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Performance of hot-mix asphalt using polymer-modified bitumen and marble dust as a filler

Journal of Traffic and Transportation Engineering(English Edition) 2023年第3期10卷 385-398页

作者： diyar khan Rawid khan Muhammad Tariq khan Muhammad Alam Tanveer Hassan Department of Civil Engineering University of Engineering and TechnologyPeshawarPakistan Faculty of Transport and Aviation Engineering Silesian University of TechnologyKatowice 40-019Poland Department of Civil Engineering University of Engineering and TechnologyMardanPakistan Department of Built Environment and Architecture London South Bank UniversityLondon SE10AAUK

Marble dust waste powder generated by the marble cutting industry has a high *** this research,the use of marble dust(MD)as a mineral filler substitute in hot mixed asphalt(HMA)was *** Marshall mix design was used to determine the optimum bitumen content(OBC)for all of the *** each of the four MD contents,i.e.,0,2%,4%,and 6%by weight of the total aggregates,four different bitumen percentages were *** results of the Marshall stability test showed that the optimum filler content was 4%*** were prepared with 0 MD in the control mix and varying percentages of MD as an alternate *** addition,MD aided in increasing the Marshall stability,rutting resistance,and permanent deformation and reduced the fatigue life of asphalt *** the percentage of MD increases,the rutting resistance and stiffness at high temperatures both *** the percentage of MD increases,the fatigue life *** resistance in high-temperature conditions can be improved by using MD in HMA as a partial substitute for stone dust(SD).In areas where extensive MD waste is present,MD can be incorporated into HMA mixtures instead of conventional fillers.

关键词： Asphalt mixture Marble dust Wheel tracker test Dynamic modulus test Four-point fatigue beam test

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Multi-Level Fusion in Ultrasound for Cancer Detection Based on Uniform LBP Features

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Computers, Materials & Continua 2021年第3期66卷 3363-3382页

作者： diyar Qader Zeebaree Adnan Mohsin Abdulazeez Dilovan Asaad Zebari Habibollah Haron Haza Nuzly Abdull Hamed Research Center of Duhok Polytechnic University DuhokKurdistan RegionIraq Presidency of Duhok Polytechnic University Duhok Kurdistan RegionIraq Center of Scientific Research and Development Nawroz University DuhokKurdistan RegionIraq School of Computing University Teknologi MalaysiaJohorMalaysia

Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains *** the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise,an enhanced technique is not *** purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern(LBP)and filtered noise *** surmount the above limitations and achieve the aim of the study,a new descriptor that enhances the LBP features based on the new threshold has been *** paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer,which was attained in two ***,several images were generated from a single image using the pre-processing *** andWiener filterswere utilized to lessen the speckle noise and enhance the ultrasound image *** strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image ***,the fusion mechanism allowed the production of diverse features from different filtered *** feasibility of using the LBP-based texture feature to categorize the ultrasound images was *** effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images,*** proposed method achieved very high accuracy(98%),sensitivity(98%),and specificity(99%).As a result,the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy,sensitivity,and specificity.

关键词： Breast cancer ultrasound image local binary pattern feature extraction noise reduction filters fusion

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