Search results for "ComputingMethodologies_PATTERNRECOGNITION"
showing 6 items of 296 documents
A novel heuristic memetic clustering algorithm
2013
In this paper we introduce a novel clustering algorithm based on the Memetic Algorithm meta-heuristic wherein clusters are iteratively evolved using a novel single operator employing a combination of heuristics. Several heuristics are described and employed for the three types of selections used in the operator. The algorithm was exhaustively tested on three benchmark problems and compared to a classical clustering algorithm (k-Medoids) using the same performance metrics. The results show that our clustering algorithm consistently provides better clustering solutions with less computational effort.
Support vector machine integrated with game-theoretic approach and genetic algorithm for the detection and classification of malware
2013
Abstract. —In the modern world, a rapid growth of mali- cious software production has become one of the most signifi- cant threats to the network security. Unfortunately, wides pread signature-based anti-malware strategies can not help to de tect malware unseen previously nor deal with code obfuscation te ch- niques employed by malware designers. In our study, the prob lem of malware detection and classification is solved by applyin g a data-mining-based approach that relies on supervised mach ine- learning. Executable files are presented in the form of byte a nd opcode sequences and n-gram models are employed to extract essential features from these sequences. Feature vectors o btained are…
It’s Not Only What You Say, But How You Say It : Investigating the Potential of Prosodic Analysis as a Method to Study Teacher’s Talk
2018
In this study, we introduce new insights into prosodic analyses as an emerging method to study what happens in classrooms interactions. We claim that the prosodic aspects (features of speech such as intonation, volume and pace) of talk are important, but under-represented in the learning sciences. These prosodic aspects may be used to complement, intensify or even reverse the linguistic content of speech. Thus far, most research on classrooms has focused on the content (what is said) rather than on understanding the meaning of the prosodic features (how it is said) of talk. In this study, we introduce prosodic analyses as a method to study classroom discussions. Our exploratory experiment f…
AI Ethics in Industry: A Research Framework
2019
Artificial Intelligence (AI) systems exert a growing influence on our society. As they become more ubiquitous, their potential negative impacts also become evident through various real-world incidents. Following such early incidents, academic and public discussion on AI ethics has highlighted the need for implementing ethics in AI system development. However, little currently exists in the way of frameworks for understanding the practical implementation of AI ethics. In this paper, we discuss a research framework for implementing AI ethics in industrial settings. The framework presents a starting point for empirical studies into AI ethics but is still being developed further based on its pr…
The Effect of Predator Population Dynamics on Batesian Mimicry Complexes.
2022
Understanding Batesian mimicry is a classic problem in evolutionary biology. In Batesian mimicry, a defended species (the model) is mimicked by an undefended species (the mimic). Prior theories have emphasized the role of predator behavior and learning as well as evolution in model-mimic complexes but have not examined the role of population dynamics in potentially governing the relative abundances and even persistence of model-mimic systems. Here, we examined the effect of the population dynamics of predators and alternative prey on the prevalence of warning-signaling prey composed of models and mimics. Using optimal foraging theory and signal detection theory, we found that the inclusion …
When more is less: The other side of artificial intelligence recommendation
2022
Based on consumers' preferences, AI (artificial intelligence) recommendation automatically filters information, which provokes scholars' debate. Supporters believe that by analyzing the consumers' preferences, AI recommendation enables consumers to choose products more quickly and with lower cost. Critics deem that consumers are more easily trapped in information cocoons because of the use of AI recommendation. This reduces the possibility of consumers contacting with a variety of commodities, thus lowering the consumer decision quality. Based on experiments, this paper discusses the moderating role of AI recommendation on the relationship of consumers' preferences and information cocoons. …