標(biāo)題: Titlebook: Artificial Intelligence and Smart Vehicles; First International Mehdi Ghatee,S. Mehdi Hashemi Conference proceedings 2023 The Editor(s) (i [打印本頁(yè)] 作者: Autonomous 時(shí)間: 2025-3-21 16:45
書目名稱Artificial Intelligence and Smart Vehicles影響因子(影響力)
書目名稱Artificial Intelligence and Smart Vehicles影響因子(影響力)學(xué)科排名
書目名稱Artificial Intelligence and Smart Vehicles網(wǎng)絡(luò)公開(kāi)度
書目名稱Artificial Intelligence and Smart Vehicles網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Artificial Intelligence and Smart Vehicles被引頻次
書目名稱Artificial Intelligence and Smart Vehicles被引頻次學(xué)科排名
書目名稱Artificial Intelligence and Smart Vehicles年度引用
書目名稱Artificial Intelligence and Smart Vehicles年度引用學(xué)科排名
書目名稱Artificial Intelligence and Smart Vehicles讀者反饋
書目名稱Artificial Intelligence and Smart Vehicles讀者反饋學(xué)科排名
作者: 敲竹杠 時(shí)間: 2025-3-21 20:20
Personal Privacy and Information Managementur results demonstrate the potential of fractal theory and deep learning techniques for developing accurate and effective spatiotemporal prediction models, which can be utilized to identify areas and time periods of heightened risk and inform targeted intervention and prevention efforts.作者: 無(wú)聊點(diǎn)好 時(shí)間: 2025-3-22 02:30
,Driver Identification by?an?Ensemble of?CNNs Obtained from?Majority-Voting Model Selection,ates that model selection using a majority vote significantly improves the accuracy of the model. Finally, the performance of this research in terms of the accuracy, precision, recall, and f1-measure are 93.22%, 95.61%, 93.22%, and 92.80% respectively when the input length is 5?min.作者: 商品 時(shí)間: 2025-3-22 07:32 作者: Resection 時(shí)間: 2025-3-22 08:51
1865-0929 023, held in Tehran, Iran,?during?May 24-25, 2023..The 14 full papers included in this book were carefully reviewed and?selected from?93?submissions. They were organized in topical sections as follows: machine learning, data mining, machine vision, image processing, signal analysis, decision support作者: 講個(gè)故事逗他 時(shí)間: 2025-3-22 16:18 作者: engrossed 時(shí)間: 2025-3-22 17:05
Generating Control Command for an Autonomous Vehicle Based on Environmental Information,utonomous vehicles and perform about 60% better than the networks designed from 2017 to 2021. In addition, the problem of overfitting in previous networks has mainly been addressed with the help of the new network architecture and different data preprocessing.作者: NATAL 時(shí)間: 2025-3-22 21:49
Personal Privacy and Information Managementn accuracy compared to the YOLOv5 algorithms. Results show that the YOLOv81 model has the highest precision value, the YOLOv8x has the highest recall value, and the YOLOv8m and YOLOv8x have the highest mAP@50 value. Also, the mAP@50–90 values of these models are approximately equal and are the highest among other models.作者: 粘連 時(shí)間: 2025-3-23 04:24
Deep Learning-Based Concrete Crack Detection Using YOLO Architecture,n accuracy compared to the YOLOv5 algorithms. Results show that the YOLOv81 model has the highest precision value, the YOLOv8x has the highest recall value, and the YOLOv8m and YOLOv8x have the highest mAP@50 value. Also, the mAP@50–90 values of these models are approximately equal and are the highest among other models.作者: 興奮過(guò)度 時(shí)間: 2025-3-23 08:55
Conference proceedings 2023organized in topical sections as follows: machine learning, data mining, machine vision, image processing, signal analysis, decision support systems, expert systems, and their applications in smart vehicles..作者: Hay-Fever 時(shí)間: 2025-3-23 11:56 作者: Projection 時(shí)間: 2025-3-23 17:14 作者: Accrue 時(shí)間: 2025-3-23 19:02 作者: 收養(yǎng) 時(shí)間: 2025-3-23 22:43 作者: 滔滔不絕地說(shuō) 時(shí)間: 2025-3-24 04:32 作者: Thymus 時(shí)間: 2025-3-24 09:32 作者: Invertebrate 時(shí)間: 2025-3-24 13:54
Road Sign Classification Using Transfer Learning and Pre-trained CNN Models, our fine-tuned VGG-16, VGG-19, ResNet50 and EfficientNetB0 models on the German Traffic Sign Recognition Benchmark (GTSRB) test dataset. Our work makes several contributions, including the utilization of transfer learning with pre-trained CNN models, the integration of augmentation techniques, and 作者: promote 時(shí)間: 2025-3-24 18:45 作者: RACE 時(shí)間: 2025-3-24 22:46 作者: Fraudulent 時(shí)間: 2025-3-25 01:48 作者: Fortuitous 時(shí)間: 2025-3-25 04:35
,Evaluation of Drivers’ Hazard Perception in Simultaneous Longitudinal and Lateral Control of Vehiclrtification process is critical in reducing traffic accidents. Most hazard perception skill assessments are based on questionnaires and button clicks. In contrast, hazard perception can be more useful in practical driving based on realistic driving assessment criteria. In this paper, understanding d作者: Needlework 時(shí)間: 2025-3-25 09:16
,Driver Identification by?an?Ensemble of?CNNs Obtained from?Majority-Voting Model Selection, public transportation control systems, and the rental car business. An critical issue of these systems is the level of privacy, which encourages a lot of research using non-visual data. This paper proposes a novel method based on IMU sensors’ data of smartphones. Also, an ensemble of convolutional 作者: 品牌 時(shí)間: 2025-3-25 12:27 作者: Crohns-disease 時(shí)間: 2025-3-25 17:07 作者: Glossy 時(shí)間: 2025-3-25 22:00 作者: interrupt 時(shí)間: 2025-3-26 03:04 作者: Cardioplegia 時(shí)間: 2025-3-26 07:23 作者: 膠狀 時(shí)間: 2025-3-26 08:56 作者: 凝視 時(shí)間: 2025-3-26 14:11
Schriften der accadis Hochschuleng new properties to the volume of moving objects and calculating the correlation coefficient and distance criterion, creating a distance matrix for both current and future states. This paper‘s findings benefit the experts in urban traffic management that can analyze and evaluate the impact of new d作者: 混合物 時(shí)間: 2025-3-26 20:47
Introducing the Facebook Platform,et..Our study contributes to the field of intelligent transportation systems by providing an efficient and accurate method for road sign classification. We believe that our approach can be easily extended to other computer vision tasks and applied to real-world scenarios.作者: diathermy 時(shí)間: 2025-3-27 00:02
https://doi.org/10.1007/978-1-4302-0970-6chnology to ensure safe driving among diabetic retinopathy patients. This innovative approach would provide real-time updates about an individual’s condition and enable them to take actions to maintain their and others’ safety while driving.作者: cajole 時(shí)間: 2025-3-27 02:47 作者: 格言 時(shí)間: 2025-3-27 07:23
Going Further with Your Application,the driver‘s behavioral anomalies are identified. Also, The drivers’ hazard perception skills were thoroughly evaluated through simulated scenarios providing insights into their ability to perceive and respond to potential hazards in diverse traffic conditions. It was demonstrated that the algorithm作者: 是突襲 時(shí)間: 2025-3-27 13:15 作者: Absenteeism 時(shí)間: 2025-3-27 16:32 作者: 輕浮女 時(shí)間: 2025-3-27 19:05 作者: 偽書 時(shí)間: 2025-3-27 21:58 作者: 夸張 時(shí)間: 2025-3-28 02:42 作者: 歌唱隊(duì) 時(shí)間: 2025-3-28 06:14
Improving Safe Driving with Diabetic Retinopathy Detection,chnology to ensure safe driving among diabetic retinopathy patients. This innovative approach would provide real-time updates about an individual’s condition and enable them to take actions to maintain their and others’ safety while driving.作者: 胰臟 時(shí)間: 2025-3-28 13:27 作者: attenuate 時(shí)間: 2025-3-28 15:29 作者: 察覺(jué) 時(shí)間: 2025-3-28 21:29 作者: 拉開(kāi)這車床 時(shí)間: 2025-3-29 00:26
Semantic Segmentation Using Events and Combination of Events and Frames,h images and events. We also introduce a novel training method (blurring module), and results show our training method boosts the performance of the network in recognition of small and far objects, and also the network could work when images suffer from blurring.作者: CREEK 時(shí)間: 2025-3-29 03:16 作者: 是限制 時(shí)間: 2025-3-29 10:29 作者: synovial-joint 時(shí)間: 2025-3-29 14:42
Yvonne Thorhauer,Christoph A. Kexelreal-world deployment. Towards boosting the appearance of AVs on the roads, the interaction of AVs with pedestrians including “prediction of the pedestrian crossing intention” deserves extensive research. This is a highly challenging task as involves multiple non-linear parameters. In this direction作者: 拖網(wǎng) 時(shí)間: 2025-3-29 16:12 作者: Trypsin 時(shí)間: 2025-3-29 21:53
Introducing the Facebook Platform, our fine-tuned VGG-16, VGG-19, ResNet50 and EfficientNetB0 models on the German Traffic Sign Recognition Benchmark (GTSRB) test dataset. Our work makes several contributions, including the utilization of transfer learning with pre-trained CNN models, the integration of augmentation techniques, and 作者: exophthalmos 時(shí)間: 2025-3-30 00:48 作者: 發(fā)展 時(shí)間: 2025-3-30 06:33
Introducing the Facebook Platform,onent of the control system for these devices, as it enables the identification of the current walking stage and facilitates appropriate ankle functionality. Gait phase detection has increasingly favored the use of inertial measurement units, which provide valuable data on angular velocity and accel作者: 惰性女人 時(shí)間: 2025-3-30 11:24
https://doi.org/10.1007/978-1-4302-0970-6ficient ALPR algorithm has been developed to detect, differentiate, and recognize the Iranian national and free zone license plates (LPs), automatically and simultaneously. Latest versions of YOLO (you only look once) has been trained based on an in-house developed dataset for Iranian motor vehicles作者: 食品室 時(shí)間: 2025-3-30 15:21
