標(biāo)題: Titlebook: Artificial Intelligence Applications and Innovations; 16th IFIP WG 12.5 In Ilias Maglogiannis,Lazaros Iliadis,Elias Pimenidis Conference pr [打印本頁] 作者: Sentry 時(shí)間: 2025-3-21 18:40
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: 全國性 時(shí)間: 2025-3-21 21:31 作者: Meditative 時(shí)間: 2025-3-22 02:45
1868-4238 entiment analysis - recommender systems.. .Part II: AI ethics - law; AI constraints; deep learning - LSTM; fuzzy algebra - fuzzy systems; machine learning; medical - health systems; and natural language..*The conference was held virtually due to the COVID-19 pandemic..978-3-030-49163-5978-3-030-49161-1Series ISSN 1868-4238 Series E-ISSN 1868-422X 作者: myelography 時(shí)間: 2025-3-22 08:36
Science of Data: A New Ladder for Causation, which is trained on the merchant names of the transactions. The other contribution is introducing a new strategy for training dataset generation employing the sliding window approach in a given time frame to adapt to the changes on the trends of fraudulent transactions. In the experiments, the feat作者: insincerity 時(shí)間: 2025-3-22 10:55
Science of Data: A New Ladder for Causation,affic in a city. Our ensemble learns the functional behaviour of an application by training on logs from normal execution time. It can then detect deviations from normal behaviour and also be retrained on false positive cases found during validation. Anomaly detection in RAN shows that our ensemble 作者: Melatonin 時(shí)間: 2025-3-22 16:06
Geetanjali Bihani,Julia Taylor Rayz HRV features were extracted in a controlled study to determine three levels of CL i.e., S0: low CL, S1: normal CL and S2: high CL. To get the best classification accuracy with the ML algorithms, different optimizations such as kernel functions were chosen with different feature matrices both for bi作者: TIGER 時(shí)間: 2025-3-22 18:35 作者: DEBT 時(shí)間: 2025-3-22 21:29
Leonida Gianfagna,Antonio Di Ceccodata ready for use. An innovative approach for analytics as a service is introduced and linked with a policy development toolkit, which is an integrated web-based environment to fulfil the requirements of the public policy ecosystem stakeholders.作者: attenuate 時(shí)間: 2025-3-23 04:44 作者: 連累 時(shí)間: 2025-3-23 07:15
Transparency: Motivations and Challengesion Challenge (BraTS) dataset, we demonstrate that it is able to outperform traditional interpolation methods by up?to 20. on SSIM scores whilst retaining generalisability on brain MRI images. We show that performance across scales is not compromised, and that it is able to achieve competitive resul作者: 前面 時(shí)間: 2025-3-23 11:07
An Adaptive Approach on Credit Card Fraud Detection Using Transaction Aggregation and Word Embedding which is trained on the merchant names of the transactions. The other contribution is introducing a new strategy for training dataset generation employing the sliding window approach in a given time frame to adapt to the changes on the trends of fraudulent transactions. In the experiments, the feat作者: meretricious 時(shí)間: 2025-3-23 17:25
Boosted Ensemble Learning for Anomaly Detection in 5G RANaffic in a city. Our ensemble learns the functional behaviour of an application by training on logs from normal execution time. It can then detect deviations from normal behaviour and also be retrained on false positive cases found during validation. Anomaly detection in RAN shows that our ensemble 作者: 看法等 時(shí)間: 2025-3-23 20:27
Machine Learning for Cognitive Load Classification – A Case Study on Contact-Free Approach HRV features were extracted in a controlled study to determine three levels of CL i.e., S0: low CL, S1: normal CL and S2: high CL. To get the best classification accuracy with the ML algorithms, different optimizations such as kernel functions were chosen with different feature matrices both for bi作者: nephritis 時(shí)間: 2025-3-24 01:53 作者: crockery 時(shí)間: 2025-3-24 04:06
PolicyCLOUD: Analytics as a Service Facilitating Efficient Data-Driven Public Policy Managementdata ready for use. An innovative approach for analytics as a service is introduced and linked with a policy development toolkit, which is an integrated web-based environment to fulfil the requirements of the public policy ecosystem stakeholders.作者: Guaff豪情痛飲 時(shí)間: 2025-3-24 09:25 作者: fatty-streak 時(shí)間: 2025-3-24 12:18 作者: HILAR 時(shí)間: 2025-3-24 18:52
An Innovative Graph-Based Approach to Advance Feature Selection from Multiple Textual Documentsost text classifiers employed and decreases the number of features required to achieve ‘state-of-the-art’ accuracy. Well-known datasets used for the experimentations reported in this paper include ., ., . and ..作者: HEED 時(shí)間: 2025-3-24 20:57 作者: MENT 時(shí)間: 2025-3-25 01:36
Explainable AI Methods and Applications,centrating in one region of the projection space. This region is identified using Density-based Spatial Clustering of Applications with Noise. Applicant companies which are projected within this region are labeled potential funding recipients and will be suggested the most competitive funding mechanisms.作者: 商品 時(shí)間: 2025-3-25 04:18
A Two-Levels Data Anonymization Approachn by incorporating the discriminative information to test the effect of labels on the quality of the anonymized data. The results show that the proposed approaches give good results in terms of utility what preserves the trade-off between data privacy and its usefulness.作者: 老人病學(xué) 時(shí)間: 2025-3-25 08:36
Manifold Learning for Innovation Funding: Identification of Potential Funding Recipientscentrating in one region of the projection space. This region is identified using Density-based Spatial Clustering of Applications with Noise. Applicant companies which are projected within this region are labeled potential funding recipients and will be suggested the most competitive funding mechanisms.作者: 容易做 時(shí)間: 2025-3-25 13:26
Conference proceedings 2020ligence Applications and Innovations, AIAI 2020, held in Neos Marmaras, Greece, in June 2020.*.The 70 full papers and 5 short papers presented were carefully reviewed and selected from 149 submissions. They cover a broad range of topics related to technical, legal, and ethical aspects of artificial 作者: 衰老 時(shí)間: 2025-3-25 19:22
The Future of AI-Enabled Cybersecurity positions and moderated results. We also focused on forecasting chart ranges, namely the top 5, 10 and 20. Given the accuracy and F-score achieved compared to previous research, our findings are deemed satisfactory, especially in predicting the top 20.作者: Pseudoephedrine 時(shí)間: 2025-3-25 23:30
Towards Explainable Artificial Intelligencetion on JPEG images. Our experimental findings on two different benchmark datasets showcase that the fused output achieves high performance and advanced interpretability by managing to leverage the correctly localized outputs of individual methods, and even detecting cases that were missed by all individual methods.作者: avarice 時(shí)間: 2025-3-26 00:55 作者: 油氈 時(shí)間: 2025-3-26 07:45
Knowledge-Based Fusion for Image Tampering Localizationtion on JPEG images. Our experimental findings on two different benchmark datasets showcase that the fused output achieves high performance and advanced interpretability by managing to leverage the correctly localized outputs of individual methods, and even detecting cases that were missed by all individual methods.作者: conscience 時(shí)間: 2025-3-26 11:29
Conference proceedings 2020nalysis - recommender systems.. .Part II: AI ethics - law; AI constraints; deep learning - LSTM; fuzzy algebra - fuzzy systems; machine learning; medical - health systems; and natural language..*The conference was held virtually due to the COVID-19 pandemic..作者: sterilization 時(shí)間: 2025-3-26 15:55 作者: 重疊 時(shí)間: 2025-3-26 18:55 作者: 招惹 時(shí)間: 2025-3-26 23:48
Cybersecurity Landscape for Computer Systemsizing on various Smart Cities components, such as data harvesting and data mining. It addresses the research question whether we can forecast traffic load based on past data, as well as meteorological conditions. Results have shown that various models can be developed based on weather data with varying level of success.作者: Neutral-Spine 時(shí)間: 2025-3-27 03:53 作者: SUGAR 時(shí)間: 2025-3-27 05:43 作者: 多產(chǎn)魚 時(shí)間: 2025-3-27 09:29 作者: 表狀態(tài) 時(shí)間: 2025-3-27 15:01
Using Classification for Traffic Prediction in Smart Citiesizing on various Smart Cities components, such as data harvesting and data mining. It addresses the research question whether we can forecast traffic load based on past data, as well as meteorological conditions. Results have shown that various models can be developed based on weather data with varying level of success.作者: 追逐 時(shí)間: 2025-3-27 19:27
Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition SystemsGoogle, and Wit, using the WER, Hper, and Rper error metrics. The experimental results show that Google’s automatic speech recognition performs better among the three systems. We intend to extend the benchmarking both to include most of the available Automated Speech Recognition systems and increase our test data.作者: padding 時(shí)間: 2025-3-28 00:01 作者: 蠟燭 時(shí)間: 2025-3-28 06:07 作者: inhumane 時(shí)間: 2025-3-28 07:35 作者: LASH 時(shí)間: 2025-3-28 13:37
https://doi.org/10.1007/978-3-030-49161-1artificial intelligence; classification; computational linguistics; computer hardware; computer networks作者: modest 時(shí)間: 2025-3-28 18:18 作者: Hallowed 時(shí)間: 2025-3-28 22:07 作者: PLE 時(shí)間: 2025-3-29 02:20
Science of Data: A New Ladder for Causation,s more difficult to troubleshoot the systems. Vendors are spending a lot of time and effort on early anomaly detection in their development cycle and majority of the time is spent on manually analyzing system logs. While main research in anomaly detection uses performance metrics, anomaly detection 作者: Proclaim 時(shí)間: 2025-3-29 07:08
Geetanjali Bihani,Julia Taylor RayzECG). However, these signals are problematic in situations e.g., in dynamic moving environments where the user cannot relax with all the sensors attached to the body and it provides significant noises in the signals. This paper presents a case study using a contact-free approach for CL classificatio作者: 管理員 時(shí)間: 2025-3-29 08:20 作者: 牛馬之尿 時(shí)間: 2025-3-29 13:30
Cybersecurity Landscape for Computer Systemsh Smart City projects, particularly focusing on traffic prediction. A systematic literature review identifies the main topics and methods used, emphasizing on various Smart Cities components, such as data harvesting and data mining. It addresses the research question whether we can forecast traffic 作者: 預(yù)感 時(shí)間: 2025-3-29 17:17 作者: Fulminate 時(shí)間: 2025-3-29 21:59 作者: 冰河期 時(shí)間: 2025-3-30 03:19 作者: 坦白 時(shí)間: 2025-3-30 07:53 作者: CHIDE 時(shí)間: 2025-3-30 08:21
Hardware Acceleration of Explainable AIn-consistency. If clusters are ball-shaped, one can derive conditions under which two clusters attain the global optimum of .-means. We show further that if the gap is sufficient for perfect separation, then an incremental .-means is able to discover perfectly separated clusters. This is in conflict作者: 靈敏 時(shí)間: 2025-3-30 16:09
Explainable AI Methods and Applications,ng mechanisms. Analysis of financial data from former funding recipients partially feeds the recommendation system. Financial company data from a representative French population are reduced and projected onto a two-dimensional space with Uniform Manifold Approximation and Projection, a manifold lea
