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检索条件"主题词=Machine Learning Algorithms"

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An Approach to Optimize Future Inbound Logistics Processes Using machine learning algorithms

An Approach to Optimize Future Inbound Logistics Processes U...

引用

IEEE International Conference on Electro-Information Technology

作者： Abdullah Albadrani Mohamed A. Zohdy Richard Olawoyin Industrial & Systems Engineering Oakland University Rochester USA Electrical & Computer Engineering Oakland University Rochester USA

ISBN: (数字)9781728153179

ISBN: (纸本)9781728153186

The manufacturing industry is in rapid change due to the increasing amount of market changes. Therefore, the accuracy of planning is critical for the manufacturers since it reflects on the global supply chain network. For inbound logistics, a variety of goods comes from different suppliers and locations to the manufacturing plants. Planning these inbound logistics relies on product readiness, manufacturing plant planning, procurement, and their continually changing information. This paper focuses on machine learning algorithms, such as K-nearest neighbors (KNN), decision trees, Support Vector machine (SVM), and Artificial Neural Network (ANN), to improve planning inbound logistics processes. These algorithms that monitor and train on customer preferences, weather, regulations, and other complex planning factors in the planning process. In the planning process, half of the time is consumed on preparing and collecting the information, and the gained knowledge is not used efficiently. Therefore, this paper proposes an approach to optimize future inbound logistics processes using machine learning algorithms such as kNN, decision trees, SVM, and ANN.

关键词： Logistics Planning Support vector machines machine learning algorithms Decision trees Prediction algorithms Task analysis

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Optimization of machine learning algorithms Hyper-Parameters for Improving the Prediction of Patients Infected with COVID-19

Optimization of Machine Learning Algorithms Hyper-Parameters...

引用

International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS)

作者： Soufiane HAMIDA Oussama EL GANNOUR Bouchaib CHERRADI Hassan OUAJJI Abdelhadi RAIHANI SSDIA Laboratory ENSET of Mohammedia Hassan II University of Casablanca Mohammedia Morocco STIE Team CRMEF Casablanca-Settat provincial section of El Jadida El Jadida Morocco

ISBN: (数字)9781728169217

ISBN: (纸本)9781728169224

In the modeling domain, the selection of appropriate hyper-parameters for classification or prediction algorithms is a difficult task, which has an impact on generalization capacity and classifier performance. In this paper, we compared the performance of five machine learning (ML) algorithms from different categories namely: SVM, AdaBoost, Random Forest, XGBoost and Decision Tree. In the first experiment, we adopt a default setting of each model for training and testing. In the second experiment, we use the GridSearch function to find an optimal configuration of the model. The experiments are performed on dataset of anonymous patients with or without COVID-19 disease. The used dataset is obtained from the Albert Einstein Hospital in Sao Paulo, Brazil. To evaluate the reached results, we used different performance evaluation metrics such as: accuracy, precision, recall, AUC and F1-score. The results of the proposed approach have shown that the optimization of the hyper-parameters of the studied learning models leads to an improvement of 18% in terms of Recall.

关键词： Classification algorithms machine learning algorithms COVID-19 Support vector machines Training Prediction algorithms Optimization

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Trust in the European Central Bank: Using Data Science and predictive machine learning algorithms

Trust in the European Central Bank: Using Data Science and p...

引用

International Conference on Advanced Computer Information Technologies (ACIT)

作者： Andrii Skirka Bogdan Adamyk Oksana Adamyk Mariana Valytska Department of Banking Ternopil National Economic University Ternopil Ukraine Department of Economic and Audit Ternopil National Economic University Ternopil Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Ternopil Ukraine

ISBN: (数字)9781728167602

ISBN: (纸本)9781728167619

Purpose: This empirical scientific research project aims to apply data science and machine learning tools to determine the influence of different factors on the level of trust in the European Central Bank. This research based on the data from the European Commission's Eurobarometer Survey 89. The paper also aims to represent some predictive analytics techniques to anticipate the level of confidence towards cental bank. Besides that, we build a couple of data visualizing plots, in order to show the main significant impact on the dependent variable. We created the ECB TrustMap Plot, correlation heatmap matrix and Alluvial diagram. Using this plots, we represented changes in network structure over people responses and decision making. Methodology: to calculate the index of trust in the central bank we used Logistic Regressioin, Decision Tree, Random Forrest and Neural Network models. Verify the output and results by using the VIF of the Logistic Model, Cross-validation, Confusion matrix, ROC-curves and accuracy estimations. Main Findings: trust in one-single currency, inflation problems, expectations about the future of EU, indicator of happiness and other indicators has a significant impact on the the level of trust in the central bank.

关键词： Europe Banking machine learning algorithms Economics Logistics Correlation Data visualization

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Chromosome Cluster Identification Framework Based on Geometric Features and machine learning algorithms*

Chromosome Cluster Identification Framework Based on Geometr...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chengchuang Lin Aihua Yin Qinglan Wu Hanbiao Chen Li Guo Gansen Zhao Xiaomao Fan Haoyu Luo Hua Tang School of Computer Science South China Normal University Guangzhou China Guangdong Women and Children Hospital Guangzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

In medicine, chromosome karyotype analysis is vital for genetic disease diagnoses, such as Edward syndrome, Patau syndrome, and Down syndrome. However, the chromosome karyotype analysis is usually manually done by extensive experienced clinical analysts, which is tedious and time-consuming. Consequently, automatic or partial automatic chromosome karyotyping is essential to alleviate clinical analysts' jobs. This paper proposes a chromosome cluster identification framework based on geometric chromosome features and machine learning algorithms to identify chromosome clusters needed to further process in the automated instance segmentation task. In the proposed framework, we first collect multiple dimensions of chromosome geometric features into a feature tuple, including object area, bounding box area, convex area, extent, solidity, perimeter, equivalent diameter, eccentricity, major axis length, minor axis length, and minor-major axis ratio. Second, we classify these chromosome feature tuples utilizing different machine learning classification algorithms. The experiment results show that our proposed method yields 96.21 ± 0.91% classification accuracy and 0.9832 ± 0.0111AUC (Area Under The Curve) value in the clinical dataset. The highlight of this paper is that the performance of the proposed approach has exceeded the existing geometric features threshold-based methods and multiple end-to-end deep learning-based baselines. Moreover, our proposed method can be deployed on any devices and platforms with a Python running environment, which significantly improves application flexibility. To facilitate peers in reproducing and employing our work, we release the code and the corresponding clinical dataset on Github.

关键词： Biological cells Clustering algorithms Task analysis machine learning algorithms Image segmentation Cells (biology) Computer science

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Assessment of machine learning algorithms for Near-Sensor Computing under Radiation Soft Errors

Assessment of Machine Learning Algorithms for Near-Sensor Co...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： M. Garay Trindade R. Possamai Bastos R. Garibotti L. Ost M. Letiche J. Beaucour Univ. Grenoble Alpes CNRS Grenoble INP* TIMA Grenoble France School of Technology Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil Loughborough University oughborough LE11 3TU U.K. Institut Laue-Langevin Grenoble France

ISBN: (数字)9781728160443

ISBN: (纸本)9781728160450

machine learning (ML) algorithms have been regaining momentum thanks to their ability to analyze substantial and complex data, supporting artificial intelligence decisions in cloud computing but also in near-sensor computing in endpoint devices. Both cloud and near-sensor computing are liable to radiation-induced soft errors, especially in automotive and aerospace safety-critical applications. In this regard, this paper contributes by comparing the accuracy of two prominent machine learning algorithms running on a low-power processor upset by radiation-induced soft errors. Both ML algorithms have been assessed with the help of a fault injection-based method able to natively emulate soft errors directly in a development board. In addition, neutron radiation test results suggest the most critical situations in which mitigation solutions should address.

关键词： Registers Support vector machines Software algorithms Computer crashes Sociology Neurons machine learning algorithms

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A Comparative Study of machine learning algorithms for Use in Breast Cancer Studies

A Comparative Study of Machine Learning Algorithms for Use i...

引用

IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Chuck Easttom Sudip Thapa Justin Lawson CEC Security LLC Plano Texas Capitol Technology University Laurel MD USA

ISBN: (数字)9781728137834

ISBN: (纸本)9781728137841

This current study examines a range of machine learning algorithms efficacy at determining malignancy from breast cancer imagery. The study uses the Wisconsin data set (diagnostic) to evaluate the algorithms performance. While there are a number of published studies regarding machine learning and breast cancer imagery data, there are gaps in the current literature. One gap is that several studies focus on a single algorithm. Another gap is that comparative studies often focus on only a few algorithms. The current study addresses these specific gaps in the existing literature. The current study looks at 5 different algorithms, then compares those to 8 algorithms from the literature. This provides a broad comparative study of the efficacy of machine learning algorithms in detecting breast cancer.

关键词： Clustering algorithms Prediction algorithms machine learning algorithms Classification algorithms Feature extraction Cancer Support vector machines

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A Novel Approach to Predict Chronic Kidney Disease using machine learning algorithms

A Novel Approach to Predict Chronic Kidney Disease using Mac...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Bhavya Gudeti Shashvi Mishra Shaveta Malik Terrance Frederick Fernandez Amit Kumar Tyagi Shabnam Kumari School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamilnadu India Terna College of Engineering University of Mumbai Maharashtra India Rajiv Gandhi College of Engineering and Technology Puducherry India Anumit Academy of Research and Research Network India

ISBN: (数字)9781728163871

ISBN: (纸本)9781728163888

A staggering 63,538 cases have been registered according to India's health statistics on Chronic Kidney Disease (CKD). The average age of nephropathy for humans lies between 48-70 years. CKD is more prevalent among males than females. Bitterly, India ranks among top 17 countries in CKD since 2015, which is characterized by a gradual loss of excretory organ performance over time. Earlier detection of the illness followed by treatment could keep this dreaded disease at the shore. machine learning, is making sensible applications in the areas such as analyzing medical science outcomes, sleuthing fraud etc. For the prediction of chronic diseases various machine learning algorithms are *** main aim is to differentiate the performance of various machine learning algorithms that are primarily based on its accuracy. This research work has idolized Rcode to compare their performance. The pivotal purpose of this study is to analyze the Chronic Kidney Disease dataset and conduct CKD and Non CKD classification cases.

关键词： Diseases Kidney Classification algorithms Support vector machines machine learning algorithms Prediction algorithms machine learning

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Using Configuration Semantic Features and machine learning algorithms to Predict Build Result in Cloud-Based Container Environment

Using Configuration Semantic Features and Machine Learning A...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yiwen Wu Yang Zhang Junsheng Chang Bo Ding Tao Wang Huaimin Wang Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Container technologies are being widely used in large scale production cloud environments, of which Docker has become the de-facto industry standard. In practice, Docker builds often break, and a large amount of efforts are put into troubleshooting broken builds. Prior studies have evaluated the rate at which builds in large organizations fail. However, there is still a lack of early warning methods for predicting the Docker build result before the build starts. This paper provides a first attempt to propose an automatic method named PDBR. It aims to use the configuration semantic features extracted by AST and the machine learning algorithms to predict build result in the cloud-based container environment. The evaluation experiments based on more than 36,000 collected Docker builds show that PDBR achieves 73.45%-91.92% in F1 and 29.72%-72.16% in AUC. We also demonstrate that different ML classifiers have significant and large effects on the PDBR AUC performance.

关键词： machine learning algorithms Semantics Standards organizations Production Organizations Containers Feature extraction

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Interpretability Analysis of machine learning algorithms in the Detection of ST-Elevation Myocardial Infarction

Interpretability Analysis of Machine Learning Algorithms in ...

引用

Computers in Cardiology (CinC)

作者： Matteo Bodini Massimo W Rivolta Roberto Sassi Universitá degli Studi di Milano Milan Italy

ISBN: (数字)9781728173825

ISBN: (纸本)9781728111056

Recent studies suggested that ST-Elevation Myocardial Infarction (STEMI) can be detected in the ECG relying on machine learning (ML) algorithms. However, most of ML algorithms lack of an interpretability analysis, since they do not provide any justification for their decisions. In this study, we trained a Random Forest (RF) on the Physionet PTB database to automatically detect STEMI patients, considering 12-lead average templates as input. Then, we used the Local Interpretable Model-agnostic Explanations (LIME) method to highlight the input parts that mostly contributed to the detection. LIME interpretations were validated with the anatomical position of the myocardial infarction available within the dataset. Experimental results showed that RF achieved a high test set accuracy (ranging from 0.84 to 0.92). However, LIME identified areas within QRS complexes as the most relevant ones for the RF decision, rather than in the ST segment as expected. Our study suggests that, despite the test set accuracy, ML algorithms for STEMI classification, trained on small or unbalanced/biased populations, may rely on features which are not clinically significant. In this regard, interpretability algorithms like LIME may help in understanding possible pitfalls.

关键词： Radio frequency machine learning algorithms Sociology Myocardium Classification algorithms Statistics Random forests

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Performance Evaluation of Supervised machine learning algorithms in Prediction of Heart Disease

Performance Evaluation of Supervised Machine Learning Algori...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： P. Sujatha K. Mahalakshmi Vels Institute of Science Technology & Advanced Studies Chennai India School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9781728197449

ISBN: (纸本)9781728197456

Big challenge in health care industry is to record and analyze the massive amount of information about patients. Innovations in technologies made revolution in the healthcare industries. In recent years the data analytics developed as promising tool for problem solving and decision making in healthcare professions. Data analytics process the data automatically to make healthcare system more dynamic and robust. It systematically uses and analyses the data of health care for better treatment with low costs. The chief applications of machine learning in healthcare are the detection and diagnosis of diseases. The heart is the chief organ of human body. Heart disease increases the mortality rate in the world. Around 90% of heart diseases are preventable. machine learning plays a remarkable role in the health care industry in prediction of heart disease. In this research paper, the presence of heart disease is predicted by employing Decision Tree, Naïve Bayes, Random Forest, Support Vector machine, K-Nearest Neighbor and logistic Regression algorithms. The performance of the algorithms was analyzed using parameters such as Accuracy, Precision, AUC and F1-score. From the experimental result, it is found that the Random Forest is more accurate for predicting the heart disease with accuracy of 83.52% compared with other supervised machine learning algorithms. The F1- Score, AUC and precision score of Random forest classifiers are 84.21%, 88.24% and 88.89% respectively.

关键词： Heart Diseases machine learning machine learning algorithms Prediction algorithms Support vector machines Tools

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