Noise Pollution Prediction in a Densely Populated City Using a Spatio-Temporal Deep Learning Approach by Marc Semper, Manuel Curado, Jose Luis Oliver, Jose F. Vicent

“…Each technique contributes specific strengths to the modeling of spatiotemporal series: CNNs are effective at capturing local spatial patterns, while LSTM networks excel at modeling long-term temporal dependencies. …”
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Source Detection and Functional Connectivity of the Sensorimotor Cortex during Actual and Imaginary Limb Movement: A Preliminary Study on the Implementation of eConnectome in Motor... by Alkinoos Athanasiou, Chrysa Lithari, Konstantina Kalogianni, Manousos A. Klados, Panagiotis D. Bamidis

“…The study of functional connectivity networks (FCNs) aims at successfully modeling the dynamics of information flow between cortical areas. …”
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RoBERTa-Based Multi-Feature Integrated BiLSTM and CNN Model for Ceramic Review Analysis by LiHua Yang, Jun Wang, WangRen Qiu

“…By feeding different outputs of RoBERTa into Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks, the model effectively captures both local static patterns and global contextual dependencies, thereby enhancing its capability to handle complex textual inputs. …”
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Optimizing learning outcomes: a deep dive into hybrid AI models for adaptive educational feedback by Hafiz Muhammad Qadir, M. Taseer Suleman, Rafaqat Alam Khan, Muhammad Sohaib, Md Junayed Hasan, Syed Abid Hussain

“…In this paper, we implement several ensemble models-AdaBoost, Gradient Boosting, XGBoost, LightGBM, and CatBoost-and deep learning architectures such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN). …”
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Assessing Classic Maya multi-scalar household inequality in southern Belize. by Amy E Thompson, Gary M Feinman, Keith M Prufer

“…We then compare our findings to Gini coefficients for other Classic Maya polities in the Maya heartland and to contemporaneous polities across Mesoamerica. We see the patterning of wealth inequality across the polities as a consequence of variable access to networks of exchange. …”
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Misinformation identification as a digital literacy skill in an ultra-orthodox community: an eye tracking study by Nili Steinfeld, Tamar Berenblum, Yehudit Miletzky, Elazar Kornfeld

“…Eye tracking technology was used to examine participants’ scan patterns and attention to information metadata. The results showed that Haredi participants were less successful in identifying false messages and less attentive to metadata. …”
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Analysis of Autism Spectrum Disorder in Children with Data Mining Methods by Sümeyye Çelik, Melike Şişeci Çeşmeli

“…Data mining techniques aim to reveal hidden patterns in data. They are widely used in many fields, such as medicine. …”
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Recent advances in the inverse design of silicon photonic devices and related platforms using deep generative models by Sun Jae Baek, Minhyeok Lee

“…Deep generative models offer additional capabilities by leveraging their ability to learn complex patterns and generate novel designs. This review examines various deep learning methodologies, including multi-layer perceptrons (MLP), convolutional neural networks (CNN), auto-encoders (AE), Generative Adversarial Networks (GAN), and reinforcement learning (RL) approaches. …”
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Analyzing the impact of COVID-19 on seasonal infectious disease outbreak detection using hybrid SARIMAX-LSTM model by Geunsoo Jang, Jeonghwa Seo, Hyojung Lee

“…It also examines the impact of the COVID-19 pandemic on their transmission patterns. Methods: We employed the Seasonal AutoRegressive Integrated Moving Average with eXogenous variables (SARIMAX) model, long short-term memory (LSTM) neural networks, and a hybrid SARIMAX-LSTM model to predict disease incidence and identify outbreak periods. …”
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Laminar differences in response to simple and spectro-temporally complex sounds in the primary auditory cortex of ketamine-anesthetized gerbils. by Markus K Schaefer, Manfred Kössl, Julio C Hechavarría

“…Recent studies have pointed out that communication-sound encoding could be based on discharge patterns of neuronal populations. Following this idea, we investigated whether the activity of local neuronal networks, such as those occurring within individual cortical columns, is sufficient for distinguishing between sounds that differed in their spectro-temporal properties. …”
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THE COMPARISON OF ARIMA AND RNN FOR FORECASTING GOLD FUTURES CLOSING PRICES by Windy Ayu Pratiwi, Anwar Fajar Rizki, Khairil Anwar Notodiputro, Yenni Angraini, Laily Nissa Atul Mualifah

“…Conversely, advanced methods such as Recurrent Neural Networks (RNN) have shown promise in handling these complexities and providing reliable long-term forecasts. …”
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Mapping the Landscape of Digital Government Transformation: A Bibliometric Analysis by Younus Muhammad, Purnomo Eko Priyo, Nurmandi Achmad, Mutiarin Dyah, Manaf Halimah Abdul, Prianto Andi Luhur, Sohsan Imron, Irawan Bambang, Salahudin Salahudin, Akbar Idil, Khairunnisa Tiara

“…The literature review makes use of citation analysis to detect patterns in the relationship between scientific papers, whereas the bibliometric analysis employs quantitative tools to evaluate published physical units. …”
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Targeting immune microenvironment in cervical cancer: current research and advances by Zhen Zhang, Miao Liu, Yu An, Chongqing Gao, Tao Wang, Zhi Zhang, Guixiang Zhang, Shuo Li, Wei Li, Mengjia Li, Gangcheng Wang

“…This review comprehensively examines the cellular and molecular components of the tumor immune microenvironment (TIME) in cervical cancer, encompassing patterns of immune cell infiltration (T cells, B cells, NK cells, DCs, TAMs), immune checkpoint molecules, and cytokine/chemokine networks. …”
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LSTM vs. Prophet: Achieving Superior Accuracy in Dynamic Electricity Demand Forecasting by Saleh Albahli

“…The LSTM component captures nonlinear dependencies and long-term temporal patterns, while Prophet models seasonal trends and event-driven fluctuations. …”
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Architecture-Aware Augmentation: A Hybrid Deep Learning and Machine Learning Approach for Enhanced Parkinson’s Disease Detection by Madjda Khedimi, Tao Zhang, Hanine Merzougui, Xin Zhao, Yanzhang Geng, Khamsa Djaroudib, Pascal Lorenz

“…We compare the accuracy of Vision Transformer (ViT) with K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN) with Support Vector Machines (SVM), and Residual Neural Networks (ResNet-50) with Logistic Regression, evaluating their performance on both augmented and non-augmented data. …”
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Deep Learning Architectures for Single-Label and Multi-Label Surgical Tool Classification in Minimally Invasive Surgeries by Hisham ElMoaqet, Hamzeh Qaddoura, Mutaz Ryalat, Natheer Almtireen, Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Thomas Neumuth, Knut Moeller

“…Nonetheless, our results demonstrated that the proposed CNN-SE-FFM-BiLSTM multi-label model achieved competitive performance to state-of-the-art methods with excellent performance in detecting tools with complex usage patterns and in minority classes. Future work should focus on optimizing models for real-time applications, and broadening dataset evaluations to improve performance in diverse surgical environments. …”
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Impact of screen time during COVID-19 on eating habits, physical activity, sleep, and depression symptoms: A cross-sectional study in Indian adolescents. by Panchali Moitra, Jagmeet Madan

“…The objectives were to 1) evaluate frequency and duration of using screens, and screen addiction behaviors in 10-15 years old adolescents in Mumbai during the COVID-19 pandemic and 2) examine the association of ST with lifestyle behaviors- eating habits, snacking patterns, physical activity (PA) levels, sleep quality and depression symptoms.…”
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Time-series visual representations for sleep stages classification. by Rebeca Padovani Ederli, Didier A Vega-Oliveros, Aurea Soriano-Vargas, Anderson Rocha, Zanoni Dias

“…Polysomnography is the standard method for sleep stage classification; however, it is costly and requires controlled environments, which can disrupt natural sleep patterns. Smartwatches offer a practical, non-invasive, and cost-effective alternative for sleep monitoring. …”
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Scale, state and the city: Transformation of Diyarbakır, Suriçi region through the framework of spatial morphology and urban resilience by Berfin Eren, Mehmet Emin Şalgamcıoğlu

“…Examining resilience at the urban scale through the street networks of different historical periods, produced via space syntax analysis, facilitates the formulation and analysis of patterns in urban movement, interactions, and past socio-economic activities. …”
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Privacy-Aware Detection for Large Language Models Using a Hybrid BiLSTM-HMM Approach by Maryam Abbasalizadeh, Sashank Narain

“…Additionally, the generated model trained on patterns derived from synthetic data, achieved <inline-formula> <tex-math notation="LaTeX">$\approx 99.99$ </tex-math></inline-formula>% accuracy when evaluated on a real-world dataset across varying sentence structures, demonstrating strong generalizability in detecting sensitive information regardless of the data source. …”
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