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Early Detection of Diabetic Retinopathy Using Machine Intelligence throughDeep Transfer and Representational Learning

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Computers, Materials & Continua 2021年第2期66卷 1631-1645页

作者： Fouzia Nawaz Muhammad Ramzan Khalid mehmood Hikmat Ullah Khan Saleem Hayat Khan Muhammad Raheel Bhutta Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan and School of Systems and TechnologyUniversity of Management and TechnologyLahore54782Pakistan Department of Computer Science COMSATS University IslamabadWah CampusIslamabadPakistan University Hospital Birmingham BirminghamUK Shifa International Hospital IslamabadPakistan Department of Computer Science and Engineering Sejong UniversitySeoulKorea

Diabetic retinopathy (DR) is a retinal disease that causes irreversible *** occurs due to the high blood sugar level of the patient, and it is clumsy tobe detected at an early stage as no early symptoms appear at the initial level. To preventblindness, early detection and regular treatment are needed. Automated detectionbased on machine intelligence may assist the ophthalmologist in examining thepatients’ condition more accurately and efficiently. The purpose of this study is toproduce an automated screening system for recognition and grading of diabetic retinopathyusing machine learning through deep transfer and representational *** artificial intelligence technique used is transfer learning on the deep neural network,Inception-v4. Two configuration variants of transfer learning are applied onInception-v4: Fine-tune mode and fixed feature extractor mode. Both configurationmodes have achieved decent accuracy values, but the fine-tuning method outperformsthe fixed feature extractor configuration mode. Fine-tune configuration modehas gained 96.6% accuracy in early detection of DR and 97.7% accuracy in gradingthe disease and has outperformed the state of the art methods in the relevant literature.

关键词： Diabetic retinopathy artificial intelligence automated screening system machine learning deep neural network transfer and representational learning

Recycled materials in railroad substructure: an energy perspective

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Railway Engineering Science 2022年第3期30卷 304-322页

作者： Buddhima Indraratna Yujie Qi Rakesh Sai Malisetty Sinniah KNavaratnarajah Fatima mehmood Miriam Tawk Transport Research Centre School of Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneySydneyNSW 2007Australia Department of Civil Engineering Faculty of EngineeringUniversity of PeradeniyaPeradeniyaSri Lanka School of Civil Mining and Environmental EngineeringUniversity of WollongongWollongongNSW 2522Australia College of Engineering and Technology American University of the Middle EastEgailaKuwait

Given that the current ballasted tracks in Australia may not be able to support faster and significantly heavier freight trains as planned for the future,the imminent need for innovative and sustainable ballasted tracks for transport infrastructure is *** the past two decades,a number of studies have been conducted by the researchers of Transport Research Centre(TRC)at the University of Technology Sydney(UTS)to investigate the ability of recycled rubber mats,as well as waste tyre cells and granulated rubber to improve the stability of track substructure including ballast and subballast *** paper reviews four applications of these novel methods,including using recycled rubber products such as CWRC mixtures(i.e.,mixtures of coal wash(CW)and rubber crumbs(RC))and SEAL mixtures(i.e.,mixtures of steel furnace slag,CW and RC)to replace subballast/capping materials,tyre cells reinforcements for subballast/capping layer and under ballast mats;and investigates the energy dissipation capacity for each application based on smallscale cyclic triaxial tests and large-scale track model *** has been found that the inclusion of these rubber products increases the energy dissipation effect of the track,hence reducing the ballast degradation efficiently and increasing the track ***,a rheological model is also proposed to investigate the effect of different rubber inclusions on their efficiency to reduce the transient motion of rail track under dynamic *** outcomes elucidated in this paper will lead to a better understanding of the performance of ballast tracks upgraded with resilient rubber products,while promoting environmentally sustainable and more affordable ballasted tracks for greater passenger comfort and increased safety.

关键词： Ballast degradation Recycled rubber Energy dissipation Laboratory testing Rheological model

Optical Characterization of rf-Magnetron Sputtered Nanostructured SnO2 Thin Films

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Chinese Physics Letters 2009年第7期26卷 316-319页

作者： Abdul Faheem Khan Mazhar mehmood A. M. Rana M. T. Bhatti A. Mahmood National Centre for Nanotechnology Department of Chemical and Materials Engineering Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad 45650 Pakistan Department of Physics Bahauddin Zakariya University Multan 60800 Pakistan NILOP Islamabad 45650 Pakistan

Tin oxide （Sn02 ） thin films are deposited by rf-magnetron sputtering and annealed at various temperatures in the range of 100-500 ℃ for 15 min. Raman spectra of the annealed films depict the formation of a small amount of SnO phase in the tetragonal Sn02 matrix, which is verified by x-ray diffraction. The average particle size is found to be about 20-30 nm, as calculated from x-ray peak broadening and SEM images. Various optical parameters such as optical band gap energy, refractive index, optical conductivity, carrier mobility, carrier concentration etc. are determined from the optical transmittance and reflectance data recorded in the wavelength range 250-2500 nm. The results are analyzed and compared with the data in the literature.

关键词： NANOCRYSTALLINE SNO2 RAMAN-SCATTERING LOW-TEMPERATURE TIN OXIDE OXYGEN OXIDATION

A Hybrid Approach for Network Intrusion Detection

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Computers, Materials & Continua 2022年第1期70卷 91-107页

作者： Mavra mehmood Talha Javed Jamel Nebhen Sidra Abbas Rabia Abid Giridhar Reddy Bojja Muhammad Rizwan Department of Computer Science Kinnaird College for WomenLahore54000Pakistan ASET Labs IslamabadPakistan Prince Sattam bin Abdulaziz University College of Computer Science and EngineeringAlkharj11942Saudi Arabia College of Business and Information Systems Dakota State UniversityMadisonUnited States of America

Due to the widespread use of the internet and smart devices,various attacks like intrusion,zero-day,Malware,and security breaches are a constant threat to any organization’s network ***,a Network Intrusion Detection System(NIDS)is required to detect attacks in network *** paper proposes a new hybrid method for intrusion detection and attack *** proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack *** the first step,the dataset is preprocessed through the data transformation technique and min-max ***,the random forest recursive feature elimination method is applied to identify optimal features that positively impact the model’s ***,we use various Support Vector Machine(SVM)types to detect intrusion and the Adaptive Neuro-Fuzzy System(ANFIS)to categorize probe,U2R,R2U,and DDOS *** validation of the proposed method is calculated through Fine Gaussian SVM(FGSVM),which is 99.3%for the binary *** Square Error(MSE)is reported as 0.084964 for training data,0.0855203 for testing,and 0.084964 to validate multiclass categorization.

关键词： Network security intrusion detection system machine learning attacks data mining classification feature selection

Abundance and Breeding of the Common Skittering Frog(Euphlyctis cyanophlyctis)and Bull Frog(Hoplobatrachus tigerinus)at Rawal Lake,Islamabad,Pakistan

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Asian Herpetological Research 2011年第4期2卷 245-250页

作者： Fozia TABASSUM Muhammad RAIS Maqsood ANWAR Tariq mehmood Iftikhar HUSSAIN Shahid ALI KHAN Department of Wildlife Management Pir Mehr Ali Shah Arid Agriculture University

The population density and breeding of two frog species, i. e., the Skittering Frog （Euphlyctis eyanophlyctis） and the Bull Frog （Hoplobatrachus tigerinus） were studied at Rawal Lake, Islamabad, Pakistan, by using visual encounter method from September 2009 to August 2010. Mean population density for the two frogs was 1.55 ± 0.44 frogs per ha, with that for the Skittering Frog being 1.09 ± 0.33 frogs/ha and that of the Bull Frog 0.46 ± 0.11 frog/ha, respectively. It is concluded that both frog species are explosive breeders, i. e., their breeding activities were confined to the first showers of the monsoon season. The mean spawn weight of the Skittering Frog was 1.5 g with more than 1000 eggs in each spawn, while that of the Bull Frog was 0.26 g with less than 1000 eggs in a spawn. The spawning sites of the two species were investigated in detail. Generally, no threat to their populations was observed. However, the opening of the spillway of Rawal Dam following the torrential rain destroyed the breeding sites of the frogs. It is maintained that the event did not produce any significant impact on their populations and breeding as the two species were quite common.

关键词： Ranidae population status breeding habitat destruction

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

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Computers, Materials & Continua 2022年第6期71卷 4609-4626页

作者： Saba Awan Zahid mehmood Hassan Nazeer Chaudhry Usman Tariq Amjad Rehman Tanzila Saba Muhammad Rashid Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Electronics Information and BioengineeringPolitecnico di MilanoMilano20122Italy College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadhSaudi Arabia Department of Computer Engineering Umm Al-Qura UniversityMakkah21421Saudi Arabia

To determine the individual circumstances that account for a road traffic accident,it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty *** of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the *** this article,we proposed a multi-model hybrid framework of the weighted majority voting(WMV)scheme with parallel structure,which is designed by integrating individually implemented multinomial logistic regression(MLR)and multilayer perceptron(MLP)classifiers using three different accident datasets i.e.,IRTAD,NCDB,and *** proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC,RMSE,Kappa rate,classification accuracy,and performs better than state-of-theart approaches for the prediction of casualty severity ***,the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road *** aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.

关键词： Prediction hybrid framework severity class casualty

Synthesis and characterization of poppy seed oil methyl esters

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Chinese Journal of Chemical Engineering 2016年第8期24卷 1087-1096页

作者： Umer Rashid Muhammad Ibrahim Imededdine Arbi Nehdi Saud Ibrahim Al-Resayes Sammi Ullah Muhammad Aamer mehmood Saira Shahzadi Institute of Advanced Technology Universiti Putra Malaysia Chemistry Department College of Science King Saud University Department of Environmental Sciences & Engineering Faculty of Engineering Government College University Faisalabad Department of Chemistry Government College University Faisalabad Department of Bioinformatics & Biotechnology Government College University Faisalabad Department of Chemistry Quaid-i-Azam University

Response surface methodology(RSM) was used to determine the optimum conditions of the methanolysis of crude poppy seed oil using Na OCH3 as catalyst. The experiments were run according to five levels, four variable central composite rotatable design(CCRD) using RSM. The reaction variables, i.e., molar ratio of methanol/oil(3:1–9:1), catalyst concentration(0.5 wt%–1.25 wt% Na OCH3), reaction temperature(25–65 °C), and reaction time(20–90 min) were studied. We demonstrated that the molar ratio of methanol/oil, catalyst concentration,and reaction temperature were the significant parameters affecting the yield of poppy seed oil methyl esters(PSOMEs). The optimum transesterification reaction conditions, established using the RSM, which offered a89.35% PSOME yield, were found to be 7.5:1 molar ratio of methanol/oil, 0.75% catalyst concentration, 45 °C reaction temperature, and 90 min reaction time. The proposed process provided an average biodiesel yield of more than 85%. A linear correlation was constructed between the observed and predicted values of the *** gas chromatography(GC) analyses have shown that PSOMEs contain linoleic-, oleic-, palmitic-, and stearic-acids as main fatty acids. The FTIR spectrum of the PSOMEs was also analyzed to confirm the completion of the transesterification reaction. The fuel properties of the PSOMEs were discussed in light of biodiesel standards(ASTM D 6751 and EN 14214).

关键词： Poppy oil Optimization Gas chromatography FTIR Fuel properties

Elemental composition of rice using calibration free laser induced breakdown spectroscopy

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Optoelectronics Letters 2019年第1期15卷 57-63页

作者： Abdul Jabbar Mahmood Akhtar Arslan Ali Shaukat mehmood Sidra Iftikhar Muhmmad Aslam Baig Department of Physics Mirpur University of Science and Technology National Centre for Physics

In this paper, we apply laser induce breakdown spectroscopy (LIBS) to determine the elemental composition of different parts(root, stem and seed) of the rice plant and determine their weighted concentration using calibration free laser induced breakdown spectroscopy (CF-LIBS) technique. Ca, Fe and K are identified as major elements, while C, Ti, Mg, Si, Li, Ba, Sr, Cr, Na and Al as minor elements. We also detect the H-alpha line of hydrogen in the spectrum and determine the electron number density. The electron number density and its behavior as a function of laser energy, laser wavelength and the detector position are investigated. The plasma temperatures of samples are determined, and the validity of the assumption of the local thermodynamic equilibrium (LTE) is discussed.

关键词： Elemental composition calibration breakdown spectroscopy

Automated White Blood Cell Disease Recognition Using Lightweight Deep Learning

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Computer Systems Science & Engineering 2023年第7期46卷 107-123页

作者： Abdullah Alqahtani Shtwai Alsubai Mohemmed Sha Muhammad Attique Khan Majed Alhaisoni Syed Rameez naqvi College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science HITEC University TaxilaTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

White blood cells(WBC)are immune system cells,which is why they are also known as immune *** protect the human body from a variety of dangerous diseases and outside *** majority of WBCs come from red bone marrow,although some come from other important organs in the *** manual diagnosis of blood disorders is difficult,it is necessary to design a computerized *** have introduced various automated strategies in recent years,but they still face several obstacles,such as imbalanced datasets,incorrect feature selection,and incorrect deep model *** proposed an automated deep learning approach for classifying white blood disorders in this *** data augmentation approach is initially used to increase the size of a ***,a Darknet-53 pre-trained deep learning model is used and finetuned according to the nature of the chosen *** the fine-tuned model,transfer learning is used,and features engineering is done on the global average pooling *** retrieved characteristics are subsequently improved with a specified number of iterations using a hybrid reformed binary grey wolf optimization *** that,machine learning classifiers are used to classify the selected best features for final *** experiment was carried out using a dataset of increased blood diseases imaging and resulted in an improved accuracy of over 99%.

关键词： White blood cells augmentation deep features feature selection classification