標(biāo)題: Titlebook: Advances in Signal Processing and Intelligent Recognition Systems; 5th International Sy Sabu M. Thampi,Rajesh M. Hegde,Juan M. Corchado Con [打印本頁] 作者: 二足動(dòng)物 時(shí)間: 2025-3-21 16:18
書目名稱Advances in Signal Processing and Intelligent Recognition Systems影響因子(影響力)
書目名稱Advances in Signal Processing and Intelligent Recognition Systems影響因子(影響力)學(xué)科排名
書目名稱Advances in Signal Processing and Intelligent Recognition Systems網(wǎng)絡(luò)公開度
書目名稱Advances in Signal Processing and Intelligent Recognition Systems網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Advances in Signal Processing and Intelligent Recognition Systems被引頻次
書目名稱Advances in Signal Processing and Intelligent Recognition Systems被引頻次學(xué)科排名
書目名稱Advances in Signal Processing and Intelligent Recognition Systems年度引用
書目名稱Advances in Signal Processing and Intelligent Recognition Systems年度引用學(xué)科排名
書目名稱Advances in Signal Processing and Intelligent Recognition Systems讀者反饋
書目名稱Advances in Signal Processing and Intelligent Recognition Systems讀者反饋學(xué)科排名
作者: resuscitation 時(shí)間: 2025-3-21 22:54 作者: Homocystinuria 時(shí)間: 2025-3-22 01:24
Artificial Intelligence Enabled Online Non-intrusive Load Monitoring Embedded in Smart Plugselectrical appliances remotely and automatically. It is promising that, the networks of smart plugs in the power system will enable autonomous demand response for optimal grid operation. This benefits power systems from several aspects, e.g., enhancing renewable penetration and reducing the peak loa作者: Blood-Vessels 時(shí)間: 2025-3-22 07:21 作者: Axillary 時(shí)間: 2025-3-22 11:35
Identifying the Influential User Based on User Interaction Model for Twitter Data entity or node can be people, groups or organizations and the links or edges are shown to represent the relationship between them. Edges are used to identify whether there is communication or social interaction between different users. Based on the user’s interest or activities made in a social gro作者: Modify 時(shí)間: 2025-3-22 15:45 作者: Itinerant 時(shí)間: 2025-3-22 20:56
Multilingual Phone Recognition: Comparison of Traditional versus Common Multilingual Phone-Set Approoffers seamless decoding of the code-switched speech. We show that this approach is superior to a more conventional front-end language-identification (LID)-switched monolingual phone recognition (LID-Mono) trained individually on each of the languages present in multilingual dataset. The state-of-th作者: 易改變 時(shí)間: 2025-3-22 21:53 作者: COM 時(shí)間: 2025-3-23 04:29
CNN Based Periocular Recognition Using Multispectral Imagesification scenario to operate reliably round the clock, it should be capable of subject recognition in multiple spectra. However, there is limited research associated with the non-ideal multispectral imaging of the periocular trait. This is critical for real life applications such as surveillance an作者: Enthralling 時(shí)間: 2025-3-23 06:25 作者: ABHOR 時(shí)間: 2025-3-23 10:44
Speaker Specific Formant Dynamics of Vowelsis speech signal, the features useful for each application/task are different. Of the different speech sounds, vowel sounds spectrally well-defined and well represented by formants. Formants which represent resonances of vocal tract are the result of physiology of individual’s speech production mech作者: Charitable 時(shí)間: 2025-3-23 16:16 作者: 割讓 時(shí)間: 2025-3-23 18:17
Indian Semi-Acted Facial Expression (iSAFE) Dataset for Human Emotions Recognitioning IoT cloud societal applications such as smart driving or smart living applications or medical applications. In fact, the dataset relating to human emotions remains as a crucial pre-requisite for designing efficient machine learning algorithms or applications. The traditionally available datasets作者: EWER 時(shí)間: 2025-3-23 22:21 作者: Guaff豪情痛飲 時(shí)間: 2025-3-24 02:48 作者: 招募 時(shí)間: 2025-3-24 09:34
Voice Controlled Media Player: A Use Case to Demonstrate an On-premise Speech Command Recognition Syamed Speech Command Recognition (SSCR) system by training Convolutional Neural Network (CNN) based model using the speech command dataset. The dataset was generated using our own crowdsourcing application and the data was further augmented for increasing the scale of it. The proposed approach result作者: 結(jié)束 時(shí)間: 2025-3-24 11:57 作者: 微枝末節(jié) 時(shí)間: 2025-3-24 16:54 作者: Delirium 時(shí)間: 2025-3-24 19:48 作者: 上下倒置 時(shí)間: 2025-3-25 01:32
Richard H. Enns,George C. McGuireologists, ecologists, and related disciplines. This work in concentrated on nocturnal species; even though the analysis of their population trends is a key indicator, there is a gap in the literature addressing their audio-based identification. After compiling a suitable dataset including six noctur作者: disciplined 時(shí)間: 2025-3-25 06:15 作者: Neutropenia 時(shí)間: 2025-3-25 08:34
https://doi.org/10.1007/0-387-31262-5electrical appliances remotely and automatically. It is promising that, the networks of smart plugs in the power system will enable autonomous demand response for optimal grid operation. This benefits power systems from several aspects, e.g., enhancing renewable penetration and reducing the peak loa作者: Hiatus 時(shí)間: 2025-3-25 11:50 作者: 遠(yuǎn)足 時(shí)間: 2025-3-25 16:42 作者: laparoscopy 時(shí)間: 2025-3-25 21:11 作者: installment 時(shí)間: 2025-3-26 01:50
Richard H. Enns,George C. McGuireoffers seamless decoding of the code-switched speech. We show that this approach is superior to a more conventional front-end language-identification (LID)-switched monolingual phone recognition (LID-Mono) trained individually on each of the languages present in multilingual dataset. The state-of-th作者: 擁擠前 時(shí)間: 2025-3-26 05:22 作者: 發(fā)怨言 時(shí)間: 2025-3-26 11:37 作者: 帶來墨水 時(shí)間: 2025-3-26 12:52 作者: Commonplace 時(shí)間: 2025-3-26 18:04 作者: 不再流行 時(shí)間: 2025-3-27 00:12
https://doi.org/10.1007/978-0-387-49333-6 behavior as it involves massive interactions and communications. Social Behavioral Biometrics is a very recently introduced research area that attempts to extract unique behavioral features from the social interactions of individuals, which are powerful enough to be used as biometric identifiers. T作者: 有害 時(shí)間: 2025-3-27 02:18
Richard H. Enns,George C. McGuireing IoT cloud societal applications such as smart driving or smart living applications or medical applications. In fact, the dataset relating to human emotions remains as a crucial pre-requisite for designing efficient machine learning algorithms or applications. The traditionally available datasets作者: dysphagia 時(shí)間: 2025-3-27 08:18
Richard H. Enns,George C. McGuireand so on has exceptionally increased. Named Entity Recognition (NER) is an initial step towards converting this unstructured data into structured data which can be used by a lot of applications. The existing methods on NER for Cyber Security data are based on rules and linguistic characteristics. A作者: 燒瓶 時(shí)間: 2025-3-27 12:46
https://doi.org/10.1007/978-0-387-49333-6t of its kind in depression detection using Audio Visual and Emotional Challenge 2011 (AVEC 2011) and Computational and Paralinguistics challenge 2016 (ComParE 2016) feature sets. A novel method of ensemble classification using simple machine learning algorithms of Instance-Based classifier with par作者: 緯線 時(shí)間: 2025-3-27 16:04 作者: ECG769 時(shí)間: 2025-3-27 19:06
https://doi.org/10.1007/978-0-387-49333-6iving system of transport vehicles. The model is built based on convolutional neural network. The German Traffic Sign Detection Benchmark (GTSDB), a standard open-source segmented image dataset with forty-three different signboard classes is considered for experimentation. Implementation of the syst作者: CLASH 時(shí)間: 2025-3-28 00:56
Richard H. Enns,George C. McGuireapplications. We introduce a novel audio database of isolated Gujarati digits in this work. This database contains recordings of digits spoken by 20 users from 5 different regions of Gujarat in practical environments. To the best of our knowledge, this is the first publicly available Gujarati spoken作者: 不可知論 時(shí)間: 2025-3-28 03:18 作者: Grandstand 時(shí)間: 2025-3-28 08:29 作者: Liberate 時(shí)間: 2025-3-28 11:48 作者: ADORE 時(shí)間: 2025-3-28 15:35 作者: 陰謀 時(shí)間: 2025-3-28 22:35
Internet of Assistants: Humans Can Get Assistance Anywhere, Anytime and Any Areasother using ontology. We provide the architectural framework called Evolutional Smart Assistant Platform (ESAP) that enables the successful implementation of smart assistant applications. IoA provides numerous possibilities of applications few of which are discussed in this paper. For instance, in a作者: 不朽中國 時(shí)間: 2025-3-29 00:54 作者: Eulogy 時(shí)間: 2025-3-29 04:41
Identifying the Influential User Based on User Interaction Model for Twitter Dataa social group. Thus, the incidence edges play an essential role in the vertex-edge incidence matrix interaction to identify the active and inactive user interactions in a social network. The Fraction of Strongly Influential (FSI) users score is used to evaluate the user interaction model by analyzi作者: 主動(dòng) 時(shí)間: 2025-3-29 08:26 作者: LURE 時(shí)間: 2025-3-29 13:36 作者: AWRY 時(shí)間: 2025-3-29 17:08
CNN Based Periocular Recognition Using Multispectral Imagescted from pretrained CNN for subject authentication. To the best of our knowledge, this is the first study of multispectral periocular recognition employing deep learning. For our work, the IIITD Multispectral Periocular (IMP) database is used. The best classification accuracy reported for this data作者: FLAT 時(shí)間: 2025-3-29 20:20 作者: 遺產(chǎn) 時(shí)間: 2025-3-30 00:01 作者: Graduated 時(shí)間: 2025-3-30 05:45
User Recognition Using Cognitive Psychology Based Behavior Modeling in?Online Social Networksata by extracting certain psychometric properties of temperament from user profiles in order to generate unique and stable user behavior patterns. Based on the Cognitive Affective System theory of Human Personality which has been proved indubitable years ago in offline social contexts, we have creat作者: 硬化 時(shí)間: 2025-3-30 09:42 作者: crescendo 時(shí)間: 2025-3-30 12:42 作者: Ovulation 時(shí)間: 2025-3-30 20:07
https://doi.org/10.1007/0-387-31262-5r, using the edge-computing capability of the smart plugs, some lightweight artificial intelligence-based NILM algorithms are developed and implemented inside the smart plugs. These practical algorithms are validated using massive hardware experiments. Case studies indicate that high accuracy can be作者: paragon 時(shí)間: 2025-3-31 00:26 作者: 清楚說話 時(shí)間: 2025-3-31 02:05
Richard H. Enns,George C. McGuire which limits the overall performance of the LID-Mono system that suffers with high PERs at small windows (poor LID performance) and mismatched decoding conditions at long windows (due to poor code-switching detection time resolution). We show that the Multi-PRS, by virtue of not having to do a fron作者: 憎惡 時(shí)間: 2025-3-31 06:53 作者: lactic 時(shí)間: 2025-3-31 12:12 作者: Defraud 時(shí)間: 2025-3-31 16:30
https://doi.org/10.1007/978-0-387-49333-6a vector. We generated our data-set consisting of 40 patients. For patient diagnosed with Obstructive Pulmonary Disease we assigned the label as +1 and for those who have other disease as ?1. Different machine learning algorithms such as kNN, SVM, Random Forest and Naive Bayes were applied to the da作者: installment 時(shí)間: 2025-3-31 18:20
