Sunflower-based butterfly optimization algorithm with enhanced RNN for the harmonics elimination in multilevel inverter by V. Mohan, G. Krithiga, M. Thamil Alagan, V. Sathya

“…Enhanced Recurrent Neural Network (ERNN) shows a kind of recurrent neural network in which the hidden neurons are tweaked by SF-BOA with the goal of minimizing THD. …”
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Optimizing Renewable Energy Systems Placement Through Advanced Deep Learning and Evolutionary Algorithms by Konstantinos Stergiou, Theodoros Karakasidis

“…This study introduces GREENIA, a novel artificial intelligence (AI)-powered framework for optimizing RES placement that holistically integrates machine learning (gated recurrent unit neural networks with swish activation functions and attention layers), evolutionary optimization algorithms (Jaya), and Shapley additive explanations (SHAPs). …”
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Machine learning for active sites prediction of quinoline derivatives by Jie Sun, Zi-Hao Li, Yi-Fei Yang, Shu-Yu Zhang

“…In this study, a generalizable approach to predict site selectivity is accomplished by using artificial neural network (ANN), which is suitable for the site prediction of derivatives of quinoline. …”
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Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load Forecasting by Mohini Yadav, Majid Jamil, Mohammad Rizwan, Richa Kapoor

“…RBFNNs and CNNs are trained in two phases using the functional link artificial neural network (FLANN) optimization method with a deep learning structure. …”
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A Deep Learning Framework for Chronic Kidney Disease stage classification by Gayathri Hegde M, P Deepa Shenoy, Venugopal KR, Arvind Canchi

“…To evaluate the proposed method, eight DL models — Feedforward Neural Network, Recurrent Neural Network, Deep Neural Network, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU) and Bidirectional GRU were trained on selected features using different FS methods, as well as complete dataset. …”
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A blockchain based deep learning framework for a smart learning environment by Shimaa Ouf, Soha Ahmed, Yehia Helmy

“…Then apply the deep learning model to this secured data to predict the learner’s performance. The smart contract functions also play a role in enabling the university to issue learners’ certificates that are stored on the blockchain to be available and verifiable by all the nodes in the network. …”
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Single Gaussian Chaotic Neuron: Numerical Study and Implementation in an Embedded System by Luis M. Torres-Treviño, Angel Rodríguez-Liñán, Luis González-Estrada, Gustavo González-Sanmiguel

“…Artificial Gaussian neurons are very common structures of artificial neural networks like radial basis function. …”
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Analysis of entropy generation in tri-hybrid nanofluid flow towards a spinning disk under the influence of hall effect and variable viscosity by Moh Yaseen, Ritu Bartwal, Anum Shafiq, Sawan Kumar Rawat, Qasem M. Al-Mdallal, Umesh Khanduri, Tabassum Naz Sindhu

“…Subsequently, an advanced artificial neural network (ANN) is employed to predict the Nusselt number. …”
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Energy Demand Forecasting Scenarios for Buildings Using Six AI Models by Khaled M. Salem, Francisco J. Rey-Martínez, A. O. Elgharib, Javier M. Rey-Hernández

“…Understanding and forecasting energy consumption patterns is crucial for improving energy efficiency and human well-being, especially in diverse infrastructures like Spain. This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. …”
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Market Regime Identification and Variable Annuity Pricing: Analysis of COVID-19-Induced Regime Shifts in the Indian Stock Market by Mohammad Sarfraz, Guglielmo D’Amico, Dharmaraja Selvamuthu

“…Advanced methodologies, including regime-switching hidden Markov models, artificial neural networks, and Monte Carlo simulations, were applied to analyze pre- and post-COVID-19 market behavior. …”
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Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study by Vladimir Koković, Kosta Pavlović, Andjela Mijanović, Slavko Kovačević, Ivan Mačužić, Vladimir Božović

“…We proposed a novel framework, employing advanced data-driven techniques like artificial neural networks (ANNs) to estimate parameters of probability distributions governing downtime lengths. …”
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Leveraging dendritic complexity for neuromorphic computing by Suma G Cardwell, Mark Plagge, Luke Parker, Claire E Plunkett, David Munkvold, Paloma T Gonzalez-Bellido, Scott Koziol, Conrad James, Frances S Chance

“…Here, we present our work that aims to incorporate dendrites for ‘compute-on-wire’ in neuromorphic architectures to increase the computational complexity (e.g. number of programmable parameters, nonlinear dynamics) as well as computational efficiency (energy/compute) of artificial neural networks (ANNs). We do this by showcasing neuromorphic dendrite elements that can be leveraged for various applications. …”
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Hierarchical Information-Extreme Machine Learning of Hand Prosthesis Control System Based on Decursive Data Structure by Anatolii Dovbysh, Vladyslav Piatachenko, Mykyta Myronenko, Mykyta Suprunenko, Julius Simonovskiy

“…The method is based on adapting the input information description to maximize the probability of correct classification decisions, similar to artificial neural networks. However, unlike neural-like structures, the proposed method was developed within a functional approach to modeling cognitive processes of natural intelligence formation and decision-making. …”
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Ancillary Voltage Control Design for Adaptive Tracking Performance of Microgrid Coupled With Industrial Loads by Subrata K. Sarker, Shahriar Rahman Fahim, Niloy Sarker, Kazi Zakaria Tayef, Abu Bakar Siddique, Dristi Datta, M. A. Parvez Mahmud, Md. Fatin Ishraque, Sajal K. Das, Md Rabiul Islam Sarker, Sk. A. Shezan, Ziaur Rahman

“…Firstly, we design an intelligent adaptive control (IAC) framework made by merging with proportional-integral (PI) regulator and artificial neural network (ANN) to sustain the regulated common bus voltage over the mentioned changes. …”
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Information-extreme machine learning of wrist prosthesis control system based on the sparse training matrix by Suprunenko M. K., Zborshchyk O. P., Sokolov O.

“…The idea of information-extreme machine learning of the control system for recognition of electromyographic biosignals, as in artificial neural networks, consists in adapting the input information description to the maximum total probability of making correct classification decisions. …”
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Ultra robust negative differential resistance memristor for hardware neuron circuit implementation by Yifei Pei, Biao Yang, Xumeng Zhang, Hui He, Yong Sun, Jianhui Zhao, Pei Chen, Zhanfeng Wang, Niefeng Sun, Shixiong Liang, Guodong Gu, Qi Liu, Shushen Li, Xiaobing Yan

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Identification and validation of ANXA3 and SOCS3 as biomarkers for acute myocardial infarction related to sphingolipid metabolism by Ling Sun, Lingyan He, Hai-Hua Pan, Chang-Lin Zhai

“…Further analyses included artificial neural networks (ANN), enrichment analysis, immune infiltration, drug prediction, and molecular docking. …”
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Swiftly accessible retinomorphic hardware for in-sensor image preprocessing and recognition: IGZO-based neuro-inspired optical image sensor arrays with metallic sensitization islan... by Kyungmoon Kwak, Kyungho Park, Jae Seong Han, Byung Ha Kang, Dong Hyun Choi, Kunho Moon, Seok Min Hong, Gwan In Kim, Ju Hyun Lee, Hyun Jae Kim

“…Here, we introduce a visible-light-driven neuromorphic vision system that integrates front-end retinomorphic photosensors with a back-end artificial neural network (ANN), employing a single neuro-inspired indium-gallium-zinc-oxide phototransistor (NIP) featuring an aluminum sensitization layer (ASL). …”
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Deconvolving X-Ray Galaxy Cluster Spectra Using a Recurrent Inference Machine by Carter Lee Rhea, Julie Hlavacek-Larrondo, Alexandre Adam, Ralph Kraft, Ákos Bogdán, Laurence Perreault-Levasseur, Marine Prunier

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Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction by Irfan Pathan, Arif Raza, Adarsh Sahu, Mohit Joshi, Yamini Sahu, Yash Patil, Mohammad Adnan Raza, Ajazuddin

“…Core AI algorithms support vector machines, random forests, graph neural networks, and transformers are examined for their applications in molecular representation, virtual screening, and ADMET property prediction. …”
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