The use of artificial intelligence to analyze and optimize financial flows by М. А. Miakisheva

“…In the scientific literature, there are contradictions between theoretical models of using AI and the practical possibilities of their implementation, as well as disagreements in evaluating the effectiveness of various types of neural networks for financial forecasting. …”
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Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neura... by Mizuho Nishio, Osamu Sugiyama, Masahiro Yakami, Syoko Ueno, Takeshi Kubo, Tomohiro Kuroda, Kaori Togashi

“…For the DCNN method, CADx was evaluated using the VGG-16 convolutional neural network with and without transfer learning, and hyperparameter optimization of the DCNN method was performed by random search. …”
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Effective Facial Expression Recognition System Using Artificial Intelligence Technique by Imad S. Yousif, Tarik A. Rashid, Ahmed S. Shamsaldin, Sabat A. Abdulhameed, Abdulhady Abas Abdullah

“…ANNS are inspired by the neural architecture of human brain capable of learning and recognizing patterns in unchartered data after trained examples, on the other hand GAs come from fundamental principles underlying natural selection perform optimization process based-on evolutionary methods which includes fitness evaluation, comparison, selection, crossover, and mutation. …”
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Impact of the STFT Window Size on Classification of Grain-Oriented Electrical Steels from Barkhausen Noise Time–Frequency Spectrograms via Deep CNNs by Michal Maciusowicz, Grzegorz Psuj

“…Due to the automation of the search for diagnostic patterns, the stage of selecting transformation parameters becomes extremely important in the process of preparing training data for evaluation algorithms. …”
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High-quality development of metropolitan regions and its driving factors: A case study of the Xuzhou Metropolitan Region by ZHU Huxiao, OU Xiangjun, YANG Zhen, TANG Shuangshuang, XIAO Weidong

“…However, there were uncoordinated growth rates among these three aspects. The spatial patterns of economic and social development quality exhibited an obvious core-periphery structure, while the eco-environmental quality showed the opposite pattern. (2) The radiation connection from the central city to the metropolitan region presented a radial network structure. …”
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Geomagnetic Field Based Indoor Landmark Classification Using Deep Learning by Bimal Bhattarai, Rohan Kumar Yadav, Hui-Seon Gang, Jae-Young Pyun

“…We present long short-term memory DRNNs for spatial/temporal sequence learning of magnetic patterns and evaluate their positioning performance on our testbed datasets. …”
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Deep learning for rice leaf disease detection: A systematic literature review on emerging trends, methodologies and techniques by Chinna Gopi Simhadri, Hari Kishan Kondaveeti, Valli Kumari Vatsavayi, Alakananda Mitra, Preethi Ananthachari

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TFTformer: A novel transformer based model for short-term load forecasting by Ahmad Ahmad, Xun Xiao, Huadong Mo, Daoyi Dong

“…A linear transformation layer post embedding improves feature representation, aligning and standardising features across sequences for improved pattern recognition. Additionally, a Temporal Convolutional Network is integrated within the Transformer’s encoder, employing causal convolutions and dilation to adapt to the sequential nature of data with an expanded receptive field. …”
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Driving Behavior Classification Using a ConvLSTM by Alberto Pingo, João Castro, Paulo Loureiro, Sílvio Mendes, Anabela Bernardino, Rolando Miragaia, Iryna Husyeva

“…This work explores the classification of driving behaviors using a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks (ConvLSTM). …”
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Navigating the road ahead: using concept mapping to assess Clinical and Translational Science Award (CTSA) program goals by Cathleen Kane, William Trochim, Haim Bar, Andie Vaught, Heather Baker, Munziba Khan, Robin Wagner, Kristi Holmes, Keith Herzog, Jamie Mihoko Doyle

“…The results also revealed a pattern where long-term impacts were ranked among the highest in importance but among the lowest in feasibility, particularly for measures tied to the Translational Science Benefits Model (TSBM), a new evaluation framework gaining popularity across the CTSA. …”
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Development of a Tool for Comprehensive Balance Assessment Based on Artificial Intelligence and Anomaly Detection by Márcio Fagundes Goethel, Klaus Magno Becker, Franciele Carvalho Santos Parolini, Ulysses Fernandes Ervilha, João Paulo Vilas-Boas

“…Existing methods of balance assessment often lack the sensitivity and specificity needed to identify subtle deviations from normal patterns, hindering early intervention. To address this gap, we introduced a novel artificial intelligence-based tool that leverages anomaly detection to provide a comprehensive assessment of balance performance across all age groups. …”
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Research on Multi-Step Prediction of Pipeline Corrosion Rate Based on Adaptive MTGNN Spatio-Temporal Correlation Analysis by Mingyang Sun, Shiwei Qin

“…Then, a dynamic adjacency matrix is adaptively learned to capture hidden spatial dependencies, while temporal convolution modules extract multi-scale temporal patterns, and the node sequences with integrated corrosion features are input into the adaptive MTGNN for prediction. …”
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An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets by Ahmad Salehiyan, Pardis Sadatian Moghaddam, Masoud Kaveh

“…While deep learning (DL) offers strong capabilities for pattern recognition, single-model architectures often lack robustness. …”
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Enhancing Magnetotelluric Data Quality Using Deep Learning-Based Denoising Models: A Study of CNN and LSTM by Widya Utama, Maman Hermana, Dwa D. Warnana, Wien Lestari, Muhammad N. A. Zakariah, Sherly A. Garini, Rista F. Indriani, Dhea P. Novian Putra, M Ulin Nuha Abduh, Alif N. F. Insani, Dandi Syahtia Pratama, Khairul Arifin Mohd Noh, Abdul Halim Abdul Latiff

“…The superior performance of CNN in mitigating noise in MT data is attributed to its architecture, which focuses on local patterns. This makes it particularly effective for handling localized and sporadic noise, as observed in the Hx and Hy channels with recurring amplitude patterns. …”
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Predicting changes of incisor and facial profile following orthodontic treatment: a machine learning approach by Jing Peng, Yan Zhang, Mengyu Zheng, Yanyan Wu, Guizhen Deng, Jun Lyu, Jianming Chen

“…MSE/MAE/R2 values for L1-MP were 0.0062/0.063/0.84, L1-MP, ANB and extraction pattern were identified as the top three influential predictors. …”
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EEG Microstate Dynamics during Different Physiological Developmental Stages and the Effects of Medication in Schizophrenia by Shihai Ling, Lingyan Du, Xi Tan, Guozhi Tang, Yue Che, Shirui Song

“…Conclusions: Alterations in microstate dynamics were observed among SCZ patients across developmental stages, suggesting potential changes in brain activity patterns. Changes in microstates A and C may serve as potential biomarkers for evaluating treatment efficacy, establishing a foundation for personalized therapeutic approaches.…”
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Comparative analysis of data transformation methods for detecting non-technical losses in electricity grids by Maria Gabriel Chuwa, Daniel Ngondya, Rukia Mwifunyi

“…Despite various proposed methods, effectively classifying normal and abnormal consumption patterns remains challenging. While feature-based detection methods perform well, they often require manual feature engineering and domain expertise. …”
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Evolution of microbial carbon sequestration potential in farmland soil driven by natural restoration in coal mine subsidence area by Chunming HAO, Yantang WANG, Sihai YI, Shuo LIU

“…This study, for the first time, systematically elucidated the evolutionary patterns and regulatory mechanisms of soil carbon sequestration potential in farmland soils during the natural recovery of coal mining subsidence areas. …”
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Histopathological Image Analysis Using Deep Learning Framework by Sudha Rani Vupulluri, Jogendra Kumar Munagala

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A Comparative Analysis of Machine Learning and Deep Learning Techniques for Accurate Market Price Forecasting by Olamilekan Shobayo, Sidikat Adeyemi-Longe, Olusogo Popoola, Obinna Okoyeigbo

“…This study compares three machine learning and deep learning models—Support Vector Regression (SVR), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM)—for predicting market prices using the NGX All-Share Index dataset. …”
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