作者：
Baba, AbdullatifAlothman, Basil
Computer Science and Engineering Department Kuwait
Computer Engineering Department Ankara Turkey
This paper explores essential aspects of autonomous underwater vehicle (AUV) design, focusing on hull structure, hydrodynamics, propulsion systems, and sensor integration. It also examines the role of underwater Simul...
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Visual information decoding aims to infer the visual content perceived by a subject based on their brain responses, representing a cutting-edge area of neuroscience research. Functional magnetic resonance imaging (fMR...
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This paper presents a comparative analysis of four generative AI models namely ChatGPT, Gemini, Copilot, and Stable Diffusion - evaluated on metrics such as visual quality, prompt adherence, creativity, usability, and...
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Gold-coated nanoparticles have garnered significant attention in recent years due to their exceptional thermal stability and distinct optical properties. This paper investigates the optical characteristics of various ...
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Recent advances in computer vision and artificial intelligence(AI)have made real-time people counting systems extremely reliable,with experts in crowd control,occupancy supervision,and *** improve the accuracy of peop...
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Recent advances in computer vision and artificial intelligence(AI)have made real-time people counting systems extremely reliable,with experts in crowd control,occupancy supervision,and *** improve the accuracy of people counting at entry and exit points,the current study proposes a deep learning model that combines You Only Look Once(YOLOv8)for object detection,ByteTrack formulti-object tracking,and a unique method for vector-based movement *** system determines if a person has entered or exited by analyzing their movement concerning a predetermined boundary *** different logical strategies are used to record entry and exit *** leveraging object tracking,cross-product analysis,and current frame state updates,the system effectively tracks human flow in and out of a roomand maintains an accurate count of the *** present approach is supervised on Alzheimer’s patients or residents in the hospital or nursing home environment where the highest level of monitoring is essential.A comparison of the two strategy frameworks reveals that robust tracking combined with deep learning detection yields 97.2%and 98.5%accuracy in both controlled and dynamic settings,*** model’s effectiveness and applicability for real-time occupancy and human management tasks are demonstrated by performance measures in terms of accuracy,computing time,and robustness in various *** integrated technique has a wide range of applications in public safety systems and smart buildings,and it shows considerable gains in counting reliability.
Risk prediction is an important task to ensuring the driving safety of railway trams. Although data-driven intelligent methods are proved to be effective for driving risk prediction, accuracy is still a top concern fo...
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Risk prediction is an important task to ensuring the driving safety of railway trams. Although data-driven intelligent methods are proved to be effective for driving risk prediction, accuracy is still a top concern for the challenges of data quality which mainly represent as the unbalanced datasets. This study focuses on applying feature extraction and data augmentation methods to achieve effective risk prediction for railway trams, and proposes an approach based on a self-adaptive K-means clustering algorithm and the least squares deep convolution generative adversarial network(LS-DCGAN). The data preprocessing methods are proposed, which include the K-means algorithm to cluster the locations of trams and the extreme gradient boosting recursive feature elimination based feature selection algorithm to retain the key features. The LS-DCGAN model is designed for sparse sample expansion, aiming to address the sample category distribution imbalance problem. The experiments implemented with the public and real datasets show that the proposed approach can reach a high accuracy of 90.69%,which can greatly enhances the tram driving safety.
Accurately segmenting brain tumors using MRI scans is essential for diagnosing, treatment planning and monitoring treatment. This research compares several U-Net based architectures, including U-Net DIU-Net, DFP-ResUN...
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The human lungs, crucial for supplying oxygen, are vulnerable to diseases such as lung cancer, a leading cause of mortality. Timely prediction of lung cancer is essential to enable early intervention by healthcare pro...
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To enhance the precision of diagnosis, this research provides a new structure for identifying brain tumors that integrates an Improved Fast Mask Region based Convolutional Neural Network (IFMRCNN) with complex image p...
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Aspect extraction plays a crucial role in understanding the fine-grained nuances of text data, allowing businesses and researchers to gain deeper insights into customer opinions, sentiment distributions, and preferenc...
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