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作者： Ilievski, G. Brno University of Technology

Cloudification of all computing environments is an undergoing process. The process has overpassed the classical Virtual Machines (VM) and Software-Defined Networking (SDN) approach and has moved towards dockerizing, microservices, app functions, network functions etc. 5G penetration is another trend, and it is built on such platforms. In this environment we are investigating the efficiency of supervised machine learning algorithms for classification of regular and encrypted Voice over IP (VoIP) traffic that 5G relies on, within a virtualized Network Functions Virtualization (NFV) environment and an east-west based network traffic. We are using statistical methods for classification of network packets without the need of inspecting the payload data and without the source, destination and port information of the packets. The efficiency is analyzed from a point of precision of the classification, but also from a point of time consumption, as adding delay to the original traffic may cause a problem, especially within 5G environments where packet delay is crucial.

关键词： VoIP classification supervised algorithms machine learning NFV 5G

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Combined Forest: a New supervised Approach for a Machine-Learning-based Botnets Detection

Combined Forest: a New Supervised Approach for a Machine-Lea...

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IEEE Global Communications Conference (GLOBECOM)

作者： Maudoux, Christophe Boumerdassi, Selma Barcello, Alex Renault, Eric CNAM Cedric Paris France ESIEE Paris Noisy Le Grand France ESIEE LIGM Paris Noisy Le Grand France

ISBN: (纸本)9781728181042

Nowadays, botnet-based attacks are the most prevalent cyber-threats type. It is therefore essential to detect this kind of malware using efficient bots detection techniques. This paper presents our security anomalies detection system, based on a model that we named Combined Forest. Our approach consists of merging some pre-processed Decision Trees to highlight different kinds of botnet by detecting their intrinsic exchanges. Using a supervised data approach, each tree is built from a labelled dataset. In order to achieve this, we aggregate the IP-flows into Traffic-flows to extract key features and avoid over-fitting. Then, we tested different machine learning algorithms and selected the most suitable one. After that, many experiments have been done to determine the best parameters and design the most accurate, adaptative and efficient model.

关键词： botnets anomalies detection machine learning supervised algorithms meta algorithms network cybersecurity

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Indicator displacement assay for freshness monitoring of green tea during storage

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FOOD RESEARCH INTERNATIONAL 2023年第1期167卷 112668页

作者： Zhang, Yiyi Yuan, Wenxuan Ren, Zhengyu Ning, Jingming Wang, Yujie Anhui Agr Univ State Key Lab Tea Plant Biol & Utilizat Hefei Peoples R China

Aging of green tea leads to reductions in its flavor and health value, yet in situ testing methods for green tea freshness are lacking. A novel sensitive indicator displacement assay (IDA) sensor was constructed and applied for monitoring of green tea freshness during storage. Low-cost pH dyes and metal ions were used as indicators and receptors, respectively, for the targeted detection of catechins in tea samples. The feasibility of the IDA reaction was verified using images and UV-vis spectroscopy, respectively. IDA combined with supervised algorithms achieved accurate identification of green tea freshness with an accuracy of 86.67%, and acceptable accuracies in the prediction of catechin monomers and total catechins with ratio of prediction to deviation values over 1.5. Thus, the developed IDA sensor is capable of qualitative and quantitative monitoring of the green tea freshness during storage, providing a new option for quality evaluation and control of green teas.

关键词： Indicator displacement array Green tea Freshness monitoring supervised algorithms Low-cost sensor

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Discrimination of Leaves in a Multi-Layered Mediterranean Forest through Machine Learning algorithms

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REMOTE SENSING 2023年第18期15卷 4450页

作者： Alvites, Cesar Maesano, Mauro Molina-Valero, Juan Alberto Lasserre, Bruno Marchetti, Marco Santopuoli, Giovanni Univ Molise Dipartimento Biosci & Terr Cda Fonte Lappone Snc I-86090 Pesche Italy Univ Tuscia Dept Innovat Biol Agrofood & Forest Syst DIBAF I-01100 Viterbo Italy Czech Univ Life Sci Prague CZU Fac Forestry & Wood Sci Prague 16500 Czech Republic Univ Molise Dipartimento Agr Ambiente & Alimenti Via De Sanctis Snc I-86100 Campobasso Italy

Terrestrial laser scanning (TLS) technology characterizes standing trees with millimetric precision. An important step to accurately quantify tree volume and above-ground biomass using TLS point clouds is the discrimination between timber and leaf components. This study evaluates the performance of machine learning (ML)-derived models aimed at discriminating timber and leaf TLS point clouds, focusing on eight Mediterranean tree species datasets. The results show the best accuracies for random forests, gradient boosting machine, stacked ensemble model, and deep learning models with an average F1 score equal to 0.92. The top-performing ML-derived models showed well-balanced average precision and recall rates, ranging from 0.86 to 0.91 and 0.92 to 0.96 for precision and recall, respectively. Our findings show that Italian maple, European beech, hazel, and small-leaf lime tree species have more accurate F1 scores, with the best average F1 score of 0.96. The factors influencing the timber-leaf discrimination include phenotypic factors, such as bark surface (i.e., roughness and smoothness), technical issues (i.e., noise points and misclassification of points), and secondary factors (i.e., bark defects, lianas, and microhabitats). The top-performing ML-derived models report a time computation ranging from 8 to 37 s for processing 2 million points. Future studies are encouraged to calibrate, configure, and validate the potential of top-performing ML-derived models on other tree species and at the plot level.

关键词： timber-leaf components TLS point clouds tree structure machine learning Mediterranean forests supervised algorithms

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Towards Data-Driven Models in the Prediction of Ship Performance (Speed-Power) in Actual Seas: A Comparative Study between Modern Approaches

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ENERGIES 2022年第16期15卷 6094-6094页

作者： Alexiou, Kiriakos Pariotis, Efthimios G. Leligou, Helen C. Zannis, Theodoros C. Univ West Attica Dept Ind Design & Prod Engn Athens 12243 Greece Hellen Naval Acad Naval Architecture & Marine Engn Sect Piraeus 18539 Greece

In the extremely competitive environment of shipping, minimizing shipping cost is the key factor for the survival and growth of shipping companies. However, stricter rules and regulations that aim at the reduction of greenhouse gas emissions published by the International Maritime Organization, force shipping companies to increase the operational efficiency of their fleet. The prediction of a ship speed in actual seas with a given power by its engine is the most important performance indicator and thus makes it the "holy grail" in pursuing better efficiency. Traditionally, tank model tests and semi-empirical formulas were the preferred solution for the aforementioned prediction and are still widely applied. However, currently, with the increased computational power that is widely available, novel and more sophisticated methods taking into consideration computational fluid dynamics (CFD) and machine learning (ML) algorithms are emerging. In this paper, we briefly present the different approaches in the prediction of a ship's speed but focus on ML methods comparing a representative number of the latest data-driven models used in papers, to provide guidelines, discover trends and identify the challenges to be faced by researchers. From this comparison, we can distinguish that artificial neural networks (ANN), being used in 73.3% of the reviewed papers, dominate as the algorithm of choice. Researchers mostly rely on physical laws governing the phenomena in the crucial part of data preprocessing tasks. Lastly, most researchers rely on data acquisition systems installed at ships in order to achieve usable results.

关键词： machine learning (ML) supervised algorithms artificial neural networks (ANN) data driven fuel oil consumption (FOC) resistance semi-empirical model

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supervised mineral exploration targeting and the challenges with the selection of deposit and non-deposit sites thereof

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APPLIED GEOCHEMISTRY 2021年 128卷 104940-104940页

作者： Rahimi, Hossain Abedi, Maysam Yousefi, Mahyar Bahroudi, Abbas Elyasi, Gholam-Reza Univ Tehran Coll Engn Sch Min Engn Geoexplorat Targeting Lab GET Lab Tehran Iran Malayer Univ Fac Engn Malayer Iran

Selection of non-deposit sites is a challenging issue affecting the application of supervised algorithms for modeling mineral exploration targets. For this, equal number of deposit and non-deposit sites has been widely applied for training purposes. In this paper, we investigated the effect of changes in the number of non-deposit sites on the effectiveness of exploration targeting models while the number of deposit sites is constant. The results obtained demonstrated that exploration targeting models are affected by the ratio of non-deposit and deposit sites. Thus, balancing between the number of deposit and non-deposit sites is an efficient way to produce more-reliable exploration targets when supervised algorithms are applied for modeling. The idea of this research came from the fact that mineralization is a rare event, and therefore, in a region of interest number of nondeposit sites is much more than that of deposit events. To illustrate the procedure proposed, we used an exploration dataset of porphyry Cu mineralization in Chahargonbad area, SE Iran. A sequence application of selforganizing map and multilayer perceptron neural network algorithm was applied to better illustration of the changing effects of the number of non-deposit sites on the ensuing exploration targeting models.

关键词： supervised algorithms Training sites Known deposit locations Non-deposit locations Balancing Mineral exploration targeting

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supervised Dynamic Probabilistic Risk Assessment of Complex Systems, Part 1: General Overview

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2021年 208卷 107406-107406页

作者： Parhizkar, Tarannom Vinnem, Jan Erik Utne, Ingrid Bouwer Mosleh, Ali Norwegian Univ Sci & Technol NTNU Dept Marine Technol Trondheim Norway Univ Calif Los Angeles B John Garrick Inst Risk Sci Los Angeles CA USA Univ Calif Los Angeles Dept Mat Sci & Engn Los Angeles CA 90024 USA

Dynamic probabilistic risk assessment (DPRA) is a systematic and comprehensive methodology that has been used and refined over the past decades to evaluate risks associated with complex systems. However, current approaches to construct and execute DPRA models are challenged by high execution time owing to numerous possible scenarios. This issue will affect the execution time of the model, which is in contrast with the aim of modeling. DPRA models must be sufficiently fast to assist decision-making processes in the required time. In this study, a new method is proposed to enhance the execution times of DPRA models. This method uses optimization algorithms to determine failure scenarios and sort scenarios based on their occurrence probabilities. The most efficient optimization algorithms, considering the nature of the DPRA models, are mixed-integer sequential quadratic programming, modified branch-and-bound algorithm, and modified particle swarm optimization, which are then compared and discussed. To validate the effectiveness of this method, a simple case study is presented. The results show the effectiveness of the method, which has high accuracy and reduces the execution time significantly (e.g. execution time of risk assessment of 16,464 possible behavior scenarios after an incident in a dynamic positioning system is one fifth of the conventional methods). A detailed supervised DPRA model of dynamic positioning systems and its application on three incidents that occurred in the Norwegian offshore sector in previous years is presented in a subsequent article (Part 2 of 1) (Parhizkar et. al.). Case study results confirm that the supervised DPRA method can be applied to other complex systems so that the dynamic probabilistic risk values can be evaluated quickly and accurately.

关键词： Dynamic probabilistic risk assessment supervised algorithms Optimization model Decision-making process Emergency situations Dynamic positioning system

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Optimize output of a piezoelectric cantilever by machine learning ensemble algorithms

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MATERIALS TODAY COMMUNICATIONS 2022年 31卷

作者： Du, Jinxu Chen, Haobin Yang, Yaodong Rao, Wei-Feng Qilu Univ Technol Shandong Inst Mech Design & Res Shandong Acad Sci Sch Mech Engn Jinan 250353 Shandong Peoples R China First Peoples Hosp Qu Jing Dept Pathol Qu Jing 655000 Yunnan Peoples R China

Due to fast development of various electronic products in our life, the demand for sustainable energy supply devices has increased. Vibration energy harvest systems made by piezoelectric materials such as lead zirconate titanate (PZT) have gradually attracted widespread attention. For specific usage scenarios, we need to establish the relationship between geometry parameters and output voltage/power quickly, which is a difficult job. Here, we demonstrated that through processing 2430 sets experimental output voltage/power data generated by PZT cantilevers, we could find out the relationship between the output, the physical parameters and working frequency via machine learning algorithms quickly. Three machine learning ensemble algorithms (gradient boosting regression tree, random forest and extreme gradient boosting) are used to process these experimental data and the optimal algorithm is found. Our work showed that machine learning ensemble algorithm can help us design energy harvest systems efficiently.

关键词： Piezoelectric properties Energy harvesters Machine learning supervised algorithms

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Interpretation of intelligence in CNN-pooling processes: a methodological survey

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NEURAL COMPUTING & APPLICATIONS 2020年第3期32卷 879-898页

作者： Akhtar, Nadeem Ragavendran, U. Deemed Univ SVKMs NMIMS Shirpur India

The convolutional neural network architecture has different components like convolution and pooling. The pooling is crucial component placed after the convolution layer. It plays a vital role in visual recognition, detection and segmentation course to overcome the concerns like overfitting, computation time and recognition accuracy. The elementary pooling process involves down sampling of feature map by piercing into subregions. This piercing and down sampling is defined by the pooling hyperparameters, viz. stride and filter size. This down sampling process discards the irrelevant information and picks the defined global feature. The generally used global feature selection methods are average and max pooling. These methods decline, when the main element has higher or lesser intensity than the nonsignificant element. It also suffers with locus and order of nominated global feature, hence not suitable for every situation. The pooling variants are proposed by numerous researchers to overcome concern. This article presents the state of the art on selection of global feature for pooling process mainly based on four categories such as value, probability, rank and transformed domain. The value and probability-based methods use the criteria such as the way of down sampling, size of kernel, input output feature map, location of pooling, number stages and random selection based on probability value. The rank-based methods assign the rank and weight to activation;the feature is selected based on the defined criteria. The transformed domain pooling methods transform the image to other domains such as wavelet, frequency for pooling the feature.

关键词： Pooling intelligence Hybrid machine learning tools supervised algorithms Stable sampling Deep learning methodology Self-learning artificial neural network

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Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study

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BMC MEDICAL RESEARCH METHODOLOGY 2020年第1期20卷 262-262页

作者： Servia, Luis Montserrat, Neus Badia, Mariona Antonio Llompart-Pou, Juan Abelardo Barea-Mendoza, Jesus Chico-Fernandez, Mario Sanchez-Casado, Marcelino Manuel Jimenez, Jose Maria Mayor, Dolores Trujillano, Javier Univ Lleida IRBLIeida Hosp Univ Arnau de Vilanova Serv Med Intens Avda Rovira Roure 80 Lleida 25198 Spain Hosp Univ Son Espases Inst Invest Sanitaria Illes Balears Serv Med Intens Palma De Mallorca Spain Hosp Univ 12 Octubre UCI Trauma & Emergencias Serv Med Intens Madrid Spain Hosp Virgen Salud Serv Med Intens Toledo Spain Hosp Univ Puerta Mar Serv Med Intens Cadiz Spain Complejo Hosp Torrecardenas Serv Med Intens Almeria Spain

Background Interest in models for calculating the risk of death in traumatic patients admitted to ICUs remains high. These models use variables derived from the deviation of physiological parameters and/or the severity of anatomical lesions with respect to the affected body areas. Our objective is to create different predictive models of the mortality of critically traumatic patients using machine learning techniques. Methods We used 9625 records from the RETRAUCI database (National Trauma Registry of 52 Spanish ICUs in the period of 2015-2019). Hospital mortality was 12.6%. Data on demographic variables, affected anatomical areas and physiological repercussions were used. The Weka Platform was used, along with a ten-fold cross-validation for the construction of nine supervised algorithms: logistic regression binary (LR), neural network (NN), sequential minimal optimization (SMO), classification rules (JRip), classification trees (CT), Bayesian networks (BN), adaptive boosting (ADABOOST), bootstrap aggregating (BAGGING) and random forest (RFOREST). The performance of the models was evaluated by accuracy, specificity, precision, recall, F-measure, and AUC. Results In all algorithms, the most important factors are those associated with traumatic brain injury (TBI) and organic failures. The LR finds thorax and limb injuries as independent protective factors of mortality. The CT generates 24 decision rules and uses those related to TBI as the first variables (range 2.0-81.6%). The JRip detects the eight rules with the highest risk of mortality (65.0-94.1%). The NN model uses a hidden layer of ten nodes, which requires 200 weights for its interpretation. The BN find the relationships between the different factors that identify different patient profiles. Models with the ensemble methodology (ADABOOST, BAGGING and RandomForest) do not have greater performance. All models obtain high values in accuracy, specificity, and AUC, but obtain lower values in recall. The greatest

关键词： Intensive care unit Machine learning techniques supervised algorithms Traumatic patient Mortality

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