Automatic detection of tire defects has become an important issue for tire production companies since these defects cause road accidents and loss of human lives. Defects in the inner structure of the ...tire cannot be detected with the naked eye; thus, a radiographic image of the tire is gathered using X-ray cameras. This image is then examined by a quality control operator, and a decision is made on whether it is a defective tire or not. Among all defect types, the foreign object type is the most common and may occur anywhere in the tire. This study proposes an explainable deep learning model based on Xception and Grad-CAM approaches. This model was fine-tuned and trained on a novel real tire dataset consisting of 2303 defective tires and 49,198 non-defective. The defective tire class was augmented using a custom augmentation technique to solve the imbalance problem of the dataset. Experimental results show that the proposed model detects foreign objects with an accuracy of 99.19%, recall of 98.75%, precision of 99.34%, and f-score of 99.05%. This study provided a clear advantage over similar literature studies.
The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for ...classifying brain tumors from Magnetic Resonance Imaging (MRI) scans. However, despite their impressive performance, the opaque nature of DL models poses challenges in understanding their decision-making mechanisms, particularly crucial in medical contexts where interpretability is essential. This paper explores the intersection of medical image analysis and DL interpretability, aiming to elucidate the decision-making rationale of DL models in brain tumor classification. Leveraging ten state-of-the-art DL frameworks with transfer learning, we conducted a comprehensive evaluation encompassing both classification accuracy and interpretability. These models underwent thorough training, testing, and fine-tuning, resulting in EfficientNetB0, DenseNet121, and Xception outperforming the other models. These top-performing models were examined using adaptive path-based techniques to understand the underlying decision-making mechanisms. Grad-CAM and Grad-CAM++ highlighted critical image regions where the models identified patterns and features associated with each class of the brain tumor. The regions where the models identified patterns and features correspond visually to the regions where the tumors are located in the images. This result shows that DL models learn important features and patterns in the regions where tumors are located for decision-making.
Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where ...it allows extending the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this article, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning, which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density.
Commercial polymers with aryl backbones such as polystyrene (PS), polycarbonate (PC), polysulfone (PSf) and poly (phenylene oxide) (PPO) were sulfonated using suitable reagents, and assessed for ...their potential to serve as proton exchange membranes (PEM) in fuel cells (FCs). The membranes thus synthesized were characterized by Fourier transform infra-red (FTIR) and
1
H
NMR to verify sulfonation and to identify the sites available for proton conduction. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) studies were carried out to investigate the thermal stability of the sulfonated membranes. Surface morphology and tensile strength were evaluated by scanning electron microscopy (SEM) and UTM, respectively. Sorption experiments were conducted to observe the interaction of sulfonated polymers with water and methanol. The ion exchange capacity (IEC), which is a measure of proton conductivity, and the degree of substitution (DS) were evaluated and found to be comparable with the commercially available Nafion membranes. Sulfonated polycarbonate (SPC) was found to possess all the requisite properties of a PEM; namely, high IEC (0.57
meq./g), tensile strength (157
N/mm
2) and thermal stability (
T
g=120
°C) besides low affinity towards methanol (%sorption=2.97). The study reveals the possibility of developing inexpensive and sturdy fuel cell membranes, which could provide attractive alternatives to substitute the expensive commercially available membranes.
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Visualisation helps explain the operating mechanisms of deep learning models, but its applications are rarely seen in traffic analysis. This paper employs a convolu-tional neural network (CNN) to ...evaluate road network performance level (NPL) and visualises the model to en-lighten how it works. A dataset of an urban road network covering a whole year is used to produce performance maps to train a CNN. In this process, a pretrained network is introduced to overcome the common issue of inadequa-cy of data in transportation research. Gradient weighted class activation mapping (Grad-CAM) is applied to vi-sualise the CNN, and four visualisation experiments are conducted. The results illustrate that the CNN focuses on different areas when it identifies the road network as dif-ferent NPLs, implying which region contributes the most to the deteriorating performance. There are particular visual patterns when the road network transits from one NPL to another, which may help performance prediction. Misclassified samples are analysed to determine how the CNN fails to make the right decisions, exposing the model’s deficiencies. The results indicate visualisation’s potential to contribute to comprehensive management strategies and effective model improvement.
Rijeka je 2020. godine nosila titulu Europske prijestolnice kulture. Višegodišnju pripremu i planirana događanja u 2020. godini autorice definiraju kao okvir unutar kojega se intenzivira propitivanje ...identiteta grada kulturom vođenom urbanom regeneracijom. Pritom je važna temporalna dimenzija, činjenica da je EPK prilika da se promišlja o budućnosti grada. Stoga, rad uključuje, uz teorijsko-metodološki uvod, opsežno predstavljanje vizije Rijeke kako je zamišljena u dokumentaciji prijave i programu EPK, kao i u drugim strateškim gradskim dokumentima. Nadalje, autorice interpretiraju doživljaj i mišljenja građana Rijeke u aspektu značenja EPK za redefiniranje identiteta grada.
In this paper, we consider the dynamics of a tagged point particle in a gas of moving hard-spheres that are non-interacting among each other. This model is known as the ideal Rayleigh gas. We add to ...this model the possibility of annihilation (ideal Rayleigh gas with annihilation), requiring that each obstacle is either annihilating or elastic, which determines whether the tagged particle is elastically reflected or removed from the system. We provide a rigorous derivation of a linear Boltzmann equation with annihilation from this particle model in the Boltzmann–Grad limit. Moreover, we give explicit estimates for the error in the kinetic limit by estimating the contributions of the configurations which prevent the Markovianity. The estimates show that the system can be approximated by the Boltzmann equation on an algebraically long time scale in the scaling parameter.
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Collaborative intelligence (CI) involves dividing an artificial intelligence (AI) model into two parts: front-end, to be deployed on an edge device, and back-end, to be deployed in the cloud. The ...deep feature tensors produced by the front-end are transmitted to the cloud through a communication channel, which may be subject to packet loss. To address this issue, in this paper, we propose a novel approach to enhance the resilience of the CI system in the presence of packet loss through Unequal Loss Protection (ULP). The proposed ULP approach involves a feature importance estimator, which estimates the importance of feature packets produced by the front-end, and then selectively applies Forward Error Correction (FEC) codes to protect important packets. Experimental results demonstrate that the proposed approach can significantly improve the reliability and robustness of the CI system in the presence of packet loss.
The COVID-19 pandemic has posed significant challenges in accurately diagnosing the disease, as severe cases may present symptoms similar to pneumonia. Real-Time Reverse Transcriptase Polymerase ...Chain Reaction (RT-PCR) is the conventional diagnostic technique; however, it has limitations in terms of time-consuming laboratory procedures and kit availability. Radiological chest images, such as X-rays and Computed Tomography (CT) scans, have been essential in aiding the diagnosis process. In this research paper, we propose a deep learning (DL) approach based on Convolutional Neural Networks (CNNs) to enhance the detection of COVID-19 and its variants from chest X-ray images. Building upon the existing research in SARS and COVID-19 identification using AI and machine learning techniques, our DL model aims to extract the most significant features from the X-ray scans of affected individuals. By employing an explanatory CNN-based technique, we achieved a promising accuracy of up to 97% in detecting COVID-19 cases, which can assist physicians in effectively screening and identifying probable COVID-19 patients. This study highlights the potential of DL in medical imaging, specifically in detecting COVID-19 from radiological images. The improved accuracy of our model demonstrates its efficacy in aiding healthcare professionals and mitigating the spread of the disease.
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