Research progress in globular fruit picking recognition algorithm based on deep learning by LI Hui, ZHANG Jun, YU Shuochen, LI Zhixin

“…When traditional fruit detection algorithms process images in complex environments, their limited expression ability and robustness are easily affected by illumination, occlusion and other factors, resulting in a decline in recognition accuracy. …”
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Survey of research on multimodal semantic communication by Zhijin QIN, Tantan ZHAO, Fan LI, Xiaoming TAO

“…With the cross-integration of artificial intelligence and communications, technologies for processing multimodal data such as text, image, audio, and video are booming, the shared dimension of modal semantics is deeply excavated, and the characteristics of multimodal semantic information such as high abstraction, intelligence and simplicity are being fully utilized, which brings new ideas and means to semantic communications.First, the fundamental theories and classifications of semantic communication were introduced, and the research status of single-modal semantic communication was reviewed for text, image, audio, and video respectively.Then, the research status of multimodal semantic communication was reviewed, and multimodal data fusion technology and secure semantic communication were introduced.Finally, the challenges faced by multimodal semantic communication were summarized.…”
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Farm-scale mapping of compost and digestate spreadings from Sentinel-2 and Sentinel-1 by Maxence Dodin, Florent Levavasseur, Antoine Savoie, Lucie Martin, Emmanuelle Vaudour

“…According to few recent studies, exogenous organic matters (EOM) can be detectable on either emerging vegetation or bare soil using optical and radar remote sensing techniques. Nevertheless, these image processing approaches considered one single EOM, one season and/or year only and were limited to one surface condition prior to spreading. …”
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Forest Change Monitoring Based on Block Instance Sampling and Homomorphic Hypothesis Margin Evaluation by Wei Feng, Fan Bu, Puxia Wu, Gabriel Dauphin, Yinghui Quan, Mengdao Xing

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A Complex Background SAR Ship Target Detection Method Based on Fusion Tensor and Cross-Domain Adversarial Learning by Haopeng Chan, Xiaolan Qiu, Xin Gao, Dongdong Lu

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Federated Learning for Grading Oil Palm Fruit Ripeness in the Oil Palm Industry by Patchanee Laddawong, Yutthapong Pianroj, Piyanart Chotikawanid, Teerasak Punvichai, Saysunee Jumrat, Atitaya Kham-Ouam, Jirapond Muangprathub

“…A predictive model was created using FL principles with a training dataset of 5209 images, which was divided into two subsets: single-palm (2571 images) and multipalm (2638 images). …”
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Interpersonal Relationship Detection Using Multi-Head Graph Attention Networks With Multi-Feature Fusion by Simge Akay, Duygu Cakir, Nafiz Arica

“…Ablation studies and attention visualization reveal that the modular architecture significantly improves feature discrimination and relationship classification compared to single-graph approaches.…”
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Key Vital Signs Monitor Based on MIMO Radar by Michael Gottinger, Nicola Notari, Samuel Dutler, Samuel Kranz, Robin Vetsch, Tindaro Pittorino, Christoph Würsch, Guido Piai

“…State-of-the-art radar systems for the contactless monitoring of vital signs and respiratory diseases are typically based on single-channel continuous wave (CW) technology. This technique allows precise measurements of respiration patterns, periods of movement, and heart rate. …”
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Comparing Cloud Mask Products for Seagrass Mapping Over Sentinel‐2 Imagery: Toward a First National Seagrass Map for Venezuela by Chengfa Benjamin Lee, Ana Carolina Peralta Brichtova, Mar Roca, Tylar Murray, Oswaldo David Bolivar Rodriguez, Daniele Cerra, Frank E. Muller‐Karger

“…We compare the performance of Cloud Score+ derived products against previously established multitemporal image composites acquired in different time ranges, and the ACOLITE‐processed single image composite. …”
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MultiDistiller: Efficient Multimodal 3D Detection via Knowledge Distillation for Drones and Autonomous Vehicles by Binghui Yang, Tao Tao, Wenfei Wu, Yongjun Zhang, Xiuyuan Meng, Jianfeng Yang

“…Additionally, during the model lightweighting process, issues such as multimodal feature coupling failure and the imbalance between classification and localization performance often arise. …”
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Basketball teaching methods based on 3D-Convolutional neural network by Chao Huang, Xian Wu

“…The results revealed that the improved single-lens multi-box detector algorithm processed images faster and with higher accuracy and F value compared to the other algorithms. …”
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Unsupervised post-training learning in spiking neural networks by Reyhaneh Naderi, Arash Rezaei, Mahmood Amiri, Herbert Peremans

“…It employs a combination of various learning strategies to facilitate complex learning processes. However, implementing biological learning mechanisms into Spiking Neural Networks (SNNs) remains challenging; thus, most SNNs are trained with only a single learning strategy such as spike timing dependent plasticity (STDP). …”
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AI-Powered Synthesis of Structured Multimodal Breast Ultrasound Reports Integrating Radiologist Annotations and Deep Learning Analysis by Khadija Azhar, Byoung-Dai Lee, Shi Sub Byon, Kyu Ran Cho, Sung Eun Song

“…Our method synthesizes comprehensive breast US reports by combining the extracted information from radiologists’ annotations during routine screenings with the analysis results from deep learning algorithms on multimodal US images. Key modules in our method include image classification using visual features (ICVF), type classification via deep learning (TCDL), and automatic report structuring and compilation (ARSC). …”
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Class-weighted Dempster–Shafer in dual-level fusion for multimodal fake real estate listings detection by Maifuza Mohd Amin, Nor Samsiah Sani, Mohammad Faidzul Nasrudin

“…The dual-level fusion allows the integration of detailed features from text and image data to be performed at an early stage, followed by the metadata fusion at the decision stage in order to obtain a more comprehensive final classification. …”
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Genomewide phenotypic analysis of growth, cell morphogenesis, and cell cycle events in Escherichia coli by Manuel Campos, Sander K Govers, Irnov Irnov, Genevieve S Dobihal, François Cornet, Christine Jacobs‐Wagner

“…Yet, a comprehensive and integrated view of these fundamental processes is lacking. Here, we describe an image‐based quantitative screen of the single‐gene knockout collection of Escherichia coli and identify many new genes involved in cell morphogenesis, population growth, nucleoid (bulk chromosome) dynamics, and cell division. …”
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Non-Intrusive Load Identification Based on Multivariate Features and Information Entropy-Weighted Ensemble by Yue Liu, Wenxia You, Miao Yang

“…Specifically, one-dimensional numerical features related to power and current are input into traditional machine learning models, and two-dimensional image features of binary V-I trajectory are processed by the deep neural network model Swin Transformer. …”
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YUNet_LLMClaimReport: An Enhanced Automobile Insurance Fraud Detection and Automated Claim Report Generation Using Large Language Models by P. Anand Kumar, S. Sountharrajan

“…The framework runs at 8.3 FPS on NVIDIA Jetson Nano and saves processing time by 60 percent. Weaknesses are single-run measures because of computational limitations and factual inconsistencies in reports (10%). …”
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Configurable Synaptic and Stochastic Neuronal Functions in ZnTe‐Based Memristor for an RBM Neural Network by Jungang Heo, Seongmin Kim, Sungjun Kim, Min‐Hwi Kim

“…Based on synaptic behavior, the Modified National Institute of Standards and Technology database image classification accuracy is up to 90%. Conversely, by setting the high‐current level (mA) in the forming process, the stable bipolar threshold switching operation and good selector characteristics (300 ns switching speed, free‐drift, recovery properties) are demonstrated. …”
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Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches by Mohsin Akram, Muhammad Adnan, Syed Farooq Ali, Jameel Ahmad, Amr Yousef, Tagrid Abdullah N. Alshalali, Zaffar Ahmed Shaikh

“…Our experiments on a combined dataset (APTOS 2019 + DDR) with pre-processed images showed that the Bayesian-augmented DenseNet-121 outperforms state-of-the-art models in test accuracy, achieving 97.68% for the Monte Carlo Dropout model, 94.23% for Mean Field Variational Inference, and 91.44% for the Deterministic model. …”
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Foundation neural-networks quantum states as a unified Ansatz for multiple hamiltonians by Riccardo Rende, Luciano Loris Viteritti, Federico Becca, Antonello Scardicchio, Alessandro Laio, Giuseppe Carleo

“…Abstract Foundation models are highly versatile neural-network architectures capable of processing different data types, such as text and images, and generalizing across various tasks like classification and generation. …”
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