Negative Selection Algorithm for Unsupervised Anomaly Detection by Michał Bereta

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Region and Sample Level Domain Adaptation for Unsupervised Infrared Target Detection in Aerial Remote Sensing Images by Lianmeng Jiao, Haifeng Wei, Quan Pan

“…Finally, the proposed region and sample level domain adaptation framework is realized based on the advanced YOLOv7 one-stage detection backbone. We conducted comprehensive experiments based on the VEDAI and DroneVehicle aerial remote sensing datasets, and the experimental results demonstrate that our algorithm achieves better performance than those state-of-the-art unsupervised domain adaptation target detection algorithms. …”
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Detecting Positive and Negative Changes From SAR Images by an Evolutionary Multi-Objective Approach by Shuang Liang, Hao Li, Yun Zhu, Maoguo Gong

“…Therefore, the changed areas can be further classified into positive and negative changed classes. This paper presents an unsupervised change detection approach for detecting the positive and negative changes based on a multi-objective evolutionary algorithm. …”
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Unsupervised Detection of Suspicious Tissue Using Data Modeling and PCA by Ikhlas Abdel-Qader, Lixin Shen, Christina Jacobs, Fadi Abu Amara, Sarah Pashaie-Rad

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Robust Predictive Maintenance for Robotics via Unsupervised Transfer Learning by Arash Golibagh Mahyari, Thomas locher

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Presenting a Text Mining Algorithm to Identify Emotion in Persian Corpus by Masoud Garshasbi, Anahid Rais-Rohani, Mohammadreza Kabaranzadeh Ghadim

“…In the first approach, the algorithm is capable of detecting only one emotional word in a sentence, and then it improves to detect boosters and negating and stop word list as well. …”
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Impact of Machine Learning on Intrusion Detection Systems for the Protection of Critical Infrastructure by Avinash Kumar, Jairo A. Gutierrez

“…They demonstrate significant capabilities in capturing spatial and temporal variables. Among the unsupervised approaches, valuable insights into anomaly detection are provided without the necessity for labeled data, although they face challenges with higher rates of false positives and negatives. …”
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Research on load frequency control system attack detection method based on multi-model fusion by Feng Zheng, Weixun Li, Huifeng Li, Libo Yang, Zengjie Sun

“…A multi-model fusion attack detection framework is proposed, integrating (Long Short-Term Memory) LSTM supervised learning and autoencoder unsupervised learning algorithms, with an adaptive weight adjustment mechanism that dynamically optimizes detection strategies. …”
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