The KNN algorithm is one of the most famous algorithms in machine learning and data mining. It does not preprocess the data before classification, which leads to longer time and more errors. To solve the problems, this paper first proposes a PK-means++ algorithm, which can better ensure the stability of a random experiment. Then, based on it and spherical region division, an improved KNNPK+ is proposed. The algorithm can select the center of the spherical region appropriately and then (...) construct an initial classifier for the training set to improve the accuracy and time of classification. (shrink)

On the basis of ecological footprint theory and tourism ecological footprint theory, the sustainable development indexes such as ecological footprint, ecological carrying capacity, ecological deficit, and ecological surplus of the research area were calculated and the long-term change pattern of each index was analyzed. This paper shows that the ecological footprint of the research area increases year by year, but the ecological footprint is always smaller than the ecological carrying capacity, indicating that the area is still in the state of (...) sustainable development. However, the per capita ecological surplus shows a decreasing trend year by year, indicating that the sustainable development of the region is getting worse. This paper proposes a reordering method of tourist attractions based on heterogeneous information fusion, and realizes the retrieval and reordering of tourist attractions based on user query and fusion of heterogeneous information, so as to help users make travel decisions. In view of the shortage of tourism commercial websites to passively provide scenic spot information, this paper puts forward a scenic spot retrieval method based on query words to enable users to obtain scenic spot information according to their needs, and constructs a tourist consumer data analysis system. The preprocessing methods and methods adopted by the data preprocessing module are analyzed in detail, and the algorithms used in the travel route analysis and consumer spending ability analysis are described in detail. The data of tourism consumers are analyzed by this system, and the results are evaluated. (shrink)

This paper takes laptops as an example to carry out research on quantitative model of brand recognition based on sentiment analysis of big data. The basic idea is to use web crawler technology to obtain the most authentic and direct information of different laptop brands from first-line consumers from public spaces such as buyer reviews of major e-commerce platforms, including review time, text reviews, satisfaction ratings and relevant user information, etc., and then analyzes consumers’ sentimental tendencies and recognition status (...) of the product brands. This study extracted a total of 437,815 user reviews of laptops from e-commerce platforms from January 1, 2019 to December 31, 2021, and performed data preprocessing on the obtained review data, followed by sentiment dictionary construction, attribute expansion, text quantification and algorithm evaluation. This paper analyzed the information receiving and processing hierarchy of the quantitative model of brand recognition, discussed the interactive relationship between brand recognition and consumer sentiment, discussed the brand recognition bias, style and demand in the context of big data, and performed the sentiment statistics and dimension analysis in the quantitative model of brand recognition. The study results show that the quantitative model of brand recognition based on sentiment analysis of big data can transform and map the keywords in text to word vectors in the high-dimensional semantic space by performing unsupervised machine learning on the text based on artificial neural network computer bionic metaphors; the model can accumulate each brand-related buyer review in the corresponding brand recognition dimension, so as to obtain the value of each product in each dimension of brand recognition; finally, the model will add the values of each dimension of brand recognition, that is, obtain the relevant value of the sum of each brand recognition. The results of this paper may provide a reference for further research on the quantitative model of brand recognition based on sentiment analysis of big data. (shrink)

Hemodynamic response to motor execution (ME) and motor imagery (MI) was investigated using functional near-infrared spectroscopy (fNIRS). We used a 31 channel fNIRS system which allows non-invasive monitoring of cerebral oxygenation changes induced by cortical activation. Sixteen healthy subjects (mean-age 24.5 yeas) were recruited and the changes in concentration of hemoglobin were examined during right and left hand finger tapping tasks and kinesthetic MI. To suppress the systemic physiological interference, we developed a preprocessing procedure which prevents over-activated reporting in (...) NIRS-SPM. In the condition of ME, more activation was observed in the anterior part of the motor cortex including the pre-motor and supplementary motor area (pre-motor and SMA), primary motor cortex (M1) and somatosensory motor cortex (SMC; t (15) > 2.27), however, in the condition of MI, more activation was found in the posterior part of motor cortex including SMC ( t (15) > 1.81), which is in line with previous observations with functional magnetic resonance imaging (fMRI). (shrink)

We describe a strategy for integration of data that is based on the idea of semantic enhancement. The strategy promises a number of benefits: it can be applied incrementally; it creates minimal barriers to the incorporation of new data into the semantically enhanced system; it preserves the existing data (including any existing data-semantics) in their original form (thus all provenance information is retained, and no heavy preprocessing is required); and it embraces the full spectrum of (...) data sources, types, models, and modalities (including text, images, audio, and signals). The result of applying this strategy to a given body of data is an evolving Dataspace that allows the application of a variety of integration and analytic processes to diverse data contents. We conceive semantic enhancement (SE) as a lightweight and flexible process that leverages the richness of the structured contents of the Dataspace without adding storage and processing burdens to what, in the intelligence domain, will be an already storage- and processing-heavy starting point. SE works not by changing the data to which it is applied, but rather by adding an extra semantic layer to this data. We sketch how the semantic enhancement approach can be applied consistently and in cumulative fashion to new data and data-models that enter the Dataspace. (shrink)

Machine stability and energy efficiency have become major issues in the manufacturing industry, primarily during the COVID-19 pandemic where fluctuations in supply and demand were common. As a result, Predictive Maintenance (PdM) has become more desirable, since predicting failures ahead of time allows to avoid downtime and improves stability and energy efficiency in machines. One type of machine failure stands out due to its impact, machine overstrain, which can occur when machines are used beyond their tolerable limit. From the current (...) literature, there are little to no relevant works that focus on machine overstrain failure detection or prediction. Accordingly, the purpose of this paper is to implement and compare four Machine Learning (ML) algorithms for PdM applied to machine overstrain failures: Artificial Neural Network (ANN), Gradient Boosting, Random Forest and Support Vector Machine (SVM). Moreover, it proposes a training methodology for imbalanced data and the automatic optimization of hyperparameters, which aims to improve performance in the ML models. To evaluate the performance of the ML models, a synthetic dataset that simulates industrial machine data is used. The obtained results show the robustness of the proposed methodology, with the ANN and SVM models achieving a perfect recall score, with 98.95% and 98.85% in accuracy, respectively. (shrink)

Designing a data-responsive system requires accurate input to ensure efficient results. The growth of technology in sensing methods and the needs of various kinds of data greatly impact data fusion (DF)-related study. A coordinative DF framework entails the participation of many subsystems or modules to produce coordinative features. These features are utilized to facilitate and improve solving certain domain problems. Consequently, this paper proposes a general Multiple Coordinative Data Fusion Modules (MCDFM) framework for real-time and heterogeneous (...) data sources. We develop the MCDFM framework to adapt various DF application domains requiring macro and micro perspectives of the observed problems. This framework consists of preprocessing, filtering, and decision as key DF processing phases. These three phases integrate specific purpose algorithms or methods such as data cleaning and windowing methods for preprocessing, extended Kalman filter (EKF) for filtering, fuzzy logic for local decision, and software agents for coordinative decision. These methods perform tasks that assist in achieving local and coordinative decisions for each node in the network of the framework application domain. We illustrate and discuss the proposed framework in detail by taking a stretch of road intersections controlled by a traffic light controller (TLC) as a case study. The case study provides a clearer view of the way the proposed framework solves traffic congestion as a domain problem. We identify the traffic features that include the average vehicle count, average vehicle speed (km/h), average density (%), interval (s), and timestamp. The framework uses these features to identify three congestion periods, which are the nonpeak period with a congestion degree of 0.178 and a variance of 0.061, a medium peak period with a congestion degree of 0.588 and a variance of 0.0593, and a peak period with a congestion degree of 0.796 and a variance of 0.0296. The results of the TLC case study show that the framework provides various capabilities and flexibility features of both micro and macro views of the scenarios being observed and clearly presents viable solutions. (shrink)

This paper conducts in-depth research and practice on the construction of medi- cal data knowledge graphs, exploring their core value in the medical field. With the explosive growth of medical data from electronic health records, genomic sequenc- ing, and medical literature, data silos and heterogeneity have restricted medical efficiency and intelligent development. Medical data knowledge graphs address this by integrating multi-source heterogeneous data into a structured semantic network, enabling effective organization and application of medical knowledge. (...) The study details the construction process, including data acquisition from clini- cal databases and literature, preprocessing (cleaning, annotation), core technologies (ontology design, entity recognition, relation extraction, knowledge fusion), and graph database-based storage and query methods. It compares technical routes (rule-based, machine learning, deep learning) and highlights innovations like com- bining BERT and graph neural networks to enhance entity extraction and relation prediction accuracy. Practical applications in clinical decision support (e.g., accelerating differen- tial diagnosis via disease-symptom-drug relationships), rare disease diagnosis, and public health management (e.g., tracking infectious disease spread) are explored through case studies. The paper also summarizes challenges such as data privacy, dynamic knowledge updates, and terminology standardization, proposing solutions and future directions. This research provides theoretical and practical support for advancing medical informatization and intelligence, contributing to improved service quality, reduced costs, and precision medicine. (shrink)

This paper examines public sentiment about Agile methodologies and Digital Transformation through high-end Twitter sentiment analysis techniques. Agile, initially developed for software development, is nowadays one of the most important drivers of innovation and adaptability in many sectors, whereas Digital Transformation reshapes the operations of organizations through the use of digital technologies. Understanding public sentiment toward these concepts is crucial for organizations that want to align their strategy with new trends and adapt to an increasingly digital world. This approach is (...) further fine-tuned in this research through the incorporation of advanced data preprocessing techniques and the application of more sophisticated analytical tools. VADER sentiment analysis is used to catch the polarity of sentiments expressed on social media. It also includes the adoption of Latent Dirichlet Allocation, or LDA, topic modeling in order to mine further into the conversations on Agile and Digital Transformation. This constituted some methodological improvements in this study that can allow nuanced understanding of emotions, opinions, and ideas that dominate public discourse on these transformative methodologies. This research performs an in-depth examination of the patterns of sentiment expressed through Twitter, termed a real-time barometer of public opinion, by analyzing a dataset of 1,014 tweets using the hashtags Agile and Digital Transformation tweets collected during the period from January 31, 2023, to August 18, 2023. On the other hand, integrating topic modeling will enable the identification of key areas of interest in public discussions: the role of AI and automation, organizational challenges, and finally best practices regarding the implementation of Agile. This paper gives a wider outlook on Agile and Digital Transformation from the perspective of public engagement and provides useful insights into their practice, research, and policy implications. The findings will be useful for organizational decisions, communications, and the development of better strategies in response to perceived public views and trends that arise with digital innovations. (shrink)

The annotated dataset is an essential requirement to develop an artificial intelligence system effectively and expect the generalization of the predictive models and to avoid overfitting. Lack of the training data is a big barrier so that AI systems can broaden in several domains which have no or missing training data. Building these datasets is a tedious and expensive task and depends on the domains and languages. This is especially a big challenge for low-resource languages. In this paper, (...) we experiment and evaluate many various approaches on sentiment analysis problems so that they can still obtain high performances under limited training data. This paper uses the preprocessing techniques to clean and normalize the data and generate the new samples from the limited training dataset based on many text augmentation techniques such as lexicon substitution, sentence shuffling, back translation, syntax-tree transformation, and embedding mixup. Several experiments have been performed for both well-known machine learning-based classifiers and deep learning models. We compare, analyze, and evaluate the results to indicate the advantage and disadvantage points of the techniques for each approach. The experimental results show that the data augmentation techniques enhance the accuracy of the predictive models; this promises that smart systems can be applied widely in several domains under limited training data. (shrink)

Nowadays, the development of big data is getting faster and faster, and the related research on motion sensing recognition and complex systems under the background of big data is gradually being valued. At present, there are relatively few related researches on vertical Baduanjin in the academic circles; research in this direction can make further breakthroughs in motion sensor recognition. In order to carry out related action recognition research on the lifting action of vertical Baduanjin, this paper uses sensor (...) technology to collect the motion video image of vertical Baduanjin based on the background of big data and uses action recognition technology and related algorithms to obtain the action. Recognize the video image to obtain the data, get the acceleration, angular velocity, and EMG data, and count the end time and duration according to the change of the action. According to the data table and graph change trend compiled at the end of the experiment, we can see the following: after the data is preprocessed, the acceleration signal change range is limited to [−1, 1], and the acceleration change has a clear directionality; and, after 15 lifts of the detected object, its angular velocity in X-axis direction is basically negative. However, when the ninth lift is performed, the angular velocity of the movement in X-axis direction is 36.09, the largest of all angular velocities. When performing the 15th lifting action, the angular velocity of this action in Z-axis direction is −26.05, which is the smallest of all angular velocities. The longest duration of the left muscle discharge during the lifting action of the subject is 15.24 for the tibial anterior muscle and 8.91 for the external oblique muscle with the shortest duration. The longest discharge duration of the right muscle is also the tibial anterior muscle with 12.15, and the shortest duration is the erector spinae with 8.79. (shrink)

This study aims to apply an Artificial Neural Network (ANN) model to forecast the movements of the Qatar Stock Market Index. The empirical analysis is based on a dataset comprising the daily closing prices of the index over the period from November 11, 2019, to December 9, 2021. For the purpose of forecasting, the study adopts a Multilayer Perceptron (MLP) network architecture, trained using the backpropagation algorithm. The modeling process involves data preprocessing, network design and training, and prediction (...) evaluation. The results indicate that the ANN model demonstrates a strong capacity to accurately forecast short-term fluctuations in the Qatar Stock Market Index. The predictive values closely match the actual index values, validating the robustness and reliability of the proposed model. These findings highlight the effectiveness of neural networks in financial time series forecasting and their potential role in supporting decision-making processes in financial. The financial sector is considered one of the leading sectors in modern economies, not only due to its critical role in mobilizing local and foreign savings and financing investments- which form the backbone of economic activity- but also because it has become the most vital connection with the global economy. With its development and financial robustness, this sector has become a benchmark for assessing the soundness of economies and their ability to attract both domestic and foreign capital. (shrink)

Financial markets are always volatile and do not depend on a single macro-economic indicator or economic data but on a whole range of factors, primarily events related to government, geopolitical issues, or corporate earnings, as well as investor sentiment. Unlike traditional quantitative models like time series and econometric models, financial text data is very complex, and most of these models are not suitable for capturing such complexities. Large Language Mod- els (LLMs) are a game changer that has brought (...) forth the effective use of NLP techniques to extract meaning from financial news, earnings reports, and social media sentiments. In this study, we test the efficacy of LLMs (Llama3, Gemma2, RoBERTa) in performing financial segment evaluation and financial market prediction. To improve sentiment classification accuracy, the structured methodology employed consists of data preprocessing, tokenization, and fine- tuning using QLoRA. According to experimental results, Llama3 performs better than other models, achieving an accuracy of 86.1%, RoBERTa attains 85.9%, and Gemma2 gets 84.4%. The study demonstrates that LLMs can effectively capture the sentiment-based market movements however, the challenge lies in the interpretability of the model, removal of the model bias, and performing inference in real time. The study findings further indicate that LLMs look very promising to use in financial forecasting, outperforming conventional models on predicting financial outcomes. (shrink)

This paper aims to introduce a robust framework for forecasting demand, including data preprocessing, data transformation and standardization, feature selection, cross-validation, and regression ensemble framework. Bagging ), boosting and extreme gradient boosting regression ), and stacking are employed as ensemble models. Different machine learning approaches, including support vector regression, extreme learning machine, and multilayer perceptron neural network, are adopted as reference models. In order to maximize the determination coefficient value and reduce the root mean square error, hyperparameters (...) are set using the grid search method. Using a steel industry dataset, all tests are carried out under identical experimental conditions. In this context, STACK1 and STACK2 models provided better performance than other models. The highest accuracies of R2 of 0.97 and 0.97 are obtained using STACK1 and STACK2, respectively. Moreover, the rank according to performances is STACK1, STACK2, XGBR, GBR, RFR, MLP, ELM, and SVR. As it improves the performance of models and reduces the risk of decision-making, the ensemble method can be used to forecast the demand in a steel industry one month ahead. (shrink)

This research aims to carve a path in understanding oral history and biographical interviews by utilizing a robust Natural Language Processing (NLP) model. This model is designed to discern and extract topics from the vast array of interview transcripts housed in the Institut für Geschichte und Biographie’s (IGB) archive “Deutsches Gedächtnis” (ADG). The research methodology has been structured meticulously into six distinct stages, underpinning the end-to-end process flow. It begins with extensive data preprocessing to ensure data quality (...) and consistency, followed by the creation of embeddings to convert the data into a numeric form for additional processing. To optimize computational efficiency, it includes the crucial step of dimension reduction. Subsequently, clustering techniques are employed, serving the dual purpose of grouping similar data and introducing structure into the large volume of data. A Large Language Model (LLM) was deployed for labeling the data, enhancing its comprehensibility and ease of retrieval. Lastly, the methodology employs classification to impose a more refined structure on the data. By diving into the depths of the interviewees’ perspectives, beliefs, and experiences, this research draws out the invaluable nuances hidden within the interviews and presents insightful perspectives on various themes or topics discussed therein. As a result, the ADG is transformed into a well-structured, intuitive, and easily navigable resource, thus significantly enriching its value to researchers and making intricate historical narratives easily accessible for exploration and study. (shrink)

Dementia, a widespread neurodegenerative condition, presents significant challenges in early diagnosis and intervention. This study investigates innovative methods for detecting dementia by analyzing linguistic patterns in speech transcripts through advanced machine learning techniques. Using datasets from DementiaBank, including the Pitt Corpus and ADReSS challenge datasets, we leveraged Large Language Models (LLMs) to assess cognitive and linguistic features. Our methodology encompassed comprehensive data preprocessing, feature extraction from the ‘Cookie Theft’ picture description test, and model fine-tuning. We explored various transfer (...) learning strategies with pre-trained models such as BERT, DistilBERT, RoBERTa, Mistral, and Llama. Our findings indicate that LLMs, particularly when optimized with Low-Rank Adaptation (LoRA) and Quantization (QLoRA), can achieve a dementia detection accuracy of 96%. (shrink)

Traffic monitoring can be supported by stationary or dynamic camera systems. Whereas the analysis of stationary camera data has a long tradition, the analysis of dynamic sensors (recorded by Unmanned Aerial Systems (UAS) or dashcams in cars or trucks) has gained interest in the recent past. This paper proposes a labeling enhancement of a dashcam video repository and the evaluation of several recent deep learning algorithm (LSTM, Inception V3, VGG16, MobileNet V2, etc.) for classifying traffic accidents in dynamic camera (...) systems. Based on fine-tuning methods and data preprocessing steps optimized results for the individual deep learning approaches could be achieved and an overall analysis is presented. (shrink)

Oil and gas consumption for power generation has caused irreversible damage to humanity. To address the attendant effects of fossil fuel utilization, renewable energy is a good alternative. International organizations give support to countries in their transition to a green energy future. This implies that the use of renewable energy is widely supported. It is therefore recommended to utilize renewable energy as it is environmentally friendly. One such type of renewables is water energy. Water cycle has streamflow fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} (...) \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left( {f_{s} } \right)$$\end{document} as its central component. Having reliable information about future fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{s}$$\end{document} data is essential in hydrological research, as it can help water managers to plan hydropower generation. To also ensure preparedness and mitigation of floods and drought as well as hydropower production planning and management, precise fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{s}$$\end{document} prediction is considered essential. Several modelling methods have been used lately to forecast fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{s}$$\end{document}, namely: physical methods, data-driven approaches such as shallow artificial neural networks (ANNs), and hybrid techniques. Nevertheless, they may not approximate complex relationships as accurate as deep learning techniques. In this study, an innovative deep learning technique based on long short-term memory (LSTM) neural network adapted with data preprocessing algorithm (DPA) is proposed for seasonal fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{s}$$\end{document} forecasting. Considering recent studies on fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{s}$$\end{document} forecasting, one can avow that researchers have been able to employ lag value predictors for future fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{s}$$\end{document} extrapolation, although deep learning techniques can offer good potentials for fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{s}$$\end{document} prediction with complex physical relationship. However, to the best knowledge of the authors, only very few studies have applied LSTM neural network for streamflow forecasting. In addition, there have been attempts to estimate river fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{s}$$\end{document} in Nigeria using some traditional methods though, but the effect of seasonal variation on fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{s}$$\end{document} forecasting has never been investigated in Nigeria. This is the maiden research in Nigeria that considers seasonal variation in LSTM neural network model-based fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{s}$$\end{document} forecasting. Accordingly, the novelty and key contribution of our state-of-the-art research is the development and implementation of a low-cost intelligent deep learning model based on the LSTM neural network enhanced with DPA for day-ahead fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{s}$$\end{document} forecasting. To further demonstrate the fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{s}$$\end{document} modelling capability of our technique, we have examined the performances of two different baseline approaches namely, the linear regression (LinReg) model and the adaptive linear element neural network (ADALINE-NN) model. The results of fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{s}$$\end{document} simulation indicated that the proposed LSTM neural network model has the capability to handle varnishing or exploding gradient conundrum. It is highly robust and steady with better accuracy when configured for 24-hour ahead fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{s}$$\end{document} forecasting. The LSTM neural network model outpaced both baseline approaches as model comparisons showed that it has the highest extrapolative accuracy. It presents the lowest RMSE and MAPE and the best NSME and CoC of [2.73, 1.28] m3/s, [11.16, 8.16] %, [0.91, 0.83], and [0.97, 0.95] for the rainy and dry season respectively. As the results of the LSTM neural network approach are observed to be more stable in general, it can be established that the proposed model is a practical daily fs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{s}$$\end{document} forecasting technique for both the rainy and dry season. (shrink)

Background: Detecting and classifying fish diseases is crucial for maintaining the health and sustainability of aquaculture systems. This study employs deep learning techniques, particularly Convolutional Neural Networks (CNNs), to automate the detection of various fish diseases using image data. Methods: The study utilizes a carefully curated dataset sourced from the Kaggle database, comprising images representing seven distinct types of fish diseases, along with images of healthy fish. Data preprocessing techniques, including resizing, rescaling, denoising, sharpening, and smoothing, are (...) applied to enhance image quality and facilitate accurate disease detection. Data augmentation is employed to increase the model's ability to generalize to unseen data. The CNN architecture is designed with cascading convolutional layers, ReLU activation functions, and pooling operations to extract high-level features associated with fish infections. The model architecture, implemented using the Keras Sequential API, includes convolutional layers, max pooling layers, and densely connected layers for classification. Results: Experimental results demonstrated promising performance across various disease categories, with high accuracy and balanced precision and recall values for most classes. The study also discussed the impact of climate change on fish disease incidence and underscores the importance of effective monitoring and management practices facilitated by technological innovations such as Big Data, IoT, sensors, and robotics in ensuring sustainable fisheries management. (shrink)

For efficient energy distribution, microgrids provide significant assistance to main grids and act as a bridge between the power generation and consumption. Renewable energy generation resources, particularly photovoltaics, are considered as a clean source of energy but are highly complex, volatile, and intermittent in nature making their forecasting challenging. Thus, a reliable, optimized, and a robust forecasting method deployed at MG objectifies these challenges by providing accurate renewable energy production forecasting and establishing a precise power generation and consumption matching at (...) MG. Furthermore, it ensures effective planning, operation, and acquisition from the main grid in the case of superior or inferior amounts of energy, respectively. Therefore, in this work, we develop an end-to-end hybrid network for automatic PV power forecasting, comprising three basic steps. Firstly, data preprocessing is performed to normalize, remove the outliers, and deal with the missing values prominently. Next, the temporal features are extracted using deep sequential modelling schemes, followed by the extraction of spatial features via convolutional neural networks. These features are then fed to fully connected layers for optimal PV power forecasting. In the third step, the proposed model is evaluated on publicly available PV power generation datasets, where its performance reveals lower error rates when compared to state-of-the-art methods. (shrink)

Background With the increasing ethical challenges and dilemmas faced by nurses due to various disasters such as COVID-19 worldwide, there is a need for a new public health ethics education curriculum to strengthen competencies for ethical responses in the nursing field. Objectives This study was aimed to identify the impact of a teaching method utilizing news articles and panel discussion material in the public health ethics education program on nursing students’ thinking regarding ethical issues. Design This was an exploratory study (...) to identify the thinking styles inherent in ethical reflection by analyzing the reflection contents written by nursing students using text mining techniques. Participants 73 among the students taking a nursing ethics course at a university in Seoul, South Korea, voluntarily participated in this study after providing informed consent. Methods The public health ethics program was conducted with sessions held once a week for a total of 7 weeks, and reflections written by nursing students were collected as text files during session 5 to 7. In this study, data preprocessing process, keyword analysis, and LDA topic modeling were sequentially conducted utilizing the R program according to the data analysis procedure of text mining techniques. Ethical considerations This study was conducted under ethics approval from the institution where participants were recruited. Findings and discussion The results of this study show that the teaching method utilizing news articles enhanced rational ethical deliberation from the cognitive aspect, whereas the teaching method utilizing panel discussion material strengthened the response to emotions on a more internal level. Conclusions The teaching method utilizing news articles and panel discussion materials in public health ethics education is expected to be mutually complementary and effective, so further studies are recommended. (shrink)

This paper first conducts knowledge point association analysis on a large amount of data collected in practical applications. Data mining includes data collection, data preprocessing, actual mining, and result analysis, establishes knowledge point association rules table, and develops college English diagnostic practice system. Then, starting from the existing paper composition mode of the system, the knowledge point association rule table is introduced, and the knowledge point association relationship mining model is constructed using the association rule (...) algorithm to explore the potential influence relationship between different knowledge points that affect the improvement of learning quality. Finally, the data collected when the system is used is preprocessed, and the three dimensions of learning status evaluation, question-type association analysis, and college English score prediction are, respectively, modeled. Finally, after combining these submodels, a relatively complete and reliable diagnosis is obtained by evaluation model and related verification. (shrink)

Many neuroanatomical alterations have been detected in patients with tinnitus in previous studies. However, little is known about the morphological and structural covariance network changes before and after long-term sound therapy. This study aimed to explore alterations in brain anatomical and SCN changes in patients with idiopathic tinnitus using voxel-based morphometry analysis 24 weeks before and after sound therapy. Thirty-three tinnitus patients underwent magnetic resonance imaging scans at baseline and after 24 weeks of sound therapy. Twenty-six age- and sex-matched healthy (...) control individuals also underwent two scans over a 24-week interval; 3.0T MRI and high-resolution 3D structural images were acquired with a 3D-BRAVO pulse sequence. Structural image data preprocessing was performed using the VBM8 toolbox. The Tinnitus Handicap Inventory score was assessed for the severity of tinnitus before and after treatment. Two-way mixed model analysis of variance and post hoc analyses were performed to determine differences between the two groups and between the two scans. Student-Newman-Keuls tests were used in the post hoc analysis. Interaction effects between the two groups and the two scans demonstrated significantly different gray matter volume in the right parahippocampus gyrus, right caudate, left superior temporal gyrus, left cuneus gyrus, and right calcarine gyrus; we found significantly decreased GM volume in the above five brain regions among the tinnitus patients before sound therapy compared to that in the HC group. The 24-week sound therapy group demonstrated significantly greater brain volume compared with the baseline group among these brain regions. We did not find significant differences in brain regions between the 24-week sound therapy and HC groups. The SCN results showed that the left superior temporal gyrus and left rolandic operculum were significantly different in nodal efficiency, nodal degree centrality, and nodal betweenness centrality after FDR correction. This study characterized the effect of sound therapy on brain GM volume, especially in the left superior temporal lobe. Notably, sound therapy had a normalizing effect on tinnitus patients. (shrink)

This paper adopts Hadoop to build and test the storage and retrieval platform for painting resources. This paper adopts Hadoop as the platform and MapReduce as the computing framework and uses Hadoop Distributed Filesystem distributed file system to store massive log data, which solves the storage problem of massive data. According to the business requirements of the system, this paper designs the system according to the process of web text mining, mainly divided into log data preprocessing (...) module, log data storage module, log data analysis module, and log data visualization module. The core part of the system is the log data analysis module. The analysis of search keywords ranking, Uniform Resource Locator, and user click relationship, URL ranking, and other dimensions are realized through data statistical analysis, and Canopy coarse clustering is performed first according to search keywords, and then K-means clustering is used for the results after Canopy clustering, and the calculation of cosine similarity is adopted to realize the grouping of users and build user portrait. The Hadoop development environment is installed and deployed, and functional and performance tests are conducted on the contents implemented in this system. The constructed private cloud platform for remote sensing image data can realize online retrieval of remote sensing image metadata and fast download of remote sensing image data and solve the problems in storage, data sharing, and management of remote sensing image data to a certain extent. (shrink)

The statistics-based method ignores the semantic constraints in the English grammar area branch training model and is unable to identify the orientation information effectively. This paper systematically discusses the close relationship between English grammar area branch training model filtering, English grammar area branch training model retrieval, and machine learning. By analyzing the role of the situation in the understanding of the English grammar area branch training model, the relationship between the English grammar area branch training model and situation model and (...) the correlation between the features of the English grammar area branch training model and situation model are determined, and then, a set of filtering methods for the English grammar area branch training model are proposed. At present, there are few research studies on bias filtering, and the method of thematic filtering is generally used, which has poor effect. This paper makes full use of the domain knowledge and adopts the semantic pattern analysis technology to establish a wealth of semantic analysis resources, including various dictionaries, rules, and weight representation, so as to effectively filter the inclined English grammar area branch training model. The introduction of semantic data sources solves the problem of data sparsity and cold start in the traditional collaborative filtering system. In addition, in order to improve the scalability and real-time performance of the recommendation system, the data mining method is used to perform fuzzy clustering for users and projects in the offline data preprocessing stage. This paper proposes a search and filter scheme based on the orientation of the training model in English grammar area, elaborates on the details, constructs a whole set of function structure from representation to weight, and gives the experimental results, which prove that the system has a good filtering effect and is fast. Compared with the traditional statistical methods, the results are satisfactory. (shrink)

Ultra-short-term wind speed prediction can assist the operation and scheduling of wind turbines in the short term and further reduce the adverse effects of wind power integration. However, as wind is irregular, nonlinear, and nonstationary, to accurately predict wind speed is a difficult task. To this end, researchers have made many attempts; however, they often use only point forecasting or interval forecasting, resulting in imperfect prediction results. Therefore, in this paper, we developed a prediction system integrating an advanced data (...) preprocessing strategy, a novel optimization model, and multiple prediction algorithms. This combined forecasting system can overcome the inherent disadvantages of the traditional forecasting methods and further improve the prediction performance. To test the effectiveness of the forecasting system, the 10-min and one-hour wind speed sequences from the Sotavento wind farm in Spain were applied for conducting comparison experiments. The results of both the interval forecasting and point forecasting indicated that, in terms of the forecasting capability and stability, the proposed system was better than the compared models. Therefore, because of the minimum prediction error and excellent generalization ability, we consider this forecasting system to be an effective tool to assist smart grid programming. (shrink)

The recent increase in the road transportation necessitates scheduling to reduce the adverse impacts of the road transportation and evaluate the effectiveness of previous actions taken in this context. However, it is impossible to undertake the scheduling and evaluation tasks unless previous information are available to predict the future. The grey model requires a limited volume of data for estimating the behavior of an unknown system. It provides high-accuracy predictions based on few data points. Various grey prediction models (...) have been proposed so far, in which three different approaches are followed to increase the accuracy: (1) data preprocessing, (2) improved equation models, and (3) error improvement or error balancing. In this paper, firstly, a theorem is proposed and proved to recognize the parameters affecting two grey models, namely GM(1, 1) and FGM(1, 1). Then, the effective parameters are adjusted through particle swarm optimization (PSO) to formulate two adjusted models, namely IGM(1, 1) and IFGM(1, 1). According to the simulation results of the proposed models, accuracy of the modeling improved by a minimum of 14.24% and a maximum of 82.39%. Finally, the number of users of a public road transportation system was predicted using the proposed models. The results showed enhanced accuracy (by 7.7%) of the proposed models for predicting the number of users of the public road transportation system. (shrink)

To reduce the workload of paper evaluation and improve the fairness and accuracy of the evaluation process, a writing assistant scoring system for English as a Foreign Language (EFL) learners is designed based on the principle of machine learning. According to the characteristics of the data processing process and the advantages and disadvantages of the Browser/server (B/s) structure, the equipment structure design of the project online evaluation teaching auxiliary system is further optimized. The panda method is used to read (...) the data, the clean method is used to realize the data preprocessing, the model test is carried out, the cross validation method is selected, the data is divided in advance, and the process of programming the problem scoring system is further optimized, the automatic scoring technology is constructed by English teaching recognition module, feature extraction module and scoring module, the table structure of programming problems is designed, the auxiliary evaluation program of English writing is designed, and the design of writing auxiliary scoring system is completed. The analysis of the experimental results shows that the accuracy of the system is close to 90%, and the total average difference is 0.56. The system can normally take out a variety of test papers. Considering the subjectivity of manual scoring and the impact of key code setting on scoring, the carefully set key code can effectively improve the scoring accuracy of the system. The scoring strategy of the automatic scoring system is effective and the scoring effect is good, and it can be used in practical application. (shrink)

Many studies have investigated the relationship between mindfulness and creativity; however, there are a limited number of studies on the neurological basis of this therapeutic approach using electroencephalogram (EEG). This study aimed at evaluating the effect of mindfulness on improving the creativity of healthy individuals. In this study, 7 healthy subjects (1 male and 6 females) with a mean age of 40.37 years and a standard deviation of 14.52 years received group mindfulness training for 8 weeks. They had no experience (...) of mindfulness training up to that time. Before and after mindfulness training, EEG signal was recorded from all participants in eyes-closed and eyes-open conditions on Fz, C3, C4, and Pz electrodes. After data preprocessing, wavelet coefficients were extracted from each frequency band of EEG signal and evaluated using paired sample t-test and correlation methods. The gamma-band on C3 (t = 2.89, p=0.03) and Pz (t= 2.54, P = 0.04) significantly increased as a result of mindfulness training. Also, significant correlations were found between the anxiety and the gamma band in Pz (r = 0.76, P = 0.04) and Fz (r = 0.75, P = 0.04) channels and between arousal and the gamma band in the Fz channel (r=0.88, P = 0.008). Mindfulness training to promote creativity leads to the increase of gamma bands in the central and parietal regions. (shrink)

The experience of the use of applied containerized biomedical software tools in cloud environment is summarized. The reproducibility of scientific computing in relation with modern technologies of scientific calculations is discussed. The main approaches to biomedical data preprocessing and integration in the framework of the intelligent analytical system are described. At the conditions of pandemic, the success of health care system depends significantly on the regular implementation of effective research tools and population monitoring. The earlier the risks of (...) disease can be identified, the more effective process of preventive measures or treatments can be. This publication is about the creation of a prototype for such a tool within the project «Development of methods, algorithms and intelligent analytical system for processing and analysis of heterogeneous clinical and biomedical data to improve the diagnosis of complex diseases», implementted by the V.M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, together with the United Institute of Informatics Problems, National Academy of Sciences of Belarus. The insurers, entering the market, can insure mostly low risks by facilitating more frequent changes of insurers by consumers and mixing the overall health insurance market. Socio-demographic variables can be risk adjusters. Since age and gender have a relatively small explanatory power, other socio-demographic variables were studied – marital status, retirement status, disability status, educational level, income level. Because insurers have an interest in beneficial diagnoses for their policyholders, they are also interested in the ability to interpret relevant information – upcoding: insurers can encourage their policyholders to consult with doctors more often to select as many diagnoses as possible. Many countries and health care systems use diagnostic information to determine the reimbursement to a service provider, revealing the necessary data. For processing and analysis of these data, software implementations of construction for classifiers, allocation of informative features, processing of heterogeneous medical and biological variables for carrying out scientific research in the field of clinical medicine are developed. The experience of the use of applied containerized biomedical software tools in cloud environment is summarized. The reproducibility of scientific computing in relation with modern technologies of scientific calculations is discussed. Particularly, attention is paid to containerization of biomedical applications, this permits to get reproducibility of the conditions in which the calculations took place, technologies of software pipelining of calculations, that allows to organize flow calculations, and technologies for parameterization of software environment, that allows to reproduce, if necessary, an identical computing environment. The main approaches to biomedical data preprocessing and integration in the framework of the intelligent analytical system are described. The experience of using the developed linear classifier, gained during its testing on artificial and real data, allows us to conclude about several advantages provided by the containerized form of the created application: it permits to provide access to real data located in cloud environment; it is possible to perform calculations to solve research problems on cloud resources both with the help of developed tools and with the help of cloud services; such a form of research organization makes numerical experiments reproducible, i.e. any other researcher can compare the results of their developments on specific data that have already been studied by others, in order to verify the conclusions and technical feasibility of new results; there exists a universal opportunity to use the developed tools on technical devices of various classes from a personal computer to powerful cluster. (shrink)

Home cage monitoring (HCM) represents a paradigm shift in rodent behavioural research, replacing brief manual observations with continuous digital surveillance that captures animal behaviour in its natural context. This technological revolution generates vast amounts of data, offering both unprecedented opportunities and substantial analytical challenges. Effective HCM research depends on careful experimental design that often recognises the cage, rather than individual animals, as the fundamental experimental unit, with profound implications for statistical power. By adjusting cage density and employing randomised block (...) designs, researchers can maximise statistical sensitivity while minimizing animal use.Translating continuous behavioural streams into meaningful biological insights requires sophisticated yet transparent preprocessing. Moving averages reveal patterns obscured by moment-to-moment variability, while normalisation techniques enable comparisons across experiments and facilities. Heatmaps elegantly capture weeks of circadian rhythms and behavioural changes in single, interpretable displays. The statistical framework emphasises linear mixed-effects models that account for correlated repeated measurements, alongside rhythmometric techniques extracting circadian parameters. A major strength of HCM is its capacity for continuous welfare assessment through composite indices that combine multiple physiological and behavioural streams, enabling early interventions and advancing refinement goals.The chapter concludes by exploring how emerging machine learning approaches can enhance pattern detection while maintaining the statistical rigor and biological interpretability essential for translational research. Overall, HCM emerges not merely as a data collection technology but as a comprehensive framework for ethical, statistically robust behavioural phenotyping that advances all three Rs (Replacement, Reduction, and Refinement) of animal research through continuous digital biomarkers, proactive welfare monitoring, and efficient experimental designs. (shrink)

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional...

In recent years, image classification has undergone significant advancements, notably through the integration of techniques inspired by principles from quantum mechanics. This chapter introduces an image classification model that combines neural network principles with concepts from quantum computing. The process encompasses two main stages: data preprocessing and image classification. During the data preprocessing phase, the original dataset is scaled and normalized. The proposed quantum neural network (QNN) approaches are then applied to the processed dataset. The proposed (...) QNN is based on using quantum circuits instead of traditional convolution filters. A quantum neural network typically consists of three components: an encoder, a quantum layer, and a measurement. The encoder component is formed from a list of gates that are passed. This part has four RY gates, one for each qubit. Then, the ansatz with six trainable parameters and Hadamard non-parameterized circuits is used in the quantum layer. Finally, each qubit is assessed using Pauli-Z. To evaluate the performance of the proposed model, four benchmark datasets are used: two medical datasets (BreastMNIST and PneumoniaMNIST) and two nonmedical datasets (FashionMNIST and MNIST). The experimental results revealed that, unlike the state-of-the-art models, the proposed image classification model demonstrated good classification accuracy despite the small input image size. Furthermore, the results showed that the proposed QNN approach can lead to a considerable improvement in the performance of traditional neural networks. (shrink)

Semantic legal data offers the basis for a methodical examination of legal provisions and is vital for comprehending and deciphering legal regulations. Nevertheless, manually adding semantic metadata to sizable criminal datasets is expensive and time-consuming. The cutting-edge study addresses two essential troubles: the requirements engineering (RE) literature lacks a standardized framework for semantic metadata types relevant to prison requirements evaluation, and there may be insufficient automatic guide for extracting those metadata types, especially while using deep learning (DL) and Natural (...) language processing (NLP) capabilities. To address those problems, a comprehensive framework was first created via reviewing and integrating the semantic criminal metadata categories determined inside the RE literature. After that, an automated extraction technique that makes use of NLP was created to help realize and examine legal texts, enabling to find and categorize critical metadata. A Binary Moth-Flame Optimized Dynamic recurrent neural Network (BMFO-DRNN) is then used to broaden an automatic extraction approach for the specified metadata lessons. To increase the satisfactory and relevance of the entered statistics, preprocessing techniques, which include tokenization, stemming, lemmatization, and word embeddings like Word2Vec, were used for characteristic extraction. Experimental result shows the BMFO-DRNN model outperforms traditional methods, achieving accuracy (95%), F1-score (92%), precision (93%), and recall (96%). In addition to demonstrating the price of NLP in automated semantic analysis legal metadata, this work additionally shows how NLP should improve the efficacy and performance of legal evaluation in an effort to assist criminal informatics advancement. (shrink)

We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with (...) class='Hi'>data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model. (shrink)

High-dimensional data analysis has become the most challenging task nowadays. Dimensionality reduction plays an important role here. It focuses on data features, which have proved their impact on accuracy, execution time, and space requirement. In this study, a dimensionality reduction method is proposed based on the convolution of input features. The experiments are carried out on minimal preprocessed nine benchmark datasets. Results show that the proposed method gives an average 38% feature reduction in the original dimensions. The algorithm (...) accuracy is tested using the decision tree, support vector machine, and K-nearest neighbor classifiers and evaluated with the existing principal component analysis algorithm. The average increase in accuracy is 8.06 for DT, 5.80 for SVM, and 18.80 for the KNN algorithm. The most significant characteristic feature of the proposed model is that it reduces attributes, leading to less computation time without loss in classifier accuracy. (shrink)

Duhem’s problem arises especially in scientific contexts where the tools and procedures of measurement and analysis are numerous and complex. Several philosophers of cognitive science have cited its manifestations in fMRI as grounds for skepticism regarding the epistemic value of neuroimaging. To address these Duhemian arguments for skepticism, I offer an alternative approach based on Deborah Mayo’s error-statistical account in which Duhem's problem is more fruitfully approached in terms of error probabilities. This is illustrated in examples such as the use (...) of probabilistic brain atlases, comparison of different preprocessing protocols with respect to their error characteristics, and statistical modeling of fMRI data. These examples demonstrate the ways in which we can better understand and formulate the general methodological problem and direct the way toward alternative approaches to neuroimaging in philosophy of cognitive science, in which we can be more balanced and productive in our scrutiny of the epistemic value of neuroimaging studies. (shrink)

& Functional brain imaging offers new opportunities for the begin with single-subject (preprocessed) scan series, and study of that most pervasive of cognitive conditions, human consider the patterns of all voxels as potential multivariate consciousness. Since consciousness is attendant to so much encodings of phenomenal information. Twenty-seven subjects of human cognitive life, its study requires secondary analysis from the four studies were analyzed with multivariate of multiple experimental datasets. Here, four preprocessed methods, revealing analogues of phenomenal structures, datasets from the (...) National fMRI Data Center are considered: particularly the structures of temporality. In a second Hazeltine et al., Neural activation during response competi- interpretive approach, artificial neural networks were used tion; Ishai et al., The representation of objects in the human to detect a more explicit prediction from phenomenology, occipital and temporal cortex; Mechelli et al., The effects of namely, that present experience contains and is inflected by presentation rate during word and pseudoword reading; and past states of awareness and anticipated events. In all of 21 Postle et al., Activity in human frontal cortex associated with subjects in this analysis, nets were successfully trained to spatial working memory and saccadic behavior. The study of extract aspects of relative past and future brain states, in consciousness also draws from multiple disciplines. In this comparison with statistically similar controls. This exploratory article, the philosophical subdiscipline of phenomenology study thus concludes that the proposed methods for provides initial characterization of phenomenal structures ‘‘neurophenomenology’’ warrant further application, includ- conceptually necessary for an analysis of consciousness. These ing the exploration of individual differences, multivariate structures include phenomenal intentionality, phenomenal differences between cognitive task conditions, and explora- superposition, and experienced temporality.. (shrink)

Theories of superior collicular and hippocampal function have remarkable similarities. Both structures have been repeatedly implicated in spatial and attentional behaviour and in inhibitory control of locomotion. Moreover, they share certain electrophysiological properties in their single unit responses and in the synchronous appearance and disappearance of slow wave activity. Both are phylogenetically old and the colliculus projects strongly to brainstem nuclei instrumental in the generation of theta rhythm in the hippocampal EECOn the other hand, close inspection of behavioural and electrophysiological (...) data reveals disparities. In particular, hippocampal processing mainly concerns stimulus ambiguity, contextual significance, and spatial relations or other subtle, higher order characteristics. This requires the use of largely preprocessed sensory information and mediation of poststimulus investigation. Although collicular activity must also be integrated with that of “higher” centres, its primary role in attention is more “peripheral” and specific in controlling orienting/localisation via eye and body movements toward egocentrically labelled spatial positions. In addition, the colliculus may exert a nonspecific influence in alerting higher centres to the imminence of information potentially worthy of focal attention. Nevertheless, it is noteworthy that collicular and hippocampal lesions produce deficits on similar tasks, although the type of deficit is usually different in each case. Functional overlap between hippocampus and colliculus is virtually certain vis-à-vis stimulus sampling, for example in the acquisition of information via vibrissal movements and visual scanning. In addition, insofar as stimulus significance is a factor in collicular orienting mechanisms, the hippocampus — cingulate – cortex — colliculus pathway may play a significant role, modulating collicular responsiveness and thus ensuring an attentional strategy appropriate to current requirements. A tentative “reciprocal loop” model is proposed which bridges physiological and behavioural levels of analysis and which would account for the observed degree and nature of functional overlap between the superior colliculus and hippocampus. (shrink)

One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study (...) is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery. (shrink)

Linguistic predictions may be generated from and evaluated against a representation of events and referents described in the discourse. Compatible with this idea, recent work shows that predictions about novel noun phrases include their definiteness. In the current follow-up study, we ask whether people engage similar prediction-related processes for definite and indefinite referents. This question is relevant for linguistic theories that imply a processing difference between definite and indefinite noun phrases, typically because definiteness is thought to require a uniquely identifiable (...) referent in the discourse. We addressed this question in an event-related potential (ERP) study (N = 48) with preregistration of data acquisition, preprocessing, and Bayesian analysis. Participants read Dutch mini-stories with a definite or indefinite novel noun phrase (e.g., “het/een huis,” the/a house), wherein (in)definiteness of the article was either expected or unexpected and the noun was always strongly expected. Unexpected articles elicited enhanced N400s, but unexpectedly indefinite articles also elicited a positive ERP effect at frontal channels compared to expectedly indefinite articles. We tentatively link this effect to an antiuniqueness violation, which may force people to introduce a new referent over and above the already anticipated one. Interestingly, expectedly definite nouns elicited larger N400s than unexpectedly definite nouns (replicating a previous surprising finding) and indefinite nouns. Although the exact nature of these noun effects remains unknown, expectedly definite nouns may have triggered the strongest semantic activation because they alone refer to specific and concrete referents. In sum, results from both the articles and nouns clearly demonstrate that definiteness marking has a rapid effect on processing, counter to recent claims regarding definiteness processing. (shrink)

Statistics constitute the social universe of which they are gathered. The foundation necessary to develop quantified knowledge about society is the population. If quantified knowledge changes society, the question arises on how individuals become to be represented as population. The population has to be extracted from individuals in a process that we call “populationisation.” This encompasses the development of the individual into a segment of a population through the compilation of individual data into population data and its analysis. (...) To describe the process of populationisation, we follow a statistical risk assessment tool, the German Diabetes Risk Score, from its inception in a cohort study to its entry into the public sphere of German society. The population is extracted from individuals that function as research subjects through an identification number. Preprocessed information is entered into a dynamic database that enables its rearrangement according to main aggregates. Through populationisation, a signified is enacted that can be sliced up and that is equal in all its parts. Now predictions can be computed and fed back to society as tools of moral judgment. The acting individual and “society” are—numerically and literally!—entangled in the production and enactment of risk knowledge. (shrink)

Slagging-off is an important preprocessing operation of steel-making to improve the purity of iron. Current manual-operated slag removal schemes are inefficient and labor-intensive. Automatic slagging-off is desirable but challenging as the reliable recognition of iron and slag is difficult. This work focuses on realizing an efficient and accurate recognition algorithm of iron and slag, which is conducive to realize automatic slagging-off operation. Motivated by the recent success of deep learning techniques in smart manufacturing, we introduce deep learning methods to (...) this field for the first time. The monotonous gray value of industry images, poor image quality, and nonrigid feature of iron and slag challenge the existing fully convolutional networks. To this end, we propose a novel spatial and feature graph convolutional network module. SFGCN module can be easily inserted in FCNs to improve the reasoning ability of global contextual information, which is helpful to enhance the segmentation accuracy of small objects and isolated areas. To verify the validity of the SFGCN module, we create an industrial dataset and conduct extensive experiments. Finally, the results show that our SFGCN module brings a consistent performance boost for a wide range of FCNs. Moreover, by adopting a lightweight network as backbone, our method achieves real-time iron and slag segmentation. In the future work, we will dedicate our efforts to the weakly supervised learning for quick annotation of big data stream to improve the generalization ability of current models. (shrink)

This paper presents a novel application of trace clustering techniques to judicial data analysis, addressing the high variability and complexity of judicial workflows, an issue that has received limited attention in prior research. Leveraging real-world data from three Brazilian Small Claims Court units, the proposed method integrates preprocessing, data encoding, and clustering algorithms to segment judicial cases into more homogeneous groups and reveal representative behavioral patterns. These patterns facilitate process simplification and enable more effective analysis. The (...) identified clusters corresponded to distinct types of judicial procedures, and their derived process models were evaluated from both structural and behavioral perspectives. Validation by legal domain experts confirmed the method’s effectiveness, highlighting its potential to expose procedural inefficiencies and enhance comprehension of judicial flows. This study contributes empirical evidence supporting the suitability of trace clustering for analyzing flexible and highly variable processes in the judicial domain. Furthermore, the proposed method offers a generalizable framework applicable to other jurisdictions and domains, providing a scalable solution for simplifying complex event logs and supporting data-driven decision-making in process management. (shrink)

Functional neurological disorder (FND), historically referred to as hysteria, is a contested illness characterised by heterogeneous and often co-occurring neurological symptoms, such as seizures, abnormal movements, and paralysis. Its diagnosis remains challenging due to the disorder’s complexity and lack of standardised procedures. Recent neuroimaging research has sought to link FND symptoms to brain dysfunction, with three pioneering studies using resting-state functional magnetic resonance imaging (fMRI) to train machine learning (ML) classifiers for diagnostic purposes. These studies have aimed to identify biomarkers (...) for future clinical application, while also providing new insights into FND’s neuropathophysiology that have eluded traditional data analyses. Combining perspectives from science and technology studies and media studies, this paper critically examines how researchers navigate the technical constraints of resting-state fMRI when constructing the datasets to train ML classifiers for diagnosing FND. Through a close reading of the published studies, it analyses operations performed during patient selection, resting-state data acquisition, preprocessing, and connectivity modelling to translate FND’s clinical complexity into ML-ready datasets. Drawing on Annemarie Mol’s concept of ontological politics, it argues that these operations enact specific versions of FND as a condition diagnosable through individual patients’ brain connectivity patterns, while excluding multiple key aspects of the disorder as algorithmically illegible. Whereas the studies present ML classifiers as tools to support future clinical decision-making, they overlook the political implications of inadvertent diagnostic exclusions these systems enact. The paper concludes that the development of neuroimaging diagnostic ML classifiers for FND and related conditions demands critical scrutiny of the new diagnostic realities they enact and their implications for patients. (shrink)

With the sustained prosperity of the Chinese film market and the deepening development of internet social platforms, Douban Movie, as one of the most influential film review platforms in China, has accumulated a vast amount of user-generated content (UGC), serving as a critical data source for understanding audience film consumption behaviors and emotional attitudes. However, traditional manual analysis methods are limited in both efficiency and depth when confronted with massive volumes of unstructured review text. This study employs text mining (...) techniques, integrating Latent Dirichlet Allocation (LDA) topic modeling with SnowNLP sentiment analysis, to systematically analyze 4,306 user reviews from the Douban Movie platform spanning 2020 to 2025. The original dataset encompasses reviews of 484 films (412 after preprocessing). Following preprocessing steps including text cleaning, jieba word segmentation, stopword filtering, and synonym normalization, the optimal number of topics was determined using the CV coherence metric, ultimately identifying five core thematic dimensions: Narrative Structure and Plot Evaluation, Acting Performance and Character Portrayal, Audio-Visual Effects and Production Quality, Social Value and Cultural Expression, and Viewing Experience and Platform Interaction. Sentiment analysis results indicate that audiences exhibit the highest emotional engagement with narrative structure and social value themes, with positive review proportions of 42.3% and 38.7%, respectively. The audio-visual effects theme shows the highest proportion of negative reviews at 58.2%, reflecting elevated audience expectations for the technical quality of domestic Chinese films. Annual trend analysis reveals fluctuating audience sentiment from 2020 to 2025, with a notable increase in review activity during 2023–2024. This study constructs a text mining analytical framework applicable to Chinese-language film reviews, providing data-driven empirical support for content creation and marketing decision-making in the film industry. (shrink)

Flooding is one of the most damaging natural hazards globally. During the past three years, floods have claimed hundreds of lives and millions of dollars of damage in Iran. In this study, we detected flood locations and mapped areas susceptible to floods using time series satellite data analysis as well as a new model of bagging ensemble-based alternating decision trees, namely, bag-ADTree. We used Sentinel-1 data for flood detection and time series analysis. We employed twelve conditioning parameters of (...) elevation, normalized difference’s vegetation index, slope, topographic wetness index, aspect, curvature, stream power index, lithology, drainage density, proximities to river, soil type, and rainfall for mapping areas susceptible to floods. ADTree and bag-ADTree models were used for flood susceptibility mapping. We used software of Sentinel application platform, Waikato Environment for Knowledge Analysis, ArcGIS, and Statistical Package for the Social Sciences for preprocessing, processing, and postprocessing of the data. We extracted 199 locations as flooded areas, which were tested using a global positioning system to ensure that flooded areas were detected correctly. Root mean square error, accuracy, and the area under the ROC curve were used to validate the models. Findings showed that root mean square error was 0.31 and 0.3 for ADTree and bag-ADTree techniques, respectively. More findings illustrated that accuracy was obtained as 86.61 for bag-ADTree model, while it was 85.44 for ADTree method. Based on AUC, success and prediction rates were 0.736 and 0.786 for bag-ADTree algorithm, in order, while these proportions were 0.714 and 0.784 for ADTree. This study can be a good source of information for crisis management in the study area. (shrink)

By reviewing the evolution and applications of computer‑vision models, this paper systematically analyzes the beneficial impact of prior knowledge on machine cognition—namely, improved data efficiency, enhanced robustness, and increased interpretability. Vision models exploit a rich set of a priori image properties—spatial locality, translational invariance, and hierarchical organization—by embedding these priors, explicitly or implicitly, into network architecture, preprocessing pipelines, and regularization terms. Such incorporation enables models to achieve high accuracy and clearer internal representations even when training datasets are scarce. (...) In parallel, we examine the cognitive constraints and potential risks of relying on a priori knowledge. Overly strong priors can restrict the expressive range of machine cognition, introduce subjectivity, and pose difficulties in formally representing the acquired knowledge. This dual perspective underscores both the promise and the pitfalls of integrating prior knowledge into visual‑perception systems. (shrink)

In this paper, the multifeature fusion music classification algorithm and its simulation results are studied by deep confidence networks, the multifeature fusion music database is established and preprocessed, and then features are extracted. The simulation is carried out using multifeature fusion music data. The multifeature fusion music preprocessing includes endpoint detection, framing, windowing, and pre-emphasis. In this paper, we extracted the rhythm features, sound quality features, and spectral features, including energy, cross-zero rate, fundamental frequency, harmonic noise ratio, and (...) 12 statistical features, including maximum value, mean value, and linear slope. A total of 384-dimensional statistical features was extracted and compared with the classification ability of different emotional features. The deficiencies of the traditional classification algorithm are first studied, and then by introducing confusion, constructing multilevel classifiers, and tuning each level of the classifier, better recognition rates than traditional primary classification are obtained. This paper introduces label information for supervised training to further improve the features of multifunctional fusion music. Experiments show that this information has excellent performance in multifunctional fusion music recognition. The experiments compare the multilevel classifier with primary classification, and the multilevel classification with the primary classification and the classification performance is improved, and the recognition rate of the multilevel classification algorithm is also improved over the multilevel classification algorithm, proving that the excellent performance with multiple levels of classification. (shrink)