Forecasting Global Ionospheric TEC Using Deep Learning Approach by Lei Liu, Shasha Zou, Yibin Yao, Zihan Wang

“…In this study, the long short‐term memory (LSTM) neural network (NN) is applied to forecast the 256 spherical harmonic (SH) coefficients that are traditionally used to construct global ionospheric maps (GIM). …”
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A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification by Himansu Das, Bighnaraj Naik, H. S. Behera

“…It helps to deal with the uncertainty issues and assists the Artificial Neural Network- (ANN-) based model to achieve better performance. …”
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A Node-Regulated Deflection Routing Framework for Contention Minimization by Bakhe Nleya, Andrew Mutsvangwa

“…This is by way of regulated deflection routing (rDr) in which neural network agents are utilized in reinforcing the deflection route choices at core nodes. …”
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Speed Distribution Prediction of Freight Vehicles on Mountainous Freeway Using Deep Learning Methods by Yuren Chen, Yu Chen, Bo Yu

“…Meanwhile, the models were mostly developed based on the regression method, which is inconsistent with natural driving process. Recurrent neural network (RNN) is a distinctive type of deep learning method to capture the temporary dependency in behavioral research. …”
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Utilizing MRAMs With Low Resistance and Limited Dynamic Range for Efficient MAC Accelerator by Sateesh, Kaustubh Chakarwar, Shubham Sahay

“…Spintronics based magnetic memory devices can emulate synaptic behavior efficiently and are hailed as one of the most promising candidates for realizing compact and ultra-energy efficient neural network accelerators. Although ultra-dense magnetic memories with multi-bit capability (MLC) were proposed recently, their application in hybrid CMOS-non-volatile memory accelerators is limited due to their low dynamic range (memory window) and high cell currents (ON/OFF-state resistance in ∼kΩ). …”
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Comparison of In Silico Tools for Splice-Altering Variant Prediction Using Established Spliceogenic Variants: An End-User’s Point of View by Woori Jang, Joonhong Park, Hyojin Chae, Myungshin Kim

“…Therefore, we evaluated the performance of 8 in silico tools (Splice Site Finder, MaxEntScan, Splice-site prediction by neural network, GeneSplicer, Human Splicing Finder, SpliceAI, Splicing Predictions in Consensus Elements, and SpliceRover) using 114 NF1 spliceogenic variants, experimentally validated at the mRNA level. …”
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Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery. by Ahmad M Al-Omari, Yazan H Akkam, Ala'a Zyout, Shayma'a Younis, Shefa M Tawalbeh, Khaled Al-Sawalmeh, Amjed Al Fahoum, Jonathan Arnold

“…In the second method, these fundamental peptide features are converted into signal images, which are then transmitted to a deep learning neural network. The first and second methods have accuracy of 74% and 92.9%, respectively. …”
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RSM-, ANN-, and GA-Based Process Optimization for Acid Centrifugation Treatment of Cane Molasses Toward Mitigating Calcium Oxide Fouling in Ethanol Plant Heat Exchanger by Lata Deso Abo, Sintayehu Mekuria Hailegiorgis, Mani Jayakumar, Sundramurthy Venkatesa Prabhu, Gadissa Tokuma Gindaba, Abas Siraj Hamda, B. S. Naveen Prasad

“…Furthermore, the implementation of an artificial neural network (ANN) provided a better prediction model for CaO reduction, with a substantial R-squared value of 0.99866. …”
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A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network by Muhammad Sibtain, Xianshan Li, Snoober Saleem

“…Therefore, to enhance the reliability and accuracy of streamflow prediction, this paper developed a three-stage hybrid model, namely, IVL (ICEEMDAN-VMD-LSTM), which integrated improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), variational mode decomposition (VMD), and long short-term memory (LSTM) neural network. Monthly data series of streamflow, temperature, and precipitation in the Swat River Watershed, Pakistan, from January 1971 to December 2015 was used as a case study. …”
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ECG heartbeat classification using progressive moving average transform by Rabah Mokhtari, Samir Brahim Belhouari, Khelil Kassoul, Abderraouf Hocini

“…Our approach integrates PMAT with a 2D-Convolutional Neural Network (CNN) model for the classification of ECG heartbeat signals. …”
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Avatar legal protection as an atypical copyright object by V. A. Kroitor

“…Creating objects with the help of a neural network, in particular, an avatar in the form of a computer copy of a person, is a complex work of different people. …”
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Optimizing hypertension prediction using ensemble learning approaches. by Isteaq Kabir Sifat, Md Kaderi Kibria

“…Five machine learning (ML) models such as logistic regression (LR), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and a stacking ensemble model were trained using selected features to predict HTN. …”
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A phase factor generation using RNNs deep learning algorithm-based PTS method for PAPR reduction of beyond 5G FBMC waveform by Aziz Nanthaamornphong, Nishant Gaur, Lakshmana Phaneendra Maguluri, Arun Kumar

“…This article proposes a hybrid method combining a partial transmission sequence and recurrent neural network (RNN) known as PTS-RNNs. RNNs improve the performance of the PTS by efficiently predicting optimal phase factors, reducing computational complexity, and lowering the PAPR of the FBMC waveform. …”
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Path Planning Optimization of Smart Vehicle With Fast Converging Distance-Dependent PSO Algorithm by Muhammad Haris, Haewoon Nam

“…This innovative algorithm is inspired by neural network activation functions to achieve faster convergence. …”
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An Optimized Deep-Learning-Based Network with an Attention Module for Efficient Fire Detection by Muhammad Altaf, Muhammad Yasir, Naqqash Dilshad, Wooseong Kim

“…In the subsequent phase, the proposed network utilizes an attention-based deep neural network (DNN) named Xception for detailed feature selection while reducing the computational cost, followed by adaptive spatial attention (ASA) to further enhance the model’s focus on a relevant spatial feature in the training data. …”
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Towards unbiased skin cancer classification using deep feature fusion by Ali Atshan Abdulredah, Mohammed A. Fadhel, Laith Alzubaidi, Ye Duan, Monji Kherallah, Faiza Charfi

“…Abstract This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. …”
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R-CNN-Based Satellite Components Detection in Optical Images by Yulang Chen, Jingmin Gao, Kebei Zhang

“…This approach is based on a regional-based convolutional neural network (R-CNN), and it can enable the accurate detection of various satellite components by using optical images. …”
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Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves by Majid Niazkar, Mohammad Zakwan

“…As a result, the rating curves of eight different rivers were developed using the conventional method, evolutionary algorithm (EA), the modified honey bee mating optimization (MHBMO) algorithm, artificial neural network (ANN), MGGP, and the hybrid MGGP-GRG technique. …”
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Bioinsecticide Production from Cigarette Wastes by Badhane Gudeta, Solomon K, M. Venkata Ratnam

“…In addition, artificial neural network (ANN) studies with MATLAB were used to accurately forecast extraction yield. …”
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An Investigation Towards Resampling Techniques and Classification Algorithms on CM1 NASA PROMISE Dataset for Software Defect Prediction by Agung Fatwanto, Muh Nur Aslam, Rebbecah Ndugi, Muhammad Syafrudin

“…Data were collected through observation towards experiments on four categories of resampling techniques (oversampling, under sampling, ensemble, and combine) combined with three categories of machine learning classification algorithms (traditional, ensemble, and neural network) to predict defective software modules on CM1 NASA PROMISE dataset. …”
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