Advanced intrusion detection systems are required due to the quick uptake of cloud computing and the growing complexity of cyber threats, especially Denial of Service and Distributed Denial of Service attacks. Deep learning architectures are becoming more popular because traditional IDS techniques frequently falter in dynamic, large-scale settings. Using datasets including CICIDS2017, NSL-KDD, and UNSW-NB15, this paper assesses the effectiveness of well-known DL architectures for intrusion detection, including Convolutional Neural Network, Recurrent Neural Networks, Long Short-Term Memory, and others. Key performance indicators such as accuracy, precision, and false positive rates are examined to compare the efficacy of these models. The findings show that some designs, like ResNet and Self-Organizing Map, perform well in structured environments but poorly on complicated datasets like KDDTest-21. Another important data gap highlighting the need for more research in this area is that most models do not automatically adjust to unexpected threats. This work aids in the creation of intelligent, scalable systems for changing network environments by evaluating the efficacy of DL-based IDS solutions.

This research investigates the effectiveness of various virtual learning platforms; Zoom, Class Dojo, Google Classroom, and VICBHE (Virtual Interactive Classroom for Bilingual Higher Education), in delivering Core Curriculum and Minimum Academic Standards (CCMAS)-aligned content. The study evaluates platform features such as multimedia support, interactivity, student engagement, ease of assignment distribution, real-time feedback, CCMAS curriculum alignment, ease of use for teachers, and content delivery. This study reveals the platforms ' strengths and weaknesses in facilitating learning outcomes through a combination of regression analysis and experimental data from multiple educational settings. The findings indicate that student engagement and curriculum alignment have the most significant impact on educational success, with Google Classroom emerging as the most effective platform overall. VICBHE, designed to deliver region-specific content, excels in curriculum alignment but faces challenges in interactivity and real-time feedback, limiting its effectiveness in dynamic learning environments. The research concludes with recommendations for platform improvements and strategies for optimizing virtual learning in diverse educational contexts.

Volume estimation of three-dimensional (3D) objects is fundamental in various scientific and engineering fields. While analytical expressions exist for the simple geometric shapes, they become impractical for complex or irregular structures. Monte Carlo simulation is a statistical method which is based on the random sampling, which offers an efficient numerical alternative. This research explores the application of Monte Carlo integration method for the estimation of the volumes of three different 3D objects viz. sphere, cylinder, and cone. The paper elaborates on the mathematical background of the simulation by presenting detailed Python implementations, and analyzes the accuracy, convergence rates, and computational efficiency of the method. The study concludes that the simulation, despite their probabilistic nature, provide an effective and scalable technique for volume estimation, particularly for the shapes without closed-form volume expressions.

The aim of this study is evaluating the efficacy of combining source-free domain adaptation techniques with quantitative uncertainty assessment, aimed at enhancing image segmentation in new domains. The research employs an uncertainty-aware source-free domain adaptation strategy, encompassing the generation of pseudo-labels, their filtration based on entropy and variance of predictions, alongside the involvement of an Exponential Moving Average (EMA) teacher and a tailored loss function. For validation purposes, segmentation models pre-trained on one image dataset were subsequently adapted to another dataset. A comprehensive comparative and ablation analysis, coupled with the visualization of the correlation between segmentation errors and the degree of uncertainty, was conducted. The ablation study corroborated that the complete configuration with the EMA teacher yielded the most favorable results. Data visualization elucidated a direct correlation between high uncertainty and an increased risk for segmentation errors. The findings of this study substantiate the viability of employing uncertainty assessment within the source-free domain adaptation process for clinical dentistry. The proposed methodology facilitates the adaptation of models to new conditions without necessitating retraining, thereby rendering the decision-making process more transparent. Future studies should consider assessing the efficacy of the proposed approach in additional dental visualization tasks, such as implant planning or orthodontic analysis.

In the context of globalisation of the scientific space and the growing role of scientometric indicators, the Hirsch index (h-index) remains one of the key tools for assessing scientific performance. At the same time, the influence of individual factors on the h-index varies significantly across the stages of a scientist's academic career, necessitating their comparative analysis. The purpose of this work is to conduct a comparative study of the Hirsch index and the factors that influence its formation, considering both novice and experienced scientits anaccounting for The study employed descriptive statistics, visual analysis, time-series smoothing (Kendall's method, Pollard's method, exponential and median smoothing), correlation analysis (Pearson's coefficients), and the k-means clustering method. The study was conducted on two large datasets representing novice and experienced scientists. It was found that the average h-index of experienced scientists is 37.78, approximately 2.6 times that of beginner scientists (14.59). Correlation analysis revealed a weak or negative relationship between the h-index and self-citation, with the strongest correlation observed between the h-index and co-authorship (r = 0.68–0.80). Medium identified 6 clusters, including one that unites scientific leaders with extremely high H-index values. The study's results confirm that, in the early stages of a scientific career, geographical and institutional factors play a significant role. In contrast, for experienced scientists, the Hirsch index becomes more predictable and is determined by the quality of scientific publications, the level of citation, and practical cooperation within scientific teams.

Lung cancer is a main reason of death globally, and reducing death rates and enhancing treatment results depend heavily on quick identification. However, medical image diagnosis, including Computed Tomography (CT) scans, is difficult and demands a high level of experience. This research proposes a comprehensive and interpretable Computer-Aided Diagnosis (CAD) structure to identify lung cancer from medical images. The workflow initiates with an Adaptive Savitzky-Golay Filter, effectively enhancing image quality by smoothing while preserving critical structural edges. Hierarchical Adaptive Cluster Refinement (HACR) is then used for precise segmentation, adaptively identifying abnormal lung regions with high accuracy. For feature extraction, the proposed system utilizes the Deep Statistical Gray-Level Co-occurrence Matrix (DS-GLCM) approach, which captures deep spatial and statistical texture features essential for distinguishing cancerous tissue. At last stage, classification is performed using a novel Deep Learning (DL) model Crested Porcupine Optimized (CPO) Channel-Attention (CA) InceptionResNet.  The CPO algorithm is exploited to tune the CA- InceptionResNet model's hyperparameters. To ensure transparency and reliability in clinical use, Explainable AI (XAI) technique- Local Interpretable Model-Agnostic Explanations (LIME) is used for visual interpretability, highlighting regions in CT images that contribute the most to model forecasts, thus boosting clinician trust and decision-making. The entire framework is implemented in Python, and experimental results on benchmark lung cancer imaging datasets demonstrate its superior performance in terms of performance metrics with an accuracy of 98.18% with sensitivity of 95.94 % and specificity of 99.10%. The combination of advanced DL and explainable AI makes the proposed framework a promising solution for lung cancer diagnosis.

Image dehazing is a critical preprocessing step in computer vision, enhancing visibility in degraded conditions. Conventional supervised methods often struggle with generalization and computational efficiency. This paper introduces a self-supervised image dehazing framework leveraging a depth-guided Swin Transformer with hybrid attention. The proposed hybrid attention explicitly integrates CNN-style channel and spatial attention with Swin Transformer window-based self-attention, enabling simultaneous local feature recalibration and global context aggregation. By integrating a pre-trained monocular depth estimation model and a Swin Transformer architecture with shifted window attention, our method efficiently models global context and preserves fine details. Here, depth is used as a relative structural prior rather than a metric quantity, enabling robust guidance without requiring haze-invariant depth estimation. Experimental results on synthetic and real-world benchmarks demonstrate superior performance, with a PSNR of 23.01 dB and SSIM of 0.879 on the RESIDE SOTS-indoor dataset, outperforming classical physics-based dehazing (DCP) and recent self-supervised approaches such as SLAD, achieving a PSNR gain of 2.52 dB over SLAD and 6.39 dB over DCP. Our approach also significantly improves object detection accuracy by 0.15 mAP@0.5 (+32.6%) under hazy conditions, and achieves near real-time inference (≈35 FPS at 256x256 resolution on a single GPU), confirming the practical utility of depth-guided features. Here, we show that our method achieves an SSIM of 0.879 on SOTS-Indoor, indicating strong structural and color fidelity for a self-supervised dehazing framework.

This study extends the one-dimensional anharmonic oscillators by implementing physics-informed transformer networks (PINNs) for multi-dimensional quantum systems. We develop a novel computational framework that combines transformer architecture with physics-informed neural networks to solve the Schrodinger equation for 2D and 3D anharmonic oscillators, addressing both perturbative and non-perturbative regimes. The methodology incorporates attention mechanisms to capture long-range quantum correlations, orthogonal loss functions for eigenfunction discovery, and adaptive training protocols for progressive dimensionality scaling. Our approach successfully computes eigenvalues and eigenfunctions for quartic anharmonic oscillators in multiple dimensions with coupling parameters ranging from weak (λ = 0.01) to strong (λ = 1000) regimes. Results demonstrate superior accuracy compared to traditional neural networks, with mean absolute errors below 10^-6 for ground state energies and the successful capture of symmetry breaking in anisotropic systems. The transformer-based architecture requires 60% fewer trainable parameters than conventional feedforward networks while maintaining comparable accuracy. Applications to molecular vibrational systems and solid-state physics demonstrate the practical utility of this approach for realistic quantum mechanical problems beyond the scope of perturbative methods.

In recent days, we have largely adopted Advanced Large Language Models (LLMs) in educational settings, where we use them as content creators, teaching assistants, and interactive conversation agents. However, the responses generated by these models are often monotonous, verbose, and ambiguous, which can hinder their effectiveness in educational contexts. Addressing these shortcomings, we introduce EduAgent, a multimodal chatbot framework specifically designed to enhance interactive learning in Electrical and Electronics Engineering (EEE) education. EduAgent can respond with pedagogically enhanced answers to electronics-related queries, complemented by relevant images and detailed explanations. It is designed to provide complete, concise, step-by-step responses, ensuring that foundational knowledge is clearly mentioned before diving deep. To develop EduAgent, we constructed a dataset comprising 596 four-turn conversations and a collection of 118 images covering a wide range of EEE concepts. The conversation dataset was used to fine-tune the open-source LLMs and facilitate in-context learning. Both images and their corresponding explanations were integrated into a knowledge base for efficient retrieval. Finally, we evaluated multiple text generation and image retrieval methods using both automatic metrics and human assessments, demonstrating the effectiveness and engagement of our approach.

Global supply chains are increasingly characterized by complexity, uncertainty, and vulnerability to disruptions, creating a pressing need for intelligent, adaptive systems that support decentralized decision-making and real-time control. This paper develops a new framework that integrates Multi-Agent Systems (MAS) with Case-Based Reasoning (CBR) to address these challenges. The model leverages autonomous agents representing suppliers, manufacturers, distributors, retailers, and coordinators that negotiate through defined protocols while embedding CBR mechanisms to retrieve and adapt historical supply chain cases for enhanced responsiveness. An optimization layer, guided by both agent heuristics and case-driven initial solutions, targets key objectives such as cost minimization, lead-time reduction, and resilience improvement. Simulation experiments were conducted under both static and dynamid environments with disruptions including supplier failures and demand fluctuations. Results demonstrate that the proposed framework achieves convergence up to 34- 41% faster than heuristic-only baselines (p<0.05) and sustains solution quality with supply chain sizes increasing from 50 to 500 agents, indicating near-linear scalability. Comparative analysis further highlights adaptability in dynamic contexts and robustness under uncertainty. A case study illustrates practical deployment and validates its effectiveness. The findings provide evidence of a powerful synergy between MAS and CBR, with implications for next-generation supply chain intelligence.