Machine Learning Optimization Techniques: A Survey, Classification, Challenges, and Future Research Issues

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Март 29, 2024

Язык: Английский

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Exploring machine learning solutions for overcoming challenges in IoT-based wireless sensor network routing: a comprehensive review

Wireless Networks, Год журнала: 2024, Номер 30(4), С. 2647 - 2673

Опубликована: Фев. 29, 2024

Язык: Английский

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Deep Learning Challenges and Prospects in Wireless Sensor Network Deployment

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер 31(6), С. 3231 - 3254

Опубликована: Март 4, 2024

Язык: Английский

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Evolution of Swarm Intelligence: A Systematic Review of Particle Swarm and Ant Colony Optimization Approaches in Modern Research

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 18, 2025

Язык: Английский

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Denoising and segmentation in medical image analysis: A comprehensive review on machine learning and deep learning approaches

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Май 17, 2024

Язык: Английский

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Techniques employed in distributed cognitive radio networks: a survey on routing intelligence

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Апрель 10, 2024

Язык: Английский

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Research on Image Recognition and Classification Algorithms in Cloud Computing Environment Based on Deep Neural Networks

IEEE Access, Год журнала: 2025, Номер 13, С. 19728 - 19754

Опубликована: Янв. 1, 2025

Язык: Английский

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Enhancing IoT security: A comprehensive exploration of privacy, security measures, and advanced routing solutions

Computer Networks, Год журнала: 2025, Номер unknown, С. 111045 - 111045

Опубликована: Янв. 1, 2025

Язык: Английский

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A multi-objective approach to load balancing in cloud environments integrating ACO and WWO techniques

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 8, 2025

Effective load balancing and resource allocation are essential in dynamic cloud computing environments, where the demand for rapidity continuous service is perpetually increasing. This paper introduces an innovative hybrid optimisation method that combines water wave optimization (WWO) ant colony (ACO) to tackle these challenges effectively. ACO acknowledged its proficiency conducting local searches effectively, facilitating swift discovery of high-quality solutions. In contrast, WWO specialises global exploration, guaranteeing extensive coverage solution space. Collectively, methods harness their distinct advantages enhance various objectives: decreasing response times, maximising efficiency, lowering operational expenses. We assessed efficacy our methodology by simulations using a cloud-sim simulator variety workload trace files. comparison well-established algorithms, such as WWO, genetic algorithm (GA), spider monkey (SMO), ACO. Key performance indicators, task scheduling duration, execution costs, energy consumption, utilisation, were meticulously assessed. The findings demonstrate WWO-ACO approach enhances efficiency 11%, decreases expenses 8%, lowers usage 12% relative conventional methods. addition, consistently achieved impressive equilibrium allocation, with balance values ranging from 0.87 0.95. results emphasise algorithm's substantial impact on improving system customer satisfaction, thereby demonstrating significant improvement techniques.

Язык: Английский

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Energy efficient cluster-based routing protocol for WSN using multi-strategy fusion snake optimizer and minimum spanning tree

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июль 22, 2024

Abstract In recent years, the widespread adoption of wireless sensor networks (WSN) has resulted in growing integration internet things (IoT). However, WSN encounters limitations related to energy and node lifespan, making development an efficient routing protocol a critical concern. Cluster technology offers promising solution this challenge. This study introduces novel cluster for WSN. The system selects heads relay nodes utilizing multi-strategy fusion snake optimizer (MSSO) employs minimum spanning tree algorithm inter-cluster planning, thereby extending system’s lifecycle conserving network energy. pursuit optimal clustering scheme, paper also tactics involving dynamic parameter updating, adaptive alpha mutation, bi-directional search optimization within MSSO. These techniques significantly increase convergence speed expand available space. Furthermore, model is presented. generates different objective functions selecting nodes, considering factors such as location, energy, base station distance, intra-cluster compactness, separation, other relevant criteria. When heads, fuzzy c-means (FCM) integrated into MSSO improve performance algorithm. planning routing, next hop selected based on residual direction.The experimental results demonstrate that proposed reduces consumption by at least 26.64% compared protocols including LEACH, ESO, EEWC, GWO, EECHS-ISSADE. Additionally, it increases lifetime 25.84%, extends stable period 52.43%, boosts throughput 40.99%.

Язык: Английский

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