Applied Intelligence
IF
5.09
Papers
3,458
Papers 3,302
1 page of 331 pages (3,302 results)
Newest
#1Dongming Zhou (GXNU: Guangxi Normal University)
#2Jing Yang (GXNU: Guangxi Normal University)
Last. Riqiang Bao (SJTU: Shanghai Jiao Tong University)
view all 3 authors...
Automatic image captioning is an interesting task that lies at the intersection of computer vision and natural language processing. Although image captioning based on reinforcement learning has made significant progress in the past few years, the problem of inconsistent evaluation indicators for training and testing remains. Reinforcement learning optimizes a single metric, and the caption generated by the model is monotonous and non-characteristics. The model cannot reflect the diversity among ...
Source
#1Anup Kumar Gupta (IITI: Indian Institute of Technology Indore)
#2Puneet Gupta (IITI: Indian Institute of Technology Indore)H-Index: 12
Last. Esa RahtuH-Index: 32
view all 3 authors...
Visual speech recognition is essential in understanding speech in several real-world applications such as surveillance systems and aiding differently-abled. It proliferates the research in the realm of visual speech recognition, also known as Automatic Lip Reading (ALR). In recent years, Deep Learning (DL) methods are being utilised for developing ALR systems. DL models tend to be vulnerable to adversarial attacks. Studying these attacks creates new research directions in designing robust DL sys...
Source
#1Ghous Ali (UE: University of Education)H-Index: 10
#2Muhammad Afzal (UE: University of Education)
Last. Adeel Shazad (NTU: National Textile University)
view all 4 authors...
The interval-valued q-rung orthopair fuzzy sets and soft sets are two different uncertainty theories to cope with incomplete and uncertain information in several real-world multi-attribute decision-making (MADM) situations. This study develops a novel hybrid model called interval-valued q-rung orthopair fuzzy soft sets (IVqROFSSs, for brevity) to generalize the interval-valued q-rung orthopair fuzzy set model and to address the decision-makers preference information more effectively in complicat...
Source
Source
#1Yinsheng Zhang (ZJSU: Zhejiang Gongshang University)H-Index: 4
#2Zheng-Yong Zhang (NUFE: Nanjing University of Finance and Economics)H-Index: 7
Last. Haiyan Wang (ZJSU: Zhejiang Gongshang University)H-Index: 7
view all 3 authors...
Spectroscopic profiling data (e.g., Raman spectroscopy and mass spectroscopy), combined with machine learning, have provided a data-driven approach for discriminative tasks. In these tasks, researchers often start with simple classification models. If one model doesn’t work, they will try more sophisticated models. If all models fail, the researchers will deem the data set as “inseparable.“ This “trial-and-error” practice reveals a fundamental question: does the dataset possess the necessary sta...
Source
#1Che Xu (HFUT: Hefei University of Technology)
#2Weiyong Liu (USTC: University of Science and Technology of China)
Last. Yushu Chen (USTC: University of Science and Technology of China)
view all 3 authors...
To solve group decision making problems with large-scale alternatives, this paper proposes a dynamic ensemble selection (DES) based group decision model by using historical decision data. The historical decision data of a group of experts are collected from the same multi-criteria decision framework and are mixed to train a set of base classifiers (BCs) to learn group preferences. For each new alternative, the predictions derived from BCs are used to determine its similar historical alternatives...
Source
#1Chenrui Yin (NUAA: Nanjing University of Aeronautics and Astronautics)
#2Qun Dai (NUAA: Nanjing University of Aeronautics and Astronautics)H-Index: 15
Due to that multivariate time series, multistep forecasting technology has a guiding role in many fields, such as electricity consumption, traffic flow detection, and stock price prediction, many approaches have been proposed, seeking to realize accurate prediction based on historical data. However, multivariate time series in real-world applications often contain complex and non-linear interdependencies between time steps and series, for which traditional approaches that just model dependencies...
Source
#1Abdelhakim Baouya (UGA: Grenoble Alpes University)H-Index: 4
#2Salim Chehida (UGA: Grenoble Alpes University)H-Index: 3
Last. Marius Bozga (UGA: Grenoble Alpes University)H-Index: 43
view all 5 authors...
Deriving an accurate behavior model from historical data of a black box for verification and feature forecasting is seen by industry as a challenging issue especially for a large featured dataset. This paper focuses on an alternative approach where stochastic automata can be learned from time-series observations captured from a set of deployed sensors. The main advantage offered by such techniques is that they enable analysis and forecasting from a formal model instead of traditional learning me...
Source
#1Wei Hua (SMU: Shanghai Maritime University)
#2Guangzhong Liu (SMU: Shanghai Maritime University)
In software engineering (SE), code classification and related tasks, such as code clone detection are still challenging problems. Due to the elusive syntax and complicated semantics in software programs, existing traditional SE approaches still have difficulty differentiating between the functionalities of code snippets at the semantic level with high accuracy. As artificial intelligence (AI) techniques have increased in recent years, exploring different machine/deep learning techniques for code...
Source
Source
12345678910
Top fields of study
This website uses cookies.
We use cookies to improve your online experience. By continuing to use our website we assume you agree to the placement of these cookies.
To learn more, you can find in our Privacy Policy.