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The smart grid communication parties need to process the data by the trusted central node, which will lead to security issues such as single‐point attacks and data tampering. This paper proposes a smart grid data access control scheme based on blockchain, the user completes the registration of the smart meter by three encryptions. After the registration is completed, the registration information will be uploaded to the blockchain. In the data access phase, the verification center verifies the user's data access request, the database will accept the user's request for data if the verification is passed, and that will be broadcasted on the entire network and uploaded to the blockchain. The security of the scheme is analyzed by using a random oracle model. Analysis shows that this scheme can resist public key replacement attacks and malicious key generation center (KGC) attacks. Compared with the existing scheme, this scheme can more effectively resist more types of attacks. It shows that the smart grid data access control scheme proposed in this paper is safe, reliable and efficient. [ABSTRACT FROM AUTHOR]

In order to solve the huge impact of the digital information age on many technical and industrial fields, a periodic fast search genetic algorithm is proposed. Based on the reconnaissance mission, this paper introduces the common allocation strategy into mission planning, and constructs the mathematical model of multi unmanned aerial vehicle (UAV) cooperative reconnaissance mission planning decision‐making. The proposed periodic fast search genetic algorithm is used to solve the problem of multi UAV cooperative reconnaissance mission planning. In 2020, the industry growth rate of global UAV technology expenditure was as high as 30.6%, and the compound growth rate of UAV in China reached 63.5%, which is enough to see the great prospect of the integrated development of UAV technology and different industries. The experiment evaluates the log verification module of UAV by comparing the two data structures of Merkle tree and linear, and the time and memory overhead of storing and verifying logs, which shows the effectiveness of the log verification scheme in this paper. [ABSTRACT FROM AUTHOR]

There is a growing trend of using artificial intelligence, particularly deep learning algorithms, in medical diagnostics, revolutionizing healthcare by improving efficiency, accuracy, and patient outcomes. However, the use of artificial intelligence in medical diagnostics comes with the critical need to explain the reasoning behind artificial intelligence‐based predictions and ensure transparency in decision‐making. Explainable artificial intelligence has emerged as a crucial research area to address the need for transparency and interpretability in medical diagnostics. Explainable artificial intelligence techniques aim to provide insights into the decision‐making process of artificial intelligence systems, enabling clinicians to understand the factors the algorithms consider in reaching their predictions. This paper presents a detailed review of saliency‐based (visual) methods, such as class activation methods, which have gained popularity in medical imaging as they provide visual explanations by highlighting the regions of an image most influential in the artificial intelligence's decision. We also present the literature on non‐visual methods, but the focus will be on visual methods. We also use the existing literature to experiment with infrared breast images for detecting breast cancer. Towards the end of this paper, we also propose an "attention guided Grad‐CAM" that enhances the visualizations for explainable artificial intelligence. The existing literature shows that explainable artificial intelligence techniques are not explored in the context of infrared medical images and opens up a wide range of opportunities for further research to make clinical thermography into assistive technology for the medical community. [ABSTRACT FROM AUTHOR]

In recent years, with the development of low‐earth orbit broadband satellites, the combination of multi‐path transmission and software‐defined networking (SDN) for satellite networks has seen rapid advancement. The integration of SDN and multi‐path transmission contributes to improving the efficiency of transmission and reducing network congestion. However, the current SDN controllers do not support the multi‐path QUIC protocol (MPQUIC), and the routing algorithm used in current satellite networks based on minimum hop count struggles to meet the real‐time requirements for some applications. Therefore, this paper designs and implements an SDN controller that supports the MPQUIC protocol and proposes a multi‐objective optimization‐based routing algorithm. This algorithm selects paths with lower propagation delays and higher available bandwidth for subflow transmission to improve transmission throughput. Considering the high‐speed mobility of satellite nodes and frequent link switching, this paper also designs a flow table update algorithm based on the predictability of satellite network topology. It enables proactive rerouting upon link switching, ensuring stable transmission. The performance of the proposed solution is evaluated through satellite network simulation environments. The experimental results highlight that SDN‐MPQUIC significantly improves performance metrics: it reduces average completion time by 37.3% to 59.3% compared to QSMPS and by 52.8% to 72.4% compared to Disjoint for files with different sizes. Additionally, SDN‐MPQUIC achieves an average throughput improvement of 81.4% compared to QSMPS and 147.8% compared to Disjoint, while demonstrating a 26.3% lower retransmission rate than QSMPS. [ABSTRACT FROM AUTHOR]

Power line inspection is an important means to eliminate hidden dangers of power lines. It is a difficult research problem how to solve the low accuracy of power line inspection based on deep neural network (DNN) due to the problems of multi‐view‐shape, small‐size object. In this paper, an automatic detection model based on Feature visual clustering network (FVCNet) for power line inspection is established. First, an unsupervised clustering method for power line inspection is proposed, and applied to construct a detection model which can recognize multi‐view‐shape objects and enhanced object features. Then, the bilinear interpolation method is used to Feature enhancement method, and the enhanced high‐level semantics and low‐level semantics are fused to solve the problems of small object size and single sample. In this paper, FVCNet is applied to the MS‐COCO 2017 data set and self‐made power line inspection data set, and the test accuracy is increased to 61.2% and 82.0%, respectively. Compared with other models, especially for the categories that are greatly affected by multi‐view‐shape, the test accuracy has been improved significantly. [ABSTRACT FROM AUTHOR]

The analysis of sentiments and mining of opinions have become more and more important in years because of the development of social media technologies. The methods that utilize natural language processing and lexicon‐based sentiment analysis techniques to analyze people's opinions in texts require the proper extraction of sentiment words to ensure accuracy. The current issue is tackled with a novel perspective in this paper by introducing a hybrid sentiment analysis technique. This technique brings together Convolutional Neural Network (CNN) and Hidden Markov Models (HMMs), to accurately categorize text data and pinpoint feelings. The proposed method involves 1D convolutional‐layer CNN to extract hidden features from comments and applying HMMs on a feature‐sentence matrix, allowing for the utilization of word sequences in extracting opinions. The method effectively captures diverse text patterns by extracting a range of features from texts using CNN. Text patterns are learned using text HMM by calculating the probabilities between sequences of feature vectors and clustering feature vectors. The paper's experimental evaluation employs benchmark datasets such as CR, MR, Subj, and SST2, demonstrating that the proposed method surpasses existing sentiment analysis techniques and traditional HMMs. One of its strengths is to analyze a range of text patterns and identify crucial features that recognize the emotion of different pieces of a sentence. Additionally, the research findings highlight the improved performance of sentiment analysis tasks through the strategic use of zero padding in conjunction with the masking technique. [ABSTRACT FROM AUTHOR]

Anomaly detection is a key step in ensuring the security and reliability of large‐scale distributed systems. Analyzing system logs through artificial intelligence methods can quickly detect anomalies and thus help maintenance personnel to maintain system security. Most of the current works only focus on the temporal or spatial features of distributed system logs, and they cannot sufficiently extract the global features of distributed system logs to achieve a good correct rate of anomaly detection. To further address the shortcomings of existing methods, this paper proposes a deep learning model with global spatiotemporal features to detect the presence of anomalies in distributed system logs. First, we extract semi‐structured log events from log templates and model them as natural language. In addition, we focus on the temporal characteristics of logs using the bidirectional long short‐term memory network and the spatial invocation characteristics of logs using the Transformer. Extensive experimental evaluations show the advantages of our proposed model for distributed system log anomaly detection tasks. The optimal F1‐Score on three open‐source datasets and our own collected distributed system datasets reach 98.04%, 94.34%, 88.16%, and 97.40%, respectively. [ABSTRACT FROM AUTHOR]

The educational sector faces a new dimension that is dominated by lifelong learning and is affected by the technical, social, and cultural changes. This pattern represents the need to improve the teaching methods for physical education and sports science. The use of computers and other information technology to increase the effectiveness of the teaching process is a modern method. This paper aims to illustrate the use of information and communication technologies (ICT) in physical education and sports. In our field, the gradual computerization results can be summed up in the following aspects: education software, design, and planning activities, recording outcomes, motion monitoring, video analysis, comparison of performance and synchronizing, measurements at distance and time and the evaluation of the activity. Although physical education and sports are practical activities, specialists can make use of modern teaching technologies. In this paper, the system of curriculum assessment for physical education has been analyzed and researched in computer assessment. The first section introduced the method of assessment of the physical education program. The second phase of the paper represents a teaching model of the physical education mathematical model utilizing the Comprehensive Adaptive Fuzzy Evaluation Theory has been proposed. A new level is the modernization of physics education with the artificial intelligence computer education system built in this paper. The experimental results have high performance in detecting the physical activity of college students. [ABSTRACT FROM AUTHOR]

Deep learning models have overcome traditional machine learning techniques for text classification domains in the field of natural language processing (NLP). Since, NLP is a branch of machine learning, used for interpreting language, classifying text of interest, and the same can be applied to analyse the medical clinical electronic health records. Medical text consists of lot of rich data which can altogether provide a good insight, by determining patterns from the clinical text data. In this paper, bidirectional‐long short‐term memory (Bi‐LSTM), bi‐LSTM attention and bidirectional encoder representations from transformers (BERT) base models are used to classify the text which are of privacy concern to a person and which should be extracted and can be tagged as sensitive. This text data which we might think not of privacy concern would majorly reveal a lot about the patient's integrity and personal life. Clinical data not only have patient demographic data but lot of hidden data which might go unseen and thus could arise privacy issues. Bi‐LSTM with attention layer is also added on top to realize the importance of critical words which will be of great importance in terms of classification, we are able to achieve accuracy of about 92%. About 206,926 sentences are used out of which 80% are used for training and rest for testing we get accuracy of 90% approx. with Bi‐LSTM alone. The same set of datasets is used for BERT model with accuracy of 93% approx. [ABSTRACT FROM AUTHOR]

Modelling one‐to‐many type mappings in problems with a temporal component can be challenging. Backpropagation is not applicable to networks that perform discrete sampling and is also susceptible to gradient instabilities, especially when applied to longer sequences. In this paper, we propose two recurrent neural network architectures that leverage stochastic units and mixture models, and are trained with target propagation. We demonstrate that these networks can model complex conditional probability distributions, outperform backpropagation‐trained alternatives, and do not rapidly degrade with increased time horizons. Our main contributions consist of the design and evaluation of the architectures that enable the networks to solve multi‐model problems with a temporal dimension. This also includes the extension of the target propagation through time algorithm to handle stochastic neurons. The use of target propagation provides an additional computational advantage, which enables the network to handle time horizons that are substantially longer compared to networks fitted using backpropagation. [ABSTRACT FROM AUTHOR]

Although the market demand for smart devices (SDs) in the Internet of Things (IoT) era is surging, the corresponding thunderstorm protection measures have rarely attracted attention. This paper presents a thunderstorm prediction method with elevation correction, to reduce the thunderstorm damage to SDs by visually tracking thunderstorm activities. First, a self‐made three‐dimensional atmospheric electric field apparatus (3DAEFA) deployed in IoT is developed to collect real‐time AEF data. A 3DAEFA‐based localization model is established, and the localization formula after correction is derived. AEF data predicted by the bi‐directional long short‐term memory (BiLSTM) model are input to this formula to obtain thunderstorm point charge localization results. Then, the localization skill is evaluated. Finally, the proposed method is assessed in experiments, under single and multiple point charge conditions. There are significant reductions of at least 33.1% and 8.8% in ranging and elevation angle errors, respectively. Particularly, this post‐prediction correction reduces the deviation of fitted point charge moving paths by at most 0.189 km, demonstrating excellent application effects. Comparisons with radar charts and existing methods testify that this method can effectively predict thunderstorms. [ABSTRACT FROM AUTHOR]

Radiologists and clinicians must automatically examine breast and tumor locations and sizes accurately. In recent years, several neural network‐based feature fusion versions have been created to improve medical image segmentation. Multi‐modal image fusion photos may efficiently identify tumors. This work uses image fusion to identify computed tomography and magnetic resonance imaging alterations. A Gauss‐log ratio operator is recommended for difference image production. The Gauss‐log ratio and log ratio difference image complement the objective of improving the difference map through image fusion. The feature change matrix extracts edge, texture, and intensity from each picture pixel. The final change detection map classifies feature vectors as "changed" or "unchanged" which has been mapped for high‐resolution or low‐resolution pixels. This paper proposes a multi‐feature blocks (MFB) based neural network for multi‐feature fusion. This neural network modeling approach globalizes pixel spatial relationships. MFB‐based feature fusion also aims to capture channel interactions between feature maps. The proposed technique outperforms state‐of‐the‐art approaches which have been discussed in detail in experimental results section. [ABSTRACT FROM AUTHOR]

Cost‐sensitive ensemble learning as a combination of two approaches, ensemble learning and cost‐sensitive learning, enables generation of cost‐sensitive tree‐based ensemble models using the cost‐sensitive decision tree (CSDT) learning algorithm. In general, tree‐based models characterize nice graphical representation that can explain a model's decision‐making process. However, the depth of the tree and the number of base models in the ensemble can be a limiting factor in comprehending the model's decision for each sample. The CSDT models are widely used in finance (e.g., credit scoring and fraud detection) but lack effective explanation methods. We previously addressed this gap with cost‐sensitive tree Shapley Additive Explanation Method (CSTreeSHAP), a cost‐sensitive tree explanation method for the single‐tree CSDT model. Here, we extend the introduced methodology to cost‐sensitive ensemble models, particularly cost‐sensitive random forest models. The paper details the theoretical foundation and implementation details of CSTreeSHAP for both single CSDT and ensemble models. The usefulness of the proposed method is demonstrated by providing explanations for single and ensemble CSDT models trained on well‐known benchmark credit scoring datasets. Finally, we apply our methodology and analyze the stability of explanations for those models compared to the cost‐insensitive tree‐based models. Our analysis reveals statistically significant differences between SHAP values despite seemingly similar global feature importance plots of the models. This highlights the value of our methodology as a comprehensive tool for explaining CSDT models. [ABSTRACT FROM AUTHOR]

To address the issue of low efficiency caused by the repeated use of quantum attack resistant static identity authentication methods in a satellite integrated smart grid, this paper proposes a quantum attack resistant continuous identity authentication protocol. First, in the initial authentication stage, in order to reduce computational complexity, the key encryption mechanism in the CRYSTALS‐Kyber algorithm was improved and combined with the NTRU message recovery digital signature scheme to construct a lattice based explicit AKE (Kyber NTRU. AKE), which achieved mutual authentication and negotiated shared tokens. Second, in the continuous authentication stage, incorporating quantum attack resistant tokens into the current algorithm to improve authentication efficiency. The formal analysis results indicate that compared to the weakly forward secure Kyber.AKE in the CRYSTALS‐Kyber algorithm, Kyber‐NTRU.AKE achieves complete forward secrecy, while the non‐formal analysis results demonstrate the security of the continuous authentication phase. Through theoretical analysis and efficiency comparison with Cyber.AKE, the analysis shows that the Cyber‐NTRU.AKE has higher computational and communication efficiency than Cyber.AKE. [ABSTRACT FROM AUTHOR]

In recent years, due to the rise of social, biological, and other rich content graphs, several novel community detection methods using structure and node attributes have been proposed. Moreover, nodes in a network are naturally characterized by multiple community memberships and there is growing interest in overlapping community detection algorithms. In this paper, we design a weighted vertex interaction model based on distance dynamics to divide the network, furthermore, we propose a distance Dynamics‐based Overlapping Semantic Community detection algorithm(DOSC) for node‐attribute networks. The method is divided into three phases: Firstly, we detect local single‐attribute subcommunities in each attribute‐induced graph based on the weighted vertex interaction model. Then, a hypergraph is constructed by using the subcommunities obtained in the previous step. Finally, the weighted vertex interaction model is used in the hypergraph to get global semantic communities. Experimental results in real‐world networks demonstrate that DOSC is a more effective semantic community detection method compared with state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Stock forecasting is difficult because of its complexity and uncertainty. To better predict stock prices and then provide stockholders with reasonable suggestions, this paper proposes an improved time convolution network (TCN) model for predicting stock prices. The model used can make up for some of the shortcomings of the traditional neural network, use the trading data in the stock market, and put the preprocessed data of financial news into the model for training to improve the accuracy of prediction. Using the Shanghai Securities Exchange (SSE) 50 Index (Shanghai Securities Exchange 50 Index selects the most representative 50 stocks with large scale and good liquidity in Shanghai stock market as sample stocks) and news text crawled from financial web pages as samples, predict the direction of the SSE 50 Index's rise and fall. After using different network structure hyperparameters to adjust the model structure, the prediction effect is compared with other models, and it is found that the proposed improved TCN model can effectively improve the effect of predicting the rise and fall of the SSE 50 index, and can complete the model training and predict the stock price faster. [ABSTRACT FROM AUTHOR]

With a broad usage of digital imaging data in hospitals, the medical image repository size is growing in a very rapid manner. This creates difficulty in querying and managing these vast databases that in turn leads to the requirement of Content‐Based Medical Image Retrieval (CBMIR) systems. The CBMIR is considered as the major ambiguous and challenging task for reducing the semantic gap among the human queries and images in the datasets having more information content. The main intention of this paper is to enhance medical image retrieval and classification using the improved pattern descriptor and deep learning. For the medical image retrieval, this paper develops the Optimized Local Directional Weber Pattern with a multi‐objective similarity function. This similarity function focuses on the measures like Structural Similarity Index Measure, Peak Signal‐to‐Noise Ratio, Mean Squared Error MSE, and correlation. Likewise, the development of an improved Faster‐Region Convolutional Neural Network (Faster‐RCNN) is adopted in the classification phase. Both the retrieval and classification are enhanced by the Modified Wind Speed‐based Deer Hunting Optimization Algorithm. Considering classification and retrieval tasks, the experiments on benchmark datasets reveal an enhancement from the introduced algorithm when compared over the existing methods. [ABSTRACT FROM AUTHOR]

Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV‐based route selection method provides better performance. [ABSTRACT FROM AUTHOR]

Random minimaxing studies the consequences of using a random number for scoring the leaf nodes of a full width game tree and then computing the best move using the standard minimax procedure. Experiments in Chess showed that the strength of play increases as the depth of the lookahead is increased. Previous research by the authors provided a partial explanation of why random minimaxing can strengthen play by showing that, when one move dominates another move, then the dominating move is more likely to be chosen by minimax. This paper examines a special case of determining the move probability when domination does not occur. Specifically, we show that, under a uniform branching game tree model, whether the probability that one move is chosen rather than another depends not only on the branching factors of the moves involved, but also on whether the number of ply searched is odd or even. This is a new type of game tree pathology, where the minimax procedure will change its mind as to which move is best, independently of the true value of the game, and oscillate between moves as the depth of lookahead alternates between odd and even. [ABSTRACT FROM AUTHOR]

Nowadays, most of the services from cloud are protuberant within the all commercial, public, and private areas. A primary difficulty of cloud computing system is making a virtualized environment safe from all intruders. The existing system uses signature‐based methods, which cannot provide accurate detection of malware. This paper put forward an approach to detect the malware by using the approach based on feature extraction and various classification techniques. Initially the clean files and malware files are extracted. The feature selection includes gain ratio to provide subset features. The classification is used to predict any malware that has been entered in the mobile device. In this paper, it is proposed to use the ensemble classifier which contains different kinds of classifiers such as Support Vector Machine, K‐Nearest Neighbor, and Naïve Bayes classification. These together are known as a meta classifier. These three classification methods had been used for proposed work and get the results with higher accuracy. This measures the correctness of the prediction happened using ensemble method with high precision and recall values which is specifically identifies the quality of the techniques used. [ABSTRACT FROM AUTHOR]

Precise modeling of a polymer electrolyte membrane fuel cell (PEMFC) is a crucial issue in analyzing and controlling electrical energy production. In this paper, a novel semiexperimental model is proposed for forecasting of PEMFC output voltage. As well, the coevolution ribonucleic acid genetic algorithm (coRNA‐GA) is presented as a novel estimation approach for determination of proposed model coefficients. This optimization method is motivated by the biological RNA, encodes the chromosomes by RNA nucleotide basics, and accepts a few RNA operations. This paper proposed several genetic operators to preserve the diversity of particles, and two sets from particles are chosen using various validation functions. In these two subpopulations, different evolutionary methods have been employed for balancing of seeking and extraction. Input pressure of cathode is chosen in this paper as a further parameter for modifying the depiction of concentration overvoltage (Vcon) in the case of conventional Amphlett's PEMFC system. Finally, the performance of the coRNA‐GA algorithm, as well as the precision of the obtained model, is authenticated via empirical results. Also, the obtained results are compared with some other methods, and the superiority of the proposed model is demonstrated in voltage prediction accuracy. [ABSTRACT FROM AUTHOR]

Abstract: How to automatically understand and answer users' questions (eg, queries issued to a search engine) expressed with natural language has become an important yet difficult problem across the research fields of information retrieval and artificial intelligence. In a typical interactive Web search scenario, namely, session search, to obtain relevant information, the user usually interacts with the search engine for several rounds in the forms of, eg, query reformulations, clicks, and skips. These interactions are usually mixed and intertwined with each other in a complex way. For the ideal goal, an intelligent search engine can be seen as an artificial intelligence agent that is able to infer what information the user needs from these interactions. However, there still exists a big gap between the current state of the art and this goal. In this paper, in order to bridge the gap, we propose a Markov random field–based approach to capture dependence relations among interactions, queries, and clicked documents for automatic query expansion (as a way of inferring the information needs of the user). An extensive empirical evaluation is conducted on large‐scale web search data sets, and the results demonstrate the effectiveness of our proposed models. [ABSTRACT FROM AUTHOR]

The traditional target recognition and classification is mostly done manually, with low efficiency and high cost. Improving the level of target recognition automatically has become an important research topic. This paper proposes a target recognition method based on transfer learning to effectively complete the classification and recognition of targets using a brain–computer interface (BCI) model. Based on the construction of the faster‐RCNN deep learning model, the pre‐training of the model is achieved by VGG‐16 and Inception‐v2, and the transfer learning algorithm is used to optimize the faster‐RCNN deep learning model based on the kinematics model. Experiments are carried out with the aim to detect tableware by the persons whose brain signals recognition rate has been substantially improved using faster‐RCNN. Compared with the traditional recognition methods, the results at the lab‐scale level illustrated that the proposed algorithm can effectively improve the speed and accuracy of target recognition by using the BCI model to classify tableware of different colors and shapes in a complex background. [ABSTRACT FROM AUTHOR]

In film and television animation works, animated characters are the soul and core of the work. The behavior, language expression, and emotional expression of animated characters play an important role in the expression of the animation theme and content. Aiming at the problem that the mobile animation system can only add and change actions for a single virtual character, and the characters cannot interact with each other, this paper analyzes the technical principles, technical characteristics, and application scope of human–computer interaction (HCI), taking sensors as the research object. An algorithm for separating the human body from the background environment in the depth image is proposed. Through the calculation of the depth value, the calculation results are compared, and the target human body and the background are effectively separated. In the depth data processing, the algorithm of judging the pixel offset value is used to identify the body part, and a sensor‐based HCI system is designed. The depth‐of‐field data map acquired by the sensor is used to identify human body parts and determine actions, thereby realizing HCI based on action recognition. Simulation test results show that the effective rate of the system is 80%, and the design of animated characters can be put into the visualization stage. Using the algorithm in this paper, the physical signs of the animated characters can be quickly identified, so that the next action of the animation can be more clearly captured. Has a certain practical value. [ABSTRACT FROM AUTHOR]

Today, with the rapid development of virtual reality technology, 3D printing technology has gradually gained people's attention. Copper alloy parts have a wide range of uses in our lives, but due to the high technology required, production is more difficult. In order to make copper alloy parts more widely used, this paper uses 3D printing technology to print copper alloy parts, and through comparative experiments, the printed copper alloy parts are analyzed in detail. In the printing process, by adjusting the printing parameters, different parts are printed, and the printing results are analyzed for different parameters. The experimental results show that the experimental 3D printing technology can successfully print copper alloy parts. During the printing process, the input parameters have an extremely important effect on the results. The experimental results show that there are related parameters in the printing process, such as layer height, length, density, etc., which will cause deviations in the printing results. Among them, the layer height and length have the most important effect on the results, accounting for more than 50% of the deviation value. This shows that based on virtual reality technology, the use of 3D printing copper alloy parts is achievable. [ABSTRACT FROM AUTHOR]

Epilepsy is a common mental disorder that affects about 70 million people worldwide. Epileptic electroencephalogram (EEG) signal, an important means to judge epileptic seizure, needs neurologists' prior knowledge to mark manually. This marking method is time‐consuming and laborious. Currently, the existing automated diagnosis methods have achieved good results on one benchmark EEG dataset, most of which can achieve accuracy of more than 0.95. However, the method has limitations on the dataset, and the accuracy of the diagnosis results on another new dataset drops sharply to nearly 0.5. Aiming at the existing EEG signal diagnosis lacks stability and generalization ability, this paper proposed a multilayer‐weighted integrated self‐learning algorithm for different classifiers. For this algorithm, weighted voting was first conducted on the the diagnostic results by different classifiers to obtain a result, which was weighted again to produce the final diagnostic results. This algorithm improves the problem that the traditional self‐learning algorithm is greatly affected by data noise, which shows a strong stability in different data sets and in clinical epileptic EEG signal data detection, so as to reduce the workload of neurologists and provide support and assistance for the diagnosis and treatment of epilepsy. The experiment result shows that the algorithm can improve the stability and reliability of EEG automatic diagnosis of epilepsy. The accuracy and AUC area of its classification in two different public data sets and clinical data can reach 0.80 to 0.95. [ABSTRACT FROM AUTHOR]

Latent Dirichlet allocation (LDA) is one of the major models used for topic modelling. A number of models have been proposed extending the basic LDA model. There has also been interesting research to replace the Dirichlet prior of LDA with other pliable distributions like generalized Dirichlet, Beta‐Liouville and so forth. Owing to the proven efficiency of using generalized Dirichlet (GD) and Beta‐Liouville (BL) priors in topic models, we use these versions of topic models in our paper. Furthermore, to enhance the support of respective topics, we integrate mixture components which gives rise to generalized Dirichlet mixture allocation and Beta‐Liouville mixture allocation models respectively. In order to improve the modelling capabilities, we use variational inference method for estimating the parameters. Additionally, we also introduce an online variational approach to cater to specific applications involving streaming data. We evaluate our models based on its performance on applications related to text classification, image categorization and genome sequence classification using a supervised approach where the labels are used as an observed variable within the model. [ABSTRACT FROM AUTHOR]

The existing YOLOv5‐based framework has achieved great success in the field of target detection. However, in forest fire detection tasks, there are few high‐quality forest fire images available, and the performance of the YOLO model has suffered a serious decline in detecting small‐scale forest fires. Making full use of context information can effectively improve the performance of small target detection. To this end, this paper proposes a new graph‐embedded YOLOv5 forest fire detection framework, which can improve the performance of small‐scale forest fire detection using different scales of context information. To mine local context information, we design a spatial graph convolution operation based on the message passing neural network (MPNN) mechanism. To utilize global context information, we introduce a multi‐head self‐attention (MSA) module before each YOLO head. The experimental results on FLAME and our self‐built fire dataset show that our proposed model improves the accuracy of small‐scale forest fire detection. The proposed model achieves high performance in real‐time performance by fully utilizing the advantages of the YOLOv5 framework. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) autonomous driving technology based on deep learning has achieved great success. However, under the tunnel environment, the computer vision‐based IoV may fail due to low illumination. In order to handle this issue, this paper deploys an image enhancement module at the terminal of the IoV to alleviate the low illumination influence. The enhanced images can be submitted through IoT to the cloud server for further processing. The core algorithm of image enhancement is implemented by a dynamic graph embedded transformer network based on federated learning which can fully utilize the data resources of multiple devices in IoV and improve the generalization. Extensive comparative experiments are conducted on the publicly available dataset and the self‐built dataset which is collected under the tunnel environment. Compared with other deep models, all results confirm that the proposed graph embedded Transformer model can effectively enhance the detail information of the low‐light image, which can greatly improve the following tasks in IoV. [ABSTRACT FROM AUTHOR]

Traffic detection (including lane detection and traffic sign detection) is one of the key technologies to realize driving assistance system and auto drive system. However, most of the existing detection methods are designed based on single‐modal visible light data, when there are dramatic changes in lighting in the scene (such as insufficient lighting in night), it is difficult for these methods to obtain good detection results. In view of multi‐modal data can provide complementary discriminative information, based on the YoLoV5 model, this paper proposes a multi‐modal fusion YoLoV5 network, which consists of three key components: the dual stream feature extraction module, the correlation feature extraction module, and the self‐attention fusion module. Specifically, the dual stream feature extraction module is used to extract the features of each of the two modalities. Secondly, input the features learned from the dual stream feature extraction module into the correlation feature extraction module to learn the features with maximum correlation. Then, the extracted maximum correlation features are used to achieve information exchange between modalities through a self‐attention mechanism, and thus obtain fused features. Finally, the fused features are inputted into the detection layer to obtain the final detection result. Experimental results on different traffic detection tasks can demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This article describes experiments on speech segmentation using long short‐term memory recurrent neural networks. The main part of the paper deals with multi‐lingual and cross‐lingual segmentation, that is, it is performed on a language different from the one on which the model was trained. The experimental data involves large Czech, English, German, and Russian speech corpora designated for speech synthesis. For optimal multi‐lingual modeling, a compact phonetic alphabet was proposed by sharing and clustering phones of particular languages. Many experiments were performed exploring various experimental conditions and data combinations. We proposed a simple procedure that iteratively adapts the inaccurate default model to the new voice/language. The segmentation accuracy was evaluated by comparison with reference segmentation created by a well‐tuned hidden Markov model‐based framework with additional manual corrections. The resulting segmentation was also employed in a unit selection text‐to‐speech system. The generated speech quality was compared with the reference segmentation by a preference listening test. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the leading causes of death among women worldwide. In most cases, the misinterpretation of medical diagnosis plays a vital role in increased fatality rates due to breast cancer. Breast cancer can be diagnosed by classifying tumors. There are two different types of tumors, such as malignant and benign tumors. Identifying the type of tumor is a tedious task, even for experts. Hence, an automated diagnosis is necessary. The role of machine learning in medical diagnosis is eminent as it provides more accurate results in classifying and predicting diseases. In this paper, we propose a deep ensemble network (DEN) method for classifying and predicting breast cancer. This method uses a stacked convolutional neural network, artificial neural network and recurrent neural network as the base classifiers in the ensemble. The random forest algorithm is used as the meta‐learner for providing the final prediction. Experimental results show that the proposed DEN technique outperforms all the existing approaches in terms of accuracy, sensitivity, specificity, F‐score and area under the curve (AUC) measures. The analysis of variance test proves that the proposed DEN model is statistically more significant than the other existing classification models; thus, the proposed approach may aid in the early detection and diagnosis of breast cancer in women, hence aiding in the development of early treatment techniques to increase survival rate. [ABSTRACT FROM AUTHOR]

There has been an explosion of interest in health sciences applications of case-based reasoning (CBR), not only in the traditional CBR in medicine domain, but also in bioinformatics, enabling home health-care technologies, CBR integration, and synergies between CBR and knowledge discovery. This special issue features the best papers from the third workshop on CBR in the health sciences, held at ICCBR-05 in Madrid. It is the third in a series of exciting workshops, the first two of which were held at ICCBR-03, in Trondheim, Norway, and at ECCBR-04, in Madrid, Spain. The nine high-quality papers introduced here represent the research and experience of twenty-two authors working in eight different countries on a wide range of problems and projects. These papers illustrate some of the major trends of current research in CBR in the health sciences, and represent overall an excellent sample of the most recent advances of CBR in the health sciences. [ABSTRACT FROM AUTHOR]

Summarizes the content of the special issue of "Computational Intelligence" journal related to various aspects of the application of agents to support electronic business (e-business). Impact of electronic commerce on traditional business processes; Issues that limit the use of Web for commercial purposes; Requirements in relation to the infrastructure for e-business; Role of agent technologies in doing business over the Internet.

Finding a number of nodes that are able to maximize the spread of influence through the social network and are called influence maximization has numerous applications in marketing. One such application is to find influential members for promoting a product across a large network. Even though numerous algorithms have been proposed, challenges such as scalability, time constraints, and low accuracy have motivated the researchers for better solutions. Some of the newly proposed algorithms are scalable, but fail to provide adequate accuracy. On the other hand, some greedy algorithms provide a good level of accuracy but are very time consuming for large networks. In this paper, an algorithm is proposed called FAIMCS that can quickly find influential nodes across large networks with high accuracy. FAIMCS, reduces computational overhead considerably by eliminating major portions of the social network graph which have little influence. FAIMCS uses community detection algorithm to determine each community's quota of influential nodes based on the structure of that community. Finally, it obtains influential nodes from the candidate nodes. Experiment results show FAIMCS is faster than current algorithms and provides a high level of accuracy for large social networks. [ABSTRACT FROM AUTHOR]

The common backfill mining technology in the green mining industry can be used for the secondary utilization of construction waste in smart cities. This measure has the advantages of low cost, fast results, and less environmental pollution. Over the past few decades, with the continuous advancement of global urbanization, the effective and environmentally friendly construction waste disposal and emission are very important for the development of urban green construction. Construction waste can be prepared as paste filling material, as one of the raw materials for backfill mining. This paper proposes a new method that can quickly and accurately predict the strength of paste filling materials with different compositions. A deep connected convolutional neural network (CNN) that can extract input parameters is used to build a prediction model. The coarse aggregate, fine aggregate, and cementing material are employed as the input variables of the CNN model, and five indicators which are generally used to evaluate the strength of filling material are selected as the output results. The experimental results show that the proposed prediction approach can obtain robust prediction results and high prediction accuracy and speed. [ABSTRACT FROM AUTHOR]

The world has enhanced the development of education with the rapid development of information technology in modern society to facilitate the information process. Educational technology has gained experience through the advancement of digital information network technology, from modern teaching technology to computer technology and communication technology. In this paper, an English learning system has been developed based on an improved fuzzy‐assisted hierarchical neural network system (IF‐HNNS). Improving the standard of English education is particularly important in improving intelligent education, especially the efficiency of classroom teaching. The development of an interactive educational system has changed the conventional way of teaching and facilitated individual independent learning. This English teaching system is a network updating computer‐aided learning platform. The computer‐aided system has the following advantages: (i) Easy‐to‐actualize computer‐aided learning media will provide more teaching content and teach satisfaction. (ii) The interaction of computer‐aided learning will raise interest in learning. (iii) Computer helped learners are rich in educational resources and are easy to set teaching goals. (iv) It will improve the monitoring of the education and teaching cycle using a computer‐aided learning with human‐computer interaction method. [ABSTRACT FROM AUTHOR]

Artificial intelligence education (AIEd) is defined in the field of education as the utilization of artificial intelligence. There are currently many AIEd‐driven applications in schools and universities. This paper applies an artificial intelligence module combined with the knowledge recommendation to the system and develops an online English teaching system in comparison with the common teaching auxiliary system. The method of English teaching is useful in investigating the potential internal connections between evaluation outcomes and various factors. This article develops deep learning‐assisted online intelligent English teaching system that utilizes to create a modern tool platform to help students improve their English language teaching efficiency in line with their mastery of knowledge and personality. The decision tree algorithm and neural networks have been used and to generate an English teaching assessment implementation model based on decision tree technologies. It provides valuable data from extensive information, summarizes rules and data, and helps teachers to improve their education and the English scores of students. This system reflects the thinking of the artificial intelligence expert system. Test application demonstrates that the system can help students improve their learning efficiency and will make learning content more relevant. Besides, the system provides an example model with similar methods and has a referential definition. [ABSTRACT FROM AUTHOR]

Role Back Access Control model (RBAC) allows devices to access cloud services after authentication of requests. However, it does not give priority in Big Data to devices located in certain geolocations. Regarding the crisis in a specific region, RBAC did not provide a facility to give priority access to such geolocations. In this paper, we planned to incorporate Location Time‐ (GEOTime) based condition alongside Priority Attribute role‐based access control model (PARBAC), so requesters can be allowed/prevented from access based on their location and time. The priority concept helped to improve the performance of the existing access model. TIME‐PARBAC also ensures service priorities based on geographical condition. For this purpose, the session is encrypted using a secret key. The secret key is created by mapping location, time, speed, acceleration and other information into a unique number, that is, K(Unique_Value) = location, time, speed, accelerator, other information. Spatial entities are used to model objects, user position, and geographically bounded roles. The role is activated based on the position and attributes of the user. To enhance usability and flexibility, we designed a role schema to include the name of the role and the type of role associated with the logical position and the rest of the PARBAC model proposed using official documentation available on the website for Azure internet of things (IoT) Cloud. The implementation results utilizing a health use case signified the importance of geology, time, priority and attribute parameters with supporting features to improve the flexibility of the existing access control model in the IoT Cloud. [ABSTRACT FROM AUTHOR]

This paper reports on our study of a sparse neural network regression method based on a single hidden layer architecture and sparsity‐inducing penalties. To determine the size of network in a data‐adaptive way, we adopt the lasso and group lasso penalty functions to simultaneously induce sparsity at node and predictor levels. We also devise several techniques to improve performance of the neural network regression estimator. We adopt a B‐spline activation function with a compact support to identify local trends of data. In addition, we develop an algorithm based on node addition process, in which additional nodes come into the network as a complexity parameter decreases. At each value of the complexity parameter, the algorithm initializes the additional nodes along the direction that best captures the unexplained functional relationship between the response and predictors. The optimization step is conducted based on an efficient coordinate descent algorithm. Numerical studies based on simulated and real datasets illustrate that the combination of the aforementioned devices significantly improves the performance of the neural network estimator. [ABSTRACT FROM AUTHOR]

Time Series Classification (TSC) started getting a lot of attention recently, mostly due to the important real‐world applications of time series such as in the financial industry. In this context, various approaches have been proposed to treat this important and challenging problem in data mining. These approaches include deep learning models that are outperformed the traditional classification techniques. However, the choice of an optimal and efficient deep neural network for TSC is still a major problem. Motivated by this challenge, we propose in this paper, an optimized deep learning framework for TSC tasks, using normal cloud representation and convolutional neural networks optimization (NCR‐CNNO). Our approach consists of two phases: In the first one, we convert the raw time series to a matrix of characteristics using two‐dimensional normal cloud representation. In the second phase, we suggest a CNN to handle the matrix generated in the first phase, as well as, an optimization model is proposed to optimize the unnecessary and redundant parameters. Experiments conducted on extensive time series datasets demonstrate that NCR‐CNNO yields significant improvement in the performance of time series classification compared to the state of the art. [ABSTRACT FROM AUTHOR]

The problem of risk classification and prediction, an essential research direction, aiming to identify and predict risks for various applications, has been researched in this paper. To identify and predict risks, numerous researchers build models on discovering hidden information of a label (positive credit or negative credit). Fuzzy logic is robust in dealing with ambiguous data and, thus, benefits the problem of classification and prediction. However, the way to apply fuzzy logic optimally depends on the characteristics of the data and the objectives, and it is extraordinarily tricky to find such a way. This paper, therefore, proposes a general membership function model for fuzzy sets (GMFMFS) in the fuzzy decision tree and extend it to the fuzzy random forest method. The proposed methods can be applied to identify and predict the credit risks with almost optimal fuzzy sets. In addition, we analyze the feasibility of our GMFMFS and prove our GMFMFS‐based linear membership function can be extended to a nonlinear membership function without a significant increase in computing complex. Our GMFMFS‐based fuzzy decision tree is tested with a real dataset of US credit, Susy dataset of UCI, and synthetic datasets of big data. The results of experiments further demonstrate the effectiveness and potential of our GMFMFS‐based fuzzy decision tree with linear membership function and nonlinear membership function. [ABSTRACT FROM AUTHOR]

The multiturn dialogue system has been prevalently used in e‐commerce websites and modern information systems, which significantly improves the efficiency of problem solving and further promotes the service quality. In a multiturn dialogue system, the problem of intention classification is a core task, as the intention of a customer is the basis of subsequent problems handling. However, traditional related methods are unsuitable for the classification of multiturn dialogues. Because traditional methods do not distinguish the importance of each sentence and concatenate all sentences in the text, which is likely to generate a model with low prediction accuracy. In this paper, we propose a method of multiturn dialogue classification based on key sentences mining. We design a keywords extraction algorithm, mining key sentences from the dialogue text. We propose an algorithm finishing the computation of the weights of each sentence. According to the sentence weight and the sentence vector, the dialogue text is transformed to a dialogue vector. The dialogue text is classified by a classifier, and the input is the dialogue vector. We conducted sufficient experiments on a real‐world dataset, evaluating the performance of the proposed method. The experimental results show that our method outperforms the related methods on a series of evaluation metrics. [ABSTRACT FROM AUTHOR]

Nowadays, manual event assignment for Chinese mayor's hotline is still a problem of low efficiency. In this paper, we propose a computer‐aided event assignment method based on hierarchical features and enhanced association. First, hierarchical features of hotline events are extracted to obtain event encoding vectors. Second, the fine‐tuned RoBERTa2RoBERTa model is used to encode the "sanding" responsibility texts of Chinese local departments. Third, an association enhanced attention (AEA) mechanism is proposed to capture the correlation information of the "event‐sanding" splicing vectors for the sake of obtaining matching results of "event‐sanding," and the matching results are input into the classifier. Finally, the assignment department for is obtained by a department selection module. Experimental results show that our method can achieve better performance compared with several baseline methods on HEAD (a dataset we construct independently). The ablation experiments also demonstrate the validity of each key module in our method. [ABSTRACT FROM AUTHOR]

Recently, graph neural networks (GNNs) have attracted much attention in the field of machine learning due to their remarkable success in learning from graph‐structured data. However, implementing GNNs in practice faces a critical bottleneck from the high complexity of communication and computation, which arises from the frequent exchange of graphic data during model training, especially in limited communication scenarios. To address this issue, we propose a novel framework of federated graph neural networks, where multiple mobile users collaboratively train the global model of graph neural networks in a federated way. The utilization of federated learning into the training of graph neural networks can help reduce the communication overhead of the system and protect the data privacy of local users. In addition, the federated training can help reduce the system computational complexity significantly. We further introduce a greedy‐based user selection for the federated graph neural networks, where the wireless bandwidth is dynamically allocated among users to encourage more users to attend the federated training of neural networks. We perform the convergence analysis on the federated training of neural networks, in order to obtain some more insights on the impact of critical parameters on the system design. Finally, we perform the simulations on the coriolis ocean for reAnalysis (CORA) dataset and show the advantages of the proposed method in this paper. [ABSTRACT FROM AUTHOR]