Search Results - "transfer learning"

Accurate and precise detection of lanes and traffic signs is predominant for the safety and efficiency of autonomous vehicles and these two significant tasks should be addressed to handle Indian traffic conditions. There are several state-of-art You Only Live Once (YOLO) models trained on benchmark...

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With advancements in computational resources, artificial intelligence has gained significant attention in motor health monitoring. These sophisticated deep learning algorithms have been widely used for induction motor health monitoring due to their autonomous feature extraction abilities and end-to-...

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Forecasting the future demand for newly launched products has been challenging for supply chain practitioners, often due to the lack of data. However, market surveys and extracting knowledge by examining similar market products to find the behaviour of a new product can be inaccurate and lead to err...

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Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer. Computational methods have been widely explored and have become increasingly accurate in recent years. However, the clinical application of prediction methods is still in it...

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Accurate short- and mid-term blood glucose predictions are crucial for patients with diabetes struggling to maintain healthy glucose levels, as well as for individuals at risk of developing the disease. Consequently, numerous efforts from the scientific community have focused on developing predictiv...

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A deep transfer learning (TL)-based comprehensive eye diagram analysis and diagnosis scheme that can output essential eye diagram parameters, estimate fiber link length, calculate Q-factor, and diagnose device imperfection-induced impairments is proposed. TL can be used to extract system information...

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No-reference Video Quality Assessment (VQA) presents a critical challenge in digital multimedia. This study explores video quality measurement using the DOVER framework combined with a transfer learning method. While existing approaches often rely on end-to-end fine-tuning that requires substantial...

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This study introduces a fall detection system utilizing an affordable consumer smartwatch and smartphone with edge computing capabilities for implementing AI algorithms. Due to the widespread use of these devices, the system as a whole is extremely accepted, easy to use, requires no tuning of any ki...

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The objective is to address the issues of data imbalance, overfitting, and inadequate generalization ability in skin disease datasets and recognition models. The proposed model for the classification of skin diseases is based on the fusion of features and the utilization of transfer learning. The mo...

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Detecting and segmenting damaged wires in substations is challenging due to varying lighting conditions and limited annotated data, which degrade model accuracy and robustness. In this paper, a novel 24 h × 7 days broken wire detection and segmentation framework based on dynamic multi-window attenti...

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Learning is a cumulative process that is shaped by past experiences. Language learning in particular puts in focus learners’ level, age, and background. Thus, acquired habits from the mother tongue will be a point of departure which builds up new learned habits. So transfer from old to new situatio...

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The primary objective of this study is to explore how machine learning techniques can be incorporated into the analysis of material deformation. Neural network algorithms are applied to the study of mechanical properties of wire rods subjected to cold plastic deformations. Specifically, this study e...

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Computer vision is increasingly used for fall safety monitoring, but it struggles in subtle hazard scenarios, causing delays in hazard detection. Therefore, this issue was addressed in this study using transfer learning on pre-trained models that were fine-tuned with target datasets to enhance accur...

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The emergence of deep neural networks has spurred progress in single hyperspectral image (HSI) superresolution. However, mainstream models prioritize network architecture and optimization, overlooking the limitations of HSI datasets’ small scale and potential data imbalance. Direct traini...

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The underwater acoustic (UWA) communication system faces challenges due to environmental factors, extensive multipath spread, and rapidly changing propagation conditions. Deep learning based solutions, especially for orthogonal frequency division multiplexing (OFDM) receivers, have been shown to imp...

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Soybeans are an important commodity in the food and feed industry, where they can be used to produce soy milk or other processed products. However, low-quality seeds can reduce the quality of the processed products and increase production costs. To address this issue, a soybean seed quality classifi...

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Skin diseases are very common all over the world. The examination can be done by photographing the relevant area or taking a tissue sample to diagnose skin diseases. Examining tissue samples allows examination at the cellular level. This study discussed three skin diseases: lichen sclerosus, morphea...

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IntroductionCarotid B-mode ultrasound (U/S) imaging provides more than the degree of stenosis in stroke risk assessment. Plaque morphology and texture have been extensively investigated in U/S images, revealing plaque components, such as juxtaluminal black areas close to lumen (JBAs), whose size is...

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Accurate detection of slope cracks plays a crucial role in early landslide disaster warning; however, traditional approaches often struggle to identify fine and irregular cracks. This study introduces a novel deep learning model, Crack-Net, which leverages a multi-modal feature fusion mechanism and...

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Identification of tomato leaf disease remains difficult because standard approaches are frequently incorrect in identifying distinct signs. Convolutional Neural Networks (CNNs) perform well in image classification and pattern identification, although they are prone to overfitting. Thus, max pooling...

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