Current Issue : April-June Volume : 2023 Issue Number : 2 Articles : 5 Articles
Wavelength selection is one of the key steps in quantitative spectral analysis, which reduces the computation time while also improving the prediction accuracy of the model. In this paper, we propose a wavelength selection algorithm based on the ant colony optimization (ACO), in which the absolute value of the regression coefficient of the multiple linear regression (MLR) model is used as the basis for evaluating the importance of wavelengths, and the absolute value of the regression coefficient after full wavelength MLR modeling is used as the initial pheromone value of the ant colony optimization (MLR-ACO). In each iteration, the absolute value of the regression coefficient corresponding to each wavelength of the individual with the highest fitness value is used as the basis for a pheromone update. The crossover operator is introduced in MLR-ACO (MLR-ACO-GA), and the individuals with the top 100 fitness values in MLR-ACO are used as the initial population of the genetic algorithm (GA). A selected frequency of wavelengths greater than the threshold among MLR-ACO individuals is calculated. A number of coarse interval points are generated according to the selected frequency, and a coarse crossover operation is performed at the coarse interval points. Fine crossover points are randomly generated within the coarse interval, and fine crossover operations are performed within the coarse interval to exploit the potential of combining excellent individuals in MLR-ACO with each other as much as possible. MLR-ACO can well solve the problem of traditional ACO initial pheromone scarcity, and MLR-ACO-GA can avoid MLR-ACO falling into a local optimum to a certain extent and be more flexible in the selection of the number of wavelengths, which can give full play to the advantages of MLR-ACO....
In agriculture farming, pests and other plant diseases are the most imperative factor that causes significant hindrance to cucumber production and its quality. Farmers around the globe are currently facing difficulty in recognizing various cucumber leaf diseases, which is imperative to preventing leaf diseases effectively. Manual techniques to diagnose cucumber diseases are often timeconsuming, subjective, and laborious. To address this issue, this paper proposes a tuned convolutional neural network (CNN) algorithm to recognise five cucumber diseases and healthy leaves that comprises image enhancement, feature extraction, and classification. Data augmentation methods were utilized as a preprocessing step to enlarge the datasets, and it was also to decrease the chance of overfitting. Automatically features are extracted by using CNN layers. Finally, five cucumber leaf diseases and one healthy leaf are classified. Furthermore, to overcome the lack of a public dataset, a new dataset of cucumber leaf diseases has been constructed that includes spider, leaf miner, downy mildew, powdery mildew, one viral disease, and healthy class leaves. The dataset has a total of 4868 cucumber leaf images. In order to prove the authenticity of the proposed CNN, comparative experiments were conducted using pretrained models (AlexNet, Inception-V3, and ResNet-50). The proposed CNN achieves a recognition accuracy of 98.19% with the augmented dataset and 100% with the publicly plant disease dataset. The experimental results confirm that the proposed CNN algorithm was efficient for recognizing the cucumber leaf diseases compared with other algorithms....
The effect of aluminum oxide nanoparticles (Al2O3) on the 60/70 penetration of asphalt cement (AC) was investigated in terms of the physical and rheological characteristics by using the Superpave testing procedures. Al2O3 at 3, 5, and 7% concentrations were blended with 60/70 penetration of grade AC. Conventional testing procedures were adopted regarding the physical characteristics, while dynamic shear rheometer (DSR) testing procedures were conducted to evaluate the high and low temperature failure parameters. In addition, heuristic modelling techniques, artificial neural networks (ANN), and support vector machines (SVM) were employed to predict the performance characteristics of AC by using the mechanical testing conditions. The frequency sweep test and multiple stress creep recovery (MSCR) test results revealed that the optimum composition of Al2O3 was at 5% concentration considering the high temperature performance characteristics since further addition of the Al2O3 resulted in degradation in the enhanced properties due to agglomeration of the nanoparticles in the blend. On the contrary, Al2O3 5% demonstrated the lowest viscoelastic behavior at intermediate temperatures. The higher complex modulus (G∗) and lower phase angle (δ) parameters indicated that the increase in stiffness due to the modification process was at the cost of losing elastic properties against fatigue cracking. Moreover, based on the statistical performance indicator, coefficient of determination (R2), it was observed that the ANN models for predicting G∗ and δ achieved a prediction accuracy of 0.989 and 0.911 while SVM models were able to achieve 0.984 and 0.929, respectively, considering the training datasets. On the other hand, it was noted that SVM models outperformed the ANN models in terms of a smaller gap between the results obtained from the training and testing datasets. The difference between the training and testing datasets for G∗ and δ parameters for the SVM models were 3.2% and 6.8% while for the ANN models, the differences were 11.6% and 9.5%, respectively, indicating that the ANN models were more prone to the overfitting phenomenon....
The design process of antenna structures that meet up-to-date requirements takes a long time and brings a high computational load. In this paper, an approach based on Soft Computing (SC) techniques was used to shorten the design time and to achieve an antenna structure that yields performance characteristics as close as possible to the desired values. In order to obtain a microstrip patch antenna with the targeted characteristics and the best accuracy in a faster way, a Support Vector Machine (SVM)-based regression model was employed. A triple-band microstrip antenna with desired resonance frequencies and gain values was designed by using the Support Vector Regression (SVR) model by introducing multiple slots and arc-truncation to the patch antenna. Simulation results of the High-Frequency Structural Simulator (HFSS) and measurements of implementation of the designed antenna are given. Performance characteristics of the obtained antenna are also compared with those given in the literature, which have triple-band properties. In addition, the antenna was redesigned using the optimization tool in HFSS for comparison. The accuracy of the results and required time for design were compared for both the SVR model approach and the HFSS optimization tool....
Atenolol (ATN) is a drug that is widely used to treat some heart diseases, and since it cannot be completely decomposed in the human body, some amounts of it are found in surface water. These amounts may bring risks to the environment and humans, and for this reason, its removal is a must. In the present study, the combined sono-electro-persulfate method was used for ATN removal. Based on the design of the experiment conducted by response surface methodology (RSM), the effects of 5 main factors (pH, time, PS concentration, current intensity, and initial ATN concentration) have been investigated at 5 levels. After passing the test steps in different conditions, the remaining amount of ATN has been measured by high-performance liquid chromatography (HPLC). Finally, an adaptive neuro-fuzzy inference system (ANFIS) with 99.63% accuracy and a genetic algorithm (GA) were used to analyze and interpret data and predict optimal conditions. The obtained results indicate the possibility of a maximum efficiency of 99.8% in the mentioned conditions (Ph of 7.4, time of 18 min, PS concentration of 2000 mg/L, current intensity of 3.35 A, and initial ATN concentration of 11.2 mg/L). According to the obtained results, the initial concentration of ATN can be considered as the most effective factor in this process, and the best Ph range for this experiment was the neutral range. The sonoelectro persulfate process can be mentioned as a new and effective method for removing ATN from water sources....
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