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Bi-linear mapping integrated machine learning based authentication routing protocol for improving quality of service in vehicular Ad-Hoc network

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2023年 4卷

作者： Satyanarayana, Raju K Selvakumar, K. Research Scholar Department of Information Technology Faculty of Engineering and Technology Annamalai University Chidambaram India Department of Information Technology Faculty of Engineering and Technology Annamalai University Chidambaram India

Recently, most of the critical problems of real-time applications have been solved by one of the fast and most hastily growing technological tools, machine learning. The novel architectures of Vehicular Ad-hoc Networks reduce traffic congestion and accidents since the number of electric vehicles is increasing daily. There is a need for an efficient and secured routing protocol for better implementation of VANET. machine learning algorithms help in achieving such efficiency and security. This paper develops a Novel machine learning based Authentication Routing protocol by integrating the Long-Short-Term-Memory model with the Bi-Lear Mapping algorithm to analyze the routing data and authenticate the network users. The bi-Linear Mapping model assigns and compares the node's private and public keys, whereas LSTM analyses the routing table information. The proposed model is simulated in NS3 software, and the performance is verified. The results show that the proposed model obtained better throughput, PDR, energy efficiency, authentication which is better than the earlier methods. © 2023 The Author(s)

关键词： Authentication Blm machine learning algorithm Mlar Routing protocol Security analysis V-2-V Vanet

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LightCUD: a program for diagnosing IBD based on human gut microbiome data

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BIODATA MINING 2021年第1期14卷 1-13页

作者： Xu, Congmin Zhou, Man Xie, Zhongjie Li, Mo Zhu, Xi Zhu, Huaiqiu Peking Univ Coll Engn Dept Biomed Engn State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Peking Univ Ctr Quantitat Biol Beijing 100871 Peoples R China Peking Univ Third Hosp Dept Crit Care Med Beijing 100191 Peoples R China

Background The diagnosis of inflammatory bowel disease (IBD) and discrimination between the types of IBD are clinically important. IBD is associated with marked changes in the intestinal microbiota. Advances in next-generation sequencing (NGS) technology and the improved hospital bioinformatics analysis ability motivated us to develop a diagnostic method based on the gut microbiome. Results Using a set of whole-genome sequencing (WGS) data from 349 human gut microbiota samples with two types of IBD and healthy controls, we assembled and aligned WGS short reads to obtain feature profiles of strains and genera. The genus and strain profiles were used for the 16S-based and WGS-based diagnostic modules construction respectively. We designed a novel feature selection procedure to select those case-specific features. With these features, we built discrimination models using different machine learning algorithms. The machine learning algorithm LightGBM outperformed other algorithms in this study and thus was chosen as the core algorithm. Specially, we identified two small sets of biomarkers (strains) separately for the WGS-based health vs IBD module and ulcerative colitis vs Crohn's disease module, which contributed to the optimization of model performance during pre-training. We released LightCUD as an IBD diagnostic program built with LightGBM. The high performance has been validated through five-fold cross-validation and using an independent test data set. LightCUD was implemented in Python and packaged free for installation with customized databases. With WGS data or 16S rRNA sequencing data of gut microbiome samples as the input, LightCUD can discriminate IBD from healthy controls with high accuracy and further identify the specific type of IBD. The executable program LightCUD was released in open source with instructions at the webpage . The identified strain biomarkers could be used to study the critical factors for disease development and recommend treatments regar

关键词： machine learning algorithm Human gut microbiome IBD Biomarker

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Estimation of Anthocyanins in Whole-Fertility Maize Leaves Based on Ground-Based Hyperspectral Measurements

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REMOTE SENSING 2023年第10期15卷

作者： Jiang, Shiyu Chang, Qingrui Wang, Xiaoping Zheng, Zhikang Zhang, Yu Wang, Qi Northwest A&F Univ Coll Nat Resources & Environm Yangling 712100 Peoples R China

The estimation of anthocyanin (Anth) content is very important for observing the physiological state of plants under environmental stress. The objective of this study was to estimate the Anth of maize leaves at different growth stages based on remote sensing methods. In this study, the hyperspectral reflectance and the corresponding Anth of maize leaves were measured at the critical growth stages of nodulation, tasseling, lactation, and finishing of maize. First-order differential spectra (FD) were derived from the original spectra (OS). First, the spectral parameters highly correlated with Anth were selected. A total of two sensitive bands (R-?), five classical vegetation indices (VIS), and six optimized vegetation indices (VIC) were selected from the original and first-order spectra. Then, univariate regression models for Anth estimation (Anth-UR models) and multivariate regression models for estimating anthocyanins (Anth-MR models) were constructed based on these parameters at different growth stages of maize. It was shown that the first-order spectral conversion could effectively improve the correlation between R-?, VIC, and Anth, and VIC are usually more sensitive to Anth than VIS. In addition, the overall performance of Anth-MR models was better than that of Anth-UR models. Among them, Anth-MR models with the combination of three types of spectral parameters (FD(R-?) + OS_VIC + FD_VIC/VIS) as inputs had the best overall performance. Moreover, different growth stages had an impact on the Anth estimation models, with tasseling and lactation stages showing better results. The best-performing Anth-MR models for these two growth stages were as follows. For the tasseling stage, the best model was the FD(R-?) + OS_VIC + VIS-based SVM model, with an R-2 of 0.868, RMSE of 0.007, and RPD of 2.19. For the lactation stage, the best-performing model was the FD(R-?) + OS_VIC + FD_VIC-based RF model, with an R-2 of 0.797, RMSE of 0.007, and RPD of 2.24. These results will pr

关键词： hyperspectral maize leaves Anth classic vegetation index optimized vegetation index first-order differential spectra machine learning algorithm

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Annual Electricity and Energy Consumption Forecasting for the UK Based on Back Propagation Neural Network, Multiple Linear Regression, and Least Square Support Vector machine

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PROCESSES 2023年第1期11卷

作者： Liu, Yinlong Li, Jinze Beihang Univ Res Inst Aeroengine Beijing 100191 Peoples R China Kings Coll London Fac Nat & Math Sci London SE1 SWA England Beihang Univ Sch Elect Informat Engn Beijing 100191 Peoples R China

The long-term demand forecast for annual national electricity and energy consumption plays a vital role in future strategic planning, power system installation programming, energy investment planning, and next-generation unit construction. Three machine learning algorithms of BP-NN, MLR, and LS-SVM were chosen for training forecasting models, with the data on population, GDP, mean temperature, sunshine, rainfall, and frost days in 1993-2019 serving as the input variables. The total data were divided by 70% into the training set (1993-2011) and 30% into the test set (2012-2019), in chronological order. RMSE, MAPE, and MaxError were adopted as the performance criteria. The statistical results show that the gross population of the UK increases year by year from 1993 to 2020. The GDP generally increases before 2007 but has a decline, and then varies with a large amplitude afterward. The electricity and energy consumption of the UK generally increase from 1993 and reach a peak around 2005. Afterward, a decline occurs basically year by year until 2019. The simulation results reveal that all three models predict well on the training set but have some overestimation on the test set. The LS-SVM model has the best forecasting performance among the three models on the training set. The results show that it is feasible to use machine learning algorithms to predict the future electricity and energy consumption of a country based on past economic and livelihood data. In this way, economic decision-makers can rely on the predicted values to make a well-founded layout for future energy construction and investment to avoid waste or a shortage of resources.

关键词： machine learning algorithm least square support vector machine multiple linear regression back propagation neural network electricity and energy consumption forecasting UK

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Battery leakage fault diagnosis based on multi-modality multi-classifier fusion decision algorithm

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JOURNAL OF ENERGY STORAGE 2023年 72卷

作者： Zhang, Zhengjie Cao, Rui Jin, Yuntao Lin, Jiayuan Zheng, Yifan Zhang, Lisheng Gao, Xinlei Yang, Shichun Beihang Univ Sch Transportat Sci & Engn Beijing 102206 Peoples R China Imperial Coll London Dept Mech Engn London England

With the rapid development of the new energy vehicle industry and the overall number of electric vehicles, the thermal runaway problem of lithium-ion batteries has become a major obstacle to the promotion of electric vehicles. During actual usage, the battery leakage problem leads to the degradation of the system performance, which may cause arcing, external short circuit or even thermal runaway. Therefore, it is essential to analyze the internal mechanism of electrolyte leakage phenomenon and design the corresponding fault diagnosis algorithm. This work tests the disassembled leaking battery module of the practical vehicle. The incremental capacity analysis of the charging process indicated that the battery had capacity loss, and the voltage signal trend analysis of the discharging process found that the leaking battery had higher voltage difference slope. The detection results based on the electrochemical impedance spectrum (EIS) test corroborate the anomaly internal impedance of the battery. Moreover, an incremental capacity bar graph analysis method based on cloud data is designed and the ohm resistance is calculated by a simplified subspace identification algorithm. On this basis, the threshold alarm information is incorporated to form a feature matrix, and a machine learning fault diagnosis algorithm based on multi-modality multi-classifier fusion decision framework is proposed, which is capable of scoring and quantifying the hazard level of fault types such as electrolyte leakage, and achieve up to 26 days advance warning on cloud-based real-vehicle data.

关键词： Lithium -ion batteries Electrolyte leakage Fault diagnosis Thermal runaway (TR) machine learning algorithm

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A methodology for diagnosing FAC induced pipe thinning using accelerometers and deep learning models

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ANNALS OF NUCLEAR ENERGY 2020年 143卷

作者： Chae, Young Ho Kim, Seung Geun Kim, Hyeonmin Kim, Jung Taek Seong, Poong Hyun Korea Adv Inst Sci & Technol Dept Nucl & Quantum Engn 291 Daehak Ro Daejeon 34141 South Korea Korea Atom Energy Res Inst 34057 111Daedeok Daero 989Beon Gil Daejeon South Korea

The flow accelerated corrosion (FAC) phenomenon is critical phenomenon that undermine the safety of nuclear power plant. The problems of FAC are not only induces pipe's thinning but occurs widely in nuclear power plants. Therefore, precise diagnosis of FAC phenomenon is important but the FAC phenomenon has a complicated mechanism. As a result, accurately diagnosing the FAC phenomenon is difficult (Kain, 2014). To overcome these difficulties, we proposed a methodology utilizing vibration data and deep learning to diagnose FAC induced thinning. To minimize the effect of outliers, Cook's distance and the Hilbert transform were used. When there was a significant difference in the pipes' thickness, the support vector machine (SVM), convolutional neural network (CNN), and long-short term memory (LSTM) network all showed good results. However, when the difference in pipes' thickness was subtle and thinning was non-uniform, only LSTM successfully diagnosed the pipe's condition. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Flow accelerated corrosion Vibration signal machine learning algorithm Deep learning

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Identification of Bacterial Pathogens at Genus and Species Levels through Combination of Raman Spectrometry and Deep-learning algorithms

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MICROBIOLOGY SPECTRUM 2022年第6期10卷 e0258022页

作者： Wang, Liang Tang, Jia-Wei Li, Fen Usman, Muhammad Wu, Chang-Yu Liu, Qing-Hua Kang, Hai-Quan Liu, Wei Gu, Bing Guangdong Acad Med Sci Guangdong Prov Peoples Hosp Lab Med Guangzhou Guangdong Peoples R China Xuzhou Med Univ Sch Med Informat & Engn Dept Intelligent Med Engn Xuzhou Jiangsu Peoples R China Fifth Peoples Hosp Huaian Lab Med Huaian Jiangsu Peoples R China Xuzhou Med Univ Sch Med Imaging Dept Biomed Engn Xuzhou Jiangsu Peoples R China Macau Univ Sci & Technol State Key Lab Qual Res Chinese Med Taipa Macao Peoples R China Xuzhou Med Univ Lab Med Affiliated Hosp Xuzhou Jiangsu Peoples R China

The rapid and accurate identification of the causing agents during bacterial infections would greatly improve pathogen transmission, prevention, patient care, and medical treatments in clinical settings. Although many conventional and molecular methods have been proven to be efficient and reliable, some of them suffer technical biases and limitations that require the development and application of novel and advanced techniques. Recently, due to its cost affordability, noninvasiveness, and label-free feature, Raman spectroscopy (RS) is emerging as a potential technique for fast bacterial detection. However, the method is still hampered by many technical issues, such as low signal intensity, poor reproducibility, and standard data set insufficiency, among others. Thus, it should be cautiously claimed that Raman spectroscopy could provide practical applications in real-world settings. In order to evaluate the implementation potentials of Raman spectroscopy in the identification of bacterial pathogens, we investigated 30 bacterial species belonging to 9 different bacterial genera that were isolated from clinical samples via surfaced enhanced Raman spectroscopy (SERS). A total of 17,149 SERS spectra were harvested from a Raman spectrometer and were further analyzed via machine learning approaches, which showed that a convolutional neural network (CNN) deep learning algorithm achieved the highest prediction accuracy for recognizing pathogenic bacteria at both the genus and species levels. In summary, the SERS technique holds a promising potential for fast bacterial pathogen identification in clinical laboratories with the integration of machine learning algorithms, which might be further developed and sharpened for the direct identification and prediction of bacterial pathogens from clinical samples. IMPORTANCE In this study, we investigated 30 bacterial species belonging to 9 different bacterial genera that were isolated from clinical samples via surfaced enhanced Raman

关键词： Raman spectroscopy SERS bacterial pathogen rapid diagnosis machine learning algorithm

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Nanopore sensing and beyond: Electrochemical systems for optically-coupled single-entity studies, stimulus-responsive gating applications, and point-of-care sensors

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SENSORS AND ACTUATORS REPORTS 2024年 8卷

作者： Reitemeier, Julius Metro, Jarek Fu, Kaiyu X. Univ Notre Dame Dept Chem & Biochem Notre Dame IN 46556 USA Univ Notre Dame Berthiaume Inst Precis Hlth Notre Dame IN 46556 USA

Nanopores play essential roles in biological processes, such as ion channels and pumps in cellular membranes, and in technological applications such as DNA sequencing. Advancements in nanofabrication techniques have enabled the routine integration of nanopores into solid-state devices, resulting in a plethora of analytical applications. This review explores recent developments in nanopore-enabled electrochemical systems, which have transcended traditional resistive pulse sensing to offer novel capabilities in single-entity studies, stimulus- responsive gating, and point-of-care diagnostics. We highlight recent studies on the design and utility of nano- pore electrode arrays, which serve as nanocontainers capable of isolating and analyzing single entities, and extend the discussion to hierarchically organized, stimulus-responsive systems that regulate species transport across nanopores, enriching analytes for ultrasensitive detection. In addition, we review the utilization of probe- assisted nanopore sensing, demonstrating its efficacy in selectively binding and detecting target molecules and ions. Finally, we outline future directions for nanopore-based systems to enhance robustness, achieve high- throughput analysis, and incorporate artificial intelligence for materials design and data analysis, promising transformative impacts on diagnostics and biological research.

关键词： Nanopore sensing Nanoelectrochemistry Aptamer-based biosensor Single-entity detection machine learning algorithm Real-time analysis

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Artificial image objects for classification of breast cancer biomarkers with transcriptome sequencing data and convolutional neural network algorithms

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BREAST CANCER RESEARCH 2021年第1期23卷 1-11页

作者： Chen, Xiangning Chen, Daniel G. Zhao, Zhongming Balko, Justin M. Chen, Jingchun 410 AI LLC Germantown MD 20876 USA Univ Texas Hlth Sci Ctr Houston Ctr Precis Hlth Sch Biomed Informat Houston TX 77030 USA Univ Texas Houston McGovern Med Sch Dept Psychiat & Behav Sci Houston TX 77030 USA Vanderbilt Univ Med Ctr Vanderbilt Ingram Canc Ctr Dept Med Nashville TN USA Vanderbilt Univ Vanderbilt Ingram Canc Ctr Med Ctr Breast Canc Res Program Nashville TN USA Vanderbilt Univ Med Ctr Vanderbilt Ingram Canc Ctr Dept Pathol Nashville TN 37232 USA Vanderbilt Univ Med Ctr Vanderbilt Ingram Canc Ctr Dept Microbiol Nashville TN USA Vanderbilt Univ Med Ctr Vanderbilt Ingram Canc Ctr Dept Immunol Nashville TN USA Univ Nevada Nevada Inst Personalized Med Las Vegas NV 89154 USA

Background Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and the highly correlated expression between individual genes. Methods We proposed a method to transform RNA sequencing data into artificial image objects (AIOs) and applied convolutional neural network (CNN) algorithms to classify these AIOs. With the AIO technique, we considered each gene as a pixel in an image and its expression level as pixel intensity. Using the GSE96058 (n = 2976), GSE81538 (n = 405), and GSE163882 (n = 222) datasets, we created AIOs for the subjects and designed CNN models to classify biomarker Ki67 and Nottingham histologic grade (NHG). Results With fivefold cross-validation, we accomplished a classification accuracy and AUC of 0.821 +/- 0.023 and 0.891 +/- 0.021 for Ki67 status. For NHG, the weighted average of categorical accuracy was 0.820 +/- 0.012, and the weighted average of AUC was 0.931 +/- 0.006. With GSE96058 as training data and GSE81538 as testing data, the accuracy and AUC for Ki67 were 0.826 +/- 0.037 and 0.883 +/- 0.016, and that for NHG were 0.764 +/- 0.052 and 0.882 +/- 0.012, respectively. These results were 10% better than the results reported in the original studies. For Ki67, the calls generated from our models had a better power for prediction of survival as compared to the calls from trained pathologists in survival analyses. Conclusions We demonstrated that RNA sequencing data could be transformed into AIOs and be used to classify Ki67 status and NHG with CNN algorithms. The AIO method could handle high-dimensional data with highly correlated variables, and there was no need for variable selection. With the AIO technique, a data-driven, consistent, and automation-ready model could be developed to classify biomarkers with RNA sequencing data and provide more efficient care for cancer patients.

关键词： RNA sequencing Breast cancer biomarker classification Artificial image object Artificial intelligence machine learning algorithm Convolutional neural network Image classification

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Silicon nanoparticles: Comprehensive review on biogenic synthesis and applications in agriculture

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ENVIRONMENTAL RESEARCH 2023年 232卷 116292页

作者： Mahawar, Lovely Ramasamy, Kesava Priyan Suhel, Mohammad Prasad, Sheo Mohan Zivcak, Marek Brestic, Marian Rastogi, Anshu Skalicky, Milan Slovak Univ Agr Inst Plant & Environm Sci Fac Agrobiol & Food Resources Nitra Slovakia Umea Univ Dept Ecol & Environm Sci Umea Sweden Univ Allahabad Dept Bot Ranjan Plant Physiol & Biochem Lab Prayagraj India Poznan Univ Life Sci Fac Environm Engn & Mech Engn Dept Ecol & Environm Protect Lab Bioclimatol Piatkowska 94 PL-60649 Poznan Poland Czech Univ Life Sci Prague Dept Bot & Plant Physiol Prague Czech Republic

Recent advancements in nanotechnology have opened new advances in agriculture. Among other nanoparticles, silicon nanoparticles (SiNPs), due to their unique physiological characteristics and structural properties, offer a significant advantage as nanofertilizers, nanopesticides, nanozeolite and targeted delivery systems in agriculture. Silicon nanoparticles are well known to improve plant growth under normal and stressful environments. Nanosilicon has been reported to enhance plant stress tolerance against various environmental stress and is considered a non-toxic and proficient alternative to control plant diseases. However, a few studies depicted the phytotoxic effects of SiNPs on specific plants. Therefore, there is a need for comprehensive research, mainly on the interaction mechanism between NPs and host plants to unravel the hidden facts about silicon nanoparticles in agriculture. The present review illustrates the potential role of silicon nanoparticles in improving plant resistance to combat different environmental (abiotic and biotic) stresses and the underlying mechanisms ***, our review focuses on providing the overview of various methods exploited in the biogenic synthesis of silicon nanoparticles. However, certain limitations exist in synthesizing the well-characterized SiNPs on a laboratory scale. To bridge this gap, in the last section of the review, we discussed the possible use of the machine learning approach in future as an effective, less labour-intensive and time-consuming method for silicon nanoparticle synthesis. The existing research gaps from our perspective and future research directions for uti-lizing SiNPs in sustainable agriculture development have also been highlighted.

关键词： Nanotechnology Silicon nanoparticles Environmental stress Biological synthesis machine learning algorithm

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