Baseline high-resolution maps of soil nutrients in Morocco to support sustainable agriculture by Yassine Bouslihim, Abdelkrim Bouasria, Ahmed Jelloul, Lotfi Khiari, Sara Dahhani, Rachid Mrabet, Rachid Moussadek

“…This paper presents the first national reference maps of available P and exchangeable K at 250 m resolution over Morocco’s croplands using digital soil mapping with machine learning algorithms and environmental covariates. Unlike previous efforts employing traditional interpolation methods, these maps were developed using Random Forest by integrating 5,276 soil samples for P and 6,978 for K with 76 environmental covariates representing climate, topography, vegetation, and parent material. …”
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Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods by Zhishui You, Yuzhu Guo, Xiulei Zhang, Yifan Zhao

“…However, despite the significant strides made, the paucity of EEG data has emerged as the main bottleneck, preventing generalization of decoding algorithms. Taking inspiration from the resounding success of generative models in computer vision and natural language processing arenas, the generation of synthetic EEG data from limited recorded samples has recently garnered burgeoning attention. …”
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Detecting Unbalanced Network Traffic Intrusions With Deep Learning by S. Pavithra, K. Venkata Vikas

“…To overcome these challenges, this project proposes a novel hybrid Intrusion Detection System using machine learning algorithms, which includes XGBoost, Long Short-Term Memory (LSTM), Mini-VGGNet, and AlexNet, which is used to handle the unbalanced network traffic data. …”
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Deep Learning Automated System for Thermal Defectometry of Multilayer Materials by A. S. Momot, R. M. Galagan, V. Yu. Gluhovskii

“…Three neural network modules are used for automated data processing, each of which performs one of the tasks: defects detection and classification, determination of the defect depth and thickness. The software algorithms and user interface for interacting with system are programmed in the NI LabVIEW development environment.Experimental studies on samples made of multilayer fiberglass have shown a significant advantage of the developed system over using traditional methods for analyzing thermal testing data. …”
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Increasing the noise immunity of radio receiving paths with automatic sensitivity control by Р. V. Zayats, I. Y. Malevich

“…An original ASC system is proposed, which is invariant to the sampling step of the transmission coefficients of controlled elements with increased performance. …”
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Neuromorphic principles for machine olfaction by Nik Dennler, Aaron True, André van Schaik, Michael Schmuker

“…Neuromorphic computing, exemplified by breakthroughs in machine vision through concepts like address-event representation and send-on-delta sampling, has revolutionised sensor technology, enabling low-latency and high dynamic range perception with minimal bandwidth. …”
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Application of Self-Organizing Maps to Explore the Interactions of Microorganisms with Soil Properties in Fruit Crops Under Different Management and Pedo-Climatic Conditions by Francesca Antonucci, Simona Violino, Loredana Canfora, Małgorzata Tartanus, Ewa M. Furmanczyk, Sara Turci, Maria G. Tommasini, Nika Cvelbar Weber, Jaka Razinger, Morgane Ourry, Samuel Bickel, Thomas A. J. Passey, Anne Bohr, Heinrich Maisel, Massimo Pugliese, Francesco Vitali, Stefano Mocali, Federico Pallottino, Simone Figorilli, Anne D. Jungblut, Hester J. van Schalkwyk, Corrado Costa, Eligio Malusà

“…Background: Self-organizing maps (SOMs) are a class of neural network algorithms able to visually describe a high-dimensional dataset onto a two-dimensional grid. …”
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Exploring fecal microbiota signatures associated with immune response and antibiotic impact in NSCLC: insights from metagenomic and machine learning approaches by Wenjie Han, Wenjie Han, Yuhang Zhou, Yuhang Zhou, Yiwen Wang, Yiwen Wang, Xiaolin Liu, Tao Sun, Tao Sun, Junnan Xu, Junnan Xu, Junnan Xu

“…By leveraging metagenomic profiling and machine learning approaches, this study aimed to elucidate gut microbial signatures associated with immune response in lung cancer (LC) and to evaluate the modulatory effects of antibiotic exposure.MethodsA systematic literature search was conducted to identify relevant datasets, resulting in the inclusion of 209 fecal metagenomic samples: 154 baseline samples (45 responders, 37 non-responders, and 72 healthy controls) and 55 longitudinal samples collected during immunotherapy. …”
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Identification of MEG3 and MAPK3 as potential therapeutic targets for osteoarthritis through multiomics integration and machine learning by Bing Ma, Xiaoru Wang, Chengfei Xu, Zelin Xu, Fei Zhang, Wendan Cheng

“…The intersecting genes were further refined using three machine learning algorithms: LASSO, random forest, and SVM–RFE. Diagnostic efficacy was assessed via ROC curve analysis and nomogram construction. …”
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High-Throughput Photonic Time-Stretch Optical Coherence Tomography with Data Compression by Chaitanya K. Mididoddi, Fangliang Bai, Guoqing Wang, Jinchao Liu, Stuart Gibson, Chao Wang

“…A number of optimization algorithms for the reconstruction of the frequency-domain OCT signals have been compared in terms of reconstruction accuracy and efficiency. …”
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Few-shot learning for novel object detection in autonomous driving by Yifan Zhuang, Pei Liu, Hao Yang, Kai Zhang, Yinhai Wang, Ziyuan Pu

“…This study focuses on enhancing perception robustness in autonomous vehicles, particularly in detecting rare objects, which pose a challenge due to limited training samples. While deep learning-based vision methods have shown promising accuracy, they struggle with rare object detection. …”
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ARGContextProfiler: extracting and scoring the genomic contexts of antibiotic resistance genes using assembly graphs by Nazifa Ahmed Moumi, Shafayat Ahmed, Connor Brown, Amy Pruden, Liqing Zhang

“…Several tools, databases, and algorithms are now available to facilitate the identification of ARGs in metagenomic sequencing data; however, direct annotation of short-read data provides limited contextual information. …”
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Enhanced Detection of Intrusion Detection System in Cloud Networks Using Time-Aware and Deep Learning Techniques by Nima Terawi, Huthaifa I. Ashqar, Omar Darwish, Anas Alsobeh, Plamen Zahariev, Yahya Tashtoush

“…We generate real DoS traffic, including normal, Internet Control Message Protocol (ICMP), Smurf attack, and Transmission Control Protocol (TCP) classes, and develop nine predictive algorithms, combining traditional machine learning and advanced deep learning techniques with optimization methods, including the synthetic minority sampling technique (SMOTE) and grid search (GS). …”
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Evaluation of Feature Transformation and Machine Learning Models on Early Detection of Diabetes Mellitus by Ahmed Ali Linkon, Inshad Rahman Noman, Md Rashedul Islam, Joy Chakra Bortty, Kanchon Kumar Bishnu, Araf Islam, Rakibul Hasan, Masuk Abdullah

“…To comprehensively evaluate the effectiveness of these preprocessing techniques, we experimented with twelve different ML models, including both traditional algorithms and ensemble methods. A publicly available dataset has been used for this research, containing 768 samples and 8 features. …”
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Simulating the root-to-shoot ratio of natural grassland biomass in China by the AutoGluon framework by Rui Guo, Xiaodong Huang, Yangjing Xiu, Minglu Che, Jinlong Gao, Shuai Fu, Qisheng Feng, Tiangang Liang

“…In this study, a high-accuracy R/S model was constructed using the AutoGluon framework and traditional machine learning (ML) algorithms with 1,367 R/S samples of grassland in China, integrating climate, soil, terrain and spectral features. …”
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Integrating artificial intelligence for sustainable waste management: Insights from machine learning and deep learning by Son V.T. Dao, Tuan M. Le, Hieu M. Tran, Hung V. Pham, Minh T. Vu, Tuan Chu

“…Then these selected features are fed into Machine Learning algorithms such as Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). …”
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Hyperparameter optimisation in deep learning from ensemble methods: applications to proton structure by Juan Cruz-Martinez, Aron Jansen, Gijs van Oord, Tanjona R Rabemananjara, Carlos M R Rocha, Juan Rojo, Roy Stegeman

“…These hyperparameters must be determined separately from the model parameters such as network weights, and are often fixed by ad-hoc methods or by manual inspection of the results. An algorithmic, objective determination of hyperparameters demands the introduction of dedicated target metrics, different from those adopted for the model training. …”
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HAF-YOLO: Dynamic Feature Aggregation Network for Object Detection in Remote-Sensing Images by Pengfei Zhang, Jian Liu, Jianqiang Zhang, Yiping Liu, Jiahao Shi

“…The growing use of remote-sensing technologies has placed greater demands on object-detection algorithms, which still face challenges. This study proposes a hierarchical adaptive feature aggregation network (HAF-YOLO) to improve detection precision in remote-sensing images. …”
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Novel channel attention-based filter pruning methods for low-complexity semantic segmentation models by Md. Bipul Hossain, Na Gong, Mohamed Shaban

“…While the aforementioned models have been deemed very successful in segmenting medical targets including organs and diseases in high resolution images, the computational complexity represents a burden for the real-time application of the algorithms or the deployment of the models on resource-constrained platforms. …”
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Development of Robust CNN Architecture for Grading and Classification of Renal Cell Carcinoma Histology Images by Amit Kumar Chanchal, Shyam Lal, Shilpa Suresh

“…Recently, deep learning algorithms have proved to be very efficient and accurate in histopathology image analysis. …”
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