A Hybrid DL Architecture for Improved Generalizability with Self-Adaptive Jaya Optimizer for Diabetic Retinopathy

Rawat, Akhilesh; Kumar, Rajeev

doi:10.1016/j.procs.2024.04.198

Procedia Computer Science

2024

DOI: 10.1016/j.procs.2024.04.198

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A Hybrid DL Architecture for Improved Generalizability with Self-Adaptive Jaya Optimizer for Diabetic Retinopathy

Akhilesh Rawat,

Rajeev Kumar

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2024

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Cited by 2 publications

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Automated brain tumor recognition using equilibrium optimizer with deep learning approach on MRI images

Ragab,

Katib,

Sharaf

et al. 2024

Sci Rep

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Brain tumours (BT) affect human health owing to their location. Artificial intelligence (AI) is intended to assist in diagnosing and treating complex diseases by combining technologies like deep learning (DL), big data analytics, and machine learning (ML). AI can identify and categorize tumours by analyzing brain imaging approaches like Magnetic Resonance Imaging (MRI). The medical sector has been promptly shifted by evolving technology, and an essential element of these transformations is AI technology. AI model determines tumours’ class, size, aggressiveness, and location. This assists medical doctors in making more exact diagnoses and treatment plans and helps patients better understand their health. Also, AI is used to track the progress of patients through treatment. AI-based analytics is used to predict potential tumour recurrence and assess treatment response. This study presents Brain Tumor Recognition using an Equilibrium Optimizer with a Deep Learning Approach (BTR-EODLA) technique for MRI images. The BTR-EODLA technique intends to recognize whether or not a BT presence exists. In the BTR-EODLA technique, median filtering (MF) is deployed to eliminate the noise in the input MRI. Besides, the squeeze-excitation ResNet (SE-ResNet50) model is applied to derive feature vectors, and its parameters are fine-tuned by the design of the EO model. The BTR-EODLA technique utilizes the stacked autoencoder (SAE) model for BT detection. A sequence of experiments is performed to ensure the improved performance of the BTR-EODLA technique. The investigational validation of the BTR-EODLA technique portrayed a superior accuracy value of 98.78% over existing models.

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Automated brain tumor recognition using equilibrium optimizer with deep learning approach on MRI images

Ragab,

Katib,

Sharaf

et al. 2024

Sci Rep

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Optimizing energy hubs with a focus on ice energy storage: a strategic approach for managing cooling, thermal, and electrical loads via an advanced slime mold algorithm

Hai,

Shami,

Saleh

et al. 2024

International Journal of Low-Carbon Technologies

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Amidst the increasing incorporation of multicarrier energy systems in the industrial sector, this article presents a detailed stochastic methodology for the optimal operation and daily planning of an integrated energy system that includes renewable energy sources, adaptive cooling, heating, and electrical loads, along with ice storage capabilities. To address this problem, it applies the 2 m + 1 point estimation method to accurately assess system uncertainties while minimizing computational complexity. The “2 m + 1 point” technique swiftly evaluates unpredictability through Taylor series calculations, capturing deviations in green energy output, and the demand for both electric and thermal energy across power networks, while also considering the oscillating costs associated with senior energy transmission systems. In addition, this article proposes a novel self-adaptive optimization technique, called the enhanced self-adaptive mucilaginous fungus optimization algorithm (SMSMA), dedicated to overcoming the intricate nonlinear challenges inherent in the optimal daily operation of an energy system. The advanced self-adaptive strategy relies on wavelet theory to enhance the capability and effectiveness of the original mucilaginous fungus algorithm in optimizing daily schedules for an integrated energy system. Numerical analyses demonstrate that the introduced stochastic daily scheduling framework, coupled with the SMSMA optimization algorithm, effectively reduces the operating costs of the energy system.

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A Hybrid DL Architecture for Improved Generalizability with Self-Adaptive Jaya Optimizer for Diabetic Retinopathy

Cited by 2 publications

References 27 publications

Automated brain tumor recognition using equilibrium optimizer with deep learning approach on MRI images

Automated brain tumor recognition using equilibrium optimizer with deep learning approach on MRI images

Optimizing energy hubs with a focus on ice energy storage: a strategic approach for managing cooling, thermal, and electrical loads via an advanced slime mold algorithm

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