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This paper presents a convolutional neural network (CNN) to classify between the conventionally and organically cultivated Memecik varieties of green olives. The image forming method called the rising paper chromatography is utilized in preparing the images of Memecik varieties of green olives for CNN. In the rising chromatography method, 20, 30, and 40% sample concentrations were determined as the suitable concentrations for both organic and conventional olives. The concentrations of AgNO3 and FeSO4 were determined as 0.25, 0.5, 0.75 and 1% for both conventional and organic samples. The visual differences used for differentiation of different types of Memecik green olives are usually determined according to the regional color differences, the vivid color occurrence, the width and the frequency of bowl occurrence, the thin line, and the picks at drop zone by the expert assessors. The testing results in this study verified the effectiveness of the CNN methodology in differentiating between the organically and conventionally cultivated Memecik green olives. The newly designed neural network achieved 100% accuracy. Furthermore, this high accuracy achieved by CNN might suggest that it can be effectively used in place of the expert assessors. [ABSTRACT FROM AUTHOR]

Natural disasters can be unpredictable and catastrophic. Even after the event, the repercussions are prolonged due to the incompetence of disaster management strategies. To mitigate the effects of a natural hazard, disaster management teams have to rapidly come forth with innovative plans of action. To contain the damage that a disaster causes, the response time, and preparedness of a disaster management team is crucial. Having a forewarning about the nature of the disaster can prove to be beneficial for the management team. Remote/unreachable areas such as deep parts of the forests, far-flung rural areas, oceans and other hard to reach locations are at a higher risk of receiving poorer aid and response due to the lack of communication and connectivity with the rest of the world. Our paper provides a solution for this particular problem by suggesting UAV/Drones for surveillance and monitoring in these inaccessible, disaster struck areas. The drones not only monitor the situation but constantly click pictures and send them to a base station, where the images are used to collect insights about the type of disaster that has to be dealt with. The paper proposes a deep learning model based on feature concatenation for classification of disasters which can be deployed at the base station, and can receive data in the form of images from a UAV/drone hovering over the affected place. The proposed model is efficient and able to achieve a higher accuracy as compared to the leading CNN models and closely related recent works as well. [ABSTRACT FROM AUTHOR]

IoT devices convert billions of objects into data-generating entities, enabling them to report status and interact with their surroundings. This data comes in various formats, like structured, semi-structured, or unstructured. In addition, it can be collected in batches or in real time. The problem now is how to benefit from all of this data gathered by sensing and monitoring changes like temperature, light, and position. In this paper, we propose a predictive analytics framework constructed on top of open-source technologies such as Apache Spark and Kafka. The framework focuses on forecasting temperature time series data using traditional and deep learning predictive analytics methods. The analysis and prediction tasks were performed using Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and a novel hybrid model based on Convolution Neural Network (CNN) and LSTM. The purpose of this paper is to determine whether and how recently developed deep learning-based models outperform traditional algorithms in the prediction of time series data. The empirical studies conducted and reported in this paper demonstrate that deep learning-based models, specifically LSTM and CNN-LSTM, exhibit superior performance compared to traditional-based algorithms, ARIMA and SARIMA. More specifically, the average reduction in error rates obtained by LSTM and CNN-LSTM models were substantial when compared to other models indicating the superiority of deep learning. Moreover, the CNN-LSTM-based deep learning model exhibits a higher degree of closeness to the actual values when compared to the LSTM-based model. [ABSTRACT FROM AUTHOR]

Microscopic image analysis technology helps solve the inadvertences of artificial traditional methods in disease, wastewater treatment, and environmental change monitoring analysis. Convolutional neural network (CNN) play an important role in microscopic image analysis. Image segmentation, in which U-Net is increasingly applied in microscopic image segmentation, is a crucial step in detection, tracking, monitoring, feature extraction, modelling, and analysis. This paper comprehensively reviews the development history of U-Net, analyses several research results of various segmentation methods since the emergence of U-Net, and conducts a comprehensive review of related papers. This paper summarised the improved methods of U-Net and then listed the existing significance of image segmentation techniques and their improvements introduced over the years. Finally, focusing on the different improvement strategies of U-Net in different papers, the related work of each application target is reviewed according to detailed technical categories to facilitate future research. Researchers can see the dynamics of the transmission of technological development and keep up with future trends in this interdisciplinary field. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to improve the training efficiency of deep convolutional neural networks. During training, the method evaluates scores to measure how much each layer's parameters change and whether the layer will continue learning or not. Based on these scores, the network is scaled down such that the number of parameters to be learned is reduced, yielding a speed-up in training. Unlike state-of-the-art methods that try to compress the network to be used in the inference phase or to limit the number of operations performed in the back-propagation phase, the proposed method is novel in that it focuses on reducing the number of operations performed by the network in the forward propagation during training. The proposed training strategy has been validated on two widely used architecture families: VGG and ResNet. Experiments on MNIST, CIFAR-10 and Imagenette show that, with the proposed method, the training time of the models is more than halved without significantly impacting accuracy. The FLOPs reduction in the forward propagation during training ranges from 17.83% for VGG-11 to 83.74% for ResNet-152. As for the accuracy, the impact depends on the depth of the model and the decrease is between 0.26% and 2.38% for VGGs and between 0.4 and 3.2% for ResNets. These results demonstrate the effectiveness of the proposed technique in speeding up learning of CNNs. The technique will be especially useful in applications where fine-tuning or online training of convolutional models is required, for instance because data arrive sequentially. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a condition that degenerates over time and impairs speech and pronunciation because brain cells have died. This research work aims to predict parkinson disease using the voice features extracted from speech signals recorded from PD individuals with dysphonic speech disorders by employing deep learning algorithms. PD is challenging to diagnose early on in the clinical presentation. To address the issue in machine learning methods, this paper proposes a neural network model by processing speech signals to classify PD using the University of California Irvine (UCI) machine learning repository dataset. Initially, a pre-loss reduction module is created by using pre-sampling to make the dataset balanced by reducing the dimensionality and maintaining the size of the space without influencing the learning process for data preparation. The relevant features are derived using a novel multi-agent salp swarm (MASS) algorithm, and a novel Parkinson classification neural network (PCNN) is proposed to classify Parkinson's patients with high accuracy employing these derived features. The result shows that the models that use MASS-PCNN produce higher classification accuracy of 99.1%, precision of 97.8%, recall of 94.7% and F1-score of 0.995 when paralleled to the existing models. As an outcome, the suggested model will perform superior to common convolutional neural networks. [ABSTRACT FROM AUTHOR]

Personalized learning has gained significant attention in education as a means to cater to the diverse needs of learners and optimize educational outcomes. However, ensuring the efficiency of personalized learning remains a challenge. It requires the ability to accurately analyze and interpret vast amounts of data collected from learners. Traditional analytical approaches often struggle to handle the complexity and heterogeneity of this data, limiting the potential for personalized learning interventions. To address these challenges, this paper proposes a personalized learning efficiency data analysis network (PLEDANet) based on machine learning. First, PLEDANet redesigns a convolutional neural network based on the ResNet structure. The network performs convolutions using multiple convolution kernels of different scales to extract diverse feature information from personalized learning efficiency data. To enhance the extraction and representation of fine-grained differentiated features, PLEDANet introduces a hybrid attention module to combine channel and spatial information among feature maps. Second, PLEDANet designs a hybrid loss function for model training, which consists of the AM-softmax loss and the Center loss. The former increases the inter-class distance of features by imposing a fixed angular margin, while the latter reduces the intra-class distance by constraining the samples and feature centers. Finally, extensive experiments are conducted on PLEDANet. The experimental results validate the superiority of PLEDANet for personalized learning efficiency analysis. [ABSTRACT FROM AUTHOR]

Breast cancer, a widespread global disease, represents a significant threat to women's health and lives. Many researchers have proposed computer-aided diagnosis systems for classifying breast cancer. The majority of the approaches primarily utilize deep learning methods, which overlook the crucial necessity of incorporating both local information for precise tumor detection. In addition, available breast cancer datasets are imbalanced in nature. Therefore, this paper presents the hybrid breast network (HBNet) for detection of breast cancer, designed to address two critical challenges: class imbalance and incorporation of both global and local information in achieving precise tumor classification. To overcome the problem of class imbalance, HBNet incorporates the borderline synthetic minority oversampling technique. Simultaneously, it employs a feature fusion approach to combine deep and handcrafted features extracted by utilizing ResNet50 and HOG which incorporates global and local information. Moreover, the proposed method integrates the block-matching and 3D denoising filter to effectively eliminate multiplicative noise that has enhanced the performance of the system. The proposed HBNet is evaluated with BUSI and UDIAT datasets and achieved an average accuracy of 95.824% and 90.37%, respectively. [ABSTRACT FROM AUTHOR]

Successful deployment of convolutional neural networks on resource-constrained hardware platforms is challenging for ubiquitous AI applications. For latency-sensitive scenarios, real-time inference requires model compression techniques such as network pruning to achieve the purpose of inference acceleration. However, many researches focus on hardware-independent filter pruning methods, which cannot balance the contribution of pruned structure to latency and the accuracy drop. Although some pruning methods have introduced latency constraints into the pruning process, most of them are based on look-up tables, which omits the key step of hardware optimization, resulting in significant deviation in latency estimation. In this paper, we propose a novel latency-constrained pruning method, named hardware-in-loop pruning (HILP). It is based on the fast optimal pruning rate search within the layer and layer-wise hybrid pruning, which can prioritize removing the less important layers with considerable latency contributions. The proposed hardware-in-loop pipeline enables the hardware optimization module to be integrated into the entire framework. During pruning, an intermediate network architecture is automatically transformed to a deployable model for accurate latency measurement. The latency-optimized intermediate architecture is then selected by traversing all layers for next progressive step. HILP is generally applicable to any platform that provides a hardware optimization toolchain, such as NVIDIA GPU and Cambricon NPU. We evaluate HILP on both image classification task using ResNet50 with ImageNet and object detection task using YOLOv3 with COCO, HILP can reduce the inference latency of these two networks to 60% and 75%, respectively, within the range of accuracy variation not exceeding 0.6%. Extensive experiment results have proven that HILP is able to achieve a significant advantage in latency-accuracy performance compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Tailoring a deep convolutional neural network (DCNN) is a tedious and time-consuming task in the field of medical image analysis. In this research paper, Deep-generative adversial neural network (Deep-GAN) based model is proposed using grid search optimization (GSO) technique for identification and classification of thyroid nodule. The main objective of this work is to propose a deep learning (DL) model for the identification and classification of thyroid nodules without user or specialist intervention. The proposed model has gone through four phases namely (i) data acquisition, (ii) pre-processing (iii) data augmentation using GAN technique and (iv) optimization and classification using Deep-GAN model. Two pre-trained architectures namely Alex-Net and Visual Geometry Group (VGG-16) are considered for the identification and classification of thyroid nodule in ultrasonography (USG) images. From the experiment, it is found that Alex-GAN model has shown an improvement of 2 to 4 percentage points in comparison with VGG-GAN model and reported literature on Thyroid digital image database (TDID) public and collected dataset. [ABSTRACT FROM AUTHOR]

Assessing the strength of complex networks is crucial for evaluating their functionality and monitoring system security. Two primary perspectives for measuring network robustness are connectivity and controllability, which quantify how well a network maintains connectivity and controllability after experiencing complete attacks. However, conventional robustness calculation methods that use simulation attacks can be impractical for large-scale networks. Recent research has explored the use of deep learning methods for network robustness prediction, including PCR (short for predictor of controllability robustness), which uses a VGG (short for visual geometry group)-based convolutional neural network (CNN) and a linear filter. However, PCR has limitations, including inaccurate edge importance differentiation and fluctuating output curves. This paper presents ATTRP, an attention-based robustness predictor, which addresses these limitations. ATTRP uses a novel convolutional neural network with channel and spatial attention mechanisms to improve prediction accuracy and a robustness filter based on Savitzky–Golay smoothing to rectify the robustness curve. Experimental studies demonstrate that ATTRP outperforms other predictors, with much lower errors, while its computational speed is far superior to that of traditional attacking simulation. Overall, our research provides a more effective and efficient means of predicting network robustness, with potential applications in monitoring system security. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) have emerged as powerful tools in diverse remote sensing domains, but their optimization remains challenging due to their complex nature and the large number of parameters involved. Researchers have been exploring more sophisticated methodologies to improve image classification accuracy. In this paper, we introduce a multi-granularity feature encoding ensemble network (MGFEEN) that is designed to fine-tune features at different levels of granularity. The network is trained in a two-step process: First, the output of granularity level i is used as the input for the next level; then, a fully connected layer is added to the pre-trained network to advance to the next level. The effectiveness of the MGFEEN's feature extraction is evaluated by feeding the globally extracted features to a softmax classifier for classification. By applying ensemble learning principles, our proposed MGFEEN achieves more accurate final predictions. We evaluate our model on three widely recognized benchmark datasets: UC-Merced, SIRIWHU, and EAC-Dataset. Notably, on the EAC-Dataset, our results show a significant 0.54% improvement in accuracy over a single-training-network setup, resulting in an impressive 98.70% accuracy level. [ABSTRACT FROM AUTHOR]

Deep learning integration in cancer diagnosis enhances accuracy and diagnosis speed which helps clinical decision-making and improves health outcomes. Despite all these benefits in cancer diagnosis, the present AI models in urology cancer diagnosis have not been sufficiently reviewed systematically. This paper reviews the artificial intelligence approaches used in cancer diagnosis, prediction, and treatment of urology cancer. AI models and their applications in urology subspecialties are evaluated and discussed. The Scopus, Microsoft Academic and PubMed/MEDLINE databases were searched in November 2022 using the terms "artificial intelligence", "neural network", "machine learning," or "deep learning" combined with the phrase "urology cancers". The search was limited to publications published within the previous 20 years to identify cutting-edge deep-learning applications published in English. Irrelevant review articles and publications were eliminated. The included research involves two kinds of research analysis: quantitative and qualitative. 48 articles were included in this survey. 25 studies proposed several approaches for prostate cancers, while 15 were for bladder cancers. 8 studies discussed renal cell carcinoma and kidney cancer. The models presented to detect urology cancers have achieved high detection accuracy (77–95%). Deep learning approaches that use convolutional neural networks have achieved the highest accuracy among other techniques. Although it is still progressing, the development of AI models for urology cancer detection, prediction, and therapy has shown significant promise. Additional research is required to employ more extensive, higher-quality, and more recent datasets to the clinical performance of the proposed AI models in urology cancer applications. [ABSTRACT FROM AUTHOR]

In areas with high tuberculosis (TB) prevalence, high mortality rate has significantly increased over the past few decades. Even though tuberculosis can be treated, areas with high disease burden continue to have insufficient screening tools, leading to diagnostic delays and incorrect diagnoses. As a result of these challenges, a computer-aided diagnostics (CAD) system has been developed that can automatically detect tuberculosis. There are few different methods that can be used to screen for tuberculosis; however, chest X-ray (CXR) is most commonly used and strongly suggested because it is so effective in identifying lung irregularities. Over past ten years, we have seen a meteoric rise in amount of research conducted into application of machine learning strategies to examination of chest X-ray images for screening regarding pulmonary abnormalities. Particularly, we have also noticed significant interest in testing for TB. This attentiveness has increased in tandem with phenomenal progress that has been made in deep learning (DL), which is predominately founded on convolutional neural networks (CNNs). Because of these advancements, significant research contributions have been made in field of DL techniques for TB screening by utilizing CXR images. The main focus of this paper is to emphasize favorable methods and data collection, as well as methodological contributions, identify data collections, and identify challenges. [ABSTRACT FROM AUTHOR]

Approximation approaches have been developed for very large-scale integration architecture to limit power consumption and increase effective throughput. The full adder (FA) and block-based designs are the approximate adder designs widely used in the existing techniques to accelerate the additions by partitioning a long carry propagation chain. However, these techniques require more power and area than the standard FA-based techniques. To overcome this issue, this paper presents an error-reduced carry prediction approximate full adder (ERCPAA) technique to achieve faster additions and performance by adding a constant truncation mechanism with an error reduction strategy. Deep learning and neural network technology had made great progress in recent years for field programmable gate array implementation and real-time object detection. It is essential to maintain high accuracy rates in image processing applications, as any errors introduced during the processing can significantly impact the quality of the output image and impact the hardware implementation. Therefore, we propose an ERCPAA that utilizes a lightweight convolutional neural network (ERCPAA–lightweight CNN) to fit well in a fixed-point CNN accelerator architecture. A lightweight CNN that is both thin (with fewer feature maps per layer) and deep (four layers) with just one fully connected hidden layer can enable faster training while achieving higher accuracy. In addition, in a few positions of the higher-order bits of the incorrect part, a full adder (FA) cell is used to simplify the traditional one-bit FA cell, creating an approximate summation and carry. The proposed method is verified by randomly selecting specific images from the Fruit 360, ImageNet, and the Caltech 256 Image Dataset. The proposed model offers an normalized mean error distance, error rate, and mean relative error distance values of 0.154, 6.55%, and 4.6E-4. The recognition accuracy of the proposed model in the ImageNet database is 96% with an execution time of 1420 µs. [ABSTRACT FROM AUTHOR]

Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network, recurrent neural network, generative adversarial network, multilayer perception, and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F-score, latency, adaptability, and scalability. [ABSTRACT FROM AUTHOR]

A fabric inspection system is a sophisticated computer vision system that detects fabric defects, automatically. In recent years, human visual inspection has traditionally been used to detect fabric defects. However, this trend is inaccurate and may be expensive due to the need for highly-trained personnel. This paper describes a deep-learning-based fabric inspection system for detecting fabric defects instead of the dependence on personnel. To find the Region of Interest (RoI) in fabric images, the system depends on a saliency-based region detection technique to localize the defected areas in fabric images. The fabric images are then classified into defect-free and defective images using a Convolutional Neural Network (CNN). Four convolutional layers and four max-pooling layers are arranged in the suggested model. A fully-connected layer and a Softmax activation function are also used in the classification task. The results of the experiments indicate that the proposed system exceeds some other state-of-the-art systems in terms of both quality and robustness. The proposed system achieves an average accuracy of 95.8%. Hence, it can be used in applications related to fabric industry. [ABSTRACT FROM AUTHOR]

Several state-of-the-art convolutional neural networks (CNNs)-based methods are available for image denoising tasks. CNNs are typically trained using the backpropagation algorithm, which requires all operations in the network to be differentiable. Most CNN operations satisfy this requirement and can be applied to backpropagation-based training algorithms. However, some transforms, including wavelet transform, which is useful for speeding up CNN computations as well as performing multi-resolution analysis, are not strictly differentiable. This paper addresses this challenge by proposing a wavelet-like transform that is differentiable. This new design is, in fact, a new CNN architecture named semi-wavelet, specific edge convolutional neural network (SW/SE-CNN), consisting of three newly designed layers. The first layer is a Semi-Wavelet (SW)-based layer which is a differential down-sampling operator for wavelet approximation. That is, the SW layer converts the input image into four channels. Three of these channels are estimations of the vertical, horizontal, and diagonal edges of the original image; and the fourth channel is a down-sampled version of it. The second proposed layer, called Semi-Wavelet Inverse (SWI), is to restore the original image by using the four SW output channels. Additionally, a specific edge extractor (SE), as another new layer, is designed on the basis of the well-known Sobel operator to extract specific edges of the image. The reason behind proposing the SE layer is to provide more edge information for the network; and the motive for including the SW layer is to speed the network up as well as multi-resolution analysis. Then, the new SW/SE-CNN architecture is implemented for Gaussian image denoising. The experimental results show that the new SW/SE-CNN outperformed the state-of-the-art methods for Gaussian image denoising based on the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) measurements for grayscale as well as color images. [ABSTRACT FROM AUTHOR]

Nowadays, the volume of online information is growing and it is difficult to find the required information. Effective strategies such as recommender systems are required to overcome information overload. Collaborative filtering is a widely used type of recommender system in e-commerce environments and can simply provide suggestions for users. Recently, deep learning approaches were applied in collaborative filtering to tackle some drawbacks. This systematic review aims to provide a comprehensive review of recent research efforts on deep learning-based collaborative filtering recommender systems. We explain the research methodology and paper selection process and the search query. 102 papers are selected out of 719 papers that were published between 2019 and May 2023. Furthermore, the approaches in the selected papers are classified into two main categories: memory-based and model-based techniques. The main ideas, advantages, disadvantages, used tools, type of neural network, applications, and evaluation parameters of each selected paper are also discussed in detail. It was found that CNN (Convolutional Neural Network), AE (Autoencoder), DNN (Deep neural network), and Hybrid networks are the four mostly used neural networks in recommender systems. Also, Python, MATLAB, and Java are the most frequently used tools in the reviewed papers. Regarding the applications of the recommender systems in the reviewed papers, movies, products, and music recommendation are three most frequent applications. We point out the open issues and future research directions. Some key challenges such as cold start, data sparsity, scalability, and accuracy are still open to be addressed. [ABSTRACT FROM AUTHOR]

Traditional sentiment analysis focuses on text-level sentiment mining, transforming sentiment mining into classification or regression problems, resulting in a sentiment analysis low accuracy rate. Sentiment analysis refers to the use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study sentimental states. Therefore, more scholars have begun to focus on speech recognition and facial expression recognition research, and extracting and analysing people's sentiment tendencies can improve sentiment recognition accuracy. Traditional single-modal sentiment analysis can no longer meet people's needs. Therefore, this paper proposes a multimodal sentiment analysis method based on the multimodal sentiment analysis method that can obtain more sentimental information sources and help people make better decisions. The experimental results in this paper show that the highest recognition rates of CNN-SVM, RNN-SVM, and CRNN-SVM were 76.8%, 71.2%, and 93.5%, respectively. It can be seen that CRNN-SVM has the highest sentiment tendency recognition rate in deep learning, so it is suitable to apply CRNN-SVM to sentiment tendency analysis system design in this paper. The average accuracy rate of the system designed in this paper was 91%, and the stability was also very strong, which shows that the system designed in this paper is meaningful. The main contribution of this paper is based on the limitations of single-mode emotion analysis. It proposes a multimode emotion analysis method and introduces a convolutional neural network to help people obtain more emotional information sources to meet their needs. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a hybrid bio-inspired algorithm which takes the merits of whale optimization algorithm (WOA) and adaptive particle swarm optimization (APSO). The proposed algorithm is referred as the hybrid WOA_APSO algorithm. We utilize a convolutional neural network (CNN) for classification purposes. Extensive experiments are performed to evaluate the performance of the proposed model. Here, pre-processing and segmentation are performed on 120 lung CT images for obtaining the segmented tumored and non-tumored region nodule. The statistical, texture, geometrical and structural features are extracted from the processed image using different techniques. The optimized feature selection plays a crucial role in determining the accuracy of the classification algorithm. The novel variant of whale optimization algorithm and adaptive particle swarm optimization, hybrid bio-inspired WOA_APSO, is proposed for selecting optimized features. The feature selection grouping is applied by embedding linear discriminant analysis which helps in determining the reduced dimensions of subsets. Twofold performance comparisons are done. First, we compare the performance against the different classification techniques such as support vector machine, artificial neural network (ANN) and CNN. Second, the computational cost of the hybrid WOA_APSO is compared with the standard WOA and APSO algorithms. The experimental result reveals that the proposed algorithm is capable of automatic lung tumor detection and it outperforms the other state-of-the-art methods on standard quality measures such as accuracy (97.18%), sensitivity (97%) and specificity (98.66%). The results reported in this paper are encouraging; hence, these results will motivate other researchers to explore more in this direction. [ABSTRACT FROM AUTHOR]

Knee ligament tear injury is frequent in many volleyball, football, basketball, and cricket players. In the past, various deep learning-based ACL tear detection schemes using knee magnetic resonance imaging (MRI) have been presented. It has shown challenges in ACL tear detection performance due to class imbalance issue arising due to uneven training samples and poor feature representation. This paper presents a simple and effective ACL knee ligament tear detection method with a convolutional neural network (ATD-CNN) to lessen the intricacy of the network. Further, the self-attention mechanism is used to improve the feature representation of MRI image information, neglect the irrelevant information in deep features, and enhance the classification accuracy. To diminish the class imbalance issue, generative adversarial network (GAN) is used to construct the synthetic database. The performance of the ATD-CNN with self-attention is assessed on the MRNet database using precision, accuracy, F1-score, and recall. ATD-CNN provides an accuracy of 90.10% for the original and 93.93% and augmented datasets. However, the ATD-CNN without attention mechanism resulted in 89.60% and 92.30% accuracy for original and augmented dataset. The proposed ATD-CNN model indicates that it can be utilized to detect ACL tears automatically and outperforms the existing schemes for tear detection. [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]

One of the significant aspects of our digital world is that data are literally everywhere, and it is increasing. On the other hand, the number of cyberattacks aiming to seize this data and use it illegally is increasing at an exponential rate, and this is the challenge. Therefore, intrusion detection systems (IDS) have attracted considerable interest from researchers and industries. In this regard, machine learning (ML) techniques are playing a pivotal role as they put the responsibility of analyzing enormous amounts of data, finding patterns, classifying intrusions, and solving issues on computers instead of humans. This paper implements two separate classification layers of ML-based algorithms with the recently published NF-UQ-NIDS-v2 dataset, preprocessing two volumes of sample records (100 k and 10 million), utilizing MinMaxScaler, LabelEncoder, selecting superlative features by recursive feature elimination, normalizing the data, and optimizing hyper-parameters for classical algorithms and neural networks. With a small dataset volume, the results of the classical algorithms layer show high detection accuracy rates for support vector (98.26%), decision tree (98.78%), random forest (99.07%), K-nearest neighbors (98.16%), CatBoost (99.04%), and gradient boosting (98.80%). In addition, the layer of neural network algorithms has proven to be a very powerful technology when using deep learning, particularly due to its unique ability to effectively handle enormous amounts of data and detect hidden correlations and patterns; it showed high detection results, which were (98.87%) for long short-term memory and (98.56%) for convolutional neural networks. [ABSTRACT FROM AUTHOR]

Knowledge about the subtypes of a disease critically affects clinical decisions ranging from the choice of therapeutic options to patient management. If the understanding of a disease is partial and the subtypes of the disease are not yet known, a traditional supervised approach becomes untenable for disease subtype classification. In these contexts, unsupervised methods for subtype discovery are essential. There has been very little prior work on the discovery of phenotypic subtypes based on imaging. Moreover, within that limited body of work, the discovered phenotypes often lack interpretability. In this paper, we present a data-driven approach to discovering interpretable imaging phenotypes in 3D image volumes. In particular, the phenotypes are discovered in a latent space learned through a 3D autoencoder. To interpret the discovered phenotypes, we learn a convolutional neural network to classify the phenotype label and present VolPAM (Volumetric Phenotype-Activation-Map) to interpret the latent phenotypes in terms of their imaging footprints. We present results and visualizations on datasets of Computed Tomography images as an example 3D imaging modality. The proposed methods can become a useful aid to further the understanding of the condition in question through phenotype discovery and interpretability in terms of distinct aspects of the discovered phenotypes. [ABSTRACT FROM AUTHOR]

This paper presents an efficient deep neural network model for diagnosing Parkinson's disease from gait. More specifically, we introduce a hybrid ConvNet-Transformer architecture to accurately diagnose the disease by detecting the severity stage. The proposed architecture exploits the strengths of both convolutional neural networks and Transformers in a single end-to-end model, where the former is able to extract relevant local features from Vertical Ground Reaction Force (VGRF) signal, while the latter allows to capture long-term spatio-temporal dependencies in data. In this manner, our hybrid architecture achieves an improved performance compared to using either models individually. Our experimental results show that our approach is effective for detecting the different stages of Parkinson's disease from gait data, with a final accuracy of 88%, outperforming other state-of-the-art AI methods on the Physionet gait dataset. Moreover, our method can be generalized and adapted for other classification problems to jointly address the feature relevance and spatio-temporal dependency problems in 1D signals. Our source code and pre-trained models are publicly available at https://github.com/SafwenNaimi. [ABSTRACT FROM AUTHOR]

Today, investigating gait recognition as a biometric technology has become necessary, especially after the COVID-19 pandemic broke out in the world. This paper proposes a deep structure procedure for precise human identification from the walking manner quickly, as each human has a unique way of walking. The proposed generative adversarial network (GAN) generates gait images from pedestrians of the CASIA and OU-ISIR gait datasets for normalizing the number of frames in each class in both datasets. Then, we extract the gait features from the normalized datasets by the pretrained convolutional neural network (CNN) models; AlexNet, Inception, VGG16, VGG19, ResNet, and Xception, and balance them using the synthetic minority oversampling technique (SMOTE) to enhance the recognition results. Several feature selectors are selecting the best features, including particle swarm optimization (PSO), grey wolf optimization (GWO), Chi-square, and genetic models. Finally, the proposed deep neural network recognizes the gait images precisely. Several performance assessment measures were generated to assess the model's quality, including accuracy, precision, sensitivity, specificity, false negative rate (FNR), intersection of union (IoU), and time. Experimental results showed that the inception model with genetic feature selector performed better in all used datasets than the current studies and was more robust against any change, achieving 99.3%, 99.1%, and 99.09% accuracy in identifying CASIA-B, CASIA-A, and OU-ISIR datasets, respectively in low time. [ABSTRACT FROM AUTHOR]

The convolutional neural network has been proven to be a robust recognition and diagnosis model for modelling diseases. In general, a convolutional neural network consists of feature learning which is considered a vital process in terms of feature extraction and classifier algorithms used for decision-making. In recent years, various pooling operations considered the spine of the feature learning layer have been developed in the literature; unfortunately, few of them can be spread to tackle the overfitting problem. In this paper, a novel pooling approach termed whole image average pooling is proposed to hold over the most significant feature information on the learning layer. The proposed method consists of two main stages: in the first stage, the average of all the values of the input image is calculated, in the second stage, the input image is divided into regions that represent the feature set of the input image, and then, the values of the regions are calculated. While obtaining the values of these features, the average of all values of the input image is taken into account. The proposed whole image average pooling approach addresses the overfitting problem by processing all pixel values in the image data and is used to get the important feature information from the medical datasets. To evaluate the performance of the proposed whole image average pooling, four clinically applicable tumour datasets as medical datasets have been trained and tested on seven convolutional neural network models AlexNet, VGG16, ResNet18, GoogleNet, ResNet50, R-CNN, and Faster R-CNN. The experimental outcomes demonstrate that the proposed whole image average pooling consistently outperformed the state-of-the-art pooling operation in terms of accuracy, sensitivity, specificity, positive predictive value, false-positive rate, and F1-measure evaluation criteria in three datasets. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals show the electrical activity of the brain, which are one of the inputs of the brain–computer interface (BCI). The BCI provides the communication path between the brain and the computer. One of the critical applications of BCI is Motor imagery (MI). MI is a mental process that a person practices or simulates a particular movement without physically acting. BCI allows the person to communicate with their environment independently of peripheral muscles and nerves, using EEG brain signals by assistive devices such as wheelchairs, robotic arms, and computers. In this paper, a space learning concept is proposed for EEG motor imagery signal classification. Our innovation in the proposed method is to increase and then, reduce the data dimensions, which has led to learning the efficient space for signals classification. It is based on two techniques: Multi-Kernel Learning (MKL) and dimension reduction. The composite kernel is made of a combination of four kernels by The Heuristic MKL Algorithm. This algorithm uses heuristic rules to estimate the weight of kernels with high accuracy and very little computational complexity. The weight associated with each base kernel and its parameters is calculated by the Equilibrium Optimizer. Dimensions of data are reduced to avoid the curse of dimensions. In this step, the number of dimensions of reduced space and the mapping matrix are learned to reduce the dimensions of data linearly. We selected ELM, KNN, and SVM classifiers for classification. The BCI Competition dataset was used for evaluation, which consists of five subsets aa, al, ay, aw, av, and two classes of the right hand and right foot. The proposed method with the ELM was improved the average classification accuracy and standard deviation by 3.9% and 2.28, respectively, and achieved 91.4% accuracy. The lower standard deviation than other methods shows that our method is more robust than all other methods to subject variety. The proposed method is compared with twelve state-of-the-art methods and has shown higher accuracy than other methods such as the deep convolutional neural networks. The results show the superiority of the proposed method over other methods in the Wilcoxon signed test. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have been widely used in image classification tasks and have achieved remarkable results compared with traditional methods. Their main advantage is the ability to extract hidden features automatically using local connectivity and spatial locality. However, CNN cannot be applied to tabular data, mainly due to the unsuitability of the tabular data structure to the CNN input. In this paper, we propose a new generic method for the representation of multidimensional tabular data as color-encoded images that can be used both for data visualization and classification with CNN. Our approach, named FC-Viz (Feature Clustering-Visualization), is based on user-oriented data visualization ideas, such as pixel-oriented techniques, feature clustering, and feature interactions. The proposed approach includes a transformation of each instance of the tabular data into a 2D pixel-based representation, where pixels representing features with strong correlation and interaction are adjacent to each other. We applied FC-Viz to ten multidimensional tabular datasets with dozens to thousands of features and compared its classification and visualization performance with a state-of-the-art tabular data transformation method. The evaluation experiments show that our approach is as accurate as the state-of-the-art, but with much smaller images resulting in much more compact and faster CNN models. [ABSTRACT FROM AUTHOR]

Deep learning techniques have gained immense popularity recently because of their remarkable capacity to learn complex patterns and features from large datasets. These techniques have revolutionized many fields by achieving advanced performance in various tasks. The availability of large datasets and the advancement of computing resources have enabled deep learning models to perform well in solving challenging problems. As a result, they have become an essential tool in many industries, including agriculture. The application of deep learning in agriculture has great potential for increasing productivity, reducing costs, and improving sustainability by aiding in the early identification and prevention of plant leaf diseases, optimizing crop yields, and facilitating precision agriculture. This paper suggests using a novel approach to automatically classify multi-class leaf diseases in tomatoes using a deep multi-scale convolutional neural network (DMCNN). The proposed DMCNN architecture consists of parallel streams of convolutional neural networks at different scales, which get merged at the end to form a single output. The images of tomato leaves are preprocessed using data augmentation techniques and fed into the DMCNN model to classify disease. The proposed approach is evaluated on a dataset of tomato plant images containing 10 distinct classes of diseases and compared with different existing models. The research results reveal that the suggested DMCNN model performs better than other models in terms of accuracy, precision, recall, and F1 score. Furthermore, the proposed model reported an overall accuracy of 99.1%, which is higher than the accuracy of existing models tested on the same dataset. The study demonstrates the potential of deep learning techniques for automated disease classification in agriculture, which can aid in early disease detection and prevent crop loss. [ABSTRACT FROM AUTHOR]

Conventionally, fine-tuning or transfer learning using a pre-trained convolutional network is adopted to design a classifier. However, when the dataset is small this can deteriorate the classifier generalization performance due to negative transfer or overfitting issues. In this paper, we suggest a flexible and high-capacity multiple kernel-based convolutional neural network (MK-CNN) to automate the pathological brain classification task. The proposed network employed different stacks of convolution with various kernels to obtain multi-scale features from the input image. The smaller kernel size provides specific information about the local features whereas the larger kernel size provides the global spatial information. Hence, the network takes into account both regional specifics and global spatial consistency thanks to this multi-scale methodology. Only the output layer is shared between each network stack. This makes it possible to specifically tweak the CNN's weights and biases for each convolution stack and associated kernel size. The results reported on real patient data from the Harvard Whole Brain Atlas reveal that our method outperforms state-of-the-art techniques. The suggested approach may be used to help experts carry out the clinical follow-up study. [ABSTRACT FROM AUTHOR]

With the advancement in modern imaging techniques like CT scan, MRI, PET scan etc., a vast amount of data is generated every day in the field of healthcare. Big data contains hidden information, which necessitates the development of intelligent systems to analyze it and extract relevant information, allowing for accurate and cost-effective decisions in the medical field. By utilizing the untapped potential of the big data available in the medical field, very precise models can be developed for the medical diagnosis of retinal diseases. Optical coherence tomography (OCT) is a non-invasive imaging test that captures different, distinctive layers of the retina and optic nerve in a living eye to map and measure their thickness, that helps diagnose various retinal disorders. With the advancement of the application of deep learning-based techniques in the field of medical sciences, the use of convolutional neural network (CNN) based approaches for disease detection is gaining popularity. While the manual examination of 3D OCT images for the diagnosis of retinal disorders requires extensive time and expert intervention, the use of CNNs provides an effective automated option that provides results with higher accuracy while also reducing the time involved in the overall process. In this paper, we have implemented the aforementioned idea by proposing OCT-CNN, a CNN architecture, that automatically classifies retinal OCT images and identifies potential disorders in a living eye. Several techniques have been employed to enhance the performance of the proposed approach, including digital enhancement of the images, dropout regularization, adaptive learning rates, and early stopping of training to attain optimal performance. The performance of the proposed OCT-CNN is evaluated on the UCSD dataset against several popular deep CNN architectures and existing state-of-the-art approaches to automatic retinal OCT classification. The proposed OCT-CNN attains the best performance on all evaluated metrics, pushing the classification accuracies to 99.28% on CNV, 99.9% on DME, 99.38% on DRUSEN, and 100% on NORMAL images, indicating its superiority over existing state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Dry eye disease (DED) is a chronic eye disease and a common complication among the world's population. Evaporation of moisture from tear film or a decrease in tear production leads to an unstable tear film which causes DED. The tear film breakup time (TBUT) test is a common clinical test used to diagnose DED. In this test, DED is diagnosed by measuring the time at which the first breakup pattern appears on the tear film. TBUT test is subjective, labour-intensive and time-consuming. These weaknesses make a computer-aided diagnosis of DED highly desirable. The existing computer-aided DED detection techniques use expensive instruments for image acquisition which may not be available in all eye clinics. Moreover, among these techniques, TBUT-based DED detection techniques are limited to finding only tear film breakup area/time and do not identify the severity of DED, which can essentially be helpful to ophthalmologists in prescribing the right treatment. Additionally, a few challenges in developing a DED detection approach are less illuminated video, constant blinking of eyes in the videos, blurred video, and lack of public datasets. This paper presents a novel TBUT-based DED detection approach that detects the presence/absence of DED from TBUT video. In addition, the proposed approach accurately identifies the severity level of DED and further categorizes it as normal, moderate or severe based on the TBUT. The proposed approach exhibits high performance in classifying TBUT frames, detecting DED, and severity grading of TBUT video with an accuracy of 83%. Also, the correlation computed between the proposed approach and the Ophthalmologist's opinion is 90%, which reflects the noteworthy contribution of our proposed approach. [ABSTRACT FROM AUTHOR]

This paper aims to propose a new technique for identifying and categorizing malevolent Internet traffic within the context of security for smart devices. Given the rising usage of smart devices, including mobile phones, wearables, smart transportation-based devices, and the Internet of Things, concerns regarding their security are increasing. The need to develop effective security measures arises from the potential for attackers to compromise user data. In this study, we introduce an innovative approach that combines deep learning techniques, specifically convolutional neural networks (CNN), with long short-term memory (LSTM) for the purpose of detecting and categorizing malevolent Internet traffic. The objective of the proposed technique is to address the challenges related to time estimation by focusing on level prediction, resulting in a substantial reduction in prediction time for the identification of malevolent traffic. We utilize bidirectional long short-term memory–CNN (BI-LSTM–CNN) to identify malevolent communication and provide support for voice input. Experimental outcomes illustrate the effectiveness of our proposed technique in terms of precision, accuracy, F1 factor, false acceptance rate (FAR), false positive rate (FPR), and detection rate. In comparison with existing methods for detecting malevolent traffic, our approach achieves a 99.62% traffic detection rate, 99.98% accuracy, and 0.01% FAR, whereas the accuracy, detection rate of malevolent traffic, and FAR of existing methods are 99.88%, 97.32%, and 4.31%, respectively. These outcomes emphasize the superior performance and analysis of our technique, rendering it a valuable contribution to the realm of smart device security. In summary, this paper proposes a novel BI-LSTM–CNN technique for detecting malevolent traffic in smart devices. The proposed methodology tackles time estimation challenges and exhibits superior performance when compared to existing techniques. [ABSTRACT FROM AUTHOR]

The usage of intelligent IoT devices is exponentially rising, and so the possibility of attacks in the IoT surfaces. The deep leaning algorithms are competent for directing the sanctuary investigation of IoT systems but have not upgraded the analysis of potential attacks in IoT. This paper aims to advance deep learning methods to create upgraded security strategies for IoT frameworks quickly. The study of the IoT security threats identified with inalienable or recently presented risks is done. Also, this paper does a quick examination of different possible attack surfaces for the IoT framework, and the potential risks identified with each character. The systematic survey of deep learning methods for IoT security and the existence of the chances, focal points, and weaknesses of every strategy opens the door significant for future research. [ABSTRACT FROM AUTHOR]

Acute lymphocytic leukemia (ALL) is a common serious cancer in white blood cells (WBC) that advances quickly and produces abnormal cells in the bone marrow. Cancerous cells associated with ALL lead to impairment of body systems. Microscopic examination of ALL in a blood sample is applied manually by hematologists with many defects. Computer-aided leukemia image detection is used to avoid human visual recognition and to provide a more accurate diagnosis. This paper employs the ensemble strategy to detect ALL cells versus normal WBCs using three stages automatically. Firstly, image pre-processing is applied to handle the unbalanced database through the oversampling process. Secondly, deep spatial features are generated using a convolution neural network (CNN). At the same time, the gated recurrent unit (GRU)-bidirectional long short-term memory (BiLSTM) architecture is utilized to extract long-distance dependent information features or temporal features to obtain active feature learning. Thirdly, a softmax function and the multiclass support vector machine (MSVM) classifier are used for the classification mission. The proposed strategy has the resilience to classify the C-NMC 2019 database into two categories by using splitting the entire dataset into 90% as training and 10% as testing datasets. The main motivation of this paper is the novelty of the proposed framework for the purposeful and accurate diagnosis of ALL images. The proposed CNN-GRU-BiLSTM-MSVM is simply stacked by existing tools. However, the empirical results on C-NMC 2019 database show that the proposed framework is useful to the ALL image recognition problem compared to previous works. The DenseNet-201 model yielded an F1-score of 96.23% and an accuracy of 96.29% using the MSVM classifier in the test dataset. The findings exhibited that the proposed strategy can be employed as a complementary diagnostic tool for ALL cells. Further, this proposed strategy will encourage researchers to augment the rare database, such as blood microscopic images by creating powerful applications in terms of combining machine learning with deep learning algorithms. [ABSTRACT FROM AUTHOR]

Image white-balancing is an integral part of every camera's processing pipeline. White-balancing is used to remove illumination chromaticity from an image. Most research in this field has been limited to images with a single uniform illuminant. In this paper, we introduce a novel method for illumination estimation for situations where the scene is illuminated by a variable number of different illuminants and where the illumination in the scene can be non-uniform. The proposed method uses a lightweight convolutional neural network that achieves state-of-the-art results. The method performs illumination estimation on a patch-by-patch basis. We use the assumption that only one illuminant affects each patch since they are so small. Unlike other such methods, our method uses features extracted from the entire image to perform patch illumination estimation. The paper also shows how the image features improve method accuracy with a minimal increase in complexity. The proposed method has around 42 k parameters, and it was tested on three different cameras from the Large-Scale Multi-Illuminant dataset. [ABSTRACT FROM AUTHOR]

Haphazard urbanization has resulted in development of unplanned informal settlements, also called slums. Citizens residing in these dense settlements are generally deprived of essential sustainable drivers, i.e., green cover and open spaces (GOS). Such limitations can have an adverse impact on various aspects such as physiological, economical, health, and quality of life. Much research has been done on applying Machine Learning (ML) techniques for identifying slums at the city scale. However, no study explicitly addressed the problem of GOS extraction inside the informal settlements. This paper aims to fill this gap by training three modified Convolution Neural Network (CNN) models, i.e., VGG16-UNet, MobileNetV2-UNet, DeepLabV3 + on manually labeled high-resolution satellite imagery for the city of Mumbai. All three models performed excellently and achieved the state-of-the art overall accuracy of around 95%, for the considered classes. Out of the three models, VGG16-UNet performed the best and hence was applied to the whole city for developing green, open, and green and open indices. The indices were further used to establish a quantitative relation between various built-up typologies and GOS. It was found that informal settlements have significantly less green and open space than other built-up typologies. Stark GOS difference between slums and its neighborhood planned residential was observed, and hence, requires urgent attention of the city managers. The outcome presented in the paper can be used as input to various decision-making systems concerning better informal space planning to better plan the GOS, which eventually can lead to development of sustainable cities. [ABSTRACT FROM AUTHOR]

Corn disease prediction is an essential part of agricultural productivity. This paper presents a novel 3D-dense convolutional neural network (3D-DCNN) optimized using the Ebola optimization search (EOS) algorithm to predict corn disease targeting the increased prediction accuracy than the conventional AI methods. Since the dataset samples are generally insufficient, the paper uses some preliminary pre-processing approaches to increase the sample set and improve the samples for corn disease. The Ebola optimization search (EOS) technique is used to reduce the classification errors of the 3D-CNN approach. As an outcome, the corn disease is predicted and classified accurately and more effectually. The accuracy of the proposed 3D-DCNN-EOS model is improved, and some necessary baseline tests are performed to project the efficacy of the anticipated model. The simulation is performed in the MATLAB 2020a environment, and the outcomes specify the significance of the proposed model over other approaches. The feature representation of the input data is learned effectually to trigger the model's performance. When the proposed method is compared to other existing techniques, it outperforms them in terms of precision, the area under receiver operating characteristics (AUC), f1 score, Kappa statistic error (KSE), accuracy, root mean square error value (RMSE), and recall. [ABSTRACT FROM AUTHOR]

Although current Siamese-based trackers have achieved impressive performance in visual object tracking by balancing accuracy and speed, their sensitivity to adversarial perturbation makes them face many risks in practical applications (e.g., automatic drive). In this paper, we first propose a general attack method against Siamese-based trackers, and then carefully design an online augmentation strategy based on adversarial examples to improve the tracking accuracy. To ensure both attack and augmentation trackers simultaneously, two opposing losses are proposed to push and pull the gap between the template patch and search regions. Specifically, most Siamese-based trackers employ the cross-correlation module to associate features of two branches. The similarity between the template patch and the search region will be reflected on the cross-correlation map, which is the focus of our method. SiamCAR is the main research object of this paper; the tracking accuracy after deception and enhancement is reduced by 64.62% and improved by 0.88%, respectively. We also transfer our attack method to SiamRPN++, SiamBAN, SiamGAT, and TrDiMP. Extensive experiments on the popular benchmark datasets indicate the effectiveness and universality of our method. [ABSTRACT FROM AUTHOR]

Chinese calligraphy is an important part of Chinese national culture and art and part of the essence of Chinese national culture. Stone calligraphy is one of the important elements of Chinese calligraphy art. Stone carved calligraphy characters have high cultural and artistic value. Therefore, accurately recognizing stone carved calligraphy characters are of great importance. Stone carved calligraphy can identify hard-to-preserve stone calligraphy paper materials in electronic data that can be preserved for a long time, thereby offering important reference materials for the study of the historical development of Chinese calligraphy art. Moreover, with the development of science and technology and the investment of China in cultural and artistic undertakings, computer-aided calligraphy character recognition technology is also constantly improving, and its application in calligraphy recognition is becoming increasingly extensive. This article aims to study a method of stone inscription calligraphy recognition based on convolutional neural networks. In this paper, we use an image recognition and optimization method consisting of a convolutional neural network to carry out an experiment with stone inscription calligraphy characters. It was concluded that the recognition accuracy of stone calligraphy characters by the convolutional neural network reached 99.2%, indicating that this stone calligraphy character recognition method based on a convolutional neural network has a good ability to recognize stone calligraphy characters. [ABSTRACT FROM AUTHOR]

The automatic reading of pointer meters is significantly important to data monitoring and efficient measurement in the industrial field. However, the existing automatic reading method can not obtain accurate performance in natural scenarios and present no satisfactory application effects in industrial fields (such as power stations and gas stations). In this paper, a novel automatic reading method for pointer meters based on deep learning is proposed, which contains five stages. Stage-1: the object detection algorithm Yolov4 and the feature optimization module IFF are used to locate the target meter. Stage-2: Semantic segmentation model is applied to extract the pointer area based on Anam-Net. Stage-3: the character detection algorithm CRAFT and the text recognition algorithm E2E-MLT are combined and used to recognize the scale text and unit on the meter. Stage-4: the scale area of the meter is converted to the polar coordinate system, and a lightweight convolutional neural network is designed to locate the main scale line. And finally in Stage-5: the reading data are calculated according to the outputs of the above-mentioned deep learning models. The experiment results show that the reading method proposed in this paper has higher accuracy and robustness than those of the existing approaches and obtains satisfactory application effects in the industrial field. [ABSTRACT FROM AUTHOR]

In big data era, multi-source heterogeneous data become the biggest obstacle to data sharing due to its high dimension and inconsistent structure. Using text classification to solve the ontology construction and mapping problem of multi-source heterogeneous data can not only reduce manual operation, but also improve the accuracy and efficiency. This paper proposes an ontology construction and mapping scheme based on hybrid neural network and autoencoder. Firstly, the proposed text classification method uses the multi-core convolutional neural network to capture local features and uses the improved Bidirectional Long Short-Term Memory network to compensate for the shortcomings of the convolutional neural network that cannot obtain context-related information. Secondly, a similarity matching method is used for ontology mapping, which integrate autoencoder to improve anti-interference ability. We have carried out several sets of experiments to test the validity of the proposed ontology construction and mapping scheme. [ABSTRACT FROM AUTHOR]

Promoting the development of enterprises is a common task of all countries in the world, and the government has always taken promoting enterprise development as a long-term strategy. With the development for market, as an integral part of the social market economy, enterprises have made great contributions to the rapid of national economy. The enterprises are increasing, but the market competition is becoming increasingly fierce, and the weaknesses of enterprises such as limited scale, lax management and difficult financing are constantly highlighted. The appearance of these problems makes enterprises encounter many bottleneck problems in the process of development. Innovation, as the inexhaustible power of enterprise development, plays a significant role in enterprise growth, it is the key to solving the bottleneck problem of enterprise development. If an enterprise wants to achieve considerable development, it must innovate and enhance its competitiveness through innovation. In this context, how to evaluate enterprise innovation has become an important work. This work proposes an IPSO-ATT-MSCNN network via PSO from swarm optimization algorithm and artificial neural network to evaluate enterprise innovation. First, this work designs a multi-scale convolutional neural network (ATT-MSNN) via attention mechanism. This method uses multi-scale convolution to extract features of different scales, which improves the richness of features. This adds attention mechanisms to enhance useful features and reduce the impact of unwanted features such as noise. Second, in view of the performance degradation caused by the random initialization, this paper uses improved PSO algorithm to optimize the initial parameters. Third, this work proposes a series of strategies for promoting enterprise innovation. Finally, this work carried out a comprehensive and systematic experiment for the designed method, and the experiment verified the superiority of this method. [ABSTRACT FROM AUTHOR]

Stereo-matching is a hot topic in the field of visual image research, to address the low image-matching accuracy of traditional algorithms. In this paper, an optimization for image stereo-matching algorithm using deep reinforcement learning (DRL) is proposed in rule constraints and parallax estimation. First, the image edge pixel constraint rules are established, and the image sample blocks are adjusted. Second, the image parallax estimation is performed by computing geometric constraint and pixel parallax probability in rule constraints, and a DRL structure is designed. Finally, the DRL analysis is performed iteratively by the convolutional neural networks feature extraction, agent training decision, and reward value accumulation, and stereo-matching images are output. Experiments show that the image structural similarity of the proposed algorithm is high, and the correct matching rate is more than 95%. The images have good interpretability, and the stereo-matching effect is good. [ABSTRACT FROM AUTHOR]

According to the coexistence of fuzziness and randomness of risk factors in engineering early warning, this paper proposes a risk early warning model based on a convolutional neural network, which identifies and analyzes the risk points in engineering through engineering site pictures and provides early warning for engineering risk points in time. The risk warning model fully characterizes the fuzziness and randomness of risk, and the warning results are more objective and in line with the actual situation, which provides a more feasible engineering risk warning method. The experiments prove that the method can accurately warn of project risks, and the warning readiness rate reaches 91.3%. [ABSTRACT FROM AUTHOR]

The recent developments in unmanned aerial vehicles (UAVs) and indoor hybrid LiFi–WiFi networks (HLWNs) present a significant opportunity for creating low-cost, power-efficient, reliable, flexible, and ad-hoc HLWN-enabled indoor flying networks (IFNs). However, to efficiently operate and practically realize indoor HLWN, a unified physical layer (UniPHY) is indispensable for joint communication (control and data transfer) and sensing (e.g., localization). A UniPHY structure reduces costs and increases overall flexibility for HLWN-based IFNs. While conventional block-based wireless transceivers independently designed for LiFi and WiFi offer mediocre performance for a composite UniPHY waveform, a machine learning-based end-to-end learning framework for UniPHY can improve overall error performance and reduce the complexity of UAV transceiver hardware. Therefore, this paper proposes a novel generic end-to-end learning framework for a UniPHY system that can efficiently enable HLWN. The performance of the proposed learning framework based on deep neural networks (DNNs) and convolutional neural networks (CNNs) is investigated. Additionally, we assess the computational complexity of the proposed DNN and CNN learning frameworks. The results demonstrate that the performance of DNNs and CNNs varies depending on the considered channel model. Specifically, the analysis reveals that CNNs outperform traditional DNNs in WiFi (Rayleigh fading-based) channels. In contrast, traditional DNNs perform better than CNNs in LiFi (additive white Gaussian noise (AWGN)-based) channels. [ABSTRACT FROM AUTHOR]

Nowadays, face recognition using video surveillance systems becomes one of the active research topics in security domains. Security plays a significant role in everyday life for secure and sustainable developments of smart cities. The conventional techniques provide efficient recognition results only when the faces are captured with complete face images. However, they suffer to handle large pose variation images extracted from video sequences. Therefore, to deal with this issue, this paper specially designed a multidimensional face recognition model to recognize faces under multiple pose variations and angles. Three face video databases, namely facesurv database, IARPA Janus benchmark database and McGill database, are utilized for experimental evaluation. The videos of these three databases are converted into number of image frames through background subtraction process. From the image frames, the large pose variation images with different angles are identified and selected to process further. The video recorded under dynamic environment conditions diminishes recognition performance, so the image frames are processed through several preprocessing pipelines. The preprocessed images are then fed into the proposed optimal mask region-based convolutional neural network with modified short-term memory (OMRCNN-MBiLSTM) model, which learns the facial patterns present in the images more efficiently. The feature vectors learned by the proposed classifier are matched with the input face database to determine the identity of the person. With the ability to handle multiview and large pose variations, the proposed model accurately recognizes faces. The simulation result manifests the superiority of proposed model over other existing methods. [ABSTRACT FROM AUTHOR]

Combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) produces a powerful architecture for video classification problems as spatial–temporal information can be processed simultaneously and effectively. Using transfer learning, this paper presents a comparative study to investigate how temporal information can be utilized to improve the performance of video classification when CNNs and RNNs are combined in various architectures. To enhance the performance of the identified architecture for effective combination of CNN and RNN, a novel action template-based keyframe extraction method is proposed by identifying the informative region of each frame and selecting keyframes based on the similarity between those regions. Extensive experiments on KTH and UCF-101 datasets with ConvLSTM-based video classifiers have been conducted. Experimental results are evaluated using one-way analysis of variance, which reveals the effectiveness of the proposed keyframe extraction method in the sense that it can significantly improve video classification accuracy. [ABSTRACT FROM AUTHOR]