Startseite A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks
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A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks

  • Wissam H. Alawee EMAIL logo , Luttfi A. Al-Haddad , Ali Basem und Abdullah A. Al-Haddad
Veröffentlicht/Copyright: 18. November 2024
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Aus der Zeitschrift Open Engineering Band 14 Heft 1

Abstract

Breast cancer is globally known to be a major health concern that necessitates advancements in detection and classification methods. This study introduces a machine learning-based approach for breast cancer diagnosis using benign and malignant mammograms of breast cancer. A two-hidden-layer artificial neural network (ANN) model was designed to categorize breast cancer from mammographic images. Prior to analysis, the images were subjected to a sophisticated data augmentation process that leveraged data denoising, contrast enhancement, and the application of a generative adversarial network (GAN). This multi-enhancement preprocessing enriched the quality of the images and transformed them into a format more amenable to analysis by vectorizing the pixel data. The methodology involved rigorous training of the ANN on input images, resulting in a significant improvement in the model’s ability to classify breast cancer accurately. Experimental results demonstrate a notable enhancement in classification performance, with an increase in accuracy ranging from 22.5 to 42.5% compared to traditional scans. The final model achieved an impressive accuracy rate of unity, which considered all stages of image processing, including normal, contrast-enhanced, denoised, and GAN-enhanced scans. The outcomes of this research underlined the effectiveness of data augmentation and ANN in medical imaging. Future innovations in breast cancer diagnostics are elaborated by the potential to improve early detection and patient outcomes. The robust offered methodology for breast cancer detection is considered to be a significant contribution to biotechnological fields of interest.

Keywords: artificial neural network; breast cancer; cancer detection; data augmentation; generative adversarial network

1 The genesis and tapestry of research

1.1 Introduction

Breast cancer is characterized by the malignant growth of cells within the breast tissue and poses a significant health challenge worldwide [1,2]. It is a complex disease, often developing in the milk ducts or lobules, and has the potential to spread throughout the body [3,4]. Early detection is key to improving survival rates and mitigating the adverse effects of the disease. The urgency for precise and early diagnosis has propelled rapid advancements in medical imaging techniques, which play a pivotal role in monitoring and guiding treatment strategies [5]. However, despite the availability of various imaging methods, challenges persist in accurately distinguishing between benign and malignant cases. This complexity is further compounded by the variability in cellular presentation, such as differences in size, shape, and location within the breast tissue [6]. The need for a more accurate and efficient diagnostic approach is evident, as it can greatly enhance treatment outcomes, reduce patient discomfort, and potentially lower mortality rates. In the context of breast cancer research, the exploration of the interplay between hormonal factors, genetics, and environmental influences continues to be of paramount importance.

The traditional techniques for breast cancer detection, primarily mammography and biopsy, each have their limitations and complexities. Mammography is widely used for early detection but faces challenges in accurately identifying cancerous tissues, especially in dense breast tissue [7,8]. Biopsy, on the other hand, provides a more definitive diagnosis but carries risks associated with invasive procedures [9,10]. The variability in mammographic interpretation and the invasive nature of biopsies necessitate the development of more sophisticated and non-invasive diagnostic tools. Deep learning (DL), which is known to be a subset of machine learning, offers a promising avenue in this regard. It has the potential to revolutionize breast cancer detection by learning directly from image data, thereby enhancing accuracy and efficiency in diagnosis. This study introduces a novel machine learning-based approach that utilizes a two-hidden-layer neural network model trained on an enriched dataset through advanced image processing techniques of data augmentation, namely data denoising, contrast enhancement, and generative adversarial network (GAN) applications. The integration of these methods aims to address the inherent challenges in breast cancer diagnosis and the limitations of small datasets, which offer more reliability and automated solutions for the early detection and classification of this prevalent disease.

1.2 Literature review

A recent study has proposed a two-stage model for breast cancer detection using thermographic images [11]. The approach is notable for its use of the VGG16 DL model combined with an optimized Dragonfly Algorithm. The innovation in the work lies in the incorporation of the Grunwald–Letnikov (GL) method to enhance the performance of the Dragonfly Algorithm. The model was evaluated using the DMR-IR standard dataset and demonstrated an impressive 100% diagnostic accuracy. A significant achievement of the model is its ability to reduce the feature set by 82% compared to the VGG16 model alone, showcasing efficiency in feature selection and potentially faster processing times. Another study has investigated the effectiveness of various DL architectures by leveraging transfer learning for breast cancer detection in histopathological images [12]. The work stands out for its use of multiple advanced architectures, including ResNet, ResNeXt, SENet, Dual Path Net, DenseNet, NASNet, and Wide ResNet. Utilizing the BreaKHis database of 7,909 histopathological images, the study demonstrated high accuracy rates, with the best models achieving up to 99.8% accuracy. The study emphasized the power of transfer learning in adapting non-specific DL models to highly specialized tasks like breast cancer detection. The automatic classification of breast cancer using histopathological images was a central focus in the study of Buvaneswari et al. [13]. The method involved preprocessing for noise removal and image resizing, then feature extraction using a 3D-convolutional neural network. The classification was performed using stochastic diffusion kernel recursive NNs (SDKRNN). The model was tested across various datasets, yielding a balanced set of performance metrics, including 98% accuracy and an F-1 score of 89%. A previous study compared six in-tuned DL models using transfer learning for breast tumor classification [14]. The study introduced a custom model trained on a public dataset with results showing that the models trained on the augmented dataset with 7,800 images had achieved up to 98.11% accuracy. Moreover, a novel approach for breast cancer detection using ensemble DL architectures integrated with the Web of Things (WoT) was presented in the study of Sheeba et al. [15]. The methodology involved collecting input images through WoT, preprocessing with Gaussian filtering, and segmentation using active contour convolutional neural networks. This study led to a high classification accuracy of 96% and a detection accuracy of 92%. The results showed the potential of combining DL with emerging technologies like WoT for enhanced breast cancer detection. Table 1 lists a simply comprehensive presentation of the comparison between the five cited papers.

Table 1

Literature survey of currently applied state-of-the-art methodologies

Ref. Methodology used Image type Key techniques/algorithms Dataset size Performance metrics
[11] Thermographic images Thermographic VGG16, Dragonfly Algorithm, GL method Standard dataset High accuracy with 82% fewer features
[12] DL with transfer learning Histopathological ResNet, ResNeXt, SENet, Dual Path Net, DenseNet, NASNet, Wide ResNet 7,909 images from 82 patients Up to 99.8% accuracy
[13] Feature extraction and classification using DL Histopathological 3D-CNN, SDKRNN Various datasets Accuracy: 98%, precision: 93.8%
[14] Comparison of DL models using ultrasound images Ultrasound ResNet-50, Inception-V3, Inception-ResNet-V2, MobileNet-V2, VGG-16, DenseNet-121 780 images, augmented to 3,900 and 7,800 Up to 98.11% accuracy
[15] DL techniques integrated with WoT for microscopic image analysis Microscopic Gaussian filtering, active contour CNN, and transfer learning with regional attention mechanism Not specified Detection accuracy: 92%, specificity: 91%

1.3 Research gap and contribution statement

Despite the advancements highlighted in the referenced studies, there remains a significant research gap in the integration and optimization of machine learning techniques for the analysis of diverse image types in breast cancer detection. Current methodologies primarily focus on single-type image analysis (thermographic, histopathological, ultrasound, or microscopic) and often employ conventional DL models without fully exploiting the potential of data augmentation and hybrid algorithmic approaches. The present study introduces a novel machine learning-based architecture that not only bridges this gap but also brings a new perspective to the field. This study employs a two-hidden-layer neural network model optimized through a comprehensive data augmentation process involving denoised data, contrast-enhanced images, and the use of a GAN. This approach allows for the effective processing of a diverse range of image types, thereby enhancing the model’s accuracy and generalizability. Furthermore, the model’s ability to efficiently vectorize images and handle complex datasets sets it apart from existing methods, offering a more robust and versatile solution for early and accurate detection of breast cancer across different imaging modalities. This innovation not only fills the identified research gap but also marks a significant step forward in the application of DL in medical imaging.

2 Developed methodology

In this study, the proposal of a marginally novel machine learning-based methodology for the classification and detection of breast cancer is given. The current approach advances such classifications by leveraging the strengths of the utilized artificial neural network (ANN) model. Generally speaking, the approach is primarily focused on processing mammographic images, which are inherently complex and require a sophisticated analysis technique to ensure accurate diagnosis. The dataset employed in the present research was acquired from a publicly available source, as mentioned in the study of Deb et al. [16]. The link to freely access the open-source data is available at the following portal: https://github.com/sagardeepdeb/rahman_xception_global, where all the scans are presented. It comprises mammographic scans of varying resolutions, predominantly larger than 4,000 × 2,000 pixels. To maintain the integrity and quality of these high-resolution images, regions of interest (ROIs) containing the mass were meticulously extracted rather than resizing the entire mammograms. Examples of these ROIs, both benign and malignant, post-preprocessing, are integral to the proposed analysis. For visual reference, representative mammographic scans from the dataset are depicted in Figure 1.

Figure 1 Acquired and randomly selected mammographic scans of breast cancer dataset: (a) benign and (b) malignant.
Figure 1

Acquired and randomly selected mammographic scans of breast cancer dataset: (a) benign and (b) malignant.

Computers are being utilized heavily in the field of biological medicine and other diagnostical approaches [17,18,19,20]. The methodology deviates from traditional approaches by utilizing a modified ANN, initially developed for image classification tasks. This network is particularly suited for deep feature extraction due to its inception modules, depth-wise separable convolution layers and residual blocks. The modification lies in the enhancements of the ROIs before processing them into classification tests. To enhance the performance of the proposed model, the study adopted contrast-enhancing, denoising, and GAN image pre-processing techniques. These techniques aid in the more effective extraction of features, which is crucial for the subsequent classification process. The GAN process is particularly adept at emphasizing the most prominent features in the mammograms, which is essential for distinguishing between benign and malignant cases. Figure 2 elaborates on the workflow process of the presented approach, where the images are progressed through a two-hidden-layer ANN after the pixels are vectorized accordingly.

Figure 2 The workflow of the ANN-based image classifying methodology.
Figure 2

The workflow of the ANN-based image classifying methodology.

3 Theoretical basis

This section describes the data augmentation process in order to enhance the classification of breast cancer scans. This part of the research also delves into the procedure followed for creating the ANN.

3.1 Data augmentation

Data augmentation is a crucial process in the field of machine learning, which is particularly used for medical imaging. It involves artificially expanding the size and variability of datasets by altering the images in ways that are plausible during real-world usage. This study covers three key data augmentation methods which are contrast enhancement, image denoising, and GANs. First, contrast enhancement is used to improve the visual quality of images by increasing the contrast between the different features in an image. A common approach to contrast enhancement is histogram equalization, which modifies the intensity distribution of an image. The transformation function T ( r ) can be defined as

(1) T ( r ) = ( L − 1 ) ∫ 0 T P r ( w ) d w ,

where r and T ( r ) are the original and transformed intensities, respectively, L is the total number of possible intensity levels in the image, and P r ( w ) is the probability density function of the pixel intensities. Moreover, as a second method, image denoising involves the removal of noise from the image while preserving important structural details. A simple yet effective method for denoising is Gaussian filtering. The Gaussian filter applies a convolution operation to each pixel with a kernel that represents a Gaussian distribution. The kernel G ( x , y ) for a 2D Gaussian filter is given by

(2) G ( x , y ) = 1 2 π σ 2 e x 2 + y 2 2 σ 2 ,

where x and y are the distances from the origin in the horizontal and vertical axes, respectively, and σ is the standard deviation of the Gaussian distribution. Finally, GANs are used to generate new data samples that are indistinguishable from the original dataset. A GAN consists of two networks: a generator G and a discriminator D. The generator creates images while the discriminator evaluates them. The objective function of a GAN is formulated as

(3) Min G Max D V ( G , D ) = E x ∼ p data [ log ⁡ ( D ( x ) ) ] + E y ∼ p g [ log ⁡ ( 1 − D ( y ) ) ] = ∫ x ∈ χ p data ( x ) log ( D ( x ) ) + p g ( x ) log ( 1 − D ( x ) ) d x .

Here, x is a real image from the dataset, z is a noise vector, p data is the data distribution, p g is the noise distribution, G ( z ) is the generated image, and D ( x ) is the discriminator’s estimate of the probability that x is a real image. Figures 3 and 4 display the results of the three methods for both the benign and malignant breast cancer scans, respectively.

Figure 3 Enhanced benign mammographic scans of breast cancer: (a) contrasted, (b) denoised, and (c) GAN analysis.
Figure 3

Enhanced benign mammographic scans of breast cancer: (a) contrasted, (b) denoised, and (c) GAN analysis.

Figure 4 Enhanced malignant mammographic breast cancer scans: (a) contrast-enhanced, (b) denoised approach, and (c) GAN analysis.
Figure 4

Enhanced malignant mammographic breast cancer scans: (a) contrast-enhanced, (b) denoised approach, and (c) GAN analysis.

3.2 ANN

Machine learning has proven to be effective in many different applications, regardless of the required regression or classification purposes [21,22,23,24,25,26,27,28,29,30,31,32]. An ANN is a computational model inspired by the way biological neural networks in the human brain process information [33,34]. It is composed of interconnected nodes or neurons, which process data and pass it through layers to produce an output [35]. The basic operation of a neuron in an ANN, the adopted activation function [35], and the stochastic gradient descent (SGD) solver [30,36] can described by the following equations:

(4) y = f ∑ i = 1 n w i x i + b ,

(5) f = tanh ( x ) = sinh ( x ) cosh ( x ) = e x − e − x e x + e − x ,

(6) w new = w old − η ▽ Q ( w old , x i , y i ) ,

where y is the output, x i are the inputs, w i are the weights, b is the bias, and f is the activation function. This mathematical formulation allows ANNs to learn complex patterns and relationships within data, making them highly effective for tasks like image classification and pattern recognition. Figure 5 depicts the adopted ANN structure, while Table 2 enlists the parameters for progressing the images.

Figure 5 The utilized ANN structure.
Figure 5

The utilized ANN structure.

Table 2

Parameter values of the adopted ANN model

Parameter No. of hidden layers No. of neurons in the first layer No. of neurons in the second layer Solver No. of iterations
Value 2 4 4 SGD 1,000

4 Results and discussion

In terms of discussing the results, it is important to point out that after the data were extracted from the images, it was turned to numerical features. The numeric value corresponds to the letter n, where the methodology has extracted countless numerical values of around 2,500. These statistics are based upon three main features, namely the size, width, and height of the image. Although some approaches would conduct important selection techniques, this current approach is to advance them for classification purposes. The first three numerical results (n0–n5) are randomly selected for depiction in Figure 6. Figure 6a visualizes the free depiction of the trained dataset scans of benign and malignant while corresponding to the five true numerical statistics. While the radial visualization is depicted in Figure 6b, it is concluded that n1 is a feature where it classifies benign tests easily, while n2 corresponds more to the malignant scans. Moreover, the violin plots for the three main features are illustrated in Figure 7. The size feature is shown in Figure 7a where it can be seen that the malignant scans are of higher diversity, which makes it a hard challenge for classification. Figure 7b, on the other hand, depicts the width violins while pointing out that the height is of the same instances because all the scans are cropped on the same width and height.

Figure 6 Visualization of the trained data: (a) free and (b) radial.
Figure 6

Visualization of the trained data: (a) free and (b) radial.

Figure 7 Violin plot of the three features: (a) size and (b) width and height.
Figure 7

Violin plot of the three features: (a) size and (b) width and height.

The classification results of all three methods, in addition to traditional datasets, are listed in Table 3, where the recognition accuracy is presented in percentages. Corresponding to this, the confusion matrix for each of the overall four techniques is elaborated upon in Figure 8. The testing dataset comprises 40 total images where half of which correspond to malignant scans, and the others are healthy. When the normal image scans were progressed into the ANN model, 57.5% was predicted correctly with 7 only identifying as malignant where 2 were wrong. This is an absolutely not dependable prediction methodology with high percentages of error. The denoised dataset, on the other hand, was of marginally similar results with a classification accuracy of 65%. Following up, the contrast-enhanced group of images was slightly better in prediction, with a recognition accuracy of 77.5%. However, this cannot be trusted among medical applications, which require a classifying near perfection. Interestingly, the proposed GAN-enhanced dataset had exhibited a remarkable classification accuracy which exceeded expectations with a value of unity. GANs can be advantageous over normal images because they have the capability to generate synthetic images; in addition, GANs could potentially handle noise in a more adaptive and dynamic way during the generation process. It is also concluded that the synthetic images produced by GANs might exhibit better contrasts and highlight relevant features for improved classification. While GANs might not bring such advantageous results in other fields of operation, it is concluded that GANs work perfectly when used for breast cancer scan classification.

Table 3

Classification-based results

Test dataset Result accuracy of classification (%)
Normal 57.5
Denoised 65.0
Contrast Enhanced 77.5
GAN 100
Figure 8 Confusion matrix for each of the four tested datasets: (a) normal scans, (b) denoised, (c) contrast-enhanced dataset, and (d) GAN.
Figure 8

Confusion matrix for each of the four tested datasets: (a) normal scans, (b) denoised, (c) contrast-enhanced dataset, and (d) GAN.

5 Conclusion

In conclusion, this study presented a robust machine learning-based approach for breast cancer diagnosis by leveraging a two-hidden-layer ANN model and a comprehensive data augmentation process. The incorporation of data denoising, contrast enhancement, and GAN techniques has significantly improved the quality of mammographic image classification. It led to a remarkable accuracy rate of 100% in the final model of which GAN-based enhancements were adopted. The detailed analysis of numerical features extracted from images, namely nx, highlighted the importance of considering size, width, and height for classification purposes. Notably, GAN-enhanced datasets demonstrated superior performance compared to normal, denoised, and contrast-enhanced images, showcasing the potential of GANs in generating synthetic images with improved contrasts for accurate breast cancer classification. This research contributes significantly to the biotechnological field by emphasizing the efficacy of data augmentation and ANN in medical imaging, particularly in the context of breast cancer diagnostics and computers in biotechnology. The findings also underscored the potentiality of future innovations to enhance early detection and improve patient outcomes in bio-related fields of breast cancer diagnosis and image processing.

  1. Funding information: Authors state no funding involved.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal, reviewed all the results and approved the final version of the manuscript. WHA: conceptualization, methodology, funding, and writing – review and editing; LAA-H: methodology, formal analysis, investigation, software, writing – original draft preparation; AB: conceptualization, methodology, and writing – review and editing; AAA-H writing – review and editing.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Data availability statement: The image dataset referenced from article [16] was used as a foundation for applying the new methodologies introduced in this study. However, all newly generated data, including images and results, are original and fully presented in this manuscript.

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Received: 2024-03-15
Revised: 2024-05-07
Accepted: 2024-05-19
Published Online: 2024-11-18

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  3. Influence of vibratory conveyor design parameters on the trough motion and the self-synchronization of inertial vibrators
  4. Application of finite element method in industrial design, example of an electric motorcycle design project
  5. Correlative evaluation of the corrosion resilience and passivation properties of zinc and aluminum alloys in neutral chloride and acid-chloride solutions
  6. Will COVID “encourage” B2B and data exchange engineering in logistic firms?
  7. Influence of unsupported sleepers on flange climb derailment of two freight wagons
  8. A hybrid detection algorithm for 5G OTFS waveform for 64 and 256 QAM with Rayleigh and Rician channels
  9. Effect of short heat treatment on mechanical properties and shape memory properties of Cu–Al–Ni shape memory alloy
  10. Exploring the potential of ammonia and hydrogen as alternative fuels for transportation
  11. Impact of insulation on energy consumption and CO2 emissions in high-rise commercial buildings at various climate zones
  12. Advanced autopilot design with extremum-seeking control for aircraft control
  13. Adaptive multidimensional trust-based recommendation model for peer to peer applications
  14. Effects of CFRP sheets on the flexural behavior of high-strength concrete beam
  15. Enhancing urban sustainability through industrial synergy: A multidisciplinary framework for integrating sustainable industrial practices within urban settings – The case of Hamadan industrial city
  16. Advanced vibrant controller results of an energetic framework structure
  17. Application of the Taguchi method and RSM for process parameter optimization in AWSJ machining of CFRP composite-based orthopedic implants
  18. Improved correlation of soil modulus with SPT N values
  19. Technologies for high-temperature batch annealing of grain-oriented electrical steel: An overview
  20. Assessing the need for the adoption of digitalization in Indian small and medium enterprises
  21. A non-ideal hybridization issue for vertical TFET-based dielectric-modulated biosensor
  22. Optimizing data retrieval for enhanced data integrity verification in cloud environments
  23. Performance analysis of nonlinear crosstalk of WDM systems using modulation schemes criteria
  24. Nonlinear finite-element analysis of RC beams with various opening near supports
  25. Thermal analysis of Fe3O4–Cu/water over a cone: a fractional Maxwell model
  26. Radial–axial runner blade design using the coordinate slice technique
  27. Theoretical and experimental comparison between straight and curved continuous box girders
  28. Effect of the reinforcement ratio on the mechanical behaviour of textile-reinforced concrete composite: Experiment and numerical modeling
  29. Experimental and numerical investigation on composite beam–column joint connection behavior using different types of connection schemes
  30. Enhanced performance and robustness in anti-lock brake systems using barrier function-based integral sliding mode control
  31. Evaluation of the creep strength of samples produced by fused deposition modeling
  32. A combined feedforward-feedback controller design for nonlinear systems
  33. Effect of adjacent structures on footing settlement for different multi-building arrangements
  34. Analyzing the impact of curved tracks on wheel flange thickness reduction in railway systems
  35. Review Articles
  36. Mechanical and smart properties of cement nanocomposites containing nanomaterials: A brief review
  37. Applications of nanotechnology and nanoproduction techniques
  38. Relationship between indoor environmental quality and guests’ comfort and satisfaction at green hotels: A comprehensive review
  39. Communication
  40. Techniques to mitigate the admission of radon inside buildings
  41. Erratum
  42. Erratum to “Effect of short heat treatment on mechanical properties and shape memory properties of Cu–Al–Ni shape memory alloy”
  43. Special Issue: AESMT-3 - Part II
  44. Integrated fuzzy logic and multicriteria decision model methods for selecting suitable sites for wastewater treatment plant: A case study in the center of Basrah, Iraq
  45. Physical and mechanical response of porous metals composites with nano-natural additives
  46. Special Issue: AESMT-4 - Part II
  47. New recycling method of lubricant oil and the effect on the viscosity and viscous shear as an environmentally friendly
  48. Identify the effect of Fe2O3 nanoparticles on mechanical and microstructural characteristics of aluminum matrix composite produced by powder metallurgy technique
  49. Static behavior of piled raft foundation in clay
  50. Ultra-low-power CMOS ring oscillator with minimum power consumption of 2.9 pW using low-voltage biasing technique
  51. Using ANN for well type identifying and increasing production from Sa’di formation of Halfaya oil field – Iraq
  52. Optimizing the performance of concrete tiles using nano-papyrus and carbon fibers
  53. Special Issue: AESMT-5 - Part II
  54. Comparative the effect of distribution transformer coil shape on electromagnetic forces and their distribution using the FEM
  55. The complex of Weyl module in free characteristic in the event of a partition (7,5,3)
  56. Restrained captive domination number
  57. Experimental study of improving hot mix asphalt reinforced with carbon fibers
  58. Asphalt binder modified with recycled tyre rubber
  59. Thermal performance of radiant floor cooling with phase change material for energy-efficient buildings
  60. Surveying the prediction of risks in cryptocurrency investments using recurrent neural networks
  61. A deep reinforcement learning framework to modify LQR for an active vibration control applied to 2D building models
  62. Evaluation of mechanically stabilized earth retaining walls for different soil–structure interaction methods: A review
  63. Assessment of heat transfer in a triangular duct with different configurations of ribs using computational fluid dynamics
  64. Sulfate removal from wastewater by using waste material as an adsorbent
  65. Experimental investigation on strengthening lap joints subjected to bending in glulam timber beams using CFRP sheets
  66. A study of the vibrations of a rotor bearing suspended by a hybrid spring system of shape memory alloys
  67. Stability analysis of Hub dam under rapid drawdown
  68. Developing ANFIS-FMEA model for assessment and prioritization of potential trouble factors in Iraqi building projects
  69. Numerical and experimental comparison study of piled raft foundation
  70. Effect of asphalt modified with waste engine oil on the durability properties of hot asphalt mixtures with reclaimed asphalt pavement
  71. Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network
  72. Numerical study on discharge capacity of piano key side weir with various ratios of the crest length to the width
  73. The optimal allocation of thyristor-controlled series compensators for enhancement HVAC transmission lines Iraqi super grid by using seeker optimization algorithm
  74. Numerical and experimental study of the impact on aerodynamic characteristics of the NACA0012 airfoil
  75. Effect of nano-TiO2 on physical and rheological properties of asphalt cement
  76. Performance evolution of novel palm leaf powder used for enhancing hot mix asphalt
  77. Performance analysis, evaluation, and improvement of selected unsignalized intersection using SIDRA software – Case study
  78. Flexural behavior of RC beams externally reinforced with CFRP composites using various strategies
  79. Influence of fiber types on the properties of the artificial cold-bonded lightweight aggregates
  80. Experimental investigation of RC beams strengthened with externally bonded BFRP composites
  81. Generalized RKM methods for solving fifth-order quasi-linear fractional partial differential equation
  82. An experimental and numerical study investigating sediment transport position in the bed of sewer pipes in Karbala
  83. Role of individual component failure in the performance of a 1-out-of-3 cold standby system: A Markov model approach
  84. Implementation for the cases (5, 4) and (5, 4)/(2, 0)
  85. Center group actions and related concepts
  86. Experimental investigation of the effect of horizontal construction joints on the behavior of deep beams
  87. Deletion of a vertex in even sum domination
  88. Deep learning techniques in concrete powder mix designing
  89. Effect of loading type in concrete deep beam with strut reinforcement
  90. Studying the effect of using CFRP warping on strength of husk rice concrete columns
  91. Parametric analysis of the influence of climatic factors on the formation of traditional buildings in the city of Al Najaf
  92. Suitability location for landfill using a fuzzy-GIS model: A case study in Hillah, Iraq
  93. Hybrid approach for cost estimation of sustainable building projects using artificial neural networks
  94. Assessment of indirect tensile stress and tensile–strength ratio and creep compliance in HMA mixes with micro-silica and PMB
  95. Density functional theory to study stopping power of proton in water, lung, bladder, and intestine
  96. A review of single flow, flow boiling, and coating microchannel studies
  97. Effect of GFRP bar length on the flexural behavior of hybrid concrete beams strengthened with NSM bars
  98. Exploring the impact of parameters on flow boiling heat transfer in microchannels and coated microtubes: A comprehensive review
  99. Crumb rubber modification for enhanced rutting resistance in asphalt mixtures
  100. Special Issue: AESMT-6
  101. Design of a new sorting colors system based on PLC, TIA portal, and factory I/O programs
  102. Forecasting empirical formula for suspended sediment load prediction at upstream of Al-Kufa barrage, Kufa City, Iraq
  103. Optimization and characterization of sustainable geopolymer mortars based on palygorskite clay, water glass, and sodium hydroxide
  104. Sediment transport modelling upstream of Al Kufa Barrage
  105. Study of energy loss, range, and stopping time for proton in germanium and copper materials
  106. Effect of internal and external recycle ratios on the nutrient removal efficiency of anaerobic/anoxic/oxic (VIP) wastewater treatment plant
  107. Enhancing structural behaviour of polypropylene fibre concrete columns longitudinally reinforced with fibreglass bars
  108. Sustainable road paving: Enhancing concrete paver blocks with zeolite-enhanced cement
  109. Evaluation of the operational performance of Karbala waste water treatment plant under variable flow using GPS-X model
  110. Design and simulation of photonic crystal fiber for highly sensitive chemical sensing applications
  111. Optimization and design of a new column sequencing for crude oil distillation at Basrah refinery
  112. Inductive 3D numerical modelling of the tibia bone using MRI to examine von Mises stress and overall deformation
  113. An image encryption method based on modified elliptic curve Diffie-Hellman key exchange protocol and Hill Cipher
  114. Experimental investigation of generating superheated steam using a parabolic dish with a cylindrical cavity receiver: A case study
  115. Effect of surface roughness on the interface behavior of clayey soils
  116. Investigated of the optical properties for SiO2 by using Lorentz model
  117. Measurements of induced vibrations due to steel pipe pile driving in Al-Fao soil: Effect of partial end closure
  118. Experimental and numerical studies of ballistic resistance of hybrid sandwich composite body armor
  119. Evaluation of clay layer presence on shallow foundation settlement in dry sand under an earthquake
  120. Optimal design of mechanical performances of asphalt mixtures comprising nano-clay additives
  121. Advancing seismic performance: Isolators, TMDs, and multi-level strategies in reinforced concrete buildings
  122. Predicted evaporation in Basrah using artificial neural networks
  123. Energy management system for a small town to enhance quality of life
  124. Numerical study on entropy minimization in pipes with helical airfoil and CuO nanoparticle integration
  125. Equations and methodologies of inlet drainage system discharge coefficients: A review
  126. Thermal buckling analysis for hybrid and composite laminated plate by using new displacement function
  127. Investigation into the mechanical and thermal properties of lightweight mortar using commercial beads or recycled expanded polystyrene
  128. Experimental and theoretical analysis of single-jet column and concrete column using double-jet grouting technique applied at Al-Rashdia site
  129. The impact of incorporating waste materials on the mechanical and physical characteristics of tile adhesive materials
  130. Seismic resilience: Innovations in structural engineering for earthquake-prone areas
  131. Automatic human identification using fingerprint images based on Gabor filter and SIFT features fusion
  132. Performance of GRKM-method for solving classes of ordinary and partial differential equations of sixth-orders
  133. Visible light-boosted photodegradation activity of Ag–AgVO3/Zn0.5Mn0.5Fe2O4 supported heterojunctions for effective degradation of organic contaminates
  134. Production of sustainable concrete with treated cement kiln dust and iron slag waste aggregate
  135. Key effects on the structural behavior of fiber-reinforced lightweight concrete-ribbed slabs: A review
  136. A comparative analysis of the energy dissipation efficiency of various piano key weir types
  137. Special Issue: Transport 2022 - Part II
  138. Variability in road surface temperature in urban road network – A case study making use of mobile measurements
  139. Special Issue: BCEE5-2023
  140. Evaluation of reclaimed asphalt mixtures rejuvenated with waste engine oil to resist rutting deformation
  141. Assessment of potential resistance to moisture damage and fatigue cracks of asphalt mixture modified with ground granulated blast furnace slag
  142. Investigating seismic response in adjacent structures: A study on the impact of buildings’ orientation and distance considering soil–structure interaction
  143. Improvement of porosity of mortar using polyethylene glycol pre-polymer-impregnated mortar
  144. Three-dimensional analysis of steel beam-column bolted connections
  145. Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery
  146. Performance evaluation of grouted porous asphalt concrete
  147. Optimization of local modified metakaolin-based geopolymer concrete by Taguchi method
  148. Effect of waste tire products on some characteristics of roller-compacted concrete
  149. Studying the lateral displacement of retaining wall supporting sandy soil under dynamic loads
  150. Seismic performance evaluation of concrete buttress dram (Dynamic linear analysis)
  151. Behavior of soil reinforced with micropiles
  152. Possibility of production high strength lightweight concrete containing organic waste aggregate and recycled steel fibers
  153. An investigation of self-sensing and mechanical properties of smart engineered cementitious composites reinforced with functional materials
  154. Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model
  155. Experimental investigation of dynamic soil properties for modeling energy-absorbing layers
  156. Numerical investigation of the effect of longitudinal steel reinforcement ratio on the ductility of concrete beams
  157. An experimental study on the tensile properties of reinforced asphalt pavement
  158. Self-sensing behavior of hot asphalt mixture with steel fiber-based additive
  159. Behavior of ultra-high-performance concrete deep beams reinforced by basalt fibers
  160. Optimizing asphalt binder performance with various PET types
  161. Investigation of the hydraulic characteristics and homogeneity of the microstructure of the air voids in the sustainable rigid pavement
  162. Enhanced biogas production from municipal solid waste via digestion with cow manure: A case study
  163. Special Issue: AESMT-7 - Part I
  164. Preparation and investigation of cobalt nanoparticles by laser ablation: Structure, linear, and nonlinear optical properties
  165. Seismic analysis of RC building with plan irregularity in Baghdad/Iraq to obtain the optimal behavior
  166. The effect of urban environment on large-scale path loss model’s main parameters for mmWave 5G mobile network in Iraq
  167. Formatting a questionnaire for the quality control of river bank roads
  168. Vibration suppression of smart composite beam using model predictive controller
  169. Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete
  170. In-depth analysis of critical factors affecting Iraqi construction projects performance
  171. Behavior of container berth structure under the influence of environmental and operational loads
  172. Energy absorption and impact response of ballistic resistance laminate
  173. Effect of water-absorbent polymer balls in internal curing on punching shear behavior of bubble slabs
  174. Effect of surface roughness on interface shear strength parameters of sandy soils
  175. Evaluating the interaction for embedded H-steel section in normal concrete under monotonic and repeated loads
  176. Estimation of the settlement of pile head using ANN and multivariate linear regression based on the results of load transfer method
  177. Enhancing communication: Deep learning for Arabic sign language translation
  178. A review of recent studies of both heat pipe and evaporative cooling in passive heat recovery
  179. Effect of nano-silica on the mechanical properties of LWC
  180. An experimental study of some mechanical properties and absorption for polymer-modified cement mortar modified with superplasticizer
  181. Digital beamforming enhancement with LSTM-based deep learning for millimeter wave transmission
  182. Developing an efficient planning process for heritage buildings maintenance in Iraq
  183. Design and optimization of two-stage controller for three-phase multi-converter/multi-machine electric vehicle
  184. Evaluation of microstructure and mechanical properties of Al1050/Al2O3/Gr composite processed by forming operation ECAP
  185. Calculations of mass stopping power and range of protons in organic compounds (CH3OH, CH2O, and CO2) at energy range of 0.01–1,000 MeV
  186. Investigation of in vitro behavior of composite coating hydroxyapatite-nano silver on 316L stainless steel substrate by electrophoretic technic for biomedical tools
  187. A review: Enhancing tribological properties of journal bearings composite materials
  188. Improvements in the randomness and security of digital currency using the photon sponge hash function through Maiorana–McFarland S-box replacement
  189. Design a new scheme for image security using a deep learning technique of hierarchical parameters
  190. Special Issue: ICES 2023
  191. Comparative geotechnical analysis for ultimate bearing capacity of precast concrete piles using cone resistance measurements
  192. Visualizing sustainable rainwater harvesting: A case study of Karbala Province
  193. Geogrid reinforcement for improving bearing capacity and stability of square foundations
  194. Evaluation of the effluent concentrations of Karbala wastewater treatment plant using reliability analysis
  195. Adsorbent made with inexpensive, local resources
  196. Effect of drain pipes on seepage and slope stability through a zoned earth dam
  197. Sediment accumulation in an 8 inch sewer pipe for a sample of various particles obtained from the streets of Karbala city, Iraq
  198. Special Issue: IETAS 2024 - Part I
  199. Analyzing the impact of transfer learning on explanation accuracy in deep learning-based ECG recognition systems
  200. Effect of scale factor on the dynamic response of frame foundations
  201. Improving multi-object detection and tracking with deep learning, DeepSORT, and frame cancellation techniques
  202. The impact of using prestressed CFRP bars on the development of flexural strength
  203. Assessment of surface hardness and impact strength of denture base resins reinforced with silver–titanium dioxide and silver–zirconium dioxide nanoparticles: In vitro study
  204. A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks
  205. Modification of the 5D Lorenz chaotic map with fuzzy numbers for video encryption in cloud computing
  206. Special Issue: 51st KKBN - Part I
  207. Evaluation of static bending caused damage of glass-fiber composite structure using terahertz inspection
https://doi.org/10.1515/eng-2024-0052
Schlagwörter für diesen Artikel
artificial neural network; breast cancer; cancer detection; data augmentation; generative adversarial network
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Regular Articles
  2. Methodology of automated quality management
  3. Influence of vibratory conveyor design parameters on the trough motion and the self-synchronization of inertial vibrators
  4. Application of finite element method in industrial design, example of an electric motorcycle design project
  5. Correlative evaluation of the corrosion resilience and passivation properties of zinc and aluminum alloys in neutral chloride and acid-chloride solutions
  6. Will COVID “encourage” B2B and data exchange engineering in logistic firms?
  7. Influence of unsupported sleepers on flange climb derailment of two freight wagons
  8. A hybrid detection algorithm for 5G OTFS waveform for 64 and 256 QAM with Rayleigh and Rician channels
  9. Effect of short heat treatment on mechanical properties and shape memory properties of Cu–Al–Ni shape memory alloy
  10. Exploring the potential of ammonia and hydrogen as alternative fuels for transportation
  11. Impact of insulation on energy consumption and CO2 emissions in high-rise commercial buildings at various climate zones
  12. Advanced autopilot design with extremum-seeking control for aircraft control
  13. Adaptive multidimensional trust-based recommendation model for peer to peer applications
  14. Effects of CFRP sheets on the flexural behavior of high-strength concrete beam
  15. Enhancing urban sustainability through industrial synergy: A multidisciplinary framework for integrating sustainable industrial practices within urban settings – The case of Hamadan industrial city
  16. Advanced vibrant controller results of an energetic framework structure
  17. Application of the Taguchi method and RSM for process parameter optimization in AWSJ machining of CFRP composite-based orthopedic implants
  18. Improved correlation of soil modulus with SPT N values
  19. Technologies for high-temperature batch annealing of grain-oriented electrical steel: An overview
  20. Assessing the need for the adoption of digitalization in Indian small and medium enterprises
  21. A non-ideal hybridization issue for vertical TFET-based dielectric-modulated biosensor
  22. Optimizing data retrieval for enhanced data integrity verification in cloud environments
  23. Performance analysis of nonlinear crosstalk of WDM systems using modulation schemes criteria
  24. Nonlinear finite-element analysis of RC beams with various opening near supports
  25. Thermal analysis of Fe3O4–Cu/water over a cone: a fractional Maxwell model
  26. Radial–axial runner blade design using the coordinate slice technique
  27. Theoretical and experimental comparison between straight and curved continuous box girders
  28. Effect of the reinforcement ratio on the mechanical behaviour of textile-reinforced concrete composite: Experiment and numerical modeling
  29. Experimental and numerical investigation on composite beam–column joint connection behavior using different types of connection schemes
  30. Enhanced performance and robustness in anti-lock brake systems using barrier function-based integral sliding mode control
  31. Evaluation of the creep strength of samples produced by fused deposition modeling
  32. A combined feedforward-feedback controller design for nonlinear systems
  33. Effect of adjacent structures on footing settlement for different multi-building arrangements
  34. Analyzing the impact of curved tracks on wheel flange thickness reduction in railway systems
  35. Review Articles
  36. Mechanical and smart properties of cement nanocomposites containing nanomaterials: A brief review
  37. Applications of nanotechnology and nanoproduction techniques
  38. Relationship between indoor environmental quality and guests’ comfort and satisfaction at green hotels: A comprehensive review
  39. Communication
  40. Techniques to mitigate the admission of radon inside buildings
  41. Erratum
  42. Erratum to “Effect of short heat treatment on mechanical properties and shape memory properties of Cu–Al–Ni shape memory alloy”
  43. Special Issue: AESMT-3 - Part II
  44. Integrated fuzzy logic and multicriteria decision model methods for selecting suitable sites for wastewater treatment plant: A case study in the center of Basrah, Iraq
  45. Physical and mechanical response of porous metals composites with nano-natural additives
  46. Special Issue: AESMT-4 - Part II
  47. New recycling method of lubricant oil and the effect on the viscosity and viscous shear as an environmentally friendly
  48. Identify the effect of Fe2O3 nanoparticles on mechanical and microstructural characteristics of aluminum matrix composite produced by powder metallurgy technique
  49. Static behavior of piled raft foundation in clay
  50. Ultra-low-power CMOS ring oscillator with minimum power consumption of 2.9 pW using low-voltage biasing technique
  51. Using ANN for well type identifying and increasing production from Sa’di formation of Halfaya oil field – Iraq
  52. Optimizing the performance of concrete tiles using nano-papyrus and carbon fibers
  53. Special Issue: AESMT-5 - Part II
  54. Comparative the effect of distribution transformer coil shape on electromagnetic forces and their distribution using the FEM
  55. The complex of Weyl module in free characteristic in the event of a partition (7,5,3)
  56. Restrained captive domination number
  57. Experimental study of improving hot mix asphalt reinforced with carbon fibers
  58. Asphalt binder modified with recycled tyre rubber
  59. Thermal performance of radiant floor cooling with phase change material for energy-efficient buildings
  60. Surveying the prediction of risks in cryptocurrency investments using recurrent neural networks
  61. A deep reinforcement learning framework to modify LQR for an active vibration control applied to 2D building models
  62. Evaluation of mechanically stabilized earth retaining walls for different soil–structure interaction methods: A review
  63. Assessment of heat transfer in a triangular duct with different configurations of ribs using computational fluid dynamics
  64. Sulfate removal from wastewater by using waste material as an adsorbent
  65. Experimental investigation on strengthening lap joints subjected to bending in glulam timber beams using CFRP sheets
  66. A study of the vibrations of a rotor bearing suspended by a hybrid spring system of shape memory alloys
  67. Stability analysis of Hub dam under rapid drawdown
  68. Developing ANFIS-FMEA model for assessment and prioritization of potential trouble factors in Iraqi building projects
  69. Numerical and experimental comparison study of piled raft foundation
  70. Effect of asphalt modified with waste engine oil on the durability properties of hot asphalt mixtures with reclaimed asphalt pavement
  71. Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network
  72. Numerical study on discharge capacity of piano key side weir with various ratios of the crest length to the width
  73. The optimal allocation of thyristor-controlled series compensators for enhancement HVAC transmission lines Iraqi super grid by using seeker optimization algorithm
  74. Numerical and experimental study of the impact on aerodynamic characteristics of the NACA0012 airfoil
  75. Effect of nano-TiO2 on physical and rheological properties of asphalt cement
  76. Performance evolution of novel palm leaf powder used for enhancing hot mix asphalt
  77. Performance analysis, evaluation, and improvement of selected unsignalized intersection using SIDRA software – Case study
  78. Flexural behavior of RC beams externally reinforced with CFRP composites using various strategies
  79. Influence of fiber types on the properties of the artificial cold-bonded lightweight aggregates
  80. Experimental investigation of RC beams strengthened with externally bonded BFRP composites
  81. Generalized RKM methods for solving fifth-order quasi-linear fractional partial differential equation
  82. An experimental and numerical study investigating sediment transport position in the bed of sewer pipes in Karbala
  83. Role of individual component failure in the performance of a 1-out-of-3 cold standby system: A Markov model approach
  84. Implementation for the cases (5, 4) and (5, 4)/(2, 0)
  85. Center group actions and related concepts
  86. Experimental investigation of the effect of horizontal construction joints on the behavior of deep beams
  87. Deletion of a vertex in even sum domination
  88. Deep learning techniques in concrete powder mix designing
  89. Effect of loading type in concrete deep beam with strut reinforcement
  90. Studying the effect of using CFRP warping on strength of husk rice concrete columns
  91. Parametric analysis of the influence of climatic factors on the formation of traditional buildings in the city of Al Najaf
  92. Suitability location for landfill using a fuzzy-GIS model: A case study in Hillah, Iraq
  93. Hybrid approach for cost estimation of sustainable building projects using artificial neural networks
  94. Assessment of indirect tensile stress and tensile–strength ratio and creep compliance in HMA mixes with micro-silica and PMB
  95. Density functional theory to study stopping power of proton in water, lung, bladder, and intestine
  96. A review of single flow, flow boiling, and coating microchannel studies
  97. Effect of GFRP bar length on the flexural behavior of hybrid concrete beams strengthened with NSM bars
  98. Exploring the impact of parameters on flow boiling heat transfer in microchannels and coated microtubes: A comprehensive review
  99. Crumb rubber modification for enhanced rutting resistance in asphalt mixtures
  100. Special Issue: AESMT-6
  101. Design of a new sorting colors system based on PLC, TIA portal, and factory I/O programs
  102. Forecasting empirical formula for suspended sediment load prediction at upstream of Al-Kufa barrage, Kufa City, Iraq
  103. Optimization and characterization of sustainable geopolymer mortars based on palygorskite clay, water glass, and sodium hydroxide
  104. Sediment transport modelling upstream of Al Kufa Barrage
  105. Study of energy loss, range, and stopping time for proton in germanium and copper materials
  106. Effect of internal and external recycle ratios on the nutrient removal efficiency of anaerobic/anoxic/oxic (VIP) wastewater treatment plant
  107. Enhancing structural behaviour of polypropylene fibre concrete columns longitudinally reinforced with fibreglass bars
  108. Sustainable road paving: Enhancing concrete paver blocks with zeolite-enhanced cement
  109. Evaluation of the operational performance of Karbala waste water treatment plant under variable flow using GPS-X model
  110. Design and simulation of photonic crystal fiber for highly sensitive chemical sensing applications
  111. Optimization and design of a new column sequencing for crude oil distillation at Basrah refinery
  112. Inductive 3D numerical modelling of the tibia bone using MRI to examine von Mises stress and overall deformation
  113. An image encryption method based on modified elliptic curve Diffie-Hellman key exchange protocol and Hill Cipher
  114. Experimental investigation of generating superheated steam using a parabolic dish with a cylindrical cavity receiver: A case study
  115. Effect of surface roughness on the interface behavior of clayey soils
  116. Investigated of the optical properties for SiO2 by using Lorentz model
  117. Measurements of induced vibrations due to steel pipe pile driving in Al-Fao soil: Effect of partial end closure
  118. Experimental and numerical studies of ballistic resistance of hybrid sandwich composite body armor
  119. Evaluation of clay layer presence on shallow foundation settlement in dry sand under an earthquake
  120. Optimal design of mechanical performances of asphalt mixtures comprising nano-clay additives
  121. Advancing seismic performance: Isolators, TMDs, and multi-level strategies in reinforced concrete buildings
  122. Predicted evaporation in Basrah using artificial neural networks
  123. Energy management system for a small town to enhance quality of life
  124. Numerical study on entropy minimization in pipes with helical airfoil and CuO nanoparticle integration
  125. Equations and methodologies of inlet drainage system discharge coefficients: A review
  126. Thermal buckling analysis for hybrid and composite laminated plate by using new displacement function
  127. Investigation into the mechanical and thermal properties of lightweight mortar using commercial beads or recycled expanded polystyrene
  128. Experimental and theoretical analysis of single-jet column and concrete column using double-jet grouting technique applied at Al-Rashdia site
  129. The impact of incorporating waste materials on the mechanical and physical characteristics of tile adhesive materials
  130. Seismic resilience: Innovations in structural engineering for earthquake-prone areas
  131. Automatic human identification using fingerprint images based on Gabor filter and SIFT features fusion
  132. Performance of GRKM-method for solving classes of ordinary and partial differential equations of sixth-orders
  133. Visible light-boosted photodegradation activity of Ag–AgVO3/Zn0.5Mn0.5Fe2O4 supported heterojunctions for effective degradation of organic contaminates
  134. Production of sustainable concrete with treated cement kiln dust and iron slag waste aggregate
  135. Key effects on the structural behavior of fiber-reinforced lightweight concrete-ribbed slabs: A review
  136. A comparative analysis of the energy dissipation efficiency of various piano key weir types
  137. Special Issue: Transport 2022 - Part II
  138. Variability in road surface temperature in urban road network – A case study making use of mobile measurements
  139. Special Issue: BCEE5-2023
  140. Evaluation of reclaimed asphalt mixtures rejuvenated with waste engine oil to resist rutting deformation
  141. Assessment of potential resistance to moisture damage and fatigue cracks of asphalt mixture modified with ground granulated blast furnace slag
  142. Investigating seismic response in adjacent structures: A study on the impact of buildings’ orientation and distance considering soil–structure interaction
  143. Improvement of porosity of mortar using polyethylene glycol pre-polymer-impregnated mortar
  144. Three-dimensional analysis of steel beam-column bolted connections
  145. Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery
  146. Performance evaluation of grouted porous asphalt concrete
  147. Optimization of local modified metakaolin-based geopolymer concrete by Taguchi method
  148. Effect of waste tire products on some characteristics of roller-compacted concrete
  149. Studying the lateral displacement of retaining wall supporting sandy soil under dynamic loads
  150. Seismic performance evaluation of concrete buttress dram (Dynamic linear analysis)
  151. Behavior of soil reinforced with micropiles
  152. Possibility of production high strength lightweight concrete containing organic waste aggregate and recycled steel fibers
  153. An investigation of self-sensing and mechanical properties of smart engineered cementitious composites reinforced with functional materials
  154. Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model
  155. Experimental investigation of dynamic soil properties for modeling energy-absorbing layers
  156. Numerical investigation of the effect of longitudinal steel reinforcement ratio on the ductility of concrete beams
  157. An experimental study on the tensile properties of reinforced asphalt pavement
  158. Self-sensing behavior of hot asphalt mixture with steel fiber-based additive
  159. Behavior of ultra-high-performance concrete deep beams reinforced by basalt fibers
  160. Optimizing asphalt binder performance with various PET types
  161. Investigation of the hydraulic characteristics and homogeneity of the microstructure of the air voids in the sustainable rigid pavement
  162. Enhanced biogas production from municipal solid waste via digestion with cow manure: A case study
  163. Special Issue: AESMT-7 - Part I
  164. Preparation and investigation of cobalt nanoparticles by laser ablation: Structure, linear, and nonlinear optical properties
  165. Seismic analysis of RC building with plan irregularity in Baghdad/Iraq to obtain the optimal behavior
  166. The effect of urban environment on large-scale path loss model’s main parameters for mmWave 5G mobile network in Iraq
  167. Formatting a questionnaire for the quality control of river bank roads
  168. Vibration suppression of smart composite beam using model predictive controller
  169. Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete
  170. In-depth analysis of critical factors affecting Iraqi construction projects performance
  171. Behavior of container berth structure under the influence of environmental and operational loads
  172. Energy absorption and impact response of ballistic resistance laminate
  173. Effect of water-absorbent polymer balls in internal curing on punching shear behavior of bubble slabs
  174. Effect of surface roughness on interface shear strength parameters of sandy soils
  175. Evaluating the interaction for embedded H-steel section in normal concrete under monotonic and repeated loads
  176. Estimation of the settlement of pile head using ANN and multivariate linear regression based on the results of load transfer method
  177. Enhancing communication: Deep learning for Arabic sign language translation
  178. A review of recent studies of both heat pipe and evaporative cooling in passive heat recovery
  179. Effect of nano-silica on the mechanical properties of LWC
  180. An experimental study of some mechanical properties and absorption for polymer-modified cement mortar modified with superplasticizer
  181. Digital beamforming enhancement with LSTM-based deep learning for millimeter wave transmission
  182. Developing an efficient planning process for heritage buildings maintenance in Iraq
  183. Design and optimization of two-stage controller for three-phase multi-converter/multi-machine electric vehicle
  184. Evaluation of microstructure and mechanical properties of Al1050/Al2O3/Gr composite processed by forming operation ECAP
  185. Calculations of mass stopping power and range of protons in organic compounds (CH3OH, CH2O, and CO2) at energy range of 0.01–1,000 MeV
  186. Investigation of in vitro behavior of composite coating hydroxyapatite-nano silver on 316L stainless steel substrate by electrophoretic technic for biomedical tools
  187. A review: Enhancing tribological properties of journal bearings composite materials
  188. Improvements in the randomness and security of digital currency using the photon sponge hash function through Maiorana–McFarland S-box replacement
  189. Design a new scheme for image security using a deep learning technique of hierarchical parameters
  190. Special Issue: ICES 2023
  191. Comparative geotechnical analysis for ultimate bearing capacity of precast concrete piles using cone resistance measurements
  192. Visualizing sustainable rainwater harvesting: A case study of Karbala Province
  193. Geogrid reinforcement for improving bearing capacity and stability of square foundations
  194. Evaluation of the effluent concentrations of Karbala wastewater treatment plant using reliability analysis
  195. Adsorbent made with inexpensive, local resources
  196. Effect of drain pipes on seepage and slope stability through a zoned earth dam
  197. Sediment accumulation in an 8 inch sewer pipe for a sample of various particles obtained from the streets of Karbala city, Iraq
  198. Special Issue: IETAS 2024 - Part I
  199. Analyzing the impact of transfer learning on explanation accuracy in deep learning-based ECG recognition systems
  200. Effect of scale factor on the dynamic response of frame foundations
  201. Improving multi-object detection and tracking with deep learning, DeepSORT, and frame cancellation techniques
  202. The impact of using prestressed CFRP bars on the development of flexural strength
  203. Assessment of surface hardness and impact strength of denture base resins reinforced with silver–titanium dioxide and silver–zirconium dioxide nanoparticles: In vitro study
  204. A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks
  205. Modification of the 5D Lorenz chaotic map with fuzzy numbers for video encryption in cloud computing
  206. Special Issue: 51st KKBN - Part I
  207. Evaluation of static bending caused damage of glass-fiber composite structure using terahertz inspection
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Heruntergeladen am 4.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/eng-2024-0052/html
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