The times of travel agents selecting tourist routes in the office seem to be finally passing. The potential of artificial intelligence (AI) technologies in the tourism industry exceeds the capabilities of traditional search engines and real people. Some travel services have already begun to use elements of artificial intelligence, which help to analyze large volumes of data and learn from their own and other people’s experience of fulfilling customer orders. Currently, the main goal for travel brands is to “learn” using personalized customer experience. Personalized services that are most suitable for a particular client are a strong competitive advantage. It is AI that helps choose such services, since it allows processing a lot of data and creating a personalized product much faster than traditional search technologies

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With the evolution of modern digital pathology, examining cancer cell tissues has paved the way to quantify subtle symptoms, for example, by means of image staining procedures using Eosin and Hematoxylin. Cancer tissues in the case of breast and lung cancer are quite challenging to examine by manual expert analysis of patients suffering from cancer. Merely relying on the observable characteristics by histopathologists for cell profiling may under-constrain the scale and diagnostic quality due to tedious repetition with constant concentration. Thus, automatic analysis of cancer cells has been proposed with algorithmic and soft-computing techniques to leverage speed and reliability. The paper’s novelty lies in the utility of Zernike image moments to extract complex features from cancer cell images and using simple neural networks for classification, followed by explainability on the test results using the Local Interpretable Model-Agnostic Explanations (LIME) technique and Explainable Artificial Intelligence (XAI). The general workflow of the proposed high throughput strategy involves acquiring the BreakHis public dataset, which consists of microscopic images, followed by the application of image processing and machine learning techniques. The recommended technique has been mathematically substantiated and compared with the state-of-the-art to justify the empirical basis in the pursuit of our algorithmic discovery. The proposed system is able to classify malignant and benign cancer cell images of 40× resolution with 100% recognition rate. XAI interprets and reasons the test results obtained from the machine learning model, making it reliable and transparent for analysis and parameter tuning.