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Deforestation is an important environmental issue that involves the removal of forests on a large scale, resulting in ecological imbalance and biodiversity loss. Synthetic Aperture Radar (SAR) images are widely used as a valuable tool to detect deforestation effectively. The SAR technology allows capturing high-resolution images irrespective of weather conditions or daylight, making it helpful to monitor remote and densely vegetated areas. Recently, deep learning techniques used on SAR images have showcased promising results in the automation of deforestation detection and mapping processes. By leveraging neural networks (NNs) and machine learning (ML) systems, these approaches examine SAR data to recognize deforestation patterns and estimate deforestation rates over time. Therefore, this study develops a cross-breed multilayer convolutional neural network (CNN) for deforestation rate estimation in the Amazon. The proposed model initially preprocesses the input SAR data to remove the speckle noise using a box car mean squared sparse coding filter (BCMSSCF). Besides, crossbreed multilayer CNN (CM_CNN) is used for mapping and segmentation of the deforested area. To determine the pace of deforestation in the Amazon region, a widespread experimental analysis was performed on the LBA-ECO LC-14 dataset. A detailed comparative result analysis of the proposed model is made with recent approaches. The experimental results stated that the proposed model shows promising results in terms of different performance measures.

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

Background With the advent of the information era, the significance of visual communication design has escalated within the realm of increasingly prevalent network applications. Addressing the deficiency observed in prevailing sentiment analysis approaches in visual communication design, which predominantly leverage the holistic image information while overlooking the nuances inherent in the localized regions that accentuate emotion, coupled with the inadequacy in semantically mining diverse channel features. Methods This paper introduces a dual-attention multilayer feature fusion-based methodology denoted as DA-MLCNN. Initially, a multilayer convolutional neural network (CNN) feature extraction architecture is devised to effectuate the amalgamation of both overall and localized features, thereby extracting both high-level and low-level features inherent in the image. Furthermore, the integration of a spatial attention mechanism fortifies the low-level features, while a channel attention mechanism bolsters the high-level features. Ultimately, the features augmented by the attention mechanisms are harmonized to yield semantically enriched discerning visual features for training sentiment classifiers. Results This culminates in attaining classification accuracies of 79.8% and 55.8% on the Twitter 2017 and Emotion ROI datasets, respectively. Furthermore, the method attains classification accuracies of 89%, 94%, and 91% for the three categories of sadness, surprise, and joy on the Emotion ROI dataset. Conclusions The efficacy demonstrated on dichotomous and multicategorical emotion image datasets underscores the capacity of the proposed approach to acquire more discriminative visual features, thereby enhancing the landscape of visual sentiment analysis. The elevated performance of the visual sentiment analysis method serves to catalyze innovative advancements in visual communication design, offering designers expanded prospects and possibilities.

Deep learning techniques and tools have experienced enormous growth and widespread diffusion in recent years. Among the areas where deep learning has become more widespread there are computational biology and cognitive neuroscience. At the same time, the need for tools able to explore, understand, and possibly manipulate, a deep learning model has strongly emerged. We propose an approach to map a deep learning model into a multilayer network. Our approach is tailored to Convolutional Neural Networks (CNN), but can be easily extended to other architectures. In order to show how our mapping approach enables the exploration and management of deep learning networks, we illustrate a technique for compressing a CNN. It detects whether there are convolutional layers that can be pruned without losing too much information and, in the affirmative case, returns a new CNN obtained from the original one by pruning such layers. We prove the effectiveness of the multilayer mapping approach and the corresponding compression algorithm on the VGG16 network and two benchmark datasets, namely MNIST, and CALTECH-101. In the former case, we obtain a 0.56% increase in accuracy, precision, and recall, and a 21.43% decrease in mean epoch time. In the latter case, we obtain an 11.09% increase in accuracy, 22.27% increase in precision, 38.66% increase in recall, and 47.22% decrease in mean epoch time. Finally, we compare our multilayer mapping approach with a similar one based on single layers and show the effectiveness of the former. We show that a multilayer network-based approach is able to capture and represent the complexity of a CNN. Furthermore, it allows several manipulations on it. An extensive experimental analysis described in the paper demonstrates the suitability of our approach and the goodness of its performance.

We present a machine learning based COVID-19 cough classifier which can discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone. This type of screening is non-contact, easy to apply, and can reduce the workload in testing centres as well as limit transmission by recommending early self-isolation to those who have a cough suggestive of COVID-19. The datasets used in this study include subjects from all six continents and contain both forced and natural coughs, indicating that the approach is widely applicable. The publicly available Coswara dataset contains 92 COVID-19 positive and 1079 healthy subjects, while the second smaller dataset was collected mostly in South Africa and contains 18 COVID-19 positive and 26 COVID-19 negative subjects who have undergone a SARS-CoV laboratory test. Both datasets indicate that COVID-19 positive coughs are 15%–20% shorter than non-COVID coughs. Dataset skew was addressed by applying the synthetic minority oversampling technique (SMOTE). A leave-p-out cross-validation scheme was used to train and evaluate seven machine learning classifiers: logistic regression (LR), k-nearest neighbour (KNN), support vector machine (SVM), multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM) and a residual-based neural network architecture (Resnet50). Our results show that although all classifiers were able to identify COVID-19 coughs, the best performance was exhibited by the Resnet50 classifier, which was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98. An LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs, with an AUC of 0.94 after selecting the best 13 features from a sequential forward selection (SFS). Since this type of cough audio classification is cost-effective and easy to deploy, it is potentially a useful and viable means of non-contact COVID-19 screening. •A machine learning based COVID-19 cough classifier has been developed.•This classifier achieves the highest AUC of 0.98 from a residual based architecture.•Cough audio recordings are collected from all six continents of the globe.•COVID-19 positive coughs are 15% to 20% shorter than non-COVID coughs.•A special feature extraction technique preserves end-to-end time-domain patterns.

Stock price prediction is a rich research topic that has attracted interest from various areas of science. The recent success of machine learning in speech and image recognition has prompted researchers to apply these methods to asset price prediction. The majority of literature has been devoted to predicting either the actual asset price or the direction of price movement. In this paper, we study a hitherto little explored question of predicting significant changes in stock price based on previous changes using machine learning algorithms. We are particularly interested in the performance of neural network classifiers in the given context. To this end, we construct and test three neural network models including multilayer perceptron, convolutional net, and long short-term memory net. As benchmark models, we use random forest and relative strength index methods. The models are tested using 10-year daily stock price data of four major US public companies. Test results show that predicting significant changes in stock price can be accomplished with a high degree of accuracy. In particular, we obtain substantially better results than similar studies that forecast the direction of price change.

Mapping deforestation is an essential step in the process of managing tropical rainforests. It lets us understand and monitor both legal and illegal deforestation and its implications, which include the effect deforestation may have on climate change through greenhouse gas emissions. Given that there is ample room for improvements when it comes to mapping deforestation using satellite imagery, in this study, we aimed to test and evaluate the use of algorithms belonging to the growing field of deep learning (DL), particularly convolutional neural networks (CNNs), to this end. Although studies have been using DL algorithms for a variety of remote sensing tasks for the past few years, they are still relatively unexplored for deforestation mapping. We attempted to map the deforestation between images approximately one year apart, specifically between 2017 and 2018 and between 2018 and 2019. Three CNN architectures that are available in the literature—SharpMask, U-Net, and ResUnet—were used to classify the change between years and were then compared to two classic machine learning (ML) algorithms—random forest (RF) and multilayer perceptron (MLP)—as points of reference. After validation, we found that the DL models were better in most performance metrics including the Kappa index, F1 score, and mean intersection over union (mIoU) measure, while the ResUnet model achieved the best overall results with a value of 0.94 in all three measures in both time sequences. Visually, the DL models also provided classifications with better defined deforestation patches and did not need any sort of post-processing to remove noise, unlike the ML models, which needed some noise removal to improve results.

CNN originates from image processing and is not commonly known as a forecasting technique in time-series analysis which depends on the quality of input data. One of the methods to improve the quality is by smoothing the data. This study introduces a novel hybrid exponential smoothing using CNN called Smoothed-CNN (S-CNN). The method of combining tactics outperforms the majority of individual solutions in forecasting. The S-CNN was compared with the original CNN method and other forecasting methods such as Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM). The dataset is a year time-series of daily website visitors. Since there are no special rules for using the number of hidden layers, the Lucas number was used. The results show that S-CNN is better than MLP and LSTM, with the best MSE of 0.012147693 using 76 hidden layers at 80%:20% data composition.

Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms.

The use of deep learning (DL) techniques for side-channel analysis (SCA) has become increasingly popular recently. This paper assesses the application of DL to non-profiled SCA attacks on AES-128 encryption, taking into consideration various challenges, including high-dimensional data, imbalanced classes, and countermeasures. The paper proposes using a multi-layer perceptron (MLP) and a convolutional neural network (CNN) to tackle hiding protection methods, such as noise generation and de-synchronization. The paper also introduces a technique called significant Hamming weight (SHW) labeling and a dataset reconstruction approach to handle imbalanced datasets, resulting in a reduction of 30% in the number of measurements required for training. The experimental results on reconstructed dataset demonstrate improved performance in DL-based SCA compared to binary labeling techniques, especially in the face of hiding countermeasures. This leads to better results for non-profiled attacks on different targets, such as ASCAD and RISC-V microcontrollers.