Deep Learning in Power Systems: A Bibliometric Analysis and Future Trends by Seyed Mahdi Miraftabzadeh, Andrea Di Martino, Michela Longo, Dario Zaninelli

“…This paper presents a bibliometric analysis and future trends of deep learning in power systems, aiming to identify its fundamental characteristics and summarize the research hot topics and future trends. …”
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Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions by Ruslan Shevchuk, Vasyl Martsenyuk, Bogdan Adamyk, Vladlena Benson, Andriy Melnyk

“…The study reveals geographical concentrations of research activity, key institutional players, the evolution of theoretical frameworks, and shifts from basic security mechanisms to sophisticated machine learning and graph neural network approaches. This research summarizes the state of the field and highlights future directions essential for blockchain security.…”
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Knowledge mapping and bibliometric analysis of medical knee magnetic resonance imaging for knee osteoarthritis (2004–2023) by Juntao Chen, Hui Xu, Hui Xu, Hang Zhou, Zheng Wang, Wanyu Li, Juan Guo, Yunfeng Zhou, Yunfeng Zhou

“…In this study, we aimed to systematically examine the global research status on the application of medical knee MRI in the treatment of KOA, analyze research hotspots, explore future trends, and present results in the form of a knowledge graph.MethodsThe Web of Science core database was searched for studies on medical knee MRI scans in patients with KOA between 2004 and 2023. …”
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Convolution of the physical point cloud for predicting the self-assembly of colloidal particles by Seunghoon Kang, Young Jin Lee, Kyung Hyun Ahn

“…The approach involves constructing a physical point cloud from inter-particle stress information extracted from randomly distributed colloidal particles and embedding it into a graph convolutional network (GCN). In the field of pattern recognition, GCNs are widely utilized to classify arbitrary 3D objects by learning multidimensional relationships within feature spaces defined by spatial coordinates. …”
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Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods by Anton S. Chepurnenko, Tatiana N. Kondratieva, Ebrahim Al-Wali

“… Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. …”
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Asynchronous Real-Time Federated Learning for Anomaly Detection in Microservice Cloud Applications by Mahsa Raeiszadeh, Amin Ebrahimzadeh, Roch H. Glitho, Johan Eker, Raquel A. F. Mini

“…In our approach, edge clients perform real-time learning with continuous streaming local data. At the edge clients, we model intra-service behaviors and inter-service dependencies in multi-source distributed data based on a Span Causal Graph (SCG) representation and train a model through a combination of Graph Neural Network (GNN) and Positive and Unlabeled (PU) learning. …”
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Education in the era of Neurosymbolic AI by Chris Davis Jaldi, Eleni Ilkou, Noah Schroeder, Cogan Shimizu

“…Education is poised for a transformative shift with the advent of neurosymbolic artificial intelligence (NAI), which will redefine how we support deeply adaptive and personalized learning experiences. The integration of Knowledge Graphs (KGs) with Large Language Models (LLMs), a significant and popular form of NAI, presents a promising avenue for advancing personalized instruction via neurosymbolic educational agents. …”
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Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases by Emily Alsentzer, Michelle M. Li, Shilpa N. Kobren, Ayush Noori, Undiagnosed Diseases Network, Isaac S. Kohane, Marinka Zitnik

“…SHEPHERD performs deep learning over a knowledge graph enriched with rare disease information and is trained on a dataset of simulated rare disease patients. …”
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An efficient swarm evolution algorithm with probability learning for the black and white coloring problem by Zhiqiang Zhang, Li Zhang, Xiujun Zhang

“…This problem is a NP-complete problem, widely used in reagent product storage in chemical industry and the solution to the problem of black and white queens in chess. The paper presents a swarm evolution algorithm based on improved simulated annealing search and evolutionary operation with probability learning mechanism. …”
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Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection by Mulugeta Weldezgina Asres, Christian Walter Omlin, Long Wang, David Yu, Pavel Parygin, Jay Dittmann, the CMS-HCAL Collaboration

“…Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. …”
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A systematic review of deep learning chemical language models in recent era by Hector Flores-Hernandez, Emmanuel Martinez-Ledesma

“…In this study, we present a systematic review that offers a statistical description and comparison of the strategies utilized to generate molecules through deep learning techniques, utilizing the metrics proposed in Molecular Sets (MOSES) or Guacamol. …”
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Analysis of Learning Obstacles for Junior High School Students in Understanding SPLDV Concepts by Firda Luthfiatika Jannah, Neneng Aminah, Surya Amami Pramuditya, Cita Dwi Rosita, M. Subali Noto

“…Tujuan penelitian ini untuk melakukan eksplorasi mengenai learning obstacle yang dialami oleh siswa SMP ketika mereka mencoba memahami konsep SPLDV. …”
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Grammar Tips Integrated with Telegram Bot: Strategies to Facilitate Learning English Grammar by Rajistha I Gusti Ngurah Adi, Claria Dewa Ayu Kadek

“…However, the visual mode is presented in the form of graphs and charts that show the progress of students learning grammar materials. …”
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Machine learning reveals the dynamic importance of accessory sequences for Salmonella outbreak clustering by Chao Chun Liu, William W. L. Hsiao

“…To quantify the ability of MGE variations to cluster outbreak clones, we devised a reference-free tree-building algorithm inspired by colored de Bruijn graphs, which enabled topological comparisons between MGE and standard typing methods. …”
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Deep learning model for patient emotion recognition using EEG-tNIRS data by Mohan Raparthi, Nischay Reddy Mitta, Vinay Kumar Dunka, Sowmya Gudekota, Sandeep Pushyamitra Pattyam, Venkata Siva Prakash Nimmagadda

“…A Modality-Attentive Multi-Channel Graph Convolution Model (MAMP-GF) is introduced, leveraging GraphSAGE-based representation learning to capture inter-channel relationships. …”
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Dueling Network Architecture for GNN in the Deep Reinforcement Learning for the Automated ICT System Design by Tianchen Zhou, Yutaka Yakuwa, Natsuki Okamura, Hiroyuki Hochigai, Takayuki Kuroda, Ikuko Eguchi Yairi

“…This paper presents an improved deep reinforcement learning-based (DRL) approach for end-to-end models using a Graph Neural Network(GNN). …”
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Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization by Shiguo Huang, Linyu Cao, Ruili Sun, Tiefeng Ma, Shuangzhe Liu

“…To address this problem, this paper presents a novel two-stage approach that integrates deep learning with portfolio optimization. …”
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Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data by Manuel Ciba, Marc Petzold, Caroline L. Alves, Francisco A. Rodrigues, Yasuhiko Jimbo, Christiane Thielemann

“…Abstract Biosensors, such as microelectrode arrays that record in vitro neuronal activity, provide powerful platforms for studying neuroactive substances. This study presents a machine learning workflow to analyze drug-induced changes in neuronal biosensor data using complex network measures from graph theory. …”
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Structure-Based Deep Learning Framework for Modeling Human–Gut Bacterial Protein Interactions by Despoina P. Kiouri, Georgios C. Batsis, Christos T. Chasapis

“…<b>Methods:</b> This study presents a deep learning-based framework for predicting PPIs between human and gut bacterial proteins using structural data. …”
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A survey of reinforcement and deep reinforcement learning for coordination in intelligent traffic light control by Aicha Saadi, Noureddine Abghour, Zouhair Chiba, Khalid Moussaid, Saadi Ali

“…Reinforcement learning (RL) enables a single agent to learn and perform optimal actions independently, whereas multi-agent reinforcement learning (MARL) enables traffic light controllers to learn, exchange and optimize their actions. …”
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