A novel GIS-MCDA-based spatial habitat suitability model for Posidonia oceanica in the Mediterranean.

Seagrasses are extremely productive flowering plants that produce oxygen by photosynthesis in the marine environment. They are globally in decline and can become endangered due to human activities and natural hazards. In order to maintain seagrass biodiversity, suitable habitats for this species must be determined and marine protected areas must be established. Recent technology allows acoustic systems to collect accurate high resolution data of the seafloor. Additionally, cost-effective optical satellite images, which provide wide coverage, have been used in various benthic studies. In this study, a habitat suitability model was developed using acoustic and optical data for Posidonia oceanica in Gulluk Bay, Turkey, SE Mediterranean, by applying the geographic information system-multi-criteria decision analysis and remote sensing techniques. Various criteria, namely, depth, sheltered area, slope, sediment yield, and topographic position index, were weighted using the analytic hierarchy process method. The model was able to identify suitable habitats for seagrass with 76% accuracy. The proposed model in the study allows fast, temporal, cost-effective, and sustainable production of seagrass habitat maps.

Comparison of low cost 3D structured light scanners for face modeling.

This study aims to compare three different structured light scanner systems to generate accurate 3D human face models. Among these systems, the most dense and expensive one was denoted as the reference and the other two that were low cost and low resolution were compared according to the reference system. One female face and one male face were scanned with three light scanner systems. Point-cloud filtering, mesh generation, and hole-filling steps were carried out using a trial version of commercial software; moreover, the data evaluation process was realized using CloudCompare open-source software. Various filtering and mesh smoothing levels were applied on reference data to compare with other low-cost systems. Thus, the optimum reduction level of reference data was evaluated to continue further processes. The outcome of the presented study shows that low-cost structured light scanners have a great potential for 3D object modeling, including the human face. A considerable cheap structured light system has been used due to its capacity to obtain spatial and morphological information in the case study of 3D human face modeling. This study also discusses the benefits and accuracy of low-cost structured light systems.

An assessment of site suitability for marina construction in Istanbul, Turkey, using GIS and AHP multicriteria decision analysis.

Marinas play a key role in sea transportation and tourism. The problem of an insufficient marina capacity has revealed in terms of sea traffic due to the demographic structure and increasing tourism potential of Istanbul which is the biggest metropolitan city of Turkey and has around 600-km-long coastline. Therefore, the study area is mainly focused on the Marmara Sea shoreline of Istanbul. Rather than traditional methods, a rapid and cost-effective solution which considers natural and urban environment conditions is essential to satisfy the need for a marina site selection. Thanks to the latest improvements in geographic information systems, it is convenient to perform location selection analysis of marinas taking advantages of geology, land use, demography and accessibility data sets. The goal of this study is to define the areas that are appropriate for building marinas, with the use of topographic and demographic data in a present shoreline applying analytical hierarchy process multicriteria decision-making method. In this study, erosion, landslide, tsunami, land use, geologically hazardous areas, transfer lines, sea traffic data, neighbourhood scale population, age patterns and house income data have been used. Analytical hierarchy process method is used to give a weight to each data set, and a grading system has been developed for the area selection of marinas. The result maps of the analysis that show study area as classified into four categories from good to not suitable are presented. It is possible to create a decision support system for upper scale plans that enable authorities to perform analysis accurately, cost and time effectively using the proposed methodology that integrates multiple data sets with different scales and types.

Deep neural network approaches for detecting gastric polyps in endoscopic images.

Gastrointestinal endoscopy is the primary method used for the diagnosis and treatment of gastric polyps. The early detection and removal of polyps is vitally important in preventing cancer development. Many studies indicate that a high workload can contribute to misdiagnosing gastric polyps, even for experienced physicians. In this study, we aimed to establish a deep learning-based computer-aided diagnosis system for automatic gastric polyp detection. A private gastric polyp dataset was generated for this purpose consisting of 2195 endoscopic images and 3031 polyp labels. Retrospective gastrointestinal endoscopy data from the Karadeniz Technical University, Farabi Hospital, were used in the study. YOLOv4, CenterNet, EfficientNet, Cross Stage ResNext50-SPP, YOLOv3, YOLOv3-SPP, Single Shot Detection, and Faster Regional CNN deep learning models were implemented and assessed to determine the most efficient model for precancerous gastric polyp detection. The dataset was split 70% and 30% for training and testing all the implemented models. YOLOv4 was determined to be the most accurate model, with an 87.95% mean average precision. We also evaluated all the deep learning models using a public gastric polyp dataset as the test data. The results show that YOLOv4 has significant potential applicability in detecting gastric polyps and can be used effectively in gastrointestinal CAD systems. Gastric Polyp Detection Process using Deep Learning with Private Dataset.