ABSTRACT This work highlights the critical need for highly reliable systems across various fields of science and technology. It emphasises the significance of achieving maximum reliability while operating within constraints such as cost, weight and volume. To address this challenge, the study introduces an innovative approach to enhance the reliability of Cold Standby systems. The proposed method involves incorporating a combination of cold standby components (RRAP‐CM‐CS) with advanced optimisation techniques, specifically utilising a hybrid of particle swarm optimisation and grey wolf optimiser (HPSGWO). The results obtained from simulations and real‐world tests demonstrate a substantial improvement in the reliability of benchmark systems. The approach not only enhances system reliability but also surpasses the performance of traditional methods. This paper provides valuable insights into a practical and effective strategy for strengthening systems by intelligently mixing components and leveraging optimisation strategies.

Reliable channel state information (CSI) is a critical enabler for low-power Internet of Things (IoT) links and emerging 6G edge devices, where receivers must operate under tight energy/latency budgets and in the presence of non-ideal noise and malicious interference. Deep learning (DL)-based channel estimators can surpass classical LS/MMSE baselines; however, they remain vulnerable to distribution shifts and adversarial attacks targeting pilot observations. This paper proposes a lightweight and robust micro-channel estimation (μ-CE) framework based ondefensive distillation, where a compact student convolutional neural network (CNN) is trained under a higher-capacity teacher estimator using a regression-oriented distillation loss. The resulting μ-CE learns a smoother input–output mapping with reduced gradient sensitivity, improving trustworthiness without sacrificing accuracy or computational efficiency. Using MATLAB-and DeepMIMO-generated 5G NR TDL-C channels, we evaluate robustness under diverse non-adversarial noise types and four white-box gradient-based attacks (Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Momentum Iterative Method (MIM), and Projected Gradient Descent (PGD)). Compared with an undefended CNN, the proposed μ-CE improves normalized mean squared error (NMSE) by approximately 0.5–1 dB under the considered non-adversarial noise conditions (including additive white Gaussian noise (AWGN) at a signal-to-noise ratio (SNR) of 15 dB in the default test setting), limits adversarial NMSE degradation to within 1–2 dB of the clean baseline for moderate perturbation budgets, and reduces attack success rate (ASR) by about 25–40%. Moreover, the distilled μ-CE requires roughly 14× fewer parameters and multiply-accumulate (MAC) operations than the teacher model, supporting practical deployment for robust CSI acquisition in resource-constrained IoT and 6G edge receivers.

The rapid development of the Internet of Medical Things (IoMT) enables automatic recording of surgical reports via interconnected medical devices. However, the reliability of these data is often compromised by missing data, frequently stemming from intermittent IoT communication issues like network disruptions or device malfunctions. This incomplete data critically hinders downstream medical applications and violates implicit network intents related to data integrity and timeliness within an Intent-based Networking (IBN), essential for supporting proactive resource allocation in operating rooms and optimized surgical scheduling. While existing studies focus on addressing missing event types, event time imputation remains a significant, underexplored challenge due to the need to capture implicit temporal contexts and complex cross surgical procedures dependencies. To tackle this for IoMT, we propose a novel Surgical event time IMputation in Intent-Based Networking(SIM-IBN) model. SIM-IBN employs continuous-time LSTMs with attention mechanisms to learn intra-and inter-sequence correlations, effectively recovering missing timestamps. By enhancing data reliability at the source, SIM-IBN serves as a crucial component enabling IBN systems to better fulfill intents for dependable IoMT operations. Rigorous evaluation on real-world surgical event datasets demonstrates SIM-IBN’s superiority over state-of-the-art baselines by up to 11.88% across various missing data scenarios, validating its potential to enable more reliable IoMT systems and enhance operational efficiency in smart healthcare environments.

ABSTRACT A conventional content‐based image retrieval system (CBIR) extracts image features from every pixel of the images, and its depiction of the feature is entirely different from human perception. Additionally, it takes a significant amount of time for retrieval. An optimal combination of appropriate image features is necessary to bridge the semantic gap between user queries and retrieval responses. Furthermore, users should require minimal interactions with the CBIR system to obtain accurate responses. Therefore, the proposed work focuses on extracting highly relevant feature information from a set of images in various natural image databases. Subsequently, a feature‐based learning/classification model is introduced before similarity measure calculations, aiming to minimise retrieval time and the number of comparisons. The proposed work analyses the learning models based on the retrieval system's performance separately for the following features: (i) dominant colour, (ii) multi‐resolution radial difference texture patterns, and a combination of both. The developed work is assessed with other techniques, and the results are reported. The results demonstrate that the implemented ensemble learning model‐based CBIR outperforms the recent CBIR techniques.

Abstract This article outlines an integrated strategy that combines fuzzy multi‐objective programming and a multi‐criteria decision‐making framework to achieve a number of transportation system management‐related objectives. To rank fleet cars using various criteria enhancement, the Fuzzy technique for order of preference by resemblance to optimum solution are initially integrated. We then offer a novel Multi‐Objective Possibilistic Linear Programming (MOPLP) model, based on the rankings of the vehicles, to determine the number of vehicles chosen for the work while taking into consideration the constraints placed on them. The search for optimal solutions to MOPs has benefited from the decades‐long development of classical optimisation techniques. As a result of its potential for use in the real world, multi‐objective optimisation (MOO) under uncertainty has gained traction in recent years. Recently, fuzzy set theory has been used to solve challenges in multi‐objective linear programming. In this paper, we present a method for solving MOPs that makes use of both linear and non‐linear membership functions to maximize user happiness. A hypothetical case study of transportation issue is taken here. This innovative approach improves management for the betterment of transportation networks in smart cities. The method is a more robust and versatile approach to the complex difficulties of contemporary urban transportation because it incorporates the TOPSIS method for vehicle ranking and then using Distance Operator and variable Membership Functions in fuzzy goal programming operation on the selected vehicles. The results provide valuable insights into the strengths and limitations of each technique, facilitating informed decision‐making in real‐world optimization scenarios.

COVID-19, a respiratory illness caused by SARS-CoV-2, has brought attention to a common symptom: loss of smell and taste. Anosmia, a prevalent symptom of COVID-19, varies in severity from mild to severe, necessitating accurate diagnostic tools. The study proposes a novel framework for predicting COVID-19 anosmia severity utilizing YOLOv8 for classification and EigenCAM for interpretability. YOLOv8, optimized for object detection, is adapted for classification tasks using advanced architectural enhancements and mosaic augmentation. EigenCAM provides interpretability by highlighting image regions crucial for predictions, aiding clinical decision-making. Evaluation across multiple YOLOv8 model sizes using DTI and FLAIR modalities reveals robust performance, with the Large model excelling in DTI and the Nano model in FLAIR. Compared to our previous work, the framework significantly enhances accuracy and interpretability in predicting anosmia severity, marking a substantial advancement in medical image analysis. This study underscores the potential of deep learning for precise and interpretable medical diagnostics, offering insights into anosmia severity prediction.

Advancements in deep learning and the Internet of Things (IoT) enable early fire detection through vision-based systems, reducing ecological, social, and economic damage. These systems necessitate lightweight, cost-effective convolutional neural networks (CNNs) for real-time operation. Effective deployment on AI-assisted edge devices is crucial for optimal performance. To mitigate this problem, we present the optimized fire attention network (OFAN) for effective and efficient fire detection. In the re-engineered attention block, we swapped the convolution layers by dilated variants and integrated additional dense layers to capture global context and refine more weight optimization. We calibrate the OFAN for real-time processing using a lightweight and efficient feature extractor backbone model. Additionally, a challenging fire dataset is a critical contribution that contains extremely diverse, blazing, and non-fire, captured in lighting and foggy environments. It advances traditional fire detection samples by considering low-light and foggy conditions. A comprehensive experiment is conducted over three widely used fire detection datasets, and our proposed OFAN outperforms state-of-the-art. The proposed OFAN achieved 96.23%, 96.54% and 94.63% accuracies over BoWFire, FD and the newly proposed DiverseFire dataset, respectively. Our research sets a standard for fire detection over edge devices, offering improved accuracy and better frames per second (FPS) performance.

Atrial Fibrillation (AF) with high mortality rate needs to be monitored and detected accurately. AF is indicated as varying pulse-to-pulse intervals in a PPG signal. To record Photoplethysmography (PPG) signal, wrist-watches are used. AF detection is made using features, discriminating AF from Normal Sinus Rhythm (NSR). The presence of Premature Atrial and Ventricular Contraction$(\text{PAC}/\text{PVC})$in subjects due to its randomness may lead to false AF detection. The proposed methodology utilizes Convolutional Neural Network (CNN) on Time-Frequency spectra (TFS) along with signal processing for classification of PPG signal into AF, NSR and$\text{PAC}/\text{PVC}$, The Poincare plot based$\text{PAC}/\text{PVC}$detection implemented in this paper not only separates$\text{PAC}/\text{PVC}$from NSR and AF but also improves the accuracy of AF and NSR detection. The results for training and testing are validated on UMass Simband dataset (Smartwatch PPG Data with AF, NSR, PAC, PVC) and MIMIC III dataset (Finger Tips Pulse Oximetry PPG data with AF, NSR,$\text{PAC}/\text{PVC})$., The experimental results have shown that the proposed system gives higher accuracy, sensitivity and specificity values for both datasets.

Abstract In this research, a novel customized deep learning model is proposed to detect Tuberculosis (TB) from chest X‐rays (CXR). The model is utilized for three experimentations: (i) classification of CXR image as healthy or TB infected, (ii) sub‐classification of infected images to TB specific manifestations, and (iii) classification of CXR image to thoracic disease manifestations. The National Institute of Health (NIH) CXR is used for experimentation. For the first two experimentations, the subset of the dataset is used containing only 10 TB specific manifestations, whereas, the entire NIH CXR dataset is used for the third experiment. The F1 score for binary classification of TB in experiment 1 is calculated as 0.92 which is higher than the average F1 score of the radiologists. The average accuracy for classifying TB specific manifestations in experiment 2 is recorded as 0.84. Finally, the average accuracy of the thoracic disease classification is recorded as 0.82 in experiment 3. The proposed system outperformed the existing approaches reporting higher AUC for each manifestation. Whereas, to the best of knowledge it is the first such attempt on NIH CXR dataset for TB and TB specific manifestation classification and the proposed system showed promising results.

The emerging of information technology integrated with healthy human lifestyle has been contributing to improving the overall quality of life. Smart devices have facilitated the digital transformation in different domains, primarily, in the health sector for the fields of management, diagnosis, and treatments. The aim of maintaining mental health is to improve human productivity and social functionality. This paper intends to study the benefits of using an intelligent application that uses a text analytical tool to support mental health. It uses innovative sensors and different technologies that are built-in smart devices. It detects anxiety and depression using the camera sensors, and by performing self-testing scales. It is a user-friendly platform providing simple bits of advice, animated breath exercises, and online text-based therapy with registered psychologists supported by the text analytical tool. We tested different machine learning classifications, and the SVM selected showed the best performance with a score of 79.81% in the text analytical tool.

The challenge of processing XML path queries with ancestor-descendant edges is that the structural constraints of the queries over recursive data cannot be easily interpreted. This is likely because the queried data may not have a structure which fits exactly with the structural constraints within the representative queries due to the ancestor-descendant edges. In this paper, we propose OXiPRto handle data with structural recursion by finding invisible recursive patterns of XML Schema with a recursive structure from the data. Our approach exploits the advantage of utilizing XML Schema as it includes all the potential structures of data in an application. The semantics-based knowledge about identifying the connectivity of XML data can be captured from the utilized schema. Experiments are conducted to proof efficiency of OXiPRwhen it processes path queries with recursive structure.

This paper presents an object-based method for indexing recursive structured XML data and process branched queries efficiently. The proposed method is called Object-based Twig Query processing for Recursive data (OTQℜ). It is an extended approach of our existing work in [1] in order to handle recursion in XML data. Our motivation of extending OTQ to OTQℜ is to support many applications that require recursive data structure to be fully functional. OTQℜ is proposed to utilize semantics of XML data to efficiently process branched queries on recursive XML data. The experiments and evaluation are presented to cover variant evaluating points and the efficiency of our approach.

The increased deployment of the XML-based standard for representation and exchange in multi-disciplinary domains has enforced the need for a more effective way to deal with XML query processing. Since very limited attention has been given to the semantic nature of the XML data being processed, this paper focuses on a technique for XML query optimization, called Object-based Twig Query (OTQ), to utilize the semantic structure of the data being queried to process twig queries. A twig query, which is a type of query with multiple branches, requires complex processing due to the joins between multiple paths. Outperforms object-based data partitioning, which aims at leveraging the notion of frequently-accessed data subsets and putting these subsets together into adjacent partitions. It evaluates branched queries through two essential components: (i) OTQ indexing, which uses an object-based connection to construct its indices i.e. Schema index and Data index, and (ii) OTQ processing to produce the final results in optimal time. At the end of this paper, a set of experimental results for the proposed approach on arange of real and synthetic XML data, as well as a comparative study of a similar work in the area, is presented to demonstrate the effectiveness of OTQ optimization.

Due to the growing use of XML data format in global information, an effective XML data management system is needed. An Enabled XML DB is one of the recent widely accepted approaches to store XML documents. This ability coupled with the increase use of XML data in different areas have triggered the need for a better method to structure a large data in order to improve query performance. Issues concerning the ways to efficiently partition large XML documents into a more manageable form are yet to be addressed. At the same time, it is essential to ensure that the partitioning method maintains the preservation of XML data hierarchical structure. For this reason, this paper introduces OXDP that structures large XML data logically by partitioning them into object based XML components. An evaluation is shown to demonstrate the effectiveness of OXDP in XML partitioning which subsequently has the potential of improving query performance in Enabled XML DB environments.