Fundus images have been established as an important factor in analyzing and recognizing many cardiovascular and ophthalmological diseases. Consequently, precise segmentation of blood using computer vision is vital in the recognition of ailments. Although clinicians have adopted computer-aided diagnostics (CAD) in day-to-day diagnosis, it is still quite difficult to conduct fully automated analysis based exclusively on information contained in fundus images. In fundus image applications, one of the methods for conducting an automatic analysis is to ascertain symmetry/asymmetry details from corresponding areas of the retina and investigate their association with positive clinical findings. In the field of diabetic retinopathy, matched filters have been shown to be an established technique for vessel extraction. However, there is reduced efficiency in matched filters due to noisy images. In this work, a joint model of a fast guided filter and a matched filter is suggested for enhancing abnormal retinal images containing low vessel contrasts. Extracting all information from an image correctly is one of the important factors in the process of image enhancement. A guided filter has an excellent property in edge-preserving, but still tends to suffer from halo artifacts near the edges. Fast guided filtering is a technique that subsamples the filtering input image and the guidance image and calculates the local linear coefficients for upsampling. In short, the proposed technique applies a fast guided filter and a matched filter for attaining improved performance measures for vessel extraction. The recommended technique was assessed on DRIVE and CHASE_DB1 datasets and achieved accuracies of 0.9613 and 0.960, respectively, both of which are higher than the accuracy of the original matched filter and other suggested vessel segmentation algorithms.

The Internet of Things (IoT) has become increasingly popular, and opened new possibilities for applications in various domains. However, the IoT also poses security challenges due to the limited resources of the devices and its dynamic network topology. Routing attacks on 6LoWPAN-based IoT devices can be particularly challenging to detect because of its unique characteristics of the network. In recent years, several techniques have been proposed for detecting routing attacks, including anomaly detection. These techniques leverage different features of network traffic to identify and classify routing attacks. This paper focuses on routing attacks that target the Routing Protocol for Low-Power and Lossy Networks (RPL), which are widely used in 6LoWPAN-based IoT systems. The attacks discussed in this paper can be categorized as either inherited from Wireless Sensor Networks or exploiting vulnerabilities unique to RPL (known as RPL-specific attacks). The paper describes various RPL attacks, including Flood Attacks, Data-DoS/DDoS Attacks, Wormhole Attacks, RPL Rank Attacks, Blackhole Attacks, Version Attacks, and Sinkhole Attacks. In this paper, a novel Hybrid Intrusion Detection System (HIDS) that combines a decision tree classifier and a one-class Support Vector Machine classifier is proposed to detect routing attacks. The HIDS draws on the strengths of both a Signature Intrusion Detection System (SIDS) and an Anomaly-based Intrusion Detection System (AIDS) to identify routing attacks with a high degree of accuracy and a low false alarm rate. The routing dataset, which features genuine IoT network traffic and various kinds of routing attacks, was used to test the proposed HIDS. According to the findings, the hybrid IDS proposed in this study outperforms SIDS and AIDS approaches, with higher detection rates and lower false positive rates.

Federated Learning (FL) is revolutionizing Machine Learning (ML) by enabling devices in different locations to collaborate and learn from user-generated data without centralizing it. In dynamic and non-stationary environments like Internet of Things (IoT), Concept Drift (CD) is the phenomenon of data changing/evolving over time. Traditional FL frameworks struggle to maintain performance when local data distributions evolve, as they lack mechanisms for detecting and adapting to concept drift. However, the use of FL in such environments, where data changing/evolving continuously and Continual Learning (CL) is required to adapt to concept drift, remains a relatively unexplored area. This study specifically addresses this gap by examining strategies for continuous adaptation within federated systems when faced with non-stationary data. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study systematically reviews existing literature on FL adaptation to concept drift. To the best of our knowledge, this is the first systematic review that consolidates and reinterprets existing studies under the emerging framework of Federated Drift-Aware Learning (FDAL), bridging Federated and Continual Learning research toward adaptive and drift-resilient federated systems. We conducted an extensive systematic survey, including an analysis of state-of-the-art methods and the latest developments in this field. Our study highlights their strengths, weaknesses, and datasets used, identifies key challenges faced by FL systems in these scenarios, and explores potential future directions. Additionally, we categorize the limitations and future directions into major thematic areas that highlight core gaps and research opportunities. The results of this study will support researchers in overcoming the adaptation challenges that FL systems face when dealing with changing environments due to concept drift and serve as a critical resource for advancing adaptive federated intelligence.

Hybrid post-quantum TLS deployments pair classical and post-quantum signature algorithms to resist future quantum adversaries. However, to the best of our knowledge, no active IETF TLS, PQUIP, or LAMPS working-group draft treats CPU-cost adversarial algorithm substitution as a first-class availability threat. We define the Cryptographic Amplification Factor ($\mathrm{CAF}$), a metric for the per-resource cost asymmetry that an adversary induces by forcing a TLS 1.3 server onto a high-cost signature algorithm (SLH-DSA) instead of a low-cost one (ECDSA or ML-DSA). We characterise four adversary classes—insider misconfiguration, supply-chain compromise, remote TLS peer, and protocol man-in-the-middle—demonstrate working proof-of-concept exploits on representative testbeds, and evaluate three defensive primitives under realistic deployment conditions. On the liboqs 0.14.0 reference implementation, ${\mathrm{CAF}}_{r_{\mathrm{cpu}}}$ reaches $\mathrm{12,255}\times$ on Apple M-series ARM64 and $\mathrm{14,272}\times$ on AWS Graviton3 (3904–$\mathrm{14,272}\times$ across five microarchitectures). Under a representative two-worker hardware security module (HSM) calibration ($\lambda =13.5$ TPS), substitution drives the modelled queue utilisation $\rho$ past the stability boundary ($1.44\times {10}^{-4}\to 1.787$), a model-independent saturation; on the testbed, we observe server-CPU saturation, accept-queue exhaustion, and dropped connections. Two enforcement defences and a Parallel Signing Architecture provably bound $\rho \le \alpha <1$ by admission control, regardless of attack rate. The exposed surface is concentrated today in high-assurance and financial-sector deployments and widens as FIPS 206 finalises.

Decision-making problems in complex environments are often characterised by uncertainty, vagueness, and dynamically evolving information. In such contexts, decision makers may express hesitant and fluctuating evaluations over time, which cannot be adequately captured by classical hesitant fuzzy frameworks. To address this limitation, time-sequential hesitant fuzzy sets (TSHFSs) have been introduced as an effective tool for modelling temporal hesitancy. However, the development of information measures for TSHFSs, particularly entropy measures for quantifying uncertainty and deriving criteria weights, remains limited. In this paper, we propose a novel class of entropy measures for TSHFSs by constructing transformation mechanisms based on proximity-driven formulations derived from similarity structures. The proposed measures are developed using arithmetic and algebraic operators to capture the dispersion of information across time sequences, enabling a more refined representation of temporal uncertainty. These entropy measures are further integrated into a multi-criteria decision-making (MCDM) framework, where they are employed to determine criteria weights under incomplete information and combined with the TOPSIS method for ranking alternatives. The effectiveness of the proposed framework is validated through comparative analysis with existing TSHFS entropy measures and sensitivity analysis under varying decision conditions. The results demonstrate that the proposed measures maintain ranking consistency while providing improved discrimination and interpretability of alternatives. In particular, the framework effectively captures fluctuating hesitancy and enhances the robustness of decision outcomes in dynamic environments. The proposed approach contributes to the advancement of TSHFS-based decision analysis by offering a mathematically grounded and practically applicable entropy-driven framework for handling time-dependent uncertainty in complex decision-making problems.

Quantum machine learning is commonly benchmarked by matching a quantum head’s parameter count to that of a classical baseline. We show that this control is insufficient. We compare a 32-parameter parameterized quantum circuit (PQC) head against a nominally matched 31-parameter multi-layer perceptron (MLP) on three paraphrase tasks—the Microsoft Research Paraphrase Corpus (MRPC), Paraphrase Adversaries from Word Scrambling (PAWS), and Quora Question Pairs (QQP)—and document two pitfalls that inflate an apparent quantum deficit. First, under the tested protocol, unweighted binary cross-entropy on imbalanced data leads to near-all-positive prediction, producing an illusory equivalence. Second, the surviving gap traces to a readout that sits offset from the decision threshold at initialization ($⟨Z_0⟩=+0.922$), and no circuit parameter acts as a threshold shift that could recenter it. A pre-specified calibration experiment removes 61% to 76% of the gap, and a training-free probe detects the condition in one forward pass. Threshold-free metrics then changed our own conclusions. On PAWS, both heads rank barely above chance (area under the receiver operating characteristic curve, AUC, at most $0.508$), so the difference there is threshold placement. Only QQP retains a residual, and a post hoc control doubling the qubit count narrows it by $42\%$ without removing it (paired $p=0.0009$). This pattern is consistent with a capacity or optimization limitation, but does not distinguish between them. Four of five pre-specified predictions failed. Parameter roles and readout offsets must be audited, not only counts.