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• Five Machine Learning (ML) models compared for wave height time series prediction. • Complex ML models do not outperform simple AR in wave height time series prediction. • Comment to related papers: signal decomposition in test set series is WRONG. Significant Wave Height (SWH) is crucial in many aspect of ocean engineering. The accurate prediction of SWH has therefore been of immense practical value. Recently, Artificial Intelligence (AI) time series prediction methods have been widely used for single-point short-term SWH time-series forecasting, resulting in many AI-based models claiming to achieve good results. However, the extent to which these complex AI models can outperform traditional methods has largely been overlooked. This study compared five different models - AutoRegressive (AR), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and WaveNet - for their performance on SWH time series prediction at 16 buoy locations. Surprisingly, the results suggest that the differences of performance among different models are negligible, indicating that all these AI models have only "learned" the linear auto-regression from the data. Additionally, we noticed that many recent studies used signal decomposition method for such time series prediction, and most of them decomposed the test sets, which is WRONG. [ABSTRACT FROM AUTHOR]

• Qualitative and quantitative detection of amine gases could be achieved by CSA. • A visible detection of beef freshness using the amine-responsive CSA was proposed. • ResNet34 had the best performance for beef freshness detection based on CSA. • T-SNE could further visualize and understand the classification process of DL. This study developed an innovative approach that combines a colourimetric sensor array (CSA) composed of twelve pH-response dyes with advanced algorithms, aiming to detect amine gases and assess the freshness of chilled beef. With the assistance of multivariate statistical analysis, the sensor array can effectively distinguish five amine gases and enable rapid quantification of trimethylamine vapour with a limit of detection (LOD) of 8.02 ppb and visually monitor the fresh levels of chilled beef. Moreover, the utilization of deep learning models (ResNet34, VGG16, and GoogleNet) for chilled beef freshness evaluation achieved an overall accuracy of 98.0 %. Furthermore, t -distributed stochastic neighbour embedding (t -SNE) visualized the feature extraction process and provided explanations to understand the classification process of deep learning. The results demonstrated that applying deep learning techniques in the process of pattern recognition of CSA can help in realizing the rapid, robust, and accurate assessment of chilled beef freshness. [ABSTRACT FROM AUTHOR]

Smartphone has long been considered as one excellent platform for disease screening and diagnosis, especially when combined with microfluidic paper-based analytical devices (μPADs) that feature low cost, ease of use, and pump-free operations. In this paper, we report a deep learning-assisted smartphone platform for ultra-accurate testing of paper-based microfluidic colorimetric enzyme-linked immunosorbent assay (c-ELISA). Different from existing smartphone-based μPAD platforms, whose sensing reliability is suffered from uncontrolled ambient lighting conditions, our platform is able to eliminate those random lighting influences for enhanced sensing accuracy. We first constructed a dataset that contains c-ELISA results (n = 2048) of rabbit IgG as the model target on μPADs under eight controlled lighting conditions. Those images are then used to train four different mainstream deep learning algorithms. By training with these images, the deep learning algorithms can well eliminate the influences of lighting conditions. Among them, the GoogLeNet algorithm gives the highest accuracy (>97%) in quantitative rabbit IgG concentration classification/prediction, which also provides 4% higher area under curve (AUC) value than that of the traditional curve fitting results analysis method. In addition, we fully automate the whole sensing process and achieve the "image in, answer out" to maximize the convenience of the smartphone. A simple and user-friendly smartphone application has been developed that controls the whole process. This newly developed platform further enhances the sensing performance of μPADs for use by laypersons in low-resource areas and can be facilely adapted to the real disease protein biomarkers detection by c-ELISA on μPADs. [Display omitted] • This deep learning-assisted smartphone platform is unaffected by ambient lighting. • A fully automated "image in, answer out" operation fashion. • A 2048 custom image dataset is used to test 4 mainstream deep learning algorithms. • GoogLeNet provides >97% accuracy in quantitative rabbit IgG testing. • The area under the curve (AUC) is 4% higher than that of conventional methods. [ABSTRACT FROM AUTHOR]

Advances in artificial intelligence (AI) and automated robotics will profoundly influence space operations. By utilising machine learning and deep learning approaches, AI-enabled systems may accomplish tasks as well as improve their own performance. These capabilities are useful in the often-remote settings of outer space and will grow in value as automated space operations become more widespread. As AI extends throughout the space domain, automated algorithms will take on many of the roles that have historically been handled by humans. Artificial intelligence is progressing from theory to implementation in the space environment by exposing new satellites and orbital autonomous vehicles to new data. Even though all initial computational parameters are provided, such systems' outputs can be very unpredictable, putting people, property, and the environment at risk. This paper investigates the application of United Nations space treaties, selected regional AI regulations, and various 'soft-law' instruments and industry initiatives focusing on responsible AI system development to space-based AI systems. Following that, reforms are proposed to clarify the practical relationship between AI systems and the international legal regime that governs space, as well as a 'bottom-up' regulatory approach to better facilitate the future development of regulation governing the use of AI by the global space sector. While this work does not purport to provide a conclusive resolution to these multifaceted matters, its objective is to underscore significant obstacles that arise at the convergence of space law and AI, serving as a preliminary foundation for subsequent discussions on this issue. • Advances in AI and automated robotics will have a profound impact on space operations. Automated algorithms will take on roles traditionally handled by humans as AI becomes more widespread in space. • However, the unpredictable outputs of AI systems can put people, property, and the environment at risk, raising questions about liability. • The paper investigates the application of UN space treaties, regional AI regulations, and industry initiatives to space-based AI systems. • Reforms are proposed to clarify the relationship between AI systems and the international legal regime governing space. • A 'bottom-up' regulatory approach is suggested to facilitate future regulation of AI in the global space sector. [ABSTRACT FROM AUTHOR]

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective. • In-depth survey on FSCIL methods, applications, performance. • Categorizing FSCIL into five subcategories for clear analysis. • Evaluating FSCIL research on benchmarks for strengths, weaknesses. • FSCIL applications in computer vision, NLP, and graph. [ABSTRACT FROM AUTHOR]

Nowadays, there has been a rapidly increasing number of scientific submissions in multiple research domains. A large number of journals have various acceptance rates, impact factors, and rankings in different publishers. It becomes time-consuming for many researchers to select the most suitable journal to submit their work with the highest acceptance rate. A paper submission recommendation system is more critical for the research community and publishers as it gives scientists another support to complete their submission conveniently. This paper investigates the submission recommendation system for two main research topics: computer science and applied mathematics. Unlike the previous works (Wang et al., 2018; Son et al., 2020) that extract TF–IDF and statistical features as well as utilize numerous machine learning algorithms (logistics regression and multiple perceptrons) for building the recommendation engine, we present an efficient paper submission recommendation algorithm by using different bidirectional transformer encoders and the Mixture of Transformer Encoders technique. We compare the performance between our methodology and other approaches by one dataset from Wang et al. (2018) with 14012 papers in computer science and another dataset collected by us with 223,782 articles in 178 Springer applied mathematics journals in terms of top K accuracy (K = 1 , 3 , 5 , 10). The experimental results show that our proposed method extensively outperforms other state-of-the-art techniques with a significant margin in all top K accuracy for both two datasets. We publish all datasets collected and our implementation codes for further references. 1 1 https://github.com/BinhMisfit/PSRMTE. • Bidirectional transformer encoders can improve the performance of the paper submission recommendation system. • The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem. • Proposed techniques can surpass other recent techniques on two datasets related. [ABSTRACT FROM AUTHOR]

Although current semantic segmentation approaches have achieved impressive performance, their ability to incrementally learn new classes is limited. Moreover, pixel-by-pixel annotations are costly and time-consuming. Therefore, a new field called Weakly-supervised Incremental Learning for Semantic Segmentation (WILSS) has emerged, which learns new classes using image-level labels. However, image-level labels do not provide sufficient detail, and we discover that the state-of-the-art of WILSS suffers from confusion between old knowledge and new knowledge. To address this issue, we propose W eakly-supervised I ncremental learning for S emantic segmentation with Class H ierarchy (WISH), a method that considers the hierarchical structure of each class when determining which knowledge to trust in cases of confusion between old and new knowledge. Our method has achieved new state-of-the-art performances in all settings compared to the previous methods on the Pascal VOC and MS COCO datasets. • Our paper introduces WISH, utilizing hierarchy and image labels for class incremental learning in semantic segmentation. • We efficiently utilize hierarchy, boosting segmentation performances without added costs. • Our method outperforms existing methods in all configurations, showcasing hierarchical integration's effectiveness. [ABSTRACT FROM AUTHOR]

A paper-based colourimetric assay for the detection of alanine transaminase has been developed. In the presence of alanine transaminase, 2,4-dinitrophenyl hydrazine changes to pyruvate hydrazone leading to a colour change from pale yellow to dark yellow. Reaction conditions were optimized using absorption spectroscopic studies. Hydrophobic patterns on the Whatman chromatographic paper were created by wax printing, and the reagents were drop cast at the reagent zone. On the paper device, the intensity of the yellow colour increases with ALT concentration in the range of 20–140 U/L in human serum. For the quantification of ALT, coloured images were captured using a digital camera and were processed with Image J software. The machine learning approach was also explored for the ALT analysis by training with colour images of the paper device and testing using a cross-validation procedure. The results obtained with real clinical samples on the paper device showed good accuracy of less than 5% relative error with the clinical lab results. Furthermore, the paper device shows high selectivity to ALT in the presence of various interfering species in blood serum with a sensitivity of 0.261 a.u/(U/L), a detection limit of 4.12 U/L, and precise results with an RSD of less than 7%. For the testing of whole blood, a plasma separation membrane was integrated with the patterned paper. [Display omitted] • Colourimetric detection of ALT on paper strip using 2,4-DNPH. • Negligible interference from other biomolecules. • Tested with serum and ALT spiked serum samples. • Plasma separation membrane incorporated strip used for testing of blood samples. • Quantification of ALT on paper strip using ImageJ and Machine learning approach. [ABSTRACT FROM AUTHOR]

• In clinical needle EMG interpretation, conventional machine learning methods were more often studied than deep learning methods. • Most studies developed models for classification of contraction signals and a few addressed the classification of rest signals. • Current research has methodological shortcomings, which prevent clinical implementation, but are surmountable. This scoping review provides an overview of artificial intelligence (AI), including machine and deep learning techniques, in the interpretation of clinical needle electromyography (nEMG) signals. A comprehensive search of Medline, Embase and Web of Science was conducted to find peer-reviewed journal articles. All papers published after 2010 were included. The methodological quality of the included studies was assessed with CLAIM (checklist for artificial intelligence in medical imaging). 51 studies were identified that fulfilled the inclusion criteria. 61% used open-source EMGlab data set to develop models to classify nEMG signal in healthy, amyotrophic lateral sclerosis (ALS) and myopathy (25 subjects). Only two articles developed models to classify signals recorded at rest. Most articles reported high performance accuracies, but many were subject to bias and overtraining. Current AI-models of nEMG signals are not sufficient for clinical implementation. Suggestions for future research include emphasizing the need for an optimal training and validation approach using large datasets of clinical nEMG data from a diverse patient population. The outcomes of this study and the suggestions made aim to contribute to developing AI-models that can effectively handle signal quality variability and are suitable for daily clinical practice in interpreting nEMG signals. [ABSTRACT FROM AUTHOR]

Decentralized deep learning algorithms leverage peer-to-peer communication of model parameters and/or gradients over communication graphs among the learning agents with access to their private data sets. The majority of the studies in this area focus on achieving high accuracy, with many at the expense of increased communication overhead among the agents. However, large peer-to-peer communication overhead often becomes a practical challenge, especially in harsh environments such as for an underwater sensor network. In this paper, we aim to reduce communication overhead while achieving similar performance as the state-of-the-art algorithms. To achieve this, we use the concept of Minimum Connected Dominating Set from graph theory that is applied in ad hoc wireless networks to address communication overhead issues. Specifically, we propose a new decentralized deep learning algorithm called minimum connected Dominating Set Model Aggregation (DSMA). We investigate the efficacy of our method for different communication graph topologies with a small to large number of agents using varied neural network model architectures. Empirical results on benchmark data sets show a significant (up to 100X) reduction in communication time while preserving the accuracy or in some cases, increasing it compared to the state-of-the-art methods. We also present an analysis to show the convergence of our proposed algorithm. • Proposing a communication-efficient protocol for decentralized learning systems. • Finding valuable connections in the network to reduce communication overhead. • Hybrid of federated learning and gossiping methods. • Verifying the method performance for different networks, datasets, and models. • Preserving accuracy while reducing communication, even in large-scale networks. [ABSTRACT FROM AUTHOR]

Building footprint information is crucial for comprehending global urban development processes. Deep learning algorithms have shown significant potential in building extraction from high spatial resolution imagery. However, the requirement for large-scale annotated data has been a limitation for applying deep learning methods to city-level or national-level building mapping. The dynamic change and distinct landscape variation of cities in different geographic locations further emphasizes the need for automatic building footprint extraction. In this paper, we propose a Self-supervised Knowledge Transfer (SKTrans) framework for extracting building footprints from unlabeled remote sensing images over large areas. To address building tone differences cross regions and time periods, a tone shift mechanism is introduced to reduce the contrast, brightness, and saturation differences between buildings. Furthermore, considering building style diversity across different cities, a knowledge integration module is proposed to develop a comprehensive depiction including tonal differences, multi-scale variance, structural attributes and semantic confusion between roads and buildings. The effectiveness of SKTrans is demonstrated on three public datasets of WHU, Massachusetts, and Inria. To further evaluate the feasibility of large-scale mapping for automatic building footprint, global generalization experiments cross regions and time periods, which is based on imagery from five countries of different continents were implemented. The comprehensive results demonstrate that SKTrans surpasses existing state-of-the-art methods, supporting the time-series city-level building mapping without labeled dataset. Additionally, the architectural landscape pattern changes can be quantitatively analyzed based on the generalization results to facilitate sustainable urban development. Overall, SKTrans provides a new insight to meet the needs of high-precision label-free building mapping and update from large-scale multi-style remote sensing images. [ABSTRACT FROM AUTHOR]

Federated learning (FL) provides autonomy and privacy by design to participating peers, who cooperatively build a machine learning (ML) model while keeping their private data in their devices. However, that same autonomy opens the door for malicious peers to poison the model by conducting either untargeted or targeted poisoning attacks. The label-flipping (LF) attack is a targeted poisoning attack where the attackers poison their training data by flipping the labels of some examples from one class (i.e., the source class) to another (i.e., the target class). Unfortunately, this attack is easy to perform and hard to detect, and it negatively impacts the performance of the global model. Existing defenses against LF are limited by assumptions on the distribution of the peers' data and/or do not perform well with high-dimensional models. In this paper, we deeply investigate the LF attack behavior. We find that the contradicting objectives of attackers and honest peers on the source class examples are reflected on the parameter gradients corresponding to the neurons of the source and target classes in the output layer. This makes those gradients good discriminative features for the attack detection. Accordingly, we propose LFighter, a novel defense against the LF attack that first dynamically extracts those gradients from the peers' local updates and then clusters the extracted gradients, analyzes the resulting clusters, and filters out potential bad updates before model aggregation. Extensive empirical analysis on three data sets shows the effectiveness of the proposed defense regardless of the data distribution or model dimensionality. Also, LFighter outperforms several state-of-the-art defenses by offering lower test error, higher overall accuracy, higher source class accuracy, lower attack success rate, and higher stability of the source class accuracy. Our code and data are available for reproducibility purposes at https://github.com/NajeebJebreel/LFighter. [ABSTRACT FROM AUTHOR]

Supervised learning in deep neural networks is commonly performed using error backpropagation. However, the sequential propagation of errors during the backward pass limits its scalability and applicability to low-powered neuromorphic hardware. Therefore, there is growing interest in finding local alternatives to backpropagation. Recently proposed methods based on forward-mode automatic differentiation suffer from high variance in large deep neural networks, which affects convergence. In this paper, we propose the Forward Direct Feedback Alignment algorithm that combines Activity-Perturbed Forward Gradients with Direct Feedback Alignment and momentum. We provide both theoretical proofs and empirical evidence that our proposed method achieves lower variance than forward gradient techniques. In this way, our approach enables faster convergence and better performance when compared to other local alternatives to backpropagation and opens a new perspective for the development of online learning algorithms compatible with neuromorphic systems. • Forward Gradient methods suffer from high variance that hinders convergence. • Activity-Perturbed Forward Gradients can be used to learn derivatives as direct feedback connections. • Feedback learning acts as a momentum that reduces the gradient variance closer to backpropagation. • Feedback learning reduces the biasness of Direct Feedback Alignment. • Feedback learning enables learning in convolutional layers. [ABSTRACT FROM AUTHOR]

Artificial Intelligence and Machine learning have been widely used in various fields of mathematical computing, physical modeling, computational science, communication science, and stochastic analysis. Approaches based on Deep Artificial Neural Networks (DANN) are very popular in our days. Depending on the learning task, the exact form of DANNs is determined via their multi-layer architecture, activation functions and the so-called loss function. However, for a majority of deep learning approaches based on DANNs, the kernel structure of neural signal processing remains the same, where the node response is encoded as a linear superposition of neural activity, while the non-linearity is triggered by the activation functions. In the current paper, we suggest to analyze the neural signal processing in DANNs from the point of view of homogeneous chaos theory as known from polynomial chaos expansion (PCE). From the PCE perspective, the (linear) response on each node of a DANN could be seen as a 1st degree multi-variate polynomial of single neurons from the previous layer, i.e. linear weighted sum of monomials. From this point of view, the conventional DANN structure relies implicitly (but erroneously) on a Gaussian distribution of neural signals. Additionally, this view revels that by design DANNs do not necessarily fulfill any orthogonality or orthonormality condition for a majority of data-driven applications. Therefore, the prevailing handling of neural signals in DANNs could lead to redundant representation as any neural signal could contain some partial information from other neural signals. To tackle that challenge, we suggest to employ the data-driven generalization of PCE theory known as arbitrary polynomial chaos (aPC) to construct a corresponding multi-variate orthonormal representations on each node of a DANN. Doing so, we generalize the conventional structure of DANNs to Deep arbitrary polynomial chaos neural networks (DaPC NN). They decompose the neural signals that travel through the multi-layer structure by an adaptive construction of data-driven multi-variate orthonormal bases for each layer. Moreover, the introduced DaPC NN provides an opportunity to go beyond the linear weighted superposition of single neurons on each node. Inheriting fundamentals of PCE theory, the DaPC NN offers an additional possibility to account for high-order neural effects reflecting simultaneous interaction in multi-layer networks. Introducing the high-order weighted superposition on each node of the network mitigates the necessity to introduce non-linearity via activation functions and, hence, reduces the room for potential subjectivity in the modeling procedure. Although the current DaPC NN framework has no theoretical restrictions on the use of activation functions. The current paper also summarizes relevant properties of DaPC NNs inherited from aPC as analytical expressions for statistical quantities and sensitivity indexes on each node. We also offer an analytical form of partial derivatives that could be used in various training algorithms. Technically, DaPC NNs require similar training procedures as conventional DANNs, and all trained weights determine automatically the corresponding multi-variate data-driven orthonormal bases for all layers of DaPC NN. The paper makes use of three test cases to illustrate the performance of DaPC NN, comparing it with the performance of the conventional DANN and also with plain aPC expansion. Evidence of convergence over the training data size against validation data sets demonstrates that the DaPC NN outperforms the conventional DANN systematically. Overall, the suggested re-formulation of the kernel network structure in terms of homogeneous chaos theory is not limited to any particular architecture or any particular definition of the loss function. The DaPC NN Matlab Toolbox is available online and users are invited to adopt it for own needs. • Kernel structure reformulation of deep artificial neural networks in terms of homogeneous chaos theory. • Response on each node of the deep network represented through polynomial chaos expansion. • Orthonormal decomposition mitigates the non-optimal representation while processing the neural signal. • Accounting for high-order effects reflecting simultaneous neural impacts. • Analytical estimation of mean and variance of neural signal on each node is provided. [ABSTRACT FROM AUTHOR]

This paper proposes a three-stage online deep learning model for time series based on the ensemble deep random vector functional link (edRVFL). The edRVFL stacks multiple randomized layers to enhance the single-layer RVFL's representation ability. Each hidden layer's representation is utilized for training an output layer, and the ensemble of all output layers forms the edRVFL's output. However, the original edRVFL is not designed for online learning, and the randomized nature of the features is harmful to extracting meaningful temporal features. In order to address the limitations and extend the edRVFL to an online learning mode, this paper proposes a dynamic edRVFL consisting of three online components, the online decomposition, the online training, and the online dynamic ensemble. First, an online decomposition is utilized as a feature engineering block for the edRVFL. Then, an online learning algorithm is designed to learn the edRVFL. Finally, an online dynamic ensemble method, which can measure the change in the distribution, is proposed for aggregating all layers' outputs. This paper evaluates and compares the proposed model with state-of-the-art methods on sixteen time series. [ABSTRACT FROM AUTHOR]

• Importance of gait recognition for security and surveillance applications. • Comparison of ML and DL methods in terms of effectiveness. • Examples of specific applications, such as fall detection and biometric identification. • Challenges and limitations of current methods. • Future directions and potential improvements. The study of gait recognition, a biometric application that identifies individuals based on their unique walking patterns, is an evolving field. In this paper, we conduct a literature review to compare the performance of machine learning and deep learning approaches in covariate conditions, focusing on the specific aspects of deep learning pipelines in gait recognition. We highlight commonly used strategies and open problems in identification based on behavioral traits and propose future perspectives for researchers in this field. Through our investigation, we aim to provide insights that will aid researchers in developing informed decisions when to take which data preprocessing technique in designing gait recognition systems. Our paper provides a comprehensive exposition of machine learning versus deep learning architectures and pipelines for biometric applications using human gait and serves as a valuable resource for researchers in this area. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has the potential to simplify and optimize various steps of the brachytherapy workflow, and this literature review aims to provide an overview of the work done in this field. We conducted a literature search in June 2022 on PubMed, Embase, and Cochrane for papers that proposed AI applications in brachytherapy. A total of 80 papers satisfied inclusion/exclusion criteria. These papers were categorized as follows: segmentation (24), registration and image processing (6), preplanning (13), dose prediction and treatment planning (11), applicator/catheter/needle reconstruction (16), and quality assurance (10). AI techniques ranged from classical models such as support vector machines and decision tree-based learning to newer techniques such as U-Net and deep reinforcement learning, and were applied to facilitate small steps of a process (e.g., optimizing applicator selection) or even automate the entire step of the workflow (e.g., end-to-end preplanning). Many of these algorithms demonstrated human-level performance and offer significant improvements in speed. AI has potential to augment, automate, and/or accelerate many steps of the brachytherapy workflow. We recommend that future studies adhere to standard reporting guidelines. We also stress the importance of using larger sample sizes and reporting results using clinically interpretable measures. [ABSTRACT FROM AUTHOR]

Seamless positioning and navigation requires an integration of outdoor and indoor positioning systems. Until recently, these systems mostly function in-silos. Though GNSS has become a standalone system for outdoors, no unified positioning modality could be found for indoor environments. Wi-Fi and Bluetooth signals are popular choices though. Increased adoption of different machine learning techniques for indoor–outdoor context detection and localization could be witnessed in the recent literature. The difficulty in precise data annotation, need for sensor fusion, the effect of different hardware configurations pose critical challenges that affect the success of indoor–outdoor (IO) positioning systems. Wireless sensor-based techniques are explicitly programmed, hence estimating locations dynamically becomes challenging. Machine learning and deep learning techniques can be used to overcome such situations and react appropriately by self-learning through experiences and actions without human intervention or reprogramming. Hence, the focus of the work is to present the readers a comprehensive survey of the applicability of machine learning and deep learning to achieve seamless navigation. The paper systematically discusses the application perspectives, research challenges, and the framework of ML (mostly) and DL (a few) based positioning approaches. The comparisons against various parameters like the technology used, the procedure applied, output metric and challenges are presented along with experimental results on benchmark datasets. The paper contributes to bridging the IO localization approaches with IO detection techniques so as to pave the way into the research domain for seamless positioning. Recent advances and hence, possible future research directions in the context of IO localization have also been articulated. • IO localization methods are presented from pragmatic view of seamless positioning. • ML techniques are categorized based on their applications in seamless positioning. • Performance results of ML techniques on benchmark datasets are reported. • The open issues along with future directions are discussed. [ABSTRACT FROM AUTHOR]

Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper. • This pre-Editorial provides an overview of some hot topics in AI for life. • The benefit for the reader is to get a short, crisp, concise intro into AI. • Authors interested in this Special Issue will get a scope of topics. • The take away message is that in the future no biotechnology can do without AI. [ABSTRACT FROM AUTHOR]

Diagnostic histopathology faces increasing demands due to aging populations and expanding healthcare programs. Semi-automated diagnostic systems employing deep learning methods are one approach to alleviate this pressure. The learning models for histopathology are inherently complex and opaque from the user's perspective. Hence different methods have been developed to interpret their behavior. However, relatively limited attention has been devoted to the connection between interpretation methods and the knowledge of experienced pathologists. The main contribution of this paper is a method for comparing morphological patterns used by expert pathologists to detect cancer with the patterns identified as important for inference of learning models. Given the patch-based nature of processing large-scale histopathological imaging, we have been able to show statistically that the VGG16 model could utilize all the structures that are observable by the pathologist, given the patch size and scan resolution. The results show that the neural network approach to recognizing prostatic cancer is similar to that of a pathologist at medium optical resolution. The saliency maps identified several prevailing histomorphological features characterizing carcinoma, e.g., single-layered epithelium, small lumina, and hyperchromatic nuclei with halo. A convincing finding was the recognition of their mimickers in non-neoplastic tissue. The method can also identify differences, i.e., standard patterns not used by the learning models and new patterns not yet used by pathologists. Saliency maps provide added value for automated digital pathology to analyze and fine-tune deep learning systems and improve trust in computer-based decisions. • Saliency maps identified histomorphological features characterizing cancer. • VGG16 model utilized all the structures that are observable by the pathologist. • The method can identify standard patterns not used by the model. • The method can also identify new patterns not yet used by human pathologists. [ABSTRACT FROM AUTHOR]

In a constant evolving world, change detection is of prime importance to keep updated maps. To better sense areas with complex geometry (urban areas in particular), considering 3D data appears to be an interesting alternative to classical 2D images. In this context, 3D point clouds (PCs), whether obtained through LiDAR or photogrammetric techniques, provide valuable information. While recent studies showed the considerable benefit of using deep learning-based methods to detect and characterize changes into raw 3D PCs, these studies rely on large annotated training data to obtain accurate results. The collection of these annotations are tricky and time-consuming. The availability of unsupervised or weakly supervised approaches is then of prime interest. In this paper, we propose an unsupervised method, called DeepCluster 3D Change Detection (DC3DCD), to detect and categorize multiclass changes at point level. We classify our approach in the unsupervised family given the fact that we extract in a completely unsupervised way a number of clusters associated with potential changes. Let us precise that in the end of the process, the user has only to assign a label to each of these clusters to derive the final change map. Our method builds upon the DeepCluster approach, originally designed for image classification, to handle complex raw 3D PCs and perform change segmentation task. An assessment of the method on both simulated and real public dataset is provided. The proposed method allows to outperform fully-supervised traditional machine learning algorithm and to be competitive with fully-supervised deep learning networks applied on rasterization of 3D PCs with a mean of IoU over classes of change of 57.06% and 66.69% for the simulated and the real datasets, respectively. The code is available at https://github.com/idegelis/torch-points3d-dc3dcd. • DC3DCD, unsupervised learning method for multiclass 3D point cloud change detection. • Revisiting the DeepCluster principle for change segmentation in raw 3D point clouds. • Results are better than classical supervised machine learning methods. • Results are equal to rasterization-based deep learning supervised networks. • In a binary-to-multiclass scenario, DC3DCD competes with fully supervised methods. [ABSTRACT FROM AUTHOR]

The ionospheric foF2 prediction model GWO-ALSTM is proposed in this paper, which combines the Gray Wolf Optimization (GWO) algorithm and the attention mechanism based on the long short-term memory (LSTM) model. The datasets used in this model were obtained from the oblique ionosondes of the China Ground-based Seismo-ionospheric Monitoring Network. The data from 2010 to 2012 was used for training, and the data of 2013 was used for verification. The input parameters of the model include local time, day number, sunspot number, F10.7 solar flux, Ap index, Dst index, and foF2 at the previous moment, and the output parameter is foF2 at the current moment. The IRI2016 model, GABP model, LSTM model and GWO-ALSTM model were compared with the observed results for analysis. The results show that the GWO-ALSTM model is superior to the LSTM model, the GABP model and the IRI2016 model. Meanwhile, the comparative analyses of the diurnal variations of foF2 show that the curve of the GWO-ALSTM model fits the observed values more closely than the curve of any other model under the geomagnetic quiet and storm conditions. [ABSTRACT FROM AUTHOR]

Multi-view learning (MVL) is a rapidly evolving direction in the field of machine learning. Despite the positive results, most algorithms that combine multi-view learning with twin support vector machines (TSVM) focus on the traditional machine learning domain. No method has been accomplished for combining MVL, TSVM, and deep learning. In this paper, we propose two novel multi-view deep models to solve the multiclass classification problem, namely deep multi-view twin support vector machines (DMvTSVM) based on deep neural network (DNN) and auto-encoder (AE) network. They find two non-parallel hyperplanes such that each hyperplane is as close to its own class as possible while being as far away from the other class as possible. Meanwhile, we apply similarity regularization to the output of the Deep TSVM classifier for each view to learn consensus information between views, and use this to refine the joint weights of the deep model and TSVM. Finally, the novel models employ the o n e − v s − r e s t strategy to allow the DMvTSVM classifier to solve the multiclass classification problems. In the experiments, the proposed methods are compared with existing state-of-the-art algorithms to prove their effectiveness. • The first combination of twin support vector machines (TSVM) and deep neural networks. • Nesting of more advanced auto encoder networks. • Incorporating multi-view learning capabilities. • Mutual negotiation and simultaneous learning between deep neural networks and TSVM. • The experiments prove the effectiveness of the algorithm in this paper. [ABSTRACT FROM AUTHOR]

In today's email dependent world, cyber criminals often target organizations using a variety of social engineering techniques and specially crafted malicious emails. When successful, such attacks can result in significant harm to physical and digital systems and assets, the leakage of sensitive information, reputation damage, and financial loss. Despite the plethora of studies on the detection of phishing attacks and malicious links in emails, there are no solutions capable of effectively, quickly, and accurately coping with more complex email-based attacks, such as malicious email attachments. This paper presents the first fully automated malicious email detection framework using deep ensemble learning to analyze all email segments (body, header, and attachments); this eliminates the need for human expert intervention for feature engineering. In this paper, we also demonstrate how an ensemble framework of deep learning classifiers each of which are trained on specific portions of an email (thereby independently utilizing the entire email) can generalize better than popular email analysis methods that analyze just a specific portion of the email for analysis. The proposed framework is evaluated comprehensively and with an AUC of 0.993, the proposed framework's results surpass state-of-the-art malicious email detection methods, including human expert feature-based machine learning models by a TPR of 5%. [ABSTRACT FROM AUTHOR]

Solar flares are a kind of violent solar eruptive activity phenomenon and an important warning device of space weather disturbance. In space weather forecasting, flare forecasting is an important forecast content. This paper proposes a flare prediction model based on long and short-term memory neural network, which uses the time sequence of magnetic field changes in the solar active region in the past 24 h to construct samples, and analyzes the time series evolution of magnetic field characteristics through the long and short-term memory neural network to predict whether ≥ M-level flares will occur in the next 48 h. This paper uses a data set for all active region samples from May 2010 to May 2017, and selects 10 magnetic field characteristic parameters of SDO/HMI SHARP. In the modeling process, six feature parameters with high weight, gain rate, and coverage rate were selected as input parameters through XGBoost method. Through test comparison, the false report rate and accuracy rate of the model are similar to the traditional machine learning model, and the accuracy rate and critical success index are better than the traditional machine learning model, which are 0.7483 and 0.7402, respectively. The overall effect of the model is better than that of the traditional machine learning model. [ABSTRACT FROM AUTHOR]

• Develop a dataset of Mars scenes from numerous raw Mars images. • Compare performance of three common deep learning models to select the appropriate one. • Conduct transfer learning and data augmentation to enhance selected model's generation capacity. • Deep learning can realize autonomous, real-time Martian terrain classification. Scene information plays a crucial role in motion control, attitude perception, and path planning for wheeled planetary rovers (WPRs). Terrain recognition is the fundamental component of scene recognition. Due to the rich information, visual sensors are usually used in terrain classification. However, teleoperation delay prevents WPRs from using visual information efficiently. End-to-end learning method of deep learning (DL) that does not need complex image preprocessing was proposed to deal with this issue. This paper first built a terrain dataset (consists of loose sand, bedrock, small rock, large rock, and outcrop) using real Mars images to directly support You Only Look Once (YOLOv5) to test its performance on terrain classification. Because the capability of end-to-end training scheme is positively correlated with dataset, the performance of YOLOv5 can be significantly improved by exploiting orders of magnitude more data. The best combination of hyperparameters and models was achieved by slightly tuning YOLOv5, and data augmentation was also applied to optimize its accuracy. Furthermore, its performance was compared with two other end-to-end network architectures. Deep learning algorithms can be used in the future planetary exploration missions, such as WPRs autonomy improvement, traversability analysis, and avoiding getting trapped. [ABSTRACT FROM AUTHOR]

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. • We categorize brain age estimation models based on the deep learning architectures. • We classify deep learning architectures based on the slice/voxel-based input data. • We examine the modalities, size of data, performance and the datasets used. • We enumerate several challenges and trace directions that can be explored further. [ABSTRACT FROM AUTHOR]

The application of neural network models to solve combinatorial optimization has recently drawn much attention and shown promising results in dealing with similar problems, like Travelling Salesman Problem. The neural network allows to learn solutions based on given problem instances, using reinforcement learning or supervised learning. In this paper, we present a novel end-to-end method to solve routing problems. In specific, we propose a gated cosine-based attention model (GCAM) to train policies, which accelerates the training process and the convergence of policy. Extensive experiments on different scale of routing problems show that the proposed method can achieve faster convergence of the training process than the state-of-the-art deep learning models while achieving solutions of the same quality. • We proposed a cosine-based neural network model based on the transformer architecture to improve node embeddings. • We use cosine decomposability to accelerate training speed of the proposed model. • We use a gating connection to stabilize training and get faster convergence, which leads to better overall performance. • We apply the proposed gated cosine-based attention model to solve several Routing Problems. [ABSTRACT FROM AUTHOR]

• An end-to-end network combining deep learning and broad learning system. • The network has an incremental learning ability. • A pose estimation method based on a cooperative mark with concentric circles. • Superior computing speed on CPU. This paper proposes an efficient relative localization and object detection network (AeroNet) based on incremental learning for minimalistic high-speed cooperative navigation of aerial-ground unmanned vehicles in cluttered environments. Due to highly limited computation capability and memory resources in micro-UAVs, YOLO series are applied as the baseline of object detection network, and a lightweight backbone is built based on the depthwise separable convolution. To improve the real-time performance, the detection head is formulated with broad learning system. Besides, 6D relative pose estimation is achieved via equation fitting of an elliptical cooperative mark. To verify the effectiveness of AeroNet, experiments are conducted on Intel NUC and NVIDIA TX2 with our self-collected dataset. Results show that AeroNet can progressively increase the accuracy of object detection to 89%, and the computational time is only 76ms on Intel NUC and 28ms on Nvidia TX2, respectively, which meet the need of real-time requirement of on-board calculation in micro-UAV avionics systems. [ABSTRACT FROM AUTHOR]

In practical applications, multi-view data depicting objects from assorted perspectives can facilitate the accuracy increase of learning algorithms. However, given multi-view data, there is limited work for learning discriminative node relationships and graph information simultaneously via graph convolutional network that has drawn the attention from considerable researchers in recent years. Most of existing methods only consider the weighted sum of adjacency matrices, yet a joint neural network of both feature and graph fusion is still under-explored. To cope with these issues, this paper proposes a joint deep learning framework called Learnable Graph Convolutional Network and Feature Fusion (LGCN-FF), consisting of two modules: feature fusion network and learnable graph convolutional network. The former aims to learn an underlying feature representation from heterogeneous views, while the latter explores a more discriminative graph fusion via learnable weights and a parametric activation function dubbed Differentiable Shrinkage Activation (DSA) function. The proposed LGCN-FF is validated to be superior to various state-of-the-art methods in multi-view semi-supervised classification. [Display omitted] • Propose a joint deep learning framework for multi-view information fusion. • Conduct feature and graph fusion simultaneously under the same framework. • Develop a multi-stage training strategy to optimize different subproblems with back propagation. • Construct a learnable GCN framework with adaptive weights and a parametric differentiable shrinkage activation function. [ABSTRACT FROM AUTHOR]

Machine learning algorithms especially deep neural networks have seen tremendous growth in their real-world deployment. While these algorithms have to yield high performances for the task at hand, it is of equal importance that they are robust against the different kinds of singularities or adversarial anomalies. Researchers have generally addressed singularity detection independently for each anomaly, however, for the algorithms to be effective in the real world, the singularity detection algorithm must be able to handle a wide variety of attacks whether trained individually on them or not seen at the time of training. With this objective, in this paper, we propose a unique end-to-end transformation domain network to detect a variety of well-known attacks. The proposed architecture utilizes a combination of two different wavelet transformations to simultaneously learn low-level and high-level image features in a deep stacked layer fashion. The proposed method is generalized to handle a broad set of singularities of several computer vision algorithms whether operating in the object recognition domain or biometrics recognition. We showcase the results on a wide range of singularity points including (i) adversarial perturbations which are learned using deep learning networks, (ii) physical presentation attacks on face recognition which aim to produce fake data for the sensor for acquisition, and identification, (iii) synthetic images, and (iv) digital retouching of the images. Even with a zero-day or open-world attack setting, the results of the proposed algorithm show improvement over state-of-the-art results. The proposed architecture is agnostic to parameters, computationally efficient to provide a sustainable detector to resource-constrained institutions, provides robustness to attacks, and supports green computing. • A novel parameter-agnostic architecture is proposed to defend anomalies. • The proposed algorithm can handle adversaries in seen, unseen, and open-set settings. • The proposed algorithm is domain agnostic i.e., can handle vision and face datasets. • Our unified algorithm surpasses the SOTA works developed to handle specific attacks. • Bring a computationally feasible algorithm, it can be deployed on mobile devices. [ABSTRACT FROM AUTHOR]

• An audio dataset with FSL and OSR considerations is proposed. • It contains real recordings from 34 classes of alarms and common domestic sounds. • Recordings were captured in real environments using the same device. • This dataset will avoid manipulations on existing datasets (common practice today). • Two baselines are proposed based on a transfer learning approach as starting points. The problem of training with a small set of positive samples is known as few-shot learning (FSL). It is widely known that traditional deep learning algorithms usually show very good performance when trained with large datasets. However, in many applications, it is not possible to obtain such a high number of samples. This paper deals with the application of FSL to the detection of specific and intentional acoustic events given by different types of sound alarms, such as door bells or fire alarms, using a limited number of samples. These sounds typically occur in domestic environments where many events corresponding to a wide variety of sound classes take place. Therefore, the detection of such alarms in a practical scenario can be considered an open-set recognition (OSR) problem. To address the lack of a dedicated public dataset for audio FSL, researchers usually make modifications on other available datasets. This paper is aimed at providing the audio recognition community with a carefully annotated dataset 1 for FSL in an OSR context comprised of 1360 clips from 34 classes divided into pattern sounds and unwanted sounds. To facilitate and promote research on this area, results with state-of-the-art baseline systems based on transfer learning are also presented. [ABSTRACT FROM AUTHOR]

• The paper fills the gap in the literature by reviewing and analyzing the current studies that apply deep learning for Covid-19 forecasting. • The initial search identified 152 studies of which 53 passed the quality control. • The existing literature is categorized using a model-based taxonomy. • The description of the models along with their performance evaluation is presented. • Recommendations for future improvements are provided. The Covid-19 pandemic has galvanized scientists to apply machine learning methods to help combat the crisis. Despite the significant amount of research there exists no comprehensive survey devoted specifically to examining deep learning methods for Covid-19 forecasting. In this paper, we fill the gap in the literature by reviewing and analyzing the current studies that use deep learning for Covid-19 forecasting. In our review, all published papers and preprints, discoverable through Google Scholar, for the period from Apr 1, 2020 to Feb 20, 2022 which describe deep learning approaches to forecasting Covid-19 were considered. Our search identified 152 studies, of which 53 passed the initial quality screening and were included in our survey. We propose a model-based taxonomy to categorize the literature. We describe each model and highlight its performance. Finally, the deficiencies of the existing approaches are identified and the necessary improvements for future research are elucidated. The study provides a gateway for researchers who are interested in forecasting Covid-19 using deep learning. [ABSTRACT FROM AUTHOR]

Foreign species can deteriorate the environment and the economy of a country. To automatically monitor biosecurity threats at country borders, this paper investigates compact deep networks for accurate and real-time object segmentation for hyperspectral images. To this end, knowledge distillation (KD) approaches compress the model by distilling the knowledge of a large teacher network to a compact student network. However, when the student is over-compressed, the performance of standard KD methods degrades significantly due to the large capacity gap between the teacher and the student. This gap can be addressed by adding medium-sized teacher assistants, but training them incurs significant computation and hence is impractical. To address this problem, this paper proposes a new framework called Knowledge Distillation from Multi-head Teacher (KDM), which distills the knowledge of a multi-head teacher to the student. By encapsulating multiple teachers in a single network, our proposed KDM assists the learning of a very compact student and significantly reduces the training time. We also introduce Bio-HSI, a new large benchmark hyperspectral image dataset of 3,125 high-resolution images with dense segmentation ground truth. This new, large dataset can be expected to advance research on deep models for hyperspectral image segmentation. Evaluated on this dataset, the student trained via our KDM has 762 times fewer parameters than the state-of-the-art segmentation model (i.e., HRNet), while achieving competitive accuracy. [ABSTRACT FROM AUTHOR]

Two participants with competing objectives are considered in one-to-one orbital pursuit-evasion problem, one of which attempts to satisfy some certain terminal conditions with a minimum cost through its own control while the other player attempts to maximize the specified payoff and avoids entering this terminal range. This paper treats the problem of minimax time to a given final distance, within which the interception between pursuer and evader is defined to occur. To achieve the successful interception with an evader that adopts any unknown maneuvers, a near-optimal interception strategy for the orbital pursuit-evasion is presented in this paper. The pursuit-evasion problem is formulated as a differential game first, and a closed-form barrier solution is provided next. A near-optimal guidance law using deep learning is proposed to intercept the evader inside the capture zone. For the games that start outside the barrier, a learning algorithm for the capture zone embedding strategy is presented based on deep reinforcement learning to help the game state cross the barrier surfaces. The simulation cases of intercepting the evaders that adopt different maneuvering strategies are provided, and the results demonstrate the effectiveness and onboard feasibility of the proposed strategy in solving the orbital interception problem. • The closed-form barrier solution for a planar orbital pursuit-evasion game is provided. • A deep neural network is trained on a dataset consisting of plenty of barrier trajectories generated through the closed-form barrier formula. • The near-optimal guidance law inside capture zone is proposed using trained neural network. • A capture zone embedding strategy is presented based on deep reinforcement learning. • The closed loop interception strategy is proposed combining the strategies outside and inside capture zone. [ABSTRACT FROM AUTHOR]

This paper presents a review of up-to-date Machine Learning (ML) techniques applied to photovoltaic (PV) systems, with a special focus on deep learning. It examines the use of ML applied to control, islanding detection, management, fault detection and diagnosis, forecasting irradiance and power generation, sizing, and site adaptation in PV systems. The contribution of this work is three fold: first, we review more than 100 research articles, most of them from the last five years, that applied state-of-the-art ML techniques in PV systems; second, we review resources where researchers can find open data-sets, source code, and simulation environments that can be used to test ML algorithms; third, we provide a case study for each of one of the topics with open-source code and data to facilitate researchers interested in learning about these topics to introduce themselves to implementations of up-to-date ML techniques applied to PV systems. Also, we provide some directions, insights, and possibilities for future development. • Review of papers applying machine learning techniques to photovoltaic systems. • Review of open datasets, code, and simulation environments in photovoltaic systems. • More than 100 research articles reviewed with particular focus on deep learning. • Study cases with open source code for each one of the challenges. • Opportunities for future research and development. [ABSTRACT FROM AUTHOR]

[Display omitted] • Integrating deep learning with 2DCOS for wheat flour spectral classification. • The improved EfficientNet model achieved 100% accuracy in wheat flour identification. • Comparison of deep learning and machine learning methods in spectral analysis. • Transforms spectra into images, applicable beyond wheat flour. Wheat flour is a ubiquitous food ingredient, yet discerning its various types can prove challenging. A practical approach for identifying wheat flour types involves analyzing one-dimensional near-infrared spectroscopy (NIRS) data. This paper introduces an innovative method for wheat flour recognition, combining deep learning (DL) with Two-dimensional correlation spectrum (2DCOS). In this investigation, 316 samples from four distinct types of wheat flour were collected using a near-infrared (NIR) spectrometer, and the raw spectra of each sample underwent preprocessing employing diverse methods. The discrete generalized 2DCOS algorithm was applied to generate 3792 2DCOS images from the preprocessed spectral data. We trained a deep learning model tailored for flour 2DCOS images – EfficientNet. Ultimately, this DL model achieved 100% accuracy in identifying wheat flour within the test set. The findings demonstrate the viability of directly transforming spectra into two-dimensional images for species recognition using 2DCOS and DL. Compared to the traditional stoichiometric method Partial Least Squares Discriminant Analysis (PLS_DA), machine learning methods Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and deep learning methods one-dimensional convolutional neural network (1DCNN) and residual neural network (ResNet), the model proposed in this paper is better suited for wheat flour identification, boasting the highest accuracy. This study offers a fresh perspective on wheat flour type identification and successfully integrates the latest advancements in deep learning with 2DCOS for spectral type identification. Furthermore, this approach can be extended to the spectral identification of other products, presenting a novel avenue for future research in the field. [ABSTRACT FROM AUTHOR]

• The proposed AP-GAN-DNN method is able to predict creep fracture life more accurately in the case of small samples, which solves the complex nonlinear problem and the problem of test data dispersion. • Compared with traditional physical methods and machine learning models, the AP-GAN-DNN model has better generalization and robustness for creep fracture life prediction. • AP-GAN data enhancement method can significantly reduce the "pattern collapse" effect of GAN and improve the performance of prediction algorithms. 7050 aluminum alloy has high strength and excellent fracture toughness. However, damage can be caused to the 7050 aluminum alloy by creep at elevated temperatures. The accuracy of conventional creep life prediction methods may fall short of meeting the desired standards. And the application of machine learning (ML) methods is limited due to the scarcity and dispersion of creep sample data. Generative Adversarial Networks (GAN) are often used for data enhancement, but suffer from "pattern collapse" under small sample conditions. To solve the above problems, this paper proposes a creep fracture life prediction method, which combines the affinity propagation (AP) clustering algorithm, GAN, and deep neural networks (DNN) for accurate creep fracture life prediction. The AP clustering algorithm for adaptive clustering was utilized to better reflect the creep sample distribution. Independent GAN models were trained for each cluster to better capture distribution characteristics of the data and generate synthetic data that was highly similar to the real data. The DNN models were trained using the synthetic data and then predicted using real creep fracture life data. The presented method was compared with traditional physical and machine learning methods, and the method combining K-Means, GAN, and DNN. The experimental results show that the method proposed in this paper has better prediction accuracy in a small sample creep fracture life dataset. [ABSTRACT FROM AUTHOR]

Property valuation research, evolving with Automated Valuation Models (AVMs) using Artificial Intelligence (AI) and Machine Learning (ML), encounters challenges in handling dynamic market conditions. While the market approach is a practical solution to complement the AVMs, it also suffers from different deficiencies, particularly in relying on subjective valuer judgment. In Australia's diverse real estate market, complete and up-to-date market data derived from recent transactions of the different property types within various suburbs can be crucial for valuers. However, accessing such data often comes at a high cost, and the availability of transaction data is limited, mainly when market analysis necessitates the consideration of property valuation across various property types and bedroom counts. Accordingly, this paper presents a novel multi-step method to estimate the median prices of different property types considering their bedroom counts at the suburban level in the Melbourne Metropolitan area to benefit valuers when adopting the market approach. Nine distinct and ensembled spatially-based imputation techniques of K-Nearest Neighbors (KNN), Inverse Distance Weighted (IDW), Weighted KNN, Weighted IDW, Weighted KNN-IDW, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), RF-IDW-KNN and XGBoost-IDW-KNN are first employed to impute missing data on six market-related parameters obtained from the Real Estate Institute of Victoria (REIV). These parameters include median price (with no consideration of bedroom counts), price change, median rent, rental yield, clearance rate and days on market for houses and units. Next, based on these parameters, three ML algorithms—RF, Support Vector Regression (SVR) and XGBoost—are developed to estimate the median prices. Subsequently, the Long Short-Term Memory (LSTM) technique is employed for Deep Learning (DL)-based spatiotemporal analysis, clustering suburbs based on property value fluctuations. Finally, these clusters are integrated into the ML models developed in the previous step as an auxiliary feature to assess their potential impact on enhancing price estimation accuracy. The results demonstrate promising accuracies for different property types based on different performance assessment metrics. The paper also underscores improved estimation accuracy by incorporating time series-based clustering as a supplementary parameter through transfer learning. [ABSTRACT FROM AUTHOR]

With the development of digital technology, the maritime industry is under continuous digital transformation. For example, from manned engine room to control room and even to remotely controlled or autonomous ships. Maintenance has also changed from being a reactive or scheduled procedure to a predictive and proactive activity. Proactive maintenance relies on the condition monitoring (CM) of equipment. Condition Based Maintenance (CBM), Reliability Centered Maintenance (RCM) and Prognostics and Health Management (PHM) primarily focusing on three technical processes, namely condition monitoring, fault diagnosis and prognosis, and maintenance decision-making. Over the recent years, research has been on these topics through two main approaches: data-driven approaches and model-based approaches. Especially data-driven approaches with Deep Learning (DL) techniques have become a popular direction with successful implementation in different domains. Classification Societies are also developing new standards and methods with the industry to provide assurance of these emerging services. This paper presents a comprehensive review of state-to-art methods for PHM techniques for the past few years (2017–2023). First, a general introduction to different approaches with a focus on data-driven methods is presented. Subsequently, a detailed review of techniques applied to PHM is provided, where unique contributions, advantages, limitations and challenges are discussed. This is followed by a chapter that discusses the technical rules and standards from classification societies (DNV and ABS). The paper then explores the benefits, challenges, existing problems, and recommendations for PHM from the perspective of classification societies. Maritime stakeholders may find this article to be a valuable guide or reference for the development of such services. • A comprehensive review of PHM in the maritime industry is implemented. • Evaluation of diverse PHM methodologies like physics-based, and data-driven methods. • Introduced a new categorization and data flow analysis for the reviewed PHM papers. • Reviewed DNV and ABS rules on PHM, summarizing classification societies' insights. • Presented challenges and future opportunities for PHM in maritime from DNV's view. [ABSTRACT FROM AUTHOR]

Automated industries lead to high quality production, lower manufacturing cost and better utilization of human resources. Robotic manipulator arms have major role in the automation process. However, for complex manipulation tasks, hard coding efficient and safe trajectories is challenging and time consuming. Machine learning methods have the potential to learn such controllers based on expert demonstrations. Despite promising advances, better approaches must be developed to improve safety, reliability, and efficiency of ML methods in both training and deployment phases. This survey aims to review cutting edge technologies and recent trends on ML methods applied to real-world manipulation tasks. After reviewing the related background on ML, the rest of the paper is devoted to ML applications in different domains such as industry, healthcare, agriculture, space, military, and search and rescue. The paper is closed with important research directions for future works. • Recent trends in applying ML to real-world manipulation tasks are reviewed. • Different application domains utilizing manipulators are considered. • Challenges of using reinforcement learning for robot control are highlighted. • Possible methods to enforce robot safety during training RL agents are investigated. [ABSTRACT FROM AUTHOR]

The field of comic recognition (CR) has gained significant attention in recent years due to the growing popularity of digital comics. CR involves the automatic identification and extraction of comic panels, speech bubbles, and text from digital comic images, which has numerous applications in the digital media industry. The objective of this review paper is to conduct a systematic literature analysis of the current state of the art in CR techniques and methods. The authors strive to present all major discoveries related to representation models, techniques, tools, datasets, and comparative evaluations of recognition models. The present study utilizes the conventional Systematic Literature Review (SLR) approach and covers a wide range of 72 articles published in 17 prominent journals and 29 notable conferences and workshops. The available literature on recognition models and techniques is broadly categorized into three groups: computer vision (CV) technique being the most commonly used (75%) in the selected studies, followed by non-CV techniques (13.88%) and hybrid techniques (11.11%). Among the widely used datasets in the selected studies, Manga 109 and Ebdtheque are recognized by 50% of them. Within the field of CR, authors such as Jean-Christophe Burie, Christophe Rigaud, Jean-Marc Ogier, and Zhi Tang have received significant attention. The paper summarizes the findings of the CR research and highlights the need for standardization of accuracy norms and datasets. It emphasizes the importance of exploring and developing hybrid techniques for efficient CR tasks. [ABSTRACT FROM AUTHOR]

Building precise machine learning and deep learning models has traditionally required a combination of mathematical skills and hands-on experience to meticulously adjust hyperparameters that significantly impact the learning process. As datasets continue to expand across various engineering domains, researchers increasingly turn to machine learning methods to uncover hidden insights that may elude classic regression techniques. This surge in adoption raises concerns about the adequacy of resultant meta-models and the interpretation of findings. In response to these challenges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. This paper benchmarks AutoML solutions by providing an overview of their principles and applying them to predict the most important mechanical properties of different concrete datasets, i.e., compressive strength. Nine datasets from various concrete types, sample sizes, and features are utilized, with a detailed discussion on the benchmark dataset from high-performance concrete, applying best practices to the other eight datasets. For each case, the importance of hyperparameter tuning is discussed, alongside the ensemble and stacking models. Tree-based models are employed for each dataset to develop SHAP plots, interpret results, and understand the contribution of each component in the mix design to the overall strength of the concrete. This paper further explores three unique aspects of benchmarking AutoML in material science: (1) "reliability" by contrasting the benchmark dataset's error metric with literature collected over the past 20 years, (2) "uncertainty" by quantifying the variability in the mean and standard deviation of the error metric from different datasets and its correlation with the sample-to-feature ratio, and (3) "dilemma" by discussing the shortcomings of AutoML in specific concrete datasets. • The first comprehensive application of AutoML is presented in the concrete regression model. • Nine different concrete datasets are compared in terms of compressive strength. • AutoML's reliability, and dilemmas are evaluated in predicting concrete strength. • A benchmark problem is discussed to quantify the AutoML uncertainty in material science. • Explainable machine learning is used to identify the key contributing factors in f' c. [ABSTRACT FROM AUTHOR]

Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis. • The contribution of deep learning is investigated for Parkinson's Disease diagnosis. • A bibliometric analysis and literature review is performed. • The available research papers retrieved and analyzed in the Scopus database. • The literature review presented a research gap in deep learning approaches related to incremental learning. [ABSTRACT FROM AUTHOR]

Food safety is an important public health issue, and deep learning (DL) algorithms can provide powerful tools and methods for food safety and authenticity detection. Compared with chemometric algorithms and traditional machine learning algorithms, the performances of DL algorithms are improved in many aspects. By learning and analyzing a large amount of data, DL models can improve the efficiency and accuracy of food safety and authenticity detection, helping to ensure the public health and safety. This paper reviews some commonly used chemometric algorithms, traditional machine learning algorithms, and popular DL algorithms. Among them, special attentions are paid to convolutional neural network (CNN), fully convolutional network (FCN) and generative adversarial network (GAN). Moreover, the auxiliary effect of GAN on CNN is highlighted. Finally, this paper revisits recent applications of DL algorithms in the field of food safety and authenticity detection, and prospects the challenges and future directions of DL algorithms in this field. Although DL has made many achievements in the field of food safety and authenticity detection, there is still a great potential for development. For example, the data augmentation function of GAN can assist CNN to obtain more training samples, thus improving the recognition rate. In addition, multimodal neural network (MNN) or multimodal attention network (MAN) can be also used to achieve the fusion of data from different modalities to further improve the robustness and accuracy of DL algorithms. [Display omitted] • Some popular DL algorithms in food safety and authenticity detection are introduced. • The improvements of DL algorithms to chemometric and ML algorithms are summarized. • Recent applications of DL algorithms in food safety and authenticity detection are reviewed. • Future prospects of DL algorithms in food safety and authenticity detection are discussed. [ABSTRACT FROM AUTHOR]