Fish disease detection is crucial for maintaining the health and sustainability of aquaculture systems. Traditional methods of disease identification in fish often rely on manual observation, which can be inefficient ...
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(纸本)9798350379945
Fish disease detection is crucial for maintaining the health and sustainability of aquaculture systems. Traditional methods of disease identification in fish often rely on manual observation, which can be inefficient and susceptible to humanly made mistakes. In recent years, machine learning techniques, particularly convolutional neural networks (CNNs), have shown promise in automating the detection process. However, the performance of CNNs can be limited by their reliance on fixed-size input images and their inability to capture long-range dependencies. In this work, five deep learning models such as the CNN, DenseNet121, ViT, VGG16 and VGG19 models are proposed to study their performance on our dataset using accuracy as the major evaluation metric. First, exploratory data analysis is carried out to understand the features and classes of our dataset, then preprocessing steps are carried out to resize them to a standard size, normalized the pixels as well as augmenting all classes to increase on variations of images for each class, to create a dataset balance for all classes. Then the dataset are splitted into training and validation sets and also extracted feature from images using VGG16 so as to capture the underlying dependencies in them. We trained all of our models on the training set then finally evaluated them to get their performance. The study implements deep transfer learning to all our models to improve the models' predictions and also solve the problem of poor generalization. Finally three different Explainable AI (XAI) techniques on each of the models and these include LIME, Grad-CAM and integrated gradient to gain an understanding on which exact features did the models base on to make predictions. Further more we investigated which XAI technique works better for each model. In our future prospects, more XAI techniques such SHAP and Layer-wise backward propagation are annalysed and also carry out our research together with engagements from domain expert
This work looks into machine learning as a means of enhancing rainfall prediction. A logistic regression model is trained using meteorological data, including meteorological parameters such as temperature, wind speed,...
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We present an algorithm for the efficient generation of all pairwise non-isomorphic cycle permutation graphs, i.e. cubic graphs with a 2-factor consisting of two chordless cycles, and non-hamiltonian cycle permutation...
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Channel prediction permits to acquire channel state information(CSI) without signaling overhead. However,almost all existing channel prediction methods necessitate the deployment of a dedicated model to accommodate a ...
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Channel prediction permits to acquire channel state information(CSI) without signaling overhead. However,almost all existing channel prediction methods necessitate the deployment of a dedicated model to accommodate a specific configuration. Leveraging the powerful modeling and multi-task learning capabilities of foundation models, we propose the first space-time-frequency(STF) wireless foundation model(WiFo) to address time-frequency channel prediction tasks in a unified manner. Specifically, WiFo is initially pre-trained over massive and extensive diverse CSI datasets. Then, the model will be instantly used for channel prediction under various CSI configurations without any fine-tuning. We propose a masked autoencoder(MAE)-based network structure for WiFo to handle heterogeneous STF CSI data, and design several mask reconstruction tasks for self-supervised pre-training to capture the inherent 3D variations of CSI. To fully unleash its predictive power, we build a large-scale heterogeneous simulated CSI dataset consisting of 160k CSI samples for *** validate its superior unified learning performance across multiple datasets and demonstrate its state-of-the-art(SOTA) zero-shot generalization performance via comparisons with other full-shot baselines.
The effectiveness and dependability of fingerprint recognition systems make them popular for biometric authentication. This research addresses the challenge of distinguishing between real and spoof fingerprint images ...
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The primary objective of a software project is to get a high-quality software product while reducing the cost and the time required to complete the project. To do that, the software needs to be tested before being rel...
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Structured regularization allows machine learning models to consider spatial relationships among parameters, leading to results that generalize better and are more interpretable compared to norm penalties. In this stu...
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Being prevalent in real-world scenarios, dynamic constrained multiobjective optimization problems (DCMOP) are hard to solve due to their continuously and slowly variable objectives and constraints. Although researches...
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Underwater images suffer from poor visibility due to absorption and scattering of light, resulting in low contrast, blurred details, and color distortions. Image enhancement techniques can improve visual quality befor...
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A comprehensive health monitoring & analyzing system that provides personalized recommendations, goal setting, and tracking based on relevant factors related to BMI & COVID-19. System aims to analyze attack ob...
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