Trees are the key components of urban vegetation in cities. The timely and accurate identification of existing urban tree species with their location is the most important task for improving air, water, and land quality; reducing carbon accumulation; mitigating urban heat island effects; and protecting soil and water balance. Light detection and ranging (LiDAR) is frequently used for extracting high-resolution structural information regarding tree objects. LiDAR systems are a cost-effective alternative to the traditional ways of identifying tree species, such as field surveys and aerial photograph interpretation. The aim of this work was to assess the usage of machine learning algorithms for classifying the deciduous (broadleaf) and coniferous tree species from 3D raw LiDAR data on the Davutpasa Campus of Yildiz Technical University, Istanbul, Turkey. First, ground, building, and low, medium, and high vegetation classes were acquired from raw LiDAR data using a hierarchical-rule-based classification method. Next, individual tree crowns were segmented using a mean shift clustering algorithm from high vegetation points. A total of 25 spatial- and intensity-based features were utilized for support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) classifiers to discriminate deciduous and coniferous tree species in the urban area. The machine learning-based classification’s overall accuracies were 80%, 83.75%, and 73.75% for the SVM, RF, and MLP classifiers, respectively, in split 70/30 (training/testing). The SVM and RF algorithms generally gave better classification results than the MLP algorithm for identifying the urban tree species.

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The Use of Machine Learning Algorithms in Urban Tree Species Classification

Land use classification of high resolution remote sensing images using an encoder based modified GAN architecture

Covid-19 merupakan virus yang menyebar dan meluas sehingga berubah menjadi suatu pandemi. Virus Covid-19 menyerang melalui organ vital manusia yaitu paru-patu, oleh karena itu peneliti lebih berfokus untuk mengidentifikasi Covid-19 pada paru-paru. Penelitian ini dilakukan dengan menggunakan citra CT Scan paru-paru dan bertujuan untuk mendeteksi ada tidaknya virus dengan cara mengklasifikasikan citra Covid-19 ke dalam tiga kelas menggunakan algoritma Random Forest serta mengkombinasikannya dengan menyertakan beberapa ekstraksi fitur yaitu Haralick , Color Histogram , dan Hu-Moments . Penelitian dimulai dengan hanya memasukkan satu fitur ke dalam percobaan, lalu mengkombinasikan dengan fitur yang lain, kemudian membandingkannya menggunakan klasifikasi oleh algoritma lain seperti K-Nearest Neighbor (KNN), Decision Tree , Linear Discriminant Analysis (LDA), Logistic Regression , Support Vector Machine (SVM), dan Naive Bayes . Hasil penelitian menunjukkan bahwa akurasi tertinggi dihasilkan oleh algoritma Random Forest dengan memasukkan fitur Haralick dan Color Histogram ke dalam proses yaitu sebesar 96,9%, diikuti oleh KNN sebesar 96,5%, Decision Tree sebesar 95,5%, dan yang paling rendah yaitu Naive Bayes sebesar 42,4%

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Random Forest Classifier untuk Deteksi Penderita COVID-19 berbasis Citra CT Scan

The mutants of novel coronavirus (COVID-19 or SARS-Cov-2) are spreading with different variants across the globe, affecting human health and the economy. Rapid detection and providing timely treatment for the COVID-19 infected is the greater challenge. For fast and cost-effective detection, artificial intelligence (AI) can perform a key role in enhancing chest X-ray images and classifying them as infected/non-infected. However, AI needs huge datasets to train and detect the COVID-19 infection, which may impact the overall system speed. Therefore, Deep Neural Network (DNN) is preferred over standard AI models to speed up the classification with a set of features from the datasets. Further, to have accurate feature extraction, an algorithm that combines Zernike Moment Feature (ZMF) and Gray Level Co-occurrence Matrix Feature (GF) is proposed and implemented. The proposed algorithm uses 36 Zernike Moment features with variance and contrast textures. This helps to detect the COVID-19 infection accurately. Finally, the Region Blocking (RB) approach with an optimum sub-image size (32 × 32) is employed to improve the processing speed up to 2.6 times per image. The performance of this implementation presents an accuracy (A) of 93.4%, sensitivity (Se) of 72.4%, specificity (Sp) of 95%, precision (Pr) of 74.9% and F1-score (F1) of 72.3%. These metrics illustrate that the proposed model can identify the COVID-19 infection with a lesser dataset and improved accuracy up to 1.3 times than state-of-the-art existing models.

https://www.mdpi.com/2079-9292/11/17/2682/pdf?version=1661931702

Rapid and Accurate Diagnosis of COVID-19 Cases from Chest X-ray Images through an Optimized Features Extraction Approach

Every biological system on the planet is severely impacted by environmental change, and its primary driver is deforestation. Meanwhile, quantitative analysis of changes in Land Use and Land Cover (LULC) is one of the prominent ways to manage and understand land transformation; thus, it is essential to inspect the performance of various techniques for LULC mapping to recognize the better classifier to more applications of earth observation. This article develops a Tunicate Swarm Algorithm with Deep Learning Enabled Land Use and Land Cover Change Detection (TSADL-LULCCD) technique in Nallamalla Forest, India. The presented TSADL-LULCCD technique mainly focuses on the identification and classification of land use in the Nallamalla forest using LANDSAT images. To accomplish this, the presented TSADL-LULCCD technique employs a dense EfficientNet model for feature extraction. In addition, the Adam optimizer is applied for the optimal hyper parameter tuning of the dense EfficientNet approach. For land cover classification, the TSADL-LULCCD technique exploits the Deep Belief Network (DBN) approach. To tune the hyper parameters related to the DBN system, the TSA is used. The experimental validation of the TSADL-LULCCD algorithm is tested on LANDSAT-7-based Nallamalla region images. The experimental results stated that the TSADL-LULCCD technique exhibits better performance over other existing models in terms of different evaluation measures.

/pdf/tunicate-swarm-algorithm-with-deep-learning-based-land-use-30u0jwo7.pdf

Tunicate Swarm Algorithm with Deep Learning Based Land Use and Cover Change Detection in Nallamalla Forest India

Fusing deep convolutional neural network features with Thepade’s SBTC for land usage identification

From staying quarantined at home, practicing work from home to moving outside wearing masks and carrying sanitizers, every individual has now become so adaptive to so called ‘New Normal’ post series of lockdowns across the countries. The situation triggered by novel Coronavirus has changed the behaviour of every individual towards every other living as well as non-living entity. In the Wuhan city of China, multiple cases were reported of pneumonia caused due to unknown reasons. The concerned medical authorities confirmed the cause to be Coronavirus. The symptoms seen in these cases were not much different than those seen in case of pneumonia. Earlier the research has been carried out in the field of pneumonia identification and classification through X-ray images of chest. The difficulty in identifying Covid19 infection at initial stage is due to high resemblance of its symptoms with the infection caused due to pneumonia. Hence it is trivial to well distinguish cases of coronavirus from pneumonia that may help in saving life of patients. The paper uses chest X-ray images to identify Covid19 infection in lungs using machine learning classifiers and ensembles with Gray-Level Cooccurrence Matrix (GLCM) features. The advocated methodology extracts statistical texture features from X-ray images by computing a GLCM for each image. The matrix is computed by considering various stride combinations. These GLCM features are used to train the machine learning classifiers and ensembles. The paper explores both the multiclass classification (X-ray images are classified into one of the three classes namely Covid19 affected, Pneumonia affected and normal lungs) and binary classification (Covid19 affected and other). The dataset used for evaluating performance of the method is open sourced and can be accessed easily. Proposed method being simple and computationally effective achieves noteworthy performance in terms of Accuracy, F-Measure, MCC, PPV and Sensitivity. In sum, the best stride combination of GLCM and ensemble of machine learning classifiers is suggested as vital outcome of the proposed method for effective Covid19 identification from chest X-ray images

https://elcvia.cvc.uab.es/article/download/v19-n3-thepade-bang-chaudari/pdf_59

Covid19 Identification from Chest X-ray Images Using Machine Learning Classifiers with GLCM Features

• Empirical performance assessment of six pre-trained deep CNN architectures (VGG16, VGG19, InceptionV3, DenseNet121, MobileNet, and Xception) for the human face liveness detection application. • Modification of all considered pre-trained DCNN models to make them suitable for human face liveness detection. • Observation of the testing accuracy & HTER observed over a diverse number of epochs for assessing the pre-trained DCNN models. • Fine-tuning of the last two convolution blocks of VGG16 and VGG19 at the constant learning rate to get better test accuracy (100%). Advancement in deep learning is mapping to every field of life and applying it to almost all research problems. Numerous Deep Convolutional Neural Network (DCNN) architectures are being proposed, giving different results based on the depth and value of hyperparameters. The entire development of such DCNN architectures from scratch needs a lot of effort, and such architectures may not be used for other applications than the one they are structured for. Transfer learning is a way to modify these pre-trained networks to make them suitable for newer diverse applications. This paper attempts to empirically assess the performance and suitability of existing pre-trained DCNN architectures for human face liveness detection. Due to the advent of ambient computing for contactless identification of humans using their biometric traits, human face liveness detection proves to be an important research area. Six pre-trained DCNN models, alias VGG16, VGG19, DensNet121, Xception, MobileNet, and InceptionV3, are considered for empirical assessment in human face liveness detection. The method is explored using two face liveness detection datasets - NUAA and Replay-Attack. Face Liveness Accuracy, and Half Total Error Rate (HTER) are considered prime performance evaluation metrics. At a learning rate of 10 −4 , the VGG19 network with scenario “Original VGG” gives the highest face liveness detection accuracy, which is the outcome of current research. 

Face presentation attack identification optimization with adjusting convolution blocks in VGG networks

The evolution and improvements of deep learning are being used to tackle any research obstacles that could be converted into classification problems in all spheres of life. Each Deep convolutional neural network (DCNN) design's output is determined by the depth and value of the hyperparameters, which explains why so many of them have been proposed. These DCNN architectures must be created entirely from scratch, and they can only be used for the applications for which they were intended. Transfer learning may be used to modify these pre-trained networks so they are more appropriate for particular purposes. This article aims to evaluate the empirical performance of the applicability of pre-trained DCNN models to identify human face presentation threats (FPAD). Human FPAD is one of the most significant and crucial areas of research right now because of the introduction of ambient computing, which necessitates contact-free identification of persons with the help of their biometric traits. Six pre-trained DCNN models are taken into account for an experimental evaluation in human FPAD alias VGG19, VGG16, DensNet121, MobileNet, Xception, and InceptionV3. The investigation makes use of the NUAA and Replay-Attack benchmark FPAD datasets. Thepade's sorted block truncation coding (SBTC) 10-ary features are merged with deep learning features produced from the finest performing finetuned DCNNs to enhance the FPAD capabilities of analyzed machine learning (ML) classifiers. The integration of features of Thepade's SBTC 10-ary and DCNN has considerably increased the FPAD accuracy of ML classifiers with slightly more computations of feature extraction. 

Enhanced Face Presentation Attack Prevention Employing Feature Fusion of Pre-trained Deep Convolutional Neural Network Model and Thepade's Sorted Block Truncation Coding

Mayuresh R. Dindorkar

Papers

Fusing deep convolutional neural network features with Thepade’s SBTC for land usage identification

Covid19 Identification from Chest X-ray Images Using Machine Learning Classifiers with GLCM Features

Face presentation attack identification optimization with adjusting convolution blocks in VGG networks

Enhanced Face Presentation Attack Prevention Employing Feature Fusion of Pre-trained Deep Convolutional Neural Network Model and Thepade's Sorted Block Truncation Coding