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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 presents a convolutional neural network (CNN) to classify between the conventionally and organically cultivated Memecik varieties of green olives. The image forming method called the rising paper chromatography is utilized in preparing the images of Memecik varieties of green olives for CNN. In the rising chromatography method, 20, 30, and 40% sample concentrations were determined as the suitable concentrations for both organic and conventional olives. The concentrations of AgNO3 and FeSO4 were determined as 0.25, 0.5, 0.75 and 1% for both conventional and organic samples. The visual differences used for differentiation of different types of Memecik green olives are usually determined according to the regional color differences, the vivid color occurrence, the width and the frequency of bowl occurrence, the thin line, and the picks at drop zone by the expert assessors. The testing results in this study verified the effectiveness of the CNN methodology in differentiating between the organically and conventionally cultivated Memecik green olives. The newly designed neural network achieved 100% accuracy. Furthermore, this high accuracy achieved by CNN might suggest that it can be effectively used in place of the expert assessors. [ABSTRACT FROM AUTHOR]

This paper investigates the art design of user interaction experience in mobile systems through the methods of contextual perception and machine learning. The theoretical foundations for the design of intangible cultural heritage interactive display resources include digital display theory, intangible cultural heritage education theory, embodied cognition theory, and gamification design theory. Based on the modelling and analysis of the theoretical foundations, the design principles are derived, including the heritage education principle, the somatic interaction design principle, and the content design principle. In this paper, we use ontologies to construct user knowledge models, fuse multi-situational similarity metrics, screen out candidate neighbour sets through preliminary screening, then combine the user's activity to construct time-based weight tensor scores, use tensor decomposition to obtain recommendation evaluation values, and finally use the recommendation evaluation values to make artistic recommendations. The experimental results show that the algorithm can still obtain a good recommendation implementation in the case of extremely sparse data. The analysis of the fit between augmented reality and folk art appreciation class, as well as the reference to relevant application cases, make design and practice of augmented reality application in folk art appreciation class, try to solve the common problems in folk art appreciation class, analyse the feedback effect and make a summary and outlook. [ABSTRACT FROM AUTHOR]

Based on the applications of BSO algorithms for KSP, the properties of different swarm optimization algorithms can be understood better. The comparison result of the proposed algorithm with other algorithms proves that EDOLSCA also has advantages in heat exchanger optimal design. Finally, the experimental results confirm the efficiency of the proposed scheme in real-world urban scenarios; The last paper by Lidong Zhang et al. on "Elite and dynamic opposite learning enhanced sine cosine algorithm for application to plat-fin heat exchangers design problem" proposes a novel variant of sine and cosine algorithm named EDOLSCA enhanced by dynamic opposite learning algorithm and the elite strategy. [Extracted from the article]

Experimental results on five public gene expression profile datasets verify that the proposed algorithm outperforms other methods in terms of diversity, distribution stability, and quality of generated samples. The fifth paper by I Tingyu Ye i et al. on "Artificial bee colony algorithm with an adaptive search manner and dimension perturbation" proposes a modified artificial bee colony (ABC) algorithm with an adaptive search manner and dimension perturbation (ASDABC). The tenth paper by I Fei Han i et al. on "Gene-CWGAN: A Data Enhancement Method for Gene Expression Profile Based on Improved CWGAN-GP" tackles the problem of high dimension and small sample size of gene expression profile data classification. [Extracted from the article]

Open peer review is a growing trend in academic publications. Public access to peer review data can benefit both the academic and publishing communities. It also serves as a great support to studies on review comment generation and further to the realization of automated scholarly paper review. However, most of the existing peer review datasets do not provide data that cover the whole peer review process. Apart from this, their data are not diversified enough as the data are mainly collected from the field of computer science. These two drawbacks of the currently available peer review datasets need to be addressed to unlock more opportunities for related studies. In response, we construct MOPRD, a multidisciplinary open peer review dataset. This dataset consists of paper metadata, multiple version manuscripts, review comments, meta-reviews, author's rebuttal letters, and editorial decisions. Moreover, we propose a modular guided review comment generation method based on MOPRD. Experiments show that our method delivers better performance as indicated by both automatic metrics and human evaluation. We also explore other potential applications of MOPRD, including meta-review generation, editorial decision prediction, author rebuttal generation, and scientometric analysis. MOPRD is a strong endorsement for further studies in peer review-related research and other applications. [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]

Manual term extraction is similar to literal meaning: A translator browses text, classifies words, and prepares for translation. Terminology, as a centralized carrier of expertise, creation, popularization, and disappearance, dynamically reflects the development and evolution of an industry. The automatic extraction of terminology is a key technology for creating a professional terminology database, and it is also a key topic in the field of natural language processing. The purpose of this paper is to study how to analyse a term extraction algorithm based on machine learning and a comprehensive feature strategy. Focusing on the problems of poor generality and single statistical features of current term extraction algorithms, this paper proposes an improved domain ontology term extraction algorithm based on a comprehensive feature strategy. Moreover, automatic term extraction experiments based on a word-based maximum entropy model and a conditional random field model based on machine learning are conducted in this paper. Its word-based conditional random field model outperforms the maximum entropy model. The experimental results show that the algorithm based on the comprehensive feature strategy improves the accuracy by 8.6% compared with the TF-IDF algorithm and the C-value term extraction algorithm. This algorithm can be used to effectively extract the terms in a text and has good generality. [ABSTRACT FROM AUTHOR]

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]

IoT-based health monitoring systems are used for the detection of pandemic diseases at early stages. The fourteenth paper "Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning" proposes Res-CovNet, an IoT-driven COVID-19 framework using transfer learning. [Extracted from the article]

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]

COVID-19 outbreak has caused a devastating impact on the daily lives of people, public health, and economic progress of infected countries. It has become a leading cause of substantial mortality and morbidity around the world. The emergence of new variants of virus has posed severe challenges for humanitarian society. Information and Communication Technology (ICT) has played a vital role in this pandemic and offered various promising innovations to control its dissemination. The current research study presents a scientometric analysis on the literature of ICT-assisted COVID-19 research. In this paper, ICT has been classified into six major categories; artificial intelligence and medical imaging, mobile technology, ubiquitous computing, big data analytics, social media platforms, and printing technology. It extensively examines the role of these technologies in COVID-19 by applying various empirical approaches such as co-citation analysis, publication and citation behavior analysis, participating nations, and knowledge mapping of scientific literature using visualization tool CiteSpace. Furthermore, it provides a visual approach to identify developing paths, evolution trends, research hotspots, cluster analysis, and potential future directions in medical informatics. [ABSTRACT FROM AUTHOR]

The emergence of digital media has changed the way people live and learn. In the context of the new era, digital media is gradually integrating into people's life and learning. Digital media contains massive images, and fine-grained image recognition for digital media has become an important topic. The challenge of fine-grained image recognition is that the difference between different categories is small, and the difference between the same categories is sometimes large. This work designs a fine-grained image recognition based on feature enhancement (FIRFE). This extracts as much information as possible from fine-grained images under weak supervision to improve the recognition accuracy. When the existing methods extract image features, the feature extraction other than the most significant local feature is not enough. This deals with local features alone and ignores the relationship between features. First, this paper designs a feature enhancement and suppression module to process image features. Secondly, this paper designs pyramid residual convolution. This uses different scale convolution kernels to capture different levels of features in the scene. Thirdly, this paper uses the softpool method to rationally allocate the information weight in the pooling process. Fourth, this paper uses feature focus module to mine more features. This focuses on obtaining similar information in multiple local features as discriminant features to further improve the recognition. Fifthly, this paper carried out systematic experiments on the designed method. The proposed method achieves 94.3%/95.7% accuracy, 92.9%/94.1% recall, and 91.4%/92.2% F1 score on different datasets. This verified the superiority of this method for fine-grained image recognition of digital media. [ABSTRACT FROM AUTHOR]

Sub-health state is a state of health and low quality between disease and health. The theoretical basis of children's sub-health is to start from the whole. The common clinical sub-health conditions cannot be explained by modern detection methods, and it can be screened and analyzed with the help of big data in medical informatization. The combination of "Internet + " and the health care model is an innovation in the construction of medical informatization. It can provide many considerate services to the masses in time and alleviate the anxiety of illness. Therefore, it is very necessary to carry out the efficacy evaluation of children's sub-health from the perspective of medical information. Therefore, this paper completes the following work with the help of AI neural network: (1) This paper proposes an improved AlexNet network evaluation method based on attention mechanism. In this study, attention mechanism is added to the original AlexNet model to weight each channel of the feature layer. At the same time, we improve the large convolution kernel of the previous layers of the original AlexNet network and use batch normalization instead of the local response normalization (LRN) layer in the original model. (2) This paper proposes an evaluation method based on improved residual network, which improves the original residual block of the residual model and widens the residual block. The residual block can effectively reduce the amount of network parameters and improve the efficiency of network training. (3) This paper proposes an evaluation method of multi-scale feature fusion (MSFF). The features extracted from the improved AlexNet and residual network are fused and then evaluated. At this time, the training time is greatly shortened, and the accuracy is higher than that of single model. [ABSTRACT FROM AUTHOR]

This paper explores the knowledge network structure of foreign research literature by applying the qualitative comparative analysis (QCA) method to the field of information science and library science (ISLS) from the perspective of the cocitation of social network actors such as authors, institutions, countries, and literature, and it further reveals the future application trends of this method. [Method/process] Based on 86 journals in the ISLS field that were downloaded from the Web of Science using the QCA method, the social network analysis (SNA) method and the visual analysis tool Gephi are used to analyse the author cooperation network, the research institution cooperation network, the national cooperation network, the cocitation network, the cutting-edge trends, etc., of journal papers. The analysis shows that the QCA method covers a wide range within the field of ISLS, but the research topics involved in this field are not concentrated, and the author cooperation network has scale-free characteristics. The application of the QCA method is still dominant in European and American countries, and China, the USA, and Italy all play key roles in the national cooperation network. Finally, the institutional cooperation network has certain small group attributes. [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 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]

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]

Deep learning-based analytical schemes combined with other traditional schemes such as neural networks, fuzzy logic reasoning and other machine learning schemes can even automate the big data processing in such smart ecosystems. The application of data science techniques in smart ecosystems has surged since the beginning of this decade as this offers potentially new and meaningful information about the considered system. The recent growth of data because of advancements in the Internet of Things (IoT) and other related technologies has made it more imperative to leverage data science techniques for understanding and interacting with the local environment (humans/devices/systems). [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]

The spread of Covid-19 misinformation on social media had significant real-world consequences, and it raised fears among internet users since the pandemic has begun. Researchers from all over the world have shown an interest in developing deception classification methods to reduce the issue. Despite numerous obstacles that can thwart the efforts, the researchers aim to create an automated, stable, accurate, and effective mechanism for misinformation classification. In this paper, a systematic literature review is conducted to analyse the state-of-the-art related to the classification of misinformation on social media. IEEE Xplore, SpringerLink, ScienceDirect, Scopus, Taylor & Francis, Wiley, Google Scholar are used as databases to find relevant papers since 2018–2021. Firstly, the study begins by reviewing the history of the issues surrounding Covid-19 misinformation and its effects on social media users. Secondly, various neuro-fuzzy and neural network classification methods are identified. Thirdly, the strength, limitations, and challenges of neuro-fuzzy and neural network approaches are verified for the classification misinformation specially in case of Covid-19. Finally, the most efficient hybrid method of neuro-fuzzy and neural networks in terms of performance accuracy is discovered. This study is wrapped up by suggesting a hybrid ANFIS-DNN model for improving Covid-19 misinformation classification. The results of this study can be served as a roadmap for future research on misinformation classification. [ABSTRACT FROM AUTHOR]

To solve the recommendation accuracy degradation problem encountered in sequential recommendation cases caused by data sparsity—such as short historical user behaviour sequences and limited information—this paper proposes a sequential recommendation model based on multipair contrastive learning with informative augmentation (IA-MPCL). The model aims to better learn user preference representations. Initially, a self-attention network is utilized to maintain the intrinsic relevance of the original sequences and introduce virtual interaction items for short sequences to achieve informative enhancement. Subsequently, multiple positive samples are generated by data augmentation methods to form multiple pairs of positive and negative samples. A multipair contrastive loss is constructed to eliminate the negative impact of fake positive and negative samples on the training process of the self-attention network. Finally, an adaptive loss weighting mechanism is proposed to dynamically regulate the role of the contrastive loss during multitask training. Through comparison experiments involving baseline methods and experiments conducted on datasets with different sparsity levels, the results show that IA-MPCL achieves significant improvements in terms of both recommendation accuracy and data sparsity resistance. [ABSTRACT FROM AUTHOR]

The metaverse network economy is built on the blockchain protocol network and occurs in the metaverse virtual space by the virtual digital avatar. This is a series of economic activities that produce, exchange, distribute and consume digital products, and interact, integrate and promote with the real economy in real space. The network economic system of the metaverse is gradually formed in the process of adapting to the development of digital economy. While these products and services bring convenience, they are accompanied by more complex and diverse economic risks. Based on transformer serialization analysis, this paper proposes an analytical method of economic risk mechanism of metacosmic network to complete the evaluation of economic risk. First, this work proposes an improved transformer (ITransformer). The probabilistic sparse self-attention (PSSA) and self-attention distillation (SAD) are proposed to improve the performance. This can mine more discriminative temporal features. Secondly, this work designed a dual-stream ITransformer (DSIT) to evaluate the economic risks of the metaverse network via ITransformer. This can process the global and local features in the metaverse economic data with high performance. Finally, this work has performed massive experiments on dual-stream ITransformer pipeline. Experimental results verify the effectiveness and feasibility of DSIT. [ABSTRACT FROM AUTHOR]

With the ongoing integration of information technology and industrialization, strategic emerging industries are becoming an increasingly important force in guiding future economic and social development. As one of the strategic emerging industries' development priorities and a replacement for scarce labor resources, industrial robots will be widely used in labor-intensive industries. It is a contentious topic how to evaluate and improve robot knowledge education. There are modular features introduced in this paper, which builds a modular network for the evaluation of robot knowledge education quality and enhances the cognitive computing ability of artificial neural networks and their ability to process complex information. A modular neural network-based model of feature combination robot knowledge education quality evaluation is developed based on the three aspects of module division method, subnet structure selection, and feature combination output. The following are some of the paper's most important contributions: K-OD algorithm of density clustering optimized by K-means is proposed. Because of its high level of modularized partition simulating, this method has an excellent clustering effect, and it identifies the core points, boundary points, as well as outliers. Using K-OD algorithm, the calculation of density radius and threshold is optimized by using density clustering, which reduces the overall computational complexity. Find out how SOM neural networks learn from competition. SOM network's competition layer neuron weights are prone to falling into local optimal solutions, so an SASOM neural network with weight adjustment simulated by an annealing algorithm is proposed to address this issue. It is more accurate in terms of prediction and error, and it is better at identifying sample attribute features. This work builds a modular neural network for evaluating robot knowledge education quality using K-OD clustering algorithm and SASOM neural network, which introduces the simulated annealing mechanism. [ABSTRACT FROM AUTHOR]

With the development of informatization, traditional industries have been seriously impacted by cross-border e-commerce (CBEC), and the financial management mode no longer meets the needs of CBEC users. As cross-border electronic trading platforms face enormous financial data challenges, they need financial supervision and effective network risk protection. Artificial intelligence (AI) can effectively improve the financial accounting ability and resource integration of platforms. However, at present, the underdeveloped financial management system of CBEC platforms and problems, such as the running records of platform funds have seriously hindered the development of the financial standardization of platforms because the financial management system affects managers' statistical analysis of financial data. Therefore, this paper analyzes the financial risks, the management problems and the causes of these problems of CBEC platforms and then uses AI to study the financial operation of CBEC platforms. Subsequently, this paper uses a machine learning (ML) algorithm to analyze the financial data clustering center and the security factor of the financial model. Finally, this paper proposes some corresponding strategies for financial model optimization and construction that can improve the information security and capital management of CBEC finance and promote the long-term development of CBEC platforms. The experimental results show that the classifier value and the safety factor of the financial model of CBEC platforms gradually increase under the ML algorithm. The mean value of the classifier value is approximately 1.14, and the mean value of the safety factor is approximately 1.37. Overall, the initial value of the classifier value of the platform financial model is 0.85, which increases to 1.41 on the seventh day, while the whole process increases by 0.56. The initial value of the safety factor of the platform financial model is 1.10, which increases to 1.64 on the seventh day, while the whole process increases by 0.54. The transaction information security and financial accounting accuracy of the CBEC platform financial model is better than that of the original financial model. The transaction information security is 10.4% higher than that of the original financial model, and the financial accounting accuracy is 10% higher than that of the original financial model. In other words, both AI and ML can promote the financial accounting standards and long-term development of CBEC platforms. [ABSTRACT FROM AUTHOR]

Nowadays, the volume of online information is growing and it is difficult to find the required information. Effective strategies such as recommender systems are required to overcome information overload. Collaborative filtering is a widely used type of recommender system in e-commerce environments and can simply provide suggestions for users. Recently, deep learning approaches were applied in collaborative filtering to tackle some drawbacks. This systematic review aims to provide a comprehensive review of recent research efforts on deep learning-based collaborative filtering recommender systems. We explain the research methodology and paper selection process and the search query. 102 papers are selected out of 719 papers that were published between 2019 and May 2023. Furthermore, the approaches in the selected papers are classified into two main categories: memory-based and model-based techniques. The main ideas, advantages, disadvantages, used tools, type of neural network, applications, and evaluation parameters of each selected paper are also discussed in detail. It was found that CNN (Convolutional Neural Network), AE (Autoencoder), DNN (Deep neural network), and Hybrid networks are the four mostly used neural networks in recommender systems. Also, Python, MATLAB, and Java are the most frequently used tools in the reviewed papers. Regarding the applications of the recommender systems in the reviewed papers, movies, products, and music recommendation are three most frequent applications. We point out the open issues and future research directions. Some key challenges such as cold start, data sparsity, scalability, and accuracy are still open to be addressed. [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]

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]

Constructing a personalized learning path is a critical way to solve the problem of cognitive difference and learning disorientation effectively. The construction process of the learning path is closely related to the internal relationship between knowledge and needs to meet different learning scenarios and learning needs. Because of the above requirements, a personalized learning path model based on a knowledge network is proposed in this paper. The algorithm begins by building a knowledge network with learning resource nodes and knowledge points. Following that, the order of the knowledge points was determined using their sequential link. A sequence of learning materials that adhere to user characteristics was eventually acquired by evaluating the learning time limit of various learning contexts. The proposed approach was tested on the data sets of open MOOPer and Python learning platforms. Compared with traditional learning path construction algorithms, the proposed algorithm improves the accuracy and relevance. [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]

It is challenging to use unsupervised machine translation models to generate ancient poems. The current method has solved the problems of Under-translation and Over-translation caused by the huge length difference between the translated sentence pairs. However, the above method lacks guidance in generating intermediate vectors, and the denoising ability of the model is very poor. In this paper, we guide vector space distribution during training to improve the quality of the generated ancient poems and the convergence speed of the model. We also introduce the target language information while adding noise, which effectively avoids the recurrence of the Under-translation problem while improving the model's denoising ability. Experiment results on the VP dataset show that our model obtains state-of-the-art results with faster convergence speed. In addition to the BLEU scores, we also made a comparative analysis of ancient poetry sentences generated by different models. The analysis results show that the optimization method proposed in this paper is indeed helpful for generating high-quality ancient poems. [ABSTRACT FROM AUTHOR]

In recent years, graph neural network (GNN) and auto-encoding (AE) have been widely utilized in multiple data mining problems. These architectures have demonstrated powers in data high-dimensional embedding for improving the performance of various task-driven learning tasks, like as clustering. However, most of recent GNN-based cluster techniques still suffered several limitations. These limitations are related to the capability of simultaneously preserving the low-levelled latent feature and global structural representations of the given network/graph. These view-varied graph representations can help to improve the performance of multi-scaled clustering task. Moreover, the achieved multi-viewed structural node embeddings which are learnt by GNN-based architectures might also involve with problems. These problems are related to feature noise and data uncertainty. These feature noise/uncertainties are occurred within representation learning process. These limitations can directly lead to the downgrade in the accuracy performance for clustering tasks. To overcome existing limitations, within this paper, we proposed a novel fuzzy-driven noise-reduced attention-based graph auto-encoding mechanism for network clustering, called as: FAGC. In general, our proposed FAGC model is as an attention-driven multi-layered graph-based AE architecture which is integrated with a custom de-noising fuzzy neural network. In our proposed FAGC model, we integrate the fuzzy neural network with GNN to eliminate problems which are related to the feature uncertainty and noise occurrence during the graph representation learning process. Later, the better quality as well as rich-structural network representations which are generated from our FAGC model are utilized to achieve state-of-the-art performances for the network clustering problem. The extensive experiments within benchmark networked datasets and comparative studies demonstrated the effectiveness and outperformance of our proposed FAGC model in comparing with state-of-the-art graph embedding techniques. [ABSTRACT FROM AUTHOR]

Today's factories are considered as smart ecosystems with humans, machines and devices interacting with each other for efficient manufacturing of products. Industry 4.0 is a suite of enabler technologies for such smart ecosystems that allow transformation of industrial processes. When implemented, Industry 4.0 technologies have a huge impact on efficiency, productivity and profitability of businesses. The adoption and implementation of Industry 4.0, however, require to overcome a number of practical challenges, in most cases, due to the lack of modernisation and automation in place with traditional manufacturers. This paper presents a first of its kind case study for moving a traditional food manufacturer, still using the machinery more than one hundred years old, a common occurrence for small- and medium-sized businesses, to adopt the Industry 4.0 technologies. The paper reports the challenges we have encountered during the transformation process and in the development stage. The paper also presents a smart production control system that we have developed by utilising AI, machine learning, Internet of things, big data analytics, cyber-physical systems and cloud computing technologies. The system provides novel data collection, information extraction and intelligent monitoring services, enabling improved efficiency and consistency as well as reduced operational cost. The platform has been developed in real-world settings offered by an Innovate UK-funded project and has been integrated into the company's existing production facilities. In this way, the company has not been required to replace old machinery outright, but rather adapted the existing machinery to an entirely new way of operating. The proposed approach and the lessons outlined can benefit similar food manufacturing industries and other SME industries. [ABSTRACT FROM AUTHOR]

Human distance estimation is essential in many vital applications, specifically, in human localisation-based systems, such as independent living for older adults applications, and making places safe through preventing the transmission of contagious diseases through social distancing alert systems. Previous approaches to estimate the distance between a reference sensing device and human subject relied on visual or high-resolution thermal cameras. However, regular visual cameras have serious concerns about people's privacy in indoor environments, and high-resolution thermal cameras are costly. This paper proposes a novel approach to estimate the distance for indoor human-centred applications using a low-resolution thermal sensor array. The proposed system presents a discrete and adaptive sensor placement continuous distance estimators using classification techniques and artificial neural network, respectively. It also proposes a real-time distance-based field of view classification through a novel image-based feature. Besides, the paper proposes a transfer application to the proposed continuous distance estimator to measure human height. The proposed approach is evaluated in different indoor environments, sensor placements with different participants. This paper shows a median overall error of ± 0.2 m in continuous-based estimation and 96.8 % achieved-accuracy in discrete distance estimation. [ABSTRACT FROM AUTHOR]

Digital image noise may be introduced during acquisition, transmission, or processing and affects readability and image processing effectiveness. The accuracy of established image processing techniques, such as segmentation, recognition, and edge detection, is adversely impacted by noise. There exists an extensive body of work which focuses on circumventing such issues through digital image enhancement and noise reduction, but this work is limited by a number of constraints including the application of non-adaptive parameters, potential loss of edge detail information, and (with supervised approaches) a requirement for clean, labeled, training data. This paper, developed on the principle of Noise2Void, presents a new unsupervised learning approach incorporating a pseudo-siamese network. Our method enables image denoising without the need for clean images or paired noise images, instead requiring only noise images. Two independent branches of the network utilize different filling strategies, namely zero filling and adjacent pixel filling. Then, the network employs a loss function to improve the similarity of the results in the two branches. We also modify the Efficient Channel Attention module to extract more diverse features and improve performance on the basis of global average pooling. Experimental results show that compared with traditional methods, the pseudo-siamese network has a greater improvement on the ADNI dataset in terms of quantitative and qualitative evaluation. Our method therefore has practical utility in cases where clean images are difficult to obtain. [ABSTRACT FROM AUTHOR]

In the era of Internet access, software is easily available on digital distribution platforms such as app stores. The distribution of software on these platforms makes user feedback more accessible and can be used from requirements engineering to software maintenance context. However, such user reviews might contain technical information about the app that can be valuable for developers and software companies. Due to pervasive use of mobile apps, a large amount of data is created by users on daily basis. Manual identification and classification of such reviews are time-consuming and laborious tasks. Hence, automating this process is essential for assisting developers in managing these reviews efficiently. Prior studies have focused on classification of these reviews into bug reports, user experience, and feature requests. Nevertheless to date, a very few research papers have extracted Non-Functional Requirements (NFRs) present in these reviews. NFRs are considered as the set of quality attributes such as reliability, performance, security and usability of the software. Previous studies have utilized machine learning techniques to classify these reviews into their respective classes. However, it was observed that existing studies treat review classification problems as single-label classification problem, and also underestimate the contextual relationship between the words of review statements. To alleviate this limitation, the proposed research work used a transfer learning model to classify multi-label app reviews into four NFRs: Dependability, Performance, Supportability, and Usability. The proposed approach evaluates the performance of the pre-trained language model for multi-label review classification. In this paper, a set of experiments are conducted to compare the performance of the proposed model against the baseline machine learning with binary relevance and keyword based approach. We evaluated our approach over a dataset of 6000 user reviews of 24 iOS apps. Experimental results show that the proposed model outperforms state-of-the-art baseline techniques with respect to precision, recall, and F1-measure. [ABSTRACT FROM AUTHOR]

Currently end-to-end deep learning based open-domain dialogue systems remain black box models, making it easy to generate irrelevant contents with data-driven models. Specifically, latent variables are highly entangled with different semantics in the latent space due to the lack of priori knowledge to guide the training. To address this problem, this paper proposes to harness the generative model with a priori knowledge through a cognitive approach involving feature disentanglement. Particularly, the model integrates the guided-category knowledge and open-domain dialogue data for the training, leveraging the priori knowledge into the latent space, which enables the model to disentangle the latent variables. Besides, this paper proposes a new metric for open-domain dialogues, which can objectively evaluate the interpretability of the latent space distribution. Finally, this paper validates our model on different datasets and experimentally demonstrate that our model is able to generate higher quality and more interpretable dialogues than other models. [ABSTRACT FROM AUTHOR]

Data missing has always occurred in data processing. To solve this problem, researchers have improved the process methods of the missing data with diverse strategies, which range from directly deleting missing data samples to using artificial intelligence technology to filling in incomplete data. The processing methods of the missing data have been improved. Generative adversarial imputation network (GAIN) is a kind of neural network which has an excellent performance in missing data imputation. A number of publications that research and cite the GAIN model show a significant growth trend after GAIN was proposed in 2018. GAIN has been studied and improved by many scholars in their specific fields. However, few studies have systematically surveyed the GAIN model's development trends on missing data from its birth to the present, which result in a lack of comprehensive information about GAINs general performance in different fields. In this review, we summarize the development of the GAIN model in missing data imputation from 2018 to 2022. Based on the WOS database, 32 publications are selected according to the PRISMA statement. The outcome of this paper is from the following aspects: (1) analyzing the publication information and application fields quantitatively; (2) expounding the GAIN-based models, classification, and research trends; (3) elaborating the model attributes and missing data mechanism; and (4) summarizing the existing issues and proposing the future directions. Above all, this paper can help scholars gain further insight into the missing data issues and better understand the optimized directions of GAIN models. [ABSTRACT FROM AUTHOR]

This paper presents a new method for detecting the amount of oxide slag on metal ingots, which is a critical indicator of the quality of the ingots. The challenge arises due to the irregular shape and small number of samples of the oxide slag, which makes it difficult to create a pixel-level label dataset for training a semantic segmentation network. To overcome this, a two-stage UNet network based on mixed supervised learning is proposed in this paper. The network uses a combination of a small number of pixel-level labels and a large number of weak label samples to reduce the cost of creating the dataset while achieving better detection accuracy. To solve the issue of overfitting due to the small number of samples, an improved generative adversarial network (GAN) is used to generate oxide slag images with arbitrary shapes to expand the dataset and improve the detection accuracy. Experiments show that the proposed network is highly effective in accurately detecting the distribution of oxide slag on metal ingots. Compared to existing segmentation algorithms, the network achieves higher accuracy while requiring a lower cost for dataset creation. The results of this work have important implications for the practical application of intelligent sensing in slag-picking robots. [ABSTRACT FROM AUTHOR]

The popularity of digital cameras and social networks has greatly enriched people's spiritual life, and we can easily obtain massive amounts of digital photos. However, due to the lack of professional guidance and differences in aesthetic appreciation, the photos taken many photographers lack aesthetics. This article is dedicated to the research of image aesthetics, using computers to simulate human perception, and realize the evaluation or beautification of images in line with human aesthetics. In terms of image classification, this article examines the unique perception of human vision on images and proposes new aesthetic features. Combining visual features and semantic features, the SVM algorithm is utilized to build an aesthetic classifier. In the aspect of image optimization, this paper uses the detection of the main image area and the division line of the area and adjusts the main body size and position of the image according to common aesthetic rules, so as to realize the optimization adjustment of the composition of the social image. The experimental results show that the accuracy of social image classification is 97.7%, and the optimized and adjusted images are more aesthetic. [ABSTRACT FROM AUTHOR]

In order to improve the traffic visualization management technology of smart cities, this paper applies machine learning to smart traffic management, and uses machine learning methods to process and analyze traffic data generated in smart cities. Moreover, this paper uses it to understand the distribution law of traffic data and the internal connections between data, excavate traffic characteristics in smart cities, and accurately predict the future traffic situation, so as to solve the problems of traffic congestion and route planning in smart cities. Moreover, this paper combines improved machine learning algorithms to construct a traffic visualization management system, and designs experiments to verify the performance of the system proposed in this paper. It can be seen from the experimental research results that the method proposed in this paper can play an important role in the traffic management of smart cities. [ABSTRACT FROM AUTHOR]

To improve the effect of commercial insurance risk decision, this paper applies neural network algorithms to commercial insurance risk decision under the guidance of machine learning ideas, and selects the neural network algorithm based on the actual situation. Moreover, this paper analyzes the nature of risks of commercial insurance, analyzes the types of risks and risk relevance, constructs a commercial insurance risk decision model based on neural network algorithms, and determines the system process. In addition, this paper uses a combination method of qualitative and quantitative to identify the influencing factors of risk estimation to obtain relevant influencing factors, and verify the model proposed in this paper in combination with experimental research. From the experimental research results, it can be seen that the commercial insurance risk decision system based on neural network algorithm is very good in terms of decision effect. [ABSTRACT FROM AUTHOR]

This paper explores the application of Visual Question-Answering (VQA) technology, which combines computer vision and natural language processing (NLP), in the medical domain, specifically for analyzing radiology scans. VQA can facilitate medical decision-making and improve patient outcomes by accurately interpreting medical imaging, which requires specialized expertise and time. The paper proposes developing an advanced VQA system for medical datasets using the Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (BLIP) architecture from Salesforce, leveraging deep learning and transfer learning techniques to handle the unique challenges of medical/radiology images. The paper discusses the underlying concepts, methodologies, and results of applying the BLIP architecture and fine-tuning approaches for VQA in the medical domain, highlighting their effectiveness in addressing the complexities of VQA tasks for radiology scans. Inspired by the BLIP architecture from Salesforce, we propose a novel multi-modal fusion approach for medical VQA and evaluating its promising potential. [ABSTRACT FROM AUTHOR]

This article is concerned with sampled-data synchronization problem of heterogeneous delays inertial neural networks (INNs) with generally uncertain semi-Markovian (GUSM) jumping. Different from traditional Markovian inertial neural networks (MINNs), the INNs with GUSM are investigated in this paper by fully considering the sojourn time and the lacking transition rates, which is more general and applicable for practical system. The new extended two-sided looped-functional (ETSLF) approach is adopted in this paper, and some improved less conservative criteria are derived to achieve the synchronization of the drive and response INNs. The controller gain matrices are acquired based on synchronization criteria. Finally, the viability of the method is presented through three examples. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to clarify the importance of explainability in the crop recommendation process and provide insights on how Explainable Artificial Intelligence (XAI) can be incorporated into existing models successfully. The objective is to increase the definition and transparency of the recommendations implemented by AI in smart agriculture, leading to a detailed analysis of the synchronization between crop recommendation systems and XAI that informs decisions as it has sustainable knowledge and practices in modern agriculture. It reviews state-of-the-art XAI techniques such as local interpretable model-agnostic interpretation (LIME), SHapley interpretation additive approach (SHAP), integrated gradients (IG), and level-wise relevance propagation (LRP). It focuses on interpretable models and critical features analysis, and XAI methods are discussed in terms of their applications, critical features, and definitions. The paper found that XAI methods such as LIME and SHAP can make AI-driven crop recommendation systems more transparent and reliable. Graphical techniques such as dependency plots, summary plots, waterfall graphs, and decision plots effectively analyze feature importance. The paper includes counterfactual explanations using dice ml and hearing with advanced techniques combining IG and LRP to provide in-depth narrative model behavior. The novelty of this study lies in a detailed investigation of how XAI can be incorporated into crop recommendation systems to address the "black box" nature of AI models. It uses a unique XAI technique and model approach to make AI-driven recommendations more meaningful and practical for farmers. The proposed systems and techniques are designed to consume agriculture, addressing the specific needs of intelligent systems, making this research a significant contribution to agricultural AI. [ABSTRACT FROM AUTHOR]

Handwritten character recognition falls under the domain of image classification, which has been under research for years. But still, specific gaps need to be highlighted as offline handwritten character recognition (OHCR) with the limitation of the unstructured hierarchy of character classification. However, the idea is to make the machine recognize handwritten human characters. The language focused on in this research paper is English, using offline handwritten character recognition for identifying English characters. There are many publicly available datasets, of which EMNIST is the most challenging. The key idea of this research paper is to recommend a deep learning-based ELBP-CNN method to help recognize English characters. This research paper proposes a deep learning CovNet with feature extraction and novel local binary pattern-based approaches, LBP (AND, OR), that is tested and compared with renowned pre-trained models using transfer learning. These parametric settings address multiple issues and are finalized after experimentation. The same hyperparametric settings were used for all the models under test and E-Character, with the same data augmentation settings. The proposed model, named the E-Character recognizer, produced 87.31% accuracy. It was better than most of the tested pre-trained models and other proposed methods by other researchers. This research paper further highlighted some problems, like misclassification due to the similar structure of characters. [ABSTRACT FROM AUTHOR]

Demand response, and artificial intelligence integration with it, have a considerable effect in optimizing energy consumption, grid stability, and promoting sustainable energy practices. Consequently, this paper presents NeuroQuMan, a comprehensive methodology for simulating demand response using a three-Qubit quantum neural network (QNN) model. NeuroQuMan integrates quantum computing and machine learning techniques to accurately predict demand based on user reaction time. The methodology encompasses an advanced structure that includes data preprocessing, three-Qubit quantum device initialization, quantum circuit definition, user decision-making, QNN predictions, loss calculations, and visualization. During the tests, NeuroQuMan achieved considerable performance values of metrics, with RMSPE of 5.41%, MAPE of 4.43%, as well as MAE of 0.37, RMSE of 0.45, and MSE of 0.21, respectively. These metrics manifest the accuracy and effectiveness of NeuroQuMan in predicting demand response. By the side of future perspectives of the work, it explores the application of advanced quantum techniques to further enhance prediction accuracy. NeuroQuMan represents the potential of quantum computing in addressing demand response challenges and provides a pathway toward more resilient and intelligent energy management systems. The findings and framework presented in this paper are utilized to advance the field of demand response and quantum-based energy management techniques using a three-Qubit structure. [ABSTRACT FROM AUTHOR]