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A report from Yangtze University in Jingzhou, China discusses the use of deep learning algorithms in food safety and authenticity detection. The researchers highlight the advantages of deep learning algorithms over traditional machine learning algorithms and chemometric algorithms. They specifically focus on convolutional neural networks (CNN), fully convolutional networks (FCN), and generative adversarial networks (GAN). The report also explores the potential for further development in this field, including the use of data augmentation and multimodal neural networks. The research concludes that deep learning algorithms have made significant progress in food safety and authenticity detection, but there is still room for improvement. [Extracted from the article]

A new report from the Chinese Academy of Sciences discusses research findings on sustainable food systems. The study focuses on crop change detection, which is crucial for agricultural sustainability. The researchers propose a fast hyperspectral change detection algorithm called FLRaMF, which improves detection accuracy and computational efficiency. Experimental results show that the FLRaMF method achieves higher detection accuracy with reduced computation cost in agricultural crop change detection tasks. For more information, the full article can be accessed for free through the provided link. [Extracted from the article]

A recent report from Poznan University of Life Sciences in Poland discusses the use of artificial intelligence (AI) and machine learning algorithms to evaluate the quality of food products. The researchers focused specifically on identifying blackcurrant powders using microscopic images and texture feature extraction. They found that ensembles of classifiers, such as random forest (RF) classifiers, were more effective in identifying blackcurrant powders compared to single neural models. This research suggests that machine learning algorithms and ensembles of classifiers could be valuable tools for real-time quality assessment of food products. [Extracted from the article]

Algorithms, Chemometric, Emerging Technologies, Food Research, Machine Learning, Technology Keywords: Algorithms; Chemometric; Emerging Technologies; Food Research; Machine Learning; Technology EN Algorithms Chemometric Emerging Technologies Food Research Machine Learning Technology 132 132 1 07/24/23 20230727 NES 230727 2023 JUL 27 (NewsRx) -- By a News Reporter-Staff News Editor at Food Weekly News -- Data detailed on food research have been presented. [Extracted from the article]

Compared with other existing segmentation algorithms, OSNC algorithm has stronger anti-interference ability, faster segmentation speed as well as more efficiently saves key information ability." Keywords: Algorithms; Food Quality EN Algorithms Food Quality 304 304 1 06/26/23 20230629 NES 230629 2023 JUN 29 (NewsRx) -- By a News Reporter-Staff News Editor at Food Weekly News -- Fresh data on algorithms are presented in a new report. [Extracted from the article]

A recent study conducted by researchers at Shihezi University in China investigated the effects of different drying conditions on yam slices. The study found that drying time, color difference, unit energy consumption, polysaccharide content, rehydration ratio, and allantoin content were all significantly affected by drying conditions. The researchers used a BP neural network wolf algorithm (GWO) model to determine the optimal drying conditions, which were found to have higher coefficients of determination and lower RMSEs than the response surface methodology (RMS). The study provides valuable insights for the optimization of the drying process for yam slices and offers a reliable model for future research in this area. [Extracted from the article]

A recent study conducted by researchers at New Horizon College of Engineering explores the use of machine learning models in predicting future food production. The study focuses on livestock production and agricultural production, specifically in Iran, and proposes a hybrid deep learning model called A-ROA. By combining the Arithmetic Optimization Algorithm (AOA) and the Rider Optimization Algorithm (ROA), the model aims to accurately estimate food output and assist policymakers in planning for future food safety and supply. The study provides valuable insights into the potential of machine learning in the field of food science and engineering. [Extracted from the article]

Due to the increase in demand for quality food products by consumers IoT-based Food Supply Chain Management (FCM) is required for satisfying the consumer's needs. Keywords for this news article include: Kaifeng, People's Republic of China, Asia, Algorithms, Food Industries, Food Poisoning, Food Products, Food Safety, Food Suppliers, Foodborne Diseases and Conditions, Gastroenterology, Yellow River Conservancy Technical Institute. Keywords: Kaifeng; People's Republic of China; Asia; Algorithms; Food Industries; Food Poisoning; Food Products; Food Safety; Food Suppliers; Foodborne Diseases and Conditions; Gastroenterology EN Kaifeng People's Republic of China Asia Algorithms Food Industries Food Poisoning Food Products Food Safety Food Suppliers Foodborne Diseases and Conditions Gastroenterology 165 165 1 05/02/23 20230504 NES 230504 2023 MAY 4 (NewsRx) -- By a News Reporter-Staff News Editor at Food Weekly News -- Data detailed on Foodborne Diseases and Conditions - Food Safety have been presented. [Extracted from the article]

Keywords: Algorithms; Computers; Food Products; Sensor Research; Technology EN Algorithms Computers Food Products Sensor Research Technology 144 144 1 04/10/23 20230413 NES 230413 2023 APR 13 (NewsRx) -- By a News Reporter-Staff News Editor at Food Weekly News -- Researchers detail new data in sensor research. Algorithms, Computers, Food Products, Sensor Research, Technology. [Extracted from the article]

A recent study conducted by researchers at Beijing University of Chemical Technology in China has proposed a new model for early warning of food safety risks. The model, called IAPSO-LSTM, combines an improved adaptive particle swarm optimization algorithm with a long short-term memory neural network. The researchers tested the model using data from a food testing agency in Guizhou Province and found that it effectively identified high-risk factors, with sodium cyclamate being the primary factor. The study concludes that the IAPSO-LSTM model outperforms other models and can help government departments effectively warn of potential food safety risks. [Extracted from the article]

Researchers at Mersin University in Turkey have conducted a study on demand forecasting for food production using machine learning algorithms. The study aimed to develop models for predicting heavy demand for food in a university refectory without pre-booking. The researchers applied various machine learning techniques, including artificial neural networks, Gauss process regression, support vector regression, regression trees, and ensemble decision trees. The study found that the ensemble decision tree boosted model had the best prediction performance. This research provides a unique contribution to the field of demand forecasting for food production. [Extracted from the article]

Keywords: Shanghai; People's Republic of China; Asia; Mathematics; Algorithms; Food Quality EN Shanghai People's Republic of China Asia Mathematics Algorithms Food Quality 170 170 1 04/24/23 20230427 NES 230427 2023 APR 27 (NewsRx) -- By a News Reporter-Staff News Editor at Food Weekly News -- Researchers detail new data in Mathematics. Keywords for this news article include: Shanghai, People's Republic of China, Asia, Mathematics, Algorithms, Food Quality, Fudan University. [Extracted from the article]

Keywords: Kent; Ohio; United States; North and Central America; Algorithms; Cyborgs; Diet and Nutrition; Emerging Technologies; Food Labeling; Health and Medicine; Inorganic Chemicals; Machine Learning; Minerals; Vitamin B12; Vitamin E; Vitamins EN Kent Ohio United States North and Central America Algorithms Cyborgs Diet and Nutrition Emerging Technologies Food Labeling Health and Medicine Inorganic Chemicals Machine Learning Minerals Vitamin B12 Vitamin E Vitamins 80 80 1 04/24/23 20230427 NES 230427 2023 APR 27 (NewsRx) -- By a News Reporter-Staff News Editor at Food Weekly News -- New research on Machine Learning is the subject of a report. The highest classification accuracies for specific micronutrients are achieved for vitamin B12 (0.94) and phosphorus (0.94), while the lowest are for vitamin E (0.81) and selenium (0.83). [Extracted from the article]