Hamiltonian Learning via Shadow Tomography of Pseudo-Choi States by Juan Castaneda, Nathan Wiebe

“…We introduce a new approach to learn Hamiltonians through a resource that we call the pseudo-Choi state, which encodes the Hamiltonian in a state using a procedure that is analogous to the Choi-Jamiolkowski isomorphism. …”
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Deep learning-driven IoT solution for smart tomato farming by Akshit Saxena, Aayushi Agarwal, Bhavya Nagrath, Carmel Sanjana Jayavanth, Shamita Thulasidoss, S. Maheswari, P. Sasikumar

“…Abstract The rising food demand and challenges with respect to the climate have made precision agriculture (PA) vital for sustainable crop production. This study presents an IoT-based smart greenhouse platform tailored for tomato farming, integrating environmental sensing and deep learning. …”
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Adversarial Multitask Learning for Domain Adaptation Through Domain Adapter by Hidayaturrahman, Agung Trisetyarso, Iman Herwidiana Kartowisastro, Widodo Budiharto

“…This study presents a technique called Adversarial Multitask Learning (AML) to enhance the effectiveness of domain adaptation methods in practical applications, which are currently highly sought after. …”
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Arabic speech recognition using end‐to‐end deep learning by Hamzah A. Alsayadi, Abdelaziz A. Abdelhamid, Islam Hegazy, Zaki T. Fayed

“…In this work, the application of state‐of‐the‐art end‐to‐end deep learning approaches is investigated to build a robust diacritised Arabic ASR. …”
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Hierarchical Service Composition via Blockchain-enabled Federated Learning by Li Huang, Lu Zhao, Yansong Liu, Yao Zhao

“…To address these limitations, we propose the Hierarchical Service Composition (HSC) approach, leveraging blockchain and federated learning to minimize computational complexity. The integration of Blockchain-enabled Federated Learning (BFL) facilitates machine learning model training with decentralized data, ensuring practicality and fairness. …”
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Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning by Xiyu Zhao, Qimei Cui, Weicai Li, Wei Ni, Ekram Hossain, Quan Z. Sheng, Xiaofeng Tao, Ping Zhang

“…Personalized federated learning (PFL), e.g., the renowned Ditto, strikes a balance between personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). …”
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The Construction of Knowledge Graphs in the Assembly Domain Based on Deep Learning by Jing Qu, Yanmei Li, Huilong Du, Wen Wang, Weiping Fu

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Neural signals, machine learning, and the future of inner speech recognition by Adiba Tabassum Chowdhury, Ahmed Hassanein, Aous N. Al Shibli, Youssuf Khanafer, Mohannad Natheef AbuHaweeleh, Shona Pedersen, Muhammad E. H. Chowdhury

“…The limitations of current technologies were also discussed, along with insights into future advancements and potential applications of machine learning in inner speech recognition (ISR). Building on prior literature, this work synthesizes and organizes existing ISR methodologies within a structured mathematical framework, reviews cognitive models of inner speech, and presents a detailed comparative analysis of existing ML approaches, thereby offering new insights into advancing the field.…”
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Application and prospects of machine learning for rockfalls, landslides and debris flows by Jiazhu WANG, Yongbo TIE, Yongjian BAI, Yanchao GAO, Donghui WANG, Mingzhi ZHANG

“…Rockfalls, landslides, and debris flows present significant threats to the safety of mountainous communities globally. …”
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Applications of deep learning in trauma radiology: A narrative review by Chi-Tung Cheng, Chun-Hsiang Ooyang, Chien-Hung Liao, Shih-Ching Kang

“…This narrative review provides the fundamental concepts for developing DL algorithms in trauma imaging and presents an overview of current progress in each modality. …”
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Ball Point Game: Playing or Learning Agile Project Management? by Maria Lydia Fioravanti, Gustavo M. N. Avellar, Bruna Oliveira Romeiro, Bruna Goncalves Rezende, Ellen Francine Barbosa, Ana M. Moreno

“…The BPG proved to be an effective tool for experiential learning by allowing students to go through all stages of the experiential learning cycle. …”
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Backdoor Attack to Giant Model in Fragment-Sharing Federated Learning by Senmao Qi, Hao Ma, Yifei Zou, Yuan Yuan, Zhenzhen Xie, Peng Li, Xiuzhen Cheng

“…With the help of fine-tuning technique, a backdoor attack method is presented, by which the malicious clients can hide the backdoor in a designated fragment that is going to be shared with the benign clients. …”
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Image Classification of Chicken Breed and Gender Using Deep Learning by Liuchao Zhu, Zixin Chen, Hanwen Zhang, Yanju Shan, Gaige Ji, Huanliang Xu, Jingting Shu, Junxian Huang

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A Hybrid Machine Learning Model for Accurate Autism Diagnosis by Durga Prasad Kavadi, Venkata Rami Reddy Chirra, Palacharla Ravi Kumar, Sai Babu Veesam, Sagar Yeruva, Lalitha Kumari Pappala

“…As such, there is a growing need for advanced techniques to handle complex data in the diagnosis of disorders like Autism Spectrum Disorder (ASD). This study presents a Big Data and Machine Learning-based Medical Data Classification (BDML-MDCASD) model aimed at improving the accuracy and efficiency of ASD diagnosis. …”
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Weakly supervised learning in thymoma histopathology classification: an interpretable approach by Chunbao Wang, Chunbao Wang, Xianglong Du, Xiaoyu Yan, Xiali Teng, Xiaolin Wang, Zhe Yang, Hongyun Chang, Yangyang Fan, Caihong Ran, Jie Lian, Chen Li, Hansheng Li, Lei Cui, Yina Jiang

“…Accurate classification is crucial for diagnosis, but current methods often struggle with complex tumor subtypes. This study presents an AI-assisted diagnostic model that combines weakly supervised learning with a divide-and-conquer multi-instance learning (MIL) approach to improve classification accuracy and interpretability.MethodsWe applied the model to 222 thymoma slides, simplifying the five-class classification into binary and ternary steps. …”
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Emotion recognition with multiple physiological parameters based on ensemble learning by Yilong Liao, Yuan Gao, Fang Wang, Li Zhang, Zhenrong Xu, Yifan Wu

“…Using a soft voting ensemble method, the proposed approach achieved a 96.21% accuracy rate in classifying seven emotions—calm, happy, disgust, surprise, anger, sad, and fear, indicating its ability to accurately capture emotional responses to short videos. This study presents an innovative approach to emotion recognition using multiple physiological parameters, demonstrating the potential of ensemble learning for complex tasks. …”
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Predicting carbon peak at the provincial level using deep learning by Xiaoyan Tang, Kunsheng Fang

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Attacks and countermeasures on federated learning via historical knowledge modeling by Songsong Zhang, Zhengliang Jiang, Hang Gao, Suying Gui, Tiegang Gao

“…Abstract Federated learning has emerged as a promising paradigm for privacy-preserving multi-source data fusion. …”
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A Comprehensive Survey on Machine and Deep Learning for Optical Communications by Mohammad Ali Amirabadi, S. Alireza Nezamalhosseini, Mohammad Hossein Kahaei, Lawrence R. Chen

“…The increasing complexity of optical communication systems and networks necessitates advanced methodologies for extracting valuable insights from vast and heterogeneous datasets. Machine learning (ML) and deep learning (DL) have emerged as pivotal tools in this domain, revolutionizing data analysis and enabling automated self-configuration in optical communication systems. …”
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Trajectory-Driven Deep Learning for UAV Location Integrity Checks by Mincheol Shin, Sang-Yoon Chang, Jonghyun Kim, Kyungmin Park, Jinoh Kim

“…., using hardware sensors, cryptographic mechanisms, and machine learning (ML) approaches, but they concentrate primarily on GPS signal-related information (e.g., jamming and noise). …”
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