Advancing soil mapping and management using geostatistics and integrated machine learning and remote sensing techniques: a synoptic review by Sunshine A. De Caires, Chaney St Martin, Melissa A. Atwell, Fuat Kaya, Glorious A. Wuddivira, Mark N. Wuddivira

“…Future research should focus on improving model accuracy, interpretability, transparency, and explainability. …”
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Development of VIIRS-OLCI chlorophyll-a product for the coastal estuaries by Alexander Gilerson, Mateusz Malinowski, Jacopo Agagliate, Eder Herrera-Estrella, Maria Tzortziou, Michelle C. Tomlinson, Andrew Meredith, Richard P. Stumpf, Michael Ondrusek, Lide Jiang, Lide Jiang, Menghua Wang

“…The NN algorithm demonstrated a significant improvement in the Chl-a retrieval capabilities in comparison with other algorithms, which utilize only reflectance bands. …”
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Thermal damage detection of EAST internal component based on machine learning by Zhongfang Guan, Bin Zhang, Jian Liu, Jinping Qian, Xianzu Gong, Runze Chen, Zuhao Wang, Binfu Gao, Yutong Guo, Chuannan Xuan, Cong Cao, Tianqi Jia, Pan Li, Wenbin Liu, Wei Wang, Yunchan Hu, Yifan He, Kangjia Yang, Wenyi Lu, Chunyu He, the EAST team

“…Improvements are needed for specialized tasks, particularly for early warning and precise identification of small internal component damage during the initial stages of EAST discharges. we enhance the YOLOv8 model by incorporating specialized layers for detecting small targets and integrating the CBAM attention mechanism. …”
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Experimental Demonstration of 73 Gb/s QPSK and 150 Gb/s QAM-32 Wireless Data Links in the Sub-THz Band Through Frequency Selective Surface Filters by Walter Fuscaldo, Daniele Pirrone, Dimitrios C. Zografopoulos, Antonio Ferraro, Pascal Szriftgiser, Guillaume Ducournau, Romeo Beccherelli

“…Preliminary results on the error performance of amplitude-phase modulations were obtained and compared with a simple model to serve as a testbed for future, more accurate analyses. …”
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Machine learning-assisted early detection of keratoconus: a comparative analysis of corneal topography and biomechanical data by Arkadiusz Syta, Arkadiusz Podkowiński, Tomasz Chorągiewicz, Robert Karpiński, Jakub Gęca, Dominika Wróbel-Dudzińska, Katarzyna E Jonak, Dariusz Głuchowski, Marcin Maciejewski, Robert Rejdak, Kamil Jonak

“…The results suggest that machine learning models, particularly Random Forest, can effectively aid in the early detection of keratoconus in young individuals, potentially improving patient outcomes through timely intervention.…”
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Using explainable machine learning and eye-tracking for diagnosing autism spectrum and developmental language disorders in social attention tasks by Adoración Antolí, Adoración Antolí, Francisco Javier Rodriguez-Lozano, José Juan Cañas, Julia Vacas, Julia Vacas, Fátima Cuadrado, Fátima Cuadrado, Araceli Sánchez-Raya, Araceli Sánchez-Raya, Carolina Pérez-Dueñas, Carolina Pérez-Dueñas, Juan Carlos Gámez-Granados

“…The most informative areas of interest were those broadly grouping social and non-social stimuli, while more specific variables did not improve classification accuracy. Naive Bayes and Logistic Model Trees (LMT) emerged as the most effective algorithms in this study. …”
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Intellectual or developmental disabilities and curative female breast cancer treatment: A population-based retrospective cohort studyLay summary by Rebecca L. Hansford, Brooke Wilson, Rebecca Griffiths, Alyson L. Mahar

“…IDD status was identified using an established algorithm. We estimated associations between IDD and surgical resection, adjuvant chemotherapy, and radiation using cause-specific hazards models in four distinct cohorts determined by stage and treatment eligibility. …”
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Underwater Target 3D Reconstruction via Integrated Laser Triangulation and Multispectral Photometric Stereo by Yang Yang, Yimei Liu, Eric Rigall, Yifan Yin, Shu Zhang, Junyu Dong

“…With the gradual application of 3D reconstruction technology in underwater scenes, the design of vision-based reconstruction models has become an important research direction for human ocean exploration and development. …”
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Design and analysis of intelligent service chain system for network security resource pool by Zenan WANG, Jiahao LI, Chaohong TAN, Dechang PI

“…The traditional network security architecture ensures network security by directing traffic through hardware based network security function devices.Since the architecture consists of fixed hardware devices, it leads to a single form of network security area deployment and poor scalability.Besides, the architecture cannot be flexibly adjusted when facing network security events, making it difficult to meet the security needs of future networks.The intelligent service chain system for network security resource pool was based on software-defined network and network function virtualization technologies, which can effectively solve the above problems.Network security functions of virtual form were added based on network function virtualization technology, combined with the existing hardware network elements to build a network security resource pool.In addition, the switching equipment connected to the network security elements can be flexibly controlled based on software-defined network technology.Then a dynamically adjustable network security service chain was built.Network security events were detected based on security log detection and a expert library consisting of security rules.This enabled dynamic and intelligent regulation of the service chain by means of centralized control in the face of network security events.The deployment process of the service chain was mathematically modeled and a heuristic algorithm was designed to realize the optimal deployment of the service chain.By building a prototype system and conducting experiments, the results show that the designed system can detect security events in seconds and automatically adjust the security service chain in minutes when facing security events, and the designed heuristic algorithm can reduce the occupation of virtual resources by 65%.The proposed system is expected to be applied to the network security area at the exit of the campus and data center network, simplifying the operation and maintenance of this area and improving the deployment flexibility of this area.…”
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Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoder by Ameet Shah, Dhanpratap Singh, Heba G. Mohamed, Salil Bharany, Ateeq Ur Rehman, Seada Hussen

“…The extracted features were used in network of neurons to execute the classification for MIT-BIH arrhythmia databases using the newly developed self-attention autoencoder (AE) algorithm. The results are compared with existing models, revealing that the proposed system outperforms the classification and prediction of cardiac arrhythmia with a precision of 99.91%, recall of 99.86%, and accuracy of 99.71%. …”
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Optimized Tile Quality Selection in Multi-User 360° Video Streaming by Moatasim Mahmoud, Stamatia Rizou, Andreas S. Panayides, Nikolaos V. Kantartzis, George K. Karagiannidis, Pavlos I. Lazaridis, Zaharias D. Zaharis

“…Simulation results of our proposed framework show significant improvement in the delivered viewports’ quality and robustness against increasing the number of users.…”
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A SAC-Bi-RRT Two-Layer Real-Time Motion Planning Approach for Robot Assembly Tasks in Unstructured Environments by Qinglei Zhang, Siyao Hu, Jianguo Duan, Jiyun Qin, Ying Zhou

“…To realize the safe assembly of assembly robots in dynamic and complex environments, a dynamic obstacle avoidance trajectory planning method for robots combining traditional planning algorithms and deep reinforcement learning algorithms is proposed to improve the robot’s agent and obstacle avoidance ability in dynamic and complex environments. …”
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Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule Generation by Matvei Anoshin, Asel Sagingalieva, Christopher Mansell, Dmitry Zhiganov, Vishal Shete, Markus Pflitsch, Alexey Melnikov

“…This work develops an application of hybrid quantum generative models based on the integration of parameterized quantum circuits into known molecular generative adversarial networks and proposes quantum cycle architectures that improve model performance and stability during training. …”
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A comprehensive review of data analytics and storage methods in geothermal energy operations by Ali Basem, Ahmed Kateb Jumaah Al-Nussairi, Dana Mohammad Khidhir, Narinderjit Singh Sawaran Singh, Mohammadreza Baghoolizadeh, Mohammad Ali Fazilati, Soheil Salahshour, S. Mohammad Sajadi, Ali Mohammadi Hasanabad

“…The study also delves into the potential of machine learning to optimize geothermal design, monitor performance, improve performance, find errors, and more. It was shown that artificial neural networks were the most common kind of trained model, while several other models were often used as benchmarks for performance. …”
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State-of-the-Art on IoV-Based Deep Learning Framework for Enhanced Driving Behavior Recognition: Recent Progress, Technology Updates, Challenges, and Future Direction by Hongguang Li, Shafrida Sahrani, Mahidur R. Sarker, Yinglin Xiao

“…Therefore, this paper proposes an active IoV based DL framework, emphasizing the feasibility of enhancing data processing techniques and algorithmic improvements to boost model accuracy and generalization ability, and highlighting the potential of integrating edge and cloud computing to support real-time data analysis and decision-making. …”
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Advanced Brain Tumor Segmentation With a Multiscale CNN and Conditional Random Fields by Ala Guennich, Mohamed Othmani, Hela Ltifi

“…The use of high-precision automatic algorithms for segmenting brain tumors has the potential to improve disease diagnosis, treatment monitoring, and large-scale pathological studies. …”
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Enhancing Visual Perception in Immersive VR and AR Environments: AI-Driven Color and Clarity Adjustments Under Dynamic Lighting Conditions by Maryam Abbasi, Paulo Váz, José Silva, Pedro Martins

“…Experimental results from 60 participants show significant improvements, with up to 41% better color accuracy and 39% enhanced clarity under dynamic lighting conditions. …”
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Data-Driven Diagnostics for Pediatric Appendicitis: Machine Learning to Minimize Misdiagnoses and Unnecessary Surgeries by Deborah Maffezzoni, Enrico Barbierato, Alice Gatti

“…In this study, we explore the potential of machine learning (ML) algorithms to improve the diagnosis, management, and prediction of appendicitis severity in pediatric patients. …”
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A Review of Research on Cloud Detection Methods for Hyperspectral Infrared Radiances by Zhuoya Ni, Mengdie Wu, Qifeng Lu, Hongyuan Huo, Chunqiang Wu, Ruixia Liu, Fu Wang, Xiaoying Xu

“…Specifically, we analyze and summarize the factors affecting cloud detection, such as surface background information, vertical cloud distribution, hyperspectral IR channel selection, improvements in cloud detection algorithms and model applicability. …”
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Predicting weather-related power outages in large scale distribution grids with deep learning ensembles by L. Prieto-Godino, C. Peláez-Rodríguez, J. Pérez-Aracil, J. Pastor-Soriano, S. Salcedo-Sanz

“…This approach not only enhances prediction accuracy compared to individual learners but also improves the generalization ability and robustness of standalone DL models. …”
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