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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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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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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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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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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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

“…This paper investigates the influence of the STFT computational window size on the material state evaluation results obtained using convolutional neural network (CNN). …”
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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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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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A Multi-Phase DRL-Driven SDN Migration Framework Addressing Budget, Legacy Service Compatibility, and Dynamic Traffic by Kai Yuan Tan, Saw Chin Tan, Teong Chee Chuah

“…By integrating a DRL model with a clustering algorithm, SMART determines the migration sequence to minimize link utilization and reduce the number of SDN-enabled nodes required for effective traffic load distribution under dynamic traffic patterns. Extensive evaluations on the Abilene and GEANT network topologies demonstrate that SMART outperforms three existing approaches, achieving most of the SDN benefits by migrating only 36% and 52% of legacy nodes, respectively. …”
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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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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

“…Data analysis, employing an artificial neural network with 19 socio-anthropometric and postural variables, showed the tool’s exceptional accuracy (R = 0.99998) in differentiating among balance profiles. …”
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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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Urban growth simulation using cellular automata model and machine learning algorithms (case study: Tabriz metropolis) by Omid Ashkriz, Babak Mirbagheri, Ali Akbar Matkan, Alireza Shakiba

“…Introduction: Urban growth has accelerated in recent decades, therefore, predicting the future growth pattern of the city is very important to prevent environmental, economic, and social problems. …”
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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

“…In order to comprehensively investigate the spatio-temporal dynamics of corrosion evolution under complex pipeline environments and improve the corrosion rate prediction accuracy, a novel framework for corrosion rate prediction based on adaptive multivariate time series graph neural network (MTGNN) multi-feature spatio-temporal correlation analysis is proposed. …”
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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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Mobile Phone Network Data in the COVID-19 era: A systematic review of applications, socioeconomic factors affecting compliance to non-pharmaceutical interventions, privacy implicat... by Mohammed Okmi, Tan Fong Ang, Muhammad Faiz Mohd Zaki, Chin Soon Ku, Koo Yuen Phan, Irfan Wahyudi, Lip Yee Por

“…However, because these datasets represent only discrete instances of measurement, they miss continuous temporal shifts in human activities, failing to record the majority of human mobility patterns in real-time. Bolstered by the rapid expansion of telecommunication networks and the ubiquitous use of smartphones, mobile phone network data (MPND) played a pivotal role in fighting and controlling the spread of COVID-19.…”
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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

“…These findings provide new insights into the comprehensive evaluation of ecological benefits associated with natural recovery processes. …”
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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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Assessment and Optimization of Hydrological Connectivity for Effective Management of Water Resources in the Samian Watershed by Zeinab Hazbavi, Nazila Alaei

“…The results showed that the hydrological network in the Samian Watershed with a total length of 1254.73 km had 173 links and 176 nodes. …”
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Histopathological Image Analysis Using Deep Learning Framework by Sudha Rani Vupulluri, Jogendra Kumar Munagala

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