Forecasting Demand in Supply Chain Using Machine Learning Algorithms

2015 ◽  
Vol 5 (1) ◽  
pp. 56-73 ◽  
Author(s):  
Nicholas Ampazis
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Learning Algorithms ◽  
Chain Structure ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Sources Of Information ◽  
Public Data ◽  
Supply Chain Structure

Managing inventory in a multi-level supply chain structure is a difficult task for big retail stores as it is particularly complex to predict demand for the majority of the items. This paper aims to highlight the potential of machine learning approaches as effective forecasting methods for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. For this purpose, we utilize Artificial Neural Networks (ANNs) trained with an effective second order algorithm, and Support Vector Machines (SVMs) for regression. We evaluated the effectiveness of the proposed approach using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during an especially critical for sales season, which is the Christmas holiday season. In our analysis we also integrated data from two other sources of information, namely an aggregator for movie reviews (Rotten Tomatoes), and a movie oriented social network (Flixster). Consequently, the approach presented in this paper combines the integration of data from various sources of information and the power of advanced machine learning algorithms for lowering the uncertainty barrier in forecasting supply chain demand.

A Computational Intelligence Approach to Supply Chain Demand Forecasting

2012 ◽  
pp. 1551-1565 ◽  
Author(s):  
Nicholas Ampazis
Keyword(s):  
Supply Chain ◽  
Computational Intelligence ◽  
Demand Forecasting ◽  
Business Environment ◽  
Chain Structure ◽  
Support Vector ◽  
Customer Demand ◽  
Public Data ◽  
Vector Machines ◽  
Supply Chain Structure

Estimating customer demand in a multi-level supply chain structure is crucial for companies seeking to maintain their competitive advantage within an uncertain business environment. This work explores the potential of computational intelligence approaches as forecasting mechanisms for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. The computational intelligence approaches that we utilize are Artificial Neural Networks (ANNs), trained with the OLMAM algorithm (Optimized Levenberg-Marquardt with Adaptive Momentum), and Support Vector Machines (SVMs) for regression. The effectiveness of the proposed approach was evaluated using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during the critical, for sales, Christmas holiday season.

A Computational Intelligence Approach to Supply Chain Demand Forecasting

2011 ◽  
pp. 110-124
Author(s):  
Nicholas Ampazis
Keyword(s):  
Supply Chain ◽  
Computational Intelligence ◽  
Demand Forecasting ◽  
Business Environment ◽  
Chain Structure ◽  
Support Vector ◽  
Customer Demand ◽  
Public Data ◽  
Vector Machines ◽  
Supply Chain Structure

Estimating customer demand in a multi-level supply chain structure is crucial for companies seeking to maintain their competitive advantage within an uncertain business environment. This work explores the potential of computational intelligence approaches as forecasting mechanisms for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. The computational intelligence approaches that we utilize are Artificial Neural Networks (ANNs), trained with the OLMAM algorithm (Optimized Levenberg-Marquardt with Adaptive Momentum), and Support Vector Machines (SVMs) for regression. The effectiveness of the proposed approach was evaluated using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during the critical, for sales, Christmas holiday season.

scholarly journals A study of deep learning approaches for medication and adverse drug event extraction from clinical text

2019 ◽  
Vol 27 (1) ◽  
pp. 13-21 ◽  
Author(s):  
Qiang Wei ◽  
Zongcheng Ji ◽  
Zhiheng Li ◽  
Jingcheng Du ◽  
Jingqi Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Joint Model ◽  
Machine Learning Algorithms ◽  
Entity Recognition ◽  
Superior Performance ◽  
Drug Event ◽  
Support Vector ◽  
Learning Approaches

AbstractObjectiveThis article presents our approaches to extraction of medications and associated adverse drug events (ADEs) from clinical documents, which is the second track of the 2018 National NLP Clinical Challenges (n2c2) shared task.Materials and MethodsThe clinical corpus used in this study was from the MIMIC-III database and the organizers annotated 303 documents for training and 202 for testing. Our system consists of 2 components: a named entity recognition (NER) and a relation classification (RC) component. For each component, we implemented deep learning-based approaches (eg, BI-LSTM-CRF) and compared them with traditional machine learning approaches, namely, conditional random fields for NER and support vector machines for RC, respectively. In addition, we developed a deep learning-based joint model that recognizes ADEs and their relations to medications in 1 step using a sequence labeling approach. To further improve the performance, we also investigated different ensemble approaches to generating optimal performance by combining outputs from multiple approaches.ResultsOur best-performing systems achieved F1 scores of 93.45% for NER, 96.30% for RC, and 89.05% for end-to-end evaluation, which ranked #2, #1, and #1 among all participants, respectively. Additional evaluations show that the deep learning-based approaches did outperform traditional machine learning algorithms in both NER and RC. The joint model that simultaneously recognizes ADEs and their relations to medications also achieved the best performance on RC, indicating its promise for relation extraction.ConclusionIn this study, we developed deep learning approaches for extracting medications and their attributes such as ADEs, and demonstrated its superior performance compared with traditional machine learning algorithms, indicating its uses in broader NER and RC tasks in the medical domain.

scholarly journals Intelligent Method of Supply Chain Circulation Industry Structure Based on Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
JingFei Ran
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Real Data ◽  
Chain Structure ◽  
Percentage Error ◽  
Support Vector ◽  
Data Simulation ◽  
Average Percentage ◽  
Four Levels ◽  
Supply Chain Structure

In the deepening of supply chain competition, whether the structure of supply chain industry is reasonable and scientific has been severely tested. For warehousing, purchase and distribution channels, and customers, it largely determines whether the structure of supply chain is stable and efficient. The rationality of structure can determine the value of supply chain. By analyzing these four levels, this paper judges whether the supply chain structure is reasonable; the judgment standard is based on the three popular machine learning models, Stochastic Forest, XGBoost, and Support Vector Machine. The three models are based on a large number of real data environments. Through data simulation and parameter optimization, four supply chain characteristics are put into the model for simulation training for many times, and the three error numbers of MAE, RMSE, and MAPE of the model are analyzed to judge the reliability of the model. On this basis, through the combination of models, it is determined that the average percentage error of the combination of the three models is higher than that of the other pairwise combinations, reaching 0.937, which completes the expectation of intelligent prediction of supply chain structure.

scholarly journals Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation

2021 ◽  
Vol 18 (4(Suppl.)) ◽  
pp. 1406
Author(s):  
Fadratul Hafinaz Hassan ◽  
Mohd Adib Omar
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Prediction Models ◽  
Recurrent Stroke ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Recurrent Strokes ◽  
Public Datasets

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning algorithms implementation in the recurrent stroke prediction models. This research aims to investigate and compare the performance of machine learning algorithms using recurrent stroke clinical public datasets. In this study, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Bayesian Rule List (BRL) are used and compared their performance in the domain of recurrent stroke prediction model. The result of the empirical experiments shows that ANN scores the highest accuracy at 80.00%, follows by BRL with 75.91% and SVM with 60.45%.

scholarly journals Determining the Tiers of a Supply Chain Using Machine Learning Algorithms

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1934
Author(s):  
Kyoung Jong Park
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Accurate Information ◽  
K Nearest Neighbor ◽  
Multi Class Classification

Companies in the same supply chain influence each other, so sharing information enables more efficient supply chain management. An efficient supply chain must have a symmetry of information between participating entities, but in reality, the information is asymmetric, causing problems. The sustainability of the supply chain continues to be threatened because companies are reluctant to disclose information to others. If companies participating in the supply chain do not disclose accurate information, the next best way to improve the sustainability of the supply chain is to use data from the supply chain to determine each enterprise’s information. This study takes data from the supply chain and then uses machine learning algorithms to find which enterprise the data refer to when new data from unknown sources arise. The machine learning algorithms used are logistic regression, random forest, naive Bayes, decision tree, support vector machine, k-nearest neighbor, and multi-layer perceptron. Indicators for evaluating the performance of multi-class classification machine learning methods are accuracy, confusion matrix, precision, recall, and F1-score. The experimental results showed that LR and MLP accurately predicted companies (tiers), but NB, DT, RF, SVM, and K-NN did not accurately predict companies. In addition, the performance similarity of machine learning algorithms through experiments was classified into LR and MLP groups, NB and DT groups, and RF, SVM, and K-NN groups.

scholarly journals Predicting Breast Cancer: A Comparative Analysis of Machine Learning Algorithms

2020 ◽  
Vol 3 ◽  
pp. 455-459
Author(s):  
Pulung Hendro Prastyo ◽  
I Gede Yudi Paramartha ◽  
Michael S. Moses Pakpahan ◽  
Igi Ardiyanto
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors ◽  
Common Cancer

Breast cancer is the most common cancer among women (43.3 incidents per 100.000 women), with the highest mortality (14.3 incidents per 100.000 women). Early detection is critical for survival. Using machine learning approaches, the problem can be effectively classified, predicted, and analyzed. In this study, we compared eight machine learning algorithms: Gaussian Naïve Bayes (GNB), k-Nearest Neighbors (K-NN), Support Vector Machine(SVM), Random Forest (RF), AdaBoost, Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). The experiment is conducted using Breast Cancer Wisconsin datasets, confusion matrix, and 5-folds cross-validation. Experimental results showed that XGBoost provides the best performance. XGBoost obtained accuracy (97,19%), recall (96,75%), precision (97,28%), F1-score (96,99%), and AUC (99,61%). Our result showed that XGBoost is the most effective method to predict breast cancer in the Breast Cancer Wisconsin dataset.

A Topical Survey: Applications of Machine Learning in Medical Issues

2020 ◽  
Vol 17 (11) ◽  
pp. 5010-5019
Author(s):  
Chapala Maharana ◽  
Bijan Bihari Mishra ◽  
Ch. Sanjeev Kumar Dash
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Image Segmentation ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Lesion Detection ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Medical Issues

Computational Intelligence methods have replaced almost all real world applications with high accuracy within the given time period. Machine Learning approaches like classification, feature selection, feature extraction have solved many problems of different domain. They use different ML models implemented with suitable ML tool or combination of tools from NN (Neural Network), SVM (Support Vector Machine), DL (Deep Learning), ELM (Extreme Learning Machine). The model is used for training with known data along with ML algorithms (fuzzy logic, genetic algorithm) to optimize the accuracy for different medical issues for example gene expression and image segmentation for information extraction and disease diagnosis, health monitoring, disease treatment. Most of the medical problems are solved using recent advances in AI (Artificial Intelligence) technologies with the biomedical systems development (e.g., Knowledge based Decision Support Systems) and AI technologies with medical informatics science. AI based methods like machine learning algorithms implemented models are increasingly found in real life applications ex. healthcare, natural calamity detection and forecasting. There are the expert systems handled by experts for knowledge gain which is used in decision making applications. The ML models are found in different medical applications like disease diagnosis (ex. cancer prediction, diabetics disease prediction) and for treatment of diseases (ex. in diabetics disease the reduction in mean glucose concentration following intermittent gastric feeds). The feature selection ML method is used for EEG classification for detection of the severity of the disease in heart related diseases and for identification of genes in different disorder like autism disorder. The ML models are found in health record systems. There are other applications of ML approaches found in image segmentation, tissue extraction, image fragmentation for disease diagnosis (ex. lesion detection in breast cancer for malignancy) and then treatment of those diseases. ML models are found in mobile health treatment, treatment of psychology patients, treatment of dumb patients etc. Medical data handling is the vital part of health care systems for the development of AI systems which can again be solved by machine learning approaches. The ML approaches for medical issues have used ensemble methods or combinations of machine learning tools and machine learning algorithms to optimize the result with good accuracy value at a faster rate.

scholarly journals Machine Learning Approaches for Inferring Liver Diseases and Detecting Blood Donors from Medical Diagnosis

2021 ◽  
Author(s):  
Fahad B. Mostafa ◽  
Easin Hasan
Keyword(s):  
Machine Learning ◽  
Health Professionals ◽  
Medical Diagnosis ◽  
Blood Donors ◽  
Medical Condition ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Accuracy Score ◽  
Learning Approaches

ABSTRACTFor a medical diagnosis, health professionals use different kinds of pathological ways to make a decision for medical reports in terms of patients’ medical condition. In the modern era, because of the advantage of computers and technologies, one can collect data and visualize many hidden outcomes from them. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning data driven algorithms can be used to validate existing methods and help researchers to suggest potential new decisions. In this paper, multiple imputation by chained equations was applied to deal with missing data, and Principal Component Analysis to reduce the dimensionality. To reveal significant findings, data visualizations were implemented. We presented and compared many binary classifier machine learning algorithms (Artificial Neural Network, Random Forest, Support Vector Machine) which were used to classify blood donors and non-blood donors with hepatitis, fibrosis and cirrhosis diseases. From the data published in UCI-MLR [1], all mentioned techniques were applied to find one better method to classify blood donors and non-blood donors (hepatitis, fibrosis, and cirrhosis) that can help health professionals in a laboratory to make better decisions. Our proposed ML-method showed better accuracy score (e.g. 98.23% for SVM). Thus, it improved the quality of classification.

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