Integrated deep network model with multi-head twofold attention for drug–target interaction prediction by Angelin Jeba P, Tamilpavai G

“…The MHTA mechanism enhances the model’s ability to focus on different aspects of drug and target features independently, effectively capturing intricate interaction patterns. Dense embeddings generated from input representations are refined using recurrent layers for long-range dependencies and convolutional layers for local patterns. …”
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Real-Time Financial Fraud Detection Using Adaptive Graph Neural Networks and Federated Learning by Milad Rahmati

“…The GNN component dynamically models relationships within financial transactions, allowing the system to detect suspicious patterns as they emerge rather than relying on historical fraud markers. …”
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Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks by Ayan Chatterjee, Vajira Thambawita, Michael A. Riegler, Pal Halvorsen

“…The model uses a contrastive loss function to compare input sequences and adjusts network weights iteratively during training to recognize intricate patterns. …”
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Versatility Evaluation of Landslide Risk with Window Sizes and Sampling Techniques Based on Deep Learning by Fudong Ren, Koichi Isobe

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Enhanced Network Traffic Classification Using Bayesian-Optimized Logistic Regression and Random Forest Algorithm by Manisankar Sannigrahi, R. Thandeeswaran

“…Compared to other machine learning algorithms, the proposed models demonstrate superior performance in balancing accuracy, computational efficiency, and detection speed, making them ideal for real-time network security applications. This approach’s flexibility to adapt to various network conditions and datasets ensures consistent optimal performance as traffic patterns change.…”
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Improving Performance of the Convolutional Neural Networks for Electricity Theft Detection by using Cheetah Optimization Algorithm by Hassan Ghaedi, Seyed Reza Kamel Tabbakh, Reza Ghaemi

“…Extensive research studies have been done to detect electricity theft by analyzing customer consumption patterns. Today, one of the most widely used methods is convolutional neural networks (CNNs). …”
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Brain Tumour Segmentation and Grading Using Local and Global Context-Aggregated Attention Network Architecture by Ahmed Abdulhakim Al-Absi, Rui Fu, Nadhem Ebrahim, Mohammed Abdulhakim Al-Absi, Dae-Ki Kang

“…The main advantage of LGCNet is its dedicated network for a specific task. The proposed model is evaluated by considering the BraTS2019 dataset with different metrics, such as the Dice score, sensitivity, specificity and Hausdorff score. …”
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Biotic interactions and stream network position affect body size of aquatic vertebrates across watersheds by Brooke E. Penaluna, Sherri L. Johnson, Amanda M. M. Pollock, Ivan Arismendi, Dana R. Warren

“…We reveal a biogeographic pattern in which body size peaks in middle stream‐network positions and plateaus or declines at lower and upper locations, proposing that stream network position also plays a role in determining body size in small watersheds. …”
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Comparative study on carbon emission spatial network and carbon emission reduction collaboration in urban agglomerations by Yongqiang Dong, Lanjian Liu

“…Mismatches exist between collaboration patterns and emission networks: central cities dominate cooperation, while peripheral cities lack initiative; intensity polarization and provincial-level mini-clubs prevail. …”
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Fairness-Aware Graph Neural Networks for ICU Length of Stay Prediction in IoT-Enabled Environments by Angelos Christos Maroudis, Konstantina Karathanasopoulou, Charithea C. Stylianides, George Dimitrakopoulos, Andreas S. Panayides

“…Motivated by the concept of “fairness through unawareness,” our proposed framework employs a demographic feature exclusion strategy, preventing access to potentially discriminatory information, and thus enforcing an inductive bias that redirects learning toward non-discriminatory pattern dependencies. To address the loss of static information, we introduce a custom graph neural network that dynamically reconstructs patient relationships over time, adapting from static demographics to evolving inter-patient correlations via multi-modal embeddings (e.g., medications, procedures, vitals, conditions) and learned feature-driven edge formation. …”
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Fractional Transfer Entropy Networks: Short- and Long-Memory Perspectives on Global Stock Market Interactions by Ömer Akgüller, Mehmet Ali Balcı, Larissa Margareta Batrancea, Lucian Gaban

“…These findings demonstrate that FTE uncovers nuanced dynamics overlooked by methods focusing solely on either current events or deep-rooted patterns. Although the method relies on price returns and does not differentiate specific shock types, it offers a versatile tool for investors, policymakers, and researchers to gauge financial stability, evaluate contagion risk, and better understand how ephemeral signals and historical legacies jointly govern global market connectivity.…”
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ChaMTeC: CHAnnel Mixing and TEmporal Convolution Network for Time-Series Anomaly Detection by Ibrahim Delibasoglu, Deniz Balta, Musa Balta

“…Time-series anomaly detection is a critical task in various domains, including industrial control systems, where the early detection of unusual patterns can prevent system failures and ensure operational reliability. …”
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Brain Network Analysis Reveals Age-Related Differences in Topological Reorganization During Vigilance Decline by Lingyun Gao, Rui Zhang, Mengru Xu, Yi Sun, Gang Li, Yu Sun

“…Moreover, age-related dysconnectivity pattern was revealed over a wide frequency range (<inline-formula> <tex-math notation="LaTeX">$1-45$ </tex-math></inline-formula> Hz) in the elderly group, which further developed toward less optimal network architecture. …”
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Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network by Andrea Colacino, Andrea Soricelli, Michele Ceccarelli, Ornella Affinito, Monica Franzese

“…Methods: To address this issue, we first performed a pan-cancer analysis to train a convolutional 1-D Neural Network (CNN) using supervised learning. Then, we evaluated the robustness of the net on an independent PTC dataset and assessed its ability to classify normal (N=56) versus tumor (N=461) samples and fvPTC (N=102) versus cvPTC (N=359). …”
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Spatial distribution and source apportionment of nitrogen in typical plain river networks and bacterial community response by Aiju You, Aiju You, Qiaoxi Zheng, Pengcheng Yao, Pengcheng Yao

“…IntroductionThe Yubei plain river network (YPRN) is confined and has poor hydrodynamics, resulting in the accumulation of pollutants. …”
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Intrusion Alert Analysis Method for Power Information Communication Networks Based on Data Processing Units by Rui Zhang, Mingxuan Zhang, Yan Liu, Zhiyi Li, Weiwei Miao, Sujie Shao

“…Finally, the suffix tree models attack activities, capturing key behavioral paths of high-severity alerts and identifying attacker patterns. Experimental evaluations on the CPTC-2017 and CPTC-2018 datasets validate the proposed method’s effectiveness in reducing alert redundancy, extracting critical attack behaviors, and constructing attack activity sequences. …”
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COVID-19 Disruption Strategy for Redesigning Global Supply Chain Network across TPP Countries by Takaki Nagao, Hiromasa Ijuin, Tetsuo Yamada, Keisuke Nagasawa, Lei Zhou

“…<i>Results</i>: Regarding the impact of disruptions on suppliers, two patterns emerge in the reconfigured network: direct changes due to supplier disruptions and indirect changes due to factory relocation. …”
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HFFC: A High-Fan-Out and Flexible-Cluster Optical Network Architecture for Data Centers by Aijun Liu, Zixuan Chen, Yucheng Zhang, Jun Qin

“…The performance of the HFFC architecture is evaluated by simulation using synthetic traffic patterns. …”
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Optimizing Fractional-Order Convolutional Neural Networks for Groove Classification in Music Using Differential Evolution by Jiangang Chen, Pei Su, Daxin Li, Junbo Han, Gaoquan Zhou, Donghui Tang

“…This study presents a differential evolution (DE)-based optimization approach for fractional-order convolutional neural networks (FOCNNs) aimed at enhancing the accuracy of groove classification in music. …”
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Deep learning based predictive models for real time accident prevention in autonomous vehicle networks by Ahmed Almutairi, Abdullah Faiz Al Asmari, Fayez Alanazi, Tariq Alqubaysi, Ammar Armghan

“…The use of autonomous vehicle (AV) networks is a potentially useful solution since they make it possible to react in real time to probable collisions. …”
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