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Teaching and exam proctoring represent key pillars of the education system. Human proctoring, which involves visually monitoring examinees throughout exams, is an important part of assessing the academic process. The capacity to proctor examinations is a critical component of educational scalability. However, such approaches are time-consuming and expensive. In this paper, we present a new framework for the learning and classification of cheating video sequences. This kind of study aids in the early detection of students' cheating. Furthermore, we introduce a new dataset, "actions of student cheating in paper-based exams". The dataset consists of suspicious actions in an exam environment. Five classes of cheating were performed by eight different actors. Each pair of subjects conducted five distinct cheating activities. To evaluate the performance of the proposed framework, we conducted experiments on action recognition tasks at the frame level using five types of well-known features. The findings from the experiments on the framework were impressive and substantial. [ABSTRACT FROM AUTHOR]

Rail surface defects typically serve as early indicators of railway malfunctions, which may compromise the quality and corrosion resistance of rails, thereby endangering the safe operation of trains. The timely detection of defects is essential to ensure the safe operation of railways. To improve the classification accuracy of rail surface defect detection, this paper proposes a rail surface defects detection algorithm based on MobileNet-YOLOv7. By integrating lightweight deep learning algorithms into the engineering application of rail surface defect detection, a MobileNetV3 lightweight network is used as the backbone network for YOLOv7 to enhance both speed and accuracy in complex defect extraction. Subsequently, the efficient intersection over union (EIOU) loss function is utilized as the positional loss function to bolster system resilience. Finally, the k-means++ clustering algorithm is applied to obtain new anchor boxes. The experimental results demonstrate the effectiveness of the proposed method, achieving superior detection accuracy compared with traditional algorithms. [ABSTRACT FROM AUTHOR]

The correct workability of concrete is an essential parameter for its placement and compaction. However, an absence of automatic and transparent measurement methods to estimate the workability of concrete hinders the adaptation from laborious traditional methods such as the slump test. In this paper, we developed a machine-learning framework for estimating the slump class of concrete in the mixer using a stereovision camera. Depth data from five different slump classes was transformed into Haralick texture features to train several machine-learning classifiers. The best-performing classifier achieved a multiclass classification accuracy of 0.8179 with the XGBoost algorithm. Furthermore, we found through statistical analysis that while the denoising of depth data has little effect on the accuracy, the feature extraction of mixer blades and the choice of region of interest significantly increase the accuracy and the efficiency of the classifiers. The proposed framework shows robust results, indicating that stereovision is a competitive solution to estimate the workability of concrete during concrete production. [ABSTRACT FROM AUTHOR]

Among various calamities, conflagrations stand out as one of the most-prevalent and -menacing adversities, posing significant perils to public safety and societal progress. Traditional fire-detection systems primarily rely on sensor-based detection techniques, which have inherent limitations in accurately and promptly detecting fires, especially in complex environments. In recent years, with the advancement of computer vision technology, video-oriented fire detection techniques, owing to their non-contact sensing, adaptability to diverse environments, and comprehensive information acquisition, have progressively emerged as a novel solution. However, approaches based on handcrafted feature extraction struggle to cope with variations in smoke or flame caused by different combustibles, lighting conditions, and other factors. As a powerful and flexible machine learning framework, deep learning has demonstrated significant advantages in video fire detection. This paper summarizes deep-learning-based video-fire-detection methods, focusing on recent advances in deep learning approaches and commonly used datasets for fire recognition, fire object detection, and fire segmentation. Furthermore, this paper provides a review and outlook on the development prospects of this field. [ABSTRACT FROM AUTHOR]

Surface imperfections in steel materials potentially degrade quality and performance, thereby escalating the risk of accidents in engineering applications. Manual inspection, while traditional, is laborious and lacks consistency. However, recent advancements in machine learning and computer vision have paved the way for automated steel defect detection, yielding superior accuracy and efficiency. This paper introduces an innovative deep learning model, GDCP-YOLO, devised for multi-category steel defect detection. We enhance the reference YOLOv8n architecture by incorporating adaptive receptive fields via the DCNV2 module and channel attention in C2f. These integrations aim to concentrate on valuable features and minimize parameters. We incorporate the efficient Faster Block and employ Ghost convolutions to generate more feature maps with reduced computation. These modifications streamline feature extraction, curtail redundant information processing, and boost detection accuracy and speed. Comparative trials on the NEU-DET dataset underscore the state-of-the-art performance of GDCP-YOLO. Ablation studies and generalization experiments reveal consistent performance across a variety of defect types. The optimized lightweight architecture facilitates real-time automated inspection without sacrificing accuracy, offering invaluable insights to further deep learning techniques for surface defect identification across manufacturing sectors. [ABSTRACT FROM AUTHOR]

Autonomous vehicles have become a topic of interest in recent times due to the rapid advancement of automobile and computer vision technology. The ability of autonomous vehicles to drive safely and efficiently relies heavily on their ability to accurately recognize traffic signs. This makes traffic sign recognition a critical component of autonomous driving systems. To address this challenge, researchers have been exploring various approaches to traffic sign recognition, including machine learning and deep learning. Despite these efforts, the variability of traffic signs across different geographical regions, complex background scenes, and changes in illumination still poses significant challenges to the development of reliable traffic sign recognition systems. This paper provides a comprehensive overview of the latest advancements in the field of traffic sign recognition, covering various key areas, including preprocessing techniques, feature extraction methods, classification techniques, datasets, and performance evaluation. The paper also delves into the commonly used traffic sign recognition datasets and their associated challenges. Additionally, this paper sheds light on the limitations and future research prospects of traffic sign recognition. [ABSTRACT FROM AUTHOR]

Deep learning, which is a subfield of machine learning, has opened a new era for the development of neural networks. The auto-encoder is a key component of deep structure, which can be used to realize transfer learning and plays an important role in both unsupervised learning and non-linear feature extraction. By highlighting the contributions and challenges of recent research papers, this work aims to review state-of-the-art auto-encoder algorithms. Firstly, we introduce the basic auto-encoder as well as its basic concept and structure. Secondly, we present a comprehensive summarization of different variants of the auto-encoder. Thirdly, we analyze and study auto-encoders from three different perspectives. We also discuss the relationships between auto-encoders, shallow models and other deep learning models. The auto-encoder and its variants have successfully been applied in a wide range of fields, such as pattern recognition, computer vision, data generation, recommender systems, etc. Then, we focus on the available toolkits for auto-encoders. Finally, this paper summarizes the future trends and challenges in designing and training auto-encoders. We hope that this survey will provide a good reference when using and designing AE models. [ABSTRACT FROM AUTHOR]

The analysis of textures is an important task in image processing and computer vision because it provides significant data for image retrieval, synthesis, segmentation, and classification. Automatic texture recognition is difficult, however, and necessitates advanced computational techniques due to the complexity and diversity of natural textures. This paper presents a method for classifying textures using graphs; specifically, natural and horizontal visibility graphs. The related image natural visibility graph (INVG) and image horizontal visibility graph (IHVG) are used to obtain features for classifying textures. These features are the clustering coefficient and the degree distribution. The suggested outcomes show that the aforementioned technique outperforms traditional ones and even comes close to matching the performance of convolutional neural networks (CNNs). Classifiers such as the support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) are utilized for the categorization. The suggested method is tested on well-known image datasets like the Brodatz texture and the Salzburg texture image (STex) datasets. The results are positive, showing the potential of graph methods for texture classification. [ABSTRACT FROM AUTHOR]

As one of the most important fields in computer vision, object detection has undergone marked development in recent years. Generally, object detection requires many labeled samples for training, but it is not easy to collect and label samples in many specialized fields. In the case of few samples, general detectors typically exhibit overfitting and poor generalizability when recognizing unknown objects, and many FSOD methods also cannot make good use of support information or manage the potential problem of information relationships between the support branch and the query branch. To address this issue, we propose in this paper a novel framework called Decoupled Multi-scale Attention (DMA-Net), the core of which is the Decoupled Multi-scale Attention Module (DMAM), which consists of three primary parts: a multi-scale feature extractor, a multi-scale attention module, and a decoupled gradient module (DGM). DMAM performs multi-scale feature extraction and layer-to-layer information fusion, which can use support information more efficiently, and DGM can reduce the impact of potential optimization information exchange between two branches. DMA-Net can implement incremental FSOD, which is suitable for practical applications. Extensive experimental results demonstrate that DMA-Net has comparable results on generic FSOD benchmarks, particularly in the incremental FSOD setting, where it achieves a state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is an important field in computer vision with many practical applications. However, one of the challenges in FER is dealing with small sample data, where the number of samples available for training machine learning algorithms is limited. To address this issue, a domain adaptive learning strategy is proposed in this paper. The approach uses a public dataset with sufficient samples as the source domain and a small sample dataset as the target domain. Furthermore, the maximum mean discrepancy with kernel mean embedding is utilized to reduce the disparity between the source and target domain data samples, thereby enhancing expression recognition accuracy. The proposed Domain Adaptive Facial Expression Recognition (DA-FER) method integrates the SSPP module and Slice module to fuse expression features of different dimensions. Moreover, this method retains the regions of interest of the five senses to accomplish more discriminative feature extraction and improve the transfer learning capability of the network. Experimental results indicate that the proposed method can effectively enhance the performance of expression recognition. Specifically, when the self-collected Selfie-Expression dataset is used as the target domain, and the public datasets RAF-DB and Fer2013 are used as the source domain, the performance of expression recognition is improved to varying degrees, which demonstrates the effectiveness of this domain adaptive method. [ABSTRACT FROM AUTHOR]

Protecting wheat yield is a top priority in agricultural production, and one of the important measures to preserve yield is the control of wheat diseases. With the maturity of computer vision technology, more possibilities have been provided to achieve plant disease detection. In this study, we propose the position attention block, which can effectively extract the position information from the feature map and construct the attention map to improve the feature extraction ability of the model for the region of interest. For training, we use transfer learning to improve the training speed of the model. In the experiment, ResNet built on positional attention blocks achieves 96.4% accuracy, which is much higher compared to other comparable models. Afterward, we optimized the undesirable detection class and validated its generalization performance on an open-source dataset. [ABSTRACT FROM AUTHOR]

Automatic brain tumor detection in MR Images is one of the basic applications of machine vision in medical image processing, which, despite much research, still needs further development. Using multiple machine learning techniques as an ensemble system is one of the solutions that can be effective in achieving this goal. In this paper, a novel method for diagnosing brain tumors by combining data mining and machine learning techniques has been proposed. In the proposed method, each image is initially pre-processed to eliminate its background region and identify brain tissue. The Social Spider Optimization (SSO) algorithm is then utilized to segment the MRI Images. The MRI Images segmentation allows for a more precise identification of the tumor region in the image. In the next step, the distinctive features of the image are extracted using the SVD technique. In addition to removing redundant information, this strategy boosts the speed of the processing at the classification stage. Finally, a combination of the algorithms Naïve Bayes, Support vector machine and K-nearest neighbor is used to classify the extracted features and detect brain tumors. Each of the three algorithms performs feature classification individually, and the final output of the proposed model is created by integrating the three independent outputs and voting the results. The results indicate that the proposed method can diagnose brain tumors in the BRATS 2014 dataset with an average accuracy of 98.61%, sensitivity of 95.79% and specificity of 99.71%. Additionally, the proposed method could diagnose brain tumors in the BTD20 database with an average accuracy of 99.13%, sensitivity of 99% and specificity of 99.26%. These results show a significant improvement compared to previous efforts. The findings confirm that using the image segmentation technique, as well as the ensemble learning, is effective in improving the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, the scientific community has placed great emphasis on the recognition of human activity, especially in the area of health and care for the elderly. There are already practical applications of activity recognition and unusual conditions that use body sensors such as wrist-worn devices or neck pendants. These relatively simple devices may be prone to errors, might be uncomfortable to wear, might be forgotten or not worn, and are unable to detect more subtle conditions such as incorrect postures. Therefore, other proposed methods are based on the use of images and videos to carry out human activity recognition, even in open spaces and with multiple people. However, the resulting increase in the size and complexity involved when using image data requires the use of the most recent advanced machine learning and deep learning techniques. This paper presents an approach based on deep learning with attention to the recognition of activities from multiple frames. Feature extraction is performed by estimating the pose of the human skeleton, and classification is performed using a neural network based on Bidirectional Encoder Representation of Transformers (BERT). This algorithm was trained with the UP-Fall public dataset, generating more balanced artificial data with a Generative Adversarial Neural network (GAN), and evaluated with real data, outperforming the results of other activity recognition methods using the same dataset. [ABSTRACT FROM AUTHOR]

Remote sensing sensors-based image processing techniques have been widely applied in non-destructive quality inspection systems of agricultural crops. Image processing and analysis were performed with computer vision and external grading systems by general and standard steps, such as image acquisition, pre-processing and segmentation, extraction and classification of image characteristics. This paper describes the design and implementation of a real-time fresh fruit bunch (FFB) maturity classification system for palm oil based on unrestricted remote sensing (CCD camera sensor) and image processing techniques using five multivariate techniques (statistics, histograms, Gabor wavelets, GLCM and BGLAM) to extract fruit image characteristics and incorporate information on palm oil species classification FFB and maturity testing. To optimize the proposed solution in terms of performance reporting and processing time, supervised classifiers, such as support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN), were performed and evaluated via ROC and AUC measurements. The experimental results showed that the FFB classification system of non-destructive palm oil maturation in real time provided a significant result. Although the SVM classifier is generally a robust classifier, ANN has better performance due to the natural noise of the data. The highest precision was obtained on the basis of the ANN and BGLAM algorithms applied to the texture of the fruit. In particular, the robust image processing algorithm based on BGLAM feature extraction technology and the ANN classifier largely provided a high AUC test accuracy of over 93% and an image-processing time of 0,44 (s) for the detection of FFB palm oil species. [ABSTRACT FROM AUTHOR]

Object detection is a computer vision based technique which is used to detect instances of semantic objects of a particular class in digital images and videos. Crowd density analysis is one of the commonly utilized applications of object detection. Since crowd density classification techniques face challenges like non-uniform density, occlusion, inter-scene, and intra-scene deviations, convolutional neural network (CNN) models are useful. This paper presents a Metaheuristics with Deep Transfer Learning Enabled Intelligent Crowd Density Detection and Classification (MDTL-ICDDC) model for video surveillance systems. The proposed MDTL-ICDDC technique mostly concentrates on the effective identification and classification of crowd density on video surveillance systems. In order to achieve this, the MDTL-ICDDC model primarily leverages a Salp Swarm Algorithm (SSA) with NASNetLarge model as a feature extraction in which the hyperparameter tuning process is performed by the SSA. Furthermore, a weighted extreme learning machine (WELM) method was utilized for crowd density and classification process. Finally, the krill swarm algorithm (KSA) is applied for an effective parameter optimization process and thereby improves the classification results. The experimental validation of the MDTL-ICDDC approach was carried out with a benchmark dataset, and the outcomes are examined under several aspects. The experimental values indicated that the MDTL-ICDDC system has accomplished enhanced performance over other models such as Gabor, BoW-SRP, Bow-LBP, GLCM-SVM, GoogleNet, and VGGNet. [ABSTRACT FROM AUTHOR]

The massive adaptation of reverse transcriptase-polymerase chain reaction (RT-PCR) has facilitated efforts to battle against the COVID-19 pandemic that has inflicted millions of individuals around the world. Besides RT-PCR, radiography imaging examinations yields valuable insight for detecting and diagnosing this infectious disease. Thus, this paper proposed a computer vision and artificial-intelligence-based hybrid approach aid in efficient detection and control of COVID-19 disease. The study utilized chest X-ray images to segregate COVID-19 positive cases among healthy individuals by exploiting several combinational structures of image filtering, feature-extraction techniques, and machine learning algorithms. It analyzed the effects of three noise removal filters and two feature-extraction techniques on performance of several machine learning and deep-learning-based classifiers. The proposed schemes first remove unnecessary noise using a conservative smoothing filter, Crimmins speckle removal, and Gaussian filter. It then employs linear discriminant analysis (LDA) as linear method and principal component analysis (PCA) as non-linear feature-extraction technique to extract highly discriminant feature sets. Finally, it uses these feature sets to train various classification models, including convolutional neural network (CNN), support vector machine (SVM), and logistic regression (LG). Evidently, the proposed conservative smoothing filter with single peak to maintain symmetry in horizontal and vertical directions for enhancement of image, along with LDA and SVM, secured an overall classification accuracy of 99.93%. Experimental results show that, besides achieving high accuracies, the incorporation of feature-extraction techniques significantly reduces the computational time of the proposed model. [ABSTRACT FROM AUTHOR]

Printed circuit board (PCB) assurance in the optical domain is a crucial field of study. Though there are many existing PCB assurance methods using image processing, computer vision (CV), and machine learning (ML), the PCB field is complex and increasingly evolving, so new techniques are required to overcome the emerging problems. Existing ML-based methods outperform traditional CV methods; however, they often require more data, have low explainability, and can be difficult to adapt when a new technology arises. To overcome these challenges, CV methods can be used in tandem with ML methods. In particular, human-interpretable CV algorithms such as those that extract color, shape, and texture features increase PCB assurance explainability. This allows for incorporation of prior knowledge, which effectively reduces the number of trainable ML parameters and, thus, the amount of data needed to achieve high accuracy when training or retraining an ML model. Hence, this study explores the benefits and limitations of a variety of common computer vision-based features for the task of PCB component detection. The study results indicate that color features demonstrate promising performance for PCB component detection. The purpose of this paper is to facilitate collaboration between the hardware assurance, computer vision, and machine learning communities. [ABSTRACT FROM AUTHOR]

As machine vision technology has advanced, pig face recognition has gained wide attention as an individual pig identification method. This study establishes an improved ResNAM network as a backbone network for pig face image feature extraction by combining an NAM (normalization-based attention module) attention mechanism and a ResNet model to probe non-contact open-set pig face recognition. Then, an open-set pig face recognition framework is designed by integrating three loss functions and two metrics to finish the task with no crossover of individuals in the training and test sets. The SphereFace loss function with the cosine distance as a metric and ResNAM are combined in the framework to obtain the optimal open-set pig face recognition model. To train our model, 37 pigs with a total of 12,993 images were randomly selected from the collected pig face images, and 9 pigs with a total of 3431 images were set as a test set. 900 pairs of positive sample pairs and 900 pairs of negative pairs were obtained from the images in the test set. A series of experimental results show that our accuracy reached 95.28%, which was 2.61% higher than that of a human face recognition model. NAM was more effective in improving the performance of the pig face recognition model than the mainstream BAM (bottleneck attention module) and CBAM (convolutional block attention module). The research results can provide technological support for non-contact open-set individual recognition for intelligent farming processes. [ABSTRACT FROM AUTHOR]

Land cover classification using high-resolution Polarimetric Synthetic Aperture Radar (PolSAR) images obtained from satellites is a challenging task. While deep learning algorithms have been extensively studied for PolSAR image land cover classification, the performance is severely constrained due to the scarcity of labeled PolSAR samples and the limited domain acceptance of models. Recently, the emergence of the Segment Anything Model (SAM) based on the vision transformer (VIT) model has brought about a revolution in the study of specific downstream tasks in computer vision. Benefiting from its millions of parameters and extensive training datasets, SAM demonstrates powerful capabilities in extracting semantic information and generalization. To this end, we propose a dual-stream feature extraction network based on SAM, i.e., BSDSNet. We change the image encoder part of SAM to a dual stream, where the ConvNext image encoder is utilized to extract local information and the VIT image encoder is used to extract global information. BSDSNet achieves an in-depth exploration of semantic and spatial information in PolSAR images. Additionally, to facilitate a fine-grained amalgamation of information, the SA-Gate module is employed to integrate local–global information. Compared to previous deep learning models, BSDSNet's impressive ability to represent features is akin to a versatile receptive field, making it well suited for classifying PolSAR images across various resolutions. Comprehensive evaluations indicate that BSDSNet achieves excellent results in qualitative and quantitative evaluation when performing classification tasks on the AIR-PolSAR-Seg dataset and the WHU-OPT-SAR dataset. Compared to the suboptimal results, our method improves the Kappa metric by 3.68% and 0.44% on the AIR-PolSAR-Seg dataset and the WHU-OPT-SAR dataset, respectively. [ABSTRACT FROM AUTHOR]

Fallen leaf disease can lead to a decrease in leaf area, a decrease in photosynthetic products, insufficient accumulation of fruit sugar, poor coloring and flavor, and a large number of fruits developing sunburn. To address the aforementioned issue, this article introduces a deep learning algorithm designed for the segmentation and recognition of agricultural disease images, particularly those involving leaf lesions. The essence of this algorithm lies in enhancing the Multi-scale Attention Net (MA-Net) encoder and attention mechanism to improve the model's performance when processing agricultural disease images. Firstly, an analysis was conducted on MA-Net, and its limitations were identified. Compared to res-block, Mix Vision Transformer (MiT) consumes relatively less time during the training process, can better capture global and contextual information in images, and has better robustness and scalability. Then, the feature extraction parts of different networks were used as encoders to join the MA-Net network. Compared to a Position-wise Attention Block (PAB), which has higher computational complexity and requires a larger amount of computing resources, Effective Channel Attention net (ECANet) reduces the number of model parameters and computation by learning the correlation between channels, as well as having a better denoising ability. The experimental results show that the proposed solution has high accuracy and stability in agricultural disease image segmentation and recognition. The mean Intersection over Union (mIoU) is 98.1%, which is 0.2% higher than traditional MA-Net; Dice Loss is 0.9%, which is 0.1% lower than traditional MA-Net. [ABSTRACT FROM AUTHOR]

Sign language recognition, an essential interface between the hearing and deaf-mute communities, faces challenges with high false positive rates and computational costs, even with the use of advanced deep learning techniques. Our proposed solution is a stacked encoded model, combining artificial intelligence (AI) with the Internet of Things (IoT), which refines feature extraction and classification to overcome these challenges. We leverage a lightweight backbone model for preliminary feature extraction and use stacked autoencoders to further refine these features. Our approach harnesses the scalability of big data, showing notable improvement in accuracy, precision, recall, F1-score, and complexity analysis. Our model's effectiveness is demonstrated through testing on the ArSL2018 benchmark dataset, showcasing superior performance compared to state-of-the-art approaches. Additional validation through an ablation study with pre-trained convolutional neural network (CNN) models affirms our model's efficacy across all evaluation metrics. Our work paves the way for the sustainable development of high-performing, IoT-based sign-language-recognition applications. [ABSTRACT FROM AUTHOR]

The existing classification methods for Panax notoginseng taproots suffer from low accuracy, low efficiency, and poor stability. In this study, a classification model based on image feature fusion is established for Panax notoginseng taproots. The images of Panax notoginseng taproots collected in the experiment are preprocessed by Gaussian filtering, binarization, and morphological methods. Then, a total of 40 features are extracted, including size and shape features, HSV and RGB color features, and texture features. Through BP neural network, extreme learning machine (ELM), and support vector machine (SVM) models, the importance of color, texture, and fusion features for the classification of the main roots of Panax notoginseng is verified. Among the three models, the SVM model performs the best, achieving an accuracy of 92.037% on the prediction set. Next, iterative retaining information variables (IRIVs), variable iterative space shrinkage approach (VISSA), and stepwise regression analysis (SRA) are used to reduce the dimension of all the features. Finally, a traditional machine learning SVM model based on feature selection and a deep learning model based on semantic segmentation are established. With the model size of only 125 kb and the training time of 3.4 s, the IRIV-SVM model achieves an accuracy of 95.370% on the test set, so IRIV-SVM is selected as the main root classification model for Panax notoginseng. After being optimized by the gray wolf optimizer, the IRIV-GWO-SVM model achieves the highest classification accuracy of 98.704% on the test set. The study results of this paper provide a basis for developing online classification methods of Panax notoginseng with different grades in actual production. [ABSTRACT FROM AUTHOR]

Reliable tools for artefact rejection and signal classification are a must for cosmic ray detection experiments based on CMOS technology. In this paper, we analyse the fitness of several feature-based statistical classifiers for the classification of particle candidate hits in four categories: spots, tracks, worms and artefacts. We use Zernike moments of the image function as feature carriers and propose a preprocessing and denoising scheme to make the feature extraction more efficient. As opposed to convolution neural network classifiers, the feature-based classifiers allow for establishing a connection between features and geometrical properties of candidate hits. Apart from basic classifiers we also consider their ensemble extensions and find these extensions generally better performing than basic versions, with an average recognition accuracy of 88%. [ABSTRACT FROM AUTHOR]

This research addressed the need to enhance template-matching performance in e-learning and automated assessments within Egypt's evolving educational landscape, marked by the importance of e-learning during the COVID-19 pandemic. Despite the widespread adoption of e-learning, robust template-matching feedback mechanisms should still be developed for personalization, engagement, and learning outcomes. This study augmented the conventional best-buddies similarity (BBS) approach with four feature descriptors, Harris, scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and maximally stable extremal regions (MSER), to enhance template-matching performance in e-learning. We systematically selected algorithms, integrated them into enhanced BBS schemes, and assessed their effectiveness against a baseline BBS approach using challenging data samples. A systematic algorithm selection process involving multiple reviewers was employed. Chosen algorithms were integrated into enhanced BBS schemes and rigorously evaluated. The results showed that the proposed schemes exhibited enhanced template-matching performance, suggesting potential improvements in personalization, engagement, and learning outcomes. Further, the study highlights the importance of robust template-matching feedback in e-learning, offering insights into improving educational quality. The findings enrich e-learning experiences, suggesting avenues for refining e-learning platforms and positively impacting the Egyptian education sector. [ABSTRACT FROM AUTHOR]

Acute lymphoblastic leukemia (ALL) is a lethal blood cancer that is characterized by an abnormal increased number of immature lymphocytes in the blood or bone marrow. For effective treatment of ALL, early assessment of the disease is essential. Manual examination of stained blood smear images is current practice for initially screening ALL. This practice is time-consuming and error-prone. In order to effectively diagnose ALL, numerous deep-learning-based computer vision systems have been developed for detecting ALL in blood peripheral images (BPIs). Such systems extract a huge number of image features and use them to perform the classification task. The extracted features may contain irrelevant or redundant features that could reduce classification accuracy and increase the running time of the classifier. Feature selection is considered an effective tool to mitigate the curse of the dimensionality problem and alleviate its corresponding shortcomings. One of the most effective dimensionality-reduction tools is principal component analysis (PCA), which maps input features into an orthogonal space and extracts the features that convey the highest variability from the data. Other feature selection approaches utilize evolutionary computation (EC) to search the feature space and localize optimal features. To profit from both feature selection approaches in improving the classification performance of ALL, in this study, a new hybrid deep-learning-based feature engineering approach is proposed. The introduced approach integrates the powerful capability of PCA and particle swarm optimization (PSO) approaches in selecting informative features from BPI mages with the power of pre-trained CNNs of feature extraction. Image features are first extracted through the feature-transfer capability of the GoogleNet convolutional neural network (CNN). PCA is utilized to generate a feature set of the principal components that covers 95% of the variability in the data. In parallel, bio-inspired particle swarm optimization is used to search for the optimal image features. The PCA and PSO-derived feature sets are then integrated to develop a hybrid set of features that are then used to train a Bayesian-based optimized support vector machine (SVM) and subspace discriminant ensemble-learning (SDEL) classifiers. The obtained results show improved classification performance for the ML classifiers trained by the proposed hybrid feature set over the original PCA, PSO, and all extracted feature sets for ALL multi-class classification. The Bayesian-optimized SVM trained with the proposed hybrid PCA-PSO feature set achieves the highest classification accuracy of 97.4%. The classification performance of the proposed feature engineering approach competes with the state of the art. [ABSTRACT FROM AUTHOR]

Drowsy driving can significantly affect driving performance and overall road safety. Statistically, the main causes are decreased alertness and attention of the drivers. The combination of deep learning and computer-vision algorithm applications has been proven to be one of the most effective approaches for the detection of drowsiness. Robust and accurate drowsiness detection systems can be developed by leveraging deep learning to learn complex coordinate patterns using visual data. Deep learning algorithms have emerged as powerful techniques for drowsiness detection because of their ability to learn automatically from given inputs and feature extractions from raw data. Eye-blinking-based drowsiness detection was applied in this study, which utilized the analysis of eye-blink patterns. In this study, we used custom data for model training and experimental results were obtained for different candidates. The blinking of the eye and mouth region coordinates were obtained by applying landmarks. The rate of eye-blinking and changes in the shape of the mouth were analyzed using computer-vision techniques by measuring eye landmarks with real-time fluctuation representations. An experimental analysis was performed in real time and the results proved the existence of a correlation between yawning and closed eyes, classified as drowsy. The overall performance of the drowsiness detection model was 95.8% accuracy for drowsy-eye detection, 97% for open-eye detection, 0.84% for yawning detection, 0.98% for right-sided falling, and 100% for left-sided falling. Furthermore, the proposed method allowed a real-time eye rate analysis, where the threshold served as a separator of the eye into two classes, the "Open" and "Closed" states. [ABSTRACT FROM AUTHOR]

This work aims to classify normal and carcinogenic cells in the oral cavity using two different approaches with an eye towards achieving high accuracy. The first approach extracts local binary patterns and metrics derived from a histogram from the dataset and is fed to several machine-learning models. The second approach uses a combination of neural networks as a backbone feature extractor and a random forest for classification. The results show that information can be learnt effectively from limited training images using these approaches. Some approaches use deep learning algorithms to generate a bounding box that can locate the suspected lesion. Other approaches use handcrafted textural feature extraction techniques and feed the resultant feature vectors to a classification model. The proposed method will extract the features pertaining to the images using pre-trained convolution neural networks (CNN) and train a classification model using the resulting feature vectors. By using the extracted features from a pre-trained CNN model to train a random forest, the problem of requiring a large amount of data to train deep learning models is bypassed. The study selected a dataset consisting of 1224 images, which were divided into two sets with varying resolutions.The performance of the model is calculated based on accuracy, specificity, sensitivity, and the area under curve (AUC). The proposed work is able to produce a highest test accuracy of 96.94% and an AUC of 0.976 using 696 images of 400× magnification and a highest test accuracy of 99.65% and an AUC of 0.9983 using only 528 images of 100× magnification images. [ABSTRACT FROM AUTHOR]

Computer vision methodologies using machine learning techniques usually consist of the following phases: pre-processing, segmentation, feature extraction, selection of relevant variables, classification, and evaluation. In this work, a methodology for object recognition is proposed. The methodology is called PSEV-BF (pre-segmentation and enhanced variables for bird features). PSEV-BF includes two new phases compared to the traditional computer vision methodologies, namely: pre-segmentation and enhancement of variables. Pre-segmentation is performed using the third version of YOLO (you only look once), a convolutional neural network (CNN) architecture designed for object detection. Additionally, a simulated annealing (SA) algorithm is proposed for the selection and enhancement of relevant variables. To test PSEV-BF, the repository commons object in Context (COCO) was used with images exhibiting uncontrolled environments. Finally, the APIoU metric (average precision intersection over union) is used as an evaluation benchmark to compare our methodology with standard configurations. The results show that PSEV-BF has the highest performance in all tests. [ABSTRACT FROM AUTHOR]

pH value is a crucial indicator for evaluating silage quality. In this study, taking maize silage as the research object, a quantitative prediction model of pH value change during the secondary fermentation of maize silage was constructed based on computer vision. Firstly, maize silage samples were collected for image acquisition and pH value determination during intermittent and always-aerobic exposure. Secondly, after preprocessing the acquired image with the region of interest (ROI) interception, smoothing, and sharpening, the color and texture features were extracted. In addition, Pearson correlation analysis and RF importance ranking were used to choose useful feature variables. Finally, based on all feature variables and useful feature variables, four regression models were constructed and compared using random forest regression (RFR) and support vector regression (SVR): RFR model 1, RFR model 2, SVR model 1, and SVR model 2. The results showed that—compared with texture features—the correlation between color features and pH value was higher, which could better reflect the dynamic changes in pH value. All four models were highly predictive. The RFR model represented the quantitative analysis relationship between image information and pH value better than the SVR model. RFR model 2 was efficient and accurate, and was the best model for pH prediction, with R c 2 , R p 2 , RMSEC, RMSEP, and RPD of 0.9891, 0.9425, 0.1758, 0.3651, and 4.2367, respectively. Overall, this study proved the feasibility of using computer vision technology to quantitatively predict pH value during the secondary fermentation of maize silage and provided new insights for monitoring the quality of maize silage. [ABSTRACT FROM AUTHOR]

With the maturity of computer vision and natural language processing technology, we are becoming more ambitious in image captioning. In particular, we are more ambitious in generating longer, richer, and more accurate sentences as image descriptions. Most existing image caption models use an encoder—decoder structure, and most of the best-performing models incorporate attention mechanisms in the encoder—decoder structure. However, existing image captioning methods focus only on visual attention mechanism and not on keywords attention mechanism, thus leading to model-generated sentences that are not rich and accurate enough, and errors in visual feature extraction can directly lead to generated caption sentences that are incorrect. To fill this gap, we propose a combination attention module. This module comprises a visual attention module and a keyword attention module. The visual attention module helps in performing fast extractions of key local features, and the keyword attention module focuses on keywords that may appear in generated sentences. The results generated by the two modules can be corrected for each other. We embed the combination attention module into the framework of the Transformer, thus constructing a new image caption model CAT (Combination Attention Transformer) to generate more accurate and rich image caption sentences. Extensive experiments on the MSCOCO dataset demonstrate the effectiveness and superiority of our method over many state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Large-scale optical sensing and precise, rapid assessment of seismic building damage in urban communities are increasingly demanded in disaster prevention and reduction. The common method is to train a convolutional neural network (CNN) in a pixel-level semantic segmentation approach and does not fully consider the characteristics of the assessment objectives. This study developed a machine-learning-derived two-stage method for post-earthquake building location and damage assessment considering the data characteristics of satellite remote sensing (SRS) optical images with dense distribution, small size, and imbalanced numbers. It included a modified You Only Look Once (YOLOv4) object detection module and a support vector machine (SVM) based classification module. In the primary step, the multiscale features were successfully extracted and fused from SRS images of densely distributed buildings by optimizing the YOLOv4 model toward the network structures, training hyperparameters, and anchor boxes. The fusion improved multi-channel features, optimization of network structure and hyperparameters have significantly enhanced the average location accuracy of post-earthquake buildings. Thereafter, three statistics (i.e., the angular second moment, dissimilarity, and inverse difference moment) were further discovered to effectively extract the characteristic value for earthquake damage from located buildings in SRS optical images based on the gray level co-occurrence matrix. They were used as the texture features to distinguish damage intensities of buildings, using the SVM model. The investigated dataset included 386 pre- and post-earthquake SRS optical images of the 2017 Mexico City earthquake, with a resolution of 1024 × 1024 pixels. Results show that the average location accuracy of post-earthquake buildings exceeds 95.7% and that the binary classification accuracy for damage assessment reaches 97.1%. The proposed two-stage method was validated by its extremely high precision in respect of densely distributed small buildings, indicating the promising potential of computer vision in large-scale disaster prevention and reduction using SRS datasets. [ABSTRACT FROM AUTHOR]