this study explores recent transformative advancements in deep learning-based healthcare technologies, revolutionizing patient care by enabling personalized precision medicine and extending access through IoT and cybe...
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this study explores recent transformative advancements in deep learning-based healthcare technologies, revolutionizing patient care by enabling personalized precision medicine and extending access through IoT and cyberphysical systems. Diagnostic methodologies for critical conditions like cancer, diabetes, and heart failure are examined, utilizing computational intelligence to enhance patient identification and treatment. the focus lies on electronic health records (EHRs), investigating contemporary deep learning techniques for cancer prediction and framework-based mechanisms for healthcare optimization. Key algorithms like SVMs, Autoencoders, and CNNs are explored, showing applicability in clinical settings and genomic sequence-based diagnostics. Healthcare's unique processing techniques, spanning gene-based strategies, clinical tests, observation, and diagnostic models, are scrutinized for predictive and treatment potential. Integration of statistical and medical references is highlighted for efficient predictions alongside patient-specific data. the research advocates for AI-powered DSS integration, with CNNs as potent tools for customized medical interventions, marking a significant step towards elevated patient care and precision medicine realization.
In subpopulation shift scenarios, a Curriculum learning (CL) approach would only serve to imprint the model weights, early on, withthe easily learnable spurious correlations featured. To the best of our knowledge, no...
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In this study, we compare multiple machine learningalgorithms for indoor positioning applications, offering insights into the application of swarm optimizationalgorithms for hyperparameter selection in indoor positi...
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the Gannet optimization Algorithm (GOA) has been proven to exhibit excellent performance in resolving issues in a range of fields. However, it is ineffective, nevertheless, when used for binary optimization issues. th...
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Computational offloading is considered a promising emerging paradigm for addressing the limited resources of edge devices in expanding power grids. However, withthe advancement of intelligent technologies such as dig...
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Computational offloading is considered a promising emerging paradigm for addressing the limited resources of edge devices in expanding power grids. However, withthe advancement of intelligent technologies such as digitalized power grids, applications often consist of several interdependent subtasks, forming interconnected automated workflows. this paper focuses on the computational offloading technique within task-dependent workflows. It proposes a multi-objective optimization problem for offloading, considering both time and energy consumption. the model takes into account the constraints of task duration, communication capacity, and computational capacity. Additionally, a predictive-guided a predictive-guided multi-objective reinforcement learning algorithm based on Pareto optimization (MORLBP) is introduced. this algorithm combines the principles of multi-objective optimization, Pareto optimality theory, and deep reinforcement learning. It utilizes the quality of the Pareto front as a metric and is compared against NSGA-II and MOPSO algorithms. the proposed algorithm's effectiveness and advancement are validated through simulations, demonstrating its efficiency and innovation in tackling the multi-objective offloading problem within task-dependent workflows.
this study proposes an intelligent car interface interaction optimization algorithm based on user experience human factors engineering. the algorithm combines the basic principles of human factors engineering, designs...
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Lidar is a kind of distance sensor, which plays an indispensable role in the realization of autonomous driving. this paper systematically introduces single photon avalanche photodiodes (SPADs) and discusses their main...
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Wireless sensor networks (WSNs) play a pivotal role in various applications, ranging from environmental monitoring to defence technology. this paper proposes a novel time synchronization protocol for WSNs inspired by ...
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Machine learning (ML) is a technique that helps applications makes accurate predictions by using input to predict output values. there are various ML models that provide different results, each representing an alterna...
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Withthe increase of data size, it has become particularly important to find the most trustworthy information among the diverse and contradictory data. the process of discerning claims consistent withthe truth from v...
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ISBN:
(纸本)9789819755547;9789819755554
Withthe increase of data size, it has become particularly important to find the most trustworthy information among the diverse and contradictory data. the process of discerning claims consistent withthe truth from various data sources is commonly referred to as truth discovery. While truth discovery has demonstrated commendable outcomes across diverse applications, most truth discovery algorithms are severely affected by hyper-parameters, thereby limiting their overall performance enhancement potential. Consequently, it is imperative to explore hyper-parameter recommendation for optimizing truth discovery. Initially, we advocate for data augmentation on the input dataset of the truth discovery. Given the limited availability of open-source datasets of truth discovery algorithms, employing data augmentation becomes crucial for enhancing data richness while upholding data quality. Subsequently, we propose a hyper-parameter recommendation method grounded in dataset similarity, model-agnostic meta learning and Bayesian optimization. the proposed method entails a multi-step process. First, a preliminary estimation of the hyper-parameter for the truth discovery algorithm is obtained through meta-learning. this initial estimation serves as input for the Bayesian optimization algorithm, which, in turn, predicts the hyper-parameter values tailored to each dataset. Leveraging similarity measures between datasets, the hyper-parameters for the target dataset are then computed. Following the hyper-parameter recommendation phase, the truth discovery algorithm attains optimal hyper-parameters, resulting in a noteworthy performance average improvement of 18.14% according to extensive experiments.
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