The popular East Asian card game Big2 involves rules that do not allow players to view each other's hand cards, making artificial intelligence face challenges in performing well in this game. Based on Markov Decis...
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The popular East Asian card game Big2 involves rules that do not allow players to view each other's hand cards, making artificial intelligence face challenges in performing well in this game. Based on Markov Decision Processes (MDPs) that can handle partially observable and stochastic information, we design the Big2MDP framework to explore card-playing strategies that minimize losing risks while maximizing scoring opportunities for the Big2 game. According to our review of relevant research, this is the first Big2 artificial intelligence framework with the following features: (1) the ability to simultaneously consider scoring and losing points to make the best winning decisions with minimal losing risk, (2) the capability to predict multiple opponents' actions to optimize the decision-making, and (3) the adaptability to compete for the free-playing right to change card combinations at the essential moment. We implement a system of four-player card game Big2 on the Android platform to validate the feasibility and effectiveness of Big2MDP. Experimental results show that Big2MDP outperforms existing artificial intelligence methods, achieving the highest win rate and the least number of losing points as competing against both computer and human players in Big2 games. IEEE
Deep learning methods have played a prominent role in the development of computer visualization in recent years. Hyperspectral imaging (HSI) is a popular analytical technique based on spectroscopy and visible imaging ...
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Many people all around the world suffer from heart disease, which is regarded as a severe illness. In healthcare, especially cardiology, it is crucial to accurately and quickly diagnose cardiac problems. In this resea...
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A novel energy-efficient clustering-based congestion-awareness routing mechanism has been developed for wireless sensor network (WSN). In the first stage, some set of sensor nodes are initialised in the WSN environmen...
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Classification of brain haemorrhage is a challenging task that needs to be solved to help advance medical treatment. Recently, it has been observed that efficient deep learning architectures have been developed to det...
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Developing manufacturing methods for flexible electronics will enable and improve the large-scale production of flexible, spatially efficient, and lightweight devices. Laser sintering is a promising postprocessing met...
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Millimeter-wave is the core technology to enable multi-Gbps throughput and ultra-low latency *** the devices need to operate at very high frequency and ultra-wide bandwidth:They consume more energy,dissipate more powe...
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Millimeter-wave is the core technology to enable multi-Gbps throughput and ultra-low latency *** the devices need to operate at very high frequency and ultra-wide bandwidth:They consume more energy,dissipate more power,and subsequently heat up *** overheating is a common concern of many users,and millimeter-wave would exacerbate the *** this work,we first thermally characterize millimeter-wave *** measurements reveal that after only 10 s of data transfer at 1.9 Gbps bit-rate,the millimeter-wave antenna temperature reaches 68◦C;it reduces the link throughput by 21%,increases the standard deviation of throughput by 6×,and takes 130 s to dissipate the heat *** degrading the user experience,exposure to high device temperature also creates *** on the measurement insights,we propose Aquilo,a temperature-aware,multi-antenna network *** maintains relatively high throughput performance but cools down the devices *** testbed experiments under both static and mobile conditions demonstrate that Aquilo achieves a median peak temperature only 0.5◦C to 2◦C above the optimal while sacrificing less than 10%of throughput.
The purpose of this article is to see how machine learning (ML) algorithms and applications are used in the COVID-19 inquiry and for other purposes. The available traditional methods for COVID-19 international epidemi...
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Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial...
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Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.
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