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Titlebook: Human-Centric Smart Computing; Proceedings of ICHCS Siddhartha Bhattacharyya,Jyoti Sekhar Banerjee,Mar Conference proceedings 2024 The Edit

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31#
發(fā)表于 2025-3-27 00:56:05 | 只看該作者
Career Change: Systematic Literature Review and Future Research Agenda,change intentions. To show the current situation of the research studies in the field of career change and to determine the scope for future study in this field, a rigorous analysis of 28 documents published during the period from 2014 to 2022 has been conducted. No systematic review of literature s
32#
發(fā)表于 2025-3-27 02:00:25 | 只看該作者
33#
發(fā)表于 2025-3-27 07:50:09 | 只看該作者
,Registration of?CT Images of?Lung Using Fireworks Algorithm, for registering lung CT images. Following that, the FWA-IATF algorithm successfully registers the images. The results of registration based on the FWA-IATF method and PSO approach are compared. The results indicate that the FWA-IATF-based registration methods outperform the PSO-based registration a
34#
發(fā)表于 2025-3-27 10:02:10 | 只看該作者
35#
發(fā)表于 2025-3-27 15:48:32 | 只看該作者
36#
發(fā)表于 2025-3-27 20:14:50 | 只看該作者
37#
發(fā)表于 2025-3-27 22:09:35 | 只看該作者
An Exploration of How Children Can Be Proactive for Their Own Digital Privacy and Security in the Phildren may establish a safer online environment for themselves by avoiding issues that compromise their privacy and security online, by discussing their cyber-activities with their parents, and by receiving proper instruction on online safety.
38#
發(fā)表于 2025-3-28 03:59:59 | 只看該作者
A Scalable URL-Based Phishing Websites Detection System Using Machine Learning Techniques,tting these harmful actions. This is because the vast majority of phishing attacks have characteristics that machine learning can detect. We‘ll offer a phishing detection system in this research that uses machine learning, specifically supervised learning, to determine whether a website is authentic
39#
發(fā)表于 2025-3-28 07:55:34 | 只看該作者
40#
發(fā)表于 2025-3-28 12:32:38 | 只看該作者
Prediction of Heart Disease and Improving Classifier Performance Using Particle Swarm Optimization,nd Dataset is used for performing the analysis with 19 features. In order to increase the performance of the classifiers, the proposed approaches also choose a significant number of features to utilize as data entries. The acquired results demonstrated that the suggested diagnostic method can accura
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