“…5 ). Afterward, the production design office guides and adjusts logistics vehicles to ensure the integrity of the entire SC (Choi et al 2020 ; Li et al 2021 ; Wu et al 2020 ; Zheng et al 2021 ). Fig.…”
Section: Economic Analysis Model Of the Iot-based Sclmmentioning
Under the development trend of economic globalization, the financial difficulties of the supply chain may cause a serious crisis to the enterprises in the whole supply chain. It is necessary to alleviate the transformation pressure faced by international trading enterprises (ITE), to enhance the core competitiveness of ITE. This paper takes the SCLM (Supply Chain and Logistic Management) of ITE as the research object. Based on the analysis of the existing SCLM experience of ITE and the Internet of Things (IoT) technology, this paper puts forward the economic analysis model of the IoT-based SCLM. Specific data of some ITE prove the effectiveness of the proposed model. Further optimization is carried out through process analysis. The results show that the proposed SCLM economic analysis model based on IoT can effectively maximize the interests of enterprises and shorten the supply chain process by adding information groups. The introduced IoT-based method reduces the Average Queue Time (AQT) of vehicles by nearly 7 times and the Average Waiting Time (AWT) by nearly 4 times. After the IoT technology is applied to the SCLM of ITE, this paper significantly improves the cooperation efficiency of various departments, optimizes the relationship between customers and suppliers, and improves the core competitiveness of the enterprise. Therefore, this paper has important reference value for promoting the development of the small and micro machinery industry in China.
“…5 ). Afterward, the production design office guides and adjusts logistics vehicles to ensure the integrity of the entire SC (Choi et al 2020 ; Li et al 2021 ; Wu et al 2020 ; Zheng et al 2021 ). Fig.…”
Section: Economic Analysis Model Of the Iot-based Sclmmentioning
Under the development trend of economic globalization, the financial difficulties of the supply chain may cause a serious crisis to the enterprises in the whole supply chain. It is necessary to alleviate the transformation pressure faced by international trading enterprises (ITE), to enhance the core competitiveness of ITE. This paper takes the SCLM (Supply Chain and Logistic Management) of ITE as the research object. Based on the analysis of the existing SCLM experience of ITE and the Internet of Things (IoT) technology, this paper puts forward the economic analysis model of the IoT-based SCLM. Specific data of some ITE prove the effectiveness of the proposed model. Further optimization is carried out through process analysis. The results show that the proposed SCLM economic analysis model based on IoT can effectively maximize the interests of enterprises and shorten the supply chain process by adding information groups. The introduced IoT-based method reduces the Average Queue Time (AQT) of vehicles by nearly 7 times and the Average Waiting Time (AWT) by nearly 4 times. After the IoT technology is applied to the SCLM of ITE, this paper significantly improves the cooperation efficiency of various departments, optimizes the relationship between customers and suppliers, and improves the core competitiveness of the enterprise. Therefore, this paper has important reference value for promoting the development of the small and micro machinery industry in China.
“…Wide ranges of supervised and unsupervised artificial intelligence techniques have been suggested and applied in different modeling studies 58 – 63 . The working procedures of the used machine learning methods, i.e., least-squares support vector regression (LSSVR), hybrid neuro-fuzzy system, and five types of artificial neural networks have been briefly explained in this section.…”
Section: Estimation Scenarios For Density Of Deep Eutectic Solventsmentioning
Deep eutectic solvents (DES) are recently synthesized to cover limitations of conventional solvents. These green solvents have wide ranges of potential usages in real-life applications. Precise measuring or accurate estimating thermophysical properties of DESs is a prerequisite for their successful applications. Density is likely the most crucial affecting characteristic on the solvation ability of DESs. This study utilizes seven machine learning techniques to estimate the density of 149 deep eutectic solvents. The density is anticipated as a function of temperature, critical pressure and temperature, and acentric factor. The LSSVR (least-squares support vector regression) presents the highest accuracy among 1530 constructed intelligent estimators. The LSSVR predicts 1239 densities with the mean absolute percentage error (MAPE) of 0.26% and R2 = 0.99798. Comparing the LSSVR and four empirical correlations revealed that the earlier possesses the highest accuracy level. The prediction accuracy of the LSSVR (i.e., MAPE = 0. 26%) is 74.5% better than the best-obtained results by the empirical correlations (i.e., MAPE = 1.02%).
“…Zhou et al [ 35 ] described an efficient blind quality assessment approach for SCIs and NSIs that is based on a dictionary of learnt local and global quality criteria. Li et al [ 36 ] created an artificial intelligence technique that is used for data-enhanced encryption at the IoT's endpoints and intermediate nodes. The technique presented in this article is an AI approach for encrypting data at the endpoints and intermediate nodes of autonomous IoT networks.…”
Early diagnosis of breast cancer is an important component of breast cancer therapy. A variety of diagnostic platforms can provide valuable information regarding breast cancer patients, including image-based diagnostic techniques. However, breast abnormalities are not always easy to identify. Mammography, ultrasound, and thermography are some of the technologies developed to detect breast cancer. Using image processing and artificial intelligence techniques, the computer enables radiologists to identify chest problems more accurately. The purpose of this article was to review various approaches to detecting breast cancer using artificial intelligence and image processing. The authors present an innovative approach for identifying breast cancer using machine learning methods. Compared to current approaches, such as CNN, our particle swarm optimized wavelet neural network (PSOWNN) method appears to be relatively superior. The use of machine learning methods is clearly beneficial in terms of improved performance, efficiency, and quality of images, which are crucial to the most innovative medical applications. According to a comparison of the process’s 905 images to those of other illnesses, 98.6% of the disorders are correctly identified. In summary, PSOWNNs, therefore, have a specificity of 98.8%. Furthermore, PSOWNNs have a precision of 98.6%, which means that, despite the high number of women diagnosed with breast cancer, only 830 (95.2%) are diagnosed. In other words, 95.2% of images are correctly classified. PSOWNNs are more accurate than other machine learning algorithms, SVM, KNN, and CNN.
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