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This paper introduces a diagnostic model that effectively diagnoses in fourteen different stages, by fusing functional magnetic resonance imaging (fMRI) and structural MRI (sMRI) information. Georgios Theodoridis and Athanasios Tsadiras from the Aristotle University of Thessaloniki, Greece, have authored the seventh paper which is entitled "Applying machine learning techniques to predict and explain subscriber churn of an online drug information platform." Machine learning (ML) and more specifically deep learning (DL) algorithms are considered among the most paramount technologies of both artificial intelligence (AI) and 4 SP th sp industrial revolution. This paper provides an in-depth comparison of various machine learning (ML) techniques and advanced preprocessing methods, in an effort to successfully perform online subscriber churn prediction. [Extracted from the article]

The model integrates artificial intelligence (AI) and big data analytics, utilizing IoMT devices for data acquisition and Hadoop ecosystem for managing big data. The field of medical diagnosis is currently undergoing a remarkable transformation with the emergence of artificial intelligence (AI) techniques, particularly deep learning and big data analytics. By harnessing the power of deep learning and big data analytics, AI-based e-diagnosis has the potential to revolutionize healthcare delivery. [Extracted from the article]

This cannot be achieved only through conventional Machine Learning (ML) frameworks and technologies. The technological advancement in recent years has seen a great shift leading to a greater magnitude of data storage, communication and subsequent processing with reduced size and cost of the systems. The thirteen paper "A Novel Emergency Situation Awareness Machine Learning Approach to Assess Flood Disaster Risk based on Chinese Weibo" is authored by Bai Hua, Yu Hualong, Yu Guang and Huang Xing. [Extracted from the article]

With the ongoing integration of information technology and industrialization, strategic emerging industries are becoming an increasingly important force in guiding future economic and social development. As one of the strategic emerging industries' development priorities and a replacement for scarce labor resources, industrial robots will be widely used in labor-intensive industries. It is a contentious topic how to evaluate and improve robot knowledge education. There are modular features introduced in this paper, which builds a modular network for the evaluation of robot knowledge education quality and enhances the cognitive computing ability of artificial neural networks and their ability to process complex information. A modular neural network-based model of feature combination robot knowledge education quality evaluation is developed based on the three aspects of module division method, subnet structure selection, and feature combination output. The following are some of the paper's most important contributions: K-OD algorithm of density clustering optimized by K-means is proposed. Because of its high level of modularized partition simulating, this method has an excellent clustering effect, and it identifies the core points, boundary points, as well as outliers. Using K-OD algorithm, the calculation of density radius and threshold is optimized by using density clustering, which reduces the overall computational complexity. Find out how SOM neural networks learn from competition. SOM network's competition layer neuron weights are prone to falling into local optimal solutions, so an SASOM neural network with weight adjustment simulated by an annealing algorithm is proposed to address this issue. It is more accurate in terms of prediction and error, and it is better at identifying sample attribute features. This work builds a modular neural network for evaluating robot knowledge education quality using K-OD clustering algorithm and SASOM neural network, which introduces the simulated annealing mechanism. [ABSTRACT FROM AUTHOR]

Promoting the development of enterprises is a common task of all countries in the world, and the government has always taken promoting enterprise development as a long-term strategy. With the development for market, as an integral part of the social market economy, enterprises have made great contributions to the rapid of national economy. The enterprises are increasing, but the market competition is becoming increasingly fierce, and the weaknesses of enterprises such as limited scale, lax management and difficult financing are constantly highlighted. The appearance of these problems makes enterprises encounter many bottleneck problems in the process of development. Innovation, as the inexhaustible power of enterprise development, plays a significant role in enterprise growth, it is the key to solving the bottleneck problem of enterprise development. If an enterprise wants to achieve considerable development, it must innovate and enhance its competitiveness through innovation. In this context, how to evaluate enterprise innovation has become an important work. This work proposes an IPSO-ATT-MSCNN network via PSO from swarm optimization algorithm and artificial neural network to evaluate enterprise innovation. First, this work designs a multi-scale convolutional neural network (ATT-MSNN) via attention mechanism. This method uses multi-scale convolution to extract features of different scales, which improves the richness of features. This adds attention mechanisms to enhance useful features and reduce the impact of unwanted features such as noise. Second, in view of the performance degradation caused by the random initialization, this paper uses improved PSO algorithm to optimize the initial parameters. Third, this work proposes a series of strategies for promoting enterprise innovation. Finally, this work carried out a comprehensive and systematic experiment for the designed method, and the experiment verified the superiority of this method. [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]

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]

Hand gestures are becoming an important part of the communication method between humans and machines in the era of fast-paced urbanization. This paper introduces a new standard dataset for hand gesture recognition, Static HAnd PosturE (SHAPE), with adequate side, variation, and practicality. Compared with the previous datasets, our dataset has more classes, subjects, or scenes than other datasets. In addition, the SHAPE dataset is also one of the first datasets to focus on Asian subjects with Asian hand gestures. The SHAPE dataset contains more than 34,000 images collected from 20 distinct subjects with different clothes and backgrounds. A recognition architecture is also presented to investigate the proposed dataset. The architecture consists of two phases that are the hand detection phase for preprocessing and the classification phase by customized state-of-the-art deep neural network models. This paper investigates not only the high accuracy, but also the lightweight hand gesture recognition models that are suitable for resource-constrained devices such as portable edge devices. The promising application of this study is to create a human–machine interface that solves the problem of insufficient space for a keyboard or a mouse in small devices. Our experiments showed that the proposed architecture could obtain high accuracy with the self-built dataset. Details of our dataset can be seen online at https://users.soict.hust.edu.vn/linhdt/dataset/ [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurological disease that has been reported to have affected most people worldwide. Recent research pointed out that about 90% of PD patients possess voice disorders. Motivated by this fact, many researchers proposed methods based on multiple types of speech data for PD prediction. However, these methods either face the problem of low rate of accuracy or lack generalization. To develop an approach that will be free of these issues, in this paper we propose a novel ensemble approach. These paper contributions are two folds. First, investigating feature selection integration with deep neural network (DNN) and validating its effectiveness by comparing its performance with conventional DNN and other similar integrated systems. Second, development of a novel ensemble model namely EOFSC (Ensemble model with Optimal Features and Sample Dependant Base Classifiers) that exploits the findings of recently published studies. Recent research pointed out that for different types of voice data, different optimal models are obtained which are sensitive to different types of samples and subsets of features. In this paper, we further consolidate the findings by utilizing the proposed integrated system and propose the development of EOFSC. For multiple types of vowel phonations, multiple base classifiers are obtained which are sensitive to different subsets of features. These features and sample-dependent base classifiers are integrated, and the proposed EOFSC model is constructed. To evaluate the final prediction of the EOFSC model, the majority voting methodology is adopted. Experimental results point out that feature selection integration with neural networks improves the performance of conventional neural networks. Additionally, feature selection integration with DNN outperforms feature selection integration with conventional machine learning models. Finally, the newly developed ensemble model is observed to improve PD detection accuracy by 6.5%. [ABSTRACT FROM AUTHOR]

IoT is an emerging technology that is rapidly gaining traction throughout the world. With the incredible power and capacity of IoT, anyone may connect to any network or service at any time, from anywhere. IoT-enabled gadgets have transformed the medical industry by granting unprecedented powers such as remote patient monitoring and self-monitoring. Accurate electrocardiogram (ECG) interpretation is critical in the clinical ECG process since it is most often connected with a condition that might create serious difficulties in the body. Cardiologists and medical practitioners frequently utilize ECG to evaluate heart health. The human heart has an electric transmission system that creates regular electrical signals unintentionally and transmits them to the whole heart. Many individuals die as a result of heart disease all around the world. The doctor will be able to provide exceptional treatment to the patients, and the patients will be able to monitor their own health. This research offers an IoT-based ECG monitoring system that uses a heart rate sensor to create data and an intelligent hybrid classification algorithm to classify the data. ECG monitoring has become a widely used method for detecting cardiac problems. The following are the primary contributions of this paper: To begin, this paper introduces WISE (wearable IoT cloud-based health monitoring system), a unique system for real-time personal health monitoring. In order to offer real-time health monitoring, WISE uses the BASN (body area sensor network) infrastructure. Data from the BASN are instantly transferred to the cloud in WISE, and a lightweight wearable LCD may be incorporated to provide rapid access to real-time data. This hybrid model can manage with the problem of class imbalance in the ECG dataset, which will aid in the development of an IoT-based smart and accurate healthcare system. This research uses ADNN, which correctly predicts an abnormal ECG 98.1% of the time. The suggested hybrid model's results are compared to those of other classification models to determine its accuracy and suitability. [ABSTRACT FROM AUTHOR]

This paper analyzes the inherent relationship between convolutional neural networks and sparse representation and proposes an improved convolutional neural network model for image synthesis in response to problems with current methods. In the testing phase, the calculation of the sparse coefficients involves the solution of complex optimization problems, which greatly reduce the operating efficiency, inspired by the successful application of convolutional neural networks in the field of image reconstruction. Compared with the traditional image portrait synthesis method, this model not only has an end-to-end closed form but also does not need to solve complex optimization problems in the synthesis stage. The synthesis experiment on an image dataset shows that this method not only improves the synthesis effect but also improves the efficiency of the traditional method by one to two orders of magnitude, demonstrating its potential application value. Blocking processing is a common method for sparse domain image modeling. It improves the computational efficiency but also decreases the global structure of the image, which is difficult to compensate for through the aggregation and overlap of image blocks. In response to this problem, this paper proposes a low-rank image inpainting method based on a Gaussian mixture model. This method embeds the local statistical characteristics of image blocks into the kernel norm model and not only uses the Gaussian mixture model to maintain the local details of the image but also describes the global low-rank structure of the image through the kernel norm, thus restoring a class of image data with a potential low-rank structure and theoretically revealing the structured sparse nature of the Gaussian mixture model. This paper optimizes the strategy based on random hidden neuron nodes and proposes a dropout anti-overfitting strategy based on sparsity. The experiments show that this strategy can effectively improve the convergence speed while ensuring good performance and can effectively prevent overfitting. [ABSTRACT FROM AUTHOR]

In order to improve the effectiveness of signal processing, according to the actual needs of signal processing and the current problems in signal processing, this paper introduces an improved LSTM recurrent neural network algorithm to construct the signal processing algorithm. Moreover, this paper sets up the functional structure of this paper based on the neural network model structure, and builds an intelligent analysis system for signal processing tasks based on the LSTM recurrent neural network algorithm. In addition, this paper analyzes the system algorithm flow in detail, and combines experimental research to carry out the effectiveness of the system constructed in this paper, and conducts quantitative analysis from two aspects of signal threshold prediction and signal processing effect. Finally, this paper conducts experimental results research with the support of mathematical statistics methods. From the research point of view, it can be known that the system constructed in this paper has good signal processing functions. [ABSTRACT FROM AUTHOR]

As a main electronic material, X-ray circuits are widely used in various electronic devices, and their quality has an important impact on the overall quality of electronic products. In the process of mass production of circuit boards, due to the large number of layers, tight lines and some harmful external factors, circuit board quality may be problematic. Detecting circuit board defects are important for improving the reliability of electronic products. This paper introduces deep learning and artificial intelligence technology to conduct research on the automatic detection of X-ray circuit board defects. The study used a defect detection system to study X-ray circuit boards as a detection object and obtained the structure, lighting system and composition of the detection system. The working principle of the detection system is explained, and the image is preprocessed. Testing the processing performance of the PCB defect detection system, when the number of pixels is 6526, 7028, 7530 and 8032, the time consumption ratios between the proposed detection system and image processing on a traditional PC are 35.17%, 35.4%, 35% and 35.28%, respectively. The experimental results make a certain contribution to the future artificial intelligence X-ray PCB defect automatic diagnosis algorithm. [ABSTRACT FROM AUTHOR]

Combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) produces a powerful architecture for video classification problems as spatial–temporal information can be processed simultaneously and effectively. Using transfer learning, this paper presents a comparative study to investigate how temporal information can be utilized to improve the performance of video classification when CNNs and RNNs are combined in various architectures. To enhance the performance of the identified architecture for effective combination of CNN and RNN, a novel action template-based keyframe extraction method is proposed by identifying the informative region of each frame and selecting keyframes based on the similarity between those regions. Extensive experiments on KTH and UCF-101 datasets with ConvLSTM-based video classifiers have been conducted. Experimental results are evaluated using one-way analysis of variance, which reveals the effectiveness of the proposed keyframe extraction method in the sense that it can significantly improve video classification accuracy. [ABSTRACT FROM AUTHOR]

Cross-domain few-shot classification aims to recognize images in the new categories and domains that only contain few but unacquainted images. Considering the problems of fine-grained recognition in cross-domain few-shot classification including marginal overall-discrepancy in feature distribution and obvious fine-grained difference in dataset, this paper proposes a simple and effective attentive fine-grained recognition (AFGR) model. Specifically, the residual attention module is stacked into the feature encoder based on the residual network, which can linearly enhance different semantic feature information to help the metric function better locate the fine-grained feature information of the image. In addition, a bilinear metric function structure is proposed to learn and fuse different fine-grained image features, respectively, since the weights of bilinear measurement functions are not shared. Eventually, the final classification result is obtained by merging the recognition of bilinear metric function through posterior probability multiplication. In this paper, ablation experiments and comparative experiments are carried out with the typical few-shot dataset mini-ImageNet as the training domain and the CUB, Cars, Places and Plantae datasets as the test domain. The experimental results demonstrate that the proposed AFGR method is effective, with the highest increase in recognition accuracy 13.82% and 7.95% compared with the latest results under the experimental settings of 5-way1-shot and 5-way5-shot, respectively, which also proves the problems of fine-grained recognition in cross-domain small sample classification. [ABSTRACT FROM AUTHOR]

English expression, language characteristics and usage norms are quite special, which is quite different from Chinese. This has special requirements for auxiliary teaching tools that use computer technology for English text processing. Based on neural network algorithm, this paper combines the actual needs of English grammar error correction to construct an English grammar error correction model based on neural network. In data processing, after feature selection, logistic regression model is used to analyze the influence of different features on article error correction. The article error correction incorporating word vector features mainly explores how to effectively express the features in English grammar error correction. In addition, this paper proposes two methods to optimize the feature representation in article error correction. One is to directly use the word vector corresponding to the word as a feature, replacing the original One-hot encoding, and the other uses a clustering method to compress the article features. Finally, this paper designs experiments to study the performance of the model constructed in this paper. The results obtained show that the model constructed in this paper has a certain effect. [ABSTRACT FROM AUTHOR]

A novel investigation of the bearing capacity of ring footings embedded in undrained anisotropic clays using a new hybrid soft computation technique that cooperates between finite element limit analysis (FELA) and artificial neural networks (ANNs) is presented in this paper. The undrained shear strength of clays is considered to be anisotropic and linearly increased with depth. The anisotropic undrained shear strength (AUS) model is employed to demonstrate the anisotropic behaviour of the undrained clays. The FLEA solutions of this problem based on lower bound (LB) and upper bound (UB) solutions are presented. The variations of bearing capacity factor (N) and the failure patterns of ring footings are examined by considering the changes in the geometric ratio of the internal radius and external radius (ri/ro), the embedment ratio (D/ro), the rates of increase of undrained shear with depth (ρro/suTC0), and the ratio of anisotropic (re). The results of the paper are presented in the simple dimensionless design charts and tables that show the relationships between N and all investigated parameters which will be beneficial to practitioners. Furthermore, the application of artificial neural networks (ANNs) is adopted to do sensitivity analysis and build an empirical equation for the complex relationship between the input variables and output bearing capacity factor. As the results, the radius ratio ri/ro is the most important variable on bearing capacity factor and the lower importance parameters are D/ro, ρro/suTC0, and re with relative importance 37.01, 30.37; 21.59; 11.03%, respectively. The proposed empirical equation is also proved to be an efficient method as evidenced by the high agreement between its predicted values and those from FELA with R2 of 99.95%. [ABSTRACT FROM AUTHOR]

Various applications of control charts in the field of health-care monitoring and surveillance can be found in the literature. As one of the major categories, monitoring binary outcomes of cardiac surgeries with the aim of logistic regression model for the patients' death probability has been extended by different researchers. For this aim, statistical control charts, such as cumulative sum (CUSUM) chart, are applied as a risk-adjusted method to monitoring patients' mortality rate. However, employing machine learning techniques such as artificial neural network (ANN) has not been paid attention. So, this paper proposes a novel ANN-based control chart with a heuristic training approach to monitor binary surgical outcomes by control charts. Performance of the proposed approach is investigated and compared with existing studies, based on the average run lengths (ARL) criterion and the results demonstrated a superior performance of the proposed approach. Nevertheless, to demonstrate the application of the proposed approach, some real-life applications are also provided in this paper. Furthermore, robustness of the proposed method is investigated by considering Beta distribution for the death rate in addition to the logistic model. [ABSTRACT FROM AUTHOR]

ExSeisDat is designed using standard message passing interface (MPI) library for seismic data processing on high-performance super-computing clusters. These clusters are generally designed for efficient execution of complex tasks including large size IO. The IO performance degradation issues arise when multiple processes try accessing data from parallel networked storage. These complications are caused by restrictive protocols running by a parallel file system (PFS) controlling the disks and due to less advancement in storage hardware itself as well. This requires and leads to the tuning of specific configuration parameters to optimize the IO performance, commonly not considered by users focused on writing parallel application. Despite its consideration, the changes in configuration parameters are required from case to case. It adds up to further degradation in IO performance for a large SEG-Y format seismic data file scaling to petabytes. The SEG-Y IO and file sorting operations are the two of the main features of ExSeisDat. This research paper proposes technique to optimize these SEG-Y operations based on artificial neural networks (ANNs). The optimization involves auto-tuning of the related configuration parameters, using IO bandwidth prediction by the trained ANN models through machine learning (ML) process. Furthermore, we discuss the impact on prediction accuracy and statistical analysis of auto-tuning bandwidth results, by the variation in hidden layers nodes configuration of the ANNs. The results have shown the overall improvement in bandwidth performance up to 108.8% and 237.4% in the combined SEG-Y IO and file sorting operations test cases, respectively. Therefore, this paper has demonstrated the significant gain in SEG-Y seismic data bandwidth performance by auto-tuning the parameters settings on runtime by using an ML approach. [ABSTRACT FROM AUTHOR]

In order to overcome the shortcomings of the current English network learning system, based on the neural network algorithm, this paper constructs an intelligent English network learning system based on the improved algorithm. Moreover, by analyzing the coupling between recurrent neural networks by contrast methods, this paper infers the coupling between recurrent neural networks. Moreover, this paper studies the continuous attractors of the autoencoder neural network and studies the continuous attractors of different types of autoencoder models. On this basis, this paper expands the existing model, adds the module of the interaction between the external input and the visible layer and studies the conditions required for the continuous attractor of the autoencoder model. In addition, on the basis of actual needs, this paper constructs the basic structure of the model and integrates it into the improved algorithm proposed in this paper to realize English online intelligent learning. Finally, this paper designs experiments to analyze the practical effects of this model and analyzes the experimental results through mathematical statistics. The research results show that the English network learning system constructed in this paper is effective. [ABSTRACT FROM AUTHOR]

Oxygen content in the flue gas system of power plants is an essential factor affecting boiler efficiency. Accurate oxygen content measurement is vital in evaluating boiler combustion efficiency. The device measuring oxygen content in flue gases at an oil refinery uses a Zirconia oxygen analyzer. This sensor utilization without sensor redundancy makes the oxygen content measurement conducted manually. Workers' manual measurement is risky because it is a high-risk work area. In addition, the oxygen content in flue gas also indicates boiler combustion efficiency and the amount of other harmful gases produced by the boiler. This paper proposes a soft sensor using artificial neural networks (ANN) and extreme gradient boosting (XGBoost) to predict oxygen content. The dataset used is collected from the historical data of the distributed control system of an oil refinery system boiler. The experimental results show that the one hidden layer ANN model achieves an MAE of 0.0715 and RMSE of 0.0935, while the XGBoost model with hyperparameter tuning and seven features achieves an MAE of 0.0452 and RMSE of 0.0642. The results suggest that the XGBoost model with hyperparameter tuning and seven features outperforms the one hidden layer ANN model. The use of the seven features of the XGBoost model is the result of optimization between computational complexity and system performance. [ABSTRACT FROM AUTHOR]

The risk of death incurred by breast cancer is rising exponentially, especially among women. This made the early breast cancer detection a crucial problem. In this paper, we propose a computer-aided diagnosis (CAD) system, called CADNet157, for mammography breast cancer based on transfer learning and fine-tuning of well-known deep learning models. Firstly, we applied hand-crafted features-based learning model using four extractors (local binary pattern, gray-level co-occurrence matrix, and Gabor) with four selected machine learning classifiers (K-nearest neighbors, support vector machine, random forests, and artificial neural networks). Then, we performed some modifications on the Basic CNN model and fine-tuned three pre-trained deep learning models: VGGNet16, InceptionResNetV2, and ResNet152. Finally, we conducted a set of experiments using two benchmark datasets: Digital Database for Screening Mammography (DDSM) and INbreast. The results of the conducted experiments showed that for the hand-crafted features based CAD system, we achieved an area under the ROC curve (AUC) of 95.28% for DDSM using random forest and 98.10% for INbreast using support vector machine with the histogram of oriented gradients extractor. On the other hand, CADNet157 model (i.e., fine-tuned ResNet152) was the best performing deep model with an AUC of 98.90% (sensitivity: 97.72%, specificity: 100%), and 98.10% (sensitivity: 100%, specificity: 96.15%) for, respectively, DDSM and INbreast. The CADNet157 model overcomes the limitations of traditional CAD systems by providing an early detection of breast cancer and reducing the risk of false diagnosis. [ABSTRACT FROM AUTHOR]

The radiotelephony communication is a voice communication mode between air traffic service unit and aircraft currently. The control instruction is a kind of unstructured data, so that the automatic systems cannot use understand its semantic. If control instruction is regarded as a sort of special "natural language," methods such as syntax analysis and sematic analysis can be adopted to generate the structured instruction. The correct recognition of the language must be important for the control instruction. However, the control instruction in Chinese is different from the general use of Chinese language in form, resulting in prepositions becoming important for semantic analysis. This paper proposes a deep neural network-based Chinese language control construction algorithm for the trajectory prediction. In particular, analysis of sematic characteristics of control instruction is realized by using cognitive linguistics theory and construction grammar theory. The control instruction is then designed by the semantic ontology. Based on the deep neural networks by considering the word sequence of instruction as the inputs. The test results have demonstrated the effectiveness of the proposed algorithm with a developed entity extracting model. (The results are quantified using the BiLSTM-LAN-CRF in detail.) [ABSTRACT FROM AUTHOR]

As a common data-driven method, artificial neural networks have been widely used in electricity spot price forecasting. To improve the accuracy of short-term forecasts, this paper proposes an optimized artificial neural network model for monthly electricity spot prices forecasting. A genetic algorithm is applied to regulate the weights and biases parameters of the artificial neural network structure. This study uses various historical dataset at monthly periods selected from Nordic electricity spot prices. For efficiency comparison, one-step ahead forecast method based on Schwartz-Smith stochastic model and two other prediction models, artificial neural network trained by Levenberg–Marquardt and particle swarm optimization algorithms are also presented. The comparison results show that the prediction model based on the genetic optimization algorithm is more accurate than the other prediction models. The proposed forecasting model can be considered as an alternative technique for the electricity spot price forecasting in the Nordic regions. [ABSTRACT FROM AUTHOR]

In this research paper, we study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history, leveraging a database of synthetic storm simulations. Traditionally, computational fluid dynamics (CFD) solvers are employed to numerically solve the storm surge governing equations that correspond to expensive to evaluate partial differential equations (PDE). This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations. This model can serve as a fast and affordable emulator for the expensive CFD solvers creating the original database. The neural network model is trained with the storm track parameters used to drive the CFD solvers, and the output of the model is the time-series evolution of the predicted storm surge across multiple nodes within the spatial domain of interest. Once the model is trained, it can be deployed for further predictions based on new storm track inputs. The developed neural network model is a time-series model, composed of a long short-term memory (LSTM), a variation of recurrent neural network (RNN), further enriched with convolutional neural networks (CNNs). The convolutional neural network is employed to capture the correlation of data spatially (across the aforementioned nodes). Therefore, the temporal and spatial correlations of data are captured by the combination of the mentioned models, representing the ConvLSTM model. As the problem is a sequence to sequence time-series problem, an encoder–decoder ConvLSTM model is designed. Furthermore, the performance of the developed convolutional recurrent neural network model is improved by residual connection networks. Additional techniques are employed in the process of model training to enrich the model performance that the model can learn from the data in a more effective way. The performance of the developed model is compared with the results provided by a Gaussian process (GP) implementation, representing a state-of-the-art alternative for establishing time-series emulation of storm surge predictions. The results show that the proposed convolutional recurrent neural network outperforms the GP implementation for the examined synthetic storm database. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) are vulnerable to being attacked by adversarial examples, leading to DNN misclassification. Perturbations in adversarial examples usually exist in the form of noise. In this paper, we proposed a lightweight joint contrastive learning and frequency domain denoising network (CFNet), which can effectively remove adversarial perturbations from adversarial examples. First, CFNet separates the channels of the features obtained by the multilayer convolution of the adversarial examples, and the separated feature maps are used to calculate the similarity with the high- and low-frequency feature maps obtained by Gaussian low-pass filtering of the clean examples. Second, by adjusting the network's attention to high-frequency feature images, CFNet can effectively remove the perturbations in adversarial examples and obtain reconstructed examples with high visual quality. Finally, to further improve the robustness of CFNet, contrastive regularization is proposed to bring the reconstructed examples back to the manifold decision boundary of clean examples, thus improving the classification accuracy of reconstructed examples. On the CIFAR-10 dataset, compared with the existing state-of-the-art defense model, the defense accuracy of CFNet is improved by 16.93% and 5.67% under untargeted and targeted projected gradient descent attacks, respectively. The AutoAttack untargeted attack defense accuracy increased by 30.81%. Experiments show that our approach provides better protection than existing state-of-the-art approaches, especially against unseen (untrained) types of attacks and adaptive attacks. [ABSTRACT FROM AUTHOR]

Quality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods—Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy. [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic has swept the world for over two years. However, a large number of infectious asymptomatic COVID-19 cases (ACCs) are still making the breaking up of the transmission chains very difficult. Efforts by epidemiological researchers in many countries have thrown light on the clinical features of ACCs, but there is still a lack of practical approaches to detect ACCs so as to help contain the pandemic. To address the issue of ACCs, this paper presents a neural network model called Spatio-Temporal Episodic Memory for COVID-19 (STEM-COVID) to identify ACCs from contact tracing data. Based on the fusion Adaptive Resonance Theory (ART), the model encodes a collective spatio-temporal episodic memory of individuals and incorporates an effective mechanism of parallel searches for ACCs. Specifically, the episodic traces of the identified positive cases are used to map out the episodic traces of suspected ACCs using a weighted evidence pooling method. To evaluate the efficacy of STEM-COVID, a realistic agent-based simulation model for COVID-19 spreading is implemented based on the recent epidemiological findings on ACCs. The experiments based on rigorous simulation scenarios, manifesting the current situation of COVID-19 spread, show that the STEM-COVID model with weighted evidence pooling has a higher level of accuracy and efficiency for identifying ACCs when compared with several baselines. Moreover, the model displays strong robustness against noisy data and different ACC proportions, which partially reflects the effect of breakthrough infections after vaccination on the virus transmission. [ABSTRACT FROM AUTHOR]

Recognizing the plant disease and pests in its golden time is a highly critical problem to be addressed, since the herbalist can apply treatments within this period and save the agricultural product. In this paper, a deep learning approach to recognize the disease from the leaves of the plants has been pursued. A client-server system is proposed in which the server-side model can leverage huge deep CNN architectures to classify the diseases, whereas the client-side model is to be chosen among small deep CNN architectures with low number of parameters in order to be easily deployed on the end-user mobile devices with poor processing powers. Here, a novel knowledge distillation technique has been leveraged that improves the accuracy level of the small client-side model significantly. This technique distills the perception knowledge of a large model classifier and transfers this knowledge to the small model in order to perform a similar prediction capability. By applying this idea on Plantvillage dataset, we could achieve 97.58 % accuracy on a small MobileNet architecture which is very close to the accuracy of a large Xception model on the server with 99.73 % accuracy. Through applying this teacher-student idea, we could improve the classification rate of the state-of-the-art tiny model by 2.12 % . [ABSTRACT FROM AUTHOR]

Bubble point oil formation volume factor (Bob) and solution gas–oil ratio (Rs) are two crucial PVT parameters used for modeling and volumetric calculations in petroleum industry. They are usually determined in laboratory or estimated using empirical correlations. Experimental methods are time-consuming and expensive where empirical correlations have limitations. Artificial intelligence can be sued overcome these limitations to develop more accurate, robust, and quick predictive tools. In this paper, we used three artificial neural network algorithms to develop intelligent models to predict Bob and Rest using 465 experimental data. Application of the Elman neural network (ENN) for this purpose is being reported for the first time. A variety of input parameters were selected based on a sensitivity analysis which include reservoir temperature (T), oil API gravity (°API), bubble point pressure (Pb), gas-specific gravity (γg), and Rs was used to predict the Bob. T, °API, Pb, γg, and Bob was used to predict the Rs. The ENN model was found superior to the other developed smart models and the empirical correlations with coefficient of determination (R2) of 0.993, root mean square error (RMSE) of 0.0093, and average absolute percent relative error (AAPRE) of 0.93% for the Bob and 0.999, 0.016, and 6.72% for the Rs, respectively. The ENN network has fewer adjustable parameters and provides faster training capabilities using fewer neurons and hidden layers compared to other ANN algorithms. The developed smart predictive tools can be safely used instead of laboratory methods and empirical correlations for a much wider ranges of input parameters and with higher accuracy and confidence. [ABSTRACT FROM AUTHOR]

The adversary makes malicious samples capable of triggering erroneous judgments in deep learning models by introducing imperceptible perturbations to the original benign texts. These malicious samples are referred to as adversarial texts. The exploration of adversarial text generation methods not only facilitates our understanding of the robustness of mainstream deep neural networks against such adversarial attacks but also aids in developing appropriate defensive strategies. Nevertheless, the mainstream research on textual adversarial attacks has mainly focused on attack effectiveness, overlooking the associated attack cost. For real-world attacks, considerations such as the time cost, material cost, manpower cost, and various constraints are also crucial. In this paper, we propose a low-cost adversarial text generation method based on the universal strategy in the black-box attack scenario, Universal Chinese Text Tricker (UCTT), for tendency classification on Chinese texts. UCTT is both text-independent and model-independent, which markedly reduces its attack cost. Instead of crafting adversarial texts for a specific text, UCTT generates universal perturbations based on a universal word substitution list, which is applicable to any data in tendency classification datasets. Given a perturbation rate, we can use the word list to craft adversarial texts by simple substitutions without accessing the target model. In the framework of adversarial text generation based on word importance, UCTT utilizes count, arithmetic progression, linear normalization, and nonlinear normalization to calculate the scores of the important words in the dataset and then computes the candidate word frequencies, which in turn constructs the universal word substitution list. Compared with other black-box methods, the experimental results on real-world tendency classification datasets show that UCTT exhibits an effective attack capability while significantly reducing the attack cost. Compared to the powerful baseline we designed that exceeds the SOTA, UCTT improves the efficiency of adversarial text generation by up to a factor of 7 without accessing the target model. In addition to demonstrating excellent attack performance on mainstream models, UCTT is also capable of attacking the powerful ChatGPT in the physical world, which cannot be directly attacked by traditional adversarial text generation methods due to the hard labels produced by the target model. [ABSTRACT FROM AUTHOR]

In 2022, the World Health Organization declared an outbreak of monkeypox, a viral zoonotic disease. With time, the number of infections with this disease began to increase in most countries. A human can contract monkeypox by direct contact with an infected human, or even by contact with animals. In this paper, a diagnostic model for early detection of monkeypox infection based on artificial intelligence methods is proposed. The proposed method is based on training the artificial neural network (ANN) with the adaptive artificial bee colony algorithm for the classification problem. In the study, the ABC algorithm was preferred instead of classical training algorithms for ANN because of its effectiveness in numerical optimization problem solutions. The ABC algorithm consists of food and limit parameters and three procedures: employed, onlooker and scout bee. In the algorithm standard, artificial onlooker bees are produced as much as the number of artificially employed bees and an equal number of limit values are assigned for all food sources. In the advanced adaptive design, different numbers of artificial onlooker bees are used in each cycle, and the limit numbers are updated. For effective exploitation, onlooker bees tend toward more successful solutions than the average fitness value of the solutions, and limit numbers are updated according to the fitness values of the solutions for efficient exploration. The performance of the proposed method was investigated on CEC 2019 test suites as examples of numerical optimization problems. Then, the system was trained and tested on a dataset representing the clinical symptoms of monkeypox infection. The dataset consists of 240 suspected cases, 120 of which are infected and 120 typical cases. The proposed model's results were compared with those of ten other machine learning models trained on the same dataset. The deep learning model achieved the best result with an accuracy of 75%. It was followed by the random forest model with an accuracy of 71.1%, while the proposed model came third with an accuracy of 71%. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have been applied in many pattern recognition or object detection applications. DNNs generally consist of millions or even billions of parameters. These demanding computational storage and requirements impede deployments of DNNs in resource-limited devices, such as mobile devices, micro-controllers. Simplification techniques such as pruning have commonly been used to slim DNN sizes. Pruning approaches generally quantify the importance of each component such as network weight. Weight values or weight gradients in training are commonly used as the importance metric. Small weights are pruned and large weights are kept. However, small weights are possible to be connected with significant weights which have impact to DNN outputs. DNN accuracy can be degraded significantly after the pruning process. This paper proposes a roulette wheel-like pruning algorithm, in order to simplify a trained DNN while keeping the DNN accuracy. The proposed algorithm generates a branch of pruned DNNs which are generated by a roulette wheel operator. Similar to the roulette wheel selection in genetic algorithms, small weights are more likely to be pruned but they can be kept; large weights are more likely to be kept but they can be pruned. The slimmest DNN with the best accuracy is selected from the branch. The performance of the proposed pruning algorithm is evaluated by two deterministic datasets and four non-deterministic datasets. Experimental results show that the proposed pruning algorithm generates simpler DNNs while DNN accuracy can be kept, compared to several existing pruning approaches. [ABSTRACT FROM AUTHOR]

Implicit discourse relation recognition (IDRR) is to detect and classify relation sense between two text segments without an explicit connective. Existing neural network models learn a semantic representation for each argument from its compositional words and classify discourse relation by the interactive semantic representation of an argument pair. As the basic English unit, a word only carries a simple and limited meaning that may be incorrectly interpreted in an argument without considering its syntactic context. This motivates us to explore whether we can learn an argument's semantic representation from another language unit phrase which usually contains full contextual meaning. We also argue that some semantic connection in between phrase pairs can be further exploited to infer the discourse relation between arguments. In this paper, we propose an Attentive Phrase Interaction Learning (APIL) model to parse and encode the interactive phrase structure for the IDRR task. In APIL, we propose a minimum subtree algorithm to obtain the phrase sequence of input arguments from its constituency syntax tree. We also design an Attentive Matching Network to learn the representation for each phrase from both arguments' semantic context and words' linguistic evidence. Furthermore, we propose a Phrase Inference Network to encode the linguistic relations of phrase pairs as semantic connections for relation inference. Experiments on the PDTB corpus show that our APIL model outperforms the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) and imitation learning (IL) are quite two useful machine learning techniques that were shown to be potential in enhancing navigation performance. Basically, both of these methods try to find a policy decision function in a reinforcement learning fashion or through imitation. In this paper, we propose a novel algorithm named Reinforcement Imitation Learning (RIL) that naturally combines RL and IL together in accelerating more reliable and efficient navigation in dynamic environments. RIL is a hybrid approach that utilizes RL for policy optimization and IL as some kind of learning from expert demonstrations with the inclusion of guidance. We present the comparison of the convergence of RIL with conventional RL and IL to provide the support for our algorithm's performance in a dynamic environment with moving obstacles. The results of the testing indicate that the RIL algorithm has better collision avoidance and navigation efficiency than traditional methods. The proposed RIL algorithm has broad application prospects in many specific areas such as an autonomous driving, unmanned aerial vehicles, and robots. [ABSTRACT FROM AUTHOR]

Automatic handwritten character recognition plays a significant role in various applications across multiple fields. With the growing interest in automatic handwriting recognition and the advancement of deep learning methods, researchers have achieved significant improvements in the development of English handwriting recognition methods. However, the recognition of Arabic handwriting has received insufficient attention. In this paper, a novel "DeepAHR" model is presented to accurately and efficiently recognize Arabic handwritten characters using deep learning techniques. The "DeepAHR" model is based on a convolutional neural network (CNN) and is trained using two recent public datasets: Hijaa and Arabic handwritten characters dataset (AHCD). The overall accuracies of the proposed model were 98.66% and 88.24% on the AHCD and Hijaa datasets, respectively.The experimental results showed that DeepAHR outperformed state-of-the-art methods in the literature. These promising results provide evidence of the successful use of the DeepAHR model for recognizing handwritten Arabic characters [ABSTRACT FROM AUTHOR]

The increasing reliance on internet-based services has heightened the vulnerability of network infrastructure to cyberattacks, particularly distributed denial of service (DDoS) attacks. These attacks can cause severe disruptions and significant financial losses. Early detection of malicious traffic is crucial in effectively combating such threats. This paper presents an innovative approach called the Encoder-Stacked deep neural networks (E-SDNN) model, which leverages Stacked/bagged multi-layer perceptrons (MLP) for accurate DDoS attack detection. The proposed method employs an encoder to select pertinent features from a preprocessed dataset, enabling precise attack detection. Extensive experiments were conducted on benchmark cybersecurity datasets, namely CICDS2017 and CICDDoS2019, encompassing various DDoS attack scenarios. The experimental results demonstrate the superiority of the E-SDNN model compared to state-of-the-art methods. The proposed E-SDNN model achieved an impressive overall accuracy rate of 99.94% and 98.86% for CICDDS2017 and CICDDoS2019, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a method to perform the real-time creation of models that are used to represent aspects of tracked objects in video frames. Object modeling is done during the task of tracking previously unseen selected objects, and both tracking and model creation are implemented using the WiSARD weightless neural network and occur in real time, starting from no prior knowledge. The main purpose of this work is to track an object through camera images and, simultaneously, create a model that describes the presented appearances along with the transitions between each learned aspect. To achieve this goal, an object tracker based on the ClusWiSARD weightless neural network model was used to determine the states that describe the observed objects. In this way, it is possible to obtain a system that capture knowledge about the visual structures of the learned objects, creating relationships between the possible appearances, and being able to transit over the model aspects in an appropriate way. Furthermore, the created models have visual representations that can be used to show the learned aspects and validate the state transitions, in addition to being able to visualize occluded parts of objects. [ABSTRACT FROM AUTHOR]

Once realized, autonomous aerial refueling will revolutionize unmanned aviation by removing current range and endurance limitations. Previous attempts at establishing vision-based solutions have come close but rely heavily on near perfect extrinsic camera calibrations that often change midflight. In this paper, we propose dual object detection, a technique that overcomes such requirement by transforming aerial refueling imagery directly into receiver aircraft reference frame probe-to-drogue vectors regardless of camera position and orientation. These vectors are precisely what autonomous agents need to successfully maneuver the tanker and receiver aircraft in synchronous flight during refueling operations. Our method follows a common 4-stage process of capturing an image, finding 2D points in the image, matching those points to 3D object features, and analytically solving for the object pose. However, we extend this pipeline by simultaneously performing these operations across two objects instead of one using machine learning and add a fifth stage that transforms the two pose estimates into a relative vector. Furthermore, we propose a novel supervised learning method using bounding box corrections such that our trained artificial neural networks can accurately predict 2D image points corresponding to known 3D object points. Simulation results show that this method is reliable, accurate (within 3 cm at contact), and fast (45.5 fps). [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a condition that degenerates over time and impairs speech and pronunciation because brain cells have died. This research work aims to predict parkinson disease using the voice features extracted from speech signals recorded from PD individuals with dysphonic speech disorders by employing deep learning algorithms. PD is challenging to diagnose early on in the clinical presentation. To address the issue in machine learning methods, this paper proposes a neural network model by processing speech signals to classify PD using the University of California Irvine (UCI) machine learning repository dataset. Initially, a pre-loss reduction module is created by using pre-sampling to make the dataset balanced by reducing the dimensionality and maintaining the size of the space without influencing the learning process for data preparation. The relevant features are derived using a novel multi-agent salp swarm (MASS) algorithm, and a novel Parkinson classification neural network (PCNN) is proposed to classify Parkinson's patients with high accuracy employing these derived features. The result shows that the models that use MASS-PCNN produce higher classification accuracy of 99.1%, precision of 97.8%, recall of 94.7% and F1-score of 0.995 when paralleled to the existing models. As an outcome, the suggested model will perform superior to common convolutional neural networks. [ABSTRACT FROM AUTHOR]

Computational Intelligence (CI) has been revealed as one of the most promising technologies to solve some complex problems. Artificial Neural Networks (ANN) are proposed in the first paper for combat Fake News (FN). Extended versions of selected papers presenting innovations in up-to-date cybersecurity challenges are compiled in this special issue. [Extracted from the article]

Personalization systems have proved to be one of the most powerful tools for e-commerce sites, assisting users in discovering the most relevant products across enormous product catalogues. The formulation of product suggestions in the most widely used collaborative filtering is dependent on ratings contributed by the customer base. Though numerous domains consider allowing users to give an overall rating to products, a burgeoning number of online platforms are allowing users to rate products on a variety of dimensions. According to previous research, these multidimensional ratings offer valuable perceptions that can be used in generating a personalization list for users. Within the personalization systems research domain, multi-criteria systems have garnered significant attention since they use multiple criteria to predict rating scores. New strategies for leveraging information produced from multi-criteria scores to increase the prediction precision of multi-criteria (MC) systems are presented in this paper. In particular, we propose to fuse deep neural networks (DNN), matrix factorization (MF), and social spider optimization (SSO) to exploit nonlinear, non-trivial, and concealed interactions between users in terms of MC preferences. Experimenting on Yahoo! and TripAdvisor datasets reveals that our proposed approach outperforms both modern single-rating recommender systems based on MF and traditional multi-criteria systems. As a result, we believe that using multi-criteria customer evaluations can help e-commerce companies enhance the quality and specificity of their recommended services. [ABSTRACT FROM AUTHOR]

Deep Neural Networks (DNNs) are widely regarded as the most effective learning tool for dealing with large datasets, and they have been successfully used in thousands of applications in a variety of fields. Based on these large datasets, they are trained to learn the relationships between various variables. The adaptive moment estimation (Adam) algorithm, a highly efficient adaptive optimization algorithm, is widely used as a learning algorithm in various fields for training DNN models. However, it needs to improve its generalization performance, especially when training with large-scale datasets. Therefore, in this paper, we propose HN Adam, a modified version of the Adam Algorithm, to improve its accuracy and convergence speed. The HN_Adam algorithm is modified by automatically adjusting the step size of the parameter updates over the training epochs. This automatic adjustment is based on the norm value of the parameter update formula according to the gradient values obtained during the training epochs. Furthermore, a hybrid mechanism was created by combining the standard Adam algorithm and the AMSGrad algorithm. As a result of these changes, the HN_Adam algorithm, like the stochastic gradient descent (SGD) algorithm, has good generalization performance and achieves fast convergence like other adaptive algorithms. To test the proposed HN_Adam algorithm performance, it is evaluated to train a deep convolutional neural network (CNN) model that classifies images using two different standard datasets: MNIST and CIFAR-10. The algorithm results are compared to the basic Adam algorithm and the SGD algorithm, in addition to other five recent SGD adaptive algorithms. In most comparisons, the HN Adam algorithm outperforms the compared algorithms in terms of accuracy and convergence speed. AdaBelief is the most competitive of the compared algorithms. In terms of testing accuracy and convergence speed (represented by the consumed training time), the HN-Adam algorithm outperforms the AdaBelief algorithm by an improvement of 1.0% and 0.29% for the MNIST dataset, and 0.93% and 1.68% for the CIFAR-10 dataset, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a supervised learning algorithm, which avoids backward recursive gradient computation, for training deep convolutional spiking neural networks (SNNs) with single-spike-based temporal coding. The algorithm employs a linear approximation to compute the derivative of the spike latency with respect to the membrane potential, and it uses spiking neurons with piecewise linear postsynaptic potential to reduce the computational cost and the complexity of neural processing. To evaluate the performance of the proposed algorithm in deep architectures, we employ it in convolutional SNNs for the image classification task. For two popular benchmarks of MNIST and Fashion-MNIST datasets, the network reaches accuracies of, respectively, 99.2 and 92.8 % . The trade-off between memory storage capacity and computational cost with accuracy is analyzed by applying two sets of weights: real-valued weights that are updated in the backward pass and their signs, binary weights, that are employed in the feedforward process. We evaluate the binary CSNN on two datasets of MNIST and Fashion-MNIST and obtain acceptable performance with a negligible accuracy drop with respect to real-valued weights (about 0.6 and 0.8 % drops, respectively). [ABSTRACT FROM AUTHOR]

Recommender systems are widely adopted as an increasing research and development area, since they provide users with diverse and useful information tailored to their needs. Several strategies have been proposed, and in most of them some concept of similarity is used as a core part of the approach, either between items or between users. At the same time, Siamese Neural Networks are being used to capture the similarity of items in the image domain, as they are defined as a subtype of Artificial Neural Networks built with (at least two) identical networks that share their weights. In this review, we study the proposals done in the intersection of these two fields, that is, how Siamese Networks are being used for recommendation. We propose a classification that considers different recommendation problems and algorithmic approaches. Some research directions are pointed out to encourage future research. To the best of our knowledge, this paper is the first comprehensive survey that focuses on the usage of Siamese Neural Networks for Recommender Systems. [ABSTRACT FROM AUTHOR]

Visual object tracking (VOT) is a fundamental and complex problem in computer vision field. In the past few years, the research focus has been shifted from template matching to deep learning models. Especially, the Siamese networks dominate tracking domain in recent years, which take the first frame as the reference and perform object detection and localization in the following frames. However, most of them could not capture target changes due to the lack of strong feature representation abilities. To address these issue, we propose an advanced tracking network in this paper based on recurrent historical localization information. Unlike traditional symmetric structures, we utilize two convolution layers to perform target classification that predicts the initial target center. Then, we apply a gated recurrent unit that fuses multi-resolution features with historical localization information to yield the final optimized target position. Extensive experiments have been conducted on six mainstream datasets: OTB100, GOT-10k, TrackingNet, LaSOT, VOT2018 and NFS, where our tracker exhibits state-of-the-art performances. [ABSTRACT FROM AUTHOR]

Academic performance, a globally understood metric, is utilized worldwide across disparate teaching and learning environments and is regarded as a quantifiable indicator of learning gain. The ability to reliably estimate student's academic performance is important and can assist academic staff to improve the provision of support. However, it is recognized that academic performance estimation is non-trivial and affected by multiple factors, including a student's engagement with learning activities and their social, geographic, and demographic characteristics. This paper investigates the opportunity to develop reliable models for predicting student performance using Artificial Intelligence. Specifically, we propose two-step academic performance prediction using feature weighted support vector machine and artificial neural network (ANN) learning. A feature weighted SVM, where the importance of different features to the outcome is calculated using information gain ratios, is employed to perform coarse-grained binary classification (pass, P 1 , or fail, P 0 ). Subsequently, detailed score levels are divided from D to A+, and ANN learning is employed for fine-grained, multi-class training of the P 1 and P 0 classes separately. The experiments and our subsequent ablation study, which are conducted on the student datasets from two Portuguese secondary schools, have proved the effectiveness of this hybridized method. [ABSTRACT FROM AUTHOR]

In this paper, conductive polymer-based composites in order to have higher electrical conductivity have been constructed using different nanoparticles and numerically considered by different classification techniques. Due to non-conducting feature of polymer-based composites, their other positive advantages (e.g., light weight and stress corrosion) underneath non-conducting defect in which this paper has tried to overcome the faced challenges. For this purpose, carbon black (CB), carbon nanotube (CNT), and expanded graphite (EG) with different weight percentages are added to the epoxy resin as input factors and the electrical conductivity of the samples are measured as response factor. The analysis of input factors is performed and the Taguchi method, artificial neural networks (ANNs) and extreme learning machine (ELM) are designed and used for the prediction of the response factor. The predicted responses using the applied methods are compared with the experimental results. In order to increase the mechanical strength, ten layers of unidirectional carbon fiber are used. The simulation results show that the ANNs and ELM provide good compatible predictions with respect to actual experiment data. Besides, obtained experimental results prove that the highest electrical conductivity has been achieved using 10, 15, and 25 percent using the CNT, EG, and CB, respectively. As a novelty of this paper, the constructed sample composite reaches the acceptable electrical conductivity suggested by United Stated Department of Energy standard considered as material development. In particular, the findings of this research can be used to construct conductive electrodes particularly in oil and gas industries. [ABSTRACT FROM AUTHOR]

Many application scenarios for image recognition require learning of deep networks from small sample sizes in the order of a few hundred samples per class. Then, avoiding overfitting is critical. Common techniques to address overfitting are transfer learning, reduction of model complexity and artificial enrichment of the available data by, e.g., data augmentation. A key idea proposed in this paper is to incorporate additional samples into the training that do not belong to the classes of the target task. This can be accomplished by formulating the original classification task as an open set classification task. While the original closed set classification task is not altered at inference time, the recast as open set classification task enables the inclusion of additional data during training. Hence, the original closed set classification task is augmented with an open set task during training. We therefore call the proposed approach open set task augmentation. In order to integrate additional task-unrelated samples into the training, we employ the entropic open set loss originally proposed for open set classification tasks and also show that similar results can be obtained with a modified sum of squared errors loss function. Learning with the proposed approach benefits from the integration of additional "unknown" samples, which are often available, e.g., from open data sets, and can then be easily integrated into the learning process. We show that this open set task augmentation can improve model performance even when these additional samples are rather few or far from the domain of the target task. The proposed approach is demonstrated on two exemplary scenarios based on subsets of the ImageNet and Food-101 data sets as well as with several network architectures and two loss functions. We further shed light on the impact of the entropic open set loss on the internal representations formed by the networks. Open set task augmentation is particularly valuable when no additional data from the target classes are available—a scenario often faced in practice. [ABSTRACT FROM AUTHOR]

Pu'er tea is a Yunnan geographical indication product, and its brand value ranks first in China. At present, qualitative and quantitative methods with low prediction accuracy are used to predict price. In this paper, based on the current situation and industry characteristics, a differential autoregressive integrated moving average model (ARIMA) is used to predict the short-term price. From the perspective of macro and micro, back-propagation neural network model (BP) was established to predict the long-term price based on the weight ranking of 16 factors affecting the price by technique for order preference by similarity to ideal solution method (TOPSIS). The future price is predicted and analyzed, and then based on the empirical results, suggestions are put forward for the industry in terms of reducing production capacity, increasing consumer demand and combining with the publicity and promotion of Internet. [ABSTRACT FROM AUTHOR]