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The use of Artificial Intelligence (AI) to preserve and promote cultural heritage has experienced significant growth in recent years. Among the various areas of cultural heritage, numismatics have emerged as a particularly promising field where we can develop AI solutions. Numismatics refers to the study of coins, tokens, paper money, and medals, which play a critical role in understanding human history and culture. However, there are still limited resources available to help researchers and collectors in the identification of coins. This is due to the vast number of coins in circulation, which presents a significant challenge in developing smart tools for classification tasks. This paper aims to provide a contribution to this setting. In particular, we start by creating a new dataset called EURO-Coin, which consists of images showing the side of coins with reliefs and is designed to facilitate the training and testing of AI models for euro coin classification. Then, we propose two approaches that leverage Convolutional Neural Networks and self-attention layers to classify the country and value of the coins. In our experiments, we obtain an accuracy of 86.9% for country classification and an accuracy of 96.4% for value classification. Finally, we conduct an ablation study to evaluate the impact of the preprocessing activities and attention layers in our approaches. [ABSTRACT FROM AUTHOR]

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

The massive number of research articles on the Web makes it troublesome for researchers to identify related works that could meet their preferences and interests. Consequently, various network representation learning-based models have been proposed to produce citation recommendations. Nevertheless, these models do not exploit semantic relations and contextual information between the objects of bibliographic papers' networks, which can result in inadequate citation recommendations. Moreover, existing citation recommendation methods face problems such as lack of personalization, cold-start, and network sparsity. To mitigate such problems and produce individualized citation recommendations, we propose a heterogeneous network embedding model that jointly learns node representations by exploiting semantics corresponding to the author, time, context, field of study, citations, and topics. Compared to baseline models, the results produced by the proposed model over the DBLP datasets prove 10% and 12% improvement on mean average precision (MAP) and normalized discounted cumulative gain (nDCG@10) metrics, respectively. Also, the effectiveness of our model is analyzed on the cold-start papers and network sparsity problems, where it gains 12% and 9% better MAP and recall@10 scores, respectively. [ABSTRACT FROM AUTHOR]

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

Traditional sentiment analysis focuses on text-level sentiment mining, transforming sentiment mining into classification or regression problems, resulting in a sentiment analysis low accuracy rate. Sentiment analysis refers to the use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study sentimental states. Therefore, more scholars have begun to focus on speech recognition and facial expression recognition research, and extracting and analysing people's sentiment tendencies can improve sentiment recognition accuracy. Traditional single-modal sentiment analysis can no longer meet people's needs. Therefore, this paper proposes a multimodal sentiment analysis method based on the multimodal sentiment analysis method that can obtain more sentimental information sources and help people make better decisions. The experimental results in this paper show that the highest recognition rates of CNN-SVM, RNN-SVM, and CRNN-SVM were 76.8%, 71.2%, and 93.5%, respectively. It can be seen that CRNN-SVM has the highest sentiment tendency recognition rate in deep learning, so it is suitable to apply CRNN-SVM to sentiment tendency analysis system design in this paper. The average accuracy rate of the system designed in this paper was 91%, and the stability was also very strong, which shows that the system designed in this paper is meaningful. The main contribution of this paper is based on the limitations of single-mode emotion analysis. It proposes a multimode emotion analysis method and introduces a convolutional neural network to help people obtain more emotional information sources to meet their needs. [ABSTRACT FROM AUTHOR]

This editorial briefly analyses, describes, and provides a short summary of a set of selected papers published in a special issue focused on deep learning methods and architectures and their application to several domains and research areas. The set of selected and published articles covers several aspects related to two basic aspects in deep learning (DL) methods, efficiency of the models and effectiveness of the architectures These papers revolve around different interesting application domains such as health (e.g. cancer, polyps, melanoma, mental health), wearable technologies solar irradiance, social networks, cloud computing, wind turbines, object detection, music, and electricity, among others. This editorial provides a short description of each published article and a brief analysis of their main contributions. [ABSTRACT FROM AUTHOR]

In big data era, multi-source heterogeneous data become the biggest obstacle to data sharing due to its high dimension and inconsistent structure. Using text classification to solve the ontology construction and mapping problem of multi-source heterogeneous data can not only reduce manual operation, but also improve the accuracy and efficiency. This paper proposes an ontology construction and mapping scheme based on hybrid neural network and autoencoder. Firstly, the proposed text classification method uses the multi-core convolutional neural network to capture local features and uses the improved Bidirectional Long Short-Term Memory network to compensate for the shortcomings of the convolutional neural network that cannot obtain context-related information. Secondly, a similarity matching method is used for ontology mapping, which integrate autoencoder to improve anti-interference ability. We have carried out several sets of experiments to test the validity of the proposed ontology construction and mapping scheme. [ABSTRACT FROM AUTHOR]

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

The deep fusion of sports and machine vision has become a research hot spot in sports video target detection, athlete state recovery and sports promotion. On the basis of in-depth study, it can detect a large number of sports videos, complete the drawing and analysis of human body detection model, and detect and evaluate the posture of corresponding athletes in the video, which can save a lot of costs and maximize the more professional training of athletes. In order to solve the above problems, this paper innovatively completes the automatic language description of sports video based on time-sharing memory algorithm. Its principle is to realize the accurate decomposition of athletes' sports data through the mapping relationship between the corresponding letter sequence and video sequence in time-sharing memory. In order to capture the key posture of athletes' sports video, this paper innovatively proposes an object extraction algorithm based on athletes' skeleton motion enhancement. In practical application, based on the key pose capture, it is necessary to train the depth selection network in time to extract the key pose of the skeleton. Based on this network, it can enhance the key posture of bone information and accurately express its related features. After extracting the actual athlete's bone information, we need to fine-tune the training network to realize the accurate recognition of key features. Based on the above key algorithms, this paper designs a sports video athlete detection system based on deep learning and makes an experimental research on the related sports video. The experimental results show that the detection accuracy of athletes' sports video is improved by nearly 10% compared with the traditional convolution network recognition algorithm, so the algorithm has obvious advantages in recognition accuracy. [ABSTRACT FROM AUTHOR]

Handwritten music recognition (HMR) is the technology of transcribing the content of images of music scores. The accurate detection of music objects at the page level is one of the main challenges of HMR. Thus far, the existing methods suffer from the tiny and dense nature of handwritten music notations and realize positive detection accuracy only on snippets. In this paper, we propose a detector that consists of a staff line removal model and a handwritten music object detection model for page-level handwritten music object recognition. First, an end-to-end staff line removal model R_Staff_Net based on residual learning reduces the complexity of page-level detection. Second, we developed an improved YOLO-V4 model for handwritten music object detection. The improvements mainly concern the adoption of a de-coupled detection head and visual attention module in the YOLO-V4, and an adaptive multi-scale feature fusion module AMFFM is used to enhance the textures and features of tiny music symbols in the deep convolution layers, and the gradient harmonized mechanism is utilized to address the inherent imbalance between music objects. We verified the R_Staff_Net and the improved YOLO-V4 model on the ICDAR/GREC staff line removal dataset and the MUSCIMA++ dataset, respectively. The experiments highlight that R_Staff_Net presents outstanding performance with an F-M score of 98.64%, and our improved YOLO-V4 model is superior to other handwritten music symbol detection methods with a mean average precision (mAP) of 91.8% when addressing page-level input. Although the experimental results of the detector show that the R_Staff_Net helps little to the overall mAP, the network is beneficial for symbols that are similar to staff lines or heavily overlap staff lines. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology and artificial intelligence today, human–computer interaction based solely on computer software operations is far from being able to meet human requirements for computer use. People are looking forward to a more convenient and fast human–computer interface. This paper studies the expression dynamic capture and 3D animation generation methods based on deep learning. For facial expression dynamic capture, this paper proposes a facial feature extraction algorithm based on deep learning and uses SVM technology for feature classification. For 3D animation, C++ and OpenGL are used for rendering simulation. The experimental results show that the face detection algorithm proposed in this paper has good performance in both accuracy and speed. It can realize real-time detection of face regions in video images. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has been widely applied to single image super-resolution(SISR). However, the majority of deep learning methods employ the Mean Square Error(MSE) loss as the objective optimization function, and the generated results are frequently too smooth and lack of details. In addition, the high-frequency information of the reconstructed image is severely lost, resulting in a generated image with poor visual effects. In order to address the aforementioned issues, this paper proposes a super-resolution network (RRFDB-GAN) with a receptive field module with a generative adversarial network as the main framework. The network adopts the receptive field block (RFB), which enables it to extract the features in multiple scales and to improve the discriminability. In this paper, the Residual in Residual Dense Block (RRDB) and the Residual of Receptive Field Dense Block (RRFDB) are combined into a new module, called Basic block. This module enhances the capability of feature reconstruction for low-resolution images. During the upsampling stage, a combination of Nearest Neighborhood Interpolation and Sub-pixel convolution is used to reduce the computational complexity and provide additional contextual information for super-resolution reconstruction, while achieving satisfactory performance. Finally, the four Basic blocks are integrated with the upsampling module into a simple end-to-end framework. Extensive experimental results demonstrate that the proposed method in this paper shows more details on the five test sets and outperforms other methods in terms of quantitative metrics and perception assessment. [ABSTRACT FROM AUTHOR]

For detecting and resolving the various types of malware, novel techniques are proposed, among which deep learning algorithms play a crucial role. Although there has been a lot of research on the development of DL-based mobile malware detection approaches, they were not reviewed in detail yet. This paper aims to identify, assess, and synthesize the reported articles related to the application of DL techniques for mobile malware detection. A Systematic Literature Review is performed in which we selected 40 journal articles for in-depth analysis. This SLR presents and categorizes these articles based on machine learning categories, data sources, DL algorithms, evaluation parameters & approaches, feature selection techniques, datasets, and DL implementation platforms. The study also highlights the challenges, proposed solutions, and future research directions on the use of DL in mobile malware detection. This study showed that Convolutional Neural Networks and Deep Neural Networks algorithms are the most used DL algorithms. API calls, Permissions, and System Calls are the most dominant features utilized. Keras and Tensorflow are the most popular platforms. Drebin and VirusShare are the most widely used datasets. Supervised learning and static features are the most preferred machine learning and data source categories. [ABSTRACT FROM AUTHOR]

Brain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI applications, requiring in-depth assessment through deep learning. EEG research frequently employs SSVEPs, which are regarded as normal brain responses to visual stimuli, particularly in investigations of visual perception and attention. This paper tries to give an in-depth analysis of the implications of deep learning for SSVEP-adapted BCI. A systematic search across four stable databases (Web of Science, PubMed, ScienceDirect, and IEEE) was developed to assemble a vast reservoir of relevant theoretical and scientific knowledge. A comprehensive search yielded 177 papers that appeared between 2010 and 2023. Thence a strict screening method from predetermined inclusion criteria finally generated 39 records. These selected works were the basis of the study, presenting alternate views, obstacles, limitations and interesting ideas. By providing a systematic presentation of the material, it has made a key scholarly contribution. It focuses on the technical aspects of SSVEP-based BCI, EEG technologies and complex applications of deep learning technology in these areas. The study delivers more penetrating reporting on the latest deep learning pattern recognition techniques than its predecessors, together with progress in data acquisition and recording means suitable for SSVEP-based BCI devices. Especially in the realms of deep learning technology orchestration, pattern recognition techniques, and EEG data collection, it has effectively closed four important research gaps. To increase the accessibility of this critical material, the results of the study take the form of easy-to-read tables just generated. Applying deep learning techniques in SSVEP-based BCI applications, as the research shows, also has its downsides. The study concludes that a radical framework will be presented which, includes intelligent decision-making tools for evaluation and benchmarking. Rather than just finding a comparable or similar analogy, this framework is intended to help guide future research and pragmatic applications, and to determine which SSVEP-based BCI applications have succeeded at responsibility for what they set out with. [ABSTRACT FROM AUTHOR]

Due to the uncertainty of geological conditions during the tunneling process, advanced prediction of TBM tunneling parameters is significant for evaluating operational safety and efficiency, especially for real-time prediction of key tunneling parameters during the steady phase of TBM operation. At present, although there are studies on constructing predictive models based on machine learning algorithms, multiparameter prediction consistent with the actual tunneling process remains challenging due to the complexity of the TBM tunneling process and the numerous tunneling parameters. Therefore, this paper proposes a real-time multiple tunneling parameters prediction method of TBM steady phase based on dual recurrent neural networks. Firstly, the irregular multidimensional time series of tunneling parameters are analyzed and processed, which are divided into an idle-push phase, a rising phase, and a steady phase; secondly, the parameters of rising phase are analyzed using a recurrent neural network, and the parameters relevant for constructing a real-time prediction model are screened; then, based on the screened parameters, the Bayesian-optimized gated recurrent unit (GRU, a kind of recursive neural network) is proposed to construct a real-time prediction model for the four key tunneling parameters during the steady phase. Finally, the effectiveness and practicality of the proposed method are demonstrated by verification on real TBM tunnel datasets and comparing it with the models constructed by six commonly used machine learning algorithms. The results of this paper show that the designed prediction method is able to achieve a good combination of performance in terms of accuracy and computational time-consumption, with an average prediction accuracy of 91.1% for the four parameters for different rock grades of geology, the multiparameter prediction time for 100 samples is only 11 ms. In addition, three current similar studies using deep learning methods were compared to demonstrate the superiority of this proposal. As a method more closer to practical application, this work provides guidance for the forward-looking prediction of TBM tunneling parameters. [ABSTRACT FROM AUTHOR]

One of the critical challenges in brain-computer interfaces is the classification of brain activities through the analysis of EEG signals. This paper seeks to improve the efficacy of deep learning-based rehabilitation systems, aiming to deliver superior services for individuals with physical disabilities. The research introduces the time distributed long short-term memory (TD-LSTM) framework, which incorporates an LSTM and a time distributed approach to classify brain activities. Learning in TD-LSTM is achieved by uncovering time-dependent semantic dependencies within EEG signals over time. By extracting all discriminative and relevant spatiotemporal dependencies via TD-LSTM, valuable information on different time steps in each sequence has been obtained. Time distributed approach shortens the input time series, making learning from long time series sequences easier, and the learning process of complex temporal and spatial dependencies in time series multi-channel EEG signals becomes more efficient. The main contributions in this paper can be outlined as follows: (1) implementation of brain activity binary classification of motor imagery/execution tasks using time distributed approach via RNN network for the first time, (2) evaluation of the performance and generalizability of the proposed method on four benchmark datasets, (3) dual-purpose classification which providing an efficient ways for classifying both types of motor imagery/execution brain activity. The experimental results show that the proposed method performs well compared to several baseline research works. [ABSTRACT FROM AUTHOR]

Mental stress is a common problem that affects individuals all over the world. Stress reduces human functionality during routine work and may lead to severe health defects. Early detection of stress is important for preventing diseases and other negative health-related consequences of stress. Several neuroimaging techniques have been utilized to assess mental stress, however, due to its ease of use, robustness, and non-invasiveness, electroencephalography (EEG) is commonly used. This paper aims to fill a knowledge gap by reviewing the different EEG-related deep learning algorithms with a focus on Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the evaluation of mental stress. The review focuses on data representation, individual deep neural network model architectures, hybrid models, and results amongst others. The contributions of the paper address important issues such as data representation and model architectures. Out of all reviewed papers, 67% used CNN, 9% LSTM, and 24% hybrid models. Based on the reviewed literature, we found that dataset size and different representations contributed to the performance of the proposed networks. Raw EEG data produced classification accuracy around 62% while using spectral and topographical representation produced up to 88%. Nevertheless, the roles of generalizability across different deep learning models and individual differences remain key areas of inquiry. The review encourages the exploration of innovative avenues, such as EEG data image representations concurrently with graph convolutional neural networks (GCN), to mitigate the impact of inter-subject variability. This novel approach not only allows us to harmonize structural nuances within the data but also facilitates the integration of temporal dynamics, thereby enabling a more comprehensive assessment of mental stress levels. [ABSTRACT FROM AUTHOR]

The inspection and diagnosis of building engineering involve rail surface defect detection, which plays a crucial role in assessing the quality of railway tracks. However, achieving accurate detection remains a significant challenge for infrastructure construction due to the challenging factors, such as complex background, poor texture, the irregular shapes and sizes of rail surface defects, etc. To address these challenges, combined with vision sensor, this paper proposes an automatic vision-based rail surface defect detection network, which leverages a combination of convolutional neural network (CNN) and transformer. Specifically, an effective encoding path based on an improved Res2N-et is presented to obtain stronger contextual information. Meanwhile, to address the limitation of feature representation on global context information, the transformer block is introduced into the bottleneck layer to capture essential global context information. Moreover, an attention-based edge enhancement block is proposed to mitigate the loss of local edge details and strengthen the focus on key feature information, to acquire more discriminant features. To further enrich the feature representation capability, this paper introduces an efficient feature aggregation block, which combines the global features extracted by the transformer and the local features extracted by CNN, to achieve effective feature complementarity and enhances overall detection performance. Combined with public NRSD-MN dataset, proposed model obtains 87.3 % and 77.9 % on PA metric, 85.9 % and 83.4 % in mIoU metric among artificial dataset with natural dataset. Meanwhile, it also obtains 84.0 % PA metric and 87.0 % mIoU metric in SEPR dataset. Experiments have proven the superiority of proposed model though the performance comparison with latest models, which presents an effective and promising rail surface defect detection solution for railway track quality assessment, and can be instrumental in ensuring safe and efficient railway operations. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning-based activity recognition for the Human–Robot Interaction environment. The observations of the object state are acquired from the vision sensor in the real-time scenario. The activity recognition system examined in this paper comprises activities labeled as classes (pour, rotate, drop objects, and open bottles). The image processing unit processes the images and predicts the activity performed by the robot using deep learning methods so that the robot will do the actions (sub-actions) according to the predicted activity. [ABSTRACT FROM AUTHOR]

The automatic reading of pointer meters is significantly important to data monitoring and efficient measurement in the industrial field. However, the existing automatic reading method can not obtain accurate performance in natural scenarios and present no satisfactory application effects in industrial fields (such as power stations and gas stations). In this paper, a novel automatic reading method for pointer meters based on deep learning is proposed, which contains five stages. Stage-1: the object detection algorithm Yolov4 and the feature optimization module IFF are used to locate the target meter. Stage-2: Semantic segmentation model is applied to extract the pointer area based on Anam-Net. Stage-3: the character detection algorithm CRAFT and the text recognition algorithm E2E-MLT are combined and used to recognize the scale text and unit on the meter. Stage-4: the scale area of the meter is converted to the polar coordinate system, and a lightweight convolutional neural network is designed to locate the main scale line. And finally in Stage-5: the reading data are calculated according to the outputs of the above-mentioned deep learning models. The experiment results show that the reading method proposed in this paper has higher accuracy and robustness than those of the existing approaches and obtains satisfactory application effects in the industrial field. [ABSTRACT FROM AUTHOR]

Dental X-ray image segmentation is helpful for assisting clinicians to examine tooth conditions and identify dental diseases. Fast and lightweight segmentation algorithms without using cloud computing may be required to be implemented in X-ray imaging systems. This paper aims to investigate lightweight deep learning methods for dental X-ray image segmentation for the purpose of deployment on edge devices, such as dental X-ray imaging systems. A novel lightweight neural network scheme using knowledge distillation is proposed in this paper. The proposed lightweight method and a number of existing lightweight deep learning methods were trained on a panoramic dental X-ray image data set. These lightweight methods were evaluated and compared by using several accuracy metrics. The proposed lightweight method only requires 0.33 million parameters ( ∼ 7.5 megabytes) for the trained model, while it achieved the best performance in terms of IoU (0.804) and Dice (0.89) comparing to other lightweight methods. This work shows that the proposed method for dental X-ray image segmentation requires small memory storage, while it achieved comparative performance. The method could be deployed on edge devices and could potentially assist clinicians to alleviate their daily workflow and improve the quality of their analysis. [ABSTRACT FROM AUTHOR]

After 70 years of intricate development, machine learning, represented by deep learning, is based on the multilevel structure of the human brain and the layer-by-layer analysis and processing mechanism of neuron connection and interaction information. The powerful parallel information processing ability of self-adaptation and self-learning has allowed for breakthroughs in many fields, among which the most representative is image recognition. Therefore, this paper proposed optimizing the RBF algorithm with machine learning (ML) to improve the recognition rate of spectrum sensing. The results showed that the average detection success rates of the RBF algorithm were 93.62%, 95.07%, 96.91%, 98.78% and 99.37% when the SNRs were − 8 dB, − 4 dB, 0 dB, 4 dB and 8 dB, respectively, and the other conditions were kept the same. The average detection success rates of the SVM/RBF algorithm were 97.65%, 99.63%, 99.76%, 99.91% and 99.88%, respectively. The average detection success rate of the SVM/RBF algorithm was significantly higher than that of the RBF algorithm. This indicates that analyzing the RBF neural network algorithm through ML can improve the success rate of spectrum sensing, which highlights a new direction for the application of ML and neural networks. [ABSTRACT FROM AUTHOR]

With the advances in technology, assistive medical systems are emerging with rapid growth and helping healthcare professionals. The proactive diagnosis of diseases with artificial intelligence (AI) and its aligned technologies has been an exciting research area in the last decade. Doctors usually detect tuberculosis (TB) by checking the lungs' X-rays. Classification using deep learning algorithms is successfully able to achieve accuracy almost similar to a doctor in detecting TB. It is found that the probability of detecting TB increases if classification algorithms are implemented on segmented lungs instead of the whole X-ray. The paper's novelty lies in detailed analysis and discussion of U-Net + + results and implementation of U-Net + + in lung segmentation using X-ray. A thorough comparison of U-Net + + with three other benchmark segmentation architectures and segmentation in diagnosing TB or other pulmonary lung diseases is also made in this paper. To the best of our knowledge, no prior research tried to implement U-Net + + for lung segmentation. Most of the papers did not even use segmentation before classification, which causes data leakage. Very few used segmentations before classification, but they only used U-Net, which U-Net + + can easily replace because accuracy and mean_iou of U-Net + + are greater than U-Net accuracy and mean_iou , discussed in results, which can minimize data leakage. The authors achieved more than 98% lung segmentation accuracy and mean_iou 0.95 using U-Net + + , and the efficacy of such comparative analysis is validated. [ABSTRACT FROM AUTHOR]

This paper focuses on improving the autonomy and efficiency of fuel-optimal powered landing guidance for reusable rockets considering aerodynamic forces. Deep-learning-based methods are developed to enable online and autonomous operation ability and to avoid the convergence problem encountered by classic indirect and direct optimal control methods. Considering the complex uncertainties of the preceding entry flight and potential unsettling hand-over conditions, a classification network is designed to classify the initial states of landing flights into different categories that correspond to bang–bang or non-bang–bang/singular thrust profiles. Thus, the subsequent online regression network can perform well for a large initial state distribution, and the algorithm adjusts to extensive landing situations. The combined application of classification and regression networks is one of the main contributions of the paper. The offline trained state-action regression networks generate guidance commands according to the real-time rocket state, obtaining a near-optimal landing trajectory. In addition, an online parallel trajectory simulation strategy is proposed to verify the trajectory quality, and an alternative trajectory optimization procedure is embedded into the proposed network-based framework to enhance the safety and accuracy of the guidance algorithm, representing another major contribution. Numerical experiments are presented to evaluate the effectiveness and accuracy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Fast motion video sequence processing is quite difficult. In order to improve the effect of fast motion video sequence processing, this paper improves the traditional codebook model algorithm and proposes an improved codebook model algorithm. Moreover, this paper analyzes and summarizes the development and application of background modeling-based methods in moving target detection, and points out the applicability and limitations of traditional methods, which lays the foundation for the further research of moving target detection based on background modeling in the complex background. In addition, this paper analyzes the characteristics of fast motion videos, and combines deep learning algorithms to improve the feature recognition effect of fast motion video sequences. Finally, this paper verifies the effect of this method through experimental research. Through experimental research, we know that the improved algorithm proposed in this paper can realize effective processing of fast motion video, and can improve the feature recognition effect of motion video frames. [ABSTRACT FROM AUTHOR]

Image classification can effectively manage and organize images, laying a good foundation for the work in multiple fields of image processing. With the rise of Internet technology and social networks, the number of digital images has increased dramatically and there are more and more applications. People also use intuitive pictures instead of words when expressing emotions and information. A large number of digital images need to be managed, analyzed, and retrieved. This urgently requires more efficient and accurate image classification technology. Deep learning is a learning method that extends traditional neural networks, simulating the process of gradual abstraction of human brain cognition. The number of hidden layers is deepened, and features can be learned automatically. This paper has completed the traditional image noise detection and segmentation experiment based on convolutional neural network. We introduced the various parts of the convolutional neural network, including the convolutional layer, activation function, fully connected layer, etc. Noise detection is completed based on Fast RCNN on the MSTAR data set containing the background. The model fully combines the advantages of the isotropic model, the Perona-Malik model, and the second-order directional derivative. In order to better maintain the edge structure information of the image, the structure information is extracted based on the original noise image, and the improved ID model and the improved PM model are directionally diffused along the edge tangent direction of the original noise image. Considering the local structure information of the image, we use the slice similarity modulus value as the edge detector of the improved PM model, and use the slice similarity modulus value to construct a new weighting function to adaptively balance the relative weights of the improved ID model and the improved PM model. Simulation and measured data verify the effectiveness of this network in removing image coherent speckle noise. We compare and analyze it with existing denoising methods. The use of visual evaluation and objective evaluation indicators to evaluate the denoising effect and calculation efficiency shows the advantages of the network in this paper in terms of denoising effect, calculation time and space complexity. [ABSTRACT FROM AUTHOR]

In cloud services and Internet-of-Things (IoT) applications, the cybersecurity in video transmission technologies has drawn much attention in recent researches due to the rapid growth of cyber-risks on both individuals and institutions. Unfortunately, the spoofing attack, a kind of cyber-risk, has increased the number of cyber-criminals in data transfer applications without being detected, especially in smart cities. Several applications based on online video communications, such as online testing and video conferences, are involved in smart cities. The video displays various variations of a person, which makes face recognition an important concept in security implementation. The face spoofing attacks are mainly based on the person's face replication by replaying a video or by printed photos. Therefore, video forgery detection and related spoof attack detection have become a new topic in cybersecurity research. From this perspective, this paper presents a deep learning approach for video face forensic detection with a cyber facial spoofing attack using two methodologies. The first methodology is based on a convolutional neural network (CNN) to extract features from the input video frames. The model has five convolutional layers followed by five pooling layers. The second methodology is based on convolutional long short-term memory (ConvLSTM). This model comprises two pooling layers, two convolutional layers, and a convolutional LSTM layer. Each methodology includes a fully-connected layer to interconnect between the feature map resulting from the feature extraction process and the classification layer. A SoftMax layer performs the classification task in each method. This paper aims to achieve an optimum modality for face forensic detection to overcome spoofing attacks. Simulation results reveal that the ConvLSTM with CNN methodology achieves better classification results as the extracted features are more comprehensive than those of other conventional approaches. Also, it achieves an accuracy of 99%, and the works presented in the literature achieve an accuracy levels up to 95%. [ABSTRACT FROM AUTHOR]

Natural disasters can be unpredictable and catastrophic. Even after the event, the repercussions are prolonged due to the incompetence of disaster management strategies. To mitigate the effects of a natural hazard, disaster management teams have to rapidly come forth with innovative plans of action. To contain the damage that a disaster causes, the response time, and preparedness of a disaster management team is crucial. Having a forewarning about the nature of the disaster can prove to be beneficial for the management team. Remote/unreachable areas such as deep parts of the forests, far-flung rural areas, oceans and other hard to reach locations are at a higher risk of receiving poorer aid and response due to the lack of communication and connectivity with the rest of the world. Our paper provides a solution for this particular problem by suggesting UAV/Drones for surveillance and monitoring in these inaccessible, disaster struck areas. The drones not only monitor the situation but constantly click pictures and send them to a base station, where the images are used to collect insights about the type of disaster that has to be dealt with. The paper proposes a deep learning model based on feature concatenation for classification of disasters which can be deployed at the base station, and can receive data in the form of images from a UAV/drone hovering over the affected place. The proposed model is efficient and able to achieve a higher accuracy as compared to the leading CNN models and closely related recent works as well. [ABSTRACT FROM AUTHOR]

Named entity recognition (NER) aims to identify the required entities and their types from unstructured text, which can be utilized for the construction of knowledge graphs. Traditional methods heavily rely on manual feature engineering and face challenges in adapting to large datasets within complex linguistic contexts. In recent years, with the development of deep learning, a plethora of NER methods based on deep learning have emerged. This paper begins by providing a succinct introduction to the definition of the problem and the limitations of traditional methods. It enumerates commonly used NER datasets suitable for deep learning methods and categorizes them into three classes based on the complexity of named entities. Then, some typical deep learning-based NER methods are summarized in detail according to the development history of deep learning models. Subsequently, an in-depth analysis and comparison of methods achieving outstanding performance on representative and widely used datasets is conducted. Furthermore, the paper reproduces and analyzes the recognition results of some typical models on three different types of typical datasets. Finally, the paper concludes by offering insights into the future trends of NER development. [ABSTRACT FROM AUTHOR]

IoT devices convert billions of objects into data-generating entities, enabling them to report status and interact with their surroundings. This data comes in various formats, like structured, semi-structured, or unstructured. In addition, it can be collected in batches or in real time. The problem now is how to benefit from all of this data gathered by sensing and monitoring changes like temperature, light, and position. In this paper, we propose a predictive analytics framework constructed on top of open-source technologies such as Apache Spark and Kafka. The framework focuses on forecasting temperature time series data using traditional and deep learning predictive analytics methods. The analysis and prediction tasks were performed using Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and a novel hybrid model based on Convolution Neural Network (CNN) and LSTM. The purpose of this paper is to determine whether and how recently developed deep learning-based models outperform traditional algorithms in the prediction of time series data. The empirical studies conducted and reported in this paper demonstrate that deep learning-based models, specifically LSTM and CNN-LSTM, exhibit superior performance compared to traditional-based algorithms, ARIMA and SARIMA. More specifically, the average reduction in error rates obtained by LSTM and CNN-LSTM models were substantial when compared to other models indicating the superiority of deep learning. Moreover, the CNN-LSTM-based deep learning model exhibits a higher degree of closeness to the actual values when compared to the LSTM-based model. [ABSTRACT FROM AUTHOR]

In an iron ore pelletization plant, pellets are produced inside a rotating disc pelletizer. Online pellet size distribution is an important performance indicator of the pelletization process. Image processing-based system is an effective solution for online size analysis of iron ore pellets. This paper proposes a machine learning algorithm for estimating the size class of the pellets during their production by imaging from an area inside the disc pelletizer. Instead of computing the size of each individual pellets in the acquired image, this method proposes a qualitative approach to get the overall size estimate of the pellets in production. The key idea of this paper is to find out whether the disc is producing VERY SMALL, SMALL, MEDIUM, or BIG-sized pellets. A weighted average ensemble of different convolutional neural networks such as VGG16, Mobilenet, and Resnet50 is used to achieve this objective. Furthermore, batch normalization is applied to improve the estimation performance of the proposed model. A novel data augmentation method is applied to the in situ captured images to create the data set used to train and evaluate the proposed ensemble of CNN models. Results of experiments indicate that it is possible to detect the operating state of the pelletization disc by acquiring images from the inside area of the disc with sufficient accuracy. [ABSTRACT FROM AUTHOR]

Deep learning models in recent times have shown promising results for solar energy forecasting. Solar energy depends heavily on local weather conditions, and as a result, typically hundreds of models are built, which need site and season-specific training. The model maintenance and management also become a tedious job with such a large number of models. Here, we are motivated to use transfer learning to accommodate local variations in the solar pattern over the available global pattern. It may also be noted that apparently transfer learning has been rarely/never used for solar forecasting. In this paper, we have proposed a bidirectional gated recurrent unit (BGRU) based model, which employs transfer learning for short-term solar energy forecasting. The said model yields better forecasting accuracy compared to site-specific models with a lower variance. It also takes 39.6% less parameters and 76.1% reduced time for training. The current literature suggests that selection of base scenario for transfer learning is an open problem and in this paper, we have also proposed an intuitive strategy for the same. The effectiveness of the same is established through empirical study. [ABSTRACT FROM AUTHOR]

Choosing the optimal portfolio is an ongoing challenging research area and a complex process involving selecting the best investment plan according to various factors, such as investor preferences for expected return, risk, and duration of investments. Although various methods have been presented so far, they failed to obtain a holistic approach to the existing data, and the need for a comprehensive mechanism based on the investor's time preferences is felt. In this paper, by considering the fundamental characteristics, technical indicators, time-series data, and budget constraints, we developed a comprehensive and time-varying methodology to forecast stock prices and form an optimal portfolio. The proposed method consists of recurrent neural networks and multi-objective mathematical programming (MOMP). In this regard, the bidirectional long short-term memory model is adopted and optimized by the particle swarm optimization (PSO) algorithm, called PSO-BiLSTM. Furthermore, the hybrid MOMP models are developed based on long-, mid-, and short-term strategies to provide the optimal portfolio of the stocks with investment constraints. The main objectives of this research were to address the following issues: (1) developing a precise and efficient model to forecast the stocks prices, taking account of fundamental characteristics, technical indicators, time-series data appropriate to the period considered by the investor, (2) providing an optimized time-varying portfolio through developing the hybrid MOMP models, and generally (3) proposing a holistic step-by-step methodology considering three groups of market data and deep learning to apply investment constraints as well as investor's time preferences in the process of building more realistic portfolios. The results highlight that the tuned PSO-BiLSTM method performs better than the conventional methods in all three constructed models using fundamental characteristics, technical indicators, and time-series data. Compared to the conventional methods, the proposed methodology outperforms in generalization power, is more precise in forecasting prices, and provides portfolios with more profit. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging is a highly advanced and sophisticated method for capturing images in hundreds of narrow, contiguous spectral bands. However, processing and analyzing such large amounts of data are challenging. Deep learning algorithms, especially those based on convolutional neural networks (CNNs), effectively extract rich feature representations from complex datasets, such as hyperspectral images (HSIs). These representations capture high-level patterns and characteristics that can facilitate accurate classification. The success of this approach has led to its widespread use in various remote sensing applications. However, the standard CNN inputs and outputs are scalars, ignoring the relative position relationships between features. In this paper, we propose a hierarchical capsule network for HSI classification. This network incorporates a multi-level convolutional structure for feature extraction and fusion. It utilizes convolutional feature maps of various depths to generate initial capsules, followed by vector computation using capsule neurons and a weight matrix to encode spatial location relationships among features. Furthermore, the shallow convolution of the hierarchical capsule network is pre-trained based on transfer learning to further improve the performance of HSI classification. According to experimental results, the proposed method for hyperspectral image classification has been found to outperform other state-of-the-art deep learning models on four benchmark datasets. [ABSTRACT FROM AUTHOR]

The usage of intelligent IoT devices is exponentially rising, and so the possibility of attacks in the IoT surfaces. The deep leaning algorithms are competent for directing the sanctuary investigation of IoT systems but have not upgraded the analysis of potential attacks in IoT. This paper aims to advance deep learning methods to create upgraded security strategies for IoT frameworks quickly. The study of the IoT security threats identified with inalienable or recently presented risks is done. Also, this paper does a quick examination of different possible attack surfaces for the IoT framework, and the potential risks identified with each character. The systematic survey of deep learning methods for IoT security and the existence of the chances, focal points, and weaknesses of every strategy opens the door significant for future research. [ABSTRACT FROM AUTHOR]

With the increased use of social network sites, such as Twitter, attackers exploit these platforms to spread counterfeit content. Such content can be fake advertisements or illegal content. Classifying such content is a challenging task, especially in Arabic. The Arabic language has a complex structure and makes classification tasks more difficult. This paper presents an approach to classifying Arabic tweets using classical machine learning (non-deep machine learning) and deep learning techniques. Tweets corpus were collected through Twitter API and labelled manually to get a reliable dataset. For an efficient classifier, feature extraction is applied to the corpus dataset. Then, two learning techniques are used for each feature extraction technique on the created dataset using N-gram models (uni-gram, bi-gram, and char-gram). The applied classical machine learning algorithms are support vector machines, neural networks, logistics regression, and naïve Bayes. Global vector (GloVe) and fastText learning models are utilised for the deep learning approaches. The Precision, Recall, and F1-score are the suggested performance measures calculated in this paper. Afterwards, the dataset is increased using the synthetic minority oversampling technique class to create a balanced dataset. After applying the classical machine learning models, the experimental results show that the neural network algorithm outperforms the other algorithms. Moreover, the GloVe outperforms the fastText model for the deep learning approach. [ABSTRACT FROM AUTHOR]

Acute lymphocytic leukemia (ALL) is a common serious cancer in white blood cells (WBC) that advances quickly and produces abnormal cells in the bone marrow. Cancerous cells associated with ALL lead to impairment of body systems. Microscopic examination of ALL in a blood sample is applied manually by hematologists with many defects. Computer-aided leukemia image detection is used to avoid human visual recognition and to provide a more accurate diagnosis. This paper employs the ensemble strategy to detect ALL cells versus normal WBCs using three stages automatically. Firstly, image pre-processing is applied to handle the unbalanced database through the oversampling process. Secondly, deep spatial features are generated using a convolution neural network (CNN). At the same time, the gated recurrent unit (GRU)-bidirectional long short-term memory (BiLSTM) architecture is utilized to extract long-distance dependent information features or temporal features to obtain active feature learning. Thirdly, a softmax function and the multiclass support vector machine (MSVM) classifier are used for the classification mission. The proposed strategy has the resilience to classify the C-NMC 2019 database into two categories by using splitting the entire dataset into 90% as training and 10% as testing datasets. The main motivation of this paper is the novelty of the proposed framework for the purposeful and accurate diagnosis of ALL images. The proposed CNN-GRU-BiLSTM-MSVM is simply stacked by existing tools. However, the empirical results on C-NMC 2019 database show that the proposed framework is useful to the ALL image recognition problem compared to previous works. The DenseNet-201 model yielded an F1-score of 96.23% and an accuracy of 96.29% using the MSVM classifier in the test dataset. The findings exhibited that the proposed strategy can be employed as a complementary diagnostic tool for ALL cells. Further, this proposed strategy will encourage researchers to augment the rare database, such as blood microscopic images by creating powerful applications in terms of combining machine learning with deep learning algorithms. [ABSTRACT FROM AUTHOR]

Computer vision combined with deep learning technologies is widely used in video surveillance. In this paper, it is applied to drowning detection video systems. Traditional drowning detection methods detect drowning mainly by monitoring the physiological condition, the time and motion of swimmers in the water. But these methods are not applicable to detect early quiet drowning phenomena. Some researchers realize supervised classification by simulative drowning features. But real drowning events are difficult to truly simulate, so these methods are not reliable. In this paper, a drowning detection video system with edge computing is proposed, and it can detect drowning events in swimming pools without any wearable devices. According to the characteristics of drowning people, the strategies for underwater near-vertical human detection are proposed, providing a reliable basis for drowning detection. A lightweight drowning detection convolutional autoencoder is proposed to achieve unsupervised drowning detection, solving the lack of drowning videos and the inauthenticity of simulative videos. Then, an edge device is designed for detecting drowning in real time at the edge. Finally, for training and experimental evaluation, a pool dataset including many pool underwater video sequences is produced. The experimental results show that the proposed drowning detection method has a good comprehensive performance. The system is feasible and valuable. [ABSTRACT FROM AUTHOR]

The paper presents a novel manifold learning algorithm, the deep Gaussian process autoencoder (DPGA), based on deep Gaussian processes. Deep Gaussian process autoencoder algorithm has the following two main characteristics. The former is a bottleneck structure, borrowed by variational autoencoders and the latter is based on the so-called doubly stochastic variational inference for deep Gaussian processes architecture (DSVI). The main novelties of the paper consist in DGPA algorithm and the experimental protocol for evaluating it. In fact, to the best of our knowledge, deep Gaussian processes algorithms have not been applied to manifold learning, yet. Besides, an experimental protocol is introduced, the so-called manifold learning performance protocol (MLPP), to compare quantitatively the geometric preserved properties of manifold learning projections of the proposed deep Gaussian process autoencoder with the ones of state-of-the-art manifold learning algorithms. Extensive experimental tests on eleven synthetic and five real datasets show that deep Gaussian process autoencoder compares favorably with the other manifold learning competitors. [ABSTRACT FROM AUTHOR]

In recent decades, the world has witnessed a great expansion in the world of technology and electronics, in addition to the tremendous development in various industries, which has led to an increase in the need for electrical energy significantly. Renewable energy generated from environmentally friendly sources such as energy (solar, water, windmills, etc.) is the solution. The alternative is to provide that energy, especially as it is clean energy that does not cause the emission of carbon dioxide, which pollutes the air and the environment in general. This work presents a software model for producing the largest amount of energy by developing one of the best prediction techniques and using multi-parameter objective functions, where the proposed model called zero to maximum energy based on developing gradient boosting machine (DMP-DGBM) consists of several stages. The problem of this work is divided into parts: The first part is related to programming challenges, while the second part is related to application challenges; as we know, the prediction techniques are split based on the scientific field into two fields: prediction techniques related to data mining and predictions related to deep learning techniques; this work deals with the first type of prediction technique. (a) One of the data mining prediction techniques is the gradient boosting machine characterized by many features that make it the best. These features are GBM gives high accuracy results and works with huge data stream of data, but on the other hand, the core of that algorithm is a decision tree (DT) that has many limitations such as requiring choosing the root of the tree, determining the maximum number of levels of the tree, and also having high computation and long time. Therefore, the first challenge of this paper is how can avoid these limitations (i.e., high computation and implementation time) of this algorithm and benefit from their features. (b) The problem of generating electrical energy from environmentally friendly sources with high efficiency is one of the most important challenges in this field. Therefore, the second challenge of this paper is how can avoid these limitations by building an efficient technique to predict maximum energy from solar energy. DMP-DGBM model consists of many stages applied through a stepwise style. The first stage presents capturing datasets from scientific site which contains the data related to both weather and solar plant. The second stage is preprocessing which contains multi-steps including: (a) merging between two datasets; (b) splitting readings into intervals and deleting the duplicate; and (c) applying Pearson's correlation to the new dataset. In the third stage, the ZME-DGBM model is constructed based on developing gradient boosting techniques by replacing its kernel (i.e., decision tree function) with multi-parameter optimization functions. The stage begins with dividing the dataset into two sets using five cross-validation methods, and the training dataset is used to construct the DMP-DGBM models, while the testing dataset is used to evaluate them. Finally, the results of the DMP-DGBM are evaluated based on three measures (i.e., coefficient of determination, mean error, and root mean square error. The stage of constructing the predictor relied on replacing the GBM kernel with four different multi-parameter functions, as these were the parameters with the highest correlation with the target, and a threshold value of 0.95 was adopted as determining the importance of the parameters. The proposed model was characterized by giving the best results using a three-parameter function MPF4 for the GBM kernel, and those parameters were AC, TEM, and IRR, where the scale was (R2 = 0.9742), while for MSE = 0.0099 and RMSE = 0.0522 also the system is taken only 80 Ms to implement. [ABSTRACT FROM AUTHOR]

The abundance of digital documents offers a valuable chance to gain insights into public opinion, social structure, and dynamics. However, the scale and volume of these digital collections makes manual analysis approaches extremely costly and not scalable. In this paper, we study the potential of using automated methods from natural language processing and machine learning, in particular weak supervision strategies, to understand how news influence decision making in society. Besides proposing a weak supervision solution for the task, which replaces manual labeling to a certain extent, we propose an improvement of a recently published economic index. This index is known as economic policy uncertainty (EPU) index and has been shown to correlate to indicators such as firm investment, employment, and excess market returns. In summary, in this paper, we present an automated data efficient approach based on weak supervision and deep learning (BERT + WS) for identification of news articles about economical uncertainty and adapt the calculation of EPU to the proposed strategy. Experimental results reveal that our approach (BERT + WS) improves over the baseline method centered in keyword search, which is currently used to construct the EPU index. The improvement is over 20 points in precision, reducing the false positive rate typical to the use of keywords. [ABSTRACT FROM AUTHOR]

Patients with deaths from COVID-19 often have co-morbid cardiovascular disease. Real-time cardiovascular disease monitoring based on wearable medical devices may effectively reduce COVID-19 mortality rates. However, due to technical limitations, there are three main issues. First, the traditional wireless communication technology for wearable medical devices is difficult to satisfy the real-time requirements fully. Second, current monitoring platforms lack efficient streaming data processing mechanisms to cope with the large amount of cardiovascular data generated in real time. Third, the diagnosis of the monitoring platform is usually manual, which is challenging to ensure that enough doctors online to provide a timely, efficient, and accurate diagnosis. To address these issues, this paper proposes a 5G-enabled real-time cardiovascular monitoring system for COVID-19 patients using deep learning. Firstly, we employ 5G to send and receive data from wearable medical devices. Secondly, Flink streaming data processing framework is applied to access electrocardiogram data. Finally, we use convolutional neural networks and long short-term memory networks model to obtain automatically predict the COVID-19 patient's cardiovascular health. Theoretical analysis and experimental results show that our proposal can well solve the above issues and improve the prediction accuracy of cardiovascular disease to 99.29%. [ABSTRACT FROM AUTHOR]

"Digital" agriculture is rapidly affecting the value of agricultural output. Robotic picking of the ripe agricultural product enables accurate and rapid picking, making agricultural harvesting intelligent. How to increase product output has also become a challenge for digital agriculture. During the cherry growth process, realizing the rapid and accurate detection of cherry fruits is the key to the development of cherry fruits in digital agriculture. Due to the inaccurate detection of cherry fruits, environmental problems such as shading have become the biggest challenge for cherry fruit detection. This paper proposes an improved YOLO-V4 deep learning algorithm to detect cherry fruits. This model is suitable for cherry fruits with a small volume. It is proposed to increase the network based on the YOLO-V4 backbone network CSPDarknet53 network, combined with DenseNet The density between layers, the a priori box in the YOLO-V4 model, is changed to a circular marker box that fits the shape of the cherry fruit. Based on the improved YOLO-V4 model, the feature extraction is enhanced, the network structure is deepened, and the detection speed is improved. To verify the effectiveness of this method, different deep learning algorithms of YOLO-V3, YOLO-V3-dense and YOLO-V4 are compared. The results show that the mAP (average accuracy) value obtained by using the improved YOLO-V4 model (YOLO-V4-dense) network in this paper is 0.15 higher than that of yolov4. In actual orchard applications, cherries with different ripeness of cherries in the same area can be detected, and the fruits with larger ripeness differences can be artificially intervened, and finally, the yield of cherry fruits can be increased. [ABSTRACT FROM AUTHOR]

Experiments on the CheXpert dataset, which is a publicly available multi-disease chest radiograph dataset, and the TBX11K dataset show that the proposed model generates identical results. The MOFPA-WT is tested using a standard EEG signal processing dataset, namely the EEG motor movement/imagery dataset. [Extracted from the article]

Credit risk assessment is at the core of modern economies. Traditionally, it is measured by statistical methods and manual auditing. Recent advances in financial artificial intelligence stemmed from a new wave of machine learning (ML)-driven credit risk models that gained tremendous attention from both industry and academia. In this paper, we systematically review a series of major research contributions (76 papers) over the past eight years using statistical, machine learning and deep learning techniques to address the problems of credit risk. Specifically, we propose a novel classification methodology for ML-driven credit risk algorithms and their performance ranking using public datasets. We further discuss the challenges including data imbalance, dataset inconsistency, model transparency, and inadequate utilization of deep learning models. The results of our review show that: 1) most deep learning models outperform classic machine learning and statistical algorithms in credit risk estimation, and 2) ensemble methods provide higher accuracy compared with single models. Finally, we present summary tables in terms of datasets and proposed models. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of forecasting multivariate aggregated human mobility while preserving the privacy of the individuals concerned. Differential privacy, a state-of-the-art formal notion, has been used as the privacy guarantee in two different and independent steps when training deep learning models. On one hand, we considered gradient perturbation, which uses the differentially private stochastic gradient descent algorithm to guarantee the privacy of each time series sample in the learning stage. On the other hand, we considered input perturbation, which adds differential privacy guarantees in each sample of the series before applying any learning. We compared four state-of-the-art recurrent neural networks: Long Short-Term Memory, Gated Recurrent Unit, and their Bidirectional architectures, i.e., Bidirectional-LSTM and Bidirectional-GRU. Extensive experiments were conducted with a real-world multivariate mobility dataset, which we published openly along with this paper. As shown in the results, differentially private deep learning models trained under gradient or input perturbation achieve nearly the same performance as non-private deep learning models, with loss in performance varying between 0.57 and 2.8 % . The contribution of this paper is significant for those involved in urban planning and decision-making, providing a solution to the human mobility multivariate forecast problem through differentially private deep learning models. [ABSTRACT FROM AUTHOR]

Microscopic image analysis technology helps solve the inadvertences of artificial traditional methods in disease, wastewater treatment, and environmental change monitoring analysis. Convolutional neural network (CNN) play an important role in microscopic image analysis. Image segmentation, in which U-Net is increasingly applied in microscopic image segmentation, is a crucial step in detection, tracking, monitoring, feature extraction, modelling, and analysis. This paper comprehensively reviews the development history of U-Net, analyses several research results of various segmentation methods since the emergence of U-Net, and conducts a comprehensive review of related papers. This paper summarised the improved methods of U-Net and then listed the existing significance of image segmentation techniques and their improvements introduced over the years. Finally, focusing on the different improvement strategies of U-Net in different papers, the related work of each application target is reviewed according to detailed technical categories to facilitate future research. Researchers can see the dynamics of the transmission of technological development and keep up with future trends in this interdisciplinary field. [ABSTRACT FROM AUTHOR]