Hybrid neural networks for continual learning inspired by corticohippocampal circuits by Qianqian Shi, Faqiang Liu, Hongyi Li, Guangyu Li, Luping Shi, Rong Zhao

“…Our CH-HNNs incorporate artificial neural networks and spiking neural networks, leveraging prior knowledge to facilitate new concept learning through episode inference, and offering insights into the neural functions of both feedforward and feedback loops within corticohippocampal circuits. …”
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Layer ensemble averaging for fault tolerance in memristive neural networks by Osama Yousuf, Brian D. Hoskins, Karthick Ramu, Mitchell Fream, William A. Borders, Advait Madhavan, Matthew W. Daniels, Andrew Dienstfrey, Jabez J. McClelland, Martin Lueker-Boden, Gina C. Adam

“…Abstract Artificial neural networks have advanced due to scaling dimensions, but conventional computing struggles with inefficiencies due to memory bottlenecks. …”
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Spectral convolutional neural network chip for in-sensor edge computing of incoherent natural light by Kaiyu Cui, Shijie Rao, Sheng Xu, Yidong Huang, Xusheng Cai, Zhilei Huang, Yu Wang, Xue Feng, Fang Liu, Wei Zhang, Yali Li, Shengjin Wang

“…Abstract Optical neural networks are considered next-generation physical implementations of artificial neural networks, but their capabilities are limited by on-chip integration scale and requirement for coherent light sources. …”
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In situ real-time measurement for electron spin polarization in atomic spin gyroscopes by Feng Li, Haoying Pang, Zhuo Wang, Wenfeng Fan, Min Zhang, Zehua Liu, Jiahang Li, Bodong Qin, Xinxiu Zhou, Xusheng Lei, Ruigang Wang

“…By utilizing artificial neural networks, we derive an output equation for electron spin polarization, using transmitted laser power and cell temperature as independent variables. …”
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Bio‐Inspired Neuromorphic Sensory Systems from Intelligent Perception to Nervetronics by Elvis K. Boahen, Hyukmin Kweon, Hayoung Oh, Ji Hong Kim, Hayoung Lim, Do Hwan Kim

“…This study explores the latest advancements in artificial synaptic properties triggered by various stimuli, including optical, auditory, mechanical, and chemical inputs, and their subsequent processing through artificial neural networks for applications in image recognition and multimodal pattern recognition. …”
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FORECASTING DEFERRED TAXES IN INTERNATIONAL ACCOUNTING WITH MACHINE LEARNING by Osman Bayri, Ahmet Çağdaş Seçkin, Feden Koç

“…Within the context of the study, the deferred tax output parameters, which companies will present in their annual financial reports in 2020, have been estimated using the following methods: the DTA value using the random forest method with an accuracy rate of 0,823, the net DTA value using the artificial neural networks method with an accuracy rate of 0,790, the DTL value using the random forest method with an accuracy rate of 0,823 and the net DTL value using the random forest method with an accuracy rate of 0,887. …”
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Key risk factors of generalized anxiety disorder in adolescents: machine learning study by Yonghwan Moon, Hyekyung Woo, Hyekyung Woo

“…Predictive models using Random Forest and Artificial Neural Networks demonstrated that the XGBoost feature selection method effectively identified key factors and showed strong performance. …”
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Transforming Cardiac Care: Machine Learning in Heart Condition Prediction Using Phonocardiograms by Sandra D’Souza, Niranjan Reddy S, Saikonda Krishna Tarun, Sohan P, aneesha acharya k

“…The developed models record a classification accuracy of 71% for logistic regression and 94% for the random forest model. Further, artificial neural networks (ANN) and Deep learning networks have been trained to improve performance and demonstrated an accuracy of 94.5%.…”
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Neural network-based robot localization using visual features by Felipe Trujillo-Romero

“…It incorporates an object recognition module that leverages local features and unsupervised artificial neural networks to identify non-dynamic elements in a room and assign them positions. …”
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Performance Sensitivity of a Wind Farm Power Curve Model to Different Signals of the Input Layer of ANNs: Case Studies in the Canary Islands by Sergio Velázquez Medina, José A. Carta, Ulises Portero Ajenjo

“…A wind farm power curve model is proposed in this paper which is developed using artificial neural networks, and a study is undertaken of the influence on model performance when parameters such as the meteorological conditions (wind speed and direction) of areas other than the wind farm location are added as signals of the input layer of the neural network. …”
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Artificial intelligence-enhanced solubility predictions of greenhouse gases in ionic liquids: A review by Bilal Kazmi, Syed Ali Ammar Taqvi, Dagmar Juchelkov, Guoxuan Li, Salman Raza Naqvi

“…It examines artificial neural networks, deep learning models, and support vector machines for predicting solubility in ILs, and presents valuable results demonstrating the potential of these techniques. …”
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Rapid Determination of the Freshness of Lotus Seeds Using Surface Desorption Atmospheric Pressure Chemical Ionization-Mass Spectrometry with Multivariate Analyses by Yunyang Chi, Liping Luo, Xueyong Huang, Meng Cui, Ximo Dai, Yingbin Hao, Xiali Guo, Huolin Luo

“…The obtained data were processed by principal component analysis (PCA) and backpropagation artificial neural networks (BP-ANNs). The result showed that DAPCI-MS could obtain abundant chemical material information from the slice surface of lotus seeds. …”
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Prevention and Detection Research of Intelligent Sports Rehabilitation under the Background of Artificial Intelligence by Qiong Huang, Fubin Wang

“…This paper is aimed at studying the prevention and detection of sports rehabilitation in the context of artificial intelligence and proposing a compliance control method for lower limb rehabilitation robots based on artificial neural networks. In this paper, a double closed-loop control system is designed: the outer loop is an adaptive impedance control model based on sEMG feedback, and the purpose is to adjust the predicted desired joint trajectories. …”
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Swarming Computational Procedures for the Coronavirus-Based Mathematical SEIR-NDC Model by Suthep Suantai, Zulqurnain Sabir, Muhammad Asif Zahoor Raja, Watcharaporn Cholamjiak

“…The numerical solutions of the SEIR-NDC model are presented by using the computational framework of artificial neural networks (ANNs) together with the swarming optimization procedures aided with the sequential quadratic programming. …”
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A Comprehensive Investigation of Fraud Detection Behavior in Federated Learning by Sun Rui

“…The comparison involves three machine learning models - Artificial Neural Networks (ANN), Random Forest (RF), and Convolutional Neural Networks (CNN) - to assess their efficacy in the FL context. …”
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Enhancing the mechanical properties’ performances coconut fiber and CDW composite in paver block: multiple AI techniques with a Performance analysis by G. Uday Kiran, G. Nakkeeran, Dipankar Roy, Sumant Nivarutti Shinde, George Uwadiegwu Alaneme

“…In this study, Response Surface Methodology (RSM), Support Vector Machine (SVM), Gradient Boosting (GB), Artificial Neural Networks (ANN), and Random Forest (RF) machine learning method for optimization and predicting the mechanical properties of natural fiber addition incorporated with construction and demolition waste (CDW) as replacement of Fine Aggregate in Paver blocks. …”
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Systematic review of machine learning applications using nonoptical motion tracking in surgery by Teona Z. Carciumaru, Cadey M. Tang, Mohsen Farsi, Wichor M. Bramer, Jenny Dankelman, Chirag Raman, Clemens M. F. Dirven, Maryam Gholinejad, Dalibor Vasilic

“…From 3632 records, 84 studies were included, with Artificial Neural Networks (38%) and Support Vector Machines (11%) being the most common ML models. …”
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Vortex generators in heat sinks: Design, optimisation, applications and future trends by Mohammad Ismail, Abdullah Masoud Ali, Sol-Carolina Costa Pereira

“…Strategies such as nanofluids, dimples, Response Surface Methodology analysis and Artificial Neural Networks are crucial to improve VG designs to maximise thermal efficiency and minimise pressure loss. …”
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Identifying and Evaluating Chaotic Behavior in Hydro-Meteorological Processes by Soojun Kim, Yonsoo Kim, Jongso Lee, Hung Soo Kim

“…The generated time series from summation of sine functions were fitted to each series and used for investigating the hypotheses. Then artificial neural networks had been built for modeling the reservoir system and the correlation dimension was analyzed for the evaluation of chaotic behavior between inputs and outputs. …”
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Neural Network-Based Analysis of Flame States in Pulverised Coal and Biomass Co-Combustion by Żaklin Grądz, Waldemar Wójcik, Baglan Imanbek, Bakhyt Yeraliyeva

“…The measurement data after preprocessing were classified using artificial neural networks to determine the conditions for flame stability. …”
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