User Trajectory Prediction in Cellular Networks Using Multi-Step LSTM Approaches: Case Study and Performance Evaluation by Iskandar, Hajiar Yuliana, Hendrawan, Adriel Timoteo, Fabian Rafinanda Benyamin, Naufal Bhanu Anargyarahman

“…These findings demonstrate the potential of Transformer-based approaches in improving prediction accuracy and enabling advanced network management strategies, such as optimized handovers and latency reduction.…”
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Multilayer Network Modeling for Brand Knowledge Discovery: Integrating TF-IDF and TextRank in Heterogeneous Semantic Space by Peng Xu, Rixu Zang, Zongshui Wang, Zhuo Sun

“…Notably, the overlap layer exhibits the highest node centrality, indicating convergent consumer focus across algorithms. The network demonstrates small-world characteristics (average path length = 1.627) with strong clustering (average clustering coefficient = 0.848), reflecting cohesive consumer discourse around key features. …”
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A Real-Time Polygonal Wheel-Rail Force Identification Method Based on Convolutional Neural Networks (CNN) by Zeteng Zhang, Jinhai Wang, Jianwei Yang, Dechen Yao

“…The experimental results demonstrate that the vibration acceleration of vehicle components along with the vertical displacement data of primary springs, exhibit optimal performance in the identification of wheel-rail forces when employed as inputs for the network. …”
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A hybrid optimization-enhanced 1D-ResCNN framework for epileptic spike detection in scalp EEG signals by Priyaranjan Kumar, Prabhat Kumar Upadhyay

“…Abstract In order to detect epileptic spikes, this paper suggests a deep learning architecture that blends 1D residual convolutional neural networks (1D-ResCNN) with a hybrid optimization strategy. …”
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A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering by Binhua Tang, Yingying Feng, Xinyu Gao

“…In addition, scCAGN combines three different loss functions to optimize clustering performance through a joint clustering approach. …”
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A Monte Carlo Tree Search-Based Load Balancing Algorithm for 6G Indoor Heterogeneous Networks by Guanghui Ma, Syifaul Fuada, Marcos Katz

“…By dynamically predicting future network states, the proposed algorithm optimally distributes traffic, improving overall network performance. …”
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Multi-criteria routing metric for supporting data-differentiated service in hybrid wireless mesh networks in coal mines by Liansheng Lu, Haifeng Jiang, Guangzhi Han, Shanshan Ma, Renke Sun

“…End-to-end delay is calculated when transmitting urgent data, and hop count and link load are measured when transmitting non-urgent data. In order to optimize the utilization of mesh clients and to prolong the network lifetime, mesh clients and mesh routers are given different weights in the calculation of hop count. …”
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Neural Network Models for Ionospheric Electron Density Prediction at a Fixed Altitude Using Neural Architecture Search by Yang Pan, Mingwu Jin, Shun‐Rong Zhang, Simon Wing, Yue Deng

“…NAS aims to find the optimal network structure through the alternate optimization of the hyperparameters and the corresponding network parameters within a pre‐defined hyperparameter search space. …”
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Elman and feedforward neural network based models for predicting mechanical properties of flow formed AA6082 tubes by Tarak Nath De, Bikramjit Podder, Nirmal Baran Hui, Chandan Mondal, Vimal Kumar Pathak

“…Three predictive models were developed and evaluated: multivariate regression (MR), feedforward neural network (FNN), and Elman neural network (ENN). Among these, the FNN demonstrated superior predictive accuracy when validated against experimental data with maximum average prediction error of 7.45%, outperforming ENN and MR having 7.64% and 12.4%, respectively.…”
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Q-Learning-Based Medium Access Technology for Minimizing AoI in LoRa Wireless Relay Networks by Dowon Kim, Geonha Hwang, Ohyun Jo, Kyungseop Shin

“…Our approach integrates LoRa relay devices and Age of Information (AoI) metrics to enhance network performance. The algorithm dynamically adjusts Spreading Factors (SFs) based on network conditions, utilizing reinforcement learning techniques for optimal SF selection. …”
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Research on the Method of Crop Pest and Disease Recognition Based on the Improved YOLOv7-U-Net Combined Network by Wenchao Xiang, Zitao Du, Xinran Liu, Zehui Lu, Yuna Yin

“…For the YOLOv7 network, a self-attention mechanism is integrated into the SPPCSPC module to dynamically adjust channel weights and suppress redundant information while optimizing the PAFPN structure to enhance cross-scale feature fusion and improve small-object detection capabilities. …”
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Predicting Wastewater Characteristics Using Artificial Neural Network and Machine Learning Methods for Enhanced Operation of Oxidation Ditch by Igor Gulshin, Nikolay Makisha

“…The SMAPE score of 1.052% on test data demonstrates the model’s accuracy and highlights the potential of integrating artificial neural networks (ANN) and machine learning (ML) with mechanistic models for optimizing wastewater treatment processes. …”
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Deep convolutional neural network (DCNN)-based model for pneumonia detection using chest x-ray images by S. I. Ele, U. R. Alo, H. F. Nweke, A. H. Okemiri, E. O. Uche-Nwachi

“…Data Preprocessing was conducted to enhance image quality and extract relevant features, followed by implementing a deep convolutional neural networks (DCNNs) model using TensorFlow’s Keras. Using pre-trained models such as Resnet, transfer learning techniques were employed to learn efficient features from large-scale datasets and optimize the model’s performance with the limited medical data available. …”
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Using Novel Optimized Deep Learning Techniques for Detecting Fungal Infections in Hazelnuts Kernels Based on Shell Color Changes by Mehdi Farahani, Hossein Bagherpour

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GA-SVM method for single-phase grounding fault line selection in distribution network based on feature fusion by ZHANG Xiaopeng, BAI Jie, SUN Naijun, LI Jie, ZHENG Shuai, WAN Qingzhu

“…Aiming at the low accuracy of line selection method when the data amount of single-phase grounding fault in distribution network is small, a genetic algorithm optimized support vector machine (GA-SVM) method for single-phase grounding fault line selection in distribution network based on feature fusion is proposed, which adopts Fourier transform, the active power method and wavelet packet transform decompose the transient zero-sequence current of each line under different fault conditions, extracts four features, including fundamental wave amplitude, fifth harmonic amplitude, average active power component and wavelet energy value. …”
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SPARQ: Efficient Entanglement Distribution and Routing in Space–Air–Ground Quantum Networks by Mohamed Shaban, Muhammad Ismail, Walid Saad

“…Additionally, SPARQ enhances the average entanglement fidelity by 23.5% compared with existing networks spanning only space and ground layers.…”
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Developing a novel layer network structure for a LSTM model to predict mean monthly river streamflow by Amin Gharehbaghi, Redvan Ghasemlounia, Shahaboddin Daneshvar, Farshad Ahmadi

“…Abstract In this research, novel innovative DDN layer network structures by hybridizing double-LSTM model with an addition layer (+) (i.e., 2LSTM and 2LSTM + layer network models) are developed purposefully to enhance prediction performance of the mean monthly Maroon River streamflow (MRSF m ) in Iran from October 1987 to September 2017. …”
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SGNet: A Structure-Guided Network with Dual-Domain Boundary Enhancement and Semantic Fusion for Skin Lesion Segmentation by Haijiao Yun, Qingyu Du, Ziqing Han, Mingjing Li, Le Yang, Xinyang Liu, Chao Wang, Weitian Ma

“…To overcome these challenges, we propose SGNet, a structure-guided network, integrating a hybrid CNN–Mamba framework for robust skin lesion segmentation. …”
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Predicting Thermal Conductivity of Nanoparticle-Doped Cutting Fluid Oils Using Feedforward Artificial Neural Networks (FFANN) by Beytullah Erdoğan, Abdulsamed Güneş, İrfan Kılıç, Orhan Yaman

“…To address these issues, a feedforward artificial neural network (FFANN) method was proposed to predict thermal conductivity. …”
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Revolutionizing Clear-Sky Humidity Profile Retrieval with Multi-Angle-Aware Networks for Ground-Based Microwave Radiometers by Yinshan Yang, Zhanqing Li, Jianping Guo, Yuying Wang, Hao Wu, Yi Shang, Ye Wang, Langfeng Zhu, Xing Yan

“…Based on the 7-year (2018–2024) in situ measurements from Beijing, Nanjing, and Shanghai, validation results reveal that AngleNet achieves substantial improvements, with an average R2 of 0.71 and a root mean square error (RMSE) of 10.39%, surpassing conventional models such as LGBM (light gradient boosting machine) and RF (random forest) by over 10% in both metrics, and demonstrating a remarkable 41% increase in R2 and a 10% reduction in RMSE compared to the previous BRNN method (batch normalization and robust neural network). …”
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