Purpose The global rise of alternative medicine, particularly the use of medicinal plants, offers promising health benefits but also poses significant risks due to limited knowledge, species misidentification and improper preparation or administration. Design/methodology/approach This paper introduces HerbOnto, a Herbal Ontology designed for the dynamic development of medicinal plant knowledge base – meaning it continuously collects, updates and categorizes data through advanced data collection techniques and classification algorithms. Findings HerbOnto aims to reduce risks and enhance safety by providing an evolving, structured and accessible knowledge base that supports the effective and responsible use of medicinal plants worldwide. Originality/value The dynamic and predictive approach to ontology development for medicinal plants, which goes beyond existing static herbal databases or taxonomies.

Purpose Path information in knowledge graphs can provide explicit explanations for recommendation decisions, thus becoming a focus in explainable recommendation research. However, limited studies about explainable recommendation problems result in low model transparency and poor persuasiveness, affecting user experience. Therefore, the goal is to provide accurate and interpretable recommendations for recommendations. Design/methodology/approach This study proposes a recommendation reasoning method based on knowledge graph and reinforcement learning. To alleviate the noise problem in the state space, a multi-head attention mechanism is used to learn state expressions. A dual critic network is used to optimize long-term and short-term rewards simultaneously, achieving path reasoning in the knowledge graph to provide short-term and long-term value explanations for recommendation decisions. Findings We conduct extensive experiments on the real-world benchmark dataset and the domain dataset to validate the effectiveness of our method on the recommendation and explanation tasks, which proves the method's ability to generate high-quality and interpretable course recommendations. Originality/value Developing explainable recommendation methods based on the combination of knowledge graph and reinforcement learning is crucial to overcome the current limitations. A recommendation system that integrates knowledge reasoning, autonomous learning and interpretability may meet the needs of the modern education field for explainable recommendation systems.

Purpose This study focused on early warning in K-12 online education and aims to address the following research gaps: timing, model performance and understandability by completing the following analytic tasks: (1) determine the best prediction timing based on changes in course requirements; (2) compare early warning models with the correct evaluation indicators; (3) interpret a complex predictive model using interpretable AI techniques. Through holistic analyses, the case study shows that the semester can be divided into three stages with different course requirements. Students who failed to meet the course requirements of individual stages would be predicted as at-risk. Through interpretable AI techniques, the key predictors of individual stages were identified, and the factors causing a student to be predicted as at-risk were also revealed. In addition, multiple at-risk types were identified through the analyses. Design/methodology/approach This study employed a variational autoencoder and time series segmentation to detect changes in learning behavioral patterns resulting from alterations in course requirements. The semester was divided into three stages. Subsequently, complex ensemble machine learning classifiers were employed for early warnings at each stage, enabling accurate prediction of students' learning performance. Interpretable AI techniques were employed to gain insight, identify characteristics of different at-risk types and suggest personalized interventions. Findings The case study analyzed 16,011 K-12 online school students. Major Findings include: (1) The semester was divided into three segments based on learning pattern transition points. XGBoost can identify the most at-risk students in each segment. Results indicate S2 is a relatively appropriate stage for early warning prediction and intervention. By the end of the second stage, the models can already identify over 75% of at-risk students. A student’s at-risk probability in previous stage has the most important influence on his performance in the next stage. If a student has high at-risk probability, he will have more possibility to be labeled as potential at-risk in the next stage, if he has low at-risk probability, he will have more possibility to be labeled as potential successful in the next stage, this reflects the influence between neighbor learning stages. We also found that, early warning can be most effectively conducted in the last week of each of these three stages. (2) From S1 to S3 stages, significant changes in the categories of key behaviors were observed, the key behavior at S1 stage was assignment viewing and submitting, at S2 stage was resource viewing and grade check, at S3 stage was assignment viewing and submitting, the discussion behavior became not important, while the testing behavior gradually became important, this reflected the changes in the course requirements and the learning strategy. It is evident that in specific stages of the semester, not all behaviors are better when more frequent; some behaviors, when excessive at the wrong time, can have a negative impact on predictive results. (3) Five types of at-risk students were found at each stage through interpretable AI analyses: high engaged at-risk, low engaged at-risk, testing at-risk, low interaction at-risk and un-persistent at-risk, which represented five types of learning patterns that conducted potential at-risk. With the progress of course, students adjusted learning strategy and might convert from one at-risk type to other at-risk type or successful type. The S2 stage was the most important stage, students had their last opportunity to reverse their at-risk trends at this stage. (4) Students who do not perform well in the early stages still have the opportunity to reverse the at-risk trend with effort in the later stages, emphasizing the importance of intervention at the end of the second stage. Similarly, students who perform well in the early stages can still face failure if they do not put in effort in the final stage. Low-engaged students tended to maintain their low-engaged status, resulting in failure at the end of the semester compared to other at-risk types, this was caused by that they had no learning activity to adjust their learning strategy. Research limitations/implications A follow-up study can focus on the development of personalized interventions and observe whether these interventions can influence potentially at-risk students' learning strategies, more effectively guiding students to align with course requirements and ultimately leading to improved performance. Originality/value This study is one of the few that focus on large-scale K-12 early warning prediction and aim to identify at-risk types via interpretable AI for personalized intervention. The authors found that the proposed method can effectively determine the best prediction timing, identify more at-risk students, and gain deep insight into students' learning processes. The authors confirm that the research in their work is original, and all the data given in the paper are real and authentic. The study has not been submitted to peer review and has not been accepted for publication in another journal.

PurposeThis paper presents pyBibX, a Python library devised to conduct comprehensive bibliometric and scientometric analyses on raw data files sourced from Scopus, Web of Science and PubMed, seamlessly integrating state-of-the-art artificial intelligence (AI) capabilities into its core functionality.Design/methodology/approachThe library executes a comprehensive exploratory data analysis (EDA), presenting outcomes via visually appealing graphical illustrations. Network capabilities have been deftly integrated, encompassing citation, collaboration and similarity analysis. Furthermore, the library incorporates AI capabilities, including embedding vectors, topic modeling, text summarization and other general natural language processing tasks, employing models such as sentence-BERT, BerTopic, BERT, chatGPT and PEGASUS.FindingsAs a demonstration, we have analyzed 184 documents associated with “multiple-criteria decision analysis” published between 1984 and 2023. The EDA emphasized a growing fascination with decision-making and fuzzy logic methodologies. Next, network analysis further accentuated the significance of central authors and intra-continental collaboration, identifying Canada and China as crucial collaboration hubs. Finally, AI analysis distinguished two primary topics and chatGPT’s preeminence in text summarization. It also proved to be an indispensable instrument for interpreting results, as our library enables researchers to pose inquiries to chatGPT regarding bibliometric outcomes. Even so, data homogeneity remains a daunting challenge due to database inconsistencies.Originality/valuePyBibX is the first application integrating cutting-edge AI capabilities for analyzing scientific publications, enabling researchers to examine and interpret these outcomes more effectively. pyBibX is freely available at https://bit.ly/442wD5z.

PurposeThis paper introduces a novel method, Variance Rule-based Window Size Tracking (VR-WT), for deriving a sequence of estimation window sizes. This approach not only identifies structural change points but also ascertains the optimal size of the estimation window. VR-WT is designed to achieve accurate model estimation and is versatile enough to be applied across a range of models in various disciplines.Design/methodology/approachThis paper proposes a new method named Variance Rule-based Window size Tracking (VR-WT), which derives a sequence of estimation window sizes. The concept of VR-WT is inspired by the Potential Scale Reduction Factor (PSRF), a tool used to evaluate the convergence and stationarity of MCMC.FindingsMonte Carlo simulation study demonstrates that VR-WT accurately detects structural change points and select appropriate window sizes. The VR-WT is essential in applications where accurate estimation of model parameters and inference about their value, sign, and significance are critical. The VR-WT has also helped us understand shifts in parameter-based inference, ensuring stability across periods and highlighting how the timing and impact of market shocks vary across fields and datasets.Originality/valueThe first distinction of the VR-WT lies in its purpose and methodological differences. The VR-WT focuses on precise parameter estimation. By dynamically tracking window sizes, VR-WT selects flexible window sizes and enables the visualization of structural changes. The second distinction of VR-WT lies in its broad applicability and versatility. We conducted empirical applications across three fields of study: CAPM; interdependence analysis between global stock markets; and the study of time-dependent energy prices.

PurposeCoal is a critical global energy source, and fluctuations in its price significantly impact related enterprises' profitability. This study aims to develop a robust model for predicting the coal price index to enhance coal purchase strategies for coal-consuming enterprises and provide crucial information for global carbon emission reduction.Design/methodology/approachThe proposed coal price forecasting system combines data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. It addresses the challenge of merging low-resolution and high-resolution data by adaptively combining both types of data and filling in missing gaps through interpolation for internal missing data and self-supervision for initiate/terminal missing data. The system employs self-supervised learning to complete the filling of complex missing data.FindingsThe ensemble model, which combines long short-term memory, XGBoost and support vector regression, demonstrated the best prediction performance among the tested models. It exhibited superior accuracy and stability across multiple indices in two datasets, namely the Bohai-Rim steam-coal price index and coal daily settlement price.Originality/valueThe proposed coal price forecasting system stands out as it integrates data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. Moreover, the system pioneers the use of self-supervised learning for filling in complex missing data, contributing to its originality and effectiveness.

PurposeSleep arousal detection is an important factor to monitor the sleep disorder.Design/methodology/approachThus, a unique nth layer one-dimensional (1D) convolutional neural network-based U-Net model for automatic sleep arousal identification has been proposed.FindingsThe proposed method has achieved area under the precision–recall curve performance score of 0.498 and area under the receiver operating characteristics performance score of 0.946.Originality/valueNo other researchers have suggested U-Net-based detection of sleep arousal.Research limitations/implicationsFrom the experimental results, it has been found that U-Net performs better accuracy as compared to the state-of-the-art methods.Practical implicationsSleep arousal detection is an important factor to monitor the sleep disorder. Objective of the work is to detect the sleep arousal using different physiological channels of human body.Social implicationsIt will help in improving mental health by monitoring a person's sleep.

PurposeProcess mining has emerged in the last decade as one of the most promising tools to discover and understand the actual execution of processes. This paper addresses the application of process mining techniques to analyze the performance of automatic guided vehicles (AGVs) in one of the Body in White circuits of the factory that Stellantis has in Vigo, Spain.Design/methodology/approachStandard process mining discovery and conformance algorithms are applied to analyze the different AGV execution paths, their lead times, main sources and identify any unexpected potential situations, such as unexpected paths or loops.FindingsResults show that this method provides very useful insights which are not evident for logistics technicians. Even with such automated devices, where the room for decreased efficiency can be apparently small, process mining shows there are cases where unexpected situations occur, leading to an increase in circuit times and different variants for the same route, which pave the road for an actual improvement in performance and efficiency.Originality/valueThis paper provides evidence of the usefulness of applying process mining in manufacturing processes. Practical applications of process mining have traditionally been focused on processes related to services and management, such as order to cash and purchase to pay in enterprise resource planning software. Despite its potential for use in industrial manufacturing, such contributions are scarce in the current state of the art and, as far as we are aware of, do not fully justify its application.

PurposeThe objective of the proposed work is to identify the most commonly occurring non–small cell carcinoma types, such as adenocarcinoma and squamous cell carcinoma, within the human population. Another objective of the work is to reduce the false positive rate during the classification.Design/methodology/approachIn this work, a hybrid method using convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) and long-short-term memory networks (LSTMs) has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. To extract features from non–small cell lung carcinoma images, a three-layer convolution and three-layer max-pooling-based CNN is used. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types. The accuracy of the proposed method is 99.57 per cent, and the false positive rate is 0.427 per cent.FindingsThe proposed CNN–XGBoost–LSTM hybrid method has significantly improved the results in distinguishing between adenocarcinoma and squamous cell carcinoma. The importance of the method can be outlined as follows: It has a very low false positive rate of 0.427 per cent. It has very high accuracy, i.e. 99.57 per cent. CNN-based features are providing accurate results in classifying lung carcinoma. It has the potential to serve as an assisting aid for doctors.Practical implicationsIt can be used by doctors as a secondary tool for the analysis of non–small cell lung cancers.Social implicationsIt can help rural doctors by sending the patients to specialized doctors for more analysis of lung cancer.Originality/valueIn this work, a hybrid method using CNN, XGBoost and LSTM has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. A three-layer convolution and three-layer max-pooling-based CNN is used to extract features from the non–small cell lung carcinoma images. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types.

PurposeThe problem of image retrieval and image description exists in various fields. In this paper, a model of content-based image retrieval and image content extraction based on the KD-Tree structure was proposed.Design/methodology/approachA Random Forest structure was built to classify the objects on each image on the basis of the balanced multibranch KD-Tree structure. From that purpose, a KD-Tree structure was generated by the Random Forest to retrieve a set of similar images for an input image. A KD-Tree structure is applied to determine a relationship word at leaves to extract the relationship between objects on an input image. An input image content is described based on class names and relationships between objects.FindingsA model of image retrieval and image content extraction was proposed based on the proposed theoretical basis; simultaneously, the experiment was built on multi-object image datasets including Microsoft COCO and Flickr with an average image retrieval precision of 0.9028 and 0.9163, respectively. The experimental results were compared with those of other works on the same image dataset to demonstrate the effectiveness of the proposed method.Originality/valueA balanced multibranch KD-Tree structure was built to apply to relationship classification on the basis of the original KD-Tree structure. Then, KD-Tree Random Forest was built to improve the classifier performance and retrieve a set of similar images for an input image. Concurrently, the image content was described in the process of combining class names and relationships between objects.