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The use of Artificial Intelligence (AI) to preserve and promote cultural heritage has experienced significant growth in recent years. Among the various areas of cultural heritage, numismatics have emerged as a particularly promising field where we can develop AI solutions. Numismatics refers to the study of coins, tokens, paper money, and medals, which play a critical role in understanding human history and culture. However, there are still limited resources available to help researchers and collectors in the identification of coins. This is due to the vast number of coins in circulation, which presents a significant challenge in developing smart tools for classification tasks. This paper aims to provide a contribution to this setting. In particular, we start by creating a new dataset called EURO-Coin, which consists of images showing the side of coins with reliefs and is designed to facilitate the training and testing of AI models for euro coin classification. Then, we propose two approaches that leverage Convolutional Neural Networks and self-attention layers to classify the country and value of the coins. In our experiments, we obtain an accuracy of 86.9% for country classification and an accuracy of 96.4% for value classification. Finally, we conduct an ablation study to evaluate the impact of the preprocessing activities and attention layers in our approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel paper currency defect detection algorithm using quaternion uniform strength. We first build paper currency image preprocessing integration framework which includes intensity balancing, paper currency location, and geometric correction. We then propose a global–local paper currency image registration algorithm by moving key areas within certain range which can eliminate the false difference effectively. Finally, the quaternion uniform strength is calculated by using quaternion convolution edge detector. The defect degree of paper currency is determined by using the quaternion uniform color difference. The proposed algorithm is tested using different datasets from five countries: CNY, USD, EUR, VND, and RUB. The experimental results demonstrate that the proposed algorithm yields better results than the existing state-of-the-art paper currency defect detection techniques. The demo of the proposed paper currency defect detection algorithm will be publicly available. [ABSTRACT FROM AUTHOR]

This paper presents a convolutional neural network (CNN) to classify between the conventionally and organically cultivated Memecik varieties of green olives. The image forming method called the rising paper chromatography is utilized in preparing the images of Memecik varieties of green olives for CNN. In the rising chromatography method, 20, 30, and 40% sample concentrations were determined as the suitable concentrations for both organic and conventional olives. The concentrations of AgNO3 and FeSO4 were determined as 0.25, 0.5, 0.75 and 1% for both conventional and organic samples. The visual differences used for differentiation of different types of Memecik green olives are usually determined according to the regional color differences, the vivid color occurrence, the width and the frequency of bowl occurrence, the thin line, and the picks at drop zone by the expert assessors. The testing results in this study verified the effectiveness of the CNN methodology in differentiating between the organically and conventionally cultivated Memecik green olives. The newly designed neural network achieved 100% accuracy. Furthermore, this high accuracy achieved by CNN might suggest that it can be effectively used in place of the expert assessors. [ABSTRACT FROM AUTHOR]

Brain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI applications, requiring in-depth assessment through deep learning. EEG research frequently employs SSVEPs, which are regarded as normal brain responses to visual stimuli, particularly in investigations of visual perception and attention. This paper tries to give an in-depth analysis of the implications of deep learning for SSVEP-adapted BCI. A systematic search across four stable databases (Web of Science, PubMed, ScienceDirect, and IEEE) was developed to assemble a vast reservoir of relevant theoretical and scientific knowledge. A comprehensive search yielded 177 papers that appeared between 2010 and 2023. Thence a strict screening method from predetermined inclusion criteria finally generated 39 records. These selected works were the basis of the study, presenting alternate views, obstacles, limitations and interesting ideas. By providing a systematic presentation of the material, it has made a key scholarly contribution. It focuses on the technical aspects of SSVEP-based BCI, EEG technologies and complex applications of deep learning technology in these areas. The study delivers more penetrating reporting on the latest deep learning pattern recognition techniques than its predecessors, together with progress in data acquisition and recording means suitable for SSVEP-based BCI devices. Especially in the realms of deep learning technology orchestration, pattern recognition techniques, and EEG data collection, it has effectively closed four important research gaps. To increase the accessibility of this critical material, the results of the study take the form of easy-to-read tables just generated. Applying deep learning techniques in SSVEP-based BCI applications, as the research shows, also has its downsides. The study concludes that a radical framework will be presented which, includes intelligent decision-making tools for evaluation and benchmarking. Rather than just finding a comparable or similar analogy, this framework is intended to help guide future research and pragmatic applications, and to determine which SSVEP-based BCI applications have succeeded at responsibility for what they set out with. [ABSTRACT FROM AUTHOR]

Research on the visual system and its development is not only crucial for understanding how we see and interpret the environment from basic visual processing to complex perception but also aids in the development of technologies like virtual reality, augmented reality, and computer vision. In our previous paper, we have proposed a novel biological motion direction detection mechanism. Based on this mechanism, the authors of this paper proposed a learning artificial visual system (AVS) and use it to achieve motion direction detection. The learning AVS consists of two layers: the local visual feature detective neuron layer and the global feature detective neuron layer. For local motion direction detection, we use dendritic neurons to implement local motion direction-detective neurons and use them to extract local motion direction information. Global feature detective neurons are simply implemented by summing the local motion direction-detective neurons in each direction of possible motion. We trained the learning AVS by eliminating unnecessary branches or synapses and keeping only those that are strongly connected to the function of motion direction detection. Computer simulations demonstrate that AVS can learn the correct motion direction of objects with different shapes, size and positions. We compare the performance of AVS with support vector machine (SVM) and traditional convolutional neural networks (CNNs)—LeNet-5 and EfficientNet-B0 (EfN)—in motion direction detection and find that our learning AVS has better recognition accuracy, noise immunity and less demands on size of dataset and learning costs. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) system performance and reliability can be improved through the detection of defects in PV modules and the evaluation of their effects on system operation. In this paper, a novel system is proposed to detect and classify defects based on electroluminescence (EL) images. This system is called Fault Detection and Classification (FDC) and splits into four modules, which are (1) Image Preprocessing Module (IPM), (2) Feature Extraction Module (FEM), (3) Feature Selection Module (FSM), and (4) Classification Module (CM). In the first module (i.e., IPM), the EL images are preprocessed to enhance the quality of the images. Next, the two types of features in these images are extracted and fused together through FEM. Then, during FSM, the most important and informative features are extracted from these features using a new feature selection methodology, namely, Feature Selection-based Chaotic Map (FS-CM). FS-CM consists of two stages: filter stage using chi-square to initially select the most effective features and a modified selection stage using an enhanced version of Butterfly Optimization Algorithm (BOA). In fact, BOA is a popular swarm-based metaheuristic optimization algorithm that has only recently found success. While BOA has many benefits, it also has some drawbacks, including a smaller population and an increased likelihood of getting stuck in a local optimum. In this paper, a new methodology is proposed to improve the performance of BOA, called chaotic-based butterfly optimization algorithm. Finally, these selected features are used to feed the proposed classification model through CM. During CM, Hybrid Classification Model (HCM) is proposed. HCM consists of two stages, which are binary classification stage using Naïve Bayes (NB) and multi-class classification stage using enhanced multi-layer perceptron. According to the experimental results, the proposed system FDC outperforms the most recent methods. FDC introduced 98.2%, 89.23%, 87.2%, 87.9%, 87.55%, and 88.20% in terms of accuracy, precision, sensitivity, specificity, g-mean, and f-measure in the same order. [ABSTRACT FROM AUTHOR]

Due to the uncertainty of geological conditions during the tunneling process, advanced prediction of TBM tunneling parameters is significant for evaluating operational safety and efficiency, especially for real-time prediction of key tunneling parameters during the steady phase of TBM operation. At present, although there are studies on constructing predictive models based on machine learning algorithms, multiparameter prediction consistent with the actual tunneling process remains challenging due to the complexity of the TBM tunneling process and the numerous tunneling parameters. Therefore, this paper proposes a real-time multiple tunneling parameters prediction method of TBM steady phase based on dual recurrent neural networks. Firstly, the irregular multidimensional time series of tunneling parameters are analyzed and processed, which are divided into an idle-push phase, a rising phase, and a steady phase; secondly, the parameters of rising phase are analyzed using a recurrent neural network, and the parameters relevant for constructing a real-time prediction model are screened; then, based on the screened parameters, the Bayesian-optimized gated recurrent unit (GRU, a kind of recursive neural network) is proposed to construct a real-time prediction model for the four key tunneling parameters during the steady phase. Finally, the effectiveness and practicality of the proposed method are demonstrated by verification on real TBM tunnel datasets and comparing it with the models constructed by six commonly used machine learning algorithms. The results of this paper show that the designed prediction method is able to achieve a good combination of performance in terms of accuracy and computational time-consumption, with an average prediction accuracy of 91.1% for the four parameters for different rock grades of geology, the multiparameter prediction time for 100 samples is only 11 ms. In addition, three current similar studies using deep learning methods were compared to demonstrate the superiority of this proposal. As a method more closer to practical application, this work provides guidance for the forward-looking prediction of TBM tunneling parameters. [ABSTRACT FROM AUTHOR]

In this paper, finite-time and fixed-time function projective synchronization of competitive neural networks with time-varying delays and noise perturbation is studied. Firstly, different from the existing papers, a more flexible Lyapunov function is constructed based on p-norm and two hybrid controllers are designed in this paper. Secondly, based on the finite-time and fixed-time stability theory and stochastic analysis theory, some new and useful finite-time and fixed-time function projective synchronization criteria are obtained. Furthermore, the settling time is derived with the help of some lemmas and mathematical inequalities under the appropriate control scheme. Finally, illustrative examples are given to show the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive radial basis function neural network (RBFNN) is investigated and presented for identifying the nonlinear dynamical system. In traditional RBFNN structure, the selections of the activated radius are decided by the designer's experience to satisfy the best approximation of nonlinear dynamical function. In order to reduce the experience error, an adaptive RBFNN mechanism that can realize auto-regulation of activated radius is proposed in this paper. Taking the lattice points as the center of activated function, we use Taylor expansion to separate the factor of activated radius in local space. In order to ensure that the identification error has the property of fast convergence, the error conversion function is used for shrinking the gain in the error differential equation. Constructing a Lyapunov function to determine the differential equation of the weights and the activated radius, it is shown that the weights and the activated radius will converge to the neighborhood of its true value and the identification error will converge to neighborhood of zero in a periodic or period-like nonlinear dynamical system. To illustrate the effectiveness of the proposed adaptive RBFNN, Vanderpol and Duffing dynamical system are used as test examples, in comparison with traditional RBFNN and wavelet neural networks (WNN). The results show that the proposed method has best accurate identification and approximating effect in all of test algorithms. [ABSTRACT FROM AUTHOR]

One of the critical challenges in brain-computer interfaces is the classification of brain activities through the analysis of EEG signals. This paper seeks to improve the efficacy of deep learning-based rehabilitation systems, aiming to deliver superior services for individuals with physical disabilities. The research introduces the time distributed long short-term memory (TD-LSTM) framework, which incorporates an LSTM and a time distributed approach to classify brain activities. Learning in TD-LSTM is achieved by uncovering time-dependent semantic dependencies within EEG signals over time. By extracting all discriminative and relevant spatiotemporal dependencies via TD-LSTM, valuable information on different time steps in each sequence has been obtained. Time distributed approach shortens the input time series, making learning from long time series sequences easier, and the learning process of complex temporal and spatial dependencies in time series multi-channel EEG signals becomes more efficient. The main contributions in this paper can be outlined as follows: (1) implementation of brain activity binary classification of motor imagery/execution tasks using time distributed approach via RNN network for the first time, (2) evaluation of the performance and generalizability of the proposed method on four benchmark datasets, (3) dual-purpose classification which providing an efficient ways for classifying both types of motor imagery/execution brain activity. The experimental results show that the proposed method performs well compared to several baseline research works. [ABSTRACT FROM AUTHOR]

This paper proposes a metaheuristic algorithm, called advanced differential evolution (ADE), by improving the DE algorithm. The ADE algorithm was developed with the goal of creating an optimization framework that addresses the challenges of exploration and exploitation balance, avoiding local minima, utilizing chaos theory for diverse initialization, and improving solution quality and convergence speed. By incorporating these features, ADE aims to enhance the effectiveness of optimization processes. The proposed algorithm utilizes chaos theory to generate the initial population, which is subsequently divided into two sub-populations with adaptive sizes. The size of each sub-population is determined using a formula based on the number of iterations during the algorithm's execution. The first sub-population has a larger size in the beginning and the second one has a smaller size, but the total size of these two populations is always constant. The main contribution of this paper is the proposal of two novel improved differential evolution algorithms, namely MDE1 and MDE2, which are utilized for exploration within these sub-populations. The proposed ADE is tested on 29 well-known benchmarks and six engineering problems, and the results are compared with seven other algorithms. Various statistical experiments are carried out showing that the proposed algorithm provides significant superiority over other well-known algorithms. [ABSTRACT FROM AUTHOR]

Visual question answering is a challenging task that necessitates for sophisticated reasoning over the visual elements to provide an accurate answer to a question. Majority of the state-of-the-art VQA models are only applicable to English questions. However, applications such as visual assistance and tourism necessitate the incorporation of multilingual VQA systems. This paper presents an effective deep learning framework for Malayalam visual question answering (MVQA), which can answer a specific natural language question about an image in Malayalam. As there is no available dataset in English–Malayalam VQA, a MVQA dataset was created by translating English question–answer pairs from the visual genome dataset. The paper proposes an attention-driven MVQA model on the developed dataset. The proposed MVQA model uses a deep learning-based co-attention mechanism to jointly learn the attention for images and Malayalam questions. A second-order multimodal factorized high-order pooling is used for multi modal feature fusion. Different VQA models using combinations of classical CNNs and RNNs were experimented on the developed MVQA dataset, and the performance was compared against the proposed attention-driven model. Experimental results show that the proposed attention-driven MVQA model achieves state-of-the-art results as compared to other models for MVQA on the custom Malayalam VQA dataset. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid Modified Coronavirus Herd Immunity Aquila Optimization Algorithm (MCHIAO) that compiles the Enhanced Coronavirus Herd Immunity Optimizer (ECHIO) algorithm and Aquila Optimizer (AO). As one of the competitive human-based optimization algorithms, the Coronavirus Herd Immunity Optimizer (CHIO) exceeds some other biological-inspired algorithms. Compared to other optimization algorithms, CHIO showed good results. However, CHIO gets confined to local optima, and the accuracy of large-scale global optimization problems is decreased. On the other hand, although AO has significant local exploitation capabilities, its global exploration capabilities are insufficient. Subsequently, a novel metaheuristic optimizer, Modified Coronavirus Herd Immunity Aquila Optimizer (MCHIAO), is presented to overcome these restrictions and adapt it to solve feature selection challenges. In this paper, MCHIAO is proposed with three main enhancements to overcome these issues and reach higher optimal results which are cases categorizing, enhancing the new genes' value equation using the chaotic system as inspired by the chaotic behavior of the coronavirus and generating a new formula to switch between expanded and narrowed exploitation. MCHIAO demonstrates it's worth contra ten well-known state-of-the-art optimization algorithms (GOA, MFO, MPA, GWO, HHO, SSA, WOA, IAO, NOA, NGO) in addition to AO and CHIO. Friedman average rank and Wilcoxon statistical analysis (p-value) are conducted on all state-of-the-art algorithms testing 23 benchmark functions. Wilcoxon test and Friedman are conducted as well on the 29 CEC2017 functions. Moreover, some statistical tests are conducted on the 10 CEC2019 benchmark functions. Six real-world problems are used to validate the proposed MCHIAO against the same twelve state-of-the-art algorithms. On classical functions, including 24 unimodal and 44 multimodal functions, respectively, the exploitative and explorative behavior of the hybrid algorithm MCHIAO is evaluated. The statistical significance of the proposed technique for all functions is demonstrated by the p-values calculated using the Wilcoxon rank-sum test, as these p-values are found to be less than 0.05. [ABSTRACT FROM AUTHOR]

The inspection and diagnosis of building engineering involve rail surface defect detection, which plays a crucial role in assessing the quality of railway tracks. However, achieving accurate detection remains a significant challenge for infrastructure construction due to the challenging factors, such as complex background, poor texture, the irregular shapes and sizes of rail surface defects, etc. To address these challenges, combined with vision sensor, this paper proposes an automatic vision-based rail surface defect detection network, which leverages a combination of convolutional neural network (CNN) and transformer. Specifically, an effective encoding path based on an improved Res2N-et is presented to obtain stronger contextual information. Meanwhile, to address the limitation of feature representation on global context information, the transformer block is introduced into the bottleneck layer to capture essential global context information. Moreover, an attention-based edge enhancement block is proposed to mitigate the loss of local edge details and strengthen the focus on key feature information, to acquire more discriminant features. To further enrich the feature representation capability, this paper introduces an efficient feature aggregation block, which combines the global features extracted by the transformer and the local features extracted by CNN, to achieve effective feature complementarity and enhances overall detection performance. Combined with public NRSD-MN dataset, proposed model obtains 87.3 % and 77.9 % on PA metric, 85.9 % and 83.4 % in mIoU metric among artificial dataset with natural dataset. Meanwhile, it also obtains 84.0 % PA metric and 87.0 % mIoU metric in SEPR dataset. Experiments have proven the superiority of proposed model though the performance comparison with latest models, which presents an effective and promising rail surface defect detection solution for railway track quality assessment, and can be instrumental in ensuring safe and efficient railway operations. [ABSTRACT FROM AUTHOR]

Mental stress is a common problem that affects individuals all over the world. Stress reduces human functionality during routine work and may lead to severe health defects. Early detection of stress is important for preventing diseases and other negative health-related consequences of stress. Several neuroimaging techniques have been utilized to assess mental stress, however, due to its ease of use, robustness, and non-invasiveness, electroencephalography (EEG) is commonly used. This paper aims to fill a knowledge gap by reviewing the different EEG-related deep learning algorithms with a focus on Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the evaluation of mental stress. The review focuses on data representation, individual deep neural network model architectures, hybrid models, and results amongst others. The contributions of the paper address important issues such as data representation and model architectures. Out of all reviewed papers, 67% used CNN, 9% LSTM, and 24% hybrid models. Based on the reviewed literature, we found that dataset size and different representations contributed to the performance of the proposed networks. Raw EEG data produced classification accuracy around 62% while using spectral and topographical representation produced up to 88%. Nevertheless, the roles of generalizability across different deep learning models and individual differences remain key areas of inquiry. The review encourages the exploration of innovative avenues, such as EEG data image representations concurrently with graph convolutional neural networks (GCN), to mitigate the impact of inter-subject variability. This novel approach not only allows us to harmonize structural nuances within the data but also facilitates the integration of temporal dynamics, thereby enabling a more comprehensive assessment of mental stress levels. [ABSTRACT FROM AUTHOR]

Communication quality is compromised when wireless sensor network (WSN) operate in harsh environments, which can be improved by supplementing the nodes. This paper proposes a deployment strategy to optimize the placement of new nodes in WSN, specifically in complex environments with limited communication. To achieve this, the paper introduces the concept of strong connectivity relationships and presents a novel metaheuristic algorithm, namely double-state differential evolution (DSDE), which divides the optimization process into two states and adopts different optimization strategies. The proposed DSDE can improve the overall network communication glowing at a lower computing cost. Extensive experiments show that the proposed DSDE has better performance than state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on the prescribed-time cluster synchronization of coupled inertial neural networks. By variable transformation, the coupled inertial neural networks are converted into high-order systems. An effective prescribed-time control applicable to high-order systems is introduced by virtue of a time-varying scaling function. Moreover, a dimension-lifting approach is utilized to derive sufficient criteria for achieving prescribed-time cluster synchronization under the proposed control. Compared with existing Lyapunov–Krasovskii functional methods, the criteria obtained in this paper are in form of low-dimensional linear matrix inequalities and therefore can be easily verified. A numerical example is provided to demonstrate the effectiveness of proposed results. [ABSTRACT FROM AUTHOR]

Nowadays, landing gears used in aircraft are the basic systems that carry the weight of aircraft systems and provide them to move on the ground. During the landing of aircraft, big and sudden vibrations occur as soon as the wheels come into contact with the runway. This situation negatively affects human safety, comfort and also the life of the landing gear system. Therefore, it is important to keep this vibration at permissible levels. This paper presents an improved vibration control method that controls the amplitudes of vibrations of landing systems. This paper presents proposed vibration control method that controls the angular position variables of the landing gear mechanism to control the vibrations occurring during landing. For different runway conditions and 200 km/h landing speed, the angular joint variables of the landing gear mechanism links are controlled using standard controller and a proposed neural controller structure. The simulation results are shown that the proposed neural controllers have better performance at vibration control of landing systems during landing. [ABSTRACT FROM AUTHOR]

This paper points out the interval form deficiencies in the recent paper "An application of parametric approach for interval differential equation in inventory model for deteriorating items with selling-price-dependent demand" by Rehman et al. (Neural Comput Appl 32(17):14069–14085, 2020). The comments in this paper pertain mainly to the drawbacks of interval arithmetic (SIA) presented in Section 3 of Rehman et al. (2020) referencing the convention of converting an interval-valued model into a parametric form. Proposed corrections to the overestimation of the interval form through examples are presented. [ABSTRACT FROM AUTHOR]

Historical architecture has different special styles attributed to each era, dynasty, or region. These styles are common features such as geometric properties, ratios, scales, colors, and artistic techniques. Historical geometric ornaments have an enormous capability for classification based on their geometric characteristics. Smart pattern recognition allows researchers to classify huge databases of heritage for useful internet searches. So, our main goal in this paper is to implement the detection of categories in geometric patterns for classification and documentation in which by the photography of ornaments in every monument, the type of patterns and the number of every type of pattern would be estimated quickly. Furthermore, due to occurring deterioration in these patterns, our method also contributes to recognizing the deteriorated patterns. When we encounter numerous pieces of deteriorated patterns, manual recognition in order to reassemble and reconstruct is usually impossible or time-consuming. With the aid of artificial intelligence, in this paper, our aim is to seek to solve the automatic recognition of historical geometric patterns, even patterns having deterioration as an occlusion via image processing and machine learning methods. A challenging issue that researchers would tackle in detecting historical geometric pattern's types is the variety in geometric textures, especially when they have occlusion such as deterioration. This issue leads to limited success in classifying via extracting only one feature. The other issue is that the extracted feature must be invariant to the transformation, such as scale, rotation, and noise variation. To cope with the challenges mentioned above and accurately classify, we plan to use the fusion method based on extracting global and local features. So, the features extracted from images in this research are based on local and global. In other words, the proposed fusion strategy lies both in feature and decision level, but the core is the proposed three combination methods in fusion decision methods. In this method, the dataset is composed of four main Persian geometric pattern types: Tond dah, Kond tablghenas, Hashtva 4 lengeh, and HashtvatablKond. So, the model will be trained by extracting global and local features of the images separately. Random forest, as the prevalent machine learning algorithm, is proposed for training data and predicting the class of input images. Finally, the probability of prediction for random forest classifiers is fused by the Decision Templates (DT) combiner, Naïve Bayes (NB) combiner, and Dempster–Shafer combination methods. In comparison with the individual classifier accuracy results of 80% and 85% for global and local features respectively, our proposed approach achieves an improved accuracy of 90%, 88%, and 90% in three fusion decision methods including DT combiner, NB combiner, and Dempster–Shafer combination methods. [ABSTRACT FROM AUTHOR]

An efficient plant disease identification is prime important for Agricultural productivity. This paper proposes a method of automatic leaf extraction and classification with improved accuracy compared with existing techniques. Images acquired from natural fields are complex, with background information that needs to be accurately segmented to identify areas of interest. Grab cut is a semi-automatic approach to extract foreground objects, which might occasionally degrade an object's properties. This paper suggests a better Grab cut for automatically extracting leaves from real-field images. The enhanced dataset of 23,617 images with eleven tomato leaf classes are created using the plant village dataset and actual field images. This paper addresses a wide range of issues related to convolutional neural network optimization, including how the number of layers affects the results of leaf disease detection and the creation of tiny models for portable devices. Five models, VGG 16, MobileNet, and custom architecture with input sizes of 98 × 98, 160 × 160, and 256 × 256, have been optimized and evaluated through several trials. Training and testing involve varying the input size, the number of layers, the optimizer, the dropout, the batch normalization, and the transfer learning. This paper presents and analyzes the findings of thirty experiments on various architecture. The proposed modified sequential eleven-layer architecture achieves accuracy compared with MobileNet and a reduced model size of 3.9 MB for input size 160 and 4.5 MB for input size 98. [ABSTRACT FROM AUTHOR]

Recent increases in global food demand have made this research and, therefore, the prediction of agricultural commodity prices, almost imperative. The aim of this paper is to build efficient artificial intelligence methods to effectively forecast commodity prices in light of these global events. Using three separate, well-structured models, the commodity prices of eleven major agricultural commodities that have recently caused crises around the world have been predicted. In achieving its objective, this paper proposes a novel forecasting model for agricultural commodity prices using the extreme learning machine technique optimized with the genetic algorithm. In predicting the eleven commodities, the proposed model, the extreme learning machine with the genetic algorithm, outperforms the model formed by the combination of long short-term memory with the genetic algorithm and the autoregressive integrated moving average model. Despite the fluctuations and changes in agricultural commodity prices in 2022, the extreme learning machine with the genetic algorithm model described in this study successfully predicts both qualitative and quantitative behavior in such a large number of commodities and over such a long period of time for the first time. It is expected that these predictions will provide benefits for the effective management, direction and, if necessary, restructuring of agricultural policies by providing food requirements that adapt to the dynamic structure of the countries. [ABSTRACT FROM AUTHOR]

To address the problem of positioning customer order decoupling point (CODP) in customized logistics service supply chain and to provide logistics service integrators (LSI) with various CODP decision-making and supplier selection solutions. We consider LSI, logistics service providers (LSPs) and customers and propose multiobjective nonlinear team optimization models for multi-CODP location, including minimizing the cost to the LSI and maximizing the synthetic satisfaction of LSPs and customers. This paper adopts an improved nondominated sorting genetic algorithm (NSGA-II), in which the key modules such as the crossover operations, chromosome structure and mutation operations are reconstructed, to solve the team models and investigate the Pareto-optimal front for the two objectives. To demonstrate the validity of NSGA-II and the reliability of mathematical models, cases from a specific dataset are generated and solved. Moreover, a classic genetic algorithm (GA) is applied to solve for the two objectives separately. The optimal solutions found by GA are f 1 = 2027.491, f 2 = 0.7704 and f 1 = 2454.512, f 2 = 0.8909. The corresponding optimal solutions in the Pareto-optimal solution set are f 1 = 2053.675, f 2 = 0.7715 and f 1 = 2427.114, f 2 = 0.8843. We find that the optimal solutions provided by GA cannot dominate any solution in the Pareto-optimal solution set, indicating that the results are valid and sufficiently convergent. In summary, the results of this paper provide LSI with easy access to various supplier selection schemes with multi-CODP, which is essential to reduce the cognitive burden on decision makers and address the diverse customization needs of many challenging real-world logistics issues. [ABSTRACT FROM AUTHOR]

Most printed material is produced by printing halftone dot patterns. One of the key issues that determine the attainable print quality is the structure of the paper surface, but the relation is non-deterministic in nature. We examine the halftone print quality and study the statistical dependence between the defects in printed dots and the topography measurement of the unprinted paper. The work concerns SC paper samples printed by an IGT gravure test printer. We have small-scale 2D measurements of the unprinted paper surface topography and the reflectance of the print result. The measurements before and after printing are aligned with subpixel resolution, and individual printed dots are detected. First, the quality of the printed dots is studied using Self Organizing Map and clustering and the properties of the corresponding areas in the unprinted topography are examined. The printed dots are divided into high and low print quality. Features from the unprinted paper surface topography are then used to classify the corresponding paper areas using Support Vector Machine classification. The results show that the topography of the paper can explain some of the print defects. However, there are many other factors that affect the print quality, and the topography alone is not adequate to predict the print quality. [ABSTRACT FROM AUTHOR]

This paper investigates the art design of user interaction experience in mobile systems through the methods of contextual perception and machine learning. The theoretical foundations for the design of intangible cultural heritage interactive display resources include digital display theory, intangible cultural heritage education theory, embodied cognition theory, and gamification design theory. Based on the modelling and analysis of the theoretical foundations, the design principles are derived, including the heritage education principle, the somatic interaction design principle, and the content design principle. In this paper, we use ontologies to construct user knowledge models, fuse multi-situational similarity metrics, screen out candidate neighbour sets through preliminary screening, then combine the user's activity to construct time-based weight tensor scores, use tensor decomposition to obtain recommendation evaluation values, and finally use the recommendation evaluation values to make artistic recommendations. The experimental results show that the algorithm can still obtain a good recommendation implementation in the case of extremely sparse data. The analysis of the fit between augmented reality and folk art appreciation class, as well as the reference to relevant application cases, make design and practice of augmented reality application in folk art appreciation class, try to solve the common problems in folk art appreciation class, analyse the feedback effect and make a summary and outlook. [ABSTRACT FROM AUTHOR]

Safety helmet wearing detection based on video surveillance is an important means of safety monitoring in many industrial scenes. The training of safety helmet wearing detection models requires large and well-labeled dataset. However, the incidence of security violations is relatively low, which results in insufficient samples for training deep detection models. Safety helmet wearing detection is a common requirement in many scenarios such as construction sites, substations, and factory workshops. Aggregating data from multiple companies for model training would improve the performance of the detection model. Traditional centralized training methods are not feasible because aggregating data in centralized locations (such as the cloud) can raise concerns about data privacy and the high cost of data communication and storage. This paper proposes FedSH, a novel cloud-edge-based federated learning framework, which learns a shared global safety helmet wearing detection model in the cloud from multiple companies at the network edges and achieves data privacy protection by keeping company data locally. In addition, this paper designs reweighting mechanisms and applies transfer learning to address class imbalance and non-IID problems in the training data, so as to obtain an accurate and personalized detection model. Extensive experiments have been conducted on real surveillance video datasets. The experimental results demonstrate that FedSH outperforms the existing widely used federated learning methods with an accuracy improvement of at least 3.4%; the reduction in accuracy is within the range of 5% compared with centralized learning methods. FedSH effectively achieves a good balance between model performance, privacy protection, and communication efficiency. [ABSTRACT FROM AUTHOR]

Natural disasters can be unpredictable and catastrophic. Even after the event, the repercussions are prolonged due to the incompetence of disaster management strategies. To mitigate the effects of a natural hazard, disaster management teams have to rapidly come forth with innovative plans of action. To contain the damage that a disaster causes, the response time, and preparedness of a disaster management team is crucial. Having a forewarning about the nature of the disaster can prove to be beneficial for the management team. Remote/unreachable areas such as deep parts of the forests, far-flung rural areas, oceans and other hard to reach locations are at a higher risk of receiving poorer aid and response due to the lack of communication and connectivity with the rest of the world. Our paper provides a solution for this particular problem by suggesting UAV/Drones for surveillance and monitoring in these inaccessible, disaster struck areas. The drones not only monitor the situation but constantly click pictures and send them to a base station, where the images are used to collect insights about the type of disaster that has to be dealt with. The paper proposes a deep learning model based on feature concatenation for classification of disasters which can be deployed at the base station, and can receive data in the form of images from a UAV/drone hovering over the affected place. The proposed model is efficient and able to achieve a higher accuracy as compared to the leading CNN models and closely related recent works as well. [ABSTRACT FROM AUTHOR]

Named entity recognition (NER) aims to identify the required entities and their types from unstructured text, which can be utilized for the construction of knowledge graphs. Traditional methods heavily rely on manual feature engineering and face challenges in adapting to large datasets within complex linguistic contexts. In recent years, with the development of deep learning, a plethora of NER methods based on deep learning have emerged. This paper begins by providing a succinct introduction to the definition of the problem and the limitations of traditional methods. It enumerates commonly used NER datasets suitable for deep learning methods and categorizes them into three classes based on the complexity of named entities. Then, some typical deep learning-based NER methods are summarized in detail according to the development history of deep learning models. Subsequently, an in-depth analysis and comparison of methods achieving outstanding performance on representative and widely used datasets is conducted. Furthermore, the paper reproduces and analyzes the recognition results of some typical models on three different types of typical datasets. Finally, the paper concludes by offering insights into the future trends of NER development. [ABSTRACT FROM AUTHOR]

In order to realize the intelligent recognition and statistics of hydrogenation catalyst image information, this paper presents a new method to judge the active phase by image recognition, which is different from traditional methods. Firstly, considering that hydrogenation catalyst image targets are small and easy to stack, the feature extraction network in the CenterNet model is optimized by adding the multi-feature fusion module to improve the accuracy of the network in edge positioning. Secondly, according to the linear shape of the hydrogenation catalyst, the mask branch is added to the CenterNet model to train the hydrogenation catalyst stripes with unclear target to reduce the leakage rate of the hydrogenation catalyst. The experimental results show that the detection accuracy of the improved CenterNet network is 91 % , 7 % higher than that of the original one, with a decline in detection rate by 12 % . The method proposed in this paper can accurately identify and segment the hydrogenation catalyst in the electron microscope image, which can provide technical support for the statistics and analysis of the hydrogenation catalyst image. [ABSTRACT FROM AUTHOR]

Medical image segmentation is crucial in using digital images for disease diagnosis, particularly in post-processing tasks such as analysis and disease identification. Segmentation of magnetic resonance imaging (MRI) and computed tomography images pose distinctive challenges attributed to factors such as inadequate illumination during the image acquisition process. Multilevel thresholding is a widely adopted method for image segmentation due to its effectiveness and ease of implementation. However, the primary challenge lies in selecting the optimal set of thresholds to achieve accurate segmentation. While Otsu's between-class variance and Kapur's entropy assist in identifying optimal thresholds, their application to cases requiring more than two thresholds can be computationally intensive. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values; however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. In this paper, we introduce an improved chameleon swarm algorithm (ICSA) to address these limitations. ICSA is designed for image segmentation and global optimization tasks, aiming to improve the precision and efficiency of threshold selection in medical image segmentation. ICSA introduces the concept of the "best random mutation strategy" to enhance the search capabilities of the standard chameleon swarm algorithm (CSA). This strategy leverages three distribution functions—Levy, Gaussian, and Cauchy—for mutating search individuals. These diverse distributions contribute to improved solution quality and help prevent premature convergence. We conduct comprehensive experiments using the IEEE CEC'20 complex optimization benchmark test suite to evaluate ICSA's performance. Additionally, we employ ICSA in image segmentation, utilizing Otsu's approach and Kapur's entropy as fitness functions to determine optimal threshold values for a set of MRI images. Comparative analysis reveals that ICSA outperforms well-known metaheuristic algorithms when applied to the CEC'20 test suite and significantly improves image segmentation performance, proving its ability to avoid local optima and overcome the original algorithm's drawbacks. Medical image segmentation is essential for employing digital images for disease diagnosis, particularly for post-processing activities such as analysis and disease identification. Due to poor illumination and other acquisition-related difficulties, radiologists are especially concerned about the optimal segmentation of brain magnetic resonance imaging (MRI). Multilevel thresholding is the most widely used image segmentation method due to its efficacy and simplicity of implementation. The issue, however, is selecting the optimum set of criteria to effectively segment each image. Although methods like Otsu's between-class variance and Kapur's entropy help locate the optimal thresholds, using them for more than two thresholds requires a significant amount of processing resources. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values; however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. Due to the aforementioned, we present an improved chameleon swarm algorithm (ICSA) in this paper for image segmentation and global optimization tasks to be able to address these weaknesses. In the ICSA method, the best random mutation strategy has been introduced to improve the searchability of the standard CSA. The best random strategy utilizes three different types of distribution: Levy, Gaussian, and Cauchy to mutate the search individuals. These distributions have different functions, which help enhance the quality of the solutions and avoid premature convergence. Using the IEEE CEC'20 test suite as a recent complex optimization benchmark, a comprehensive set of experiments is carried out in order to evaluate the ICSA method and demonstrate the impact of combining the best random mutation strategy with the original CSA in improving both the performance of the solutions and the rate at which they converge. Furthermore, utilizing the Otsu approach and Kapur's entropy as a fitness function, ICSA is used as an image segmentation method to select the ideal threshold values for segmenting a set of MRI images. Within the experiments, the ICSA findings are compared with well-known metaheuristic algorithms. The comparative findings showed that ICSA performs better than other competitors in solving the CEC'20 test suite and has a significant performance boost in image segmentation. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of fixed-time synchronization of time-varying coupled competitive neural networks with impulsive effects. Synchronizing impulses and desynchronizing impulses are each taken into account. The innovative pure power-law control is designed in this paper to avoid vibratory behaviors, which is more straightforward and effective than the common design including the linear and power-law terms. Note that the key difficulty is determining how to ensure fixed-time synchronization and estimate the settling time in an impulsive sense since the impulsive influences may lead to the discontinuity of the states. However, the theoretical framework of fixed-time synchronization under desynchronizing impulses has not been constructed much in the existing literature, which deeply motivated us to contribute to this analysis. By using the average impulsive intervals method, graph theory, and the Lyapunov method, two cases of the settling time along with the fixed-time synchronization criteria regarding the synchronizing and desynchronizing impulses are given. Lastly, a numerical example is provided to show the viability and accuracy of the discovered theoretical results. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a crucial preprocessing step that aims to eliminate irrelevant and redundant features, reduce the dimensionality of the feature space, and enhance clustering efficiency and effectiveness. FS is categorized as NP-Hard due to the high number of existing solutions. Various metaheuristic methods have been developed to address the FS problem, yielding promising results. Particularly, particle swarm optimization (PSO), an evolutionary computing (EC) approach guided by swarm intelligence, has gained widespread adoption owing to its implementation simplicity and potential for global search. This paper analyzes several variants of PSO algorithms and introduces a new FS method called HPSO. The proposed approach utilizes an asynchronously adaptive inertia weight and an improved constriction factor. Additionally, it incorporates a chaotic map and a MAD fitness function with a feature count penalty to tackle the clustering FS problem. The efficiency of the developed method is evaluated against the genetic algorithm (GA) and well-known variants of PSO algorithms, including PSOs with fixed inertia weights, PSOs with improved inertia weights, PSOs with fixed constriction factors, PSOs with improved constriction factors, PSOs with adaptive inertia weights, and PSO's includes advanced learning exemplars and sophisticated structure topologies. This paper assesses two different reference text data sets, Reuters-21578 and Webkb. In comparison with competitive methods, the proposed HPSO method achieves higher clustering precision and selects a more informative feature set. [ABSTRACT FROM AUTHOR]

In this paper, we primarily concentrate on the quasi-projective synchronization of master–slave discrete-time BAM neural networks. Without using the LMI and matrix measure methods, by applying discrete inequalities and the Lyapunov sequences, constructing a discrete inequality group, then solving the inequality group, two novel criteria to guarantee the quasi-projective synchronization are obtained for the considered networks. The controllers designed, the approach, and the results in the paper are fully novel, which advance the development of the study of synchronization of discrete neural networks. [ABSTRACT FROM AUTHOR]

In recent years, the proliferation of vehicles on roadways, accompanied by an escalating demand for augmented safety and refined traffic management, has fueled substantial progress in the domains of computer vision and deep learning methodologies. This technological surge has precipitated ground-breaking applications, with real-time vehicle detection emerging as a linchpin. Capability to accurately detect vehicles in a real-time holds profound implications across diverse domains. In the context of autonomous driving, it underpins essential functions such as path planning, collision avoidance, and decision-making, essential for the safe operation of autonomous vehicles. In the arena of traffic monitoring, it empowers transportation authorities to acquire real-time insights into traffic dynamics, congestion patterns, and incident detection, thereby optimizing traffic flow and resource allocation strategies. Furthermore, within surveillance systems, real-time vehicle detection equips security personnel with the means to swiftly identify potential security threats, enhancing overall situational awareness and safety. This paper contributes to this dynamic landscape by introducing an innovative architecture expressly crafted for vehicle detection. Leveraging state-of-the-art computer vision techniques and deep learning methodologies, this architecture addresses the evolving challenges posed by contemporary traffic scenarios. Through the enhancement of real-time vehicle detection capabilities, our aim is to fortify the foundations of safety, efficiency, and security within vehicular contexts. The paper includes a fusion of AlexNet and Darknet accompanied by a comparative analysis. It is expected to deliver unmatched dependability and accuracy in real-time traffic monitoring, setting a new standard for performance in this application. [ABSTRACT FROM AUTHOR]

Recently, fully transformer-based trackers have achieved impressive tracking results, but this also brings a great deal of computational complexity. Some researchers have applied token pruning techniques to fully transformer-based trackers to diminish the computational complexity, but this leads to missing contextual information that is important for the regression task in the tracker. In response to the above issue, this paper proposes a token fusion method that speeds up inference while avoiding information loss and thus improving the robustness of the tracker. Specifically, the input of the transformer's encoder contains search tokens and exemplar tokens, and the search tokens are divided into tracking object tokens and background tokens according to the similarity between search tokens and exemplar tokens. The tokens with greater similarity to the exemplar tokens are identified as tracking object tokens, and those with smaller similarity to the exemplar tokens are identified as background tokens. The tracking object tokens contain the discriminative features of the tracking object, for the sake of making the tracker pay more attention to the tracking object tokens while reducing the computational effort. All the tracking object tokens are kept, and then, the background tokens are weighted and fused to form new background tokens according to the attention weight of the background tokens to prevent the loss of contextual information. The token fusion method presented in this paper not only provides efficient inference of the tracker but also makes the tracker more robust. Extensive experiments are carried out on popular tracking benchmark datasets to verify the validity of the token fusion method. [ABSTRACT FROM AUTHOR]

With the introduction of ChatGPT, the public's perception of AI-generated content has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field's development prospects. [ABSTRACT FROM AUTHOR]

IoT devices convert billions of objects into data-generating entities, enabling them to report status and interact with their surroundings. This data comes in various formats, like structured, semi-structured, or unstructured. In addition, it can be collected in batches or in real time. The problem now is how to benefit from all of this data gathered by sensing and monitoring changes like temperature, light, and position. In this paper, we propose a predictive analytics framework constructed on top of open-source technologies such as Apache Spark and Kafka. The framework focuses on forecasting temperature time series data using traditional and deep learning predictive analytics methods. The analysis and prediction tasks were performed using Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and a novel hybrid model based on Convolution Neural Network (CNN) and LSTM. The purpose of this paper is to determine whether and how recently developed deep learning-based models outperform traditional algorithms in the prediction of time series data. The empirical studies conducted and reported in this paper demonstrate that deep learning-based models, specifically LSTM and CNN-LSTM, exhibit superior performance compared to traditional-based algorithms, ARIMA and SARIMA. More specifically, the average reduction in error rates obtained by LSTM and CNN-LSTM models were substantial when compared to other models indicating the superiority of deep learning. Moreover, the CNN-LSTM-based deep learning model exhibits a higher degree of closeness to the actual values when compared to the LSTM-based model. [ABSTRACT FROM AUTHOR]

Single-image haze removal is an essential preprocessing phase in many object detection and segmentation approaches. Recently, end-to-end deep learning-based approaches have dominated the field of single-image dehazing because of their superiority in recovering clear images corrupted by different types of degradation. However, training an effective dehazing network remains challenging, particularly in the absence of high-quality realistic training datasets. In this paper, a novel approach called a subspace-based dehazing generative adversarial network (SuDGAN) is proposed. Traditional training methods attempt to apply changes to pixel intensities, whereas SuDGAN adopts a novel training approach using existing synthetic datasets to learn the adjustment of subspace components related to haze. This approach enables the network to learn more discriminative haze-aware features and focus on adjusting the components that are more affected by haze (luminance) while preserving those that are less influenced by haze (structure). The proposed SuDGAN, along with several state-of-the-art approaches, is evaluated on various challenging synthetic and realistic datasets using haze-related and traditional evaluation metrics. The experimental results demonstrate the efficiency of SuDGAN in removing haze and producing visually pleasing results. Furthermore, the results show that SuDGAN has clear quantitative and qualitative improvements over most state-of-the-art dehazing approaches. [ABSTRACT FROM AUTHOR]

We aim to investigate how closely neural networks (NNs) mimic human thinking. As a step in this direction, we study the behavior of artificial neuron(s) that fire most when the input data score high on some specific emergent concepts. In this paper, we focus on music, where the emergent concepts are those of rhythm, pitch and melody as commonly used by humans. As a black box to pry open, we focus on Google's MusicVAE, a pre-trained NN that handles music tracks by encoding them in terms of 512 latent variables. We show that several hundreds of these latent variables are "irrelevant" in the sense that can be set to zero with minimal impact on the reconstruction accuracy. The remaining few dozens of latent variables can be sorted by order of relevance by comparing their variance. We show that the first few most relevant variables, and only those, correlate highly with dozens of human-defined measures that describe rhythm and pitch in music pieces, thereby efficiently encapsulating many of these human-understandable concepts in a few nonlinear variables. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) is a developmental condition resulting from abnormalities in brain structure and function, which can manifest as communication and social interaction difficulties. Conventional methods for diagnosing ASD may not be effective in the early stages of the disorder. Hence, early diagnosis is crucial to improving the patient's overall health and well-being. One alternative and effective method for early autism diagnosis is facial expression recognition since autistic children typically exhibit distinct facial expressions that can aid in distinguishing them from other children. This paper provides a deep convolutional neural network (DCNN)-based real-time emotion recognition system for autistic kids. The proposed system is designed to identify six facial emotions, including surprise, delight, sadness, fear, joy, and natural, and to assist medical professionals and families in recognizing facial expressions in autistic children for early diagnosis and intervention. In this study, an attention-based YOLOv8 (AutYOLO-ATT) algorithm for facial expression recognition is proposed, which enhances the YOLOv8 model's performance by integrating an attention mechanism. The proposed method (AutYOLO-ATT) outperforms all other classifiers in all metrics, achieving a precision of 93.97%, recall of 97.5%, F1-score of 92.99%, and accuracy of 97.2%. These results highlight the potential of the proposed method for real-world applications, particularly in fields where high accuracy is essential. [ABSTRACT FROM AUTHOR]

The importance of technology and managerial risk management in banks has increased due to the financial crisis. Banks are the most affected since there are so many of them with poor financial standing. Due to this problem, an unstable and inefficient financial system causes economic stagnation in both the banking sector and overall economy. Data envelopment analysis (DEA) has been used to examine decision-making units (DMUs) performance to enhance efficiency. Currently, with the rapid growth of big data, adding more DMUs will likely require a large amount of memory and CPU time on the computer system, which will be the biggest challenge. As a result, machine learning (ML) approaches have been used to analyze financial institution performance, but many of them have variances in predictions or model stability, making measuring bank efficiency extremely difficult. For this, ensemble learning is commonly used to evaluate the performance of financial institutions in this context. This paper presents a robust super learner ensemble technique for assessing bank efficiency, with four machine learning models serving as base learners. These models are the support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and AdaBoost classifier (ADA) which represent the base learners and their results utilized to train the meta-learner. The super learner (SL) approach is an extension of the stacking technique, which generates an ensemble based on cross-validation. One important benefit of this cross-validation theory-based technique is that it can overcome the overfitting issue that plagues most other ensemble approaches. When SL and base learners were compared for their forecasting abilities using different statistical standards, the results showed that the SL is superior to the base learners, where different variable combinations were used. The SL had accuracy (ACC) of 0.8636–0.9545 and F1-score (F1) of 0.9143–0.9714, while the basic learners had ACC of 0.5909–0.8182 and F1 of 0.6897–0.9143. So, SL is highly recommended for improving the accuracy of financial data forecasts, even with limited financial data. [ABSTRACT FROM AUTHOR]

Sustainable Development Goal 7 is dedicated to ensuring access to clean and affordable energy that can be utilized in various applications. Solar panels (SP) are utilized to convert sunlight into electricity, acting as a renewable energy source. It is important to keep SP clean to obtain the required performance, as the accumulation of snow and dust on SP greatly affects the amount of electricity generated. On the other hand, excessive cleaning has some detrimental effects on the SP, therefore cleaning should only be done when necessary and not on a regular basis. Consequently, it is critical to determine whether the cleaning procedure is necessary by automatically detecting the presence of dust or snow on the panels while avoiding inaccurate predictions. Research efforts have been made to detect the presence of dust and snow on SP, but most of the proposed methods do not guarantee accurate detection results. This paper proposes an accurate, reliable, and interpretable approach called Solar-OBNet. The proposed Solar-OBNet can detect dusty SP and snow-covered SP very efficiently and be used in conjunction with the methods used to clean SP. The proposed Solar-OBNet is based on a Bayesian convolutional neural network, which enables it to express the amount of confidence in its predictions. Two measurements are used to estimate the uncertainty in the outcomes of the proposed Solar-OBNet, namely predictive entropy and standard deviation. The proposed Solar-OBNet can express confidence in the correct predictions by showing low values for predictive entropy and standard deviation. The proposed Solar-OBNet can also give an uncertainty warning in the case of erroneous predictions by showing high values of predictive entropy and standard deviation. The proposed Solar-OBNet's efficacy was verified by interpreting its results using a method called Weighted Gradient-Directed Class Activation Mapping (Grad-CAM). The proposed Solar-OBNet has achieved a balanced accuracy of 94.07% and an average specificity 95.83%, outperforming other comparable methods. [ABSTRACT FROM AUTHOR]

Long time series forecasting (LTSF), which involves modeling relationships within long time series to predict future values, has extensive applications in domains such as weather forecasting, financial analysis, and traffic prediction. Recently, numerous transformer-based models have been developed to address the challenges in LTSF. These models employ methods such as sparse attention to alleviate the inefficiencies associated with the attention mechanism and utilize decomposition architecture to enhance the predictability of the series. However, these complexity reduction methods necessitate additional calculations, and the series decomposition architecture overlooks the random components. To overcome these limitations, this paper proposes the Frequency Enhanced Decomposed Attention Free Transformer (FEDAF). FEDAF introduces two variants of the Frequency Enhanced Attention Free Mechanism (FEAFM), namely FEAFM-s and FEAFM-c, which seamlessly replace self-attention and cross-attention. Both variants perform calculations in the frequency domain without incurring additional costs, with the time and space complexity of FEAFM-s being O (L log L) . Additionally, FEDAF incorporates a time series decomposition architecture that considers random components. Unlike other models that solely decompose the series into trend and seasonal components, FEDAF also eliminates random terms by applying Fourier denoising. Our study quantifies data drift and validates that the proposed decomposition structure can mitigate the adverse effects caused by data shift. Overall, FEDAF demonstrates superior forecasting performance compared to state-of-the-art models across various domains, achieving a remarkable improvement of 19.49% for Traffic in particular. Furthermore, an efficiency analysis reveals that FEAFM enhances space efficiency by 12.8% compared to the vanilla attention mechanism and improves time efficiency by 43.63% compared to other attention mechanism variants. [ABSTRACT FROM AUTHOR]

Retinal fundus image classification is pivotal for the early detection of various eye disorders. Leveraging advancements in deep learning, this research introduces a robust framework for retinal fundus image classification. In this research paper, we introduce a novel methodology that integrates the Active Gradient Deep Convolutional Neural Network (AG-DCNN) architecture with the Red Spider Optimization (RSO) algorithm. Extensive evaluations conducted on well-established retinal image datasets such as DRIVE, STARE, CHASE DB1, HRF, DRISHTI-GS, and RFMiD illustrate substantial enhancements in classification accuracy, sensitivity, and specificity when compared to conventional approaches. The AG-DCNN with RSO exhibits superior performance and remarkable generalization abilities across diverse datasets. This novel approach not only enhances the latest advances in retinal image classification but also holds promise for early disease diagnosis and improved patient care. Through extensive examinations, our proposed method consistently outperforms established techniques, establishing itself as the benchmark in retinal fundus image classification. The integration of AG-DCNN and RSO showcases its efficacy and potential impact in advancing the field of medical image analysis. [ABSTRACT FROM AUTHOR]

The CoAtNet deep neural model has been shown to achieve state-of-the-art performance by stacking convolutional and self-attention layers. In particular, the initial layers of CoAtNet apply efficient convolutions for extracting local features out of the input image and the initial fine-resolution feature maps. In turn, the final layers apply more cumbersome Transformers in order to extract global features from the coarse-resolution feature maps. The model's outcome directly depends on those final global features. This paper proposes an extension of the original CoAtNet model based on the introduction of a dual stream of convolution and self-attention blocks applied at the final layers of CoAtNet. In this way, those final layers automatically aggregate both local and global features extracted from the initial feature maps. Two dual-stream topologies have been proposed and evaluated. This Dual-Stream CoAtNet model exhibits a significant improvement on the segmentation accuracy of breast ultrasound images, thus contributing to the development of more robust tumor detection methods. [ABSTRACT FROM AUTHOR]

To improve the motivation and enthusiasm of subjects during active rehabilitation training, this paper proposes a novel training task planning-based adaptive assist-as-needed (TTP-AAAN) control algorithm for an upper limb exoskeleton. The overall controller contains an outer control loop to determine the required assistive force, and an inner control loop to drive the exoskeleton to track subject motion and to provide desired assistive force obtained from the outer control loop. In the outer control loop, a motion intention and task performance evaluation (MITPE) strategy is established to learn the motor capability of the subject. Based on the obtained evaluation result, the radius and frequency of multi-periodic trajectory tracking task, and the gain of the assistive force are adaptively adjusted by using the adaptive central pattern generator (ACPG) algorithm. Then, in the inner control loop, an asymmetric barrier Lyapunov function-based adaptive output feedback (ABLF-AOF) controller, in combination with a neural network (NN) state observer, is developed. The exoskeleton tracking errors are constrained by the asymmetric barrier Lyapunov function, and the state variables and uncertainty terms of the exoskeleton are simultaneously estimated by the NN state observer. Experiments are carried out with an upper limb exoskeleton to demonstrate the effectiveness of the proposed control strategy. The experimental results show that the developed control scheme can provide assistance and achieve task parameter adaption for the subjects with different motion patterns. In addition, the proposed controller has better training performance than task performance-based adaptive velocity assist-as-needed (AAN) controller and minimal AAN controller. [ABSTRACT FROM AUTHOR]

Hybrid energy system (HES) is considered a solution to the energy supply issue, particularly in rural areas to achieve their sustainable development goals. The rise in energy consumption has increased the appeal of renewable resources, because of their potential to supply consumers with competitive, carbon-free electricity. This paper suggests strategies for managing energy and the most recently published optimizers for designing a stand-alone HES positioned in a remote region of southwest Egypt. This HES includes two green energy sources (wind and solar) and a storage system for energy (battery) as the first backup in addition to a second backup (diesel). The most recent sizing techniques employing the Chernobyl disaster optimizer, dynamic control cuckoo search (DCCS), and gold rush optimizer have been suggested to obtain the optimal design of the utilized HES. Furthermore, an in-depth evaluation of the applied optimization approaches has been achieved based on a comparative study. A detailed analysis of the studied algorithms aims to identify the optimum algorithm that provides the lowest possible cost at the highest level of reliability for the proposed HES. The simulation results verified that, the DCCS algorithm outperformed other algorithms, indicating its potential for achieving promising solutions. [ABSTRACT FROM AUTHOR]

Invasive sea lamprey (Petromyzon marinus) has historically inflicted considerable economic and ecological damage in the Great Lakes and continues to be a major threat. Accurately monitoring sea lampreys are critical to enabling the deployment of more targeted and effective control measures to minimize the impact associated with this species. This paper presents the first stand-alone system for real-time detection of sea lamprey attachment on underwater surfaces through the use of classifier models deployed on a microcontroller system. A range of low-complexity models was explored: single-layer artificial neural networks, logistic regression, Gaussian Naive-Bayes, decision trees, random forest, and Scalable, Efficient, and Fast classifieR (SEFR). Threshold models tuned using a multi-objective optimization formulation were also considered. Classifier models were trained with a dataset generated through live animal testing and presented accuracies between 80 and 86%. The models were deployed on an Arduino microcontroller platform and compared in classification accuracy, detection performance, time complexity, and memory size using real-time detection testing. Classification accuracies between 65 and 75% were observed during validation. Models demonstrated good capture rates for lamprey attachments (63–85%), and average detection delays ranging from 9 to 36 s. A video demonstrating the operation of the system during a real-time validation test is also included in this work. While there is room for improving the accuracy of the system, this research presents the first step toward an electronic sea lamprey monitoring system that can provide a detailed view of sea lamprey activity enhancing control and conservation efforts across its entire range. [ABSTRACT FROM AUTHOR]

Entrepreneurs and small businesses have increasingly used reward-based crowdfunding to raise capital for their creative projects, whose success is central to this industry. Thus, predicting the success of crowdfunding campaigns is a topic of great importance for entrepreneurs and platform managers. The literature that employs monolithic classifiers and static ensemble learning for crowdfunding success prediction are scarce. In contrast, the dynamic selection (DS) algorithm, which belongs to the ensemble learning category, deserves a particular remark since it has overcome traditional monolithic classifiers and static ensembles in many applications. This paper proposes a dynamic selection framework for reward crowdfunding prediction. DS algorithms select a competent subset of the classifier per query instance. This procedure is performed during the generalization, and the subset is composed of local experts, favoring an increase in accuracy. Fifteen machine learning models are evaluated using three metrics (accuracy, area under the ROC curve and F-score), and ensemble learning obtained better results than traditional classifiers. In particular, Meta-DES, which performs dynamic selection, obtains the best overall results among the evaluated models. Furthermore, since usually interpreting the output of ML models is considered to be very difficult due to their complex "black box" architecture, we also use Shapley additive explanations to interpret the perdition's outputs. Among variables evaluated in our models, the textual sentiment of the mass media, the number of pledges, and the target amount of the campaign deserve a highlight when predicting the campaign's success. The source-code and further details about the experimental analysis are available at https://github.com/las33/Crowdfunding. [ABSTRACT FROM AUTHOR]

Most operational weather and climate models carry out forecasts or simulations of atmospheric variables at low spatial resolutions, but we often need high-resolution projections of such variables. The process of mapping low-resolution projections to high-resolution projections is called spatial downscaling. This is analogous to the computer vision task of single-image super-resolution (SISR). In recent studies, convolution-based architectures including UNet and its variants have emerged as a good choice for SISR. Since the gridded spatial map of any climate variable is analogous to an image, we can use the SISR-based models for spatial downscaling. In this paper, we present a novel UNet-based architecture called max-average UNet (MAUNet) to downscale the precipitation. We have proposed Max-Average Units (MAUs) that include a max-pooling, an average-pooling, and an averaging unit for the encoding part of the UNet. For the decoding part, we develop UpSampler Unit (USU), which too utilizes averaging. We demonstrate the importance of max-averaging units through toy experiments on the MNIST and CelebA dataset. We also examine the role of dropouts for this task and experimentally demonstrate their skill to improve the model's performance on different datasets. The task examined here is to obtain fourfold resolution enhancement of monsoon precipitation over the Indian landmass and also over Southeast Continental USA (CONUS) region. The models are evaluated using RMSE, PSNR, MSSIM, and correlation coefficient as evaluation matrices. We have made detailed comparisons to show that MAUNet can produce superior downscaling than standard interpolation techniques as well as previously used deep learning models like UNet, EDSR, and SRDRN. [ABSTRACT FROM AUTHOR]