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: 哄笑 時(shí)間: 2025-3-21 17:08
書(shū)目名稱Artificial Intelligence Applications and Innovations影響因子(影響力)
書(shū)目名稱Artificial Intelligence Applications and Innovations影響因子(影響力)學(xué)科排名
書(shū)目名稱Artificial Intelligence Applications and Innovations網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Artificial Intelligence Applications and Innovations網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Artificial Intelligence Applications and Innovations被引頻次
書(shū)目名稱Artificial Intelligence Applications and Innovations被引頻次學(xué)科排名
書(shū)目名稱Artificial Intelligence Applications and Innovations年度引用
書(shū)目名稱Artificial Intelligence Applications and Innovations年度引用學(xué)科排名
書(shū)目名稱Artificial Intelligence Applications and Innovations讀者反饋
書(shū)目名稱Artificial Intelligence Applications and Innovations讀者反饋學(xué)科排名
作者: 爆炸 時(shí)間: 2025-3-21 23:25 作者: 吹牛大王 時(shí)間: 2025-3-22 04:26 作者: 閑聊 時(shí)間: 2025-3-22 07:32 作者: NUL 時(shí)間: 2025-3-22 10:17
Hand Function in Rheumatoid Arthritisalance between data diversity and information preservation, KeepOriginalAugment enables models to leverage both diverse salient and non-salient regions, leading to enhanced performance. We explore three strategies for determining the placement of the salient region—minimum, maximum, or random—and in作者: 不給啤 時(shí)間: 2025-3-22 14:27 作者: 輕推 時(shí)間: 2025-3-22 20:04 作者: 菊花 時(shí)間: 2025-3-23 00:30 作者: Asseverate 時(shí)間: 2025-3-23 03:56 作者: Interdict 時(shí)間: 2025-3-23 05:42
Bruce W. Conolly,Mario Benanzioeve a reliable result, the output of the ANN model should be evaluated as an average of at least 50 runs, with reinitiating the starting values of the weights and biases in each run. The model was also used to study the effect of airflow on the performance of the AC and identify the conditions leadi作者: jettison 時(shí)間: 2025-3-23 12:11 作者: 疼死我了 時(shí)間: 2025-3-23 14:54 作者: 角斗士 時(shí)間: 2025-3-23 19:12 作者: 假 時(shí)間: 2025-3-23 22:14 作者: 裙帶關(guān)系 時(shí)間: 2025-3-24 04:13
Gamified Crowd Management Utilizing AR and?Computer Vision on?the?Edgehm and a prototype system for a museum exhibition which operates at the edge of the network utilizing the mobile devices as execution environments. As part of the prototype, we propose the integration of a series of serious games that are supplementary to the exhibits and have the goal of delaying t作者: 設(shè)想 時(shí)間: 2025-3-24 07:51
KeepOriginalAugment: Single Image-Based Better Information-Preserving Data Augmentation Approachalance between data diversity and information preservation, KeepOriginalAugment enables models to leverage both diverse salient and non-salient regions, leading to enhanced performance. We explore three strategies for determining the placement of the salient region—minimum, maximum, or random—and in作者: 該得 時(shí)間: 2025-3-24 13:36
Using DCGANs and HOG?+?Patch-Based CNN for Face Spoofing Mitigation biometric samples. We then proposed a HOG?+?Patch-based CNN structure for spoofing mitigation on the generated spoofing datasets. Our proposed CNN model outperforms notable VGG-16 and ResNet-50 models in classification and verification accuracies on our spoofing dataset.作者: PATHY 時(shí)間: 2025-3-24 18:48 作者: bromide 時(shí)間: 2025-3-24 21:09
A Machine Learning Approach for?Points of?Interest Extraction and?Event Classificationsion of routine predictions but also enhances the adaptability of the system to changes in mobility behavior over time. The incorporation of a cognitive module, based on Dynamic Neural Fields (DNF), further allows for personalized predictions regarding the timing, duration, and nature of trips. Vali作者: OCTO 時(shí)間: 2025-3-25 00:30
Controlling Popularity Bias in?Sequential Recommendation Models prioritizes being most correct rather than trying to find a truly fitting recommendation. Popularity bias is a main cause of echo chambers within the current media landscape, which unfortunately has led to less critical thinking and more divisiveness our communities. To counter this issue, we prese作者: meditation 時(shí)間: 2025-3-25 03:43 作者: Alopecia-Areata 時(shí)間: 2025-3-25 09:03
Dynamic Stacking Optimization in?Unpredictable Environments: A Focus on?Crane Schedulings operating on the same girder, multiple arrival and handover stacks, and various stacks in the buffer area. Our approach employs a role-based solver, efficiently planning crane assignments to manage container movements within the terminal. The performance of the solvers is compared with other solve作者: Conserve 時(shí)間: 2025-3-25 15:21 作者: PATRI 時(shí)間: 2025-3-25 16:39
Optimizations for?Learning from?Linear Feedback Shift Register Variations with?Artificial Neural Netneeded for learning to predict the outputs of the Geffe generator through the introduction of an ANN pipeline model. Performed experiments display the strength of the approach that is able to maintain up to . accuracy for predicting Geffe outputs while reducing the amount of training bits to the deg作者: Benzodiazepines 時(shí)間: 2025-3-25 20:37
Deep Sleep Recognition Based on?CNNs and?Data Augmentation of collecting sleep EEG data in real life, which can also pose unnecessary inconvenience to subjects over long periods, we employed three data augmentation methods to enrich the dataset and introduce more variability under limited data capabilities. In this paper, we explore the application of data作者: nocturia 時(shí)間: 2025-3-26 01:15
Gamified Crowd Management Utilizing AR and?Computer Vision on?the?Edgeital technologies and tools. Among others, the experience is affected by the number of visitors in the exhibition area, making the indoor crowd management a challenging topic which cross cuts several scientific areas..Various methods have been developed to overcome this issue especially in museum ex作者: 反感 時(shí)間: 2025-3-26 04:23 作者: inquisitive 時(shí)間: 2025-3-26 10:07 作者: 敬禮 時(shí)間: 2025-3-26 15:53
Vertical Federated Image Segmentationon is located on separate data silos and it can be difficult for a machine learning engineer to consolidate all of it in a fashion that is appropriate for model development. Additionally, some of these localized data regions may not have access to a labelled ground truth, rendering conventional mode作者: 水土 時(shí)間: 2025-3-26 19:32 作者: terazosin 時(shí)間: 2025-3-26 21:14
An Empirical Analysis of?Data Reduction Techniques for?k-NN Classificationd Prototype Generation (PG) methods. The research provides an in-depth examination of these methodologies, categorizing DRTs into two primary categories: PS and PG, and further dividing them into three sub-categories: condensation methods, edition methods, and hybrid methods. An experimental study c作者: 織布機(jī) 時(shí)間: 2025-3-27 03:19
Controlling Popularity Bias in?Sequential Recommendation Models result, through the way that people consume news and media, we are transitioning from a static media delivery model to a dynamic, personalized system which many are adapting and even enjoying the resulted changes. Personalized recommendations are mostly made with the help of the machine learning mo作者: 突變 時(shí)間: 2025-3-27 07:28
Enhanced Item Recommendation via?Graph Properties in?Sparse Data and analytical perspectives. The latest works focus on ranking-based personalized recommenders. However, they recommend the same number of items for everyone and still suffer from the interaction sparsity issue. We propose a complex-graph-oriented supervised learning-based link prediction with a re作者: Nutrient 時(shí)間: 2025-3-27 13:10
Modeling the Air Conditioner Performance Tests Using Artificial Neural Network Simulator (ANNS-AC)oses. This helps save time and effort instead of repeating the test for validation. A backpropagation ANN models with multiple hidden layers were trained using 22 input variables and three targets. More than 800 test reports were used to train the ANN model. The input processing functions, neuron si作者: demote 時(shí)間: 2025-3-27 17:10 作者: Aspirin 時(shí)間: 2025-3-27 18:52
A Voting Approach for?Explainable Classification with?Rule Learning instance. Contrarily, in this paper, we investigate the application of rule learning methods in such a context. Thus, classifications become based on comprehensible (first-order) rules, explaining the predictions made. In general, however, rule-based classifications are less accurate than state-of-作者: 抱怨 時(shí)間: 2025-3-27 22:36
Dynamic Stacking Optimization in?Unpredictable Environments: A Focus on?Crane SchedulingIt entails the utilization of cranes to relocate products, with the relocation needing to be scheduled while adhering to various time constraints. This paper addresses the challenge of developing solution approaches for such dynamic stacking problems in uncertain environments, particularly the envir作者: 顯赫的人 時(shí)間: 2025-3-28 06:04
FASTER-CE: Fast, Sparse, Transparent, and?Robust Counterfactual Explanationsal explanation definition, researchers have also identified other desirable properties that make counterfactual explanations more usable on the deployment and the end-user sides: speed of explanation generation, robustness/sensitivity, and succinctness of explanations. Motivated by the need to make 作者: boisterous 時(shí)間: 2025-3-28 07:28
Optimizations for?Learning from?Linear Feedback Shift Register Variations with?Artificial Neural Netest results have been obtained for learning on Linear Feedback Shift Registers (LFSRs). Due to the deterministic nature of LFSRs, Decision Trees (DTs) and Artificial Neural Networks (ANNs) were able to reach up to . test accuracy for next bit prediction tasks. Despite important advances, a number of作者: neoplasm 時(shí)間: 2025-3-28 13:03 作者: 挫敗 時(shí)間: 2025-3-28 16:19 作者: 表否定 時(shí)間: 2025-3-28 19:55 作者: vasculitis 時(shí)間: 2025-3-29 01:49 作者: Irascible 時(shí)間: 2025-3-29 05:30
Pattern Matching in?Polyphonic Musical Sequencested pieces as well as well-known pieces. The algorithm consistently was able to detect both exact and approximate pattern occurrences accurately, even when the pieces were subject to changes in rhythm and key. A series of testing rounds involving manipulation of . and . values, showcases the algorithms’ adaptability and efficiency.作者: 古董 時(shí)間: 2025-3-29 08:05 作者: Defiance 時(shí)間: 2025-3-29 15:05 作者: reflection 時(shí)間: 2025-3-29 17:43 作者: 我不怕?tīng)奚?nbsp; 時(shí)間: 2025-3-29 23:14
Conference proceedings 2024and selected from 213 submissions. The diverse nature of papers presented demonstrates the vitality of AI algorithms and approaches. It certainly proves the very wide range of AI applications as well..作者: 過(guò)份艷麗 時(shí)間: 2025-3-30 02:41
1868-4238 reviewed and selected from 213 submissions. The diverse nature of papers presented demonstrates the vitality of AI algorithms and approaches. It certainly proves the very wide range of AI applications as well..978-3-031-63225-9978-3-031-63223-5Series ISSN 1868-4238 Series E-ISSN 1868-422X 作者: NAV 時(shí)間: 2025-3-30 04:57
Rollin K. Daniel,Kevin A. Brenneralistic negative sampling application for overcoming these problems. We employ the power-law degree distribution property of the complex graphs to sample the negative instances. The experiments show that our method outperforms ranking-based personalized recommenders with a 20% increase in recommendation success in multiple evaluation metrics.作者: capsaicin 時(shí)間: 2025-3-30 08:20 作者: 滔滔不絕地講 時(shí)間: 2025-3-30 14:35
Rehabilitation in the Athletes,tensive experimentation taking into consideration multiple scenarios pertaining to varying number of stations and available drones indicates that the reposition of the launch pad as center for all drones’ routes, obtained significant improvements/minimisation in the total distance of the path ranging from 4% to 22%.作者: GLIDE 時(shí)間: 2025-3-30 17:19
A Voting Approach for?Explainable Classification with?Rule Learningark data sets including a use case of significant interest to insurance industries, we prove that our approach not only clearly outperforms ordinary rule learning methods, but also yields results on a par with state-of-the-art outcomes.作者: Tinea-Capitis 時(shí)間: 2025-3-30 23:24 作者: Gratuitous 時(shí)間: 2025-3-31 03:06 作者: 胰臟 時(shí)間: 2025-3-31 05:19 作者: Trypsin 時(shí)間: 2025-3-31 11:32
