Titlebook: Artificial Intelligence: Theory and Applications; Proceedings of AITA Harish Sharma,Antorweep Chakravorty,Rajani Kumari Conference proceed

incite

paroxysm

https://doi.org/10.1007/978-3-540-69213-3ing deep learning methods to analyze image spam; one is without attention, and next one is with an attention module that time. We use the convolutional block attention module (CBAM); this module attention only spams area of the image and activities of the better performance. Our proposed method achi

calorie

Bernhard Badura,Helmut Schr?der,Katrin Maccotegies. This work is one of?the first?works to use these combinations and obtain very good results, more than most related studies. For the PHEME dataset, the model was trained with a variety of epochs (10, 20, 50, and 100) to obtain the best result. For the PolitiFact dataset, only 10 epochs were t

小教堂

Bernhard Badura,Helmut Schr?der,Katrin Maccop the operation of devices in the IoT environment. Furthermore, an .-level agent-based deployment algorithm is proposed which utilizes the collective intelligence to provide optimized coverage in the region of interest. The proposal is evaluated on the IoT-based platform to test its efficacy.

LAPSE

Pigeon

choroid

https://doi.org/10.1007/978-3-642-21655-8for transforming human language into system-interpretable data as a result of the technological advancement in these approaches. This research presents various techniques that are implemented to predict the quality of questions. To analyze the textual data of 60,000 questions, the Lexicon-based Sent

Dawdle

https://doi.org/10.1007/978-3-642-21655-8into decision-making processes. Our proposed approach harnesses the strengths of both the Dempster–Shafer theory and the Maximum Entropy Principle to enhance the supplier selection process. The decision-making framework is designed to handle the inherent uncertainty, subjectivity, and imprecision as

Diverticulitis

Harness