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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Modeling turning performance of Inconel 718 with hybrid nanofluid under MQL using ANN and ANFIS by Paresh Kulkarni, Satish Chinchanikar

“…This study develops artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models to predict cutting force, surface roughness, and tool life during Inconel 718 turning with a hybrid nanofluid under minimum quantity lubrication. …”
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Performance Investigation of Coated Carbide Tools in Milling Procedures by Paschalis Charalampous

“…Based on this dataset, artificial intelligence (AI) models, including artificial neural network (ANN), k-nearest neighbors (KNN), and support vector regression (SVR), were developed in order to predict the tool’s life as a function of the milling conditions. …”
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Rapid diagnosis of rheumatoid arthritis and ankylosing spondylitis based on Fourier transform infrared spectroscopy and deep learning by Wei Shuai, Xue Wu, Chen Chen, Enguang Zuo, Xiaomei Chen, Zhengfang Li, Xiaoyi Lv, Lijun Wu, Cheng Chen

“…Four classification models, namely artificial neural network (ANN), convolutional neural network (CNN), improved AlexNet model, and multi-scale convolutional neural network (MSCNN) were established. …”
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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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Development Trend of Information Technology and AdvancedNuclear Power Generation by Tao ZHOU, Haolei ZHANG, Yao YAO, Chunmei LIU

“…Quantum technology can enhance the core fuel function. Artificial intelligence machine data capture and neural networks learn to process and apply information more precisely. …”
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Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction by Chenxi Wang, Weiwei Zhang, Ming Ni, Qiong Wang, Chang Liu, Linbin Dai, Mengguo Zhang, Yong Shen, Feng Gao

“…We designed a multi-modal deep-learning framework that employs 3D convolutional neural networks to analyze MRI and additional neural networks to evaluate demographic data. …”
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Opportunities and Challenges of Brain-on-a-Chip Interfaces by Wenwei Shao, Weiwei Meng, Jiachen Zuo, Xiaohong Li, Dong Ming

“…Biological neural networks inherently grant BoCI systems neuro-inspired computational properties—such as ultralow energy consumption and dynamic plasticity—that surpass those of conventional artificial intelligence. …”
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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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A compositional model for effort‐aware Just‐In‐Time defect prediction on android apps by Kunsong Zhao, Zhou Xu, Meng Yan, Lei Xue, Wei Li, Gemma Catolino

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Multimodal Data Fusion Framework for Early Prediction of Autism Spectrum Disorder by Mohemmed Sha, Hussein Al-Dossary, Mohamudha Parveen Rahamathulla

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The underlying molecular mechanisms and biomarkers of Hip fracture combined with deep vein thrombosis based on self sequencing bioinformatics analysis by Guanghua Shi, Xiaocui Shi, Meng Zhang, Rui Cheng, Mengqing Hu, Yu Zhao, Shimei Li, Xiuxiu Li, Haiyun Ma, Pengcui Li

“…Feature genes were further refined by intersecting results from three machine learning algorithms and constructing an artificial neural network (ANN). Diagnostic performance was assessed using receiver operating characteristic (ROC) curves. …”
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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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General Cultural Competence of a Future Teacher in a Changing World by O. R. Neradovskaya, V. A. Starodubtsev

“…The paper analyzes the content оf the concept of “general cultural competence of a future teacher” and its dialectical development in a changing world under the influence of various factors: the new technological order; the complexity and diversity of various social environments; the risks of environmental, energy and epidemic crises; the emergence of artificial intelligence and learning neural networks; and the large consequences of small (at first glance) events in public life. …”
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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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