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Advanced data modeling for industrial drying machine energy optimization

JOURNAL OF SUPERCOMPUTING 2022年第15期78卷 16820-16840页

作者： Barriga, R. Romero, M. Nettleton, D. Hassan, H. Ind Farmaceut Almirall Ctra 2Km 593 St Andreu De La Barca 08740 Spain Univ Politecn Valencia Dept Informat Sistemas & Comp Camino Vera 14 Valencia 46022 Spain IRIS Technol Solut Ctra Esplugues 39 Cornella De Llobregat 08940 Spain

In this article, a digital twin approach is proposed for modeling a pharmaceutical drying process using machine learning techniques, driven by data from different sensors captured in-line. The current difficulty with the drying process is mainly due to the manual operator control for choosing the end-point for terminating the drying step. This results in significant variability, depending on which human operator is supervising, and due to the multi-tasking nature of the job, generally allows a longer processing time to be sure the material is completely dry. The objective is to automate the end-point identification, thus optimizing the processing duration and therefore the overall energy consumption of the process. The point at which the drying is complete is indicated by the temperature difference between the ingoing and outgoing air flow. However, the stochastic nature of the process makes the data modeling a challenge. Firstly, a wide selection of supervised statistical and machine learning algorithms was benchmarked to find the one (CatBoost) which gave the best performance with the data. Next, the set of hyper-parameters was found for CatBoost which gave the optimum performance. This gave a best performance of 0.788 (R-2) fitting of the drying end-point prediction with the real values, for a large number of batches (over 700 K records). This is considered a good result taking into account the high residual of data models for these data and the stochastic nature of the process. The approach has been deployed in a real setting digital twin to control the drying process cutoff in the production plant. The results show the viability of the approach for modeling the process and automatically identifying the optimum end-point for the drying process, thus achieving significant energy savings which have been quantified as approximately 3.7 MWh per year for the pharmaceutical company.

关键词： Data models Digital simulation Energy consumption supervised learning algorithms Machine learning Predictive control Pharmaceutical technology Process modeling

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Cloud classification through machine learning and global horizontal irradiance data analysis

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QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 2024年第765期150卷 5435-5451页

作者： Lusi, Anabela Rocio Orte, Pablo Facundo Wolfram, Elian Orlando, Jose Ignacio UNIDEF CONICET Dept Invest Laseres & Aplicac DEILAP CITEDEFCNRSIFAECIIRL 3351 RA-3351 Villa Martelli Argentina Consejo Nacl Invest Cient & Tecn CONICET C1425 Buenos Aires Argentina Serv Meteorol Nacl SMN Direcc Redes Observac Buenos Aires Argentina UNCPBA CONICET Inst PLADEMA Yatiris Grp Tandil Argentina

Cloud observations and characterization are crucial owing to their influence on energy balance, climate, and weather. Their particular effects on radiation vary depending on different cloud parameters, such as cloud base or top height, water content, and cloud optical thickness, all of them closely related to the specific cloud type. Cloud classification therefore becomes a crucial task in meteorology, although it remains challenging for weather services worldwide owing to the intensive associated labor and cost. In this study we introduce a new low-cost method for automating cloud classification based on a combination of ground-based global horizontal irradiance (GHI) measurements, a clear-sky model, and machine learning. Based on the hypothesis that different cloud types have their own GHI signatures, we trained different supervised learning algorithms using GHI data manually labeled by meteorological observers from time-synchronized all-sky images. Multiple time windows were extracted from each GHI series, with eight features defined in each case to characterize the sequence. The best outcome was achieved using an XGBoost model on features extracted on time windows of 33 min, obtaining an accuracy of 0.88 and a Cohen's kappa of 0.84 in a held-out test set. The development presented in this study has the ability to provide low-cost cloud classification from ground-based observations, which is a challenge for weather services worldwide owing to intensive labor and cost.

关键词： all-sky images cloud classification ground-based measurements solar irradiance supervised learning algorithms

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Fake News Classification: Past, Current, and Future

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Computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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Predicting Patent Quality Based on Machine learning Approach

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2024年 71卷 3144-3157页

作者： Erdogan, Zulfiye Altuntas, Serkan Dereli, Turkay Iskenderun Tech Univ Dept Ind Engn TR-31200 Antakya Turkiye Yildiz Tech Univ Dept Ind Engn TR-34349 Istanbul Turkiye Hasan Kalyoncu Univ Off President TR-27000 Gaziantep Turkiye

The investment budget allocated by companies in R&D activities has increased due to increased competition in the market. Applications for industrial property rights by countries, investors, companies, and universities to protect inventions obtained as an outcome of investments have also increased. The selection of the patent to be invested becomes more difficult with the increasing number of applications. Therefore, predicting patent quality is quite significant for companies to be successful in the future. The level to which a patent meets the expectations of decision makers is referred to as patent quality. Patent indices represent decision makers' expectations. In this study, an approach is proposed to predict patent quality in practice. The proposed approach uses supervised learning algorithms and analytic hierarchy process (AHP) method. The proposed approach is applied to patents related to personal digital assistant technologies. The performances of individual and ensemble machine learning methods have been also analyzed to establish the prediction model. In addition, 75% split ratio and the five-fold cross-validation methods have been used to verify the prediction model. The multilayer perceptron algorithm has 76% accuracy value. The proposed prediction model is essential in directing R&D studies to the right technology areas and transferring the incentives to patent applications with a high quality rate.

关键词： Patents Codes Clustering algorithms Prediction algorithms Machine learning algorithms Predictive models Technological innovation Analytic hierarchy process (AHP) machine learning multilayer perceptron patent indices supervised learning algorithms

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Inferring Urban Land Use Using Large-Scale Social Media Check-in Data

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NETWORKS & SPATIAL ECONOMICS 2014年第3-4期14卷 647-667页

作者： Zhan, Xianyuan Ukkusuri, Satish V. Zhu, Feng Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA

Emerging location-based services in social media tools such as Foursquare and Twitter are providing an unprecedented amount of public-generated data on human movements and activities. This novel data source contains valuable information (e.g., geo-location, time and date, type of places) on human activities. While the data is tremendously beneficial in modeling human activity patterns, it is also greatly useful in inferring planning related variables such as a city's land use characteristics. This paper provides a comprehensive investigation on the possibility and validity of utilizing large-scale social media check-in data to infer land use types by applying the state-of-art data mining techniques. Two inference approaches are proposed and tested in this paper: the unsupervised clustering method and supervised learning method. The land use inference is conducted in a uniform grid level of 200 by 200 m. The methods are applied to a case study of New York City. The validation result confirms that the two approaches effectively infer different land use types given sufficient check-in data. The encouraging result demonstrates the potential of using social media check-in data in urban land use inference, and also reveals the hidden linkage between the human activity pattern and the underlying urban land use pattern.

关键词： Land use Social media Foursquare Geo-location data Data mining Clustering algorithms supervised learning algorithms

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A single supervised learning model to detect fake access points, frequency sweeping jamming and deauthentication attacks in IEEE 802.11 networks

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MACHINE learning WITH APPLICATIONS 2022年 10卷

作者： Amoordon, Andy Deniau, Virginie Fleury, Anthony Gransart, Christophe Univ Gustave Eiffel Campus Villeneuve DAscq Champs Sur Marne France Inst Mines Telecom IMT Nord Europe CERI SN Paris France Univ Lille Douai France

Wireless networks are nowadays indispensable components of telecommunication infrastructures. They offer flexibility, mobility and rapid expansion of telecommunication infrastructures. In wireless networks, transmissions are unisolated and most commonly emitted using omnidirectional antennas. This makes wireless networks more vulnerable to some specific attacks as compared to wired networks. For instance, attacks such as fake access points, intentional jamming and deauthentication can be easily perpetrated against IEEE 802.11 networks using freely accessible software and cheap hardware. Intentional jamming and deauthentication attacks are standalone attacks, but they can be combined with the fake access point attack to increase the latter's effectiveness. In our research, we work on methods to detect the three different attacks when they are perpetrated independently (one at a time) or concurrently (several at the same time). In this contribution, we present a model that can detect the three attacks, when perpetrated independently, by analysing a set of features (frame interval, Received Signal Strength Indicator, sequence number gap and management frame subtype) extracted from IEEE 802.11 management frame and radiotap headers. We have implemented the model using several supervised learning algorithms. The model with Random Forest and the K -Nearest Neighbour predictors have best detection precision (over 96 %) for fake access point and deauthentication attacks and perfectible detection precision for the intentional jamming attack (over 81%).

关键词： IEEE 802.11 networks Attacks Jamming Fake access point Deauthentication supervised learning algorithms Network Intrusion Detection System

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Forecasting hourly particulate matter concentrations based on the advanced multivariate methods

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2017年第5期14卷 1047-1054页

作者： Perisic, M. Maletic, D. Stojic, S. S. Rajsic, S. Stojic, A. Univ Belgrade Inst Phys Belgrade Pregrevica 118 Belgrade 11080 Serbia Singidunum Univ Danijelova 32 Belgrade 11010 Serbia

In this study, several multivariate methods were used for forecasting hourly PM10 concentrations at four locations based on SO2 and meteorological data from the previous period. According to the results, boosted decision trees and multi-layer perceptrons yielded the best predictions. The forecasting performances were similar for all examined locations, despite the additional PM10 spatio-temporal analysis showed that the sites were affected by different emission sources, topographic and microclimatic conditions. The best prediction of PM10 concentrations was obtained for industrial sites, probably due to the simplicity and regularity of dominant pollutant emissions on a daily basis. Conversely, somewhat weaker forecast accuracy was achieved at urban canyon avenue, which can be attributed to the specific urban morphology and most diverse emission sources. In conclusion to this, the integration of advanced multivariate methods in air quality forecasting systems could enhance accuracy and provide the basis for efficient decision-making in environmental regulatory management.

关键词： Air quality Environmental pollution Regulatory management supervised learning algorithms

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Structural damage assessment using linear approximation with maximum entropy and transmissibility data

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2015年 54-55卷 210-223页

作者： Meruane, V. Ortiz-Bernardin, A. Univ Chile Dept Mech Engn Santiago Chile

supervised learning algorithms have been proposed as a suitable alternative to model updating methods in structural damage assessment, being Artificial Neural Networks the most frequently used. Notwithstanding, the slow learning speed and the large number of parameters that need to be tuned within the training stage have been a major bottleneck in their application. This article presents a new algorithm for real-time damage assessment that uses a linear approximation method in conjunction with antiresonant frequencies that are identified from transmissibility functions. The linear approximation is handled by a statistical inference model based on the maximum-entropy principle. The merits of this new approach are twofold: training is avoided and data is processed in a period of time that is comparable to the one of Neural Networks. The performance of the proposed methodology is validated by considering three experimental structures: an eight-degree-of-freedom (DOE) mass-spring system, a beam, and an exhaust system of a car. To demonstrate the potential of the proposed algorithm over existing ones, the obtained results are compared with those of a model updating method based on parallel genetic algorithms and a multilayer feedforward neural network approach. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Structural damage assessment supervised learning algorithms Maximum-entropy principle Linear approximation

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Direct evidence that late Neanderthal occupation precedes a technological shift in southwestern Italy

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AMERICAN JOURNAL OF BIOLOGICAL ANTHROPOLOGY 2022年第1期179卷 18-30页

作者： Oxilia, Gregorio Bortolini, Eugenio Marciani, Giulia Menghi Sartorio, Jessica Cristina Vazzana, Antonino Bettuzzi, Matteo Panetta, Daniele Arrighi, Simona Badino, Federica Figus, Carla Lugli, Federico Romandini, Matteo Silvestrini, Sara Sorrentino, Rita Moroni, Adriana Donadio, Carlo Morigi, Maria Pia Slon, Viviane Piperno, Marcello Talamo, Sahra Collina, Carmine Benazzi, Stefano Univ Bologna Dept Cultural Heritage Via Ariani 1 I-48121 Ravenna Italy CSIC Inst Mila & Fontanals Invest Humanidades Dept Archaeol & Anthropol Barcelona Spain Univ Siena UR Preistoria & Antropol Dept Phys Sci Earth & Environm Siena Italy Univ Roma Tor Vergata Dept Enterprise Engn Mario Lucertini Rome Italy Univ Bologna Dept Phys & Astron Augusto Righi Bologna Italy CNR Inst Clin Physiol Pisa Italy CNR Inst Environm Geol & Geoengn Milan Italy Univ Modena & Reggio Emilia Dept Chem & Geol Sci Modena Italy Univ Naples Federico II Dept Earth Sci Environm & Resources Naples Italy Max Planck Inst Evolutionary Anthropol Dept Evolutionary Genet Leipzig Germany Tel Aviv Univ Dept Anat & Anthropol Tel Aviv Israel Tel Aviv Univ Dept Human Mol Genet & Biochem Sackler Fac Med Tel Aviv Israel Tel Aviv Univ Dan David Ctr Human Evolut & Biohist Res Tel Aviv Israel Museo Civ Archeol Biagio Greco Mondragone Italy Univ Bologna Dept Chem G Ciamician Bologna Italy Max Planck Inst Evolutionary Anthropol Dept Human Evolut Leipzig Germany

Objectives During the middle-to-upper Paleolithic transition (50,000 and 40,000 years ago), interaction between Neanderthals and Homo sapiens varied across Europe. In southern Italy, the association between Homo sapiens fossils and non-Mousterian material culture, as well as the mode and tempo of Neanderthal demise, are still vividly debated. In this research, we focus on the study of two human teeth by using 3D geometric morphometric approaches for a reliable taxonomical attribution as well as obtaining new radiometric dates on the archeological sequence. Material and Methods This work presents two lower deciduous molars uncovered at Roccia San Sebastiano (Mondragone-Caserta, Italy), stratigraphically associated with Mousterian (RSS1) and Uluzzian (RSS2) artifacts. To obtain a probabilistic attribution of the two RSS teeth to each reference taxa group composed of Neanderthals and Homo sapiens, we performed and compared the performance of three supervised learning algorithms (flexible discriminant analysis, multiadaptive regression splines, and random forest) on both crown and cervical outlines obtained by virtual morphometric methods. Results We show that RSS1, whose Mousterian context appears more recent than 44,800-44,230 cal BP, can be attributed to a Neanderthal, while RSS2, found in an Uluzzian context that we dated to 42,640-42,380 cal BP, is attributed to Homo sapiens. Discussion This site yields the most recent direct evidence for a Neanderthal presence in southern Italy and confirms a later shift to upper Paleolithic technology in southwestern Italy compared to the earliest Uluzzian evidence at Grotta del Cavallo (Puglia, Italy).

关键词： deciduous human molars Mediterranean Europe Neanderthal supervised learning algorithms Uluzzian virtual analysis

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Explainable artificial intelligence and machine learning algorithms for classification of thyroid disease

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DISCOVER APPLIED SCIENCES 2024年第7期6卷 1-17页

作者： Kumari, Priyanka Kaur, Baljinder Rakhra, Manik Deka, Aniruddha Byeon, Haewon Asenso, Evans Rawat, Anil Kumar Lovely Profess Univ Comp Sci & Engn Phagwara India Assam Town Univ Fac Engn Comp Sci & Engn Gauhati Assam India Inje Univ Dept AI & Software Gimhae 50834 South Korea Inje Univ Med Big Data Res Ctr Gimhae 50834 South Korea Univ Ghana Accra Ghana Lovely Profess Univ Elect & Commun Engn Phagwara India

A common endocrine issue affecting millions globally is thyroid illness. For this ailment to be effectively treated and managed, an early and accurate diagnosis is essential. Machine learning algorithms have attracted a lot of attention recently in the healthcare industry and have the potential to improve thyroid disease diagnosis and categorization. The implementation of machine learning methods for the classification of thyroid disease is presented in this study. To create predictive models, the study makes use of a dataset that includes a variety of thyroid-related factors, including age, gender, and hormone levels. To evaluate the effectiveness of several machine learning techniques in classifying thyroid diseases, including random forest, support vector machines, XG-Boost, and ensemble classifier, they are implemented and compared. To ensure robust model performance, the methodology includes data preparation, feature selection, and model training, as well as strategies for hyperparameter adjustment and cross-validation. To assess the algorithms' efficiency in differentiating between several thyroid illness classifications, such as hyperthyroidism, hypothyroidism, and the study measures the algorithms' accuracy, precision, recall, F1-score, voting, and area under the ROC curve. Machine learning models has been proven important tool for disease diagnosis and ***, in this research various machine learning models are implemented for thyroid disease *** analysis between various machine learning models is done to choose the best model for classification.

关键词： Thyroid disease Prediction Machine learning Data cleaning SVM supervised learning algorithms Ensemble classifier and their comparison

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