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Artificial Intelligence Applications for Producing Glycosylated Biopharmaceutical Drug Modalities
Biopharmaceutical drug modalities derived from animal cell culture are inherently complex and heterogeneous by nature. Among the most complex of...
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An Adaptive Simulated Annealing-Based Machine Learning Approach for Develo** an E-Triage Tool for Hospital Emergency Operations
Patient triage at emergency departments (EDs) is necessary to prioritize care for patients with critical and time-sensitive conditions. In this...
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Ensemble Learning in Investment Appraisal
The work is a continuation of the previous research of the authors. In this paper, the ensemble learning method was chosen from the machine learning...
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Analysis on the Balance of Health Care Resource Allocation Based on Improved Machine Learning
Health resource planning is an important means for the government to adjust resource allocation and achieve fair and efficient development of health....
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Introduction
The innovation of express delivery networks management mode is to adapt to the rapid development of e-commerce and the transformation and upgrading...
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Research on Secure Storage of Healthcare Data in the Environment of Internet of Things
In order to improve the security and storage efficiency of medical care data, a safe storage method of medical care data in the Internet of Things...
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DeepKPred: Prediction and Functional Analysis of Lysine 2-Hydroxyisobutyrylation Sites Based on Deep Learning
Protein 2-hydroxyisobutyrylation (Khib), a newly identified post-translational modification, plays a role in various cellular processes. To gain a...
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Effect of factors on company’s goodwill
The super profit method (SPM) is a valuation technique used to estimate the value of goodwill. It is commonly used in the context of valuing small...
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Analytical Foundations: Predictive and Prescriptive Analytics
In this chapter, we review important predictive and prescriptive models that can be applied to supply chain problems. We begin with linear models and...
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Efficient quality variable prediction of industrial process via fuzzy neural network with lightweight structure
Quality Variables of industrial processes generally require to be obtained as fast as possible. In this paper, a correlation-wise self-organizing...
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Incremental transfer learning for robot drilling state monitoring under multiple working conditions
Robot drilling is widely used in industrial scenarios, and the quality of the hole affects the quality of the finished product. Drilling for...
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Evaluation of stochastic flow lines with provisioning of auxiliary material
Flow lines are often used to perform assembly operations in multi-stage processes. During these assembly operations, components that are relatively...
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Machine-learning based process monitoring for automated composites manufacturing
Automated fibre placement (AFP) is an advanced robotic manufacturing technique which can overcome the challenges of traditional composite...
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Prediction of cutting force via machine learning: state of the art, challenges and potentials
Cutting force is a critical factor that reflects the machining states and affects tool wear, cutting stability, and the quality of the machined...
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Data-driven prediction of keyhole features in metal additive manufacturing based on physics-based simulation
The defect formation is closely related to molten pool and keyhole features in metal additive manufacturing. Experimentation and physics-based...
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Big Data Analytics in Healthcare
A vast volume of digitized clinical data has been generated and accumulated rapidly since the widespread adoption of Electronic Medical Records...
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An automatic inspection system for the detection of tire surface defects and their severity classification through a two-stage multimodal deep learning approach
In the tire manufacturing field, the pursuit of uncompromised product quality stands as a cornerstone. This paper introduces an innovative multimodal...
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Data Analysis Applications in the Optimal Integration of Energy Supply Chain
The demand for fuel sources grows as human civilizations evolve, which leads to a faster depletion of energy resources. Since households and...
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Machine Learning and Supply Chain Management
Scholars have turned to highly capable machine learning (ML) approaches for analyzing and interpreting huge amounts of data due to the limitations of...
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Balanced weighted extreme learning machine for imbalance learning of credit default risk and manufacturing productivity
Imbalanced class distribution exists in real world problems and is considered an important research topic. The weighted extreme learning machine...
