A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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Diagnosis of COVID-19 Using a Deep Learning Model in Various Radiology Domains by Yousef Alhwaiti, Muhammad Hameed Siddiqi, Madallah Alruwaili, Ibrahim Alrashdi, Saad Alanazi, Muhammad Hasan Jamal

“…In this work, we designed a model using convolutional neural network in order to detect COVID-19 from X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) images. …”
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Weakly-Supervised Deep Shape-From-Template by Sara Luengo-Sanchez, David Fuentes-Jimenez, Cristina Losada-Gutierrez, Daniel Pizarro, Adrien Bartoli

“…WS-DeepSfT addresses the limitations of existing SfT techniques by combining a weakly-supervised deep neural network (DNN) for registration and a classical As-Rigid-As-Possible (ARAP) algorithm for 3D reconstruction. …”
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Caption Generation Based on Emotions Using CSPDenseNet and BiLSTM with Self-Attention by Kavi Priya S, Pon Karthika K, Jayakumar Kaliappan, Senthil Kumaran Selvaraj, Nagalakshmi R, Baye Molla

“…The encoding unit captures the facial expressions and dense image features using a Facial Expression Recognition (FER) model and CSPDense neural network, respectively. Further, the word embedding vectors of the ground truth image captions are created and learned using the Word2Vec embedding technique. …”
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Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals by Yan Zhao, Ming Guo, Xiangyong Chen, Jianqiang Sun, Jianlong Qiu

“…These serve as input to the attention-based convolutional neural network (CNN) fusion model. The designed network structure is simple and lightweight while integrating the channel attention mechanism to extract and enhance features. …”
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Estimation of Bearing Capacity of Strip Footing Rested on Bilayered Soil Profile Using FEM-AI-Coupled Techniques by Ahmed M. Ebid, Kennedy C. Onyelowe, M. Salah

“…Multiple numerical data were generated for the case under study and artificial intelligence (AI)-based techniques; generalized reduced gradient (GRG), genetic programming (GP), artificial neural network (ANN), and evolutionary polynomial regression (EPR) were used to predict the UBC. …”
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Learning to Boost the Performance of Stable Nonlinear Systems by Luca Furieri, Clara Lucia Galimberti, Giancarlo Ferrari-Trecate

“…Our methods enable learning over specific classes of deep neural network performance-boosting controllers for stable nonlinear systems; crucially, we guarantee <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_{p}$</tex-math></inline-formula> closed-loop stability even if optimization is halted prematurely. …”
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An Estimation Model on Electricity Consumption of New Metro Stations by Zhao Yu, Yun Bai, Qian Fu, Yao Chen, Baohua Mao

“…It was demonstrated that the proposed model could outperform the traditional methods which use a back-propagation neural network or multivariate linear regression. The method presented in this paper can be an adequate tool for estimating the ECMS and should further assist in the delivery of new, energy-efficient metro stations.…”
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Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning by Wei Hao, Donglei Rong, Kefu Yi, Qiang Zeng, Zhibo Gao, Wenguang Wu, Chongfeng Wei, Biljana Scepanovic

“…The research results show that the prediction of the traffic delay index is within a reasonable interval, and it is significantly better than traditional prediction methods such as the LSTM, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), exponential smoothing method, and Back Propagation (BP) neural network, which shows that the model proposed in this paper has the feasibility of application.…”
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Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python by Polina Lemenkova

“…Image processing using Machine Learning (ML) and Artificial Neural Network (ANN) methods was investigated by employing the algorithms of Geographic Resources Analysis Support System (GRASS) Geographic Information System GIS with embedded Scikit-Learn library of Python language. …”
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Research on Feature Extracted Method for Flutter Test Based on EMD and CNN by Hua Zheng, Zhenglong Wu, Shiqiang Duan, Jiangtao Zhou

“…Inspired by deep learning concepts, a novel feature extraction method for flutter signal analysis was established in this study by combining the convolutional neural network (CNN) with empirical mode decomposition (EMD). …”
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A Metaheuristic Approach to Detecting and Mitigating DDoS Attacks in Blockchain-Integrated Deep Learning Models for IoT Applications by Manal Alkhammash

“…Besides, the MHADMA-BCIDL technique employs an attention-based convolutional neural network with bi-directional long short-term memory (CNN-BiLSTM-Attention) method for the detection and classification of attacks. …”
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Safety assessment of rosuvastatin-fenofibrate combination in the treatment of hyperlipidemia based on FDA’s adverse event reporting system database by Qun Li, Wenya Shan, Saiwei Wu

“…The proportional report ratio (PRR), reporting odds ratio (ROR), and Bayesian Confidence Propagation Neural Network (BCPNN) analysis were used to extract data from FAERS for suspected signals referring to the combination of rosuvastatin and fenofibrate.ResultsA total of 68 safety signals were detected from the top 250 AEs in 3,587 reports, of which 28 signals were not included in the drug labels. …”
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Human Posture Recognition and Estimation Method Based on 3D Multiview Basketball Sports Dataset by Xuhui Song, Linyuan Fan

“…The convolutional neural network framework used in this research is VGG11, and the basketball dataset Image Net is used for pretraining. …”
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XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot Stereograms by Tingting Zhang, Xu Yan, Xin Chen, Yi Mao

“…To effectively classify the EEG signals induced by DRDS, we introduced a novel hybrid neural network model, XCF-LSTMSATNet, which integrates an XGBoost Channel Feature Optimization Module with the EEGNet and an LSTM Self-Attention Modules. …”
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Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism by Aarthi Chelladurai, D.P. Manoj Kumar, S. S. Askar, Mohamed Abouhawwash, Mohamed Abouhawwash

“…The experimental outcome shows that the proposed classifier achieved an accuracy of 99.98% which is comparably better than existing convolutional neural network models with transfer learning and IBSA-NET.…”
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A hybrid CNN-LSTM model with adaptive instance normalization for one shot singing voice conversion by Assila Yousuf, David Solomon George

“…In the proposed singing voice conversion technique, an encoder decoder framework was implemented using a hybrid model of convolutional neural network (CNN) accompanied by long short term memory (LSTM). …”
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Evaluating the predictive potential of RSM and ANN models in treatment of greywater-syrup mixture using Ekowe clay-PEM microbial fuel cell by Livinus A. Obasi, Cornelius O. Nevo

“… This study provides a comparative evaluation of the ability of response surface methodology (RSM) and artificial neural network (ANN) to predict the performance of microbial fuel cell (MFC) driven by greywater-syrup substrate system as anolyte with respect to power generation and wastewater treatment. …”
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Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data by Johnny Downs, Robert Stewart, Alice Wickersham, Sumithra Velupillai, Lucile Ter-Minassian, Natalia Viani, Lauren Cross

“…Using a unique linked health and education data resource, we examined how machine learning (ML) approaches can predict risk of ADHD.Design Retrospective population cohort study.Setting South London (2007–2013).Participants n=56 258 pupils with linked education and health data.Primary outcome measures Using area under the curve (AUC), we compared the predictive accuracy of four ML models and one neural network for ADHD diagnosis. Ethnic group and language biases were weighted using a fair pre-processing algorithm.Results Random forest and logistic regression prediction models provided the highest predictive accuracy for ADHD in population samples (AUC 0.86 and 0.86, respectively) and clinical samples (AUC 0.72 and 0.70). …”
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Accurate inversion of chlorophyll content based on PROSPECT-LSROGF-BAS-BP method by Shengfan Zhu, Jin Zhang, Dan Wang, Rui Ding

“…Next, to improve the retrieval accuracy of traditional BP neural networks for chlorophyll content, the Beetle Antennae Search (BAS) algorithm is used to optimize the weights and thresholds of the BP neural network, forming the BAS-BP model. …”
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