Face recognition (FR) has received considerable attention in the field of security, especially in the use of closed-circuit television (CCTV) cameras in security monitoring. Although significant advances in the field of computer vision are made, advanced face recognition systems provide satisfactory performance only in controlled conditions. They deteriorate significantly in the face of real-world scenarios such as lighting conditions, motion blur, camera resolution, etc. This article shows how we design, implement, and conduct the empirical comparisons of machine learning open libraries in building attendance taking (AT) support systems using indoor security cameras called ATSS. Our trial system was deployed to record the appearances of 120 students in five classes who study on the third floor of FPT Polytechnic College building. Our design allows for flexible system scaling, and it is not only usable for a school but a generic attendance system with CCTV. The measurement results show that the accuracy is suitable for many different environments.

In real life, many problems are instances of combinatorial optimization. Cross-functional team selection is one of the typical issues. The decision-maker has to select solutions among $\left(\begin{array}{c}k\\ h\end{array}\right)$ solutions in the decision space, where k is the number of all candidates, and h is the number of members in the selected team. This paper is our continuing work since 2018; here, we introduce the completed version of the Min Distance to the Boundary model (MDSB) that allows access to both the “deep” and “wide” aspects of the selected team. The compromise programming approach enables decision-makers to ignore the parameters in the decision-making process. Instead, they point to the one scenario they expect. The aim of model construction focuses on finding the solution that matched the most to the expectation. We develop two algorithms: one is the genetic algorithm and another based on the philosophy of DC programming (DC) and its algorithm (DCA) to find the optimal solution. We also compared the introduced algorithms with the MIQP-CPLEX search algorithm to show their effectiveness.

The efficient manifold ranking (EMR) algorithm has been widely utilized in content-based image retrieval (CBIR). In this algorithm, each image is represented by low-level features that describe color, texture, and shape. However, low-level features have limitations in capturing semantic meaning. To enhance EMR performance in CBIR, this research proposes a fusion method called CoEMR. CoEMR combines multi-rankings on low-level features with CNN features extracted from a CNN model to enhance the discriminative power of a query image compared to dataset images. Furthermore, CoEMR generates a similarity score between two input images, constructing a similarity learning model. Experiments demonstrate the effectiveness of the proposed methods in improving EMR quality. Additionally, the potential integration of CBIR with Large Language Models in Medical Image Diagnosis Systems is discussed. Key words: Content-based medical image retrieval, Medical image, Efficient manifold ranking, Deep Metric Learning, Contrastive loss, Triplet loss, EMR learning, LLMs, Semantic similarity.