[Retracted] Dynamic Systems Enhanced by Electronic Circuits on 7D

Abstract

Hybrid synchronization is one of the most significant aspects of a dynamic system. We achieve nonlinear control unit results to synchronize two comparable 7D structures in this study. Many dynamic systems are directly connected to health care and directly enhance health. We employed linearization and Lyapunov as analytical methods, and since the linearization method does not need updating the Lyapunov function, it is more successful in achieving synchronization phenomena with better outcomes than the Lyapunov method. The two methods were combined, and the result was a striking resemblance to the dynamic system’s mistake. The mathematical system with control and error of the dynamic system was subjected to digital emulation. The digital good outcomes were comparable to the two methods previously stated. We compared the outcomes of three hybrid synchronizations based on Lyapunov and linearization methods. Finally, we used the existing system, presenting it in a new attractor and comparing the findings to those of other similar systems.

Comparative Study of Artificial Intelligence Techniques for the Diagnosis of Chronic Nerve Diseases

“…Fuzzy cognitive maps (FCMs) extend cognitive maps to the fuzzy domain in the interval [−1, 1] to determine the strength in causal relationships [ 14 ]. FCM refines cognitive maps, which describe joint strength through fuzzy data in the interval [−1, 1] [ 2 ].…”

“…Artificial intelligence tries to emulate the natural ability that human beings have to make decisions of any archetype, simulating in their way of learning how that instruction is based on reaching decision-making [ 1 ]. The agricultural sector is one of the essential sectors globally speaking; however, it has been the victim of losses due to diseases, taking into consideration that people with problems of poverty live in these areas, which makes this group of farmers very vulnerable being interrupted in the supply of food products derived from pathogens [ 2 , 3 ]. For this purpose, various artificial intelligence techniques have been used, including neural networks, expert systems (already mentioned above), data mining, and intelligent agents.…”

“…As a result of this great uncertainty, the determinations that the different specialists have adopted in each stage of the diagnostic procedure are not always the same, since each specific incident entails a different decision procedure for each specialist, although this is trying the assessment of the same type of condition. There are ways to identify any plant's conditions, such as examining plant tissues in an equipped laboratory or the presence of a specialist agronomist at the planting site; in either case, the problem is the time it takes to get the results [ 2 ]. Among the techniques that have been used most frequently for the recognition of diseases, the following stand out: fuzzy logic (diagnosis based on classification), expert systems (based on rules, probabilities, based on cases), neural networks (diagnosis based on training and recognition), and applied data mining (diagnosis based on pattern recognition) [ 1 ].…”

[Retracted] Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams

“…The disease diagnosis is performed by examining the parasite load collected from the feces. For the process of detecting and counting eggs in a sample, the Kato-Katz method [ 7 ] is used, which is currently considered the gold standard for egg counting and egg preparation of the blades. After this preparation, an analysis is performed using an optical microscope with a magnification of the objective lens 100 times.…”

“…The acute forms of SM are closely related to ecological tourism and poor sanitation conditions. In contrast, individuals residing in endemic areas do not usually exhibit the manifestations of the acute phase [ 7 , 8 ].…”