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Most existing studies and public datasets for handwritten Chinese text recognition are based on the regular documents with clean and blank background, lacking research reports for handwritten text recognition on challenging areas such as educational documents and financial bills. In this paper, we focus on examination paper text recognition and construct a challenging dataset named examination paper text (SCUT-EPT) dataset, which contains 50 000 text line images (40 000 for training and 10 000 for testing) selected from the examination papers of 2 986 volunteers. The proposed SCUT-EPT dataset presents numerous novel challenges, including character erasure, text line supplement, character/phrase switching, noised background, nonuniform word size, and unbalanced text length. In our experiments, the current advanced text recognition methods, such as convolutional recurrent neural network (CRNN) exhibits poor performance on the proposed SCUT-EPT dataset, proving the challenge and significance of the dataset. Nevertheless, through visualizing and error analysis, we observe that humans can avoid vast majority of the error predictions, which reveal the limitations and drawbacks of the current methods for handwritten Chinese text recognition (HCTR). Finally, three popular sequence transcription methods, connectionist temporal classification (CTC), attention mechanism, and cascaded attention-CTC are investigated for HCTR problem. It is interesting to observe that although the attention mechanism has been proved to be very effective in English scene text recognition, its performance is far inferior to the CTC method in the case of HCTR with large-scale character set.

This paper tackles the problem of detecting incongruities between headlines and body text, where a news headline is irrelevant or even in opposition to the information in its body. Our model, called the graph-based hierarchical dual encoder (GHDE), utilizes a graph neural network to efficiently learn the content similarity between news headlines and long body paragraphs. This paper also releases a million-item-scale dataset of incongruity labels that can be used for training. The experimental results show that the proposed graph-based neural network model outperforms previous state-of-the-art models by a substantial margin (5.3%) on the area under the receiver operating characteristic (AUROC) curve. Real-world experiments on recent news articles confirm that the trained model successfully detects headline incongruities. We discuss the implications of these findings for combating infodemics and news fatigue.

An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms have been proposed for implementing anomaly-based IDS (AIDS). Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results. This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters, and testing criteria. Specifically, this paper applies 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML-AIDS of networks and computers. These supervised ML algorithms include the artificial neural network (ANN), decision tree (DT), k-nearest neighbor (k-NN), naive Bayes (NB), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms, whereas the unsupervised ML algorithms include the expectation-maximization (EM), k-means, and self-organizing maps (SOM) algorithms. Several models of these algorithms are introduced, and the turning and training parameters of each algorithm are examined to achieve an optimal classifier evaluation. Unlike previous studies, this study evaluates the performance of AIDS by measuring the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models. The training and testing time for ML-AIDS models are also considered in measuring their performance efficiency given that time complexity is an important factor in AIDSs. The ML-AIDS models are tested by using a recent and highly unbalanced multiclass CICIDS2017 dataset that involves real-world network attacks. In general, the k-NN-AIDS, DT-AIDS, and NB-AIDS models obtain the best results and show a greater capability in detecting web attacks compared with other models that demonstrate irregular and inferior results.

Handwritten mathematical expressions are an essential part of many domains, including education, engineering, and science. The pervasive availability of computationally powerful touch-screen devices, similar to the recent emergence of deep neural networks as high-quality sequence recognition models, result in the widespread adoption of online recognition of handwritten mathematical expressions. Also, a deeper study and improvement of such technologies is necessary to address the current challenges posed by the extensive usage of distance learning, and remote work due to the world pandemic. This paper delineates the state-of-the-art recognition methods along with the user's experience in pen-centric applications for operating with handwritten mathematical expressions. Recognition methods have been categorized into classes, with a description of their merits and limitations. Particular attention is paid to end-to-end approaches based on encoder-decoder architecture and multi-modal input. Evaluation protocols and open benchmark datasets are considered as well as the comparison of the recognition performance, based on open competition results. The use of handwritten math recognition is illustrated by examples of applications for various fields and platforms. A distinctive part of the survey is that we also considered how UI design relies on the use of different recognition approaches, which is aimed at helping potential researchers improve the performance of the introduced approaches toward the best responses in practical applications. Finally, this paper presents the prospective survey of future research directions in handwritten mathematical expression recognition and their applications.

The Internet of Things is based on a communication network to automatically receive various information from the natural world. It uses intelligent objects with perception, communication and computers to automatically receive various information from nature. All independently managed physical objects are connected to each other to achieve integrated perception, reliable transmission and intelligent processing, and to establish intelligent information between people and objects, and objects and object service systems. This article mainly introduces the impact of industrial agglomeration on the regional economy in a simulated intelligent environment based on machine learning. This paper proposes a method to detect industrial agglomeration index to analyze industrial agglomeration. Through the establishment of the industrial agglomeration index system, the level of integration of the manufacturing industry in our city was objectively analyzed, and the impact of the integration of manufacturing industry on our city was tested empirically. Finally, the relationship between industrial integration and regional economic development was tested. The experimental results in this paper show that industrial integration in a smart environment based on simulation learning has a significant positive impact on regional economic development. Among them, the level of cooperation between the manufacturing service industry and the manufacturing industry increased by 1%, and the level of regional economic development increased by 0.025%.

Technology forecasting not only helps business managers to make the right decisions but also helps researchers to grasp the direction of technology development. Technology forecasting, which facilitates the identification of the development technologies with high potential, can be an effective tool to support the management and plan for the future research activities. For this purpose, this paper firstly constructs Multi-modal input based on deep learning (MIDL) text classification model to extract relevant SCI papers from Web of Science database from 1996 to 2019 for topic classification, and then apply the Ensemble Empirical Mode Decomposition (EEMD) and Long Short-Term Memory (LSTM) neural networks to build an EEMD-LSTM technology forecasting model to predict the future development of each research field. Besides, we verify the validity of the method by taking robotics as an example in this paper. The results show that the accuracy, recall and F1 of MIDL text classification model are 0.826, 0.822 and 0.824 respectively. As compared with the optimal results of other classification models, the accuracy, recall and F1 are improved by 4.1%, 3.5%, and 3.8% respectively. The mean MAPE of the EEMD-LSTM model is 7%, which is 11% lower than ARIMA, 8% lower than LSTM, and 10% lower than 2-layer-LSTM.

In recent years, a variety of supervised manifold learning techniques have been proposed to outperform their unsupervised alternative versions in terms of classification accuracy and data structure capturing. Some dissimilarity measures have been used in these techniques to guide the dimensionality reduction process. Their good performance was empirically demonstrated; however, the relevant analysis is still missing. This paper contributes to a theoretical analysis on a) how dissimilarity measures affect maintaining manifold neighbourhood structure and b) how supervised manifold learning techniques could contribute to the reduction of classification error. This paper also provides a cross-comparison between supervised and unsupervised manifold learning approaches in terms of structure capturing using Kendall’s Tau coefficients and co-ranking matrices. Four different metrics (including three dissimilarity measures and Euclidean distance) have been considered along with manifold learning methods such as Isomap, ${t}$ -Stochastic Neighbour Embedding ( ${t}$ -SNE), and Laplacian Eigenmaps (LE), in two datasets: Breast Cancer and Swiss-Roll. This paper concludes that although the dissimilarity measures used in the manifold learning techniques can reduce classification error, they do not learn well or preserve the structure of the hidden manifold in the high dimensional space, but instead, they destroy the structure of the data. Based on the findings of this paper, it is advisable to use supervised manifold learning techniques as a pre-processing step in classification. In addition, it is not advisable to apply supervised manifold learning for visualization purposes since the two-dimensional representation using supervised manifold learning does not improve the preservation of data structure.

It is expected that peer to peer energy trading will constitute a significant share of research in upcoming generation power systems due to the rising demand of energy in smart microgrids. However, the on-demand use of energy is considered a big challenge to achieve the optimal cost for households. This paper proposes a blockchain-based predictive energy trading platform to provide real-time support, day-ahead controlling, and generation scheduling of distributed energy resources. The proposed blockchain-based platform consists of two modules; blockchain-based energy trading and smart contract enabled predictive analytics modules. The blockchain module allows peers with real-time energy consumption monitoring, easy energy trading control, reward model, and unchangeable energy trading transaction logs. The smart contract enabled predictive analytics module aims to build a prediction model based on historical energy consumption data to predict short-term energy consumption. This paper uses real energy consumption data acquired from the Jeju province energy department, the Republic of Korea. This study aims to achieve optimal power flow and energy crowdsourcing, supporting energy trading among the consumer and prosumer. Energy trading is based on day-ahead, real-time control, and scheduling of distributed energy resources to meet the smart grid’s load demand. Moreover, we use data mining techniques to perform time-series analysis to extract and analyze underlying patterns from the historical energy consumption data. The time-series analysis supports energy management to devise better future decisions to plan and manage energy resources effectively. To evaluate the proposed predictive model’s performance, we have used several statistical measures, such as mean square error and root mean square error on various machine learning models, namely recurrent neural networks and alike. Moreover, we also evaluate the blockchain platform’s effectiveness through hyperledger calliper in terms of latency, throughput, and resource utilization. Based on the experimental results, the proposed model is effectively used for energy crowdsourcing between the prosumer and consumer to attain service quality.

The epidemic increase in online reviews’ growth made the sentiment classification a fascinating domain in academic and industrial research. The reviews assist several domains, which is complicated to gather annotated training data. Several sentiment classification methodologies are devised for performing the sentiment analysis, but retrieval of information is not accurately performed, less effective, and less convergence speed. In this paper, we propose a sentiment paper proposes a sentiment classification model, namely Spider Monkey Crow Optimization algorithm (SMCA), for training the deep recurrent neural network (DeepRNN). In this method, the telecom review is employed to remove stop words and stemming to eliminate inappropriate data to minimize user’s seeking time. Meanwhile, the feature extraction is performed using SentiWordNet to derive the sentiments from the reviews. The extracted SentiWordNet features and other features, like elongated words, punctuation, hashtag, and numerical values, are employed in the DeepRNN for classifying sentiments. To retrieve the required review, the Fuzzy K-Nearest neighbor (Fuzzy-KNN) is employed to retrieve the review based on a distance measure. With rigorous assessments and experimentation, it is observed that the proposed SMCA-based DeepRNN performs better in terms of accuracy of 97.7%, precision of 95.5%, recall of 94.6%, and F1-score 96.7%, respectively.

Hyperspectral and multispectral information processing systems and technologies have demonstrated its usefulness for the improvement of agricultural productivity and practices by providing useful information to farmers and crop managers on the factors affecting crop status and growth. These technologies are widely used in a range of agriculture applications such as crop management, crop yield forecasting, crop disease detection, and the monitoring of agriculture land usage, water, and soil conditions. Hyperspectral information sensing can acquire several hundred spectral bands that cover the electromagnetic spectrum of an observational scene in a single acquisition. The resulting hyperspectral data cube contains a large volume of spatial and spectral information. The hyperspectral sequence of images or video further increases the data generation velocity and volume which lead to the Big data challenges particularly in agricultural remote sensing applications. This paper is structured to first give a comprehensive review of representative studies to provide insights into significant research efforts in agriculture using Big data, machine learning and deep learning with the focus on frameworks or architectures, information processing and analytics with hyperspectral and multispectral data. The potential for utilizing Big data, machine learning and deep learning for hyperspectral and multispectral data in agriculture is very promising. The paper then further explores the potential of using ensemble machine learning and scalable parallel discriminant analysis which takes into consideration the spatial and spectral components for Big data in agriculture. To the best of our knowledge, no similar review study on agriculture with Big data, machine learning and deep learning for hyperspectral and multispectral information processing has been reported. Furthermore, the potential of ensemble machine learning and scalable parallel discriminant analysis has not been explored in agriculture information processing. Experiments and data analytics have been performed on hyperspectral data from agriculture for validation. The results have shown the good performance of our approach.

Dyslexia is a neurological disorder that is characterized by imprecise comprehension of words and generally poor reading performance. It affects a significant population of school-age children, with more occurrences in males, thus, putting them at risk of poor academic performance and low self-esteem for a lifetime. The long-term hope is to have a dyslexia diagnostic method that is informed by neural-biomarkers. In this regard, large numbers of machine learning methods and, more recently, deep learning methods have been implemented across various types of dataset with the above-chance classification accuracy. However, attainment of clinical acceptability of these state-of-the-art methods is bedeviled by certain challenges including lack of biologically-interpretable biomarkers, privacy of dataset and classifiers, hyper-parameter selection/optimization, and overfitting problem among others. This review paper critically analyzes recent machine learning methods for detecting dyslexia and its biomarkers and discusses challenges that require proper attentions from the users of deep learning methods in order to enable them to attain clinically relevance and acceptable level. The review is conducted within the premise of implementation and experimental outcomes for each of the 22 selected articles using the Preferred Reporting Items for Systematic review and Meta-Analyses (PRISMA) protocol, with a view to outlining some critical challenges for achieving high accuracy and reliability of the state-of-the-art machine learning methods. As an evidence-based protocol for reporting in systematic reviews and meta-analyses, PRISMA helps to ensure clarity and transparency of this paper by showing a four-phase flow diagram of the selection process for articles used in this review. It is therefore, envisaged that higher classification performance of clinical relevance can be achieved using deep learning models for dyslexia and its biomarkers by addressing identified potential challenges.

Several hypovigilance detection systems (HDx) were developed to avoid road-side accidents due to driver fatigue. They have suffered from several limitations. Notably many of these are focused on center-head position to define an area of interest (often referred to as PERCLOS (percentage eye closure)) without considering the face occlusion problem, light illumination, and suffer poor response time. These HDx systems mostly depend on image processing, vision-based, and multisensor-based features. To address these problems, the author utilized vision, sensors, environmental, and vehicular-based features that integrated together by fusion to predict multistage of HDx. Lately, few studies have utilized the combination of multimodal features and deep learning (DL) architectures. Those multimodal-based features (M-HDx) were feasible to predict stages of driver fatigue (multi-stage). However, there is a need to critically measure the performance of these M-HDx by carrying out a comparative analysis to recognize multi-stage of fatigue in terms of hardware-based benchmarks. Moreover, it is important to evaluate the M-HDx systems using different features-set with respect to traditional and advanced machine learning techniques. Therefore, the primary aim of this work is in algorithm and feature modeling, then compare the advantages and differences with other work. In this paper, a different study is conducted compare to state-of-the-art survey articles by statistically measuring the performance. After experiments on M-HDx systems, this paper concludes that there is still a research gap to real-time development of multistage M-HDx systems. In the end, the paper summarizes the directions, challenges, and applications in the development of HDx systems to assist other researchers for further research.

Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a large impact on Smart Farming and involve the whole supply chain, particularly for rice production. The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the rice smart farming strategy new abilities to predict changes and identify opportunities. The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms. These three elements (e.g., BD, ML and IoT) have been used tremendously to improve all areas of rice production processes in agriculture, which transform traditional rice farming practices into a new era of rice smart farming or rice precision agriculture. In this paper, we perform a survey of the latest research on intelligent data processing technology applied in agriculture, particularly in rice production. We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification. This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice. Based on the proposed mapping framework, our conclusion is that an efficient and effective integration of all these three technologies is very crucial that transform traditional rice cultivation practices into a new perspective of intelligence in rice precision agriculture. Finally, this paper also summarizes all the challenges and technological trends towards the exploitation of multiple sources in the era of big data in agriculture.

The standardization and digitalization of nine constitutions of traditional Chinese medicine (TCM) have promoted the development of TCM automation. The keys to constitution identification are tongue diagnosis and consultation diagnosis. In this paper, a tongue detection method based on the combination of the histogram of oriented gradients (HOG) and the support vector machine (SVM) is proposed. To separate the tongue body and tongue coating, a k-means segmentation method based on the Lab color space is proposed. Based on the clustering analysis of the difference between the color components of the tongue body and tongue coating in the Lab color space, the separation is realized. Thus, the relationship between the tongue image and constitution can be analyzed. The method for consultation diagnosis is divided into a questionnaire scale and a question answering system. The questionnaire includes 29 quantified items with corresponding weights and answer scores, so the final score of each type of constitution is calculated. The final constitution type is determined on the basis of the scores of both diagnoses. In addition, the key of question answering system is text similarity calculation. First, a sentence similarity calculation method combining the n-gram language model and word2vec is proposed. Then, a multifeature fusion sentence similarity calculation method combining the Word Mover’s Distance (WMD) and editing distance is proposed. Finally, the experimental results show the effectiveness of the proposed method, and a constitution identification application is developed based on the content of this paper.

Context: User stories have been widely accepted as artifacts to capture the user requirements in agile software development. They are short pieces of texts in a semi-structured format that express requirements. Natural language processing (NLP) techniques offer a potential advantage in user story applications. Objective: Conduct a systematic literature review to capture the current state-of-the-art of NLP research on user stories. Method: The search strategy is used to obtain relevant papers from SCOPUS, ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar. Inclusion and exclusion criteria are applied to filter the search results. We also use the forward and backward snowballing techniques to obtain more comprehensive results. Results: The search results identified 718 papers published between January 2009 to December 2020. After applying the inclusion/exclusion criteria and the snowballing technique, we identified 38 primary studies that discuss NLP techniques in user stories. Most studies used NLP techniques to extract aspects of who, what, and why from user stories. The purpose of NLP studies in user stories is broad, ranging from discovering defects, generating software artifacts, identifying the key abstraction of user stories, and tracing links between model and user stories. Conclusion: NLP can help system analysts manage user stories. Implementing NLP in user stories has many opportunities and challenges. Considering the exploration of NLP techniques and rigorous evaluation methods is required to obtain quality research. As with NLP research in general, the ability to understand a sentence’s context continues to be a challenge.

Semantic segmentation is a task that covers most of the perception needs of intelligent vehicles in an unified way. Recent studies witnessed that attention mechanisms achieve impressive performance in computer vision task. Current attention mechanisms based segmentation methods differ with each other in position and form of the attention mechanism, and perform differently in practice. This paper firstly introduces the effectiveness of multi-scale context features and attention mechanisms in segmentation tasks. We find that multi-scale and channel attention can play a vital role in constructing effective context features. Based on this analysis, this paper proposes an efficient attention pyramid network (EAPNet) for semantic segmentation. Specifically, to efficient handle the problem of segmenting objects at multiple scales, we design efficient channel attention pyramid (ECAP) which employ atrous convolution with channel attention in cascade or in parallel to capture multi-scale context by using multiple atrous rates. Furthermore, we propose a residual attention fusion block (RAFB), whose purpose is to simultaneously focus on meaningful low-level feature maps and spatial location information. At the same time, we will explore different channel attention modules and spatial attention modules, and describe their impact on network performance. We empirically evaluate our EAPNet on two semantic segmentation datasets, including PASCAL VOC 2012 and Cityscapes datasets. Experimental results show that without MS COCO pre-training and any post-processing, EAPNet achieved 81.7% mIoU on the PASCAL VOC 2012 validation set. With deeplabv3+ as the benchmark, EAPNet improve the model performance of more than 1.50% mIoU.

Recognizing the current weather conditions from a single image is of great theoretical significance. It also has potential practical value for daily life and traffic scheduling. To achieve that, typical weather recognition methods focus on learning a general weather description, e.g., sunny, cloudy, foggy, rainy and snowy etc, for the overall weather condition. However, it is far away from being sufficient for many tasks especially traffic management and control. To solve this key problem, this paper proposes a Global-Similarity Local-Salience Network (abbreviated as GSLSNet) for traffic weather recognition. Specifically, a simple but effective Global-Similarity Module (GSM) is proposed to recognize the overall weather condition and a Local-Salience Module (LSM) is presented to restrict the network to focus on road weather details. Besides, this paper also provides a new traffic weather dataset, named TWData, which is the first fine categorized dataset especially for highway weather recognition. Experimental results compared with state-of-the-art methods on both public datasets and TWData demonstrate the superiority of the proposed GSLSNet.

A distributed denial of service (DDoS) attack represents a major threat to service providers. More specifically, a DDoS attack aims to disrupt and deny services to legitimate users by overwhelming the target with a massive number of malicious requests. A cyberattack of this kind is likely to result in tremendous economic losses for businesses and service providers due to increasing both operating and financial costs. In recent years, machine learning (ML) techniques have been widely used to prevent DDoS attacks. Indeed, many defense systems have been transformed into smart and intelligent systems through the use of ML techniques, which allow them to defeat DDoS attacks. This paper analyzes recent studies concerning DDoS detection methods that have adapted single and hybrid ML approaches in modern networking environments. Additionally, the paper discusses different DDoS defense systems based on ML techniques that make use of a virtualized environment, including cloud computing, software-defined network, and network functions virtualization environments. As the development of the Internet of Things (IoT) has been the subject of significant research attention in recent years, the paper also discusses ML approaches as security solutions against DDoS attacks in IoT environments. Furthermore, the paper recommends a number of directions for future research. This paper is intended to assist the research community with the design and development of effective defense systems capable of overcoming different types of DDoS attacks.

Graph embedding models are widely used in knowledge graph completion (KGC) task. However, most models are based on the assumption that knowledge is completely certain, and this is inconsistent with real-world situations. Although there are multiple studies on uncertain knowledge embedding tasks, they often use knowledge confidence to learn embedding and cannot make full use the structural and uncertain information of knowledge. This paper presents a new embedding model named Structural and Uncertain Knowledge Embedding (SUKE), which comprises two components: an evaluator and a confidence generator. For unknown triples, the evaluator learns the structural and uncertain information to evaluate its rationality and obtain a candidate set. The confidence generator then determines the confidence of the candidate set to achieve KGC. To verify the effectiveness of the proposed model, confidence prediction, triple evaluation, and fact classification tasks are performed on three data sets. Experimental results show that SUKE performs better than mainstream embedding methods. The model proposed in this paper can help advance the research on the embedding of uncertain knowledge graphs.

Wireless communication systems play a very crucial role in modern society for entertainment, business, commercial, health and safety applications. These systems keep evolving from one generation to next generation and currently we are seeing deployment of fifth generation (5G) wireless systems around the world. Academics and industries are already discussing beyond 5G wireless systems which will be sixth generation (6G) of the evolution. One of the main and key components of 6G systems will be the use of Artificial Intelligence (AI) and Machine Learning (ML) for such wireless networks. Every component and building block of a wireless system that we currently are familiar with from our knowledge of wireless technologies up to 5G, such as physical, network and application layers, will involve one or another AI/ML techniques. This overview paper, presents an up-to-date review of future wireless system concepts such as 6G and role of ML techniques in these future wireless systems. In particular, we present a conceptual model for 6G and show the use and role of ML techniques in each layer of the model. We review some classical and contemporary ML techniques such as supervised and un-supervised learning, Reinforcement Learning (RL), Deep Learning (DL) and Federated Learning (FL) in the context of wireless communication systems. We conclude the paper with some future applications and research challenges in the area of ML and AI for 6G networks.

Sentiment Analysis is an important research direction of natural language processing, and it is widely used in politics, news and other fields. Word embeddings play a significant role in sentiment analysis. The existing sentiment embeddings methods directly embed the sentiment lexicons into traditional word representation. This sentiment representation methods can only differentiate the sentiment information of different words, not the same word in different contexts, so it cannot provide accurate sentiment information for word in different contexts. This paper proposes sentiment concept to solve the problem. First, we found the optimal sentiment concept of words in Microsoft Concept Graph according to the context of words. Then we obtained the sentiment information of words under optimal sentiment concept from the multi-semantics sentiment intensity lexicon which we constructed in this paper to achieve accurate embedding of sentiment information and provide more accurate semantics and sentiment representation for words. Finally, we combined two refined word embeddings methods to achieve a more comprehensive word representation. Compared with traditional and sentiment embeddings methods on six representative datasets, the validity of the word embeddings method based on sentiment concept proposed in this paper is verified.

Nowadays, rehabilitation training for stroke survivors is mainly completed under the guidance of the physician. There are various treatment ways, however, most of them are affected by various factors such as experience of physician and training intensity. The treatment effect cannot be fed back in time, and objective evaluation data is lacking. In addition, the treatment method is complicated, costly, and highly dependent on physicians. Moreover, stroke survivors’ compliance is poor, which leads to various limitations. This paper combines the Internet-of-Things, machine learning, and intelligence system technologies to design a smartphone-based intelligence system to help stroke survivors to improve upper limb rehabilitation. With the built-in multi-modal sensors of the smart phone, training action data of users can be obtained, and then transfer to the server through the Internet. This research presents a DTW-KNN joint algorithm to recognize accuracy of rehabilitation actions and classify to multiple training completion levels. The experimental results show that the DTW-KNN algorithm can evaluate the rehabilitation actions, the accuracy rates of the classification in excellent, good, and normal are 85.7%, 66.7%, and 80% respectively. The intelligence system presented in this paper can help stroke survivors to proceed rehabilitation training independently and remotely, which reduces medical costs and psychological burden.

The popularity and usage of Cloud computing is increasing rapidly. Several companies are investing in this field either for their own use or to provide it as a service for others. One of the results of Cloud development is the emergence of various security problems for both industry and consumer. One of the ways to secure Cloud is by using Machine Learning (ML). ML techniques have been used in various ways to prevent or detect attacks and security gaps on the Cloud. In this paper, we provide a Systematic Literature Review (SLR) of ML and Cloud security methodologies and techniques. We analyzed 63 relevant studies and the results of the SLR are categorized into three main research areas: (i) the different types of Cloud security threats, (ii) ML techniques used, and (iii) the performance outcomes. We have defined 11 Cloud security areas. Moreover, distributed denial-of-service (DDoS) and data privacy are the most common Cloud security areas, with a 16% level of use and 14%respectively. On the other hand, we found 30 ML techniques used, some used hybrid and others as standalone. The most popular ML used is SVM in both hybrid and standalone models. Furthermore, 60% of the papers compared their models with other models to prove the efficiency of their proposed model. Moreover, 13 different evaluation metrics were enumerated. The most applied metric is true positive rate and least used is training time. Lastly, from 20 datasets found, KDD and KDD CUP'99 are the most used among relevant studies.

In the text sentiment classification task, some words are seemingly unrelated to the classification task, but they have a direct impact on the performance of classification model. For example, in the sentences “I have terminal cancer” and “Cancer is a very common disease”, it can be clearly found that the word “cancer” has two different sentiment tendencies in the daily life domain and the medical domain. In the daily life domain, the word “cancer” shows an extremely negative sentiment tendency. While in the medical domain, the word “cancer” is just a simple term with a relatively neutral sentiment tendency. Although current deep learning models have already achieved good performance through their powerful feature learning capabilities, there are serious deficiencies in dealing with the above problem. Therefore, from a new perspective, this paper proposes the Graph Domain Adversarial Transfer Network (GDATN) based on the idea of adversarial learning, which uses the labeled source domain data to predict the sentiment label of unlabeled target domain data. Firstly, GDATN extracts feature representations through the Bidirectional Long Short-Term Memory (BiLSTM) Network and Graph Attention Network (GAT) successively. Then, GDATN introduces the domain classifier to capture the domain-shared text feature representation with the Gradient Reversal Layer (GRL). In addition, an auxiliary task named the projection mechanism is constructed to further capture the domain-specific text feature representation in response to the text domain problem. Extensive experimental results on two benchmark datasets show that GDATN proposed in this paper outperforms the other six benchmark sentiment classification models, and GDATN has a better stability on different cross-domain pairs.

An abundance of various segmentation techniques are available in the literature, that cater to wide range of image understanding applications. The paper proposes a unified way of systematic categorization of the research work on image segmentation called Multi-Faceted Hierarchical Image Segmentation Taxonomy (MFHIST), which consist of six facets presented in a hierarchical manner - scope, requirement, control, feature, image representation and approach specifications. Every scope is exemplified with research works from the literature for better understanding. The paper gives an illustration of populating MFHIST, to provide the reader a quick grasp of few important state-of-art image segmentation research works and their adaptations. As a case study, the illustrations display a limited version to uncover the journey of basic to modern adaptations in the areas region based segmentation approach, such as Markov Random Fields, Spectral Clustering, Active Contour Model, Mean Shift Clustering. The other segmentation approaches have not been considered here, owing to the enormous volume of works in the past four to five decades and limitation in articulating all of them using MFHIST. The performance analysis of the algorithms using quantitative metrics is not in the present scope and will be considered in future version of MFHIST.

A new approach that drastically improves cross-modal retrieval performance in vision and language (hereinafter referred to as & x201C;vision and language retrieval & x201D;) is proposed in this paper. Vision and language retrieval takes data of one modality as a query to retrieve relevant data of another modality, and it enables flexible retrieval across different modalities. Most of the existing methods learn optimal embeddings of visual and lingual information to a single common representation space. However, we argue that the forced embedding optimization results in loss of key information for sentences and images. In this paper, we propose an effective utilization of representation spaces in a simple but robust vision and language retrieval method. The proposed method makes use of multiple individual representation spaces through text-to-image and image-to-text models. Experimental results showed that the proposed approach enhances the performance of existing methods that embed visual and lingual information to a single common representation space.

The goal of this paper is to conduct the study on the text classification for financial news based on partial information. By a fact that an indispensable step for the efficient use of topic information embedded in financial news is the text classification, a new neural network called “All Dataset based on CharCNN (Character Convolutional Neural Networks) and GRU (Gated Recurrent Unit)” (in short, AD-CharCGNN) which extracts a part of the financial article and incorporates both time domain and spatial domain to classify financial texts is proposed. In the study of this paper, we first build a character level vocabulary by reading all characters of the financial dataset, part of each financial text which will be classified is mapped to a high-dimensional spatial vector based on the vocabulary. Then, the vectors are convoluted in the spatial domain to get the text local features, and next, the features are processed by the gated recurrent units to get the features contained time information. Finally, the features which contain spatial and time information will be classified through softmax function to get the text classification results. Our results on the experiments confirm that the network proposed in this paper works effectively with the accuracy of 96.45%, and it seems that the text classification algorithm with the feature by taking only partial text part is more suitable for the application of the practice. Meanwhile, for the input with character level vector, the network is not only suitable for Chinese but also for other languages.

Recently, Deep Learning (DL) approaches have been applied to solve the Sentiment Classification (SC) problem, which is a core task in reviews mining or Sentiment Analysis (SA). The performances of these approaches are affected by different factors. This paper addresses these factors and classifies them into three categories: data preparation based factors, feature representation based factors and the classification techniques based factors. The paper is a comprehensive literature-based survey that compares the performance of more than 100 DL-based SC approaches by using 21 public datasets of reviews given by customers within three specific application domains (products, movies and restaurants). These 21 datasets have different characteristics (balanced/imbalanced, size, etc.) to give a global vision for our study. The comparison explains how the proposed factors quantitatively affect the performance of the studied DL-based SC approaches. Xunta de Galicia Agencia Estatal de Investigación | Ref. TEC2017-84197-C4-2-R

The early and accurate detection of the onset of acute myocardial infarction (AMI) is imperative for the timely provision of medical intervention and the reduction of its mortality rate. Machine learning techniques have demonstrated great potential in aiding disease diagnosis. In this paper, we present a framework to predict the onset of AMI using 713,447 extracted ECG samples and associated auxiliary data from the longitudinal and comprehensive ECG-ViEW II database, previously unexplored in the field of machine learning in healthcare. The framework is realized with two deep learning models, a convolutional neural network (CNN) and a recurrent neural network (RNN), and a decision-tree based model, XGBoost. Synthetic minority oversampling technique (SMOTE) was utilized to address class imbalance. High prediction accuracy of 89.9%, 84.6%, 97.5% and ROC curve areas of 90.7%, 82.9%, 96.5% have been achieved for the best CNN, RNN, and XGBoost models, respectively. Shapley values were utilized to identify the features that contributed most to the classification decision with XGBoost, demonstrating the high impact of auxiliary inputs such as age and sex. This paper demonstrates the promising application of explainable machine learning in the field of cardiovascular disease prediction.

Understanding the peak time of popularity evolution can provide insights on recommendation systems and online advertising campaigns. Although popularity evolution has been largely studied, the problem of how to predict its peak time remains unexplored. Taking Twitter hashtags as case study, the goal of this study is to predict when popularity reaches the peak for Twitter hashtags, from the perspective of an online social network application, in the context of the Twitter social network. On the whole, this paper includes three research aspects. Firstly, this paper investigates how early popularity reaches its peaks. Then, it is found that popularity tends to peak in the early stage of its evolution. Secondly, this paper discusses when a peak time prediction should be triggered. Thirdly, this paper designs a multi-modal based deep learning method, where the state-of-art deep learning techniques, such as multi-modal embedding and attention mechanisms, are adopted. We find that in the early stage of popularity evolution, no matter which factor is used as the input, the prediction effect is poor. By contrast, the hashtag string factor has the weakest contribution to popularity prediction in the middle and late stages of popularity evolution. The overall performance of our proposed method is evaluated in terms of the minimum, quartiles, and maximum values of absolute errors. From the experimental results, the prediction method we designed is superior.

This paper describes the impact of big data and machine learning (ML) on digital transformation of the marketing industry and the challenges it faces from a data and information management perspective. To do this, the study identified areas of digital transformation in marketing that have not yet been sufficiently covered by existing peer-reviewed academic research. Research papers were retrieved from five databases, namely Web of Science, Scopus, ScienceDirect, Emerald, and ProQuest. Screening of 214 articles resulted in 69 articles being selected for this literature review. A gap in the existing research was identified, with five areas directly related to digital transformation in marketing. Another five potential opportunities for future research in the area of big data utilisation and application of ML-driven technologies in the marketing field were also identified. An investigation of the prospects of digital transformation in marketing can benefit both academic researchers and business practitioners working in the domain of information technologies (IT), information systems (IS), business, and marketing, by identifying areas needing primary research, systemisation, follow-up research, validation or gathering of empirical data Refereed/Peer-reviewed

In recent years, with the rapid development of Internet technology, online shopping has become a mainstream way for users to purchase and consume. Sentiment analysis of a large number of user reviews on e-commerce platforms can effectively improve user satisfaction. This paper proposes a new sentiment analysis model-SLCABG, which is based on the sentiment lexicon and combines Convolutional Neural Network (CNN) and attention-based Bidirectional Gated Recurrent Unit (BiGRU). In terms of methods, the SLCABG model combines the advantages of sentiment lexicon and deep learning technology, and overcomes the shortcomings of existing sentiment analysis model of product reviews. The SLCABG model combines the advantages of the sentiment lexicon and deep learning techniques. First, the sentiment lexicon is used to enhance the sentiment features in the reviews. Then the CNN and the Gated Recurrent Unit (GRU) network are used to extract the main sentiment features and context features in the reviews and use the attention mechanism to weight. And finally classify the weighted sentiment features. In terms of data, this paper crawls and cleans the real book evaluation of dangdang.com, a famous Chinese e-commerce website, for training and testing, all of which are based on Chinese. The scale of the data has reached 100000 orders of magnitude, which can be widely used in the field of Chinese sentiment analysis. The experimental results show that the model can effectively improve the performance of text sentiment analysis.

In this paper, we propose a new method for CT pathological image analysis of brain and chest to extract image features and classify images. Because the deep neural network needs a large number of labeled samples to complete the training, and the cost of medical image labeling is very high, the training samples needed to train the deep neural network are insufficient. In this paper, a semi supervised learning based image classification method is proposed, which uses a small amount of labeled pathological image data to train the network model, and then integrates the features extracted by the network to classify the image. The results show that the classification effect of the neural network is better than convolution neural network and other traditional image classification models. To some extent, it can reduce the dependence of neural network on a large number of training samples, and effectively reduce the over fitting phenomenon of the network. Through the analysis of the overall classification accuracy and kappa coefficient of different classification methods under different sample numbers, it is found that the overall classification accuracy and kappa coefficient are increasing with the increasing number of training samples. Especially in the case of a small number of training samples, compared with other deep neural networks and traditional classification methods, the classification accuracy of the counter neural network is about 10% higher than that of other neural networks and traditional classification methods, and the advantages are more obvious.

Conventional speaker verification systems become frail or incompetent while facing attack from spoofed speech. Presently many anti-spoofing countermeasures have been studied for automatic speaker verification. It has been known that the salient feature is of a more important role rather than the selection of classifiers in the current research field of spoofing detection. The effectiveness of constant-Q transform (CQT) has been demonstrated for anti-spoofing feature analysis in many research literatures on automatic speaker verification. On the basis of CQT-based information sub-features, i.e. octave-band principal information (OPI), full-band principal information (FPI), short-term spectral statistics information (STSSI) and magnitude-phase energy information (MPEI), three concatenated features are proposed by investigating their information complementarity in this paper, the first one is constant-Q statistics-plus-principal information coefficients (CQSPIC) by combining OPI, FPI and STSSI; the second one is constant-Q energy-plus-principal information coefficients (CQEPIC) by combining OPI, FPI and MPEI and the third one is constant-Q energy-statistics-principal information coefficients (CESPIC) by combining OPI, FPI, MPEI and STSSI. In this paper, we set up deep neural network (DNN) classifiers for evaluation of the proposed features. Experiments show that the proposed features can outperform some commonly used features meanwhile the proposed systems give better or comparable performance comparing with state-of-the-art performance on ASVspoof 2019 logical access and physical access corpus.

Analysis and collection of time-series data as a major role of machine learning has been emphasized with an important key in cognitive science. Because the cognitive mechanisms such as human sensation and perception from cognitive science are fast responses ranging from a few milliseconds to hundreds of milliseconds, the method of pattern recognition and analysis of these brain signals must be done and it is necessary to derive some information. In this paper, we investigated time-series data of cognitive function of the brain obtained using a non-invasive technique on multiple channels via signal classification and analysis, using a cognitive science approach and experiments. The test dataset was collected in 19 channels using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) techniques with multiple rests and working conditions on eight subjects. From this perspective, the main contributions of this paper are that it completes the collection and analysis of cognitive-scientific time-series data and has scientific implications that extend to other integrated domains, energy, manufacturing, bioinformatics, and finance area. The use of Shapelet and DTW (Dynamic Time Warping) classification techniques on brain signal time-series shows the potential to identify neuro-biological phenomena that can proactively signal a disease or disorder. EEG bandwidth and frequency-specific data have also been categorized as machine learning algorithms and have shown accurate patterns and trends in measuring cognitive functions of scientific, biological and academic importance.

Nature-inspired optimization is a modern technique in the past decades. Researchers report their successful applications in various fields such as manufacturing, biomedical, and environmental engineering, while other researchers doubt its applicability. In this paper, we collect newly emerging nature-inspired optimization algorithms proposed after 2008, present them in a unified way, implement them, and evaluate them on benchmark functions. Moreover, we optimize the behavioural parameters for these algorithms. Since it is impossible to cover all interesting topics regarding nature-inspired optimization, this paper only focuses on the continuous encoding algorithms for single objective global problems, which is fundamental for other related topics.

Reinforcement Learning (RL) systems achieved outstanding performance in different domains such as Atari games, finance, healthcare, and self-driving cars. However, their black-box nature complicates their use, especially in critical applications such as healthcare. To solve this problem, researchers have proposed different approaches to interpret RL models. Some of these methods were adopted from machine learning, while others were designed specifically for RL. The main objective of this paper is to show and explain RL interpretation methods, the metrics used to classify them, and how these metrics were applied to understand the internal details of RL models. We reviewed papers that propose new RL interpretation methods, improve the old ones, or discuss the pros and cons of the existing methods.

This paper addresses the use in different stages of pregnancy of ultrasound imaging and to examine the tumors diagnosed during lactation or pregnancy. There are recent advancements in the application of obstetric ultrasound and imaging techniques helpful for improving the outcome of the pregnancy using various Learning techniques. This paper addresses the need to implement sustainable ultrasound standards with an acceptably high maternal and perinatal mortality rates to provide better and more affordable, quality Ultrasonic Flaw (UT) equipment which can improve Obstetric health care. The state-of-the-art learning approach for obstetric ultrasound is a category of methods in machine learning that are gaining popularity and attracting interest in various fields, including image processing and computer vision. In this paper advanced Machine learning processes map a raw input image to the desired output image using logistic regression classifier(LRC) and Convolution neural networks (CNNs) are of particular interest among all Machine learning methods. Furthermore, we have utilized the Internet of Medical Things (IoMT) for obstetric tumor image segmentation and identification of tumors for the medical experts. The experimental results show the LRC based on CNN can be utilized to predict the output of the ultrasound of obstetric with increased maternal and perinatal mobility rates.

With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.

In recent years, the class imbalance problem that aims to correctly classify imbalanced data sets and improve the classification performance of minority instances has received attention. Such problem can be roughly described as one of the class(es) termed as minority class(es) contains much smaller instances than the others, also referred as majority class(es). To address this problem, a novel classification method called focused online random forest based on synthetic minority oversampling technique (FORF-SMOTE) is proposed in this paper and simply expressed as FORF-S, which constructs two online random forests respectively trained by original training dataset and new generated dataset, then further jointly consititute the model. Instead of oversampling the minority instances in data level, the algorithm in this paper is motivated by making the sampling strategies integrated into algorithm level to create classifiers, which can better identify the minority class. Moreover, the method is also compared with other state-of-the-art methods and the results have demonstrated that the proposed algorithm takes advantages of the aforementioned methods.

Gait recognition aims to identify person by walking pattern of individuals, which has a valuable application prospect in the fields of identity identification, public security and medical diagnosis. At present, most of the gait recognition methods are designed based on the human gait information. However, this paper focuses on the feature space of gait recognition feature learning, and optimizes the feature space by introducing some general criteria to improve the discrimination of depth features. In recent literature, optimizing the feature space has been proven to be beneficial for personal identification. Taken above insights together, we propose a novel gait recognition method based on joint metric learning and hierarchical network in an end-to-end manner, for improving the ability of feature identification. The contribution of this paper is two-fold. First, a joint metric learning and hierarchical network is used to realize the learning of fine-grained information and accelerated the discriminative feature learning. Second, to learn discriminative feature, we use a fuse loss function, the triplet loss and classifier loss. It guides the training process, pulls the samples in the same set close to each other, pushes the samples in different sets away from each other, and takes the silhouettes in each set as a whole. We evaluate the proposed method on two widely-used gait datasets, ie. CASIA-B and OU-LP-Bag. Experimental results demonstrate the effectiveness of the proposed method, significantly improve gait recognition performances under cross-view and bag-carrying walking conditions, respectively. Particularly, we achieves Rank-1 accuracy of 93.2% on OU-LP-Bag dataset, better than existing state-of-the art.

This paper proposes inverse feature learning (IFL) as a novel supervised feature learning technique that learns a set of high-level features for classification based on an error representation approach. The key contribution of this method is to learn the representation of error as high-level features, while current representation learning methods interpret error by loss functions which are obtained as a function of differences between the true labels and the predicted ones. One advantage of this error representation is that the learned features for each class can be obtained independently of learned features for other classes; therefore, IFL can learn simultaneously meaning that it can learn new classes’ features without retraining. Error representation learning can also help with generalization and reduce the chance of over-fitting by adding a set of impactful features to the original data set which capture the relationships between each instance and different classes through an error generation and analysis process. This method can be particularly effective in data sets, where the instances of each class have diverse feature representations or the ones with imbalanced classes. The experimental results show that the proposed IFL results in better performance compared to the state-of-the-art classification techniques for several popular data sets. We hope this paper can open a new path to utilize the proposed perspective of error representation learning in different feature learning domains.

Sentiment analysis is one of the significant application fields of natural language processing. Researchers divide the sentiment into explicit sentiment and implicit sentiment according to whether the expression contains sentiment words or not. Most of the existing research focuses on explicit sentiment tasks. However, implicit sentiment analysis is difficult to study because of its implicit tendency to express sentiment and the problem of “weak features” and “multi-confounding weak features”. We develop a novel model based on “Hierarchical Knowledge Enhancement and Multi-pooling”(HKEM), which fully integrates the knowledge information of different levels in the text through hierarchical knowledge enhancement to alleviate the “weak feature” problem. At the same time, the multi-pooling method is used to extract and analyze multiple features, and the “multi-confounding weak features” are modeled. In this paper, comparative experiments have been conducted on the SMP-ECISA2019 dataset, and the results show that the accuracy of this model is comparable to that of the current best model, and the F1 score is 5.9% higher than that of the best model, indicating that the model proposed in this paper can accurately analyze Chinese implicit sentiment and achieve the state-of-the-art in the comprehensive performance evaluation, which proves the effectiveness and superiority of the model in this paper.

Charging of large-scale electric vehicles (EVs) will have a serious impact on the power grid. In the environment of power market, the orderly scheduling of EVs through agents is an effective way to solve this problem. Due to the uncertainty of EV travels, how to participate and profit in demand response has become urgent. In this paper, a set of optimal bidding method for agents participating in the market is proposed. Firstly, because the EV service provider cannot collect the historical travel data of all the owners in the whole region, a multi-layer machine learning algorithm is used to simulate the travel behavior of EV in this paper. Secondly, based on the demand response rules of the current electricity market, the optimal bidding model for EV service providers is proposed. In addition, considering the offset between the predicted and the actual electricity price, the model is designed based on interval optimization. Finally, the behavior model and the bidding method are applied to IEEE30 bus system and a provincial practical system. It is shown that the proposed method can bring greater benefits to EV service providers.

Chinese authors indirectly portray real social relationships in society by incorporating their own experiences and feelings about politics, economic life, and cultural habits. Social and natural knowledge are mixed and hidden in natural literary language. Therefore, by well defining the type of original text, the pipeline for processing the text and the rules to build the graph, we can extract enough valid and useful information. Based on higher-level information, we can build a literary character graph to promote computers’ comprehension of literature. The identification of opposite characters is a challenging topic because the character relationships are intricate and complex. The cryptic expression in literary works can enhance the readability of the plot, but it is obvious that it increases the cost of understanding. Even for human readers different types of characters are difficult to classify and identify, and it usually needs careful reading for several times to get the subtle differences among the characters. Though extracting knowledge from Chinese literature is a complicated and difficult topic, this paper models the graph of polar literary characters and divides polar communities by extracting polar vertices and polar edges based on the concept of literary sentiment polarity. The results show that using a long-sentence window is a good trade-off. The experiments of modern Chinese polar literature show that the accuracy of the community division method for the integrated graph polarity is obviously better than the method based on the co-occurrence network, and it can automatically match the positive and negative communities as well as build the complete graph structure. It clearly reflects the meaning of literary works from the perspective of polarity. This systematic model describes general standard operation steps for the machine to understand complex Chinese literature. The experiments conducted on seven benchmark Chinese novel datasets demonstrate that the method based on emotional polarity shows a significant improvement compared to baseline performance. Though we use Chinese datasets in this paper, the model and methods are important references for literature analysis and graph extraction in other languages.

Due to the rapid development of information technology and network technology, there is a lot of data, but the phenomenon of lack of knowledge is becoming more and more serious. Data mining technology has developed vigorously in this environment, and it has shown more and more vitality. Based on Spark programming model, this paper designs the parallel extension of fuzzy c-means. In order to enhance the performance of fuzzy c-means parallel expansion, the improvement strategy of k-means during the initialization phase is borrowed, and k-means// is extended to fuzzy c-means to obtain better clustering performance. Combined with Spark’s programming model, this paper can obtain extended parallel fuzzy c-means algorithm. Several experiments on the data set of the algorithm proposed in this paper have shown good scalability and parallelism, effectively expanding fuzzy c-means clustering to distributed applications, greatly increasing the scale of the data processed by the algorithm. This improves the robustness of the algorithm and the adaptability of the algorithm to the shape and structure of the data, so that the parallel and scalable clustering algorithm can more effectively perform cluster analysis on big data. Three algorithms were simulated on MATLAB platform. We use simple data sets and complex two-dimensional data sets, and compare with the traditional fuzzy c-means algorithm and fuzzy c-means algorithm based on fuzzy entropy. Experiments show that the scalable parallel fuzzy c-means algorithm not only greatly improves the anti-noise performance, but also improves the convergence speed, and it can automatically determine the optimal number of clusters.

Student performance prediction is a fundamental task in online learning systems, which aims to provide students with access to active learning. Generally, student performance prediction is achieved by tracing the evolution of each student's knowledge states via a series of learning activities. Every learning activity record has two types of feature data: student behavior and exercise features. However, most methods use features that are related to exercises, such as correctness and concepts, while other student behavior features are usually ignored. The few studies that have focused on student behavior features through subjective manual selection argue that different student behavior features can be used in an equivalent manner to predict student performance. In this paper, we assume that the integration of student behavior features and exercise features is crucial to improve the precision of prediction, and each feature has a different impact on student performance. Therefore, this paper proposes a novel framework for student performance prediction by making full use of both student behavior features and exercise features and combining the attention mechanism with the knowledge tracing model. Specifically, we first exploit machine learning to capture feature representation automatically. Then, a fusion attention mechanism based on recurrent neural network architecture is used for student performance prediction. Extensive experiments on a real-world dataset show the effectiveness and practicability of our approach. The accuracy of our method is up to 98%, which is superior to previous methods.

In the era of the fourth industrial revolution (Industry 4.0) and the Internet of Things (IoT), real-time data is enormously collected and analyzed from mechanical equipment. By classifying and characterizing the measured signals, the fault condition of mechanical components could be identified. However, most current health monitoring techniques utilize time-consuming and labor-intensive feature engineering, i.e., feature extraction and selection, that are carried out by experts. This paper, on the contrary, deals with an automatic diagnosis method of machine monitoring using a convolutional neural network (CNN) with class activation maps (CAM). A class activation map enables us to discriminate the fault region in the images, thus allowing us to localize the fault precisely. The goal of the paper is to demonstrate how CNN and CAM could be employed to real-world vibration video to characterize the machine’s status, representing normal or fault conditions. The performance of the proposed model is validated with a base-excited cantilever beam dataset and a water pump dataset. This paper presents a novel industrial application by developing a promising method for automatic machine condition-based monitoring.

Nowadays, the intelligent fault diagnosis based on deep learning have achieved remarkable results in the fields of the industrial equipment health monitoring and management. To implement the adaptive feature extraction and fault isolation for key components of rotating machinery (rolling bearings, etc.), two new algorithms, Adaptive Sparse Contrative Auto-encoder (ASCAE) algorithm and Optimized Unsupervised Extreme Learning Machine (OUSELM) classifier by Cuckoo Search Algorithm (CSA), can be firstly designed in this paper, respectively. Furthermore, a new rolling bearing fault diagnosis framework based on ASCAE combined with OUSELM is first of all proposed in this paper. Accordingly, this designed fault diagnosis framework can be divided into three main steps: i). Firstly, the vibration signals of rolling bearings can be collected and processed on the key components of rotating machinery, and then the collected vibration signals can be accordingly converted into frequency signals; ii). Secondly, the transformed spectral signals can be entered into the constructed ASCAE for feature learning to exploit the multi-layer sensitive features from the hidden raw data; iii). Thirdly, the extracted multi-layer sensitive features can be flowed into the trained OUSELM classifier for unsupervised fault state separation and diagnosis. More specifically, our designed fault diagnosis framework (ASCAE-OUSELM) can employ the homotopy regularization theory, sparse theory, intelligent optimization algorithm and other tools to optimize the parameters and improve the performance of the original Contrative Auto-encoder (CAE) algorithm and Unsupervised Extreme Learning Machine (USLEM) algorithm, respectively. At the same time, the proposed fault diagnosis framework can achieve effective sparse and sensitive feature information extraction in the feature extraction stage (ASCAE) to avoid over-fitting. In fault isolation stage, the issue of the supervised and low training efficiency caused by traditional deep learning model can be perfectly addressed by OUSELM. Eventually, the experimental data of rolling bearings validated the effectiveness of the proposed fault diagnosis framework and two deigned algorithms.

This paper addresses road anomaly detection by formulating it as a classification problem and applying deep learning approaches to solve it. Besides conventional road anomalies, additional ones are introduced from the perspective of a vehicle. In order to facilitate the learning process, the paper pays a close attention to pattern representation, and proposes three sets of numeric features for representing road conditions. Also, three deep learning approaches, i.e. Deep Feedforward Network (DFN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), are considered to tackle the classification problem. The detectors, with respect to the three deep learning approaches, are trained and evaluated through data collected from a test vehicle driven on various road anomaly conditions. The comparison study on the detection performances is conducted by setting key hyper-parameters to certain sets of fixed values. Also, the comparison study on performances of each detector with respect to different pattern representations is conducted. The results have shown the effectiveness of the proposed approaches and the efficiency of the proposed feature representations in road anomaly detection.