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In an era overwhelmed by academic big data, students grapple with identifying academic papers that resonate with their learning objectives and research interests, due to the sheer volume and complexity of available information. This study addresses the challenge by proposing a novel academic paper recommendation system designed to enhance personalized learning through the nuanced understanding of academic social networks. Utilizing the theory of social homogeneity, the research first constructs a sophisticated academic social network, capturing high-order social relationships, such as co-authorship and advisor–advisee connections, through hypergraph modeling and advanced network representation learning techniques. The methodology encompasses the development and integration of a hypergraph convolutional neural network and a contrastive learning framework to accurately model and recommend academic papers, focusing on aligning with students' unique preferences and reducing reliance on sparse interaction data. The findings, validated across multiple real-world datasets, demonstrate a significant improvement in recommendation accuracy, particularly in addressing the cold-start problem and effectively mapping advisor–advisee relationships. The study concludes that leveraging complex academic social networks can substantially enhance the personalization and precision of academic paper recommendations, offering a promising avenue for addressing the challenges of academic information overload and fostering more effective personalized learning environments. [ABSTRACT FROM AUTHOR]

The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its nascent phase. There have been significant advancements in deep learning specifically for three-dimensional data, offering a range of ways to address these issues. This study offers a comprehensive examination of the latest advancements in deep learning methodologies. We examine many benchmark models for the tasks of 3D object registration, augmentation, and reconstruction. We thoroughly analyse their architectures, advantages, and constraints. In summary, this report provides a comprehensive overview of recent advancements in three-dimensional deep learning and highlights unresolved research areas that will need to be addressed in the future. [ABSTRACT FROM AUTHOR]

In the final manuscript [[33]], presented as a communication, the author indicates that unmanned aircraft systems (UASs), commonly referred to as drones, are an emerging technology that has changed the way that many industries conduct business. When used on a state-by-state basis, the normalized UAS adoption index shows that there are regional differences in UAS adoption, with western and eastern states having higher UAS adoption rates and central states having significantly lower UAS adoption rates. The aim of this second I Eng i Special Issue is to collect experimental and theoretical re-search relating to engineering science and technology. Telecommunication companies collect a deluge of subscriber data without retrieving substantial information. [Extracted from the article]

10.3390/rs13214334 7 Park S.-H., Yoo J., Son D., Kim J., Jung H.-S. Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network. Lee and Choi [[4]] proposed a daytime cloud detection algorithm using a multi-temporal Geostationary Korea Multi-Purpose Satellite 2A (GEO-KOMPSAT-2A, GK-2A) dataset. 10.3390/rs13214282 9 Park S.-H., Jung H.-S., Lee S., Kim E.-S. Mapping Forest Vertical Structure in Sogwang-ri Forest from Full-Waveform Lidar Point Clouds Using Deep Neural Network. [Extracted from the article]

Sensor data fusion analysis is a crucial process that integrates data from multiple sensors to provide more accurate and comprehensive information. This analysis has various applications in fields such as industry, medicine, robotics, transportation, and information systems. The article highlights sixteen high-quality papers that explore different aspects of sensor data fusion, including facial detection, object detection, odometry systems, geospatial data evaluation, and fault detection. The papers demonstrate the potential of sensor data fusion in improving decision-making, efficiency, and productivity in various industries. Future research in this area will focus on miniaturization, multi-sensor systems, and addressing challenges related to data quality in mobile sensors. [Extracted from the article]

We present in this paper the state of the art and an analysis of recent research work and achievements performed in the domain of AI-based and vision-based systems for helping blind and visually impaired people (BVIP). We start by highlighting the recent and tremendous importance that AI has acquired following the use of convolutional neural networks (CNN) and their ability to solve image classification tasks efficiently. After that, we also note that VIP have high expectations about AI-based systems as a possible way to ease the perception of their environment and to improve their everyday life. Then, we set the scope of our survey: we concentrate our investigations on the use of CNN or related methods in a vision-based system for helping BVIP. We analyze the existing surveys, and we study the current work (a selection of 30 case studies) using several dimensions such as acquired data, learned models, and human–computer interfaces. We compare the different approaches, and conclude by analyzing future trends in this domain. [ABSTRACT FROM AUTHOR]

This study compares the performance of graph convolutional neural network (GCN) models with conventional natural language processing (NLP) models for classifying scientific literature related to radio frequency electromagnetic field (RF-EMF). Specifically, the study examines two GCN models: BertGCN and the citation-based GCN. The study concludes that the model achieves consistently good performance when the input text is long enough, based on the attention mechanism of BERT. When the input sequence is short, the composition parameter  λ , which combines output values of the two subnetworks of BertGCN, plays a crucial role in achieving high classification accuracy. As the value of  λ  increases, the classification accuracy also increases. The study also proposes and tests a simplified variant of BertGCN, revealing performance differences among the models under two different data conditions by the existence of keywords. This study has two main contributions: (1) the implementation and testing of a variant of BertGCN and citation-based GCN for document classification tasks related to radio frequency electromagnetic fields publications, and (2) the confirmation of the impact of model conditions, such as the existence of keywords and input sequence length, in the original BertGCN. Although this study focused on a specific domain, our approaches have broader implications that extend beyond scientific publications to general text classification. [ABSTRACT FROM AUTHOR]

Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases. [ABSTRACT FROM AUTHOR]

This editorial note is dedicated to the 10th Scientific Conference which was held on June 2022 in Galati, Romania, and was organized by the Council of Doctoral Schools of the "Dunarea de Jos" University of Galati (SCDS-UDJG). Three articles in this Special Issue present findings in the field of mechanical and industrial engineering. Four articles present studies related to artificial intelligence technologies, representing the majority of papers published in this Special Issue. [Extracted from the article]

Rockfall movement is a significant hazard for infrastructure located in mountainous regions, and Paper [[9]] presents a numerical investigation on the relationship between damping and coefficient of restitution (COR) in rockfall movement. Geotechnical engineering is a complex field that deals with various hazards that can impact soil, rock, and other geologic materials. [Extracted from the article]

10.3390/math10091364 18 Wang Y., Yang L., Wu J., Song Z., Shi L. Mining Campus Big Data: Prediction of Career Choice Using Interpretable Machine Learning Method. 10.3390/math10193659 6 Cao X., Luo Q., Wu P. Filter-GAN: Imbalanced Malicious Traffic Classification Based on Generative Adversarial Networks with Filter. With the emergence of big data and the resulting information explosion, computational and mathematical methods provide effective tools to handle the vast amounts of data and information used in big data analytics, knowledge discovery and distillation, and decision-making for solving complex problems in the world. The results show that the feature image representation method can effectively characterize the original session traffic, and Filter-GAN can generate more efficient samples. [Extracted from the article]

To protect our planet, the material recycling of domestic waste is necessary. Since the COVID-19 pandemic began, the volume of domestic waste has surged overwhelmingly, and many countries suffered from poor waste management. Increased demand for food delivery and online shopping led to a huge surge in plastic and paper waste which came from natural resources. To reduce the consumption of resources and protect the environment from pollution, such as that from landfills, waste should be recycled. One of precious recyclable materials from household waste is liquid cartons that are made of high-quality paper. To promote sustainable recycling, this paper proposes a vision-based inspection module based on convolutional neural networks via transfer learning (CNN-TL) for collecting liquid packaging cartons in the reverse vending machine (RVM). The RVM is an unmanned automatic waste collector, and thus it needs the intelligence to inspect whether a deposited item is acceptable or not. The whole processing algorithm for collecting cartons, including the inspection step, is presented. When the waste is inserted into the RVM by a user after scanning the barcode on the waste, it is relocated to the inspection module, and the item is weighed. To develop the inspector, an experimental set-up with a video camera was built for image data generation and preparation. Using the image data, the inspection agent was trained. To make a good selection for the model, 17 pretrained CNN models were evaluated, and DenseNet121 was selected. To access the performance of the cameras, four different types were also evaluated. With the same CNN model, this paper found the effect of the number of training epochs being set to 10, 100, and 500. In the results, the most accurate agent was the 500-epoch model, as expected. By using the RVM process logic with this model, the results showed that the accuracy of detection was over 99% (overall probability from three inspections), and the time to inspect one item was less than 2 s. In conclusion, the proposed model was verified for whether it would be applicable to the RVM, as it could distinguish liquid cartons from other types of paper waste. [ABSTRACT FROM AUTHOR]

Long Short-Term Memory (LSTM)-Based Dog Activity Detection Using Accelerometer and Gyroscope. The third paper, authored by Jeong et al. [[14]], introduced a fall detection system that combines a simple threshold method (STM) and LSTM, creating the STM-LSTM-based fall detection system. With rapid advancements in artificial intelligence (AI), the Internet of Things (IOTs), and communication networks, the future of information and communication engineering (ICE) promises remarkable opportunities and challenges. 10.3390/app12199801 18 Zhang M., Wang C., Zhu W., Shen Y. An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning. [Extracted from the article]

Applied computing and artificial intelligence methods have been attracting growing interest in recent years due to their effectiveness in solving technical problems. Hao et al. [[14]] present an unsupervised fault diagnosis methodology to leverage the generated MPCs of different working conditions to diagnose the actual unlabeled MPCs. The paper by Ainapure et al. [[17]] proposes a new cross-domain fault diagnosis method with enhanced robustness. The paper by Saeed et al. [[1]] proposes an approach to building an AutoML data-dependent CNN model (DeepPCANet) customized for DR screening automatically. [Extracted from the article]

This paper addresses the problem of real-time onboard routing for dynamic low earth orbit (LEO) satellite networks. It is difficult to apply general routing algorithms to dynamic LEO networks due to the frequent changes in satellite topology caused by the disconnection between moving satellites. Deep reinforcement learning (DRL) models trained by various dynamic networks can be considered. However, since the inference process with the DRL model requires too long a computation time due to multiple convolutional layer operations, it is not practical to apply to a real-time on-board computer (OBC) with limited computing resources. To solve the problem, this paper proposes a practical co-design method with heterogeneous processors to parallelize and accelerate a part of the multiple convolutional layer operations on a field-programmable gate array (FPGA). The proposed method was tested with a real heterogeneous processor-based OBC and showed that the proposed method was about 3.10 times faster than the conventional method while achieving the same routing results. [ABSTRACT FROM AUTHOR]

Because of the difficulty in fault detection for and diagnosing the adjustment hydraulic servomotor, this paper uses feature extraction technology to extract the time domain and frequency domain features of the pressure signal of the adjustment hydraulic servomotor and splice the features of multiple pressure signals through the Multi-source Information Fusion (MSIF) method. The comprehensive expression of device status information is obtained. After that, this paper proposes a fault detection Algorithm GA-SVDD-neg, which uses Genetic Algorithm (GA) to optimize Support Vector Data Description with negative examples (SVDD-neg). Through joint optimization with the Mutual Information (MI) feature selection algorithm, the features that are most sensitive to the state deterioration of the adjustment hydraulic servomotor are selected. Experiments show that the MI algorithm has a better performance than other feature dimensionality reduction algorithms in the field of the abnormal detection of adjustment hydraulic servomotors, and the GA-SVDD-neg algorithm has a stronger robustness and generality than other anomaly detection algorithms. In addition, to make full use of the advantages of deep learning in automatic feature extraction and classification, this paper realizes the fault diagnosis of the adjustment hydraulic servomotor based on 1D Convolutional Neural Network (1DCNN). The experimental results show that this algorithm has the same superior performance as the traditional algorithm in feature extraction and can accurately diagnose the known faults of the adjustment hydraulic servomotor. This research is of great significance for the intelligent transformation of adjustment hydraulic servomotors and can also provide a reference for the fault warning and diagnosis of the Electro-Hydraulic (EH) system of the same type of steam turbine. [ABSTRACT FROM AUTHOR]

In the dynamic visual cabinet, the occurrence of motion blur when consumers take out commodities will reduce the accuracy of commodity detection. Recently, although Transformer-based video deblurring networks have achieved results compared to Convolutional Neural Networks in some blurring scenarios, they are still challenging for the non-uniform blurring problem that occurs when consumers pick up the commodities, such as the problem of difficult alignment of blurred video frames of small commodities and the problem of underutilizing the effective information between the video frames of commodities. Therefore, an improved Transformer video deblurring network is proposed. Firstly, a multi-scale Transformer feature extraction method is utilized for non-uniform blurring. Secondly, for the problem of difficult alignment of small-item-blurred video frames, a temporal interactive attention mechanism is designed for video frame alignment. Finally, a feature recurrent fusion mechanism is introduced to supplement the effective information of commodity features. The experimental results show that the proposed method has practical significance in improving the accuracy of commodity detection. Moreover, compared with the recent Transformer deblurring algorithm Video Restoration Transformer, the Peak Signal-to-Noise Ratio of this paper's algorithm is higher than that of the Deep Video Deblurring dataset and the Fuzzy Commodity Dataset by 0.23 dB and 0.81 dB, respectively. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations. Therefore, the application of image technology for vehicle-type recognition is of great significance for integrated traffic management. In this paper, an improved faster region with convolutional neural network features (Faster R-CNN) model was proposed for vehicle-type recognition. Firstly, the output features of different convolution layers were combined to improve the recognition accuracy. Then, the average precision (AP) of the recognition model was improved through the contextual features of the original image and the object bounding box optimization strategy. Finally, the comparison experiment used the vehicle image dataset of three vehicle types, including cars, sports utility vehicles (SUVs), and vans. The experimental results show that the improved recognition model can effectively identify vehicle types in the images. The AP of the three vehicle types is 83.2%, 79.2%, and 78.4%, respectively, and the mean average precision (mAP) is 1.7% higher than that of the traditional Faster R-CNN model. [ABSTRACT FROM AUTHOR]

In this paper, we utilize a machine learning model (the convolutional neural network) to analyze aerial images of winter hard red wheat planted areas and cloud coverage over the planted areas as a proxy for future yield forecasts. We trained our model to forecast the futures price 20 days ahead and provide recommendations for either a long or short position on wheat futures. Our method shows that achieving positive alpha within a short time window is possible if the algorithm and data choice are unique. However, the model's performance can deteriorate quickly if the input data become more easily available and/or the trading strategy becomes crowded, as was the case with the aerial imagery we utilized in this paper. [ABSTRACT FROM AUTHOR]

Since plant diseases occurring during the growth process are a significant factor leading to the decline in both yield and quality, the classification and detection of plant leaf diseases, followed by timely prevention and control measures, are crucial for safeguarding plant productivity and quality. As the traditional convolutional neural network structure cannot effectively recognize similar plant leaf diseases, in order to more accurately identify the diseases on plant leaves, this paper proposes an effective plant disease image recognition method aECA-ResNet34. This method is based on ResNet34, and in the first and the last layers of this network, respectively, we add this paper's improved aECAnet with the symmetric structure. aECA-ResNet34 is compared with different plant disease classification models on the peanut dataset constructed in this paper and the open-source PlantVillage dataset. The experimental results show that the aECA-ResNet34 model proposed in this paper has higher accuracy, better performance, and better robustness. The results show that the aECA-ResNet34 model proposed in this paper is able to recognize diseases of multiple plant leaves very accurately. [ABSTRACT FROM AUTHOR]

This document is a special issue of the journal Biomimetics, focusing on the design and control of bio-inspired robots. It explores various aspects of bionics in robotics, including robot design, perception, control, and decision-making, as well as incorporating neuroscience and brain science. The issue covers a wide range of topics, such as stiffness adjustment for continuum robots, biomimetic motor control, stroke rehabilitation, reinforcement learning for quadruped robots, improved spiking neural networks, energy-efficient image segmentation, kinematics analysis, synthetic nervous systems for robotic control, online running-gait generation, and bio-inspired perception and navigation for service robots. The document also discusses specific papers within the special issue that address challenges in robotic perception and navigation, legged robot control, and motion control of continuum robots and robotic arms. It concludes by announcing plans for a second special issue on related topics. [Extracted from the article]

This paper [[7]] addresses the challenges Intrusion Detection Systems (IDS) face in the Internet of Things (IoT) context due to IoT data's high dimensionality and diversity. The Internet of Things (IoT) has become an increasingly popular technology in recent years, enabling interconnectivity and communication between devices and systems. As a result, there is a growing need for advanced data analysis techniques and artificial intelligence (AI) methods to process, analyze, and extract valuable insights from IoT data. The proposed technique for tracking a moving object of interest in a noisy video using a modified simplest color balance algorithm and a binarization algorithm proved to be effective in building training data for machine learning. [Extracted from the article]

This document is a special issue of the journal "Processes" focused on the application of power electronics technologies in power systems. It contains 15 articles divided into four sections: inverter, converter, control technique, and others. The articles cover topics such as optimal control of inverters, high-efficiency power converters, control methods for power electronics, and various applications of power electronics. The articles provide theoretical analysis, model derivation, actual measurement, and verification, making them valuable for research in this field. The authors express gratitude to all contributors and declare no conflicts of interest. [Extracted from the article]

The maturity grading of walnuts during harvesting relies on experience. In this paper, walnut images in a natural environment were collected to construct a dataset, and deep learning algorithms were utilized to combine walnut internal physical and chemical indicators to carry out research on walnut maturity detection methods and further research on walnut oil content prediction by combining walnut images with walnut oil content indicators. The main contents of this paper include collecting walnut images in a natural environment, constructing datasets, and using deep learning algorithms combined with internal physical and chemical indexes of walnuts to study walnut maturity detection and oil content prediction methods. First, two walnut image acquisition schemes were designed, and a total of 9504 images were collected from 23 August to 21 September 2021. The dataset was expanded to 18,504 images through data preprocessing and image enhancement. A self-supervised Gaussian attention network (GATCluster) walnut ripeness detection method based on image clustering is proposed to develop ripeness criteria through unsupervised clustering, and the accuracy of the criteria is verified by analysis of variance (ANOVA). The maturity detection accuracy of the test set of 1500 images is 88.33%. Secondly, a walnut oil content prediction method based on improved ResNet34 is proposed. The feature extraction capability is improved by introducing the Squeeze-and-Excitation Networks (SENet) channel attention mechanism and the convolutional self-attention module. The prediction results on 50 images show that the root mean square error, average absolute percentage error, and regression coefficient are 2.96, 0.103, and 0.8822, respectively. The experiments show that the method performs well in predicting the oil content of walnuts at different maturity levels. [ABSTRACT FROM AUTHOR]

Self-interference (SI) represents a bottleneck in the performance of full-duplex (FD) communication systems, necessitating robust offsetting techniques to unlock the potential of FD systems. Currently, deep learning has been leveraged within the communication domain to address specific challenges and enhance efficiency. Inspired by this, this paper reviews the self-interference cancellation (SIC) process in the digital domain focusing on SIC capability. The paper introduces a model architecture that integrates CNN and gated recurrent unit (GRU), while also incorporating residual networks and self-attention mechanisms to enhance the identification and elimination of SI. This model is named CGRSA-Net. Firstly, CNN is employed to capture local signal features in the time–frequency domain. Subsequently, a ResNet module is introduced to mitigate the gradient vanishing problem. Concurrently, GRU is utilized to dynamically capture and retain both long- and short-term dependencies during the communication process. Lastly, by integrating the self-attention mechanism, attention weights are flexibly assigned when processing sequence data, thereby focusing on the most important parts of the input sequence. Experimental results demonstrate that the proposed CGRSA-Net model achieves a minimum of 28% improvement in nonlinear SIC capability compared to polynomial and existing neural network-based eliminator. Additionally, through ablation experiments, we demonstrate that the various modules utilized in this paper effectively learn signal features and further enhance SIC performance. [ABSTRACT FROM AUTHOR]

With the rapid advancement of digital technology, three-dimensional designs of Grid Information Models (GIMs) are increasingly applied in the power industry. Addressing the challenges of extracting key parameters during the GIM's process for power grid equipment, this paper explores an innovative approach that integrates artificial intelligence with image recognition technologies into power design engineering. The traditional methods of "template matching, feature extraction and classification, and symbol recognition" have enabled the automated processing of electrical grid equipment engineering drawings, allowing for the extraction of key information related to grid equipment. However, these methods still rely on manually designed and selected feature regions, which limits their potential for achieving full automation. This study introduces an optimized algorithm that combines enhanced Convolutional Neural Networks (CNNs) with Depth-First Search (DFS) strategies, and is specifically designed for the automated extraction of crucial GIM parameters from power grid equipment. Implemented on the design schematics of power engineering projects, this algorithm utilizes an improved CNN to precisely identify component symbols on schematics, and continues to extract text data associated with these symbols. Utilizing a scene text detector, the text data are matched with corresponding component symbols. Finally, the DFS strategy is applied to identify connections between these component symbols in the diagram, thus facilitating the automatic extraction of key GIM parameters. Experimental results demonstrate that this optimized algorithm can accurately identify basic GIM parameters, providing technical support for the automated extraction of parameters using the GIM. This study's recognition accuracy is 91.31%, while a traditional CNN achieves 71.23% and a Faster R-CNN achieves 89.59%. Compared to existing research, the main innovation of this paper lies in the application of the combined enhanced CNN and DFS strategies for the extraction of GIM parameters in the power industry. This method not only improves the accuracy of parameter extraction but also significantly enhances processing speed, enabling the rapid and effective identification and extraction of critical information in complex power design environments. Moreover, the automated process reduces manual intervention, offering a novel solution in the field of power design. These features make this research broadly applicable and of significant practical value in the construction and maintenance of smart grids. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Processes titled "Process Monitoring and Fault Diagnosis." The issue explores the application of data analytic techniques to enhance stable operation and safety in chemical processes and related industries. The collection of research papers covers various topics, including process fault detection, bearing fault diagnosis, remaining useful life prediction, and more. The papers introduce cutting-edge methodologies and demonstrate their reliability through validation. The issue aims to foster communication and the development of advanced process monitoring techniques. [Extracted from the article]

Marine anchor buoys, as fixed-point profile observation platforms, are highly susceptible to the threat of ship collisions. Installing cameras on buoys can effectively monitor and collect evidence from ships. However, when using a camera to capture images, it is often affected by the continuous shaking of buoys and rainy and foggy weather, resulting in problems such as blurred images and rain and fog occlusion. To address these problems, this paper proposes an improved YOLOv8 algorithm. Firstly, the polarized self-attention (PSA) mechanism is introduced to preserve the high-resolution features of the original deep convolutional neural network and solve the problem of image spatial resolution degradation caused by shaking. Secondly, by introducing the multi-head self-attention (MHSA) mechanism in the neck network, the interference of rain and fog background is weakened, and the feature fusion ability of the network is improved. Finally, in the head network, this model combines additional small object detection heads to improve the accuracy of small object detection. Additionally, to enhance the algorithm's adaptability to camera detection scenarios, this paper simulates scenarios, including shaking blur, rain, and foggy conditions. In the end, numerous comparative experiments on a self-made dataset show that the algorithm proposed in this study achieved 94.2% mAP50 and 73.2% mAP50:95 in various complex environments, which is superior to other advanced object detection algorithms. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) have been widely used in ocean missions. When they fail in the ocean, it is important to predict their trajectory. Existing methods rely heavily on historical trajectory data while overlooking the influence of the ocean environment on an AUV's trajectory. At the same time, these methods fail to use the dependency between variables in the trajectory. To address these challenges, this paper proposes an AUV trajectory prediction model known as the nonlinear Kepler optimization algorithm–bidirectional long short-term memory–time-variable attention (NKOA-BiLSTM-TVA) model. This paper introduces opposition-based learning during the initialization process of the KOA and improves the algorithm by incorporating a nonlinear factor into the planet position update process. We designed an attention mechanism layer that spans both time and variable dimensions, called TVA. TVA can extract features from both the time and variable dimensions of the trajectory and use the dependency between trajectory variables to predict the trajectory. First, the model uses a convolutional neural network (CNN) to extract spatial features from the trajectory. Next, it combines a BiLSTM network with TVA to predict the AUV's trajectory. Finally, the improved NKOA is used to optimize the model's hyperparameters. Experimental results show that the NKOA-BiLSTM-TVA model has an excellent parameter optimization effect and higher prediction accuracy in AUV trajectory prediction tasks. It also achieves excellent results in ship trajectory prediction. [ABSTRACT FROM AUTHOR]

Electric vehicles can reduce the dependence on limited resources such as oil, which is conducive to the development of clean energy. An accurate battery state of health (SOH) is beneficial for the safety of electric vehicles. A multi-feature and Convolutional Neural Network–Bidirectional Long Short-Term Memory–Multi-head Attention (CNN-BiLSTM-MHA)-based lithium-ion battery SOH estimation method is proposed in this paper. First, the voltage, energy, and temperature data of the battery in the constant current charging phase are measured. Then, based on the voltage and energy data, the incremental energy analysis (IEA) is performed to calculate the incremental energy (IE) curve. The IE curve features including IE, peak value, average value, and standard deviation are extracted and combined with the thermal features of the battery to form a complete multi-feature sequence. A CNN-BiLSTM-MHA model is set up to map the features to the battery SOH. Experiments were conducted using batteries with different charging currents, and the results showed that even if the nonlinearity of battery SOH degradation is significant, this method can still achieve a fast and accurate estimation of the battery SOH. The Mean Absolute Error (MAE) is 0.1982%, 0.1873%, 0.1652%, and 0.1968%, and the Root-Mean-Square Error (RMSE) is 0.2921%, 0.2997%, 0.2130%, and 0.2625%, respectively. The average Coefficient of Determination (R2) is above 96%. Compared to the BiLSTM model, the training time is reduced by an average of about 36%. [ABSTRACT FROM AUTHOR]

With the continuous advancement of the economy and technology, the number of cars continues to increase, and the traffic congestion problem on some key roads is becoming increasingly serious. This paper proposes a new vehicle information feature map (VIFM) method and a multi-branch convolutional neural network (MBCNN) model and applies it to the problem of traffic congestion detection based on camera image data. The aim of this study is to build a deep learning model with traffic images as input and congestion detection results as output. It aims to provide a new method for automatic detection of traffic congestion. The deep learning-based method in this article can effectively utilize the existing massive camera network in the transportation system without requiring too much investment in hardware. This study first uses an object detection model to identify vehicles in images. Then, a method for extracting a VIFM is proposed. Finally, a traffic congestion detection model based on MBCNN is constructed. This paper verifies the application effect of this method in the Chinese City Traffic Image Database (CCTRIB). Compared to other convolutional neural networks, other deep learning models, and baseline models, the method proposed in this paper yields superior results. The method in this article obtained an F1 score of 98.61% and an accuracy of 98.62%. Experimental results show that this method effectively solves the problem of traffic congestion detection and provides a powerful tool for traffic management. [ABSTRACT FROM AUTHOR]

Ground-glass nodules (GGN) are the main manifestation of early lung cancer, and accurate and efficient identification of ground-glass pulmonary nodules is of great significance for the treatment of lung diseases. In response to the problem of traditional machine learning requiring manual feature extraction, and most deep learning models applied to 2D image classification, this paper proposes a Transformer-based recognition model for ground-glass nodules from the view of global 3D asymmetry feature representation. Firstly, a 3D convolutional neural network is used as the backbone to extract the features of the three-dimensional CT-image block of pulmonary nodules automatically; secondly, positional encoding information is added to the extracted feature map and input into the Transformer encoder layer for further extraction of global 3D asymmetry features, which can preserve more spatial information and obtain higher-order asymmetry feature representation; finally, the extracted asymmetry features are entered into a support vector machine or ELM-KNN model to further improve the recognition ability of the model. The experimental results show that the recognition accuracy of the proposed method reaches 95.89%, which is 4.79, 2.05, 4.11, and 2.74 percentage points higher than the common deep learning models of AlexNet, DenseNet121, GoogLeNet, and VGG19, respectively; compared with the latest models proposed in the field of pulmonary nodule classification, the accuracy has been improved by 2.05, 2.05, and 0.68 percentage points, respectively, which can effectively improve the recognition accuracy of ground-glass nodules. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of cybersecurity, the integration of multi-source, heterogeneous, and fragmented data into a coherent knowledge graph has garnered considerable attention. Such a graph elucidates semantic interconnections, thereby facilitating sophisticated analytical decision support. Central to the construction of a cybersecurity knowledge graph is Named Entity Recognition (NER), a critical technology that converts unstructured text into structured data. The efficacy of NER is pivotal, as it directly influences the integrity of the knowledge graph. The task of NER in cybersecurity, particularly within the Chinese linguistic context, presents distinct challenges. Chinese text lacks explicit space delimiters and features complex contextual dependencies, exacerbating the difficulty in discerning and categorizing named entities. These linguistic characteristics contribute to errors in word segmentation and semantic ambiguities, impeding NER accuracy. This paper introduces a novel NER methodology tailored for the Chinese cybersecurity corpus, termed CSBERT-IDCNN-BiLSTM-CRF. This approach harnesses Iterative Dilated Convolutional Neural Networks (IDCNN) for extracting local features, and Bi-directional Long Short-Term Memory networks (BiLSTM) for contextual understanding. It incorporates CSBERT, a pre-trained model adept at processing few-shot data, to derive input feature representations. The process culminates with Conditional Random Fields (CRF) for precise sequence labeling. To compensate for the scarcity of publicly accessible Chinese cybersecurity datasets, this paper synthesizes a bespoke dataset, authenticated by data from the China National Vulnerability Database, processed via the YEDDA annotation tool. Empirical analysis affirms that the proposed CSBERT-IDCNN-BiLSTM-CRF model surpasses existing Chinese NER frameworks, with an F1-score of 87.30% and a precision rate of 85.89%. This marks a significant advancement in the accurate identification of cybersecurity entities in Chinese text, reflecting the model's robust capability to address the unique challenges presented by the language's structural intricacies. [ABSTRACT FROM AUTHOR]

Under the conditions of a mechanical fault in a motor, mechanical vibration of a specific frequency can be generated. The electrical contact points directly connected to the motor can vibrate at the same frequency. The electrical contact points with poor contact can easily produce a series arc fault under vibration conditions, which affects the reliability of the power supply. In order to detect the series arc fault under different vibration conditions, the arc fault generator is connected between the back end of the frequency converter and the motor. An arc fault experiment under different vibration conditions was carried out and the fault phase current and arc voltage signals were collected. In this paper, the noise-assisted multivariate empirical mode decomposition and the correlation coefficient between each intrinsic mode function are used to select the fault feature signals. Then, the reconstructed signal is input into the series arc fault model combining a multi-scale convolutional neural network and a bidirectional long short-term memory network for training. The research results show that the series arc fault detection method proposed in this paper can effectively detect the series arc fault and can preliminarily identify the type of motor fault causing the mechanical vibration of the motor; the model has good noise immunity and generalization. [ABSTRACT FROM AUTHOR]

This paper is on the autonomous detection of humans in off-limits mountains. In off-limits mountains, a human rarely exists, thus human detection is an extremely rare event. Due to the advances in artificial intelligence, object detection–classification algorithms based on a Convolution Neural Network (CNN) can be used for this application. However, considering off-limits mountains, there should be no person in general. Thus, it is not desirable to run object detection–classification algorithms continuously, since they are computationally heavy. This paper addresses a time-efficient human detector system, based on both motion detection and object classification. The proposed scheme is to run a motion detection algorithm from time to time. In the camera image, we define a feasible human space where a human can appear. Once motion is detected inside the feasible human space, one enables the object classification, only inside the bounding box where motion is detected. Since motion detection inside the feasible human space runs much faster than an object detection–classification method, the proposed approach is suitable for real-time human detection with low computational loads. As far as we know, no paper in the literature used the feasible human space, as in our paper. The outperformance of our human detector system is verified by comparing it with other state-of-the-art object detection–classification algorithms (HOG detector, YOLOv7 and YOLOv7-tiny) under experiments. This paper demonstrates that the accuracy of the proposed human detector system is comparable to other state-of-the-art algorithms, while outperforming in computational speed. Our experiments show that in environments with no humans, the proposed human detector runs 62 times faster than YOLOv7 method, while showing comparable accuracy. [ABSTRACT FROM AUTHOR]

To enhance the accuracy of theft detection for electricity consumers, this paper introduces a novel strategy based on the fusion of the dual-time feature and deep learning methods. Initially, considering electricity-consumption features at dual temporal scales, the paper employs temporal convolutional networks (TCN) with a long short-term memory (LSTM) multi-level feature extraction module (LSTM-TCN) and deep convolutional neural network (DCNN) to parallelly extract features at these scales. Subsequently, the extracted features are coupled and input into a fully connected (FC) layer for classification, enabling the precise detection of theft users. To validate the method's effectiveness, real electricity-consumption data from the State Grid Corporation of China (SGCC) is used for testing. The experimental results demonstrate that the proposed method achieves a remarkable detection accuracy of up to 94.7% during testing, showcasing excellent performance across various evaluation metrics. Specifically, it attained values of 0.932, 0.964, 0.948, and 0.986 for precision, recall, F1 score, and AUC, respectively. Additionally, the paper conducts a comparative analysis with mainstream theft identification approaches. In the comparison of training processes, the proposed method exhibits significant advantages in terms of identification accuracy and fitting degree. Moreover, with adjustments to the training set proportions, the proposed method shows minimal impact, indicating robustness. [ABSTRACT FROM AUTHOR]

Handwriting is a biometric behavioral characteristic with evident individual distinctiveness. With the rise of the deep learning trend and demands for forensic identification, handwriting identification has become one of the focal points of research in the field of pattern recognition. Research in handwriting identification for major global languages has matured. However, in China, there is limited attention in the field of writer identification for minority languages such as Mongolian, making it challenging to resolve criminal cases involving handwriting issues. This paper initiates an initial exploration of Mongolian handwriting identification by constructing a structurally simple convolutional neural network. This convolutional neural network, consisting of 12 convolution operations and designed for Mongolian handwriting identification, is referred to as MWInet-12. In this paper, the model evaluation experiments were conducted using a dataset comprising 156,372 samples contributed by 125 writers from the MOLHW dataset. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio. The final results of the experiments reveal impressive accuracy on the test set, achieving a top-1 accuracy of 89.60% and a top-5 accuracy of 97.53%. Furthermore, through comparative experiments involving Resnet50, Fragnet, GRRNN, VGG16, and VGG19 models, this paper establishes that the proposed model yields the most favorable results for Mongolian handwriting identification. The exploratory research on Mongolian handwriting identification in this paper contributes to increasing awareness of information processing for minority languages. It aids in advancing research on classifying writers of Mongolian historical texts and provides technical support for judicial authentication involving handwriting issues. [ABSTRACT FROM AUTHOR]

The fifth IEEE International Conference on Applied System Innovation 2019 (IEEE ICASI 2019, https://2019.icasi-conf.net/), which was held in Fukuoka, Japan, on April 11-15, 2019, provided a unified communication platform for a wide range of topics. Technological innovation by mechanical engineering includes information technology (IT)-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanoscience and nanotechnology. The first technique is an algorithm for dynamic FCU temperature setting, which enables the FCU temperature to be set in accordance with changes in the outdoor temperature to satisfy the indoor thermal comfort of occupants. 10.3390/app9142792 9 Lin C.M., Liu H.Y., Tseng K.Y., Lin S.F. Heating, Ventilation, and Air Conditioning System Optimization Control Strategy Involving Fan Coil Unit Temperature Control. [Extracted from the article]

Emotion recognition commonly relies on single-modal recognition methods, such as voice and video signals, which demonstrate a good practicability and universality in some scenarios. Nevertheless, as emotion-recognition application scenarios continue to expand and the data volume surges, single-modal emotion recognition proves insufficient to meet people's needs for accuracy and comprehensiveness when the amount of data reaches a certain scale. Thus, this paper proposes the application of multimodal thought to enhance emotion-recognition accuracy and conducts corresponding data preprocessing on the selected dataset. Appropriate models are constructed for both audio and video modalities: for the audio-modality emotion-recognition task, this paper adopts the "time-distributed CNNs + LSTMs" model construction scheme; for the video-modality emotion-recognition task, the "DeepID V3 + Xception architecture" model construction scheme is selected. Furthermore, each model construction scheme undergoes experimental verification and comparison with existing emotion-recognition algorithms. Finally, this paper attempts late fusion and proposes and implements a late-fusion method based on the idea of weight adaptation. The experimental results demonstrate the superiority of the multimodal fusion algorithm proposed in this paper. When compared to the single-modal emotion-recognition algorithm, the accuracy of recognition is increased by almost 4%, reaching 84.33%. [ABSTRACT FROM AUTHOR]

The past few decades have witnessed remarkable progress in the application of artificial intelligence (AI) and machine learning (ML) in medicine, notably in medical imaging. The application of ML to dental and oral imaging has also been developed, powered by the availability of clinical dental images. The present work aims to investigate recent progress concerning the application of ML in the diagnosis of oral diseases using oral X-ray imaging, namely the quality and outcome of such methods. The specific research question was developed using the PICOT methodology. The review was conducted in the Web of Science, Science Direct, and IEEE Xplore databases, for articles reporting the use of ML and AI for diagnostic purposes in X-ray-based oral imaging. Imaging types included panoramic, periapical, bitewing X-ray images, and oral cone beam computed tomography (CBCT). The search was limited to papers published in the English language from 2018 to 2022. The initial search included 104 papers that were assessed for eligibility. Of these, 22 were included for a final appraisal. The full text of the articles was carefully analyzed and the relevant data such as the clinical application, the ML models, the metrics used to assess their performance, and the characteristics of the datasets, were registered for further analysis. The paper discusses the opportunities, challenges, and limitations found. [ABSTRACT FROM AUTHOR]

Multi-class geospatial object detection in high-resolution remote sensing images has significant potential in various domains such as industrial production, military warning, disaster monitoring, and urban planning. However, the traditional process of remote sensing object detection involves several time-consuming steps, including image acquisition, image download, ground processing, and object detection. These steps may not be suitable for tasks with shorter timeliness requirements, such as military warning and disaster monitoring. Additionally, the transmission of massive data from satellites to the ground is limited by bandwidth, resulting in time delays and redundant information, such as cloud coverage images. To address these challenges and achieve efficient utilization of information, this paper proposes a comprehensive on-board multi-class geospatial object detection scheme. The proposed scheme consists of several steps. Firstly, the satellite imagery is sliced, and the PID-Net (Proportional-Integral-Derivative Network) method is employed to detect and filter out cloud-covered tiles. Subsequently, our Manhattan Intersection over Union (MIOU) loss-based YOLO (You Only Look Once) v7-Tiny method is used to detect remote-sensing objects in the remaining tiles. Finally, the detection results are mapped back to the original image, and the truncated NMS (Non-Maximum Suppression) method is utilized to filter out repeated and noisy boxes. To validate the reliability of the scheme, this paper creates a new dataset called DOTA-CD (Dataset for Object Detection in Aerial Images-Cloud Detection). Experiments were conducted on both ground and on-board equipment using the AIR-CD dataset, DOTA dataset, and DOTA-CD dataset. The results demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Intelligent image and optical information processing have paved the way for the recent epoch of new intelligence and information. Three-Dimensional Imaging Advances in three-dimensional imaging have enabled the capture and reconstruction of three-dimensional information. This Special Issue focuses on the intelligent processing of images and optical information such as object detection and classification and three-dimensional imaging. [Extracted from the article]

With the rapid popularity and continuous development of social networks, users' communication and interaction through platforms such as microblogs and forums have become more and more frequent. The comment data on these platforms reflect users' opinions and sentiment tendencies, and sentiment analysis of comment data has become one of the hot spots and difficulties in current research. In this paper, we propose a BERT-ETextCNN-ELSTM (Bidirectional Encoder Representations from Transformers–Enhanced Convolution Neural Networks–Enhanced Long Short-Term Memory) model for sentiment analysis. The model takes text after word embedding and BERT encoder processing and feeds it to an optimized CNN layer for convolutional operations in order to extract local features of the text. The features from the CNN layer are then fed into the LSTM layer for time-series modeling to capture long-term dependencies in the text. The experimental results proved that compared with TextCNN (Convolution Neural Networks), LSTM (Long Short-Term Memory), TextCNN-LSTM (Convolution Neural Networks–Long Short-Term Memory), and BiLSTM-ATT (Bidirectional Long Short-Term Memory Network–Attention), the model proposed in this paper was more effective in sentiment analysis. In the experimental data, the model reached a maximum of 0.89, 0.88, and 0.86 in terms of accuracy, F1 value, and macro-average F1 value, respectively, on both datasets, proving that the model proposed in this paper was more effective in sentiment analysis of comment data. The proposed model achieved better performance in the review sentiment analysis task and significantly outperformed the other comparable models. [ABSTRACT FROM AUTHOR]

This Special Issue contains 2 reviews and 17 research papers related to the following topics: Time series forecasting [[1], [3], [5]]; Image analysis [[6]]; Medical applications [[7]]; Knowledge graph analysis [[9]]; Cybersecurity [[11], [13]]; Traffic analysis [[14]]; Agriculture [[16]]; Environmental data analysis [[17]]. In [[2]], a time series analysis was applied in a different manner: their prediction of the high stock dividend (HSD) was based on a sequence of typical machine learning approaches instead of state-of-the-art methods such as ARIMA or SMA. The authors of [[1]] focused on short time series forecasting in the domain of crime data (thefts, shoplifting, vehicular crimes, and burglaries in Mexico). In this paper, the authors attempt to answer the questions what is air traffic complexity and which air traffic data variables have greater impacts on increases in complexity?. [Extracted from the article]

The occurrence of maize diseases is frequent but challenging to manage. Traditional identification methods have low accuracy and complex model structures with numerous parameters, making them difficult to implement on mobile devices. To address these challenges, this paper proposes a corn leaf disease recognition model SNMPF based on convolutional neural network ShuffleNetV2. In the down-sampling module of the ShuffleNet model, the max pooling layer replaces the deep convolutional layer to perform down-sampling. This improvement helps to extract key features from images, reduce the overfitting of the model, and improve the model's generalization ability. In addition, to enhance the model's ability to express features in complex backgrounds, the Sim AM attention mechanism was introduced. This mechanism enables the model to adaptively adjust focus and pay more attention to local discriminative features. The results on a maize disease image dataset demonstrate that the SNMPF model achieves a recognition accuracy of 98.40%, representing a 4.1 percentage point improvement over the original model, while its size is only 1.56 MB. Compared with existing convolutional neural network models such as EfficientNet, MobileViT, EfficientNetV2, RegNet, and DenseNet, this model offers higher accuracy and a more compact size. As a result, it can automatically detect and classify maize leaf diseases under natural field conditions, boasting high-precision recognition capabilities. Its accurate identification results provide scientific guidance for preventing corn leaf disease and promote the development of precision agriculture. [ABSTRACT FROM AUTHOR]

This paper aims to use deep learning algorithms to identify and study the stability of breakwater armor blocks. It introduces a posture identification model for fender blocks using a Mask Region-based Convolutional Neural Network (R-CNN), which has been enhanced by considering factors affecting breakwater fender blocks. Furthermore, a wave prediction model for breakwaters is developed by integrating Bidirectional Encoder Representations from Transformers (BERTs) with Bidirectional Long Short-Term Memory (BiLSTM). The performance of these models is evaluated. The results show that the accuracy of the Mask R-CNN and its comparison algorithms initially increases and then decreases with higher Intersection Over Union (IOU) thresholds, peaking at 95.16% accuracy at an IOU threshold of 0.5. The BERT-BiLSTM wave prediction model maintains a loss value around 0.01 and an accuracy of approximately 90.00%. These results suggest that the proposed models offer more accurate stability assessments of breakwater armor blocks. By combining the random forest prediction model with BiLSTM, the wave characteristics and fender posture can be predicted better, offering reliable decision support for breakwater engineering. [ABSTRACT FROM AUTHOR]

For visible and Synthetic Aperture Radar (SAR) image fusion, this paper proposes a visible and SAR image fusion algorithm based on a Transformer and a Convolutional Neural Network (CNN). Firstly, in this paper, the Restormer Block is used to extract cross-modal shallow features. Then, we introduce an improved Transformer–CNN Feature Extractor (TCFE) with a two-branch residual structure. This includes a Transformer branch that introduces the Lite Transformer (LT) and DropKey for extracting global features and a CNN branch that introduces the Convolutional Block Attention Module (CBAM) for extracting local features. Finally, the fused image is output based on global features extracted by the Transformer branch and local features extracted by the CNN branch. The experiments show that the algorithm proposed in this paper can effectively achieve the extraction and fusion of global and local features of visible and SAR images, so that high-quality visible and SAR fusion images can be obtained. [ABSTRACT FROM AUTHOR]

The rapid advancement of autonomous vehicle technology has brought into focus the critical need for enhanced road safety systems, particularly in the areas of road damage detection and surface classification. This paper explores these two essential components, highlighting their importance in autonomous driving. In the domain of road damage detection, this study explores a range of deep learning methods, particularly focusing on one-stage and two-stage detectors. These methodologies, including notable ones like YOLO and SSD for one-stage detection and Faster R-CNN for two-stage detection, are critically analyzed for their efficacy in identifying various road damages under diverse conditions. The review provides insights into their comparative advantages, balancing between real-time processing and accuracy in damage localization. For road surface classification, the paper investigates the classification techniques based on both environmental conditions and material road composition. It highlights the role of different convolutional neural network architectures and innovations at the neural level in enhancing classification accuracy under varying road and weather conditions. The main finding of this work is that it offers a comprehensive overview of the current state of the art, showcasing significant strides in utilizing deep learning for road analysis in autonomous vehicle systems. The study concludes by underscoring the importance of continued research in these areas to further refine and improve the safety and efficiency of autonomous driving. [ABSTRACT FROM AUTHOR]

To solve the problem of the Morlet wavelet transform not satisfying wavelet admissibility conditions, which results in fuzzy and aliasing time–frequency image features, this paper proposes an improved Morlet wavelet. The scale parameter is the wavelet's dominant frequency parameter. It largely satisfies the admissibility conditions and effectively improves the fuzzy aliasing effect of time–frequency image features. To further improve feature learning, this paper proposes a combined Morlet wavelet and residual network. The experimental results show that the accuracy of the proposed method is 10%, 4.5%, and 3% higher than that of LeNets, CNNs, and ResNets under varying load and noise disturbance levels, respectively. [ABSTRACT FROM AUTHOR]