Internet of Things Assisted Plant Disease Detection and Crop Management Using Deep Learning for Sustainable Agriculture by Eman A. Al-Shahari, Ghadah Aldehim, Mohammed Aljebreen, Jehad Saad Alqurni, Ahmed S. Salama, Sitelbanat Abdelbagi

“…Furthermore, the classification algorithm is implemented by using the recurrent spiking neural network (RSNN) model. …”
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Implementing Remote Radiotherapy Planning to Increase Patient Flow at a Johannesburg Academic Hospital, South Africa: Protocol for a Prospective Feasibility Study by Duvern Ramiah, Sonwabile Ngcezu, Oluwatosin Ayeni, Okechinyere Achilonu, Mariam Adeleke, Theo Nair, Joseph Otten, Daniel Mmereki

“…Phase 1 (feasibility) encompasses system commissioning, including beam modeling, computed tomography (CT)-to-electron density calibration, multileaf collimator (MLC) optimization, and dose calculations using the anisotropic analytical algorithm. …”
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Enhanced CLIP-GPT Framework for Cross-Lingual Remote Sensing Image Captioning by Rui Song, Beigeng Zhao, Lizhi Yu

“…Our approach utilizes CLIP for zero-shot multimodal feature extraction of remote sensing images, followed by the design and optimization of a mapping network based on an improved Transformer with adaptive multi-head attention to align these features with the text space of GPT-2, facilitating the generation of high-quality descriptive text. …”
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A pregeneration–recognition method of detecting weak seafloor echoes for full-waveform airborne LiDAR bathymetry by Yadong Guo, Wenxue Xu, Yanxiong Liu, Fanlin Yang, Xue Ji, Yikai Feng, Qiuhua Tang

“…Finally, a back propagation neural network (BPNN) model is constructed using the points generated from surface–seafloor shots, and the seafloor points in seafloor-undefined shots are obtained by optimizing the BPNN results. …”
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Predicting hydrocarbon reservoir quality in deepwater sedimentary systems using sequential deep learning techniques by Xiao Hu, Jun Xie, Xiwei Li, Junzheng Han, Zhengquan Zhao, Hamzeh Ghorbani

“…Three sequential deep learning models—Recurrent Neural Network and Gated Recurrent Unit—were developed and optimized using the Adam algorithm. …”
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To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions by Pengyu Chen, Zhenwei Yang, Peigang Ning, Hao Yuan, Zuochao Qi, Qingshan Li, Bo Meng, Xianzhou Zhang, Haibo Yu

“…This model is expected to provide personalized decision support to clinicians and help to optimize treatment plans and improve patient outcomes.…”
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The ensemble transform Schmidt–Kalman filter: A novel method to compensate for observation uncertainty due to unresolved scales by Zackary Bell, Sarah L. Dance, Joanne A. Waller

“…Observation error due to unresolved scales occurs when there is a difference in scales observed and modeled. To obtain an optimal estimate through data assimilation, the error due to unresolved scales must be accounted for in the algorithm. …”
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Intelligent Charging Navigation for Electric Vehicles Based on Real-time Electricity Price by Rui WU, Liangsong ZHOU, Zhandong YAO

“…Regarding this kind of problem, an electric vehicle intelligent charging navigation strategy based on real-time electricity price is proposed, where the PSO-GA algorithm is used to solve the optimal scheme. The motivation is to guide the EV charging toward an orderly way by virtue of the real-time electricity price mechanism. …”
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Game-Theoretic Cooperative Task Allocation for Multiple-Mobile-Robot Systems by Lixiang Liu, Peng Li

“…In contrast, under larger and more complex problem instances, the proposed algorithm can achieve up to a 50% performance improvement over the benchmarks. …”
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AMNED: An Efficient Framework for Spiking Neuron Coding in AirComp Federated Learning by Juncheng Ji, Chan-Tong Lam, Ke Wang, Benjamin K. Ng

“…This paper advances ACFL technologies by proposing the Adaptive Memristor Neuron Encoding-Decoding (AMNED) framework for AirComp Federated Learning (ACFL), enabling efficient, privacy-preserving model aggregation optimized for resource-constrained wireless environments. …”
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Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques by Athanasios Angeioplastis, John Aliprantis, Markos Konstantakis, Alkiviadis Tsimpiris

“…The results indicated that courses with strong correlations (+0.3 and above) significantly enhanced predictive accuracy, particularly in binary classification tasks. kNN and neural networks emerged as the most robust models, achieving F1 scores exceeding 0.8. …”
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Research on Concrete Crack and Depression Detection Method Based on Multi-Level Defect Fusion Segmentation Network by Zhaochen Yao, Yanjuan Li, Hao Fu, Jun Tian, Yang Zhou, Chee-Loong Chin, Chau-Khun Ma

“…Joint detection loss and segmentation loss are used for task co-optimization. On our self-constructed concrete defect dataset, MDFNet significantly outperforms the baseline model. …”
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Transactional configuration of innovative development of business ecosystem by E. V. Popov, A. D. Tikhonova, A. S. Zyrianov

“…As a result, the authors have developed an algorithm that aims to create a pool of stakeholders in the business ecosystem in order to achieve sustainable development within the innovative economy through optimized transaction costs. …”
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Powdery mildew resistance prediction in Barley (Hordeum Vulgare L) with emphasis on machine learning approaches by Farveh Vahidpour, Hossein Sabouri, Fakhtak Taliei, Sayed Javad Sajadi, Saeed Yarahmadi, Hossein Hosseini Moghaddam

“…The Bayesian algorithm was utilized to optimize the parameters of the machine-learning models. …”
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High‐Precision Prediction of Ionospheric TEC in the China Region Based on CMONOC High‐Resolution Data and an Auxiliary Attention Temporal Convolutional Network by Jianghe Chen, Pan Xiong, Haochen Wu, Xiaoran Zhang, Xuemin Zhang, Rongzi Chai, Ting Zhang, Kaixin Wang, Chaoyu Wang

“…At the data level, a non‐integrated spherical harmonic model and Differential Code Bias correction method are employed to significantly reduce interpolation errors and improve model accuracy. …”
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In silico assessment of biocompatibility and toxicity: molecular docking and dynamics simulation of PMMA-based dental materials for interim prosthetic restorations by Ravinder S. Saini, Rayan Ibrahim H. Binduhayyim, Vishwanath Gurumurthy, Abdulkhaliq Ali F. Alshadidi, Shashit Shetty Bavabeedu, Rajesh Vyas, Doni Dermawan, Punnoth Poonkuzhi Naseef, Seyed Ali Mosaddad, Artak Heboyan

“…Future directions include experimental validation of computational findings and the development of PMMA-based dental materials with improved biocompatibility and clinical performance. …”
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Enhancing prediction of crop yield and soil health assessment for sustainable agriculture using machine learning approach by Kapil Netaji Vhatkar, Shweta Ashish Koparde, Sonali Kothari, Jayesh Sarwade, Kishor Sakur

“…The goal of this research is • to make sophisticated models for precise crop production forecasting and thorough evaluation of soil health, • to improve sustainability by optimize farming methods, and • to assist farmers in making well-informed decisions.Iterative Partitioning-Ensemble Filter (IP-EF) is a technique used for feature selection, enhancing model performance by iteratively partitioning data and refining feature subsets. …”
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Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis by Emre Bilgin

“…Predictive models have guided treatment decisions such as—response to methotrexate and biologics—while wearable devices and electronic health records (EHR) improve disease activity monitoring. …”
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SCL-YOLOv11: A Lightweight Object Detection Network for Low-Illumination Environments by Shulong Zhuo, Hao Bai, Lifeng Jiang, Xiaojian Zhou, Xu Duan, Yiqun Ma, Zihan Zhou

“…Furthermore, a lightweight detail-enhancement convolution layer and a shared-convolution detection head are designed to improve the model’s capability in capturing fine-grained details. …”
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Artificial Neural Network Framework for Hybrid Control and Monitoring in Turning Operations by Bogdan Felician Abaza, Vlad Gheorghita

“…This paper proposes a hybrid control and monitoring framework designed to enhance turning operations by integrating artificial neural networks (ANNs) for predictive modeling and adaptive recalibration. The system leverages machine learning (ML) to improve machining efficiency, tool longevity, and energy consumption optimization. …”
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