標(biāo)題: Titlebook: Computational Vision and Bio-Inspired Computing; Proceedings of ICCVB S. Smys,Jo?o Manuel R. S. Tavares,Fuqian Shi Conference proceedings 2 [打印本頁(yè)] 作者: 教條 時(shí)間: 2025-3-21 19:21
書目名稱Computational Vision and Bio-Inspired Computing影響因子(影響力)
書目名稱Computational Vision and Bio-Inspired Computing影響因子(影響力)學(xué)科排名
書目名稱Computational Vision and Bio-Inspired Computing網(wǎng)絡(luò)公開度
書目名稱Computational Vision and Bio-Inspired Computing網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computational Vision and Bio-Inspired Computing被引頻次
書目名稱Computational Vision and Bio-Inspired Computing被引頻次學(xué)科排名
書目名稱Computational Vision and Bio-Inspired Computing年度引用
書目名稱Computational Vision and Bio-Inspired Computing年度引用學(xué)科排名
書目名稱Computational Vision and Bio-Inspired Computing讀者反饋
書目名稱Computational Vision and Bio-Inspired Computing讀者反饋學(xué)科排名
作者: 玩忽職守 時(shí)間: 2025-3-21 21:35 作者: Ebct207 時(shí)間: 2025-3-22 01:47
Lecture Notes in Computer Science machine learning algorithm is used for Classification. The comparative analysis of features extraction using DWT?+?KSVM, DWT?+?Otsu?+?KSVM, and DWT?+?Otsu?+?Kmeans?+?KSVM is done. The proposed model DWT?+?Otsu?+?Kmeans?+?KSVM gives promising results compared to the prior two approaches.作者: 鍵琴 時(shí)間: 2025-3-22 06:09 作者: Asseverate 時(shí)間: 2025-3-22 11:02 作者: 無節(jié)奏 時(shí)間: 2025-3-22 15:41
Review of Machine Learning-Based Disease Diagnosis and Severity Estimation of COVID-19,c search of online research databases including Google Scholar, PubMed, and Web of Science was conducted in accordance with the “Preferred Reporting Items for Systematic Reviews and Meta-Analysis” guidelines. The word “machine learning” was combined with COVID-19-specific keywords to construct the search syntax.作者: 無節(jié)奏 時(shí)間: 2025-3-22 19:46 作者: Inelasticity 時(shí)間: 2025-3-22 21:12
,Using Inception Modules to?Improve the?Accuracy of?Image Classification,icine, and advertising. In this paper, the authors attempt to use inception modules to improve the accuracy of image classification. They come forward with a model based on inception modules that significantly improve the accuracy of baseline models.作者: Expand 時(shí)間: 2025-3-23 03:51
Random Matrix Theory Application for Denoising Gene Co-expression Data,s correlations are eliminated and only strongly correlated elements are retained and the network transitions to a system of highly correlated genes as described by the Poisson statistics of RMT which can be used for further analysis.作者: 憤憤不平 時(shí)間: 2025-3-23 06:48 作者: inscribe 時(shí)間: 2025-3-23 10:13 作者: lacrimal-gland 時(shí)間: 2025-3-23 17:31 作者: SIT 時(shí)間: 2025-3-23 21:57
Tommaso Flaminio,Lluis Godo,Hykel Hosninsformer outperform MobileNet using the RAVDESS dataset. We achieved an accuracy of 92% on vision transformer, 91% on SWIN transformer, and 86% on MobileNet when tested on the RAVDESS dataset. The proposed robust emotion recognition system also enables shorter training and testing times, which is ideal for various applications.作者: Onerous 時(shí)間: 2025-3-24 00:14
Linda C. van der Gaag,Hans L. Bodlaenderf the plant so that the disease may not spread to the whole plant. However, in case the leaf is healthy, the system sends a message to the farmers for watering the plants after checking different parameters like soil moisture level, temperature, and humidity.作者: Melatonin 時(shí)間: 2025-3-24 03:39 作者: 拋物線 時(shí)間: 2025-3-24 08:20
Sébastien Destercke,Thierry Denoeuxd is low. The main goal of this project is to create a facial recognition-based attendance monitoring system to help educational institutions improve and upgrade their current systems and make them more efficient and effective.作者: Exposure 時(shí)間: 2025-3-24 14:40
Deep Web Search Log Mining Using Spark for Efficient and Scalable Web Information Retrieval,ge ranking. Experiment results show the use of proposed framework reduces the training time of deep learning model CNN and model prediction accuracy is increased. The average precision of the ranked search results based on document semantic vector is improved efficiently using CNN with spark in comparison with CNN without spark.作者: Blasphemy 時(shí)間: 2025-3-24 15:34
Emotion Recognition from Videos Using Transformer Models,nsformer outperform MobileNet using the RAVDESS dataset. We achieved an accuracy of 92% on vision transformer, 91% on SWIN transformer, and 86% on MobileNet when tested on the RAVDESS dataset. The proposed robust emotion recognition system also enables shorter training and testing times, which is ideal for various applications.作者: 輕打 時(shí)間: 2025-3-24 22:32
Smart Plant Health Monitoring and Leaf Disease Detection,f the plant so that the disease may not spread to the whole plant. However, in case the leaf is healthy, the system sends a message to the farmers for watering the plants after checking different parameters like soil moisture level, temperature, and humidity.作者: TIGER 時(shí)間: 2025-3-25 00:48
Virtual Game Play and Pose Detection Based on Computer Vision,ments of the human body. Data extracted from the image/videos by using OpenPose connects the obtained data with the virtual environment. The result analysis shows that the proposed SVM algorithm outperforms all other algorithms in terms of recognition rate.作者: 欲望 時(shí)間: 2025-3-25 04:53
Face Recognition-Based Attendance System,d is low. The main goal of this project is to create a facial recognition-based attendance monitoring system to help educational institutions improve and upgrade their current systems and make them more efficient and effective.作者: FLIRT 時(shí)間: 2025-3-25 09:35 作者: 嚴(yán)厲批評(píng) 時(shí)間: 2025-3-25 15:03
Conference proceedings 2023imbatore, India, from November 18 to 19, 2022. This volume presents state-of-the-art research innovations in computational vision and bio-inspired techniques. It includes theoretical and practical aspects of bio-inspired computing techniques, like machine learning, sensor-based models, evolutionary 作者: Binge-Drinking 時(shí)間: 2025-3-25 15:54
2194-5357 a reference resource for researchers and practitioners in acThis book includes selected papers from the 6th International Conference on Computational Vision and Bio Inspired Computing (ICCVBIC 2022), held in Coimbatore, India, from November 18 to 19, 2022. This volume presents state-of-the-art resea作者: galley 時(shí)間: 2025-3-25 21:37 作者: VEIL 時(shí)間: 2025-3-26 01:46
Daniel Neugebauer,J?rg Rothe,Kenneth Skibalowing stage, we use a refinement network. Our method performs better than traditional methods, according to the experimental findings. We also show that feature point matching, simultaneous localization and mapping in dimly lit environments can all be significantly enhanced by our method.作者: 易碎 時(shí)間: 2025-3-26 07:35 作者: Barter 時(shí)間: 2025-3-26 09:00 作者: Fraudulent 時(shí)間: 2025-3-26 13:06 作者: 接觸 時(shí)間: 2025-3-26 19:23 作者: GAVEL 時(shí)間: 2025-3-26 22:41 作者: obligation 時(shí)間: 2025-3-27 03:11 作者: asthma 時(shí)間: 2025-3-27 05:36
Tan Kiat Shi,Willi-Hans Steeb,Yorick Hardyn of house number is the noise which is predominant. Hence, this work concentrates on the preprocessing of the data which is done twice to eliminate noise. Further XGBoosting and deep random forest approaches are utilised to improve the accuracy in recognition which utilizes less memory. Results sho作者: Anguish 時(shí)間: 2025-3-27 09:52 作者: ACE-inhibitor 時(shí)間: 2025-3-27 13:55 作者: 關(guān)節(jié)炎 時(shí)間: 2025-3-27 18:09
Early Detection of ColoRectal Cancer Using Patch-Based Hybrid Model and Transfer Learning, and low-level features are examined for early prediction. The dataset used for analysis of this model is taken from the hospital comprising the samples of 100,000 images where 54,000 images are the ColoRectal cancer affected patients and 46,000 images are non-cancerous. This method is performing th作者: 不透明性 時(shí)間: 2025-3-27 22:04
Biomaterials Analysis and Deep Learning Outperforms Clinical Experts in Diagnosis and Classificatioorked. Also, the proposed system has yielded 99.54% accuracy with the help of 5-fold validation while classifying both healthy and fractured bones. Both algorithms gave better accuracy results than comparable existing algorithms in terms of performance.作者: Odyssey 時(shí)間: 2025-3-28 02:06
Classification of Epileptic Seizures Using EEMD with Multi-entropy Features Integrating Different Tseizures with accuracy of 95.33%, specificity of 97.6%, and sensitivity of 95.3%. Thus, the current method would be an efficient for automated prediction and detection of epileptic seizures in real-time.作者: Jejune 時(shí)間: 2025-3-28 06:39 作者: 帽子 時(shí)間: 2025-3-28 12:40 作者: 咽下 時(shí)間: 2025-3-28 16:20 作者: 辯論的終結(jié) 時(shí)間: 2025-3-28 21:58
XG Boosting and Deep Random Forest Based House Number Detection,n of house number is the noise which is predominant. Hence, this work concentrates on the preprocessing of the data which is done twice to eliminate noise. Further XGBoosting and deep random forest approaches are utilised to improve the accuracy in recognition which utilizes less memory. Results sho作者: exhibit 時(shí)間: 2025-3-29 02:34
https://doi.org/10.1007/978-981-19-9819-5Bioinformatics; Evolutionary Computing; Capsule Networks; Ambient Intelligence; Computational Neuro Scie作者: 無能性 時(shí)間: 2025-3-29 04:38
978-981-19-9818-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: Interregnum 時(shí)間: 2025-3-29 11:15 作者: DOTE 時(shí)間: 2025-3-29 11:54 作者: 植物茂盛 時(shí)間: 2025-3-29 18:42 作者: 新娘 時(shí)間: 2025-3-29 23:18 作者: sigmoid-colon 時(shí)間: 2025-3-30 02:34
Thomas Lukasiewicz,Umberto Straccia or perception of information in images for human viewers and understanding of imagery. Using the pixel values existing in the digital image, the details of low-light images are boosted under low illumination. The primary purpose of enhancing low-vision image is to increase visibility of the image. 作者: 商議 時(shí)間: 2025-3-30 07:48 作者: Detonate 時(shí)間: 2025-3-30 10:57 作者: Finasteride 時(shí)間: 2025-3-30 15:37 作者: DEBT 時(shí)間: 2025-3-30 19:59 作者: 古代 時(shí)間: 2025-3-31 00:26
Lecture Notes in Computer Sciences invasive, considered as the best image scanning technique in medical imaging for diagnosing various diseases. However, filtering and denoising MR Images remain a challenge. There is no direction in the literature regarding the proper technique for MR image processing. The present article proposes 作者: 機(jī)械 時(shí)間: 2025-3-31 02:12 作者: 玩笑 時(shí)間: 2025-3-31 05:42 作者: 可行 時(shí)間: 2025-3-31 12:37
https://doi.org/10.1007/978-3-319-20807-7seizures or occurring seizures. Therefore, this paper proposes an approach for automated prediction and detection of epileptic seizure stages using electroencephalogram (EEG) signals with ensemble empirical mode decomposition (EEMD) technique. The acquired EEG signals are disintegrated by EEMD to va作者: 太空 時(shí)間: 2025-3-31 16:42
Sébastien Destercke,Thierry Denoeux possibility of errors during data entry into the machine count. Real-time face recognition is a real-world solution that includes daily activities to manage multiple students attending school. Facial recognition is a process that recognizes students‘ faces to take attendance using facial biometrics作者: 背叛者 時(shí)間: 2025-3-31 19:18
Leila Amgoud,Philippe Besnard,Anthony Hunterlaptop. However, one input device that has not changed in the last 50?years is the QWERTY keyboard. With the recent innovation in sensor technology and artificial intelligence (AI), users can type on any surface as if it was a keyboard by using the virtual keyboards. This research study utilizes the作者: Delude 時(shí)間: 2025-3-31 23:40 作者: sebaceous-gland 時(shí)間: 2025-4-1 02:06 作者: 鴕鳥 時(shí)間: 2025-4-1 08:12
Lecture Notes in Computer Sciencesignificant and challenging task for diagnosis. In this present article, an efficient prediction methodology developed using Elman recurrent neural network (ERNN) with bacterial colony optimization (BCO) named BCO-ERNN. The BCO is used to train the ERNN to obtain high prediction accuracy and escape 作者: Left-Atrium 時(shí)間: 2025-4-1 10:17 作者: BILE 時(shí)間: 2025-4-1 17:37 作者: RUPT 時(shí)間: 2025-4-1 19:30
Tan Kiat Shi,Willi-Hans Steeb,Yorick Hardyarticular set by appropriate methods. Classification of numbers automatically is carried out in this work. This work mainly concentrates on the identification of house numbers to classify the houses. The applications include tax payments, etc. Hence, in order to classify such data XGboost is used. T作者: murmur 時(shí)間: 2025-4-2 01:29
Computational Vision and Bio-Inspired Computing978-981-19-9819-5Series ISSN 2194-5357 Series E-ISSN 2194-5365
