Leveraging Graph Networks to Model Environments in Reinforcement Learning by Viswanath Chadalapaka, Volkan Ustun, Lixing Liu

“…This paper proposes leveraging graph neural networks (GNNs) to model an agent’s environment to construct superior policy networks in reinforcement learning (RL). To this end, we explore the effects of different combinations of GNNs and graph network pooling functions on policy performance. …”
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Insights into the role of dopamine in olfactory learning behavior of honeybee by Muhammad Fahad Raza, Zhiguo Li, Hongyi Nie, Songkun Su

“… One of the brain's primary functions is remembering and learning information related to food and odour. …”
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MLPro 2.0 - Online machine learning in Python by Detlef Arend, Laxmikant Shrikant Baheti, Steve Yuwono, Syamraj Purushamparambil Satheesh Kumar, Andreas Schwung

“…In this paper, we present version 2.0 of the open-source middleware MLPro for applied machine learning in Python. Notably, it introduces the new sub-framework MLPro-OA for online machine learning, focusing on standards and templates for classic and online-adaptive data stream processing (DSP/OADSP). …”
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Deep Learning in Archiving Indus Script and Motif Information by Vaishnavi Dixit, Nushrat Hussain, Shubham Basak, Deva Atturu, Debasis Mitra, Ujjwal Bhattacharya

“…The system operates as a pipeline comprising three deep learning models integrated with a custom-designed database. …”
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Goistrat: gene-of-interest-based sample stratification for the evaluation of functional differences by Carlos Uziel Pérez Malla, Jessica Kalla, Andreas Tiefenbacher, Gabriel Wasinger, Kilian Kluge, Gerda Egger, Raheleh Sheibani-Tezerji

“…Conclusion Our comprehensive approach provides a novel tool to identify disease-relevant functions of genes of interest (GOI) in large datasets. …”
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Artificial Intelligence in SMEs: Enhancing Business Functions Through Technologies and Applications by Thang Le Dinh, Manh-Chiên Vu, Giang T.C. Tran

“…Through a systematic literature review of 50 studies published between 2016 and 2025, we identify prominent AI technologies, including machine learning, natural language processing, and generative AI, and their applications in enhancing efficiency, decision-making, and innovation across sales and marketing, operations and logistics, finance and other business functions. …”
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Cellular senescence-associated genes in rheumatoid arthritis: Identification and functional analysis. by You Ao, Qing Lan, Tianhua Yu, Zhichao Wang, Jing Zhang

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Colloidal Magnetoelectric Shape Recognition Based on Machine Learning by Xichen Hu, Xianhu Liu, Olli Ikkala, Bo Peng

“…Functionalized particles ranging from nanoscale to microscale and their assemblies have facilitated a wide variety of sensing concepts, from molecular‐scale chemical and biological detection to large‐scale engineering defect testing. …”
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Active learning model used for android malware detection by Md․Habibullah Shakib, Md. Yeasin, Kh. Mustafizur Rahman, Fabiha Faiz Mahi, Md. Mahsin-Ul-Islam, Saddam Hossain

“…The results of the experiments demonstrated the effectiveness of our proposed model, which outperformed both supervised machine learning and the traditional machine learning technique in terms of training and test accuracies of 92.36 % and 85.9 % and loss functions of 22.5 % and 33.2 %, respectively, and achieved the highest degree of accuracy.…”
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Detection of phishing portals through machine learning algorithms by E. A. Trushnikov

“…Objective Analysis and practical implementation of the phishing portal detection functionality through machine learning algorithms. Method. …”
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Unsupervised Machine Learning Approaches for Test Suite Reduction by Anila Sebastian, Hira Naseem, Cagatay Catal

“…As software systems grow in size, complexity, and functionality, the parallel expansion of the test suite leads to an inefficient utilization of computational power and time, presenting challenges to optimization. …”
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Learning-induced remodelling of inhibitory synapses in the motor cortex by Nishita Bhembre, Annalisa Paolino, Sooraj S. Das, Sumasri Guntupalli, Laura R. Fenlon, Victor Anggono

“…Robust structural and functional plasticity occurs at excitatory synapses in the motor cortex in response to learning. …”
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Machine Learning‐Assisted Prediction and Generation of Antimicrobial Peptides by Sukhvir Kaur Bhangu, Nicholas Welch, Morgan Lewis, Fanyi Li, Brint Gardner, Helmut Thissen, Wioleta Kowalczyk

“…In summary, in silico predictive and generative models for functional motif and AMP discovery are powerful tools for engineering highly effective AMPs to combat multidrug resistant pathogens.…”
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Investigating the Relationship Between Circadian Rhythm and Learning-Memory by Tayfun Başer, Mustafa Saygın

“…Many experimental models on learning and memory have been created so far. The general conclusion is the necessity of the circadian rhythm for learning and memory formation. …”
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Contrastive learning-driven framework for neuron morphology classification by Yikang Jiang, Hao Tian, Quanbing Zhang

“…Abstract The Neuron morphology classification is a critical task in neuroscience research, as the morphological features of neurons are closely linked to the functional characteristics of neural circuits. However, traditional classification methods often struggle with the complexity and diversity of neuronal morphologies. …”
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Classification of Biological Data using Deep Learning Technique by Azha Javed, Muhammad Javed Iqbal

“…Recently machine learning algorithms got huge attention and are widely used. …”
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Memory consolidation from a reinforcement learning perspective by Jong Won Lee, Min Whan Jung, Min Whan Jung

“…From this perspective, memory consolidation functions as a form of offline reinforcement learning, aimed at enhancing adaptive decision-making.…”
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Multiplatform ecosystems of professional learning: The case of the #CharlasEducativas by Jeffrey P. Carpenter, Ingrid Mosquera-Gende, Paula Marcelo-Martínez

“…Abstract Self-directed educator professional learning is commonplace, and such activities increasingly span multiple digital spaces and formats, and blur boundaries between online and offline. …”
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Automated Generation of Hybrid Metaheuristics Using Learning-to-Rank by Xinru Xue, Ting Shu, Jinsong Xia

“…The effectiveness and efficiency of the LTR-MHA method are evaluated using the CEC2017 benchmark functions. The experiments confirm the effectiveness of the proposed method. …”
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ROBOT NAVIGATION IN INDOOR ENVIRONMENT THROUGH SELF LEARNING by Chandan Kalita, Kishore Kashyap, Mirzanur Rahman, Satyajit Sarma, Parvez Aziz Boruah

“…Indoor robot navigation is a critical functionality for the effective use of mobile robots within indoor environments, especially when GPS signals are unavailable. …”
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