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Analysing samples in their original form is increasingly crucial in analytical chemistry due to the need for efficient and sustainable practices. Analytical chemists face the dual challenge of achieving accuracy while detecting minute analyte quantities in complex matrices, often requiring sample pretreatment. This necessitates the use of advanced techniques with low detection limits, but the emphasis on sensitivity can conflict with efforts to simplify procedures and reduce solvent use. This article discusses the shift towards green analytical methods, focusing on portable spectroscopic techniques in the near-infrared (NIR) region. A case study involving the prediction of adulteration in almond flour with bitter almond flour illustrates the importance of particle size and the integration between the sample and the instrument. The study emphasizes the necessity of investigating the multivariate error associated with raw data to enhance data preprocessing strategies. This research provides valuable insights for professionals in the field, presenting a methodology applicable to a broad range of analytical applications while underscoring the critical role of raw data analysis in achieving accurate and reliable results. [ABSTRACT FROM AUTHOR]

To shed light on the dynamics of psychological processes, researchers often collect intensive longitudinal (IL) data by asking people to repeatedly report on their momentary experiences in daily life. Two important decisions when designing an IL study concern the number of persons and the number of measurement occasions to be included. These sample size decisions are ideally based on statistical power considerations. When conducting statistical power analysis, it is necessary to provide the value of the effect size of interest as well as of all other model parameters. In IL research, these values have to be based on previous studies. This implies that these values are subject to large heterogeneity due to differences in study design and preprocessing choices. This between-study heterogeneity can severely impact power-based sample size recommendations. In this article, we introduce an approach to investigate the impact of study design and pre-processing of previous studies and to determine a recommended sample size to account for this impact. We demonstrate how to use this approach to investigate the effect of different construct operationalizations, study duration, and preprocessing choices. This approach paves the way for more thoughtful and robust sample-size decisions. [ABSTRACT FROM AUTHOR]

The rapid increase in digital content, particularly in text-based tasks like news classification, has significantly amplified the demand for automated classification methods. At this point, Natural Language Processing (NLP) techniques offer the potential to efficiently generate results from large datasets without human intervention. This study presents a Naive Bayes (NB)-based classification system, developed using Python, aimed at categorizing news headlines. NB algorithms are favored for text classification problems due to their simplicity and fast computation. The dataset used, derived from BBC News headlines, covers diverse categories such as technology, business, sports, entertainment, and politics. The data preprocessing phase included steps such as text cleaning, removing stop words, and converting the text into numerical data using Count Vectorization. This process plays a critical role in ensuring accurate and effective classification. Five different NB variants were examined in this study: Gaussian, Multinomial, Complement, Bernoulli, and Tree-Augmented Naive Bayes (TAN). The results showed that Multinomial NB delivered the best performance with an accuracy rate of 98.53%. Complement NB achieved 98.31%, TAN 98.20%, Bernoulli 96.74%, while Gaussian NB ranged between 91.79% and 92.92%. Additionally, NB algorithms were compared with advanced machine learning algorithms such as Logistic Regression, Random Forest, Linear Support Vector Classifier, and Multi-Layer Perceptron. The Multi-Layer Perceptron stood out with an accuracy rate of 98.31%, while the other algorithms also surpassed 97% accuracy. This study demonstrates that NB algorithms provide a robust, reliable, and effective solution for news classification problems, with the Multinomial and Complement variants showing particularly high accuracy. Future research will aim to further enhance the performance of these algorithms using larger datasets and new approaches. [ABSTRACT FROM AUTHOR]

Deformation is one of the critical response quantities reflecting the structural safety of dams. To enhance outlier identification and denoising in dam deformation monitoring data, this study proposes a novel preprocessing method based on optimized Variational Mode Decomposition (VMD) and Kernel Density Estimation (KDE). The approach systematically processes data in three steps: First, VMD decomposes raw data into intrinsic mode functions without recursion. The parallel Jaya algorithm is used to adaptively optimize VMD parameters for improved decomposition. Second, the intrinsic mode functions containing outlier and noise characteristics are identified and separated using sample entropy and correlation coefficients. Finally, KDE thresholds are applied for outlier localization, while a data superposition method ensures effective denoising. Validation using simulated deformation data and Global Navigation Satellite Systems (GNSS)-based observed horizontal deformation from dam engineering demonstrates the method's robustness in accurately identifying outliers and denoising data, achieving superior preprocessing performance. [ABSTRACT FROM AUTHOR]

Predicting traffic speed is an important issue, especially in urban regions. Precise long-term forecasts would enable individuals to conserve time and financial resources while diminishing air pollution. Despite extensive research on this subject, to our knowledge, no publications investigate or tackle the issue of imbalanced datasets in traffic speed prediction. Traffic speed data are often biased toward high numbers because low traffic speeds are infrequent. The temporal aspect of traffic carries two important factors for low-speed value. The daily population movement, captured by the time of day, and the weather data, recorded by month, are both considered in this study. Hour-wise Pattern Organization and Month-wise Pattern Organization techniques were devised, which organize the speed data using these two factors as a metric with a view to providing a superior representation of data characteristics that are in the minority. In addition to these two methods, a Speed-wise Pattern Organization strategy is proposed, which arranges train and test samples by setting boundaries on speed while taking the volatile nature of traffic into consideration. We evaluated these strategies using four popular model types: long short-term memory (LSTM), gated recurrent unit networks (GRUs), bi-directional LSTM, and convolutional neural networks (CNNs). GRU had the best performance, achieving a MAPE (Mean Absolute Percentage Error) of 13.51%, whereas LSTM demonstrated the lowest performance, with a MAPE of 13.74%. We validated their robustness through our studies and observed improvements in model accuracy across all categories. While the average improvement was approximately 4%, our methodologies demonstrated superior performance in low-traffic speed scenarios, augmenting model prediction accuracy by 11.2%. The presented methodologies in this study are applied in the pre-processing steps, allowing their application with various models and additional pre-processing procedures to attain comparable performance improvements. [ABSTRACT FROM AUTHOR]

The accurate prediction of the next location in a sequence is highly beneficial for users of mobile applications. In this study, we investigate how various data preprocessing techniques affect the performance of location recommendation systems. We utilize datasets from Foursquare and Twitter, incorporating users' historical check-ins. Key preprocessing steps include filtering datasets to users with common features, analyzing user location preferences, varying sequence lengths and location categories, and integrating time-of-day information. Our findings reveal that proper data preprocessing significantly enhances the accuracy of recommendations by addressing key challenges such as data sparsity and user heterogeneity. Specifically, tailoring datasets to individual user attributes improves model personalization, while restructuring category hierarchies balances precision and diversity in the recommendations that are given. Integrating temporal data further refines the predictions that are made by accounting for time-based user behavior. Recommendations are generated using recurrent neural networks (RNNs) and hidden Markov models (HMMs), with the experimental results showing up to 20% improvement in the precision of personalized models compared to global ones. [ABSTRACT FROM AUTHOR]

Background: Fluorescent Timer proteins, which display time-dependent changes in their emission spectra, are invaluable for analyzing the temporal dynamics of cellular events at the single-cell level. We previously developed the Timer-of-cell-kinetics-and-activity (Tocky) tools, utilizing a specific Timer protein, Fast-FT, to monitor temporal changes in cellular activities. Despite their potential, the analysis of Timer fluorescence in flow cytometry is frequently compromised by variability in instrument settings and the absence of standardized preprocessing methods. The development and implementation of effective data preprocessing methods remain to be achieved. Results: In this study, we introduce the R package that automates the data preprocessing of Timer fluorescence data from flow cytometry experiments for quantitative analysis at single-cell level. Our aim is to standardize Timer data analysis to enhance reproducibility and accuracy across different experimental setups. The package includes a trigonometric transformation method to elucidate the dynamics of Fluorescent Timer proteins. We have identified the normalization of immature and mature Timer fluorescence data as essential for robust analysis, clarifying how this normalization affects the analysis of Timer maturation. These preprocessing methods are all encapsulated within the TockyPrep package. Conclusions: TockyPrep is available for distribution via GitHub at https://github.com/MonoTockyLab/TockyPrep, providing tools for data preprocessing and basic visualization of Timer fluorescence data. This toolkit is expected to enhance the utility of experimental systems utilizing Fluorescent Timer proteins, including the Tocky tools. [ABSTRACT FROM AUTHOR]

With the proliferation of Internet of Things (IoT) devices across various applications, for example, smart homes, drones, and healthcare, the security vulnerabilities have also increased, necessitating robust network intrusion detection systems (NIDS). This study focuses on the classification of cyberattacks, including denial of service (DoS), worms, and backdoor attacks, from normal network traffic using the UNSW-NB15 dataset. machine learning (ML) and deep learning (DL) models, such as decision tree (DT) classifier, K-nearest-neighbor (KNN) classifier, linear regression, linear support vector machine, logistic regression (LR), multi-layer perceptron (MLP), and random forest (RF), were employed for both binary and multi-class classification. Data preprocessing involved handling null values, one-hot encoding categorical variables, and normalizing numerical features. Feature selection was performed using the Pearson correlation coefficient method, reducing the dataset attributes significantly. The models demonstrated high accuracy in detecting anomalies, with the RF classifier achieving the highest accuracy of 98.64% for binary classification and notable performance across multi-class classifications. This study underscores the effectiveness of ML techniques in enhancing IoT network security and offers comprehensive insights. [ABSTRACT FROM AUTHOR]

The importance of enhancing the accuracy of time-series forecasting using artificial intelligence tools is increasingly critical in light of the rapid advancements in modern technologies, particularly deep learning and neural networks. These approaches have already shown considerable advantages over traditional methods, especially due to their capacity to efficiently process large datasets and detect complex patterns. A crucial step in the forecasting process is the preprocessing of time-series data, which can greatly improve the training quality of neural networks and the precision of their predictions. This paper introduces a novel preprocessing technique that integrates information from both the time and frequency domains. To achieve this, the authors developed a feature extraction–extension scheme, where the extraction component focuses on obtaining the phase and amplitude of complex numbers through fast Fourier transform (FFT) and the extension component expands the time intervals by enriching them with the corresponding frequency characteristics of each individual time point. Building upon this preprocessing method, the FFT-LSTM forecasting model, which combines the strengths of FFT and Long Short-Term Memory (LSTM) recurrent neural networks, was enhanced. The simulation of the improved FFT-LSTM model was carried out on two time series with distinct characteristics. The results revealed a substantial improvement in forecasting accuracy compared to established methods in this domain, with about a 5% improvement in MAE and RMSE, thereby validating the effectiveness of the proposed approach for forecasting applications across various fields. [ABSTRACT FROM AUTHOR]

Android malware detection using artificial intelligence today is a mandatory tool to prevent cyber attacks. To address this problem in this paper the proposed methodology consists of the application of genetic programming symbolic classifier (GPSC) to obtain symbolic expressions (SEs) that can detect if the android is malware or not. To find the optimal combination of GPSC hyperparameter values the random hyperparameter values search method (RHVS) method and the GPSC were trained using 5-fold cross-validation (5FCV). It should be noted that the initial dataset is highly imbalanced (publicly available dataset). This problem was addressed by applying various preprocessing and oversampling techniques thus creating a huge number of balanced dataset variations and on each dataset variation the GPSC was trained. Since the dataset has many input variables three different approaches were considered: the initial investigation with all input variables, input variables with high feature importance, application of principal component analysis. After the SEs with the highest classification performance were obtained they were used in threshold-based voting ensembles and the threshold values were adjusted to improve classification performance. Multi-TBVE has been developed and using them the robust system for Android malware detection was achieved with the highest accuracy of 0.98 was obtained. [ABSTRACT FROM AUTHOR]

The development and maturation of TBM (tunnel boring machine) technology have significantly improved the accuracy and richness of excavation data, driving advancements in intelligent tunneling research. However, challenges remain in managing data noise and parameter coupling, limiting the interpretability of traditional machine learning models regarding TBM parameter relationships. This study proposes a cutterhead rotation speed prediction model based on dimensional analysis. By utilizing boxplot methods and low-pass filtering techniques, excavation data were preprocessed to select appropriate operational and mechanical parameters. A dimensionless model was established and integrated with elastic net regression to quantify parameters. Using TBM cluster data from a water diversion tunnel project in Xinjiang, the accuracy and generalizability of the model were validated. Results indicate that the proposed model achieves high prediction accuracy, effectively capturing trends in cutterhead rotation speed while demonstrating strong generalizability. [ABSTRACT FROM AUTHOR]

Traffic prediction is essential for transportation planning, resource allocation, congestion management and enhancing travel experiences. This study optimizes data preprocessing techniques to improve machine learning-based traffic prediction models. Data preprocessing is critical in preparing the data for machine learning models. This study proposes an approach that optimizes data preprocessing techniques, focusing on flow-based analysis and optimization, to enhance traffic prediction models. The proposed approach explores fixed and variable orders of data preprocessing using a genetic algorithm across five diverse datasets. Evaluation metrics such as root mean squared error (RMSE), mean absolute error (MAE) and R-squared error assess model performance. The results indicate that the genetic algorithm's variable order achieves the best performance for the ArcGIS Hub and Frementon Bridge Cycle datasets, fixed order one preprocessing for the Traffic Prediction dataset and variable order using the genetic algorithm for the PeMS08 dataset. Fixed order 2 preprocessing yields the best performance for the XI AN Traffic dataset. These findings highlight the importance of selecting the appropriate data preprocessing flow order for each dataset, improving traffic prediction accuracy and reliability. The proposed approach advances traffic prediction methodologies, enabling more precise and reliable traffic forecasts for transportation planning and management applications. [ABSTRACT FROM AUTHOR]