Pseudorandom Function from Learning Burnside Problem by Dhiraj K. Pandey, Antonio R. Nicolosi

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Reinforcement learning:toward action-knowledge merged intelligent mechanisms and algorithms by Fei-Yue WANG, Dongpu CAO, Qinglai WEI

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Teaching AI awareness in middle school classrooms: Design, implementation and evaluation of two education modules on algorithmic bias and filter bubbles by Mehtab Iqbal, Khushbu Singh, Sushmita Khan, Oluwafemi Osho, Emily Sidnam-Mauch, Nicole Bannister, Kelly Caine, Bart Knijnenburg

“…We design, implement, and evaluate two educational modules on the causes and consequences of two particularly salient types of AI misrepresentation: algorithmic biases and filter bubbles. We implement these modules in middle school math and computer science classrooms by integrating our materials with the existing curricula and curricular requirements of the partner school. …”
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Komparasi Machine Learning Berbasis Pso Untuk Prediksi Tingkat Keberhasilan Belajar Berbasis E-Learning by Elin Panca Saputra, Siti Nurajizah, Mawadatul Maulidah, Nadiyah Hidayati, Taufik Rahman

“…Then the application key using the PSO-based Naïve Bayes (NB) algorithm can get performance results with a weight of 94.40% and an-AUC number of 94.50%, then the PSO-based Support Vectore Machine (SVM) Algorithm with a performance result of 88.20 and an AUC value of 91.10%, and Artificial Neural Network-(NN) based on Particle Swarm Optimizatio (PSO) produces an accuracy performance score with a weight of 99.20% and an accuracy value of 98.50%. …”
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Time-Distributed Vision Transformer Stacked With Transformer for Heart Failure Detection Based on Echocardiography Video by Mgs M. Luthfi Ramadhan, Adyatma W. A. Nugraha Yudha, Muhammad Febrian Rachmadi, Kevin Moses Hanky Jr Tandayu, Lies Dina Liastuti, Wisnu Jatmiko

“…Specifically, most existing studies use a convolutional neural network that only captures the local context of an image hindering it from learning the global context of an image. Moreover, the frame sampling algorithms only sample certain consecutive frames which makes it questionable whether the dynamic of the left ventricle during a cardiac cycle is included. …”
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Quantum ensemble learning with a programmable superconducting processor by Jiachen Chen, Yaozu Wu, Zhen Yang, Shibo Xu, Xuan Ye, Daili Li, Ke Wang, Chuanyu Zhang, Feitong Jin, Xuhao Zhu, Yu Gao, Ziqi Tan, Zhengyi Cui, Aosai Zhang, Ning Wang, Yiren Zou, Tingting Li, Fanhao Shen, Jiarun Zhong, Zehang Bao, Zitian Zhu, Zixuan Song, Jinfeng Deng, Hang Dong, Pengfei Zhang, Wei Zhang, Hekang Li, Qiujiang Guo, Zhen Wang, Ying Li, Xiaoting Wang, Chao Song, H. Wang

“…However, the vulnerability of quantum information to environmental noises and the consequent high cost for realizing fault tolerance has impeded the quantum models from learning complex datasets. Here, we introduce AdaBoost.Q, a quantum adaptation of the classical adaptive boosting (AdaBoost) algorithm designed to enhance learning capabilities of quantum classifiers. …”
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Modeling Air Combat Behavior for Simulation-Based Pilot Training: A Survey of Machine Learning Approaches by Andreas Strand, Patrick Ribu Gorton, Karsten Brathen

“…Recent advancements in machine learning, specifically reinforcement learning, imitation learning, and evolutionary algorithms, offer scalable alternatives by enabling agents to learn complex behavior from data. …”
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