QoS Analysis for Cloud-Based IoT Data Using Multicriteria-Based Optimization Approach

In the expanding field of medical imaging, precise segmentation of anatomical structures is critical for accurate diagnosis and therapeutic interventions. This research paper introduces an innovative approach, building upon the established U-Net architecture, to enhance lung segmentation techniques applied to Computed Tomography (CT) images. Traditional methods of lung segmentation in CT scans often confront challenges such as heterogeneous tissue densities, variability in human anatomy, and pathological alterations, necessitating an approach that embodies greater robustness and precision. Our study presents a modified U-Net model, characterized by an integration of advanced convolutional layers and innovative skip connections, improving the reception field and facilitating the retention of high-frequency details essential for capturing the lung's intricate structures. The enhanced U-Net architecture demonstrates substantial improvements in dealing with the subtleties of lung parenchyma, effectively distinguishing between precarious nuances of tissues, and pathologies. Rigorous quantitative evaluations showcase a significant increase in the Dice coefficient and a decrease in the Hausdorff distance, indicating a more refined segmentation output compared to predecessor models. Additionally, the proposed model manifests exceptional versatility and computational efficiency, making it conducive for real-time clinical applications. This research underlines the transformative potential of employing advanced deep learning architectures for biomedical imaging, paving the way for early intervention, accurate diagnosis, and personalized treatment paradigms in pulmonary disorders. The findings have profound implications, propelling forward the nexus of artificial intelligence and healthcare towards unprecedented horizons.

Section: Discussionmentioning

confidence: 99%

Section: J Regulatory and Ethical Considerations In Ai-integrated Hea...mentioning

confidence: 99%

Enhanced U-Net Architecture for Lung Segmentation on Computed Tomography and X-Ray Images

Saimassay,

Begenov,

Sadyk

et al. 2024

“…Word2Vec is a widely used feature representation technique in NLP [13]. It belongs to the family of word embedding methods that transform words into continuous vector representations in a high-dimensional space.…”

Section: Methodsmentioning

confidence: 99%

Offensive Language Detection on Social Media using Machine Learning

Abdrakhmanov,

Kenesbayev,

Berkimbayev

et al. 2024

This research paper addresses the critical issue of cyberbullying detection within the realm of social networks, employing a comprehensive examination of various machine learning and deep learning techniques. The study investigates the performance of these methodologies through rigorous evaluation using standard metrics, including Accuracy, Precision, Recall, Fmeasure, and AUC-ROC. The findings highlight the notable efficacy of deep learning models, particularly the Bidirectional Long Short-Term Memory (BiLSTM) architecture, in consistently outperforming alternative methods across diverse classification tasks. Confusion matrices and graphical representations further elucidate model performance, emphasizing the BiLSTM-based model's remarkable capacity to discern and classify cyberbullying instances accurately. These results underscore the significance of advanced neural network structures in capturing the complexities of online hate speech and offensive content. This research contributes valuable insights toward fostering safer and more inclusive online communities by facilitating early identification and mitigation of cyberbullying. Future investigations may explore hybrid approaches, additional feature integration, or realtime detection systems to further refine and advance the state-ofthe-art in addressing this critical societal concern.

“…Despite the success of Mask R-CNN-based approaches, real-time performance remains a critical concern for practical deployment in autonomous vehicles [25]. Existing implementations often suffer from high computational overhead, limiting their applicability in real-time systems [26].…”

Section: Related Workmentioning

confidence: 99%

Real-Time Road Lane-Lines Detection using Mask-RCNN Approach

Beissenova,

Ussipbekova,

Sultanova

et al. 2024

This paper presents a novel approach to real-time road lane-line detection using the Mask R-CNN framework, with the aim of enhancing the safety and efficiency of autonomous driving systems. Through extensive experimentation and analysis, the proposed system demonstrates robust performance in accurately detecting and segmenting lane boundaries under diverse driving conditions. Leveraging deep learning techniques, the system exhibits a high level of accuracy in handling complex scenarios, including variations in lighting conditions and occlusions.Real-time processing capabilities enable instantaneous feedback, contributing to improved driving safety and efficiency. However, challenges such as model generalizability, interpretability, computational efficiency, and resilience to adverse weather conditions remain to be addressed. Future research directions include optimizing the system's performance across different geographic regions and road types and enhancing its adaptability to adverse weather conditions. The findings presented in this paper contribute to the ongoing efforts to advance autonomous driving technology, with implications for improving road safety and transportation efficiency in real-world settings. The proposed system holds promise for practical deployment in autonomous vehicles, paving the way for safer and more efficient transportation systems in the future.