標(biāo)題: Titlebook: Data Science and Artificial Intelligence; First International Chutiporn Anutariya,Marcello M. Bonsangue Conference proceedings 2023 The Ed [打印本頁] 作者: Opulent 時(shí)間: 2025-3-21 18:14
書目名稱Data Science and Artificial Intelligence影響因子(影響力)
書目名稱Data Science and Artificial Intelligence影響因子(影響力)學(xué)科排名
書目名稱Data Science and Artificial Intelligence網(wǎng)絡(luò)公開度
書目名稱Data Science and Artificial Intelligence網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Science and Artificial Intelligence被引頻次
書目名稱Data Science and Artificial Intelligence被引頻次學(xué)科排名
書目名稱Data Science and Artificial Intelligence年度引用
書目名稱Data Science and Artificial Intelligence年度引用學(xué)科排名
書目名稱Data Science and Artificial Intelligence讀者反饋
書目名稱Data Science and Artificial Intelligence讀者反饋學(xué)科排名
作者: Provenance 時(shí)間: 2025-3-21 20:58 作者: Shuttle 時(shí)間: 2025-3-22 03:56 作者: 有罪 時(shí)間: 2025-3-22 05:27
A Modified Hybrid RBF-BP Network Classifier for?Nonlinear Estimation/Classification and?Its Applicat[., .] is proposed. The modified hybrid RBF-BP network is formulated as an adaptive incremental learning algorithm for a single-layer RBF hidden neuron layer. The algorithm uses a density clustering approach to determine the number of RBF hidden neurons and it maintains the self-learning process of 作者: 發(fā)微光 時(shí)間: 2025-3-22 12:03 作者: Infiltrate 時(shí)間: 2025-3-22 16:09
Exploration of?the?Feasibility and?Applicability of?Domain Adaptation in?Machine Learning-Based Codee to limited choices of the publicly available datasets, most of the machine learning-based classifiers were trained by the earlier versions of open-source projects that no longer represent the characteristics and properties of modern programming languages. Our experiments exhibit the feasibility an作者: Infiltrate 時(shí)間: 2025-3-22 17:05
Web Usage Mining for?Determining a?Website’s Usage Pattern: A Case Study of?Government Websiteyze a website’s usage. This study examined web usage mining to discover online users’ usage patterns and used the results to redesign and improve the government website. This study aims to help online customers obtain a better experience. A dataset was collected from the Metropolitan Electricity Aut作者: carbohydrate 時(shí)間: 2025-3-23 00:12
Deep-Learning-Based LSTM Model for Predicting a Tidal River’s Water Levels: A Case Study of the Kapuing method to forecast the water level dynamics of the Kapuas Kecil River and determine the optimal window size for precise predictions. Our results reveal an optimal window size of 336 h (equivalent to 14?days) for water level prediction using LSTM in this coastal region. Using this optimal window 作者: 門窗的側(cè)柱 時(shí)間: 2025-3-23 01:22
Data Augmentation for?EEG Motor Imagery Classification Using Diffusion Model brain-computer interfaces (BCIs). However, due to the limited amount of available data, overfitting is a common problem, especially when using a deep-learning classifier. One way to address this is by performing data augmentation. In this paper, we investigate the efficacy of the diffusion model as作者: Obstacle 時(shí)間: 2025-3-23 06:12 作者: galley 時(shí)間: 2025-3-23 13:14 作者: 清洗 時(shí)間: 2025-3-23 15:20
Using the New YoLo Models in Detecting Small-Sized Objects in the Case of Rice Grains on Branche the grain quality. In identifying rice seeds, there are also some difficulties in separating components such as branches and spikes. The study uses a dataset of images of rice branches with differences in shape, state, and size. After the pre-processing steps, the obtained data has images of relati作者: Throttle 時(shí)間: 2025-3-23 19:44 作者: PHON 時(shí)間: 2025-3-23 23:06 作者: 討好女人 時(shí)間: 2025-3-24 03:08
Improving Low Light Object Detection Using Image Enhancement Modelsoorly. We present a comparative study of various state-of-the-art image enhancement models for the purpose of facilitating object detection under low light. We also propose a new method for robust low-light object detection that shows substantial improvements over state-of-the-art baselines. The pro作者: 機(jī)構(gòu) 時(shí)間: 2025-3-24 10:11
Data Science and Artificial Intelligence978-981-99-7969-1Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: 尋找 時(shí)間: 2025-3-24 13:44 作者: 使顯得不重要 時(shí)間: 2025-3-24 17:02
978-981-99-7968-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: Morsel 時(shí)間: 2025-3-24 21:18
Conference proceedings 2023 full papers and the 4 short papers included in this volume were carefully reviewed and selected from 70 submissions.?This volume focuses on ideas, methodologies, and cutting-edge research that can drive progress and foster interdisciplinary collaboration in the fields of data science and artificial intelligence.作者: 捕鯨魚叉 時(shí)間: 2025-3-25 00:31 作者: Granular 時(shí)間: 2025-3-25 04:40 作者: 全神貫注于 時(shí)間: 2025-3-25 08:49 作者: 脆弱么 時(shí)間: 2025-3-25 14:01 作者: inflame 時(shí)間: 2025-3-25 19:38 作者: Tempor 時(shí)間: 2025-3-25 23:15
Complications of Regional Anesthesiae to limited choices of the publicly available datasets, most of the machine learning-based classifiers were trained by the earlier versions of open-source projects that no longer represent the characteristics and properties of modern programming languages. Our experiments exhibit the feasibility an作者: Innocence 時(shí)間: 2025-3-26 03:00 作者: organism 時(shí)間: 2025-3-26 05:26
https://doi.org/10.1007/978-3-319-98264-9ing method to forecast the water level dynamics of the Kapuas Kecil River and determine the optimal window size for precise predictions. Our results reveal an optimal window size of 336 h (equivalent to 14?days) for water level prediction using LSTM in this coastal region. Using this optimal window 作者: Scintillations 時(shí)間: 2025-3-26 09:37
Maria Angela Cerruto,Alessandra Masin brain-computer interfaces (BCIs). However, due to the limited amount of available data, overfitting is a common problem, especially when using a deep-learning classifier. One way to address this is by performing data augmentation. In this paper, we investigate the efficacy of the diffusion model as作者: Euphonious 時(shí)間: 2025-3-26 13:05 作者: eustachian-tube 時(shí)間: 2025-3-26 18:46
https://doi.org/10.1007/978-3-031-35575-2l network (GAN) to create and alter images that are practically impossible for humans to distinguish from authentic ones. The development of GAN technology has led to significant improvements in image generation. This progress has made it difficult for humans to differentiate between generated image作者: Prostaglandins 時(shí)間: 2025-3-26 21:06
Small Footprint JavaScript Engine the grain quality. In identifying rice seeds, there are also some difficulties in separating components such as branches and spikes. The study uses a dataset of images of rice branches with differences in shape, state, and size. After the pre-processing steps, the obtained data has images of relati作者: B-cell 時(shí)間: 2025-3-27 01:06
Gabriel Mujica,Jorge Portilla,Teresa Riesgo2.5 gene is a key transcription factor that regulates cardiomyocyte differentiation. A human embryonic stem cell (hESC) reporter line with NKX2.5 in GFP signal allows us to monitor the specificity and efficiency of human cardiac differentiation. We intend to develop an automatic analysis pipeline fo作者: 刺激 時(shí)間: 2025-3-27 08:07
Components and Services for IoT Platformsng price. Typically, a pineapple’s sweetness is determined manually, which is time-consuming and prone to human error, resulting in a lower selling price and pineapple waste due to unsold fruit. To address this issue, we developed a deep learning-based algorithm for identifying the sweetness of pine作者: 浮夸 時(shí)間: 2025-3-27 11:29 作者: Cultivate 時(shí)間: 2025-3-27 16:40 作者: Paradox 時(shí)間: 2025-3-27 18:41
Conference proceedings 2023 full papers and the 4 short papers included in this volume were carefully reviewed and selected from 70 submissions.?This volume focuses on ideas, methodologies, and cutting-edge research that can drive progress and foster interdisciplinary collaboration in the fields of data science and artificial作者: SSRIS 時(shí)間: 2025-3-28 00:24
Unable to Intubate the Left Coronary Systemrtificial and real-life datasets, for example, Double Moon, Concentric Circle, No Structure and UCI datasets, are used to test the effectiveness of our homemade implementation strategies. The experimental results showed that the implemented algorithm has significant accuracy improvement and reliability.作者: 制定 時(shí)間: 2025-3-28 03:39
Maria Angela Cerruto,Alessandra Masinthod outperformed other methods in terms of classification accuracy by 17.49%. The Kullback-Leibler (KL) divergence is used for assessing the similarity between the training set (with and without augmentation) and validation set, thus showing the effectiveness of the diffusion approach compared to other techniques.作者: Influx 時(shí)間: 2025-3-28 06:45
Maria Angela Cerruto,Alessandra Masin2vec from Thai2Fit and English-Thai machine translation models proposed by VISTEC. Based on the augmented messages, a Deep Learning technique, BiLSTM, is used to construct a chatbot classification model. The experimental obtained results demonstrate that data augmentation can help to increase the classification performance.作者: Accomplish 時(shí)間: 2025-3-28 10:49 作者: 拱形大橋 時(shí)間: 2025-3-28 16:57 作者: 吸氣 時(shí)間: 2025-3-28 21:51
Data Augmentation for?EEG Motor Imagery Classification Using Diffusion Modelthod outperformed other methods in terms of classification accuracy by 17.49%. The Kullback-Leibler (KL) divergence is used for assessing the similarity between the training set (with and without augmentation) and validation set, thus showing the effectiveness of the diffusion approach compared to other techniques.作者: Largess 時(shí)間: 2025-3-29 01:43
Thai Conversational Chatbot Classification Using BiLSTM and Data Augmentation2vec from Thai2Fit and English-Thai machine translation models proposed by VISTEC. Based on the augmented messages, a Deep Learning technique, BiLSTM, is used to construct a chatbot classification model. The experimental obtained results demonstrate that data augmentation can help to increase the classification performance.作者: Gene408 時(shí)間: 2025-3-29 06:37
Deep Learning Implementation for?Pineapple Sweetness ClassificationtNet-50, both with a learning rate of 0.000001, to differentiate between sweet and not-so-sweet pineapple. Both algorithms detected pineapple sweetness with the same accuracy of 84.09%. However, RestNet50 had a greater loss than EffisintNetB4.作者: enchant 時(shí)間: 2025-3-29 07:28
Small Footprint JavaScript Engineet of 150 images and nearly 6000 instances, and the results are evaluated on many different epochs, the results show that the highest accuracy belongs to YOLOv7, which is 89.93% (Precision), 87.96% (Recall), and 91.33% (mAP). The study also opens up further studies in detecting diseases on rice, such as grain blight, cotton neck blast, etc.作者: Prosaic 時(shí)間: 2025-3-29 12:40 作者: BRACE 時(shí)間: 2025-3-29 18:13
Improving Low Light Object Detection Using Image Enhancement Modelslight. We also propose a new method for robust low-light object detection that shows substantial improvements over state-of-the-art baselines. The proposed approach increases detection robustness to different lighting conditions and establishes a state-of-the-art mAP. of 79.5% on the ExDark dataset.作者: CURT 時(shí)間: 2025-3-29 21:23 作者: Feedback 時(shí)間: 2025-3-30 02:10
Ricardo A. Natalin MD,Jaime Landmanrtinent features. Our experimental results showcase robust model performance, achieving an F1-score of 90% on our experimental dataset, surpassing other approaches. Further results and discussions are provided in this paper, .作者: 裝飾 時(shí)間: 2025-3-30 04:03 作者: PAD416 時(shí)間: 2025-3-30 11:09
1865-0929 23..The 22 full papers and the 4 short papers included in this volume were carefully reviewed and selected from 70 submissions.?This volume focuses on ideas, methodologies, and cutting-edge research that can drive progress and foster interdisciplinary collaboration in the fields of data science and 作者: JAUNT 時(shí)間: 2025-3-30 15:46 作者: ARY 時(shí)間: 2025-3-30 18:23
Hybridization of Modified Grey Wolf Optimizer and Dragonfly for Feature Selectionrtinent features. Our experimental results showcase robust model performance, achieving an F1-score of 90% on our experimental dataset, surpassing other approaches. Further results and discussions are provided in this paper, .作者: incision 時(shí)間: 2025-3-30 23:21
Deep-Learning-Based LSTM Model for Predicting a Tidal River’s Water Levels: A Case Study of the Kapusize, the LSTM model consistently outperforms GRU and RNN models in comparative assessments. These findings offer not only valuable insights into water level prediction in the study area but also the potential of deep learning to enhance flood and disaster management in similar river systems globally.作者: 暖昧關(guān)系 時(shí)間: 2025-3-31 02:44
SecureQNN: Introducing a?Privacy-Preserving Framework for?QNNs at?the?Deep Edgehe number of epochs an attacker requires to build a model with the same accuracy as the target with the information disclosed. The set of layers whose information makes the attacker spend less training effort than the owner training from scratch is protected in an isolated environment, i.e., the sec作者: 過份 時(shí)間: 2025-3-31 05:56
Chaotic Mountain Gazelle Optimizer (CMGO): A Robust Optimization Algorithm for K-Means Clustering of outperforms the original MGO and other tested algorithms in clustering pure numeric and categorical data, securing first place, and third for mixed data. Thus, CMGO emerges as a robust, efficient K-means optimizing method for complex, diverse datasets.
