Classical optimization and search algorithms are not effective for nonlinear, complex, dynamic large-scaled problems with incomplete information. Hence, intelligent optimization algorithms, which are inspired by natural phenomena such as physics, biology, chemistry, mathematics, and so on have been proposed as working solutions over time. Many of the intelligent optimization algorithms are based on physics and biology, and they work by modelling or simulating different nature-based processes. Due to philosophy of constantly researching the best and absence of the most effective algorithm for all kinds of problems, new methods or new versions of existing methods are proposed to see if they can cope with very complex optimization problems. Two recently proposed algorithms, namely ray optimization and optics inspired optimization, seem to be inspired by light, and they are entitled as light-based intelligent optimization algorithms in this paper. These newer intelligent search and optimization algorithms are inspired by the law of refraction and reflection of light. Studies of these algorithms are compiled and the performance analysis of light-based i ntelligent optimization algorithms on unconstrained benchmark functions and constrained real engineering design problems is performed under equal conditions for the first time in this article. The results obtained show that ray optimization is superior, and effectively solves many complex problems.
Computational intelligence search and optimization algorithms have been efficiently adopted and used for many types of complex problems. Optics Inspired Optimization (OIO) is one of the most recent physics inspired computational intelligence methods which treats the search space of the problem to be optimized as a wavy mirror in which each peak is assumed to reflect as a convex mirror and each valley to reflect as a concave one. Each candidate solution is treated as an artificial light point that its glittered ray is reflected back by the search space of the problem and the artificial image is formed based on mirror equations adopted from Optics, as a new candidate solution. In this study, OIO for the first time has been designed as solution search strategy for travelling tournament problem which is one of the current sports problems and aids to minimize transportation and total movement of teams. Furthermore, this problem has been firstly solved by League Championship Algorithm and obtained results from both synthetic and real datasets have been compared in this study for the first time. Obtained results show the superiority of OIO which is a novel algorithm and seems to efficiently solve many complex problems.
Cervical cancer is a very serious disease that deeply affects women's lives, often resulting in death. This type of cancer, which is very common in women, is diagnosed at an early stage and is of vital importance for the success of the treatment. Pap-smear tests are used by physicians as the primary diagnostic tool to diagnose the disease. In this study, a hybrid deep model is proposed to classify pap-smear images to detect cervical cancer. In addition, the Gaussian method was applied to improve the images in the original dataset. Feature maps were taken from both the original dataset and the Gaussian-enhanced dataset in the built hybrid architecture, which used Darknet53 and Mobilenetv2 models as the base. After these obtained feature maps were combined, useless features were extracted and the number of features was reduced by using the Neighborhood Component Analysis (NCA) dimension reduction method. Finally, this optimized feature map was classified into different classifiers. As a result of the experimental studies, it was determined that the proposed hybrid model performed better when compared to other studies in the literature and the accuracy rate was 98.90% in the Support Vector Machines (SVM) classifier.
Otitis media with effusion (OME) is defined as a middle ear disease that occurs with the accumulation of fluid in the posterior part of the eardrum, usually without any symptoms. When OME disease is not treated, some negative consequences arise that deeply affect the education, social and cultural life of the patient. OME disease is a difficult issue to diagnose by specialists. In this article, autoendoscopic images of the eardrum have been classified using deep learning methods to help specialists in the diagnosis of OME. In this study, a hybrid deep model based on artificial intelligence is proposed. In the proposed hybrid model, feature maps were obtained using Efficientnetb0 and Densenet201 architectures from both the original dataset and the improved dataset using the gaussian method. Then, the merging process was applied to these feature maps. Unnecessary features are eliminated by applying NCA dimension reduction to the combined feature map. The most valuable features obtained at the end of the optimization process are classified in different machine learning classifiers. The proposed model reached a very competitive accuracy value of 98.20% in the SVM classifier.
Urinary system stone disease is a common disease group all over the world. Ureteral stones constitute 20% of all urinary system stones. Ureteral stones are important because they can cause hydronephrosis and related renal parenchymal damage in the kidneys. In the study, a hybrid model was developed to detect hydronephrosis and ureteral stones from kidney images. In the developed model, heat maps of the original images were obtained by using gradient‐weighted class activation mapping (Grad‐CAM) technology. Then, feature maps were extracted from both the original and heatmap datasets using the Efficientnetb0 architecture. Extracted feature maps were concatenated using a multimodal fusion technique. In this way, different features of an image are obtained. This has a positive effect on the performance of the model. The Relief dimension reduction technique was used to eliminate unnecessary features in the obtained feature map so that the proposed model can work faster and more effectively. Finally, the optimized feature map is classified in the support vector machine (SVM) classifier. To compare the performance of the proposed hybrid model, results were obtained with 8 state‐of‐the‐art models accepted in the literature. Among these models, the highest accuracy value was achieved in the Efficientnetb0 architecture with 67.98%, whereas the accuracy of the proposed hybrid model was 91.1%. This value indicates that the proposed model can be used for HUN diagnosis.
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