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mAISs differ from conventional AISs in that multiple-detector sets are evolved concurrently via negative selection.

The differences between Zhao's and our approach lie in feature extraction and the use of multiple-detector sets.

In this paper, we introduce the Multiple-Detector Set Artificial Immune System (mAIS).

The development of a Multiple-Detector Set Artificial Immune System model for Android app classification into malicious or benign categories.

Therefore, to address the growing threat of Android malware, we developed a Multiple-Detector Set Artificial Immune System (mAIS) and a validation scheme.

The contributions of this research are as follows: The development of a Multiple-Detector Set Artificial Immune System model for Android app classification into malicious or benign categories.

Inspired by the human immune system, we explore the development of a new Multiple-Detector Set Artificial Immune System (mAIS) for the detection of mobile malware based on the information flows in Android apps.

This applies to both non-self detector sets and self detector sets.

A tree-structured detector hierarchy is designed to organize multiple detector nodes identifying pose ranges of faces.

Carrier et al. demonstrated in a systematic review that multiple detector CTPA (2 – 64 detectors) might increase the rate of subsegmental PE diagnosis as compared with single detector CTPA.

All other schemes consider multiple detectors working sequentially.

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