Fast dentification of overlapping fluorescence spectra of oil species based on LDA and two-dimensional convolutional neural network.

Although most petroleum oil species can be identified by their fluorescence spectra, overlapping fluorescence spectra make identification difficult. This study aims to address the issue that fluorescence spectroscopy is ineffective in identifying overlapping oil species. In this study, an equivalent model of overlapping oil species with fluorescence spectra was established. The linear discriminant analysis (LDA)-assisted machine learning (ML) algorithms K nearest neighbor (KNN), decision tree (DT), and random forest (RF) improved the identification of fluorescent spectrally overlapping oil species for diesel-lubricant oils. The identification accuracies of two-dimensional convolutional neural network (2DCNN), LDA combined with the ML algorithms effectively all 100 %. Furthermore, Partial Least Squares Regression (PLSR) algorithm, Support Vector Regression (SVR) algorithm, DT regression algorithm, and RF regression algorithm were also used to identify the lubricant concentration in diesel-lubricant oils. The coefficient of determination of the DT was 1, and the root-mean-square error was 0, which identified the concentration of lubricant oils in them accurately and without error.

Unsupervised meta-analysis on chemical elements and atomic energy prediction: A case study on the periodic table.

This paper presents an artificial classification and atomic energy correlation analysis of the chemical components. The choice of data mining is due to its robustness, which can explore intrinsic or hidden relationships between chemical components and their properties. The Mendeleev table is conceivably the earliest example of the data analysis technique in materials science. However, the classical periodic table represents the arrangement of chemical elements based on particular periodicities, which has the issue of property progression for a few chemical components. In this investigation, we utilized one of the unsupervised data mining methods (principal component analysis) to explore knowledge from the chemical components database based on all the prepared properties. The main objective is to make an artificial classification of chemical components depending on their accessible physical and energetic properties. The results revealed the effectiveness of the data mining method in appreciating the relationships between the variables and properties that offer a new approach to seeing a Mendeleev table. The final step of this work highlights the significance of predictive polynomials that permit the scientific community to make atomic total energy predictions for each chemical component, from helium to lawrencium.

Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation.

BACKGROUND: Aging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging study by providing gene expression profile of the entire transcriptome of individual cells. Many interesting information could be inferred from this new type of data with the help of novel computational methods. RESULTS: In this manuscript a novel statistical method, penalized Latent Dirichlet Allocation (pLDA), is applied to an aging mouse blood scRNA-seq data set. A pipeline is built for cell type and aging prediction. The sequence of models in the pipeline take scRNA-seq expression counts as input, preprocess the data using pLDA and predict the cell type and aging status. CONCLUSIONS: pLDA learns a dimension reduced representation of the expression profile. This representation allows identification of cell types and has predictability of the age of cells.

Vibrational spectroscopy data fusion for enhanced classification of different milk types.

The aim of this study is to classify seven types of Irish milk (butter, fresh, heart active, lactose free, light, protein, and slimline), supplied by a specific company, using vibrational spectroscopy methods: Near infrared (NIR), mid infrared (MIR), and Raman spectroscopy. In this regard, chemometric methods were used, and the impact of spectral data fusion on prediction accuracy was evaluated. A total of 105 samples were tested, with 21 used in the test set. The study assessed principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and sequential and orthogonalized partial least squares linear discriminant analysis (SO-PLS-LDA) for classifying different milk types. The prediction accuracy, when applying PLS-DA on individual blocks of data and low-level fused data, did not exceed 85.71 %. However, implementing the SO-PLS-LDA strategy significantly improved the accuracy to 95 %, suggesting a promising method for the development of classification models for milk using data fusion strategies.

FL-DSFA: Securing RPL-Based IoT Networks against Selective Forwarding Attacks Using Federated Learning.

The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks. In this paper, we present a federated learning-based detection technique for detecting selective forwarding attacks, termed FL-DSFA. A lightweight model involving the IoT Routing Attack Dataset (IRAD), which comprises Hello Flood (HF), Decreased Rank (DR), and Version Number (VN), is used in this technique to increase the detection efficiency. The attacks on IoT threaten the security of the IoT system since they mainly focus on essential elements of RPL. The components include control messages, routing topologies, repair procedures, and resources within sensor networks. Binary classification approaches have been used to assess the training efficiency of the proposed model. The training step includes the implementation of machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB). The comparative analysis illustrates that this study, with SVM and KNN classifiers, exhibits the highest accuracy during training and achieves the most efficient runtime performance. The proposed system demonstrates exceptional performance, achieving a prediction precision of 97.50%, an accuracy of 95%, a recall rate of 98.33%, and an F1 score of 97.01%. It outperforms the current leading research in this field, with its classification results, scalability, and enhanced privacy.

A method combining LDA and neural networks for antitumor drug efficacy prediction.

Background: Personalized medicine has gained more attention for cancer precision treatment due to patient genetic heterogeneity in recent years. However, predicting the efficacy of antitumor drugs in advance remains a significant challenge to achieve this task. Objective: This study aims to predict the efficacy of antitumor drugs in individual cancer patients based on clinical data. Methods: This paper proposes to predict personalized antitumor drug efficacy based on clinical data. Specifically, we encode the clinical text of cancer patients as a probability distribution vector in hidden topics space using the Latent Dirichlet Allocation (LDA) model, named LDA representation. Then, a neural network is designed, and the LDA representation is input into the neural network to predict drug response in cancer patients treated with platinum drugs. To evaluate the effectiveness of the proposed method, we gathered and organized clinical records of lung and bowel cancer patients who underwent platinum-based treatment. The prediction performance is assessed using the following metrics: Precision, Recall, F1-score, Accuracy, and Area Under the ROC Curve (AUC). Results: The study analyzed a dataset of 958 patients with non-small cell cancer treated with antitumor drugs. The proposed method achieved a stratified 5-fold cross-validation average Precision of 0.81, Recall of 0.89, F1-score of 0.85, Accuracy of 0.77, and AUC of 0.81 for cisplatin efficacy prediction on the data, which most are better than those of previous methods. Of these, the AUC value is at least 4% higher than those of the previous. At the same time, the superior result over the previous method persisted on an independent dataset of 266 bowel cancer patients, showing the generalizability of the proposed method. These results demonstrate the potential value of precise tumor treatment in clinical practice. Conclusions: Combining LDA and neural networks can help predict the efficacy of antitumor drugs based on clinical text. Our approach outperforms previous methods in predicting drug clinical efficacy.

Guests' Aesthetic experience with lifestyle hotels: An application of LDA topic modelling analysis.

The purpose of this study is to systematically explore lifestyle hotel guests' aesthetic experiences. This study adopts word frequency analysis, latent Dirichlet allocation (LDA) topic modelling analysis and manual coding to systematically analyse 11,239 online reviews posted by guests from 131 lifestyle hotels in eight cities in China. A framework is developed to organize the identified themes and illustrate lifestyle hotel guests' aesthetic experiences. The framework revealed that lifestyle hotels embrace the concept of "bleisure" travel-blending business and leisure by offering high-end lodging, flexible tourism destination elements, and event services that cater to the needs of today's independent guests. The findings suggest that lifestyle hotel guests stress multiple functions of a hotel, especially the spiritual. Guided by the aesthetic experience at lifestyle hotels, hotel managers can cater to the full spectrum of hotel guests' aesthetic experience when implementing marketing strategies.

Pre-Processing of Categorical Features Within Medical Analysis Systems.

The complexity of the cancer problem domain presents challenges not only to the medical analysis systems tasked with its analysis, but also to the users of such systems. While it is desirable to assist users in operating these medical analysis systems, prior groundwork is required before this can be achieved, such as recognising patterns in the way users create certain analyses within these systems. In this paper, we use machine learning algorithms to analyse user behaviour patterns and attempt to predict the next user interaction within the CARESS medical analysis system. Since an appropriate pre-processing scheme is essential for the performance of these algorithms, we propose the usage of a Natural Language Processing (NLP)- inspired approach to preserve some semantic cohesion of the mostly categorical features of these user interactions. Furthermore, we propose to use a sliding window that contains information about the latest user interactions in combination with Latent Dirichlet Allocation (LDA) to extract a latent topic from these last interactions and use it as additional input to the machine learning models. We compare this pre-processing scheme with other approaches that utilise one-hot encoding and feature hashing. The results of our experiments show that the sliding window LDA scheme is a promising solution, that performs better for our use case than the other evaluated pre-processing schemes. Overall, our results provide an important piece for further research and development in the area of assisting users in operating analysis systems in complex problem domains.

Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain-Computer Interface System.

The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain-computer interfaces (BCIs). BCIs allow people with motor impairments to control external devices directly with their brains using electroencephalogram (EEG) patterns that translate brain activity into control signals. Many researchers have been working to develop MI-based BCI recognition systems using various time-frequency feature extraction and classification approaches. However, the existing systems still face challenges in achieving satisfactory performance due to large amount of non-discriminative and ineffective features. To get around these problems, we suggested a multiband decomposition-based feature extraction and classification method that works well, along with a strong feature selection method for MI tasks. Our method starts by splitting the preprocessed EEG signal into four sub-bands. In each sub-band, we then used a common spatial pattern (CSP) technique to pull out narrowband-oriented useful features, which gives us a high-dimensional feature vector. Subsequently, we utilized an effective feature selection method, Relief-F, which reduces the dimensionality of the final features. Finally, incorporating advanced classification techniques, we classified the final reduced feature vector. To evaluate the proposed model, we used the three different EEG-based MI benchmark datasets, and our proposed model achieved better performance accuracy than existing systems. Our model's strong points include its ability to effectively reduce feature dimensionality and improve classification accuracy through advanced feature extraction and selection methods.

A topic modeling-based analysis for the outcomes of psychological contract breaches and violations in organizations: Current research trends and future agenda.

The purpose of this study is to examine the temporal trends of conceptualizations of psychological contract breaches and violations in organizations and their outcomes. Thus, it is envisaged to provide a deep insight into the related topic and to contribute to the elucidation of unexplored aspects by guiding a comprehensive understanding of the perceptual underlying phenomenon. In this study, the topic-modeling method, one of the text-mining methods, using the Latent Dirichlet Allocation (LDA) algorithm was used to gain insights into the main topics on which studies on psychological contract breaches and violations were conducted. Within the framework of the purpose, this study reveals the topics belonging to the concept of psychological contract breaches and violations in organizations. In addition, the titles of these topics, the changes in the interest in these topics over the years, and especially during the COVID-19 outbreak, have been revealed. The 'findings of the study indicate that future research should focus on the affective outcomes of psychological contract breaches and violations in organizations. In addition, future studies can be conducted in which the topics that have reached research saturation in the current study are addressed together with the topics that need research attention.

HGATT_LR: transforming review text classification with hypergraphs attention layer and logistic regression.

Text classification plays a major role in research such as sentiment analysis, opinion mining, and customer feedback analysis. Text classification using hypergraph algorithms is effective in capturing the intricate relationships between words and phrases in documents. The method entails text preprocessing, keyword extraction, feature selection, text classification, and performance metric evaluation. Here, we proposed a Hypergraph Attention Layer with Logistic Regression (HGATT_LR) for text classification in the Amazon review data set. The essential keywords are extracted by utilizing the Latent Dirichlet Allocation (LDA) technique. To build a hypergraph attention layer, feature selection based on node-level and edge-level attention is assessed. The resultant features are passed as an input of Logistic regression for text classification. Through a comparison analysis of different text classifiers on the Amazon data set, the performance metrics are assessed. Text classification using hypergraph Attention Network has been shown to achieve 88% accuracy which is better compared to other state-of-the-art algorithms. The proposed model is scalable and may be easily enhanced with more training data. The solution highlights the utility of hypergraph approaches for text classification as well as their applicability to real-world datasets with complicated interactions between text parts. This type of analysis will empower the business people will improve the quality of the product.

Non-destructive prediction of fertility and sex in chicken eggs using the short wave near-infrared region.

The objective of this study was to evaluate the ability of a handheld near-infrared device (900-1600 nm) to predict fertility and sex (male and female) traits in-ovo. The NIR reflectance spectra of the egg samples were collected on days 0, 7, 14 and 18 of incubation and the data was analysed using principal component analysis (PCA), linear discriminant analysis (LDA) and support vector machines classification (SVM). The overall classification rates for the prediction of fertile and infertile egg samples ranged from 73 % to 84 % and between 93 % to 95 % using LDA and SVM classification, respectively. The highest classification rate was obtained on day 7 of incubation. The classification between male and female embryos achieved lower classification rates, between 62 % and 68 % using LDA and SVM classification, respectively. Although the classification rates for in-ovo sexing obtained in this study are higher than those obtained by chance (50 %), the classification results are currently not sufficient for industrial in-ovo sexing of chicken eggs. These results demonstrated that short wavelengths in the NIR range may be useful to distinguish between fertile and infertile egg samples at days 7 and 14 during incubation.

Exploring climate change discourse on social media and blogs using a topic modeling analysis.

Climate change is one of the most pressing global issues of our time, and understanding public perception and awareness of the topic is crucial for developing effective policies to mitigate its effects. While traditional survey methods have been used to gauge public opinion, advances in natural language processing (NLP) and data visualization techniques offer new opportunities to analyze user-generated content from social media and blog posts. In this study, a new dataset of climate change-related texts was collected from social media sources and various blogs. The dataset was analyzed using BERTopic and LDA to identify and visualize the most important topics related to climate change. The study also used sentence similarity to determine the similarities in the comments written and which topic categories they belonged to. The performance of different techniques for keyword extraction and text representation, including OpenAI, Maximal Marginal Relevance (MMR), and KeyBERT, was compared for topic modeling with BERTopic. It was seen that the best coherence score and topic diversity metric were obtained with OpenAI-based BERTopic. The results provide insights into the public's attitudes and perceptions towards climate change, which can inform policy development and contribute to efforts to reduce activities that cause climate change.

A Latent Dirichlet Allocation approach to understanding students' perceptions of Automated Writing Evaluation.

Automated writing evaluation (AWE) has shown promise in enhancing students' writing outcomes. However, further research is needed to understand how AWE is perceived by middle school students in the United States, as they have received less attention in this field. This study investigated U.S. middle school students' perceptions of the MI Write AWE system. Students reported their perceptions of MI Write's usefulness using Likert-scale items and an open-ended survey question. We used Latent Dirichlet Allocation (LDA) to identify latent topics in students' comments, followed by qualitative analysis to interpret the themes related to those topics. We then examined whether these themes differed among students who agreed or disagreed that MI Write was a useful learning tool. The LDA analysis revealed four latent topics: (1) students desire more in-depth feedback, (2) students desire an enhanced user experience, (3) students value MI Write as a learning tool but desire greater personalization, and (4) students desire increased fairness in automated scoring. The distribution of these topics varied based on students' ratings of MI Write's usefulness, with Topic 1 more prevalent among students who generally did not find MI Write useful and Topic 3 more prominent among those who found MI Write useful. Our findings contribute to the enhancement and implementation of AWE systems, guide future AWE technology development, and highlight the efficacy of LDA in uncovering latent topics and patterns within textual data to explore students' perspectives of AWE.

Nasal Drug Delivery and Nose-to-Brain Delivery Technology Development Status and Trend Analysis: Based on Questionnaire Survey and Patent Analysis.

Nasal administration is a non-invasive method of drug delivery that offers several advantages, including rapid onset of action, ease of use, no first-pass effect, and fewer side effects. On this basis, nose-to-brain delivery technology offers a new method for drug delivery to the brain and central nervous system, which has attracted widespread attention. In this paper, the development status and trends of nasal drug delivery and nose-to-brain delivery technology are deeply analyzed through multiple dimensions: literature research, questionnaire surveys, and patent analysis. First, FDA-approved nasal formulations for nose-to-brain delivery were combed. Second, we collected a large amount of relevant information about nasal drug delivery through a questionnaire survey of 165 pharmaceutical industry practitioners in 28 provinces and 161 different organizations in China. Third, and most importantly, we conducted a patent analysis of approximately 700+ patents related to nose-to-brain delivery, both domestically and internationally. This analysis was conducted in terms of patent application trends, technology life cycle, technology composition, and technology evolution. The LDA topic model was employed to identify technological topics in each time window (1990-2023), and the five key major evolution paths were extracted. The research results in this paper will provide useful references for relevant researchers and enterprises in the pharmaceutical industry, promoting the further development and application of nasal drug delivery and nose-to-brain delivery technology.

Diagnosis of Rotor Component Shedding in Rotating Machinery: A Data-Driven Approach.

The potential for rotor component shedding in rotating machinery poses significant risks, necessitating the development of an early and precise fault diagnosis technique to prevent catastrophic failures and reduce maintenance costs. This study introduces a data-driven approach to detect rotor component shedding at its inception, thereby enhancing operational safety and minimizing downtime. Utilizing frequency analysis, this research identifies harmonic amplitudes within rotor vibration data as key indicators of impending faults. The methodology employs principal component analysis (PCA) to orthogonalize and reduce the dimensionality of vibration data from rotor sensors, followed by k-fold cross-validation to select a subset of significant features, ensuring the detection algorithm's robustness and generalizability. These features are then integrated into a linear discriminant analysis (LDA) model, which serves as the diagnostic engine to predict the probability of rotor component shedding. The efficacy of the approach is demonstrated through its application to 16 industrial compressors and turbines, proving its value in providing timely fault warnings and enhancing operational reliability.

An integrated framework for flood disaster information extraction and analysis leveraging social media data: A case study of the Shouguang flood in China.

Flood disasters cause significant casualties and economic losses annually worldwide. During disasters, accurate and timely information is crucial for disaster management. However, remote sensing cannot balance temporal and spatial resolution, and the coverage of specialized equipment is limited, making continuous monitoring challenging. Real-time disaster-related information shared by social media users offers new possibilities for monitoring. We propose a framework for extracting and analyzing flood information from social media, validated through the 2018 Shouguang flood in China. This framework innovatively combines deep learning techniques and regular expression matching techniques to automatically extract key flood-related information from Weibo textual data, such as problems, floodings, needs, rescues, and measures, achieving an accuracy of 83 %, surpassing traditional models like the Biterm Topic Model (BTM). In the spatiotemporal analysis of the disaster, our research identifies critical time points during the disaster through quantitative analysis of the information and explores the spatial distribution of calls for help using Kernel Density Estimation (KDE), followed by identifying the core affected areas using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm. For semantic analysis, we adopt the Latent Dirichlet Allocation (LDA) algorithm to perform topic modeling on Weibo texts from different regions, identifying the types of disasters affecting each township. Additionally, through correlation analysis, we investigate the relationship between disaster rescue requests and response measures to evaluate the adequacy of flood response measures in each township. The research results demonstrate that this analytical framework can accurately extract disaster information, precisely identify critical time points in flood disasters, locate core affected areas, uncover primary regional issues, and further validate the sufficiency of response measures, therefore enhancing the efficiency in collecting disaster information and analytical capabilities.

Factors influencing contractors' low-carbon construction behaviors in China: a LDA-DEMATEL-ISM approach.

Contractors' low-carbon construction behaviors (CLCB) are pivotal in advancing decarbonization during the construction phase. However, there exists a notable gap in the comprehensive exploration of the multifaceted factors and mechanisms influencing CLCB. Therefore, this study aims to systematically identify the factors influencing CLCB in China, examine the interrelationships among these factors, and pinpoint the key determinants. Based on topic modeling of Latent Dirichlet Allocation (LDA), influencing factors are identified firstly from the pertinent literature. Subsequently, the causality degree and centrality degree between these factors are assessed by the Decision-Making Trial and Evaluation Laboratory (DEMATEL), followed by the establishment of a hierarchical structure using the Interpretive Structural Modeling (ISM) method, culminating in the identification of pivotal factors. Findings reveal that (1) 21 influential factors influencing CLCB are identified. (2) "Incentive policies for relevant stakeholders" and "Low-carbon regulation and supervision" emerge as key influences. (3) CLCB should be guided by policy and subjective awareness, fortified by market and management support, underpinned by technology, and directly driven by economic considerations. This research furnishes valuable insights for promoting low-carbon development during the construction phase, thereby assisting the construction sector in achieving carbon peak and carbon neutrality.

Statistical methods for discrimination of STR genotypes using high resolution melt curve data.

Despite the improvements in forensic DNA quantification methods that allow for the early detection of low template/challenged DNA samples, complicating stochastic effects are not revealed until the final stage of the DNA analysis workflow. An assay that would provide genotyping information at the earlier stage of quantification would allow examiners to make critical adjustments prior to STR amplification allowing for potentially exclusionary information to be immediately reported. Specifically, qPCR instruments often have dissociation curve and/or high-resolution melt curve (HRM) capabilities; this, coupled with statistical prediction analysis, could provide additional information regarding STR genotypes present. Thus, this study aimed to evaluate Qiagen's principal component analysis (PCA)-based ScreenClust® HRM® software and a linear discriminant analysis (LDA)-based technique for their abilities to accurately predict genotypes and similar groups of genotypes from HRM data. Melt curves from single source samples were generated from STR D5S818 and D18S51 amplicons using a Rotor-Gene® Q qPCR instrument and EvaGreen® intercalating dye. When used to predict D5S818 genotypes for unknown samples, LDA analysis outperformed the PCA-based method whether predictions were for individual genotypes (58.92% accuracy) or for geno-groups (81.00% accuracy). However, when a locus with increased heterogeneity was tested (D18S51), PCA-based prediction accuracy rates improved to rates similar to those obtained using LDA (45.10% and 63.46%, respectively). This study provides foundational data documenting the performance of prediction modeling for STR genotyping based on qPCR-HRM data. In order to expand the forensic applicability of this HRM assay, the method could be tested with a more commonly utilized qPCR platform.

Cross-cultural study of hotel practices as perceived by Chinese and North American travellers: A data mining analysis.

This paper aims to unearth the different perception styles of Chinese and North American travellers from analytic versus holistic thinking perspectives. Python was utilized to gather online textual data from Chinese and North American travellers, while word frequency analysis, latent Dirichlet allocation (LDA) topic modelling analysis and content analysis were employed to elucidate the perception styles in a cross-cultural context. In general, North American travellers mainly leaned towards analytic thinking, whereas Chinese travellers showcased a blend of holistic and analytic thought processes. The topic of travel, leisure and accommodation showed both holistic and analytic thinking styles. The topics of nature and environment, front desk service, and travel routes and scenic spot areas mainly represented a holistic thinking style. The topics of convenience and facilities, breakfast, transportation, hotel theme and features, and decoration and amenities mainly suggested an analytic thinking style. Hotels should consider the different perception styles of Chinese and North American travellers to facilitate strategies accordingly and to maximize the experience of travellers from different cultural backgrounds.