Journal of Artificial Intelligence in Electrical Engineering
Volume:11 Issue: 42, Summer 2022

One of the most famous algorithms in the field of focused exploration of data mining correlation rules is the Apriori algorithm and its many developed versions. But what can be raised as a major challenge in this field is the proper application of this algorithm in the distributed environments of today's world. In this research, a parallelization-based approach is proposed to improve the performance of the Apriori algorithm in the process of exploring recurring patterns on network topologies. The proposed approach includes two major features: (1) combining the node centrality criterion and the Apriori algorithm to identify frequent patterns, (2) using the mapping/reduction method in order to create parallel processing and achieve optimal values in the shortest time. Also, this approach pursues three main goals: reducing the temporal and spatial complexity of the Apriori algorithm, improving the process of extracting dependency rules and identifying recurring patterns, comparing the performance of the proposed approach on different network topologies in order to determine the advantages and disadvantages of each topology. To prove the superiority of the proposed method, a comparison has been made between our approach and the basic Apriori algorithm. The evaluation results of the methods prove that the proposed approach provides an acceptable performance in terms of execution time criteria compared to other methods.

In this brief presents, first a basic structure of 15-level single-phase inverter is presented. The proposed topology works asymmetrically and is capable of producing positive and negative voltage levels at the output. Then, to produce more output voltage levels, the basic structure is expanded and a cascaded multi-level structure is obtained. The new structure is analyzed and compared with several structures in terms of the number of power electronic components used. The result of the comparison shows that the proposed structure uses less power electronic components and is more cost-effective compared to others. Considering the state of the switches and depends on the application, it is possible to use the different kinds of intelligent control methods. Finally, to prove the results, the 15-level inverters is simulated in PSCAD/EMTDC software program.

Fuzzy c-means (FCM) is assumed that all the features are of equal importance. In real applications, however, the importance of the features is different and there exist some features that are more important than the others. These important features should basically have more effects than the other features in the forming of optimal clusters. The basic FCM algorithm does not support this idea. Also, the FCM algorithm suffers from another problem; the algorithm is very sensitive to initialization, whereas a bad initialization leads to a poor local optimum. In this paper, motivated by these weaknesses of the FCM, the goal is to solve the two problems at the same time. In doing so, an automatic local feature weighting scheme is proposed to properly weight the features of each clusters. And, a cluster weighting process is performed to mitigate the initialization sensitivity of the FCM. Feature weighting and cluster weighting are performed simultaneously and automatically during the clustering process resulting in high quality clusters, regardless of the initial centers. Extensive experiments conducted on a synthetic dataset and 16 real world datasets indicate that the proposed algorithm outperforms the state-of-the-arts algorithms. The convergence proof of the proposed algorithm is also provided.

In sliding mode control, the sliding movement can be divided into two phases: reaching phase and sliding phase. In each phase, we face a series of problems. In the sliding phase, switching leads to the occurrence of undesirable chattering phenomenon, so that such high frequency oscillations stimulate the unmodeled dynamics of the system and may cause damage to the controlled device. In this paper, a fuzzy-sliding model controller (FSMC) is presented to solve this problem. On the other hand, during the reaching phase, SMC is sensitive to parameter uncertainty and external disturbance. In the continuation of the paper, a sliding mode fuzzy controller (SMFC) with a moving sliding surface to minimize or even eliminate the reaching phase is introduced.

Retinal blood vessels include arteries and veins and are usually next to each other. Blood vessels are used to classify the severity of the disease and are also used for guidance during surgery, as retinopathy is one of the dangerous diseases.Diabetic retinopathy can cause the formation of new vessels (neoangiogenesis). This condition causes low vision and even blindness. Therefore, a reliable method for diagnosing and classifying the vessel is needed in order to avoid these complications. Retinopathy is one of the hidden diseases that is usually not known. prevent the next possibility.There are several methods for diagnosis, the most common of which is the use of traditional methods based on manual feature extraction, which requires a lot of feature geometry and expertise, and is usually dependent on data.From this method, neural convolution is a reliable, efficient and reliable method for extracting features without manual intervention, which requires a lot of expertise, which also reduces the dependence on data.In this article, using convolutional neural network, diabetic retinopathy has been diagnosed with accuracy and sensitivity of 98.8% and 97.5%, respectively.The obtained results indicate that the proposed method is suitable for locating blood vessels automatically.

The emergence and increasing progress of artificial intelligence has faced the legal science with unsolvable challenges. Artificial intelligence systems, like other new technologies, have faced serious challenges to the principle of accountability and legal rules about civil responsibilities (compensation for damages caused by artificial intelligence systems). This is an important issue that ensures the confidence of potential victims of these systems and trust in the artificial intelligence industry. In the face of changes in smart technology, the courts experience challenges in applying traditional laws that the current laws are unable to respond to, and regulatory organizations and legislators must pay attention to the fact that the current laws are not responsive in monitoring artificial intelligence and exercising legal responsibilities. They need to pay attention to the special and new law. But the important issue that the legislators in all legal systems are concerned with is whether artificial intelligence is considered a legal entity or not, and whether artificial intelligence can be tried, which has not yet been answered. This article, while reviewing the nature and elements of artificial intelligence, which it is necessary for lawyers and lawyers to know, examines the various aspects of the challenges facing the science of law in the field of artificial intelligence and examines the ineffectiveness of the laws governing the damages caused by artificial intelligence. The result is that the rules of audience need to be revised in dealing with the responsibilities arising from artificial intelligence.