Advanced neural network-based buckling analysis of functionally graded plates with elliptical holes: boosted encoder-decoder architecture

Data-driven multi-objective optimization with neural network-based sensitivity analysis for semiconductor devices

The growing use of artificial intelligence (AI) in physical education (PE) has led to an urgent need to develop robust methodologies that can be used to choose the most suitable algorithms in uncertain and vague environments. This paper introduces a new hybrid decision-making (DM) model that incorporates the weighted aggregated sum product assessment (WASPAS) technique into the q-rung linear Diophantine fuzzy set (q-RLDFS) framework. The primary objective is to address the gap in the lack of structured and uncertainty-resistant methods for assessing AI models based on multiple, frequently conflicting criteria in the domain of PE. The proposed model presents a two-layer framework, where the WASPAS method enables a flexible scoring system by combining both the weighted sum model (WSM) and the weighted product model (WPM). On the other hand, the q-RLDFS framework facilitates high-order fuzzy modelling and accounts for hesitation in expert ratings, thereby making the decision results more interpretable and robust. To determine the effectiveness of the model, five AI algorithms named convolutional neural network-based motion analysis (CNN-MA), reinforcement learning based training optimizer (RL-TO), expert system for exercise prescription (ES-EP), hybrid AI tutor with natural language processing (HAI-NLP), and wearable sensor data mining algorithm (WSDMA) are evaluated against eight key criteria relevant to PE. Results revealed that CNN-MA is the most effective solution to implement, followed by RL-TO. The sensitivity and comparative analysis are thoroughly conducted to determine the validity of the model in terms of its robustness and reliability. The research offers distinct practical implications and actionable recommendations to educators, administrators, and policymakers, informing the strategic implementation of AI technologies within the PE domain. In general, the research makes a significant contribution to the adoption of AI in PE, as it provides a scalable, transparent, and practically applicable decision-support model.

Log analysis plays a crucial role in understanding system behavior and detecting anomalies in various domains. This article presents the results of our autoencoder type neural network trained for log analysis and anomaly detection based on the reconstruction error of each entry. Furthermore, the article focuses on the challenge of text vectorization, specifically employing a bag-of-words method for representing log messages in a numerical format suitable for neural network models. The bag-of-words approach captures the frequency-based representation of log messages. The study also acknowledges the limitations of this method in capturing semantic information. It is important to note that this study represents only a starting point in the journey of log analysis and text vectorization. The field encompasses a wide range of existing working methods, and further research and exploration are necessary to uncover the full potential of log analysis techniques for various applications.

Air pollution is one of the most common concerns for human health. Air contamination has many pollutants like NO2, PM2.5, PM10, and other harmful gases, which are harmful to the living system on the earth. The quality of air gets affected by these particulate matters. This paper proposes a neural network-based prediction model for detection and prediction of air pollution. Performance measurement of Bayesian Regularization (BR) algorithm, Levenberg–Marquardt (LM) algorithm, and Scaled Conjugate Gradient (SCG) algorithm has been analyzed.

The factor evaluation for sustainability implementation in post-disaster reconstruction projects was conducted using artificial neural network (ANN)-based sensitivity analysis. The current study involves the utilization of Levenberg-Marquardt algorithm (LMA) for the ANN model development. The findings demonstrated that the governing network structure is 10-3-1 utilizing input parameters (IP) including waste management (Sl), environmental impact management (S2), support of management (S3), construction cost (S4), public health and safety (S5), user security (S6), noise pollution (S7), energy consumption (S8), public services (S9), and recycling (S10). The results revealed that the governing model (GM) has a performance of correlation (R) = 0.99813 and Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) of 0.00110 and 0.4240/0, respectively. Moreover, a sensitivity analysis (SA) using Garson's algorithm (GA) reveals a trend of the relative value (RV) was observed to be construction cost > public services > support of management > user security > environmental impact management> noise pollution> public health and safety> energy consumption > recycling > waste management wherein the construction cost is the most influential parameter to the sustainability rating in post-disaster reconstruction projects (PDRP). The study shows how well ANN can detect the crucial elements that determine a project's sustainability rating in the post-disaster reconstruction projects.

Global or continental-scale land cover mapping with remote sensing data is limited by the spatial characteristics of satellites. Subpixel-level mapping is essential for the successful description of many land cover patterns with spatial resolution of less than /spl sim/1 km and also useful for finer resolution data. This paper presents a novel adaptive resonance theory MAP (ARTMAP) neural network-based mixture analysis model-ART mixture MAP (ART-MMAP). Compared to the ARTMAP model, ART-MMAP has an enhanced interpolation function that decreases the effect of category proliferation in ART/sub a/ and overcomes the limitation of class category in ART/sub b/. Results from experiments demonstrate the superiority of ART-MMAP in terms of estimating the fraction of land cover within a single pixel.

In this paper, the proxy model of the functional function is constructed by using the BP neural network fitting method, and the inertia weight index of the traditional particle swarm algorithm is improved, and the slope safety factor solution method of the improved particle swarm algorithm combined with the BP neural network is formed, which is verified and solved through practical engineering cases, and the method of fitting the response surface of the functional function using the BP neural network is proposed. In this study, the LSTM deep learning network model is introduced, combined with the chaotic characteristic analysis of slope deformation. An effective slope deformation prediction method was successfully constructed. This method not only has high prediction accuracy and generalization ability, but also has important practical value and application prospects. In the future, we will continue to study the application of LSTM model in slope deformation prediction, in order to further improve the prediction accuracy and practicability. It can effectively solve the problem that the functional function cannot be explicitly expressed when solving the reliability of complex structures.

Ash content is a critical coal quality indicator that determines coal grade and commercial applications, making its rapid and accurate measurement essential. Traditional slow ashing methods are operationally cumbersome, time-consuming, and pose environmental and safety concerns. Hyperspectral imaging technology offers a promising alternative with its non-contact detection and superior spatial-spectral information fusion capabilities. However, existing machine learning and deep learning approaches, while capable of modeling complex spectral-analyte relationships, often overlook spatial information and struggle to effectively capture the heterogeneous distribution characteristics of ash minerals in coal samples. Therefore, this study proposes a fast estimation method of hyperspectral coal ash based on graph neural network, using a hybrid GCN-GAT encoder and an adaptive spatial-spectral edge construction strategy. This method models the mineral distribution pattern by constructing a spatial graph, constructs a spectrogram to capture the mineral spectral characteristics, and optimizes it for different ash content ranges. In the experiments of concentrated coal (ash range from 9% to 18%) and tailings coal samples (ash range from 49% to 77 %), the average absolute errors of this method are 0.862 % and 1.18 %, respectively, which are significantly better than traditional machine learning methods and deep learning methods. The interpretability analysis shows that the absorption characteristics of hydroxyl-containing minerals corresponding to the 1200-1400 nm band contribute the most to the ash estimation, which verifies the effectiveness of the graph neural network in coal quality analysis and provides practical guidance for coal automatic characterization and industrial quality control.

Lung cancer continues to be a leading contributor to global cancer mortality, highlighting the need for rapid and reliable diagnostic techniques to enhance patient survival rates. Non-Small Cell Lung Cancer (NSCLC), the most prevalent subtype, requires advanced computational methods to support accurate classification and clinical interpretation. Despite remarkable progress in neural network-based imaging analysis, the opacity of these models often limits their clinical acceptance. This work introduces an explainable neural network framework designed to categorize NSCLC subtypes while ensuring interpretability in its decision process. The model integrates knowledge transfer from pre-trained DenseNet, ResNet50, and VGG16 architectures to distinguish among adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and normal tissue in histopathological scans. To promote transparency, the GradCAM technique is incorporated to generate color-weighted overlays that reveal the image regions most influential in each classification. Experimental findings demonstrate that DenseNet achieved an overall accuracy of 93% with balanced precision, recall, and F1-scores across all subtypes. The interpretive heatmaps confirmed that the model's focus corresponded with pathologically relevant regions, improving both trust and diagnostic utility. The proposed approach highlights the potential of explainable neural networks as dependable tools for computer-assisted lung cancer diagnosis.

The efficacy of a novel fuzzy neural network classifier for the characterization of ultrasonic liver images based on texture analysis techniques is investigated. Classification features are extracted with the use of image texture analysis techniques such as fractal dimension texture analysis, spatial gray-level dependence matrices, gray-level difference statistics, gray-level run-length statistics, and first-order gray-level parameters. These features are fed to a neural network classifier based on geometrical fuzzy sets. Starting from the construction of the Voronoi diagram of the training patterns, an aggregation of Voronoi regions is performed, leading to the identification of larger regions belonging exclusively to one of the pattern classes. The resulting scheme is a constructive algorithm that defines fuzzy clusters of patterns. Based on observations concerning the grade of membership of the training patterns to the created regions, decision probabilities are computed through which the final classification is performed.

A simplified example of building a neural network using TensorFlow is presented in this article.A single-layer neural network, trained with a small dataset of seven data points and optimized with Stochastic Gradient Descent and a mean squared error loss function, is defined for predicting house prices based on the number of rooms.A prediction for a new value is then made with the trained model.This example serves as a demonstration of the potential for neural networks, in combination with TensorFlow, to bring about increased efficiency, productivity, and improved decision making in business and the economy through automation of tasks and processes.This article demonstrates how the use of neural networks can bring increased efficiency and productivity in business operations through the automation of data processing tasks.The implications and possibilities of using neural networks are also analyzed.This paper will serve as a resource for researchers and practitioners interested in using TensorFlow and neural networks for machine learning and data processing.By automating data analysis and prediction, businesses can make more informed decisions and respond more quickly to market changes, resulting in improved financial performance.

Diabetic Retinopathy (DR) is a leading cause of vision im-pairment and blindness among diabetic patients worldwide.Early detec-tion and timely intervention are crucial to preventing severe visual loss.Convolutional Neural Networks (CNNs) have emerged as a powerful tool for image analysis and have shown great potential in medical image di-agnostics.This study explores the application of CNNs for the early diagnosis of DR using retinal fundus images.By leveraging deep learn-ing techniques, our CNN model is trained to automatically detect and classify various stages of DR with high accuracy.The proposed method utilizes a large dataset of annotated retinal images to ensure robustness and generalizability.Experimental results demonstrate that the CNN-based approach significantly outperforms traditional methods, offering a reliable and efficient solution for early DR detection.This advancement in automated image analysis can facilitate timely and precise diagnosis, potentially reducing the burden of diabetic complications and improving patient outcomes.

With the surge in population growth, the demand for electricity has escalated, necessitating efficient solutions to enhance the reliability and security of electrical systems. Smart grids, functioning as self-sufficient systems, offer a promising avenue by facilitating bi-directional communication between producers and consumers. Ensuring the stability and predictability of smart grid operations is paramount to evaluating their efficacy and usability. Machine learning emerges as a crucial tool for decision-making amidst fluctuating consumer demand and power supplies, thereby bolstering the stability and reliability of smart grids. This study explores the performance of various machine learning classifiers in predicting the stability of smart grid systems. Utilizing a smart grid dataset obtained from the University of California’s machine learning repository, classifiers such as logistic regression (LR), XGBoost, linear support vector machine (Linear SVM), and SVM with radial basis function (SVM-RBF) were evaluated. Evaluation metrics, including accuracy, precision, recall, and F1 score, were employed to assess classifier performance. The results demonstrate high accuracy across all models, with the Deep Neural Network (DNN) model achieving the highest accuracy of 99.5%. Additionally, LR, linear SVM, and SVM-RBF exhibited comparable accuracy levels of 98.9%, highlighting their efficacy in smart grid stability prediction. These findings underscore the utility of machine learning techniques in enhancing the reliability and efficiency of smart grid systems.

Marker-less analysis of human gait has made considerable progress in recent years. However, developing a gait analysis system capable of extracting reliable and precise kinematic data in a standard and unobtrusive manner remains an open challenge. This narrative review considers the transformation of methods for extracting gait extremity information from videos or images, perceived how analysis methods have improved from arduous manual procedures to semi-objective and objective marker-based systems and then marker-less systems. The gait analysis systems widely used restrict the analysis process with the use of markers, inhibited environmental conditions, and long processing duration. Such limitations can impede the use of a gait analysis system in multiple applications. Advancement in marker-less pose estimation and Q-learning-based techniques are opening the possibility of adopting productive methods for estimating precise poses of humans and information of movement from video frames. Vision-Based gait analysis techniques are capable of providing a cost-effective, unobtrusive solution for estimation of stick images and thus the analysis of the gait. This work provides a comprehensive review of marker-less computer vision and deep neural network-based gait analysis, parameters, design specifications, and the latest trends. This survey provides a birds-eye view of the domain. This review aims to introduce the latest trends in gait analysis using computer vision methods thus provide a single platform to learn various marker-less methods for the analysis of the gait that is likely to have a future impact in bio-mechanics while considering the challenges with accuracy and robustness that are yet to be addressed.KeywordsHuman Gait analysisMarker-less techniquesPose estimationStick imagesAbnormality detection

Dental implant-associated infections increase the risk of implant failure, presenting significant challenges in modern dentistry. The host-microbe interaction plays a crucial role in the development of implant-associated infections. To gain a deeper understanding of the underlying mechanisms, numerous studies have been conducted using in vitro co-culture models of bacteria and human cells or in situ samples. Due to the complexity of the images generated throughout these studies, however, the analysis by means of classical image processing techniques is challenging. This study proposes a workflow—based on two custom Cellpose models—that, for the first time, allows the analysis of microbial surface coverage in microscopy images of fluorescent-stained and co-localized microorganisms and human cells with substantial background signals. The first Cellpose model demonstrated its efficacy in the analysis of individual bacteria within images derived from an 3D implant-tissue-oral biofilm in vitro co-culture model. In combination with the second custom model, which was trained to recognize microcolonies, images obtained from an in situ study could also be automatically segmented. The model’s segmentation accuracy could be further enhanced by acquiring additional training images and improving image quality, making the proposed workflow now valuable for a range of dental implant-related and other co-culture images.