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13th International Conference on Information and Communication Technology and System (ICTS)

作者： Siek, Michael Guswanto, Kevin Bina Nusantara Univ Fac Comp & Media Sch Informat Syst Business Informat Syst Program Jakarta 11480 Indonesia

ISBN: (纸本)9781665440592

People are all currently living in the world where data has changed how company think, act and plan. Data, if used correctly, might be able to become a company's sharpest weapon in fighting the competition with other companies. Inventory cost is one of the most burdening costs in the food and beverage industry with the items like degradable raw materials or fresh ingredients. If not managed correctly might become a waste causing loss to the company. Degraded ingredients also might lower the overall food quality which might result in unsatisfied customers. Managing inventory, however, is not as easy as it seems, especially with the traditional method. This paper focuses on development of accurate predictive model using computational intelligence for optimal inventory management with a case study of restaurant ingredient management. Several machine learning algorithms like linear regression, multi-layer perceptron, random tree, random forest, and model trees were utilized to build accurate predictive models from time series data of the restaurant inventory. With good prediction system using computational intelligence, the inventory cost and wasted ingredients can be significantly reduced, which this eventually maximizes the profit.

关键词： machine learning algorithm time series forecasting inventory optimization and control

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Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms

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JOURNAL OF CLEANER PRODUCTION 2021年 285卷

作者： Bakay, Melahat Sevgul Agbulut, Umit Duzce Univ Fac Engn Dept Biomed Engn TR-81620 Duzce Turkey Duzce Univ Fac Engn Dept Mech Engn TR-81620 Duzce Turkey

Today, the world's primary energy demand has been met by the burning of fossil-based fuels at a rate of 85%. This dominant use of fossil-based fuels has led to an accelerating increase in the release of greenhouse gases (GHG) all across the world. The largest share in total GHG emissions belongs to the electricity and heat production sector with a rate of 25%. With this viewpoint, this paper is aiming to forecast the GHG emissions (CO2, CH4, N2O, F-gases, and total GHG) using deep learning (DL), support vector machine (SVM), and artificial neural network (ANN) algorithms from the electricity production sector in Turkey. The dataset is supplied from the Turkish Statistical Institute and covers the years 1990-2018. In the study, the last four years (2015-2018) is being forecasted. To evaluate the performance success of the algorithms, five metrics (RMSE, MBE, rRMSE, R-2, and MAPE) are discussed in detail. In the results, this research is reporting that all algorithms used in the study are giving separately satisfying results for the forecasting of GHG emissions in Turkey. Based on the forecasting results, it is seen that the highest R-2 value for the emissions varies from 0.861 to 0.998 and all results are categorized as "excellent" in terms of rRMSE (all rRMSE values < 10%). Besides, MBE changes between -2.427 and 2.235, and all MAPE values are smaller than 1.2%. Total GHG emission is forecasted in DL algorithm with very satisfied R-2, RMSE, MBE, rRMSE, and MAPE of 0.998, 2.046, 0.419, 0.406%, and 0.021%, respectively. On the other hand, CO2 accounted for 69.05% of total GHG emissions of Turkey in 1990 but rising by 80.48% in the year 2018. In comparison with those of 1990, electricity production and total GHG emissions of Turkey in 2018 increased by 429.7% and 137.4%, respectively. Total GHG emission corresponding to electricity production is calculated to be 0.3813 Mt-total GHG/MWh in 1990 and 0.1709 Mt-total GHG/MWh in 2018. In conclusion, GHG emissions have recen

关键词： CO2 emission Electricity production GHG Greenhouse gases machine learning algorithm

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machine learning assisted prediction of exhaust gas temperature of a heavy-duty natural gas spark ignition engine

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APPLIED ENERGY 2021年 300卷

作者： Liu, Jinlong Huang, Qiao Ulishney, Christopher Dumitrescu, Cosmin E. West Virginia Univ Mech & Aerosp Engn Morgantown WV 26506 USA West Virginia Univ Chem & Biomed Engn Morgantown WV 26506 USA

Exhaust gas temperature is a key parameter for optimizing engine performance and emissions. Of particular interest is forecasting the exhaust gas temperature in a diesel engine converted to spark ignition natural gas operation, as the combustion process in this engine is significantly different from the one in a conventional engine. The goal was to assess four different machine learning algorithms namely the artificial neural network, random forest, support vector regression, and gradient boosting regression trees, with respect to a physical 1D CFD model and relative to one another, when predicting the exhaust temperature. The spark timing, equivalence ratio, and engine speed were model inputs. First, the artificial neural network predicted the exhaust temperature more accurately than the physical model, because of the complex premixed combustion phenomena inside a conventional diesel chamber. When compared relative to one another, all machine learning models predicted the exhaust gas temperature with acceptable error while also capturing its relationship with the three model inputs. The gradient boosting regression trees predicted the best, but it usually requires high quality noise-free data. The random forest had the least accuracy, but it required the least amount of calibration. The support vector regression had the smallest error, but it required the highest computational resources. The artificial neural network algorithm was the most appropriate, but it required effort in tuning its hyperparameters. Overall, the results showed that well-trained machine learning models can complement more complex physical model while also helping with optimizing the engine performance, emissions, and life.

关键词： Physics-based modeling Data-based modeling Exhaust gas temperature Natural gas spark ignition engine machine learning algorithm

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Predictive Analytics of Donors in crowdfunding platforms: A case study on *** 11

Predictive Analytics of Donors in crowdfunding platforms: A ...

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IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Dirisam, Joel Varma Bein, Doina Verma, Abhishek Calif State Univ Fullerton Dept Comp Sci Fullerton CA 92634 USA New Jersey City Univ Dept Comp Sci Jersey City NJ USA

ISBN: (纸本)9781665414906

Every year donors support crowdfunding organizations like ***. With the decline in the number of contributions received every year, initiatives in United States schools are affected. Donorschoose is a readily available dataset which contains data about US school projects proposed by teachers. In this paper, we apply machine learning algorithms, and we analyze each feature of the Donorschoose including the actions of donors for every project to encourage school's funding. We also provide probability and statistical modeling techniques to compare with the machine learning results. Some of the findings include that donor will be retained if they had a positive interaction overall with the organization, and the targeting donors specific to location also improves new donors to join. Finally, we present some main facets of the dataset concerning the approval of the project donations.

关键词： Crowdfunding machine learning algorithm natural language processing NLP probabilistic model statistical model

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Modelling Residential-Scale Consumer Demographics using Monthly Electricity Consumption Data

Modelling Residential-Scale Consumer Demographics using Mont...

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IEEE Electrical Power and Energy Conference (EPEC)

作者： Rahman, Abdur Arif, Ameera Nadeem, Ahmad Arshad, Naveed LUMS Sch Sci & Engn Lahore Pakistan

ISBN: (纸本)9781665429283

Electricity is one of the most widely used forms of energy that plays a significant part in sufficing the fundamental energy demand based on contemporary human needs. It is becoming highly tedious for the energy sector to manage and surveil the modern energy demands based on the constantly changing consumer demographics. In order to progress as a business, it has become pertinent for the distribution companies to evolve their development plans, tariffs, and business models according to the consumer requirements. This article proposes three different models to predict the attributes of a consumer household, namely the multivariate linear regression (MLR), the support vector regression (SVR), and the artificial neural network (ANN). The study uses the PRECON dataset, which is based on the monthly electricity consumption of households in Lahore, Pakistan. All of the proposed models play significant roles in predicting the required consumer demographics for forecasting. The linear model shows the ability to predict the number of people with very low MAPE of 3.57% as compared to other models. So far, ANN has shown the best results in predicting the number of fans, air conditioners, and rooms. However, the MAPE reports extracted from this study show the inability of the used models to explain the variation of property area confidently.

关键词： energy prediction residential building machine learning algorithm electricity consumption demographics consumer profile

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NIR spectroscopy and artificial neural network for seaweed protein content assessment in-situ

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 201卷

作者： Shalev, Niva Tadmor Ghermandi, Andrea Tchernov, Dan Shemesh, Eli Israel, Alvaro Brook, Anna Univ Haifa Dept Nat Resources & Environm Management 199 Abba Khoushy Ave IL-3498838 Har Hakarmel Israel Univ Haifa Leon H Charney Sch Marine Sci Dept Marine Biol Morris Kahn Marine Res Stn IL-3498838 Haifa Israel Israel Oceanog & Limnol Res Ltd PBC Tel Shikmona POB 9753 IL-3109701 Haifa Israel Univ Haifa Dept Geog & Environm Studies Spect & Remote Sensing Lab 199 Abba Khoushy Ave IL-3498838 Har Hakarmel Israel

Determining seaweed protein concentration and the associated phenotype is critical for food industries that require precise tools to moderate concentration fluctuations and attenuate risks. Algal protein extraction and profiling have been widely investigated, but content determination involves a costly, time-consuming and high-energy, laboratory-based fractionation technique. The present study examines the potential of a field spectroscopy technology as a precise, non-destructive tool for on-site detection of red seaweed protein concentration. By using information from a large dataset of 144 Gracilaria sp. specimens, studied in a land-based cultivation set-up, under six treatment regimes during two cultivation seasons, and an artificial neural network, machine learning algorithm and diffuse visible-near infrared reflectance spectroscopy, predicted protein concentrations in the algae were obtained. The prediction results were highly accurate (R-2 = 0.95;RMSE = 0.84), exhibiting a high correlation with the analytically determined values. External validation of the model derived from a separate trial, exhibited even better results (R-2 = 0.99;RMSE = 0.45). This model, trained to convert phenotypic spectral measurements and pigment intensity into accurate protein content predictions, can be adapted to include diversified algae species and usages.

关键词： Artificial neural network Diffuse reflectance spectroscopy machine learning algorithm Phycobiliprotein Protein content Seaweed

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Predicting stress-dependent gas permeability of cement mortar with different relative moisture contents based on hybrid ensemble artificial intelligence algorithms

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CONSTRUCTION AND BUILDING MATERIALS 2022年 348卷

作者： Chao, Zhiming Wang, Mingyang Sun, Yinuo Xu, Xu Yue, Wenhan Yang, Chuanxin Hu, Tianxiang Shanghai Maritime Univ Shanghai 200135 Peoples R China Sichuan Univ Key Lab Sichuan Prov Failure Mech & Engn Disaster Prevent Chengdu 610065 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China China Inst Water Resources & Hydropower Res Res Ctr Flood & Drought Disaster Reduct Minist Wat Beijing 100038 Peoples R China Anhui Prov Port & Shipping Grp Co LTD Wuhu Peoples R China IXM Shanghai Corp Management Co Ltd Shanghai 200120 Peoples R China

The gas permeability of cement mortar is a vital parameter for the reasonable design of relevant buildings. However, due to the large number of influence factors and the complex interaction mechanism, the correct prediction of the gas permeability for cement mortar is always a huge challenge. In this paper, a novel machine learning model was established by combining Mind Evolutionary algorithm (MEA) and the ensemble algorithm of Adaptive Boosting algorithm (ADA)-Back Propagation Artificial Neural Network (BPANN) to predict the stress-dependent gas permeability of cement mortar with different moisture contents based on the results of 1890 laboratory gas permeability experiments. The novel machine learning model combined by MEA and ADA-BPANN is firstly adopted in the prediction of the gas permeability of cement mortar. By comparing with the conventional machine learning algorithms, including Particle Swarm Optimisation algorithm (PSO) and Genetic algorithm (GA) tuned ADA-BPANN, MEA tuned Extreme learning machine (ELM) and Random Forest (RF), the superior performance of MEA tuned ADA-BPANN has been validated. This new algorithm is with higher predicting precision, shorter training time, and the avoidance of local optimum and overfitting. Secondly, sensitivity analysis was carried out by adopting this proposed novel model, which indicates that the impact of relative moisture content on the gas permeability is the highest, followed by confining pressure, cyclic time and confining pressure loading/unloading stage. Thirdly, an analytical equation was proposed to assess the gas permeability that allows the usage of machine learning skills for the practitioners with limited machine learning knowledge. The present research highlights the potential of the MEA tuned ADA-BPANN model as a useful tool to assist in preciously estimating the stress-dependent gas permeability of cement mortar with different moisture contents. This can provide huge help for the reasonable design of re

关键词： Cement mortar Permeability Relative moisture content machine learning algorithm Optimisation algorithm

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A Scheme for Quickly Simulating Extraterrestrial Solar Radiation over Complex Terrain on a Large Spatial-Temporal Span-A Case Study over the Entirety of China

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REMOTE SENSING 2022年第7期14卷

作者： Lin, Siwei Chen, Nan Zhou, Qianqian Lin, Tinmin Li, Huange Fuzhou Univ Minist Educ Key Lab Spatial Data Min & Informat Sharing Fuzhou 350116 Peoples R China Fuzhou Univ Acad Digital China Fujian Fuzhou 350116 Peoples R China

Extraterrestrial solar radiation (ESR) is the essential basic background for solar radiation, which determines the occurrence of the weather and atmospheric phenomena. Since the influence of ESR variation on actual rugged terrain is a diverse, complex, and dynamic process, simulating ESR over a large spatial-temporal span, especially with a high-resolution digital elevation model (DEM), is a significant challenge. In this paper, we developed a new scheme for simulating ESR over the entirety of China using a DEM with a resolution of 30 m. To fully consider regional terrain status, the feature variables used were elevation, slope, and aspects of the located grid and the surrounding four grids to reveal the topography. In addition, latitude was used as a feature variable to consider the geographical location, and the month number was used to consider the duration. On the basis of different geographical locations, the training dataset was established from 20,000 grids. With the feature variable composition and training dataset, a backpropagation artificial neural network (BP ANN) was found to have the best performance compared with the other three machine learning methods in simulating ESR for a DEM. In terms of the proposed scheme and BP ANN, we drew an ESR map of China with a resolution of 30 m. The determination coefficient of the simulation result achieved 0.99 and the root-mean-square error was less than 50 MJ/m(2) in all sample areas, confirming its remarkable accuracy. In terms of efficiency, the time consumption of ESR simulated using the proposed scheme shrinks over 150 times in all sample areas compared to that simulated via the theoretical model. Simultaneously, the developed scheme was also used to simulate an ESR for a DEM with a resolution of 90 m to verify the universality and robustness of the developed scheme. In addition, we used the proposed scheme to derive the direct solar radiation and global solar radiation, thereby further proving the reliability

关键词： extraterrestrial solar radiation DEM terrain shielding effect spatial-temporal machine learning algorithm

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Automatic medical protocol classification using machine learning approaches

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021年 200卷 105939-105939页

作者： Lopez-Ubeda, Pilar Diaz-Galiano, Manuel Carlos Martin-Noguerol, Teodoro Luna, Antonio Urena-Lopez, L. Alfonso Martin-Valdivia, M. Teresa Univ Jaen SINAI Grp CEATIC Campus Las Lagunillas S-N E-23071 Jaen Spain Radiol Dept MRI Unit HT Med Carmelo Torres 2 Jaen 23007 Spain

Background and objective: Assignment of medical imaging procedure protocols requires extensive knowledge about patient's data, usually included in radiological request forms and radiological reports. Assignment of protocol is required prior to radiological study acquisition, determining procedure for each patient. The automation of this protocol assignment process could improve the efficiency of patient's diagnosis. Artificial intelligence has proven to be of great help in these healthcare-related problems, and specifically the application of Natural Language Processing (NLP) techniques for extracting information from text reports has been successfully used in automatic text classification tasks. Methods: In this paper, machine learning classification models based on NLP have been developed using patient's data present in radiological reports and radiological imaging protocols. We have used a real corpus provided by the private medical center "HT medica" composed of almost 70 0,0 0 0 Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) examinations obtained during routine clinical use. We have compared several models including traditional machine learning methods such as support vector machine and random forest, neural networks and transfer language techniques. Results: The results obtained are encouraging taking into account that the system is performing a complex text multiclass classification task. Specifically, for the best proposed system we obtain 92.2% accuracy in the CT dataset and 86.9% in the MRI dataset. Conclusions: The best machine learning system is potentially efficient, quality and cost effective. For this reason it is currently used in real scenarios by radiologists as decision support tool for assigning protocols of CT and MRI studies. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Spanish radiological report Image protocol Automatic protocol assignment Natural language processing machine learning algorithm Textual multiclass classification task

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A data-driven approach to predict hourly bill rates for US contingent workers 21

A data-driven approach to predict hourly bill rates for US c...

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21st IEEE International Conference on Data Mining (IEEE ICDM)

作者： Giuliani, Giuliano Giuliani, Fabrizio Husband, Summer M. Randstad Sourceright Data Engn & Analyt Amsterdam Netherlands Randstad Sourceright Data Engn & Analyt Houston TX USA

ISBN: (纸本)9781665424271

Contingent workers make up a large portion of the service industry workforce. A bill rate is the hourly fee a company pays to a staffing agency for the services of a temporary worker. In reality, the market for contract labor is fluid and bill rates sometimes fluctuate from week to week. Having a systematic approach that proactively aligns pay and bill rates with shifting market conditions can keep clients from underpaying or overpaying for contract talent and create a competitive advantage for recruiters and hiring managers. By using Randstad historical data, we build a machine learning model for the US labor market that predicts high confidence bill rates for a vast range of job titles at different geographical granularities (national, state and MSA) and that takes into account different experience levels.

关键词： bill rate prediction machine learning algorithm XGBoost data augmentation model evaluation

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