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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VulMPFF: A Vulnerability Detection Method for Fusing Code Features in Multiple Perspectives by Xiansheng Cao, Junfeng Wang, Peng Wu, Zhiyang Fang

“…Specifically, VulMPFF extracts serialized abstract syntax tree as IRC from code sequence, lexical and syntactic relation perspective, and code property graph as IRC from graph structure perspective, and uses Bi-LSTM model with attention mechanism and graph neural network with attention mechanism to learn the code features from multiple perspectives and fuse them to detect the vulnerabilities in the code, respectively. …”
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Assessment of Rear-End Collision Risk Based on a Deep Reinforcement Learning Technique: A Break Reaction Assessment Approach by Muhammad Sameer Sheikh, Yinqiao Peng

“…Firstly, we introduce the deep neural network (DNN) to learn the movements of LAV. Then, a collision-free modeling based on the deep reinforcement model (DRM) is proposed to mitigate the collision risks associated with LAV movements to nearby vehicles thus improving traffic safety. …”
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Design of circulating temperature control system for rubber and plastic industry based on mechatronics by Hexun Cao, Weiming Liu

“…Aiming at the problem of poor control effect of current temperature control system, a new circulating temperature control system for plastic industry was proposed in this study.MethodsFirstly, fuzzy neural network and improved particle swarm optimization algorithm were introduced in this study, and then the hybrid algorithm was combined with mechatronics technology to design and implement a set of rubber and plastic industry cycle temperature control system.ResultsAccording to the test data, the improved algorithm performed well in both scenarios. …”
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Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach by Sumaira Tabassum, M. Jawad Khan, Javaid Iqbal, Asim Waris, M. Adeel Ijaz

“…It is fine-tuned on labeled data, followed by a classification step using a Convolutional Neural Network (CNN). A unique dataset of 234,259 chromosome images, including the training, validation, and test sets, was used. …”
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Male-assisted training and injury patterns: hypergraph-enhanced analysis of injuries in women’s water polo by Xuehui Feng, Xuehui Feng, Zhibin Wang, Zhibin Wang, Zheng Wang, Zheng Wang, Chen He, Chen He, Hongxing Xun, Yuanfa Chen, Jie Ding, Jie Ding, Gen Chen, Gen Chen, Zhe Liu, Zhe Liu

“…The study seeks to identify key factors influencing these changes and propose corresponding injury prevention measures.MethodsWe utilized pattern analysis and classification techniques to explore the injury data. A Hypergraph Neural Network (HGNN) was employed for pattern extraction, where each athlete was represented as a node in a hypergraph, with node dimensions capturing high-order relational embedding information. …”
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Microgrid system for electric vehicle charging stations integrated with renewable energy sources using a hybrid DOA–SBNN approach by Kommoju Naga Durga Veera Sai Eswar, M. Arun Noyal Doss, Mohammad Shorfuzzaman, Ali Elrashidi

“…The manuscript introduces a hybrid technique for efficient electric vehicle (EV) charging integrating the Dollmaker Optimization algorithm (DOA) and spatial Bayesian neural network (SBNN). This method optimizes the joint operation of photovoltaic (PV), wind turbines (WTs), supercapacitors (SCs), and battery energy storage systems (BESSs) in microgrids to enhance EV charging station efficiency, reliability, and power quality while reducing grid outages. …”
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Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals by Ihtisham Ul Haq, Ghassan Husnain, Yazeed Yasin Ghadi, Nisreen Innab, Masoud Alajmi, Hanan Aljuaid

“…This research presents a tailored one-dimensional convolutional neural network (1D-CNN) for the classification of phonocardiogram (PCG) signals into normal or abnormal categories, providing an automated and efficient solution for congenital heart disease (CHD) diagnosis. …”
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Metabolomics Biomarkers in Prediction of Sudden Infant Death Syndrome: The Role of Short Chain Fatty Acids by Maria Aslam, Omer Riaz, Jawaria Aslam, Dost Muhammad Khan, Mustafa Hameed, Muhammad Suleman, Rizwan Shahid, Turke Althobaiti, Naeem Ramzan

“…The application of ML, particularly the Artificial Neural Network (ANN) and Stacking model, demonstrated exceptional accuracy of 94% and 96.15% with a recall of 100% and 92.31%, respectively. …”
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Template switching during DNA replication is a prevalent source of adaptive gene amplification by Julie N Chuong, Nadav Ben Nun, Ina Suresh, Julia Cano Matthews, Titir De, Grace Avecilla, Farah Abdul-Rahman, Nathan Brandt, Yoav Ram, David Gresham

“…Using a CNV reporter system and neural network simulation-based inference (nnSBI) we quantified the formation rate and fitness effect of CNVs for each strain. …”
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Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in Nort... by Xiuyu Liu, Zhen Zhang, Tao Jiang, Xuehua Li, Yanyi Li

“…Then, seven methods such as linear regression, BP neural network (BP), KNN, random forest (RF), and random forest based on genetic algorithm optimization (GA_RF) are used to construct the inversion model of TSM concentration. …”
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An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method by Shuangshuang Shi, Kefei Zhang, Suqin Wu, Jiaqi Shi, Andong Hu, Huajing Wu, Yu Li

“…The root mean square errors of the bi‐LSTM‐based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI‐2016, artificial neural network and LSTM‐based models, correspondingly. …”
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Deep learning-based prediction of HER2 status and trastuzumab treatment efficacy of gastric adenocarcinoma based on morphological features by Zhida Wu, Tao Wang, Junlin Lan, Jianchao Wang, Gang Chen, Tong Tong, Hejun Zhang

“…Results We developed a convolutional neural network (CNN) model using surgical specimens that achieved an area under the curve (AUC) value of 0.847 in predicting HER2 amplification, and achieved an AUC of 0.903 in predicting HER2 status specifically in patients with HER2 2 + expression. …”
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Indonesia rupiah currency detection for visually impaired people using transfer learning VGG-19 by Raissa Alfatikarani, Laras Suciningtyas, Genta Garuda Bimasakti, Faqisna Putra Mardhatillah, Jessie R. Paragas, Hapsari Peni Agustin Tjahyaningtijas

“…Previous methods of currency detection using Convolutional Neural Network (CNN) techniques, including the VGG-19 architecture, have often encountered challenges, particularly the long training times required. …”
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Leveraging AI to improve disease screening among American Indians: insights from the Strong Heart Study by Paul Rogers, Thomas McCall, Ying Zhang, Jessica Reese, Dong Wang, Weida Tong

“…Logistic Regression, Artificial Neural Network, and Random Forest were utilized as in silico screening tests within the SHS group. …”
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Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms by Sobhan Maleky, Maryam Faraji, Majid Hashemi, Akbar Esfandyari

“…The random forest model with the highest accuracy (R 2 = 0.986) was the best prediction model, while logistic regression (R 2 = 0.98), decision tree (R 2 = 0.979), K-nearest neighbor (R 2 = 0.968), artificial neural network (R 2 = 0.955), and support vector machine (R 2 = 0.928) predicted GWQI with lower accuracy. …”
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Robust Forest Sound Classification Using Pareto-Mordukhovich Optimized MFCC in Environmental Monitoring by Ahmad Qurthobi, Robertas Damasevicius, Vytautas Barzdaitis, Rytis Maskeliunas

“…To improve classification capabilities, the study introduces a hybrid model that combines neural network (CNN) with a Bidirectional Long-Short-Term Memory (BiLSTM) layer, designed to capture both spatial and temporal features of the sound data. …”
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