious games in science education in particular and attitudes towards the use of serious games in education in general. | Mohib ULLAH Sareer Ul AMIN Muhammad MUNSIF Muhammad Mudassar YAMIN Utkurbek SAFAEV Habib KHAN Salman KHAN Habib ULLAH | 2022 | Virtual Reality & Intelligent Hardware2022，4，3： | 2 |
| 4 | Support-Vector-Machine-based Adaptive Scheduling in Mode 4 Communication显示文摘Vehicular ad-hoc networks(VANETs)are mobile networks that use and transfer data with vehicles as the network nodes.Thus,VANETs are essentially mobile ad-hoc networks(MANETs).They allow all the nodes to communicate and connect with one another.One of the main requirements in a VANET is to provide self-decision capability to the vehicles.Cognitive memory,which stores all the previous routes,is used by the vehicles to choose the optimal route.In networks,communication is crucial.In cellular-based vehicle-to-everything(CV2X)communication,vital information is shared using the cooperative awareness message(CAM)that is broadcast by each vehicle.Resources are allocated in a distributed manner,which is known as Mode 4 communication.The support vector machine(SVM)algorithm is used in the SVM-CV2X-M4 system proposed in this study.The k-fold model with different values of k is used to evaluate the accuracy of the SVM-CV2XM4 system.The results show that the proposed system achieves an accuracy of 99.6%.Thus,the proposed system allows vehicles to choose the optimal route and is highly convenient for users. | Muhammad Adnan Khan Ahmed Abu-Khadrah Shahan Yamin Siddiqui Taher M.Ghazal Tauqeer Faiz Munir Ahmad Sang-Woong Lee | 2022 | Computers, Materials & Continua2022，，11： | 1 |
| 5 | Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning显示文摘Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally.According to clinical statistics,one woman out of eight is under the threat of breast cancer.Lifestyle and inheritance patterns may be a reason behind its spread among women.However,some preventive measures,such as tests and periodic clinical checks can mitigate its risk thereby,improving its survival chances substantially.Early diagnosis and initial stage treatment can help increase the survival rate.For that purpose,pathologists can gather support from nondestructive and efficient computer-aided diagnosis(CAD)systems.This study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based fusion.In multimodal medical imaging fusion,a deep learning approach is applied,obtaining 97.5%accuracy with a 2.5%miss rate for breast cancer prediction.A deep extreme learning machine technique applied on feature-based data provided a 97.41%accuracy.Finally,decisionbased fusion applied to both breast cancer prediction models to diagnose its stages,resulted in an overall accuracy of 97.97%.The proposed system model provides more accurate results compared with other state-of-the-art approaches,rapidly diagnosing breast cancer to decrease its mortality rate. | Shahan Yamin Siddiqui Iftikhar Naseer Muhammad Adnan Khan Muhammad Faheem Mushtaq Rizwan Ali Naqvi Dildar Hussain Amir Haider | 2021 | Computers, Materials & Continua2021，，4： | 1 |
| 6 | Topological evolution of laminar juncture flows under different critical parameters显示文摘Horseshoe vortex topological structure has been studied extensively in the past,traditional'saddle of separation'and new'attachment saddle point'topologies found in literature both have theoretical basis and experimental and computational evidences for support.The laminar incompressible juncture flows at low Reynolds numbers especially are observed to have new topology.Studies concerning the existence of the new topology though found in literature,the topological evolution and its dependency on various critical flow parameters require further investigation.A Particle Image Velocimetry based analysis is carried out to observe the effect of aspect ratio,?*/D and shape of the obstacle on laminar horseshoe vortex topology for small obstacles.Rise in aspect ratio evolves the topology from the traditional to new for all the cases observed.The circular cross section obstacles are found more apt to having the new topology compared to square cross sections.It is noted that the sweeping effect of the fluid above the vortex system in which horseshoe vortex is immersed plays a critical role in this evolution.Topological evolution is observed not only in the most upstream singular point region of horseshoe vortex system but also in the corner region.The corner vortex topology evolves from the traditional type to new one before the topological evolution of the most upstream singular point,resulting in a new topological pattern of the laminar juncture flows'separation-attachment combination'.The study may help extend the understanding of the three-dimensional boundary layer separation phenomenon. | YOUNIS Muhammad Yamin ZHANG Hua HU Bo MEHMOOD Saqib | 2014 | Science China(Technological Sciences)2014，57，7： | 1 |
| 7 | Chronic complications risk among type 2 diabetes patients with a family history of diabetes显示文摘Family history of diabetes(FH+)has been associated with early metabolic alteration including insulin resistance,lipid metabolism,and ectopic fat accumulation even in healthy individuals.1-3 Furthermore,normoglycemic firstdegree relatives of type 2 diabetes mellitus(T2DM)have been documented having increased carotid intimamedia thickness_and pro-inflammatory cytokines.4.s Taken together,individuals with FH+,who were otherwise healthy,have shown to possess susceptibility for diabetes mellitus(DM)chronic complication.Hence,this study aimed to investigate whether FH+increased the risk of chronic complications in patients with overt T2DM. | Dicky L.Tahapary Syahidatul Wafa Christian Tricaesario Felix F.Widjaja Jimmy Tandradynata Rudy Kurniawan William Djauhari Afif H.Maruf Muhammad Yamin Sidartawan Soegondo | 2023 | Chronic Diseases and Translational Medicine2023，9，4： | 0 |
| 8 | Modification of colorimetric method based digital soil test kit for determination of macronutrients in oil palm plantation显示文摘It is the need of time that oil palm farmers must perform the spatially planned soil analysis to know about the fertilizer sufficient and deficient zones of land.Colorimetric method is a suitable and fast solution of soil analysis for NPK determination using the digital soil test kit.NPK determination procedure with a digital soil test kit was undefined for oil palm.Furthermore,the digital soil test kit determines the passage of light through an opaque medium of soil solution with a specified reagent.Therefore,environmental light may interfere leading to wrong results of NPK measurement.Likewise,this equipment was non-incorporable with the controller of any VRT fertilizer applicator.In this research,these issues were addressed and the NPK measurement procedure was defined for oil palm plantation by modifying the‘soil to water’ratio in sample soil solution with an optimum environmental light range of 18-23 W/m^(2).‘Soil to water’ratios were found for nitrogen,phosphorus and potassium as 0.31 to 5.00,1.00 to 5.00 and 4.50 to 5.00,respectively to fit the requirement of NPK for oil palm in the prescribed range of the equipment.Validation study of modified digital soil test kit showed that 91.7%N,89.6%P and 93.8%K results of modified digital soil test kit were matched with analytical laboratory method.Thus,the reliability of NPK results using digital soil test kit was enhanced,making the kit incorporable with the controller of variable rate fertilizer applicator through remote monitoring based data acquisition system.The outcome of this research can be used in the development of an IoT network data fusion for dynamic assessment of the NPK variation in the soil and nutrient management in oil palm plantations. | Muhammad Yamin Wan Ishak bin Wan Ismail Muhamad Saufi bin Mohd Kassim Samsuzana Binti Abd Aziz Farah Naz Akbar Redmond R.Shamshiri Muhammad Ibrahim Benjamin Mahns | 2020 | International Journal of Agricultural and Biological Engineering2020，13，4： | 0 |
| 9 | Intelligent Decision Support System for COVID-19 Empowered with Deep Learning显示文摘The prompt spread of Coronavirus(COVID-19)subsequently adorns a big threat to the people around the globe.The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector.Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected.Lately,the testing kits for COVID-19 are not available to deal it with required proficiency,along with-it countries have been widely hit by the COVID-19 disruption.To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19.It would be a feather in the cap if the early diagnosis of COVID-19 could reveal that how it has been affecting the masses immensely.According to the apparent clinical research,it has unleashed that most of the COVID-19 cases are more likely to fall for a lung infection.The abrupt changes do require a solution so the technology is out there to pace up,Chest X-ray and Computer tomography(CT)scan images could significantly identify the preliminaries of COVID-19 like lungs infection.CT scan and X-ray images could flourish the cause of detecting at an early stage and it has proved to be helpful to radiologists and the medical practitioners.The unbearable circumstances compel us to flatten the curve of the sufferers so a need to develop is obvious,a quick and highly responsive automatic system based on Artificial Intelligence(AI)is always there to aid against the masses to be prone to COVID-19.The proposed Intelligent decision support system for COVID-19 empowered with deep learning(ID2S-COVID19-DL)study suggests Deep learning(DL)based Convolutional neural network(CNN)approaches for effective and accurate detection to the maximum extent it could be,detection of coronavirus is assisted by using X-ray and CT-scan images.The primary experimental results here have depicted the maximum accuracy for training and is around 98.11 percent and for validation it comes out to be approximately 95.5 percent while statistical parameters like sensitivity and specificity for training is 98.03 percent and 98.20 percent respectively,and for validation 94.38 percent and 97.06 percent respectively.The suggested Deep Learning-based CNN model unleashed here opts for a comparable performance with medical experts and it ishelpful to enhance the working productivity of radiologists. It could take the curvedown with the downright contribution of radiologists, rapid detection ofCOVID-19, and to overcome this current pandemic with the proven efficacy. | Shahan Yamin Siddiqui Sagheer Abbas Muhammad Adnan Khan Iftikhar Naseer Tehreem Masood Khalid Masood Khan Mohammed A.Al Ghamdi Sultan H.Almotiri | 2021 | Computers, Materials & Continua2021，，2： | 0 |
| 10 | Supervised Machine Learning-Based Prediction of COVID-19显示文摘COVID-19 turned out to be an infectious and life-threatening viral disease,and its swift and overwhelming spread has become one of the greatest challenges for the world.As yet,no satisfactory vaccine or medication has been developed that could guarantee its mitigation,though several efforts and trials are underway.Countries around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely treatment.In this regard,healthcare experts,researchers and scientists have delved into the investigation of existing as well as new technologies.The situation demands development of a clinical decision support system to equip the medical staff ways to timely detect this disease.The state-of-the-art research in Artificial intelligence(AI),Machine learning(ML)and cloud computing have encouraged healthcare experts to find effective detection schemes.This study aims to provide a comprehensive review of the role of AI&ML in investigating prediction techniques for the COVID-19.A mathematical model has been formulated to analyze and detect its potential threat.The proposed model is a cloud-based smart detection algorithm using support vector machine(CSDC-SVM)with cross-fold validation testing.The experimental results have achieved an accuracy of 98.4%with 15-fold cross-validation strategy.The comparison with similar state-of-the-art methods reveals that the proposed CSDC-SVM model possesses better accuracy and efficiency. | Atta-ur-Rahman Kiran Sultan Iftikhar Naseer Rizwan Majeed Dhiaa Musleh Mohammed Abdul Salam Gollapalli Sghaier Chabani Nehad Ibrahim Shahan Yamin Siddiqui Muhammad Adnan Khan | 2021 | Computers, Materials & Continua2021，，10： | 0 |
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