In this paper, which consists of two parts The first part is implemented on object detection in the surrounding with Yolo (You Only Look Once)Algorithm provides exact classification and position which is configured on newly created datasets for classes of object: a car, a person, a truck, a bus, traffic light, motorcycle, pothole, wetland uses the Convolutional Neural Network and max-polling layer for prediction that improves detecting of small target and these deep learning technique provides a high accuracy for detecting real world.
In this article, we present an approach to detect basic movements of cyclists in real world traffic situations based on image sequences, optical flow (OF) sequences, and past positions using a multi-stream 3D convolutional neural network (3D-ConvNet) architecture.
This project was the first to explore the use of AI agents trained in simulation using deep reinforcement learning and tested on a high-performance aircraft in the real world performing routing and computer vision tasks.
The goal of this survey is to state the current state of the art of deep reinforcement learning (DRL) research in the real-time strategy game “StarCraft” introducing the creative ways in which neural networks can be used to bring us steps closer to create AI agents that can deal with the real world.
On this basis, a real world typical flexible flow shop, the ball grinder mill of ceramic and cement industry, is taken as a case study to show the modeling process and verify the performance of the artificial intelligence algorithm-based multi-objective optimization model.
The goal of this study is to examine the ctDNA mut profile, and the impact of commonly mutated genes (ESR1, TP53 and PIK3CA) on progression free survival (PFS) in pts received pal as SOC in a real world experience.
The results of experiments executed over 23 real world datasets have shown that Enlarged Hedge Algebras based classifier with our proposed co-optimization PSO algorithm outperforms the existing classifiers which are designed based on Enlarged Hedge Algebras methodology with two phase optimization process and the existing fuzzy set theory based classifiers.
e18733 Background: Although PD-1/PD-L1 inhibitors have become the standard treatment for patients with advanced non-small cell lung cancer (NSCLC), data from clinical trials are difficult to be verified in the real world.
This single center study retrospectively analyzed the efficacy and safety of preoperative chemotherapy combined with PD-1 antibody in patients with locally advanced operable or potentially resectable ESCC in the real world.
At the same time, these trials do provide a lot of good data, but it needs to be interpreted well, and extrapolated appropriately to patients in practice as there are differences between what happens in a randomized control trial and in the real world.
While vaccine efficacy is studied in an ideal setting, as a prospective randomized control trial (where effect of vaccination in prevention of infection or disease can be proven), vaccine effectiveness studies are done in real world settings in prospective cohort or retrospective caseecontrol studies.
The aims of this study were; firstly, to replicate results of inter-brain synchrony reported in existing literature for a real world task and secondly, to explore whether the inter-brain synchrony could be elicited in a Virtual Environment (VE).
Real world problems related to decision making contain uncertainty in data which cannot be very precise as per our choice; as it is seen that the interval valued fuzzy logic deals greatly with such imprecise data and gives the best outcome.
In this paper, interval-valued fuzzy cliques (IVFQs) and interval-valued fuzzy clique covers (IVFQCs) of an interval-valued fuzzy graph (IVFG) are introduced by introducing the fuzziness because, the crisp graphs has some limitations in real world due to uncertainty of vagueness.
This approach relies heavily on normative (rational) decision-making processes, and often leaves out descriptive influences that stem from personal, social, and environmental factors and explain how decisions are typically made in the real world.
However, intricate reading methods are required to obtain multi-level information stored in different colors, which greatly limits the application of anti-counterfeiting technology on solving real world problems.
In this paper, we introduce a novel end-to-end simulation tool- Behavior Simulation from User Equipment to Edge and Cloud (BSUEEC)- which can offload computing, communication, and traffic simulation to different simulators for the deployment and resource planning of MEC, applicable to various real world scenarios.
The comparison, carried out on three different real world datasets, reveals similar predictive performance, although the shallow approach seems to be more robust and less demanding in terms of time-to-predict.
This study describes Comparative Effectiveness Research of CMM versus SCS to provide real world evidence regarding the appropriate means for FBSS management, in terms of Patient-Reported Outcomes Measures.
Using real world, in situ measurements of circadian thermal fluctuations of beach sediment on Henderson Island and Cocos (Keeling) Islands, we demonstrate that plastics increase circadian temperature extremes.