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The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals. [ABSTRACT FROM AUTHOR]

Automatic handwritten character recognition plays a significant role in various applications across multiple fields. With the growing interest in automatic handwriting recognition and the advancement of deep learning methods, researchers have achieved significant improvements in the development of English handwriting recognition methods. However, the recognition of Arabic handwriting has received insufficient attention. In this paper, a novel "DeepAHR" model is presented to accurately and efficiently recognize Arabic handwritten characters using deep learning techniques. The "DeepAHR" model is based on a convolutional neural network (CNN) and is trained using two recent public datasets: Hijaa and Arabic handwritten characters dataset (AHCD). The overall accuracies of the proposed model were 98.66% and 88.24% on the AHCD and Hijaa datasets, respectively.The experimental results showed that DeepAHR outperformed state-of-the-art methods in the literature. These promising results provide evidence of the successful use of the DeepAHR model for recognizing handwritten Arabic characters [ABSTRACT FROM AUTHOR]

The crucial part of the software development cycle is software maintenance. The demands included in the software management are fault fixes and request to change or bring a new feature. If priority is not given to these demands, then it may lead to customer dissatisfaction, inefficient planning, and software failure as well. Therefore, it is important to study the severity of the bug reports to maintain the efficiency of the software. Various research has been conducted in the past to predict the severity of the paper using text mining focusing only on the content of the bug reports. The sentiment of the user while reporting a bug also plays a vital role. In this study, we will be focusing on two aspects, that is, sentiment and content to improve the prediction. We propose a prediction model based on LDA to study the content aspect and emotion analysis to study the sentiment aspect. The model is validated on the datasets collected from the Eclipse project using Convolutional Neural Network (CNN). The results show that the CNN model effectively utilizes the content and sentiment aspect of the data to handle the severity prediction. CNN has weight sharing feature that decreases the number of parameters used for training. It also improves generalization and overfitting is avoided The Accuracy, Precision, Recall, and F‐measure are improved when both aspects are taken into account rather than considering only content. [ABSTRACT FROM AUTHOR]

Over the past decades, the software industry has expanded to include all industries. Since stakeholders tend to use it to get their work done, software houses seek to estimate the cost of the software, which includes calculating the effort, time, and resources required. Although many researchers have worked to estimate it, the prediction accuracy results are still inaccurate and unstable. Estimating it requires a lot of effort. Therefore, there is an urgent need for modern techniques that contribute to cost estimation. This paper seeks to present a model based on deep learning and machine learning techniques by combining convolutional neural networks (CNN) and the particle swarm algorithm (PSO) in the context of time series forecasting, which enables feature extraction and automatic tuning of hyperparameters, which reduces the manual effort of selecting parameters and contributes to fine-tuning. The use of PSO also enhances the robustness and generalization ability of the CNN model and its iterative nature allows for efficient discovery of hyperparameter similarity. The model was trained and tested on 13 different benchmark datasets and evaluated through six metrics: mean absolute error (MAE), mean square error (MSE), mean magnitude relative error (MMRE), root mean square error (RMSE), median magnitude relative error (MdMRE), and prediction accuracy (PRED). Comparative results reveal that the performance of the proposed model is better than other methods for all datasets and evaluation criteria. The results were very promising for predicting software cost estimation. [ABSTRACT FROM AUTHOR]

Document classification is becoming increasingly essential for the vast number of documents available in digital libraries, emails, the Internet, etc. Textual records frequently contain non-discriminative (noisy and irrelevant) terms that are also high-dimensional, resulting in higher computing costs and poorer learning performance in Text Classification (TC). Feature selection (FS), which tries to discover discriminate terms or features from the textual data, is one of the most effective tasks for this issue. This paper introduces a novel multi-stage term-weighting scheme-based FS model designed for the single-label TC system to obtain the optimal set of features. We have also developed a hybrid deep learning fine-tuning network based on Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) for the classification stage. The FS approach is worked on two-stage criteria. The filter model is used in the first stage, and the multi-objective wrapper model, an upgraded version of the Whale Optimization Algorithm (WOA) with Particle Swarm Optimization (PSO), is used in the second stage. The objective function in the above wrapper model is based on a tri-objective principle. It uses the Pareto front technique to discover the optimal set of features. Here in the wrapper model, a novel selection strategy has been introduced to select the whale instead of the random whale. The proposed work is evaluated on four popular benchmark text corpora, of which two are binary class, and two are multi-class. The suggested FS technique is compared against classic Machine Learning (ML) and deep learning classifiers. The results of the experiments reveal that the recommended FS technique is more effective in obtaining better results than the other results. [ABSTRACT FROM AUTHOR]

Sentiment analysis has increasingly gained significance in commercial settings, driven by the rising impact of reviews on purchase decision-making in recent years. This research conducts a thorough examination of the suitability of machine learning and deep learning approaches for sentiment analysis, using Romanian reviews as a case study, with the aim of gaining insights into their practical utility. A comprehensive, multi-level analysis is performed, covering the document, sentence, and aspect levels. The main contributions of the paper refer to the in-depth exploration of multiple sentiment analysis models at three different textual levels and the subsequent improvements brought with respect to these standard models. Additionally, a balanced dataset of Romanian reviews from twelve product categories is introduced. The results indicate that, at the document level, supervised deep learning techniques yield the best outcomes (specifically, a convolutional neural network model that obtains an AUC value of 0.93 for binary classification and a weighted average F1-score of 0.77 in a multi-class setting with 5 target classes), albeit with increased resource consumption. Favorable results are achieved at the sentence level, as well, despite the heightened complexity of sentiment identification. In this case, the best-performing model is logistic regression, for which a weighted average F1-score of 0.77 is obtained in a multi-class polarity classification task with three classes. Finally, at the aspect level, promising outcomes are observed in both aspect term extraction and aspect category detection tasks, in the form of coherent and easily interpretable word clusters, encouraging further exploration in the context of aspect-based sentiment analysis for the Romanian language. [ABSTRACT FROM AUTHOR]

Mineral particle size is an important parameter in the mineral beneficiation process. In industrial processes, the grinding process produces pulp with qualified particle size for subsequent flotation processes. In this paper, a hierarchical intelligent control method for mineral particle size based on machine learning is proposed. In the machine learning layer, artificial intelligence technologies such as long and short memory neural networks (LSTM) and convolution neural networks (CNN) are used to solve the multi-source ore blending prediction and intelligent classification of dry and rainy season conditions, and then the ore-feeding intelligent expert control system and grinding process intelligent expert system are used to coordinate the production of semi-autogenous mill and Ball mill and Hydrocyclone (SAB) process and intelligently adjust the control parameters of DCS layer. This paper presents the practical application of the method in the SAB production process of an international mine to realize automation and intelligence. The process throughput is increased by 6.05%, the power consumption is reduced by 7.25%, and the annual economic benefit has been significantly improved. [ABSTRACT FROM AUTHOR]

Multi-modal information outbreak is consistently increasing in social media. Classification of tweet sentiments using various information modalities will help the recommender systems to achieve success in digital marketing. Moreover, aspect-level sentiment analysis categorizes a target's sentiment polarity in a specific environment. Using topic modelling in aspect-level sentiment analysis enables the identification of more accurate aspect-based tweet sentiments. The existing sentiment classification techniques used for the development of recommendation systems do not focus on the aspect-based approach modelled using deep learning classifier with temporal analysis on the social media data. Hence, this paper proposes an efficient sentiment classification model that highlights the impact of topic modelling-based word feature embedding for improvising the classification of Twitter sentiments and product reviews based on temporal reasoning and analysis for performing predictive analysis. For tweets context analysis, Latent Dirichlet Allocation based topic modelling is used in this work which generates the topics. For each topic, the sentiment is calculated separately and the topic guided feature expansion is done using Senti-wordnet. Moreover, an extended deep learning classification algorithm called Long Short-Term Memory (LSTM) with word embedding and temporal reasoning(LSTMWTR) is proposed in this paper for improving the classification accuracy. Finally, the labelled data are classified using the existing machine learning algorithms namely Naïve Bayes, Support Vector Machines and also using the deep learning models such as Convolution Neural Network(CNN),LSTM, Recurrent Neural Networks (RNN) and the transformer model namelyBi-directional Encoder Representation from Transformers (BERT),Convolution Bi-directional Recurrent Neural Network (CBRNN) and the proposed deep learning algorithm namelyLSTMWTR. These sentiment classification algorithms have been evaluated with word embedding for tweet sentiment classification and product review classification. The results obtained from this work show that the proposed LSTMWTR algorithm emerges as the highly accurate model for tweet sentiment and product review classification. [ABSTRACT FROM AUTHOR]

The popularity and demand for high-quality date palm fruits (Phoenix dactylifera L.) have been growing, and their quality largely depends on the type of handling, storage, and processing methods. The current methods of geometric evaluation and classification of date palm fruits are characterised by high labour intensity and are usually performed mechanically, which may cause additional damage and reduce the quality and value of the product. Therefore, non-contact methods are being sought based on image analysis, with digital solutions controlling the evaluation and classification processes. The main objective of this paper is to develop an automatic classification model for varieties of date palm fruits using a convolutional neural network (CNN) based on two fundamental criteria, i.e., colour difference and evaluation of geometric parameters of dates. A CNN with a fixed architecture was built, marked as DateNET, consisting of a system of five alternating Conv2D, MaxPooling2D, and Dropout classes. The validation accuracy of the model presented in this study depended on the selection of classification criteria. It was 85.24% for fruit colour-based classification and 87.62% for the geometric parameters only; however, it increased considerably to 93.41% when both the colour and geometry of dates were considered. [ABSTRACT FROM AUTHOR]

Adolescent mental health issues like depression and anxiety are rising among adolescents worldwide, leading to serious long-term consequences if left untreated. However, stigma and lack of resources delay access to timely screening and care. Traditional subjective methods fail to detect early warning signs and risk factors. This paper develops a novel hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model to enable early warning of common mental health risks like depression in teenagers. The model is trained on a large clinical dataset with over 50,000 adolescents encompassing electronic health records and neuroimaging data. This allows discovering subtle predictive patterns within clinical encounters and brain structure or function at the population level. The methodology centers on a tailored CNN and LSTM architecture to comprehensively model the multivariate clinical time series data, combining CNN spatial feature learning with LSTM temporal sequence modeling. The hybrid approach leverages complementary techniques to analyze the neuroimaging and electronic health record data. The model achieves an accuracy of 95%, AUC of 97%, precision of 94%, recall of 91%, and F1 score of 92% on held-out test data, significantly outperforming prior state-of-the-art models which had lower accuracy. Detailed predictive feature analysis and clinical validation confirm the model's utility for early mental health risk screening and targeted intervention in teenagers. Overall, the study demonstrates an effective approach to integrating big data, deep learning and rigorous evaluation for developing more accurate, explainable and useful models to support adolescent mental healthcare through data-driven early warning systems. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications due to narrow spectral features that allow multiplex analysis. We have previously developed a multiplexed, SERS-based nanosensor for micro-RNA (miRNA) detection called the inverse molecular sentinel (iMS). Machine learning (ML) algorithms have been increasingly adopted for spectral analysis due to their ability to discover underlying patterns and relationships within large and complex data sets. However, the high dimensionality of SERS data poses a challenge for traditional ML techniques, which can be prone to overfitting and poor generalization. Non-negative matrix factorization (NMF) reduces the dimensionality of SERS data while preserving information content. In this paper, we compared the performance of ML methods including convolutional neural network (CNN), support vector regression, and extreme gradient boosting combined with and without NMF for spectral unmixing of four-way multiplexed SERS spectra from iMS assays used for miRNA detection. CNN achieved high accuracy in spectral unmixing. Incorporating NMF before CNN drastically decreased memory and training demands without sacrificing model performance on SERS spectral unmixing. Additionally, models were interpreted using gradient class activation maps and partial dependency plots to understand predictions. These models were used to analyze clinical SERS data from single-plexed iMS in RNA extracted from 17 endoscopic tissue biopsies. CNN and CNN-NMF, trained on multiplexed data, performed most accurately with RMSElabel = 0.101 and 9.68 × 10–2, respectively. We demonstrated that CNN-based ML shows great promise in spectral unmixing of multiplexed SERS spectra, and the effect of dimensionality reduction on performance and training speed. Graphical abstract This is a visual representation of the abstract. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a complication of prolonged diabetic mellitus causing damage to the micro-vascular system in retina. Computer-aided detection of DR can significantly reduce time, effort, and cost in early diagnosis and thereby restrict further complications. In this paper, we present a deep learning based framework for automated DR stage classification from fundus retinal images.We design and develop a custom build Convolutional Neural Network (CNN) model named Model-IVS to extract meaningful and discriminative features from the retinal fundus images. The proposed hybrid-CNN combines the multi-scale pyramidal feature processing concept of inception model with the convolutional pipeline based feature extraction concept of VGG-Nets and adopts multi-scale feature concatenation based short-cut connections of DenseNet with learnable convolutions. For the binary classification tasks, we employ Model-IVS as a feature extractor and use Support Vector Machine (SVM) classifier on the reduced features obtained through principal component analysis. The proposed hybrid model is end-to-end trained incrementally on a class-balanced dataset curated from the train set of EyePACS dataset. The cross-dataset generalization of the proposed hybrid CNN based DR classification framework is evaluated on APTOS, MESSIDOR-1 and MESSIDOR-2 datasets. Experimental results demonstrate that our model has outperformed all the existing state-of-the-art methods for DR severity grading task on APTOS dataset. The proposed Model-IVS has achieved best accuracy, AUC-ROC, and kappa score of 84.33%, 0.978 and 0.899, respectively in DR severity grading task. In binary classification tasks like DR screening and referable DR predictions, our Model-IVS feature extraction network with SVM classifier on PCA features has also achieved comparable performance results w.r.t. the state-of-the-art methods. In DR screening task, we recorded an accuracy of 97.84%, 87.42%, and 89.67% on APTOS, MESSIDOR-1, and MESSIDOR-2 datasets, respectively. In referable DR prediction, we achieved AUC-ROC score of 0.987, 0.928, and 0.873 on APTOS, MESSIDOR-1, and MESSIDOR-2 datasets, respectively. [ABSTRACT FROM AUTHOR]

Crack detection on roads' surfaces is an important issue in pavement management, as it provides an indication of the quality of the road and its deterioration over time. Pavement cracks are one of the most common types of damage observed on roads, and they can be seen visually. Despite the fact that it does not provide immediate resolution to the issue, understanding the extent of crack damage is essential for the upkeep of roads. This paper presents a novel approach to automatically detecting pavement cracks using the orthoimage generated by a consumer-grade photogrammetric Unmanned Aerial Vehicle (UAV) and a deep learning algorithm. We used an autoencoder Convolutional Neural Network (CNN) to train a dataset full of challenging factors such as road lines and marks, oil and colour spots, and water stains. The model was tested on a dataset of RGB patches of different patterns of cracks and achieved an overall accuracy (OA) and F1 score of about 0.98. The results demonstrate the effectiveness of the proposed method in accurately detecting pavement cracks in challenging real-world conditions. This approach provides an efficient and cost-effective solution for pavement crack detection, that can be used for measuring the road's quality and monitoring it. [ABSTRACT FROM AUTHOR]

The escalating global water usage and the increasing strain on major cities due to water shortages highlights the critical need for efficient water management practices. In water-stressed regions worldwide, significant water wastage is primarily attributed to leakages, inefficient use, and aging infrastructure. Undetected water leakages in buildings' pipelines contribute to the water waste problem. To address this issue, an effective water leak detection method is required. In this paper, we explore the application of edge computing in smart buildings to enhance water management. By integrating sensors and embedded Machine Learning models, known as TinyML, smart water management systems can collect real-time data, analyze it, and make accurate decisions for efficient water utilization. The transition to TinyML enables faster and more cost-effective local decision-making, reducing the dependence on centralized entities. In this work, we propose a solution that can be adapted for effective leakage detection in real-world scenarios with minimum human intervention using TinyML. We follow an approach that is similar to a typical machine learning lifecycle in production, spanning stages including data collection, training, hyperparameter tuning, offline evaluation and model optimization for on-device resource efficiency before deployment. In this work, we considered an existing water leakage acoustic dataset for polyvinyl chloride pipelines. To prepare the acoustic data for analysis, we performed preprocessing to transform it into scalograms. We devised a water leak detection method by applying transfer learning to five distinct Convolutional Neural Network (CNN) variants, which are namely EfficientNet, ResNet, AlexNet, MobileNet V1, and MobileNet V2. The CNN models were found to be able to detect leakages where a maximum testing accuracy, recall, precision, and F1 score of 97.45%, 98.57%, 96.70%, and 97.63%, respectively, were observed using the EfficientNet model. To enable seamless deployment on the Arduino Nano 33 BLE edge device, the EfficientNet model is compressed using quantization resulting in a low inference time of 1932 ms, a peak RAM usage of 255.3 kilobytes, and a flash usage requirement of merely 48.7 kilobytes. [ABSTRACT FROM AUTHOR]

Crack detection on roads' surfaces is an important issue in pavement management, as it provides an indication of the quality of the road and its deterioration over time. Pavement cracks are one of the most common types of damage observed on roads, and they can be seen visually. Despite the fact that it does not provide immediate resolution to the issue, understanding the extent of crack damage is essential for the upkeep of roads. This paper presents a novel approach to automatically detecting pavement cracks using the orthoimage generated by a consumer-grade photogrammetric Unmanned Aerial Vehicle (UAV) and a deep learning algorithm. We used an autoencoder Convolutional Neural Network (CNN) to train a dataset full of challenging factors such as road lines and marks, oil and colour spots, and water stains. The model was tested on a dataset of RGB patches of different patterns of cracks and achieved an overall accuracy (OA) and F1 score of about 0.98. The results demonstrate the effectiveness of the proposed method in accurately detecting pavement cracks in challenging real-world conditions. This approach provides an efficient and cost-effective solution for pavement crack detection, that can be used for measuring the road's quality and monitoring it. [ABSTRACT FROM AUTHOR]

In recent years, deep neural networks have enabled countless innovations in the field of image classification. Encouraged by success in this field, researchers worldwide have demonstrated how to use Convolutional Neural Network techniques in medical imaging problems. In this article, the results were obtained through the use of the EfficientNet in the task of classifying 14 different diseases based on chest X-ray images coming from the NIH (National Institutes of Health) ChestX-ray14 dataset. The approach addresses dataset imbalances by introducing a custom split to ensure fair representation. Binary cross entropy loss is utilized to handle the multi-label difficulty. The model architecture comprises an EfficientNet backbone for feature extraction, succeeded by sequential layers including GlobalAveragePooling, Dense, and BatchNormalization. The main contribution of this paper is a proposed solution that outperforms previous state-of-the-art deep learning models average area under the receiver operating characteristic curve—AUC-ROC (score: 84.28%). The usage of the transfer-learning technique and traditional deep learning engineering techniques was shown to enable us to obtain such results on consumer-class GPUs (graphics processing units). [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disease that can cause dementia and result in a severe reduction in brain function, inhibiting simple tasks, especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD-induced dementia, and unpaid care for people with AD-related dementia is valued at USD 271.6 billion. Hence, various approaches have been developed for early AD diagnosis to prevent its further progression. In this paper, we first review other approaches that could be used for the early detection of AD. We then give an overview of our dataset and propose a deep convolutional neural network (CNN) architecture consisting of 7,866,819 parameters. This model comprises three different convolutional branches, each having a different length. Each branch is comprised of different kernel sizes. This model can predict whether a patient is non-demented, mild-demented, or moderately demented with a 99.05% three-class accuracy. In summary, the deep CNN model demonstrated exceptional accuracy in the early diagnosis of AD, offering a significant advancement in the field and the potential to improve patient care. [ABSTRACT FROM AUTHOR]

Seismic trace editing is a tedious process in data preprocessing that can incur high time costs, especially when handling large 3D datasets. In addition, existing methods to edit seismic traces may miss vital information when killing noisy traces simply. Thus, in this paper, we propose an automated method to edit seismic traces based on machine learning. The proposed method combines the Hough transform technique and a convolutional neural network (CNN) to improve the feasibility of the scheme. The Hough transform is a feature extraction technique that helps identify anomaly lines in images, and we employ it in the proposed method to ascertain the prospective positions of noisy and bad traces. We then implement a bandpass filter and the trained CNN model to identify the precise noisy traces in the target region indicated by the Hough transform process. Upon identification, automated processing is applied to determine whether the processed traces can be useful or should be discarded. This comprehensive framework includes four main steps, i.e., data preprocessing, Hough transform detection, network training, and network prediction. Experiments conducted on real-world data yielded 98% accuracy, which indicates the potential efficacy of the proposed automated trace editing method in practical applications. [ABSTRACT FROM AUTHOR]

Waste disposal remains a challenge due to land availability, and environmental and health issues related to the main disposal method, landfilling. Combining computer vision (machine learning) and robotics to sort waste is a cost-effective solution for landfilling activities limitation. The objective of this study was to combine transfer and ensemble learning to process collected waste images and classify landfill waste into nine classes. Pretrained CNN models (Inception–ResNet-v2, EfficientNetb3, and DenseNet201) were used as base models to develop the ensemble network, and three other single CNN models (Models 1, 2, and 3). The single network performances were compared to the ensemble model. The waste dataset, initially grouped in two classes, was obtained from Kaggle, and reorganized into nine classes. Classes with a low number of data were improved by downloading additional images from Google search. The Ensemble Model showed the highest prediction precision (90%) compared to the precision of Models 1, 2, and 3, 86%, 87%, and 88%, respectively. All models had difficulties predicting overlapping classes, such as glass and plastics, and wood and paper/cardboard. The environmental costs for the Ensemble network, and Models 2 and 3, approximately 15 g CO2 equivalent per training, were lower than the 19.23 g CO2 equivalent per training for Model 1. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology, network attacks occur frequently and numerous hidden dangers appear in network security. Therefore, improving the performance of intrusion detection systems to detect and defend against attacks is the key to ensuring network security. However, in the face of complex and massive network data feature information, traditional machine learning methods suffer from data imbalance and feature redundancy, which results in low detection rates, high false alarm rates and poor real-time performance of intrusion detection systems. Therefore, to address these problems, this paper proposes a data imbalance-based Convolutional Neural Network Intrusion Detection Method (CNN-IDMDI). First, an oversampling method is used to solve the data imbalance problem by decomposing the increased number of samples for the few attacks with multiple sampling to form multiple sub-samples. Second, the gradient coordination mechanism and the improved loss function Focal Loss are combined to calculate the loss between the actual and expected values to detect network malicious attacks in high-dimensional and unbalanced data. Finally, the methods in this paper are compared with the current mainstream intrusion detection methods on the NSL-KDD dataset for binary and multi-classification detection. The experimental results show that the method in this paper can effectively improve the effectiveness of CNN intrusion detection and network anomaly. The average accuracy of the CNN intrusion detection method based on data imbalance for binary intrusion detection is 98.73% and the implementation time of the method is 1.42 s, which is 15.45%, 12.76%, and 2.91% higher than the average accuracy of the CNN, the CNN Long Short-Term Memory (CNN-LSTM) and the CNN Neural-induced Support Vector Machine (CNN-NSVM) methods, respectively, and the detection time is saved by 0.82 s, 0.72 s, and 0.61 s, respectively. The average accuracy of the CNN intrusion detection method based on data imbalance for multi-classification intrusion detection is 94.55% and the time required to complete the detection is 2.96 s. This improves the average accuracy by 16.09%, 12.71%, and 3.66% compared with the CNN, CNN-LSTM and CNN-NSVM methods, respectively. It is also quicker, as the time consumption of CNN is 8.84 s, CNN-LSTM is 8.31 s and CNN-NSVM is 6.43 s. Therefore, the CNN-IDMDI method for intrusion detection proposed in this paper has higher accuracy and faster speed. [ABSTRACT FROM AUTHOR]

Readability of Chinese texts considered in this paper is a multi-class classification problem with 12 grade classes corresponding to 6 grades in primary schools, 3 grades in middle schools, and 3 grades in high schools. A special property of this problem is the strong ambiguity in determining the grades. To overcome the difficulty, a measurement of readability assessment methods used empirically in practice is adjacent accuracy in addition to exact accuracy. In this paper we give mathematical definitions of these concepts in a learning theory framework and compare these two quantities in terms of the ambiguity level of texts. A deep learning algorithm is proposed for readability of Chinese texts, based on convolutional neural networks and a pre-trained BERT model for vector representations of Chinese characters. The proposed CNN model can extract sentence and text features by convolutions of sentence representations with filters and is efficient for readability assessment, which is demonstrated with some numerical experiments. [ABSTRACT FROM AUTHOR]

Satellite-Derived Bathymetry (SDB) can be calculated using analytical or empirical approaches. Analytical approaches require several water properties and assumptions, which might not be known. Empirical approaches rely on the linear relationship between reflectances and in-situ depths, but the relationship may not be entirely linear due to bottom type variation, water column effect, and noise. Machine learning approaches have been used to address nonlinearity, but those treat pixels independently, while adjacent pixels are spatially correlated in depth. Convolutional Neural Networks (CNN) can detect this characteristic of the local connectivity. Therefore, this paper conducts a study of SDB using CNN and compares the accuracies between different areas and different amounts of training data, i.e., single and multi-temporal images. Furthermore, this paper discusses the accuracies of SDB when a pre-trained CNN model from one or a combination of multiple locations is applied to a new location. The results show that the accuracy of SDB using the CNN method outperforms existing works with other methods. Multi-temporal images enhance the variety in the training data and improve the CNN accuracy. SDB computation using the pre-trained model shows several limitations at particular depths or when water conditions differ. [ABSTRACT FROM AUTHOR]

Classifying epitopes is essential since they can be applied in various fields, including therapeutics, diagnostics and peptide-based vaccines. To determine the epitope or peptide against an antibody, epitope mapping with peptides is the most extensively used method. However, this method is more time-consuming and inefficient than using present methods. The ability to retrieve data on protein sequences through laboratory procedures has led to the development of computational models that predict epitope binding based on machine learning and deep learning (DL). It has also evolved to become a crucial part of developing effective cancer immunotherapies. This paper proposes an architecture to generalize this case since various research strives to solve a low-performance classification problem. A proposed DL model is the fusion architecture, which combines two architectures: Transformer architecture and convolutional neural network (CNN), called MITNet and MITNet-Fusion. Combining these two architectures enriches feature space to correlate epitope labels with the binary classification method. The selected epitope–T-cell receptor (TCR) interactions are GILG, GLCT and NLVP, acquired from three databases: IEDB, VDJdb and McPAS-TCR. The previous input data was extracted using amino acid composition, dipeptide composition, spectrum descriptor and the combination of all those features called AADIP composition to encode the input data to DL architecture. For ensuring consistency, fivefold cross-validations were performed using the area under curve metric. Results showed that GILG, GLCT and NLVP received scores of 0.85, 0.87 and 0.86, respectively. Those results were compared to prior architecture and outperformed other similar deep learning models. [ABSTRACT FROM AUTHOR]

As one of the main methods of information fusion, artificial intelligence class fusion algorithm not only inherits the powerful skills of artificial intelligence, but also inherits many advantages of information fusion. Similarly, as an important sub-field of artificial intelligence class fusion algorithm, deep learning class fusion algorithm also inherits advantages of deep learning and information fusion. Hence, deep learning fusion algorithm has become one of the research hotspots of many scholars. To solve the problem that the existing neural networks are input into multiple channels as a whole and cannot fully learn information of multichannel images, Shan et al. proposed multichannel concat-fusional convolutional neural networks. To mine more multichannel images' information and further explore the performance of different fusion types, the paper proposes new fusional neural networks called multichannel cross-fusion convolutional neural networks (McCfCNNs) with fusion types of "R+G+B/R+G+B/R+G+B" and "R+G/G+B/B+R" based on the tremendous strengths of information fusion. Experiments show that McCfCNNs obtain 0.07-6.09% relative performance improvement in comparison with their corresponding non-fusion convolutional neural networks (CNNs) on diverse datasets (such as CIFAR100, SVHN, CALTECH256, and IMAGENET) under a certain computational complexity. Hence, McCfCNNs with fusion types of "R+G+B/R+G+B/R+G+B" and "R+G/G+B/B+R" can learn more fully multichannel images' information, which provide a method and idea for processing multichannel information fusion, for example, remote sensing satellite images. [ABSTRACT FROM AUTHOR]

Fixture layout is a complex task that significantly impacts manufacturing costs and requires the expertise of well-trained engineers. While most research approaches to automating the fixture layout process use optimization or rule-based frameworks, this paper presents a novel approach using supervised learning. The proposed framework replicates the 3-2-1 locating principle to layout fixtures for sheet metal designs. This principle ensures the correct fixing of an object by restricting its degrees of freedom. One main novelty of the proposed framework is the use of topographic maps generated from sheet metal design data as input for a convolutional neural network (CNN). These maps are created by projecting the geometry onto a plane and converting the Z coordinate into gray-scale pixel values. The framework is also novel in its ability to reuse knowledge about fixturing to lay out new workpieces and in its integration with a CAD environment as an add-in. The results of the hyperparameter-tuned CNN for regression show high accuracy and fast convergence, demonstrating the usability of the model for industrial applications. The framework was first tested using automotive b-pillar designs and was found to have high accuracy (≈ 100%) in classifying these designs. The proposed framework offers a promising approach for automating the complex task of fixture layout in sheet metal design. [ABSTRACT FROM AUTHOR]

Network Application Classification (NAC) is a vital technology for intrusion detection, Quality-of-Service (QoS)-aware traffic engineering, traffic analysis, and network anomalies. Researchers have focused on designing algorithms using deep learning models based on statistical information to address the challenges of traditional payload and port-based traffic classification techniques. Internet of Things (IoT) and Software Defined Network (SDN) are two popular technologies nowadays and aims to connect devices over the internet and intelligently control networks from a centralized space. IoT aims to connect billions of devices; therefore, classification is essential for efficient processing. SDN is a new networking paradigm, which separates data plane measurement from the control plane. The emergence of deep learning algorithms with SDN provides a scalable traffic classification architecture. Due to the inadequate results of payload and port-based approaches, a statistical technique to classify network traffic into different classes using a Convolution Neural Network (CNN) and a Recurrent Neural Network (RNN) is presented in this paper. This paper provides a classification method for software defined IoT networks. The results show that, contrary to other traffic classification methods, the proposed approach offered a better accuracy rate of over 99 %, which is promising. [ABSTRACT FROM AUTHOR]

Children who have Attention-Deficit/Hyperactivity Disorder (ADHD) have a chronic behavioral disease. Children with ADHD have a hard time focusing and controlling their actions. One of the most difficult problems in controlling and treating this condition is early detection. There is yet to be discovered a reliable professional procedure for early detection of this condition. The electroencephalogram (EEG) is a useful neuroimaging technique for researching ADHD; one of the key goals is to define the EEG of ADHD youngsters. Numerous methods based on EEG signals have been put out in the literature to address this issue since they are an effective neuroimaging approach for studying ADHD. The best recording formats and channels for diagnosing ADHD, however, have not been the subject of many research. Machine learning (ML) and Artificial Intelligence (AI) strategies for identifying ADHD using EEG-based tools are discussed in this paper. Although, in the case of ADHD, the utilization of ML and AI approaches is restricted. However, the data clearly imply that combining EEG technologies with ML/AI may be utilized to detect ADHD. For categorizing adult ADHD subtypes based on EEG power spectra, ML algorithms that incorporate several classifiers are presented. A widely used deep learning (DL) method is the convolutional neural network (CNN). The use of DL approaches in ADHD research, on the other hand, is currently restricted. EEG has been used in studies to look for ADHD neurological connections. Recent advances in deep learning algorithms, particularly CNN, are anticipated to overcome the issue. [ABSTRACT FROM AUTHOR]

Traditional video surveillance, temperature-based or smoke-based fire source location methods are difficult to timely and accurately locate the fire source in warehouses with the characteristics of burning intensely, smoke spreading quickly, and being sheltered by shelves and goods. To overcome the drawbacks, a deep-learning-based fire source localization algorithm with temperature and smoke sensor data fusion according to the different stages of the combustion process is proposed in this paper. The temperature and smoke concentration information are collected from sensors distributed in different spatial locations of a warehouse. A convolutional neural network is used to exact the fusion data feature. The deep learning algorithm is adopted to construct the fire source localization model where the fusion data feature of temperature and smoke concentrations are the inputs and the fire source coordinates are the outputs. By using Fire Dynamics Simulator, a warehouse that meets the practical application is constructed and kinds of fire scenes are simulated. The experimental results show that the RMSE of the model localization reaches 0.63, 0.08, and 0.17 in three stages respectively, which verifies the effectiveness of the proposed fire source localization algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a computer vision-based cashew nut grading system has been designed and implemented for classifying different grades of cashew nuts using combined features and machine learning approaches. The important task in the cashew nut grading system is to classify the whole and split down cashew nuts. Since these cashew nuts look very similar from the top view, it is a challenging task to classify the whole cashew nut and split down cashew nuts. Hence, a single-view image of cashew nut has been captured by placing a camera with a distance of 17 cm (from the right side of the conveyor belt). The captured red, blue and green images are normalized and converted into hue, saturation and value color space. S channel from HSV image is used for segmentation process using Otsu threshold technique. The total numbers of features extracted are 275 and the features are texture (180), color (90), and shape (5). The constrained optimization-based feature selection method is used and 30 features are selected for further process. The Support Vector Machine (SVM) classifier is used for the classification, and the results obtained from different kernel functions are computed and compared. The 8-layer convolutional neural network (CNN) has been developed in this work for classification and to analyze the performance and accuracy. The accuracy of different machine learning classifiers like SVM 1-1, SVM 1-All and CNN model is also evaluated and compared. The overall accuracy obtained by SVM 1-All with kernel function radial basis for classification is 98.93%. [ABSTRACT FROM AUTHOR]

Histology images are extensively used by pathologists to assess abnormalities and detect malignancy in breast tissues. On the other hand, Convolutional Neural Networks (CNN) are by far, the privileged models for image classification and interpretation. Based on these two facts, we surveyed the recent CNN-based methods for breast cancer histology image analysis. The survey focuses on two major issues usually faced by CNN-based methods namely the design of an appropriate CNN architecture and the lack of a sufficient labelled dataset for training the model. Regarding the design of the CNN architecture, methods examining breast histology images adopt three main approaches: Designing manually from scratch the CNN architecture, using pre-trained models and adopting an automatic architecture design. Methods addressing the lack of labelled datasets are grouped into four categories: methods using pre-trained models, methods using data augmentation, methods adopting weakly supervised learning and those adopting feedforward filter learning. Research works from each category and reported performance are presented in this paper. We conclude the paper by indicating some future research directions related to the analysis of histology images. [ABSTRACT FROM AUTHOR]

Simple Summary: Excessive use of groundwater resources in the Central Valley in California has led to major land sinking over the years. In this study, we rely on satellite imagery to monitor and assess the extent of this sinking. Specifically, we use images from a satellite that emits microwaves which allows us to directly obtain the deformation at a specific time. Then, we apply a machine learning algorithm to the resulting data in an attempt to effectively predict short-term future deformation. We find that the algorithm applied has a low error when compared to the actual data. This shows that machine learning algorithms could be incorporated into models that assess potential hazards associated with land sinking. Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers' integrity. [ABSTRACT FROM AUTHOR]

This paper describes the process of developing a classification model for the effective detection of malignant melanoma, an aggressive type of cancer in skin lesions. Primary focus is given on fine-tuning and improving a state-of-the-art convolutional neural network (CNN) to obtain the optimal ROC-AUC score. The study investigates a variety of artificial intelligence (AI) clustering techniques to train the developed models on a combined dataset of images across data from the 2019 and 2020 IIM-ISIC Melanoma Classification Challenges. The models were evaluated using varying cross-fold validations, with the highest ROC-AUC reaching a score of 99.48%. [ABSTRACT FROM AUTHOR]

Large bowel obstruction (LBO) occurs when there is a blockage or twisting in the large bowel that prevents wastes and gas from passing through. If left untreated, the blockage cuts off blood supply to the colon, causing sections of it to die which results in high rates of morbidity and fatality. The examination of clinical symptoms of LBO involves careful inspection of the cecum and colon. Radiologists use X‐rays to inspect the clinical signs. Some research has been done to automate the detection of related abdominal and intestinal diseases. However, all these studies concentrate only on detecting Crohn's, ulcerative colitis, Acute Appendicitis, colorectal cancer, celiac diseases, liver diseases, and chronic kidney diseases. Automatic detection and classification of LBO has not been given due attention so far to the best of the authors knowledge. To address this challenge, we have designed a model for the detection and classification of LBO. The models development comprises of stages such as preprocessing, detection, segmentation, feature extraction, and classification. We used YOLOv3 for detection and used a gray scale level co‐occurrence matrix (GLCM), and a convolutional neural network for feature extraction, while support vector machine (SVM) and softmax were used for classification. The proposed model achieved a diagnostic accuracy of 89% when feature extraction methods such as CNN and median filter with softmax classifier were used. CNN and Gaussian filter with soft max classifier achieved 91%, while CNN and anisotropic filter with soft max classifier achieved 92%. GLCM with threshold segmentation and Gaussian filter with SVM classifier achieved 87%, while CNN with watershed segmentation and Gaussian filter with SVM classifier achieved 97% and CNN‐GLCM with watershed segmentation and anisotropic diffusion filter with SVM classifier achieved 98% for detection and classification of LBO. Finally, this paper presented a performance analysis of various machine learning approaches for detection and classification of LBO. Hence, our model is designed to assist human experts (Radiologists) in diagnosing LBO. [ABSTRACT FROM AUTHOR]

Drowning is one of the top five causes of death for children aged 1–14 worldwide. According to data from the World Health Organization (WHO), drowning is the third most common reason for unintentional fatalities. Designing a drowning detection system is becoming increasingly necessary in order to ensure the safety of swimmers, particularly children. This paper presents a computer vision and deep learning-based early drowning detection approach. We utilized five convolutional neural network models and trained them on our data. These models are SqueezeNet, GoogleNet, AlexNet, ShuffleNet, and ResNet50. ResNet50 showed the best performance, as it achieved 100% prediction accuracy with a reasonable training time. When compared to other approaches, the proposed approach performed exceptionally well in terms of prediction accuracy and computational cost. [ABSTRACT FROM AUTHOR]

In smart cities, health care, industrial production, and many other fields, the Internet of Things (IoT) have had significant success. Protected agriculture has numerous IoT applications, a highly effective style of modern agriculture development that uses artificial ways to manipulate climatic parameters such as temperature to create ideal circumstances for the growth of animals and plants. Convolutional Neural Networks (CNNs) is a deep learning approach that has made significant progress in image processing. From 2016 to the present, various applications for the automatic diagnosis of agricultural diseases, identifying plant pests, predicting the number of crops, etc., have been developed. This paper involves a presentation of the Internet of Things system in agriculture and its deep learning applications. It summarizes the most essential sensors used and methods of communication between them, in addition to the most important deep learning algorithms devoted to intelligent agriculture. [ABSTRACT FROM AUTHOR]

Emotion recognition, or the ability of computers to interpret people's emotional states, is a very active research area with vast applications to improve people's lives. However, most image-based emotion recognition techniques are flawed, as humans can intentionally hide their emotions by changing facial expressions. Consequently, brain signals are being used to detect human emotions with improved accuracy, but most proposed systems demonstrate poor performance as EEG signals are difficult to classify using standard machine learning and deep learning techniques. This paper proposes two convolutional neural network (CNN) models (M1: heavily parameterized CNN model and M2: lightly parameterized CNN model) coupled with elegant feature extraction methods for effective recognition. In this study, the most popular EEG benchmark dataset, the DEAP, is utilized with two of its labels, valence, and arousal, for binary classification. We use Fast Fourier Transformation to extract the frequency domain features, convolutional layers for deep features, and complementary features to represent the dataset. The M1 and M2 CNN models achieve nearly perfect accuracy of 99.89% and 99.22%, respectively, which outperform every previous state-of-the-art model. We empirically demonstrate that the M2 model requires only 2 seconds of EEG signal for 99.22% accuracy, and it can achieve over 96% accuracy with only 125 milliseconds of EEG data for valence classification. Moreover, the proposed M2 model achieves 96.8% accuracy on valence using only 10% of the training dataset, demonstrating our proposed system's effectiveness. Documented implementation codes for every experiment are published for reproducibility. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has great potential to implement the fifth-generation (5G) requirements of wireless communication. For a NOMA traditional detection method, successive interference cancellation (SIC) plays a vital role at the receiver side for both uplink and downlink transmission. Due to the complex multipath channel environment and prorogation of error problems, the traditional SIC method has a limited performance. To overcome the limitation of traditional detection methods, the deep-learning method has an advantage for the highly efficient tool. In this paper, a deep neural network which has bi-directional long short-term memory (Bi-LSTM) for multiuser uplink channel estimation (CE) and signal detection of the originally transmitted signal is proposed. Unlike the traditional CE schemes, the proposed Bi-LSTM model can directly recover multiuser transmission signals suffering from channel distortion. In the offline training stage, the Bi-LTSM model is trained using simulation data based on channel statistics. Then, the trained model is used to recover the transmitted symbols in the online deployment stage. In the simulation results, the performance of the proposed model is compared with the convolutional neural network model and traditional CE schemes such as MMSE and LS. It is shown that the proposed method provides feasible improvements in performance in terms of symbol-error rate and signal-to-noise ratio, making it suitable for 5G wireless communication and beyond. [ABSTRACT FROM AUTHOR]

Mechanical equipment failure may cause massive economic and even life loss. Therefore, the diagnosis of the failures of machine parts in time is crucial. The rolling bearings are one of the most valuable parts, which have attracted the focus of fault diagnosis. Many successful rolling bearing fault diagnoses have been made based on machine learning and deep learning. However, most diagnosis methods still rely on complex signal processing and artificial features, bringing many costs to the deployment and migration of diagnostic models. This paper proposes an end-to-end continuous/discontinuous feature fusion method for rolling bearing fault diagnosis (C/D-FUSA). This method comprises long short-term memory (LSTM), convolutional neural networks (CNN) and attention mechanism, which automatically extracts the continuous and discontinuous features from vibration signals for fault diagnosis. We also propose a contextual-dependent attention module for the LSTM layers. We compare the method with the other simpler deep learning methods and state-of-the-art methods in rolling bearing fault data sets with different sample rates. The results show that our method is more accurate than the other methods with real-time inference. It is also easy to be deployed and trained in a new environment. [ABSTRACT FROM AUTHOR]

Due to the strong penetration ability and high transmission rate of ultra-wideband (UWB) signals, UWB technology plays a very significant role in the field of precise indoor positioning. However, the harsh and volatile indoor environment leads to non-line-of-sight (NLOS) propagation and severe attenuation of UWB signals, which may generate significant ranging and positioning errors. To mitigate NLOS effect and improve positioning accuracy, existing methods use ranging information and channel impulse response (CIR) to identify UWB signal propagation channels. However, these NLOS identification methods often require a priori knowledge and suitable thresholds, and most of them only perform a binary classification between LOS and NLOS in a particular scenario. To address these disadvantages, this paper proposes a novel multi-classification method to classify UWB signal propagation channels based on one-dimensional wavelet packet analysis (ODWPA) and convolutional neural network (CNN). This method first decomposes the CIR of known categories into colored coefficients images using a one-dimensional wavelet packet function, then uses these images to train CNNs with different structures, and finally selects CNN model with the best performance to identify the unknown category of UWB signal propagation channels. The experimental results show that the performance of the proposed method is always the best, regardless of the type of experimental scenario, and the identification accuracy is always higher than 90% and up to 100%. [ABSTRACT FROM AUTHOR]

Myocardial infarction (MI) is an acute interruption of blood flow to the heart, which causes the heart to suffer from a deficiency of blood and ischemia, so the heart muscle is damaged, and cells can die and lose their function. Despite the low incidence of MI in the world, it is still a common disease-causing death. Therefore, detecting the MI signals early can reduce mortality. This paper presented a method based on a deep convolutional neural network (CNN) for the detection of MI automatically. The proposed CNN is an end-to-end model without requiring any stages of machine learning and requires only one stage to detect MI from the input signals. In the case of imbalanced data, we optimize our deep model with a new loss function named the focal loss to deal with this case by constituting the loss indirectly the focus in those difficult classes. The Physikalisch-Technische Bundesanstalt (PTB) dataset was employed in the validation to classify the signals to normal and MI. The performance of our technique alongside state-of-the-art in the area shows an increase in terms of average accuracy and F1 score. Results show that focal loss improves the detection accuracy by 9% for detecting MI signals. In summary, the proposed method achieved an overall accuracy, precision, F1 score, and recall of 98.84%, 98.31%, 97.92%, and 97.63, respectively using focal loss and overall accuracy of 89.72%, a precision of 88.52%, a recall of 81.11% and F1 score of 83.02% without using focal loss. Our method using focal loss is an effective tool to perform a fast and reliable MI diagnosis to assist the cardiologists in detecting MI early. [ABSTRACT FROM AUTHOR]

In today's digital age, the proliferation of network-connected devices has triggered a surge in cyberattacks. Distributed Denial-of-Service (DDoS) attacks pose a particularly formidable challenge to network security by disrupting access to vital services. While numerous researchers have proposed DDoS detection methods utilizing machine learning and deep learning techniques, developing a robust and reliable DDoS intrusion detection system remains challenging. This challenge is exacerbated by issues such as highly imbalanced data, multi-classification, and computational complexity. This paper proposes an innovative feature selection approach to create a robust intrusion detection system capable of detecting and classifying recent common DDoS attack types. We evaluate the performance of our model on the CICDDoS2019 benchmark dataset. Our experimental results demonstrate that our proposed model outperforms existing methods, achieving a detection accuracy of 96.82%, a recall of 96.82%, a precision of 96.76%, and an F1 score of 96.50%. Additionally, our model exhibits faster prediction times, with the ability to predict an attack in just 0.189 ms. Notably, our approach, combined with preprocessing and feature selection techniques, outperforms previous works and baseline models in DDoS attack classification. [Display omitted] • An innovative hybrid feature extraction method for training of DDoS attack detection model. • We propose a robust CNN model integrated with an Inception mechanism designed to classify various types of DDoS attacks. • Evaluated the network performance of the proposed system in terms of various parameters and compared the results with existing studies and baseline models. • The proposed model outperforms more than 96% in all performance metrics (Precision, Recall, Accuracy, and F1 Score). • The proposed model achieves the fastest prediction time of 0.189 ms among existing models in the literature. [ABSTRACT FROM AUTHOR]

With the significant increase in photovoltaic (PV) electricity generation, more attention has been given to PV power forecasting. Indeed, accurate forecasting allows power grid operators to better schedule and dispatch their assets, such as energy storage systems and reserve. In this paper, a hybrid deep learning model and a convolutional neural network with memory is proposed, to provide intraday (2 h) solar irradiance forecasts using sequentially-collected images from public webcams. The performance of the proposed model is compared to those of a standard time-series forecast models, a linear regression as well as state-of-the-art neural networks. All models are trained and tested using images collected from two webcams on EPFL's campus for just over a year. The results show that the proposed method outperforms all other models and matches the state-of-the-art methodology while providing simplicity of implementation and efficient computation. • A novel method for estimating global horizontal irradiance is proposed. • The method uses images public webcams instead of all-sky cameras. • The method combines images from different cameras to improve forecasts' accuracy. • The method can be extended to include other types of data (meteorological data). • This simple method matches state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Accurately predicting wind and photovoltaic power is one of the keys to improving the economy of wind-solar complementary power generation system, reducing scheduling costs and no-load losses, and ensuring grid stability. However, the natural properties of energy result in complex fluctuations in their corresponding power sequences, making accurate predictions difficult. Therefore, this paper proposes an intelligent prediction system that combines decomposition algorithms and deep learning for ultra-short-term prediction of wind and photovoltaic power. First, an improved decomposition algorithm is proposed, based on fuzzy entropy's property that its value increases with the increase of sequence uncertainty, particle swarm optimization (PSO) is used to search for the optimal parameter combinations of variational modal decomposition (VMD), so that it can automatically adjust the parameters for energy data with different characteristics to reduce the human error. Then, a convolutional neural network (CNN) architecture that balances operational efficiency and prediction performance is constructed, and the hyperparameters of the CNN are optimized using the wild horse optimization algorithm (WHO) to improve the stability and accuracy of the prediction model. In this paper, real data from wind power plants and photovoltaic power plants in China are used as experimental objects, and experiments are carried out in three aspects, namely, benchmark model selection, decomposition algorithm comparison and combined model comparison. The results show that selecting CNN as the benchmark model is a good choice; the improved VMD has better decomposition performance than other state-of-the-art decomposition algorithms. The system proposed in this paper is highly generalizable and adaptive, and its prediction performance and accuracy greatly outperform that of other comparative models, with prediction accuracies improved by 72% and 79%, respectively, compared to a single CNN model. • The hyperparameters of VMD are determined by using PSO based on fuzzy entropy. • Optimize convolutional neural network using the wild horse optimization algorithm. • The intelligent prediction system can accurately predict wind power and PV power. • Experiments based on power data from actual wind farms and PV plants. • A deep learning prediction method applied to wind and solar complementary systems. [ABSTRACT FROM AUTHOR]

The vibration signals for offshore wind-turbine high-speed bearings are often contaminated with noises due to complex environmental and structural loads, which increase the difficulty of fault detection and diagnosis. In view of this problem, we propose a fault-diagnosis strategy with good noise immunity in this paper by integrating the two-dimensional convolutional neural network (2DCNN) with random forest (RF), which is supposed to utilize both CNN's automatic feature-extraction capability and the robust discrimination performance of RF classifiers. More specifically, the raw 1D time-domain bearing-vibration signals are transformed into 2D grayscale images at first, which are then fed to the 2DCNN-RF model for fault diagnosis. At the same time, three procedures, including exponential linear unit (ELU), batch normalization (BN), and dropout, are introduced in the model to improve feature-extraction performance and the noise immune capability. In addition, when the 2DCNN feature extractor is trained, the obtained feature vectors are passed to the RF classifier to improve the classification accuracy and generalization ability of the model. The experimental results show that the diagnostic accuracy of the 2DCNN-RF model could achieve 99.548% on the CWRU high-speed bearing dataset, which outperforms the standard CNN and other standard machine-learning and deep-learning algorithms. Furthermore, when the vibration signals are polluted with noises, the 2DCNN-RF model, without retraining the model or any denoising process, still achieves satisfying performance with higher accuracy than the other methods. [ABSTRACT FROM AUTHOR]

With the development of Industry 4.0 technology including the Internet of Things (IoT) and deep learning techniques, it is important to reduce maintenance costs and ensure the safety of manufacturing process. The cutting tool degradation can cause significant economic losses and risks for machine users. Accurate prediction of cutting tool is important for making full use of cutter life. Deep learning plays an important role for tool condition monitoring. To overcome these difficulties, this paper aims to propose a new approach in the application of deep learning to estimate the tool wear during the milling process. The proposed methodology is based on the data-driven approach using Variational Mode Decomposition (VMD) and deep learning. Two deep learning machines used in this study, Convolutional Neural Networks (CNN) and Bidirectional long short-term memory (BiLSTM) to achieve collaborative data mining on (VMD) and to enhance the accuracy of modeling. VMD is a new decomposition technique used to decompose signal into sub-time series called intrinsic mode functions (IMFs). However, the VMD performances specifically depend on the constraints parameters that need to be pre-determined for VMD method especially the number of modes. The model development based on 1D-CNN and BiLSTM are selected by using the IMFs as inputs. The performance of the proposed approach is further improved by using the combined CNN and BiLSTM and has shown higher performances in prediction, compared to traditional learning techniques and adopted in previous works highlight the proposed prognostics method's superiority. Among all models, the VMD-CNN-BiLSTM achieve the best performance of modeling with respect to efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Although there have been various researches in the detection of different figurative language, there is no single work in the automatic classification of euphemisms. Our primary work is to present a system for the automatic classification of euphemistic phrases in a document. In this research, a large dataset consisting of 100,000 sentences is collected from different resources for identifying euphemism or non-euphemism utterances. In this work, several approaches are focused to improve the euphemism classification: 1. A Combination of lexical n-gram features 2.Three Feature-weighting schemes 3.Deep learning classification algorithms. In this paper, four machine learning (J48, Random Forest, Multinomial Naïve Bayes, and SVM) and three deep learning algorithms (Multilayer Perceptron, Convolutional Neural Network, and Long Short-Term Memory) are investigated with various combinations of features and feature weighting schemes to classify the sentences. According to our experiments, Convolutional Neural Network (CNN) achieves precision 95.43%, recall 95.06%, F-Score 95.25%, accuracy 95.26%, and Kappa 0.905 by using a combination of unigram and bigram features with TF-IDF feature weighting scheme in the classification of euphemism. These results of experiments show CNN with a strong combination of unigram and bigram features set with TF-IDF feature weighting scheme outperforms another six classification algorithms in detecting the euphemisms in our dataset. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) revolutionizes the field of education by providing a personalized, adaptive and online learning environment. Domains of AI such as Natural Language Processing (NLP), Machine Learning (ML) and Deep Learning (DL) were used in Educational data mining approach. The learning nature of Special Children's is heterogeneous in nature as they are in need of different types of educational need. Traditional way of teaching is not possible for these children. Tutoring Systems with intelligent incorporated with the help of AI, ML, DL can motivating and reinforce children towards teaching and learning environment. In this paper the application of Convolution Neural Network algorithm, SVM algorithm and decision tree algorithm in recognizing the MNIST Sign Language was compared. Results show that a maximum of 86% accuracy is achieved using Convolution Neural Network algorithm, and among the machine learning algorithm an accuracy of 84% is achieved by using the Support Vector Classification algorithm. [ABSTRACT FROM AUTHOR]

The availability of techniques for driver distraction detection has been difficult to put to use because of delays caused due to lag in inferencing the model. Distractions caused due to handheld devices have been major causes of traffic accidents as they affect the decision-making capabilities of the driver and gives them less time to react to difficult situations. Often drivers try to multitask which reduces their reaction time leading to accidents, which can easily be avoided if they had been attentive. As such, problems related to the driver's negligence towards safety a possible solution is to monitor the driver and driving behavior and alerting them if they are distracted. In this paper, we propose a novel approach for detecting when a driver is distracted due to in hand electronic devices which is not only able to detect the distraction with high accuracy but also is energy and memory efficient. Our proposed compressed neural got an accuracy of 0.83 in comparison to 0.86 of heavyweight network. [ABSTRACT FROM AUTHOR]

Machine learning models are widely used for identifying malicious software. However, existing models suffer from issues such as imprecise polysemous representations and a lack of contextual semantic representations, leading to the failure to recognize certain types of malicious software. In this paper, we propose a semantic-based intelligent malware detection model called SeMalBERT for identifying malicious software in Windows systems. Specifically, the model utilizes the API function sequences of malicious software as the learned features. Firstly, BERT is applied to accomplish word representation tasks and extract semantic information from the sequences. Secondly, a hybrid discriminator based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is used to explore the chaining relationships between functions. Lastly, an external attention mechanism is added after the LSTM to enable the model to better focus on key information within the text. Experimental results demonstrate that SeMalBERT outperforms existing malware detection techniques in terms of accuracy, F1 score, and loss function value on a general dataset. [ABSTRACT FROM AUTHOR]

Plant classification is an interesting problem in Computer Vision. Several researchers are completed to classification of plant by leaf of plant and flower of plant. After several research efforts, it has been confirmed that leaf of plant is the best and consistent source for classification of plant. However it is interesting to classify a plant through structure of leaf. Hence, it is mandatory to normalize the leaves of plant into the same size to acquire better performance. In this paper, we have proposed a hybrid method namely CNN-KNN based hybrid method. This method used on two datasets. These datasets are LeafSnap and Flavia. CNN + KNN achieved to get maximum accuracy of 98.4% and 96.5%, respectively, in LeafSnap and Flavia datasets. [ABSTRACT FROM AUTHOR]