Identification of oil authenticity and adulteration using deep long short-term memory-based neural network with seagull optimization algorithm

Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms

Traditional oil quality measurement is mostly based on chemical indicators such as acid value, peroxide value, and p-anisidine value. This process requires specialized knowledge and involves complex steps. Hence, this study designs and proposes a Sesame Oil Quality Assessment Service Platform, which is composed of an Intelligent Sesame Oil Evaluator (ISO Evaluator) and a Cloud Service Platform. Users can quickly assess the quality of sesame oil using this platform. The ISO Evaluator employs Artificial Intelligence of Things (AIoT) sensors to detect changes in volatile gases and the color of the oil during storage. It utilizes deep learning mechanisms, including Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) to determine and evaluate the quality of the sesame oil. Evaluation results demonstrate that the linear discriminant analysis (LDA) value is 95.13. The MQ2, MQ3, MQ4, MQ7, and MQ8 sensors have a positive correlation. The CNN combined with an ANN model achieves a Mean Absolute Percentage Error (MAPE) of 8.1820% for predicting oil quality, while the LSTM model predicts future variations in oil quality indicators with a MAPE of 0.44%. Finally, the designed Sesame Oil Quality Assessment Service Platform effectively addresses issues related to digitization, quality measurement, supply quality observation, and scalability.

Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

The learning process and hyper-parameter optimization of artificial neural networks (ANNs) and deep learning (DL) architectures is considered one of the most challenging machine learning problems. Several past studies have used gradient-based back propagation methods to train DL architectures. However, gradient-based methods have major drawbacks such as stucking at local minimums in multi-objective cost functions, expensive execution time due to calculating gradient information with thousands of iterations and needing the cost functions to be continuous. Since training the ANNs and DLs is an NP-hard optimization problem, their structure and parameters optimization using the meta-heuristic (MH) algorithms has been considerably raised. MH algorithms can accurately formulate the optimal estimation of DL components (such as hyper-parameter, weights, number of layers, number of neurons, learning rate, etc.). This paper provides a comprehensive review of the optimization of ANNs and DLs using MH algorithms. In this paper, we have reviewed the latest developments in the use of MH algorithms in the DL and ANN methods, presented their disadvantages and advantages, and pointed out some research directions to fill the gaps between MHs and DL methods. Moreover, it has been explained that the evolutionary hybrid architecture still has limited applicability in the literature. Also, this paper classifies the latest MH algorithms in the literature to demonstrate their effectiveness in DL and ANN training for various applications. Most researchers tend to extend novel hybrid algorithms by combining MHs to optimize the hyper-parameters of DLs and ANNs. The development of hybrid MHs helps improving algorithms performance and capable of solving complex optimization problems. In general, the optimal performance of the MHs should be able to achieve a suitable trade-off between exploration and exploitation features. Hence, this paper tries to summarize various MH algorithms in terms of the convergence trend, exploration, exploitation, and the ability to avoid local minima. The integration of MH with DLs is expected to accelerate the training process in the coming few years. However, relevant publications in this way are still rare.

The Optimal and Economic Planning of a Power System Based on the Microgrid Concept with a Modified Seagull Optimization Algorithm Integrating Renewable Resources

In the past, planning to develop an electricity generation capacity supply of consumable load, an acceptable level of reliability, and minimum cost has played significant roles. Due to technological development in energy and the support of energy policymakers to make the most of these clean and cheap resources, a significant amount of research has been conducted to make the most of such energy. Constraints such as low capacity, output power uncertainty, and sustainability problems have made using distributed energy sources costly and complex. Theoretically, capacity development planning in a power system is part of macro-energy planning. It is generally based on specific development policies in each country’s national interest. In addition to being economical, the purpose of this planning was to find the best capacity development plan commensurate with the amount of consumption so that the development plan does not go beyond the permissible limits of reliability, environmental issues, and other constraints. On the other hand, due to the considerable growth of divided production, especially energy sources, it is essential to use microgrids. Accordingly, in this research study, in the process of solving the problem of planning and providing load growth by the distributed generation units to maximize reliability and minimize investment costs, the creation of smaller networks was investigated. To optimize zoning, the weighted graph theory method, in which the weight of the edges is the apparent power passing through the lines, was adopted. In addition, reactive power reliability was included in the calculations to improve the economic aspects. Probabilistic modeling for the presence of renewable resources was employed to bring the model to reality. Since the above problem is very complex, a Seagull-based algorithm and chaos theory were utilized to solve this matter. Finally, the suggested method for the sample system is discussed in different scenarios, indicating an improvement in the system’s performance. According to the numerical results, the NSGA, SPEA, and MOPSO have mean values of 68.3%, 50.2%, and 48.3%, which are covered by the proposed optimization algorithm.