Deep Learning-Driven Multi-Temporal Detection: Leveraging DeeplabV3+/Efficientnet-B08 Semantic Segmentation for Deforestation and Forest Fire Detection by Joe Soundararajan, Andrew Kalukin, Jordan Malof, Dong Xu

“…In FireDataset_20m, which presents a severe class imbalance between fire and non-fire pixels, the framework achieves 99.95% accuracy, 93.16% precision, and 91.47% recall. A qualitative analysis confirms the model’s ability to accurately localize fire hotspots and deforested areas. …”
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Towards precision diagnosis: a novel hybrid DC-CAD model for lung disease detection leveraging multi-scale capsule networks and temporal dynamics by Esther Stacy E. B. Aggrey, Qin Zhen, Seth Larweh Kodjiku, Linda Delali Fiasam, Collins Sey, Chiagoziem C. Ukwuoma, Evans Aidoo, Emmanuel Osei-Mensah

“…DC-CAD is innovative in its ability to combine multi-scale feature extraction and temporal dynamic analysis, enabling the model to capture intricate spatial relationships and sequential changes in lung tissue. …”
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Spatial Resilience Modelling of Merapi Volcano Ecosystem Post-Eruption by Emma Soraya, Wahyu Wardhana, Ronggo Sadono

“…This study aimed to model spatial resilience of Merapi volcano ecosystem within the area of National Park of Merapi Volcano (TNGM) five years post 2010 eruption and restoration intervention. Analysis was conducted using multi temporal remote sensing and spatial analysis using geographic information system to draw the changes of the ecosystem over time, particularly post eruption and restoration actions. …”
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Neural entrainment to pitch changes of auditory targets in noise by Xiaoxuan Guo, Guangting Mai, Yousef Mohammadi, Ester Benzaquén, Ekaterina A Yukhnovich, Will Sedley, Timothy D Griffiths

“…Source-space analysis revealed bilateral temporal lobe and hippocampal generators, and strong tracking in the superior parietal lobe for auditory figures and natural speech. …”
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Autobiographical memory in Alzheimer’s disease: a systematic review by Chiara Stramba-Badiale, Fabio Frisone, Fabio Frisone, Fabio Frisone, Fabio Frisone, Diana Biondi, Giuseppe Riva, Giuseppe Riva

“…IntroductionAutobiographical memory impairment is a significant feature of Alzheimer’s disease (AD), affecting patients’ ability to recall personal life events and maintain their sense of self. …”
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Vector Autoregressive Spatio-Temporal (VAST) models for biomass distribution of pacific cod Gadus macrocephalus (Gadidae) considering water temperature at the sea bottom in the Wes... by V. V. Kulik, A. B. Savin

“…The vector autoregressive spatio-temporal (VAST) models which included nonlinear dependencies of cod catches from T and D have the best generalization ability. …”
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Research on the Resilience Evaluation and Spatiotemporal Evolution Characteristics of China’s Marine Fishery Economy by GUAN Mei, CHEN Xiaoyu, LI Chunlin

“…Utilizing the GIS spatial analysis technology, this paper explores the temporal and spatial characteristics of the economic resilience of the marine fishery, and applies the Markov chain to analyze the characteristics of its dynamic evolution. …”
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An exploration of modeling approaches for capturing seasonal transmission in stochastic epidemic models by Mahmudul Bari Hridoy

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Spatial Conflicts in ‘Production, Living, and Ecological Space’ Functions at Urban Fringes: The Case of Zengcheng, Guangzhou by Ziqing Feng, Shaoqiu Long, Yilun Liu

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Adaptive ensemble spatial analysis by Alvaro F. Egaña, María Jesús Valenzuela, Mohammad Maleki, Juan F. Sánchez-Pérez, Gonzalo Díaz

“…This has limited a greater use of these techniques in disciplines that work with spatial or spatio-temporal information. This paper presents a novel spatial analysis technique, which is an extension of a previously proposed ensemble spatial interpolation model. …”
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Drilling Rate of Penetration Prediction Based on CBT-LSTM Neural Network by Kai Bai, Siyi Jin, Zhaoshuo Zhang, Shengsheng Dai

“…This model integrates the capability of a two-dimensional convolutional neural network (2D-CNN) for multi-feature extraction, the advantages of bidirectional long short-term memory networks (BiLSTM) for processing bidirectional temporal information, and the dynamic weight adjustment of the time pattern attention mechanism (TPA) for extracting crucial information in BiLSTM, effectively capturing key features in temporal data. …”
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A Multi-Scale Adaptive Fusion Network: End-to-End Interpretable Small-Sample Classifier for Motor Imagery EEG by Qiulei Han, Yan Sun, Ze Song, Hongbiao Ye, Tingwei Chen, Jian Zhao

“…The SCA module strengthens the model’s ability to perceive features of different frequencies and spatio-temporal information through multi-scale feature interactions to extract richer primary feature representations. …”
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Analysis of Rehabilitation Occupational Therapy Techniques Based on Instrumental Music Chinese Tonal Language Spectrogram Analysis by Ying Liao

“…This paper provides an in-depth analysis of timbre-speech spectrograms in instrumental music, designs a model analysis of rehabilitation occupational therapy techniques based on the analysis of timbre-speech spectrograms in instrumental music, and tests the models for comparison. …”
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Multitask semantic change detection guided by spatiotemporal semantic interaction by Yinqing Wang, Liangjun Zhao, Yueming Hu, Hui Dai, Yuanyang Zhang

“…Accurately capturing the change information of land cover types is crucial for remote sensing image analysis and subsequent decision-making applications. …”
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Anomaly detection method for power system information based on multimodal data by Liyue Chen, XuXiang Zhou, Peng Zhou, Xin Sun, SenSen Zheng

“…By leveraging this integration, our method captures both temporal patterns and spectral signatures, offering a more comprehensive analysis of system behavior—an advancement that significantly enhances detection performance compared to traditional techniques. …”
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Complexity synchronization analysis of neurophysiological data: Theory and methods by Ioannis Schizas, Sabrina Sullivan, Scott Kerick, Korosh Mahmoodi, J. Cortney Bradford, David L. Boothe, Piotr J. Franaszczuk, Piotr J. Franaszczuk, Paolo Grigolini, Bruce J. West, Bruce J. West

“…The purpose of this paper is to advance the validation, standardization, and reconstruct-ability of MDEA and CS analysis of heterogeneous neurophysiological time series data.ResultsResults from processing these datasets show that the complexity of brain, heart, and lung ONTS co-vary over time during cognitive task performance in 44% of subjects, while complexity of brain-heart interactions significantly co-vary in 85% of subjects.DiscussionWe conclude that certain principles, guidelines, and strategies for the application of MDEA analysis need further consideration. …”
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Research on Hybrid Architecture Neural Networks for Time Series Prediction by Fujin Zhuang, Xiao Chen, Punyaphol Horata, Khamron Sunat

“…To address these challenges, this paper proposes a novel time series prediction model based on Temporal Convolutional Network (TCN), Multi-Layer Perceptron (MLP), and attention mechanism (referred to as “TCN-MLP-Attention”), using daily average price data from the U.S. avocado market from 2015 to 2023. …”
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Data-Driven Optimized Load Forecasting: An LSTM-Based RNN Approach for Smart Grids by Muhammad Asghar Majeed, Sotdhipong Phichaisawat, Furqan Asghar, Umair Hussan

“…Compared to other deep learning techniques such as GRU, standard RNN, and CNN, RNN-LSTM outperforms in capturing long-range dependencies and learning from temporal patterns effectively. The convergence analysis further confirms the robustness of RNN-LSTM, making it the most suitable model for dynamic and complex power system environments. …”
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Comparative Analysis of the Performance of Gridded Precipitation Products Over Iran by Ali Gorjizadeh, Ali Moridi

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A network analysis of facial and vocal emotion recognition deficits in schizophrenia by Wenxuan Zhao, Qi Zhang, Long Gao, Ning Fan, Yajun Yun, Jiaqi Song, Yunhe Ji, Yongqian Wang, Meng Zhang, Fude Yang, Shuping Tan

“…Emotion recognition deficits and demographic or clinical characteristics were not significantly correlated. Network analysis revealed strong intercorrelations among different cognitive domains, linking MCCB performance to emotion recognition abilities (r>0.9; p<0.001). …”
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