Acute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.

The global prevalence of allergic diseases has increased dramatically in recent years and are now recognized as significant chronic diseases worldwide. One of the most important allergens that causes allergic diseases is house dust mites. This study aims to present a bibliometric overview of research published on dust mites between 1980 and 2018. Articles published from 1980 to 2018 were analyzed using bibliometric methods. The keywords "Dust mite*," and "Dermatophagoides" were used in the Web of Science (WoS). Simple linear regression analysis was used to estimate the number of future publications on this subject. A total of 4742 publications were found, 2552 (53.8%) of them were articles. Most of the articles were on subjects related to immunology (1274; 49.9%) and allergy (1229; 48.1%). Clinical and Experimental Allergy (222; 8.7%) was the journal with the most publications. The USA was the country that most contributed to the literature with 461 (18.1%) articles. The countries producing the most publications on this subject were developed countries. The most active author was W.R. Thomas (66; 2.5%). The most productive institution was the University of Western Australia (91; 3.6%). The most cited article was published in the New England Journal of Medicine. According to the findings, developed countries were the most productive in publishing on house dust mites. By planning multinational research rather than regional studies, it may be suggested that researchers in underdeveloped or developing countries could also conduct more research on this subject.

The machine learning method, which is a sub-branch of artificial intelligence and which makes predictions with mathematical and statistical operations, is used frequently in education as in every field of life. Nowadays, it is seen that millions of data are recorded continuously, and a large amount of data accumulation has occurred. Although data accumulation increases exponentially, the number of analysts and their capabilities to process these data are insufficient. Although we live in the information age, it is more accurate to say that we live in the data age. By using stored and accumulated data, it is becoming increasingly essential to reveal meaningful relationships and trends and to make predictions for the future. It is important to analyze the data obtained from the education process and to evaluate the success of the students and the factors affecting success. These analyses may also contribute to future training activities. In this study, a data set, including socio-demographic variables of students enrolled in distance education at Hitit University, was used. The authors estimated the success of the students with demographic and social variables such as age, gender, city, family income, family education level. The primary purpose is to provide students with information about their estimated academic achievement at the beginning of the process. Thus, at the beginning of the education process, students' success can be increased by informing the students who are predicted to be unsuccessful. Diversification and enhancement of this data may also support other decision-making mechanisms in the training process. Additionally, the factors affecting students’ academic success were researched, and the students' educational outcomes were evaluated. Prediction success was compared using various machine learning algorithms. As a result of the analysis, it was determined that the Random Forest algorithm was more predictive of student achievement than others.

Pancreatic cancer is one of the most aggressive types of cancer, known for its high mortality rate, as it is often diagnosed at an advanced stage. Early diagnosis holds the potential to prolong patients' lifespans and improve treatment success rates. In this study, an innovative method is proposed to enhance the diagnosis of pancreatic cancer. Computed tomography (CT) images were converted into graphs using the Harris Corner Detection Algorithm and analyzed using deep learning models via transfer learning. DenseNet121 and InceptionV3 transfer learning models were trained on graph‐based data, and model parameters were optimized using the Whale Optimization Algorithm (WOA). Additionally, classification algorithms such as k‐Nearest Neighbors (k‐NN), Support Vector Machines (SVM), and Random Forests (RF) were integrated into the analysis of the extracted features. The best results were achieved using the k‐NN classification algorithm on features optimized by WOA, yielding an accuracy of 92.10% and an F1 score of 92.74%. The study demonstrated that graph‐based transformation enabled more effective modeling of spatial relationships, thereby enhancing the performance of deep learning models. WOA offered significant superiority compared to other methods in parameter optimization. This study aims to contribute to the development of a reliable diagnostic system that can be integrated into clinical applications. In the future, the use of larger and more diverse datasets, along with different graph‐based methods, could enhance the generalizability and performance of the proposed approach. The proposed model has the potential to serve as a decision support tool for physicians, particularly in early diagnosis, offering an opportunity to improve patients' quality of life.

Pancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its advanced stage diagnosis. The five-year survival rate after diagnosis is less than 10%. However, if diagnosed early, the five-year survival rate can reach up to 70%. Early diagnosis of PDAC can aid treatment and improve survival rates by taking necessary precautions. The challenge is to develop a reliable, data privacy-aware machine learning approach that can accurately diagnose pancreatic cancer with biomarkers. The study aims to diagnose a patient’s pancreatic cancer while ensuring the confidentiality of patient records. In addition, the study aims to guide researchers and clinicians in developing innovative methods for diagnosing pancreatic cancer. Machine learning, a branch of artificial intelligence, can identify patterns by analyzing large datasets. The study pre-processed a dataset containing urine biomarkers with operations such as filling in missing values, cleaning outliers, and feature selection. The data was encrypted using the Fernet encryption algorithm to ensure confidentiality. Ten separate machine learning models were applied to predict individuals with PDAC. Performance metrics such as F1 score, recall, precision, and accuracy were used in the modeling process. Among the 590 clinical records analyzed, 199 (33.7%) belonged to patients with pancreatic cancer, 208 (35.3%) to patients with non-cancerous pancreatic disorders (such as benign hepatobiliary disease), and 183 (31%) to healthy individuals. The LGBM algorithm showed the highest efficiency by achieving an accuracy of 98.8%. The accuracy of the other algorithms ranged from 98 to 86%. In order to understand which features are more critical and which data the model is based on, the analysis found that the features “plasma_CA19_9”, REG1A, TFF1, and LYVE1 have high importance levels. The LIME analysis also analyzed which features of the model are important in the decision-making process. This research outlines a data privacy-aware machine learning tool for predicting PDAC. The results show that a promising approach can be presented for clinical application. Future research should expand the dataset and focus on validation by applying it to various populations.

Watermarking is one of the most common techniques used to protect data’s authenticity, integrity, and security. The obfuscation in the frequency domain used in the watermarking method makes the watermarking stronger than the obfuscation in the spatial domain. It occupies an important place in watermarking works in imperceptibility, capacity, and robustness. Finding the optimal location to hide the watermarking is one of the most challenging tasks in these methods and affects the method’s performance. In this article, sample identification information is processed with the method of watermaking on the hiding environment created by using a chaos‐based random number generator on biomedical data to provide solutions to problems such as visual attack, identity theft, and information confusion. In order to obtain biomedical data, a lensless digital in‐line holographic microscopy (DIHM) setup was designed, and holographic data of human blood and cancer cell lines, which are widely used in the laboratory environment, were obtained. The standard USAF 1951 target was used to evaluate the resolution of our imaging setup. Various QR codes were generated for medical sample identification, and the captured medical data were processed by watermarking it with chaos‐based random number generators. A new method using chaos‐based discrete wavelet transform (DWT) and singular value decomposition (SVD) has been developed and applied to high‐resolution data to eliminate the problem of encrypted data being directly targeted by third‐party attacks. The performance of the proposed new watermarking method has been demonstrated by various robustness and invisibility tests. Experimental results showed that the proposed scheme reached an average PSNR value of 564588 dB and SSIM value of 0.9972 against several geometric and destructive attacks, which means that the proposed method does not affect the image quality and also ensures the security of the watermarking information. The results of the proposed method have shown that it can be used efficiently in various fields.