Jpg Format Research Articles

A Novel Ensemble Approach for Rib Fracture Detection and Visualization using CNNs and Grad-CAM.

This study aimed to develop a reliable and efficient system for predicting and locating rib fractures in medical images using an ensemble of convolutional neural networks (CNNs). We employed five CNN architectures-Visual Geometry Group Network 16 (VGG16), Densely Connected Convolutional Network 169 (DenseNet169), Inception Version 4 (Inception V4), Efficient Network B7 (EfficientNet-B7), and Residual Network Next 50 layers (ResNeXt-50)-trained on a dataset of 840 grayscale computed tomography (CT) scan images in .jpg format collected from 42 patients at a local hospital. The images were categorized into two groups representing healed and fresh fractures. The ensemble model was designed to improve predictive accuracy and robustness, utilizing techniques like gradient-weighted class activation mapping (Grad-CAM) for visualization of fracture locations. The ensemble model achieved an accuracy of 0.96, area under the curve (AUC) of 0.97, recall of 0.97, and F1 score of 0.96. Grad-CAM visualizations could effectively locate rib fractures, providing crucial assistance in diagnostics. The ensemble model demonstrates high accuracy and robustness in fracture detection, underscoring its potential for enhancing diagnostic processes in clinical settings. Despite limitations such as the small dataset size and lack of diverse demographic representation, the results are promising for future clinical application.

Classification of Plant Diseases Based on Rice Leaf Image Analysis Using SVM and CLAHE Methods

Rice disease has been found in Indonesia and needs to be identified early for the prevention process. Blast and brown spot disease in rice is considered the most prominent and dangerous disease. This study will focus on identifying four rice leaf disease detections, including Bacterial Blight, Blast, Brown Spot and Tungro. This study aims to determine the effect of contrast enhancement method on SVM method for plant disease classification based on rice leaf image analysis using SVM and CLAHE methods. The dataset consists of four classes namely: Bacterial Blight, Blast, Brown Spot and Tungro with .jpg format. The dataset consists of 480 data for each class. Based on the experimental results, the accuracy of the SVM model reached 100% for the training stage and 94.81% for the testing stage. The accuracy of the CLAHE-SVM model reaches 100% for the training stage and 95.95% for the testing stage. Based on the accuracy value, the CLAHE-SVM model has better performance than the SVM model

Comparative Study: Flower Classification using Deep Learning, SMOTE and Fine-Tuning

Deep learning is a technology that can be used to classify flowers. In this research, flower type classification using the CNN method with several existing CNN architectures will be discussed. The data consists of 4317 images in .jpg format, covering 5 classes that is sunflower, dandelion, daisy, tulip and rose. The distribution of data for each class is daisy with 764 pictures, dandelion with 1052 pictures, rose with 784 pictures, sunflower with 733 pictures, and tulip with 984 pictures. With total dataset of 4317 pictures is further split to training data with ratio of 60%, validation with ratio of 10%, and testing with ratio of 30% to process with the CNN method and CNN framework. Due to the imbalance data distribution, the SMOTE method is applied to balancing number of samples in each class. This research compares CNN architectures, including CNN, GoogleNet, DenseNet, and MobileNet, where each transfer learning model undergoes fine-tuning to improve performance. At the classification stage, performance will be measured based on model testing accuracy. The accuracy obtained using CNN is 74.61%, using GoogleNet is 87.45%, DenseNet is 93.92%, and MobileNet is 88.34%.

CONCEALING TEXT AND EXECUTABLE FILES IN JPG FORMAT FILES WITH A STEGANOGRAPHIC COMMAND-LINE TOOL IN THE OPERATING SYSTEM KALI LINUX

In this scientific paper concealing text and executable files in JPG format files with a steganographic command-line tool in the Kali Linux operating system is presented. Among the myriad of steganographic tools, Steghide has gained prominence for its robust features and ease of use. Primarily focused on embedding and extracting secret messages within image files, the software tool Steghide enables users to encode text data within JPEG and BMP image formats while maintaining data integrity and minimizing visual alterations. This paper provides an in-depth examination of Steghide’s functionality, workflow, and security considerations in the context of digital steganography. It is also discussed the algorithms underpinning Steghide’s operations, its strengths, limitations and common use cases.

A UAV Thermal Imaging Format Conversion System and Its Application in Mosaic Surface Microthermal Environment Analysis.

UAV thermal infrared remote sensing technology, with its high flexibility and high temporal and spatial resolution, is crucial for understanding surface microthermal environments. Despite DJI Drones' industry-leading position, the JPG format of their thermal images limits direct image stitching and further analysis, hindering their broad application. To address this, a format conversion system, ThermoSwitcher, was developed for DJI thermal JPG images, and this system was applied to surface microthermal environment analysis, taking two regions with various local zones in Nanjing as the research area. The results showed that ThermoSwitcher can quickly and losslessly convert thermal JPG images to the Geotiff format, which is further convenient for producing image mosaics and for local temperature extraction. The results also indicated significant heterogeneity in the study area's temperature distribution, with high temperatures concentrated on sunlit artificial surfaces, and low temperatures corresponding to building shadows, dense vegetation, and water areas. The temperature distribution and change rates in different local zones were significantly influenced by surface cover type, material thermal properties, vegetation coverage, and building layout. Higher temperature change rates were observed in high-rise building and subway station areas, while lower rates were noted in water and vegetation-covered areas. Additionally, comparing the temperature distribution before and after image stitching revealed that the stitching process affected the temperature uniformity to some extent. The described format conversion system significantly enhances preprocessing efficiency, promoting advancements in drone remote sensing and refined surface microthermal environment research.

Image Quality Restoration on Historical Artifacts Using Histogram Equalization and Contrast Stretching Methods

This research was carried out to improve the image quality of historical artifacts that experience quality degradation such as noise, unclear or dark image contrast, and poor sharpness. Contrast Stretching and Histogram Equalization are image quality improvement methods to expand and even out image grayscale. This research aims to build a system for improving image quality on historical artifacts using contrast stretching and histogram equalization methods. The sample used in this research is RGB image data with 10 test images in .jpg format, while the process uses the Matlab programming language. The results of improving image quality using the contrast stretching and histogram equalization methods from sample 1 to image sample 10 showed the best results in test image_citra_1 with contrast stretching results based on PSNR = 32.61 dB and MSE = 35.86 Db and PSNR = 19.13 Db and MSE = 794.76 in histogram equalization calculations.

Classification of Alzheimer’s Disease Based on Deep Learning Using Medical Images

Neurodegenerative disorders, notably Alzheimer’s, pose an escalating global health challenge. Marked by the degeneration of brain neurons, these conditions lead to a gradual decline in nerve cells. Worldwide, over 55 million people grapple with dementia, with Alzheimer’s prominently impacting the aging demographic. The primary hurdle to early Alzheimer’s detection is the widespread lack of awareness. The main goal is to design and implement an artificial intelligence system using deep learning (DL) to detect Alzheimer’s disease (AD) through medical images and classify them into various stages, such as non-demented, moderate dementia, mild dementia, and very mild dementia. The dataset contains 6400 magnetic resonance images in .jpg format, with standardized dimensions of 176 × 208 pixels. To demonstrate the advantages of data augmentation and transformation techniques, four scenarios were created: two without these techniques, utilizing the Adam and SGD optimizers, and two with these techniques, also employing the Adam and SGD optimizers, respectively. The main results revealed that scenarios utilizing these techniques exhibited more stable performance when validated with a new dataset. Scenario 3, using the Adam optimizer, achieved a weighted average accuracy of 91.83%, whereas scenario 4, employing the SGD optimizer, reached 87.58% accuracy. In contrast, scenarios 1 and 2, which omitted these techniques, obtained low accuracies below 55%. It is concluded that classifying AD with a DL model exceeding 90% accuracy is feasible. This is the importance of utilizing data augmentation and transformation techniques to improve generalizability to input image variations, which is a consistent factor in the healthcare sector.

SurvdigitizeR: an algorithm for automated survival curve digitization

BackgroundDecision analytic models and meta-analyses often rely on survival probabilities that are digitized from published Kaplan–Meier (KM) curves. However, manually extracting these probabilities from KM curves is time-consuming, expensive, and error-prone. We developed an efficient and accurate algorithm that automates extraction of survival probabilities from KM curves.MethodsThe automated digitization algorithm processes images from a JPG or PNG format, converts them in their hue, saturation, and lightness scale and uses optical character recognition to detect axis location and labels. It also uses a k-medoids clustering algorithm to separate multiple overlapping curves on the same figure. To validate performance, we generated survival plots form random time-to-event data from a sample size of 25, 50, 150, and 250, 1000 individuals split into 1,2, or 3 treatment arms. We assumed an exponential distribution and applied random censoring. We compared automated digitization and manual digitization performed by well-trained researchers. We calculated the root mean squared error (RMSE) at 100-time points for both methods. The algorithm’s performance was also evaluated by Bland–Altman analysis for the agreement between automated and manual digitization on a real-world set of published KM curves.ResultsThe automated digitizer accurately identified survival probabilities over time in the simulated KM curves. The average RMSE for automated digitization was 0.012, while manual digitization had an average RMSE of 0.014. Its performance was negatively correlated with the number of curves in a figure and the presence of censoring markers. In real-world scenarios, automated digitization and manual digitization showed very close agreement.ConclusionsThe algorithm streamlines the digitization process and requires minimal user input. It effectively digitized KM curves in simulated and real-world scenarios, demonstrating accuracy comparable to conventional manual digitization. The algorithm has been developed as an open-source R package and as a Shiny application and is available on GitHub: https://github.com/Pechli-Lab/SurvdigitizeR and https://pechlilab.shinyapps.io/SurvdigitizeR/.

EXPLORING CYTOLOGICAL ASPECTS OF SOCKET HEALING OPTIMIZATION AFTER ATYPICALREMOVAL OF THE THIRD LOWER MOLAR

This study aims to investigate the cytological characteristics of socket healing after atypical removal of the lower third molar, utilizing osteoplastic synthetic material in conjunction with platelet-rich gel and plasma enriched with thrombocyte factors, to enhance local osteogenesis. Materials and Methods For this comparative clinical study, smears of alveolar content were collected following atypical extraction of the lower third molar. Cell elements were quantified for cytological analysis. Data collection utilized a template developed in Microsoft Excel version 16.34. Statistical processing was performed using RStudio 2023.03.1 Build 446 (Posit Software, PBC). Smears were air-dried, fixed in a 1:1 mixture of alcohol and acetone for 5 minutes, stained first with May-Grünwald’s methylene blue (15 minutes), followed by Romanowsky-Giemsa’s azure-eosin stain (30 minutes). Smear images were captured with the Leica morpho densitometric complex: DM 1000 microscope, DFC-320 camera, images were obtained in jpg format at 400x magnification. Results The study encompassed 30 patients divided into control and experimental groups. Cytological analysis was conducted on the 2nd, 5th, and 10th days of post- operation. In both groups, on the 2nd day, alveolar smears showed the presence of erythrocytes (n = 6) and neutrophils (n = 2). On the 5th day, the experimental group exhibited neutrophils (n = 3) and abundant cocci microflora (n = 6). On the 10th day, epitheliocytes (n = 8) primarily indicated proliferation. Conclusions The obtained results underscore the effectiveness of employing osteoplastic synthetic material in combination with plateletrich gel and plasma enriched with thrombocyte factors for enhancing local osteogenesis following atypical removal of the third molar. This approach demonstrates significant elevation in epithelialization activity, fibroblastic activity, and other markers of the reparative process in comparison to the control group.

Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study

BackgroundDeep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future.MethodsA total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions. The labeled images were resized to 640 × 320 while maintaining their aspect ratio before model training. The labeled images were randomly divided into three groups (Training:349 images (1589 labels), Validation:43 images (181 labels), Test:43 images (215 labels)). YOLOv5x algorithm was used to perform deep learning. The segmentation performance of the tested model was visualized and analyzed using ROC analysis and a confusion matrix. True Positive (TP), False Positive (FP), and False Negative (FN) values were determined.ResultsAmong the test group images, there were 133 TPs, 36 FPs, and 82 FNs. The model’s performance metrics include precision, recall, and F1 score values of detecting white spot lesions were 0.786, 0.618, and 0.692. The AUC value obtained from the ROC analysis was 0.712. The mAP value obtained from the Precision-Recall curve graph was 0.425.ConclusionsThe model’s accuracy and sensitivity in detecting white spot lesions remained lower than expected for practical application, but is a promising and acceptable detection rate compared to previous study. The current study provides a preliminary insight to further improved by increasing the dataset for training, and applying modifications to the deep learning algorithm.Clinical revelanceDeep learning systems can help clinicians to distinguish white spot lesions that may be missed during visual inspection.

PERANCANGAN DAN IMPLEMENTASI PHOTOSTOCK DI SEKOLAH SMP CHARITAS BATAM DENGAN PENERAPAN METODE 4D (DEFINE, DESIGN, DEVELOP, DISSEMINATE)

Charitas Batam Junior High School, an A accredited institution in Batam City, Riau Islands, was established in 2007 with a vision of providing transformative, compassionate, and fraternal education. In order to maintain its commitment to delivering innovative and high-quality education, the school is currently facing challenges concerning the insufficient quality and variety of suitable photo stock, necessitating updates. This study aims to enhance the school's operational sustainability through the enhancement by creating a new photostock repository, which will be applies the 4D method (Define, Design, Develop, Disseminate) and stored in a Google Drive folder presented as .jpg format. The photostock design process involves the use of Adobe Photoshop 2021 and Adobe Lightroom Classic software. The implementation of the photostock is expected to streamline the school's operational processes.

Quantitative anatomy of the extensor digiti minimi muscle in the growing human fetus.

Introduction Age-specific reference intervals for the extensor digiti minimi muscle (EDMM) in the human fetus may be relevant in the detailed evaluation of the musculoskeletal systems with potential relevant aspects for surgical treatment. The aim of the study was to examine the age-specific reference intervals and growth dynamics of the EDMM in relation to its length, width, projection surface area and volume. Material and methods The examined material included 70 human formalin-fixed fetuses of both sexes (37♀, 33♂) aged from 17 to 29 weeks. With the use of anatomical dissection every EDMM was visualized, recorded in a form of JPG formats and analyzed by the digital image analysis system and statistical methods. Results No variability of the EDMM was found. All the morphometric parameters of the EDMM revealed neither sex nor laterality differences. With fetal age most linear parameters of the EDMM concerning its examined lengths and widths increased in accordance with natural logarithmic functions. The only two exceptions to this referred to the belly width of EDMM measured at its mid-length and the tendon width of EDMM measured proximal to the extensor retinaculum of wrist, which both followed square root functions. The projection surface areas of the EDMM followed natural logarithmic functions, while the volumetric growth of the EDMM was proportionate to fetal age. Conclusions The variability of the EDMM in the human fetus is minimal. The morphometric data of the EDMM represents age-specific reference intervals of clinical significance. Morphometric parameters of the EDMM reveal neither sex nor laterality differences. The EDMM displays three different growth dynamics: from gradual growth deceleration according to both natural logarithmic functions (total length of the muscle and its tendons, belly length, tendon lengths, belly width at its origin, tendon width at its insertion, and projection surface areas) and square root functions (belly width at its mid-length and tendon width in the pre-retinacular segment) to a proportionate growth (total volume).

Edge impulse-based convolutional neural network for Hand Posture Recognition

Hand Posture Recognition (HPR) plays a crucial role in enabling effective human-computer interaction, particularly for individuals with hearing disabilities. The study compares five models, including MobileNetV2 96x96 0.35, MobileNetV1 96x96 0.25, MobileNetV1 96x96 0.1, self-designed Network 1, and self-designed Network 2, based on the Sbastien Marcel Static Hand Posture Database. Evaluation metrics - infserencing time, peak RAM usage, flash usage, and accuracy - are used to analyze the performance. The experiment workflow for each model comprises five major steps. Firstly, a random selection of 120 images from the Sbastien Marcel Static Hand Posture Database is converted to JPG format. Then, the images are divided into 80% training data and 20% testing data. Subsequently, the original images are normalized, and features are extracted for further processing. Subsequently, the models are individually trained using the preprocessed data, optimizing their parameters. Finally, the trained models are evaluated using the testing data set to assess their performance in hand posture recognition. The results indicate that MobileNetV2 96x96 0.35 achieves the highest accuracy of 96.69% while consuming fewer hardware resources compared to other models. MobileNetV1 96x96 0.1 demonstrates the lowest inferencing time and peak RAM usage, making it suitable for real-time applications. Furthermore, self-designed Model 1 exhibits the lowest flash usage, making it a viable option for resource-constrained devices. This study provides valuable insights into the selection of CNN architectures for HPR, offering guidance for practitioners to choose models based on specific application requirements.

Identification of Fencing Athletes Based on Anthropometric Measurements Using MediaPipe Pose

Over time, numerous developments in digital technology have benefited people, including anthropometric measurements that provide information on an athlete’s ability in sports. The use of digital technology in sports must continue, particularly in the National Sports Committee of Indonesia (Komite Olahraga Nasional Indonesia, KONI) of Bandung City. This study proposed a technique for classifying and identifying fencing athletes’ talents. This work developed a methodology for evaluating sports talent based on anthropometric measurements of athletes’ bodies using the posture detection approach. Fencing and nonfencing athletes in KONI Bandung City were categorized using this talent identification. This study used 36 datasets of body posture images from various skills of the sport. These images were in JPEG or JPG format with a resolution of 3,024 × 4,032 and were acquired using a Canon EOS 1300D camera. This study utilized four points landmarks, which are usually used as measurement components in KONI, to categorize fencing athletes. The four points are shoulder (S), elbow (E), index (I), and hip (H) landmarks. The testing was done using three different dataset settings. According to the test results of all scenarios, scenario 2 had the highest accuracy. This scenario was able to categorize fencing and nonfencing athletes with an accuracy rate of 89% and an average processing time of less than 3 s per image.

Utilization of Spiking Neural Network (SNN) in X-Ray Image for Lung Disease Detection

The large number of cases of lung disease means that doctors have difficulty in making initial diagnoses, making them prone to misdiagnoses. One type of lung disease that is included in the vulnerable category is pneumonia. Early detection of the condition of the lungs affected by bacterial pneumonia can be carried out by screening using the X-Ray examination modality, namely Digital Radiography (DR). However, in practice, the diagnosis process on Citra DR takes a long time because it requires competent medical personnel (specialists). A system is needed that can help medical personnel to speed up the process of diagnosing lung disease and get accurate results so that misdiagnosis does not occur. The aim of this research is to utilize the Spiking Neural Network (SNN) method for classifying lung disease from DR images. The system was created using MATLAB with the initial step of creating a read data program, namely reading DR image secondary data in .jpg format taken from Kaggle.com. This research uses DR image data totaling 200 images. Next, standardize the size to 50 x 50 pixels. Then segmenting the image divides the gray level histogram into two different parts of the image automatically without requiring user assistance to enter threshold values ​​for normal and pneumonia images. Then convert the image to 1 dimension and create a manual program for the training data using 50 normal images and 50 pneumonia images. Lastly, create a program to test the data using 100 normal images and 100 pneumonia images. Based on the results of data testing, a confusion matrix was obtained from 200 images with sensitivity of 87%, specificity of 69%, precision of 73.7288%, recall of 69%, and accuracy of 78%

Algoritma Principal Component Analysis (PCA) dan Metode Bounding Box pada Pengenalan Citra Wajah

Current facial recognition system utilizes the Principal Component Algorithm (PCA) and the Bounding Box method to recognize facial locations based on brightness levels. The problem that was found in the experiment was that unclear or illuminated light factors could cause inaccuracies in facial area recognition. PCA is an algorithm capable of performing dimensional reduction to recognize the face area. The recognition process involves image pre-processing, PCA analysis to produce vectors, and application of a Bounding Box to focus on critical areas. This research contributes to the development of reliable and efficient facial recognition systems, potentially applied in security and access management. The experiment used the Grimace dataset using the .jpg format, with tests on normal brightness and -50 decreases in brightness level. At the decrease in -50, the result shows that the smallest distance value is 3540.1, and the greatest distance is 6849.4 with the average value being 5810.110. face recognition results can recognize face images with the original image

Design and Production Skills of Modern Physics Experiment Report Website

In view of the large number of modern physics experiment reports, the situation is complex, and it is inconvenient to browse, specifically, there are 35 students in class 1, 42 students in class 2, and students do all 12 experiments, but one student chooses to do 9 experiments, and the normal modern physics experiment report has 8 pages, and the pictures provided by the students are some big and some are small; Some are in .jpg format, some are in .png format; some figures are skewed. To this end, it is proposed to make a modern physics experiment report website based on Dreamweaver that can quickly browse all students, and the problem that needs to be solved is that the pictures are named according to the student number, the experimental serial number, and the page serial number, the size of the picture is unified, and the crooked pictures are taken for correction, and the top, left, and main three frame websites are designed to be easy to browse, and the website data produced is large, but neat and easy to browse.

Comparative Deep Learning Models in Applications of Steganography Detection

This paper explores the use of deep learning algorithms in steganography detection. More specifically, it examines deep learning-based binary classification to distinguish between stego and non-stego images from the three steganography algorithms, The Wavelet Obtained Weights (WOW), Spatial Universal Wavelet Relative Distortion (S-UNIWARD), Highly Undetectable Steganography (HUGO). It also highlights the lack of research to develop a practical universal image steganography detection system using trained deep learning. The proposed farmwork combines multiple detection deep learning architectures to create a universal Deep Convolutional Neural Network (Deep-CNN). In this paper, we evaluate Deep-CNN-based image steganography detection techniques trained on images extracted from the three steganography algorithms. The dataset consists of 10,000 images in PGM format, which is converted to JPG format with a size of 256256 pixels. The data set is classified into clear and stego images, which are the same image samples used in each category, with the three separate data sets for stego images created using three algorithms (WOW, S-UNIWARD, and HUGO). The results show a slight decrease in detection accuracy, but the fine-tuning of the improved deep-CNN architecture performs better than other methods.

Recognizing surface edges and prediction of terrain deformation by analyzing LANDSAT-8 raster in QGIS- A case study of Coimbatore, India

Convolutional Neural Network (CNN) is one of the deep learning algorithms generally used for image recognition and allocation. These neural networks are developed in multi-layers which reasonably reduces complex dimensions of any input without ruining original information. The satellite images are obtained in .jpg format with potential resolutions. The land usage of the given area is estimated and the objects present in the image are identified using the Canny Edge detection algorithm. It extracts useful data in terms of structures and scales down the size of the data. Raster data in.tif format from LANDSAT-8 is collected over a year. With the Semi-automated classification plugin (SCP) in QGIS, the signatures are created. Signatures are pixelated polygons that are classified to store land attributes. The Normalized indices of vegetation (NDVI), water (NDWI), and built-up (NDBI) are calculated. Land use land cover area was developed. Multi-layer perceptron has numerous hidden layers, and the iterations can be fixed in the MOLUSCE plugin. The land cover for two years, 2020 and 2023 is given along with spatial variables such as precipitation and elevation. The changes in each category of land are identified. In the last three years, the area covered by buildings has increased from 25% to 31%. The area under water bodies had a slight decrease from 1.46% to 1.25%. The land cover for the year 2026 is predicted. From the predictions, it is conclusive that, our research supports the changes between 3 years had not much difference, but above 6 years, it is evident that land will be deformed from most of the vegetation area into built-up.

Exploration of Species Diversity as Ecological Conditions Vary to Determine the Possible Areas for Collecting Tree Seeds in Amhara Region, Ethiopia

The aim of this study was to determine the forest species trees and shrubs that occur in the Amhara region, Ethiopia, for determining the possible areas for collecting seeds and propagations to meet genetic conservation and use programs as well as the recovery of degraded areas. The study was conducted at Fudi Natural Forest in Fagta Lekoma district, Amhara region, northwestern Ethiopia. Using geographic information systems (GISs), the points of presence of species were plotted on the USGS SRTM map (GTOPO30) at 1 : 250,000 scale (USGS, 2018) using Arc GIS 10.1 software (ESRI, 2011). The map has been cropped and presented only for the citizens that contained points of presence of the species. Figures were generated in the JPG format for each species individually, presenting the distribution of each according to the altitude of the region. The maps were elaborated using multiple linear regressions, relating the bioclimatic variables with the numerical models of latitude, longitude, and altitude. Descriptive statistical analysis was initially performed. This was followed by performing a normality test to observe the data distribution. In the region, 1250 individuals of 32 families and 46 species were surveyed. The families most found in the northeast, north, northwest, southwest, southeast, and west regions were Mimosoideae, Euphorbiaceae, Celastraceae, and Rubiaceae.