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Comparative evaluation of intelligent regression algorithms for performance and emissions prediction of a hydrogen-enriched Wankel engine

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FUEL 2021年 290卷 120005-120005页

作者： Shi, Cheng Ji, Changwei Wang, Huaiyu Wang, Shuofeng Yang, Jinxin Ge, Yunshan Beijing Univ Technol Coll Energy & Power Engn Beijing Lab New Energy Vehicles Beijing 100124 Peoples R China Beijing Univ Technol Key Lab Reg Air Pollut Control Beijing 100124 Peoples R China Beijing Inst Technol Beijing Collaborat Innovat Ctr Elect Vehicles Beijing 100081 Peoples R China

In order to satisfy the heightened emissions regulation and further enhance the performance of the engine, efforts on amelioration of the engine management system are of great significance besides using intelligent regression algorithms. Based on a hydrogen-enriched Wankel rotary engine, multiple engine operations with variable excess air ratios, variable ignition timing, and variable hydrogen enrichment have been carried out a series of engine calibration tests in detail. After recording the required experimental data, three different methods, including quadratic polynomial, artificial neural networks (ANN), and support vector machine (SVM) are applied to construct a multi-objective regression model which gives a unique insight into the mathematical relationship between the engine performance and the operation and control parameters. For the ANN, the effect of the number of nodes in the hidden layer on the regression performance was discussed, and the weight values of the ANN was optimized using a genetic algorithm. For the SVM, the effects of the kernel function and three optimization methods on regression performance were discussed. The results indicated that the SVM exhibited the best fitting results among the three methods. The optimal R-2 for brake thermal efficiency, fuel energy flow rate, nitrogen oxide, carbon monoxide, and unburned hydrocarbon is 0.9877, 0.9840, 0.9949, 0.9937, and 0.9992, respectively. It is highly recommended that the SVM method as a generic methodology may be a new direction for nonlinear control system modeling of the Wankel engine.

关键词： Wankel engine Hydrogen enrichment regression algorithm Support vector machine Artificial neural network

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Mental health status monitoring for people with autism spectrum disorder using machine learning

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International Journal of Information Technology (Singapore) 2024年第1期16卷 43-51页

作者： Jayanthi, S. Priyadharshini, V. Kirithiga, V. Premalatha, S. Department of Electronics and Communication Engineering Sri Manakula Vinayagar Engineering College Puducherry India

In this article, an assistive technology in the form of a stress monitoring system customized exclusively for people affected by autism spectrum disorder (ASD) is proposed. ASD tends to impact the linguistic and motor skills and the effects of stress often has a negative impact on the physical and mental health of the individuals. The suppressed heart rate variability (HRV) is found to be lesser in ASD affected individuals. This is considered as a key factor and the constraints of the parameters to be monitored such as heart rate, body temperature and body movements is set accordingly. The hardware module encompasses various sensors such as pulse sensor, temperature sensor and accelerometers and can be developed into a variety of wearables specific to the user. The acquired data is transmitted to a non-relational database whose data is extracted and processed by a machine learning model consisting of a linear regression algorithm and random forest classifier algorithm to generate and analyse reports to support the mental stress management of the user. This wearable device can be accommodated for remote monitoring of the users in mundane day to day situations and even in social habitats such as healthcare establishments and even in educational institutions. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Assistive technology Autism spectrum disorder Machine learning Random forest classifier regression algorithm Sensors

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Estimating the total nitrogen content of Aquilaria sinensis leaves based on a hybrid feature selection algorithm and image data from a modified digital camera

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BIOSYSTEMS ENGINEERING 2022年第0期213卷 89-104页

作者： Chen, Zhulin Wang, Xuefeng Sun, Shanshan Chinese Acad Forestry Res Inst Forestry Informat Tech Beijing 100091 Peoples R China Natl Forestry & Grassland Adm Key Lab Forest Management & Growth Modelling Beijing 100091 Peoples R China Beijing Normal Univ Fac Geog Sci State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China Ceovis Technol Co Ltd Beijing 101399 Peoples R China

With the development of imaging devices and image processing algorithms, numerous features have come to be used for the estimation of total nitrogen content (TNC) in plants. However, higher-dimensional inputs contain more correlated variables that can detrimentally affect model performance. In this study, a hybrid feature selection approach was developed for TNC estimation in Aquilaria sinensis. A low-cost modified digital camera with external filters was used to capture canopy images. Three feature selection methods, namely, random forest (RF), Pearson correlation coefficient (PCC)-based feature selection, and sequential backward selection (SBS), were combined into two hybrid feature selection algorithms (RF_SBS and PCC_SBS). In addition, three regression algorithms were used in hybrid feature selection process: random forest regression (RFR), support vector regression (SVR), and partial least squares regression (PLSR). The hybrid feature selection process consists of two steps. First, the lowest number of dimensions is sought based on the feature ranking. Then, SBS is used to find the best feature combinations. Compared with the original models, the R(2 )values of the RF-SBS-based models are improved by 0.094 (RF_SBS_RFR), 0.190 (RF_SBS_SVR), and 0.116 (RF_SBS_PLSR), while the R2 values of the PCCSBS-based models are improved by 0.055 (PCC_SBS_RFR), 0.092 (PCC_SBS_SVR) and 0.128 (PCC_SBS_PLSR). Finally, the two best TNC estimation models are found to be PCC_SBS_PLSR, with an R2 of 0.863, and RF_SBS_SVR, with an R-2 of 0.872. The proposed hybrid feature selection approach not only has great capacity to improve estimation accuracy but also can reduce model complexity by choosing the best feature subset. (C) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： Total nitrogen content Modified digital camera Feature selection regression algorithm

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Machine learning model design for high performance cloud computing & load balancing resiliency: An innovative approach

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第10期34卷 9991-10009页

作者： Kamila, Nilayam Kumar Frnda, Jaroslav Pani, Subhendu Kumar Das, Rashmi Islam, Sardar M. N. Bharti, P. K. Muduli, Kamalakanta Shri Venkateshwara Univ Dept Comp Sci & Engn Gajraula India Univ Zilina Dept Quantitat Methods & Econ Informat Zilina Slovakia VSB Tech Univ Ostrava Dept Telecommunicat Ostrava Czech Republic Biju Patnaik Univ Technol Krupajal Engn Coll Rourkela Odisha India CV Raman Global Univ Bhubaneswar Odisha India Bhubaneswar Engn Coll Dept Comp Sci & Engn Bhubaneswar Odisha India Victoria Univ Melbourne Vic Australia Papua New Guinea Univ Technol Lae Papua N Guinea

High performance computing is now a major area where business and computing technologies need resilient high performance to meet business continuity and real-time needs. However, many top-level business and technology organizations are still in the process of improving high performance and traffic resiliency to ensure the availability of the system at all times. Machine learning is an important advancement of computer technology that helps in decision making by prediction and classification mechanism based on historical data. In this paper, we propose and integrate the concept of high-performance computing with artificial intelligence machine learning techniques in cloud platforms. The networking and computing performance data are used to validate, predict and classify the traffic and performance patterns and ensure system performance and continuous traffic flow resiliency decisions. The proposed integrated design approach has been analyzed on different step actions and decisions based on machine learning regression and classification models, which auto-correct the performance of the system at real run time instances. Our machine learning integrated design simulated results show its traffic resilience performs proactively 38.15% faster with respect to the failure point recovery along with 7.5% business cost savings as compared to today's existing non-machine learning based design models.

关键词： Artificial intelligence Machine learning Cloud computing Load balancing regression algorithm

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Eggplant Yield Prediction Utilizing 130 Locally Collected Genotypes and Machine Learning Model 26

Eggplant Yield Prediction Utilizing 130 Locally Collected Ge...

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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Islam, Arfanul Islam Shanto, Mohammad Naimul Mahabub Rabby, Md. Sorowar Sikder, Arif Rahman Sayem Uddin, Md. Arefin, Muhammed Nazmul Patwary, Muhammed J. A. International Islamic University Chittagong Department of Computer Science and Engineering Kumira Chattogram4318 Bangladesh

ISBN: (纸本)9798350359015

Accurate crop yield prediction has long been a challenge for the agricultural community, with significant consequences for food security, farmer livelihoods, and strategic planning. Traditional forecasting methods based on agro-meteorological data have proven inadequate for capturing the wide variety of factors impacting production. Eggplant is also well-known throughout the world as one of the most popular fruit vegetables, and demand for this vegetable is growing as the world's population grows. Yield, i.e agricultural production per plant is significantly related to fruit diameter, fruits per plant, percent of fruit infestation by brinjal shoot and fruit borer, and fruit weight attributes, indicating that direct selection based on fruit number and fruit weight may be adequate for improving other traits. This study demonstrates a machine learning approach for precise eggplant yield prediction using data from different regions of Bangladesh, analyzing three regression algorithms such as categorical boosting regression (CatBR), light gradient boosting regression (LGBMR), and extreme gradient boosting regression (XGBR), as well as the Bayesian approach used for choosing optimal hyperparameters. CatBR emerged as the best-performing algorithm for eggplant yield prediction, utilizing 10 genetic parameters. The CatBR model we used performed admirably, displaying both reliability and speed in forecasting crop yields based on the most important variables. It achieved an R2 score of 68.02%, as well as an MSE score of 3.8117, an MAE score of 1.3090, and a MedAE score of 0.9949. Notably, our proposed model is capable of flawlessly combining and analyzing data sourced from various regions across Bangladesh, ensuring high precision and avoiding model overfitting. © 2023 IEEE.

关键词： Crop yield Eggplant Machine Learning regression algorithm Yield prediction

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Crime Rate Prediction Using Machine Learning 6

Crime Rate Prediction Using Machine Learning

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Sharaff, Ankit Kushwaha, Pradeep Kumar Dwivedi, Shashi Prakash Krishna, Om Singh, Shivam Thakur, Dinesh Bachelor of Technology Amity University Uttar Pradesh Greater Noida India Amity University Department of Computer Science & Engineering Uttar Pradesh Greater Noida India Lloyd Institute of Engineering and Technology Uttar Pradesh Greater Noida India Lloyd Law College Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

In our culture, crime is a major problem that affects everyone. Many crimes take place every day. The dataset utilized in this study includes data on each year's date and crime rate. This initiative mainly focuses on the crime rate related to robberies. Using historical data, a linear regression technique is used to forecast future crime rates. The programmer accepts a date as input and outputs the year's matching percentage of crime rate. © 2023 IEEE.

关键词： crime rate Machine learning number of offences regression algorithm

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Occlusive target recognition method of sorting robot based on anchor-free detection network

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2024年

作者： Wang, Fuzhi Song, Changlin Xihua Univ Sch Mech Engn Chengdu 610039 Peoples R China

The rapid development of industry intelligence has promoted the importance of sorting robots, but the shortcomings of sorting tasks are also gradually obvious. The target detection of the sorting robot in the process of sorting and grasping is affected by the anchor frame setting, and its grasping efficiency and recognition effect are low. Therefore, a detection network based on an anchor frame-free regression algorithm is proposed in this study, and a positioning and grasping method of the sorting robot based on the Deep Belief Network (DBN) algorithm is proposed. The algorithm experiment shows that the prediction accuracy of the proposed anchor-free detection network in the PASCAL VOC dataset reaches 92.91, significantly higher than that of other detection networks. In addition, the sorting robot was researched and designed with high accuracy in occlusion target recognition. When the occlusion area reaches 70%, the accuracy rate is as high as 81.26%, which is far higher than other detection schemes. The above results show that in improving the sorting robot's target recognition ability, the anchor-free detection network can significantly improve its recognition accuracy. At the same time, introducing the DBN network can improve the robot's recognition ability for occluded targets, which is of great significance to the development of the sorting robot and the intelligent industry.

关键词： Sorting robot no anchor frame target detection regression algorithm DBN

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Predictive Analytics for Future Air Pollution Levels Based on Population Growth

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Procedia Computer Science 2024年 245卷 834-843页

作者： Darel Yeshe Irma Kartika Wairooy Brilly Andro Makalew Computer Science Department School of Computer Science Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Jakarta Indonesia 11480

Air is very beneficial and crucial for every living creature on earth, hence it is very important to protect the air quality in order to avoid diseases. However, due to the increase in population, some human activities have been causing detrimental damage to the air quality. Some people have realized the potential impact of this problem and have done studies on predicting future air pollution. Some implemented machine learning models such as random forest regression, support vector machine, etc, while others have utilized deep learning models in their research.. This study will implement five different models, specifically linear regression, ridge regression, random forest regression, and multilayer perceptron regression. There will be two datasets used in this paper which will be merged and processed. There will be three key evaluation metrics being used in this paper namely root mean squared error, mean absolute error, and r-squared. The results from all of the models have concluded that more optimization and factors are needed in order to boost the final result of this study.

关键词： Machine Learning Predicting regression algorithm Technology Education Artificial Intelligence

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Novel Python-based " all-regressor model " application for photovoltaic plant-specific yield estimation and systematic analysis

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2021年第1期47卷 7480-7498页

作者： Adun, Humphrey Bamisile, Olusola Mukhtar, Mustapha Dagbasi, Mustafa Kavaz, Doga Oluwasanmi, Ariyo Cyprus Int Univ Energy Syst Engn Dept Haspolat Lefkosa Turkey Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu Sichuan Peoples R China Guangdong Univ Petrochem Technol Sch Econ & Management Maoming Peoples R China Cyprus Int Univ Environm Sci Dept Haspolat Lefkosa Turkey Univ Elect Sci & Technol China Sch Software Engn Chengdu Peoples R China

The deployment of photovoltaic technology in global electricity generation has reduced the dependence on fossil fuels and the adverse effect of global warming climate changes. However, the variability of PV output due to its reliance on meteorological parameters hinders the total maximization of the technology. In this paper, the interdependence of meteorological parameters on accurate estimation of the specific yield of the PV system is examined using machine learning algorithms. The analysis uses 2 years of meteorological data in a 10-min interval. The meteorological parameters used as input are global solar radiation (GR), wind speed (WS), wind direction (WD), the standard deviation of wind direction (WSD), air temperature (AT), and relative humidity (RH), while the output variable is the PV specific yield (PVSY). This study uses a Python-based "all regressor" machine learning algorithm, to analyze their accuracy for the estimation analysis at different input feature space. The accuracy of the models and algorithms is analyzed using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of correlation (R-2). The best performance in the two feature space was retrieved for AT, and GR with MSE and MAE values of 0.113 and 0.252, respectively. The best performance in the three feature space was retrieved for WSD, AT, and GR with MSE and MAE values of 0.109 and 0.255, respectively. The results show that the most accurate PVSY estimation was for the four input feature space with input variables of WS, AT, GR, and RH. The K-nearest neighbor (KNN) proved to be the most appropriate algorithm for this feature space with an RMSE and MAE score of 0.101 and 0.266, respectively. The study also shows that the most significant meteorological inputs in PV yield estimation are air temperature and global solar radiation. The study also noted that a large feature space adds uncertainties to the training process. Finally, the paper concludes

关键词： PV specific yield machine learning python meteorological parameters regression algorithm

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Double plasmonic peak shift sensitivity: an analysis of a highly sensitive LSPR-PCF sensor for a diverse range of analyte detection

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APPLIED PHYSICS A-MATERIALS SCIENCE & PROCESSING 2023年第8期129卷 571-571页

作者： Islam, Mohammad Rakibul Iftekhar, A. N. M. Hassan, Ali Ahnaf Zaman, Safin Al Hosain, Muhammad Alif Islamic Univ Technol Dept Elect & Elect Engn Gazipur 1704 Bangladesh

In this study, a unique combination of Silver (Ag) and Ga-doped ZnO (GZO) is used as plasmonic materials where both materials can be used for analyte detection. The sensor exhibits two distinct peaks within the same Refractive Index (RI) by which the sensing applications enhance. GZO is responsible for the first of the two peaks, while Ag contributes to the latter one. Along y-polarization, the highest value of double peak shift sensitivity (DPSS) known to date is found: 27,341.5 nm/RIU. The sensor also displays a high wavelength sensitivity (WS) of 27,360 nm/RIU and an amplitude sensitivity (AS) of 875.72 RIU-1. Additionally, the sensor demonstrates a high wavelength resolution of 6.032 x 10(-6) and an amplitude resolution of 1.496 x 10(-)5. The sensor exhibits a linearity of R-2 = 0.9973 and a figure of merit (FOM) of 243.4 RIU-1. Again, a & PLUSMN;10 tolerance limit is tested without showing any significant change in confinement loss and resonant wavelength shift. The sensor is examined for a wide array of RI ranging from 1.27 to 1.41 extending its application to the detection of pharmaceutical products and various chemicals. Furthermore, machine learning regression algorithms have been explored in this work to find out the sensing parameters in RIs that were not numerically investigated. Random Forrest Regressor and K-Neighbors Regressor were found to be showing high accuracies of 90.176% and 95.54%, respectively. Using these two algorithms, the performance of the sensor in detecting various chemicals were predicted.

关键词： Localized surface plasmon resonance (LSPR) Double resonance Double peak shift sensitivity (DPSS) Wavelength sensitivity Machine learning regression algorithm

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