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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]

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]

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]

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]

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 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]

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]

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]

• 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]

• 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]

Building occupancy information could aid energy preservation while simultaneously maintaining the end-user comfort level. Energy conservation becomes essential since energy resources are scarce and human dependency on appliances is only exponentially increasing. While intrusive sensors (i.e., cameras and microphones) can raise privacy concerns, this paper presents an innovative non-intrusive occupancy detection approach using environmental sensor data (e.g., temperature, humidity, carbon dioxide (CO 2), and light sensors). The proposed scheme transforms multivariate time-series data into images for better encoding and extracting relevant features. The utilized image transformation method is based on data normalization and matrix conversion. Precisely, by representing time-series in 2D space, an encoding kernel can move in two directions while it can move only in one direction when applied to a 1D signal. Moreover, machine learning (ML) and deep learning (DL) techniques are utilized to classify occupancy patterns. Several simulations are used to evaluate the approach; mainly, we investigated pre-trained and custom convolutional neural network (CNN) models. The latter attained an accuracy of 99.00%. Additionally, pixel data are extracted from the generated images and subjected to traditional ML methods. Throughout the numerous comparison settings, it was observed that the latter strategy provided the optimal balance of 99.42% accuracy performance and minimal training time across the occupancy datasets. • Exhaustive literature review on occupancy detection schemes based on sensor data. • Image transformation technique based on data normalization and matrix conversion. • Assessing a variety of pre-trained and custom CNN models to predict occupancy status. • Pixel extraction to gather values and integrate them with conventional ML algorithms. [ABSTRACT FROM AUTHOR]

• Feature engineering based framework for location-sensitive disaster event detection. • Augmentation of context-free and context-based features with place of occurrence. • Evaluation with unsupervised machine learning algorithm with 09 performance metrics. • AHP-TOPSIS based selection of an efficient machine learning algorithm and feature set. • Location-augmented context-based features outperformed traditional textual features. Disaster event detection aims to identify events like terrorist attacks, fire incidents, stampede incidents, building collapse, etc., reported in the online news articles or social media. Place of occurrence of disaster event is a significant feature associated with events for location-sensitive disaster event detection. Efficient feature selection and their augmentation with location information can contribute towards the evolution of traditional approaches and their adoption for location-sensitive disaster event detection leading to improvement in the overall process as a whole. Since the evaluation of event detection techniques deliberates various intrinsic and extrinsic performance metrics, the decision-making for the selection of feature sets is treated as a Multiple-Criteria Decision Making (MCDM) problem. This paper proposes a framework, GeoClust , that is based on feature engineering of traditional textual features in order to enhance their capability for improved location-sensitive disaster event detection. The framework augments context-free and context-based textual feature sets with feature sets of place of occurrence of the events and evaluates their performance using unsupervised machine learning algorithms for various performance metrics. Finally, the best feature set is selected using AHP-TOPSIS technique of MCDM in order to tune the system for automatic and efficient location-sensitive disaster event detection in real-time. Extensive set of experiments have been performed in order to evaluate the framework on a dataset of online news articles reporting disaster events about terrorist attacks, fire incidents, stampede incidents, building collapse and maoist attacks happened at different locations in India. The results show that the location-augmented feature sets significantly improve performance of location-sensitive disaster event detection as compared with traditional feature sets. The results also demonstrate that the context-based feature sets with location-augmentation are ranked higher than the context-free feature sets in MCDM analysis. [ABSTRACT FROM AUTHOR]

[Display omitted] • Alternative to the timely and costly finite element-based simulations of the thermal history in additive manufacturing. • Generic, physics-based features that performs well on various geometries, deposition patterns, meshes, and other simulation properties. • High precision machine learning predictions of thermal fields in additive manufacturing that achieved mean absolute percentage error below 5%. Additive manufacturing (AM) is an emerging manufacturing technology that constructs complex parts through layer-by-layer deposition. The prediction and control of thermal fields during production of AM parts are of crucial importance as the temperature distribution and gradient dictates the microstructures, properties, and performance. Finite element (FE) analyses are commonly conducted to simulate the thermal history of the AM process, but are known to be costly and time-consuming. This paper aims to address the challenge by presenting the essential components of a generic data-driven control framework. The proposed framework utilizes extremely randomized trees and is trained and tested on datasets generated through FE simulations. The datasets contain generic, engineered features constructed based on the physics of the underlying thermal process. The features are transferable between a wide range of cases and have achieved mean absolute percentage errors (MAPE) below 2.5% for predicting nodal temperature profiles. In addition, predictions of entire simulations with machine learning (ML) models trained on datasets from different cases have been conducted with MAPE below 5%. The results demonstrate the transferability of thermal histories between several geometries and significantly reduce the need for expensive FE simulations. We believe that these findings are an important step towards real-time optimization in AM. [ABSTRACT FROM AUTHOR]

A cerebrovascular accident or stroke is the second commonest cause of death in the world. If it is not fatal, it can result in paralysis, sensory impairment and significant disability. Rehabilitation plays an important role to help survivors relearn lost skills and assist them to regain independence and thus ameliorate their quality of life. With the development of technology, researchers have come up with new solutions to assist clinicians in monitoring and assessing their patients; as well as making physiotherapy available to all. The objective of this review is to assess the recent developments made in the field of post-stroke rehabilitation using wearable devices for data collection and machine learning algorithms for the exercises' evaluation. To do so, PRISMA guidelines for systematic reviews were followed. Scopus, Lens, PubMed, ScienceDirect and Microsoft academic were electronically searched. Peer-reviewed papers using sensors in post-stroke rehabilitation were included, for the period between 2015 to August 2021. Thirty-three publications that used wearable sensors for patients' assessment were included. Based on that, we have proposed a taxonomy that divided the assessment systems into three categories namely activity recognition, movement classification, and clinical assessment emulation. Moreover, The most commonly employed sensors as well as the most targeted body–limbs, outcome measures, and study designs are reviewed, in addition to the examination of the machine learning approaches starting from the feature engineering to the classification done. Finally, limitations and potential study directions in the field are presented. [ABSTRACT FROM AUTHOR]

Feature selection in real-world data mining problems is essential to make the learning task efficient and more accurate. Identifying the best feature selection algorithm, among the many available, is a complex activity that still relies heavily on human experts or some random trial-and-error procedure. Thus, the automated machine learning community has taken some steps towards the automation of this process. In this paper, we address the metalearning challenge of recommending feature selection algorithms by proposing a novel meta-feature engineering model. Our model considers a broad collection of meta-features that enable the study of the relationship between the dataset properties and the feature selection algorithm performance in terms of several criteria. We arrange the input meta-features into eight categories: (i) simple, (ii) statistical, (iii) information-theoretical, (iv) complexity, (v) landmarking, (vi) based on symbolic models, (vii) based on images, and (viii) based on complex networks (graphs). The target meta-features emerge from a multi-criteria performance measure, based on five individual performance indexes, that assesses feature selection methods grounded in information, distance, dependence, consistency, and precision measures. We evaluate our proposal using a recently developed framework that extracts the input meta-features from 213 benchmark datasets, and ranks the assessed feature selection algorithms, to fill in the target meta-features in meta-bases. This evaluation uses five state-of-the-art classification methods to induce recommendation models from meta-bases: C4.5, Random Forest, XGBoost, ANN, and SVM. The results showed that it is possible to reach an average accuracy of up to 90% applying our meta-feature engineering model. This work is the first to use an extensive empirical evaluation to provide a careful discussion of the strengths and limitations of more than 160 meta-features. These meta-features, while designed to aid the task of feature selection algorithm recommendation, can readily be employed in other metalearning scenarios. Therefore, we believe our findings are a valuable contribution to the fields of automated machine learning and data mining, as well as to the feature extraction and pattern recognition communities. • A novel meta-feature engineering model recommends feature selection algorithms. • The proposal obtains promising results from 213 datasets with hit rates of up to 90%. • Some simple, landmarking, image, and graph-based input meta-features highlighted. • A multi-criteria performance measure rigorously assesses candidate algorithms. • Chains of binary or multiclass classifiers can efficiently rank candidate algorithms. [ABSTRACT FROM AUTHOR]

With the increasing transportation activities, road freight transportation has caused significant impacts on sustainability. The necessity of establishing sustainable road freight transportation plans have emerged for businesses. Therefore, it is important to develop decision support models that can be used by managers for sustainable road freight transportation. The objective of study is to predict the profit margin given to customers for freight trucking in sustainable road transportation. For this purpose, the variables affecting the profit margin to be given to the customers in sustainable road freight transportation are determined in the light of experts and managers. A data framework has been created using three-dimensional sustainability factors and customer-based variables. A machine learning-based methodology is developed. The scenario-based empirical investigation has been performed. Two different streams have been developed. The first stream is based on traditional importance analysis and second stream considers Recency, Frequency and Monetary based feature engineering and Discrete Wavelet Transform for noise reduction. Three different machine learning algorithms which are Random Forest, Robust Regression and XGBoost used for these two streams. Six different scenarios are considered. Finally, the proposed methodology is applied to two sustainable road freight transportation firms in Turkey. It is aimed to achieve the best performance for profit margin prediction analysis along with demonstrating that the proposed approach provides higher evaluation accuracy. The benchmarking results revealed the superiority of the proposed approach, which trained and tested the prediction model using the stream of data coming from a combination of Discrete Wavelet Transform and feature extraction. The benchmarking and comparisons enact that enhanced XGBoost algorithms provides the best prediction result. This paper presents a novel approach to predict customer-based profit margin in sustainable road freight transportation sector by combining different machine-learning methods for the first time. This study also provides useful insights about strategic and sustainable development perspectives to managers. • Predicting the profit margin in sustainable road freight transportation. • Three-dimensional sustainability factors and customer-based variables are used. • Developing a new methodology based on machine learning. • Best performing machine learning algorithm is highlighted. • Provides managerial and sustainable development implications. [ABSTRACT FROM AUTHOR]

• Electricity fraud detection in conventional meters. • Combining ML: supervised and unsupervised algorithms. • Extensive feature engineering. • Multivariate Gaussian distribution for anomaly detection. • Light Gradient Boosting algorithm: performance and tuning. Although fraud in electricity consumption is easier to detect when consumption is recorded hourly by smart meters, in most developing countries, where the propensity for fraud is higher, conventional meters are not yet affordable. Fraud detection is easier with time series data-logging due to the periodicity and variability of consumption that reveals deviations from a regular consumption pattern. In contrast, fraud detection with conventional meters remains a significant challenge because anomalies in consumption are well hidden within the normal consumption of other consumers. In this paper, large datasets regarding consumers and invoice data from Tunisia are combined and investigated with several Machine Learning (ML) classification algorithms, to detect irregularities in electricity consumption. By performing extensive feature engineering, including multivariate Gaussian distribution, the efficiency of ensemble classifiers such as Light Gradient Boosting (LGB) outperforms other algorithms and achieves realistic performance from challenging, unbalanced and uncorrelated input datasets. [ABSTRACT FROM AUTHOR]

Modular construction is an attractive building method due to its advantages over traditional stick-built methods in terms of reduced waste and construction time, more control over resources and environment, and easier implementation of novel techniques and technologies in a controlled factory setting. However, efficient and timely decision-making in modular factories requires spatiotemporal information about the resources regarding their locations and activities which motivates the necessity for an automated activity identification framework. Thus, this paper utilizes sound, a ubiquitous data source present in every modular construction factory, for the automatic identification of commonly performed manual activities such as hammering, nailing, sawing, etc. To develop a robust activity identification model, it is imperative to engineer the appropriate features of the data source (i.e., traits of the signal) that provides a compact yet descriptive representation of the parameterized audio signal based on the nature of the sound, which is very dependent on the application domain. In-depth analysis regarding appropriate features selection and engineering for audio-based activity identification in construction is missing from current research. Thus, this research extensively investigates the effects of various features extracted from four different domains related to audio signals (time-, time-frequency-, cepstral-, and wavelet-domains), in the overall performance of the activity identification model. The effect of these features on activity identification performance was tested by collecting and analyzing audio data generated from manual activities at a modular construction factory. The collected audio signals were first balanced using time-series data augmentation techniques and then used to extract a 318-dimensional feature vector containing 18 different feature sets from the abovementioned four domains. Several sensitivity analyses were performed to optimize the feature space using a feature ranking technique (i.e., Relief algorithm), and the contribution of features in the top feature sets using a support vector machine (SVM). Eventually, a final feature space was designed containing a 130-dimensional feature vector and 0.5-second window size yielding about 97% F-1 score for identifying different activities. The contributions of this study are two-fold: 1. A novel means of automated manual construction activity identification using audio signal is presented; and 2. Foundational knowledge on the selection and optimization of the feature space from four domains is provided for future work in this research field. The result of this study demonstrates the potential of the proposed system to be applied for automated monitoring and data collection in modular construction factory in conjunction with other activity recognition frameworks based on computer vision (CV) and/or inertial measurement units (IMU). • An audio-based activity identification framework for manual activities in modular construction is presented. • Methods for feature extraction, optimization, and synthesis for classifying audio-signals is provided. • A multi-class SVM model with 5-fold cross-validation is used to validate the designed feature space. • Designed feature space and window-size are used to evaluate the SVM classification model to identify different activities. [ABSTRACT FROM AUTHOR]

Data driven material research is a hot topic in the cross field of artificial intelligence and materials science. The core of new material prediction is to find the relationship between material structure and properties. In this research, machine learning will have important advantages and play an important role for materials data. In this paper,we put forward a framework combining feature engineering and linear regression to find the correlation between structure and properties from materials data. High temperature superconductor and double perovskites for solar cells were employed to test the feasibility of the method. In the former, we successfully rebuilt a descriptor (ℓζ)−1 from data mining which is consistent with the theoretical formula. In the latter, as an exploration, we obtain a new descriptor χ b 2 rs x 2 e rs x − 1 from data mining which expresses the heat of formation (ΔH F) in the double perovskite. By our experiment, the method can obtain related expressions of structure-property relationship for material.The results show that the method is a simple yet efficient paradigm to construct the structure-property relationship and provides valuable hints to accelerate the process of materials design. Unlabelled Image • Construct appropriate descriptors with feature preprocessing and feature engineering. • Non-linear combination of descriptors can help build structure-property relationship. • The linear regression model combined with descriptors can be used to fit the structure-property relationship of materials. [ABSTRACT FROM AUTHOR]

• Accurate transferrable machine learning based load forecasting of machine tools. • Automated data preprocessing, feature construction and selection process. • Time lag and moving average feature construction increases forecasting accuracy. • Autocorrelation function is a valuable information source of feature construction. With the ongoing integration of renewable energies into the electrical power grid, industrial energy flexibility gains importance. To enable demand response applications, knowledge about the future energy demand is necessary. This paper presents a machine learning process to forecast the very short term load of two machine tools, which can be utilized as a decision support basis for control schemes and measures to increase energy flexibility and decrease energy cost in manufacturing. The presented process is developed and evaluated on production machines in a research factory. The results indicate that the developed machine learning process is feasible and creates an accurate very short term load forecasting model for different production machines. It can be used as a blueprint to develop load forecasting models for other production machines using the historic load profile and various machine and process data. A combination of time series features and an Artificial Neural Network proves to be the most robust model regarding the presented machine tools with achieved coefficients of determination between 0.57 and 0.64 for a 100 step forecast. Improvements are still needed regarding the forecasting accuracy, especially of load peaks, for which different measures are proposed. [ABSTRACT FROM AUTHOR]