標(biāo)題: Titlebook: Artificial Intelligence Applications and Innovations; 20th IFIP WG 12.5 In Ilias Maglogiannis,Lazaros Iliadis,Antonios Papale Conference pr [打印本頁(yè)] 作者: 恰當(dāng) 時(shí)間: 2025-3-21 17:17
書目名稱Artificial Intelligence Applications and Innovations影響因子(影響力)
書目名稱Artificial Intelligence Applications and Innovations影響因子(影響力)學(xué)科排名
書目名稱Artificial Intelligence Applications and Innovations網(wǎng)絡(luò)公開度
書目名稱Artificial Intelligence Applications and Innovations網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Intelligence Applications and Innovations被引頻次
書目名稱Artificial Intelligence Applications and Innovations被引頻次學(xué)科排名
書目名稱Artificial Intelligence Applications and Innovations年度引用
書目名稱Artificial Intelligence Applications and Innovations年度引用學(xué)科排名
書目名稱Artificial Intelligence Applications and Innovations讀者反饋
書目名稱Artificial Intelligence Applications and Innovations讀者反饋學(xué)科排名
作者: 召集 時(shí)間: 2025-3-21 20:56 作者: 小淡水魚 時(shí)間: 2025-3-22 01:22 作者: Flatter 時(shí)間: 2025-3-22 06:54 作者: 簡(jiǎn)略 時(shí)間: 2025-3-22 09:23 作者: Eosinophils 時(shí)間: 2025-3-22 16:09 作者: forecast 時(shí)間: 2025-3-22 18:44
Enhancing Predictive Process Monitoring with Conformal Predictionty in predictions, providing a substantial contribution to the areas of PPM and CP. It also offers a solid and trustworthy approach for integrating uncertainty quantification into process mining predictive models that contributes to significantly enhanced decision-support.作者: Incompetent 時(shí)間: 2025-3-23 00:50
Conference proceedings 2024ons, AIAI 2024, held in?Corfu, Greece, during June 27–30, 2024...The 100 full papers and 8 short papers included in this book were carefully reviewed and selected from 213 submissions. The diverse nature of papers presented demonstrates the vitality of AI algorithms and approaches. It certainly prov作者: guardianship 時(shí)間: 2025-3-23 03:28
https://doi.org/10.1007/b138568etection of under the system mounted with Raspberry Pi, automatic data collection, preparing of a dataset, training and testing of the model, as well as leak detection are ensured. In this regard, DeepDetector offers a viable way of enhancing Android user security.作者: fibula 時(shí)間: 2025-3-23 09:20
Detecting Illicit Data Leaks on?Android Smartphones Using an?Artificial Intelligence Modelsetection of under the system mounted with Raspberry Pi, automatic data collection, preparing of a dataset, training and testing of the model, as well as leak detection are ensured. In this regard, DeepDetector offers a viable way of enhancing Android user security.作者: parsimony 時(shí)間: 2025-3-23 11:16
AI-Driven Sentiment Trend Analysis: Enhancing Topic Modeling Interpretation with?ChatGPTiment analysis and topic modeling, extracting meaningful insights from the vast amount of textual data generated on social media platforms presents unique challenges due to the short and noisy nature of the text. In this study, we propose a methodology for analyzing sentiment trends in social media,作者: 開始發(fā)作 時(shí)間: 2025-3-23 15:18
An Algorithmic Data Pipeline Architecture for the Production of Personalized Telecom Product Offerst” product at the “best” price, choosing from an increasingly complex collection of offers and tariff packages. To this end, various methods are aiming to understand and estimate the user‘s behavior, predict traffic and willingness to pay. Based on such information, sales channels select and propose作者: 不容置疑 時(shí)間: 2025-3-23 22:00 作者: 確定方向 時(shí)間: 2025-3-24 02:03 作者: nuclear-tests 時(shí)間: 2025-3-24 05:00 作者: 先兆 時(shí)間: 2025-3-24 10:03 作者: HAIRY 時(shí)間: 2025-3-24 13:56 作者: unstable-angina 時(shí)間: 2025-3-24 18:21
Multi-dimensional Classification on?Social Media Data for?Detailed Reporting with?Large Language Mod engage with others. All this knowledge can then be transformed into informative reports with the assistance of Large Language Models (LLMs), like ChatGPT, which leverage deep learning techniques to analyze data and generate comprehensive analyses. By effectively classifying user-generated posts bas作者: 脖子 時(shí)間: 2025-3-24 21:38 作者: Entirety 時(shí)間: 2025-3-25 02:07
Strategizing the Shallows: Leveraging Multi-Agent Reinforcement Learning for Enhanced Tactical Deciscal evidence. These conflicts are well-represented within the framework of Partially Observable Stochastic Games (POSG), which models the adversarial interactions between contending forces through decision-making agents, possible states, actions, observations, and probabilistic state transitions..Th作者: Allodynia 時(shí)間: 2025-3-25 03:30 作者: PUT 時(shí)間: 2025-3-25 07:56 作者: 木質(zhì) 時(shí)間: 2025-3-25 15:08 作者: agnostic 時(shí)間: 2025-3-25 18:30 作者: Temporal-Lobe 時(shí)間: 2025-3-26 00:00 作者: CYT 時(shí)間: 2025-3-26 02:07
Improved , Prediction Using Machine Learning Algorithmsir quality, particularly the concentration of . in the air, is crucial especially for urban settings due to direct health implications. Various machine and deep learning models have been used for air quality prediction. However, the application of these approaches on . concentration levels, focusing作者: 蘑菇 時(shí)間: 2025-3-26 07:20
Improving Agricultural Image Classification by?Mining Imagesds have emerged to address this task. However, as these methods have expanded, they have become more reliant on data and require additional external information to improve performance. In reality, agricultural images often have low quality and lack annotations, and it is challenging to obtain clear 作者: 起波瀾 時(shí)間: 2025-3-26 11:09 作者: Increment 時(shí)間: 2025-3-26 14:56
IFIP Advances in Information and Communication Technologyhttp://image.papertrans.cn/b/image/167565.jpg作者: Allure 時(shí)間: 2025-3-26 19:55
David A. Hart,Joan Stein-Streileiniment analysis and topic modeling, extracting meaningful insights from the vast amount of textual data generated on social media platforms presents unique challenges due to the short and noisy nature of the text. In this study, we propose a methodology for analyzing sentiment trends in social media,作者: AVID 時(shí)間: 2025-3-26 23:49
David A. Hart,Joan Stein-Streileint” product at the “best” price, choosing from an increasingly complex collection of offers and tariff packages. To this end, various methods are aiming to understand and estimate the user‘s behavior, predict traffic and willingness to pay. Based on such information, sales channels select and propose作者: 頑固 時(shí)間: 2025-3-27 03:08
Mitchell J. Nelles,J. Wayne Streileinused methods by investors. However, machine learning models are now widely applied to predict stock prices and trends, among which reinforcement learning has received significant attention. Previous studies have integrated additional technical indicator features combined with historical price inform作者: Manifest 時(shí)間: 2025-3-27 07:37
Hamster Lymphoid Cell Responses in Vitroa host of other fields concern themselves with extracting, predicting, and reacting to, changes in the topics being discussed by online users, and the disposition these users have with respect to topics of interest. Creating systems that can automate or simplify this process would have an immediate 作者: defeatist 時(shí)間: 2025-3-27 11:07 作者: Synchronism 時(shí)間: 2025-3-27 14:36
Peter Hoth MD,Annunziato Amendola MDCurrent RAG models primarily rely on vector similarity matching, which limits their ability to uncover latent semantic relationships between queries and documents. To enhance the retrieval phase of RAG, we propose a framework that incorporates topic modeling in the RAG pipeline for semantically rera作者: lethal 時(shí)間: 2025-3-27 17:58
https://doi.org/10.1007/b138568A dataset comprising 1000 customer surveys from 2020–2022 was crafted by annotating keywords gleaned from open-ended questions. The research employs the efficacy of fine-tuning Pre-trained Language Models (PLMs) and employing Large Language Models (LLMs) through prompting for keyword generation. The作者: Influx 時(shí)間: 2025-3-28 01:05 作者: granite 時(shí)間: 2025-3-28 03:02 作者: 領(lǐng)先 時(shí)間: 2025-3-28 10:16 作者: 帶來 時(shí)間: 2025-3-28 13:01 作者: Cervical-Spine 時(shí)間: 2025-3-28 15:42
https://doi.org/10.1007/b138568 as well as in organizations across different industry fields. Moreover, also AI-support is a necessary requirement for modern (big data) analysis applications nowadays. An exemplar industrial application domain highlighting the necessity of AI-supported data exploration in a real-world big data ana作者: PACK 時(shí)間: 2025-3-28 21:15
https://doi.org/10.1007/b138568us underlying neurological and vestibular disorders, impacting visual stability and affecting an individual’s perception of their surroundings. Benign Paroxysmal Positional Vertigo (BPPV) is a special case of nystagmus where brief episodes of dizziness are triggered by specific head movements. Howev作者: glucagon 時(shí)間: 2025-3-29 00:21 作者: 小步走路 時(shí)間: 2025-3-29 04:37 作者: 入會(huì) 時(shí)間: 2025-3-29 07:49 作者: 生氣地 時(shí)間: 2025-3-29 13:19
Janet L. Poole Ph.D., O.T.R./L.ds have emerged to address this task. However, as these methods have expanded, they have become more reliant on data and require additional external information to improve performance. In reality, agricultural images often have low quality and lack annotations, and it is challenging to obtain clear 作者: nascent 時(shí)間: 2025-3-29 15:47
https://doi.org/10.1007/978-3-031-63215-0Machine Learning; Anomaly Detection; Generative/Adversarial Neural Networks; Sentiment Analysis; Convolu作者: Hyperalgesia 時(shí)間: 2025-3-29 21:55 作者: 顛簸下上 時(shí)間: 2025-3-30 02:36 作者: insular 時(shí)間: 2025-3-30 04:20 作者: Rotator-Cuff 時(shí)間: 2025-3-30 08:44
Enhancing Financial Market Prediction with?Reinforcement Learning and?Ensemble Learningreinforcement learning model for the final prediction outcome. The experimental results show that integrating these two signal sources as input into the deep reinforcement learning model yields higher profits than the baseline model and achieves state-of-the-art performance in effectively integratin作者: 雜色 時(shí)間: 2025-3-30 13:29
Generating Profiles of?News Commentators with?Language Models a corpus of news articles and their associated comments. To evaluate their utility, learned topic models are fit to the article and comment data, as well as manually constructed sets of comparison profiles. The learned topic models are used to evaluate perplexity and coherence metrics between the g作者: 制定法律 時(shí)間: 2025-3-30 18:47 作者: 共和國(guó) 時(shí)間: 2025-3-31 00:02 作者: intoxicate 時(shí)間: 2025-3-31 03:05 作者: 搜尋 時(shí)間: 2025-3-31 08:53 作者: Glucocorticoids 時(shí)間: 2025-3-31 12:17
