This paper presents the concept and the modern advances in architecture, models and services in cloud. The goal of the paper is the development of cloud architecture and to show how an absolutely secure, fast and powerful cloud computing environment can be built in an easy and economical way to serve as an end product. For this purpose, it will be created the cloud environment by using a new, modern and evolving, optimistic approach, infrastructure as code (IaC) by writing a Terraform code with the corresponding cloud source, AWS “Amazon Web Services”. The developed cloud architecture will provide the PaaS (Platform as a Service) for medical imaging analytics and will support all necessary software tools and packages.

This paper presents the concept and the modern advances in personalized medicine that rely on technology and review the existing tools for early detection of breast cancer. The breast cancer types and distribution worldwide is discussed. It is spent time to explain the importance of identifying the normality and to specify the main classes in breast cancer, benign or malignant. The main purpose of the paper is to propose a conceptual model for early detection of breast cancer based on machine learning for processing and analysis of medical big dataand further knowledge discovery for personalized treatment. The proposed conceptual model is realized by using Naive Bayes classifier. The software is written in python programming language and for the experiments the Wisconsin breast cancer database is used. Finally, the experimental results are presented and discussed.

Целта на тази статия е да представи основните резултати от научните изследвания на автора в областта на анализа на големи биомедицински данни в подкрепа на прецизната медицина, по-специално геномиката и онкологията. Ос- новните части на статията разкриват най-съвременните технологии за големи данни и тяхното значение за напредъка на молекулярната биология и медицина в контекста на изчислителната парадигма за научни изследвания. Освен това, статията представя подходи за откриване на гени и ранно откриване на рак на гърдата и рак на щитовидната жлеза въз основа на анализ на големи биомедицински данни. The goal of this paper is to present the major outcomes of the author scientific research in the area of Big Biomedical Data analytics in support of precision medicine, specifically genomics and oncology. The main parts of the paper reveal the state-of-the art in big data technologies and their significance for the advance of molecular biology and medicine in the context of the computational paradigm for scientific research. Furthermore, it presents approaches for gene finding and early detection of breast cancer and thyroid cancer based on big biomedical data analytics.

This paper examines the analysis of the different data types within the field of Bioinformatics. The aim is to study and handpick machine learning algorithms th

Hybrid quantum–classical machine learning offers a promising direction for advancing automated quality control in industrial settings. In this study, we investigate two hybrid quantum–classical approaches for classifying defects in aluminum TIG welding images and benchmarking their performance against a conventional deep learning model. A convolutional neural network is used to extract compact and informative feature vectors from weld images, effectively reducing the higher-dimensional pixel space to a lower-dimensional feature space. Our first quantum approach encodes these features into quantum states using a parameterized quantum feature map composed of rotation and entangling gates. We compute a quantum kernel matrix from the inner products of these states, defining a linear system in a higher-dimensional Hilbert space corresponding to the support vector machine (SVM) optimization problem and solving it using a Variational Quantum Linear Solver (VQLS). We also examine the effect of the quantum kernel condition number on classification performance. In our second method, we apply angle encoding to the extracted features in a variational quantum circuit and use a classical optimizer for model training. Both quantum models are tested on binary and multiclass classification tasks, and the performance is compared with the classical CNN model. Our results show that while the CNN model demonstrates robust performance, hybrid quantum–classical models perform competitively. This highlights the potential of hybrid quantum–classical approaches for near-term real-world applications in industrial defect detection and quality assurance.