泥土谦卑
发表于 2025-3-23 18:56:32
Arkar Minn,Tofael Ahamedtscheiders in Verbindung gebracht werden kann. Abschließend stellt dieses Kapitel dar, welche irrationalen Verhaltensmuster sich durch die Wahrscheinlichkeitsgewichtefunktion erklären lassen. Hierzu gehört beispielsweise die Tendenz, zu viele kleine Versicherungen abzuschließen. Ebenso lässt sich di
图画文字
发表于 2025-3-24 01:18:59
Linhuan Zhang,Tofael Ahamed,Yan Zhang,Pengbo Gao,Tomohiro Takigawat wird. Ebenso können die Erkenntnisse genutzt werden, um das eigene Verhalten zu lenken oder im Sinne eines Hedonic Framing die Wahrnehmung so zu beeinflussen, dass die eigene Zufriedenheit gesteigert wird..In diesem Kapitel werden für diese Anwendungsfelder jeweils Beispiele präsentiert, wie aus d
EPT
发表于 2025-3-24 04:02:51
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Pander
发表于 2025-3-24 08:02:06
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抱怨
发表于 2025-3-24 12:45:51
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评论者
发表于 2025-3-24 15:05:41
Long Range Wide Area Network (LoRaWAN) for Oil Palm Soil Monitoring,he template for LoRaWAN network is laid out in four parts; sensor node, gateway, network server, and application server. LoRaWAN is perfect for outlying regions without cellular network coverage or for establishing private networks covering long distances with minimum power consumption and maintenan
伤心
发表于 2025-3-24 22:30:54
Artificial Intelligence in Agriculture: Commitment to Establish Society 5.0: An Analytical Conceptsge and its consequences over crops is demanding innovative solutions to keep on increasing yield while mitigating the adverse effects on the ecosystem. The aim of this chapter is to provide an analytical concept mapping and framework about AI-based learning systems, in a quasi-philosophical way to e
小淡水鱼
发表于 2025-3-24 23:09:14
Potentials of Deep Learning Frameworks for Tree Trunk Detection in Orchard to Enable Autonomous Nav (7–8 PM) conditions in August and September (summertime) in Japan. Thermal imagery datasets were augmented to train, validate, and test using the faster R-CNN, YOLO-v3, and CenterNet deep learning model to detect a tree trunk. A total of 12,876 images were used to train the model, 9270 images were
与野兽博斗者
发表于 2025-3-25 04:27:49
Real-Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT,e unique ID method was found to be more reliable, with an F1. of 87.85%. This was because YOLOv4 had a very low false negative in detecting pear fruits. The ROI line is more reliable because of its more restrictive nature, but due to flickering in detection it was not able to count some pears despit
collagenase
发表于 2025-3-25 08:32:23
Pear Recognition System in an Orchard from 3D Stereo Camera Datasets Using Deep Learning Algorithms) conditions at JST, Tokyo Time, August 2021 (summertime) to prepare training, validation, and test datasets at a ratio of 6:3:1. All the images were taken by a 3D stereo camera which included PERFORMANCE, QUALITY, and ULTRA models. We used the PERFORMANCE model to capture images to make the dataset