Dear Editor,This letter presents a latent-factorization-of-tensors (LFT)-incorporated battery cycle life prediction framework. Data-driven prognosis and health management (PHM) for battery pack (BP) can boost the safe...
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Dear Editor,This letter presents a latent-factorization-of-tensors (LFT)-incorporated battery cycle life prediction framework. Data-driven prognosis and health management (PHM) for battery pack (BP) can boost the safety and sustainability of a battery management system (BMS),which relies heavily on the quality of the measured BP data like the voltage (V), current (I), and temperature (T).
Damage to the retinal blood vessels is critical in diabetic retinopathy, a progressively emerging health concern that often advances quietly without explicit symptoms. Optical coherence tomography-OCT has emerged as a...
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Damage to the retinal blood vessels is critical in diabetic retinopathy, a progressively emerging health concern that often advances quietly without explicit symptoms. Optical coherence tomography-OCT has emerged as a favored noninvasive imaging technique for diagnosing diabetic retinopathy promptly and accurately. However, timely and precise diagnoses from OCT images are essential in prevention of blindness. Moreover, accurate interpretation of OCT images is challenging. Single model learning debilitates in managing diverse data types and structures, constraining its adaptability to varied environments. Its limitations become apparent in tasks requiring expertise from multiple domains, delaying overall performance. Moreover, learning may exhibit susceptibility to overfitting with large and heterogeneous datasets, resulting in compromised generalization capabilities. In this study, we propose a hybrid learning model for the classification of four distinct classes of retinal diseases in OCT images with improved generalization capabilities. Our hybrid model is constructed upon the well-established architectural foundations of ResNet50 and EfficientNetB0. By pre-training the hybrid model on extensive datasets like ImageNet and then fine-tuning it on publicly available OCT image datasets, we capitalize on the strengths of both architectures. This empowers the hybrid model to excel in discerning intricate image patterns while efficiently extracting hierarchical prediction from various regions within the images. To enhance classification accuracy and mitigate overfitting, we eliminate the fully connected layer from the base model and introduce a concatenate layer to combine two objective learning prediction. A dataset comprising 84,452 OCT images, each expertly graded for illnesses. we conducted training and evaluation of our proposed model, which demonstrated superior performance compared to existing methods, achieving an impressive overall classification accuracy of 97.
Brain tumors,one of the most lethal diseases with low survival rates,require early detection and accurate diagnosis to enable effective treatment *** deep learning architectures,particularly Convolutional Neural Netwo...
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Brain tumors,one of the most lethal diseases with low survival rates,require early detection and accurate diagnosis to enable effective treatment *** deep learning architectures,particularly Convolutional Neural Networks(CNNs),have shown significant performance improvements over traditional methods,they struggle to capture the subtle pathological variations between different brain tumor *** attention-based models have attempted to address this by focusing on global features,but they come with high computational *** address these challenges,this paper introduces a novel parallel architecture,ParMamba,which uniquely integrates Convolutional Attention Patch Embedding(CAPE)and the Conv Mamba block including CNN,Mamba and the channel enhancement module,marking a significant advancement in the *** unique design of ConvMamba block enhances the ability of model to capture both local features and long-range dependencies,improving the detection of subtle differences between tumor *** channel enhancement module refines feature interactions across ***,CAPE is employed as a downsampling layer that extracts both local and global features,further improving classification *** results on two publicly available brain tumor datasets demonstrate that ParMamba achieves classification accuracies of 99.62%and 99.35%,outperforming existing ***,ParMamba surpasses vision transformers(ViT)by 1.37%in accuracy,with a throughput improvement of over 30%.These results demonstrate that ParMamba delivers superior performance while operating faster than traditional attention-based methods.
This research focuses on improving the Harris’Hawks Optimization algorithm(HHO)by tackling several of its shortcomings,including insufficient population diversity,an imbalance in exploration ***,and a lack of thoroug...
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This research focuses on improving the Harris’Hawks Optimization algorithm(HHO)by tackling several of its shortcomings,including insufficient population diversity,an imbalance in exploration ***,and a lack of thorough exploitation *** tackle these shortcomings,it proposes enhancements from three distinct perspectives:an initialization technique for populations grounded in opposition-based learning,a strategy for updating escape energy factors to improve the equilibrium between exploitation and exploration,and a comprehensive exploitation approach that utilizes variable neighborhood search along with mutation *** effectiveness of the Improved Harris Hawks Optimization algorithm(IHHO)is assessed by comparing it to five leading algorithms across 23 benchmark test *** findings indicate that the IHHO surpasses several contemporary algorithms its problem-solving ***,this paper introduces a feature selection method leveraging the IHHO algorithm(IHHO-FS)to address challenges such as low efficiency in feature selection and high computational costs(time to find the optimal feature combination and model response time)associated with high-dimensional *** analyses between IHHO-FS and six other advanced feature selection methods are conducted across eight *** results demonstrate that IHHO-FS significantly reduces the computational costs associated with classification models by lowering data dimensionality,while also enhancing the efficiency of feature ***,IHHO-FS shows strong competitiveness relative to numerous algorithms.
As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive...
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As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.
In recent years, IoT has transformed personal environments by integrating diverse smart devices. This paper presents an advanced IoT architecture that optimizes network infrastructure, focusing on the adoption of MQTT...
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Recently, deep learning neural networks have been widely used in object classification. The process of object classification typically involves extracting features from the point cloud using neural networks and integr...
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The Social Internet of Things (SIoT) is an innovative fusion of IoT and smart devices that enable them to establish dynamic relationships. Securing sensitive data in a smart environment requires a model to determine t...
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Diabetes is a chronic disease characterized by the inability of the pancreas to produce enough insulin or the body’s inability to use insulin efficiently. This disease is becoming increasingly prevalent worldwide and...
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Accurate gas concentration prediction is crucial for air quality management and environmental protection. However, the complexity of spatiotemporal dependencies and data gaps within air quality datasets pose significa...
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