Underwater image quality evaluation (UIQE) is crucial in improving image processing techniques and optimizing the design of the imaging system to obtain object information more accurately. However, existing UIQE metho...
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Link prediction is a central downstream assignment in network analysis, which denotes an attempt to assess the probability of a connection between two nodes based on observed link and node properties, which can be uti...
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In recent years, data generated from diverse sources has grown exponentially, giving rise to new challenges for processing and analysis under the umbrella of “big data". Although various methods and platforms ha...
In recent years, data generated from diverse sources has grown exponentially, giving rise to new challenges for processing and analysis under the umbrella of “big data". Although various methods and platforms have been proposed to tackle these issues, there is still a need for a conceptual guide that synthesizes state-of-the-art tools and techniques across the full machine learning (ML) workflow. In this survey, we consolidate research on big data infrastructures, distributed processing frameworks, and ML methods, mapping out an end-to-end conceptual framework that can serve as a reference architecture for end-to-end deployment. Specifically, we discuss key aspects of big data analytics (storage solutions, platforms like Hadoop and Spark, NoSQL databases), preprocessing approaches (dimensionality reduction, instance selection, noise handling), ML algorithms (supervised, unsupervised, and emerging deep learning paradigms), and deployment considerations (monitoring, continuous integration, and versioning). By gathering and integrating these elements, our survey provides a comprehensive overview of existing solutions, clarifies the design choices developers must consider, and identifies research gaps that remain to be addressed.
Teaching is a process that requires permanent observation and improvement. With the rapid development of e-learning, there was a need to review, improve and optimize the process of evaluating students’ performance. T...
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The increasing prevalence of deep hoaxes, such as fake news and phishing schemes, poses a significant threat to cybersecurity, undermining trust and spreading misinformation. In Indonesia, surveys indicate that more t...
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Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This pa...
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ISBN:
(数字)9798331507817
ISBN:
(纸本)9798331507824
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The study proposes an ensemble model that integrates key modalities-demographics, cognitive scores, neuropsychological assessments, and MRI data-via early fusion to enhance AD detection. Various machine learning models, including Logistic Regression, Gradient Boosting, AdaBoost, XGBoost, Support Vector Machine, and Neural Networks, are compared, and feature selection is optimized through Recursive Feature Elimination, ANOVA F-value, and mutual information methods, with mutual information showing the most efficacy. With further hyperparameter tuning, the highest accuracy is achieved at 95.85%, with Gradient Boosting as the top-performing model. Additional experiments assess and evaluate individual modality contributions, underscoring the predictive value of cognitive scores and neuropsychological features. To enhance clinical interpretability, we incorporate model explainability using LIME and SHAP, which identify key features influencing model predictions across different AD stages. Explainable AI (XAI) is essential in healthcare for providing transparency and supporting clinician confidence by highlighting which biomarkers, such as MMSE, CDRSB, and PTAU, contribute most to diagnostic outcomes. This study demonstrates classical machine learning's potential in AD diagnostics, offering a pathway to cost-effective and clinically viable models for neurodegenerative disease management.
Polyps are critical abnormalities that may indicate the initial phases of colon cancer, making accurate detection and analysis essential in medical diagnostics. Traditional 2D medical imaging techniques provide limite...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Polyps are critical abnormalities that may indicate the initial phases of colon cancer, making accurate detection and analysis essential in medical diagnostics. Traditional 2D medical imaging techniques provide limited insight into the shape and structure of polyps, which is where 3D reconstruction can play a transformative role. By converting 2D polyp images into 3D models, healthcare professionals can gain enhanced visualization, enabling better diagnosis, treatment planning, and surgical precision. This study demonstrates a robust process for reconstructing polyp images in 3D using the Kvasir-Seg dataset, which includes 1,000 polyp images with expertly annotated ground truth masks. Our methodology includes the generation of depth maps using the Intel DPT-Large model, the extraction of point clouds with a customized approach, and the construction of precise mesh objects with Open3D. We extract geometric properties, including linearity, planarity, and curvature change, from the point clouds and meshes for a more in-depth analysis of the 3D objects. The unsupervised analysis of the resulting density plots indicates the presence of two distinct clusters within the feature distributions, highlighting the significance of 3D reconstruction for improved polyp characterization. Furthermore, the evaluation included 2D features such as edge density, contrast, dissimilarity, homogeneity, energy, correlation, entropy, number of lines, circularity, and Gabor energy. The analysis of p-values reveals that the 3D features related to these two clusters show notably lower p-values, thereby demonstrating their significance in the comprehensive analysis.
Web tracking (WT) systems are advanced technologies used to monitor and analyze online user behavior. Initially focused on HTML and static webpages, these systems have evolved with the proliferation of IoT, edge compu...
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Early-stage 3D brain tumor segmentation from magnetic resonance imaging (MRI) scans is crucial for prompt and effective treatment. However, this process faces the challenge of precise delineation due to the tumors’ c...
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