首页|New Findings from Hebei Agricultural University Describe Advances in Machine Learning (A Recognition Method for Aggressive Chicken Behavior Based on Machine Learning)
New Findings from Hebei Agricultural University Describe Advances in Machine Learning (A Recognition Method for Aggressive Chicken Behavior Based on Machine Learning)
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A new study on artificial intelligence is now available. According to news originating from Baoding, People’s Republic of China, by NewsRx correspondents, research stated, “Aggressive behavior is an important indicator of chicken welfare assessment. At present, the aggressive behavior of chickens typically requires human observation for welfare assessment, and the assessment results are influenced by the subjective judgment of humans.” Funders for this research include National Natural Science Foundation of China; Hebei Province Layer/broiler Industry Technology System. Our news correspondents obtained a quote from the research from Hebei Agricultural University: “This paper proposes an aggressive chicken behavior identification method based on a hybrid strategy improved Sparrow Search Algorithm combined with Support Vector Machine (ISSA-SVM). Nine-axis inertial sensors were used to collect the behavioral data of chickens. A total of 231-dimensional feature data in the time and frequency domains of the behavioral data were extracted through a 1 s sliding window. To reduce feature redundancy, the initial population is initialized using circle chaotic mapping instead of random initialization of the original sparrow algorithm to increase the uniformity of the initial population distribution in the feature space; adaptive weights are introduced to increase the search range of the early iteration, and the global optimal solution of the previous generation is introduced to improve the global search capability of the algorithm; the optimal solution is perturbed using the dimension-by-dimension mutation strategy of adaptive t-distribution to increase the diversity of the feature distribution. ISSA-SVM reduced the feature dimensionality from 231 to 17, indicating a reduction of 92.6%. The recognition overall accuracy of ISSA-SVM for aggressive chicken behavior was 94.27%, which improved by 1.33% compared to SVM.”
Hebei Agricultural UniversityBaodingPeople’s Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
IEEE
