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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]

Stock prices forecasting has always been a challenging task. Although many research projects adopt machine learning and deep learning algorithms to address the problem, few of them pay attention to the varying degrees of dependencies between stock prices. In this paper we introduce a hybrid model that improves stock price prediction by emphasizing the dependencies between adjacent stock prices. The proposed model, ResNLS, is mainly composed of two neural architectures, ResNet and LSTM. ResNet serves as a feature extractor to identify dependencies between stock prices across time windows, while LSTM analyses the initial time‐series data with the combination of dependencies which considered as residuals. In predicting the SSE Composite Index, our experiment reveals that when the closing price data for the previous five consecutive trading days is used as the input, the performance of the model (ResNLS‐5) is optimal compared to those with other inputs. Furthermore, ResNLS‐5 outperforms vanilla CNN, RNN, LSTM, and BiLSTM models in terms of prediction accuracy. It also demonstrates at least a 20% improvement over the current state‐of‐the‐art baselines. To verify whether ResNLS‐5 can help clients effectively avoid risks and earn profits in the stock market, we construct a quantitative trading framework for back testing. The experimental results show that the trading strategy based on predictions from ResNLS‐5 can successfully mitigate losses during declining stock prices and generate profits in the periods of rising stock prices. [ABSTRACT FROM AUTHOR]

Skin cancer, also known as melanoma, is a deadly form of skin cancer that can significantly improve survival rates when diagnosed at an early stage. It is usually diagnosed visually from dermoscopic images, and such visual assessment of skin cancer by the naked eye is a challenging and arduous task. Therefore, the detection of melanoma from dermoscopic images using trained artificial intelligence models is of great importance today. However, since melanoma is a rare disease, existing databases of skin lesions often contain highly unbalanced numbers of benign and malignant samples. In this paper, we propose a new one‐class classification‐based skin lesion classification strategy for small and unbalanced datasets. One‐class classification (OCC) is a special case of multi‐classification. OCC aims to learn a descriptive paradigm from positive class data (true data) during training and reject pseudo data (fake data) that do not conform to the paradigm during inference. OCC has great potential for application in anomaly detection problems. We have analyzed several approaches to the OCC task in recent years and propose a new design paradigm for the OCC problem, taking into account the unbalanced data set of the melanoma classification task. We have designed an improved OCC network based on this design paradigm, where the network is based on the architecture of a residual neural network, combining the coding and decoding idea of variational self‐encoder and the adversarial training idea of an adversarial neural network, using binary cross‐entropy as the loss function and introducing the channel attention mechanism. Tests on several publicly available dermatology datasets show that this improved OCC network addresses the unbalanced dataset situation in melanoma image classification to some extent while having relatively excellent performance. Compared with some traditional networks, it can obtain more stable training results and perform more consistently on complex datasets. [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]

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]

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]

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]

Intermittency in wind offers the major challenge in accomplishing the wind energy as a dependable sustainable energy resource in power grid. Fluctuations in wind speed occur seasonally over a year and if this seasonality is considered, the prediction of the speed of wind can be made more accurate. In this paper, an attempt is made to apply a signal decomposition technique called Variational Mode Decomposition (VMD), which decomposes series of wind speed data into several intrinsic mode functions (IMFs) to make the data more regular thereby enhancing the accuracy of the wind speed forecast model. Then, artificial intelligence technique, Adaptive neuro fuzzy inference system (ANFIS) is applied for the wind speed prediction by combining the obtained modes from VMD. Here, wind data of two sites in India, Jogimatti and Lamba are taken for the study. Each site data is grouped into high and low wind speed months and later, this series is decomposed into regular modes using VMD. Later, ANFIS is applied for training and predicting the wind speed for different time horizons. [ABSTRACT FROM AUTHOR]

Abstract: In distributed and open environments, MASs (multiagent systems) generally have no mechanisms for prior coordination and self‐organization has been believed to be the necessary selection to achieve the coordination of agents. This paper first presents a values‐driven model for self‐organization in which the expected emergent properties of a system are specified as the social values while the social values are realized via implicitly inducing members to regulate their individual values and adjust their behaviors to fit the expectations of the system. Based on the values‐driven self‐organization, this paper proposes an automated coordination mechanism for decentralized MASs. In this mechanism, by indirectly changing the difficulties in acquiring resources (which may be delegated to some special agents since MASs generally do not have substantial bodies), MASs can lead agents to regulate their values to be consistent with the social values of MASs so that the coordination of MASs can spontaneously emerge from the local behaviors of agents. Finally, this paper implements a simulation traffic system using the coordination mechanism based on values‐driven self‐organization to validate the emergence of coordination among multiple agents. [ABSTRACT FROM AUTHOR]

Abstract: Track‐before‐detect algorithm based on the particle filter algorithm has the problems of low tracking precision, poor particles, and requiring a large amount of particles to be calculated in a low signal‐to‐noise ratio, which is difficult to meet the accuracy and speed required by the modern infrared search and tracking system. In this paper, an improved infrared small target detection and tracking method based on a new particle filter is proposed. This is where particles are used to represent an individual bat to imitate the hunting process of bats. By adjusting loudness, frequency, and impulse emissivity of a particle swarm, the optimal particle at that time is followed to search in the solution space. In addition, the global search and the local search can also be dynamically switched to improve the quality and distribution of the particle swarm. The performance of the proposed algorithm is tested in a simulation scene and the real scene of the infrared small target detection and tracking. Experimental results show that the proposed algorithm improves the performance of the infrared searching and tracking system. [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]

Plan similarity measures play a key role in many areas of artificial intelligence, such as case-based planning, plan recognition, ambient intelligence, or digital storytelling. In this paper, we present 2 novel structural similarity measures to compare plans based on a search process in the space of partial plans. Partial plans are compact representations of sets of plans with some common structure and can be organized in a lattice so that the most general partial plans are above the most specific ones. To compute our similarity measures, we traverse this space of partial plans from the most general to the most specific using successive refinements. Our first similarity measure is designed for propositional plan formalisms, and the second is designed for classical planning formalisms (including variables and types). We also introduce 2 novel refinement operators used to traverse the space of plans: an ideal downward refinement operator for propositional partial plans and a finite and complete downward refinement operator for classical partial plans. Finally, we evaluate our similarity measures in the context of a nearest neighbor classifier using 2 datasets commonly used in the plan recognition literature (Linux and Monroe), showing good results in both synthetic and real data. [ABSTRACT FROM AUTHOR]