Accurate segmentation of brain tumors in magnetic resonance imaging is essential for diagnosis, treatment planning, and surgical guidance. Although deep learning-based segmentation methods have achieved strong performance, their clinical adoption remains limited due to the lack of reliable uncertainty estimation. To address this challenge, we propose an uncertainty-aware framework for multi-class brain tumor sub-region segmentation and evaluate it across several U-Net variants. The study investigates how architectural design choices influence segmentation accuracy and prediction reliability. Specifically, we analyze Attention U-Net, Residual U-Net, squeeze-attention U-Net, and convolutional block attention module U-Net, each incorporating distinct attention mechanisms and feature reuse strategies. All architectures are integrated within a unified Bayesian inference framework using Monte Carlo dropout at test time to approximate posterior sampling. This approach enables pixel-wise and class-specific estimation of epistemic uncertainty along with segmentation outputs. Experiments conducted on the BraTS 2020 dataset show that segmentation accuracy across U-Net variants remains competitive for whole tumor, tumor core, and enhancing tumor regions. However, the generated uncertainty maps reveal distinct spatial patterns, particularly near ambiguous tumor boundaries and anatomically complex regions. These findings demonstrate that uncertainty-aware evaluation provides complementary insights beyond conventional accuracy metrics, improving model interpretability and reliability. Clinically, the integration of Bayesian uncertainty estimation with multiple U-Net backbones produces interpretable segmentation results accompanied by pixel-wise confidence information. Such uncertainty maps can assist radiologists in identifying low-confidence regions, supporting more informed decision-making, reducing inter-observer variability, and increasing trust in automated brain tumor segmentation systems.

The current study proposes an improved DenseNet-based Sequential Multimodal Biometric Authentication System, involving face and ear modality for better human identification. The architecture is composed of three convolutional layers and two dense layers, which are optimized for obtaining the discriminative spatial representations in 200 × 200 pixel facial and ear images. Evaluation is performed based on strict 5-fold subject disjoint cross-validation data to ensure the unbiased assessment. The model proposed attained a steady classification accuracy of 97.1 ± 0.79%, and balanced values for Precision, Recall and F1-score under controlled validation conditions, while the Performance analysis including False Acceptance (FAR), False Rejection (FRR) and Equal Error Rate (EER) showed that the EER found is around 1.05% at the optimum operating value. Comparative experiments between parallel feature concatenation and sequential verification techniques show that the sequential framework yields decreased FAR, when compared to the parallel framework, without having a detrimental effect on overall accuracy, while the Statistical validation by analysis of variance shows that the incremental architectural improvements have a significant impact on performance improvements. Findings of this analysis show a “score distribution” that both “single-trait and traditional multifactor systems” exceed the presentation of a novel method for Nex-G authentication solutions. This study advances biometric security by demonstrating how multimodal fusion may address the increasing global demand for robust and privacy-aware authentication methods, thereby setting a standard for intelligent multimodal recognition systems.

Seismic-induced vibration mitigation in multi-degree-of-freedom (MDOF) building structures calls for efficient and adaptive control strategies. Fractional-order PIλDμ controllers allow increased flexibility in tuning when compared with the conventional proportional integral derivative (PID) controllers. However, considering highly dynamic seismic conditions, selecting their optimal parameters remains challenging. A Particle Swarm Optimization (PSO)-based fractional order controller approach is presented in this paper for the optimal tuning of five key parameters of the PIλDμ controller using a two-story building model subjected to the 1940 El Centro earthquake. The controller structure is formulated using fractional-order calculus, while PSO is utilized to determine optimal gains and fractional orders without prior knowledge about the model. Simulation results indicate that the proposed fractional order proportional integral derivative (FOPID) controller is effective in suppressing structural vibrations, outperforming both classical PID control and the uncontrolled case. It is demonstrated that incorporating intelligent optimization techniques along with fractional-order control can be a promising approach toward enhancing seismic resilience in civil structures.

Accurate differentiation of brain tumors in MRI images is essential for diagnosis, therapy planning, and surgical intervention. We suggest an Attention U-Net that is aware of uncertainty for binary brain tumor segmentation in this work. By adding spatial attention mechanisms, our model improves the traditional U-Net and helps it focus better on tumor areas that matter. In addition to segmentation maps, the model also makes uncertainty maps that show areas where the prediction may not be as reliable. Our method does very well on the BraTS 2020 dataset and gives meaningful uncertainty responses in areas where the tumor boundaries are not clear. This extra layer of uncertainty gives clinical decision-makers useful confidence cues, which makes AI-based segmentation more reliable and easy to understand.

In the realm of 3D curve reconstruction, Non-Uniform Rational B-Splines (NURBSs) offer a versatile mathematical tool due to their ability to precisely represent complex geometries. However, achieving high fitting accuracy in stereo-based applications remains challenging, primarily due to the nonlinear nature of weight optimization. This study introduces an enhanced iterative strategy that leverages the geometric significance of NURBS weights to incrementally refine curve fitting. By formulating an inverse optimization problem guided by model deformation principles, the proposed method progressively adjusts weights to minimize reprojection error. Experimental evaluations confirm the method’s convergence and demonstrate its superiority in fitting accuracy when compared to conventional optimization techniques.

The rise of Internet of Things (IoT) devices has brought about an increase in security risks, emphasizing the need for effective anomaly detection systems. Previous research introduced a dynamic voting classifier to overcome overfitting or inaccurate accuracies caused by dataset imbalance. This article introduces a new method for IoT anomaly detection that employs a hybrid voting classifier, which combines several machine learning models. To solve the overfitting and class weight issues, an adaptive voting classifier is used that adjusts weights according to the highest preference for accuracy. The developing voting system increases the effectiveness of more accurate classifiers, enhancing the group's overall capability. A proposed combined classifier combines Logistic Regression, AdaBoost, Gradient Boosting, and Multi-Layer Perceptron models using a soft voting method. To develop and assess this method, the CIC-IoT-2023 dataset is utilized, which contains 33 types of IoT attacks across 7 categories. This process includes thorough data preprocessing and feature selection from a pool of 42 available attributes. The performance of this approach is measured against individual classifiers across binary, 8-class, and 34-class classification tasks. The results highlight the effectiveness of the hybrid model. It achieves 98.95% accuracy, 76.72% recall, and 72.01% F1-score in the 34-class problem, surpassing the performance of all individual models. For the 8-class task, the hybrid classifier attains 99.39% accuracy, 90.89% recall, and an 83.01% F1-score. This demonstrates the high potential of the hybrid approach for IoT anomaly detection.

An investigation of the impact that watermarking has on image statistics is presented here. This paper provides a fresh methodology that is based on the degrees of visibility graphs, in contrast to other methods that investigate variations in intensity levels. The experiment utilizes the use of twelve standard images in addition to three watermarks. The findings indicate that the conventional method of intensity difference analysis produces distributions that are inconsistent across images. As an alternative, the method that has been offered, which makes use of degrees of visibility graphs, displays normal distributions that are more consistent across all images, regardless of the watermark. In light of this, it appears that the degrees of visibility graph provides a promising independent metric for watermarking analysis that is independent of both the image and the watermark. According to the findings, the difference in degrees between the original and watermarked images occurs according to a normal distribution, with the mean falling somewhere between - 0.98 and - 25.93. This range appears to be quite broad, and the reason for this is the outlier. After the outliers have been removed, this range is now - 0.98 to - 3.54 degrees. Overall, the study indicates that analyzing the degrees of visibility graph is a more robust and informative method of evaluating the effectiveness of watermarking than directly comparing intensity levels.

Due to the ever increasing number of closed circuit television (CCTV) cameras worldwide, it is the need of the hour to automate the screening of video content. Still, the majority of video content is manually screened to detect some anomalous incidence or activity. Automatic abnormal event detection such as theft, burglary, or accidents may be helpful in many situations. However, there are significant difficulties in processing video data acquired by several cameras at a central location, such as bandwidth, latency, large computing resource needs, and so on. To address this issue, an edge-based visual surveillance technique has been implemented, in which video analytics are performed on the edge nodes to detect aberrant incidents in the video stream. Various deep learning models were trained to distinguish 13 different categories of aberrant incidences in video. A customized Bi-LSTM model outperforms existing cutting-edge approaches. This approach is used on edge nodes to process video locally. The user can receive analytics reports and notifications. The experimental findings suggest that the proposed system is appropriate for visual surveillance with increased accuracy and lower cost and processing resources.

This study investigates the performance of a polypropylene myristic acid-modified copper (Cu) superhydrophobic coating under various conditions, including immersion in NaCl solution, abrasion tests, pH levels, and temperature variations. We utilized specific machine learning models, such as random forest (RF) and XGBoost, to predict the wettability behavior of materials under varying voltage and temperature conditions. Gaussian noise data augmentation was employed to improve the model accuracy and prevent overfitting. The RF model demonstrated strong generalization capabilities, with consistently low MSE values and high R-2 values across training and testing data sets. In contrast, the XGBoost model showed a slight performance decline when transitioning from training data to testing data, indicating potential generalization limitations. For variables such as immersion days, abrasion cycles, pH levels, and temperatures, multiple polynomial regression models were developed to predict the contact angle, showing a high degree of alignment with experimental data. The predictions of contact angle after 122 days of immersion in the NaCl solution (3.5 wt %), 350 abrasion cycles (each cycle of 18 cm), and exposure to 400 degrees C indicated sustained hydrophobic properties. The predicted contact angles were 110 degrees for immersion in NaCl, 400 degrees C, and for abrasion cycles it is 99 degrees. These predictions closely match with the experimental results, which showed contact angles of 103.7 degrees for immersion, abrasion cycles and 97 degrees for temperature exposure. The coating's robustness is attributed to its micro/nanostructured surface, which traps air and reduces water contact, and the chemical stability imparted by polypropylene myristic acid modification, ensuring low surface energy and durability. This study demonstrates the effectiveness of higher-degree polynomial models in accurately predicting the performance of superhydrophobic coatings, underscoring the potential of advanced modeling techniques in the development of durable, high-performance coatings.

Image moments are an important tool used for image reconstruction. An image can be represented in terms of image moments, which is known as image reconstruction from its moments. To construct an image from the moments, a question often arises that how many moments are required for the reconstruction of image. Theoretically, many image moments may be required for accurately reconstructing an image. However, since image moment construction can be computationally challenging, often in practice only a finite number of moments are used for the image reconstruction. The difference in reconstructed and original image for many numbers of images can lead to a satisfying answer. However, accurate reconstruction may not often be possible, and we are often relying on other approaches to find similarity between the original image and reconstructed image. We used a similarity based on a topological data analysis tool known as the persistence diagram, determining the bottleneck distance between the original and the reconstructed image as the measure of similarity. Our investigation was conducted on the common images utilized in image processing tasks. The findings indicate that there is no direct correlation between the number of moments and the quality of image reconstruction. It is necessary to choose an appropriate number of moments, which may be very small, instead of calculating a high number of image moments. Received: 30 April 2024 | Revised: 2 July 2024 | Accepted: 29 July 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Manoj Kumar Singh: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization, Project administration. Deepika Saini: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Sanoj Kumar: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.

Currently, rational curves such as the Non-Uniform Rational B-Spline (NURBS) play a significant role in both shape representation and shape reconstruction. NURBS weights are often real in nature and are referred to as challenging to assign, with the exception of conics. ‘Matrix Weighted Rational Curves’ are the expanded form of rational curves that result from replacing these real weights with matrices, or matrix weights. The only difference between these curves and conventional curves is the geometric definition of the matrix weights. In this paper, MW-NURBS curves are used to reconstruct space curves from their stereo perspectives. In particular, MW-NURBS fitting is carried out in stereo views, and the weight matrices for the MW-NURBS curves are produced using the normal vectors provided at the control points. Instead of needing to solve a complicated system, the MW-NURBS model can reconstruct curves by choosing control points and control normals from the input data. The efficacy of the proposed strategy is verified by using many examples based on both synthetic and real images. The various error types are compared to those of conventional methods like point-based and NURBS-based approaches. The results demonstrate that the errors acquired from the proposed approach are much fewer than those obtained from the point-based method and the NURBS-based method.

In recent years, there has been a substantial surge in the application of image watermarking, which has evolved into an essential tool for identifying multimedia material, ensuring security, and protecting copyright. Singular value decomposition (SVD) and discrete cosine transform (DCT) are widely utilized in digital image watermarking despite the considerable computational burden they involve. By combining block-based direct current (DC) values with matrix norm, this research article presents a novel, robust zero-watermarking approach. It generates a zero-watermark without attempting to modify the contents of the image. The image is partitioned into non-overlapping blocks, and DC values are computed without applying DCT. This sub-image is further partitioned into non-overlapping blocks, and the maximum singular value of each block is calculated by matrix norm instead of SVD to obtain the binary feature matrix. A piecewise linear chaotic map encryption technique is utilized to improve the security of the watermark image. After that, the feature image is created via XOR procedure between the encrypted watermark image and the binary feature matrix. The proposed scheme is tested using a variety of distortion attacks including noise, filter, geometric, and compression attacks. It is also compared with the other relevant image watermarking methods and outperformed them in most cases.

In this study, a method for classifying textures based on image visibility graphs and topological data analysis is given. To improve texture classification result, we propose a new method that uses topological data analysis together with image visibility graphs. The present study involves the analysis of the degree distribution obtained from the image visibility graph along with the extraction of seven distinct topological features that are subsequently utilized for classification purposes. The proposed approach has been evaluated on established image texture datasets, such as the Salzburg texture image dataset. The results show an improvement in performance, suggesting the possibility of integrating graph-based techniques and topological characteristics in the process of texture classification.

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.