Time Perception Test in IntelliCage System for Preclinical Study: Linking Depression and Serotonergic Modulation by Olga Sysoeva, Rauf Akhmirov, Maria Zaichenko, Ivan Lazarenko, Anastasiya Rebik, Nadezhda Broshevitskaja, Inna Midzyanovskaya, Kirill Smirnov

“…Disturbances in time perception are also reported in depression with one of the behavioral schedules used to study interval timing, differential-reinforcement-learning-of-low-rate, having been shown to have high predictive validity for an antidepressant effect. …”
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Advances in machine learning applications to resource technology for organic solid waste by Hongzhi MA, Yichan LIU, Jihua ZHAO, Fan FEI, Ming GAO, Qunhui WANG

“…Another strategy is the use of reinforcement learning and transfer learning, which effectively address dynamic environments and small datasets, respectively. …”
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The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance by Hazrat Bilal, Muhammad Nadeem Khan, Sabir Khan, Muhammad Shafiq, Wenjie Fang, Rahat Ullah Khan, Mujeeb Ur Rahman, Xiaohui Li, Qiao-Li Lv, Bin Xu

“…Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. …”
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Homogenized Point Mutual Information and Deep Quantum Reinforced Wind Power Prediction by W. G. Jency, J. E. Judith

“…With the relevant features selected, in the second section, the actual wind power prediction is made using the Deep Quantum Reinforced Learning model. To validate the proposed method, Wind Turbine SCADA Dataset is used for constructing and testing. …”
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