Breast cancer diagnosis from histopathology images is often time consuming and prone to human error, impacting treatment and prognosis. Deep learning diagnostic methods offer the potential for improved accuracy and efficiency in breast cancer detection and classification. However, they struggle with limited data and subtle variations within and between cancer types. Attention mechanisms provide feature refinement capabilities that have shown promise in overcoming such challenges. To this end, this paper proposes the Efficient Channel Spatial Attention Network (ECSAnet), an architecture built on EfficientNetV2 and augmented with a convolutional block attention module (CBAM) and additional fully connected layers. ECSAnet was fine-tuned using the BreakHis dataset, employing Reinhard stain normalization and image augmentation techniques to minimize overfitting and enhance generalizability. In testing, ECSAnet outperformed AlexNet, DenseNet121, EfficientNetV2-S, InceptionNetV3, ResNet50, and VGG16 in most settings, achieving accuracies of 94.2% at 40×, 92.96% at 100×, 88.41% at 200×, and 89.42% at 400× magnifications. The results highlight the effectiveness of CBAM in improving classification accuracy and the importance of stain normalization for generalizability.

Background: Dental care has been transformed by neural networks, introducing advanced methods for improving patient outcomes. By leveraging technological innovation, dental informatics aims to enhance treatment and diagnostic processes. Early diagnosis of dental problems is crucial, as it can substantially reduce dental disease incidence by ensuring timely and appropriate treatment. The use of artificial intelligence (AI) within dental informatics is a pivotal tool that has applications across all dental specialties. This systematic literature review aims to comprehensively summarize existing research on AI implementation in dentistry. It explores various techniques used for detecting oral features such as teeth, fillings, caries, prostheses, crowns, implants, and endodontic treatments. AI plays a vital role in the diagnosis of dental diseases by enabling precise and quick identification of issues that may be difficult to detect through traditional methods. Its ability to analyze large volumes of data enhances diagnostic accuracy and efficiency, leading to better patient outcomes. Methods: An extensive search was conducted across a number of databases, including Science Direct, PubMed (MEDLINE), arXiv.org, MDPI, Nature, Web of Science, Google Scholar, Scopus, and Wiley Online Library. Results: The studies included in this review employed a wide range of neural networks, showcasing their versatility in detecting the dental categories mentioned above. Additionally, the use of diverse datasets underscores the adaptability of these AI models to different clinical scenarios. This study highlights the compatibility, robustness, and heterogeneity among the reviewed studies. This indicates that AI technologies can be effectively integrated into current dental practices. The review also discusses potential challenges and future directions for AI in dentistry. It emphasizes the need for further research to optimize these technologies for broader clinical applications. Conclusions: By providing a detailed overview of AI’s role in dentistry, this review aims to inform practitioners and researchers about the current capabilities and future potential of AI-driven dental care, ultimately contributing to improved patient outcomes and more efficient dental practices.

Dental caries is one of the most prevalent and chronic diseases worldwide. Dental X-ray radiography is considered a standard tool and a valuable resource for radiologists to identify dental diseases and problems that are hard to recognize by visual inspection alone. However, the available dental panoramic image datasets are extremely limited and only include a small number of images. U-Net is one of the deep learning networks that are showing promising performance in medical image segmentation. In this work, different U-Net models are applied to dental panoramic X-ray images to detect caries lesions. The Detection, Numbering, and Segmentation Panoramic Images (DNS) dataset, which includes 1500 panoramic X-ray images obtained from Ivisionlab, is used in this experiment. The major objective of this work is to extend the DNS Panoramic Images dataset by detecting the cavities in the panoramic image and generating the binary ground truth of this image to use as the ground truth for the evaluation of models. These ground truths are revised by experts to ensure their robustness and correctness. Firstly, we expand the Panoramic Images (DNS) dataset by detecting the cavities in the panoramic images and generating the images’ binary ground truth. Secondly, we apply U-Net, U-Net++ and U-Net3+ to the expanded DNS dataset to learn the hierarchical features and to enhance the cavity boundary. The results show that U-Net3+ outperforms the other versions of U-Net with 95% in testing accuracy.

In the domain of action recognition using multisensory data, the integration of RGB and sig-nal-based modalities offers a promising approach to enhance the accuracy of action classification systems. Our system was developed through experimentation on three benchmark datasets: UTD-MHAD (University of Texas at Dallas Multimodal Human Action Dataset), HWU-USP and LaRa. Initially, the data undergoes preprocessing, where gaussian and butterworth filters are applied to the RGB and signal data, respectively. Following this, windowing/segmentation is applied to signals and RGB data. After that, features are extracted from the signal data, including auto-regression, MFCC (Mel-frequency Cepstral Coefficients), and transient detection principle, while the RGB (Red Green Blue) was processed as a combined input to extract features such as angles, velocity, full-body elliptical modeling, fiducial points, and a 2.5D point cloud of the entire body. These features are then fused, followed by the application of the Yeo-Johnson power optimizer to refine the data. The optimized data is subsequently classified using a Neurofuzzy classifier to recognize different actions. This classifier is chosen for its ability to adapt to the heterogeneous nature of multimodal data, where features are spread across different domains, making traditional classifiers less effective. The Neurofuzzy model employs cross-validation for training and testing to ensure reliable results. The results also suggest that the proposed model yields a higher accuracy than the existing models. More specifically, in the HWU-USP dataset, the accuracy amounts to mean 89%, in the LaRa, to mean 91% and 88% over the UTD-MHAD dataset. The system under study effectively distinguishes related actions, but its efficiency is hindered by the complexity of individual actions and the increased noise in the dataset.

Speech recognition is a captivating process that revolutionizes human-computer interactions, allowing us to interact and control machines through spoken commands. The foundation of speech recognition lies in understanding a given language’s linguistic and textual characteristics. Although automatic speech recognition (ASR) systems flawlessly convert speech into text for various international languages, their implementation for Arabic remains inadequate. In this research, we diligently explore the current state of Arabic ASR systems and unveil the challenges encountered during their development. We categorize these challenges into two groups: those specific to the Arabic language and those that are more general. Additionally, we propose strategies to overcome these obstacles and emphasize the need for ASR architectures tailored to the Arabic language’s unique grammatical and phonetic structure. In addition, we provide a comprehensive and explicit description of various feature extraction methods, language models, and acoustic models utilized in the Arabic ASR system.

This paper introduces an advanced framework for Human Pose Estimation (HPE) and Semantic Event Classification (SEC), addressing the growing demand for sophisticated human skeleton modeling, context-aware feature extraction, and machine learning techniques for accurate event recognition in real-world scenarios. HPE, a fundamental task in applications such as sports analytics and surveillance systems, involves predicting human joint locations from visual data. Despite substantial progress driven by deep learning advancements, particularly in handling occlusions and crowded environments, a comprehensive integration of these innovations remains limited in current literature. To bridge this gap, we propose a novel HPE and SEC system featuring a robust six-stage pipeline. The workflow begins with preprocessing steps, including video-to-image sequence conversion, floor removal to reduce noise, grayscale transformation, and human silhouette extraction via binary masks. Human detection is performed using the GrabCut algorithm. Full-body feature extraction incorporates 1-D Fourier Transform, 2-D energy-based motion analysis, 0−180° intensity mapping via the Radon Transform algorithm, and movable body part detection using Fourier-Based Correlation. Key point features are characterized through the degree of freedom, human landmark detection via the HSV algorithm, and angular point analysis using the media pipe algorithm. Features are then optimized using the Ray Optimizer and classified through a Neuro-Fuzzy classifier. The proposed system achieves classification accuracies of 94.82%, 92.33%, and 93.63% on the UCF Sports Actions, Human Action Recognition (HAR), and MPII Human Pose datasets, respectively, outperforming state-of-the-art methods and demonstrating its effectiveness in tackling real-world challenges.