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• To design an automatic multi-disease detection in CXR images by ensemble model. • To adopt ODeepLabv3 to perform segmentation process for multi-disease detection. • To adopt a new ensemble model named Optimized Ensemble Transfer Learning (OETL). • To design a Mutation Rate-based Lion Algorithm (MR-LA) to obtain high accuracy. • To optimize the epochs in ensemble classifier with the help of MR-LA algorithm. Chest radiography is a comparatively low-cost, most probably applied in a medical process that brings crucial information to discover diagnostic conclusions. Chest X-rays (CXR) are used in the prognosis of chest diseases that includes asthma, lung cancer, pneumonia, and COVID-19. Artificial intelligence detects the multi-disease automatically to enhance its proficiency and performance by solving image detection complications with various machine learning and deep learning approaches. Convolutional Neural Network (CNN) has been designed for the advancement of computerized recognition systems. For the classification of medical images, texture, shape, size, and tissue constitution are essential features for detecting diseases. Hence, huge input features are merged with deep CNN validated to increase the effectiveness of CXR analysis. In addition, multi-scale features are visualized in CNN for the detection of variable sizes of thoracic diseases. This paper has implemented a novel multi-disease diagnosis model using chest X-ray images through deep learning approaches, and thus, it helps to minimize the computational cost. The standard CXR images are gathered from the standard datasets. After, the pre-processing is performed for cleaning and contrast the images. Further, the image segmentation is carried out using Optimized DeepLabv3 (ODeepLabv3), where the optimization of parameters in DeepLabv3 is done by a new Mutation Rate-based Lion Algorithm (MR-LA). Then, multi-disease classification is carried out through Optimized Ensemble Transfer Learning (OETL), where the OETL is carried out through VGG16, ResNet, ImageNet, MobileNet, GoogleNet, Inception, and Xception. Here, the parameter optimization of all models is done by the same MR-LA. The proposed model improved the effectiveness and performance in expressions of sensitivity, precision, and specificity factors and get higher classification and detection accuracy. The outcome of the recommended method over other models is identified by the relative analysis conducted. [ABSTRACT FROM AUTHOR]

Seamless positioning ability has become an essential requirement in large-scale smart city scenes with the development of Artificial Intelligence of Things technology. The performance of seamless positioning is limited by the inaccurate crowdsourced navigation database, cumulative error of built-in sensors, and changeable measurement errors of different location sources. In order to solve these problems, this paper presents the CrowdLOC-S framework, which provides a concrete and accurate indoor/outdoor localization performance using the combination of crowdsourced Wi-Fi fingerprinting, Global Navigation Satellite System (GNSS), and low-cost sensors. A data and model dual-driven based trajectory estimator is developed for improving the long-term positioning performance of built-in sensors, and a hybrid one-dimensional convolutional neural network (1D-CNN), Bi-directional Long Short-Term Memory (Bi-LSTM), and Multilayer Perceptron (MLP) enhanced quality indicator is proposed for quality evaluation of crowdsourced trajectories and further Wi-Fi fingerprinting database construction. Besides, the transfer learning approach is applied in the quality indicator for autonomously predicting the location errors towards different indoor and outdoor location sources and realizing seamless scenes switching. Finally, a unified extended Kalman filter is developed to realize multi-source integration-based seamless localization using the positioning information provided by indoor and outdoor location sources and corresponding quality indicator results. Comprehensive experiments demonstrate that the presented CrowdLOC-S system is proven to realize precise and efficient indoor and outdoor positioning performance in complex and large-scale urban environments. [ABSTRACT FROM AUTHOR]

The Computational Fluid Dynamics (CFD) simulations at different fidelity levels are a common tool for predicting the aerodynamic performance in many engineering applications such as; automotive and aerospace to cite a few. However, the associated computational costs with these simulations are not trivial, and using the CFD simulations might easily become unpractical specially at the early design stages. The number of the required CFD simulations could be reduced immensely by using Artificial Intelligence (AI) methods which can predict, for example, the aerodynamic coefficients based on previous and limited CFD simulations. Having discussed the AI potential to minimize the CFD usage, the objective of this work is to investigate and compare the ability of different AI methods to predict the aerodynamic coefficients using the same CFD database from the literature. In this study, three different AI methods, namely; machine learning (e.g. Extreme Gradient Boosting (XGB), CatBoost, Bagging, Light Gradient Boosting Machine (Light-GBM), Random Forest (RFR), Gradient Boosting), deep learning (e.g. One Dimensional Convolutional Neural Network (Conv1D)) and surrogate models (e.g. Gaussian Process Regression (GPR)) have been utilized to estimate a sample CFD database (e.g. NACA0012, RAE2822) from the existing literature, and the prediction performance of these methods have been compared. The results show that, the aerodynamic coefficients predicted using CatBoost, XGB and Bagging are in good agreement with the reference CFD results. Furthermore, the proposed Conv1D method with LeakyReLU activation function showed a promising results in the development of aerodynamic models which can be used in the early stages of the air vehicles design. The obtained results from this work indicate that the CatBoost and the XGB models required much smaller training time compared to the Conv1D method, moreover, the proposed regression trees; CatBoost, Bagging and XGB can reduce the required number of CFD simulations significantly. • Various artificial intelligence methods were studied to predict aerodynamic data. • Regression tree models such as XGB, Catboost and LightGBM were used. • Surrogate models and deep learning models (i.e. Conv1D) also were used. • Detailed comparison were presented between the aforementioned models. • The paper suggests the suitability of XGB and Catboost for aerodynamics cases. [ABSTRACT FROM AUTHOR]

Applying artificial intelligence in education is relevant to addressing the current educational crises. Many available solutions apply Convolutional Neural Networks (CNNs) to help improve educational outcomes. Therefore, a series of works have been developed integrating techniques in different educational contexts, for instance, in online teaching practices. Given the various studies and the relevance of CNNs for educational applications, this paper presents a systematic literature review to discuss the state-of-the-art. We reviewed 133 papers from the IEEE Xplore, ACM Digital Library, and Scopus databases. Based on our revision, we discuss characteristics of studies such as publication venues, educational context, datasets, types of CNNs models, and performance of models. We evidence that the literature regarding CNNs still misses more studies discussing educational problems faced by Global South students, considering both teaching and learning perspectives. Such a population cannot be neglected during experiments due to specific educational weaknesses (for example, basic skills) demanding personalized solutions. • A systematic literature review of 133 published papers. • China covered most publications, followed by India. • There is a research gap regarding using CNNs in some regions of the Global South. • The main educational context with CNN applications is students' performance. [ABSTRACT FROM AUTHOR]

Breakthrough performances have been achieved in computer vision by utilizing deep neural networks. In this paper we propose to use random forest to classify image representations obtained by concatenating multiple layers of learned features of deep convolutional neural networks for scene classification. Specifically, we first use deep convolutional neural networks pre-trained on the large-scale image database Places to extract features from scene images. Then, we concatenate multiple layers of features of the deep neural networks as image representations. After that, we use random forest as the classifier for scene classification. Moreover, to reduce feature redundancy in image representations we derived a novel feature selection method for selecting features that are suitable for random forest classification. Extensive experiments are conducted on two benchmark datasets, i.e. MIT-Indoor and UIUC-Sports. Obtained results demonstrated the effectiveness of the proposed method. The contributions of the paper are as follows. First, by extracting multiple layers of deep neural networks, we can explore more information of image contents for determining their categories. Second, we proposed a novel feature selection method that can be used to reduce redundancy in features obtained by deep neural networks for classification based on random forest. In particular, since deep learning methods can be used to augment expert systems by having the systems essentially training themselves, and the proposed framework is general, which can be easily extended to other intelligent systems that utilize deep learning methods, the proposed method provide a potential way for improving performances of other expert and intelligent systems. [ABSTRACT FROM AUTHOR]

With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND. • This paper proposes a solution for fake news detection based on deep learning. • The proposed deep learning approach comprises both sequential and recurrent modalities. • The proposed sequential models includes CNNs and concatenated CNNs, while the recurrent includes LSTM and GRU models. • Each model is compared with the other proposed models as well as the works in the literature. [ABSTRACT FROM AUTHOR]

Many intelligent systems, such as assistive robots, augmented reality trainers or unmanned vehicles, need to know their physical location in the environment in order to fulfill their task. While relying exclusively on natural landmarks for that task is the preferred option, their use is somewhat limited because the proposed methods are complex, require high computational power, and are not reliable in all environments. On the other hand, artificial landmarks can be placed in order to alleviate these problems. In particular, square fiducial markers are one of the most popular tools for camera pose estimation due to their high performance and precision. However, the state-of-the-art methods still perform poorly under difficult image conditions, such as camera defocus, motion blur, small scale or non-uniform lighting. This paper proposes a method to robustly detect this type of landmarks under challenging image conditions present in realistic scenarios. To do so, we re-define the marker identification problem as a classification one based on state-of-the-art machine learning techniques. Second, we propose a procedure to create a training dataset of synthetically generated images affected by several challenging transformations. Third, we show that, in this problem, a classifier can be trained using exclusively synthetic data, performing well in real and challenging conditions. Different types of classifiers have been tested to prove the validity of our proposal (namely, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN) and Support Vector Machine (SVM)), and statistical analyses have been performed in order to determine the best approach for our problem. Finally, the obtained classifiers have been compared to the ArUco and AprilTags fiducial marker systems in challenging video sequences. The results obtained show that the proposed method performs significantly better than previous approaches, making the use of this technology more reliable in a wider range of realistic scenarios such as outdoor scenes or fast moving cameras. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new technique that applies automated image analysis in the area of structural corrosion monitoring and demonstrate improved efficacy compared to existing approaches. Structural corrosion monitoring is the initial step of the risk-based maintenance philosophy and depends on an engineer's assessment regarding the risk of building failure balanced against the fiscal cost of maintenance. This introduces the opportunity for human error which is further complicated when restricted to assessment using drone captured images for those areas not reachable by humans due to many background noises. The importance of this problem has promoted an active research community aiming to support the engineer through the use of artificial intelligence (AI) image analysis for corrosion detection. In this paper, we advance this area of research with the development of a framework, CorrDetector. CorrDetector uses a novel ensemble deep learning approach underpinned by convolutional neural networks (CNNs) for structural identification and corrosion feature extraction. We provide an empirical evaluation using real-world images of a complicated structure (e.g. telecommunication tower) captured by drones, a typical scenario for engineers. Our study demonstrates that the ensemble approach of CorrDetector significantly outperforms the state-of-the-art in terms of classification accuracy. • We presented a novel framework for structural corrosion detection in drone images. • We developed an ensemble deep learning approach to recognise corrosion. • We conducted a comprehensive evaluation using real-world industrial datasets. • We demonstrated the efficacy by comparing with current state-of-the-art models. [ABSTRACT FROM AUTHOR]

Machine intelligence based automation plays a significant role in many modern applications, and vision based understanding is a significant element of this. To meet the goals of vision based understanding of images, segmentation plays a vital role through partitioning of regions of interest for further processing. Much research, including basic statistical and modern convolutional neural network based techniques, has been reported for segmentation. However, application based fine tuning is always essential for effective results in complex applications. In this paper, we have proposed a score-based mask edge improvement of Mask-RCNN to segment fruit and vegetable images in a supermarket environment. A modular score-based edge improvement head is proposed for Mask-RCNN to improve the segmentation of fruit and vegetable images. The edge difference between ground truth and estimated edges is filled with a pre-defined score proportional to the pixel-level difference. A cosine similarity based loss function is reduced to improve the edge details following segmentation. A significant improvement has been reported based on the proposed technique. • Cosine similarity based mask edge improvement for Mask-RCNN. • Fruit and vegetable segmentation in a supermarket environment. • Score-based mask edge improvement for effective segmentation of fruit and vegetables. [ABSTRACT FROM AUTHOR]

• Deep learning for identifying papillary thyroid carcinomas and computing certainty. • Ensemble technique combines multiple deep CNNs to significantly increase accuracies. • GRAD-CAM method highlights dominant tissues for the discriminative decision-making. • Effectiveness of stain normalization methods on predictive performances is evaluated. • Proposed ensemble learning is superior to current models for automatic diagnosis. Accurately cytopathological diagnosis of Papillary Thyroid Carcinoma (PTC) is of importance for reducing costs and increasing efficiency of treatments. In this paper, we pursue that goal by introducing artificial intelligence (AI) for automatic classification of malignant PTC cell clusters from Fine Needle Aspiration Cytology (FNAC) processed by ThinPrep. High-resolution cytological images obtained with a 40 × objective lens digital camera attached to an Olympus microscope were segmented into fragments and then divided into training, validation, and testing subsets. Fragments are non-overlapped patches containing only regions-of-interest that cover informative tissue structures for making proper diagnoses. Deep learning CNN models were pre-trained and fine-tuned on large-scale ImageNet domain before they were re-trained on cytology fragments. Moreover, we proposed a method to compute certainty of the patient-level prediction that undoubtedly provides additional evidence for reliability and confidence of the prediction. Results showed that the best classification performance on digital FNAC images achieved using DenseNet161, obtaining a mean accuracy of 0.9556 (p < 0.01), a mean sensitivity of 0.9734, and a mean specificity of 0.9405 on yet-to-be-seen test-set. Ensemble learning findings suggested combinations of AdaBoost classifier with multiple CNN models boosted predictive performances, up to 0.9971 accuracy. Moreover, stain normalization introduced by Reinhard increased the predictive ability, outperforming histogram specification, and Macenko methods. Presented findings demonstrate deep learning can integrate into computer-aided diagnosis systems to support cytopathologists in accurate diagnosis of PTC. [ABSTRACT FROM AUTHOR]