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With the emergence and vigorous development of 5G technology, there is a significant surge in network usage and traffic, resulting in heightened complexity within network and IT environments. This exponential increase in activity produces a plethora of events, making conventional systems inadequate for the efficient management of 5G networks. In comparison to 4G technology, 5G technology brings forth a host of new features, one of which is the network data analytics function (NWDAF). This function grants network operators the flexibility to either employ their own data analytics methodologies based on machine learning (ML) and deep learning (DL) into their networks. In this paper, we present a dataset named "NWDAF-NFPP" for network function performance time series prediction, collected from a laboratory at China Telecom. The dataset is carefully anonymized to ensure maximum realism and comprehensiveness, while safeguarding sensitive information. It encompasses eight categories of network functions, with data collected at five-minute intervals. The availability of this dataset provides valuable resources for researchers to conduct time series prediction research on network element performance. Following data collection, a total of six models were employed for network element performance prediction, encompassing both machine learning and deep learning approaches. This diverse set of models was carefully chosen to ensure comprehensive coverage of different techniques and algorithms. Through the comparison and analysis of these models, we aim to evaluate their predictive capabilities and identify the most effective approach for network element performance prediction. This comparative analysis will provide valuable insights into the strengths and limitations of each model, aiding in informed decision-making for network optimization and management strategies in the future. [ABSTRACT FROM AUTHOR]

Exploring the improvement of classroom teaching effect under the background of sports education integration in China has important practical significance and theoretical value. Integration of yard sports education and classroom course education in the school is a novel concept put forward by China's sports and education circles to deepen the combination of sports and education. Although it is only a word different from the combination of sports and education to integrating sports and course education in the school, it is a groundbreaking theoretical and practical innovation for China in terms of changing the competitive sports development model and cultivating exceptional athletes. It is a new way to promote the sustainable development of sports and education according to the scientific outlook on development. Its fundamental significance is to change the closed state of sports and education departments, put sports and education in the background of economic and social development in a certain region, and fundamentally reform the content and mode of education. Therefore, this paper proposes a comprehensive rating model of classroom teaching effects based on deep learning (DL) and machine learning (ML) techniques. In this paper, the Jaffe expression dataset is used to train and test the utilized ML and DL models such as ResNet50, random forest (RF), logistic regression (LR), K-nearest neighbor (k-NN), and decision tree (DT). Further, with the help of artificial intelligence (AI) techniques, the algorithms can objectively evaluate the classroom teaching effect after the integration of physical education with classroom education and provide important guidance for the modernization and intellectualization of China's educational methods in the future. [ABSTRACT FROM AUTHOR]

The speech enhancement effect of traditional deep learning algorithms is not ideal under low signal-to-noise ratios (SNR). Skip connections-deep neural network (Skip-DNN) improves the traditional deep neural network (DNN) by adding skip connections between each layer of the neural network to solve the degradation problem of DNN. In this paper, the Multiresolution Cochleagram (MRCG) features in the gammachirp transform domain are denoised to obtain the improved MRCG (I-MRCG). The noise reduction method adopts the Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator (MMSE-STSA) and takes I-MRCG as the input feature and Skip-DNN as the training network to improve the speech enhancement effect of the model. This paper also proposes an improved source-to-distortion ratio (SDR) loss function. When the loss function uses the improved SDR, it will improve the performance of Skip-DNN speech enhancement model. The experiments in this paper are performed on the Edinburgh dataset. When using I-MRCG as the input feature of Skip-DNN, the average perceptual evaluation of speech quality (PESQ) is 2.9137, and the average short-time objective intelligibility (STOI) is 0.8515. Compared with MRCG as Skip-DNN input features, the improvements are 0.91% and 0.71%, respectively. When the improved SDR is used as the loss function of the speech model, the average PESQ is 2.9699 and the average STOI is 0.8547. Compared with other loss functions, the improved SDR has a better enhancement effect when used as the loss function of the speech enhancement model. [ABSTRACT FROM AUTHOR]

Irony recognition is an important research direction in text sentiment analysis, which contributes to discover ironic tone to judge text emotion correctly. The object of this paper is the application of deep learning to irony recognition, and the research aims to solve the problem that existing machine learning algorithms have difficulty in discriminating ironic tones. Aiming at the irony recognition of Chinese social comments, DC-BiGRU-CNN is proposed from the perspective of structural optimization rather than a text vectorization mechanism point of view, which is a dual-channel CNN combined with BiGRU, and incorporates attention mechanism and multigranularity convolutional neural network as the main framework. This paper first briefly discusses the difficulties of irony recognition and provides an overview of existing text sentiment analysis algorithms. This is followed by a detailed discussion of DC-BiGRU-CNN. Furtherly, it is compared with the main irony recognition methods on a social comment dataset containing Chinese ironic comments, and the experimental results show that DC-BiGRU-CNN can improve the accuracy of irony recognition. [ABSTRACT FROM AUTHOR]

The communication recognition of mobile phone core is a test of the development of machine vision. The size of mobile phone core is very small, so it is difficult to identify small defects. Based on the in-depth study of the algorithm, combined with the actual needs of core identification, this paper improves the algorithm and proposes an intelligent algorithm suitable for core identification. In addition, according to the actual needs of core wire recognition, this paper makes an intelligent analysis of the core wire recognition process. In addition, this paper improves the traditional communication image recognition algorithm and analyzes the data of the recognition algorithm according to the shape and image characteristics of the mobile phone core. Finally, after constructing the functional structure of the system model constructed in this paper, the system model is verified and analyzed, and on this basis, the performance of the improved core recognition algorithm proposed in this paper is verified and analyzed. From the results of online monitoring and recognition, the statistical accuracy of mobile phone core video recognition is about 90%, which has higher accuracy in mobile phone core image recognition than traditional recognition algorithms. The core line recognition algorithm based on deep learning and machine vision is effective and has a good practical effect. [ABSTRACT FROM AUTHOR]

Microexpressions have extremely high due value in national security, public safety, medical, and other fields. However, microexpressions have characteristics that are obviously different from macroexpressions, such as short duration and weak changes, which greatly increase the difficulty of microexpression recognition work. In this paper, we propose a microexpression recognition method based on multimodal fusion through a comparative study of traditional microexpression recognition algorithms such as LBP algorithm and CNN and LSTM deep learning algorithms. The method couples the separate microexpression image information with the corresponding body temperature information to establish a multimodal fusion microexpression database. This paper firstly introduces how to build a multimodal fusion microexpression database in a laboratory environment, secondly compares the recognition accuracy of LBP, LSTM, and CNN + LSTM networks for microexpressions, and finally selects the superior CNN + LSTM network in the comparison results for model training and testing on the test set under separate microexpression database and multimodal fusion database. The experimental results show that a microexpression recognition method based on multimodal fusion designed in this paper is more accurate than unimodal recognition in multimodal recognition after feature fusion, and its recognition rate reaches 75.1%, which proves that the method is feasible and effective in improving microexpression recognition rate and has good practical value. [ABSTRACT FROM AUTHOR]

Ship radiated noise is an important information source of underwater acoustic targets, and it is of great significance to the identification and classification of ship targets. However, there are a lot of interference noises in the water, which leads to the reduction of the model recognition rate. Therefore, the recognition results of radiated noise targets are severely affected. This paper proposes a machine learning Dempster–Shafer (ML-DS) decision fusion method. The algorithm combines the recognition results of machine learning and deep learning. It uses evidence-based decision-making theory to realize feature fusion under different neural network classifiers and improve the accuracy of judgment. First, deep learning algorithms are used to classify two-dimensional spectrogram features and one-dimensional amplitude features extracted from CNN and LSTM networks. The machine learning algorithm SVM is used to classify the chromaticity characteristics of radiated noise. Then, according to the classification results of different classifiers, a basic probability assignment model (BPA) was designed to fuse the recognition results of the classifiers. Finally, according to the classification characteristics of machine learning and deep learning, combined with the decision-making of D-S evidence theory of different times, the decision-making fusion of radiated noise is realized. The results of the experiment show that the two fusions of deep learning combined with one fusion of machine learning can significantly improve the recognition results of low signal-to-noise ratio (SNR) datasets. The lowest fusion recognition result can reach 76.01%, and the average fusion recognition rate can reach 94.92%. Compared with the traditional single feature recognition algorithm, the recognition accuracy is greatly improved. Compared with the traditional one-step fusion algorithm, it can effectively integrate the recognition results of heterogeneous data and heterogeneous networks. The identification method based on ML-DS proposed in this paper can be applied in the field of ship radiated noise identification. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a distributed machine learning (ML) framework. In FL, multiple clients collaborate to solve traditional distributed ML problems under the coordination of the central server without sharing their local private data with others. This paper mainly sorts out FLs based on machine learning and deep learning. First of all, this paper introduces the development process, definition, architecture, and classification of FL and explains the concept of FL by comparing it with traditional distributed learning. Then, it describes typical problems of FL that need to be solved. On the basis of classical FL algorithms, several federated machine learning algorithms are briefly introduced, with emphasis on deep learning and classification and comparisons of those algorithms are carried out. Finally, this paper discusses possible future developments of FL based on deep learning. [ABSTRACT FROM AUTHOR]

Since the breakthrough of deep learning in object classification in 2012, extraordinary achievements have been made in the field of target detection, but the high time and space complexity of the target detection network based on deep learning has hindered the technology from application in actual product. To solve this problem, first of all, this paper uses the MobileNet classification network to optimize the Faster R-CNN target detection network. The experimental results on the rare earth element detection data set show that the MobileNet classification network is not suitable for optimizing the Faster R-CNN network. After that, this paper proposes a classification network that combines VGG16 and MobileNet, and uses the fusion network to optimize the Faster R-CNN target detection network. The experimental results on the rare earth element detection data set show that the Faster R-CNN target detection network optimized by the fusion classification network has the advantages of using VGG16 and MobileNet's Faster R-CNN target detection network to detect rare earth elements. The innovation of this article is that the results on 5 time series data sets show that CDA-WR has better predictive performance than other ELM variant models. The effect of determining trace cerium elements in rocks and minerals is increased by more than 50%, based on deep learning. The algorithm studies the methods of target detection and recognition and integrates it into the intelligent robot used in this subject, giving the robot the ability to accurately detect the target object in real time. [ABSTRACT FROM AUTHOR]

As a branch of the field of machine learning, deep learning technology is abrupt in various computer vision tasks with its powerful functional learning functions. The deep learning method can extract the required features from the original data and dynamically adjust and update the parameters of the neural network through the backpropagation algorithm so as to achieve the purpose of automatically learning features. Compared with the method of extracting features manually, the recognition accuracy is improved, and it can be used for the segmentation of copperplate printing images. This article mainly introduces the research on the key algorithm of the copperplate printing image segmentation based on deep learning and intends to provide some ideas and directions for improving the copperplate printing image segmentation technology. This paper introduces the related principles, watershed algorithm, and guided filtering algorithm of copperplate printing image synthesis process and establishes an image segmentation model. As a result, a deep learning-based optimization algorithm mechanism for the segmentation of copper engraving printing images is proposed, and experimental steps such as main color extraction in the segmentation of copper engraving printing images, adaptive main color extraction based on fuzzy set 2, and main color extraction based on fuzzy set 2 are proposed. Experimental results show that the average processing time of each image segmentation model in this paper is 0.39 seconds, which is relatively short. [ABSTRACT FROM AUTHOR]

The demand of C language informatization for English learning has increased greatly, and strengthening the application of informatization in teaching has become a current trend, while deep learning algorithms have been applied in various tasks due to their obvious advantages. In this paper, an English score prediction method based on the XGBoost algorithm is proposed. In order to verify the effectiveness of the model in English score prediction, the principle of the XGBoost algorithm is firstly analyzed as a basis. The English test scores of a university for 2019–2021 were used as the basic data source, and the output probabilities of the proposed model were used to compare the results under different conditions. The experimental results show that the predicted scores are basically consistent with the actual scores. From a scientific point of view, the ability to predict unknown data with low error suggests that it enables students and teachers to identify the underlying factors that make it difficult for students to answer questions. Understanding these causes is useful for designing high-quality courses and lesson plans. [ABSTRACT FROM AUTHOR]

Aiming at the high miss detection and false detection rate of traditional SSD (single shot multibox detector) target detection algorithm in target detection, this paper proposes a lightweight detection algorithm for deep learning target detection model in order to improve the detection accuracy. Firstly, the real-time and efficient target detection backbone network VoVNet is used to replace the feature extraction network VGG16. The residual structure is integrated to solve the problem of VoVNet network degradation to improve network performance. Secondly, self-attention mechanism is introduced to capture multiscale local and global information to obtain richer image semantic features. According to the characteristics of the target sample size, the anchor frame is designed by using a priori information. In the network training, the anchor frame by selection enhancement based on IoU optimization is used to fully train the target information and strengthen the reading ability of the network to small-scale targets. Experiments on the public data set AI-TOD to show that the target detection lightweight model of deep learning has stronger detection ability and higher average detection accuracy than other algorithms, which proves the applicability and effectiveness of this algorithm. [ABSTRACT FROM AUTHOR]

With the improvement of computer computing power and storage capacity, the emergence of massive data makes the methods based on human action feature detection and recognition unable to meet people's needs due to poor generalization ability. Based on the detection and recognition of human action features based on deep learning algorithms, a suitable neural network can be constructed to identify locked human action features from surveillance video and analyze whether it is a specific behavior. In this paper, a deep learning algorithm is proposed to optimize the detection of human action features, and a multiview reobservation fusion action recognition model of 3D pose is designed. Several factors affecting the recognition of human action features are analyzed, and a detailed summary is made from the detection environment. Experiments show that adding one or two layers of feature attention enhancement to the multiview observation fusion network can improve the accuracy by 1% to 3%. In this way, the model can integrate action features from multiple observation angles to judge actions and learn to find observation angles suitable for action recognition, thereby improving the performance of action recognition. [ABSTRACT FROM AUTHOR]

The latest development of neuroscience has deepened the understanding of the information-processing mechanisms in the human brain and inspired a couple of sophisticated computational methods, such as deep learning, memory networks, and hierarchical temporal memory. However, it remains a challenge to explore simpler models due to the high computational cost of the above-mentioned methods. This paper proposes recall network (RN), an intuitive and simple model, that initializes itself by constructing the network path derived from the correlation of features in the training dataset and then makes classification decisions by recalling the paths that are relevant to the features in the test set. The algorithm has been applied to 263 datasets available from UCI Machine Learning Repository, and the classification results of repeated 10-fold cross-validation experiments on Weka demonstrate its competitive performance with prestigious classification algorithms, such as ANN, J48, and KNN. [ABSTRACT FROM AUTHOR]

In order to explore the analysis and research on the development of digital industry and further understand the industrial development trend, in view of the continuous growth of the scale and quantity of digital industry and the continuous increase and expansion of digital industry economy, this paper uses the most popular intelligent deep learning algorithm to verify and research the efficiency of digital industry development under intelligent technology. Finally, it is considered that the application of intelligent deep learning algorithm not only meets the development needs of digital industry in the intelligent era but also improves the accuracy of economic development of digital industry and the overall development power. It has important strategic significance in promoting the economic prosperity of digital industry in the later stage, improving the advantages of industrial development and improving people's livelihood, and has a new awareness of China's economic development in the world. [ABSTRACT FROM AUTHOR]

As a complex machine learning algorithm, deep learning can extract object shape information and more complex and advanced information in images by using a deep learning model. In order to solve some problems of deep learning in image feature extraction and classification, this paper designs a modeling method of multifocus image segmentation algorithm based on deep learning. The acceleration effect of FPGA (field programmable gate array) on deep learning and weight sharing is analyzed. By introducing deep learning, the trouble of determining the weight coefficient is eliminated, and the energy function is simplified. Therefore, the relevant parameters of multifocus image segmentation can be easily set, and better results can be obtained. The multifocus image segmentation algorithm based on deep learning can not only obtain closed and smooth segmentation curves but also adaptively deal with topology changes due to high segmentation accuracy and stable algorithm. The results show that the model effectively combines the local and global information of the image, so that the model has good robustness. The depth learning algorithm is used to calculate the average value of local inner and outer pixels of an image. Even for complex images, relatively simple contour curves can be obtained. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has been widely used all over the world. AI can be applied not only in mechanical learning and expert system but also in knowledge engineering and intelligent information retrieval and has achieved amazing results. This article aims to study the relevant knowledge of deep learning algorithms and multisensor information fusion and how to use deep learning algorithms and multisensor information fusion to study AI algorithms. This paper raises the question of whether the improved multisensor information fusion will affect the AI algorithm. From the data in the experiment of this article, the accuracy of the neural network before the improvement was 4.1%. With the development of society, the traditional algorithm finally dropped to 1.3%. The accuracy of the multisensor information fusion algorithm before the improvement was 3.1% at the beginning; with the development of society, it finally dropped to 1%; it can be known that the accuracy of the improved neural network is 4.6%, and with continuous improvement, it finally increased to 9.8%. The improved multisensor information fusion algorithm is the same, the accuracy at the beginning was 3.9%, and gradually increased to 9.5%. From this set of data, it can be known that the improved convolutional neural network (CNN) algorithm, and the improved multisensor information fusion algorithm should be used to study AI algorithms. [ABSTRACT FROM AUTHOR]

Gesture recognition utilizes deep learning network model to automatically extract deep features of data; however, traditional machine learning algorithms rely on manual feature extraction and poor model generalization ability. In this paper, a multimodal gesture recognition algorithm based on convolutional long-term memory network is proposed. First, a convolutional neural network (CNN) is employed to automatically extract the deeply hidden features of multimodal gesture data. Then, a time series model is constructed using a long short-term memory (LSTM) network to learn the long-term dependence of multimodal gesture features on the time series. On this basis, the classification of multimodal gestures is realized by the SoftMax classifier. Finally, the method is experimented and evaluated on two dynamic gesture datasets, VIVA and NVGesture. Experimental results indicate that the accuracy rates of the proposed method on the VIVA and NVGesture datasets are 92.55% and 87.38%, respectively, and its recognition accuracy and convergence performance are better than those of other comparison algorithms. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD. This paper explores the current cutting edge applications of CNN on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of AD. An exhaustive systematic search is conducted on four notable databases: Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed in June 2021. The objective of this study is to examine the effectiveness of classification approaches on AD to analyze different kinds of datasets, neuroimaging modalities, preprocessing techniques, and data handling methods. However, CNN has achieved great success in the classification of AD; still, there are a lot of challenges particularly due to scarcity of medical imaging data and its possible scope in this field. [ABSTRACT FROM AUTHOR]

Deep learning has played an important role in many fields, which shows significant potential for cryptanalysis. Although these existing works opened a new direction of machine learning aided cryptanalysis, there is still a research gap that researchers are eager to fill. How to further improve neural distinguishers? In this paper, we propose a new algorithm and model to improve neural distinguishers in terms of accuracy and the number of rounds. First, we design an algorithm based on SAT to improve neural distinguishers. With the help of SAT/SMT solver, we obtain new effective neural distinguishers of SIMON using the input differences of high-probability differential characteristics. Second, we propose a new neural distinguisher model using multiple output differences. Inspired by the existing works and data augmentation in deep learning, we use the output differences to exploit more derived features and train neural distinguishers, by splicing output differences into a matrix as a sample. Based on the new model, we construct neural distinguishers of SIMON and SPECK with round and accuracy promotion. Utilizing our neural distinguishers, we can distinguish reduced-round SIMON or SPECK from pseudorandom permutation better. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning algorithms, it is gradually applied in UAV (Unmanned Aerial Vehicle) driving, visual recognition, target tracking, behavior recognition, and other fields. In the field of sports, many scientists put forward the research of target tracking and recognition technology based on deep learning algorithms for athletes' trajectory and behavior capture. Based on the target tracking algorithm, a regional proposal network RPN algorithm combined with the twin regional proposal network Siamese algorithm is proposed to study the tracking and recognition technology of athletes' behavior. Then, the adaptive updating network is used to track the behavior target of athletes, and the simulation model of behavior recognition is established. This algorithm is different from the traditional twin network algorithm. It can accurately take the athlete's behavior as the target candidate box in model training and reduce the interference of environment and other factors on model recognition. The results show that the Siamese-RPN algorithm can reduce the interference from the background and environment when tracking the athletes' target behavior trajectory. This algorithm can improve the training behavior recognition model, ignore the background interference elements of the behavior image, and improve the accuracy and overall performance of the model. Compared with the traditional twin network method for sports behavior recognition, the Siamese-RPN algorithm studied in this paper can perform offline operations and distinguish the interference factors of athletes' background environment. It can quickly capture the characteristic points of athletes' behavior as the data input of the tracking model, so it has excellent popularization and application value. [ABSTRACT FROM AUTHOR]

Film and television literature recommendation is an AI algorithm that recommends related content according to user preferences and records. The wide application in various APPs and websites provides users with great convenience. This article aims to study the Internet of Things and machine learning technology, combining deep learning, reinforcement learning, and recommendation algorithms, to achieve accurate recommendation of film and television literature. This paper proposes to use the ConvMF-KNN recommendation model to verify and analyze the four models of PMF, ConvM, ConvMF-word2vec, and ConvMF-KNN, respectively, on public datasets. Using the path information between vertices in bipartite graph and considering the degree of vertices, the similarity between items is calculated, and the neighbor item set of items is obtained. The experimental results show that the ConvMF-KNN model combined with the KNN idea effectively improves the recommendation accuracy. Compared with the accuracy of the PMF model on the MovieLens 100 k, MovieLens 1 M, and AIV datasets, the accuracy of the ConvMF model on the above three datasets is 5.26%, 6.31%, and 26.71%, respectively, an increase of 2.26%, 1.22%, and 7.96%. This model is of great significance. [ABSTRACT FROM AUTHOR]

Healthcare sector is one of the prominent sectors in which a lot of data can be collected not only in terms of health but also in terms of finances. Major frauds happen in the healthcare sector due to the utilization of credit cards as the continuous enhancement of electronic payments, and credit card fraud monitoring has been a challenge in terms of financial condition to the different service providers. Hence, continuous enhancement is necessary for the system for detecting frauds. Various fraud scenarios happen continuously, which has a massive impact on financial losses. Many technologies such as phishing or virus-like Trojans are mostly used to collect sensitive information about credit cards and their owner details. Therefore, efficient technology should be there for identifying the different types of fraudulent conduct in credit cards. In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds in credit cards. For evaluating the accuracy of the model, publicly available data are used. The different algorithm results visualized the accuracy as 96.1%, 94.8%, 95.89%, 97.58%, and 92.3%, corresponding to various methodologies such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network, respectively. The comparative analysis visualized that the KNN algorithm generates better results than other approaches. [ABSTRACT FROM AUTHOR]

The reinforcement learning algorithms based on policy gradient may fall into local optimal due to gradient disappearance during the update process, which in turn affects the exploration ability of the reinforcement learning agent. In order to solve the above problem, in this paper, the cross-entropy method (CEM) in evolution policy, maximum mean difference (MMD), and twin delayed deep deterministic policy gradient algorithm (TD3) are combined to propose a diversity evolutionary policy deep reinforcement learning (DEPRL) algorithm. By using the maximum mean discrepancy as a measure of the distance between different policies, some of the policies in the population maximize the distance between them and the previous generation of policies while maximizing the cumulative return during the gradient update. Furthermore, combining the cumulative returns and the distance between policies as the fitness of the population encourages more diversity in the offspring policies, which in turn can reduce the risk of falling into local optimal due to the disappearance of the gradient. The results in the MuJoCo test environment show that DEPRL has achieved excellent performance on continuous control tasks; especially in the Ant-v2 environment, the return of DEPRL ultimately achieved a nearly 20% improvement compared to TD3. [ABSTRACT FROM AUTHOR]

This paper addresses a new machine learning-based behavioral strategy using the deep Q-learning algorithm for the RoboCode simulation platform. According to this strategy, a new model is proposed for the RoboCode platform, providing an environment for simulated robots that can be programmed to battle against other robots. Compared to Atari Games, RoboCode has a fairly wide set of actions and situations. Due to the challenges of training a CNN model for such a continuous action space problem, the inputs obtained from the simulation environment were generated dynamically, and the proposed model was trained by using these inputs. The trained model battled against the predefined rival robots of the environment (standard robots) by cumulatively benefiting from the experience of these robots. The comparison between the proposed model and standard robots of RoboCode Platform was statistically verified. Finally, the performance of the proposed model was compared with machine learning based-customized robots (community robots). Experimental results reveal that the proposed model is mostly superior to community robots. Therefore, the deep Q-learning-based model has proven to be successful in such a complex simulation environment. It should also be noted that this new model facilitates simulation performance in adaptive and partially cluttered environments. [ABSTRACT FROM AUTHOR]

3D multiobject tracking (MOT) is an important part of road condition detection and hazard warning algorithm in roadside systems and autonomous driving systems. There is a tricky problem in 3D MOT that the identity of occluded object switches after it reappears. Given the good performance of the 2D MOT, this paper proposes a 3D MOT algorithm with deep learning based on the multiobject tracking algorithm. Firstly, a 3D object detector was used to obtain oriented 3D bounding boxes from point clouds. Secondly, a 3D Kalman filter was used for state estimation, and reidentification algorithm was used to match feature similarity. Finally, data association was conducted by combining Hungarian algorithm. Experiments show that the proposed method can still match the original trajectory after the occluded object reappears and run at a rate of 59 FPS, which has achieved advanced results in the existing 3D MOT system. [ABSTRACT FROM AUTHOR]

Summary: Microgrid is a new era in the power system and it has more scope of investigation on research. Due to an increase in demand and future expansion of the power system, analyzing the complexities of the network becomes a challenging task. Artificial intelligence plays a vital role in resolving such issues in a microgrid in various aspects. Owing to the rapid growth of periodical update in computational cost reduction, enhanced data analysis‐based algorithm artificial intelligence enters into new epoch Artificial Intelligence AI 2.0. Based on such approach, machine learning has been evolved as AI 2.0 initially. Now, it develops branches like deep learning, reinforcement learning, and a combination of both deep reinforcement learning algorithms. These algorithms are precise to attain higher priority in decision‐making under a complex network. This paper deals with numerous challenges of the above algorithm to state the significance of AI 2.0 and summarization of their application toward microgrid is useful to analyze the power system. [ABSTRACT FROM AUTHOR]

The restricted Boltzmann machine (RBM) is one of the widely used basic models in the field of deep learning. Although many indexes are available for evaluating the advantages of RBM training algorithms, the classification accuracy is the most convincing index that can most effectively reflect its advantages. RBM training algorithms are sampling algorithms essentially based on Gibbs sampling. Studies focused on algorithmic improvements have mainly faced challenges in improving the classification accuracy of the RBM training algorithms. To address the above problem, in this paper, we propose a fast Gibbs sampling (FGS) algorithm to learn the RBM by adding accelerated weights and adjustment coefficient. An important link based on Gibbs sampling theory was established between the update of the network weights and mixing rate of Gibbs sampling chain. The proposed FGS method was used to accelerate the mixing rate of Gibbs sampling chain by adding accelerated weights and adjustment coefficients. To further validate the FGS method, numerous experiments were performed to facilitate comparisons with the classical RBM algorithm. The experiments involved learning the RBM based on standard data. The results showed that the proposed FGS method outperformed the CD, PCD, PT5, PT10, and DGS algorithms, particularly with respect to the handwriting database. The findings of our study suggest the potential applications of FGS to real-world problems and demonstrate that the proposed method can build an improved RBM for classification. [ABSTRACT FROM AUTHOR]