Three-way multi-label classification: A review, a framework, and new challenges

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112757 - 112757

Опубликована: Янв. 1, 2025

Язык: Английский

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Multi-label learning for fault diagnosis of pumping units with one positive label

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113014 - 113014

Опубликована: Март 1, 2025

Язык: Английский

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Evolutionary Cost Analysis and Computational Intelligence for Energy Efficiency in Internet of Things-Enabled Smart Cities: Multi-Sensor Data Fusion and Resilience to Link and Device Failures

Smart Cities, Год журнала: 2025, Номер 8(2), С. 64 - 64

Опубликована: Апрель 9, 2025

This work presents an innovative, energy-efficient IoT routing protocol that combines advanced data fusion grouping and strategies to effectively tackle the challenges of management in smart cities. Our employs hierarchical Data Fusion Head (DFH), relay DFHs, marine predators algorithm, latter which is a reliable metaheuristic algorithm incorporates fitness function optimizes parameters such as how closely Sensor Nodes (SNs) group (DFG) are gathered together, distance sink node, proximity SNs within group, remaining energy (RE), Average Scale Building Occlusions (ASBO), Primary DFH (PDFH) rotation frequency. A key innovation our approach introduction techniques minimize redundant transmissions enhance quality DFG. By consolidating from multiple using algorithms, reduces volume transmitted information, leading significant savings. supports both direct routing, where fused flow straight multi-hop PDF chosen based on influential cost considers RE, ASBO. Given proposed efficient failure recovery strategies, redundancy management, techniques, it enhances overall system resilience, thereby ensuring high performance even unforeseen circumstances. Thorough simulations comparative analysis reveal protocol’s superior across metrics, namely, network lifespan, consumption, throughput, average delay. When compared most recent relevant protocols, including Particle Swarm Optimization-based clustering (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), Novel PSO-based Protocol (NPSOP), achieves very promising results. Specifically, extends lifespan by 299% over PSO-EEC, 264% LDIWPSO, 306% OFCA, 249% NPSOP. It also consumption 254% relative 247% against 253% The throughput improvements reach 67% 59% 53% 50% fusing optimizing sets new benchmark for DFG, offering robust solution diverse deployments.

Язык: Английский

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Optimal meso-granularity selection for classification based on Bayesian optimization

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113552 - 113552

Опубликована: Апрель 1, 2025

Язык: Английский

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