Kappa Coefficient Values Research Articles

Revealing Cropping Intensity Dynamics Using High-Resolution Imagery: A Case Study in Shaanxi Province, China

Reliable and continuous information on cropping intensity is crucial for assessing cropland utilization and formulating policies regarding cropland protection and management. However, there is still a lack of high-resolution cropping intensity maps for recent years, particularly in fragmented agricultural regions. In this study, we combined Landsat-8 and Sentinel-2 imagery to generate cropping intensity maps from 2019 to 2023 at a 10 m resolution for Shaanxi Province, China. First, the satellite imagery was harmonized to construct 10-day composite enhanced vegetation index (EVI) time series. Then, the cropping intensity was determined by counting the number of valid EVI peaks within a year. Assessment based on 578 sample points showed a high level of accuracy, with overall accuracy and Kappa coefficient values exceeding 0.96 and 0.93, respectively. We further analyzed the spatiotemporal patterns of cropping intensity and generated a map of abandoned cropland in Shaanxi. The results indicated that cropland in Shaanxi Province was mainly utilized for single-cropping (52.9% of area), followed by double-cropping (35.2%), with non-cropping accounting for 11.9%. Cropping intensity tended to be lower in the north and higher in the south. Temporally, the average cropping intensity of Shaanxi increased from 1.1 to over 1.3 from 2019 to 2023. Despite this upward trend, large areas of cropland were abandoned in northern Shaanxi. These results demonstrate the potential of utilizing Landsat-8 and Sentinel-2 imagery to identify cropping intensity dynamics in fragmented agricultural regions and to guide more efficient cropland management.

How reliable is the distinction between thoracolumbar AO type A3 and A4 fractures? A systematic literature review.

The AOSpine classification divides thoracolumbar burst fractures into A3 and A4 fractures; nevertheless, past research has found inconsistent interobserver reliability in detecting those two fracture patterns. This systematic analysis aims to synthesize data on the reliability of discriminating between A3 and A4 fractures. We searched PubMed, Scopus, and the Web of Science for studies reporting the inter- and intra-observer reliability of detecting thoracolumbar AO A3 and A4 fractures using computed tomography (CT). The search spanned 2013 to 2023 and included both primarily reliability and observational comparative studies. We followed the PRISMA guidelines and used the modified COSMIN checklist to assess the studies' quality. Kappa coefficient (k) values were categorized according to Landis and Koch, from slight to excellent. Of the 396 identified studies, nine met the eligibilitycriteria; all were primarily reliability studies except one observational study. Interobserver k values for A3/A4 fracturesvaried widely among studies (0.19-86). The interobserver reliability was poor in two studies, fair in one study, moderate in four studies, and excellent in two studies. Only two studies reported intra-observer reliability, showing fair and excellent agreement. The included studies revealed significant heterogeneity in study design, sample size, and interpretation methods. Considerable variability exists in interobserver reliability for distinguishing A3 and A4 fractures from slight to excellent agreement. This variability might be attributed to methodological heterogeneity among studies, limitations of reliability analysis, or diagnostic pitfalls in differentiating between A3 and A4. Most observational studies comparing the outcome of A3 and A4 fractures do not report interobserver agreement, and this should be considered when interpreting their results.

Interobserver variability in the histopathological classification and grading of dysplasia in elevated colon lesions in the city of Lima

Colonic polyp refers to lesions that exhibit a protrusion of the mucosa, regardless of histology. The most recent WHO classification is based on a better understanding of these lesions; however, its application in daily practice could be subject to interobserver variability biases that could have clinical implications. Objectives: To determine the interobserver variability in the histopathological reporting and grading of dysplasia of samples obtained from elevated colon lesions in a private laboratory in the city of Lima. Materials and methods: Observational, descriptive, and prospective study: Case series type. All biopsies of elevated colon lesions received over a period of 3 months were evaluated by two observers without clinical information of the cases, to diagnose the lesions according to the WHO classification. In cases of diagnostic differences, the cases were evaluated together to reach a consensus. Results: A Kappa coefficient value of 0.458 was obtained in the diagnostic classification of elevated colon lesions, while a Kappa value of 0.416 in the evaluation of dysplasia; indicating moderate agreement. Conclusions: Despite achieving moderate agreement between evaluators, this work demonstrates the importance of not only relying on morphological criteria for diagnostic classification, but also including criteria of location and size of these lesions to increase diagnostic accuracy.

Synergistic approach for land use and land cover dynamics prediction in Uttarakhand using cellular automata and Artificial neural network

Alterations in Land use and Land cover (LULC) stand out as a key catalyst for shifts in global climate patterns, environmental conditions, and ecological dynamics. In order to further enhance our comprehension of the effects of variability and climate shifts on the environment, Remote sensing and GIS analytical approaches have been thoroughly explored and are reflected in an imperative vision. Thus, the objective of this study is to model Uttarakhand’s LULC pattern for 2032 and analyse changes in Uttarakhand's LULC trend between 1992 and 2022. LULC change mapping was conducted utilizing a semi-automated hybrid classification approach for high level of accuracy which integrates both Maximum likelihood and Object based image analysis classification techniques on Landsat datasets. The machine learning techniques of Cellular automata and Artificial neural networks (CA-ANN) within MOLUSCE plugin of QGIS have been applied to model future LULC patterns. The accuracy assessment results showed that the overall accuracy of LULC for the years 1992, 2002, 2012, and 2022 was 96.94 %, 97.77 %, 98.61 % and 98.87 % respectively, and the overall kappa statistics coefficient was 0.92, 0.95, 0.94 and 0.95 respectively. The simulated and projected LULC map for 2022 implies a substantially high level of accuracy, with an overall Kappa coefficient value of 0.77 % and 85.39 % of overall correctness. Then, LULC changes for the year 2032 are predicted using CA-ANN. The observed alterations between 1992 and 2032 are significant, characterized by an augmentation in built-up areas, open land, and water bodies, alongside a decline in snow-covered regions, vegetation cover. Whereas, a slight increase is seen in Forested areas. Planners and policy makers aiming to accomplish a more sustainable future and efficient management of the environment will find the changes in LULC over a prolonged period of time to be a useful asset for optimal land use planning.

Fresh Meat Classification Using Laser-Induced Breakdown Spectroscopy Assisted by LightGBM and Optuna.

To enhance the accuracy of identifying fresh meat varieties using laser-induced breakdown spectroscopy (LIBS), we utilized the LightGBM model in combination with the Optuna algorithm. The procedure involved flattening fresh meat slices with glass slides and collecting spectral data of the plasma from the surfaces of the fresh meat tissues (pork, beef, and chicken) using LIBS technology. A total of 900 spectra were collected. Initially, we established LightGBM and SVM (support vector machine) models for the collected spectra. Subsequently, we applied information gain and peak extraction algorithms to select the features for each model. We then employed Optuna to optimize the hyperparameters of the LightGBM model, while a 10-fold cross-validation was conducted to determine the optimal parameters for SVM. Ultimately, the LightGBM model achieved higher accuracy, macro-F1, and Cohen's kappa coefficient (kappa coefficient) values of 0.9370, 0.9364, and 0.9244, respectively, compared to the SVM model's values of 0.8888, 0.8881, and 0.8666. This study provides a novel method for the rapid classification of fresh meat varieties using LIBS.

Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms

In this study, the satellite data of ASTER and Landsat 8 OLI were used for the discrimination of lithological units covering the Khyber range. Of the 24 tested band combinations, the most suitable include 632 and 468 of ASTER and 754 and 147 of OLI in the RGB sequence. The data were also tested with two conventional machine learning algorithms (MLAs), namely maximum likelihood classification (MLC) and support vector machine (SVM), for lithological mapping. Principal component analysis (PCA), minimum noise fraction (MNF), band ratios, and color composites in combination with available lithological maps and field data were utilized for training sample collection for the MLC and SVM models to classify the lithological units. The accuracy assessment of SVM and MLC was performed using a confusion matrix, which revealed a higher accuracy of 74.8419% and 72.1217% for ASTER and an accuracy of 58.4833% and 60.0257% for OLI, respectively. The results indicate that ASTER imagery is more suitable for lithological discrimination in the study area due to its high spectral resolution in the VNIR to SWIR range. The experiment revealed that the SVM classification offered the highest overall accuracy of nearly 75% and the kappa coefficient value of 0.7 on ASTER data. This demonstrates the effectiveness of SVM classification in exploring lithological mapping in dry to semi-arid regions.

Preclinical and Preliminary Evaluation of Perceived Image Quality of AI-Processed Low-Dose CBCT Analysis of a Single Tooth.

This study assessed AI-processed low-dose cone-beam computed tomography (CBCT) images for single-tooth diagnosis. Human-equivalent phantoms were used to evaluate CBCT image quality with a focus on the right mandibular first molar. Two CBCT machines were used for evaluation. The first CBCT machine was used for the experimental group, in which images were acquired using four protocols and enhanced with AI processing to improve quality. The other machine was used for the control group, where images were taken in one protocol without AI processing. The dose-area product (DAP) was measured for each protocol. Subjective clinical image quality was assessed twice by five dentists, with a 2-month interval in between, using 11 parameters and a six-point rating scale. Agreement and statistical significance were assessed with Fleiss' kappa coefficient and intra-class correlation coefficient. The AI-processed protocols exhibited lower DAP/field of view values than non-processed protocols, while demonstrating subjective clinical evaluation results comparable to those of non-processed protocols. The Fleiss' kappa coefficient value revealed statistical significance and substantial agreement. The intra-class correlation coefficient showed statistical significance and almost perfect agreement. These findings highlight the importance of minimizing radiation exposure while maintaining diagnostic quality as the usage of CBCT increases in single-tooth diagnosis.

Land use and land cover changes and their driving forces in selected forest reserves in Central Tanzania

Miombo woodlands benefit humans in a variety of ways, including by providing food and ecosystem services. However, Tanzania's Miombo woodlands are declining due to ever-increasing unsustainable human activities. This study used landsat images for the years 1996, 2006, and 2021 to quantify the extent of land use and cover change in the Mgori and Minyughe forest reserves of the Singida region in Central Tanzania. We conducted interviews with 154 villagers living near the forest reserves to determine the drivers of land use and cover change. The findings showed that increased cultivation, illegal settlements, forest harvesting for timber, charcoal production, and livestock grazing are the primary drivers of land use and cover changes. Over the study period, cultivated land in the Mgori forest reserve increased by 33.56%, settlements by 32.67%, and bushland by 0.52%, while woodlands and grassland decreased by −0.60% and −0.22%, respectively. In the Minyughe forest reserve, cultivated land has grown by 23.81%, scattered cultivation by 16.79%, settlements by 6.58%, and water by 1.02%. During the same time period, significant loss occurred in woodland with a dynamic index of (−3.50%), bushland (−0.75%), grassland (−0.40%), and seasonally inundated grassland (−1.86%). User accuracy was highly reliable in cultivated land, bushland, woodland, and grassland cover. The overall Kappa coefficient values provided substantial accuracy for a specific land cover category. In order to combat deforestation and forest cover loss, the study recommends that the Tanzanian government strengthen conservation in both forest reserves.

A comparative study of SAR-CoV-2 detection using RT-PCR based testing from a nasopharyngeal swab versus indwelling tracheostomy in patients with a tracheostomy

Background Nasopharyngeal swab RT-PCR is the standard procedure for COVID-19 testing. In patients with tracheostomy with an altered airway that bypasses the nasopharynx, the yield of samples from different sites is unknown. This information will be essential for formulating a sample collection method for COVID-19 RT-PCR testing in patients undergoing tracheostomy. Methods This was a cross-sectional study. Nasopharyngeal swabs and tracheal secretion (via closed-circuit secretion suction) samples were collected from 100 patients with a non-plugging tracheostomy tube at Thammasat University Hospital, Pathum Thani, Thailand. The participants were aged between 1-96 years old for screening purposes during the pandemic. The detection results and cycle threshold (Ct) values from each site were analyzed using McNemar’s test with a 95% confidence interval. Results Four participants had positive results. One patient tested positive only for the nasopharyngeal swab, whereas the other tested positive only for the tracheal secretion sample. No statistically significant difference was found between the discordant and concordant groups (P = 1). Conclusions We found two discordant results among the four positive cases in the 100 patients. One patient tested positive only from the nasopharyngeal swab, whereas the other tested positive only from the tracheal sample. The percentage of agreement was 98, and the kappa coefficient value was 65.64% (p <0.001). According to these results, one sample from the nasopharynx or tracheal tube should be sufficient to determine the infection status of low-risk patients. For highly suspicious cases, multisite sampling should be performed. This study showed discordance in COVID-19 RT-PCR screening results using samples from nasopharyngeal swabs and tracheal secretions in tracheotomized patients. A multiple-site sample is suggested for highly suspicious patients with tracheostomy.

How natural are the forests in Rajiv Gandhi (Nagarhole) Tiger Reserve? A multi-source data approach.

This study aimed to monitor long-term land use dynamics for understanding the natural forestintegrity and intactness of the Rajiv Gandhi (Nagarhole) Tiger Reserve (RTR) pre- and post-declarations as TR. We employed multi-source data from Survey of India Toposheets (1:50k), Landsat-7, and Sentinel-2A along with Global Ecosystem Dynamics Investigation (GEDI) vegetation canopy height (10m) data, a high-spatial resolution CORONA (1970) images and temporal Google Earth Pro images for mapping and validation. To map vegetation type, land use and land cover (LULC) transitions, and fragmentation (1980-2022) we employed a hybrid classification approach. This study also analyzed decadal forest dynamics within TRsusing India's State of Forest Reports (ISFR). Findings reveal significant forest fragmentation and habitat loss due to anthropogenic activities in the TR. Mono-plantations (teak and eucalyptus) were found inside TR, while the buffer (1km) was occupied mainly with coffee and orange plantations which indicates the prevalence of human footprint. The overall accuracy of the current period (2022) is 92.0% with a kappa coefficient value of 0.90. These plantations were established during the British colonial period (early 1900s) for commercial purposes by clearing natural forests. The present study highlights that mono-plantations have not transitioned into natural forests even after a century. This lack of transformation could potentially compromise the integrity of the native forest. Despite its ecological significance, RTR has experienced disturbance due to human footprint. Hence, there is a need for an action plan to protect this vital landscape by replacing mono-plantations with suitable species to preserve the integrity of the forest. These issues extend beyond the protected areas, impacting surrounding regions and require regular monitoring. The proposed methods can be applied to other protected areas facing similar problems in the countryand world.

Non-suicidal self-injury at a Canadian paediatric emergency department.

Our primary objective was to determine agreement between non-suicidal self-injury recorded at triage and during subsequent mental health assessment. The secondary objective was to describe patients who reported non-suicidal self-injury. This is a health records review of patients aged 12-18years who had an Emergency Mental Health Triage form on their health record from an ED visit June 1, 2017-May 31, 2018. We excluded patients with diagnoses of autism spectrum disorder or schizophrenia. We abstracted data from the Mental Health Triage form, Emergency Mental Health and Addictions Service Assessment forms and Assessment of Suicide and Risk Inventory. We calculated Cohen's Kappa coefficient, sensitivity, and negative predictive value to describe the extent to which the forms agreed and the performance of triage for identifying non-suicidal self-injury. We compared the cohort who reported non-suicidal self-injury with those who did not, using t-tests, Wilcoxon rank-sum tests, and chi-square tests. We screened 955 ED visits and included 914 ED visits where 558 (58.4%) reported a history of non-suicidal self-injury. There were significantly more females in the group reporting non-suicidal self-injury (82.1%, n = 458) compared to the group not reporting non-suicidal self-injury (45.8%, n = 163). Patients reporting non-suicidal self-injury did so in triage and detailed Mental Health Assessment 64.7% of the time (Cohen's Kappa Coefficient 0.6); triage had sensitivity of 71.5% (95% CI 67.3-75.4) and negative predictive value of 71.2% (95% CI 68.2-74.0). Cutting was the most common method of non-suicidal self-injury (80.3%). Screening at triage was moderately effective in identifying non-suicidal self-injury compared to a detailed assessment by a specialised mental health team. More than half of children and adolescents with a mental health-related concern in our ED reported a history of non-suicidal self-injury, most of which were female. This symptom is important for delineating patients' coping strategies.

Evaluating ASTER data and field spectrometry for lithological discrimination in semi-arid region, Northeast Kohat Plateau, Pakistan

This study employs data of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor to delineate and map the distribution of sedimentary lithologies in the semi-arid region of Kohat Plateau, Pakistan. False color composites (FCC) and various image transformation and enhancement techniques including the principal component analysis (PCA), minimum noise fraction (MNF), and band rationing (BR) were used successfully to differentiate four lithological classes. These lithologies include chemically/biochemically formed beds of the marine environment and detrital sequences of marginal marine to the riverine environment. FCC from original reflectance data, PCA, and BR techniques displayed more prominent lithological variation. To map the lithology and show the potential of ASTER data, field spectrometry over the barren lithologies was carried out. The end-member spectra from field spectrometry shows strong agreement with the pixels spectra from ASTER scene. The Spectral Angle Mapper (SAM) mapping method was then used to produce a classified lithological map, where the image pixels spectra proved more suitable reference, in comparison to the end-member spectra. The accuracy of the classified lithological map was evaluated based on field-based point data, which resulted an overall accuracy of 70% and a Kappa coefficient value of 0.679. Carbonates and evaporites showed relatively higher user and producer accuracies which are attributed to their topographic behavior and weathered scree over the adjacent rock unit. The final lithological map provided a clearer picture of surface geology where the existing geological maps lacked lithological continuity.

Effect of intracavitary electrocardiographic localization on the success rate and complications of PICC in infants.

Peripherally inserted central catheter (PICC) is widely used in chemotherapy of children with malignant tumors because of its safe operation and long indwelling time. To investigate the effect of intracavitary electroencephalogram (CEEG) localization technique on the success rate and complications of PICC in infants. A total of 180 children with PICC catheterization and maintenance at Shijiazhuang People's Hospital First Hospital from January 2017 to January 2020 were selected and divided into control group (n= 90 cases) and observation group (n= 90 cases). The control group observed the tip position of the fixed catheter through X-ray film and adjusted the catheter until its tip was located in the superior vena cava. The observation group used intracavitary electrocardiogram positioning technology. Comparison of the effects of two groups on the success rate and complications of PICC puncture in infants and young children. The success rate of one puncture in the observation group was significantly higher than that in the control group (P< 0.05). Within one month of catheterization, 13 cases had complications, with an incidence rate of 16.00% lower than the control group's 34.00% (27/80) (P< 0.05). The screening test results showed that the specificity, sensitivity, Youden index, accuracy, kappa coefficient, positive and negative predictive value were 88.89%, 97.56%, 0.86, 96.00%, 0.86, 0.86, respectively. The measured values were 97.56% and 88.89% respectively, and the cost and time of localization were lower than those of X-ray. The technique of intracavitary electrogram can be more accurate for infants to place the tip of central venous catheter through peripheral vein, which can effectively improve the success rate of one puncture with low cost, and has high reliability, accuracy and practicability, which is safe and effective.

Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data

The rapid increase in population accelerates the rate of change of Land use/Land cover (LULC) in various parts of the world. This phenomenon caused a huge strain for natural resources. Hence, continues monitoring of LULC changes gained a significant importance for management of natural resources and assessing the climate change impacts. Recently, application of machine learning algorithms on RS (remote sensing) data for rapid and accurate mapping of LULC gained significant importance due to growing need of LULC estimation for ecosystem services, natural resource management and environmental management. Hence, it is crucial to access and compare the performance of different machine learning classifiers for accurate mapping of LULC. The primary objective of this study was to compare the performance of CART (Classification and Regression Tree), RF (Random Forest) and SVM (Support Vector Machine) for LULC estimation by processing RS data on Google Earth Engine (GEE). In total four classes of LULC (Water Bodies, Vegetation Cover, Urban Land and Barren Land) for city of Lahore were extracted using satellite images from Landsat-7, Landsat-8 and Landsat-9 for years 2008, 2015 and 2022, respectively. According to results, RF is the best performing classifier with maximum overall accuracy of 95.2% and highest Kappa coefficient value of 0.87, SVM achieved maximum accuracy of 89.8% with highest Kappa of 0.84 and CART showed maximum overall accuracy of 89.7% with Kappa value of 0.79. Results from this study can give assistance for decision makers, planners and RS experts to choose a suitable machine learning algorithm for LULC classification in an unplanned urbanized city like Lahore.

Vegetation Type Mapping in Southern Patagonia and Its Relationship with Ecosystem Services, Soil Carbon Stock, and Biodiversity

Vegetation Type (VT) mapping using Optical Earth observation data is essential for the management and conservation of natural resources, as well as for the evaluation of the supply of provisioning ecosystem services (ESs), the maintenance of ecosystem functions, and the conservation of biodiversity in anthropized environments. The main objective of the present work was to determine the spatial patterns of VTs related to climatic, topographic, and spectral variables across Santa Cruz province (Southern Patagonia, Argentina) in order to improve our understanding of land use cover at the regional scale. Also, we examined the spatial relationship between VTs and potential biodiversity (PB), ESs, and soil organic content (SOC) across our study region. We sampled 59,285 sites sorted into 19 major categories of land cover with a reliable discrimination level from field measurements. We selected 31 potential predictive environmental dataset covariates, which represent key factors for the spatial distribution of land cover such as climate (four), topography (three), and spectral (24) factors. All covariate maps were generated or uploaded to the Google Earth Engine cloud-based computing platform for subsequent modeling. A total of 270,292 sampling points were used for validation of the obtained classification map. The main land cover area estimates extracted from the map at the regional level identified about 142,085 km2 of grasslands (representing 58.1% of the total area), 38,355 km2 of Mata Negra Matorral thicket (15.7%), and about 25,189 km2 of bare soil (10.3%). From validation, the Overall Accuracy and the Kappa coefficient values for the classification map were 90.40% and 0.87, respectively. Pure and mixed forests presented the maximum SOC (11.3–11.8 kg m−2), followed by peatlands (10.6 kg m−2) and deciduous Nothofagus forests (10.5 kg m−2). The potential biodiversity was higher in some shrublands (64.1% in Mata Verde shrublands and 63.7% in mixed shrublands) and was comparable to those values found for open deciduous forests (Nothofagus antarctica forest with 60.4%). The provision of ESs presented maximum values at pure evergreen forests (56.7%) and minimum values at some shrubland types (Mata Negra Matorral thicket and mixed shrubland) and steppe grasslands (29.7–30.9%). This study has provided an accurate land cover and VT map that provides crucial information for ecological studies, biodiversity conservation, vegetation management and restoration, and regional strategic decision-making.

Spatiotemporal changes of desertification degree in the Algerian green barrier over the last four decades (1984–2020)

Desertification is a major challenge for the sustainable development of arid and semiarid regions worldwide. The major novelty of this study lies in assessing, for the first time, the desertification degree changes in the Algerian green barrier using remote sensing and the Analytic Hierarchy Process (AHP) approach from 1984 through 2020. Four widely used remote sensing indices (LSA, NDVI, TGSI, and PDSI) were used in the Google Earth Engine (GEE) cloud computing platform as indicators of desertification in the study area. The results of the accuracy assessment of the generated desertification degree maps for 1984, 2000, and 2020 show high Kappa coefficient values exceeding 80%. Overall, the degree of desertification of the green barrier increased from 1984 to 2020. The area of medium degree has decreased from 15393.21 km2 (37.70%) to 9624.43 km2 (23.57%) between 1984 and 2020, respectively; whereas the area of high degree has increased from 23907.08 km2 (58.55%) to 30502.52 km2 (74.70%) for the same period. The combination of remote sensing and the AHP technique has proven to be effective in the spatiotemporal monitoring of the desertification degree particularly in data–scarce regions. Our findings emphasize the high expansion of desertification across the green barrier area, which information is essential for adopting adequate strategies for desertification control in the future.

Temporal Analysis of Mangrove Forest Extent in Restoration Initiatives: A Remote Sensing Approach Using Sentinel-2 Imagery

The significance of mangroves and the associated risks they face have prompted government and the private sector to invest in projects aimed at conserving and restoring mangroves. Despite this interest, there is currently little information available on the effectiveness of these investments in mangrove conservation and restoration efforts. Therefore, this study aimed to use Sentinel-2 imagery with 10-m resolution through the Google Earth Engine to evaluate the effectiveness of these projects in mangrove areas in two regions: the Tahiry Honko project in Madagascar and the Abu Dhabi Blue Carbon Demonstration Project in the United Arab Emirates. The study compared the U-Net and SVM for mangrove classification. The U-Net model demonstrated superior performance, achieving an accuracy of 90%, with a Kappa coefficient value of 0.84. In contrast, the SVM had an overall accuracy of 86% and Kappa coefficient of 0.78. The analysis of changes in the mangrove area using U-Net model revealed a decline of 355 ha over four years in the Tahiry Honko project, while in the Abu Dhabi Project, the mangrove area increased by 5857 ha over 5 years. These findings can provide valuable information for policy-makers and management strategies.

ATSFCNN: a novel attention-based triple-stream fused CNN model for hyperspectral image classification

Abstract Recently, the convolutional neural network (CNN) has gained increasing importance in hyperspectral image (HSI) classification thanks to its superior performance. However, most of the previous research has mainly focused on 2D-CNN, and the limited applications of 3D-CNN have been attributed to its complexity, despite its potential to enhance information extraction between adjacent channels of the image. Moreover, 1D-CNN is typically restricted to the field of signal processing as it ignores the spatial information of HSIs. In this paper, we propose a novel CNN model named attention-based triple-stream fused CNN (ATSFCNN) that fuses the features of 1D-CNN, 2D-CNN, and 3D-CNN to consider all the relevant information of the hyperspectral dataset. Our contributions are twofold: First, we propose a strategy to extract and homogenize features from 1D, 2D, and 3D CNN. Secondly, we propose a way to efficiently fuse these features. This attention-based methodology adeptly integrates features from the triple streams, thereby transcending the former limitations of singular stream utilization. Consequently, it becomes capable of attaining elevated outcomes in the context of hyperspectral classification, marked by increased levels of both accuracy and stability. We compared the results of ATSFCNN with those of other deep learning models, including 1D-CNN, 2D-CNN, 2D-CNN+PCA, 3D-CNN, and 3D-CNN+PCA, and demonstrated its superior performance and robustness. Quantitative assessments, predicated on the metrics of overall accuracy (OA), average accuracy (AA), and kappa coefficient (κ) emphatically corroborate the preeminence of ATSFCNN. Notably, spanning the three remote sensing datasets, ATSFCNN consistently achieves peak levels of OA, quantified at 98.38%, 97.09%, and 96.93% respectively. This prowess is further accentuated by concomitant AA scores of 98.47%, 95.80%, and 95.80%, as well as kappa coefficient values amounting to 97.41%, 96.14%, and 95.21%.

Dual Vegetation Index Analysis and Spatial Assessment in Kota Bharu, Kelantan using GIS and Remote Sensing

Vegetation serves as an indicator of environmental conditions in ecological classifications. In addition, vegetation index analysis can also benefit farmers and agricultural planners by optimising crop selection and irrigation practices. The spatial distribution of healthy vegetation can increase agricultural productivity. This study focuses on the Kota Bharu district in the state of Kelantan, Malaysia that aims to recognise the vegetation indices Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI). NDVI analysis measures reflected visible and near-infrared light to identify and evaluate living green plants. The Green Normalized Difference Vegetation Index (GNDVI) has a higher saturation threshold and is more sensitive to plant chlorophyll levels than NDVI. This approach works in agricultural environments with dense canopies or advanced crop development. The average accuracy level for NDVI 2023 is 78% while the average accuracy level for GNDVI 2023 is 76%. The value of kappa coefficient for NDVI and GNDVI for 2023 respectively are 0.73 and 0.72 which considered to be acceptable and represents the good correspondence.

Application of Metacombination Technique in the Financial Flow Based on Blockchain Technology in the Hospital Ecosystem

Using blockchain technology, citizens can participate in health study programs. In addition, better research and shared data on public welfare will enhance treatment for diverse communities. A centralized database is used to manage the entire health care system and organizations. The aim of the research is to apply the hybrid technique for financial flow based on blockchain technology in the hospital ecosystem. The researcher has analyzed the results and findings of previous researchers by applying a systematic review approach and identified the effective factors by performing the 7 steps of the Sandelovski and Barroso method. Among the 270 articles, 25 articles were selected based on the CASP method, and the validity of the analysis was confirmed with the Kappa coefficient value of 0.776. In this context, in order to measure reliability and quality control, the transcription method was used, and its value was identified for the indicators identified at the level of excellent agreement. The results of data analysis collected in MAXQDA software led to the identification of 42 primary codes in 9 categories. Based on the metacomposition technique, 9 categories were also categorized based on these concepts. The 9 categories are: access control and privacy, data integration, emergency and specific healthcare, data compatibility, governance and compliance, payment management, cost management, asset management and medication management. The use of blockchain technology in the hospital ecosystem, as a new solution in financial and operational flow management, leads to significant benefits. This technology allows hospitals to track their assets and drugs more accurately and provide more efficient management of supply and resource utilization. Also, by providing a reliable platform for recording and transferring various data, blockchain can play an effective role in creating digital governance systems and increasing data privacy. In addition, blockchain is also very effective in managing hospital payments and costs, as it can help track and verify payments in a transparent and highly reliable manner. This technology can also be effective in increasing the hospital's financial efficiency and reducing resource wastage, which improves healthcare services and enhances the patient experience.