Brain tumors are abnormal cell growths in the brain, which can be cancerous or non-cancerous. They have diverse effects, including cognitive and neurological impairments, as well as personality and behavioral changes....
Brain tumors are abnormal cell growths in the brain, which can be cancerous or non-cancerous. They have diverse effects, including cognitive and neurological impairments, as well as personality and behavioral changes. This study proposes a multiclass classification framework utilizing a plain CNN architecture and Transfer learning to accurately identify brain tumor types: meningioma, glioma, pituitary tumor, and no tumor. We have explored transfer learning models such as VGG16, AlexNet, and ResNet50, these are initialized with pre-trained weights and fine-tuned on a comprehensive brain tumor dataset which has an equal distribution among classes. Optimizers such as Adam, AdaDelta, and SGD are modified to enhance performance. AdaDelta was used as the VGG16 optimizer, and it helped us achieve the highest accuracy of 99.83% with a loss of 0.74%. ResNet50 on the other hand, had the lowest accuracy 70% and highest loss 66.68%. While AlexNet without dropouts and AdaDelta as optimizer gives highest loss of 92.83%. The custom CNN model's loss was 0.04%, which is the lowest of any of them. The findings contribute to robust tools for accurate brain tumor diagnosis and treatment planning.
data quality is critical across many applications. The utility of data is undermined by various errors, making rigorous data cleaning a necessity. Traditional data cleaning systems depend heavily on predefined rules a...
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data quality is critical across many applications. The utility of data is undermined by various errors, making rigorous data cleaning a necessity. Traditional data cleaning systems depend heavily on predefined rules and constraints, which necessitate significant domain knowledge and manual effort. Moreover, while configuration-free approaches and deep learning methods have been explored, they struggle with complex error patterns, lacking interpretability, requiring extensive feature engineering or labeled data. This paper introduces GIDCL (Graph-enhanced Interpretable data Cleaning with Large language models), a pioneering framework that harnesses the capabilities of Large Language Models (LLMs) alongside Graph Neural Network (GNN) to address the challenges of traditional and machine learning-based data cleaning methods. By converting relational tables into graph structures, GIDCL utilizes GNN to effectively capture and leverage structural correlations among data, enhancing the model's ability to understand and rectify complex dependencies and errors. The framework's creator-critic workflow innovatively employs LLMs to automatically generate interpretable data cleaning rules and tailor feature engineering with minimal labeled data. This process includes the iterative refinement of error detection and correction models through few-shot learning, significantly reducing the need for extensive manual configuration. GIDCL not only improves the precision and efficiency of data cleaning but also enhances its interpretability, making it accessible and practical for non-expert users. Our extensive experiments demonstrate that GIDCL significantly outperforms existing methods, improving F1-scores by 10% on average while requiring only 20 labeled tuples.
Using machine learning techniques, this article examines cancer patients' DNA sequences. Cancer is certainly one of the most common and devastating diseases in the contemporary era, with a large number of new case...
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Due to privacy and security concerns, recent advancements in group fairness advocate for model training regardless of demographic information. However, most methods still require prior knowledge of demographics. In th...
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Deep learning has facilitated the advancement and wide application of speech recognition technology. While mainstream speech recognition models are usually trained with adult standard speech data, which do not take in...
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The Internet of Things (IoT) and Machine-to-Machine (M2M) communication have connected devices, enabling major advances in various fields. The priority now is secure and efficient management of these interconnected sy...
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ISBN:
(纸本)9798400709418
The Internet of Things (IoT) and Machine-to-Machine (M2M) communication have connected devices, enabling major advances in various fields. The priority now is secure and efficient management of these interconnected systems. IoT ecosystems need access control to manage interactions. Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Policy-Based Access Control (PBAC), and Context-Based Access Control (CBAC) are examined in the context of Industrial IoT (IIoT) in this paper. Intelligent manufacturing and anticipatory maintenance are among the many opportunities created by IoT and IIoT rapid growth. However, these advances present new security and access control challenges. Access control is essential for managing user, device, and application authorization in complex, interconnected environments. Our goal is to analyze their suitability, pros, and cons in industrial settings where strict access control is needed to ensure system safety, confidentiality, and efficiency. The IIoT case study implementation of these access control mechanisms is the focus of this paper. We examine an IIoT scenario in which a manufacturing plant has a network of machinery, sensors, and control systems. The case study shows how RBAC, ABAC, PBAC, and CBAC apply pragmatically. We evaluate their ability to manage access to critical machinery, data, and devices. We analyze and compare these access control mechanisms within the IIoT framework to determine the best one for the industrial environment, taking into account scalability, security, real-time decision making, and industrial process complexity. This paper examines access control mechanisms and practical observations from a real-world IIoT case study to improve IIoT security discussions. The findings can help IIoT practitioners, researchers, and decision-makers choose access control solutions. Industrial operations can become safer and more efficient.
Online social networks have emerged as a significant data source, but the extensive collection and utilization of personal information have given rise to profound concerns regarding privacy. From a legislative and pol...
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Adaptive Random Testing (ART) enhances the testing effectiveness (including fault-detection capability) of Random Testing (RT) by increasing the diversity of the random test cases throughout the input domain. Many ART...
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Hypergraphs provide a superior modeling frame-work for representing complex multidimensional relationships in the context of real-world interactions that often occur in groups, overcoming the limitations of traditiona...
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ISBN:
(数字)9798350362480
ISBN:
(纸本)9798350362497
Hypergraphs provide a superior modeling frame-work for representing complex multidimensional relationships in the context of real-world interactions that often occur in groups, overcoming the limitations of traditional homogeneous graphs. However, there have been few studies on hypergraph-based contrastive learning, and existing graph-based contrastive learning methods have not been able to fully exploit the high-order correlation information in hypergraphs. Here, we propose a Hypergraph Fine-grained contrastive learning (HyFi) method designed to exploit the complex high-dimensional information inherent in hypergraphs. While avoiding traditional graph augmentation methods that corrupt the hypergraph topology, the proposed method provides a simple and efficient learning augmentation function by adding noise to node features. Furthermore, we expands beyond the traditional dichotomous relationship between positive and negative samples in contrastive learning by introducing a new relationship of weak positives. It demonstrates the importance of fine-graining positive samples in contrastive learning. Therefore, HyFi is able to produce high-quality embeddings, and outperforms both supervised and unsupervised baselines in average rank on node classification across 10 datasets. Our approach effectively exploits high-dimensional hypergraph information, shows significant improvement over existing graph-based contrastive learning methods, and is efficient in terms of training speed and GPU memory cost. The source code is available at https://***/Noverse0/***.
Generating textual interpretability using recent advancements in large language models (LLMs) is crucial for enhancing the efficiency of comprehensive computer-aided diagnosis (CAD) systems. This improves transparency...
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ISBN:
(数字)9798331531492
ISBN:
(纸本)9798331531508
Generating textual interpretability using recent advancements in large language models (LLMs) is crucial for enhancing the efficiency of comprehensive computer-aided diagnosis (CAD) systems. This improves transparency between medical staff, intelligent CAD systems, and end-users by creating a trustworthy and effective intermediate medical diagnosis environment. In this paper, an innovative explainable throughout CAD system is introduced, designed to predict diseases from Chest X-rays (CXR) in a comprehensive scenario. The primary goal is to undertake multiple tasks that reduce the burden on medical staff and enrich CAD outcomes, including classification, visual explanations (heatmaps), and textual report generation. The proposed CAD system is developed through eight key steps: data Collection and Annotation, data Preparation, Text Vectorizations (Indexing), Visual Encoder, RAG-Fusion, Structural Prompt, XAI LLmTextual Reasoning (LLM Model), and Final Output (LLM textual report, image classification, and heatmap localization). The AI-based CAD system is trained and evaluated using the public benchmark MIMIC-CXR dataset with 14 different classes. The classification performance achieved an overall accuracy of $70 \%$, precision of $70 \%$, and F1-score of $0.60 \%$, while for text report generation, the system obtained an average BERTScore precision of 0.83, RougeL 0.16, and a Meteor score of 0.28. These promising results suggest the potential for further improvement of the CAD system and its applicability to real-world medical tasks.
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