Abstract This study addresses the problem of detecting pancreatic cancer by classifying computed tomography (CT) images into cancerous and non‐cancerous classes using the proposed deep learning‐based aggregate analysis framework. The application of deep learning, as a branch of machine learning and artificial intelligence, to specific medical challenges can lead to the early detection of diseases, thus accelerating the process towards timely and effective intervention. The concept of classification is to reasonably select an optimal cut‐off point, which is used as a threshold for evaluating the model results. The choice of this point is key to ensure efficient evaluation of the classification results, which directly affects the diagnostic accuracy. A significant aspect of this research is the incorporation of private CT images from Vilnius University Hospital Santaros Klinikos, combined with publicly available data sets. To investigate the capabilities of the deep learning‐based framework and to maximize pancreatic cancer diagnostic performance, experimental studies were carried out combining data from different sources. Classification accuracy metrics such as the Youden index, (0, 1)‐criterion, Matthew's correlation coefficient, the F1 score, LR+, LR−, balanced accuracy, and g‐mean were used to find the optimal cut‐off point in order to balance sensitivity and specificity. By carefully analyzing and comparing the obtained results, we aim to develop a reliable system that will not only improve the accuracy of pancreatic cancer detection but also have wider application in the early diagnosis of other malignancies.

Multiple-criteria decision-making (MCDM) typically assumes that crowds make completely rational decisions. In MCDM, a crowd as a whole, or its individual members, generally make decisions free from any influence of valence, arousal, emotional state or environment. In contrast, various theories dealing with crowd psychology (Gustave Le Bon, Freudian, Deindividuation, Convergence, Emergent norm, Social identity) analyze, in one form or another, the emotions of the crowd. According to above theories, crowd is influenced by a range of behavioral factors, such as physical, social, psychological, culture, norms, and emotions. It can be argued that the emotional state, valence and arousal of crowds affect their decision making to a considerable degree and multiple criteria crowd behavior modeling must, therefore, consider this impact as well. In this light, the integration of crowd simulation and biometric methods, behavioral operations research and emotions in decision making has taken a prominent place as it leads to a better understanding of crowd emotions and crowd decision making. In this context, the authors developed the Affective Analytics of Demonstration Sites (ANDES) that added to this body of research in four ways. The crowd analysis and simulations conducted with ANDES used a neuro decision matrix. The matrix contains a detailed description of demonstration sites (public spaces) in question and the emotions, valence, arousal and physiological parameters of people present there. With ANDES’s Remote Sensor Network, emotional (emotions, valence, arousal) and physiological (average crowd facial temperature, crowd composition by gender and age group, etc.) parameters of people present at demonstration sites can be mapped. ANDES can assist experts in more effective implementations of public spaces planning and a participation process by attendees by collecting and examining various layers of data on the emotional and physiological parameters of visitors based on a visitors-centric public spaces planning approach. ANDES can determine the public space and real estate values.

The goal of investigation was to seek new features far the analysis of extremal problems. A method of visual analysis of a set of objective function values is proposed. It allows us to find a direction where the variation of function is maximal. The method ensures a high quality of analysis when the number of used values of the objective function is small, and a possibility of identifying the specific character of the objective function. The results of analysis are used in the search for a new coordinate system of the extremal problem and in a graphical representation of the observed data. The analysis would allow us to find a better optimization strategy.