Skin cancer occurs when abnormal cells in the top layer of the skin, known as the epidermis, undergo uncontrolled growth due to unrepaired DNA damage, leading to the development of mutations. These mutations lead to rapid cell growth and development of cancerous tumors. The type of cancerous tumor depends on the cells of origin. Overexposure to ultraviolet rays from the sun, tanning beds, or sunlamps is a primary factor in the occurrence of skin cancer. Since skin cancer is one of the most common types of cancer and has a high mortality, early diagnosis is extremely important. The dermatology literature has many studies of computer‐aided diagnosis for early and highly accurate skin cancer detection. In this study, the classification of skin cancer was provided by Regnet x006, EfficientNetv2 B0, and InceptionResnetv2 deep learning methods. To increase the classification performance, hairs and black pixels in the corners due to the nature of dermoscopic images, which could create noise for deep learning, were eliminated in the preprocessing step. Preprocessing was done by hair removal, cropping, segmentation, and applying a median filter to dermoscopic images. To measure the performance of the proposed preprocessing technique, the results were obtained with both raw images and preprocessed images. The model developed to provide a solution to the classification problem is based on deep learning architectures. In the four experiments carried out within the scope of the study, classification was made for the eight classes in the dataset, squamous cell carcinoma and basal cell carcinoma classification, benign keratosis and actinic keratosis classification, and finally benign and malignant disease classification. According to the results obtained, the best accuracy values of the experiments were obtained as 0.858, 0.929, 0.917, and 0.906, respectively. The study underscores the significance of early and accurate diagnosis in addressing skin cancer, a prevalent and potentially fatal condition. The primary aim of the preprocessing procedures was to attain enhanced performance results by concentrating solely on the area spanning the lesion instead of analyzing the complete image. Combining the suggested preprocessing strategy with deep learning techniques shows potential for enhancing skin cancer diagnosis, particularly in terms of sensitivity and specificity.

Early diagnosis of diabetes, which can cause death, is very important for the health of the person. In the literature, machine learning techniques are frequently used in diagnosis of many diseases, including diabetes. The aim of the study is to predict diabetes with high accuracy by using machine learning and preprocessing techniques. Pima Indian Diabetes dataset was used in the study. J48 (Decision Tree), Naïve Bayes, Support Vector Machine, Logistic Regression, Multilayer Perceptron, K Nearest Neighbor, Logistic Model Tree, and Random Forest were used for classification. Of the preprocessing methods, feature selection, imputing missing values, normalization and standardization are performed. According to the results obtained, the highest accuracy value got with the Random Forest algorithm as 80.869.

The cohesion value is one of the important factors used to evaluate software maintainability. However, measuring the cohesion value is a relatively difficult issue when tracing the source code manually. Although there are many static code analysis tools, not every tool measures every metric. The user should apply different tools for different metrics. In this study, besides the use of these tools, we predicted the cohesion values (LCOM2, TCC, LCC, and LSCC) with machine learning techniques (KNN, REPTree, multi-layer perceptron, linear regression (LR), support vector machine, and random forest (RF)) to solve them alternatively. We created two datasets utilizing two different open-source software projects. According to the obtained results, for the LCOM2 and TCC metrics, the KNN algorithm provided the best results, and for LCC and LSCC metrics, the REPTree algorithm was the best. However, out of all the metrics, RF, REPTree, and KNN had close performances with each other, and therefore any of the RF, REPTree, and KNN techniques can be used for software cohesion metric prediction.

As the need for software has increased, the maintainability of software has become an important issue and the need for the qualitative study of source codes has arisen. For this reason, we used software quality metrics to measure software quality. Moreover, for object-oriented software development projects, cohesion is an important factor. The main purpose of cohesion is to provide the rule that each class should serve a single purpose. Every class should have a single responsibility. In this study, types of Lack of Cohesion in Methods (LCOM) metrics were examined and compared using a sample Java code. As a result, it was observed that the LCOM5 metric gave the best result.