Reservoir Evaporation Prediction Modeling Based on Artificial Intelligence Methods

The current study explored the impact of climatic conditions on predicting evaporation from a reservoir. Several models have been developed for evaporation prediction under different scenarios, with artificial intelligence (AI) methods being the most popular. However, the existing models rely on several climatic parameters as inputs to achieve an acceptable accuracy level, some of which have been unavailable in certain case studies. In addition, the existing AI-based models for evaporation prediction have paid less attention to the influence of the time increment rate on the prediction accuracy level. This study investigated the ability of the radial basis function neural network (RBF-NN) and support vector regression (SVR) methods to develop an evaporation rate prediction model for a tropical area at the Layang Reservoir, Johor River, Malaysia. Two scenarios for input architecture were explored in order to examine the effectiveness of different input variable patterns on the model prediction accuracy. For the first scenario, the input architecture considered only the historical evaporation rate time series, while the mean temperature and evaporation rate were used as input variables for the second scenario. For both scenarios, three time-increment series (daily, weekly, and monthly) were considered.

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NIMG-46. SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE ALGORITHMS USING PRE-THERAPY MR IMAGING FOR GLIOMA MOLECULAR SUBTYPE CLASSIFICATION

Neuro-Oncology ◽

10.1093/neuonc/noab196.545 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi139-vi139

Author(s):

Jan Lost ◽

Tej Verma ◽

Niklas Tillmanns ◽

W R Brim ◽

Harry Subramanian ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Literature Review ◽

Prediction Accuracy ◽

Molecular Subtypes ◽

Molecular Subtype ◽

Tumor Resection ◽

Support Vector ◽

Hospital Data

Abstract PURPOSE Identifying molecular subtypes in gliomas has prognostic and therapeutic value, traditionally after invasive neurosurgical tumor resection or biopsy. Recent advances using artificial intelligence (AI) show promise in using pre-therapy imaging for predicting molecular subtype. We performed a systematic review of recent literature on AI methods used to predict molecular subtypes of gliomas. METHODS Literature review conforming to PRSIMA guidelines was performed for publications prior to February 2021 using 4 databases: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science core-collection. Keywords included: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Non-machine learning and non-human studies were excluded. Screening was performed using Covidence software. Bias analysis was done using TRIPOD guidelines. RESULTS 11,727 abstracts were retrieved. After applying initial screening exclusion criteria, 1,135 full text reviews were performed, with 82 papers remaining for data extraction. 57% used retrospective single center hospital data, 31.6% used TCIA and BRATS, and 11.4% analyzed multicenter hospital data. An average of 146 patients (range 34-462 patients) were included. Algorithms predicting IDH status comprised 51.8% of studies, MGMT 18.1%, and 1p19q 6.0%. Machine learning methods were used in 71.4%, deep learning in 27.4%, and 1.2% directly compared both methods. The most common algorithm for machine learning were support vector machine (43.3%), and for deep learning convolutional neural network (68.4%). Mean prediction accuracy was 76.6%. CONCLUSION Machine learning is the predominant method for image-based prediction of glioma molecular subtypes. Major limitations include limited datasets (60.2% with under 150 patients) and thus limited generalizability of findings. We recommend using larger annotated datasets for AI network training and testing in order to create more robust AI algorithms, which will provide better prediction accuracy to real world clinical datasets and provide tools that can be translated to clinical practice.

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Evaluation of the impact of explanatory variables on the accuracy of prediction of daily inflow to the sewage treatment plant by selected models nonlinear

Archives of Environmental Protection ◽

10.1515/aep-2017-0030 ◽

2017 ◽

Vol 43 (3) ◽

pp. 74-81 ◽

Cited By ~ 5

Author(s):

Bartosz Szeląg ◽

Lidia Bartkiewicz ◽

Jan Studziński ◽

Krzysztof Barbusiński

Keyword(s):

Prediction Models ◽

Sewage Treatment ◽

Sewage Treatment Plant ◽

Treatment Plant ◽

Municipal Sewage ◽

Support Vector ◽

Prediction Errors ◽

Explanatory Variables ◽

Input Variables ◽

The Impact

AbstractThe aim of the study was to evaluate the possibility of applying different methods of data mining to model the inflow of sewage into the municipal sewage treatment plant. Prediction models were elaborated using methods of support vector machines (SVM), random forests (RF), k-nearest neighbour (k-NN) and of Kernel regression (K). Data consisted of the time series of daily rainfalls, water level measurements in the clarified sewage recipient and the wastewater inflow into the Rzeszow city plant. Results indicate that the best models with one input delayed by 1 day were obtained using the k-NN method while the worst with the K method. For the models with two input variables and one explanatory one the smallest errors were obtained if model inputs were sewage inflow and rainfall data delayed by 1 day and the best fit is provided using RF method while the worst with the K method. In the case of models with three inputs and two explanatory variables, the best results were reported for the SVM and the worst for the K method. In the most of the modelling runs the smallest prediction errors are obtained using the SVM method and the biggest ones with the K method. In the case of the simplest model with one input delayed by 1 day the best results are provided using k-NN method and by the models with two inputs in two modelling runs the RF method appeared as the best.

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p-norms of histogram of oriented gradients for X-ray images

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i5.pp4423-4430 ◽

2021 ◽

Vol 11 (5) ◽

pp. 4423

Author(s):

Nuha H. Hamada ◽

Faten F. Kharbat

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Euclidean Distance ◽

Support Vector ◽

Histogram Of Oriented Gradients ◽

Lebesgue Spaces ◽

X Ray ◽

Original Algorithm ◽

Norm Values ◽

The Impact

Lebesgue spaces (Lp over Rn) play a significant role in mathematical analysis. They are widely used in machine learning and artificial intelligence to maximize performance or minimize error. The well-known histogram of oriented gradients (HOG) algorithm applies the 2-norm (Euclidean distance) to detect features in images. In this paper, we apply different p-norm values to identify the impact that changing these norms has on the original algorithm. The aim of this modification is to achieve better performance in classifying X-ray medical images related to of COVID-19 patients. The efficiency of the p-HOG algorithm is compared with the original HOG descriptor using a support vector machine implemented in Python. The results of the comparisons are promising, and the p-HOG algorithm shows greater efficiency in most cases.

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Short-Term Wind Speed Forecasting Based on Optimizated Support Vector Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.300-301.189 ◽

2013 ◽

Vol 300-301 ◽

pp. 189-194 ◽

Cited By ~ 1

Author(s):

Yu Sun ◽

Ling Ling Li ◽

Xiao Song Huang ◽

Chao Ying Duan

Keyword(s):

Support Vector Machine ◽

Wind Speed ◽

Prediction Model ◽

Prediction Accuracy ◽

Support Vector ◽

Particle Swarm Algorithm ◽

Short Term ◽

Neural Network Prediction ◽

Random Fluctuations ◽

The Impact

To avoid the impact which is caused by the characteristics of the random fluctuations of the wind speed to grid-connected wind power generation system, accurately prediction of short-term wind speed is needed. This paper designed a combination prediction model which used the theories of wavelet transformation and support vector machine (SVM). This improved the model’s prediction accuracy through the method of achiving change character in wind speed sequences in different scales by wavelet transform and optimizing the parameters of support vector machines through the improved particle swarm algorithm. The model showed great generalization ability and high prediction accuracy through the experiment. The lowest root-mean-square error of 200 samples was up to 0.0932 and the model’s effect was much stronger than the BP neural network prediction model. It provided an effective method for predicting wind speed.

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An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

Materials ◽

10.3390/ma14185342 ◽

2021 ◽

Vol 14 (18) ◽

pp. 5342

Author(s):

Sunil Kumar Panigrahy ◽

Yi-Chieh Tseng ◽

Bo-Ruei Lai ◽

Kuo-Ning Chiang

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Electronic Packaging ◽

Prediction Accuracy ◽

Chip Thickness ◽

Design Parameters ◽

Support Vector ◽

K Nearest Neighbor ◽

Wafer Level ◽

Advanced Packaging

Several design parameters affect the reliability of wafer-level type advanced packaging, such as upper and lower pad sizes, solder volume, buffer layer thickness, and chip thickness, etc. Conventionally, the accelerated thermal cycling test (ATCT) is used to evaluate the reliability life of electronic packaging; however, optimizing the design parameters through ATCT is time-consuming and expensive, reducing the number of experiments becomes a critical issue. In recent years, many researchers have adopted the finite-element-based design-on-simulation (DoS) technology for the reliability assessment of electronic packaging. DoS technology can effectively shorten the design cycle, reduce costs, and effectively optimize the packaging structure. However, the simulation analysis results are highly dependent on the individual researcher and are usually inconsistent between them. Artificial intelligence (AI) can help researchers avoid the shortcomings of the human factor. This study demonstrates AI-assisted DoS technology by combining artificial intelligence and simulation technologies to predict wafer level package (WLP) reliability. In order to ensure reliability prediction accuracy, the simulation procedure was validated by several experiments prior to creating a large AI training database. This research studies several machine learning models, including artificial neural network (ANN), recurrent neural network (RNN), support vector regression (SVR), kernel ridge regression (KRR), K-nearest neighbor (KNN), and random forest (RF). These models are evaluated in this study based on prediction accuracy and CPU time consumption.

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Artificial intelligence prediction of adverse outcomes in emergency department patients with hyperglycemic crises in real-time

10.21203/rs.3.rs-73620/v1 ◽

2020 ◽

Author(s):

Chin-Chuan Hsu ◽

Yuan Kao ◽

Chien-Chin Hsu ◽

Chia-Jung Chen ◽

Shu-Lien Hsu ◽

...

Keyword(s):

Artificial Intelligence ◽

Septic Shock ◽

Logistic Regression ◽

Prediction Model ◽

Real Time ◽

Adverse Outcomes ◽

Support Vector ◽

Icu Admission ◽

All Cause Mortality ◽

The Impact

Abstract Background Hyperglycemic crises are associated with high morbidity and mortality. Previous studies proposed methods for predicting adverse outcome in hyperglycemic crises, artificial intelligence (AI) has however never been tried. We implemented an AI prediction model integrated with hospital information system (HIS) to clarify this issue. Methods We included 3,715 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. Patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from their electronic medical records were collected, and multilayer perceptron (MLP) was used to construct an AI prediction model to predict sepsis or septic shock, intensive care unit (ICU) admission, and all-cause mortality within 1 month. Comparisons of the performance among random forest, logistic regression, support vector machine (SVM), K-nearest neighbor (KNN), Light Gradient Boosting Machine (LightGBM), and MLP algorithms were also done. Results Using the MLP model, the areas under the curves (AUCs) were 0.808 for sepsis or septic shock, 0.688 for ICU admission, and 0.770 for all-cause mortality. MLP had the best performance in predicting sepsis or septic shock and all-cause mortality, compared with logistic regression, SVM, KNN, and LightGBM. Furthermore, we integrated the AI prediction model with the HIS to assist physicians for decision making in real-time. Conclusions A real-time AI prediction model is a promising method to assist physicians in predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies on the impact on clinical practice and patient outcome are warranted.

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Improving the Accuracy of Text Classification using Stemming Method, A Case of Non-Formal Indonesian Conversation

10.21203/rs.3.rs-41431/v3 ◽

2021 ◽

Author(s):

Rianto Rianto ◽

Achmad Benny Mutiara ◽

Eri Prasetyo Wibowo ◽

Paulus Insap Santosa

Keyword(s):

Text Classification ◽

Hate Speech ◽

Text Processing ◽

High Accuracy ◽

Small Error ◽

Support Vector ◽

Support Vector Machine Algorithm ◽

Text Data ◽

Accuracy Level ◽

The Impact

Abstract Background: Stemming has long been used in data pre-processing to retrieve information by tracking affixed words back into their root. In an Indonesian setting, existing stemming methods have been observed, and the existing stemming methods are proven to result in high accuracy level. However, there are not many stemming methods for non-formal Indonesian text processing. This study introduces a new stemming method to solve problems in the non-formal Indonesian text data pre-processing. Furthermore, this study aims to improve the accuracy of text classifier models by strengthening stemming method. Using the Support Vector Machine algorithm, a text classifier model is developed, and its accuracy is checked. The experimental evaluation was done by testing 550 datasets in Indonesian using two different stemming methods. Findings: The results show that using the proposed stemming method, the text classifier model has higher accuracy than the existing methods with a score of 0.85 and 0.73, respectively. These results indicate that the proposed stemming methods produces a classifier model with a small error rate, so it will be more accurate to predict a class of objects. Conclusion: The existing Indonesian stemming methods are still oriented towards Indonesian formal sentences, therefore the method has limitations to be used in Indonesian non-formal sentences. This phenomenon underlies the suggestion of developing a corpus by normalizing Indonesian non-formal into formal to be used as a better stemming method. The impact of using the corpus as a stemming method is that it can improve the accuracy of the classifier model. In the future, the proposed corpus and stemming methods can be used for various purposes including text clustering, summarizing, detecting hate speech, and other text processing applications in Indonesian.

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Identifying impact of variables in deep learning models on bankruptcy prediction of construction contractors

Engineering Construction & Architectural Management ◽

10.1108/ecam-06-2020-0386 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Youjin Jang ◽

Inbae Jeong ◽

Yong K. Cho

Keyword(s):

Deep Learning ◽

Prediction Model ◽

Shapley Value ◽

Prediction Accuracy ◽

Prediction Models ◽

Bankruptcy Prediction ◽

Content Type ◽

Macroeconomic Variables ◽

Input Variables ◽

The Impact

PurposeThe study seeks to identify the impact of variables in a deep learning-based bankruptcy prediction model, which has achieved superior performance to other prediction models but cannot easily interpret hidden processes.Design/methodology/approachThis study developed three LSTM-RNN–based models that predicted the probability of bankruptcy before 1, 2 and 3 years using financial, the construction market and macroeconomic variables as input variables. Then, the impacts of the input variables that affected prediction accuracy in each model were identified by using Shapley value and compared among the three models. This study also investigated the prediction accuracy using variants of input variables grouped sequentially by high-impact ranking.FindingsThe results showed that the prediction accuracies were largely impacted by “housing starts” in all models. As the prediction period increased, the effects of macroeconomic variables on prediction accuracy increased, whereas the impact of “return on assets” on prediction accuracy decreased. It also found that the “current ratio” and “debt ratio” significantly influenced the prediction accuracies in all models. Also, the results revealed that similar prediction accuracies could be achieved using only 8, 10, and 10 variables out of a total of 18 variables for the 1-, 2-, and 3-year prediction models, respectively.Originality/valueThis study provides a Shapley value-based approach to identify how each input variable in a deep-learning bankruptcy prediction model. The findings of this study can not only assist in obtaining better insights into the underlying concept of bankruptcy but also use to select variables by removing those identified as less significant.

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Ultra-Short-Term Prediction of Wind Power Based on Error Following Forget Gate-Based Long Short-Term Memory

Energies ◽

10.3390/en13205400 ◽

2020 ◽

Vol 13 (20) ◽

pp. 5400

Author(s):

Pei Zhang ◽

Chunping Li ◽

Chunhua Peng ◽

Jiangang Tian

Keyword(s):

Wind Power ◽

Prediction Accuracy ◽

Short Term Memory ◽

Support Vector ◽

Power Prediction ◽

Short Term ◽

Term Memory ◽

Wind Power Prediction ◽

Long Short Term Memory ◽

The Impact

To improve the accuracy of ultra-short-term wind power prediction, this paper proposed a model using modified long short-term memory (LSTM) to predict ultra-short-term wind power. Because the forget gate of standard LSTM cannot reflect the correction effect of prediction errors on model prediction in ultra-short-term, this paper develops the error following forget gate (EFFG)-based LSTM model for ultra-short-term wind power prediction. The proposed EFFG-based LSTM model updates the output of the forget gate using the difference between the predicted value and the actual value, thereby reducing the impact of the prediction error at the previous moment on the prediction accuracy of wind power at this time, and improving the rolling prediction accuracy of wind power. A case study is performed using historical wind power data and numerical prediction meteorological data of an actual wind farm. Study results indicate that the root mean square error of the wind power prediction model based on EFFG-based LSTM is less than 3%, while the accuracy rate and qualified rate are more than 90%. The EFFG-based LSTM model provides better performance than the support vector machine (SVM) and standard LSTM model.

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Predictions of Apoptosis Proteins by Integrating Different Features Based on Improving Pseudo-Position-Specific Scoring Matrix

BioMed Research International ◽

10.1155/2020/4071508 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Xiaoli Ruan ◽

Dongming Zhou ◽

Rencan Nie ◽

Yanbu Guo

Keyword(s):

Prediction Accuracy ◽

Classification Model ◽

Position Specific Scoring Matrix ◽

Support Vector ◽

Data Imbalance ◽

Apoptosis Protein ◽

Scoring Matrix ◽

The Impact ◽

Apoptosis Proteins

Apoptosis proteins are strongly related to many diseases and play an indispensable role in maintaining the dynamic balance between cell death and division in vivo. Obtaining localization information on apoptosis proteins is necessary in understanding their function. To date, few researchers have focused on the problem of apoptosis data imbalance before classification, while this data imbalance is prone to misclassification. Therefore, in this work, we introduce a method to resolve this problem and to enhance prediction accuracy. Firstly, the features of the protein sequence are captured by combining Improving Pseudo-Position-Specific Scoring Matrix (IM-Psepssm) with the Bidirectional Correlation Coefficient (Bid-CC) algorithm from position-specific scoring matrix. Secondly, different features of fusion and resampling strategies are used to reduce the impact of imbalance on apoptosis protein datasets. Finally, the eigenvector adopts the Support Vector Machine (SVM) to the training classification model, and the prediction accuracy is evaluated by jackknife cross-validation tests. The experimental results indicate that, under the same feature vector, adopting resampling methods remarkably boosts many significant indicators in the unsampling method for predicting the localization of apoptosis proteins in the ZD98, ZW225, and CL317 databases. Additionally, we also present new user-friendly local software for readers to apply; the codes and software can be freely accessed at https://github.com/ruanxiaoli/Im-Psepssm.

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