Reinforcement learning in artificial intelligence and neurobiology by Tursun Alkam, Andrew H Van Benschoten, Ebrahim Tarshizi

“…In parallel, we discuss how neurobiological findings, especially the role of dopamine in encoding reward prediction errors, have inspired biologically grounded RL models. …”
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Petri graph neural networks advance learning higher order multimodal complex interactions in graph structured data by Alma Ademovic Tahirovic, David Angeli, Adnan Tahirovic, Goran Strbac

“…The work opens new directions in learning over complex structures, transcending transformer-based and traditional hypergraph-based algorithms. We validate results through theoretical analysis and real-world experiments, while demonstrating superior performance in, e.g., stock market prediction.…”
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Cable Force Optimization of Circular Ring Pylon Cable-Stayed Bridges Based on Response Surface Methodology and Multi-Objective Particle Swarm Optimization by Shengdong Liu, Fei Chen, Qingfu Li, Xiyu Ma

“…The response surface model demonstrates prediction errors of 0.35% for strain energy and 5.1% for maximum vertical mid-span deflection. …”
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Investigation of Underlying Biological Association and Targets between Rejection of Renal Transplant and Renal Cancer by Yinwei Chen, Zhanpeng Liu, Qian Yu, Xu Sun, Shuai Wang, Qingyi Zhu, Jian Yang, Rongjiang Jiang

“…The prognosis model showed excellent performance in prognosis prediction in a ccRCC cohort. In addition, the machine learning algorithms identified 19 rejection-related genes, including PLAC8, involved in ccRCC occurrence. …”
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Artificial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review. by Muhammad Ibtsaam Qadir, Jackson A Baril, Michele T Yip-Schneider, Duane Schonlau, Thi Thanh Thoa Tran, C Max Schmidt, Fiona R Kolbinger

“…The studies were stratified based on prediction target, underlying data type and imaging modality, patient cohort size, and stage of clinical translation and were subsequently analyzed to identify trends and gaps in the field. …”
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Strategic forecasting of renewable energy production for sustainable electricity supply: A machine learning approach considering environmental, economic, and oil factors in Türkiye... by Yasemin Ayaz Atalan, Hasan Şahin, Abdulkadir Keskin, Abdulkadir Atalan

“…The AB model, on the other hand, has the highest error values in the test data set, but still provides an acceptable prediction accuracy. The R2 value was 0.792% and the MAPE value (0.371%) of this model was calculated to be in the range of 20% < MAPE≤50%. …”
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Artificial intelligence derived grading of mustard gas induced corneal injury and opacity by Rajnish Kumar, Devansh M. Sinha, Nishant R. Sinha, Ratnakar Tripathi, Nathan Hesemann, Suneel Gupta, Anil Tiwari, Rajiv R. Mohan

“…The ResNet50-based model demonstrated the best performance, achieving 87% training accuracy, and 85% and 83% prediction accuracies on two independent test sets. …”
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System Optimization of Talent Life Cycle Management Platform Based on Decision Tree Model by Jiaonan Han

“…Decision tree algorithm is a widely used classification and prediction method. …”
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A Theoretical Study of Structural, Electronic, and Vibration Properties of Acetylcholine Chloride and Bromide by Kirill A. Gordienko, Aleksei B. Gordienko, Yurii N. Zhuravlev

“…They also facilitate the target search and prediction of new forms. The purpose of the study was to define the structural parameters and basic electronic and vibration characteristics of crystal acetylcholine halides (ACh-Hal). …”
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Indoor Positioning System based on SSA-ELM Neural Network for Visible Light by JIA Kejun, NIU Zhen, YU Kai, ZHANG Zhicong, PENG Duo, CAO Minghua

“…In this paper, a Sparrow Search Algorithm (SSA)-ELM neural network algorithm was proposed by introducing the SSA to determine the initial weights and thresholds of the ELM neural network.…”
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Identifying Precipitation Types From Surface Meteorological Variables With Machine Learning by Adrienn VARGA-BALOGH, Ádám LEELŐSSY, Róbert MÉSZÁROS

“…Precipitation type prediction is crucial for various sectors, including aviation, agriculture, and public safety. …”
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Ion channel classification through machine learning and protein language model embeddings by Ghazikhani Hamed, Butler Gregory

“…This study extends our previous work on protein language models for ion channel prediction, significantly advancing the methodology and performance. …”
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Preference learning based deep reinforcement learning for flexible job shop scheduling problem by Xinning Liu, Li Han, Ling Kang, Jiannan Liu, Huadong Miao

“…First, a reward predictor based on preference learning is designed to model reward prediction by comparing random fragments, eliminating the need for complex reward function design. …”
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Melanoma Skin Lesion Classification Using Neural Networks: A systematic review by ahmed hammo, Mohammed Younis

“…Given the growing interest in cancer prediction, we have presented this paper, a systematic review of recent developments, using artificial intelligence focusing on melanoma skin lesion detection, particularly systems designed on neural networks. …”
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Dead Sea Stromatolite Reefs: Testing Ground for Remote Sensing Automated Detection of Life Forms and Their Traces in Harsh Environments by Nuphar Gedulter, Amotz Agnon, Noam Levin

“…The results show the successful detection of stromatolites, with all three prediction methods giving high accuracy rates (stromatolite > 0.9, bedrock dolomite > 0.8). …”
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A Triple-Optimized Extreme Learning Machine Model for Power Load Forecasting by Haoxiang Gao, Weixin Kang, Miao Fan

“…This approach disregards the pathological characteristics and overfitting that arise from the simultaneous optimization of the three, the challenges of calculation, and the deviation of the prediction results. This paper proposes a seamless enhanced incremental ELM triple optimization model (SBOA-SEI-MRU-ELM) based on the Secretary bird optimization algorithm and the MINres regularization under the U-curve method to solve the above problem. …”
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The evolutionary analysis of emerging low frequency HIV-1 CXCR4 using variants through time--an ultra-deep approach. by John Archer, Andrew Rambaut, Bruce E Taillon, P Richard Harrigan, Marilyn Lewis, David L Robertson

“…Then, in conjunction with coreceptor prediction algorithms that infer HIV tropism, our software was used to quantify the viral population structure pre- and post-treatment. …”
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Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Dmitriy A. Martyushev

“…In this study, a comprehensive dataset consisting of 30,660 independent data points was utilized to develop machine learning (ML) models for Sw prediction. Nine well log parameters—Depth (DEPT), High-Temperature Neutron Porosity, True Resistivity, Computed Gamma Ray, Spectral Gamma Ray, Hole Caliper, Compressional Sonic Travel Time, Bulk Density, and Temperature—were used as input features to train and test five ML algorithms: Linear Regression, Support Vector Machine (SVM), Random Forest, Least Squares Boosting, and Bayesian methods. …”
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Calculation of the Respiratory Modulation of the Photoplethysmogram (DPOP) Incorporating a Correction for Low Perfusion by Paul S. Addison, Rui Wang, Scott J. McGonigle, Alberto A. Uribe, Sergio D. Bergese

“…It has been proposed as a noninvasive surrogate for pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. …”
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Mir-494-3p enhances aggressive phenotype of non-small cell lung cancer cells by regulating SET/I2PP2A by Yuwen Du, Taisuke Kajino, Yukako Shimada, Takashi Takahashi, Ayumu Taguchi

“…Integration of RNA sequencing analysis of NSCLC cells with miR-494-3p inhibition and a bioinformatic search of miRNA target prediction algorithms resulted in identification of SET/I2PP2A as a direct target of miR-494-3p. …”
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