Industry 4.0 Adoption in Food Supply Chain to Improve Visibility and Operational Efficiency-A Content Analysis

A Kaur, V Potdar, H Agrawal - IEEE Access, 2023 - ieeexplore.ieee.org
Food can become unsafe or contaminated at any point from farm to fork. Customers and
stakeholders are concerned about food safety and prompt delivery. Hence, there is a need …

Navigation Sensor Data Reliability Model based on Self-Evaluation and Mutual-Evaluation

W Li, Z Zhang, Y Liang, F Shen… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Multisource navigation involves using multiple available sensors to achieve high-precision
location services, but navigation devices and data are susceptible to various environments …

A quasi-qualitative strategy for FT-NIR discriminant prediction: Case study on rapid detection of soil organic matter

H Chen, L Xu, J Gu, F Meng, H Qiao - Chemometrics and Intelligent …, 2022 - Elsevier
Fourier transform near infrared (FT-NIR) is a technology to provide direct and rapid
quantitative determinations of soil organic matter (SOM). In this paper, a new discriminant …

Comparison between ideal and estimated pv parameters using evolutionary algorithms to save the economic costs

J Rahmani, E Sadeghian, S Dolatiary - 2018 - mpra.ub.uni-muenchen.de
Micro grids are now emerging from lab modules sites into commercial markets, driven by
technological improvements, falling costs, a proven track record, and growing recognition of …

Explainability with Semantic Concept Composition and Zero-Shot Learning for Anomaly Detection

NS Bendre - 2021 - search.proquest.com
Video analytics, an important research problem, has been well-studied within diverse
research areas and application domains like anomaly detection, safety and explainability …

Robust Countermeasures for Adversarial Attacks on Deep Learning, Deep Reinforcement Learning, and Deepfake

SH Silva - 2021 - search.proquest.com
Abstract Machine Learning (ML) algorithms are in demand in almost every field. Yet, even as
they become commonplace, they are hardly understood. Their complex architecture makes …

Data-Driven Decision-Making Based on Noisy Data Samples---Studies in the Machine Learning Applications

F Tao - 2021 - search.proquest.com
Modern machine learning research falls into the field of data-driven decision-making, which
inevitably requires consideration of the negative effect of noisy samples. However, majority …