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International Conference on Next Generation Computing and Information Systems (ICNGCIS)

作者： Gupta, Shreya Mittal, Mohit Padha, Anupama Guru Nanak Dev Univ Dept CET Amritsar Punjab India MIET Dept CSE Jammu India

ISBN: (纸本)9780769563619

Wireless sensor network (WSN) is one of emerging technologies in today's scenario. Due to progressive advancement in micro-electro-mechanical system (MEMS) technology it can easily deployed in harsh environment. Sensor node communicates with their neighboring sensor nodes via radio frequencies and has many notable capabilities like self-configurable, self-manageable and monitoring physical phenomenon. Wireless sensor network is gaining popularity due to presence of many characteristics like cheap, cost-effective, reliable etc. along with this it has one major challenge that is limited battery life. To overcome this challenge, many solutions have found till date such as improvising routing protocols, reduction in computation of data, modification in time-stamp synchronization etc but still need more work. In this paper, our major focus is on processing of sensor dataset using various machine learning algorithms. We have managed different range of datasets from hundreds to thousands values and processed with various supervised machine learning algorithms. Simulation result shows that Gaussian Naive Bayes algorithm prominently gives better results than other algorithms in terms of accuracy parameter.

关键词： Wireless sensor network supervised machine learning algorithms MEMS

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Design of 2D/2D β-Ni(OH)₂/ZnO heterostructures via photocatalytic deposition of nickel for sonophotocatalytic degradation of tetracycline and modeling with three supervised machine learning algorithms

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Chemosphere 2024年 352卷 141328页

作者： Ajami Yazdi, Aghil Ebrahimian Pirbazari, Azadeh Esmaeili Khalil Saraei, Fatemeh Esmaeili, Amin Ebrahimian Pirbazari, Ali Akbari Kohnehsari, Atena Derakhshesh, Ali Hybrid Nanomaterials & Environment Lab Fouman Faculty of Engineering College of Engineering University of Tehran Fouman 43581- 39115 Iran Data Mining Research Group Fouman Faculty of Engineering College of Engineering University of Tehran Fouman 43581-39115 Iran Department of Chemical Engineering College of Engineering Technology University of Doha for Science and Technology Arab League St Doha 24449 Qatar Environment Lab Eshtehard Industrial Park Alborz Eshtehard 31881336 Iran

Due to the expansive use of tetracycline antibiotics (TCs) to treat various infectious diseases in humans and animals, their presence in the environment has created many challenges for human societies. Therefore, providing green and cost-effective solutions for their effective removal has become an urgent need. Here, we will introduce 2D/2D p-n heterostructures that exhibit excellent sonophotocatalytic/photocatalytic properties for water-soluble pollutant removal. In this contribution, for the first time, β- Ni(OH)₂ nanosheets were synthesized through visible-light-induced photodeposition of different amounts of nickel on ZnO nanosheets (β-Ni(x)/ZNs) to fabricate 2D/2D p-n heterostructures. The PXRD patterns confirmed the formation of wurtzite phase for ZNs and the hexagonal crystal structure of β-Ni(OH)₂. The FESEM and TEM micrographs showed that the β-Ni(OH)₂ sheets were dispersed on the surface of ZNs and formed 2D/2D p-n heterojunction in β-Ni(x)/ZNs samples. With the photodeposition of β-Ni(OH)₂ nanosheets on ZNs, the surface area, pore volume, and pore diameter of β-Ni(x)/ZNs heterostructures have increased compared to ZNs, which can have a positive effect on the sonophotocatalytic/photocatalytic performance of ZNs. The degradation experiments showed that β-Ni(0.1)/ZNs and β-Ni(0.4)/ZNs have the highest degradation percentage in photocatalytic (51 %) and sonophotocatalytic (71 %) degradation of TC, respectively. Finally, the sonophotocatalytic/photocatalytic degradation process of TC was systematically validated through modeling with three powerful and supervised machine learning algorithms, including Support Vector Regression (SVR), Artificial Neural Networks (ANNs), and Stochastic Gradient Boosting (SGB). Five statistical criteria including R², SAE, MSE, SSE, and RMSE were calculated for model validation. It was observed that the developed SGB algorithm was the most reliable model for predicting the degr

关键词： 2D/2d p-n heterojunctions Data validation Nickel photodeposition Sonophotocatalytic/photocatalytic degradation supervised machine learning algorithms ZnO nanosheets

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Site adaptation of global horizontal irradiance from the Copernicus Atmospheric Monitoring Service for radiation using supervised machine learning techniques

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RENEWABLE ENERGY 2022年 195卷 92-106页

作者： Salamalikis, Vasileios Tzoumanikas, Panayiotis Argiriou, Athanassios A. Kazantzidis, Andreas Univ Patras Dept Phys Lab Atmospher Phys Patras 26500 Greece

Satellite and reanalysis-derived solar products have gained great attention due to the inadequate number of radiometric stations worldwide, however, they are associated with considerable uncertainties. This study deals with the ground-based validation of Global Horizontal Irradiance from CAMS radiation service (GHICAMS) and the application of supervised machine learning algorithms (MLAs) to site-adapt GHICAMS. The validation of GHICAMS against measurements shows significant systematic and dispersion errors for all-sky (nMBE = 4.9% and nRMSE = 15.7%) and cloudy conditions (nMBE = 17.6% and nRMSE = 38.8%). Under clear skies, CAMS performs adequately (nMBE < 1% and nRMSE < 5%). All MLAs lead to reduced errors for the site-adapted irradiances. MBE is improved by more than 50%, accompanied by significant reductions in RMSE for various solar zenith angles and cloud fractions. The best results are revealed for the tree-based MLAs and especially for Random Forests. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Global horizontal irradiance CAMS-Rad Clear sky detection Site-adaptation supervised machine learning algorithms Goodness-of-fit statistics

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Performance of the supervised learning algorithms in sex estimation of the proximal femur: A comparative study in contemporary Egyptian and Turkish samples

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SCIENCE & JUSTICE 2022年第3期62卷 288-309页

作者： Attia, MennattAllah H. Attia, Mohamed H. Farghaly, Yasmin Tarek Abulnoor, Bassam Ahmed El-Sayed Curate, Francisco Alexandria Univ Fac Med Forens Med & Clin Toxicol Alexandria Egypt Alexandria Univ Med Res Inst Biomed Engn Alexandria Egypt Deakin Univ Inst Intelligent Syst Res & Innovat Burwood Australia Alexandria Univ Fac Med Diagnost Radiol Alexandria Egypt Ain Shams Univ Fac Dent Fixed Prosthodont Cairo Egypt Univ Coimbra Res Ctr Anthropol & Hlth Dept Life Sci Coimbra Portugal Univ Coimbra Dept Life Sci Lab Forens Anthropol Coimbra Portugal

Sex estimation standards are population specific however, we argue that machine learning techniques (ML) may enhance the biological sex determination on trans-population application. Linear discriminant analysis (LDA) versus nine ML including quadratic discriminant analysis (QDA), support vector machine (SVM), Decision Tree (DT), Gaussian process (GPC), Naive Bayesian (NBC), K-Nearest Neighbor (KNN), Random Forest (RFM) and Adaptive boosting (Adaboost) were compared. The experiments involve two contemporary populations: Turkish (n = 300) and Egyptian populations (n = 100) for training and validation, respectively. Base models were calibrated using isotonic and sigmoid calibration schemes. Results were analyzed at posterior probabilities (pp) thresholds > 0.95 and > 0.80. At pp = 0.5, ML algorithms yielded comparable accuracies in the training (90% to 97%) and test sets (81% to 88%) which are not modified after employing the calibration techniques. At pp > 0.95, the raw RFM, LDA, QDA, and SVM models have shown the best performance however, calibration techniques improved the performance of various classifier especially NBC and Adaboost. By contrast, the performance of GPC, KNN, QDA models worsened by calibration. RFM has shown the best performance among all models at both thresholds whereas LDA benefited the best from using both calibration methods at pp > 0.80. Complex ML models are not necessarily achieving better performance metrics. LDA and QDA remain the fastest and simplest classifiers. We demonstrated the capability of enhancing sex estimation using ML on an independent population sample however, differences in the underlying probability distribution generated by models were detected which warranted more cautious application by forensic practitioners.

关键词： Forensic anthropology supervised machine learning algorithms Femur sexual dimorphism Regional sex estimation standards Contemporary metapopulations skeletal database

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Effect of normalization methods on the performance of supervised learning algorithms applied to HTSeq-FPKM-UQ data sets: 7SK RNA expression as a predictor of survival in patients with colon adenocarcinoma

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BRIEFINGS IN BIOINFORMATICS 2019年第3期20卷 985-994页

作者： Shahriyari, Leili Ohio State Univ Math Biosci Inst Columbus OH 43210 USA

Motivation: One of the main challenges in machine learning (ML) is choosing an appropriate normalization method. Here, we examine the effect of various normalization methods on analyzing FPKM upper quartile (FPKM-UQ) RNA sequencing data sets. We collect the HTSeq-FPKM-UQ files of patients with colon adenocarcinoma from TCGA-COAD project. We compare three most common normalization methods: scaling, standardizing using z-score and vector normalization by visualizing the normalized data set and evaluating the performance of 12 supervised learning algorithms on the normalized data set. Additionally, for each of these normalization methods, we use two different normalization strategies: normalizing samples (files) or normalizing features (genes). Results: Regardless of normalization methods, a support vector machine (SVM) model with the radial basis function kernel had the maximum accuracy (78%) in predicting the vital status of the patients. However, the fitting time of SVM depended on the normalization methods, and it reached its minimum fitting time when files were normalized to the unit length. Furthermore, among all 12 learning algorithms and 6 different normalization techniques, the Bernoulli naive Bayes model after standardizing files had the best performance in terms of maximizing the accuracy as well as minimizing the fitting time. We also investigated the effect of dimensionality reduction methods on the performance of the supervised ML algorithms. Reducing the dimension of the data set did not increase the maximum accuracy of 78%. However, it leaded to discovery of the 7SK RNA gene expression as a predictor of survival in patients with colon adenocarcinoma with accuracy of 78%.

关键词： 7SK RNA gene expression colon adenocarcinoma normalization methods supervised machine learning algorithms TCGA HTSeq-FPKM-UQ data sets

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Link Prediction with supervised learning on an Industry 4.0 related Knowledge Graph 26

Link Prediction with Supervised Learning on an Industry 4.0 ...

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26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Grangel-Gonzalez, Irlan Shah, Fasal Robert Bosch Corp Res Renningen Germany Robert Bosch Engn & Business Solut Private Ltd Bangalore Karnataka India

ISBN: (纸本)9781728129891

Industry 4.0 requires the integration of many actors to provide correct, personalized, and quick answers to customers. In order to meet this integration, data coming from different actors demand to be semantically integrated and harmonized. In these settings, knowledge graphs have proven to be successful in the task of semantic data integration of distinct data silos. Despite the increasing adoption of knowledge graphs in the Industry 4.0 domain for integrating and harmonizing data, still, all the power of the integrated data is not exploited. In this article, we tackle the problem of knowledge graph completion presenting an approach that applies supervised machine learning algorithms on top of the knowledge graph. In general, observed results indicate that supervised machine learning algorithms perform with an AUC of more than 88%. These outcomes suggest that knowledge graph completion enables to unveil new relations by connecting entities in the knowledge graph. Thus, the discovered relations in the knowledge graph bring added value to the Industry 4.0 domain.

关键词： Industry 4.0 smart manufacturing knowledge graphs supervised machine learning algorithms knowledge graph embeddings link prediction knowledge graph completion

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Intrusion Detection Systems using supervised machine learning Techniques: A survey

Intrusion Detection Systems using Supervised Machine Learnin...

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13th International Conference on Ambient Systems, Networks and Technologies, ANT 2022 / 5th International Conference on Emerging Data and Industry 4.0, EDI40 2022

作者： Abdallah, Emad E. Eleisah, Wafa' Otoom, Ahmed Fawzi Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University P.O. Box 330127 Zarqa13133 Jordan

In this paper, we investigate the subject of intrusion detection using supervised machine learning methods. The main goal is to provide a taxonomy for linked intrusion detection systems and supervised machine learning algorithms. For this purpose, we provide a deep discussion of the concepts of intrusion detection systems, supervised machine learning techniques, and cyber-security attacks. Then, concerning the application of supervised learning for intrusion detection, we cover relevant efforts. Finally, a taxonomy is provided based on these related works. Based on this taxonomy, we can conclude that the classification performance of supervised learning algorithms is high and promising based on a study of four popular data sets in this domain: KDD'99, NSL-KDD, CICIDS2017, and UNSW-NB15. Moreover, feature selection is important and, in many cases, is needed for an enhancement in performance. Furthermore, data imbalance can be a concern, and sampling approaches can help resolve the issue. Finally, for good performance, large intrusion detection data sets necessitate a deep learning technique. © 2022 Elsevier B.V.. All rights reserved.

关键词： cyber security intrusion detection systems supervised machine learning algorithms

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supervised machine learning Approach for Modeling Hot Deformation Behavior of Medium Carbon Steel

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STEEL RESEARCH INTERNATIONAL 2023年第2期94卷

作者： Murugesan, Mohanraj Yu, Jae-Hyeong Jung, Kyu-Seok Cho, Sung-Min Bhandari, Krishna Singh Chung, Wanjin Lee, Chang-Whan Seoul Natl Univ Sci & Technol Dept Mech Syst Design Engn Seoul 01811 South Korea Seoul Natl Univ Sci & Technol Dept Mech Informat Engn Seoul 01811 South Korea Jeju Natl Univ Dept Mech Engn Jeju Do 63243 South Korea

Metal forming process parameters selection highly depends on the consistent and realistic characterization of material behavior under the combined effects of strain, strain rate, and temperature on the material flow stress. Hot deformation tensile tests are performed for AISI 1045 steel at deformation temperatures and strain rates ranges from 650 to 950 degrees C and 0.05 to 1.0 s(-1), respectively. The received flow curves indicate that flow stress increases with a decrease in deformation temperature and an increase in strain rate. In this study, it is investigated the supervised machine learning techniques such as support vector regression, single decision tree, and random forest regression (RFR) models to characterize material-flow behavior during hot deformation. Overall, the proposed RFR model results are in good agreement with the experimental observations. Besides, the proposed model's predictability is assessed using graphical and numerical validations. The numerical quantification confirms that the RFR models perform significantly better with a higher coefficient of determination (R (2)), 0.9983, and low prediction error, 1.021%. Furthermore, it is revealed through the comparison with previous findings, that the proposed machine learning models can precisely calculate flow stress better than conventional models.

关键词： AISI 1045 steels flow stresses random forest regression supervised machine learning algorithms

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Intrusion Detection Systems using supervised machine learning Techniques: A survey

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Procedia Computer Science 2022年 201卷 205-212页

作者： Emad E. Abdallah Wafa’ Eleisah Ahmed Fawzi Otoom Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University P.O. Box 330127 Zarqa 13133 Jordan

In this paper, we investigate the subject of intrusion detection using supervised machine learning methods. The main goal is to provide a taxonomy for linked intrusion detection systems and supervised machine learning algorithms. For this purpose, we provide a deep discussion of the concepts of intrusion detection systems, supervised machine learning techniques, and cyber-security attacks. Then, concerning the application of supervised learning for intrusion detection, we cover relevant efforts. Finally, a taxonomy is provided based on these related works. Based on this taxonomy, we can conclude that the classification performance of supervised learning algorithms is high and promising based on a study of four popular data sets in this domain: KDD’99, NSL-KDD, CICIDS2017, and UNSW-NB15. Moreover, feature selection is important and, in many cases, is needed for an enhancement in performance. Furthermore, data imbalance can be a concern, and sampling approaches can help resolve the issue. Finally, for good performance, large intrusion detection data sets necessitate a deep learning technique.

关键词： intrusion detection systems cyber security supervised machine learning algorithms

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An associative memory approach to medical decision support systems

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012年第3期106卷 287-307页

作者： Aldape-Perez, Mario Yanez-Marquez, Cornelio Camacho-Nieto, Oscar Argueelles-Cruz, Amadeo J. Ctr Res Comp GA Madero Mexico City 07738 DF Mexico Super Sch Comp GA Madero Mexico City 07738 DF Mexico

Classification is one of the key issues in medical diagnosis. In this paper, a novel approach to perform pattern classification tasks is presented. This model is called Associative Memory based Classifier (AMBC). Throughout the experimental phase, the proposed algorithm is applied to help diagnose diseases;particularly, it is applied in the diagnosis of seven different problems in the medical field. The performance of the proposed model is validated by comparing classification accuracy of AMBC against the performance achieved by other twenty well known algorithms. Experimental results have shown that AMBC achieved the best performance in three of the seven pattern classification problems in the medical field. Similarly, it should be noted that our proposal achieved the best classification accuracy averaged over all datasets. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

关键词： Associative memories Decision support systems supervised machine learning algorithms Pattern classification

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