Whale Optimization Algorithm (WOA) is a fairly new algorithm developed in 2016. WOA was applied to continuous optimization problems and engineering problems in the literature. However, WOA demonstrates lower performance than others in traveling salesman problems. Therefore, in this study, an application of the hybrid algorithm (WOA+NN) has been done in the traveling salesman problem. A set of classical datasets which have cities scale ranged from 51 to 150 was used in the application. The results show that the hybrid algorithm (WOA+NN) outperforms AS (Ant system), WOA, GA, and SA for 50% of all datasets. Ant system (AS) is the second algorithm that is better than other metaheuristics for 40% of all datasets. In addition, it was given that a detailed analysis presents the number of best, worst, average solutions, standard deviation, and the average CPU time concerning meta-heuristics. The metrics stress that the hybrid algorithm (WOA+NN) demonstrates a performance rate over 50% in finding optimal solutions. AS (Ant system) is better at 40% of all optimal solutions. Finally, the hybrid algorithm solves the discrete problem in reasonable times in comparison to other algorithms for medium-scale datasets.

Modified Camel Algorithm (MCA) is a challenging algorithm applied to engineering problems in 2016, 2019, and 2021. MCA can be implemented to the field of business, economics, labor management, and science compared to the other techniques. The pure MCA solves optimization problems effectively and quite fast in literature. To develop and apply the mathematical model in labor management using the modified camel algorithm, it was combined with popular heuristics, such as constructive heuristic (MC), and then improved with local searches, for instance 2-opt, 3-opt, and k-opt. The suggested hybrid algorithms are tested under proper parameters. In the experimental study, random model datasets, and suitable parameters are used via uniform distribution. The experimental outcomes are given as best, average, std. deviation and CPU time for sample datasets with proper parameters. In short, the suggested hybrid metaheuristics find reasonable solutions of labor management in acceptable CPU time for all random datasets.

Beyin tümörleri kafatası içinde anormal hücre ve kitle büyümesinin genel adıdır. Beyin tümörü tanısı konulabilmesi için en yaygın tetkik beyin dokusu ve dokudaki yabancı kitleleri gösteren MR (manyetik rezonans) görüntülemesinin yapılmasıdır. Tanı konduktan sonra hızlıca tedavi süreci planlamalıdır. MR görüntüleri çekildikten sonra uzman radyologlar tarafından görüntülerin incelenerek raporlanması zaman alabilmektedir. Son yıllarda hızla gelişen derin öğrenme teknolojileri ile tıp alanında bulunan yenilikler sayesinde hastalıkların erken ve doğru teşhis edilmesi için çeşitli çalışmalar yapılmaktadır. İnsan kaynaklı hataların en aza indirilmesi bu çalışmalar içerisinde önemli bir yere sahiptir. Bu çalışmada MRI görüntülerinin işaretlenerek uzmanlara yardımcı olması için yapay zekâ tekniklerinden yararlanılarak yeni bir evrişimli sinir ağı modeli eğitilmiştir. Eğitim aşamasında U-Net modelinden yararlanılarak, BRAST veri kümesinin %80’i kullanılmıştır. Veri kümesi içerisindeki örneklerin %20’si modelin performansının değerlendirilmesi için kullanılmıştır. Eğitim ve test işlemleri sonucunda elde edilen bulgular incelendiğinde eğitilen modelin tüm tümör, tümör çekirdeği ve genişleyen tümör bölgelerini sırayla 0.908, 0.807 ve 0.877 Benzerlik oranı (BO, Dice Coefficent Score) ile başarılı bir şekilde işaretleme yapabilen bir model eğitildiği görülmektedir.

Today, with the increasing interest in technology, very useful studies are carried out in the field of image processing. Image technologies are also used in many fields such as security, defense, medicine, and industry. In this study, age, gender, and ethnicity were found in the image by using different deep learning techniques and by building our own model in CNN. The 23705 images taken from the csv file named Face Data taken from Kaggle were categorized as different gender, race, and age within the application and the accuracy and losses of the results were transferred with graphs. In addition, by creating an interface with the help of the Python flask library, the results of the snapshot taken from the camera (age, gender, and race) can also be found. Out of the 23705 images, approximately 12000 male and 11000 female profiles were obtained. These profiles were classified according to 5 different genetics specified in the dataset. The genetics in the application (0 represented White, 1 represented Black, 2 represented Asian, 3 represented Indian, 4 represented Others.) The most difficult part of this study is that the picture changes depending on many factors such as posture, pose angle, brightness, and resolution at the time of shooting..

Black hole algorithm (BHA) is a popular metaheuristic algorithm proposed and applied for data clustering in 2013. BHA was applied to continuous and discrete problems; it is also hybridized with some algorithms in the literature. The pure BHA shows better performance than others in discrete optimization, such as traveling salesman problems. However, it requires improving the algorithm with competitive heuristics. Many heuristics have often been used to construct the initial tour of a salesman, such as the nearest neighbor algorithm (NN), nearest insertion algorithm (NI), cheapest insertion algorithm (CI), random insertion algorithm (RI), furthest insertion algorithm (FI), and minimal spanning tree algorithm (MST). In addition, the black hole algorithm is combined with popular heuristics, such as swap/or insert, reverse/or 2-opt swap, and swap-reverse/or 3-opt swap, and tested with proper parameters in this study. In the experimentation, classical datasets are used via TSP-library. The experimental results are given as best, average solutions/or deviations, and CPU time for all datasets. Besides, the hybrid algorithms demonstrate a better performance rate to get optimality. Finally, hybrid algorithms solve the discrete optimization problem in a short computing time for all datasets.