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Higher Education Institutions must define and monitor strategies and policies essential for decision-making in their various areas and levels, in which Business Intelligence plays a leading role. This research addresses the problem of Business Intelligence system adoption in Higher Education Institutions, with a view, in the first instance, to identify and characterise the strategic objectives that underpin decision-making, activities, processes, indicators and information in Higher Education Institutions. After a literature review, it was found that the absence of a roadmap that can serve as a reference to implement a Business Intelligence system in Higher Education Institutions may limit the adoption of this type of solution. Therefore, this research intends to present the methodology of a proposed roadmap for the implementation of Business Intelligence systems in Higher Education Institutions, that allows for increasing its capacity for analysis and evaluation of the data and information available in the various systems and platforms. © 2023 ICSBT International Conference on Smart Business Technologies. All rights reserved.

Abstract
Gastric Cancer (GC) and Colorectal Cancer (CRC) are some of the most common cancers in the world. The most common diagnostic methods are upper endoscopy and biopsy. Possible expert distractions can lead to late diagnosis. GC is a less studied malignancy than CRC, leading to scarce public data that difficult the use of AI detection methods, unlike CRC where public data are available. Considering that CRC endoscopic images present some similarities with GC, a CRC Transfer Learning approach could be used to improve AI GC detectors. This paper evaluates a novel Transfer Learning approach for real-time GC detection, using a YOLOv4 model pre-trained on CRC detection. The results achieved are promising since GC detection improved relatively to the traditional Transfer Learning strategy.

Abstract
The increasing number of Power-Electronic (PE) interfaced devices in the new generation of distribution systems results in concerns about the power quality of modern grids. Besides the loads, the harmonic-injecting devices are increasingly penetrating the generation, storage, and delivering levels of energy dispatch systems in the microgrids and the LV networks which can be easily reflected in the primary distribution systems. As an economic, applicable, and efficient solution, the passive filters can be optimized and added to the grid to absorb the harmonics. Furthermore, in the presence of controllable devices such as PE-interfaced DGs and storage units, a coordination strategy can be implemented to actively decrease the effect of the nonlinear loads. Accordingly, the idea of a virtually-hybrid filter can be developed by the use of passive filters and the coordinated active harmonic filtering strategy. In this paper, by providing an explanation for the developed coordination strategy of active filters, the probabilistic techno-economic planning of virtually-hybrid filters is studied considering the different combinations of the linear and nonlinear loads in a modern primary distribution system. Simulation results have proved that the proposed method is capable of minimizing harmonic distortions and grid loss by the use of the optimal passive filters and the suggested coordination strategy of the active devices. © 2023 IEEE.

Abstract
Training machine learning models for artificial intelligence (AI) applications in pathology often requires extensive annotation by human experts, but there is little guidance on the subject. In this work, we aimed to describe our experience and provide a simple, useful, and practical guide addressing annotation strategies for AI development in computational pathology. Annotation methodology will vary significantly depending on the specific study's objectives, but common difficulties will be present across different settings. We summarize key aspects and issue guiding principles regarding team interaction, ground-truth quality assessment, different annotation types, and available software and hardware options and address common difficulties while annotating. This guide was specifically designed for pathology annotation, intending to help pathologists, other researchers, and AI developers with this process.(c) 2022 THE AUTHORS. Published by Elsevier Inc. on behalf of the United States & Canadian Academy of Pathology. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).