Deep-Learning-Based Solar Flare Prediction Model: The Influence of the Magnetic Field Height by Lei Hu, Zhongqin Chen, Long Xu, Xin Huang

“…The results show that predictions at around 7200 km above the photosphere outperform other heights, aligning with physical method analysis. At this altitude, the average AUC of the predictions from the three models reaches 0.788.…”
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Мethods of Machine Learning in Ophthalmology: Review by D. D. Garri, S. V. Saakyan, I. P. Khoroshilova-Maslova, A. Yu. Tsygankov, O. I. Nikitin, G. Yu. Tarasov

“…Artificial neural networks have the potential to be used in automated screening, determining the stage of diseases, predicting the therapeutic effect of treatment and the diseases outcome in the analysis of clinical data in patients with diabetic retinopathy, age-related macular degeneration, glaucoma, cataracts, ocular tumors and concomitant pathology. …”
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Statistical analysis of the current state and forecasting the results of marketing enterprises in the conditions of application of information and communication technologies during... by T.P. Ostapchuk, S.F. Lehenchuk, P. Ucheklak-Yezh, I.R. Polishchuk

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Association of Social Media Addiction, Weight Perception, and Lifestyle in Mexican Nursing Students by Vanessa Patiño-Jaimes, María Camila Giraldo-Suarez, Geu S. Mendoza-Catalán, Julieta Angel-Garcia, Diego Estrada-Luna, Angélica Saraí Jiménez-Osorio

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Intermittent hypoxemia during hemodialysis: AI-based identification of arterial oxygen saturation saw-tooth pattern by Hanjie Zhang, Andrea Nandorine Ban, Peter Kotanko

“…We built one-dimensional convolutional neural networks (1D-CNN), a state-of-the-art deep learning method, for SaO2 pattern classification and randomly assigned SaO2 time series segments to either a training (80%) or a test (20%) set. …”
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SBAS-InSAR Analysis of tectonic derived ground deformation and subsidence susceptibility mapping via machine learning in Quetta City, Pakistan by Sajid Hussain, Bin Pan, Zeeshan Afzal, Meer Muhammad Sajjad, Najeebullah Kakar, Nisar Ahmed, Wajid Hussain, Muhammad Ali

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Multi-objective optimization prediction model for building parameters of photovoltaic windows based on NSGA II-BP by Jiran Zhang, Lingling Zhang, Panpan Ren, Wengang Hao, Ao Xu

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Machine learning-based estimation of crude oil-nitrogen interfacial tension by Safia Obaidur Rab, Subhash Chandra, Abhinav Kumar, Pinank Patel, Mohammed Al-Farouni, Soumya V. Menon, Bandar R. Alsehli, Mamata Chahar, Manmeet Singh, Mahmood Kiani

“…In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil – nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs. …”
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Real-time mobile broadband quality of service prediction using AI-driven customer-centric approach by Ayokunle A. Akinlabi, Folasade M. Dahunsi, Jide J. Popoola, Lawrence B. Okegbemi

“…<p>Statistical methods employed in evaluating the quality of service (performance) of mobile broadband (MBB) networks face drawbacks relating to the accurate and reliable processing of the huge amounts of heterogenous real time traffic data generated from MBB networks. …”
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Secure OFDM Transmission Precoded by Chaotic Discrete Hartley Transform by Adnan A.E. Hajomer, Xuelin Yang, Weisheng Hu

“…We propose a chaotic discrete Hartley transform (DHT) to enhance the physical-layer security and simultaneously improve the transmission performance of optical orthogonal frequency division multiplexing (OFDM) in passive optical network. The theoretical analysis and numerical simulation show that the chaotic DHT precoding matrix after independent row&#x002F;column permutations can also effectively reduce the peak-to-average power ratio (PAPR) of OFDM signals, which can be generated for OFDM data encryption using digital chaos. …”
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Developing an artificial intelligence-based progressive growing GAN for high-quality facial profile generation and evaluation through turing test and aesthetic analysis by Shahab Kavousinejad, Kazem Dalaie, Mohammad Behnaz, Soodeh Tahmasbi, Asghar Ebadifar, Hoori Mirmohammadsadeghi

“…Abstract This study aimed to develop a Progressive Growing Generative Adversarial Network with Gradient Penalty (WPGGAN-GP) to generate high-quality facial profile images, addressing the scarcity of diverse training data in orthodontics. …”
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Dissemination of Evidence-Based Recommendations for Sickle Cell Disease to Primary Care and Emergency Department Providers in North Carolina: A Cost Benefit Analysis by Paula Tanabe, Audrey L. Blewer, Emily Bonnabeau, Hayden B. Bosworth, Denise H. Clayton, Nancy Crego, Marian F. Earls, Kern Eason, Grayson Forlines, Gary Rains, Matthew Young, Nirmish Shah

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Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques by Manjog Padhy, Umar Muhammad Modibbo, Rasmita Rautray, Subhranshu Sekhar Tripathy, Sujit Bebortta

“…Traditional Twitter Sentiment Analysis (TSA) faces challenges due to rule-based or dictionary algorithms, dealing with feature selection, ambiguity, sparse data, and language variations. …”
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MSBiLSTM-Attention: EEG Emotion Recognition Model Based on Spatiotemporal Feature Fusion by Yahong Ma, Zhentao Huang, Yuyao Yang, Zuowen Chen, Qi Dong, Shanwen Zhang, Yuan Li

“…By using raw EEG data, the method applies multi-scale convolutional neural networks and bidirectional long short-term memory networks to extract and merge features, selects key features via an attention mechanism, and classifies emotional EEG signals through a fully connected layer. …”
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A scenario-based approach for probabilistic load flow analysis of electric vehicle charging and photovoltaic integration in distribution systems: An Indonesian case study by Jimmy Trio Putra, M. Isnaeni Bambang Setyonegoro, Sasongko Pramono Hadi, Sarjiya

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Fault diagnosis and inference of hoist main bearing based on transfer learning and ontology by Fei DONG, Di ZHANG, Kunpeng GE, Junjie CHEN, Xinyue XU

“…To overcome the challenges still faced by data-driven hoist main bearing fault diagnosis methods, including data imbalance due to a lack of fault samples under real operating conditions, diagnostic performance degradation of fault diagnosis models caused by significant differences in data sample distribution under varying conditions, single fault diagnosis function, and a lack of reasoning analysis and localization for the causes of hoist main bearing system failures, a new fault diagnosis and reasoning method for hoist main bearing systems is studied, which includes two aspects: ① Bearing fault diagnosis based on convolutional neural network transfer learning and domain adaptation. …”
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Prediction of Remaining Useful Life of Packing Sets in a Plunger-Type High-Pressure Compressor Based on the PCA/SVD Analysis and NN Model by Jin-Wei Liang, Shyh-Chin Huang, Chun-Ling Lin

“…In order to enhance the prediction accuracy of the RUL of high-pressure packing, a linear regression algorithm and a two-term power series regression algorithm are both integrated into the NN (neural network) model. The effectiveness of the method is examined using the averaged difference (over 13 data sets) between predicted and real failure events. …”
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Design of an Iterative Method for Time Series Forecasting Using Temporal Attention and Hybrid Deep Learning Architectures by Yuvaraja Boddu, A. Manimaran

“…Additionally, by representing the multivariate time series data as a graph in which variables are nodes connected by edges denoting temporal relationships, TGAMTSA leverages Graph Neural Networks (GNNs) to decode complex inter-variable dependencies, resulting in a 20% improvement in prediction accuracy over traditional methods. …”
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Predictors of fairness assessment for social media screening in employee selection by Alicja Balcerak, Jacek Woźniak, Alexandra Zbuchea

“…It expands the knowledge about the applicability of social networking site content analysis of Polish users, especially of innovative candidates. …”
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Segmentation Techniques Applied to CNNs for Cervical Cancer Classification by Ana Ortiz-Gonzalez, Raquel Martinez-Espana, Juan Morales-Garcia, Baldomero Imbernon, Jose Martinez-Mas, Mauricio A. Alvarez, Oscar David Romero, Juan Pedro Martinez-Cendan, Andres Bueno-Crespo

“…This automatic segmentation is combined in a classification model that allows the models to improve their performance thanks to the morphological information provided by the combined segmentation in a Global Average Pooling layer with the convolutional network analysis of the original image. …”
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