The increasing prevalence of colon and lung cancer presents a considerable challenge to healthcare systems worldwide, emphasizing the critical necessity for early and accurate diagnosis to enhance patient outcomes. The precision of diagnosis heavily relies on the expertise of histopathologists, constituting a demanding task. The health and well‐being of patients are jeopardized in the absence of adequately trained histopathologists, potentially leading to misdiagnoses, unnecessary treatments, and tests, resulting in the inefficient utilization of healthcare resources. However, with substantial technological advancements, deep learning (DL) has emerged as a potent tool in clinical settings, particularly in the realm of medical imaging. This study leveraged the LC25000 dataset, encompassing 25,000 images of lung and colon tissue, introducing an innovative approach by employing a self‐organized operational neural network (Self‐ONN) to accurately detect lung and colon cancer in histopathology images. Subsequently, our novel model underwent comparison with five pretrained convolutional neural network (CNN) models: MobileNetV2‐SelfMLP, Resnet18‐SelfMLP, DenseNet201‐SelfMLP, InceptionV3‐SelfMLP, and MobileViTv2_200‐SelfMLP, where each multilayer perceptron (MLP) was replaced with Self‐MLP. The models’ performance was meticulously assessed using key metrics such as precision, recall, F1 score, accuracy, and area under the receiver operating characteristic (ROC) curve. The proposed model demonstrated exceptional overall accuracy, precision, sensitivity, F1 score, and specificity, achieving 99.74%, 99.74%, 99.74%, 99.74%, and 99.94%, respectively. This underscores the potential of artificial intelligence (AI) to significantly enhance diagnostic precision within clinical settings, portraying a promising avenue for improving patient care and outcomes. The synopsis of the literature provides a thorough examination of several DL and digital image processing methods used in the identification of cancer, with a primary emphasis on lung and colon cancer. The experiments use the LC25000 dataset, which consists of 25,000 photos, for the purposes of training and testing. Various techniques, such as CNNs, transfer learning, ensemble models, and lightweight DL architectures, have been used to accomplish accurate categorization of cancer tissue. Various investigations regularly show exceptional performance, with accuracy rates ranging from 96.19% to 99.97%. DL models such as EfficientNetV2, DHS‐CapsNet, and CNN‐based architectures such as VGG16 and GoogleNet variations have shown remarkable performance in obtaining high levels of accuracy. In addition, methods such as SSL and lightweight DL models provide encouraging outcomes in effectively managing large datasets. In general, the research emphasizes the efficacy of DL methods in successfully diagnosing cancer from histopathological pictures. It therefore indicates that DL has the potential to greatly improve medical diagnostic techniques.

Classroom EEG recordings classification has the capacity to significantly enhance comprehension and learning by revealing complex neural patterns linked to various cognitive processes. Electroencephalography (EEG) in academic settings allows researchers to study brain activity while students are in class, revealing learning preferences. The purpose of this study was to develop a machine learning framework to automatically classify different learning-style EEG patterns in real classroom environments. Method: In this study, a set of EEG features was investigated, including statistical features, fractal dimension, higher-order spectra, entropy, and a combination of all sets. Three different machine learning classifiers, random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), were used to evaluate the performance. The proposed framework was evaluated on the real classroom EEG dataset, involving EEG recordings featuring different teaching blocks: reading, discussion, lecture, and video. Results: The findings revealed that statistical features are the most sensitive feature metric in distinguishing learning patterns from EEG. The statistical features and RF classifier method tested in this study achieved an overall best average accuracy of 78.45% when estimated by fivefold cross-validation. Conclusions: Our results suggest that EEG time domain statistics have a substantial role and are more reliable for internal state classification. This study might be used to highlight the importance of using EEG signals in the education context, opening the path for educational automation research and development.

Unmanned aerial vehicles have revolutionized logistics, environmental monitoring, and aerial surveillance. Their widespread use has created security concerns, specifically regarding illegal spying, smuggling, and hazardous substance movement. Maintaining public safety and protecting sensitive locations requires effective drone detection and payload assessment. Our article proposes a vision-based system for real-time drone identification and classification utilizing YOLOv5, YOLOv8, and GELAN-E deep learning models, enhanced with novel attention mechanisms and interpretability techniques. By integrating the Convolutional Block Attention Module into the YOLO architecture, which is named AttnYOLO, the system enhances feature extraction and focuses on the most relevant regions in an image. This improvement in spatial and channel attention significantly boosts detection performance, particularly for small and occluded drones. Additionally, we employ Gradient-weighted Class Activation Mapping (EigenCAM) for visualizing model focus during detection, increasing the system’s transparency and interpretability. VisioDECT comprises 20,924 annotated photographs of six drone models in overcast, sunny, and evening circumstances. Under cloudy conditions, DenseNet201 achieved 100% classification accuracy, while DarkNet53 and InceptionV3 reached 99.99% and 99.9%, respectively. In evening scenarios, InceptionV3 had 100% accuracy, followed by DarkNet53 with 99.98%. Our proposed model, GELAN-E, excelled in detection and classification. In overcast settings, GELAN-E outperformed YOLOv8 with an accuracy of 0.988, a recall of 0.994, and a mAP50-95 score of 0.688. For evening conditions, GELAN-E achieved a higher mAP50-95 score of 0.642 compared to YOLOv8. These results demonstrate that the inclusion of attention mechanisms, along with visual interpretability, enhances drone detection performance, particularly in low-light and challenging environments, making this system ideal for real-time drone detection in civilian and military applications .

Background/Objectives: As the primary cause of cancer-related death globally, lung cancer highlights the critical need for early identification, precise staging, and individualized treatment planning. By enabling automated diagnosis, staging, and prognostic evaluation, recent developments in artificial intelligence (AI) and machine learning (ML) have completely changed the treatment of lung cancer. The goal of this narrative review is to compile the most recent data on uses of AI and ML throughout the lung cancer care continuum. Methods: A comprehensive literature search was conducted across major scientific databases to identify peer-reviewed studies focused on AI-based imaging, detection, and prognostic modeling in lung cancer. Studies were categorized into three thematic domains: (1) detection and screening, (2) staging and diagnosis, and (3) risk prediction and prognosis. Results: Convolutional neural networks (CNNs), in particular, have shown significant sensitivity and specificity in nodule recognition, segmentation, and false-positive reduction. Radiomics-based models and other multimodal frameworks combining imaging and clinical data have great promise for forecasting treatment outcomes and survival rates. The accuracy of non-small-cell lung cancer (NSCLC) staging, lymph node evaluation, and malignancy classification were regularly improved by AI algorithms, frequently matching or exceeding radiologist performance. Conclusions: There are still issues with data heterogeneity, interpretability, repeatability, and clinical acceptability despite significant advancements. Standardized datasets, ethical AI implementation, and transparent model evaluation should be the top priorities for future initiatives. AI and ML have revolutionary potential for intelligent, personalized, and real-time lung cancer treatment by connecting computational innovation with precision oncology.

Sepsis remains a leading cause of morbidity and mortality, yet routine diagnostics are slow, culture-dependent, and often lack the sensitivity or specificity required for early intervention. Prior studies rarely demonstrate clinical-grade performance on blood culture samples or in independent external cohorts. We address these gaps with a surface-enhanced Raman spectroscopy and deep learning workflow (SERS-DL) that performs sepsis instance recognition and causative pathogen identification directly from target-analyte enriched blood. We assembled a primary dataset of SERS spectra acquired from 653 analyte-enriched blood samples collected at a tertiary hospital in Qatar and an external blind cohort of 70 independent samples. After rigorous preprocessing and class-weighted augmentation of SERS spectra, we trained SuperRamanNet, a lightweight one-dimensional classifier based on super operational neural networks. In five-fold, sample-contained cross-validation, the system achieved 99.67 % accuracy for binary sepsis recognition and 98.84 % accuracy for six-class pathogen identification. On the external cohort, performance remained high at 98.28 % for pathogen typing, indicating robust generalizability. Comparative benchmarks and ablation studies confirmed consistent gains over convolutional and operational baselines and quantified the impact of augmentation and architectural choices. Residual confusions were concentrated between control and Escherichia coli and among certain Gram-negative classes, underscoring the need for improved raw class balance during blood sample collection. Overall, this rapid, culture-free, and portable SERS-DL pipeline delivers near clinical-grade accuracy for sepsis detection and pathogen identification directly from blood. The compact model and streamlined workflow support point-of-care translation, with potential to accelerate triage, guide early therapy, and reduce the global sepsis burden.