Kappa Coefficient Values Research Articles

Agricultural crop pattern mapping and change analysis at a sub-district level in South-eastern region, Bangladesh using Landsat satellite data from 2010 to 2019

The study first time identified and analyzed winter season agricultural crop patterns (ACP) derived from Land use (LU) maps in between 2010 to 2019 of south-eastern regions of Chittagong, Bangladesh. ACP identification was a challenging task in the worldwide research relevant to crop-related studies. To overcome this, we have considered frequently used traditional unsupervised classifier, such as K-means clustering algorithm technique. This has been applied on 30m pixel Landsat satellite reflectance images to identify crop pattern of the study area using the ENVI 5.3 and ArcGIS 10.8 software’s, respectively. Multiple crops with seven classes were identified with the validation of in-situ ground-truth data and Google Earth (GE) images. The overall accuracy and kappa coefficient values were found at 81.96% and 0.79, respectively. The results suggest a significant variation of crop patterns in the study area and in recent time, the area largely dependent on mixed irrigation approach. Moreover, the crop pattern change was observed in the studied period as mixed crop 19% (9282.17 ha), Lentils (Pelon) 24.80% (11594.38 ha), Melon (Bangi) 22.37% (10461.08 ha), Chilis 17.90% (8367.48 ha), Paddy rice, unused land, and other crops, respectively. Among them, Lentils (Pelon) and Melon (Bangi) are identified as two common crops followed by mixed crops category, cultivated in the winter season as it required less irrigation compared to paddy rice area

Comparison of contralateral acceleromyography and electromyography for posttetanic count measurement.

Electromyography has advantages over mechanomyography and acceleromyography. Previously, agreement of the train-of-four counts between acceleromyography and electromyography was found to be fair. The objective of this study was to assess the agreement of posttetanic count including agreement of neuromuscular blockade status (intense block; posttetanic count = 0 or deep block; posttetanic count ≥ 1 and train-of-four count = 0) between acceleromyography and electromyography. Thirty-six patients, aged 20-65 years, participated in this study. Rocuronium 0.6 mg/kg, with additional dose of 0.3 mg/kg if required, was administered to the patients. The train-of-four and posttetanic counts were monitored in the contralateral arm using electromyography at the first dorsal interosseus or adductor pollicis, and acceleromyography at the adductor pollicis. Posttetanic count measurements were performed at 6 min intervals; the responses were recorded until the train-of-four count reached 1. We evaluated the agreement of degree of neuromuscular blockade (intense or deep block) and that of posttetanic count between acceleromyography and electromyography. We analyzed 226 pairs of measurements. The percentage agreement indicating the same neuromuscular blockade status (intense or deep block) between acceleromyography and electromyography was 73%. Cohen's kappa coefficient value was 0.26. After excluding data with acceleromyography-posttetanic counts over 15, 184 pairs of posttetanic counts were used to evaluate the agreement between the two monitoring methods. For acceleromyography-posttetanic count, 42 (23%) pairs had the same electromyography-posttetanic count, and 93 (50%) pairs had more than the electromyography-posttetanic count. The mean posttetanic count on electromyography was 38% (95% CI: 20% to 51%) lower than that on acceleromyography (P = 0.0002). Acceleromyography frequently counted more twitches than electromyography in posttetanic count monitoring. Acceleromyography- and electromyography-posttetanic counts cannot be used interchangeably to assess the degree of neuromuscular blockade.

Simulation analysis of rural land use using rate of change driven by population and economic dynamics - A case study of Huangguashan village in Chongqing, China

The conflicts between various types of land use in rural areas of China are becoming more and more obvious, therefore, how to more accurately simulate and predict the future land cover changes in rural areas has become an important topic of current academic research. In this research, the dynamic multi-year data was used to replace the static one-year data and brought into the future land use simulation (FLUS) model for the simulation of land use in Huangguashan village, Chongqing, China. The accuracy results of the two sets of simulations are compared in terms of accuracy kappa coefficient and Overall Accuracy (OA). Next, using the improved FLUS model, four different future development scenarios of priority ecological conservation, priority economic development, priority farmland conservation and priority integrated development were set up to simulate the land use data of Huangguashan village in 2030. The results of the research found that (1) the kappa coefficient and OA values were higher for the simulation of FLUS model using dynamic multi-year data. The simulation results are closer to the actual situation, especially the simulation enhancement effect is more obvious for the long-term; (2) by comparing the land use types of four different future development scenarios, it is suggested that Huangguashan village should adopt the integrated development priority scenario as the direction of future policy formulation and planning development in the future. This research can provide more effective scientific decision support and significance for governments to formulate multi-scheme rural land use development policies.

Identifying hair fescue in wild blueberry fields using drone images for precise application of granular herbicide

Hair fescue is a common perennial grass weed in wild blueberry fields in Nova Scotia, Canada and reduces yield if not adequately controlled. Hair fescue is typically managed with a broadcast propyzamide application which is costly for growers. Current research on the management of hair fescue in wild blueberry fields focuses on the development of a precision granular herbicide applicator with individual nozzle control. The integration of aerial images could benefit current work by providing a large-scale perspective on hair fescue location. This research suggests a new strategy to identify infield areas with hair fescue infestation, generate prescription maps, and integrate them into a widely available navigation system of heavy farming equipment to improve the processing workflow of hair fescue detection on a larger scale. This method proposes the integration of multi-resolution segmentation on multispectral (Blue, Green, Red, Red Edge, Near-Infrared) drone images to group hair fescue plants into segments with similar spectral characteristics. The best performing segmentation was classified and tested on wild blueberry fields in Nova Scotia. Results were assessed by comparing classified boundaries to reference data collected with high resolution RGB images. Findings from hair fescue and wild blueberry spectral profiles showed that hair fescue could be identified using blue, green, and red bands and showed no advantages over multispectral cameras. Results from the classification of wild blueberry and hair fescue showed high Kappa coefficient values of 0.90 and 0.94 and overall accuracy of 95 and 97% were achieved for both fields surveyed. Results also demonstrated an average potential cost saving of $CAD1246.43 ha-1 when compared to a broadcast application of granular dichlobenil. This work represents the first attempt to integrate aerial imagery and a precision granular herbicide applicator to manage important weeds in wild blueberry fields.

Climate change and dispersion dynamics of the invasive plant species Chromolaena odorata and Lantana camara in parts of the central and eastern India

Lantana camara and Chromolaena odorata are categorized as the most obnoxious invasive flora globally. Their ability to combat the regeneration and proliferation of neighbouring flora, expansive nature, and robust adaptability to diverse habitats, drew global attention. Investigating the potential mutual dispersion phenomenon of these two invasive species under the climate change scenario was the primary objective of this study. The present and future (2050) prospective distribution scenarios for these two species were determined using MaxEnt in the eastern and central Indian regions encompassing the states of Jharkhand, Chhattisgarh, and West Bengal. Future projections for 2050 were derived using IPSL-CM5A-LR & MIROC5 and IPSL-CM6A-LR & MIROC6 models for different representative concentration pathways (RCPs 2.6, 4.5, 6.0, and 8.5), and Shared Socioeconomic Pathways (SSPs 126, 245, 370 and 585), respectively. The investigation revealed that currently ∼31% and ∼ 24% of the study area are susceptible to infestation of L. camara and C. odorata, respectively. Compared to the current scenario, the results showed a probable future increase of ∼1.53% in C. odorata infestation and a decrease of ∼4.95% for L. camara. The True Skill Statistics (TSS) and Kappa coefficient (in %) values of 0.71 & 76.50 for L. camara and 0.52 & 63.38 for C. odorata indicated a good model fit. Collectively, both the species exhibited robust resilience to climate change, with C. odorata outcompeting L. camara. Using both RCP and SSP pathways under the multiple climate scenarios offered a comprehensive and novel approach to acquiring greater insights into likely interactions, dominance, and distribution scenarios of these species. The results provide prior information on sensitive sites prone to future invasion, allowing management to formulate preventative measures to control infestation.

The application of the FLOTAC technique for detection of helminth eggs of medical and veterinary importance in soil samples

The FLOTAC technique was initially developed to detected infective stages of parasites in animal and human feces. Recently, its applicability has been extended by the use in pastures and vegetables for human consumption. However, its use for the detection of parasites in the soil has never been investigated. In this study, we assessed the performance of the FLOTAC and compared with centrifugal flotation (CFT) and spontaneous sedimentation (SST) techniques. A total of 50 soil samples were collected from the Metropolitan region of Recife, Northeastern Brazil. Initially, samples were standardized, and then assessed in duplicate by the FLOTAC, CFT and SST. Cohen's kappa coefficient, sensitivity, specificity, positive and negative predictive values, and accuracy of the FLOTAC, CFT and SST were assessed using the results of the combination of all three techniques as gold standard. Out of all analyses performed, in 96% (48/50), helminth eggs of parasites were detected. In particular, 96% (48/50) of samples analysed through the FLOTAC technique, 76% (38/50) for CFT and 28% (14/50) for SST were positive. Nematode eggs of the Ancylostomatidae and Ascarididae families were those mostly detected in this study. Overall, the FLOTAC presented high values of sensitivity, demonstrating that this method may also be employed for detection of parasites in soil samples. This study opens new possibilities for the use of the FLOTAC technique, highlighting its role as a potential tool for detecting environmental contamination by parasites of medical and veterinary importance.

Spatio-Temporal Classification and Prediction of Land Use and Land Cover Change in Finima Nature Park Bonny Island, Nigeria

Land Use Land Cover (LULC) is naturally dynamic, thus change is inevitable. However, its changes have greatly increased to a frightening proportion as a result of high rate of anthropogenic and natural processes. Consequently, land use and land cover classes of Finima Nature Park were classified and changes were observed within the last 33 years (1987 - 2021) and predicted for the next 33 years (2021 – 2054).
 Remote sensing and Geographic Information System (GIS) were used to achieve the goal. Coordinate points were collected from the various land use and land cover classes as a reference data for accuracy assessment of the classification. Landsat imageries of 1987, 1999, 2010 and 2021 were acquired from The United States Geological Survey (USGS). The imageries were pre-processed, processed and classified into various LULC classes using Maximum Likelihood Classification in Idrisi and ArcGis10.5. Confusion matrix and Cellular Automata (CA) Markov Chain algorithm were used for accuracy assessment and prediction of LULC respectively.
 Results showed that dense vegetation, sparse vegetation, bare land, and water body were the main LULC class in 1987 and 1999, while dense vegetation, sparse vegetation, bare land, water body and built up were observed in the years 2010, 2021 and 2054. The Kappa Coefficient values were 93%, 81%, 83% and 90% for 1987, 1999, 2010 and 2021 respectively; an indication of strong accuracy of the classification. Generally, there were changes in land use and land cover within the study periods. However, changes were mostly observed in the areas of water body and bare lands closer to the sea coast. Hence, the sea was implicated as the major driver of land use and land cover change in the park. The slight decrease in dense vegetation and sparse vegetation from 1999 to 2021, and 2021 to 2054 underscores the importance and benefit of conservation.

Clinical performance of SARS-CoV-2 detection on the cobas Liat using water gargle samples

Spring water gargle (SWG) is a suitable, non-invasive, alternative specimen for SARS-CoV-2 detection by RT-PCR. This study sought to evaluate the performance of the cobas Liat point-of-care system for the detection of SARS-CoV-2 in SWG samples. SWG samples and standard oral and nasopharyngeal swab (ONPS) were collected simultaneously from participants in a COVID-19 screening clinic, in November and December 2020. Both sample types were analyzed in parallel on the cobas Liat platform and with the Seegene Allplex 2019-nCoV assay. Among the 110 participants, 53% had compatible symptoms and 71% had a contact with a confirmed COVID-19 case. Only two (1.8%) individuals had neither symptoms nor contact. Amongst 110 paired samples, 25 (23%) were positive for SARS-CoV-2 on the cobas Liat for a least one sample type, with a kappa coefficient of 0.92. Agreement between the cobas Liat platform and the Seegene assay was also excellent (kappa coefficient values of 0.94 and 0.95). Two SWG samples failed to provide a positive result when their ONPS pair was positive, but their cycle threshold (Ct) values were >35 on the Seegene assay, reflecting a low viral load. Overall, the performance of the cobas Liat platform is excellent for the detection of SARS-CoV-2 in SWG samples in a high pre-test probability population.

The influence of urban expansion on landslide evolution. A case study in Cluj-Napoca Municipality.

In the case of Cluj-Napoca Municipality, the analysis of the landslides is a frequently discussed topic due to its complex geology and urban expansion tendency. In this study, we chose the Gruia Neighbourhood in order to map the landslide evolution and land-use/land-cover (LULC) changes which occured between 2003 and 2021. In order to use the post-classification Change Detection method, we applied the supervised classification technique on three satellite imageries (2003, 2009, 2021), which were validated by high Kappa coefficient values, as it follows: 0,892, 0,879 and 0,931. Comparing the three classified imageries, we could conclude that a transition between agricultural land to urban areas is visible, with a total decrease of the agricultural land by 40,69% and a total increase of the urban area by 24,47%. In the evaluation process of the influence of the LULC change on the landslide evolution, we could observe that the increase of the urban areas led to the increase of the surface of the landslides, moreover, the slided territories on urban areas have increased by 200%.

Multi-decadal land cover change analysis over Sundarbans Mangrove Forest of Bangladesh: A GIS and remote sensing based approach

Both natural processes and anthropogenic actions alter land use and land cover but the impact of anthropogenic actions has dramatically increased since the last century in many regions of the world. This study attempts to investigate the land cover change of the Sundarbans and surrounding areas of Bangladesh in the last forty years by analyzing Landsat images. Sundarbans, having the richest and exceptional biodiversity, is the largest remaining tract of mangrove forest on earth facing multidimensional threats of degradation. Time series Landsat satellite images collected for five years are 1980 (MSS), 1990 (TM), 2000 (TM), 2010 (TM) and 2020 (OLI). By analyzing, comparing and visualizing the classified images using ArcMap 10.5 software the historical trend of land cover change of the study area is exhibited. The result revealed that a large amount of non-vegetated areas located outside of Sundarbans mangrove forest converted into water bodies due to shrimp cultivation. During this long time, the Sundarbans mangrove forest area remained unchanged except for little change occurred by soil erosion along the river bank and coastline. Kappa coefficient revealed the accuracy of the land cover map with a coefficient value of 0.88, 0.92, 0.90, 0.93 and 0.92 for 1980, 1990, 2000, 2010 and 2020 images respectively. This research will support policymakers and the forest department to improve management policy. Also, influence researchers to conduct case-specific micro-scale research in the region. • Land cover change of Sundarbans Mangrove forest and surrounding area was analysed using GIS and RS techniques. • Excessive shrimp cultivation altered land cover outside of Sundarbans. • Sundarbans Mangrove forest remained unchanged over the last 40 years except for little change occurred by soil erosion. • Cyclone, sea level rise and decreased river water flow affecting the coastal island and the river bank. • Kappa coefficient value of 0.88, 0.92, 0.90, 0.93 and 0.92 found for 1980, 1990, 2000, 2010 and 2020 images respectively.

Crop type discrimination using Geo-Stat Endmember extraction and machine learning algorithms

The identification of crop diversity in today's world is very crucial to ensure adaptation of the crop with changing climate for better productivity as well as food security. Towards this, Hyperspectral Remote Sensing (HRS) is an efficient technique that offers the opportunity to discriminate crop types based on morphological as well as physiological features due to availability of contiguous spectral bands. The current work utilized the benefits of Airborne Visible Infrared Imaging Spectrometer- New Generation (AVIRIS-NG) data and explored the techniques for classification and identification of crop types. The endmembers were identified using the Geo-Stat Endmember Extraction (GSEE) algorithm for pure pixels identification and to generate the spectral library of the different crop types. Spectral feature comparison was done among AVIRIS-NG, Analytical Spectral Device (ASD)-Spectroradiometer and Continuum Removed (CR) spectra. The best-fit spectra obtained with the Reference ASD-Spectroradiometer and Pure Pixel spectral library were then used for crop discrimination using the ten supervised classifiers namely Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), Support Vector Machine (SVM), Minimum Distance Classifier (MDC), Binary Encoding, deep learning-based Convolution Neural Network (CNN) and different algorithms of Ensemble learning such as Tree Bag, AdaBoost (Adaptive Boosting), Discriminant and RUSBoost (Random Under Sampling). In total, nine crop types were identified, namely, wheat, maize, tobacco, sorghum, linseed, castor, pigeon pea, fennel and chickpea. The performance evaluation of the classifiers was made using various metrics like Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score. The classifier 2D-CNN was found to be the best with Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score values of 89.065 %, 0.871, 87.565%, 89.541% and 88.678% respectively. The output of this work can be utilized for large scale mapping of crop types at the species level in a short interval of time with high accuracy.

Comparing Delphi-fuzzy AHP and fuzzy logic membership in soil fertility assessment: a study of an active Ganga Delta in Sundarban Biosphere Reserve, India.

The present study led to setting up a grid-based soil fertility map along with the best fit model in the coastal regions based on soil physical (coarse, sand, silt, clay, bulk density), chemical (CEC, pH, and soil organic carbon), topographic (elevation), and nutrient elements (P2O5, K2O, Na, Zn, B) in the active Ganga deltaic region of Sundarban Biosphere Reserve, India. Soil samples have been collected from 30 soil grids, and 0-15 cm soil depth was preferred for fertility analysis because most essential soil chemical and nutrient elements affecting soil fertility are concentrated in this depth range. We have used the fuzzy-AHP-Delphi (FAHP) and fuzzy logic-Delphi (FL) methods to determine the soil fertility zone. The rules are generated on the MATLAB interface in the text form; the words "IF," "THEN," "IS," "AND," etc., are used to complete the mode-building process. The weights and the desirable limits for each criterion were set based on the expert opinions and existing literature. The kriging interpolation method and natural break classification were used to represent the soil fertility maps into five classes, namely very high fertility (0.80-1.0), high fertility (0.60-0.80), moderate fertility (0.40-0.60), low fertility (0.20-0.40), and very low fertility (0.00-0.20) respectively. Both the models show that soil fertility is respectively higher near the Hooghly River bank. In many cases, the results obtained from FAHP and FL are quite similar but huge dissimilarity has been noticed in grid numbers G2, G3, G4, F1, and F2. Since the FAHP method has been used for the weight of each criterion, therefore, it only prefers those more important parameters over others. The overall accuracy of the soil fertility map was 82.16% for the fuzzy logic model, and 79.62% for the FAHP model and the kappa coefficient value was determined as 0.82 for the fuzzy logic model and 0.79 for the FAHP model. The soil fertility map was validated using the success rate curve under the ROC technique, and the area under curve (AUC) was calculated as 84.02% for the fuzzy logic model and 81.60% for the FAHP model. Since the standard limits for each criterion were known, therefore, fuzzy logic was found to best fit the model for analyzing soil fertility for each grid.

Gully erosion vulnerability modelling, estimation of soil loss and assessment of gully morphology: a study from cratonic part of eastern India.

A highly visible form of soil erosion is gully, a significant geomorphological feature, resulting from water erosion and causing land degradation and deterioration. In arid and semi-arid environment, gully erosion is conceived as an important source of sediment supply washing out the top fertile soil and exposing lower soil layers. The present study is conducted on the lateritic terrain of Rupai watershed of eastern plateau fringe of India, where water erosion is a serious concern. In order to prepare a gully erosion vulnerability mapping, the analytical hierarchy process (AHP) model coupled with geospatial technology is adopted taking into account thirteen bio-physical factors. It is revealed that around 49% area of the watershed belongs to high to very high gully erosion vulnerability zone (GEVZ) followed by moderate risk zone of 31.64%. This model is validated performing an accuracy assessment, which is calculated to be 90.91%, and the value of Kappa co-efficient is 0.86. It is imperative to estimate the average annual soil loss alongside of delineating GEVZ; thus, the revised universal soil loss equation (RUSLE) model is used with geospatial technology. It unveils that the average estimated soil loss of the watershed varies from < 15 to 431 t ha-1 y-1. Around 29% of the study area experiences high to very high (57 to > 147 t ha-1 y-1) soil erosion risk, where 68% area endures low level of soil erosion risk (< 15 t ha-1 y-1). The study of gully morphology depicts gully depth ranging from < 1 to 5m (small to medium gully) with V and U shapes. Results obtained from this study may help in planning and management of land use and soil erosion conservation.

COMPARISON OF LANDSAT-9 AND PRISMA SATELLITE DATA FOR LAND USE / LAND COVER CLASSIFICATION

Abstract. Land use and land cover (LU/LC) detection has great significance in management of natural resources and protection of environment. Hence, monitoring LU/LC with the state-of-the-art approaches has gained importance during the recent years and free access satellite images have become valuable data source. The aim of this study is to compare classification abilities of Landsat-9 and PRISMA satellite images while applying Support Vector Machine (SVM) algorithm to distinguish different LU/LC classes. For this purpose, the study area was chosen to be of heterogeneous character that includes industrial area, roads, residential area, airport, sea, forest, vegetation and barren land. When the classification results were visually examined, it was seen that forest, industrial area and airport classes were distinguished more accurately than other classes. On the other hand, qualitative results were validated with quantitative accuracy assessment results. The overall accuracy (OA) and Kappa coefficient values were calculated as 89.33 and 0.88 for Landsat-9 satellite image and as 92.33 and 0.91 for the PRISMA satellite image, respectively. In the accuracy assessment results, although Landsat-9 and PRISMA satellite images showed similar classification performances, a slight improvement was observed by using the PRISMA satellite image. The findings indicated that although both of the Landsat-9 and PRISMA satellite images were proper data to assess the LU/LC of the complex region, a slightly more performance could be achieved by using the PRISMA satellite image.

Landslide Hazard Assessment Methods along Fault Zones Based on Multiple Working Conditions: A Case Study of the Lixian–Luojiabu Fault Zone in Gansu Province (China)

Traditional landslide hazard assessment methods generally use the same evaluation model to carry out assessments under different working conditions. Due to the differences in landslide influence factors and model calculation methods considered under different working conditions, the evaluation results obtained using traditional methods are different from those of real-world scenarios. Therefore, research on optimal landslide hazard assessment methods for different working conditions is important for disaster prevention and mitigation in areas along fault zones. Taking the section along the Lixian–Luojiabu fault zone in Gansu province in China as the research area, a landslide hazard assessment was carried out under rainfall and earthquake conditions. A method based on the fractal theory–information coupling model is proposed for the rainfall condition, and a method based on an improved Newmark model considering matric suction is proposed for the earthquake condition. Under the rainfall condition, a landslide hazard assessment was carried out using the information model, the logistic regression model, the fractal theory model, the logistic regression–information coupling model and the fractal theory–information coupling model. Meanwhile, under the earthquake condition, an assessment was carried out using the traditional Newmark model and the improved Newmark model considering matric suction. Finally, the ROC curve and Kappa coefficient were used to test the accuracy of these evaluation models and to determine the optimal model under different working conditions. The results showed that the fractal theory–information coupling model had the largest AUC value and Kappa coefficient value under the rainfall condition (0.856 and 0.807, respectively). The test value of the logistic regression–information coupling model was second, and the values of the other three models were all lower than 0.8. This shows that the evaluation of the fractal theory–information coupling model is better than those of the other models under the rainfall condition. The AUC value and Kappa coefficient of the improved Newmark model under the earthquake condition were 0.805 and 0.794, respectively, which were larger than the test values of the traditional Newmark model. This shows that the evaluation of the proposed model is better than that of the traditional Newmark model under the earthquake condition. These research results provide a reference for landslide hazard assessments in areas with similar characteristics.

A Comparison of Two Scales to Determine Prevalence of Mood Disorders in Omani Patients Recently Diagnosed with Cancer.

Studies on the prevalence rate of mood disorders in patients recently diagnosed with cancer from Middle East are scare in the literature. Therefore, this study assesses the prevalence rates of anxiety and depression, and their associations with socio-demographic factors, in recently diagnosed patients with cancer living in the Sultanate of Oman. In this prospective study, adult patients were interviewed within the first three months of diagnosis of cancer using the Hospital Anxiety and Depression Scale (HADS), and the Centre for Epidemiological Studies Depression (CES-D) Scale. Associations were studied among symptoms of anxiety and depression, and the socio-demographic factors, along with levels of agreement between the two scales. Eighty-nine patients were interviewed, and 65% were females. Using the HADS tool, 41.6% of patients had anxiety, 28% had depression, whereas 5.6% displayed severe depression. Using the CES-D tool, 41.6% of patients had depression, and 11.2% had severe depression. A fair correlation between the CES-D and HADS tools was evidenced with a Cohen's Kappa coefficient value of 0.37 (P<0.001). The socio-demographic factors were not significantly associated with the presence of anxiety and depression (P >0.05). Collectively, these findings indicate high prevalence rates of anxiety and depression in Omani patients recently diagnosed with cancer along with a significant correlation between the two scales. These results support the implementation of screening tools early in the trajectory of cancer illness to improve the overall healthcare of these patients.

An improved deep convolutional neural network architecture for chromosome abnormality detection using hybrid optimization model.

Chromosomes are thread-like structures located in the cell nucleus that contains the human body blueprint. Chromosome analysis is also known as karyotyping is the test taken to detect the abnormalities identified in the human chromosome. The two types of widely known chromosome abnormalities are structural and numerical abnormalities. Manual karyotyping is complex, time-consuming, and error-prone. To overcome these complexities, automated chromosome karyotype architecture is proposed using the deep convolutional neural network (DCNN) architecture. Training the DCNN architecture from scratch needs a huge dataset and to overcome this problem a generative adversarial networks is used to create adversarial samples that resemble the images in the actual dataset. The time-consuming hyperparameter tuning in the DCNN architecture is overcome using the hybrid moth-flame optimization integrated with the hill-climbing strategy (HMFOHC). The HMFOHC algorithm is mainly utilized in this article to minimize the huge number of parameters associated with the DCNN architecture. The efficiency of the proposed methodology is evaluated using two datasets namely the BioImLab chromosome dataset and hospital dataset. The proposed HMFOHC optimized DCNN architecture is mainly utilized for multiclass classification where it differentiates five numerical chromosome abnormalities, namely Trisomy 13, Trisomy 18, Trisomy 21, Trisomy XXY syndrome, and Monosomy X. The proposed model offers an accuracy, F1-score, and kappa coefficient value of 98.65%, 98.86%, and 0.9894, respectively. The results obtained show that the proposed model achieves higher classification accuracy when compared with the different state-of-art techniques such as deep learning, random forest, and CNN. The inference time of the proposed methodology is 12.5s which is relatively lower than the state-of-art techniques. The proposed approach can help cytogenetics forensic experts make better decisions and save time by automating manual karyotyping. RESEARCH HIGHLIGHTS: Manual karyotyping is complex, time-consuming, and error-prone. To overcome these complexities, automated chromosome karyotype architecture is proposed using the DCNN architecture The proposed method is mainly applied for multiclass classification where the different types of numerical abnormalities are addressed such as Trisomy 13, Trisomy 18, Trisomy 21, Trisomy XXY syndrome, and Monosomy X.

A comprehensible machine learning tool to differentially diagnose idiopathic pulmonary fibrosis from other chronic interstitial lung diseases.

Idiopathic pulmonary fibrosis (IPF) has poor prognosis, and the multidisciplinary diagnostic agreement is low. Moreover, surgical lung biopsies pose comorbidity risks. Therefore, using data from non-invasive tests usually employed to assess interstitial lung diseases (ILDs), we aimed to develop an automated algorithm combining deep learning and machine learning that would be capable of detecting and differentiating IPF from other ILDs. We retrospectively analysed consecutive patients presenting with ILD between April 2007 and July 2017. Deep learning was used for semantic image segmentation of HRCT based on the corresponding labelled images. A diagnostic algorithm was then trained using the semantic results and non-invasive findings. Diagnostic accuracy was assessed using five-fold cross-validation. In total, 646,800 HRCT images and the corresponding labelled images were acquired from 1068 patients with ILD, of whom 42.7% had IPF. The average segmentation accuracy was 96.1%. The machine learning algorithm had an average diagnostic accuracy of 83.6%, with high sensitivity, specificity and kappa coefficient values (80.7%, 85.8% and 0.665, respectively). Using Cox hazard analysis, IPF diagnosed using this algorithm was a significant prognostic factor (hazard ratio, 2.593; 95% CI, 2.069-3.250; p < 0.001). Diagnostic accuracy was good even in patients with usual interstitial pneumonia patterns on HRCT and those with surgical lung biopsies. Using data from non-invasive examinations, the combined deep learning and machine learning algorithm accurately, easily and quickly diagnosed IPF in a population with various ILDs.

Limiting the Collection of Ground Truth Data for Land Use and Land Cover Maps with Machine Learning Algorithms

Land use and land cover (LULC) classification maps help understand the state and trends of agricultural production and provide insights for applications in environmental monitoring. One of the major downfalls of the LULC technique is inherently linked to its need for ground truth data to cross-validate maps. This paper aimed at evaluating the efficiency of machine learning (ML) in limiting the use of ground truth data for LULC maps. This was accomplished by (1) extracting reliable LULC information from Sentinel-2 and Landsat-8 s images, (2) generating remote sensing indices used to train ML algorithms, and (3) comparing the results with ground truth data. The remote sensing indices that were tested include the difference vegetation index (DVI), the normalized difference vegetation index (NDVI), the normalized built-up index (NDBI), the urban index (UI), and the normalized bare land index (NBLI). Extracted vegetation indices were evaluated on three ML algorithms, namely, random forest (RF), k-nearest neighbour (K-NN), and k dimensional-tree (KD-Tree). The accuracy of these algorithms was assessed with standard statistical measures and ground truth data randomly collected in Prince Edward Island, Canada. Results showed that high kappa coefficient values were achieved by K-NN (82% and 74%), KD-Tree (80% and 78%), and RF (83% and 73%) for Sentinel-2A and Landsat-8 imagery, respectively. RF was a better classifier than K-NN and KD-Tree and had the highest overall accuracy with Sentinel-2A satellite images (92%). This approach provides the basis for limiting the collection of ground truth data and thus reduces the labour cost, time, and resources needed to collect ground truth data for LULC maps.

A Flood Mapping Method for Land Use Management in Small-Size Water Bodies: Validation of Spectral Indexes and a Machine Learning Technique

The assessment of flood disasters is considered an essential factor in land use management, being necessary to understand and define the magnitude of past events. In this regard, several flood diagnoses have been developed using Sentinel-2 multispectral imagery, especially in large water bodies. However, one of the main challenges is still related to floods, where water surfaces have sizes similar to the spatial resolution of the analyzed satellite images, being difficult to detect and map. Therefore, the present study developed a combined methodology for flood mapping in small-sized water bodies using Sentinel-2 MSI imagery. The method consisted of evaluating the effectiveness of the application and combination of (a) a super-resolution algorithm to improve image resolution, (b) a set of seven spectral indices for highlighting water-covered areas, such as AWE indices, and (c) two methods for flood mapping, including a machine learning method based on unsupervised classification (EM cluster) and 14 thresholding methods for automatic determination. The processes were evaluated in the Carrión River, Palencia, Spain. It was determined that the approach with the best results in flood mapping was the one that combined AWE spectral indices with methods such as Huang and Wang, Li and Tam, Otsu, moment preservation, and EM cluster classification, showing global accuracy and Kappa coefficient values higher than 0.88 and 0.75, respectively, when applying the quantitative accuracy index.