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Gestational diabetes mellitus (GDM) is one of the pregnancy complications that poses a significant risk on mothers and babies as well. GDM usually diagnosed at 22–26 of gestation. However, the early prediction is desirable as it may contribute to decrease the risk. The continuous monitoring for mother's vital signs helps in predicting any deterioration during pregnancy. The originality of this paper is to provide comprehensive framework for pregnancy women monitoring. The proposed Data Replacement and Prediction Framework consists of three layers which are: (i) IoT Layer, (ii) Fog Layer, and (iii) Cloud Layer. The first layer used IOT sensors to aggregate vital sings from pregnancies using invasive and noninvasive sensors. Then the vital signs transmitted to fog nodes to processed and finally stored in the cloud layer. The main contribution in this paper is located in the fog layer producing GDM module to implement two influential tasks which are: (i) Data Finding Methodology (DFM), and (ii) Explainable Prediction Algorithm (EPM) using DNN. First, the DFM is used to replace the unused data to free the cache space for the new incoming data items. The cache replacement is very important in the case of healthcare system as the incoming vital signs are frequent and must be replaced continuously. Second, the EPM is used to predict the incidence of GDM that may occur in the second trimester of the pregnancy. To evaluate our model, we extract data of 16,354 pregnancy women from medical information mart for intensive care (MIMIC III) benchmark dataset. For each woman, vital signs, demographic data and laboratory tests was aggregated. The results of the prediction model superior the state of the art (ACC = 0.957, AUC = 0.942). Regarding to explainability, we utilized Shapley additive explanation framework to provide local and global explanation for the developed models. Overall, the proposed framework is medically intuitive, allow the early prediction of GDM with cost effective solution. [ABSTRACT FROM AUTHOR]

Availability is one of the primary goals of smart networks, especially, if the network is under heavy video streaming traffic. In this paper, we propose a deep learning based methodology to enhance availability of video streaming systems by developing a prediction model for video streaming quality, required power consumption, and required bandwidth based on video codec parameters. The H.264/AVC codec, which is one of the most popular codecs used in video steaming and conferencing communications, is chosen as a case study in this paper. We model the predicted consumed power, the predicted perceived video quality, and the predicted required bandwidth for the video codec based on video resolution and quantization parameters. We train, validate, and test the developed models through extensive experiments using several video contents. Results show that an accurate model can be built for the needed purpose and the video streaming quality, required power consumption, and required bandwidth can be predicted accurately which can be utilized to enhance network availability in a cooperative environment. [ABSTRACT FROM AUTHOR]

Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field—integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research. [ABSTRACT FROM AUTHOR]

Scholarly work points to 5S + 1, a simple yet powerful method of initiating quality in manufacturing, as one of the foundations of Lean manufacturing and the Toyota Production Systems. The 6th S, safety, is often used to prevent future occupational hazards, therefore, reducing the loss of time, money, and human resources. This paper aims to show how Industry 4.0 technologies such as computer-based vision and object detection algorithms can help implement the 6th S in 5S + 1 through monitoring and detecting workers who fail to adhere to standard safety practices such as wearing personal protective equipment (PPE). The paper evaluated and analyzed three different detection approaches and compared their performance metrics. In total, seven models were proposed to perform such a task. All the proposed models utilized You-Only-Look-Once (YOLO v7) architecture to verify workers' PPE compliance. In approach I, three models were used to detect workers, safety helmets and safety vests. Then, a machine learning algorithm was used to verify if each detected worker is in PPE compliance. In approach II, the model simultaneously detects individual workers and verifies PPE compliance. In approach III, three different models were used to detect workers in the input feed. Then, a deep learning algorithm was used to verify the safety. All models were trained on Pictor-v3 dataset. It is found that the third approach, when utilizing VGG-16 algorithm, achieves the best performance, i.e., 80% F1 score, and can process 11.79 frames per second (FPS), making it suitable for real-time detection. [ABSTRACT FROM AUTHOR]

Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. However, the understanding of ISF's influence on the compressive strength (CS) behavior of concrete is still questioned by the scientific society. The presented paper aims to use machine learning (ML) and deep learning (DL) algorithms to predict the CS of steel fiber reinforced concrete (SFRC) incorporating hooked ISF based on the data collected from the open literature. Accordingly, 176 sets of data are collected from different journals and conference papers. Based upon the initial sensitivity analysis, the most influential parameters like water-to-cement (W/C) ratio and content of fine aggregates (FA) tend to decrease the CS of SFRC. Meanwhile, the CS of SFRC could be enhanced by increasing the amount of superplasticizer (SP), fly ash, and cement (C). The least contributing factors include the maximum size of aggregates (Dmax) and the length-to-diameter ratio of hooked ISFs (L/DISF). Several statistical parameters are also used as metrics to evaluate the performance of implemented models, such as coefficient of determination (R2), mean absolute error (MAE), and mean of squared error (MSE). Among different ML algorithms, convolutional neural network (CNN) with R2 = 0.928, RMSE = 5.043, and MAE = 3.833 shows higher accuracy. On the other hand, K-nearest neighbor (KNN) algorithm with R2 = 0.881, RMSE = 6.477, and MAE = 4.648 results in the weakest performance. [ABSTRACT FROM AUTHOR]

Insufficient data is always a challenge for developing an accurate machine learning or deep learning model in manufacturing processes, especially in tool wear monitoring under varied cutting conditions. This paper presents a Random Forest model for predicting tool wear under varied cutting conditions as well as studies extracted signal features. The Random Forest algorithm was chosen as the machine learning model, rather than the novel deep learning model. This was due to the feature importance investigation, which was embedded in the Random Forest algorithm, thereby making it easier to study the physical meanings of signal features. The frequency domain signals were rearranged as features related to spindle speeds and machine tool structure based on domain knowledge. This is the first paper to rearrange the frequency domain signals for observing the physical meanings of selected features. When data normalization was adopted, frequency domain signals related to spindle speeds were excluded from important features. Only spectrum energy related to structure vibration and time domain signals were important features. Data normalization enhanced the weighting of structure vibration features in a machine learning model. This study showed that feature normalization made the machine learning model more adaptable to different cutting conditions. Furthermore, prediction accuracy for cutting condition of spindle speed = 42,000 rpm and feed = 1.5 μm/rev (lowest prediction accuracy among cutting tests in this study) showed an increase from 68.0 to 84.1%. In addition, spindle speed had a more significant effect than feed on classification accuracy in tool wear monitoring based on experimental results. As a result, at least two data sets of the same spindle speed as in tool wear prediction were recommended to be used for model training. When there were at least two data sets in training data with the same spindle speed as in testing data, the study showed prediction accuracies were greater than 75% without data normalization and 81% with data normalization. [ABSTRACT FROM AUTHOR]

Fast motion video sequence processing is quite difficult. In order to improve the effect of fast motion video sequence processing, this paper improves the traditional codebook model algorithm and proposes an improved codebook model algorithm. Moreover, this paper analyzes and summarizes the development and application of background modeling-based methods in moving target detection, and points out the applicability and limitations of traditional methods, which lays the foundation for the further research of moving target detection based on background modeling in the complex background. In addition, this paper analyzes the characteristics of fast motion videos, and combines deep learning algorithms to improve the feature recognition effect of fast motion video sequences. Finally, this paper verifies the effect of this method through experimental research. Through experimental research, we know that the improved algorithm proposed in this paper can realize effective processing of fast motion video, and can improve the feature recognition effect of motion video frames. [ABSTRACT FROM AUTHOR]

Skin cancer, primarily resulting from the abnormal growth of skin cells, is among the most common cancer types. In recent decades, the incidence of skin cancer cases worldwide has risen significantly (one in every three newly diagnosed cancer cases is a skin cancer). Such an increase can be attributed to changes in our social and lifestyle habits coupled with devastating man-made alterations to the global ecosystem. Despite such a notable increase, diagnosis of skin cancer is still challenging, which becomes critical as its early detection is crucial for increasing the overall survival rate. This calls for advancements of innovative computer-aided systems to assist medical experts with their decision making. In this context, there has been a recent surge of interest in machine learning (ML), in particular, deep neural networks (DNNs), to provide complementary assistance to expert physicians. While DNNs have a high processing capacity far beyond that of human experts, their outputs are deterministic, i.e., providing estimates without prediction confidence. Therefore, it is of paramount importance to develop DNNs with uncertainty-awareness to provide confidence in their predictions. Monte Carlo dropout (MCD) is vastly used for uncertainty quantification; however, MCD suffers from overconfidence and being miss calibrated. In this paper, we use MCD algorithm to develop an uncertainty-aware DNN that assigns high predictive entropy to erroneous predictions and enable the model to optimize the hyper-parameters during training, which leads to more accurate uncertainty quantification. We use two synthetic (two moons and blobs) and a real dataset (skin cancer) to validate our algorithm. Our experiments on these datasets prove effectiveness of our approach in quantifying reliable uncertainty. Our method achieved 85.65 ± 0.18 prediction accuracy, 83.03 ± 0.25 uncertainty accuracy, and 1.93 ± 0.3 expected calibration error outperforming vanilla MCD and MCD with loss enhanced based on predicted entropy. [ABSTRACT FROM AUTHOR]

Analyzing and understanding the sentiments of social media documents on Twitter, Facebook, and Instagram has become a very important task at present. Analyzing the sentiment of these documents gives meaningful knowledge about the user opinions, which will help understand the overall view on these platforms. The problem of sentiment analysis (SA) can be regarded as a classification problem in which the text is classified as positive, negative, or neutral. This paper aims to give an intensive, but not exhaustive, review of the main concepts of SA and the state-of-the-art techniques; other aims are to make a comparative study of their performances, the main applications of SA as well as the limitations and the future directions for SA. Based on our analysis, researchers have utilized three main approaches for SA, namely lexicon/rules, machine learning (ML), and deep learning (DL). The performance of lexicon/rules-based models typically falls within the range of 55–85%. ML models, on the other hand, generally exhibit performance ranging from 55% to 90%, while DL models tend to achieve higher performance, ranging from 70% to 95%. These ranges are estimated and may be higher or lower depending on various factors, including the quality of the datasets, the chosen model architecture, the preprocessing techniques employed, as well as the quality and coverage of the lexicon utilized. Moreover, to further enhance models' performance, researchers have delved into the implementation of hybrid models and optimization techniques which have demonstrated an ability to enhance the overall performance of SA models. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is one of the key applications of health monitoring that requires continuous use of wearable devices to track daily activities. The most efficient supervised machine learning (ML)-based approaches for predicting human activity are based on a continuous stream of sensor data. Sensor data analysis for human activity recognition using conventional algorithms and deep learning (DL) models shows promising results, but evaluating their ambiguity in decision-making is still challenging. In order to solve these issues, the paper proposes a novel Wasserstein gradient flow legonet WGF-LN-based human activity recognition system. At first, the input data is pre-processed. From the pre-processed data, the features are extracted using Haar Wavelet mother- Symlet wavelet coefficient scattering feature extraction (HS-WSFE). After that, the interest features are selected from the extracted features using (Binomial Distribution integrated-Golden Eagle Optimization) BD-GEO. The important features are then post-processed using the scatter plot matrix method. Obtained post-processing features are finally given into the WGF-LN for classifying human activities. From these experiments, the results can be obtained and showed the efficacy of the proposed model. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has gained significant popularity through wearable technologies. Several IoT devices are used in educational institutions, smart homes, and security management. Their widespread use in sports training applications using IoT (WS-IoT)-based wearable sensors is noteworthy. Meanwhile, artificial intelligence (AI) has evolved rapidly, and its applications have expanded across many diverse fields. Over the past few years, China has placed greater emphasis on physical education to enhance students' physical fitness and overall capabilities. A significant number of colleges and universities have adopted artificial intelligence to transform traditional single-mode sports training methods. This paper introduces a continuous monitoring system for sports individuals using the WS-IoT. The goal of coaches is to maximize the results of athlete training across various sports events using AI technology and their sports expertise. In this study, we examine the use of AI technology in sports and assess the practical implications of its implementation. To achieve the objective function, we used a Bayesian deep classifier and deep learning algorithms that utilize adaptive optimization. Utilizing artificial intelligence in Chinese educational institutions improves training efficiency and effectiveness. The system was then rigorously tested and analyzed in terms of the quality-of-service parameters, including communication collisions and packet loss rates. It is shown that packet loss rates increase with increasing packet numbers, but collision rates remain unchanged. Furthermore, a practical application of the system in colleges and universities showed that 14.4% of the students were extremely satisfied with the use of teaching materials, 24.5% were satisfied with the materials, 30.1% were satisfied with the training methods, and 17.9% were dissatisfied. [ABSTRACT FROM AUTHOR]

We briefly review the state-of-the-art machine learning (ML) algorithms for mineral exploration, which mainly include random forest (RF), convolutional neural network (CNN), and graph convolutional network (GCN). In recent years, RF, a representative shallow machine learning algorithm, and CNN, a representative deep learning approach, have been proved to be powerful tools for ML-based mapping for mineral exploration. In the future, GCN deserves more attention for ML-based mapping for mineral exploration because of its ability to capture the spatial anisotropy of mineralization and its applicability within irregular study areas. Finally, we summarize the original contributions of the six papers comprising this special issue. [ABSTRACT FROM AUTHOR]

Deep learning has been postulated as a solution for numerous problems in different branches of science. Given the resource-intensive nature of these models, they often need to be executed on specialized hardware such graphical processing units (GPUs) in a distributed manner. In the academic field, researchers get access to this kind of resources through High Performance Computing (HPC) clusters. This kind of infrastructures make the training of these models difficult due to their multi-user nature and limited user permission. In addition, different HPC clusters may possess different peculiarities that can entangle the research cycle (e.g., libraries dependencies). In this paper we develop a workflow and methodology for the distributed training of deep learning models in HPC clusters which provides researchers with a series of novel advantages. It relies on udocker as containerization tool and on Horovod as library for the distribution of the models across multiple GPUs. udocker does not need any special permission, allowing researchers to run the entire workflow without relying on any administrator. Horovod ensures the efficient distribution of the training independently of the deep learning framework used. Additionally, due to containerization and specific features of the workflow, it provides researchers with a cluster-agnostic way of running their models. The experiments carried out show that the workflow offers good scalability in the distributed training of the models and that it easily adapts to different clusters. [ABSTRACT FROM AUTHOR]

Recently, improving the overall resource utilization through efficient scheduling of applications on graphic processing unit (GPU) clusters has been a concern. Traditional cluster-orchestration platforms providing GPUs exclusively for applications constrain high resource utilization. Co-execution of GPU applications is suggested to utilize limited resources. However, the co-execution of GPU applications without considering their diverse characteristics can lead to their unpredictable performances owing to interference resulting from contention and unbalanced usage of resources among applications. This paper proposes an interference-aware execution framework with Co-scheML for various GPU applications such as high performance computing (HPC), deep learning (DL) training, and DL inference. Various resource-usage characteristics of GPU applications are analyzed and profiled to identify various degrees of their application interference. As interference prediction is challenging owing to the complexity of GPU systems, an interference model is generated by applying defined GPU metrics to machine learning (ML) models. A Co-scheML scheduler deploys applications to minimize the interference using the predicted interference from the constructed model. Experimental results of our framework demonstrated that the resource utilization improved by 24%, the average job completion time (JCT) improved by 23%, and the makespan shortened by 22% on average, compared to baseline schedulers. [ABSTRACT FROM AUTHOR]

Improving and enhancing the effectiveness of software vulnerability detection methods is urgently needed today. In this study, we propose a new source code vulnerability detection method based on intelligent and advanced computational algorithms. It's a combination of four main processing techniques including (i) Source Embedding, (ii) Feature Learning, (iii) Resampling Data, and (iv) Classification. The Source Embedding method will perform the task of analyzing and standardizing the source code based on the Joern tool and the data mining algorithm. The Feature Learning model has the function of aggregating and extracting source code attribute based on node using machine learning and deep learning methods. The Resampling Data technique will perform equalization of the experimental dataset. Finally, the Classification model has the function of detecting source code vulnerabilities. The novelty and uniqueness of the new intelligent cognitive computing method is the combination and synchronous use of many different data extracting techniques to compute, represent, and extract the properties of the source code. With this new calculation method, many significant unusual properties and features of the vulnerability have been synthesized and extracted. To prove the superiority of the proposed method, we experiment to detect source code vulnerabilities based on the Verum dataset, details of this part are presented in the experimental section. The experimental results show that the method proposed in the paper has brought good results on all measures. These results have shown to be the best research results for the source code vulnerability detection task using the Verum dataset according to our survey to date. With such results, the proposal in this study is not only meaningful in terms of science but also in practical terms when the method of using intelligent cognitive computing techniques to analyze and evaluate source code has helped to improve the efficiency of the source code analysis and vulnerability detection process. [ABSTRACT FROM AUTHOR]

Despite the fact that satellite-terrestrial systems have advantages such as high throughput, low latency, and low energy consumption, as well as low exposure to physical threats and natural disasters and cost-effective global coverage, their integration exposes both of them to particular security challenges that can arise due to the migration of security challenges from one to another. Intrusion Detection Systems (IDS) can also be used to provide a high level of protection for modern network environments such as satellite-terrestrial integrated networks (STINs). To optimize the detection performance of malicious activities in network traffic, four hybrid intrusion detection systems for satellite-terrestrial communication systems (SAT-IDSs) are proposed in this paper. All the proposed systems exploit the sequential forward feature selection (SFS) method based on random forest (RF) to select important features from the dataset that increase relevance and reduce complexity and then combine them with a machine learning (ML) or deep learning (DL) model; Random Forest (RF), Long Short-Term memory (LSTM), Artificial Neural Networks (ANN), and Gated Recurrent Unit (GRU). Two datasets—STIN, which simulates satellite networks, and UNSW-NB15, which simulates terrestrial networks—were used to evaluate the performance of the proposed SAT-IDSs. The experimental results indicate that selecting significant and crucial features produced by RF-SFS vastly improves detection accuracy and computational efficiency. In the first dataset (STIN), the proposed hybrid ML system SFS-RF achieved an accuracy of 90.5% after using 10 selected features, compared to 85.41% when using the whole dataset. Furthermore, the RF-SFS-GRU model achieved the highest performance of the three proposed hybrid DL-based SAT-IDS with an accuracy of 87% after using 10 selected features, compared to 79% when using the entire dataset. In the second dataset (UNSW-NB15), the proposed hybrid ML system SFS-RF achieved an accuracy of 78.52% after using 10 selected features, compared to 75.4% when using the whole dataset. The model with the highest accuracy of the three proposed hybrid DL-based SAT-IDS was the RF-SFS-GRU model. It achieved an accuracy of 79% after using 10 selected features, compared to 74% when using the whole dataset. [ABSTRACT FROM AUTHOR]

Accurate early warning systems for landslides are a reliable risk-reduction strategy that may significantly reduce fatalities and economic losses. Several machine learning methods have been examined for this purpose, underlying deep learning (DL) models' remarkable prediction capabilities. The long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms are the sole DL model studied in the extant comparisons. However, several other DL algorithms are suitable for time series forecasting tasks. In this paper, we assess, compare, and describe seven DL methods for forecasting future landslide displacement: multi-layer perception (MLP), LSTM, GRU, 1D convolutional neural network (1D CNN), 2xLSTM, bidirectional LSTM (bi-LSTM), and an architecture composed of 1D CNN and LSTM (Conv-LSTM). The investigation focuses on four landslides with different geographic locations, geological settings, time step dimensions, and measurement instruments. Two landslides are located in an artificial reservoir context, while the displacement of the other two is influenced just by rainfall. The results reveal that the MLP, GRU, and LSTM models can make reliable predictions in all four scenarios, while the Conv-LSTM model outperforms the others in the Baishuihe landslide, where the landslide is highly seasonal. No evident performance differences were found for landslides inside artificial reservoirs rather than outside. Furthermore, the research shows that MLP is better adapted to forecast the highest displacement peaks, while LSTM and GRU are better suited to model lower displacement peaks. We believe the findings of this research will serve as a precious aid when implementing a DL-based landslide early warning system (LEWS). [ABSTRACT FROM AUTHOR]

Earlier, a method for constructing an initial approximation for solving the inverse problem of acoustics by a gradient method based on a convolutional neural network trained to predict the distribution of velocities in a medium from wave response was proposed [9]. It was shown that the neural network trained on responses from simple layered media can be successfully used for solving the inverse problem for a significantly more complex model. In this paper, we present algorithms for processing data about epidemics and an example of applying a neural network for modeling the propagation of COVID-19 in Novosibirsk region (Russia) based only on data. A neural network NN-COVID-19 that uses data about the epidemics is constructed. It is shown that this neural network predicts the propagation of COVID-19 for five days by an order of magnitude better than SEIR-HCD. When a new variant (Omicron) appeared, this neural network was able to predict (after retraining) the propagation of the epidemics more accurately. Note that the proposed neural network uses not only epidemiological data but also social ones (such as holidays, restrictive measures, etc.). The proposed approach makes it possible to refine mathematical models. A comparison of the curves constructed by SEIR-HCD model and by the neural network shows that the plots of solutions of the direct problem almost coincide with the plots constructed by the neural network. This helps refine coefficients of the differential model. [ABSTRACT FROM AUTHOR]

Cognitive radio network is a promising technology to enhance the spectrum utilization and to resolve the spectrum scarcity issues. But the malicious users play havoc with the network during spectrum sensing and demean the network performance. It is mandatory to identify such malicious attacks and address it. There have been many traditional methods to mitigate the cognitive radio network attacks. In this paper, we have surveyed advanced attack mitigation techniques like machine learning, deep learning and blockchain. Thus, by detecting and addressing the malicious activities, the throughput and overall network performance can be improved. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman spectroscopy (SERS), as a rapid, non-invasive and reliable spectroscopic detection technique, has promising applications in disease screening and diagnosis. In this paper, an annealed silver nanoparticles/porous silicon Bragg reflector (AgNPs/PSB) composite SERS substrate with high sensitivity and strong stability was prepared by immersion plating and heat treatment using porous silicon Bragg reflector (PSB) as the substrate. The substrate combines the five deep learning algorithms of the improved AlexNet, ResNet, SqueezeNet, temporal convolutional network (TCN) and multiscale fusion convolutional neural network (MCNN). We constructed rapid screening models for patients with primary Sjögren's syndrome (pSS) and healthy controls (HC), diabetic nephropathy patients (DN) and healthy controls (HC), respectively. The results showed that the annealed AgNPs/PSB composite SERS substrates performed well in diagnosing. Among them, the MCNN model had the best classification effect in the two groups of experiments, with an accuracy rate of 94.7% and 92.0%, respectively. Previous studies have indicated that the AgNPs/PSB composite SERS substrate, combined with machine learning algorithms, has achieved promising classification results in disease diagnosis. This study shows that SERS technology based on annealed AgNPs/PSB composite substrate combined with deep learning algorithm has a greater developmental prospect and research value in the early identification and screening of immune diseases and chronic kidney disease, providing reference ideas for non-invasive and rapid clinical medical diagnosis of patients. [ABSTRACT FROM AUTHOR]

An increasing and aging patient population poses a growing burden on healthcare professionals. Automation of medical imaging diagnostics holds promise for enhancing patient care and reducing manpower required to accommodate an increasing patient-population. Deep learning, a subset of machine learning, has the potential to facilitate automated diagnostics, but commonly requires large-scaled labeled datasets. In medical domains, data is often abundant but labeling is a laborious and costly task. Active learning provides a method to optimize the selection of unlabeled samples that are most suitable for improvement of the model and incorporate them into the model training process. This approach proves beneficial when only a small number of labeled samples are available. Various selection methods currently exist, but most of them employ fixed querying schedules. There is limited research on how the timing of a query can impact performance in relation to the number of queried samples. This paper proposes a novel approach called dynamic querying, which aims to optimize the timing of queries to enhance model development while utilizing as few labeled images as possible. The performance of the proposed model is compared to a model trained utilizing a fully-supervised training method, and its effectiveness is assessed based on dataset size requirements and loss rates. Dynamic querying demonstrates a considerably faster learning curve in relation to the number of labeled samples used, achieving an accuracy of 70% using only 24 samples, compared to 82% for a fully-supervised model trained on the complete training dataset of 1017 images. [ABSTRACT FROM AUTHOR]

In this paper, we describe a review concerning the Quantum Computing (QC) and Deep Learning (DL) areas and their applications in Computational Intelligence (CI). Quantum algorithms (QAs), engage the rules of quantum mechanics to solve problems using quantum information, where the quantum information is concerning the state of a quantum system, which can be manipulated using quantum information algorithms and other processing techniques. Nowadays, many QAs have been proposed, whose general conclusion is that using the effects of quantum mechanics results in a significant speedup (exponential, polynomial, super polynomial) over the traditional algorithms. This implies that some complex problems currently intractable with traditional algorithms can be solved with QA. On the other hand, DL algorithms offer what is known as machine learning techniques. DL is concerned with teaching a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of plain text, images, or sound. The inspiration for deep learning is the way that the human brain filters information. Therefore, in this research, we analyzed these two areas to observe the most relevant works and applications developed by the researchers in the world. [ABSTRACT FROM AUTHOR]

In this paper, the quantum technology is exploited to empower the OPTICS unsupervised learning algorithm, which is a density-based clustering algorithm with numerous applications in the real world. We design an algorithm called Quantum Ordering Points To Identify the Clustering Structure (QOPTICS) and demonstrate that its computational complexity outperforms that of its classical counterpart. On the other hand, we propose a Deep self-learning approach for modeling the improvement of two Swarm Intelligence Algorithms, namely Artificial Orca Algorithm (AOA) and Elephant Herding Optimization (EHO) in order to improve their effectiveness. The deep self-learning approach is based on two well-known dynamic mutation operators, namely Cauchy mutation operator and Gaussian mutation operator. And in order to improve the efficiency of these algorithms, they are hybridized with QOPTICS and executed on just one cluster it yields. This way, both effectiveness and efficiency are handled. To evaluate the proposed approaches, an intelligent application is developed to manage the dispatching of emergency vehicles in a large geographic region and in the context of Covid-19 crisis in order to avoid an important loss in human lives. A theoretical model is designed to describe the issue mathematically. Extensive experiments are then performed to validate the mathematical model and evaluate the performance of the proposed deep self-learning algorithms. Comparison with a state-of-the-art technique shows a significant positive impact of hybridizing Quantum Machine Learning (QML) with Deep Self Learning (DSL) on solving the Covid-19 EMS transportation. [ABSTRACT FROM AUTHOR]

Gravitational lensing is the relativistic effect generated by massive bodies, which bend the space-time surrounding them. It is a deeply investigated topic in astrophysics and allows validating theoretical relativistic results and studying faint astrophysical objects that would not be visible otherwise. In recent years, machine learning methods have been applied to support the analysis of the gravitational lensing phenomena by detecting lensing effects in datasets consisting of images associated with brightness variation time series. However, the state-of-the-art approaches either consider only images and neglect time-series data or achieve relatively low accuracy on the most difficult datasets. This paper introduces DeepGraviLens, a novel multi-modal network that classifies spatio-temporal data belonging to one non-lensed system type and three lensed system types. It surpasses the current state-of-the-art accuracy results by ≈ 3 % to ≈ 11 % , depending on the considered data set. Such an improvement will enable the acceleration of the analysis of lensed objects in upcoming astrophysical surveys, which will exploit the petabytes of data collected, e.g., from the Vera C. Rubin Observatory. [ABSTRACT FROM AUTHOR]

Automatic captioning of images contributes to identifying features of multimedia content and helps in the detection of interesting patterns, trends, and occurrences. English image captioning has recently made incredible progress; however, Arabic image captioning is still lagging. In the field of machine learning, Arabic image-caption generation is generally a very difficult problem. This paper presents a more accurate model for Arabic image captioning by using transformer models in both the encoder and decoder phases as feature extractors from images in the encoder phase and a pre-trained word embedding model in the decoder phase. The models are demonstrated, and all of them are implemented, trained, and tested on Arabic Flickr8k datasets. For the image feature extraction subsystem, we compared using three different individual vision models (SWIN, XCIT, and ConvNexT) with concatenation to get among them the most expressive extracted feature vector of the image, and for the caption generation lingual subsystem, which is tested by four different pre-trained language embedding models: (ARABERT, ARAELECTRA, MARBERTv2, and CamelBERT), to select from them the most accurate pre-trained language embedding model. Our experiments showed that building an Arabic image captioning system that uses a concatenation of the three transformer-based models ConvNexT combined with SWIN and XCIT as an image feature extractor, combined with the CamelBERT language embedding model produces the best results among the other combinations, having scores of 0.5980 with BLEU-1 and with ConvNexT combined with SWIN the araelectra language embedding model having a score of 0.1664 with BLEU-4 which are higher than the previously reported values of 0.443 and 0.157. [ABSTRACT FROM AUTHOR]

As a common data-driven method, artificial neural networks have been widely used in electricity spot price forecasting. To improve the accuracy of short-term forecasts, this paper proposes an optimized artificial neural network model for monthly electricity spot prices forecasting. A genetic algorithm is applied to regulate the weights and biases parameters of the artificial neural network structure. This study uses various historical dataset at monthly periods selected from Nordic electricity spot prices. For efficiency comparison, one-step ahead forecast method based on Schwartz-Smith stochastic model and two other prediction models, artificial neural network trained by Levenberg–Marquardt and particle swarm optimization algorithms are also presented. The comparison results show that the prediction model based on the genetic optimization algorithm is more accurate than the other prediction models. The proposed forecasting model can be considered as an alternative technique for the electricity spot price forecasting in the Nordic regions. [ABSTRACT FROM AUTHOR]

Sentence-level sentiment analysis (SLSA) aims to identify the overall sentiment polarity conveyed in a given sentence. The state-of-the-art performance of SLSA has been achieved by deep learning models. However, depending on the i.i.d (independent and identically distributed) assumption, the performance of these deep learning models may fall short in real scenarios, where the distributions of training and target data are almost certainly different to some extent. In this paper, we propose a supervised solution based on the non-i.i.d paradigm of gradual machine learning (GML) for SLSA. It begins with some labeled observations, and gradually labels target instances in the order of increasing hardness by iterative knowledge conveyance. It leverages labeled samples for supervised deep feature extraction, and constructs a factor graph based on the extracted features to enable gradual knowledge conveyance. Specifically, it employs a polarity classifier to detect polarity similarity between close neighbors in an embedding space, and a separate binary semantic network to extract implicit polarity relations between arbitrary instances. Our extensive experiments on benchmark datasets show that the proposed approach achieves the state-of-the-art performance on all benchmark datasets. Our work clearly demonstrates that by leveraging DNN for feature extraction, GML can easily outperform the pure DNN solutions. [ABSTRACT FROM AUTHOR]

Heartbeat classification plays an important role in identifying cardiac arrhythmias. Although automated heartbeat classification approaches have been broadly reported, they still suffer from some disadvantages, such as the inescapable domain knowledge and human ingenuity for hand-crafted heartbeat feature engineering, and the slow learning speed for iterative deep learning heartbeat classification approach. To address the above issues, for the first time, this paper proposes a novel multilayer extreme learning machine (ML-ELM) heartbeat classification approach for feature representation and classification of the heartbeat signals. Different from the iterative optimization scheme in traditional neural networks, ELM autoencoder (ELM-AE) is employed in ML-ELM for random mapping of the input weights and non-iterative learning is implemented for output weights. To realize unsupervised self-encoding heartbeat feature mapping, the output of ELM-AE is consistent with the incoming heartbeat signal, and the optimization objective is to minimize the reconstruction error. Heartbeat feature extraction is efficiently performed by layer-by-layer ELM-AE stacking. In the stage of heartbeat classification, to solve the unstable performance caused by random parameter initialization in ELM hidden nodes, bagging-based ensemble learning is employed to combine several ELM classifiers and thus develops a well-performed heartbeat classification approach. The proposed approach is applied to two-lead ECG signals, which is obtained from the MIT-BIH arrhythmia public dataset. The experimental results show that the ELM-AE based feature extraction can effectively characterize the characteristics of the heartbeat signal with high efficiency compared with other state-of-the-art approaches. Applying the ensemble decision fusion to two leads, the final classification accuracy reaches 99.41%. [ABSTRACT FROM AUTHOR]

Wind power generation helps to save energy and reduce emissions. Accurate wind speed prediction can ensure the safe integration of wind power into the energy grid, but due to the intermittent and volatile nature of wind, it brings great difficulties to wind speed prediction. To solve this issue, this paper develops a hybrid forecasting model called WT-VMD-LSTM-TCN, where the wavelet transform (WT) is used to decompose the original wind speed into multiple detailed components and an approximate component, then the variational mode decomposition (VMD) is employed to further decompose the first detailed component, and several intrinsic mode functions and a residual term are obtained. Subsequently, the LSTM-TCN model is established on each component obtained by WT and VMD, where the component is first input into the long short-term memory (LSTM), and then the output sequence of LSTM is input into the temporal convolutional network (TCN) for processing. Ultimately, the predicted results of all components from WT-VMD are reconstructed to obtain the predicted value of wind speed. The performance of the model is verified by simulation experiments with three datasets. The experimental results show that: (a) The developed LSTM-TCN model integrates the feature extraction methods of LSTM and TCN, and the feature extraction ability is more comprehensive, which can improve the forecasting performance of a single model; (b) VMD is used to decompose the subsequence with the largest error, which reduces the difficulty of the prediction of this subsequence; (c) WT-VMD-LSTM-TCN is superior to other comparison models in three datasets, which verifies that the model has good applicability. [ABSTRACT FROM AUTHOR]

Cardiac-related disorders are rapidly growing throughout the world. Accurate classification of cardiovascular diseases is an important research topic in healthcare. During COVID-19, auscultating heart sounds was challenging as health workers and doctors wear protective clothing, and direct contact with patients can spread the outbreak. Thus, contactless auscultation of heart sound is necessary. In this paper, a low-cost ear contactless stethoscope is designed where auscultation is done with the help of a bluetooth-enabled micro speaker instead of an earpiece. The PCG recordings are further compared with other standard electronic stethoscopes like Littman 3 M. This work is made to improve the performance of deep learning-based classifiers like recurrent neural networks (RNN) and convolutional neural networks (CNN) for different valvular heart problems using tuning of hyperparameters like learning rate of optimizers, dropout rate, and hidden layer. Hyper-parameter tuning is used to optimize the performances of various deep learning models and their learning curves for real-time analysis. The acoustic, time, and frequency domain features are used in this research. The investigation is made on the heart sounds of normal and diseased patients available from the standard data repository to train the software models. The proposed CNN-based inception network model achieved an accuracy of 99.65 ± 0.06% on the test dataset with a sensitivity of 98.8 ± 0.05% and specificity of 98.2 ± 0.19%. The proposed hybrid CNN-RNN architecture attained 91.17 ± 0.03% accuracy on test data after hyperparameter optimization, whereas the LSTM-based RNN model achieved 82.32 ± 0.11% accuracy. Finally, the evaluated results were compared with machine learning algorithms, and the improved CNN-based Inception Net model is the most effective among others. [ABSTRACT FROM AUTHOR]

Accurate short-term electricity load forecasts are critical for the secure and economic operation of power systems. This paper presents a computationally efficient and powerful three-stage model to accurately forecast short-term electricity load. Variational mode decomposition (VMD) was used in the first stage to extract features from the historical load signal. The stacked kernel extreme learning machine (KELM)-based auto-encoders were utilized in the unsupervised feature learning-based second stage. In the third stage, the high-order learned features were used as the inputs for the KELM-based regression model. The proposed deep KELM architecture combines stacked KELM-based auto-encoders and a KELM-based regression model to forecast short-term electricity load effectively. In order to examine the performance improvement of the proposed forecasting model, several performance comparison tests were realized using publicly available electricity load and day-ahead forecast data from the Turkish transmission system operator (TSO). The proposed model results were compared with state-of-the-art deep extreme learning machine (ELM) architectures as well as the benchmark forecasting models based on original ELM, KELM, artificial neural network (ANN), support vector machine (SVM), and regression tree (RT). The comparison results indicated that the proposed model outperformed state-of-the-art architectures and was significantly more successful than the TSO model. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging is a highly advanced and sophisticated method for capturing images in hundreds of narrow, contiguous spectral bands. However, processing and analyzing such large amounts of data are challenging. Deep learning algorithms, especially those based on convolutional neural networks (CNNs), effectively extract rich feature representations from complex datasets, such as hyperspectral images (HSIs). These representations capture high-level patterns and characteristics that can facilitate accurate classification. The success of this approach has led to its widespread use in various remote sensing applications. However, the standard CNN inputs and outputs are scalars, ignoring the relative position relationships between features. In this paper, we propose a hierarchical capsule network for HSI classification. This network incorporates a multi-level convolutional structure for feature extraction and fusion. It utilizes convolutional feature maps of various depths to generate initial capsules, followed by vector computation using capsule neurons and a weight matrix to encode spatial location relationships among features. Furthermore, the shallow convolution of the hierarchical capsule network is pre-trained based on transfer learning to further improve the performance of HSI classification. According to experimental results, the proposed method for hyperspectral image classification has been found to outperform other state-of-the-art deep learning models on four benchmark datasets. [ABSTRACT FROM AUTHOR]

It is standard cardiology practice for patients suffering from ventricular arrhythmias (the main cause of sudden cardiac death) belonging to high risk populations to be treated via the implantation of Subcutaneous Implantable cardioverter-defibrillators (S-ICDs). S-ICDs carry a risk of so-called T wave over sensing (TWOS), which can lead to inappropriate shocks that carry an inherent health risk. For this reason, according to current practice patients' Electrocardiograms (ECGs) are manually screened by a cardiologist over 10 s to assess the T:R ratio—the ratio between the amplitudes of the T and R waves which is used as a marker for the likelihood of TWOS—with a plastic template. Unfortunately, the temporal variability of a patient' T:R ratio can render such a screening procedure, which relies on an inevitably short ECG segment due to its manual nature, unreliable. In this paper, we propose and investigate a tool based on deep learning for the automatic prediction of the T:R ratios from multiple 10-second segments of ECG recordings capable of carrying out a 24-hour automated screening. Thanks to the significantly increased screening window, such a screening would provide far more reliable T:R ratio predictions than the currently utilized 10-second, template-based, manual screening is capable of. Our tool is the first, to the best of our knowledge, to fully automate such an otherwise manual and potentially inaccurate procedure. From a methodological perspective, we evaluate different deep learning model architectures for our tool, assess a range of stochastic-gradient-descent-based optimization methods for training their underlying deep-learning model, perform hyperparameter tuning, and create ensembles of the best performing models in order to identify which combination leads to the best performance. We find that the resulting model, which has been integrated into a prototypical tool for use by clinicians, is able to predict T:R ratios with very high accuracy. Thanks to this, our automated T:R ratio detection tool will enable clinicians to provide a completely automated assessment of whether a patient is eligible for S-ICD implantation which is more reliable than current practice thanks to adopting a significantly longer ECG screening window which better and more accurately captures the behavior of the patient's T:R ratio than the current manual practice. [ABSTRACT FROM AUTHOR]

Tunnelling-induced ground deformations inevitably affect the safety of adjacent infrastructures. Accurate prediction of tunnelling-induced deformations is of great importance to engineering construction, which has historically been dependent on numerical simulations or field measurements. Recently, some surrogate models originating from machine learning methods have been developed, showing satisfactory prediction performance with high computational efficiency. However, these purely data-driven models show weak robustness in the absence of sufficient training data. This study proposed a hybrid deep learning model integrating both data-driven and physics-based strategies to decrease calculation costs and eliminate the dependence on large numbers of training data. The underlying physical mechanism of ground deformation due to tunnel excavation is coupled into the deep learning framework to form a physics-informed neural network (PINN) model. The performance of the hybrid model is first assessed by comparing it with the classical Verruijt-Booker solution and a conventional purely data-driven model. The potential of the proposed PINN model for engineering applications is then illustrated. It is found that the proposed PINN model can reasonably reproduce ground deformation fields obtained numerically with only a small amount of training data. This paper provides a new paradigm for incorporating hybrid deep learning frameworks and field monitoring systems to predict ground deformation fields in real time. [ABSTRACT FROM AUTHOR]

Web robots are automated computer programs that can be exploited for benign and malicious activities such as website indexing, monitoring, or unauthorized content scraping and scalping. Several methods are available to detect automated web robots through their footprints and behaviors. Although the accuracy and efficiency of existing methods depend highly on the labeled web log data, countless web requests are generated daily with the help of web robots. Exhaustive and accurate manual labeling of reconstructed sessions is time-consuming and challenging. Further, effective detection of web robots is more challenging with unlabeled or partially labeled data. To address the aforementioned issues, we reformulated web robot detection as a semi-supervised learning problem. In this paper, we propose a deep learning-based Semi-Supervised Stacked Sparse AutoEncoder (Web-S4AE) for web robot detection. The proposed model uses content-based features and features extracted from web access log data to effectively classify web robots. The experiments were conducted on publicly available web log data from a library and information portal to assess the performance of Web-S4AE. The Web-S4AE model was trained in two phases. The first phase; comprises training the model with unlabeled data to extract the hidden information, and in the second phase, the model is fine-tuned using labeled data. The results suggest that incorporating more unlabeled data can significantly improve the classifier's performance. The Web-S4AE model's performance was also compared with other models such as the Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP). [ABSTRACT FROM AUTHOR]

As it is widely known, big data can comprehensively describe the inherent laws governing various phenomena. However, the effective and efficient analysis of available data has become a major challenge in the fields of artificial intelligence, machine learning, data mining, and others. Deep learning, with its powerful learning ability and effective data-processing methods, has been extensively researched and applied in numerous academic domains. Nevertheless, the data obtained during the deep learning process often exhibits feature homogenization, resulting in highly redundant features in the hidden layers, which, in turn, affects the learning process. Therefore, this paper proposes an algorithm based on graph clustering to optimize the features of hidden layer units, with the aim of eliminating redundancy and improving learner generation. [ABSTRACT FROM AUTHOR]

Aphid infestation poses a significant threat to crop production, rural communities, and global food security. While chemical pest control is crucial for maximizing yields, applying chemicals across entire fields is both environmentally unsustainable and costly. Hence, precise localization and management of aphids are essential for targeted pesticide application. The paper primarily focuses on using deep learning models for detecting aphid clusters. We propose a novel approach for estimating infection levels by detecting aphid clusters. To facilitate this research, we have captured a large-scale dataset from sorghum fields, manually selected 5447 images containing aphids, and annotated each individual aphid cluster within these images. To facilitate the use of machine learning models, we further process the images by cropping them into patches, resulting in a labeled dataset comprising 151,380 image patches. Then, we implemented and compared the performance of four state-of-the-art object detection models (VFNet, GFLV2, PAA, and ATSS) on the aphid dataset. Extensive experimental results show that all models yield stable similar performance in terms of average precision and recall. We then propose to merge close neighboring clusters and remove tiny clusters caused by cropping, and the performance is further boosted by around 17%. The study demonstrates the feasibility of automatically detecting and managing insects using machine learning models. The labeled dataset will be made openly available to the research community. [ABSTRACT FROM AUTHOR]

The spread of the COVID-19 started back in 2019; and so far, more than 4 million people around the world have lost their lives to this deadly virus and its variants. In view of the high transmissibility of the Corona virus, which has turned this disease into a global pandemic, artificial intelligence can be employed as an effective tool for an earlier detection and treatment of this illness. In this review paper, we evaluate the performance of the deep learning models in processing the X-Ray and CT-Scan images of the Corona patients' lungs and describe the changes made to these models in order to enhance their Corona detection accuracy. To this end, we introduce the famous deep learning models such as VGGNet, GoogleNet and ResNet and after reviewing the research works in which these models have been used for the detection of COVID-19, we compare the performances of the newer models such as DenseNet, CapsNet, MobileNet and EfficientNet. We then present the deep learning techniques of GAN, transfer learning, and data augmentation and examine the statistics of using these techniques. Here, we also describe the datasets introduced since the onset of the COVID-19. These datasets contain the lung images of Corona patients, healthy individuals, and the patients with non-Corona pulmonary diseases. Lastly, we elaborate on the existing challenges in the use of artificial intelligence for COVID-19 detection and the prospective trends of using this method in similar situations and conditions. [ABSTRACT FROM AUTHOR]

In this paper, we propose an Intelligent Decision Support System (IDSS) for the design of new textile fabrics. The IDSS uses predictive analytics to estimate fabric properties (e.g., elasticity) and composition values (% cotton) and then prescriptive techniques to optimize the fabric design inputs that feed the predictive models (e.g., types of yarns used). Using thousands of data records from a Portuguese textile company, we compared two distinct Machine Learning (ML) predictive approaches: Single-Target Regression (STR), via an Automated ML (AutoML) tool, and Multi-target Regression, via a deep learning Artificial Neural Network. For the prescriptive analytics, we compared two Evolutionary Multi-objective Optimization (EMO) methods (NSGA-II and R-NSGA-II) when optimizing 100 new fabrics, aiming to simultaneously minimize the physical property predictive error and the distance of the optimized values when compared with the learned input space. The two EMO methods were applied to design of 100 new fabrics. Overall, the STR approach provided the best results for both prediction tasks, with Normalized Mean Absolute Error values that range from 4% (weft elasticity) to 11% (pilling) in terms of the fabric properties and a textile composition classification accuracy of 87% when adopting a small tolerance of 0.01 for predicting the percentages of six types of fibers (e.g., cotton). As for the prescriptive results, they favored the R-NSGA-II EMO method, which tends to select Pareto curves that are associated with an average 11% predictive error and 16% distance. [ABSTRACT FROM AUTHOR]

Prediction of conformational B-cell epitopes (CBCE) is an essential phase for vaccine design, drug invention, and accurate disease diagnosis. Many laboratorial and computational approaches have been developed to predict CBCE. However, laboratorial experiments are costly and time consuming, leading to the popularity of Machine Learning (ML)-based computational methods. Although ML methods have succeeded in many domains, achieving higher accuracy in CBCE prediction remains a challenge. To overcome this drawback and consider the limitations of ML methods, this paper proposes a novel DL-based framework for CBCE prediction, leveraging the capabilities of deep learning in the medical domain. The proposed model is named Deep Learning-based Temporal Convolutional Neural Network (DL-TCNN), which hybridizes empirical hyper-tuned 1D-CNN and TCN. TCN is an architecture that employs causal convolutions and dilations, adapting well to sequential input with extensive receptive fields. To train the proposed model, physicochemical features are firstly extracted from antigen sequences. Next, the Synthetic Minority Oversampling Technique (SMOTE) is applied to address the class imbalance problem. Finally, the proposed DL-TCNN is employed for the prediction of CBCE. The model's performance is evaluated and validated on a benchmark antigen–antibody dataset. The DL-TCNN achieves 94.44% accuracy, and 0.989 AUC score for the training dataset, 78.53% accuracy, and 0.661 AUC score for the validation dataset; and 85.10% accuracy, 0.855 AUC score for the testing dataset. The proposed model outperforms all the existing CBCE methods. [ABSTRACT FROM AUTHOR]

The paper considers the problem of choosing an initial approximation for gradient optimization methods as applied to the inverse problem of restoring the velocity distribution in a heterogeneous continuous medium. The behavior of the medium is described by a system of acoustic equations, and the direct problem is solved by applying a finite-difference scheme. L-BFGS-B is used as a gradient optimization method. The gradient of the error functional with respect to the medium parameters is calculated by applying the adjoint state method. An initial approximation for the gradient method is obtained using a convolutional neural network trained to predict the velocity distribution in a medium from its wave response. It is shown that a neural network trained on responses of simple layered structures can be successfully used to solve the inverse problem for a much more complex Marmousi model. [ABSTRACT FROM AUTHOR]

Surface Water Bodies (SWB) are a renewable water source crucial for maintaining ecosystems and the water cycle. The declining rate of SWB increases owing to the overutilization of these resources, especially for agriculture. A timely and accurate Surface Water Body Extraction (SWBE) is necessary for water resource conservation and planning. Recently, Deep Learning (DL), a subset of Machine Learning (ML) algorithm, got remarkable attention in SWBE. It learns inherent features directly from the images at the expense of time and data. But, the ML algorithms such as K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGB) use optimal hand-crafted features to produce better results with fewer data and time. In this paper, SWBE is performed through two steps: (1) Use of spectral indices and Gabor filters for obtaining Pixel Level Feature (PLF) maps from the multispectral image; (2) Prediction of water and non-water pixels based on PLF maps using the KNN, DT, RF, SVM, and XGB classifiers. The proposed framework has experimented with Resoucesat-2 imagery over major reservoirs in Tamil Nadu and India. The results show that the proposed PLF + XGB outperforms in accuracy, recall, F1-score, kappa, False Negative Rate, Mathews Correlation Coefficient, and mean Intersection over Union with the metric value of 0.995, 0.990, 0.983, 0.979, 0.009, 0.979, and 0.969 with other existing and proposed models. Also, the surface water extent of Bhavani Sagar and Sathanur reservoirs is predicted for 4 years (2016–2019) and the causes of surface water dynamics were analyzed. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a disruptive technology for the future decades. Due to its pervasive growth, it is susceptible to cyber-attacks, and hence the significance of Intrusion Detection Systems (IDSs) for IoT is pertinent. The viability of machine learning has encouraged analysts to apply learning techniques to intelligently discover and recognize cyber attacks and unusual behavior among the IoTs. This paper proposes an enhanced anomaly-based Intrusion Detection Deep learning Multi-class classification model (EIDM) that can classify 15 traffic behaviors including 14 attack types with the accuracy of 95% contained in the CICIDS2017 dataset. Four state-of-the-art deep learning models are also customized to classify six classes of network traffic behavior. An extensive comparative study in terms of classification accuracy and efficiency metrics is conducted between EIDM and several state-of-the-art deep learning-based IDSs showing that EIDM has achieved accurate detection results. [ABSTRACT FROM AUTHOR]

Water resources, consisting of surface water and groundwater, are considered to be among the crucial natural resources in most arid and semiarid regions. Groundwater resources as the sustainable yields can be predicted, whereas this is one of the important stages in water resource management. To this end, several models such as mathematical, statistical, empirical, and conceptual can be employed. In this paper, machine learning and deep learning methods as conceptual ones are applied for the simulations. The selected models are support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), and multilayer perceptron (MLP). Next, these models are optimized with the adaptive moment estimation (ADAM) optimization algorithm which results in hybrid models. The hyper-parameters of the stated models are optimized with the ADAM method. The root mean squared error (RMSE), mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2) are used to evaluate the accuracy of the simulated groundwater level. To this end, the aquifer hydrograph is used to compare the results with observations data. So, the RMSE and R2 show that the accuracy of the machine learning and deep learning models is better than the numerical model for the given data. Moreover, the MSE is approximately the same in all three cases (ranging from 0.7113 to 0.6504). Also, the total value of R2 and RMSE for the best hybrid model is 0.9617 and 0.7313, respectively, which are obtained from the model output. The results show that all three techniques are useful tools for modeling hydrological processes in agriculture and their computational capabilities and memory are similar. [ABSTRACT FROM AUTHOR]

There has been a rapid development of THz technology—sources, detectors and various THz imaging and sensing techniques. The THz technology demonstrates great potential as a modality for early, label free, non-ionizing and non-invasive detection of cancer. Some progressive technological development milestones have been achieved in this regard, however, to become clinically competitive and to provide the sought after real operational convenience, there is need for further research and development to overcome the existing challenges. This paper provides recent trends and perspectives through identification of existing challenges for the development of THz imaging and sensing systems that can evolve into actual medical modalities. We provide an overview of various aspects of THz technology, including techniques for imaging and sensing, mechanisms for THz image contrast and models for tissue dielectric responses to THz waves. The THz imaging application for detection of various cancers is briefed. The advantages of THz cancer imaging and sensing as well as the existing challenges are identified, with recommendations provided in contribution to future research. Further, some recent THz imaging and sensing developments such as the near-field methods to break the diffraction limit including waveguides, resonance and plasmonic metasurfaces are discussed. We emphasize the contribution of analytical algorithms that are based on machine learning, in particular, deep learning for the development of THz technology. [ABSTRACT FROM AUTHOR]

The spreading of machine learning (ML) and deep learning (DL) methods in different and critical application domains, like medicine and healthcare, introduces many opportunities but raises risks and opens ethical issues, mainly attaining to the lack of transparency. This contribution deals with the lack of transparency of ML and DL models focusing on the lack of trust in predictions and decisions generated. In this sense, this paper establishes a measure, namely Congruity, to provide information about the reliability of ML/DL model results. Congruity is defined by the lattice extracted through the formal concept analysis built on the training data. It measures how much the incoming data items are close to the ones used at the training stage of the ML and DL models. The general idea is that the reliability of trained model results is highly correlated with the similarity of input data and the training set. The objective of the paper is to demonstrate the correlation between the Congruity and the well-known Accuracy of the whole ML/DL model. Experimental results reveal that the value of correlation between Congruity and Accuracy of ML model is greater than 80% by varying ML models. [ABSTRACT FROM AUTHOR]

This paper proposes to predict traffic accidents based on IoTs and deep learning to address the current problem of inaccurate traffic accident prediction. Since traditional traffic accident prediction often applies classical prediction algorithms to a small portion of data, the obtained models can only predict a small range of traffic accidents. Most accident prediction models are limited by the lack of data features, do not consider the problems of practical application scenarios, and do not incorporate regional heterogeneity, so the prediction accuracy of accident prediction models is poor. This paper analyzes and summarizes the relationship between traffic accidents and influencing factors from five aspects, such as people, vehicles, roads and environment, and proves the influence of regional heterogeneity on accidents, which paves the way for traffic accident prediction. The data and heterogeneous spatial data are preprocessed and feature selected, respectively. Logistic regression and random forest algorithm are used to train the corresponding prediction models. The results show that the prediction model combined with regional heterogeneity has better comprehensive performance than the original data. [ABSTRACT FROM AUTHOR]

Buiding data-driven models using machine learning methods has gradually become a common approach for studying reservoir parameters. Among these methods, deep learning methods are highly effective. From the perspective of multi-task learning, this paper uses six types of logging data—acoustic logging (AC), gamma ray (GR), compensated neutron porosity (CNL), density (DEN), deep and shallow lateral resistivity (LLD) and shallow lateral resistivity (LLS) —that are inputs and three reservoir parameters that are outputs to build a porosity saturation permeability network (PSP-Net) that can predict porosity, saturation, and permeability values simultaneously. These logging data are obtained from 108 training wells in a medium-low permeability oilfield block in the western district of China. PSP-Net method adopts a serial structure to realize transfer learning of reservoir-parameter characteristics. Compared with other existing methods at the stage of academic exploration to simulating industrial applications, the proposed method overcomes the disadvantages inherent in single-task learning reservoir-parameter prediction models, including easily overfitting and heavy model-training workload. Additionally, the proposed method demonstrates good anti-overfitting and generalization capabilities, integrating professional knowledge and experience. In 37 test wells, compared with the existing method, the proposed method exhibited an average error reduction of 10.44%, 27.79%, and 28.83% from porosity, saturation, permeability calculation. The prediction and actual permeabilities are within one order of magnitude. The training on PSP-Net are simpler and more convenient than other single-task learning methods discussed in this paper. Furthermore, the findings of this paper can help in the re-examination of old oilfield wells and the completion of logging data. [ABSTRACT FROM AUTHOR]

The World Health Organization declared the novel coronavirus disease 2019 a pandemic on March 11, 2020. Along with the coronavirus pandemic, a new crisis has emerged, characterized by widespread fear and panic caused by a lack of information or, in some cases, outright fake messages. In these circumstances, Twitter is one of the most eminent and trusted social media platforms. Fake tweets, on the other hand, are challenging to detect and differentiate. The primary goal of this paper is to educate society about the importance of accurate information and prevent the spread of fake information. This paper has investigated COVID-19 fake data from various social media platforms such as Twitter, Facebook, and Instagram. The objective of this paper is to categorize given tweets as either fake or real news. The authors have tested various deep learning models on the COVID-19 fake dataset. Finally, the CT-BERT and RoBERTa deep learning models outperformed other deep learning models like BERT, BERTweet, AlBERT, and DistlBERT. The proposed ensemble deep learning architecture outperformed CT-BERT and RoBERTa on the COVID-19 fake news dataset using the multiplicative fusion technique. The proposed model's performance in this technique was determined by the multiplicative product of the final predictive values of CT-BERT and RoBERTa. This technique overcomes the disadvantage of these CT-BERT and RoBERTa models' incorrect predictive nature. The proposed architecture outperforms both well-known ML and DL models, with 98.88% accuracy and a 98.93% F1-score. [ABSTRACT FROM AUTHOR]

Semantic image segmentation in computer networks is designed to determine the category to which each pixel in an image belongs. It is a basic computer vision task and has a very wide range of applications in practice. In recent years, semantic image segmentation algorithms in computer networks based on deep learning have attracted widespread attention due to their fast speed and high accuracy. However, due to the large number of downsampling layers in a deep learning model, the segmentation results are usually poor at the edge of an object, and there is currently no universal quantitative evaluation index to measure the performance of segmentation at the edge of an object. Solving these two problems is of great significance to semantic image segmentation algorithms in China. Based on traditional evaluation indicators, this paper proposes a region-based evaluation index to quantitatively measure the performance of segmentation at the edge of an object and proposes an improved loss function to improve model performance. The existing semantic image segmentation methods are summarized. This paper proposes regional-based evaluation indicators. Taking advantage of the particularity of semantic image segmentation tasks, this paper presents an efficient and accurate method for extracting the edges of objects. By defining the distance from pixels to the edges of objects, this paper proposes a fast algorithm for calculating the edge area. Based on this, three methods are proposed as well as an area-based evaluation indicator. The experimental results show that the accuracy of the loss function proposed in this paper, compared with that of the current mainstream cross-entropy loss function, is improved by 1% on the DeepLab model. For area-based evaluation indicators, a 4% accuracy improvement can be achieved, and on other segmentation models, there is also a significant improvement. [ABSTRACT FROM AUTHOR]