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This study investigated the influence of red, green, blue, and white paper bags on the free volatile compound development of Kyoho grape berries from green to harvest. Seven functional groups of volatiles were identified during the development of Kyoho grape berries including esters, aldehydes, alcohols, terpenes, ketones, acids, and hydrocarbons. Esters and aldehydes were abundant in Kyoho grape berries, mainly represented by ethyl acetate, ethyl butyrate, and (E)-2-hexenal. They accumulated quickly after veraison and slightly decreased toward maturation. Red, green, blue, and white paper bags promoted the accumulation of esters and inhibited the accumulation of aldehydes, also inhibited the accumulation of alcohols, tepenes, ketones, and acids. Their effect from strong to weak was green, blue, red, and white paper bags. The expression profiles of genes in the lipoxygenase-hydroperoxide lyase (LOX-HPL) pathway were also analyzed and the results indicated that the regulation of red, green, blue, and white paper bags on aldehydes, alcohols, and esters volatile aromas was at transcriptional level. The results expanded our comprehension in grape aroma biosynthesis and berry bagging technique in table grape cultivation.

Cork spot-like physiological disorder (CSPD) is a newly identified issue in 'Kurenainoyume' apples, yet its mechanism remains unclear. To investigate CSPD, we conducted morphological observations on 'Kurenainoyume' apples with and without pre-harvest fruit-bagging treatment using light-impermeable paper bags. Non-bagged fruit developed CSPD in mid-August, while no CSPD symptoms were observed in bagged fruit. The bagging treatment significantly reduced the proportion of opened lenticels, with only 17.9% in bagged fruit compared to 52.0% in non-bagged fruits. In non-bagged fruit, CSPD spots tended to increase from the lenticels, growing in size during fruit development. The cuticular thickness and cross-sectional area of fresh cells in CSPD spots were approximately 16 µm and 1600 µm², respectively. Healthy non-bagged fruit reached these values around 100 to 115 days after full bloom from mid- to late August. Microscopic and computerized tomography scanning observations revealed that many CSPD spots developed at the tips of vascular bundles. Therefore, CSPD initiation between opened lenticels and vascular bundle tips may be influenced by water stress, which is potentially caused by water loss, leading to cell death and the formation of CSPD spots. [ABSTRACT FROM AUTHOR]

We looked into bagging and spraying as potential methods for increasing the yield and quality of Manfaloty pomegranate trees. During the 2020 and 2021 seasons, 7-year-old trees were used in the investigation. They were grown in sandy, calcareous soil with high temperatures. Six treatments were used to examine the effects on fruit yield and quality parameters at harvest time in the first week of September. These treatments included foliar spraying with kaolin and potassium silicate treatments, polypropylene bags and bagging with white and yellow paper. Results indicated that white paper bagging resulted in the lowest percentage of fruit cracking and sunburn compared to the control and other treatments. White paper bagging increased fruit yield, weight, length, and diameter. Additionally, compared to the other treatments, the application of kaolin spray increased the biochemical contents of the fruit (TSS, total sugars, vitamin C, anthocyanin, and juice) while lowering acidity and tannins. We concluded that using kaolin spray and white paper bags could enhance the yield and quality of Manfaloty pomegranate grown under challenging conditions. [ABSTRACT FROM AUTHOR]

In order to explore the effect of bagging on the wax compositions and storage quality of ‘Yali’ pear fruit, the differences and correlations of wax content and distribution, nutrition and quality indicators, and antioxidant properties of the two different treatments (polyethylene, (PE) plastic film bags and three-layer paper bags) stored at 25 ℃±1 ℃ were analyzed. The fruits were bagged at day 60 after blooming, then the bags were removed at harvest, and no-bagged fruit acted as the control. The results showed that the highest rate of fruit weight loss was found in PE plastic film bags-treated fruit, while there was no difference in fruit hardness, brittleness and compactness between the control and the two treatments within 14 days of storage. The increases in contents of wax compositions alkenes, fatty aldehydes, fatty alcohols, esters, and total wax were significantly delayed in the two treatments. The contents of alkanes C27 and C29, alkenes C24-C26, fatty acid C18:1, fatty aldehydes C26 and C34, even number fatty alcohols C22-C28, seven esters, and triterpenoids (such as α-farnesene, β-amyrin, lupeol, and β-amyrenone) in the two treatments were lower than the control. In addition, compared to the PE plastic film bags treatment, the decline in contents of wax compositions alkene C26, fatty acids C16 and C18:1, even number fatty aldehydes C28-C32, two esters (C22:1 alcohol-C18:1 acid and C24:1 alcohol-C18:1 acid), triterpenoids (Urs-12-en-28-al,3-(acetyloxy)-,(3á)-and Urs-12-en-28-al) were delayed by the three-layer paper bags treatment. Furthermore, in comparison with PE plastic film bags treatment, higher contents of peel flavonoids, peel total phenols (at day 14), fruit titratable acid and Vc (at day 14), and higher DPPH clearance rate were found in three-layer paper bags-treated fruit. According to PCA analysis of wax components, the control and the three-layer paper bags treatment were belonged to one group, while PE plastic film bags treatment was divided into the other group. The correlation analysis showed that wax components alkanes, fatty aldehydes, triterpenes and total wax in different treatments were positively correlated with the content of peel flavonoids (P

The rainy season crop of guava is severely infested by fruit fly and fetches lower economic returns. The farmers going in for rainy season crop have to use aggressive chemical measures, which may render serious health hazard to the consumers. In the present study, different bagging materials for guava fruits viz. non-woven bags of green, white, red and blue colour, leno bags, butter paper bags, perforated polythene bags and newspaper bags were put on immature green fruits at marble stage of development and evaluated for their efficacy to prevent fruit fly damage. The fruit size, weight, organoleptic rating and yield improved significantly in all the non-woven bagged fruits as compared to non-bagged fruits. The fruit bagging especially in non-woven bags eliminated the incidence of fruit fly infestation. Thus, 98-99 per cent fruits bagged in non-woven bags were healthy, free of fruit fly infestation and marketable while non-bagged fruits exhibited 100 per cent damage. Though, the effective yield obtained from blue and white non-woven bagged treatments was >12 kg but the white coloured bags proved to be the best in terms of fruit size, yield and quality. The benefit cost (B:C) ratio exhibited by newspaper bags was highest (2.11) because they were cheaper but were not very effective in controlling the fruit fly damage. The overall costs of the bagging treatments with non-woven white bags amounted to Rs 120 per 100 fruits which is affordable as the benefits are greater compared to non-bagged fruits which were unmarketable. [ABSTRACT FROM AUTHOR]

The convolutional neural network (CNN) has brought about a drastic change in the field of image processing and pattern recognition. The filters of CNN model correspond to the activation maps that extract features from the input images. Thus, the number of filters and filter size are of significant importance to learning and recognition accuracy of CNN model-based systems such as the biometric-based person authentication system. The present paper proposes to analyze the impact of varying the number of filters of CNN models on the accuracy of the biometric-based single classifiers using human face, fingerprint and iris for person identification, and also biometric-based super-classification using both bagging- and programming-based boosting methods. The present paper gives an insight to the optimal set of filters in CNN model that gives the maximum overall accuracy of the classifier system. [ABSTRACT FROM AUTHOR]

Current study was conducted at Citrus Research Institute, Sargodha, Pakistan during 2020 to determine the effectiveness of different fruit bagging materials to reduce the fruit fly infestation and also to check their impact on skin color of developing fruits and also upon the fruit quality in citrus variety "kinnow mandarin". The fruits were covered with four types of bagging materials such as perforated polyethylene, butter paper, muslin cloth bag and brown paper. No fruit fly infestation was observed in fruits covered with different types of bags as compared with unwrapped fruits. However, 12.43% fruit fly infestation was recorded in non wrapped bags. Fruits covered with brown paper bags and butter paper bags did not show any blemish problem and were also free from citrus canker. Bagging with perforated polyethylene, brown paper and butter paper materials also resulted in improving the fruit colour. None of the bagging material resulted in increase in fruit size (length x width) and fruit weight in citrus fruits. Statistically, the highest amount of juice weight (54.22 g) was detected in fruits enveloped in brown papers followed by butter paper bag (53.31 g), perforated polyethylene bags (50.89 g), muslin cloth bag (48.43 g) and non-bagged (47.43 g) fruits. The highest amount of TSS was found in fruits wrapped in brown paper bags (11.67%) followed by muslin cloth bag (11.41%) and were statistically at par with each other but significantly different with perforated polyethylene bags (11.11%), butter paper bags (10.35%) and un-wrapped fruits (10.60%). Lowest (0.87%) and highest (1.02%) acidity was observed in fruits wrapped with brown paper and muslin cloth bags, respectively was observed. In short bagging material could be used to protect citrus from fruitfly attack. [ABSTRACT FROM AUTHOR]

The 'Huangguan' pear is one of the high-quality pear cultivars produced in China. However, the bagged fruit of the 'Huangguan' pear often suffers from peel browning spots after rain during their mature period. In this study, in an effort to discover the impact of bagging treatments on the occurrence of peel browning spots and fruit quality, fruits were covered by single-layer, two-layer, or triple-layer paper bags six weeks after reaching full bloom. The results showed that the bagged fruits were characterized by smooth surfaces and reduced lenticels compared with the unbagged ones. The unbagged and the two-layer bagged fruits had yellow/green peels, while the single- and triple-layer bagged ones had yellow/white peels. Compared with the unbagged fruits, the bagged fruits had higher vitamin C (Vc) contents and values of peel color indexes L and a and lower soluble solid contents (SSCs), titratable acid (TA) contents, absorbance index differences (IAD), and b values. Additionally, the triple-layer bagged group was superior to other groups in terms of fruit quality, but it also had the maximum incidence of peel browning spots. Before and after the appearance of peel browning spots, the bagged fruits had smoother and thinner cuticles compared with the unbagged ones. Furthermore, the triple-layer bagged fruits had minimum lignin contents and maximum phenolic contents in their peels, with minimum activity of lignin synthesis-related enzymes such as phenylalanine ammonia lyase (PAL), peroxidase (POD), and polyphenol oxidase (PPO), as well as minimum expressions of relevant genes such as cinnamyl alcohol dehydrogenase (CAD), cinnamoyl CoA reductase (CCR), 4-coumarate: coenzyme A ligase (4CL6), and cinnamate 4-hydroxylase (C4H1). It was deduced that POD activity and the relative expressions of CAD9, CCR3, CCR4, and CCR5 may play key roles in the occurrence of peel browning spots. In summary, lignin synthesis affected the incidence of peel browning spots in bagged 'Huangguan' pears. This study provides a theoretical basis for understanding the incidence of peel browning spots in 'Huangguan' pears. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation prediction constitutes a significant research area within the realm of power system artificial intelligence. Accurate prediction of future photovoltaic output is imperative for the optimal dispatchment and secure operation of the power grid. This study introduces a photovoltaic prediction model, termed ICEEMDAN-Bagging-XGBoost, aimed at enhancing the accuracy of photovoltaic power generation predictions. In this paper, the original photovoltaic power data initially undergo decomposition utilizing the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm, with each intrinsic mode function (IMF) derived from this decomposition subsequently reconstructed into high-frequency, medium-frequency, and low-frequency components. Targeting the high-frequency and medium-frequency components of photovoltaic power, a limiting gradient boosting tree (XGBoost) is employed as the foundational learner in the Bagging parallel ensemble learning method, with the incorporation of a sparrow search algorithm (SSA) to refine the hyperparameters of XGBoost, thereby facilitating more nuanced tracking of the changes in the photovoltaic power's high-frequency and medium-frequency components. Regarding the low-frequency components, XGBoost-Linear is utilized to enable rapid and precise prediction. In contrast with the conventional superposition reconstruction approach, this study employs XGBoost for the reconstruction of the prediction output's high-frequency, intermediate-frequency, and low-frequency components. Ultimately, the efficacy of the proposed methodology is substantiated by the empirical operation data from a photovoltaic power station in Hebei Province, China. Relative to integrated and traditional single models, this paper's model exhibits a markedly enhanced prediction accuracy, thereby offering greater applicational value in scenarios involving short-term photovoltaic power prediction. [ABSTRACT FROM AUTHOR]

Phishing attacks remain a significant cybersecurity threat in the digital landscape, leading to the development of defense mechanisms. This paper presents a thorough examination of Artificial Intelligence (AI)-based ensemble methods for detecting phishing attacks, including websites, emails, and SMS. Through the screening of research articles published between 2019 and 2023, 37 relevant studies were identified and analyzed. Key findings highlight the prevalence of ensemble methods such as AdaBoost, Bagging, and Gradient Boosting in phishing attack detection models. Adaboost emerged as the most used method for website phishing detection, while Stacking and Adaboost were prominent choices for email phishing detection. The majority-voting ensemble method was frequently employed in SMS phishing detection models. The performance evaluation of these ensemble methods involves metrics, such as accuracy, ROC-AUC, and F-score, underscoring their effectiveness in mitigating phishing threats. This study also underscores the availability of credible open-access datasets for the progressive development and benchmarking of phishing attack detection models. The findings of this study suggest the development of new and optimized ensemble methods for phishing attack detection. [ABSTRACT FROM AUTHOR]

Advancements in imaging, computer vision, and automation have revolutionized various fields, including field-based high-throughput plant phenotyping (FHTPP). This integration allows for the rapid and accurate measurement of plant traits. Deep Convolutional Neural Networks (DCNNs) have emerged as a powerful tool in FHTPP, particularly in crop segmentation—identifying crops from the background—crucial for trait analysis. However, the effectiveness of DCNNs often hinges on the availability of large, labeled datasets, which poses a challenge due to the high cost of labeling. In this study, a deep learning with bagging approach is introduced to enhance crop segmentation using high-resolution RGB images, tested on the NU-Spidercam dataset from maize plots. The proposed method outperforms traditional machine learning and deep learning models in prediction accuracy and speed. Remarkably, it achieves up to 40 % higher Intersection-over-Union (IoU) than the threshold method and 11 % over conventional machine learning, with significantly faster prediction times and manageable training duration. Crucially, it demonstrates that even small labeled datasets can yield high accuracy in semantic segmentation. This approach not only proves effective for FHTPP but also suggests potential for broader application in remote sensing, offering a scalable solution to semantic segmentation challenges. This paper is accompanied by publicly available source code. [ABSTRACT FROM AUTHOR]

Heavy rainfall and precipitation play a massive role in shaping the socio-agricultural landscape of a country. Being one of the key indicators of climate change, natural disasters, and of the general topology of a region, rainfall prediction is a gift of estimation that can be used for multiple beneficial causes. Machine learning has an impressive repertoire in aiding prediction and estimation of rainfall. This paper aims to find the effect of ensemble learning, a subset of machine learning, on a rainfall prediction dataset, to increase the predictability of the models used. The classification models used in this paper were tested once individually, and then with applied ensemble techniques like bagging and boosting, on a rainfall dataset based in Australia. The objective of this paper is to demonstrate a reduction in bias and variance via ensemble learning techniques while also analyzing the increase or decrease in the aforementioned metrics. The study shows an overall reduction in bias by an average of 6% using boosting, and an average reduction in variance by 13.6%. Model performance was observed to become more generalized by lowering the false negative rate by an average of more than 20%. The techniques explored in this paper can be further utilized to improve model performance even further via hyper-parameter tuning. [ABSTRACT FROM AUTHOR]

The rapid and accurate estimation of forest carbon stock is important for analyzing the carbon cycle. In order to obtain forest carbon stock efficiently, this paper utilizes airborne LiDAR data to research the applicability of different feature screening methods in combination with machine learning in the carbon stock estimation model. First, Spearman's Correlation Coefficient (SCC) and Extreme Gradient Boosting tree (XGBoost) were used to screen out the variables that were extracted via Airborne LiDAR with a higher correlation with carbon stock. Then, Bagging, K-nearest neighbor (KNN), and Random Forest (RF) were used to construct the carbon stock estimation model. The results show that the height statistical variable is more strongly correlated with carbon stocks than the density statistical variables are. RF is more suitable for the construction of the carbon stock estimation model compared to the instance-based KNN algorithm. Furthermore, the combination of the XGBoost algorithm and the RF algorithm performs best, with an R2 of 0.85 and an MSE of 10.74 on the training set and an R2 of 0.53 and an MSE of 21.81 on the testing set. This study demonstrates the effectiveness of statistical feature screening methods and Random Forest for carbon stock estimation model construction. The XGBoost algorithm has a wider applicability for feature screening. [ABSTRACT FROM AUTHOR]

In the context of optimization under uncertainty, we consider various combinations of distribution estimation and resampling (bootstrap and bagging) for obtaining samples used to estimate a confidence interval for an optimality gap. This paper makes three experimental contributions to on-going research in data driven stochastic programming: (a) most of the combinations of distribution estimation and resampling result in algorithms that have not been published before, (b) within the algorithms, we describe innovations that improve performance, and (c) we provide open-source software implementations of the algorithms. Among others, three important conclusions can be drawn: using a smoothed point estimate for the optimality gap for the center of the confidence interval is preferable to a purely empirical estimate, bagging often performs better than bootstrap, and smoothed bagging sometimes performs better than bagging based directly on the data. [ABSTRACT FROM AUTHOR]

Copyright of Big Data Research (2096-0271) is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sample average approximation (SAA), the standard approach to stochastic mixed-integer programming, does not provide guidance for cases with limited computational budgets. In such settings, variance reduction is critical in identifying good decisions. This paper explores two closely related ensemble methods to determine effective decisions with a probabilistic guarantee. (a) The first approach recommends a decision by coordinating aggregation in the space of decisions as well as aggregation of objective values. This combination of aggregation methods generalizes the bagging method and the "compromise decision" of stochastic linear programming. Combining these concepts, we propose a stopping rule that provides an upper bound on the probability of early termination. (b) The second approach applies efficient computational budget allocation for objective function evaluation and contributes to identifying the best solution with a predicted lower bound on the probability of correct selection. It also reduces the variance of the upper bound estimate at optimality. Furthermore, it adaptively selects the evaluation sample size. Both approaches provide approximately optimal solutions even in cases with a huge number of scenarios, especially when scenarios are generated by using oracles/simulators. Finally, we demonstrate the effectiveness of these methods via extensive computational results for "megascale" (extremely large scale) stochastic facility location problems. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by The Office of Naval Research [Grant N00014-20-1-2077] and the Air Force Office of Scientific Research [Grant FA9550-20-1-0006]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0324) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0324). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

The paper considers the problem of short- and middle-term forecasting in the financial sphere. To solve this problem, a hybrid system of computational intelligence based on the group method of data handling (GMDH) and bagging, as well as an algorithm for its training, is proposed. The odd stacks of the hybrid system are formed by ensembles of parallel connected subsystems. ARIMA and the GMDH-neo-fuzzy hybrid network were chosen as such subsystems. The proposed system does not require a large training data set, automatically determines the number of stacks during training, and provides online operation. The experimental investigations were conducted using the proposed hybrid system, as well as separately using ARIMA and GMDH-neo-fuzzy. The accuracy of the predictions obtained is compared, based on which the feasibility of using the proposed hybrid system is substantiated. [ABSTRACT FROM AUTHOR]

The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to be fully unearthed. Consequently, this paper advocates a novel methodology for prognosticating mining area surface deformation by integrating ensemble learning with MT-InSAR technology. Initially predicated upon the MT-InSAR monitoring outcomes, the target variables for the ensemble learning dataset were procured by melding distance-based features with spatial autocorrelation theory. In the ensuing phase, spatial stratified sampling alongside mutual information methodologies were deployed to select the features of the dataset. Utilizing the MT-InSAR monitoring data from the Zixing coal mine in Hunan, China, the relationship between fault slippage and coal extraction in the study area was rigorously analyzed using Granger causality tests and Johansen cointegration assays, thereby acquiring the dataset requisite for training the Bagging model. Subsequently, leveraging the Bagging technique, ensemble models were constructed employing Decision Trees, Support Vector Regression, and Multi-layer Perceptron as foundational estimators. Furthermore, the Tree-structured Parzen Estimator (TPE) optimization algorithm was applied to the Bagging model, resulting in an optimal model for predicting fault slip in mining areas. In comparison with the baseline model, the performance increased by 25.88%, confirming the effectiveness of the data preprocessing method outlined in this study. This result also demonstrates the innovation and feasibility of combining ensemble learning with MT-InSAR technology for predicting mining area surface deformation. This investigation is the first to integrate TPE-optimized ensemble models with MT-InSAR technology, offering a new perspective for predicting surface deformation in mining territories and providing valuable insights for further uncovering the hidden information in MT-InSAR monitoring data. [ABSTRACT FROM AUTHOR]

A number of risks exist in commercial housing, and it is critical for the government, the real estate industry, and consumers to establish an objective early warning indicator system for commercial housing risks and to conduct research regarding its measurement and early warning. In this paper, we examine the commodity housing market and construct a risk index for the commodity housing market at three levels: market level, the real estate industry and the national economy. Using the Bootstrap aggregating-grey wolf optimizer-support vector machine (Bagging-GWO-SVM) model after synthesizing the risk index by applying the CRITIC objective weighting method, the commercial housing market can be monitored for risks and early warnings. Based on the empirical study, the following conclusions have been drawn: (1) The commodity housing market risk index accurately reflect the actual risk situation in Tianjin; (2) Based on comparisons with other models, the Bagging-GWO-SVM model provides higher accuracy in early warning. A final set of suggestions is presented based on the empirical study. [ABSTRACT FROM AUTHOR]

Copyright of Guangdong Agricultural Sciences is the property of South China Agricultural University, Guangdong Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The uranium required for power plants is mainly extracted by two methods in roughly equal amounts: quarries (underground and open pit) and in situ leaching (ISL). Uranium mining by in situ leaching is extremely attractive because it is economical and has a minimal impact on the region's ecology. The effective use of ISL requires, among other things, the accurate assessment of the host rocks' filtration characteristics. An accurate assessment of the filtration properties of the host rocks allows optimizing the mining process and improving the quality of the ore reserve prediction. At the same time, in Kazakhstan, this calculation is still based on methods that were developed more than 50 years ago and, in some cases, produce inaccurate results. According to our estimates, this method provides a prediction of filtration properties with a determination coefficient R 2 = 0.32. This paper describes a method of calculating the filtration coefficient of ore-bearing rocks using machine learning methods. The proposed approach was based on nonlinear regression models providing a 20–75% increase in the accuracy of the filtration coefficient assessment compared with the current methodology. The work used different types of machine learning algorithms based on the gradient boosting technique, bagging technique, feed-forward neural networks, support vector machines, etc. The results of logging, core sampling, and hydrogeological studies obtained during the exploration stage of the Inkai deposit were used as the initial data. All used machine learning models demonstrated significantly better results than the old method. This resulted in improved results compared with previous studies. The LightGBM regressor demonstrated the best result ( R 2 = 0.710). [ABSTRACT FROM AUTHOR]

In the ever-evolving landscape of IoT sensor networks, ensuring robust information security is imperative. This paper introduces a novel approach, the Temporal GAN Ensemble with Bagging (TGE-Bag), designed to fortify the security framework of IoT sensor networks. TGE-Bag leverages the power of Generative Adversarial Networks (GANs) with a temporal dimension, addressing the dynamic nature of IoT data streams. The ensemble aspect incorporates Bagging, enhancing the overall resilience and robustness of the security model. The temporal dimension in TGE-Bag recognizes the time-sensitive nature of IoT data, acknowledging that threats and anomalies may manifest differently over time. By incorporating GANs, the model can effectively generate synthetic data representative of the temporal patterns, allowing for more comprehensive training and robust anomaly detection. The ensemble approach further contributes to the model robustness by aggregating diverse GANs, each specialized in capturing specific temporal nuances. This paper evaluates TGE-Bag efficacy through extensive simulations on real-world IoT datasets, demonstrating its superior performance in detecting and mitigating security threats. The ensemble ability to generalize across diverse temporal patterns contributes to its adaptability in various IoT sensor network scenarios. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) technology presents an intelligent way to improve our lives and contributes to many fields such as industry, communications, agriculture, etc. Unfortunately, IoT networks are exposed to many attacks that may destroy the entire network and consume network resources. This paper aims to propose intelligent process automation and an auto-configured intelligent automation detection model (IADM) to detect and prevent malicious network traffic and behaviors/events at distributed multi-access edge computing in an IoT-based smart city. The proposed model consists of two phases. The first phase relies on the intelligent process automation (IPA) technique and contains five modules named, specifically, dataset collection and pre-processing module, intelligent automation detection module, analysis module, detection rules and action module, and database module. In the first phase, each module composes an intelligent connecting module to give feedback reports about each module and send information to the next modules. Therefore, any change in each process can be easily detected and labeled as an intrusion. The intelligent connection module (ICM) may reduce the search time, increase the speed, and increase the security level. The second phase is the dynamic adaptation of the attack detection model based on reinforcement one-shot learning. The first phase is based on a multi-classification technique using Random Forest Trees (RFT), k-Nearest Neighbor (K-NN), J48, AdaBoost, and Bagging. The second phase can learn the new changed behaviors based on reinforced learning to detect zero-day attacks and malicious events in IoT-based smart cities. The experiments are implemented using a UNSW-NB 15 dataset. The proposed model achieves high accuracy rates using RFT, K-NN, and AdaBoost of approximately 98.8%. It is noted that the accuracy rate of the J48 classifier achieves 85.51%, which is lower than the others. Subsequently, the accuracy rates of AdaBoost and Bagging based on J48 are 98.9% and 91.41%, respectively. Additionally, the error rates of RFT, K-NN, and AdaBoost are very low. Similarly, the proposed model achieves high precision, recall, and F1-measure high rates using RFT, K-NN, AdaBoost, and Bagging. The second phase depends on creating an auto-adaptive model through the dynamic adaptation of the attack detection model based on reinforcement one-shot learning using a small number of instances to conserve the memory of any smart device in an IoT network. The proposed auto-adaptive model may reduce false rates of reporting by the intrusion detection system (IDS). It can detect any change in the behaviors of smart devices quickly and easily. The IADM can improve the performance rates for IDS by maintaining the memory consumption, time consumption, and speed of the detection process. [ABSTRACT FROM AUTHOR]

The knowledge-based economy has drawn increasing attention recently, particularly in online shopping applications where all the transactions and consumer opinions are logged. Machine learning methods could be used to extract implicit knowledge from the logs. Industries and businesses use the knowledge to better understand the consumer behavior, and opportunities and threats correspondingly. The outbreak of coronavirus (COVID-19) pandemic has a great impact on the different aspects of our daily life, in particular, on our shopping behaviour. To predict electronic consumer behaviour could be of valuable help for managers in government, supply chain and retail industry. Although, before coronavirus pandemic we have experienced online shopping, during the disease the number of online shopping increased dramatically. Due to high speed transmission of COVID-19, we have to observe personal and social health issues such as social distancing and staying at home. These issues have direct effect on consumer behaviour in online shopping. In this paper, a prediction model is proposed to anticipate the consumers behaviour using machine learning methods. Five individual classifiers, and their ensembles with Bagging and Boosting are examined on the dataset collected from an online shopping site. The results indicate the model constructed using decision tree ensembles with Bagging achieved the best prediction of consumer behavior with the accuracy of 95.3%. In addition, correlation analysis is performed to determine the most important features influencing the volume of online purchase during coronavirus pandemic. [ABSTRACT FROM AUTHOR]

At the heart of India's livelihood lies agriculture, a foundational and ever-evolving sector. The realm of agriculture faces modern challenges, encompassing unpredictable climate fluctuations, insufficient irrigation infrastructure, and erratic weather conditions. Amidst these challenges, machine learning has emerged as a valuable tool for evaluating soil fertility and Crop assessment in agriculture. Ensemble machine learning methodologies have garnered attention for their potential to enhance predictive capabilities. These techniques involve constructing meta-classifiers that collectively contribute to improved predictive accuracy. This paper centers on the analysis of soil data acquired from testing laboratories, to predict fertility based on a comprehensive dataset. The study employs prominent ensemble machine learning algorithms, specifically focusing on boosting techniques, to elevate predictive accuracy and ensure heightened consistency. The assessment of soil fertility categories involved the examination of 12 carefully chosen attributes. Diverse soil parameters were measured to facilitate the prediction of soil fertility levels. The outcomes of the experimentation revealed that the application of the boosting technique using the Xgboost algorithm yielded superior accuracy compared to alternative ensemble classifiers, achieving a remarkable 96% accuracy rate. [ABSTRACT FROM AUTHOR]

Learning from imbalanced data is a challenging task, as with this type of data, most conventional supervised learning algorithms tend to favor the majority class, which has significantly more instances than the other classes. Ensemble learning is a robust solution for addressing the imbalanced classification problem. To construct a successful ensemble classifier, the diversity of base classifiers should receive specific attention. In this paper, we present a novel ensemble learning algorithm called Selective Evolutionary Heterogeneous Ensemble (SEHE), which produces diversity by two ways, as follows: 1) adopting multiple different sampling strategies to generate diverse training subsets and 2) training multiple heterogeneous base classifiers to construct an ensemble. In addition, considering that some low-quality base classifiers may pull down the performance of an ensemble and that it is difficult to estimate the potential of each base classifier directly, we profit from the idea of a selective ensemble to adaptively select base classifiers for constructing an ensemble. In particular, an evolutionary algorithm is adopted to conduct the procedure of adaptive selection in SEHE. The experimental results on 42 imbalanced data sets show that the SEHE is significantly superior to some state-of-the-art ensemble learning algorithms which are specifically designed for addressing the class imbalance problem, indicating its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

We examine the main effects of ICT penetration and the shadow economy on sovereign credit ratings and the cost of debt, along with possible second-order effects between the two variables, on a dataset of 65 countries from 2001 to 2016. The paper presents a range of machine-learning approaches, including bagging, random forests, gradient-boosting machines, and recurrent neural networks. Furthermore, following recent trends in the emerging field of interpretable ML, based on model-agnostic methods such as feature importance and accumulated local effects, we attempt to explain which factors drive the predictions of the so-called ML black box models. We show that policies facilitating the penetration and use of ICT and aiming to curb the shadow economy may exert an asymmetric impact on sovereign ratings and the cost of debt depending on their present magnitudes, not only independently but also in interaction. [ABSTRACT FROM AUTHOR]

The planning and management of traffic flow networks with multiple input data sources for decision-making generate the need for a mathematical approach. The program of measures for the development of the transport infrastructure of the Russian Federation provides for the selection of pilot regions for the creation of intelligent transportation systems. With extensive knowledge of theoretical and applied mathematics, it is important to select and adapt mathematical methods for solving problems. In this regard, the aim of the study is to develop and validate an algorithm for solving the problem of classifying objects according to the potential of creating intelligent transportation systems. The main mathematical apparatus for classification is the «random forest» machine learning algorithm method. A bagging machine learning meta-algorithm for high accuracy of the algorithm was used. This paper proposes the author's method of sequential classification analysis for identifying objects with the potential to create intelligent transportation systems. The choice of using this method is justified by its best behavior under the large number of predictor variables required for an objective aggregate assessment of digital development and quality of territories. The proposed algorithm on the example of Russian regions was tested. A technique and algorithm for statistical data processing based on descriptive analytics tools have been developed. The quality of the classification analysis algorithm was assessed by the random forest method based on misclassification coefficients. The admissibility of retrained algorithms and formation of a «fine-grained» «random forest» model for solving classification problems under the condition of no prediction was proven to be successful. The most productive models with the highest probability of correct classification were «reached» and «finalized» on the basis of logistic regression analysis of relationships between predictors and categorical dependent variables. The regions of class 1 with «high potential for the creation of intelligent transportation systems» are most likely to be ready for the reorganization of infrastructure facilities; the introduction of digital technologies in the management of traffic flows was found. [ABSTRACT FROM AUTHOR]

In the construction process of an intelligent pumping station, the parameter calibration of the pumping station unit is very important. In actual engineering, the working parameters of the pumping station are affected by complex working conditions and natural factors, so that it is difficult to establish a traditional physical model for the pumping station. This paper uses a data-driven method to apply the hybrid model of the convolutional neural network (CNN) and long-term short-term memory network (LSTM) to water level prediction in pumping stations and adds self-attention mechanism feature selection and a bagging optimization algorithm. Then, after an error analysis of the hybrid model, a performance comparison experiment with the separate model was conducted. The historical data of the pumping station project provided by the Tuancheng Lake Management Office of Beijing South-to-North Water Diversion Project was used to train and verify the proposed pumping station water level prediction model. The results show that the CNN–LSTM model based on the self-attention mechanism has higher accuracy than the separate CNN model and LSTM model, with a correlation coefficient (R2) of 0.72 and a mean absolute error (MAE) of 19.14. The model can effectively solve the problem of water level prediction in the front and rear pools under complex pumping station conditions. [ABSTRACT FROM AUTHOR]

High-Frequency Trading utilizes powerful mathematical algorithms to execute transactions at an extremely rapid pace, which makes the use of machine learning techniques for prediction necessary. This paper evaluates the effectiveness of various ensemble learning algorithms, including Boosting (Adaboost and XGBoost), Bagging (Random Forest and Bagging-LSVM), and Stacking, in predicting stock prices using High-Frequency Trading (HFT) data from the Casablanca Stock Exchange. The data contains 311.812 transactions at the millisecond precision level and was obtained under a non-disclosure agreement. Three performance metrics are used: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean of the Squared Errors (MSE) and accuracy of predictions is assessed daily, monthly and annually. The study shows that the Stacking Model outperforms other algorithms in forecasting prices across different periods due to its ability to generate profit from multiple learners and construct a robust model. These results provide valuable insights for improving High-Frequency Trading (HFT) strategies, including the accuracy and robustness of the algorithms in predicting stock prices, which are crucial for effective HFT strategies. The findings highlight how each algorithm characteristics and performance metrics can enhance decision-making in HFT. The novelty of this study lies in its unique combination of a focus on an emerging market, the application of advanced ensemble learning algorithms to high-frequency data, and its comprehensive approach to evaluating algorithm performance. Also, this paper is the first study that focuses on applying ensemble learning algorithms to high-frequency data in Casablanca Stock Exchange and one of the rare that focuses on African stock markets. Thus, this study is pioneering in its comparative analysis of machine learning techniques for HFT in Morocco, introducing sophisticated and more accurate tools for analyzing and predicting Casablanca Stock Exchange trends.

Though the convex optimization has been widely used in power systems, it still cannot guarantee to yield a tight (accurate) solution to some problems. To mitigate this issue, this paper proposes an ensemble learning based convex approximation for alternating current (AC) power flow equations that differs from the existing convex relaxations. The proposed approach is based on three-phase quadratic power flow equations in rectangular coordinates. To develop this data-driven convex approximation of power flows, the polynomial regression (PR) is first deployed as a basic learner to fit convex relationships between the independent and dependent variables. Then, ensemble learning algorithms such as gradient boosting (GB) and bagging are introduced to combine learners to boost model performance. Based on the learned convex approximation of power flow, optimal power flow (OPF) is formulated as a convex quadratic programming problem. The simulation results on IEEE standard cases of both balanced and unbalanced systems show that, in the context of solving OPF, the proposed data-driven convex approximation outperforms the conventional semi-definite programming (SDP) relaxation in both accuracy and computational efficiency, especially in the cases that the conventional SDP relaxation fails. [ABSTRACT FROM AUTHOR]

The Internet-of-Things (IoT) massive access is a significant scenario for sixth-generation (6G) communications. However, low-power IoT devices easily suffer from remote interference caused by the atmospheric duct under the 6G time-division duplex (TDD) mode. It causes distant downlink wireless signals to propagate beyond the designed protection distance and interfere with local uplink signals, leading to a large outage probability. In this paper, a remote interference discrimination testbed is originally proposed to detect interference, which supports the comparison of different types of algorithms on the testbed. Specifically, 5,520,000 TDD network-side data collected by real sensors are used to validate the interference discrimination capabilities of nine promising AI algorithms. Moreover, a consistent comparison of the testbed shows that the ensemble algorithm achieves an average accuracy of 12% higher than the single model algorithm. [ABSTRACT FROM AUTHOR]

IoT-based applications have witnessed a rapid surge in deployment in various domains. IoT infrastructure is the nervous system responsible for the effective functioning of Smart Cities. Nevertheless, the full-fledged deployment of IoT-based applications exposes this infrastructure to a high risk of cyberattacks. Since IoT devices establish communication with cloud services via inbuilt sensors, the probability of sabotaging the communication channel by malicious attacks is always high. This paper aims to explore an anomaly detection method that makes use of techniques like Support Vector Machine (SVM), Artificial Neural Networks (ANN), k- Nearest Neighbor (KNN), Linear Regression (LR), Decision Trees (DT), and Random Forest (RF) to neutralize threats and boost the cybersecurity of a smart city. The paper goes on to examine the role of ensemble techniques like bagging and boosting to provide an additional security layer to the detection architecture. This is where the paper departs from the erstwhile approaches that have revolved around single classifiers to boost the detection system and have not considered the integration of cross-validation and feature selection. The experimental results conducted on the datasets UNSW-BC15 and CICIDS2017 and several measures like Accuracy, Precision, Recall, and F1 Score establish that the proposed approach outperforms various state-of-the-art methods used in detecting rare attacks. [ABSTRACT FROM AUTHOR]

There has been a sharp increase in liver disease globally, and many people are dying without even knowing that they have it. As a result of its limited symptoms, it is extremely difficult to detect liver disease until the very last stage. In the event of early detection, patients can begin treatment earlier, thereby saving their lives. It has become increasingly popular to use ensemble learning algorithms since they perform better than traditional machine learning algorithms. In this context, this paper proposes a novel architecture based on ensemble learning and enhanced preprocessing to predict liver disease using the Indian Liver Patient Dataset (ILPD). Six ensemble learning algorithms are applied to the ILPD, and their results are compared to those obtained with existing studies. The proposed model uses several data preprocessing methods, such as data balancing, feature scaling, and feature selection, to improve the accuracy with appropriate imputations. Multivariate imputation is applied to fill in missing values. On skewed columns, log1p transformation was applied, along with standardization, min–max scaling, maximum absolute scaling, and robust scaling techniques. The selection of features is carried out based on several methods including univariate selection, feature importance, and correlation matrix. These enhanced preprocessed data are trained on Gradient boosting, XGBoost, Bagging, Random Forest, Extra Tree, and Stacking ensemble learning algorithms. The results of the six models were compared with each other, as well as with the models used in other research works. The proposed model using extra tree classifier and random forest, outperformed the other methods with the highest testing accuracy of 91.82% and 86.06%, respectively, portraying our method as a real-world solution for detecting liver disease. [ABSTRACT FROM AUTHOR]

One solution to the challenge of choosing an appropriate clustering algorithm is to combine different clusterings into a single consensus clustering result, known as cluster ensemble (CE). This ensemble learning strategy can provide more robust and stable solutions across different domains and datasets. Unfortunately, not all clusterings in the ensemble contribute to the final data partition. Cluster ensemble selection (CES) aims at selecting a subset from a large library of clustering solutions to form a smaller cluster ensemble that performs as well as or better than the set of all available clustering solutions. In this paper, we investigate four CES methods for the categorization of structurally distinct organic compounds using high-dimensional IR and Raman spectroscopy data. Single quality selection (SQI) forms a subset of the ensemble by selecting the highest quality ensemble members. The Single Quality Selection (SQI) method is used with various quality indices to select subsets by including the highest quality ensemble members. The Bagging method, usually applied in supervised learning, ranks ensemble members by calculating the normalized mutual information (NMI) between ensemble members and consensus solutions generated from a randomly sampled subset of the full ensemble. The hierarchical cluster and select method (HCAS-SQI) uses the diversity matrix of ensemble members to select a diverse set of ensemble members with the highest quality. Furthermore, a combining strategy can be used to combine subsets selected using multiple quality indices (HCAS-MQI) for the refinement of clustering solutions in the ensemble. The IR + Raman hybrid ensemble library is created by merging two complementary "views" of the organic compounds. This inherently more diverse library gives the best full ensemble consensus results. Overall, the Bagging method is recommended because it provides the most robust results that are better than or comparable to the full ensemble consensus solutions. [ABSTRACT FROM AUTHOR]

There are many dangerous diseases and high mortality rates for women (including breast cancer). If the disease is detected early, correctly diagnosed and treated at the right time, the likelihood of illness and death is reduced. Previous disease prediction models have mainly focused on methods for building individual models. However, these predictive models do not yet have high accuracy and high generalization performance. In this paper, we focus on combining these individual models together to create a combined model, which is more generalizable than the individual models. Three ensemble techniques used in the experiment are: Bagging; Boosting and Stacking (Stacking include three models: Gradient Boost, Random Forest, Logistic Regression) to deploy and apply to breast cancer prediction problem. The experimental results show the combined model with the ensemble methods based on the Breast Cancer Wisconsin dataset; this combined model has a higher predictive performance than the commonly used individual prediction models. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are the key underlying technology of the Internet of Things (IoT); however, these networks are energy constrained. Security has become a major challenge with the significant increase in deployed sensors, necessitating effective detection and mitigation approaches. Machine learning (ML) is one of the most effective methods for building cyber-attack detection systems. This paper presents a lightweight ensemble-based ML approach, Weighted Score Selector (WSS), for detecting cyber-attacks in WSNs. The proposed approach is implemented using a blend of supervised ML classifiers, in which the most effective classifier is promoted dynamically for the detection process to gain higher detection performance quickly. We compared the performance of the proposed approach to three classical ensemble techniques: Boosting-based, Bagging-based, and Stacking-based. The performance comparison was conducted in terms of accuracy, probability of false alarm, probability of detection, probability of misdetection, model size, processing time, and average prediction time per sample. We applied two independent feature selection techniques. We utilized the simulation-based labeled dataset, WSN-DS, that comprises samples of four internal network-layer Denial of Service attack types: Grayhole, Blackhole, Flooding, and TDMA scheduling, in addition to normal traffic. The simulation revealed promising results for our proposed approach. [ABSTRACT FROM AUTHOR]

Broad learning system (BLS) is a novel and efficient model, which facilitates representation learning and classification by concatenating feature nodes and enhancement nodes. In spite of the efficient properties, BLS is still suboptimal when facing with imbalance problem. Besides, outliers and noises in imbalanced data remain a challenge for BLS. To address the above issues, in this paper we first propose a weighted BLS, which assigns a weight to each training sample, and adopt a general weighting scheme, which augments the weight of samples from the minority class. To further explore the prior distribution of original data, we design a density based weight generation mechanism to guide the specific weight matrix generation and propose the adaptive weighted broad learning system (AWBLS). This mechanism considers the inter-class and intra-class distance simultaneously in the density calculation. Finally, we propose the incremental weighted ensemble broad learning system (IWEB) by utilizing a progressive mechanism to further improve the stability and robustness of AWBLS. Extensive comparative experiments on 38 real-world data sets verfy that IWEB outperforms most of the imbalance ensemble classification methods. [ABSTRACT FROM AUTHOR]

Gold is often used by investors as a hedge against inflation or adverse economic times. Consequently, it is important for investors to have accurate forecasts of gold prices. This paper uses several machine learning tree-based classifiers (bagging, stochastic gradient boosting, random forests) to predict the price direction of gold and silver exchange traded funds. Decision tree bagging, stochastic gradient boosting, and random forests predictions of gold and silver price direction are much more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging, stochastic gradient boosting, and random forests produce accuracy rates of between 85% and 90% while logit models produce accuracy rates of between 55% and 60%. Stochastic gradient boosting accuracy is a few percentage points less than that of random forests for forecast horizons over 10 days. For those looking to forecast the direction of gold and silver prices, tree bagging and random forests offer an attractive combination of accuracy and ease of estimation. For each of gold and silver, a portfolio based on the random forests price direction forecasts outperformed a buy and hold portfolio. [ABSTRACT FROM AUTHOR]

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems. [ABSTRACT FROM AUTHOR]

Online learning is gradually becoming popular with the continuous development of Internet technology and the rapid development of educational informatization. It plays a key role in predicting students' course performance based on their online learning behavior. It can optimize the effects of teaching and improve teaching strategies. Student performance prediction models that are built with a single algorithm currently have limited prediction accuracy. Meanwhile, model fusion improvement technology can combine many algorithms into a single model, thereby enhancing the overall effect of the model and providing better performance. In this paper, a stacking fusion model based on RF-CART–XGBoost–LightGBM is proposed. The first layer of the model uses a decision tree (CART), random forest, XGBoost and LightGBM as the base models. The second layer uses the LightGBM model. We used the Kalboard360 student achievement dataset, and features related to online learning behavior were selected as the model's input for model training. Finally, we employed five-fold cross-validation to assess the model's performance. In comparison with the four single models, the two fusion models based on the four single models both show significantly better performance. The prediction accuracies of the bagging fusion model and stacking fusion model are 83% and 84%, respectively. This proves that the proposed stacking fusion model has better performance, which helps to improve the accuracy of the performance prediction model further. It also provides an effective basis for optimizing the effects of teaching. [ABSTRACT FROM AUTHOR]

In the medical field, distinguishing genes that are relevant to a specific disease, let's say colon cancer, is crucial to finding a cure and understanding its causes and subsequent complications. Usually, medical datasets are comprised of immensely complex dimensions with considerably small sample size. Thus, for domain experts, such as biologists, the task of identifying these genes have become a very challenging one, to say the least. Feature selection is a technique that aims to select these genes, or features in machine learning field with respect to the disease. However, learning from a medical dataset to identify relevant features suffers from the curse-of-dimensionality. Due to a large number of features with a small sample size, the selection usually returns a different subset each time a new sample is introduced into the dataset. This selection instability is intrinsically related to data variance. We assume that reducing data variance improves selection stability. In this paper, we propose an ensemble approach based on the bagging technique to improve feature selection stability in medical datasets via data variance reduction. We conducted an experiment using four microarray datasets each of which suffers from high dimensionality and relatively small sample size. On each dataset, we applied five well-known feature selection algorithms to select varying number of features. The proposed technique shows a significant improvement in selection stability while at least maintaining the classification accuracy. The stability improvement ranges from 20 to 50 percent in all cases. This implies that the likelihood of selecting the same features increased 20 to 50 percent more. This is accompanied with the increase of classification accuracy in most cases, which signifies the stated results of stability. [ABSTRACT FROM AUTHOR]

Abstract Heavy rainfall and precipitation play a massive role in shaping the socio-agricultural landscape of a country. Being one of the key indicators of climate change, natural disasters, and of the general topology of a region, rainfall prediction is a gift of estimation that can be used for multiple beneficial causes. Machine learning has an impressive repertoire in aiding prediction and estimation of rainfall. This paper aims to find the effect of ensemble learning, a subset of machine learning, on a rainfall prediction dataset, to increase the predictability of the models used. The classification models used in this paper were tested once individually, and then with applied ensemble techniques like bagging and boosting, on a rainfall dataset based in Australia. The objective of this paper is to demonstrate a reduction in bias and variance via ensemble learning techniques while also analyzing the increase or decrease in the aforementioned metrics. The study shows an overall reduction in bias by an average of 6% using boosting, and an average reduction in variance by 13.6%. Model performance was observed to become more generalized by lowering the false negative rate by an average of more than 20%. The techniques explored in this paper can be further utilized to improve model performance even further via hyper-parameter tuning.

Applications challenged by the joint problem of concept drift and class imbalance are attracting increasing research interest. This paper proposes a novel Reinforcement Online Active Learning Ensemble for Drifting Imbalanced data stream (ROALE-DI). The ensemble classifier has a long-term stable classifier and a dynamic classifier group which applies a reinforcement mechanism to increase the weight of the dynamic classifiers, which perform better on the minority class, and decreases the weight of the opposite. When the data stream is class imbalanced, the classifiers will lack the training samples of the minority class. To supply training samples, when creating a new classifier, the labeled instances buffer is used to provide instances of the minority class. Then, a hybrid labeling strategy that combines the uncertainty strategy and imbalance strategy is proposed to define whether to obtain the real label of an instance. An experimental evaluation compares the classification performance of the proposed method with semi-supervised and supervised algorithms on both real-world and synthetic data streams. The results show that the ROALE-DI achieves higher Area Under the ROC Curve (AUC) and accuracy values with even fewer real labels, and the labeling cost dynamically adjusts according to the concept drift and class imbalance ratio. [ABSTRACT FROM AUTHOR]

The performance of a six-axis force/torque sensor (F/T sensor) severely decreased when working in an extreme environment due to its sensitivity to ambient temperature. This paper puts forward an ensemble temperature compensation method based on the whale optimization algorithm (WOA) tuning the least-square support vector machine (LSSVM) and trimmed bagging. To be specific, the stimulated annealing algorithm (SA) was hybridized to the WOA to solve the local entrapment problem, and an adaptive trimming strategy is proposed to obtain the optimal trim portion for the trimmed bagging. In addition, inverse quote error (invQE) and cross-validation are employed to estimate the fitness better in training process. The maximum absolute measurement error caused by temperature decreased from 3.34 % to 3.9 × 10 − 3 % of full scale after being compensated by the proposed method. The analyses of experiments illustrate the ensemble hWOA-LSSVM based on improved trimmed bagging improves the precision and stability of F/T sensors and possesses the strengths of local search ability and better adaptability. [ABSTRACT FROM AUTHOR]

Ensemble tree machine learning models have proved useful for solving poorly understood and complex problems. This paper aims to calibrate the Eurocode 2 creep model by inserting a correction coefficient to the model. The correction coefficient is calculated using ensemble tree (bagging and boosting) models. The results showed that the insertion of the correction coefficient obtained by both the bagging and boosting models into the Eurocode 2 model led to significantly higher prediction accuracy. These approaches may lead to a better performance prediction, thereby reducing the effect of the time-dependent deformation on concrete structures. [ABSTRACT FROM AUTHOR]

Real-world classification data usually contain noise, which can affect the accuracy of the models and their complexity. In this context, an interesting approach to reduce the effects of noise is building ensembles of classifiers, which traditionally have been credited with the ability to tackle difficult problems. Among the alternatives to build ensembles with noisy data, bagging has shown some potential in the specialized literature. However, existing works in this field are limited and only focus on the study of noise based on a random mislabeling, which is unlikely to occur in real-world applications. Recent research shows that other types of noise, such as that occurring at class boundaries, are more common and challenging for classification algorithms. This paper delves into the analysis of the usage of bagging techniques in these complex problems, in which noise affects the decision boundaries among classes. In order to investigate whether bagging is able to reduce the impact of borderline noise, an experimental study is carried out considering a large number of datasets with different noise levels, and several noise models and classification algorithms. The results obtained reflect that bagging obtains a better accuracy and robustness than the individual models with this complex type of noise. The highest improvements in average accuracy are around 2–4% and are generally found at medium-high noise levels (from 15–20% onwards). The partial consideration of noisy samples when creating the subsamples from the original training set in bagging can make it so that only some parts of the decision boundaries among classes are impaired when building each model, reducing the impact of noise in the global system. [ABSTRACT FROM AUTHOR]

For the fault classification of chemical industries, the typical Fisher discriminant analysis (FDA) model requires that all the training samples should be correctly labeled. Actually, training samples tend to be polluted by mislabeled samples for some unavoidable reasons, and thus the performance of the FDA may be severely affected. However, in the chemical process monitoring community, there is little attention to fault classification with label-noise in training samples. To settle this open issue, a manifold-preserving sparse graph (MPSG)-based ensemble FDA model is initially proposed in this paper. First, under the condition of maintaining the underlying manifold structure of training samples, an MPSG is utilized to filter some mislabeled samples. Second, to improve model robustness to the left mislabeled samples, Bagging based on FDA is used to construct several subclassifiers, which are combined to form a robust classifier. Experiments on the benchmark Tennessee Eastman process and a real industrial air separation unit demonstrate the effectiveness and the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning systems for identifying Android malware related to a substring-based feature selection (SBFS) strategy for the classifiers. In the investigation, we have broadened our previous work where it has been seen that the ensemble-based learning techniques acquire preferred outcome over the recently revealed outcome by directing the DREBIN dataset, and in this manner they give a solid premise to building compelling instruments for Android malware detection. [ABSTRACT FROM AUTHOR]

Accurate and reliable low-voltage load forecasting is critical to optimal operation and control of distribution network and smart grid. However, compared to traditional regional load forecasting at high-voltage level, it faces tough challenges due to the inherent high uncertainty of the low-capacity load and distributed renewable energy integrated in the demand side. This paper proposes a novel hybrid ensemble deep learning (HEDL) approach for deterministic and probabilistic low-voltage load forecasting. The deep belief network (DBN) is applied to low-voltage load point prediction with the strong ability of approximating nonlinear mapping. A series of ensemble learning methods including bagging and boosting variants are introduced to improve the regression ability of DBN. In addition, the differencing transformation technique is utilized to ensure the stationarity of load time series for the application bagging and boosting methods. On the basis of the integrated thought of ensemble learning, a new hybrid ensemble algorithm is developed via integrating multiple separate ensemble methods. Considering the diversity in various ensemble algorithms, an effective K nearest neighbor classification method is utilized to adaptively determine the weights of sub-models. Furthermore, HEDL based probabilistic forecasting is proposed by taking advantage of the inherent resample idea in bagging and boosting. The effectiveness of the HEDL method for both deterministic and probabilistic forecasting has been systematically verified based on realistic load data from East China and Australia, indicating its promising prospective for practical applications in distribution networks. [ABSTRACT FROM AUTHOR]