A novel twin time series network for building energy consumption predicting. by Zhixin Sun, Han Cui, Xiangxiang Mei, Hailei Yuan

“…To overcome these issues, the study proposes Twin Time-Series Networks (T2SNET), which incorporates a time-embedding layer and a Temporal Convolutional Network (TCN) to extract patterns from Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), along with an adaptive fusion gate to combine energy consumption and meteorological data. …”
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Assessment and Variation of Water Quality in Urban Distribution Networks: From Reservoir to Faucet by Eunhye Jeong, Kyung-Yup Hwang, Sumin Lee, Kwangjun Jung, Hyunjun Kim

“…This study focuses on evaluating the spatiotemporal variations in water quality across a potable water distribution network in D City, South Korea, spanning from a reservoir to a large consumer’s tap. …”
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A hybrid ensemble framework with particle swarm optimization for network anomaly detection by Narinder Verma, Neerendra Kumar, Gourav Kumar, Kuljeet Singh

“…Our approach leverages the NSL-KDD and CICIDS datasets to ensure the IDS is trained and evaluated on data reflecting current network behaviours and threat landscapes. …”
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Neural network-based symmetric encryption algorithm with encrypted traffic protocol identification by Jiakai Hao, Ming Jin, Yuting Li, Yuxin Yang

“…In this study, we first introduce a plaintext guessing model (SCGM model) based on symmetric encryption algorithms, leveraging the strengths of convolutional neural networks to evaluate the plaintext guessing capabilities of four symmetric encryption algorithms. …”
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Dynamic graph convolutional networks with Temporal representation learning for traffic flow prediction by Aihua Zhang

“…Abstract In the realm of traffic prediction, emerging are methodologies founded on graph convolutional networks. Nonetheless, existing approaches grapple with issues encompassing insufficient sharing patterns, dependence on static relationship presumptions, and an inability to effectively grasp the intricate trends and cyclic attributes of traffic flow. …”
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Mobile Secured IoT Sensors-Driven Network Using Efficient QoS Management by Mohammad Siraj, Majid Altamimi, Zeeshan Ahmad Abbasi

“…With the use of mobile agents, this research offers quality-aware services and a cooperative protocol for unbalanced IoT networks. Examining the mobility patterns of devices, it effectively achieves massive amounts of data across established connections and lowers communication faults for diverse services. …”
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Optimized deep neural network architectures for energy consumption and PV production forecasting by Eghbal Hosseini, Barzan Saeedpour, Mohsen Banaei, Razgar Ebrahimy

“…Accurate time-series forecasting of energy consumption and photovoltaic (PV) production is essential for effective energy management and sustainability. Deep Neural Networks (DNNs) are effective tools for learning complex patterns in such data; however, optimizing their architecture remains a significant challenge. …”
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Analysis and Quantification of Demand Flexibility for Resilient Distribution Networks: A Systematic Review by Mohamed Massaoudi, Katherine R. Davis, Khandaker Akramul Haque

“…This review systematically investigates DF in distribution networks through three critical dimensions: quantification methodologies, regulatory frameworks, and techno-economic impacts. …”
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Rumor detection using dual embeddings and text-based graph convolutional network by Barsha Pattanaik, Sourav Mandal, Rudra M. Tripathy, Arif Ahmed Sekh

“…Currently, graph convolutional networks (GCNs), particularly TextGCN, have shown promise in text classification tasks, including rumor detection. …”
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Bayesian network modeling of flood cascade and climate risks in the Pearl River Delta by Wen Zhang, Jianglong Cui, Weike Yao, Mariavittoria Guida, Frederick Kwame Yeboah, Xuanru Zhou, Yafei Li, Lixiao Zhang, Gengyuan Liu

“…To evaluate future flood hazards, downscaled climate projections from global climate models and a stochastic weather generator were employed to simulate extreme precipitation patterns. …”
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A Lightweight Tri-Stream Feature Fusion Network for Speech Emotion Recognition by Ronghe Cao, Yunxing Wang, Xiaolong Wu, Shuang Jin, Huiling Niu

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Optimizing Personalized and Context-Aware Recommendations in Pervasive Computing Environments by A. C. Kaladevi, V. Vinoth Kumar, T. R. Mahesh, Suresh Guluwadi

“…Additionally, metrics, such as precision, recall, and F1-score, are used to evaluate the effectiveness of the hybrid approach in capturing latent factors and patterns. …”
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Social Network Community Detection by Combining Self-Organizing Maps and Genetic Algorithms by Mehdi Ellouze

“…Community detection is one of these tools and aims to detect a set of entities that share some features within a social network. We have taken part in this effort, and we proposed an approach mainly based on pattern recognition techniques. …”
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Wind Speed Forecasting in the Greek Seas Using Hybrid Artificial Neural Networks by Lateef Adesola Afolabi, Takvor Soukissian, Diego Vicinanza, Pasquale Contestabile

“…In this work, various artificial neural networks (ANNs) were developed and evaluated for their wind speed prediction ability using the ERA5 historical reanalysis data for four potential Offshore Wind Farm Organized Development Areas in Greece, selected as suitable for floating wind installations. …”
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A coupled model of zebra mussels and chlorine in collective pressurized irrigation networks by J. Burguete, B. Latorre, P. Paniagua, E.T. Medina, J. Fernández-Pato, E. Playán, N. Zapata

“…Simulations predicted similar mussel settlement patterns across all scenarios, suggesting that network morphology and total larval abundance primarily influence settlement distribution. …”
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Enhancing Network Security: A Study on Classification Models for Intrusion Detection Systems by Abeer Abd Alhameed Mahmood, Azhar A. Hadi, Wasan Hashim Al-Masoody

“…The authors utilize three datasets (Knowledge Discovery in Databases 1999 dataset, used for network intrusion detection research), UNSW-NB15 (a dataset capturing contemporary network attack patterns generated at the University of New South Wales), and CICIDS2017 (Canadian Institute for Cybersecurity Intrusion Detection System dataset, containing modern attack scenarios)(KDD99, UNSW NB15, and CICIDS2017) with varying train-test ratios to train the classifiers. …”
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Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions by Qingchun Guo, Zhenfang He, Shanshan Li, Xinzhou Li, Jingjing Meng, Zhanfang Hou, Jiazhen Liu, Yongjin Chen

“…Evaluating twelve algorithms and nineteen network topologies for the ANN and WANN models, we discovered that the optimal input variables for an API forecasting model were the APIs from the 3 preceding days and sixteen selected meteorological factors. …”
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An ingredient co-occurrence network gives insight into e-liquid flavor complexity by Jeroen L. A. Pennings, Ina M. Hellmich, Sanne Boesveldt, Reinskje Talhout

“…We identified two densely connected regions (clusters) in the network. One consisted of six ingredients with sweet-vanilla-creamy flavors. …”
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A Single RF MIMO Loading Network for High-Order Modulation Schemes by Bo Han, Vlasis I. Barousis, Antonis Kalis, Constantinos B. Papadias, Athanasios G. Kanatas, Ramjee Prasad

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Adaptive multi-scale phase-aware fusion network for EEG seizure recognition by Yanting Liang, Jingyuan Liu, Xinzhou Zhang

“…However, traditional methods rely heavily on manual feature extraction, and current deep learning-based approaches still face challenges in frequency adaptability, multi-scale feature integration, and phase alignment.MethodsTo address these limitations, we propose an Adaptive Multi-Scale Phase-Aware Fusion Network (AMS-PAFN). The framework integrates three novel components: (1) a Dynamic Frequency Selection (DFS) module employing Gumbel-SoftMax for adaptive spectral filtering to enhance seizure-related frequency bands; (2) a Multi-Scale Feature Extraction (MCFE) module using hierarchical downsampling and temperature-controlled multi-head attention to capture both macro-rhythmic and micro-transient EEG patterns; and (3) a Multi-Scale Phase-Aware Fusion (MCPA) module that aligns temporal features across scales through phase-sensitive weighting.ResultsThe AMS-PAFN was evaluated on the CHB-MIT dataset and achieved state-of-the-art performance, with 98.97% accuracy, 99.53% sensitivity, and 95.21% specificity (Subset 1). …”
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