Machine Learning for Promoting Environmental Sustainability in Ports by Meead Mansoursamaei, Mahmoud Moradi, Rosa G. González-Ramírez, Eduardo Lalla-Ruiz

“…The research findings indicate that the articles using polynomial regression models are dominant in the literature, and the recurrent neural network (RNN) and long short-term memory (LSTM) are the most recent approaches. …”
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An interpretable and stacking ensemble model for predicting heat and mass transfer of desiccant wheel by Mengyang Li, Liu Chen

“…The model uses an integration approach, Light Gradient Boosting Machine, Random Forest and Back Propagation Neural Network models are used as the first-level base models to learn the data, and the Linear Regression model as a meta-model integrates the output of the base model to obtain the final prediction results. …”
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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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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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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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Apply a deep learning hybrid model optimized by an Improved Chimp Optimization Algorithm in PM2.5 prediction by Ming Wei, Xiaopeng Du

“…Subsequently, a one-dimensional convolutional neural network (1DCNN) with efficient feature extraction capability is used to extract dynamic features from sequences. …”
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Prediction of Grade Classification of Rock Burst Based on PCA-SSA-PNN Architecture by Zhenyi Wang, Yalei Wang, Xiaoliang Jin

“…In order to estimate the risk grades of rock burst, an integrated method combining principal component analysis (PCA) and sparrow search algorithm (SSA) with probabilistic neural network (PNN) was proposed. Considering that the in situ stress of rock mass, the strength of rock, and the strength of rock mass are the key influencing factors of rock bursts, the maximum in situ stress σmax, maximum tangential stress σθ, rock strength σci, rock mass strength σcm, and three rock burst evaluation indexes (σθ/σci, σci/σmax, and σcm/σmax) were selected to constitute the rock burst grade evaluation index system. …”
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Enhancing prostate cancer segmentation in bpMRI: Integrating zonal awareness into attention-guided U-Net by Chao Wei, Zheng Liu, Yibo Zhang, Lianhui Fan

“…First, pretraining a convolutional neural network (CNN)-based attention-guided U-Net model for segmenting the region of interest which is carried out in the prostate zone. …”
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ADA-NAF: Semi-Supervised Anomaly Detection Based on the Neural Attention Forest by Andrey Ageev, Andrei Konstantinov, Lev Utkin

“…In this study, we present a novel model called ADA-NAF (Anomaly Detection Autoencoder with the Neural Attention Forest) for semi-supervised anomaly detection that uniquely integrates the Neural Attention Forest (NAF) architecture which has been developed to combine a random forest classifier with a neural network computing attention weights to aggregate decision tree predictions. …”
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