As possible diseases develop on plant leaves, classification is constantly hampered by obstacles such as overfitting and low accuracy. To distinguish healthy products from defective ones, the agricultural industry requires precise and error-free analysis. Deep convolutional neural networks are an efficient model of autonomous feature extraction that has been shown to be fairly effective for detection and classification tasks. However, deep convolutional neural networks often require a large amount of training data, cannot be translated, and need a number of parameters to be specified and tweaked. This paper proposes a highly effective structure that can be applied to classifying multiple leaf diseases of plants and fruits during the feature extraction step. It uses a deep transfer learning model that has been modified to serve this purpose. In summary, we use model engineering (ME) to extract features. Multiple support vector machine (SVM) models are employed to enhance feature discrimination and processing speed. The kernel parameters of the radial basis function (RBF) are determined based on the selected model in the training step. PlantVillage and UCI datasets were used to analyze six leaf image sets containing healthy and diseased leaves of apple, corn, cotton, grape, pepper, and rice. The classification process resulted in approximately 90,000 images. During the experimental implementation phase, the results show the potential of a powerful model in classification operations, which will be beneficial for a variety of future leaf disease diagnostic applications for the agricultural industry.
The high mortality rate and prevalence of cardiovascular disease (CVD) make early detection of the disease essential. Due to its simplicity and low cost, the phonocardiogram (PCG) system is widely used in healthcare applications for the recognition of CVD in multiclass problems. On the basis of the PCG signal, this paper proposes a hybrid method for classifying cardiac sounds with deep extracted features through two‐step learning. For fine‐grained features in Graph Convolutional Networks (GCNs), sampling and prior layers are employed. A PCG signal is divided into equal parts with overlap using the windowing process. L‐spectrograms extract frequency‐domain information from signals to figure out their power spectrum. Furthermore, the deep GCN tries to determine the association between CVD and spectrogram images to recognize CVD signals better. Combining retrieved features with convolutional neural network (CNN) characteristics reveals an image's intrinsic associations. To generate relational feature representations, correlations between clusters and GCN are visualized using a graph structure. CNN's discriminative ability has been enhanced by incorporating GCN attributes. Using Michigan Heart Sound and Murmur Database and PhysioNet/CinC 2016 Challenge results, we are 99.44% and 96.16% accurate, respectively. Through a combination of GCN architecture, CNN design, and deep features, the hybrid model significantly improves CVD classification accuracy. Measuring metrics demonstrate that the proposed approach detects CVD more effectively than previous approaches.
RFID is one of the modern technologies that many industries have benefited from it, and there are many opportunities in the tourism industry to benefit from it. Although there are many potential opportunities, no survey has been done on factors influencing this technology acceptance in tourism industry. So despite the lack of theories, the present article is aimed to evaluate factors influencing RFID acceptance in tourism businesses, focusing on the population of executive operating in Yazd tourism sector and using Davis technology Acceptance model (TAM) as well as Tornatzkey and Flisher's technology – organization – environment model (TOE) and using equation modeling techniques. Then, using hierarchical analysis approach, this article specifies the priority of identified factors influencing decision making of this technology acceptance. According to the results, senior management support and technical knowledge from organizational dimensions, compatibility, usefulness, ease of use, security risks and lack of standards from technological dimension, external pressure from environmental dimensions and costs from an economic dimension are factors influencing RFID technology acceptance in tourism industry. Economical dimension, among the others, is the most important, and after that organizational, environmental and technological dimension respecting an important.
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