A comparative study of optimization algorithms for feature selection on ML-based classification of agricultural data DOI
Zeynep Garip, Ekin Ekıncı, Murat Erhan Çimen

et al.

Cluster Computing, Journal Year: 2023, Volume and Issue: 27(3), P. 3341 - 3362

Published: Oct. 3, 2023

Language: Английский

Evaluation of visible contamination on power grid insulators using convolutional neural networks DOI
Marcelo Picolotto Corso, Stéfano Frizzo Stefenon, Gurmail Singh

et al.

Electrical Engineering, Journal Year: 2023, Volume and Issue: 105(6), P. 3881 - 3894

Published: July 14, 2023

Language: Английский

Citations

28
Detection of fish freshness using artificial intelligence methods DOI
Elham Tahsin Yasin, İlker Ali Özkan, Murat Köklü

et al.

European Food Research and Technology, Journal Year: 2023, Volume and Issue: 249(8), P. 1979 - 1990

Published: April 27, 2023

Language: Английский

Citations

27
Wildfire fuels mapping through artificial intelligence-based methods: A review DOI Creative Commons
Riyaaz Uddien Shaik, Mohamad Alipour, Kasra Shamsaei

et al.

Earth-Science Reviews, Journal Year: 2025, Volume and Issue: unknown, P. 105064 - 105064

Published: Feb. 1, 2025

Language: Английский

Citations

1
Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types DOI Creative Commons
Ahmet Feyzioğlu, Yavuz Selim Taşpınar

Sensors, Journal Year: 2023, Volume and Issue: 23(4), P. 2222 - 2222

Published: Feb. 16, 2023

Ensuring safe food supplies has recently become a serious problem all over the world. Controlling quality, spoilage, and standing time for products with short shelf life is quite difficult problem. However, electronic noses can make these controls possible. In this study, which aims to develop different approach solution of problem, nose data obtained from 12 beef cuts were classified. dataset, there are four classes (1: excellent, 2: good, 3: acceptable, 4: spoiled) indicating quality. The classifications performed separately each cut shapes. ANOVA method was used determine active features in dataset features. same classification processes carried out by using three selected method. Three machine learning methods, Artificial Neural Network, K Nearest Neighbor, Logistic Regression, frequently literature, classifications. experimental studies, accuracy 100% as result ANN combining tables dataset.

Language: Английский

Citations

22
Segmentation of dry bean (Phaseolus vulgaris L.) leaf disease images with U-Net and classification using deep learning algorithms DOI
Ramazan Kursun, Kubilay Kurtuluş Baştaş, Murat Köklü

et al.

European Food Research and Technology, Journal Year: 2023, Volume and Issue: 249(10), P. 2543 - 2558

Published: June 26, 2023

Language: Английский

Citations

17
Fisheye freshness detection using common deep learning algorithms and machine learning methods with a developed mobile application DOI Creative Commons
Müslüme Beyza Yıldız, Elham Tahsin Yasin, Murat Köklü

et al.

European Food Research and Technology, Journal Year: 2024, Volume and Issue: 250(7), P. 1919 - 1932

Published: April 18, 2024

Abstract Fish is commonly ingested as a source of protein and essential nutrients for humans. To fully benefit from the proteins substances in fish it crucial to ensure its freshness. If stored an extended period, freshness deteriorates. Determining can be done by examining eyes, smell, skin, gills. In this study, artificial intelligence techniques are employed assess The author’s objective evaluate analyzing eye characteristics. achieve this, we have developed combination deep machine learning models that accurately classify fish. Furthermore, application utilizes both learning, instantly detect any given sample was created. Two algorithms (SqueezeNet, VGG19) were implemented extract features image data. Additionally, five levels samples applied. Machine include (k-NN, RF, SVM, LR, ANN). Based on results, inferred employing VGG19 model feature selection conjunction with Artificial Neural Network (ANN) classification yields most favorable success rate 77.3% FFE dataset. Graphical

Language: Английский

Citations

7
An efficient deepfake video detection using robust deep learning DOI Creative Commons
Abdul Qadir,

Rabbia Mahum,

Mohammed A. El-Meligy

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(5), P. e25757 - e25757

Published: Feb. 9, 2024

The creation and manipulation of synthetic images have evolved rapidly, causing serious concerns about their effects on society. Although there been various attempts to identify deep fake videos, these approaches are not universal. Identifying misleading deepfakes is the first step in preventing them from spreading social media sites. We introduce a unique deep-learning technique fraudulent clips. Most deepfake identifiers currently focus identifying face exchange, lip synchronous, expression modification, puppeteers, other factors. However, exploring consistent basis for all forms videos real-time forensics challenging. propose hybrid that takes input successive targeted frames, then feeds frames ResNet-Swish-BiLSTM, an optimized convolutional BiLSTM-based residual network training classification. This proposed method helps artifacts do seem real. To assess robustness our model, we used open detection challenge dataset (DFDC) Face Forensics collections (FF++). achieved 96.23% accuracy when using FF++ digital record. In contrast, attained 78.33% aggregated records DFDC. performed extensive experiments believe provides more significant results than existing techniques.

Language: Английский

Citations

6
Benchmarking analysis of CNN models for bread wheat varieties DOI
Ali Yaşar

European Food Research and Technology, Journal Year: 2022, Volume and Issue: 249(3), P. 749 - 758

Published: Nov. 16, 2022

Language: Английский

Citations

19
Digital Technology Increases the Sustainability of Cross-Border Agro-Food Supply Chains: A Review DOI Creative Commons
Gaofeng Wang, Shuai Li,

Yi Yang

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(6), P. 900 - 900

Published: June 6, 2024

The increasing prominence of climate change, geopolitical crises, and global economic slowdown highlights the challenges structural deficiencies traditional cross-border agro-food supply chains. As a result, there has been growing consensus on need to leverage digital technology rebuild innovate safe, stable, sustainable food system. This study assessed knowledge progress development trends in chains enabled by technology. A total 352 authoritative papers from core Web Science database were selected for analysis. Citespace tool was utilized visually examine research elements. findings reveal that outcomes this territory experienced significant period rapid growth, particularly after 2020. Sustainability IEEE Access are journals with highest second-highest number publications. China France National Institute countries institutions largest publications field. hotspots mainly application technologies, safety, chain system model innovation. In past ten years, gone through three stages: precise timeliness orientation, intelligent strategic decision-making predictability orientation. We further construct ‘antecedent–practice–performance’ conceptual framework sustainability technology-enabled chain. Finally, paper presents potential directions territory, focusing four aspects: method, mechanism, topic, frontier.

Language: Английский

Citations

4
Classification of chicken Eimeria species through deep transfer learning models: A comparative study on model efficacy DOI
Zeki KÜÇÜKKARA, İlker Ali Özkan, Şakir Taşdemir

et al.

Veterinary Parasitology, Journal Year: 2025, Volume and Issue: 334, P. 110400 - 110400

Published: Jan. 20, 2025

Language: Английский

Citations

0