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Application of hybrid capsule network model for malaria parasite detection on microscopic blood smear images
Today, malaria is a dangerous disease caused by Plasmodium parasites and transmitted by the bite of Anopheles mosquitoes. It is estimated that more...
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Handling class overlap and imbalance using overlap driven under-sampling with balanced random forest in software defect prediction
Various techniques in machine learning have been used for building software defect prediction (SDP) models to identify the defective software...
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GAS-AU: an average uncertainty-based general adaptive sampling approach
Currently, surrogate models have been used in various fields due to their ability to save high computational cost of simulation. However, in...
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Classification of Hybrid Quantum-Classical Computing
As quantum computers mature, the applicability in practice becomes more important. Quantum computers will often be used in a hybrid setting, where...
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A smart waste classification model using hybrid CNN-LSTM with transfer learning for sustainable environment
Waste collection, classification, and planning have become crucial as industrialization and smart city advancement activities have increased. A...
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Sampling-Like Dynamics of the Nonlinear Dynamical System Combined with Optimization
When considering computation using physical phenomena beyond established digital circuits, the variability of the device must be addressed. In this...
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The Impact of Data Locality on the Performance of Cluster-Based Under-Sampling
Class-imbalanced classification is one of the most challenging issues in supervised learning. Traditional machine learning classifiers are generally...
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FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling
Data scarcity is a significant obstacle hindering the learning of powerful machine learning models in critical healthcare applications. Data-sharing...
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An adaptive time integration procedure for automated extended-explicit/implicit hybrid analyses
This paper introduces a new explicit-implicit time-marching formulation, presenting a novel hybrid approach for wave propagation analysis. The...
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Development of an offline OOH advertising recommendation system using negative sampling and deep interest network
The out-of-home (OOH) advertising market has been operated exclusively following the know-how of salespeople. Thus, it is difficult to make...
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SRIME: a strengthened RIME with Latin hypercube sampling and embedded distance-based selection for engineering optimization problems
This paper proposes a strengthened RIME algorithm to tackle continuous optimization problems. RIME is a newly proposed physical-based evolutionary...
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Hybrid deep learning model for multi biotic lesions detection in solanum lycopersicum leaves
Farmers are concerned about the automatic detection of lesions and pests that threaten tomato plants. Traditional computer vision and pattern...
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Localization Through Deep Learning in New and Low Sampling Rate Environments
Source localization in wireless networks is essential for spectrum utilization optimization. Traditional methods often require extensive transmitter...
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Hybrid deep learning models for time series forecasting of solar power
Forecasting solar power production accurately is critical for effectively planning and managing renewable energy systems. This paper introduces and...
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On Gaussian Sampling, Smoothing Parameter and Application to Signatures
We present a general framework for polynomial-time lattice Gaussian sampling. It revolves around a systematic study of the discrete Gaussian measure...
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Consensus of hybrid linear multi-agent systems with periodic jumps
The consensus problem for a class of hybrid linear multi-agent systems has been studied. By utilizing the modified H ∞ type Riccati inequalities, both...
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Automated approach for skin lesion segmentation utilizing a hybrid deep learning algorithm
In computer vision segmenting a digital image into multiple segments is a common objective for which convolutional neural networks have been proven...
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An Adaptive Drilling Sampling Method and Evaluation Model for Large-Scale Streaming Data
The sampling methods for real-time and high-speed changing streaming data are prone to lose a large amount of valuable discrete data. The SDDS...
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A Novel Risk Assessment Method Based on Hybrid Algorithm for SCADA
With the frequent occurrence of cyber attacks in recent years, cyber attacks have become a major factor affecting the security and reliability of...
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Hybrid physics-infused 1D-CNN based deep learning framework for diesel engine fault diagnostics
Fault diagnosis is required to ensure the safe operation of various equipment and enables real-time monitoring of associated components. As a result,...
