The properties of steels are intrinsically dependent on their microstructural components, known as phases, which form during the manufacturing process. Different steel phases can be observed in microscopic images of steel surfaces. Automatic detection and classification of these phases from images can significantly enhance the understanding of steel properties with improved speed and accuracy. This paper introduces, for the first time, an intelligent and automated method for classifying steel phases from microscopic images. This process requires defining and extracting suitable texture features unique to these images and segmenting the images into highly irregular regions based on the extracted features. To achieve this, the input image is initially divided into blocks, and texture features are extracted independently for each block. The dimensionality of these features is then reduced using Principal Component Analysis, and the refined features are subsequently fed into a Softmax neural network for classification. The implementation results indicate that the proposed method achieves an accuracy of over 99% in distinguishing between two phases: acicular ferrite and granular ferrite. Furthermore, it attains an accuracy exceeding 86% when classifying three phases: granular ferrite, acicular ferrite, and Widmanstätten ferrite. This suggests that the widely used and conventional k-means clustering method, as a traditional machine learning approach, is incapable of effectively distinguishing microscopic steel phase blocks using extracted texture features. Notably, as of the writing of this paper, no prior research has been conducted on the automatic classification of different ferrite phases, making this study a novel contribution to the field. In this research, an automated classification algorithm for ferrite phase structures in SEM images of steel is proposed using texture feature extraction methods and machine learning models. The dataset comprises images of 1024×768 resolution, which were divided into 128×128 blocks, with classification performed independently for each block. Due to the limited number of blocks available for training machine learning models, data augmentation techniques such as rotation and scaling were applied to increase the dataset size. Various image processing methods were used to extract 128 texture features. These extracted features were then used to classify different ferrite phases using two machine learning models: k-means clustering and the Softmax neural network. Additionally, PCA was employed to reduce feature dimensionality, which positively impacted the classification of granular and acicular ferrite. While k-means clustering, as a conventional and widely used machine learning method, failed to achieve satisfactory classification accuracy, the proposed approach using a smooth maximum neural network demonstrated exceptional performance. Despite the complex and irregular nature of ferrite shapes, the selected features and the proposed algorithm successfully achieved over 99% accuracy for two-phase classification and over 86% accuracy for three-phase classification.

Automated clinical coding, facilitated by artificial intelligence (AI) techniques like natural language processing and machine learning, has emerged as a promising approach to enhance coding efficiency and accuracy in healthcare. This review synthesizes current knowledge about AI-based automated coding of the International Classification of Diseases (ICD), with a focus on its challenges, benefits, and future research directions. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic search was conducted across PubMed, Embase, Scopus, and Web of Science databases on January 1, 2024. Studies discussing challenges, advantages, and research gaps in AI-driven ICD coding were included. Out of 12, 641 identified records, eight studies met the inclusion criteria. These studies highlighted six key challenges: extensive label space, imbalanced label distribution, lengthy documents, coding interpretability issues, ethical concerns, and lack of transparency. Ten major benefits of AI-based ICD coding were identified, including improved decision-making, data standardization, and increased coding accuracy. In addition, eight future directions were proposed, emphasizing interdisciplinary collaboration, transfer learning, transparency enhancement, and active learning techniques. Despite significant challenges, AI-based ICD coding holds substantial potential to revolutionize clinical coding by improving efficiency and accuracy. This review provides a comprehensive synthesis of current evidence and actionable insights for advancing research and practical implementation of automated ICD coding systems.

Objectives: This study aims to develop an Automated Traffic Prediction and Classification (ATPC) system that leverages advanced feature extraction, optimization, and classification techniques to address the related challenges.Methods: This framework takes from the Lebanon traffic and the road traffic severity prediction datasets for training and evaluation. First, a set of features is extracted from the data using the Behavioral Time Scale (BTS) feature extraction model, which identifies traffic occurrence. This algorithm improves prediction accuracy via evaluating the Kendall rank correlation measure, homogeneity, and time-series correlation values. Second, the Monkey Tree Selection Optimization (MTSO) algorithm, which estimates the best fitness value based on the position update of global "monkeys," is used to select the optimal subset of extracted features. Third, these optimal features are used to train the classifier, which reduces processing time while preserving—or even enhancing—prediction accuracy. Finally, the Propagated Deep Recurrent Network (PDRN) model is trained using the selected features to classify samples into 'normal' and 'traffic' categories.Results: Evaluations demonstrate that the proposed system achieves an accuracy of 98.86%, similarity coefficients of up to 98.9%, and an error rate as low as 2.5%, indicating significant improvements in both performance and efficiency. When compared to models such as Decision Tree, Logistic Regression, Random Forest, and XGBoost, the proposed MTSO-PDRN technique demonstrates higher accuracy, kappa coefficient, and specificity, along with a lower error rate. Furthermore, the PDRN method proved highly precise in classifying injury types.Conclusion: The enhanced performance and efficiency of the proposed system are promising, indicating its strong potential for predicting and classifying road traffic conditions to help prevent accidents.

Background: Automatic detectionandclassification of breast masses inmammogramsare still challenging tasks. Today, computeraided diagnosis (CAD) systems are being developed to assist radiologists in interpreting mammograms. Objectives: This study aimed to provide a novel method for automatic segmentation and classification of masses in mammograms to help radiologists make an accurate diagnosis. MaterialsandMethods: Foranefficientmassdiagnosis inmammograms, weproposedanautomaticschemeto perform bothmass detection and classification. First, a combination of several image enhancement algorithms, including contrast-limited adaptive histogram equalization (CLAHE), guided imaging, and median filtering, was investigated to enhance the visual features of breast area and increase the accuracy of segmentation outcomes. Second, the density of discrete wavelet coefficient density (DDWCs), based on the quincunx lifting scheme (QLS), was proposed to find suspicious mass regions or regions of interest (ROIs). Finally, mass lesions that appeared in the mammogram were classified into four categories of benign, probably benign, malignant, and probably malignant, based on the morphological shape. The proposed method was evaluated among 1593 images from the Curated Breast Imaging Subset-Digital Database for Screening Mammography (CBIS-DDSM) dataset. Results: The experimental results revealed that the suspected region localization had 100% sensitivity, with amean of 6. 4 4. 5 false positive (FP) detections per image. Moreover, the results showed an overall accuracy of 85. 9% and an area under the curve (AUC) of 0. 901 for the mass classification algorithm. Conclusion: The present results showed the comparable performance of our proposed method to that of the state-of-the-art methods.