MSIMRS: Multi-Scale Superpixel Segmentation Integrating Multi-Source Remote Sensing Data for Lithology Identification in Semi-Arid Area by Jiaxin Lu, Liangzhi Li, Junfeng Wang, Ling Han, Zhaode Xia, Hongjie He, Zongfan Bai

“…In addition, pixel-level K-Nearest Neighbor (KNN), Random Forest (RF) and SVM classification algorithms, as well as deep-learning models including Resnet50 (Res50), Efficientnet_B8 (Effi_B8), and Vision Transformer (ViT) were chosen for a comparative analysis. …”
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Machine learning based identification of an amino acid metabolism related signature for predicting prognosis and immune microenvironment in pancreatic cancer by Xiaohong Liu, Xing Wang, Jie Ren, Yuan Fang, Minzhi Gu, Feihan Zhou, Ruiling Xiao, Xiyuan Luo, Jialu Bai, Decheng Jiang, Yuemeng Tang, Bo Ren, Lei You, Yupei Zhao

“…Methods A comprehensive analysis integrating 10 machine learning algorithms was executed to pinpoint amino acid metabolic signature. …”
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Prognostic significance of calcium-related genes in lung adenocarcinoma and the role of TNNC1 in macrophage polarization and erlotinib resistance by Jian Feng, Zhe Chen, Gaoming Wang, Yu Yao, Xuewen Min, Jing Luo, Kai Xie

“…Differences in immune infiltration and potential mechanisms in LUAD were explored using seven algorithms. The relationship between signature genes, chemotherapy sensitivity, and potential targeted therapies was evaluated. …”
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Optimizing Stroke Risk Prediction: A Primary Dataset‐Driven Ensemble Classifier With Explainable Artificial Intelligence by Md. Maruf Hossain, Md. Mahfuz Ahmed, Md. Rakibul Hasan Rakib, Mohammad Osama Zia, Rakib Hasan, Md. Rakibul Islam, Md. Shohidul Islam, Md Shahariar Alam, Md. Khairul Islam

“…A novel ensemble classifier was developed, combining AdaBoost, Gradient Boosting Machine (GBM), Multilayer Perceptron (MLP), and Random Forest (RF) algorithms to enhance predictive accuracy. Additionally, Explainable Artificial Intelligence (XAI) techniques such as SHAP and LIME were integrated to elucidate key features influencing stroke prediction. …”
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Comprehensive analysis of single-cell and bulk RNA sequencing data reveals an EGFR signature for predicting immunotherapy response and prognosis in pan-cancer by Changchun Ye, Changchun Ye, Xiaoya Chen, Zilu Chen, Shiyuan Liu, Ranran Kong, Wenhao Lin, Minxia Zhu, Xuejun Sun, Zhengshui Xu

“…Therefore, it is necessary to develop a new biomarker for combined immunotherapy strategies to maximize the clinical benefits.MethodsWe collected and investigated 34 pan-cancer scRNA-Seq cohorts from The Cancer Genome Atlas (TCGA) and 10 bulk RNA-Seq cohorts utilizing multiple machine learning (ML) algorithms to identify and verify a representative EGFR-related gene signature (EGFR.Sig) as a predictive biomarker for immunotherapy response. …”
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Integrating Information Gain and Chi-Square for Enhanced Malware Detection Performance by Fauzi Adi Rafrastara, Wildanil Ghozi, Ramadhan Rakhmat Sani, Lekso Budi Handoko, Abdussalam Abdussalam, Elkaf Rahmawan Pramudya, Faizal M. Abdollah

“…Recent studies have shown that this challenge can be addressed by employing machine learning algorithms for detection. Some studies have also implemented various feature selection methods to optimize detection efficiency. …”
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A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography by Kyu-Jin Jung, Thierry G. Meerbothe, Chuanjiang Cui, Mina Park, Cornelis A.T. van den Berg, Stefano Mandija, Dong-Hyun Kim

“…To estimate tissue electrical properties, various reconstruction algorithms have been proposed. However, physics-based reconstructions are prone to various artifacts such as noise amplification and boundary artifact. …”
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Potential impacts of future climate on twelve key multipurpose tree species in Benin: Insights from species distribution modeling for biodiversity conservation by Sèdoami Flora Dogbo, Kolawolé Valère Salako, Gafarou Agoundé, Kangbéni Dimobe, Adjo Estelle Geneviève Adiko, Jens Gebauer, Constant Yves Adou Yao, Romain Glèlè Kakaï

“…We evaluated environmental variables influencing MPTS distribution, projected habitat changes, identified hotspots, and compared impacts on native versus non-native species. Four modelling algorithms—Generalized Additive Models, Generalized Linear Models, Maximum Entropy, and Random Forest—were used. …”
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Leveraging computer-aided drug design for the discovery of phytohormone analogs: A review by Yuling Guo, Ghulam Qanmber, Zhao Liu, Han Yang, Jun Li, Zhikun Yang, Zhongxian Li, Linlin Yang, Zuoren Yang

“…These include the need for more accurate models and advanced algorithms to predict hormone-receptor interactions and metabolic pathways. …”
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Comparing the potential of tree-based and area-based forest height metrics for aboveground biomass estimation in complex forest landscapes by Weiyan Liu, Yuling Chen, Haitao Yang, Guangcai Xu, Shiyu Yan, Lingjun Li, Ling Chen, Qinghua Guo

“…Two modeling approaches—parametric mixed-effects models (MEM) and non-parametric machine learning (ML) algorithms—were applied to evaluate predictive accuracy. …”
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Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks by Ahmed Abd El Moaty Mohamed Gouda, Ehab K. I. Hamad, Aziza I. Hussein, M. Mourad Mabrook, A. A. Donkol

“…The proposed E-CNN model has been investigated across nine different scenarios from the DeepSense 6G dataset and compared against the conventional algorithms. For 64-beams Scenario 1, the E-CNN model showed an increase in average top-1 accuracy from 55.57% to 63.92%, and in case of 32-beams, the accuracy increased from 71.34 % to 82.06%. …”
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Federated Deep Learning for Scalable and Privacy-Preserving Distributed Denial-of-Service Attack Detection in Internet of Things Networks by Abdulrahman A. Alshdadi, Abdulwahab Ali Almazroi, Nasir Ayub, Miltiadis D. Lytras, Eesa Alsolami, Faisal S. Alsubaei, Riad Alharbey

“…We need scalable, privacy-preserving, and resource-efficient IoT intrusion detection algorithms to solve this essential problem. This paper presents a Federated-Learning (FL) framework using ResVGG-SwinNet, a hybrid deep-learning architecture, for multi-label DDoS attack detection. …”
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Enhancing Cross-Modal Camera Image and LiDAR Data Registration Using Feature-Based Matching by Jennifer Leahy, Shabnam Jabari, Derek Lichti, Abbas Salehitangrizi

“…Various LiDAR feature layers, including intensity, bearing angle, depth, and different weighted combinations, are used to find correspondence with camera images utilizing state-of-the-art deep learning matching algorithms, i.e., SuperGlue and LoFTR. Registration is achieved using collinearity equations and RANSAC to remove false matches. …”
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Enhancing Security in DNP3 Communication for Smart Grids: A Segmented Neural Network Approach by Shahid Allah Bakhsh, Muhammad Shahbaz Khan, Oumaima Saidani, Nada Alasbali, S. Qasim Abbas, Muhammad Almas Khan, Jawad Ahmad

“…This study explores the potential for enhancing intrusion detection in DNP3 communications and the associated industrial control system traffic through the application of state-of-the-art deep learning (DL) algorithms. A Segmented Neural Network (SNN) architecture is employed to analyze the DNP3 dataset, which is captured using CICFlowMeter3 and a DNP3 Parser, integrating Deep Neural Network (DNN), Long Short Term Memory (LSTM), and Random Neural Network (RandNN) models. …”
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UEF-HOCUrdu: Unified Embeddings Ensemble Framework for Hate and Offensive Text Classification in Urdu by Kifayat Ullah, Muhammad Aslam, Muhammad Usman Ghani Khan, Faten S. Alamri, Amjad Rehman Khan

“…For this purpose, an extensive comparison of different learning algorithms were conducted. As a result, the most efficient models, namely FastText, XLM-RoBERTa, ULMFiT, and XGBoost were incorporated in the proposed ensemble approach to achieve the best results in both classification and mitigation of NLP issues. …”
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Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment by Amer Tahseen Abu-Jassar, Hani Attar, Ayman Amer, Vyacheslav Lyashenko, Vladyslav Yevsieiev, Ahmed Solyman

“…A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. …”
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A Comparative Performance Evaluation of OFDM, GFDM, and OTFS in Impulsive Noise Channels by Mohsen Sheikh-Hosseini, Farhad Rahdari, Hazhir Ghasemnezhad, Somayeh Ahmadi, Murat Uysal

“…This method examines the impact of variations in the precoder order and explores the application of iterative algorithms for more optimal designing of the precoder. …”
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Accelerated development of multi-component alloys in discrete design space using Bayesian multi-objective optimisation by Osman Mamun, Markus Bause, Bhuiyan Shameem Mahmood Ebna Hai

“…Recent advancements have demonstrated that expected hypervolume-based geometrical acquisition functions outperform other multiobjective optimisation algorithms, such as Thompson Sampling Efficient Multiobjective optimisation and pareto efficient global optimisation (parEGO), in both performance and speed. …”
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Integrating Drone-Based LiDAR and Multispectral Data for Tree Monitoring by Beatrice Savinelli, Giulia Tagliabue, Luigi Vignali, Roberto Garzonio, Rodolfo Gentili, Cinzia Panigada, Micol Rossini

“…Key forest traits were then retrieved from the multispectral data using machine learning regression algorithms, which showed satisfactory performance in estimating the LAI (R² = 0.83, RMSE = 0.44 m² m⁻²) and CCC (R² = 0.80, RMSE = 0.33 g m⁻²). …”
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Field Ridge Segmentation and Navigation Line Coordinate Extraction of Paddy Field Images Based on Machine Vision Fused with GNSS by Muhua Liu, Xulong Wu, Peng Fang, Wenyu Zhang, Xiongfei Chen, Runmao Zhao, Zhaopeng Liu

“…Finally, a homogeneous coordinate transformation method was used to extract the navigation line coordinates, with the model and algorithms deployed on the Jetson AGX Xavier platform Field tests demonstrated a real-time segmentation speed of 26.31 fps, pixel segmentation accuracy of 92.43%, and an average intersection ratio of 90.62%. …”
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