Learning Machine For Image Classification Research Articles

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment. The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising. The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future research and advancements in this area.

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India.

Alterations in zonal distribution and plasma membrane localization of hepatocyte bile acid transporters in patients with NAFLD.

NAFLD is highly prevalent with limited treatment options. Bile acids (BAs) increase in the systemic circulation and liver during NAFLD progression. Changes in plasma membrane localization and zonal distribution of BA transporters can influence transport function and BA homeostasis. However, a thorough characterization of how NAFLD influences these factors is currently lacking. This study aimed to evaluate the impact of NAFLD and the accompanying histologic features on the functional capacity of key hepatocyte BA transporters across zonal regions in human liver biopsies. A novel machine learning image classification approach was used to quantify relative zonal abundance and plasma membrane localization of BA transporters (bile salt export pump [BSEP], sodium-taurocholate cotransporting polypeptide, organic anion transporting polypeptide [OATP] 1B1 and OATP1B3) in non-diseased (n = 10), NAFL (n = 9), and NASH (n = 11) liver biopsies. Based on these data, membrane-localized zonal abundance (MZA) measures were developed to estimate transporter functional capacity. NAFLD diagnosis and histologic scoring were associated with changes in transporter membrane localization and zonation. Increased periportal BSEPMZA (mean proportional difference compared to non-diseased liver of 0.090) and decreased pericentral BSEPMZA (-0.065) were observed with NASH and also in biopsies with higher histologic scores. Compared to Non-diseased Liver, periportal OATP1B3MZA was increased in NAFL (0.041) and NASH (0.047). Grade 2 steatosis (mean proportional difference of 0.043 when compared to grade 0) and grade 1 lobular inflammation (0.043) were associated with increased periportal OATP1B3MZA. These findings provide novel mechanistic insight into specific transporter alterations that impact BA homeostasis in NAFLD. Changes in BSEPMZA likely contribute to altered BA disposition and pericentral microcholestasis previously reported in some patients with NAFLD. BSEPMZA assessment could inform future development and optimization of NASH-related pharmacotherapies.

Machine learning classification based on k-Nearest Neighbors for PolSAR data.

In this work, we focus on obtaining insights of the performances of some well-known machine learning image classification techniques (k-NN, Support Vector Machine, randomized decision tree and one based on stochastic distances) for PolSAR (Polarimetric Synthetic Aperture Radar) imagery. We test the classifiers methods on a set of actual PolSAR data and provide some conclusions. The aim of this work is to show that suitable adapted standard machine learning methods offer excellent performances vs.computational complexity trade-off for PolSAR image classification. In this work, we evaluate well-known machine learning techniques for PolSAR (Polarimetric Synthetic Aperture Radar) image classification, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), randomized decision tree, and a method based on the Kullback-Leibler stochastic distance. Our experiments with real PolSAR data show that standard machine learning methods, when adapted appropriately, offer a favourable trade-off between performance and computational complexity. The KNN and SVM perform poorly on these data, likely due to their failure to account for the inherent speckle presence and properties of the studied reliefs. Overall, our findings highlight the potential of the Kullback-Leibler stochastic distance method for PolSAR image classification.

Improving Performance of Impact Point Determination Algorithms of Gamma Interaction in a Monolithic Scintillator Using ML and Numerical Methods

Two algorithms for classification of the number of interaction points of gamma-ray in a Compton camera with scintillator crystal are developed, taking into account the limited processing power of the edge electronics in the space environment. One of the algorithms is relaying on a machine learning image classification convolution network, and the other is a numerical method, searching for multiple maxima peaks in the light intensity over the detector plane. Preliminary results of the accuracy of the two approaches are provided and discussed.

Comparative Analysis of Machine Learning Models for Image Detection of Colonic Polyps vs. Resected Polyps.

(1) Background: Colon polyps are common protrusions in the colon's lumen, with potential risks of developing colorectal cancer. Early detection and intervention of these polyps are vital for reducing colorectal cancer incidence and mortality rates. This research aims to evaluate and compare the performance of three machine learning image classification models' performance in detecting and classifying colon polyps. (2) Methods: The performance of three machine learning image classification models, Google Teachable Machine (GTM), Roboflow3 (RF3), and You Only Look Once version 8 (YOLOv8n), in the detection and classification of colon polyps was evaluated using the testing split for each model. The external validity of the test was analyzed using 90 images that were not used to test, train, or validate the model. The study used a dataset of colonoscopy images of normal colon, polyps, and resected polyps. The study assessed the models' ability to correctly classify the images into their respective classes using precision, recall, and F1 score generated from confusion matrix analysis and performance graphs. (3) Results: All three models successfully distinguished between normal colon, polyps, and resected polyps in colonoscopy images. GTM achieved the highest accuracies: 0.99, with consistent precision, recall, and F1 scores of 1.00 for the 'normal' class, 0.97-1.00 for 'polyps', and 0.97-1.00 for 'resected polyps'. While GTM exclusively classified images into these three categories, both YOLOv8n and RF3 were able to detect and specify the location of normal colonic tissue, polyps, and resected polyps, with YOLOv8n and RF3 achieving overall accuracies of 0.84 and 0.87, respectively. (4) Conclusions: Machine learning, particularly models like GTM, shows promising results in ensuring comprehensive detection of polyps during colonoscopies.

Efficacy of machine learning image classification for automated occupancy‐based monitoring

AbstractRemote cameras have become a widespread data‐collection tool for terrestrial mammals, but classifying images can be labor intensive and limit the usefulness of cameras for broad‐scale population monitoring. Machine learning algorithms for automated image classification can expedite data processing, but image misclassifications may influence inferences. Here, we used camera data for three sympatric species with disparate body sizes and life histories – black‐tailed jackrabbits (Lepus californicus), kit foxes (Vulpes macrotis), and pronghorns (Antilocapra americana) – as a model system to evaluate the influence of competing image classification approaches on estimates of occupancy and inferences about space use. We classified images with: (i) single review (manual), (ii) double review (manual by two observers), (iii) an automated‐manual review (machine learning to cull empty images and single review of remaining images), (iv) a pretrained machine‐learning algorithm that classifies images to species (base model), (v) the base model accepting only classifications with ≥95% confidence, (vi) the base model trained with regional images (trained model), and (vii) the trained model accepting only classifications with ≥95% confidence. We compared species‐specific results from alternative approaches to results from double review, which reduces the potential for misclassifications and was assumed to be the best approximation of truth. Despite high classification success, species‐level misclassification rates for the base and trained models were sufficiently high to produce erroneous occupancy estimates and inferences related to space use across species. Increasing the confidence thresholds for image classification to 95% did not consistently improve performance. Classifying images as empty (or not) offered a reasonable approach to reduce effort (by 97.7%) and facilitated a semi‐automated workflow that produced reliable estimates and inferences. Thus, camera‐based monitoring combined with machine learning algorithms for image classification could facilitate monitoring with limited manual image classification.

E-commerce assistant application incorporating machine learning image classification

The rise of mobile applications has helped to provide information in a broader network of products remotely. They simplify the identification of products by using their barcode or even an image of the item. This paper, therefore, aims to create an e-commerce assistant Android application that incorporates machine learning, more precisely, image classification, to assist potentially disadvantaged people on a tight budget in looking to save money. This is achieved by collecting an image dataset of essential products, training a machine learning model, and applying it to the developed application. Although the proposed solution appears to be useful and consistent with all the defined goals, the available datasets are currently insufficient, taking into account both their size and quality of individual images, which negatively affect the machine learning model and limit the potential of the solution being developed. We concluded that our final product succeeds in serving the basic functionality of the app's requirements. In the future, we will reach a wider network of users and investigate their needs, then develop these functions into the application.

Deep and Machine Learning Image Classification of Coastal Wetlands Using Unpiloted Aircraft System Multispectral Images and Lidar Datasets

The recent developments of new deep learning architectures create opportunities to accurately classify high-resolution unoccupied aerial system (UAS) images of natural coastal systems and mandate continuous evaluation of algorithm performance. We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). Twelve combinations of spectral bands and CHMs were used. Our results using the spectral bands showed that the U-Net (83.80–85.27% overall accuracy) and the DeepLabV3 (75.20–83.50% overall accuracy) deep learning techniques outperformed the SVM (60.50–71.10% overall accuracy) and the RF (57.40–71.0%) machine learning algorithms. The addition of the CHM to the spectral bands slightly increased the overall accuracy as a whole in the deep learning models, while the addition of a CHM notably improved the SVM and RF results. Similarly, using bands outside the three spectral bands, namely, near-infrared and red edge, increased the performance of the machine learning classifiers but had minimal impact on the deep learning classification results. The difference in the overall accuracies produced by using UAS-based lidar and SfM point clouds, as supplementary geometrical information, in the classification process was minimal across all classification techniques. Our results highlight the advantage of using deep learning networks to classify high-resolution UAS images in highly diverse coastal landscapes. We also found that low-cost, three-visible-band imagery produces results comparable to multispectral imagery that do not risk a significant reduction in classification accuracy when adopting deep learning models.

Estimation of area and volume change in the glaciers of the Columbia Icefield, Canada using machine learning algorithms and Landsat images

Machine learning image classification algorithms offer a potential for effective and efficient classification of remotely sensed images covering glaciated areas. The Columbia Icefield in Canada is one such place where glaciers are retreating and losing mass over the years. The Columbia Icefield plays an important role in the region's water budget. In this study, the accuracy of three machine learning algorithms, namely, SVM, RF and MLC, were assessed for the classification of snow/ice area on 2020 Landsat 8 OLI image. All three algorithms classified the image with over 99 percent accuracies, but the SVM classifier showed a higher accuracy in debris covered areas on glaciers. Further, we used SVM algorithm to classify Landsat 5 TM - Sept 10, 1985, Landsat 5 TM - Sept 27, 1991, Landsat 8 OLI - Aug 22, 2013, and Landsat 8 OLI - Sept 10, 2020 images in the Columbia Icefield. Among nine glaciers, Saskatchewan (- 4.57 km2), Dome (−2.03 km2), Columbia (−2.06 km2), Stutfield (−2.17 km2), G242655E52112N (−1.39 km2), Athabasca (−1.39 km2), Castleguard (−1.3 km2), and G242614E52109N (−0.54 km2) measured less ice and snow-covered areas between 1985 and 2020. For these nine glaciers, there was a total decrease of 2.01 ± 0.24 km3 volume between 1985 and 2020, which is about 1.81 ± 0.22 km3 water equivalent or 0.12 ± 0.015 km3 water equivalent per year. On average, Saskatchewan (−0.699 km3) and Columbia (−0.307 km3) Glaciers lost the highest volume of snow and ice between 1985 and 2020. This study also concluded that all SVM, RF and MLC produce highly accurate satellite image classification in the glaciated areas.

Mapping Sugarcane in Central India with Smartphone Crowdsourcing

In India, the second-largest sugarcane producing country in the world, accurate mapping of sugarcane land is a key to designing targeted agricultural policies. Such a map is not available, however, as it is challenging to reliably identify sugarcane areas using remote sensing due to sugarcane’s phenological characteristics, coupled with a range of cultivation periods for different varieties. To produce a modern sugarcane map for the Bhima Basin in central India, we utilized crowdsourced data and applied supervised machine learning (neural network) and unsupervised classification methods individually and in combination. We highlight four points. First, smartphone crowdsourced data can be used as an alternative ground truth for sugarcane mapping but requires careful correction of potential errors. Second, although the supervised machine learning method performs best for sugarcane mapping, the combined use of both classification methods improves sugarcane mapping precision at the cost of worsening sugarcane recall and missing some actual sugarcane area. Third, machine learning image classification using high-resolution satellite imagery showed significant potential for sugarcane mapping. Fourth, our best estimate of the sugarcane area in the Bhima Basin is twice that shown in government statistics. This study provides useful insights into sugarcane mapping that can improve the approaches taken in other regions.

E-Commerce Assistant Application incorporating Machine Learning Image Classification

E-Commerce Assistant Application incorporating Machine Learning Image Classification

Ultimate limits of thermal pattern recognition

Quantum Channel Discrimination (QCD) presents a fundamental task in quantum information theory, with critical applications in quantum reading, illumination, data-readout and more. The extension to multiple quantum channel discrimination has seen a recent focus to characterise potential quantum advantage associated with quantum enhanced discriminatory protocols. In this paper, we study thermal imaging as an environment localisation task, in which thermal images are modelled as ensembles of Gaussian phase insensitive channels with identical transmissivity, and pixels possess properties according to background (cold) or target (warm) thermal channels. Via the teleportation stretching of adaptive quantum protocols, we derive ultimate limits on the precision of pattern classification of abstract, binary thermal image spaces, and show that quantum enhanced strategies may be used to provide significant quantum advantage over known optimal classical strategies. The environmental conditions and necessary resources for which advantage may be obtained are studied and discussed. We then numerically investigate the use of quantum enhanced statistical classifiers, in which quantum sensors are used in conjunction with machine learning image classification methods. Proving definitive advantage in the low loss regime, this work motivates the use of quantum enhanced sources for short-range thermal imaging and detection techniques for future quantum technologies.

Use of high-resolution unmanned aerial systems imagery and machine learning to evaluate grain sorghum tolerance to mesotrione

Manual evaluation of crop injury to herbicides is time-consuming. Unmanned aerial systems (UAS) and high-resolution multispectral sensors and machine learning classification techniques have the potential to save time and improve precision in the evaluation of herbicide injury in crops, including grain sorghum (Sorghum bicolor L. Moench). The objectives of our research are to (1) evaluate three supervised classification algorithms [support vector machine (SVM), maximum likelihood, and random forest] for categorizing high-resolution UAS imagery to aid in data extraction and (2) evaluate the use of vegetative indices (VIs) collected from UAS imagery as an alternative to traditional methods of visible herbicide injury assessment in mesotrione-tolerant grain sorghum breeding trials. An experiment was conducted in a randomized complete block design using a factorial treatment arrangement of three genotypes by four mesotrione doses. Herbicide injury was rated visually on a scale of 0 (no injury) to 100 (complete plant mortality). The UAS flights were flown at 9, 15, 21, 27, and 35 days after treatment. Results show the SVM algorithm to be the most consistently accurate, and high correlations (r = − 0.83 to −0.94; p < 0.0001) were observed between the normalized difference VI and ground-measured herbicide injury. Therefore, we conclude that VIs collected with UAS coupled with machine learning image classification has the potential to be an effective method of evaluating mesotrione injury in grain sorghum.

Middle-Level Features for the Explanation of Classification Systems by Sparse Dictionary Methods.

Machine learning (ML) systems are affected by a pervasive lack of transparency. The eXplainable Artificial Intelligence (XAI) research area addresses this problem and the related issue of explaining the behavior of ML systems in terms that are understandable to human beings. In many explanation of XAI approaches, the output of ML systems are explained in terms of low-level features of their inputs. However, these approaches leave a substantive explanatory burden with human users, insofar as the latter are required to map low-level properties into more salient and readily understandable parts of the input. To alleviate this cognitive burden, an alternative model-agnostic framework is proposed here. This framework is instantiated to address explanation problems in the context of ML image classification systems, without relying on pixel relevance maps and other low-level features of the input. More specifically, one obtains sets of middle-level properties of classification inputs that are perceptually salient by applying sparse dictionary learning techniques. These middle-level properties are used as building blocks for explanations of image classifications. The achieved explanations are parsimonious, for their reliance on a limited set of middle-level image properties. And they can be contrastive, because the set of middle-level image properties can be used to explain why the system advanced the proposed classification over other antagonist classifications. In view of its model-agnostic character, the proposed framework is adaptable to a variety of other ML systems and explanation problems.

Deep and wide feature based extreme learning machine for image classification

Extreme Learning Machine (ELM) is a powerful and favorable classifier used in various applications due to its fast speed and good generalization capability. However, when dealing with complex visual tasks, the shallow architecture of ELM makes it infeasible to have good performance when raw image data are directly fed in as input. Therefore, several works tried to make use of deep neural networks (DNNs) to extract features before ELM classification. On the other hand, when the depth of DNN is too deep, the ELM classifier may suffer from overfitting problem. To solve this issue, a novel deep and wide feature based Extreme Learning Machine (DW-ELM) has been proposed in this research work. We show that the overfitting problem can be largely remedied by employing a “widened” convolutional neural network (CNN) for feature extraction, in the sense that the number of feature maps for each convolutional layer is increased by factor of k compared to a reference model, i.e. deep residual networks (ResNets). While the wide design of residual networks has been shown to benefit image classification in terms of accuracy and efficiency, its application for feature extraction is not fully investigated.We provide an extensive experimental study in this work, showing that when combined with ELM that serves as a classifier, using wide ResNets (WRNs) for feature extraction can produce a performance leap on all benchmark datasets compared to a plain end-to-end trained network over a wide range of selections regardless of architecture choices and ELM designs, while normal ResNets as feature extractors do not provide a performance gain. The gap is even larger when fewer training iterations are employed. This indicates that a good feature extractor for ELM must be wide and deep. Experiments conducted on five benchmark datasets (CIFAR-100, CIFAR-10, STL-10, Flower-102 and Fashion-MNIST) have shown significant accuracy enhancement as well as training stability of the proposed DW-ELM. Ablation studies also demonstrate that the ELM classifier is an important component for DW-ELM which enables superior performance compared with SVM based image classification approaches.

Containergy—A Container-Based Energy and Performance Profiling Tool for Next Generation Workloads

Run-time profiling of software applications is key to energy efficiency. Even the most optimized hardware combined to an optimally designed software may become inefficient if operated poorly. Moreover, the diversification of modern computing platforms and broadening of their run-time configuration space make the task of optimally operating software ever more complex. With the growing financial and environmental impact of data center operation and cloud-based applications, optimal software operation becomes increasingly more relevant to existing and next-generation workloads. In order to guide software operation towards energy savings, energy and performance data must be gathered to provide a meaningful assessment of the application behavior under different system configurations, which is not appropriately addressed in existing tools. In this work we present Containergy, a new performance evaluation and profiling tool that uses software containers to perform application run-time assessment, providing energy and performance profiling data with negligible overhead (below 2%). It is focused on energy efficiency for next generation workloads. Practical experiments with emerging workloads, such as video transcoding and machine-learning image classification, are presented. The profiling results are analyzed in terms of performance and energy savings under a Quality-of-Service (QoS) perspective. For video transcoding, we verified that wrong choices in the configuration space can lead to an increase above 300% in energy consumption for the same task and operational levels. Considering the image classification case study, the results show that the choice of the machine-learning algorithm and model affect significantly the energy efficiency. Profiling datasets of AlexNet and SqueezeNet, which present similar accuracy, indicate that the latter represents 55.8% in energy saving compared to the former.

Discriminative globality-locality preserving extreme learning machine for image classification

Extreme learning machines (ELM) have been widely used in classification due to their simple theory and good generalization ability. However, there remains a major challenge: it is difficult for ELM algorithms to maintain the manifold structure and the discriminant information contained in the data. To address this issue, we propose a discriminant globality-locality preserving extreme learning machine (DGLELM) in this paper. In contrast to ELM, DGLELM not only considers the global discriminative structure of the dataset but also makes the best use of the local discriminative geometry information. DGLELM optimizes the projection direction of the ELM output weights by maximizing the inter-class dispersion and minimizing the intra-class dispersion for global and local data. Experiments on several widely used image databases validate the performance of DGLELM. The experimental results show that our approach achieves significant improvements over state-of-the-art ELM algorithms.

Monitoring land-cover and land-use dynamics in Fanjingshan National Nature Reserve

Fanjingshan National Nature Reserve (FNNR) in China is a biodiversity hotspot that is part of a larger, multi-use landscape where tourism, farming, grazing, and other land uses occur. Payment for ecosystem services (PES) programs that encourage afforestation on farmlands may be important drivers of land-cover and land-use change in the region that surrounds FNNR. Our objective is to monitor and examine vegetation and land-use changes, including PES-related afforestation, between 1989 and 2017. We utilize several image processing techniques, such as illumination normalization approaches to suppress terrain effects, and multi-seasonal image compositing to minimize persistent cloud cover. Ancillary data were also incorporated to generate reliable vegetation and land-use change information. A random forest machine learning image classification routine is implemented through the cloud-based Google Earth Engine platform and refined using optimal classifier parameter tuning. Land-use transitions are identified and mapped with the implementation of stable training sites, discrete image classification, and logical land-use transition rules. Accuracy assessment results indicate our change detection workflow provides a reliable methodology to remotely monitor long-term forest cover and land-use changes in this mountainous, forested, and cloud prevalent region. We quantify the area of new built development and afforestation land and found that most of the land transitions took place in reserve buffer and its adjacent environs. For example, less than 2 km2 of new built was identified within the reserve boundary compared to 25 km2 for the entire study area between 1995 and 2016. We also shed light on the strengths and weaknesses of using Google Earth Engine for land-cover and land-use change studies. This efficient and open-access technique is important not only for assessing environmental changes and PES efficacy, but also for evaluating other conservation policies elsewhere.

Convolutional neural network based on an extreme learning machine for image classification

Over the last decade, substantial advances have been made in various computer vision technologies and many of them are based on convolutional neural network (CNN) architecture. Typically, CNN is trained by a stochastic gradient descent algorithm using Back-Propagation (BP) but the training process is adversely affected by slow convergence and the need for extensive parameter tuning. In this paper, we propose a new CNN architecture and training algorithm based on an Extreme Learning Machine (ELM) to overcome these drawbacks. The proposed training algorithm is a layer-wise training method for CNN, and uses an alternating strategy of random convolutional filters and semi-supervised filters to combine the advantages of both approaches. On each semi-supervised layer, the CNN efficiently solves a convex optimization problem based on nonlinear random projection. It is faster and requires less human effort than BP-based training. We experimentally validated the proposed method using a well-known character and object recognition benchmark. In our experiment, the performance of our method is comparable to approaches based on deep features and has higher accuracy than other unsupervised feature-learning methods.