Overall Accuracy Of Classification Research Articles

DeepLTSC: Long-Tail Service Classification via Integrating Category Attentive Deep Neural Network and Feature Augmentation

With the explosive growth in the number and diversity of Web services, correlative research has been investigated on Web service classification, as it fundamentally promotes advanced service-oriented applications, such as service discovery, selection, composition and recommendation. However, conventional approaches are restricted to indiscriminatingly classify Web services, which can trigger many challenges. First, they have not made full advantage of the implicit relationships among multi-dimensional information of Web services, such as the increasing number of service categories. Thus, it leads to low effectiveness of learning and representing service features, failing to ensure the overall accuracy of service classification. Second, the imbalance of service distributions has been ignored, while it is observed that service categories reveal distinct long-tail characteristics. That results in low accuracy on service classification for those categories that contain fewer Web services. To handle the challenges of more effectively learning implicit service features across the service repository, and with a particular concentration on those tail categories that contain fewer Web services, we propose a novel framework called DeepLTSC to more accurately perform the task of Web service classification under long-tail distributions. In DeepLTSC, we first present an improved label attentive convolutional deep neural network (LACNN) with service categories, which can generate deep service features to improve the overall classification performance. Then, a proposed service feature augmentation model (SFA) together with focal loss function is integrated into DeepLTSC to further optimize service features, aiming to boost the classification accuracy on tail service categories. Extensive experiments are conducted on three large-scale real-world services datasets with different long-tail distributions. The results demonstrate that DeepLTSC significantly outperforms state-of-the-art approaches for Web service classification on both overall and tail categories.

Classifying Breast Density from Mammogram with Pretrained CNNs and Weighted Average Ensembles

We are currently experiencing a revolution in data production and artificial intelligence (AI) applications. Data are produced much faster than they can be consumed. Thus, there is an urgent need to develop AI algorithms for all aspects of modern life. Furthermore, the medical field is a fertile field in which to apply AI techniques. Breast cancer is one of the most common cancers and a leading cause of death around the world. Early detection is critical to treating the disease effectively. Breast density plays a significant role in determining the likelihood and risk of breast cancer. Breast density describes the amount of fibrous and glandular tissue compared with the amount of fatty tissue in the breast. Breast density is categorized using a system called the ACR BI-RADS. The ACR assigns breast density to one of four classes. In class A, breasts are almost entirely fatty. In class B, scattered areas of fibroglandular density appear in the breasts. In class C, the breasts are heterogeneously dense. In class D, the breasts are extremely dense. This paper applies pre-trained Convolutional Neural Network (CNN) on a local mammogram dataset to classify breast density. Several transfer learning models were tested on a dataset consisting of more than 800 mammogram screenings from King Abdulaziz Medical City (KAMC). Inception V3, EfficientNet 2B0, and Xception gave the highest accuracy for both four- and two-class classification. To enhance the accuracy of density classification, we applied weighted average ensembles, and performance was visibly improved. The overall accuracy of ACR classification with weighted average ensembles was 78.11%.

The contribution of landsat 8 oli multispectral data to the lithological mapping of the Ait Ourir basin (Western High Atlas, Morocco)

The present work consists in identifying the geological facies and defining the dominant orientation of the lineaments in order to improve the knowledge on the local geodynamics in the Northern Sub-Atlasic zone of the Marrakesh High Atlas, the Ait Ourir basin region, The latter is characterized by a post-Hercynian Mesozoic cover folded into broad synclines with flat and subhorizontal bottom, separated by ejective and sharp anticlines. For this purpose, multispectral data from Landsat 8 operational land imager (OLI) have been applied in a platform of ArcGIS software and through the treatments offered by this software, namely, the principal component analysis (PCA), the band ratios (BR) and the transformation of the minimum noise fraction (MNF), a better lithological discrimination and description was brought to the set of geological formations that form the studied basins. In parallel, the results obtained by the method of classification by Support Vector Machine (SVM), which is based on the joint use of geomorphic features, textures and multispectral data from Landsat 8 satellite, showed an excellent correlation. With the simplified geological map of the northern slope of the High Atlas of Marrakesh (1/10000) and field data from the study area. The result shows an overall accuracy of classification of Landsat8 OLI data by SVM which is 96.64% and the Kappa coefficient which is 0.98 and a positive correlation is shown between lineament structures and dominant orientations of the extracted lineaments. The highest densities obtained are N-S, NE-SW and NNE-SSW oriented with the predominance of the first direction.

Monitoring land use land cover changes and its impacts on land surface temperature over Mardan and Charsadda Districts, Khyber Pakhtunkhwa (KP), Pakistan.

Land use/land cover (LULC) changes due to urban growth on the regional scale affect land surface temperature (LST). The present study aims to assess the LULC changes and their impact on LST over Mardan and Charsadda districts of Khyber Pakhtunkhwa (KP), Pakistan, in the period from 1990 to 2019. Landsat satellite (TM& ETM +) datasets in the period from 1990 to 2010 and Sentinel-2 images from 2016 to 2019 were used in this study. All the datasets were pre-processed and the LULC types were classified by maximum likelihood classification algorithm. The vegetation degradation was computed from normalized difference vegetation index (NDVI), and the LST was derived based on the LULC changes. The results showed that the overall accuracy of LULC classification was 87.84%. Dramatic LULC changes were observed during the last three decades, where the vegetation degradation area was decreased from 1307.8 (59.27%) to 1147.6 km2 (52.1%) and the barren land area increased from 816.6 (37.07%) to 961.4 km2 (42.64%). Similarly, the built-up area has also increased from 57.2 (2.5%) to 104.3 km2 (4.73%) in the years 1990 and 2019, respectively. These variations in LULC types have significantly influenced the LST from 1990 to 2019; specifically, the LST of built-up area, barren land, and vegetation cover increased from 20.1 to 32.1°C, 21.5 to 35.5°C, and 17.1 to 28.2°C, respectively. The regression line plotted defines that the LST has a negative correlation with NDVI and a positive correlation with normalized difference of built-up index (NDBI). In particular, the vegetation and land covers dramatically transformed to barren land and/or to urban development over the study area in the period from 1990 to2019, which has severely affected the LST and the natural resources of the study area. Therefore, our study will be very helpful for managing the rapid environmental changes and urban planning.

A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer's Disease.

With the advances in machine learning for the diagnosis of Alzheimer’s disease (AD), most studies have focused on either identifying the subject’s status through classification algorithms or on predicting their cognitive scores through regression methods, neglecting the potential association between these two tasks. Motivated by the need to enhance the prospects for early diagnosis along with the ability to predict future disease states, this study proposes a deep neural network based on modality fusion, kernelization, and tensorization that perform multiclass classification and longitudinal regression simultaneously within a unified multitask framework. This relationship between multiclass classification and longitudinal regression is found to boost the efficacy of the final model in dealing with both tasks. Different multimodality scenarios are investigated, and complementary aspects of the multimodal features are exploited to simultaneously delineate the subject’s label and predict related cognitive scores at future timepoints using baseline data. The main intent in this multitask framework is to consolidate the highest accuracy possible in terms of precision, sensitivity, F1 score, and area under the curve (AUC) in the multiclass classification task while maintaining the highest similarity in the MMSE score as measured through the correlation coefficient and the RMSE for all time points under the prediction task, with both tasks, run simultaneously under the same set of hyperparameters. The overall accuracy for multiclass classification of the proposed KTMnet method is 66.85 ± 3.77. The prediction results show an average RMSE of 2.32 ± 0.52 and a correlation of 0.71 ± 5.98 for predicting MMSE throughout the time points. These results are compared to state-of-the-art techniques reported in the literature. A discovery from the multitasking of this consolidated machine learning framework is that a set of hyperparameters that optimize the prediction results may not necessarily be the same as those that would optimize the multiclass classification. In other words, there is a breakpoint beyond which enhancing further the results of one process could lead to the downgrading in accuracy for the other.

Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series

Coastal tidal wetlands are highly altered ecosystems exposed to substantial risk due to widespread and frequent land-use change coupled with sea-level rise, leading to disrupted hydrologic and ecologic functions and ultimately, significant reduction in climate resiliency. Knowing where and when the changes have occurred, and the nature of those changes, is important for coastal communities and natural resource management. Large-scale mapping of coastal tidal wetland changes is extremely difficult due to their inherent dynamic nature. To bridge this gap, we developed an automated algorithm for DEtection and Characterization of cOastal tiDal wEtlands change (DECODE) using dense Landsat time series. DECODE consists of three elements, including spectral break detection, land cover classification and change characterization. DECODE assembles all available Landsat observations and introduces a water level regressor for each pixel to flag the spectral breaks and estimate harmonic time-series models for the divided temporal segments. Each temporal segment is classified (e.g., vegetated wetlands, open water, and others – including unvegetated areas and uplands) based on the phenological characteristics and the synthetic surface reflectance values calculated from the harmonic model coefficients, as well as a generic rule-based classification system. This harmonic model-based approach has the advantage of not needing the acquisition of satellite images at optimal conditions (i.e., low tide status) to avoid underestimating coastal vegetation caused by the tidal fluctuation. At the same time, DECODE can also characterize different kinds of changes including land cover change and condition change (i.e., land cover modification without conversion). We used DECODE to track status of coastal tidal wetlands in the northeastern United States from 1986 to 2020. The overall accuracy of land cover classification and change detection is approximately 95.8% and 99.8%, respectively. The vegetated wetlands and open water were mapped with user's accuracy of 94.6% and 99.0%, and producer's accuracy of 98.1% and 93.5%, respectively. The cover change and condition change were mapped with user's accuracy of 68.0% and 80.0%, and producer's accuracy of 80.5% and 97.1%, respectively. Approximately 3283 km2 of the coastal landscape within our study area in the northeastern United States changed at least once (12% of the study area), and condition changes were the dominant change type (84.3%). Vegetated coastal tidal wetland decreased consistently (~2.6 km2 per year) in the past 35 years, largely due to conversion to open water in the context of sea-level rise.

Towards a sustainable monitoring: A self-powered smart transportation infrastructure skin

Sustainable monitoring of traffic using clean energy supply has always been a significant problem for engineers. In this study, we proposed a self-powered smart transportation infrastructure skin (SSTIS) as an innovative and bionic system for the traffic classification of a smart city. This system incorporated the self-powered flexible sensors with net-zero power consumption based on the Triboelectric Nanogenerator (TENG) and an intelligent analysis system based on artificial intelligence (AI). The feasibility of the SSTIS was tested using the full-scale accelerated pavement tests (APT) and the long-short term memory (LSTM) deep learning model with a vehicle axle load classification accuracy up to 89.06%. This robust SSTIS was later tested on highway and collected around 869,600 pieces of signals data. The generative adversarial networks (GAN) WGAN-GP (Wasserstein GAN - Gradient Penalty) was used for data augmentation, due to the imbalanced data of different vehicle types in actual traffic. The overall accuracy for on-road vehicle type classification improved to 81.06% using the convolutional neural network ResNet. Finally, we developed a mobile traffic signal information monitoring system based on cloud platform and Android framework, which enabled engineers to obtain the vehicle axle-load information mobilely. This study is the emerging design and engineering application of the self-powered flexible sensors for smart traffic monitoring, which provides a significant advance for intelligent transportation and smart cities in future.

Application of normalized difference vegetation index in classifying land cover change over Bangli regency by using Landsat 8 imagery

The information on land cover changes is very important in regional spatial planning. Remote sensing technology can minimize the cost and time in analyzing land cover changes. Normalized Difference Vegetation Index (NDVI) is a vegetation index that combines red and near-infrared channels so that it can provide approximate information about land cover in an area. The objective of this study is to extract land cover change information from Landsat 8 images based on NDVI values in Bangli Regency in 2015 and 2021. The classification method used to estimate the type of land cover is supervised classification. The results reveal the decrease of the land cover in the category of water body, sand, dry land/soil, rice fields, and vegetation, which are 1.62%, 14.14%, 7.93%, 8.63%, and 2.45%, respectively, while an increase in the settlement category by 30.12%. The overall accuracy of land cover classification result based on NDVI value is 86.54%.

Locomotion Modes and Environmental Features Recognition Using Laser Distance Sensors

Recognition of locomotion mode (LM) has been used to control the lower limb powered prosthetic and orthotic devices for navigating autonomously on a variety of terrains. In addition, the knowledge of environmental features (EFs) can be utilized to modulate the joint kinetics of prosthetic devices in a precise manner. In this article, a novel LM recognition strategy has been proposed along with EFs estimation utilizing a combination of laser distance sensors (LDSs) and inertial measurement units (IMUs). LM was classified from outputs of eight sensors, i.e., six LDSs and two IMUs, utilizing support vector machine (SVM) followed by a majority voting scheme. A cloud of points was generated in the sagittal plane, which was further used to determine the EFs leveraging curve fitting techniques. The efficiency of the proposed method was tested with six healthy subjects and for a variety of LMs, such as walking on the level-ground, ramp, and stairs. The overall accuracy of LM classification and EF estimation was found to be equal to 97.85% and 95.36%, respectively. The proposed study is an accurate, computationally inexpensive, and cost-effective solution and can be used efficiently to control lower limb prosthetic devices. The novelty of this study is the simplification of the LM classification and EF estimation methods compared to a camera-based technique to facilitate real-time control of powered prostheses.

Adoption of Machine Learning Technique in Nile River Islands Classification

AbstractWider adoption of machine learning methods in water resources has the potential to greatly accelerate the efficiency and quality of analysis. The Nile River is one of the major fluvial hydro-systems in the world. Fluvial islands are present in nearly all natural and regulated rivers. The Nile River is characterized by numerous natural phenomena and human interventions represented in multiple islands characteristics. This paper investigates the formation and development of the Nile River islands in the fourth reach, which extends between Assuit and Delta barrages. A machine learning (ML) technique, with the Random Forest (RF) algorithm, has been introduced as a potential technique to replace the traditional ones, to extract and classify the land cover types and the geometrical characteristics of the Nile River islands. The assessment of the results of extracting the Nile River islands and the land cover types are included. The accuracy of the extracted boundaries of the islands is assessed using field surveying data. The classification of the islands based on the islands' geometric characteristics represented that 70% of the extracted islands are classified as Wide Island, 20% are classified as Equal Island, and 10% as Narrow Island. The islands’ classification, based on the land cover, results show that there is only 5% of the islands that are urban areas, 5% of the islands are mixed class (both vegetation and urban), and the rest of the islands 90% have a vegetation land cover type. The accuracy assessment was performed using the error matrix, the results show that the overall accuracy of the land cover classification is greater than 84%. The proposed islands’ classification scheme can become an important tool that provides the decision-makers with more detailed information to improve the planning of the Nile River islands development projects. Furthermore, this schema can be expanded to other climatic and topographic regions.

Multispectral Image Classification Based on the Bat Algorithm

There are many traditional classification algorithms used to classify multispectral images, especially those used in remote sensing. But the challenges of using these algorithms for multispectral image classification are that they are slow to implement and have poor classification accuracy. With the development of technologies that mimic nature, many researchers have resorted to using intelligent algorithms instead of traditional algorithms because of their great importance, especially when dealing with large amounts of data. The bat algorithm (BA) is one of the most important of these algorithms. This study aims to verify the possibility of using the BA to classify the multispectral images captured by the Landsat-5 TM satellite image of the study area. The study area represents the Mosul area located in the Nineveh Governorate in northwestern Iraq. The purpose is not only to study the ability of the BA to classify multispectral images but also to obtain a land cover map of this region. The BA showed efficiency in the classification results compared to Maximum Likelihood (ML), where the overall accuracy of classification when using the BA reached (82.136%), while MLreached (79.64%)

Machine learning for atherosclerotic tissue component classification in combined near-infrared spectroscopy intravascular ultrasound imaging: Validation against histology

Background and aimsAccurate classification of plaque composition is essential for treatment planning. Intravascular ultrasound (IVUS) has limited efficacy in assessing tissue types, while near-infrared spectroscopy (NIRS) provides complementary information to IVUS but lacks depth information. The aim of this study is to train and assess the efficacy of a machine learning classifier for plaque component classification that relies on IVUS echogenicity and NIRS-signal, using histology as reference standard. MethodsMatched NIRS-IVUS and histology images from 15 cadaveric human coronary arteries were analyzed (10 vessels were used for training and 5 for testing). Fibrous/pathological intimal thickening (F-PIT), early necrotic core (ENC), late necrotic core (LNC), and calcific tissue regions-of-interest were detected on histology and superimposed onto IVUS frames. The pixel intensities of these tissue types from the training set were used to train a J48 classifier for plaque characterization (ECHO-classification). To aid differentiation of F-PIT from necrotic cores, the NIRS-signal was used to classify non-calcific pixels outside yellow-spot regions as F-PIT (ECHO-NIRS classification). The performance of ECHO and ECHO-NIRS classifications were validated against histology. Results262 matched frames were included in the analysis (162 constituted the training set and 100 the test set). The pixel intensities of F-PIT and ENC were similar and thus these two tissues could not be differentiated by echogenicity. With ENC and LNC as a single class, ECHO-classification showed good agreement with histology for detecting calcific and F-PIT tissues but had poor efficacy for necrotic cores (recall 0.59 and precision 0.29). Similar results were found when F-PIT and ENC were treated as a single class (recall and precision for LNC 0.78 and 0.33, respectively). ECHO-NIRS classification improved necrotic core and LNC detection, resulting in an increase of the overall accuracy of both models, from 81.4% to 91.8%, and from 87.9% to 94.7%, respectively. Comparable performance of the two models was seen in the test set where the overall accuracy of ECHO-NIRS classification was 95.0% and 95.5%, respectively. ConclusionsThe combination of echogenicity with NIRS-signal appears capable of overcoming limitations of echogenicity, enabling more accurate characterization of plaque components.

Hurricane damage assessment using coupled convolutional neural networks: a case study of hurricane Michael

Remote sensing provides crucial support for building damage assessment in the wake of hurricanes. This article proposes a coupled deep learning-based model for damage assessment that leverages a large very high-resolution satellite images dataset and a flexibility of building footprint source. Convolutional Neural Networks were used to generate building footprints from pre-hurricane satellite imagery and conduct a classification of incurred damage. We emphasize the advantages of multiclass classification in comparison with traditional binary classification of damage and propose resolving dataset imbalances due to unequal damage impact distribution with a focal loss function. We also investigate differences between relying on learned features using a deep learning approach for damage classification versus a commonly used shallow machine learning classifier, Support Vector Machines, that requires manual feature engineering. The proposed model leads to an 86.3% overall accuracy of damage classification for a case event of Hurricane Michael and an 11% overall accuracy improvement from the Support Vector Machines classifier, suggesting better applicability of such an open-source deep learning-based workflow in disaster management and recovery. Furthermore, the findings can be integrated into emergency response frameworks for automated damage assessment and prioritization of relief efforts.

Monitoring Urban Expansion and Loss of Agriculture on the North Coast of West Java Province, Indonesia, Using Google Earth Engine and Intensity Analysis.

Uncontrolled urban expansion resulting from urbanization has a disastrous impact on agricultural land. This situation is being experienced by the densely populated and fertile island Java in Indonesia. Remote sensing technologies have developed rapidly in recent years, including the creation of Google Earth Engine (GEE). Intensity analysis (IA) is increasingly being used to systematically and substantially analyze land-use/land-cover (LULC) change. As yet, however, no study of land conversion from agriculture to urban areas in Indonesia has adopted GEE and IA approaches simultaneously. Therefore, this study aims to monitor urban penetration to agricultural land in the north coastal region of West Java Province by applying both methods to two time intervals: 2003–2013 and 2013–2020. Landsat data and a robust random forest (RF) classifier available in GEE were chosen for producing LULC maps. Monitoring LULC change using GEE and IA has demonstrated reliable findings. The overall accuracy of Landsat image classification results for 2003, 2013, and 2020 were 88%, 87%, and 88%, respectively. IA outputs at interval levels for all categories showed that the annual change-of-area rate was higher during 2013–2020 than during 2003–2013. At the category level, IA results showed that the area of agricultural land experienced net losses in both periods, with net loss in 2013–2020 being 2.3 times greater than that in 2003–2013 (∼1,850 ha per year). In contrast, the built-up area made net gains in both periods, reaching almost twice as much in the second period as in the first (∼2,030 ha per year). The transition-level IA performed proved that agricultural land had been the primary target for the expansion of built-up areas. The most extensive spatial distribution of land conversion from agriculture to built-up area was concentrated in the regencies of Bekasi, Karawang, and Cirebon. These findings are intended to provide stakeholders with enrichment in terms of available literature and with valuable inputs useful for identifying better urban and regional planning policies in Indonesia and similar regions.

Tensor-Based Robust Principal Component Analysis With Locality Preserving Graph and Frontal Slice Sparsity for Hyperspectral Image Classification

Tensor-based robust principal component analysis (PCA) methods are efficient to discover the low-rank part of a hyperspectral image for reducing redundant information and guarantee good classification results. However, current methods cannot remove noise adequately, and the residual noise remaining in the low-rank image limits the further improvement of classification performance. Thus, enhancing the robustness to noise is important and helpful for tensor-based robust PCA (RPCA) methods to process hyperspectral images. To this end, we propose a tensor-based RPCA method with a locality preserving graph and frontal slice sparsity (LPGTRPCA) for hyperspectral image classification. Specifically, a tensor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2,2,1}$ </tex-math></inline-formula> norm that requires the frontal slice sparsity of a tensor is defined to extract the noise in the hyperspectral image from the frontal direction. What is more, a position-based Laplacian graph that preserves the local structures of a tensor according to the spatial position is designed for relieving the impact of the residual noise remaining in the low-rank image. Based on the tensor nuclear norm, the tensor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2,2,1}$ </tex-math></inline-formula> norm, and the position-based Laplacian graph, LPGTRPCA efficiently separates the low-rank part with little noise from a raw hyperspectral image and achieves more robust classification results than current methods. LPGTRPCA is optimized by the alternative direction multiplier method (ADMM), and the convergence of solutions is experimentally demonstrated. In the experiments conducted on Indian Pines, Pavia University, and Salinas datasets, LPGTRPCA outperformed various state-of-the-art and classical tensor-based RPCA methods in terms of average class classification accuracy (AA), overall classification accuracy (OA), and kappa coefficient (KC).

Adaptive Resource Allocation Neural Network-Based Mammogram Image Segmentation and Classification

Image processing innovations assume a significant part in diagnosing and distinguishing diseases and monitoring these diseases’ quality. In Medical Images, detection of breast cancer in its earlier stage is most important in this field. Because of the low contrast and uncertain design of the tumor cells in breast images, it is still challenging to classify breast tumors only by visual testing by the radiologists. Hence, improvement of computer-supported strategies has been introduced for breast cancer identification. This work presents an efficient computer-assisted method for breast cancer classification of digital mammograms using Adaptive Resource Allocation Network (ARAN). At first, breast cancer images were taken as input, preprocessing step is utilized to eliminate the noise and unimportant data from the image utilizing a Butterworth filter. Adaptive histogram equalization is utilized to improve the contrast of the image. Multimodal clustering segmentation has been applied, and Tetrolet transformation based feature extraction is applied at various levels, based on this, data classification is implemented. For exact classification, ARAN is utilized to predict if the patient is influenced by breast cancer. Compared with other current research techniques, the proposed strategy predicts the results efficiently. The overall accuracy of ARAN-based mammogram classification is 93.3%.

Detection of Root, Butt, and Stem Rot presence in Norway spruce with hyperspectral imagery

Pathogenic wood decay fungi such as species of are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of and , these fungi are commonly confined to heartwood and thus external infection signs on the bark or foliage of trees are normally absent. Consequently, determining the extent of disease presence in a forest stand with field surveys is not practical for guiding forest management decisions such as optimal rotation time. Remote sensing technologies such as airborne laser scanning and aerial imagery are already used to reduce the reliance on fieldwork in forest inventories. This study aimed to use remote sensing to detect rot in spruce ( L. Karst.) forests in Norway. An airborne hyperspectral imager provided information for classifying the presence or absence of rot in a single-tree-based framework. Ground reference data showing the presence of rot were collected by harvest machine operators during the harvest of forest stands. Random forest and support vector machine algorithms were used to classify the presence and absence of rot. Results indicate a 64% overall classification accuracy for presence-absence classification of rot, although additional work remains to make the classifications usable for practical forest management.HeterobasidionPiceaAbiesPicea abies

Examining the Integration of Landsat Operational Land Imager with Sentinel-1 and Vegetation Indices in Mapping Southern Yellow Pines (Loblolly, Shortleaf, and Virginia Pines)

The mapping of southern yellow pines (loblolly, shortleaf, and Virginia pines) is important to supporting forest inventory and the management of forest resources. The overall aim of this study was to examine the integration of Landsat Operational Land Imager (OLI ) optical data with Sentinel-1 microwave C-band satellite data and vegetation indices in mapping the canopy cover of southern yellow pines. Specifically, this study assessed the overall mapping accuracies of the canopy cover classification of southern yellow pines derived using four data-integration scenarios: Landsat OLI alone; Landsat OLI and Sentinel-1; Landsat OLI with vegetation indices derived from satellite data—normalized difference vegetation index, soil-adjusted vegetation index, modified soil-adjusted vegetation index, transformed soil-adjusted vegetation index, and infrared percentage vegetation index; and 4) Landsat OLI with Sentinel-1 and vegetation indices. The results showed that the integration of Landsat OLI reflectance bands with Sentinel-1 backscattering coefficients and vegetation indices yielded the best overall classification accuracy, about 77%, and standalone Landsat OLI the weakest accuracy, approximately 67%. The findings in this study demonstrate that the addition of backscattering coefficients from Sentinel-1 and vegetation indices positively contributed to the mapping of southern yellow pines.

Impact of mining on land use land cover change and water quality in the Asutifi North District of Ghana, West Africa

Surface and groundwater are important resources for human life and health. Land use changes and mining activities, however, have the potential to compromise the safety of these resources. This study aims to investigate the land cover conditions before and during mining activities and assess the impact on forests, surface, and groundwater resources in the Asutifi North District, Ghana. Landsat data of 30 m resolution was used to assess the land cover change from 2000 to 2020. A cross-sectional analysis of data between April to June 2004 to 2019 was used to assess water quality. The overall accuracy of the land cover classification ranged between (91.33–99.41)% and (78.01–85.95)% respectively for ground-truth validation and validation using the MODIS annual 500 m land cover dataset. Forest cover decreased by 11.47% while agriculture doubled from 12.13% to 24.06% between 2000 and 2020. The introduction of mining is responsible for major land cover changes including the conversion of forest to agricultural land through shifting cultivation. For a general assessment of water quality, the percentage of pH, TB, TDS, NH3, Mg and SO4 measurements outside the EPA admissible limits for drinking water is 16.67%, 83.33%, 0.69%, 0%, 10.42%, and 16.67% respectively. Turbidity is the most impacted water quality parameter affected by land cover changes while NH3 is the least impacted parameter. The incidences of water quality outside EPA/WHO limits for drinking water at all monitoring points call for education and collective action to safeguard water sources from further deterioration. It is necessary mining companies adhere to environmental protection guidelines.

Adaptive Protection Scheme for FREEDM Microgrid Based on Convolutional Neural Network and Gorilla Troops Optimization Technique

Microgrids (MGs) suffer from unpredictable faults on the feeders for various random reasons. These faults could obstruct the stability of the MG operation and damage the components. Moreover, many uncertainties elements affect the MG&#x2019;s response to faults, such as faults&#x2019; types and locations and resistances, MG operation modes, DG penetration levels, load variations, and system topologies. Therefore, fault detection, classification, and location are vital for the MGs as they provide rapid restoration and protect the components. This paper proposes an adaptive protection (AP) scheme for the future renewable electric energy delivery and management (FREEDM) system. The proposed scheme is based on the convolution neural network (CNN), in which the measured current and voltage at buses are processed in multidimensional arrays for the images&#x2019; identification and classification. The gorilla troops optimization (GTO) technique has been used to improve the CNN by acquiring the optimal architecture and hyperparameters of the proposed CNN. The proposed protection scheme can detect the system fault, classify the fault type, and determine the fault location using three proposed CNN-GTO protection scheme models. A communication channel has been performed to transfer the data, information, and tripping signals between the different devices in the FREEDM system. The proposed method is tested using a hypothetical FREEDM microgrid system under different fault conditions. The results show that the proposed CNN-GTO models can detect, classify, and location feeder faults in the FREEDM system with high accuracy. A comparison with the existing schemes such as Support vector machine, Fuzzy logic, conventional CNN, and wavelet-based CNN is performed. The optimized CNN-based GTO models can achieve an overall accuracy for fault detection, classification, and location of 99.37, 99, and 98.2%, respectively.