Cocaine trafficking threatens countries' national security and is a major public health challenge. Cocaine is transported from producer countries to consumer markets using various routes, methods, ...and transportation means. These routes develop in the geographical environment, are carefully planned and are geo-strategic objects that respond to the opportunities that drug trafficking organisations (DTOs) find to reduce the risks of interdiction. In this sense, individual drug seizure data (IDS) become essential indicators for identifying trends and understanding trafficking flows associated with drug trafficking routes. However, due to the illicit nature of DTOs, the availability of these data is considerably limited, hindering the ability to analyse and identify trends. This study presents a methodology for collecting and processing data from open-source information reported by Brazil's federal government news website. Using geospatial intelligence and natural language processing methods, we created a dataset with 939 records and 44 variables related to cocaine seizures in Brazil in 2022. We applied geospatial analysis techniques from this dataset to identify trends and potential cocaine trafficking flows. The results were broadly consistent with existing literature on drug trafficking. They demonstrated the potential of open-source information for environmental scanning and knowledge generation through geographic information science. The approach proposed in our research provides tools that can be used to complement drug trafficking monitoring and formulate public policies to strengthen prevention and enforcement strategies.
To determine the efficacy of losartan vs. atenolol in aortic dilation progression in Marfan syndrome (MFS) patients.
A phase IIIb, randomized, parallel, double-blind study was conducted in 140 MFS ...patients, age range: 5-60 years, with maximum aortic diameter <45 mm who received losartan (n = 70) or atenolol (n = 70). Doses were raised to a maximum of 1.4 mg/kg/day or 100 mg/day. The primary end-point was the change in aortic root and ascending aorta maximum diameter indexed by body surface area on magnetic resonance imaging after 36 months of treatment. No serious drug-related adverse effects were observed. Five patients presented aortic events during a follow-up (one in the losartan and four in the atenolol groups, P = 0.366). After 3 years of follow-up, aortic root diameter increased significantly in both groups: 1.1 mm (95% CI 0.6-1.6) in the losartan and 1.4 mm (95% CI 0.9-1.9) in the atenolol group, with aortic dilatation progression being similar in both groups: absolute difference between losartan and atenolol -0.3 mm (95% CI -1.1 to 0.4, P = 0.382) and indexed by BSA -0.5 mm/m2 (95% CI -1.2 to 0.1, P = 0.092). Similarly, no significant differences were found in indexed ascending aorta diameter changes between the losartan and atenolol groups: -0.3 mm/m2 (95% CI -0.8 to 0.3, P = 0.326).
Among patients with MFS, the use of losartan compared with atenolol did not result in significant differences in the progression of aortic root and ascending aorta diameters over 3 years of follow-up.
Data mining approach for dry bean seeds classification Macuácua, Jaime Carlos; Centeno, Jorge António Silva; Amisse, Caísse
Smart agricultural technology,
October 2023, 2023-10-00, 2023-10-01, Letnik:
5
Journal Article
Recenzirano
Odprti dostop
•Data mining with an emphasis on principal component analysis.•Machine learning used to predict seed quality: random forest - RF, support vector machine - SVM and k-nearest neighbors - KNN.•Hyper ...parameter tuning in machine learning algorithms.•Dataset balancing based on synthetic minority super sampling -SMOTE and applied three machine learning techniques.•Dry bean grains.
Product quality certification is an important process in agricultural production and productivity. Traditional methods for seed quality classification have shown limitations such as complex steps, low precision, and slow inspection for large production volumes. Automatic classification techniques based on machine learning and computer vision offer fast and high throughput solutions. Despite the major advances in state-of-the-art automatic classification models, there is still a need to improve these models by incorporating other techniques. In this article, we developed a computer vision system for the automatic classification of different seed varieties based on machine learning models, combined with data mining techniques using a set of features related to the geometry of bean seeds, extracted from binary images. Three machine learning techniques were compared, namely: Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), including Principal Component Analysis (PCA), Hyperparameter tuning in machine learning algorithms, and dataset balancing based on Synthetic Minority Oversampling Technique (SMOTE). The results showed that data mining processes, such as Principal Component Analysis, Hyperparameter tuning, and application of the SMOTE technique, help to improve the quality of classification results. The KNN classifier showed better performance, with around 95% accuracy and 96% precision and recall. The best results were obtained applying hyperparameter tuning and the SMOTE technique, in the preprocessing step, obtaining an increase around 2.6%. The results proved that the combined use of data mining in the preprocessing step and machine learning classification methods can effectively and efficiently increase the classification accuracy and help automatic bean seed selection based on digital images. This can help small farmers and/or agricultural managers make decisions regarding seed selection to increase production.
Hyperspectral remote sensing enables a detailed spectral description of the object’s surface, but it also introduces high redundancy because the narrow contiguous spectral bands are highly ...correlated. This has two consequences, the Hughes phenomenon and increased processing effort due to the amount of data. In the present study, it is introduced a model that integrates stacked-autoencoders and convolutional neural networks to solve the spectral redundancy problem based on the feature selection approach. Feature selection has a great advantage over feature extraction in that it does not perform any transformation on the original data and avoids the loss of information in such a transformation. The proposed model used a convolutional stacked-autoencoder to learn to represent the input data into an optimized set of high-level features. Once the SAE is learned to represent the optimal features, the decoder part is replaced with regular layers of neurons for reduce redundancy. The advantage of the proposed model is that it allows the automatic selection and extraction of representative features from a dataset preserving the meaningful information of the original bands to improve the thematic classification of hyperspectral images. Several experiments were performed using two hyperspectral data sets (Indian Pines and Salinas) belonging to the AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensor to evaluate the performance of the proposed method. The analysis of the results showed precision and effectiveness in the proposed model when compared with other feature selection approaches for dimensionality reduction. This model can therefore be used as an alternative for dimensionality reduction.
3D Silva Centeno, Jorge Antonio; Bugalski de Andrade Peixoto, Elizabete
Revista de Geociências do Nordeste,
12/2023, Letnik:
9, Številka:
2
Journal Article
Recenzirano
A mobile laser scanner is a valuable tool to collect 3D information especially in urban regions, where vertical objects, like walls, poles and trees, need to be mapped. The collected point clouds can ...be used to segment objects and classify them according to their shape. Nevertheless, the segmentation and classification steps still need tools to analyze 3D point clouds. In this paper it is introduces a method to describe 3D shape from point clouds obtained by mobile laser scanner within the context of classification of urban furniture. The initial aim is to describe the 3D shape of objects located at the top of poles, but the approach can be extended to other objects. For this purpose, the distribution of the points is analyzed with help of the eigenvalues of the variance-covariance matrix. It is proposed the use of two parameters, one related to planarity and another to elongation, that are normalized in the range between zero and one, which allows easier description of the shape in terms of just two well-known terms.
Object detection in high resolution images is a new challenge that the remote sensing community is facing thanks to introduction of unmanned aerial vehicles and monitoring cameras. One of the ...interests is to detect and trace persons in the images. Different from general objects, pedestrians can have different poses and are undergoing constant morphological changes while moving, this task needs an intelligent solution. Fine-tuning has woken up great interest among researchers due to its relevance for retraining convolutional networks for many and interesting applications. For object classification, detection, and segmentation fine-tuned models have shown state-of-the-art performance. In the present work, we evaluate the performance of fine-tuned models with a variation of training data by comparing Faster Region-based Convolutional Neural Network (Faster R-CNN) Inception v2, Single Shot MultiBox Detector (SSD) Inception v2, and SSD Mobilenet v2. To achieve the goal, the effect of varying training data on performance metrics such as accuracy, precision, F1-score, and recall are taken into account. After testing the detectors, it was identified that the precision and recall are more sensitive on the variation of the amount of training data. Under five variation of the amount of training data, we observe that the proportion of 60%-80% consistently achieve highly comparable performance, whereas in all variation of training data Faster R-CNN Inception v2 outperforms SSD Inception v2 and SSD Mobilenet v2 in evaluated metrics, but the SSD converges relatively quickly during the training phase. Overall, partitioning 80% of total data for fine-tuning trained models produces efficient detectors even with only 700 data samples.
LiDAR se mostrou valioso na análise do meio urbano, pois permite a captura de informações tridimensionais para detectar e modelar edifícios. O desenvolvimento do LiDAR móvel terrestre abriu novas ...possibilidades para a modelagem 3D em áreas urbanas. Neste artigo é apresentada uma metodologia semiautomática que visa calcular modelos tridimensionais de edifícios a partir de nuvens de pontos obtidas com LiDAR terrestres móveis. Para tanto, a nuvem de pontos é segmentada em blocos de planos uniformes analisando a variação da densidade de pontos ao longo das principais direções da fachada. Isso permite segmentar a nuvem de pontos em regiões planas que posteriormente são combinadas para construir o modelo 3D, mesmo quando a fachada possui diferentes planos paralelos. O principal diferencial da metodologia é a utilização dos histogramas de frequência para segmentar a nuvem de pontos e detectar as bordas da fachada, garantindo economia de tempo em relação aos métodos tradicionais.
Background Surgery for intervalvular fibrous body reconstruction in aortic and mitral valve replacement is a complex operation, although mandatory in some circumstances. The long-term result of this ...operation remains unknown. The objective of this study was to analyze the outcomes of this technique. Methods A descriptive and retrospective study was carried out to analyze operative morbidity and mortality in fibrous body reconstruction with the “David technique” and to evaluate the midterm and long-term results regarding durability and survival. Results A total of 40 consecutive patients underwent the David technique between 1997 and 2014. The mean age was 58 ± 15 years and 62.5% were male. The indications were active endocarditis with paravalvular and fibrous body abscesses in 26 patients (group A) and massive calcification of the intervalvular fibrous body in 14 patients (group B). Mean European system for cardiac operative risk evaluation I predicted risk of mortality was 36 ± 24 and 16 ± 15, respectively. The hospital mortality rate was 15.3% in group A and 7.1% in group B. Survival rate after 1, 5, and 10 years was 65.4%, 57.7%, and 50% for group A and 92.9%, 85.7%, and 78.6% for group B. Freedom from reoperation at 1, 5, and 10 years was 92.3%, 84.6%, and 76.9% for group A and 90.9%, 90.9%, and 90.9% for group B. Mean follow-up was 53 ± 8 months. Conclusions Although this complex operation is associated with high perioperative mortality, the long-term results are acceptable in patients where there are not suitable alternative procedures.
Urban infrastructure element detection is important for the domain of public management in large urban centres. The diversity of objects in the urban environment makes object detection and ...classification a challenging task, requiring fast and accurate methods. Advances in deep learning methods have driven improvement in detection techniques (processing, speed, accuracy) that do not rely on manually crafted models, but, instead, use learning approaches with corresponding large training sets to detect and classify objects in images. We applied an object detection model to identify and classify four urban infrastructure elements in the Mappilary dataset. We use YOLOv5, one of the top-performing object detection models, a recent release of the YOLO family, pre-trained on the COCO dataset but fine-tuned on Mappilary dataset. Experimental results from the dataset show that YOLOv5 can make qualitative predictions, for example, the power grid pole class presented the mean Average Precision (mAP) of 78% and the crosswalk class showed mAP around 79%. A lower degree of certainty was verified in the detection of public lighting (mAP=64%) and accessibility (mAP=61%) classes due to the low resolution of certain objects. However, the proposed method showed the capability of automatically detection and location of urban infrastructure elements in real-time, which could contribute to improve decision-making.
Tomatoes are widely cultivated, both by family farmers and corporate producers. During the tomato growth cycle, several diseases can affect the plant. The identification of these diseases through ...short-range images is significant, and computer vision techniques are commonly used to identify diseases in plant leaves. In this paper, a hybrid model that combines a convolutional neural network (CNN) and a Random Forest (RF) decision tree is used for foliar spot detection in tomato leaves. High-level features learned and extracted from CNN are used as input for the RF classifier. To evaluate the proposed model’s performance for plant disease identification, a case study of 2480 low-cost digital RGB images collected in actual field conditions, under different intensities of light exposure, were used, including healthy tomato leaves and leaves with visible symptoms of powdery mildew fungus, which attacks the tomato leaf. The results were compared with six conventional machine learning classifiers: Logistic Regression (LR), Linear Discriminant Analysis (LDA), K- Nearest Neighbors (KNN), Naive Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF). The results show that the proposed model outperformed conventional classifiers, reaching an accuracy of 98%. The results highlight the importance of fusing models to improve the detection plant´s diseases.
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