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• Explore qualitative aspects of citations to measure the influence of a research article. • Apply a feature representation technique in combination with machine learning models to find the sentiment of citation. • Determine the sentiment of citation instances into positive, negative, or neutral. • Analyze the efficacy of SMOTE in balancing the citation sentiment dataset. Ascertaining the impact of research is significant for the research community and academia of all disciplines. The only prevalent measure associated with the quantification of research quality is the citation-count. Although a number of citations play a significant role in academic research, sometimes citations can be biased or made to discuss only the weaknesses and shortcomings of the research. By considering the sentiment of citations and recognizing patterns in text can aid in understanding the opinion of the peer research community and will also help in quantifying the quality of research articles. Efficient feature representation combined with machine learning classifiers has yielded significant improvement in text classification. However, the effectiveness of such combinations has not been analyzed for citation sentiment analysis. This study aims to investigate pattern recognition using machine learning models in combination with frequency-based and prediction-based feature representation techniques with and without using Synthetic Minority Oversampling Technique (SMOTE) on publicly available citation sentiment dataset. Sentiment of citation instances are classified into positive, negative or neutral. Results indicate that the Extra tree classifier in combination with Term Frequency-Inverse Document Frequency achieved 98.26% accuracy on the SMOTE-balanced dataset. [ABSTRACT FROM AUTHOR]

General physicians misclassify depression in more than half of the cases. Researchers have explored the feasibility of leveraging passively collected data points, also called digital biomarkers, to provide more granular understanding of depression phenotypes as well as a more objective assessment of disease. This paper provides a systematic review following the PRISMA guidelines (Page et al., 2021) to understand which digital biomarkers might be relevant for passive screening of depression. Pubmed and PsycInfo were systematically searched for studies published from 2019 to early 2024, resulting in 161 records assessed for eligibility. Excluded were intervention studies, studies focusing on a different disease or those with a lack of passive data collection. 74 studies remained for a quality assessment, after which 27 studies were included. The review shows that depressed participants' real-life behavior such as reduced communication with others can be tracked by passive data. Machine learning models for the classification of depression have shown accuracies up to 0.98, surpassing the quality of many standardized assessment methods. Inconsistency of outcome reporting of current studies does not allow for drawing statistical conclusions regarding effectiveness of individual included features. The Covid-19 pandemic might have impacted the ongoing studies between 2020 and 2022. While digital biomarkers allow real-life tracking of participant's behavior and symptoms, further work is required to align the feature engineering of digital biomarkers. With shown high accuracies of assessments, connecting digital biomarkers with clinical practice can be a promising method of detecting symptoms of depression automatically. • This paper provides a PRISMA-guided systematic review of studies (2019-2024), showing model accuracy ranges of 0.52-0.98. • The review summarises constructs related to mood, location, smartphone use, communication, sleep quality and heart rate. • To be integrated into clinical practice, captured data needs to be reduced to the most relevant features. [ABSTRACT FROM AUTHOR]

Accurate and swift estimation of the future performance degradation of Proton Exchange Membrane Fuel Cells (PEMFCs) is crucial for the health management of PEMFC. However, most existing studies primarily concentrate on uncovering the inherent patterns of performance degradation parameters, neglecting the fundamental relationships and pattern variations between performance degradation and other characteristic parameters (such as flow rate, temperature, and pressure). This paper seeks to address this research gap. To that end, we delve into feature parameters, extracting underlying patterns of feature evolution to construct a feature exploration-based framework for predicting PEMFC performance degradation. Initially, Time-Varying Filtered Empirical Mode Decomposition (TVF-EMD) is employed to decompose each feature variable, isolating the core information of the features. Furthermore, to reduce the computational burden caused by redundant features, Uniform Manifold Approximation and Projection (UMAP) is used to perform dimensionality reduction on the decomposed feature subsequences, ensuring the efficiency and representativeness of the extracted features. Finally, to overcome the shortcomings of long short-term memory networks (LSTM) in terms of convergence and the effective use of time series information, we introduce an innovative Mogrifier LSTM (M-LSTM) model to enhance the accuracy of PEMFC performance degradation predictions. The experimental and validation results demonstrate that the extraction of feature parameters significantly enhances the predictive precision of the baseline model. Relative to the compared models, there was an improvement of at least 16.859% in Root Mean Square Error (RMSE), a minimum increase of 23.333% in Mean Absolute Error (MAE), and a boost of no less than 0.913% in the coefficient of determination (R 2 ). Simultaneously, the proposed predictive framework demonstrates good stability and accuracy in predicting PEMFC performance degradation. • Developed a novel feature processing framework for predicting the performance degradation of PEMFC. • Employed TVF-EMD in conjunction with UMAP to extract evolving trends and cyclical patterns of feature parameters. • Introduced an innovative M-LSTM network to enhance the interaction between historical information and current inputs. • Feature processing framework improves single-model performance degradation predictions [ABSTRACT FROM AUTHOR]

The existing broiler health monitoring technology has problems such as low automation, unstable monitoring results, and low practical value, making it difficult to provide timely and reliable broiler health monitoring results. The broiler sound signal can provide feedback on their health. A widely validated and correct experience is to analyze the frequency of coughs in a segment of broiler sound signal to determine the health of the broiler group. Based on this, in this paper, the authors proposed a new broiler health monitoring technology based on sound detection. The broiler health monitoring problem is cleverly transformed into a multi-classification problem, which can be solved by identifying the sound types in broiler sound signals. Specifically, the audio signal collection system was designed to complete signal collection and preliminary signal filtering. Wiener filtering was used for deep signal filtering. The 60-dimensional sound features with good performance from three aspects, time-frequency domain, Mel-Frequency Cepstral Coefficients, and sparse representation were extracted, and a preliminary data set was created. Min-max normalization was used to align the numerical distribution of the data set, and a high-quality data set was created. Multi-classification models based on different classification algorithms and neural networks were trained, and the best-performing Random Forest was obtained, thus parameter optimization was carried out, and the optimal multi-classification model was obtained, achieving a classification accuracy of 91.14%. The visualization platform was built to process the classification results of the multi-classification model, completing majority voting processing and cough rate calculation, thereby achieving broiler health monitoring. In addition, the definitions of cough rate and prediction accuracy were newly proposed. A large number of experiments have verified the feasibility of the broiler health monitoring technology proposed in this paper, with an average prediction accuracy of 98.97% achieved. • Newly propose a complete broiler health monitoring technology based on sound detection. • Transform the broiler health monitoring problem into the sound type identification problem. • Newly propose an index of cough rate to evaluate the health of broiler groups. • Newly propose a data quality improvement scheme. • Obtain the highest prediction accuracy of broiler health monitoring in this field, currently. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for estimating the burned area of forest fires using the TabNet transformer-based architecture. Forest fires pose a significant threat to ecosystems, and accurate estimation of the affected area is essential for effective disaster management and resource allocation. We conducted a comprehensive analysis of various Machine Learning (ML) and Deep Learning (DL) methods, including Random Forest, Neural Networks, Neural Architecture Search (NAS), TabNet with Transformers, and Self-Supervised Learning with Autoencoders, to identify the most accurate and efficient model for area estimation. Our experiments employed a publicly available dataset, UCI Forest Fires, containing a combination of meteorological, geospatial, and categorical data. We implemented a thorough preprocessing pipeline that included handling categorical variables, standardization, and feature engineering. The results demonstrate that TabNet outperforms other methods, achieving state-of-the-art accuracy and generalization in predicting the target variable with a Mean Squared Error (MSE) of 2319 in training and 7781 in testing. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) offers significant potential for quality management in production with predictive analytics. Key aspects to building ML models are the selection and engineering of features from data. They allow the usage of relevant data for training ML models. Using the right features consequently improves the quality of the ML models. However, feature engineering requires knowledge of the data, data preprocessing techniques, algorithms, the domain, and use case. Hence, automatic feature engineering tools have become popular. In this paper, we investigate how domain experts and automatic tools compare for engineering features based on a time series dataset from production. [ABSTRACT FROM AUTHOR]

This study introduces an innovative framework designed to forecast the fluctuating short-term generation of photovoltaic (PV) energy in isolated microgrids. The framework relies entirely on solar irradiance data obtained from weather stations located far from the target microgrid. It implements a sophisticated decomposition approach to effectively extract an enriched collection of features from the dataset. Subsequently, a machine learning-based clustering method further enhances the forecasting process by accurately separating the relevant data points. The approach utilizes an advanced two-stage Hybrid Data Linked Model (HDLM) architecture, integrating a Layered Recurrent Neural Framework (LRNF) for prediction and a pattern identification network unit for pattern extraction. This paper demonstrates significant improvements in both the accuracy and effectiveness of estimating PV generation, achieving a mean absolute error of 1.02, a root mean square error of 2.176, and an R-squared score of 0.991. Additionally, the method reduces computing time by 15% after finalizing the input features. A comparative analysis evaluates the superior forecasting capabilities of the HDLM in remote microgrids by benchmarking it against other advanced hybrid deep learning models. The findings highlight the HDLM's potential to greatly enhance and revolutionize the management and operation of renewable energy systems in remote microgrids. [Display omitted] • Forecast remote microgrid PV power with solar irradiance data and feature engineering. • Employed a tailored decomposition approach to unveil significant patterns. • Improved prediction by assigning features using machine learning-based clustering. • Advanced Hybrid Model's feature extraction and prediction outperform other models. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) energy plays a vital role in global renewable energy generation. Accurate and reliable solar energy forecasting is the key to improving energy scheduling, planning, and intelligent decision-making. However, existing research mainly focuses on short-term solar energy forecasting and lacks exploration of long-term forecasting. To fill the research gap, this paper proposes a complementary fused method using GPU and XGBoost models to improve the performance of long-term solar energy hourly forecasting. The proposed method includes data preprocessing, feature engineering, training of GRU and XGBoost models, and fusion of the forecasting results. Based on historical solar energy, solar irradiance and numerical weather prediction (NWP) data (e.g. temperature, wind direction, wind speed, etc.) and features extended by feature engineering are used as input data to forecast the hourly solar energy production for the next ten days. In order to reduce the risk of overfitting individual models, a simple but effective fusion technique is used to combine the forecasting results of the XGBoost and GRU models to enhance the robustness of the model. In the 2022 Tianchi UNiLAB Algorithm Competition Track 3: Renewable Energy Power Generation Forecast, first place was won by us among 513 participating teams. To validate the scalability of the proposed method, experiments were conducted with various forecasting horizons using the competition dataset and the additional GEFCom2014 dataset. The proposed method demonstrates superior performance when compared with the state-of-the-art long-term series forecasting model DLinear, with MSE and MAE metrics relatively reduced by 28.3% and 17.4% in forecasting the future 150 steps. • Ensembled XGBoost and GRU models for long-term solar energy hourly forecasting. • A well-designed feature engineering is introduced in this study. • Achieved first place in the UNiLAB Algorithm Competition Track 3 competition. [ABSTRACT FROM AUTHOR]

Due to the advantages of indicating variable structure and efficient reasoning, Bayesian Networks (BN) have been widely used in data-driven soft sensor applications. However, restricted to linear and conditional Gaussian property, BN-based soft sensors rarely achieve high prediction accuracy. In this paper, an ensemble learning framework – additive dynamic Bayesian networks (ADBN) is proposed for enhanced feature learning, in which Dynamic Bayesian networks are used to learn the conditional independencies among variables and construct feature sets for the following base learners. Additional DBNs are constructed upon the residual information from the past model, to carry out feature learning to fit the residuals. The procedure is repeated and a termination rule from the perspective of feature learning is proposed to end this process, and thus the model complexity can be well restricted. The proposed method is validated on two actual industrial cases. It reveals that the ADBN feature learning method has obtained great improvements. Compared to the single DBN feature engineering method, the root mean square error (RMSE) performance has been improved by 20% and 13% on the two cases, respectively. [ABSTRACT FROM AUTHOR]

Paying attention to the feature engineering problems is the basis for constructing a more accurate building energy consumption prediction model, which helps debug, control, and operate building energy management systems. Therefore, in this paper, an integrated energy consumption prediction model considering spatial characteristics in time series data is proposed to predict the short-term energy consumption of educational buildings, and the influence of features on the model is analyzed using the cooperative game theory SHAP method, and the optimal number of features is determined by ablation analysis. The proposed model is validated by an educational building in Xi'an, Shaanxi Province. The results show that compared with other energy consumption prediction models, the RMSE value of the integrated energy consumption prediction model is reduced by 13.64%–34.55%, and the MAE value is reduced by 10.25%–30.54%, which has higher prediction accuracy. In addition, this paper also investigates the minimum amount of data and the number of features required for the training of the building energy prediction model, and the integrated energy prediction model can still effectively predict building energy consumption when the training samples are minimal and the number of features is appropriate. • Feature engineering method using deep learning to mine features. • Analyzed the impact of features on predictive models using cooperative game theory. • Analyzed the impact of dataset size and number of features on model predictions. • Developed an integrated model for building energy consumption prediction. • The main factors affecting energy consumption in educational buildings are discussed. [ABSTRACT FROM AUTHOR]

Understanding the underlying dynamics of building energy consumption is the very first step towards energy saving in building sector; as a powerful tool for knowledge discovery, data mining is being applied to this domain more and more frequently. However, most of previous researchers focus on model development during the pipeline of data mining, with feature engineering simply being overlooked. To fill this gap, three different feature engineering approaches, namely exploratory data analysis (EDA) as a feature visualization method, random forest (RF) as a feature selection method and principal component analysis (PCA) as a feature extraction method, are investigated in the paper. These feature engineering methods are tested with a building energy consumption dataset with 124 features, which describe the building physics, weather condition, and occupant behavior. The 124 features are analyzed and ranked in this paper. It is found that although feature importance depends on specific machine learning model, yet certain features will always dominate the feature space. The outcome of this study favors the usage of effective yet computationally cheap feature engineering methods such as EDA; for other building energy data mining problems, the method proposed in this study still holds important implications since it provides a starting point where efficient feature engineering and machine learning models could be further developed. [ABSTRACT FROM AUTHOR]

• Proposed model fuses deep learning-based and handcrafted audio features. • The model achieved an accuracy of 92.55 % on the constructed multi-corpus dataset. • The model ensures obtaining the best features using feature selection methods. • The proposed model demonstrated an improvement in classification accuracy of 3 %. In this paper, we introduce an innovative investigation in speech emotion recognition (SER). The proposed model combines deep learning-based and handcrafted audio features to achieve optimal accuracy. The proposed model employs an iterative feature selection and majority voting pipeline to obtain better results by fusing deep learning-based and handcrafted features. The wav2vec2 model and the openSmile audio processing library are used in order to extract audio features from audio data. Then the feature selection and majority voting techniques are used to identify the optimal feature selection methods for diverse feature sets and to combine their strengths. The experiments are performed using a diverse and extensive corpus to ensure the robustness of the proposed method. In the construction of this multi-corpus dataset, we used four well-known benchmark datasets, namely Ravdess, Savee, Crema-D, and Tess. All of these datasets are publicly available. These datasets are combined on six common emotions: sadness, happiness, fear, anger, surprise, and disgust. The resultant dataset comprises 11,511 samples across these categories. The proposed method has been shown to achieve results comparable to those reported in the existing literature. The experimental results indicate that the proposed pipeline leads to a 3 % improvement in classification accuracy. The highest achieved accuracy on the multi-corpus dataset is 92.55 %. [ABSTRACT FROM AUTHOR]

To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor data, if modelled correctly, can be used to generate a situational awareness map. Existing techniques apply traditional supervised learning methods onto historical data, contextual features and route information among different airports to predict flight delay are inaccurate and only predict arrival delay but not departure delay, which is essential to airlines. In this paper, we propose a vision-based solution to achieve a high forecasting accuracy, applicable to the airport. Our solution leverages a snapshot of the airport situational awareness map, which contains various trajectories of aircraft and contextual features such as weather and airline schedules. We propose an end-to-end deep learning architecture, TrajCNN, which captures both the spatial and temporal information from the situational awareness map. Additionally, we reveal that the situational awareness map of the airport has a vital impact on estimating flight departure delay. Our proposed framework obtained a good result (around 18 min error) for predicting flight departure delay at Los Angeles International Airport. [ABSTRACT FROM AUTHOR]

To increase the reliability of motor drive system, many fault diagnosis approaches have been reported with regard to three-phase Pulse Width Modulation Voltage Source Inverter (PWM-VSI). Based on feature engineering and deep neural network, this paper proposes a fault diagnosis approach for the VSI in three-phase Permanent-magnet Synchronous Motor (PMSM) drive. The three-phase current signals are used for the fault diagnosis of VSI. 10 typical signal features are extracted from the three-phase current signals and used as the input of deep neural network. To improve the diagnostic performance, the network structure of the presented deep neural network is designed like a pyramid. Experimental results show that the presented method can detect not only the single open-circuit faults but also the double open-circuit faults in power switches, with high diagnostic accuracy (more than 95%). Besides, comparison results show that the presented method has strong generalization performance. This paper provides theoretical guidance for the fault diagnosis of VSI in PMSM. [ABSTRACT FROM AUTHOR]

Making full use of semantic and structure information in a sentence is critical to support entity relation extraction. Neural networks use stacked neural layers to perform designated feature transformations and can automatically extract high-order abstract feature representations from raw inputs. However, because a sentence usually contains several pairs of named entities, the networks are weak when encoding semantic and structure information of a relation instance. In this paper, we propose a neuralized feature engineering approach for entity relation extraction. This approach enhances the neural network by manually designed features, which have the advantage of using prior knowledge and experience developed in feature-based models. Neuralized feature engineering encodes manually designed features into distributed representations to increase the discriminability of a neural network. Experiments show that this approach considerably improves the performance compared to that of neural networks or feature-based models alone, exceeding state-of-the-art performance by more than 8% and 16.5% in terms of F1-score on the ACE corpus and the Chinese literature text corpus, respectively. • Feature engineering is employed to generate manually designed features to enhance a neural network. • Neutralized feature engineering has the advantage of using prior knowledge and experience developed in feature-based models. • A neutralized feature engineering model is designed to support relation extraction. • This model considerably outperforms the related works more than 8% and 16.5% in terms of F1-score on two open evaluation data sets. [ABSTRACT FROM AUTHOR]

Author profiling consists of extracting their demographic and psychographic information by examining their writings. This information can then be used to improve the reader experience and to detect bots or propagators of hoaxes and/or hate speech. Therefore, author profiling can be applied to build more robust and efficient Knowledge-Based Systems for tasks such as content moderation, user profiling, and information retrieval. Author profiling is typically performed automatically as a document classification task. Recently, language models based on transformers have also proven to be quite effective in this task. However, the size and heterogeneity of novel language models, makes it necessary to evaluate them in context. The contributions we make in this paper are four-fold: First, we evaluate which language models are best suited to perform author profiling in Spanish. These experiments include basic, distilled, and multilingual models. Second, we evaluate how feature integration can improve performance for this task. We evaluate two distinct strategies: knowledge integration and ensemble learning. Third, we evaluate the ability of linguistic features to improve the interpretability of the results. Fourth, we evaluate the performance of each language model in terms of memory, training, and inference times. Our results indicate that the use of lightweight models can indeed achieve similar performance to heavy models and that multilingual models are actually less effective than models trained with one language. Finally, we confirm that the best models and strategies for integrating features ultimately depend on the context of the task. • Study of large language models for conducting Author Profiling in Spanish. • Feature integration improves the performance of large language models. • Interpretability of profiles using linguistic features. • Hyperlinks and hashtag are strong features when discerning between bots and humans. • Stylometry is the linguistic category most relevant in Author Profiling. [ABSTRACT FROM AUTHOR]

Developing a high-performing traffic incident-duration prediction model is considered a key component for evaluating the impact of these incidents on the roadway network. Various research studies have developed robust incident-duration prediction models. Still, they have faced many issues in providing an accurate prediction result due to the countless data modeling issues, such as complex correlations, highly skewed data distributions, heteroscedasticity, and outliers. This paper investigates the impact of feature optimization (FO) - a relatively new term encompassing two already-known topics: feature engineering (FE) and feature selection (FS) techniques - on the performance of several machine learning models developed for predicting incident durations. The models developed included multivariate linear regression, decision trees, support vector regressors, K-Nearest Neighbors regression, ensembles, and artificial neural networks. Various FO techniques have been used for each model to derive the massive traffic incidents dataset and repeat the prediction process. Our results show that the proposed filtering, wrapper, and embedded FS techniques can successfully reduce the number of features without sacrificing the prediction performance. Using log-normal transformation to deal with continuous data skewness, min-max normalization to deal with data variability, and principal component analysis (PCA) to reform the dataset into a smaller independent feature subset, FE techniques can enhance the accuracy of incident duration estimation over the assessed ML models. The best-performing FE technique was the PCA since performance improvements were observed across all developed ML models. The best-performing FS technique was the Recursive Feature Elimination, outperforming other tested techniques in reducing model complexity while maintaining model accuracy. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries age continuously during usage due to their characteristics and the influence of various external factors, but as degradation deepens, it can lead to an apparent decrease in battery safety and reliability. Therefore, predicting remaining useful life from the current to end state and preventing possible dangerous incidents are essential for battery health management. A novel method from the perspective of providing aging features reference and balance prognostics speed and precision is proposed. Firstly, the used dataset is preprocessed in many ways. Then, through feature engineering, 79 features are extracted in the dataset from three perspectives: direct, evolution, statistics, and most representative 16 features are filtered to form the final feature set based on the method of combining discretization and importance. Feature engineering is established to find crucial information within the dataset that correlates highly with remaining life and represents most battery recession paths. Finally, the sparse autoencoder and Transformer integrated approach is proposed to build the life prediction model, and it can learn the temporal relationship between the feature set and remaining cycles quickly and accurately. According to the forecasting results of 62 batteries with different aging conditions, the error can reach 7.43% by only using the feature sequence under 30 cycle lengths. For early life prediction, the error of using the first 100 cycles data is at most 2.6%. • A novel aging feature filtering approach is proposed based on feature engineering. • A prediction model is established by using a sparse autoencoder and Transformer. • More accurate RUL and early life of a battery can be predicted by using fewer cycle data. • After the feature engineering presented in this paper, the final aging feature set selected has strong applicability. [ABSTRACT FROM AUTHOR]

In order to identify the shared subspace between two views, in Canonical Correlation Analysis (CCA), a popular multi-view dimension reduction technique tries to maximize correlation between the views. Although, there are frequently more than two views in many actual applications, it can only process data with two views. Earlier studies, data with more than two viewpoints were managed using either linear correlation or higher degree polynomial correlation. These two forms of correlation – pairwise and high-order – have distinct impacts on perspective consistency, with their effectiveness varying depending on the dataset. In some cases, both correlations can be beneficial, while in others, only one may be relevant. Therefore, leveraging both types of correlations is necessary to achieve flexible and comprehensive view consistency in data analysis. The link between multiview data viewed from diverse perspectives is established by these two types of correlation, that is linear correlation and high order correlation, each of which has a different effect on view consistency. In this paper, we propose a Multi-view Uncorrelated Neighborhood Preserving Embedding (MUNPE), which simultaneously considers two distinct types of correlation to give flexible view consistency. While keeping the local structures of each perspective, the MUNPE also takes into account the complementaries of numerous viewpoints. The MUNPE makes the characteristics gathered by numerous projections for each view uncorrelated in order to get many projections and reduce the duplication of low-dimensional data. Iterative methods are used to resolve the MUNPE, and the algorithm's convergence has been demonstrated. The testing on the Multiple Features with real data sets were successful for MUNPE. It is observed that performance is better than CWMvEF, MULPP, MLLE, GCCA, DTCCA algorithms. [ABSTRACT FROM AUTHOR]

Time-based pricing for residents is regarded as a critical component to facilitate integration of renewable energy, but it remains one of the significant barriers to target high potential customers. Randomly recruiting customers into time-based pricing may lead to customers' bad experiences and utility companies' economic losses. Focusing on this issue, this paper proposes a new customer selection framework for time-based pricing using the smart meter data. To utilize smart meter data effectively, a novel feature engineering procedure is designed, covering both of statistical characteristics and load patterns. Then, a Bayesian neural network (BNN) based prediction model is developed by integrating the Bayesian theory into neural networks for identifying high-potential customers. Finally, the prediction uncertainty is quantified to help utility companies to identify when to trust the prediction results. Case studies on an actual time-based pricing pilot demonstrate the effectiveness of the proposed framework. As for the comprehensive metric F1-score, BNN is in the lead, which is 9.0 % higher than neural network, 6.0 % higher than convolutional neural network (CNN), and 3.3 % higher than long short-term memory (LSTM). In addition, by leveraging the quantified uncertainties, the percentage of low-potential households among selected customers drops by 8.92 %, compared to blindly trusting the prediction results. Furthermore, it's found that load pattern features contribute more to model performance than statistical features, revealing that it's the shape, rather than the amplitude, of daily load curves that determines residential price responsiveness. © 2017 Elsevier Inc. All rights reserved. • A novel feature engineering approach is proposed to dig out information from smart meter data. • A Bayesian neural network-based prediction model is developed to identify customers with great price responsiveness. • The prediction uncertainty is quantified to determine when to trust the prediction results. • The effectiveness of the proposed customer selection framework is validated on an actual time-based pricing dataset. [ABSTRACT FROM AUTHOR]

Although black-box models such as ensemble learning models often provide better predictive performance than intrinsic interpretable models such as logistic regression, black-box models are not still applicable due to the lack of interpretability. Recently, there has been an explosion of work on explainable machine learning techniques, which utilize external algorithms or models to explain the behavior of black-box models. However, it is problematic to explain the black-box model behavior because the explanation provided might not reveal the real mechanism or decision process of black-box models. In this study, instead of using explainable machine learning techniques, an automated feature engineering task was formulated to help logistic regression achieve predictive performance comparable to or even better than black-box models while maintaining interpretability. In this paper, an INterpretable Automated Feature ENgineering (INAFEN) framework was designed for logistic regression. This framework automatically transforms the nonlinear relationships between numerical features and labels into linear relationships, conducts feature cross through association rule mining, and distills knowledge from black-box models. A case study was performed on gastric survival prediction to present the rationality of the feature transformations through INAFEN and benchmark experiments to show the validity of INAFEN. Experimental results on 10 classification tasks demonstrated that INAFEN achieved an average ranking of 2.60 in area under the ROC curve (AUROC), 3.35 in area under the PR curve (AUROC), 3.70 in F1 score and 3.00 in Brier score (among 13 models), outperforming other interpretable baselines and even black-box models. In addition, the interpretability measurement of INAFEN is significantly better than that of black-box models. • An interpretable automated feature engineering framework to improve logistic regression. • Transform non-linear relationships using decision tree-based feature transformation. • Association rule-based feature cross to create new features containing interaction information. • Knowledge distillation generates soft labels from black-box models to guide linear model training. [ABSTRACT FROM AUTHOR]

Optical network performance monitoring technology, which can effectively identify physical impairment in the network, is of great significance to ensure network stability and prevent accidents. In order to establish a high-precision performance monitoring framework for optical networks, a three-step feature engineering and deep attention mechanism approach is proposed in the paper. The main modeling process consists of the following three steps: In Step I, the AAH (Asynchronous Amplitude Histogram) method is used to initially extract features from the original data. In Step II, the KPCA (Kernel Principal Component Analysis) method and the Q-learning method are utilized to reduce the feature dimension and transmit the optimized feature to the downstream predictor. In step III, the downstream predictor based on the LSTM (long short-term memory network) and attention mechanism can effectively construct the mapping between features and labels and realize data prediction. After several groups of comparative experiments, the following conclusions can be obtained: (a) Ablation experiments and component comparisons demonstrate that the proposed framework is able to effectively combine feature engineering and predictors, which can get excellent results. (b) The optical network performance detection framework proposed in the paper achieves better results than three SOTA (state-of-the-art) models and fifteen alternative frameworks. • LSTM-attention is used to establish the mapping between inputs and targets. • A new three-step feature engineering technique is proposed. • The proposed model is compared with eighteen mainstream models. [ABSTRACT FROM AUTHOR]

The global ocean is the largest ecosystem on our planet, the scientific understanding of which is the first step towards the sustainable development of the perpetual ocean. With the continuous advance of observation technology, ocean science has entered the era of big data which significantly facilitates its junction with artificial intelligence (AI). This paper aims to answer the question "can AI serve as a new bridge from oceanic data to knowledge?" by summarizing a large number of investigations and implementing experiments of application examples. First, the AI frameworks of features engineering, classification, and detection, as well as time series forecasting that explores the emerging AI oceanography, are systematically deliberated; Then several case studies with different depths of the ocean are presented to demonstrate the effectiveness of these new data-to-knowledge bridges, including ocean current reconstruction, Arctic sea ice prediction, oceanic eddy identification with profiling data, and deep-sea debris detection for oceanic ecology; Finally, recommendations to strengthen the ocean AI in several typical application areas are proposed. Consensus has been reached that big data science is one of the most dynamic branches in interdisciplinary fields, of which deep blue AI (DBAI) might be one of the most universal methodologies. Consequently, the potential of DBAI needs to be widely and urgently explored and revealed. Taking advantage of exponentially increasing big ocean data, it is highly anticipated that many oceanography problems will be successfully solved by using AI-based knowledge discovery methodologies, through which the DBAI technology itself will be significantly advanced. [Display omitted] • DBAI is becoming a new bridge between ocean data and knowledge. • DBAI is systematically deliberated as features engineering, phenomenon detection, and ocean forecasting. • Selected application examples covering from the sea surface to deep seafloor are provided. • Data is the benchmark on which DBAI will help us save our precious marine environment in many ways. [ABSTRACT FROM AUTHOR]

• A novel machine learning approach is proposed for detecting anomalies in AGC loop. • The proposed approach combines K-means, SMOTE, and multiple SVDD models. • For training, the proposed approach requires only one class data. • Feature Engineering concept is used to devise a new feature. • A financially oriented market operation attack is presented that earn illegal profits. This paper focuses on detecting cyber-attacks targeting the Automatic Generation Control (AGC) loop and market operation. To achieve this, a new data-driven learning algorithm is proposed that ensembles various learning tools such as K-Means clustering, Synthetic Minority Oversampling Technique oversampling, and Support Vector Data Description models as the base learners. Next, this paper devises a new feature variable, EF , which generates a relatively higher value for attack scenarios than normal grid states, thus aiding the classifier in predictions with low false alarms. The proposed approach can detect a wide range of cyber-attacks, including unseen attack cases. This paper further modelled and validated the detection of profit-oriented intelligent market operation attacks, absent in most of the previous works. Such attacks project themselves within the proximity of nominal grid states, making them difficult to predict. The algorithm's performance is finally compared with other learning models, and it is found that the proposed technique has superior prediction with True Positive Rate, True Negative Rate, and Geometric Accuracy as 98.13%, 99.85%, and 98.98%, respectively. [ABSTRACT FROM AUTHOR]

The increasing amount of available data in production systems is associated with great potential for process optimization. Due to lack of a data analytics methodology and low data quality within production these potentials often remain unused. Therefore, in this paper we present a model for data acquisition and data preparation including feature engineering for characteristic sensor signals of production machines. The model allows the extraction of relevant process information from the signal, which can be used for monitoring, KPI tracking, trend analysis and anomaly detection. The approach is evaluated on an industrial turning process. [ABSTRACT FROM AUTHOR]

The growing quality of computer graphics makes it difficult to classify real photos from rendered images. At the same time, a high-quality classifier that separates real-world photos from the images created by computer graphics allow solving many applied problems: from automatic evaluation of the quality of the image to detecting manually modified photos. The main directions of research in this area are associated with the identification of distinctive features in the images for use in classification models. In this paper, we provide experimental studies of various classification models and groups of features on the image dataset from Vkontakte social network. We show that the best choice from a variety of features for solving the classification problem is the combination of Haralick features, Bag of Visual Words (BoVW) with Speeded-Up Robust Features (SURF) descriptors and Local Binary Pattern (LBP) histograms. In addition, we show that Gradient Boosting model being trained on the specified combination of feature groups outperform other machine learning models with 90% accuracy. [ABSTRACT FROM AUTHOR]

With the continuous development of machine learning, enterprises using machine learning methods to mine potential data information has become a hot topic in the research of major insurance companies. In this paper, the features of auto insurance data are analyzed, and the most important features affecting auto renewal are mined. The random forest (RF), gradient lifting tree (GBDT) and lifting machine algorithm (LightGBM) are compared. The test results show that: LightGBM model with the best superiority and robustness. Features of car insurance business channel, NCD, car age and new car purchase price have a greater impact on whether to renew insurance or not. [ABSTRACT FROM AUTHOR]

This paper reports a new knowledge-driven engineered feature learning approach in response to the Global AI Challenge for Building E&M Facilities held by the Electrical and Mechanical Service Department (EMSD) of the Hong Kong SAR. The results were awarded with a Grand Prize by the competition organizer. A dynamically engineered multi-modal feature learning (DEMMFL) method is proposed for predicting the cooling load of two office buildings. The DEMMFL model is estimated with the Lasso-ridge regression and compared with other well-known methods such as the Lasso. The novel approach applies control system knowledge to engineer useful features and explore load patterns for multi-mode modeling. Deep learning methods including LSTM, GRU, and AutoGluon are implemented for automated machine learning and tested in parallel to compare the performance of the proposed model with existing methods. The proposed model is demonstrated to predict long-term cooling load most accurately using engineered features from weather information only. • A new dynamic feature engineering method for office building cooling load prediction. • Slow and fast dynamic features engineered from available weather data only. • Control-knowledge based feature to learn on-hours dynamic responses. • Multi-modal approach with eight day-type features to learn occupancy loads. • Deep recurrent learning and AutoML methods compared to the proposed method. [ABSTRACT FROM AUTHOR]

Demand-side management (DSM) is crucial to smart energy systems. This paper presents a data-driven approach for understanding the relationship between energy consumption patterns and household characteristics to better provide DSM services. The proposed method uses a robust learning fuzzy c-Means clustering algorithm to automatically generate the optimal number of customer groups for DSM, and then uses symmetric uncertainty techniques to identify the identified load patterns and socio-demographic characteristics as the features for training a deep learning model. The model obtained can be used to predict the possibility of DSM group membership for a given household. This approach can be applied even in situations where smart meter data are not available, such as when new customers are added to the system or when residents change, or due to privacy concerns. The proposed model is evaluated comprehensively, including prediction accuracy, comparison with other baselines, and case studies for DSM. The results demonstrate the usefulness of weekly energy consumption data and associated household socio-demographic information for distinguishing between different consumer groups, the effectiveness of the proposed model, and the potential for targeted DSM strategies such as time-of-use pricing, energy efficiency measures, and demand response programs. • Data-driven methods optimize residential energy management. • Robust fuzzy c-Means algorithm for cluster optimization. • Symmetric uncertainty identifies crucial energy factors.. • Deep learning model predicts household energy cluster membership. [ABSTRACT FROM AUTHOR]

In this study, the authors combined the research on loose particle signal and component signal identification with the research on loose particle material identification for the first time, providing comprehensive and reliable loose particle detection results. Based on this, a signal detection and material identification method for loose particles inside sealed relays based on fusion classification model is proposed. Due to the limitations of technical means and confidentiality management, the authors made a real sealed relay sample, and took it as the research object. Through the steps of data acquisition, signal processing, feature engineering, and model training, the dedicated component identification feature library and material identification feature library was constructed, respectively, the component identification model and material identification model based on parameter-optimized SVM with linear kernel and XGBoost was trained, respectively. For the seal relay to be tested, through the steps of data acquisition, signal processing and feature engineering, the data set to be tested was created. The component identification model was used to identify component signals with loose particle signals, and the material identification model was used to identify the materials of loose particles. The majority voting process was used to convert the classification results into identification results, resulting in loose particle detection and material identification results. In addition, the general procedure steps of the proposed method for physical testing were given, and the identification accuracy for device-level loose particle detection was newly proposed. The loose particle testing event containing thirty-seven identification tasks shows that, the achieved identification accuracy was 97.30%, and 92.16% of the average classification accuracy was achieved by the component identification model, 80.41% of the average classification accuracy was achieved by the material identification model. This effectively demonstrates the feasibility and practicality of the proposed method in this paper. It is an important supplement to the loose particle detection research, and provides references for signal detection in similar fields. • First combine the loose particle signal detection and material identification study. • First construct the dedicated feature library and perform feature optimization. • Train the optimal component identification model and material identification model. • Newly add majority voting steps to obtain the identification results. • Newly propose the identification accuracy for device-level loose particle detection. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) connects daily use devices to the Internet, such as home appliances, health care equipment, sensors, and industrial devices. Concurrently, numerous cyber-attacks target those objects and their backbone IoT networks consecutively. Therefore, several researchers have adopted Machine Learning (ML) and Deep Learning (DL) algorithms to develop efficient Intrusion Detection Systems (IDSs). However, the restricted resources of IoT devices hinder integrating those systems with those tiny devices. Hence, designing lightweight IDSs gets more interest from researchers to build efficient detection models to discard attacks in IoT networks. To give a holistic insight into this research domain, this paper presents a Systematic Literature Review (SLR) to review and analyse the recent ML and DL techniques to lighten the IDS models for detecting attacks in IoT devices. In addition, the literature studies were retrieved from six scientific databases Google Scholar, Science Direct, IEEE Xplore®, Scopus, Web of Science, and Springer. From 4,703 identified records, 57 studies were adopted based on predesigned research questions and inclusion/exclusion criteria. The study's findings illustrate the most recently used ML and DL mechanisms and feature engineering techniques to lighten the proposed IDS models. It also shows the most attacks detected, datasets used, tools and network simulators employed, and evaluation metrics and parameters. Furthermore, it suggests the research challenges and future direction after discussing the limitations of the currently proposed techniques. This study shows that most selected studies are journal articles published in IEEE Xplore®. Furthermore, the most used feature engineering techniques are filter-based, as they deliver better performance and lightness than the developed models. Most studies use correlation algorithms as a feature selection technique. Finally, the most discussed attack in the selected studies is the DoS attack. [ABSTRACT FROM AUTHOR]

• Feature construction for fire detection based on thermal images. • Characterization of the response of thermal imaging sensors in fire scenarios. • Data-driven fuzzy modeling with high transparency and interpretability. • Low complexity models with low computational load for implementation. Fire outbreaks are a serious risk in campsites due to the surroundings and dynamic environment of these areas. Due to climate change, conditions of high ignition propensity are becoming more frequent, leading to an increased need for the development of alternative fire prevention systems that can mitigate the consequences of fire incidents. In this context, this work explores thermal imaging data for early detection of fire outbreaks, aiming for application in a real context. To that end, experimental trials were conducted in laboratory and at the venue of a summer festival under real operation conditions. The datasets acquired are characterized in detail, and a feature engineering process is devised for the analysis of the response of thermal imaging sensors to a fire ignition. To deal with high-dimensional data, the feature construction method follows a statistical color-based approach, that characterizes the dynamic changes in the data using three features. Subsequently, this paper proposes a fuzzy modeling approach based on these variables, which is transparent to interpretation and enables the assessment of patterns modeled. The performance of the classification algorithm for detection of fire outbreaks is evaluated and framed with works in the state-of-the-art for this application. [ABSTRACT FROM AUTHOR]

Highlights • Line-scanning hyperspectral imaging for weedy rice and rice recognition. • Spectral features were automated extracted. • The performances of random forests and support vector machines were compared. • Important spectral features were selected for weed and rice recognition. • Recognition rates ranged from 0.92 to 1.0. Abstract Barnyard grass (Echinochloa crusgalli) and weedy rice (Oryza sativa f. spontanea) are two common and troublesome weed species in rice (Oryza sativa L.) crop. They cause significant yield loss in rice production while it is difficult to differentiate them for site-specific weed management. In this paper, we aimed to develop a classification model with important spectral features to recognize these two weeds and rice based on hyperspectral imaging techniques. There were 287 plant leaf samples in total which were scanned by the hyperspectral imaging systems within the spectral range from 380 nm to 1080 nm. After obtaining hyperspectral images, we first developed an algorithmic pipeline to automatically extract spectral features from line scan hyperspectral images. Then the raw spectral features were subjected to wavelet transformation for noise reduction. Random forests and support vector machine models were developed with the optimal hyperparameters to compare their performances in the test set. Moreover, feature selection was explored through successive projection algorithm (SPA). It is shown that the weighted support vector machine with 6 spectral features selected by SPA can achieve 100%, 100%, and 92% recognition rates for barnyard grass, weedy rice and rice, respectively. Furthermore, the selected 6 wavelengths (415 nm, 561 nm, 687 nm, 705 nm, 735 nm, 1007 nm) have the potential to design a customized optical sensor for these two weeds and rice discrimination in practice. [ABSTRACT FROM AUTHOR]

In the context of energy transition in Germany, precise load forecasting enables reducing the impact of increased volatility in power generation induced by renewable energies. This paper presents a machine learning approach to generate a 15 minutes forecasting model of the electric load for the ETA research factory at TU Darmstadt on a factory level. In the first iteration, a feature selection process was conducted to select significant features for machine learning datasets. The raw data contained 1,554 features from machine tools, technical building equipment, the building itself and external factors like the weather. The second iteration examined the forecasting capabilities of six hyperparameter tuned algorithms on the feature selected datasets. In the third iteration, feature engineering and hyperparameter tuning led to an optimized Gradient Boosting Regression Trees (GBRT) algorithm. The results indicate that the utilized machine learning approach is feasible and creates a precise very short term load forecasting model, depending on the use case of the load forecast. [ABSTRACT FROM AUTHOR]

Abstract Stock price modeling and prediction have been challenging objectives for researchers and speculators because of noisy and non-stationary characteristics of samples. With the growth in deep learning, the task of feature learning can be performed more effectively by purposely designed network. In this paper, we propose a novel end-to-end model named multi-filters neural network (MFNN) specifically for feature extraction on financial time series samples and price movement prediction task. Both convolutional and recurrent neurons are integrated to build the multi-filters structure, so that the information from different feature spaces and market views can be obtained. We apply our MFNN for extreme market prediction and signal-based trading simulation tasks on Chinese stock market index CSI 300. Experimental results show that our network outperforms traditional machine learning models, statistical models, and single-structure(convolutional, recurrent, and LSTM) networks in terms of the accuracy, profitability, and stability. [ABSTRACT FROM AUTHOR]

• A deep convolutional neural network model based on multiple pattern representation algorithms dynamic was proposed for the fault diagnosis. • The multiple pattern representation achieves an increase in the thickness of the data matrix, making the feature information embedded in it more specific. • The model proposed in this paper has outstanding performance in fault diagnosis. • Achieved an average fault diagnosis rate of 96.1% in the TE processes and 98.1% in the semi-batch crystallization processes. Fault diagnosis rate (FDR) and fault diagnosis timeliness (FDT) are critical to a fault diagnosis model applied to industrial processes. Generally, high FDR requires process data of longer time, which means a low timeliness; while, the time-length reduction of data will result in a decreased FDR as learning features from less data is more difficult. To address this problem, an idea is proposed that the time-length reduction of process data could be compensated by enhancing the data "thickness", i.e., the shortage of feature information of data could be compensated by investigating more patterns from multiple different aspects of the data. Based on this, the multiple pattern representation-convolutional neural network (MPR-CNN) is proposed, in which multiple pattern representation algorithms are used first to pre-extract features from data. The resulting three-dimensional matrix contains richer features, which could be learned more efficiently by the CNN. The diagnosis results from a continuous and a batch chemical process show that MPR-CNN significantly improves the fault diagnosis performance, which achieved 96.1% of FDR in the Tennessee Eastman process and 98.1% of FDR in a semi-batch crystallization process, and outperforms other CNN structure-based models. [Display omitted] [ABSTRACT FROM AUTHOR]

Feature engineering (or creation of new features) that strengthens the importance of fundamental variables (e.g., cloud cover, partial shading, solar irradiance) is essential in gaining an accurate PV forecast for different use cases. On-grid and off-grid PV systems for residential prosumers, and larger scale PV systems are investigated from the PV forecast point of view. Therefore, in this paper, we propose a robust and scalable method designed for PV systems with various sizes and connectivity that provides accurate predictions. The originality of our research consists in feature engineering and combining the deterministic and stochastic models into a meta-learning Stacked Ensemble Forecast (SEF) method that is properly adjusted considering the connectivity of the PV system. The predictions of several standout Machine Learning (ML) algorithms are stacked and scaled to create an accurate forecast model. The proposed methodology is tested on three case studies that cover different types of PV systems: small (residential) on-grid, off-grid, and industrial PV power plants. In all cases, the SEF model provides accurate predictions with an R2 between 0.91 and 0.99 and MAPE between 0.1 and 0.29, followed closely by the stochastic models that have an average R2 of 0.85 and MAPE of 0.3. The deterministic model provides less accurate predictions with an average R2 of 0.8 and MAPE of 0.37. Furthermore, RMSE and MAE improved with more than 16% using the SEF model compared to the stochastic models and with more than 45% compared to the deterministic model. • Forecast for on-grid/off-grid PV systems for prosumers and larger scale PV systems. • Robust PV forecasting method applied for six PV systems with various connectivity. • Feature engineering: effective solar irradiance, combination and analytical features. • A stacked forecasting method combining of deterministic and stochastic models. [ABSTRACT FROM AUTHOR]

• A novel swin-based feature engineering model has been presented. • A new CT image dataset was collected. • Swin-PHOG-LPQ is a lightweight model and an advanced signal processing model. • Our proposal attained over 95% classification accuracies. Computed tomography (CT) image classification has been the subject of intense research in the area of biomedical image classification with the objective of developing intelligent disorder detection models. In this paper, we aim to detect three disorders in lung CT images: hemothorax, contusion, and pneumothorax. Deep learning models are particularly effective for computer vision tasks. Thus our second goal is to propose a new hand-modeled image classification model that achieves high performance using the shifted windows (swin) architecture. We collected a new lung CT image dataset containing four classes – hemothorax, contusion, pneumothorax, and control – with 2730 CT images. Our proposed swin architecture-based CT image classification model is designed to extract features from patches using the Pyramidal histogram-oriented gradient (PHOG) and local phase quantization (LPQ) methods for directional and textural features. We utilized an iterative neighborhood component analysis (INCA) feature selector for feature selection and classified the chosen features using the k-nearest neighbors (kNN) classifier with 10-fold cross-validation. Finally, majority voting was employed to obtain the final classification. Our proposed Swin-PHOG-LPQ achieved a classification accuracy of 95.53%. We also evaluated our model on two publicly available CT image datasets and achieved classification accuracies of 95.31% and 97.63%, respectively. The high classification accuracies obtained by our proposed Swin-PHOG-LPQ model demonstrate its efficacy in detecting the three disorders in lung CT images. [ABSTRACT FROM AUTHOR]

Online reviews significantly impact consumers who are purchasing or seeking services via the Internet. Businesses and review platforms need to manage these online reviews to avoid misleading customers through fake ones. This necessitates developing intelligent solutions to detect these fake reviews and prevent their negative impact on businesses and customers. Therefore, many fake review detection models have been proposed to help distinguish fake reviews from genuine ones. However, these techniques depend on a limited perspective of features, mainly review content, to detect fake reviews, leading to poor performance in discovering the new patterns of fake review content and the dynamic behaviour of spammers. Therefore, there is still a need to develop new solutions to detect the new patterns of fake reviews. Hence, this paper proposes an explainable multi-view deep learning model to identify fake reviews based on different feature perspectives and classifiers. The proposed model can extract essential features from different perspectives, including review content, reviewer data, and product description. Moreover, we employ an ensemble approach that combines three popular deep learning algorithms: Bi-LSTM, CNN, and DNN, to enhance the performance of the fake review detection model. The results of two real-life datasets presented demonstrated the efficiency of our proposed model, where it outperformed the state-of-the-art methods with improvements ranging from 1% to 7% in terms of the AUC metric. To provide visibility into the outcomes of our proposed model and demonstrate the trust and transparency in the obtained results, we also offer a comprehensive explanation for our model results using Shapely Additive Explanations (SHAP) method and attention techniques. The experimental results prove that our proposed model can provide reasonable explanations that help users understand why specific reviews are classified as fake. [ABSTRACT FROM AUTHOR]

• Scientific theory of pitting corrosion is firstly integrated into machine learning model. • The prediction model of maximum pitting corrosion depth is established via automatic model generation. • A real-world pitting corrosion dataset is employed to verify the superiority of the proposed model. • The proposed model achieves better accuracy and efficiency than other models. Accurate monitoring of pipeline corrosion is important and necessary not only for the normal operation of oil and gas pipelines but also for the reliable and stable supply of energy. To avoid failures of buried steel pipelines, the precise prediction of maximum pitting corrosion depth should be conducted to prevent accidents. In this paper, an automatic machine learning (AML) based approach is developed to automate the construction of corrosion depth prediction model. The engineering theory and domain knowledge are integrated into feature engineering, which is an important part of the machine learning (ML) modeling process, to overcome the drawback of the conventional modeling method of ML. Subsequently, a novel prediction method, so-called theory-guided AML (Tg-AML) is proposed for the maximum depth prediction of pitting corrosion pipeline. Firstly, several new feature variables are constructed based on the corrosion mechanism (empirical model and interaction between input variables). Then, seven different feature subsets are developed based on correlation analysis. To select the suitable feature subset and verify the superiority of Tg-AML, a real-world pitting corrosion dataset is utilized for performance comparison based on evaluation metrics. After acquiring the suitable feature subset, the maximum pitting corrosion depth prediction model that fits the data and is guided by both engineering theory and domain knowledge is established. The results indicate that the proposed model achieves better accuracy and efficiency than other models, such as neural network, and decision tree, with root mean square error (RMSE) being 0.288, mean absolute error (MAE) being 0.174, confidence index (Cl) being 0.933. [ABSTRACT FROM AUTHOR]

Construction productivity estimation lacks a comprehensive, standard, and task-type-independent framework to generate and serialize Machine Learning (ML) models. This research aims to develop an ML management framework for estimating the work crew productivity (crew outputs over their working hours) by addressing various operation and project types. The framework takes advantage of historical data, including information regarding operations' progress, weather conditions, the number of resources, and their composition in a work crew. Daily work reports are used as a principal source of historical data. Various hyperparameters-tuned ML algorithms are adopted and ranked based on their computational complexity and prediction accuracy. The generated productivity prediction models have the flexibility to be reused for the effective planning of various construction projects. Applying the proposed framework to a case study of nine disciplines provided estimation models with high accuracies. This study also discusses the theoretical and practical implications of the presented model development procedure. • This paper provides a ML-based framework for construction crew productivity prediction. • The framework aims to address various operation and project types using daily work reports. • Factors, such as site conditions, resource quantity and their work crew composition are considered. • Various techniques and hyperparameters-tuned ML algorithms are developed and adopted. • The generated models can be reused for the effective planning of various construction projects. [ABSTRACT FROM AUTHOR]

State of health (SOH) estimation is essential for lithium-ion battery systems to ensure safe and reliable operation. The existing SOH estimation considers a few available signals, such as voltage and current, to extract specified and limited capacity-related features. Once the cell or materials is changed, features require manual re-built as the construction is specific and unsystematic. This paper proposes a novel aging information-based feature engineering framework for SOH diagnosis, which combines a comprehensive feature library driven by three-step construction strategy and an automatic feature selection pipeline fused with embedded-based and filter-based methods. In the feature space, the role played by each feature type and the extent to which the combination of features affects SOH estimation are explored by accuracy and robustness. For the collected datasets, a library of 206 features is generated as inputs for feature selection which eventually output a space with 7 features to track SOH change. These features perform well under all three typical machine learning models, with the maximum absolute error within 1% and the root mean square error (RMSE) below 0.29% for all cells of transfer operations. Compared to the existing literature using the features of discharge capacity differences between two cycles [ΔQ(V) curve], the RMSE is reduced by up to 85.1%. The approach is automated to produce a highly robust feature subset for accurate SOH estimation across usage protocols and multiple battery chemistries due to the wide range of feature sets and the superiority of feature selection. • A comprehensive feature library driven by a three-step construction method is generated. • An automatic feature selection pipeline for generating an algorithm-free feature subset is developed. • The performance of feature space-based SOH estimation is verified by three machine learning models on cross-service cells. • The role played by each feature type and the influence of feature combination on SOH are investigated. [ABSTRACT FROM AUTHOR]

As the importance of personal data privacy increases, traffic encryption has become an important topic in network communication. In the field of network security and management, the development of encrypted traffic classification technology has drawn attention to deep learning methods. For raw encrypted traffic, deep learning models can realize end-to-end classification with high accuracy. However, deep learning methods do not explain which part of the encrypted traffic is critical to classification, and that will limit their application in some cyber security scenarios that demand high interpretability. The approach proposed in this paper features a novel feature engineering method named "BITization" to determine the encoding method of features and a sliding window technique to explore which bytes contribute the most to the classification. The accuracy of classical machine learning methods in encrypted traffic is improved by at least 14.1% through the proposed feature engineering approach. In the experiments, the enhanced methods achieve a 98.6% average accuracy and a 98.5% average F1-score on the ISCX-VPN-Service, Cross-Platform-IOS, Cross-Platform-Android, and USTC-TFC2016 datasets, from which we believe a state-of-the-art performance is achieved. [ABSTRACT FROM AUTHOR]

• This paper reviews building load prediction with machine learning techniques. • Review and technical papers are searched by Sub-keyword Synonym Searching method. • Technical papers are reviewed in terms of application, algorithms, and data. • Primary limitations and gaps are identified; future trends are predicted. • A guidance for future technical paper on building load prediction is proposed. The surge of machine learning and increasing data accessibility in buildings provide great opportunities for applying machine learning to building energy system modeling and analysis. Building load prediction is one of the most critical components for many building control and analytics activities, as well as grid-interactive and energy efficiency building operation. While a large number of research papers exist on the topic of machine-learning-based building load prediction, a comprehensive review from the perspective of machine learning is missing. In this paper, we review the application of machine learning techniques in building load prediction under the organization and logic of the machine learning, which is to perform tasks T using Performance measure P and based on learning from Experience E. Firstly, we review the applications of building load prediction model (task T). Then, we review the modeling algorithms that improve machine learning performance and accuracy (performance P). Throughout the papers, we also review the literature from the data perspective for modeling (experience E), including data engineering from the sensor level to data level, pre-processing, feature extraction and selection. Finally, we conclude with a discussion of well-studied and relatively unexplored fields for future research reference. We also identify the gaps in current machine learning application and predict for future trends and development. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of safety monitoring in underground coal mines. In particular, we investigate and compare practical methods for the assessment of seismic hazards using analytical models constructed based on sensory data and domain knowledge. For our case study, we use a rich data set collected during a period of over five years from several active Polish coal mines. We focus on comparing the prediction quality between expert methods which serve as a standard in the coal mining industry and state-of-the-art machine learning methods for mining high-dimensional time series data. We describe an international data mining challenge organized to facilitate our study. We also demonstrate a technique which we employed to construct an ensemble of regression models able to outperform other approaches used by participants of the challenge. Finally, we explain how we utilized the data obtained during the competition for the purpose of research on the cold start problem in deploying decision support systems at new mining sites. [ABSTRACT FROM AUTHOR]

• Presented an extensive overview on different stages of electrocardiogram analysis. • Summarized the most notable developments of smart health monitoring systems. • Discussed the latest hardware implementations of real-time ECG monitoring systems. • Identified challenges and limitations for various automatic ECG diagnosis systems. • Outlines the future vision of next generation ECG monitoring systems for healthcare. Over the last decade, cardiovascular diseases (CVD's) are the leading cause of death globally. Early prediction of CVD's can help in reducing the complications of high-risk patients. The electrocardiogram (ECG) is an efficient aiding tool, provides accurate information about various cardiac conditions of the human heart. Evaluation and interpretation of ECG signal has become the major goal in current research to identify and mitigate risky cardiovascular conditions. The ECG signal is efficiently analysed and classified using signal processing techniques, ranging from traditional to machine learning approaches and its subbranches, such as deep learning are used for the early detection and diagnosis of cardiac conditions and arrhythmias. The development of novel types of body sensors increases the need for automated, low-cost, real-time, and efficient ECG monitoring systems that can be used at home or in ambulatory settings. IoT in healthcare industry enables the doctors and specialists to diagnose the patient status remotely in a smart and efficient manner. Motivated with all the research done in this area for early diagnosis of cardiac abnormalities and save the life of high-risk patients, we surveyed numerous research works reported in the literature and provide a glance on several aspects of ECG signal monitoring systems. We first introduce the step-by-step framework of ECG signal analysis starting data acquisition to classification, describes each stage analysis of both traditional and advanced machine learning models that have been reported in the literature. We provide a deep discussion on ECG signal acquisition, pre-recorded clinical ECG data taken from the databases, signal processing and denoising, detection of feature based on feature engineering, and signal classification along with the comparative assessment amongst reviewed studies. This work also presents the detailed analysis of smart health care system that employs portable and wearable devices, as well as the Internet of Things (IoT) and wireless technologies, as an innovative medium for data transmission that allows for remote access to a patient's health status at lower costs and with greater efficiency. Moreover, this work provides an extensive knowledge on various hardware platforms that have been adopted for the development of biomedical processors including microcontrollers, FPGA and ASIC. Additionally, the challenges and limitations are discussed in this research field and directions for future work are suggested. This is the first review paper of its kind to present a comprehensive discussion on all aspects of cardiovascular disease monitoring systems in one place. [ABSTRACT FROM AUTHOR]

Energy consumption prediction is critical to intelligent power dispatching and smart grid optimization. However, the task remains challenging due to big data fluctuation and the low accuracy of a single feature. This paper proposes a novel short-term energy consumption prediction method for electric vehicle charging stations based on attention feature engineering and stacked GRU (Gated Recurrent Unit). First, we select several handcrafted features from historical energy consumption data. Second, we assign weights to these handcrafted features using the attention mechanism. Third, we propose a multi-sequence stacked GRU in parallel architecture as the network core to extract the correlations between the base and handcraft features. We mitigate the data fluctuations by modifying the prediction resolution and evaluate our method by comparing it with RNN, stacked-GRU, and DeepDeff GRU. The experiment results demonstrate that our method outperforms the state-of-the-art method with a prediction accuracy improvement of 27.2%. [Display omitted] • A feature processing method based on feature engineering and attention mechanism is proposed. • A multi-sequence stacking GRU training structure is proposed. • The proposed method was tested on the energy consumption dataset of charging stations and compared with other methods. [ABSTRACT FROM AUTHOR]

Dark web is an obscured part of the Internet, specifically used for sharing exploits, data breaches, and other means of cybercrime. Dark web forums provide opportunities to share such data and exploits and assign user reputation and credibility through participation in discussions and sharing data, exploits, and hacks. Such activities can help develop metrics to enable identification of influential mal-actors facilitating efficient and effective defense against emerging cyber threats, particularly zero-day exploits. This paper proposes an AI-inspired framework to identify influencers on dark web social networks (INSPECT) through intelligent analysis of user-profiles, interactions, and other activities. INSPECT framework leverages Feature Engineering, Social Network Analysis, Semantic Analysis, and K-means clustering and calculates an influencer score representing the users' significance within these forums. INSPECT has been evaluated using CrimeBB dataset comprising user profiles and activities within dark web forums to assess its effectiveness in identifying influential users on the dark web forums. [ABSTRACT FROM AUTHOR]

• A dual-staged heterogeneous stacked ensemble model is introduced. • A variance-based feature selection provides the lower computational complexity model. • Meta-learner-controlled the over-fitting and improved the predictive accuracy. • The proposed technique obtaining better performance for gender recognition. Gender classification is one of the most popular topics in the field of machine learning. It can be done with images or sounds. With the aim of developing a low-complexity and highly accurate gender recognition model, this paper proposes a Dual-staged Heterogeneous Stacked Ensemble Model (DH-SEM). In the proposed ensemble model, the classification problem is addressed by the amalgamation of three supervised base learners at stage-1 and random forest as a Meta learner in stage-2. The proposed work uses a speech dataset compiled from various sources (Harvard-Haskins, Carnegie Mellon and McGill University) and consists of statistical features. Rigorous feature engineering has been performed to select the most discriminative information from the dataset. For gender recognition, the DH-SEM method adopts the reduced feature space. The gender recognition accuracy of the proposed DH-SEM model is 99.36%, which is highest as compared to the state-of-the-art methods. The robustness of the proposed technique is also validated by other performance evaluation metrics and receiver operating characteristics. [ABSTRACT FROM AUTHOR]