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Artificial Intelligence Review
Kluwer Academic Publishers
 
   
 
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  1-10 of 46 reviews Date Reviewed 
  Deep learning based single sample face recognition: a survey
Liu F., Chen D., Wang F., Li Z., Xu F. Artificial Intelligence Review 1(1): 1-26, 2022.  Type: Article

Deep learning (DL) methods, and in particular convolutional neural networks (CNNs), provide the most used and most effective face recognition systems. However, when “each identity has only a single sample available for training,” perfo...

Dec 27 2022
  A study on software fault prediction techniques
Rathore S., Kumar S. Artificial Intelligence Review 51(2): 255-327, 2019.  Type: Article

Software developers and managers struggle with the increasing number of software problems and defects. Such problems can rapidly increase the costs of software maintenance and development. Traditional defect management models cannot de...

Jun 24 2019
  A comprehensive review of krill herd algorithm: variants, hybrids and applications
Wang G., Gandomi A., Alavi A., Gong D. Artificial Intelligence Review 51(1): 119-148, 2019.  Type: Article

This paper reviews a list of krill herd (KH)-style algorithms and the associated variants. As a general overview of this algorithm, the paper presents a systematic approach for cataloguing and classifying the algorithms into three area...

Apr 4 2019
  A survey of virtual sample generation technology for face recognition
Li L., Peng Y., Qiu G., Sun Z., Liu S. Artificial Intelligence Review 50(1): 1-20, 2018.  Type: Article

Just as the authors state that “virtual sample generation technology belongs to the category of machine learning,” nobody ever has enough real data to train a face recognition model, and they need synthesized data. ...

Feb 7 2019
  A review on the applications of neuro-fuzzy systems in business
Rajab S., Sharma V. Artificial Intelligence Review 49(4): 481-510, 2018.  Type: Article

Neuro-fuzzy systems (NFS) combine two artificial intelligence (AI) techniques, namely neural networks and fuzzy logic systems. This approach takes advantage of the strengths of each technique, while simultaneously overcoming their resp...

Jul 19 2018
  A metamodeling approach for the identification of organizational smells in multi-agent systems: application to ASPECS
Araujo P., Rodríguez S., Hilaire V. Artificial Intelligence Review 49(2): 183-210, 2018.  Type: Article

The concept of a code smell is defined as representing a potential problem within a system, typically a programming design system. The idea of a code smell was introduced in 1999 with a list of 22 low-level code smells and an associate...

May 18 2018
  Evolution or revolution: the critical need in genetic algorithm based testing
Surendran A., Samuel P. Artificial Intelligence Review 48(3): 349-395, 2017.  Type: Article

Software testing consumes a considerable amount of the cost and time of software development, but it is one of the most crucial phases; it cannot be skipped. Software testing is therefore referred to as “a necessary evil.&...

Dec 1 2017
  A survey of imperatives and action representation formalisms
Srinivasan B., Parthasarathi R. Artificial Intelligence Review 48(2): 263-297, 2017.  Type: Article

What is an action? The agent observes two states at different times; if there is a change, then an action occurred. This implicit definition of action is adopted in this paper. At the beginning of artificial intelligence (AI), first-or...

Nov 20 2017
  A multiagent, dynamic rank-driven multi-deme architecture for real-valued multiobjective optimization
Acan A., Lotfi N. Artificial Intelligence Review 48(1): 1-29, 2017.  Type: Article

Solutions for multiobjective optimization problems find use in architectures that support parallel processing. A new method proposed by Acan and Lotfi is seen to dominate over most other optimization solutions, as is the case with a ty...

Nov 1 2017
  A comparative empirical study on social media sentiment analysis over various genres and languages
Hangya V., Farkas R. Artificial Intelligence Review 47(4): 485-505, 2017.  Type: Article

This paper compares two types of sentiment analysis (SA), global sentiments and the opinions of people regarding a specific target topic. Document-level SA can decide the polarity of a document at a global level, while target-level SA ...

May 1 2017
 
 
 
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