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Understanding and forecasting outer radiation belt electron flux dropouts is one of the top concerns in space physics. By constructing Support Vector Machine (SVM) models to predict storm‐time dropouts for both relativistic and ultra‐relativistic electrons over L = 4.0–6.0, we investigate the nonlinear correlations between various driving factors (model inputs) and dropouts (model output) and rank their relative importance. Only time series of geomagnetic indices and solar wind parameters are adopted as model inputs. A comparison of the performance of the SVM models that uses only one driving factor at a time enables us to identify the most informative parameter and its optimal length of time history. Its accuracy and the ability to correctly predict dropouts identifies the SYM‐H index as the governing factor at L = 4.0–4.5, while solar wind parameters dominate the dropouts at higher L‐shells (L = 6.0). Our SVM model also gives good prediction of dropouts during completely out‐of‐sample storms. Plain Language Summary The outer belt relativistic and ultra‐relativistic electrons, also known as “killer” electrons due to their deleterious effects on satellites, can exhibit fast and significant losses (also called dropouts), which can result from the combined effects of various physical processes. This study aims to identify the critical driving factors controlling dropouts using a machine‐learning approach, which enables us to extract physical insights by isolating different drivers, and ranking their importance by comparing the model performance. Our study adopts a unique way to relate the inputs to dropouts in a nonlinear way compared to the traditional statistical method. We construct Support Vector Machine models using a time series of geomagnetic indices and solar wind parameters as inputs to predict storm‐time dropouts based on 5‐year Van Allen Probes observations. Our results demonstrate that the SYM‐H index is the most informative input at L = 4.0–4.5, suggesting the dominant effects of the ring current in the inner magnetosphere. Solar wind pressure and density are regarded as the governing factor at L = 6.0, indicating the important impacts of solar wind drivers at higher L‐shells. Our SVM models give good predictions of dropouts during completely unseen storms, which are crucial for the understanding and forecasting of outer belt electron flux dropouts. Key Points We investigate the critical driving factors controlling dropouts by constructing dropout prediction models using Support Vector Machines (SVMs) The most informative (critical) inputs controlling dropouts are SYM‐H at L ≤ 4.5 and solar wind drivers at L = 6.0 with mixed impact in between Our ultimate best SVM models can capture the observed relativistic and ultra‐relativistic dropouts during completely unseen storm events

Forecasting energy demand within a distribution network is essential for developing strategies to manage and optimize available energy resources and the associated infrastructure. In this study, we consider remote communities in the Arctic located at the end of the radial distribution network without alternative energy supply. Therefore, it is crucial to develop an accurate forecasting model to manage and optimize the limited energy resources available. We first compare the accuracy of several models that perform short-and medium-term load forecasts in rural areas, where a single industrial customer dominates the electricity consumption. We consider both statistical methods and machine learning models to predict energy demand. Then, we evaluate the transferability of each method to a geographical rural area different from the one considered for training. Our results indicate that statistical models achieve higher accuracy on longer forecast horizons relative to neural networks, while the machine-learning approaches perform better in predicting load at shorter time intervals. The machine learning models also exhibit good transferability, as they manage to predict well the load at new locations that were not accounted for during training. Our work will serve as a guide for selecting the appropriate prediction model and apply it to perform energy load forecasting in rural areas and in locations where historical consumption data may be limited or even not available.

Background Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown great improvement in predicting the metastasis of several types of cancers. However, it is little reported for CAD. This study therefore aimed to screen an optimal classier model of CAD with liver metastasis and explore the metastatic mechanisms of genes when applying this classier model. Methods The differentially expressed genes between primary CAD samples and CAD with metastasis samples were screened from the Moffitt Cancer Center (MCC) dataset GSE131418. The classification performances of six selected algorithms, namely, LR, RF, SVM, GBDT, NN, and CatBoost, for classification of CAD with liver metastasis samples were compared using the MCC dataset GSE131418 by detecting their classification test accuracy. In addition, the consortium datasets of GSE131418 and GSE81558 were used as internal and external validation sets to screen the optimal method. Subsequently, functional analyses and a drug‐targeted network construction of the feature genes when applying the optimal method were conducted. Results The optimal CatBoost model with the highest accuracy of 99%, and an area under the curve of 1, was screened, which consisted of 33 feature genes. A functional analysis showed that the feature genes were closely associated with a “steroid metabolic process” and “lipoprotein particle receptor binding” (eg APOB and APOC3). In addition, the feature genes were significantly enriched in the “complement and coagulation cascade” pathways (eg FGA, F2, and F9). In a drug‐target interaction network, F2 and F9 were predicted as targets of menadione. Conclusion The CatBoost model constructed using 33 feature genes showed the optimal classification performance for identifying CAD with liver metastasis. APOB, APOC3, FGA, F2, F9, and NKX2‐3 were potential biomarkers for classification of CAD with liver metastasis. Menadione might be a promising anti‐metastatic drug of CAD cells through functioning its role at sites of F2 and F9. CatBoost model constructed by 33 feature genes showed the optimal classification performance for identifying CAD liver metastasis.

Cloud computing is currently the most cost-effective means of providing commercial and consumer IT services online. However, it is prone to new flaws. An economic denial of sustainability attack (EDoS) specifically leverages the pay-per-use paradigm in building up resource demands over time, culminating in unanticipated usage charges to the cloud customer. We present an effective approach to mitigating EDoS attacks in cloud computing. To mitigate such distributed attacks, methods for detecting them on different cloud computing smart grids have been suggested. These include hard-threshold, machine, and deep learning, support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF) tree algorithms, namely convolutional neural network (CNN), and long short-term memory (LSTM). These algorithms have greater accuracies and lower false alarm rates and are essential for improving the cloud computing service provider security system. The dataset of nine injection attacks for testing machine and deep learning algorithms was obtained from the Cyber Range Lab at the University of New South Wales (UNSW), Canberra. The experiments were conducted in two categories: binary classification, which included normal and attack datasets, and multi-classification, which included nine classes of attack data. The results of the proposed algorithms showed that the RF approach achieved accuracy of 98% with binary classification, whereas the SVM model achieved accuracy of 97.54% with multi-classification. Moreover, statistical analyses, such as mean square error (MSE), Pearson correlation coefficient (R), and the root mean square error (RMSE), were applied in evaluating the prediction errors between the input data and the prediction values from different machine and deep learning algorithms. The RF tree algorithm achieved a very low prediction level (MSE = 0.01465) and a correlation R2 (R squared) level of 92.02% with the binary classification dataset, whereas the algorithm attained an R2 level of 89.35% with a multi-classification dataset. The findings of the proposed system were compared with different existing EDoS attack detection systems. The proposed attack mitigation algorithms, which were developed based on artificial intelligence, outperformed the few existing systems. The goal of this research is to enable the detection and effective mitigation of EDoS attacks.

Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approaches—partial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)—were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R2 = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ significantly between the model with GA (R2 = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R2 = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R2 = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R2 = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R2 = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain.

The utilisation of DM (Data Mining) and ML (Machine Learning) approaches in the BC (Breast Cancer) diagnosis has recently gained a lot of consideration. However, most of these works still need enhancement since either they were assessed utilising insufficient evaluation-metrics, or they weren’t statistically-assessed, or both. Lately, one-of-the-most effective and well-known ML approaches is OSELM (Online Sequential Extreme Learning Machine), it has seen as an efficient and reputable technique for classifying-data, however it has not been implemented in BC diagnosis problem. Consequently, this research proposes the OSELM approach in-order-to enhance the rate of accuracy for the BC diagnosis. The OSELM technique has the ability to (a) capability to be applied on both (multi-class and binary) classification, (b) prevent overfitting, as well as (c) It has a comparable ability to kernel-based SVM (Support Vector Machine) and operates with a neural-network-structure. In this research, two different BC datasets (WDBC (Wisconsin Diagnostic Breast Cancer) and WBCD (Wisconsin Breast Cancer Database)) were utilised to evaluate the OSELM approach performance. The experiments outcomes have revealed the outstanding-performance of the proposed OSELM approach, which attained an average of precision 94.09%, recall 95.57%, accuracy 96.13%, G -Mean 94.82%, F -Measure 94.80%, specificity 96.51%, and MCC 91.76% using WDBC dataset. Besides, attained an average of precision 95.08%, recall 98.89%, accuracy 97.89%, G -Mean 96.96%, F -Measure 96.93%, specificity 97.41%, and MCC 95.39% using WBCD dataset. This indicates that the OSELM approach is a reliable technique for the BC diagnosis and might be suitable for solving other-applications-related issues in the sector of healthcare. Besides, it can serve as a valuable decision-support tool for oncologists, providing additional information and insights to aid in their diagnoses and treatment plans.

Purpose>This paper provides a structured literature review of blockchain in accounting. The authors identify current trends, analyse and critique the key topics of research and discuss the future of this nascent field of inquiry.Design/methodology/approach>This study’s analysis combined a structured literature review with citation analysis, topic modelling using a machine learning approach and a manual review of selected articles. The corpus comprised 153 academic papers from two ranked journal lists, the Association of Business Schools (ABS) and the Australian Business Deans Council (ABDC), and from the Social Science Research Network (SSRN). From this, the authors analysed and critiqued the current and future research trends in the four most predominant topics of research in blockchain for accounting.Findings>Blockchain is not yet a mainstream accounting topic, and most of the current literature is normative. The four most commonly discussed areas of blockchain include the changing role of accountants; new challenges for auditors; opportunities and challenges of blockchain technology application; and the regulation of cryptoassets. While blockchain will likely be disruptive to accounting and auditing, there will still be a need for these roles. With the sheer volume of information that blockchain records, both professions may shift out of the back-office toward higher-profile advisory roles where accountants try to align competitive intelligence with business strategy, and auditors are called on ex ante to verify transactions and even whole ecosystems.Research limitations/implications>The authors identify several challenges that will need to be examined in future research. Challenges include skilling up for a new paradigm, the logistical issues associated with managing and monitoring multiple parties all contributing to various public and private blockchains, and the pressing need for legal frameworks to regulate cryptoassets.Practical implications>The possibilities that blockchain brings to information disclosure, fraud detection and overcoming the threat of shadow dealings in developing countries all contribute to the importance of further investigation into blockchain in accounting.Originality/value>The authors’ structured literature review uniquely identifies critical research topics for developing future research directions related to blockchain in accounting.

Particularly in the last decade, Internet usage has been growing rapidly. However, as the Internet becomes a part of the day to day activities, cybercrime is also on the rise. Cybercrime will cost nearly $6 trillion per annum by 2021 as per the cybersecurity ventures report in 2020. For illegal activities, cybercriminals utilize any network computing devices as a primary means of communication with a victims’ devices, so attackers get profit in terms of finance, publicity and others by exploiting the vulnerabilities over the system. Cybercrimes are steadily increasing daily. Evaluating cybercrime attacks and providing protective measures by manual methods using existing technical approaches and also investigations has often failed to control cybercrime attacks. Existing literature in the area of cybercrime offenses suffers from a lack of a computation methods to predict cybercrime, especially on unstructured data. Therefore, this study proposes a flexible computational tool using machine learning techniques to analyze cybercrimes rate at a state wise in a country that helps to classify cybercrimes. Security analytics with the association of data analytic approaches help us for analyzing and classifying offenses from India-based integrated data that may be either structured or unstructured. The main strength of this work is testing analysis reports, which classify the offenses accurately with 99 percent accuracy.

Fake news creates social turbulence, which may hamper our social or economic equilibrium. Researchers have harnessed machine learning (ML) and deep learning (DL) algorithms to combat this challenge, particularly in disparate environments. Numerous techniques have been created to classify false news based on various textual features, including deep learning, machine learning, and evolutionary methodologies. Although fake news sentiment analysis is not entirely new, sentiment score-based artificial news analysis is rarely used. Our method incorporates machine learning techniques and deep learning techniques, such as LSTM-BiLSTM, with SentiWordNet parser-obtained sentiment scores. This integration improves feature sets and enables a more detailed analysis of emotional context. This research pioneers using machine learning along with deep learning techniques based on sentiment scores, an innovative approach within the field. Our research substantially improves the detection of false news. Recall and F-measure are significantly enhanced using machine learning techniques with the COVID-19 dataset. Moreover, sentiment-based deep learning techniques used for both the LIAR and COVID-19 datasets surpass previous benchmarks, obtaining a remarkable accuracy improvement of over 15% on the LIAR dataset compared to existing literature. This pioneering sentiment score-based approach enhances fake news detection accuracy, offering a potent tool to counter misinformation and safeguard societal equilibrium.