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Combining computational technologies and humanities is an ongoing effort aimed at making resources such as texts, images, audio, video, and other artifacts digitally available, searchable, and analyzable. In recent years, deep neural networks (DNN) dominate the field of automatic text analysis and natural language processing (NLP), in some cases presenting a super‐human performance. DNNs are the state‐of‐the‐art machine learning algorithms solving many NLP tasks that are relevant for Digital Humanities (DH) research, such as spell checking, language detection, entity extraction, author detection, question answering, and other tasks. These supervised algorithms learn patterns from a large number of "right" and "wrong" examples and apply them to new examples. However, using DNNs for analyzing the text resources in DH research presents two main challenges: (un)availability of training data and a need for domain adaptation. This paper explores these challenges by analyzing multiple use‐cases of DH studies in recent literature and their possible solutions and lays out a practical decision model for DH experts for when and how to choose the appropriate deep learning approaches for their research. Moreover, in this paper, we aim to raise awareness of the benefits of utilizing deep learning models in the DH community. [ABSTRACT FROM AUTHOR]

The quality of scientific publications can be measured by quantitative indices such as the h‐index, Source Normalized Impact per Paper, or g‐index. However, these measures lack to explain the function or reasons for citations and the context of citations from citing publication to cited publication. We argue that citation context may be considered while calculating the impact of research work. However, mining citation context from unstructured full‐text publications is a challenging task. In this paper, we compiled a data set comprising 9,518 citations context. We developed a deep learning‐based architecture for citation context classification. Unlike feature‐based state‐of‐the‐art models, our proposed focal‐loss and class‐weight‐aware BiLSTM model with pretrained GloVe embedding vectors use citation context as input to outperform them in multiclass citation context classification tasks. Our model improves on the baseline state‐of‐the‐art by achieving an F1 score of 0.80 with an accuracy of 0.81 for citation context classification. Moreover, we delve into the effects of using different word embeddings on the performance of the classification model and draw a comparison between fastText, GloVe, and spaCy pretrained word embeddings. [ABSTRACT FROM AUTHOR]

Accurate prostate segmentation in ultrasound images is crucial for the clinical diagnosis of prostate cancer and for performing image-guided prostate surgery. However, it is challenging to accurately segment the prostate in ultrasound images due to their low signal-to-noise ratio, the low contrast between the prostate and neighboring tissues, and the diffuse or invisible boundaries of the prostate. In this paper, we develop a novel hybrid method for segmentation of the prostate in ultrasound images that generates accurate contours of the prostate from a range of datasets. Our method involves three key steps: (1) application of a principal curve-based method to obtain a data sequence comprising data coordinates and their corresponding projection index; (2) use of the projection index as training input for a fractional-order-based neural network that increases the accuracy of results; and (3) generation of a smooth mathematical map (expressed via the parameters of the neural network) that affords a smooth prostate boundary, which represents the output of the neural network (i.e., optimized vertices) and matches the ground truth contour. Experimental evaluation of our method and several other state-of-the-art segmentation methods on datasets of prostate ultrasound images generated at multiple institutions demonstrated that our method exhibited the best capability. Furthermore, our method is robust as it can be applied to segment prostate ultrasound images obtained at multiple institutions based on various evaluation metrics. [ABSTRACT FROM AUTHOR]

Brain tumors (BTs) are an uncommon but fatal kind of cancer. Therefore, the development of computer-aided diagnosis (CAD) systems for classifying brain tumors in magnetic resonance imaging (MRI) has been the subject of many research papers so far. However, research in this sector is still in its early stage. The ultimate goal of this research is to develop a lightweight effective implementation of the U-Net deep network for use in performing exact real-time segmentation. Moreover, a simplified deep convolutional neural network (DCNN) architecture for the BT classification is presented for automatic feature extraction and classification of the segmented regions of interest (ROIs). Five convolutional layers, rectified linear unit, normalization, and max-pooling layers make up the DCNN's proposed simplified architecture. The introduced method was verified on multimodal brain tumor segmentation (BRATS 2015) datasets. Our experimental results on BRATS 2015 acquired Dice similarity coefficient (DSC) scores, sensitivity, and classification accuracy of 88.8%, 89.4%, and 88.6% for high-grade gliomas. When it comes to segmenting BRATS 2015 BT images, the performance of our proposed CAD framework is on par with existing state-of-the-art methods. However, the accuracy achieved in this study for the classification of BT images has improved upon the accuracy reported in prior studies. Image classification accuracy for BRATS 2015 BT has been improved from 88% to 88.6%. [ABSTRACT FROM AUTHOR]

As the competition between enterprises intensifies, employees have witnessed a decline of psychological health year by year and severe anxiety and depression. To ensure the normal work of employees, it is important to implement psychological occupational therapy intervention (POTI). The previous studies on corporate occupational values focus on the behavioral variables and attitude variables related to occupations. The research paradigm does not suit a specific group of employees. Therefore, this paper explores the individual differences in POTI guided by the realization of occupational values. For a specific group of employees, the correlations of the psychological problems with job attitude, job involvement, and psychological health level were reasoned logically, a theoretical model was established, and the full-model map was plotted to illustrate the influence of the realization of occupational values over POTI. Then, the evaluation data on the psychological intervention effect of 2,800 employees were explored based on multikernel learning, and an individual difference prediction model was constructed through multikernel learning. Finally, the predicted results of different groups of employees were compared, revealing the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

The spiculation sign is one of the main signs to distinguish benign and malignant pulmonary nodules. In order to effectively extract the image feature of a pulmonary nodule for the spiculation sign distinguishment, a new spiculation sign recognition model is proposed based on the doctors' diagnosis process of pulmonary nodules. A maximum density projection model is established to fuse the local three-dimensional information into the two-dimensional image. The complete boundary of a pulmonary nodule is extracted by the improved Snake model, which can take full advantage of the parallel calculation of the Spike Neural P Systems to build a new neural network structure. In this paper, our experiments show that the proposed algorithm can accurately extract the boundary of a pulmonary nodule and effectively improve the recognition rate of the spiculation sign. [ABSTRACT FROM AUTHOR]

Background: Although Pneumatic Artificial Muscle (PAM) has a promising future in rehabilitation robots, it's difficult to realize accurate position control due to its highly nonlinear properties.Objective: This paper deals with position control of PAM.Methods: To describe the hysteresis inside PAM, a polynomial based phenomenological function is developed. Based on the phenomenological model for PAM and analysis of pressure dynamics within PAM, an adaptive cascade controller is proposed. Both outer loop and inner loop employ BP Neural Network tuned PID algorithm. The outer loop is to handle high nonlinearities and unmodeled dynamics of PAM, while the inner loop is responsible for nonlinearities caused by pressure dynamics.Results: Experimental results show high tracking accuracy as compared with a convention PID controller.Conclusion: The proposed controller is effective in improving performance of PAM and will be implemented in a rehabilitation robot. [ABSTRACT FROM AUTHOR]

Cardiac digital twins provide a physics and physiology informed framework to deliver personalized medicine. However, high-fidelity multi-scale cardiac models remain a barrier to adoption due to their extensive computational costs. Artificial Intelligence-based methods can make the creation of fast and accurate whole-heart digital twins feasible. We use Latent Neural Ordinary Differential Equations (LNODEs) to learn the pressure-volume dynamics of a heart failure patient. Our surrogate model is trained from 400 simulations while accounting for 43 parameters describing cell-to-organ cardiac electromechanics and cardiovascular hemodynamics. LNODEs provide a compact representation of the 3D-0D model in a latent space by means of an Artificial Neural Network that retains only 3 hidden layers with 13 neurons per layer and allows for numerical simulations of cardiac function on a single processor. We employ LNODEs to perform global sensitivity analysis and parameter estimation with uncertainty quantification in 3 hours of computations, still on a single processor. [ABSTRACT FROM AUTHOR]

Ambient Assisted Living (AAL) is a medical surveillance system comprised of connected devices, healthcare sensor systems, wireless communications, computer hardware, and software implementations. AAL could be used for an extensive variety of purposes, comprising preventing, healing, as well as improving the health and wellness of elderly individuals. AAL intends to ensure the wellbeing of elderly persons while also spanning the number of years seniors can remain independent in their preferred surroundings. It also decreases the quantity of family caregivers by giving patients control over their health situations. To avert huge costs as well as possible adverse effects on standard of living, classifiers must be used to distinguish between adopters as well as nonadopters of such innovations. With the development of numerous classification algorithms, selecting the best classifier became a vital and challenging step in technology acceptance. Decision makers must consider several criteria from different domains when selecting the best classifier. Furthermore, it is critical to define the best multicriteria decision-making strategy for modelling technology acceptance. Considering the foregoing, this research reports the incorporation of the multicriteria decision-making (MCDM) method which is founded on the fuzzy method for order of preference by similarity to ideal solution (TOPSIS) to identify the top classifier for continuing toward supporting AAL implementation research. The results indicate that the classification algorithm KNN is the preferred technique among the collection of different classification algorithms for the ambient assisted living system. [ABSTRACT FROM AUTHOR]

The article presents the discussion on competitiveness being a determining factor for the survival of businesses. Topics include sciences research to combine experimental approaches with theoretical modelling tools from systems theory, physics, engineering, computer science; and situation driven organizations on the search for simulation models.

In this paper, we propose a new Internet of Things (IoT) based predictive modelling by using fuzzy cluster based augmentation and classification for predicting the lung cancer disease through continuous monitoring and also to improve the healthcare by providing medical instructions. Here, the fuzzy clustering method is used and which is based on transition region extraction for effective image segmentation. Moreover, Fuzzy C-Means Clustering algorithm is used to categorize the transitional region features from the feature of lung cancer image. In this work, Otsu thresholding method is used for extracting the transition region from lung cancer image. Moreover, the right edge image and the morphological thinning operation are used for enhancing the performance of segmentation. In addition, the morphological cleaning and the image region filling operations are performed over an edge lung cancer image for getting the object regions. In addition, we also propose a new incremental classification algorithm which combines the existing Association Rule Mining (ARM), the standard Decision Tree (DT) with temporal features and the CNN. The experiments have been conducted by using the standard images that are collected from database and the current health data which are collected from patient through IoT devices. The results proved that the performance of the proposed prediction model which is able to achieve the better accuracy when it is compared with other existing prediction model. [ABSTRACT FROM AUTHOR]

Background: Hepatitis B virus (HBV) infection is an important health problem worldwide with critical outcomes. The nucleoside analog lamivudine (LMV) is a potent inhibitor of HBV polymer­ase and impedes HBV replication in patients with chronic hepatitis B. Treatment with LMV for long periods causes the appearance and reproduction of drug-resistant strains, rising to more than 40% after 2 years and to over 50% and 70% after 3 and 4 years, respectively.Objectives: Artificial neural networks (ANNs) were used to make predictions with regard to re­sistance phenotypes using biochemical and biophysical features of the YMDD sequence.Patients and Methods: The study population comprised patients who were intended for surgery in various hospitals in Tehran-Iran. An ACRS-PCR method was performed to distinguish muta­tions in the YMDD motif of HBV polymerase. In the training and testing stages, these parameters were used to identify the most promising optimal network. The ideal values of RMSE and MAE are zero, and a value near zero indicates better performance. The selection was performed using statistical accuracy measures, such as root mean square error (RMSE), coefficient of determina­tion (R2), and mean absolute error (MAE). The main purpose of this paper was to develop a new method based on ANNs to simulate HBV drug resistance using the physiochemical properties of the YMDD motif and compare its results with multiple regression models. Results: The results of the MLP in the training stage were 0.8834, 0.07, and 0.09 and 0.8465, 0.160.04 in the testing stage; for the total data, the values were 0.8549, 0.115, and 0.065, respec­tively. The MLP model predicts lamivudine resistance in HBV better than the MLR model. Conclusions: The ANN model can be used as an alternative method of predicting the outcome of HBV therapy. In a case study, the proposed model showed vigorous clusterization of predicted and observed drug responses. The current study was designed to develop an algorithm for pre­dicting drug resistance using chemiophysical data with artificially created neural networks. To this end, an intelligent and multidisciplinary program should be developed on the basis of the information to be gained on the essentials of different applications by similar investigations. This program will help design expert neural network architectures for each application auto­matically. [ABSTRACT FROM AUTHOR]

The inherent black-box nature of neural networks is an important drawback with respect to the problem of explanation of neural network responses. Although several articles have tackled the problem of rule extraction from a single neural network, just a few papers have investigated rule extraction from several combined neural networks. In this article we describe how to translate symbolic rules into the Discretized Interpretable Multi-Layer Perceptron (DIMLP) and how to extract rules from one or several combined neural networks. Our approach consists of characterizing discriminant hyperplane frontiers. Unordered rules are extracted in polynomial time with respect to the size of the problem and the size of the network. Moreover, the degree of matching between extracted rules and neural network responses is 100% on training examples. We applied single DIMLP networks to 17 data sets related to medical diagnosis and medical prognosis problems. Results based on 10-fold cross-validation showed that the DIMLP model was on average as accurate as standard multi-layer perceptrons (MLP). Furthermore, DIMLP networks were significantly more accurate than CN2 on eight problems, whereas only on one problem CN2 was better than DIMLP. Finally, a non-Hodgkin lymphoma diagnosis problem based on classification of electrophoresis gels was defined. It turned out that ensembles of DIMLP networks were significantly more accurate than CN2 (96.1%±1.4 versus 82.7%±4.0). Finally, symbolic rules revealed the presence of five important spots for the discrimination of the class of Lymphocyte Leukemia/Chronic Lymphoid Leukemia (Lc/LLc), and the class of Centrocytic Lymphoma (Cc). [Copyright &y& Elsevier]

Due to insufficient samples, the generalization performance of deep network is insufficient. In order to solve this problem, an improved U-net based image automatic segmentation and diagnosis algorithm was proposed, in which the max-pooling operation in original U-net model was replaced by the convolution operation to keep more feature information. Firstly, the regions of 128×128 were extracted from all slices of the patients as data samples. Secondly, the patient samples were divided into training sample set and testing sample set, and data augmentation was performed on the training samples. Finally, all the training samples were adopted to train the model. Compared with Fully Convolutional Network (FCN) model and max-pooling based U-net model, DSC and CR coefficients of the proposed method achieve the best results, while PM coefficient is 2.55 percentage lower than the maximum value in the two comparison models, and Average Symmetric Surface Distance is slightly higher than the minimum value of the two comparison models by 0.004. The experimental results show that the proposed model can achieve good segmentation and diagnosis results. [ABSTRACT FROM AUTHOR]

The ultraviolet spectrophotometric method is often used for determining the content of glycyrrhizic acid from Chinese herbal medicine Glycyrrhiza glabra. Based on the traditional single variable approach, four extraction parameters of ammonia concentration, ethanol concentration, circumfluence time, and liquid-solid ratio are adopted as the independent extraction variables. In the present work, central composite design of four factors and five levels is applied to design the extraction experiments. Subsequently, the prediction models of response surface methodology, artificial neural networks, and genetic algorithm-artificial neural networks are developed to analyze the obtained experimental data, while the genetic algorithm is utilized to find the optimal extraction parameters for the above well-established models. It is found that the optimization of extraction technology is presented as ammonia concentration 0.595%, ethanol concentration 58.45%, return time 2.5 h, and liquid-solid ratio 11.065 : 1. Under these conditions, the model predictive value is 381.24 mg, the experimental average value is 376.46 mg, and the expectation discrepancy is 4.78 mg. For the first time, a comparative study of these three approaches is conducted for the evaluation and optimization of the effects of the extraction independent variables. Furthermore, it is demonstrated that the combinational method of genetic algorithm and artificial neural networks provides a more reliable and more accurate strategy for design and optimization of glycyrrhizic acid extraction from Glycyrrhiza glabra. [ABSTRACT FROM AUTHOR]

Background: The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances.Methods: In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted.Results: Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values.Conclusions: An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy. [ABSTRACT FROM AUTHOR]

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo–based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Practitioner Summary: Many relationships among variables (e.g. stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods. [ABSTRACT FROM AUTHOR]

The authors describe a methodology for helping computational biologists diagnose discrepancies they encounter between experimental data and the predictions of scientific models. The authors call these discrepancies data-model conflicts. They have built a prototype system to help scientists resolve these conflicts in a more systematic, evidence-based manner. In computational biology, data-model conflicts are the result of complex computations in which data and models are transformed and evaluated. Increasingly, the data, models, and tools employed in these computations come from diverse and distributed resources, contributing to a widening gap between the scientist and the original context in which these resources were produced. This contextual rift can contribute to the misuse of scientific data or tools and amplifies the problem of diagnosing data-model conflicts. The authors' hypothesis is that systematic collection of metadata about a computational process can help bridge the contextual rift and provide information for supporting automated diagnosis of these conflicts. The methodology involves three major steps. First, the authors decompose the data-model evaluation process into abstract functional components. Next, they use this process decomposition to enumerate the possible causes of the data-model conflict and direct the acquisition of diagnostically relevant metadata. Finally, they use evidence statically and dynamically generated from the metadata collected to identify the most likely causes of the given conflict. They describe how these methods are implemented in a knowledge-based system called GRENDEL and show how GRENDEL can be used to help diagnose conflicts between experimental data and computationally built structural models of the 30S ribosomal subunit. [ABSTRACT FROM AUTHOR]