This comprehensive review starts with diving into the progress and real-world applications of combining multi-omics data analysis with machine learning techniques in cancer research. Multi-omics involves examining var...
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Alzheimer's disease (AD) is a slowly progressing, irreversible brain condition that weakens memory and negatively affects the patient's quality of life. Alzheimer's disease (AD) can be identified using Mag...
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ISBN:
(纸本)9798350306231
Alzheimer's disease (AD) is a slowly progressing, irreversible brain condition that weakens memory and negatively affects the patient's quality of life. Alzheimer's disease (AD) can be identified using Magnetic Resonance Imaging (MRI) data. For an early diagnosis of the disease, various medical and diagnostic approaches are being investigated. Even while MRI is a useful tool for locating AD-related brain symptoms, the acquisition process is time-consuming, largely because workflow bottlenecks must be manually evaluated. In order to find the best effective method for detecting the disease, this research examines the basic technique for analyzing MRI images. To carry out our study to slow progression of the disease by the use of Alzheimer's disease (AD) prognosis, a dataset from The Alzheimer's Disease Neuroimaging Initiative (ADNI) will be imported and fitted. The outcomes highlight the tremendous potential of integrating imaging data for automated categorization of Alzheimer's disease (AD) using multidisciplinary AI techniques. With a deep three-dimensional convolutional network (3D CNN) being used to handle the three-dimensional MRI input and a Transformer encoder being applied to manage the genetic sequence input, the suggested solution merges machine learning, bioinformatics, and other image processing techniques. After various experiments by checking the results accuracy, it is stated that the CNN model is never enough to provide us with the desired accuracy either by training on both skull stripped data or the GM tissue segmented data. Although, it is relatively better at the skull stripped dataset training, but the results accuracy and predicted classes show that inferring some classifiers after extracting the features from the CNN would increase the accuracy and results. After applying Support Vector Machine SVM-RBF, SVM-POLY, and XGBoost, it is concluded that the training of the Skull Stripped Dataset with features extracted from the CNN model we provided an
Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many proje...
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Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.
Cybersecurity has become a significant concern for automotive manufacturers as modern cars increasingly incorporate electronic components. Electronic Control Units (ECUs) have evolved to become the central control uni...
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Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare syste...
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With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It ...
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With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.
Hardware Verification of Deep Learning Accelerators (DLAs) has become critically important for testing the reliability and trustworthiness of Learning Enabled Autonomous systems (LEAS). In this paper, we introduce a s...
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The CloudIoT paradigm has profoundly transformed the healthcare industry, providing outstanding innovation and practical applications. However, despite its many advantages, the adoption of this paradigm in healthcare ...
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Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms...
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The proliferation of data generation devices, including IoT and edge computing has led to the big data paradigm, which has considerably placed pressure on well-established relational databases during the last decade. ...
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