2D-Variation convolution-based generative adversarial network for unsupervised time series anomaly detection: a MSTL enhanced data preprocessing approach

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(7)

Published: April 9, 2025

Language: Английский

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Adaptive estimation of instance-dependent noise transition matrix for learning with instance-dependent label noise

Neural Networks, Journal Year: 2025, Volume and Issue: 188, P. 107464 - 107464

Published: April 13, 2025

Language: Английский

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A novel CNN-GRU-LSTM based deep learning model for accurate traffic prediction

Deleted Journal, Journal Year: 2025, Volume and Issue: 28(1)

Published: April 15, 2025

Language: Английский

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An improved and decentralized/distributed healthcare framework for disabled people through AI models

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 125, P. 441 - 448

Published: April 22, 2025

Language: Английский

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CPJN: news recommendation with a content and popularity joint network

Neural Networks, Journal Year: 2025, Volume and Issue: 185, P. 107177 - 107177

Published: Jan. 17, 2025

Language: Английский

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Interpretable unsupervised neural network structure for data clustering via differentiable reconstruction of ONMF and sparse autoencoder

Neural Networks, Journal Year: 2025, Volume and Issue: 188, P. 107504 - 107504

Published: April 29, 2025

Language: Английский

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Deep learning BiLSTM and Branch-and-Bound based multi-objective virtual machine allocation and migration with profit, energy, and SLA constraints

Sustainable Computing Informatics and Systems, Journal Year: 2025, Volume and Issue: unknown, P. 101128 - 101128

Published: May 1, 2025

Language: Английский

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Metaheuristic assisted hybrid deep classifiers for intrusion detection: a bigdata perspective

Wireless Networks, Journal Year: 2024, Volume and Issue: unknown

Published: July 24, 2024

Language: Английский

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Energy Efficient Real-Time Tasks Scheduling on High-Performance Edge-Computing Systems Using Genetic Algorithm

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 54879 - 54892

Published: Jan. 1, 2024

With an increase in the number of processing cores or systems, high-performance edge-computing system's power consumption along with its computational speed will increase, essentially. However, this comes at expense high-energy utilization. One notable solution to reduce energy these systems is execute slowest feasible so that job's deadline times are met. Unfortunately, method more response time and performance loss. To resolve issue, paper, we propose a scheduling approach associates genetic algorithm (GA) first (FiFeS) technique i.e. GA-FiFeS algorithm. This does not jeopardize real-time tasks' deadlines. The proposes energy-efficient schedule while still ensuring high times. results proposed approach, using plausible assumptions experimental parameters, compared currently in-practice approaches, FiFeS LeFeS (least speed) approaches. Using numerical simulations assumptions, our investigation suggests outperforms terms (~18.56%) (~2.78%). Furthermore, has comparable outcomes taking expected execution as assessment feature for analysis.

Language: Английский

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Exploiting data transmission for route discoveries in mobile ad hoc networks

Wireless Networks, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 31, 2024

Language: Английский

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