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In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market. [ABSTRACT FROM AUTHOR]

To study the intelligent decision making methods under limited decision samples, aiming at the problems that operational decision-making experience is difficult to express and the training samples for intelligent decision learning are limited, based on the joint operational simulation and drill environment, a decision-making method based on generative adversarial imitation learning is proposed. This method integrates the operational decision-making experience representation and learning process. On the basis of high-level decision-making and low-level action, rule definitions are used to specify the logic of task execution, and generative adversarial imitation learning algorithms are utilized to improve the generalization ability of intelligent agents in scenarios. This method achieved expected results in the constructed typical adversarial scenarios. The algorithm training converged and the decisions output by the intelligent agent are reasonable. Preliminary experimental results indicate that generative adversarial imitation learning, as an intelligent operational decision-making method, has value for further research.

To study the intelligent decision making methods under limited decision samples, aiming at the problems that operational decision-making experience is difficult to express and the training samples for intelligent decision learning are limited, based on the joint operational simulation and drill environment, a decision-making method based on generative adversarial imitation learning is proposed. This method integrates the operational decision-making experience representation and learning process. On the basis of high-level decision-making and low-level action, rule definitions are used to specify the logic of task execution, and generative adversarial imitation learning algorithms are utilized to improve the generalization ability of intelligent agents in scenarios. This method achieved expected results in the constructed typical adversarial scenarios. The algorithm training converged and the decisions output by the intelligent agent are reasonable. Preliminary experimental results indicate that generative adversarial imitation learning, as an intelligent operational decision-making method, has value for further research. [ABSTRACT FROM AUTHOR]

Language is a primary means of communication, but it is not the only means; knowing a language does, however, assist speed up the process. Many distinct languages are spoken worldwide, and people use them to communicate. This is only one of the many reasons why language is so crucial. Based on the literature survey, it is evident that there is a lack of available translators for the Tulu language. Despite being prevalent predominantly in Karnataka, the Tulu language has not been as widely spoken as other Indian languages until recently, although it gained enough recognition to become the second language in Karnataka. The purpose of our research work aims at translating the English language into the Tulu language. During the evaluation the system was tested on a dataset consisting of handwritten characters during the evaluation process Convolutional Neural Networks used achieved an accuracy rate of 92%. To translate English to the Tulu language, we employed a parallel sentence dataset for the neural approach and a parallel word dataset for the rule-based approach. The rule-based approach resulted in an 89% accuracy rate for word-based analysis and an 81% accuracy rate for sentence-based analysis for the English-to-Tulu language translation. The neural machine translation approach of the Encoder-Decoder model with LSTM is been used to accomplish translation from English to Tulu with a BLEU score of 0.83 and Tulu to English with a BLUE score of 0.65. The model also employed hybrid machine translation to enhance the translation.

In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market.

Background Traditional computer simulation studies of radiofrequency catheter ablation (RFCA) usually neglect the anisotropy in myocardial electrical conductivity (MEC), which is likely an essential factor in governing the ablation outcome. Here, a numerical study of lesion characteristics during RFCA based on an anatomy-based model incorporating fiber orientation was performed to investigate the anisotropy in MEC. Methods A three-dimensional thorax model including atria, blood, connective tissue, muscle, fat, and skin was constructed. The myocardial fiber was established through a rule-based method (RBM) based on the anatomical structure of the heart. The anisotropic MEC were 0.40 and 0.28 S m−1 in longitudinal and transverse directions, respectively. The ablation result was compared with the isotropic scenario where the isotropic MEC was the average of the anisotropic conductivities as 0.34 S m−1. Results The complexity of fiber architecture varied with that of the local anatomical structure. At RF power of 20 W for 30 s, the tissue temperature and lesion volume were reduced by 2.8 ± 0.1% and 6.9 ± 0.5%, respectively, under anisotropic MEC around the ostium of the pulmonary vein and left atrial appendage. Those for the posterior wall and roof of the left atrium, and the inside of the superior vena cava were 1.9 ± 0.3% and 5.6 ± 1.2%, respectively. Conclusions Anisotropy in MEC has a greater reduction effect on lesion volume than on tissue temperature during RFCA; this effect tends to be restrained at positions with more uniform fiber distributions and can be enhanced where significant variation in fiber architecture occurred.

The variety and difference between domains for textual data require customization in the Natural Language Processing component especially in Named Entity Recognition where different domains contain several types of entities. The current NER model is deemed not fit to accurately extract entities from Quranic text due to its unique content. This paper describes the building of a rule-based Named Entity Recognition method to extract the entities that exist in the English translation to the meaning of the Quranic text and its performance evaluation. Named entity tagging, a common task in-text annotation, in which entities (nouns) in the unstructured text are identified and assigned a class. A few rules are built to extract several types of entities such as the name of prophets and people, creation, location, time, and the various names of God. The rules are built mainly using regular expressions and gazetteers. The rules that have been built result in high precision and recall as well as a satisfactory F-score of over 90%. The results from this experiment can be used as annotation in building a machine learning model to extract entities from the same type of domain specifically on the Quranic text or generally in the Islamic domain text. [ABSTRACT FROM AUTHOR]

Understanding occupants' behaviours (OBs) of heating and cooling use in dwellings is essential for effectively promoting occupants' behavioural change for energy saving and achieving efficient demand response operation. Thus, intensive research has been conducted on data collection, statistical analysis, and modelling of OBs. However, the majority of smart metres currently deployed worldwide monitor only the total household consumption rather than appliance-level load. Therefore, estimating the turn-on/off state of specific home appliances from the measured household total electricity referred to as non-intrusive load monitoring (NILM), has gained research attention. However, the current NILM methods overlook the specific features of inverter-controlled heat pumps (IHPs) used for space heating/cooling; thus, they are unsuitable for detecting OBs. This study presents a rule-based method for identifying the occupants' intended operation states of IHPs based on a statistical analysis of load data monitored at 423 dwellings. This method detects the state of IHPs by subtracting the power of sequential-operation appliances other than IHPs from the total household power. Three time-series characteristics, including the durations of power-on/off states and power differences between power-off/on states, were used for this purpose. The performance of the proposed method was validated, indicating an F-score of 0.834. [ABSTRACT FROM AUTHOR]

In the present article an attempt has been made to predict the occurrences of customers leaving or 'churning' a business enterprise and explain the possible causes for the customer churning. Three different algorithms are used to predict churn, viz. decision tree, support vector machine and rough set theory. While two are rule-based learning methods which lead to more interpretable results that might help the marketing division to retain or hasten cross-sell of customers, one of them is a kernel-based classification that separates the customers on a feature hyperplane. The nature of predictions and rules obtained from them are able to provide a choice between a more focused or more extensive program the company may wish to implement as part of its customer retention program. [ABSTRACT FROM AUTHOR]

Dike-ponds in fisheries often present multiple pond conditions such as pure, suspended sediment, water bloom, semidry conditions, etc. However, the impact of these conditions on the performance of extracting dike-pond from remote sensing images has not been studied. To solve this problem, we explore the existence of such impacts by comparing the performance of four rule-based methods in two groups of test regions. The first group has few multiple pond conditions, while the second has more. The results show that various measure values deteriorate as the proportion of multiple pond conditions in the regions increases. All four methods performed worse in the second group than the first, where the overall accuracy decreased by 8.80%, misclassification error increased by 3.69%, omission error raised by 10.53%, and correct quantity rate dropped by 8.23%, respectively. The extraction method that ingested multiple pond conditions performed indistinguishably from the other methods in the first group. However, it outperformed the other methods in the second group, with a 4.22% improvement in overall accuracy, a 10.25% decrease in misclassification error, and a 19.03% increase in the correct quantity rate. These findings suggest that multiple pond conditions can negatively impact the extraction performance and should be considered in dike-pond applications that require a precise pond size, number, and shape. [ABSTRACT FROM AUTHOR]

Mobility data, based on global positioning system (GPS) tracking, have been widely used in many areas, such as analyzing travel patterns, investigating transport safety and efficiency, and evaluating travel impacts. Transport modes are essential factors in understanding mobility within the transport system. Therefore, in this study, a significant number of algorithms were tested for transport mode detection. However, no conclusive recommendations can be drawn regarding which method should be used. The evaluation of the performance of the algorithms was not discussed systematically either in current literature. This paper aims to provide an in-depth review of the methods applied in transport mode detection based on GPS tracking data. The performances of the reviewed methods are then compared and evaluated to provide guidance in choosing algorithms for transport mode detection based on GPS tracking data. The results indicate that the majority of current studies are based on a supervised learning method for transport mode detection. Many of the reviewed methods first require manual dataset labeling, which can produce major drawbacks, such as inefficiency and human errors. It was also found that deep learning approaches have the potential to deal with large amounts of unlabeled raw GPS datasets and increase the accuracy and efficiency of transport mode detection.

Cardiac electrophysiology and cardiac mechanics both depend on the average cardiomyocyte long‐axis orientation. In the realm of personalized medicine, knowledge of the patient‐specific changes in cardiac microstructure plays a crucial role. Patient‐specific computational modelling has emerged as a tool to better understand disease progression. In vivo cardiac diffusion tensor imaging (cDTI) is a vital tool to non‐destructively measure the average cardiomyocyte long‐axis orientation in the heart. However, cDTI suffers from long scan times, rendering volumetric, high‐resolution acquisitions challenging. Consequently, interpolation techniques are needed to populate bio‐mechanical models with patient‐specific average cardiomyocyte long‐axis orientations. In this work, we compare five interpolation techniques applied to in vivo and ex vivo porcine input data. We compare two tensor interpolation approaches, one rule‐based approximation, and two data‐driven, low‐rank models. We demonstrate the advantage of tensor interpolation techniques, resulting in lower interpolation errors than do low‐rank models and rule‐based methods adapted to cDTI data. In an ex vivo comparison, we study the influence of three imaging parameters that can be traded off against acquisition time: in‐plane resolution, signal to noise ratio, and number of acquired short‐axis imaging slices. [ABSTRACT FROM AUTHOR]

The Word Sense Disambiguation (WSD) is a process of disambiguating the sense of the text according to its context. Machine translation is one of the challenging task since it requires effective representation of the text to capture semantic relation between Hindi lyrics in English normal language behaviour. This paper focuses on WSD methods to deal with dialects that convert Hindi lyrics to English in its syntactic structure of the words. WSD is a phenomenon for disambiguating the text so that machine would be capable to deduce correct sense of individual given words. WSD is critical for solving natural language tasks such as Machine Translation (MT) and speech processing. The distinguishing proof of significant words in Hindi as the language is not as simple as that of dialects in English. The interpretations of sonnets through the machines are exceptionally essential and deliberate about mind-blowing events. The interpretation of English ballads into other local dialects can turn out to be very straightforward, however, vice-versa is troublesome. This is due to the assortment of structures, classes, and feelings of the local dialects. Various endeavours have been connected far and wide towards the programmed interpretation of ballads from local dialects into English. In this paper, we propose a half breed MT (HBMT) procedure driven by the standard based MT together with measurements based on statistical machine translation (SMT) and rule-based machine translation (RBMT) for WSD in natural script Hindi in English Lyrics. This proposed method improves the semantic and syntactic accuracy of a machine interpretation framework. Finally, the proposed approach result is compared with the machine translation methods such a Google and Microsoft Bing Babylonian and HMT translators provided achieves a better outcome compared to the existing standards. [ABSTRACT FROM AUTHOR]

Abstract Background There are often multiple lesions in breast magnetic resonance imaging (MRI) reports and radiologists usually focus on describing the index lesion that is most crucial to clinicians in determining the management and prognosis of patients. Natural language processing (NLP) has been used for information extraction from mammography reports. However, few studies have investigated NLP in breast MRI data based on free-form text. The objective of the current study was to assess the validity of our NLP program to accurately extract index lesions and their corresponding imaging features from free-form text of breast MRI reports. Methods This cross-sectional study examined 1633 free-form text reports of breast MRIs from 2014 to 2017. First, the NLP system was used to extract 9 features from all the lesions in the reports according to the Breast Imaging Reporting and Data System (BI-RADS) descriptors. Second, the index lesion was defined as the lesion with the largest number of imaging features. Third, we extracted the values of each imaging feature and the BI-RADS category from each index lesion. To evaluate the accuracy of our system, 478 reports were manually reviewed by two individuals. The time taken to extract data by NLP was compared with that by reviewers. Results The NLP system extracted 889 lesions from 478 reports. The mean number of imaging features per lesion was 6.5 ± 2.1 (range: 3–9; 95% CI: 6.362–6.638). The mean number of imaging features per index lesion was 8.0 ± 1.1 (range: 5–9; 95% CI: 7.901–8.099). The NLP system demonstrated a recall of 100.0% and a precision of 99.6% for correct identification of the index lesion. The recall and precision of NLP to correctly extract the value of imaging features from the index lesions were 91.0 and 92.6%, respectively. The recall and precision for the correct identification of the BI-RADS categories were 96.6 and 94.8%, respectively. NLP generated the total results in less than 1 s, whereas the manual reviewers averaged 4.47 min and 4.56 min per report. Conclusions Our NLP method successfully extracted the index lesion and its corresponding information from free-form text.

Narrative reports in medical records contain abundant clinical information that may be converted into structured data for managing patient information and predicting trends in diseases. Though various rule-based and machine-learning methods are available in electronic medical records (EMRs), a few works have explored the hybrid methods in extracting information from the Chinese EMRs. In this paper, we developed a novel hybrid approach which integrates the rules and bidirectional long short-term memory with a conditional random field layer (BiLSTM-CRF) model to extract clinical entities and attributes. A corpus of 1509 electronic notes (discharge summaries and operation notes) was annotated. Annotation from three clinicians was reconciled to form a gold standard dataset. The performance of our method was assessed by calculating the precision, recall, and F-measure for two boundary matching strategies. The experimental results demonstrate the effectiveness of our method in clinical information extraction from the Chinese EMRs.

Dike-ponds in fisheries often present multiple pond conditions such as pure, suspended sediment, water bloom, semidry conditions, etc. However, the impact of these conditions on the performance of extracting dike-pond from remote sensing images has not been studied. To solve this problem, we explore the existence of such impacts by comparing the performance of four rule-based methods in two groups of test regions. The first group has few multiple pond conditions, while the second has more. The results show that various measure values deteriorate as the proportion of multiple pond conditions in the regions increases. All four methods performed worse in the second group than the first, where the overall accuracy decreased by 8.80%, misclassification error increased by 3.69%, omission error raised by 10.53%, and correct quantity rate dropped by 8.23%, respectively. The extraction method that ingested multiple pond conditions performed indistinguishably from the other methods in the first group. However, it outperformed the other methods in the second group, with a 4.22% improvement in overall accuracy, a 10.25% decrease in misclassification error, and a 19.03% increase in the correct quantity rate. These findings suggest that multiple pond conditions can negatively impact the extraction performance and should be considered in dike-pond applications that require a precise pond size, number, and shape.

Few data‐driven approaches are available to estimate the risk of conclusion change in systematic review updates. We developed a rule‐based approach to automatically extract information from reviews and updates to be used as features for modelling conclusion change risk. Rules were developed to extract relevant information from published Cochrane reviews and used to construct four features: the number of included trials and participants in the reviews, a measure based on the number of participants, and the time elapsed between the search dates. We compared the performance of random forest, decision tree, and logistic regression to predict the conclusion change risk. The performance was measured by accuracy, precision, recall, F1‐score, and area under ROC (AU‐ROC). One rule was developed to extract the conclusion change information (96% accuracy, 100 reviews), one for the search date (100% accuracy, 100 reviews), one for the number of included clinical trials (100% accuracy, 100 reviews), and 22 for the number of participants (97.3% accuracy, 200 reviews). For unseen reviews, the random forest classifier showed the highest accuracy (80.8%) and AU‐ROC (0.80). All classifiers showed relatively similar performance with overlapping 95% confidence interval (CI). The coverage score was shown to be the most useful feature for predicting the conclusion change risk. Features mined from Cochrane reviews and updates can estimate conclusion change risk. If data from more published reviews and updates were made accessible, data‐driven methods to predict the conclusion change risk may be a feasible way to support decisions about updating reviews. [ABSTRACT FROM AUTHOR]

Copyright of Architectural Journal / Jian Zhu Xue Bao is the property of Architectural Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Background Despite widespread use, the safety of dietary supplements is open to doubt due to the fact that they can interact with prescribed medications, leading to dangerous clinical outcomes. Electronic health records (EHRs) provide a potential way for active pharmacovigilance on dietary supplements since a fair amount of dietary supplement information, especially those on use status, can be found in clinical notes. Extracting such information is extremely significant for subsequent supplement safety research. Methods In this study, we collected 2500 sentences for 25 commonly used dietary supplements and annotated into four classes: Continuing (C), Discontinued (D), Started (S) and Unclassified (U). Both rule-based and machine learning-based classifiers were developed on the same training set and evaluated using the hold-out test set. The performances of the two classifiers were also compared. Results The rule-based classifier achieved F-measure of 0.90, 0.85, 0.90, and 0.86 in C, D, S, and U status, respectively. The optimal machine learning-based classifier (Maximum Entropy) achieved F-measure of 0.90, 0.92, 0.91 and 0.88 in C, D, S, and U status, respectively. The comparison result shows that the machine learning-based classifier has a better performance, which is more efficient and scalable especially when the sample size doubles. Conclusions Machine learning-based classifier outperforms rule-based classifier in categorization of the use status of dietary supplements in clinical notes. Future work includes applying deep learning methods and developing a hybrid system to approach use status classification task.

Nowadays, sentiment analysis has been a very active research area with the increase of social media data. It presents a very useful task for product evaluation, social recommendation and popularity analysis. It constitutes also a crucial move towards natural language processing (NLP) domains. Our main goal is to identify sentiments towards aspect in the sentence. An aspect presents a specific entity or object features (price, product quality, etc.). Therefore, its analysis requires two primordial steps: extract entity aspects and identify the sentiments from all these aspects. In our research work, we propose a new hybrid method to detect sentiments towards aspects. We start with a machine learning method to detect the different aspects within a given sentence, followed by a rule based method to identify sentiments within these aspects. The evaluation of our hybrid system based on a reference dataset of Arabic Hotels' reviews Semantic Evaluation-2016 shows that our system outperforms baseline research to achieve encouraging results (96% of F-score). [ABSTRACT FROM AUTHOR]

This paper presents a comparatively contemporary easy to use technique for the identification and classification of voltage variations. The technique was established based on the Gabor Transform and the rule-based classification method. The technique was tested by using mathematical model of Power Quality (PQ) disturbances based on the IEEE Std 519-2009. The PQ disturbances focused were the voltage variations, which included voltage sag, swell and interruption. A total of 80 signals were simulated from the mathematical model in MATLAB and used in this study. The signals were analyzed by using Gabor Transform and the signal pattern, timefrequency representation (TFR) and root-mean-square voltage graph were presented in this paper. The features of the analysis were extracted, and rules were implemented in rule-based classification to identify and classify the voltage variation accordingly. The results showed that this method is easy to be used and has good accuracy in classifying the voltage variation. [ABSTRACT FROM AUTHOR]

Background: There are often multiple lesions in breast magnetic resonance imaging (MRI) reports and radiologists usually focus on describing the index lesion that is most crucial to clinicians in determining the management and prognosis of patients. Natural language processing (NLP) has been used for information extraction from mammography reports. However, few studies have investigated NLP in breast MRI data based on free-form text. The objective of the current study was to assess the validity of our NLP program to accurately extract index lesions and their corresponding imaging features from free-form text of breast MRI reports.Methods: This cross-sectional study examined 1633 free-form text reports of breast MRIs from 2014 to 2017. First, the NLP system was used to extract 9 features from all the lesions in the reports according to the Breast Imaging Reporting and Data System (BI-RADS) descriptors. Second, the index lesion was defined as the lesion with the largest number of imaging features. Third, we extracted the values of each imaging feature and the BI-RADS category from each index lesion. To evaluate the accuracy of our system, 478 reports were manually reviewed by two individuals. The time taken to extract data by NLP was compared with that by reviewers.Results: The NLP system extracted 889 lesions from 478 reports. The mean number of imaging features per lesion was 6.5 ± 2.1 (range: 3-9; 95% CI: 6.362-6.638). The mean number of imaging features per index lesion was 8.0 ± 1.1 (range: 5-9; 95% CI: 7.901-8.099). The NLP system demonstrated a recall of 100.0% and a precision of 99.6% for correct identification of the index lesion. The recall and precision of NLP to correctly extract the value of imaging features from the index lesions were 91.0 and 92.6%, respectively. The recall and precision for the correct identification of the BI-RADS categories were 96.6 and 94.8%, respectively. NLP generated the total results in less than 1 s, whereas the manual reviewers averaged 4.47 min and 4.56 min per report.Conclusions: Our NLP method successfully extracted the index lesion and its corresponding information from free-form text. [ABSTRACT FROM AUTHOR]

The main objective of this study is to propose relatively simple techniques for the automatic diagnosis of electrocardiogram (ECG) signals based on a classical rule-based method and a convolutional deep learning architecture. The validation task was performed in the framework of the PhysioNet/Computing in Cardiology Challenge 2020, where seven databases consisting of 66,361 recordings with 12-lead ECGs were considered for training, validation and test sets. A total of 24 different diagnostic classes are considered in the entire training set. The rule-based method uses morphological and time-frequency ECG descriptors that are defined for each diagnostic label. These rules are extracted from the knowledge base of a cardiologist or from a textbook, with no direct learning procedure in the first phase, whereas a refinement was tested in the second phase. The deep learning method considers both raw ECG and median beat signals. These data are processed via continuous wavelet transform analysis, obtaining a time-frequency domain representation, with the generation of specific images (ECG scalograms). These images are then used for the training of a convolutional neural network based on GoogLeNet topology for ECG diagnostic classification. Cross-validation evaluation was performed for testing purposes. A total of 217 teams submitted 1395 algorithms during the Challenge. The diagnostic accuracy of our algorithm produced a challenge validation score of 0.325 (CPU time = 35 min) for the rule-based method, and a 0.426 (CPU time = 1664 min) for the deep learning method, which resulted in our team attaining 12th place in the competition.

The simultaneous uptake of residential photovoltaic (PV) systems and electric vehicles (EVs) can lead to contrasting voltage problems (i.e. voltage rise and drop) in low‐voltage (LV) networks throughout the day. While solutions that directly manage these technologies can be effective in mitigating their impacts, they affect or require interactions with customers. A more practical alternative for distribution network operators (DNOs) is, therefore, to manage voltages using only network assets. For this purpose, two advanced methods to control on‐load tap changer fitted transformers, owned and operated by DNOs, are proposed: a rule‐based control method that only requires monitoring from remote points and a three‐phase optimal power flow‐based control method that requires full observability and adopts a simple – yet effective – forecasting to cater for future uncertainties. The performance of the proposed methods is thoroughly assessed within a stochastic approach on a real UK LV network considering realistic 1 min resolution demand, PV, and EV profiles. Results show that both methods are effective in managing contrasting voltage problems caused by both PV systems and EVs. While the optimisation method leads to the lowest number of tap changes, the rule‐based method, given its limited observability requirement, could be considered a practical interim solution. [ABSTRACT FROM AUTHOR]

Aims: To develop a natural language processing (NLP)-based algorithm for extracting clinically useful information for patients with hepatocellular carcinoma (HCC) from Chinese electronic medical records (EMRs) and use these data for the assessment of HCC staging.Materials and Methods: Clinical documents, including operation notes, radiology and pathology reports, of 92 HCC patients were collected from Chinese EMRs. We randomly grouped these patients into training (n = 60) and testing (n = 32) datasets. Rule-based and hybrid methods for extracting information were developed using the training set of manually-annotated operation notes. The method with better performance was used to process other documents. The performance of the algorithm was assessed via calculating the precision, recall and F-score for exact-boundary and partial-boundary matching strategies. The utility of clinically useful information for the HCC staging was assessed in comparison with that manually reviewed.Results: For operation notes, the rule-based and hybrid methods had a precision, recall and F-score ≥80% when the exact-boundary and partial-boundary matching strategies were applied to the testing dataset. By using the rule-based method (which has better performance than the hybrid method), three other types of documents also obtained good performance. When the extracted clinically useful information was applied for the HCC staging, the concordance rate with the manual review was 75%.Conclusion: A NLP system was developed for clinical information extraction and HCC staging based on EMRs, and the results indicate that Chinese NLP has potential utility in clinical research. [ABSTRACT FROM AUTHOR]

Sentiment analysis is one of the most important and interesting tasks in natural languages. A number of resources and tools have been developed for sentiment analysis of English, Turkish, Russian and other languages. Unfortunately, there were no data and tools available for sentiment analysis in Kazakh. The Dictionary of Kazakh sentiment words has been created during this study. In this work, we described the rule-based method using a dictionary of emotional words for sentiment analysis of texts in the Kazakh language, based on the morphological rules and ontological model. We studied the texts in Kazakh and determined the parts of speech that define the text mood. Based on the conducted studies, a lot of phrases were identified as determining the text polarity. This paper is an extended version of the paper published in [in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS, 2017, pp. 669–677]. In addition to the original material, the paper includes additional rules for determining sentiment on a 5-point scale. [ABSTRACT FROM AUTHOR]

Relation extraction is a very useful task for several natural language processing applications, such as automatic summarization and question answering. In this paper, we present our hybrid approach to extracting relations between Arabic named entities. Given that Arabic is a rich morphological language, we build a linguistic and learning model to predict the positions of words that express a semantic relation within a clause. The main idea is to employ linguistic modules to ameliorate the results that are obtained from a machine learning-based method. Our method achieves encouraging performance. The empirical results indicate that the hybrid approach outperformed both the rule-based system (by 12%) and the machine learning-based approaches (by 9%) in terms of the F-score, to achieve 75.2% when applied to the same standard testing dataset, ANERCorp.

The past few years have witnessed the rapid growth in the collection of large-scale GPS data via smartphone-based travel surveys around the world, following which transportation modes detection received significant attention. A mass of methods varying from Criteria-based rules to Machine Learning technology were employed to recognize the travel modes. However, the limited sample size, deficient feature selection and the less emphasis on addressing confusion modes, which leave room for improvement. This paper therefore sought to develop and evaluate a Random Forest classifier combined with a rule-based method to detect six travel modes (subway, walking, bicycle, e-bike, bus and car). Seven GPS-related variables are selected as feature set from the initial list of 22 variables. Consequently, more than 98% subway trips were correctly identified and the overall accuracy of the rest five modes classification is obtained as high as 93.11%. More than 85% trips were successfully identified for each mode except for the bus. More importantly, results show that socioeconomic attributes data could significantly improve the prediction of e-bike and address the confusion between bus and car modes. The employment of ROC curve provides a statistical proof to the excellent classification capacity of Random Forest in this study. Besides, the comparison with two representative classifiers demonstrates the applicability of Random Forest classifier for travel modes detection incorporating multi-source attributes. [ABSTRACT FROM PUBLISHER]

Data repairing aims at discovering and correcting erroneous data in databases. In this paper, we develop Web-ADARE, an end-to-end web-aided data repairing system, to provide a feasible way to involve the vast data sources on the Web in data repairing. Our main attention in developing Web-ADARE is paid on the interaction problem between web-aided repairing and rule-based repairing, in order to minimize the Web consultation cost while reaching predefined quality requirements. The same interaction problem also exists in crowd-based methods but this is not yet formally defined and addressed. We first prove in theory that the optimal interaction scheme is not feasible to be achieved, and then propose an algorithm to identify a scheme for efficient interaction by investigating the inconsistencies and the dependencies between values in the repairing process. Extensive experiments on three data collections demonstrate the high repairing precision and recall of Web-ADARE, and the efficiency of the generated interaction scheme over several baseline ones. [ABSTRACT FROM AUTHOR]

Scene change detection between multitemporal image scenes can be used to interpret the variation of regional land use, and has significant potential in the application of urban development monitoring at the semantic level. The traditional methods directly comparing the independent semantic classes neglect the temporal correlation, and thus suffer from accumulated classification errors. In this paper, we propose a novel scene change detection method via kernel slow feature analysis (KSFA) and postclassification fusion, which integrates independent scene classification with scene change detection to accurately determine scene changes and identify the “from-to” transition type. After representation with the bag-of-visual-words model, KSFA is proposed to extract the nonlinear temporally invariant features, to better measure the change probability between corresponding multitemporal image scenes. Two postclassification fusion methods, which are based on Bayesian theory and predefined rules, respectively, are then employed to identify the optimal coupled class combinations of multitemporal scene pairs. Furthermore, in addition to identifying semantic changes, the proposed method can also improve the performance of scene classification, since the unchanged scenes are more likely to belong to the same class. Two experiments with high-resolution remote sensing image scene data sets confirm that the proposed method can increase the accuracy of scene change detection, scene transition identification, and scene classification. [ABSTRACT FROM PUBLISHER]