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Detailed analysis of mass transfer in solar food dryer with different methods

INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2021年 128卷

作者： Das, Mehmet Alic, Erdem Akpinar, Ebru Kavak Tokat Gaziosmanpasa Univ Mech Engn Dept TR-60250 Tokat Turkey Kahramanmaras Sutcu Imam Univ Andirin Vocat High Sch TR-46410 Andirin Kahramanmaras Turkey Firat Univ Mech Engn Dept TR-23279 Elazig Turkey

In this study, the mass transfer, effective diffusion coefficient (Deff), activation energy (Ea), and drying efficiency values of the product dried in the solar food drying system were experimentally investigated. It was determined that Deff values, Ea values, and drying efficiency values calculated with experimental data varied between 0.15 x 10(-10) -0.1 x 10(-10) m(2)/s, between 21.6 and 15.9 kJ/mol, and between 51.8% and 15.9%, respectively. Mass transfer values in the drying system are modeled in detail with four different methods such as numerical analysis method (CFD), machine learning algorithms (ANN, SVM) and drying kinetics mathematical models (Newton, Page, Midilli and Kucuk, Thompson). The root means square error (RMSE) error analysis method was used to determine the validation of the models. For mass transfer studies, the moisture content (MR) values of the 10 mm thick product were taken into account. The novelty of this study is that mass transfer, which is an important parameter for drying system design and product selection, is expressed by mathematical empirical equations, artificial intelligence methods, and numerical analysis methods. The mass transfer of the product is shown by numerical analysis simulation with moisture contours. The method that models the MR values with the least error (0.01 RMSE) is the SVM algorithm.

关键词： Mass transfer Solar energy CFD machine learning algorithm

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Big data simulation for financial risk assessment of real estate bubble based on embedded system and artificial intelligence algorithm

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MICROPROCESSORS AND MICROSYSTEMS 2021年 82卷

作者： Zhang, Nannan BoHai Univ Sch Management Jinzhou 121000 Liaoning Peoples R China

The land is accepted as having oscillation examples and slacks from business change. Driving pointers and a few slacks of each wheel somewhat increase the risk of backing acceptance and silly desire inland giving companies. The board should consider the risks from diversity and desire and the risks from business tasks and administrations. Different types and risk levels for land board companies may differ from single financial experts. The administration of these business activities' risk is exceptional between the sizes of the companies, the scope of administration, and the geographical area and board of less land, working on and adjusting to private property. Therefore, options regarding land venture valuation should not simply be considered. Benefits, yet additionally accurately assess risk. The advantages and disadvantages are simultaneous-the more important, the more noticeable the comparable risk. Many domestic and unknown developing and economists are concerned about this problem. Develop a hypothesis and strategy for land venture risk - this will give land speculators some significant help in their venture options.

关键词： Financial risk assessment Real estate bubble Embedded system machine learning algorithm

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Modified Maximum Curvature Method (MMCM) and Logistic Regression: A Hybrid Architecture for Finger Vein Biometric Recognition System 23

Modified Maximum Curvature Method (MMCM) and Logistic Regres...

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23rd International Conference on Computer and Information Technology (ICCIT)

作者： Naquib, Faizah Binte Tabassom, Sharika Elahee, Fariha Mim, Farhana Hossain, Tonmoy Kalpoma, Kazi A. Ahsanullah Univ Sci & Technol Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781665422444

The finger vein authentication system is a prominent field in biometric-based research that prevents identity theft by forgery or spoofing. However, as the finger images are affected by many environmental factors such as illumination or shifting during imaging, they are often noisy and have irregularity in thickness or brightness which can cause a decline in the verification accuracy. Therefore, a meticulous finger vein pattern extraction method along with an accurate classification is necessary. Though the Maximum Curvature Method (MCM) gives promising verification accuracy, it fails to tackle the stated limitations. For this purpose, we proposed a Modified Maximum Curvature Method (MMCM) for vein extraction. In this paper, a hybrid architecture for finger vein biometric recognition system is stated with the combination of proposed MMCM and Logistic Regression (LR) machine learning classifier. Proposed MMCM incorporates Finger Region Extraction, Image Enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE), and Affine transform Normalization. The authentication is then carried out by fusing the proposed feature extraction with a set of machine learning Classifiers and evaluated based on their Equal Error Rate (EER) on the public database SDUMLA-HMT. The combination of MMCM vein extraction and LR classifier gives a satisfactory low EER of 0.043.

关键词： Maximum Curvature Method machine learning algorithm Logistic Regression Finger Vein Extraction CLAHE Biometric Recognition System

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machine learning Prediction Models for Diagnosing Hepatocellular Carcinoma with HCV-related Chronic Liver Disease

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020年 196卷 105551-105551页

作者： Hashem, Somaya ElHefnawi, Mahmoud Habashy, Shahira El-Adawy, Mohamed Esmat, Gamal Elakel, Wafaa Abdelazziz, Ashraf Omar Nabeel, Mohamed Mahmoud Abdelmaksoud, Ahmed Hosni Elbaz, Tamer Mahmoud Shousha, Hend Ibrahim Natl Res Ctr Syst & Informat Dept Engn Res Div Giza Egypt Helwan Univ Fac Engn Commun Elect & Comp Dept Cairo Egypt Cairo Univ Diagnost & Intervent Radiol Dept Cairo Egypt Cairo Univ Fac Med Endem Hepatogastroenterol Dept Cairo Egypt Natl Res Ctr Ctr Excellence Med Res Biomed Informat & Chemoinformat Grp Cairo Egypt

Background and Objective: Considered as one of the most recurrent types of liver malignancy, Hepatocellular Carcinoma (HCC) needs to be assessed in a non-invasive way. The objective of the current study is to develop prediction models for Chronic Hepatitis C (CHC)-related HCC using machine learning techniques. Methods: A dataset, for 4423 CHC patients, was investigated to identify the significant parameters for predicting HCC presence. In this study, several machine learning techniques (Classification and regression tree, alternating decision tree, reduce pruning error tree and linear regression algorithm) were used to build HCC classification models for prediction of HCC presence. Results: Age, alpha-fetoprotein (AFP), alkaline phosphate (ALP), albumin, and total bilirubin attributes were statistically found to be associated with HCC presence. Several HCC classification models were constructed using several machine learning algorithms. The proposed HCC classification models provide adequate area under the receiver operating characteristic curve (AUROC) and high accuracy of HCC diagnosis. AUROC ranges between 95.5% and 99%, plus overall accuracy between 93.2% and 95.6%. Conclusion: Models with simplistic factors have the power to predict the existence of HCC with outstanding performance. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Hepatocellular Carcinoma prediction machine learning algorithm Decision learning Tree Serum marker Hepatitis C virus

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machine-learning-based damage identification methods with features derived from moving principal component analysis

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2020年第21期27卷 1789-1802页

作者： Zhang, Ge Tang, Liqun Liu, Zejia Zhou, Licheng Liu, Yiping Jiang, Zhenyu South China Univ Technol Sch Civil Engn & Transportat State Key Lab Subtrop Bldg Sci Guangzhou 510640 Peoples R China

This paper aims to propose machine-learning-based damage identification methods with features derived from moving principal component analysis (MPCA) to improve the damage identification performance for engineering structures. Previously, machine learning algorithms have usually used structural responses as inputs directly. These methods show low damage identification capabilities and are susceptible to noise. In this paper, the eigenvectors of structural responses derived from MPCA are employed as inputs instead. Several traditional machine learning algorithms are applied for verification. The results demonstrate that as compared to strains and frequencies, their eigenvectors as inputs for machine learning algorithms render better performances for damage identification.

关键词： machine learning algorithm principal component analysis eigenvector time window damage localization damage quantification

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Validation of a Sensor-Based Gait Analysis System with a Gold-Standard Motion Capture System in Patients with Parkinson's Disease

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SENSORS 2021年第22期21卷 7680页

作者： Jakob, Verena Kuederle, Arne Kluge, Felix Klucken, Jochen Eskofier, Bjoern M. Winkler, Juergen Winterholler, Martin Gassner, Heiko Sana Krankenhaus Rummelsberg Movement & Gait Lab D-90592 Schwarzenbruck Germany Friedrich Alexander Univ Erlangen Nurnberg FAU Dept Artificial Intelligence Biomed Engn AIBE Machine Learning & Data Analyt Lab D-91052 Erlangen Germany Univ Luxembourg Digital Med L-4365 Luxembourg Luxembourg Luxembourg Inst Hlth Digital Med 1445 Str Luxembourg Luxembourg Friedrich Alexander Univ Erlangen Nurnberg FAU Univ Hosp Erlangen Dept Mol Neurol D-91054 Erlangen Germany Sana Krankenhaus Rummelsberg Dept Neurol D-90592 Schwarzenbruck Germany Fraunhofer Inst Integrated Circuits IIS Fraunhofer IIS D-91058 Erlangen Germany

Digital technologies provide the opportunity to analyze gait patterns in patients with Parkinson's Disease using wearable sensors in clinical settings and a home environment. Confirming the technical validity of inertial sensors with a 3D motion capture system is a necessary step for the clinical application of sensor-based gait analysis. Therefore, the objective of this study was to compare gait parameters measured by a mobile sensor-based gait analysis system and a motion capture system as the gold standard. Gait parameters of 37 patients were compared between both systems after performing a standardized 5 x 10 m walking test by reliability analysis using intra-class correlation and Bland-Altman plots. Additionally, gait parameters of an age-matched healthy control group (n = 14) were compared to the Parkinson cohort. Gait parameters representing bradykinesia and short steps showed excellent reliability (ICC > 0.96). Shuffling gait parameters reached ICC > 0.82. In a stridewise synchronization, no differences were observed for gait speed, stride length, stride time, relative stance and swing time (p > 0.05). In contrast, heel strike, toe off and toe clearance significantly differed between both systems (p < 0.01). Both gait analysis systems distinguish Parkinson patients from controls. Our results indicate that wearable sensors generate valid gait parameters compared to the motion capture system and can consequently be used for clinically relevant gait recordings in flexible environments.

关键词： Parkinson's disease wearables inertial sensors three-dimensional gait analysis machine learning algorithm spatiotemporal gait parameters

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Detection of Fake Tweets Using Sentiment Analysis

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SN Computer Science 2020年第2期1卷 1-7页

作者： Monica, C. Nagarathna, N. Department of Computer Science B. M. S. College of Engineering Bengaluru 560019 India

Social platform is one of the most commonly used sites in the today’s world, and people from different places exchange information, express opinions, etc. Twitter is one of the most convenient platforms where Twitter data are frequently used for research and are one of the largest micro-blogging platforms. As there is an increase in number of spam accounts, there is a need to distinguish between genuine and fake user accounts. In Twitter, there is rise in the number of spam accounts, to spread malicious information among the users. So, to overcome the problem of spam in Twitter, an analysis of the opinions and the emotions of users in the form of tweets have been carried out. In this paper, we present a model which performs sentimental analysis on real-time data that are collected on a particular topic of interest for the users who recently posted on that topic. Using this model, we compute the sentiment score of each user with the content-based features to detect spam in Twitter. The proposed method makes use of a custom rule-based algorithm for detection of bots and compares it with few other algorithms like MLP, decision tree and random forest to see how efficient the model is in detection of spam accounts. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep learning algorithm machine learning algorithm Rule-based algorithm Sentiment analysis Spam Twitter

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Applying separately cost-sensitive learning and Fisher's discriminant analysis to address the class imbalance problem: A case study involving a virtual gas pipeline SCADA system

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INTERNATIONAL JOURNAL OF CRITICAL INFRASTRUCTURE PROTECTION 2020年 29卷

作者： Choubineh, Abouzar Wood, David A. Choubineh, Zahak Petr Univ Technol Petr Engn Dept Abadan Iran DWA Energy Ltd Lincoln England Univ Kharazmi Dept Ind Engn Tehran Iran

Critical infrastructure, including refineries, pipelines and power grids are routinely monitored by supervisory control and data acquisition (SCADA) systems. The information exchange and communication aspects of such systems and their connected networks make them prone to cyberattacks. Providing SCADA systems with robust security and rapid cyber-attack detection is therefore imperative. Automatic intrusion detection can be provided by some machine learning methods, in particular, classification algorithms. However, such algorithms commonly disregard the difference between various misclassification errors. The techniques of cost-sensitive learning and Fisher's (linear) discriminant analysis (FDA) are separately investigated to overcome class imbalance issues in SCADA system datasets using five different machine learning algorithms applied to a well-studied gas pipeline dataset. The results reveal that the cost-sensitive learning is able to increase the performance of all the algorithms evaluated, especially their true positive rate. On the other hand, the FDA method can favorably influence only the HoeffdingTree and OneR algorithms. This suggests that the FDA method is not as powerful as the cost-sensitive learning in addressing class imbalance issues. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Intrusion detection Class imbalance problem Cost-sensitive learning, Fisher's discriminant analysis Cyber-attack assessment machine learning algorithm

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Novel machine learning Approaches for Modelling the Gully Erosion Susceptibility

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REMOTE SENSING 2020年第17期12卷 1-32页

作者： Arabameri, Alireza Nalivan, Omid Asadi Pal, Subodh Chandra Chakrabortty, Rabin Saha, Asish Lee, Saro Pradhan, Biswajeet Dieu Tien Bui Tarbiat Modares Univ Dept Geomorphol Tehran *** Iran Gorgan Univ Agr Sci & Nat Resources GUASNR Dept Watershed Management Gorgan *** Golestan Iran Univ Burdwan Dept Geog Bardhaman 713104 W Bengal India Korea Inst Geosci & Mineral Resources KIGAM Geosci Platform Res Div 124 Gwahak Ro Daejeon 34132 South Korea Korea Univ Sci & Technol Dept Geophys Explorat 217 Gajeong Ro Daejeon South Korea Univ Technol Sydney Fac Engn & Informat Technol Ctr Adv Modelling & Geospatial Informat Syst CAMG Ultimo NSW 2007 Australia Sejong Univ Dept Energy & Mineral Resources Engn 209 Neungdong Ro Seoul 05006 South Korea King Abdulaziz Univ Ctr Excellence Climate Change Res POB 80234 Jeddah 21589 Saudi Arabia Univ Kebangsaan Malaysia Earth Observat Ctr Inst Climate Change Bangi 43600 Selangor Malaysia Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agriculture and allied economic activities. Appropriate modeling of such erosion is therefore needed with optimum accuracy for estimating vulnerable regions and taking appropriate initiatives. The Golestan Dam has faced an acute problem of gully erosion over the last decade and has adversely affected society. Here, the artificial neural network (ANN), general linear model (GLM), maximum entropy (MaxEnt), and support vector machine (SVM) machine learning algorithm with 90/10, 80/20, 70/30, 60/40, and 50/50 random partitioning of training and validation samples was selected purposively for estimating the gully erosion susceptibility. The main objective of this work was to predict the susceptible zone with the maximum possible accuracy. For this purpose, random partitioning approaches were implemented. For this purpose, 20 gully erosion conditioning factors were considered for predicting the susceptible areas by considering the multi-collinearity test. The variance inflation factor (VIF) and tolerance (TOL) limit were considered for multi-collinearity assessment for reducing the error of the models and increase the efficiency of the outcome. The ANN with 50/50 random partitioning of the sample is the most optimal model in this analysis. The area under curve (AUC) values of receiver operating characteristics (ROC) in ANN (50/50) for the training and validation data are 0.918 and 0.868, respectively. The importance of the causative factors was estimated with the help of the Jackknife test, which reveals that the most important factor is the topography position index (TPI). Apart from this, the prioritization of all predicted models was estimated taking into account the training and validation data set, which should help future researchers to sel

关键词： land degradation gully erosion random partitioning approaches machine learning algorithm jackknife test

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Visual-tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2020年第5期17卷

作者： Jiao, Chenlei Lian, Binbin Wang, Zhe Song, Yimin Sun, Tao Tianjin Univ Key Lab Mech Theory & Equipment Design Minist Educ Tianjin 300072 Peoples R China

Object recognition is a prerequisite to control a soft gripper successfully grasping an unknown object. Visual and tactile recognitions are two commonly used methods in a grasping system. Visual recognition is limited if the size and weight of the objects are involved, whereas the efficiency of tactile recognition is a problem. A visual-tactile recognition method is proposed to overcome the disadvantages of both methods in this article. The design and fabrication of the soft gripper considering the visual and tactile sensors are implemented, where the Kinect v2 is adopted for visual information, bending and pressure sensors are embedded to the soft fingers for tactile information. The proposed method is divided into three steps: initial recognition by vision, detail recognition by touch, and a data fusion decision making. Experiments show that the visual-tactile recognition has the best results. The average recognition accuracy of the daily objects by the proposed method is also the highest. The feasibility of the visual-tactile recognition is verified.

关键词： Soft gripper visual-tactile recognition faster RCNN machine learning algorithm

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