Enhancing Visitor Forecasting with Target-Concatenated Autoencoder and Ensemble Learning by Ray-I Chang, Chih-Yung Tsai, Yu-Wei Chang

“…The TCA method integrates the prediction target into the training process, ensuring that the learned feature representations are optimized for specific forecasting tasks. Extensive experiments conducted on the Taiwan and Hawaii datasets demonstrate that the proposed TCA method significantly outperforms traditional feature selection techniques and other advanced algorithms in terms of the mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R²). …”
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Advanced Technologies for Smart Fertilizer Management in Agriculture: A Review by Jian-Jun Liu, Hao Wu, Imran Riaz

“…Precision fertilization has emerged as a critical component of modern smart agriculture, providing a transformative approach to optimizing nutrient application, enhancing crop productivity, and minimizing environmental harm while improving resource use efficiency. …”
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AI-powered Somatic Cancer Cell Analysis for Early Detection of Metastasis: The 62 principal Cancer Types by Sandile Buthelezi, Solly Matshonisa Seeletse, Taurai Hungwe, Vimbai Mbirimi-Hungwe

“…Conclusion: Integrating AI capabilities into clinical workflows holds the promise of significantly enhancing early detection and treatment of metastatic cancer, thereby improving patient outcomes and optimizing therapeutic interventions.…”
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A Review of Enhancement Techniques for Cone Beam Computed Tomography Images by Hassn Mazin Al-alaaf, Mohammed Sabah Jarjees

“…These methods encompass various mathematical algorithms, machine learning approaches, and hybrid models, which aim to mitigate the imperfections present in CBCT data while preserving diagnostically relevant information. …”
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Research status and progress on key technologies of intelligent orchard by CUI Hongwei, LU Xiaoxuan, YANG Yaqing, LU Xinyi, MA Hao, JI Jiangtao, JIN Xin, LI Xiuzhen, ZHAO Zimeng, ZENG Ningning

“…The fully intelligent management system will optimize the production process, reduce operating costs, improve the overall production efficiency and fruit quality of the orchard through technologies such as big data analysis and cloud computing, in order to provide reference and guidance for the development of key technologies in standardized orchards. …”
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Advances and challenges in neoantigen prediction for cancer immunotherapy by Yi Zhang, Ting-Ting Chen, Xiong Li, Ai-Lin Lan, Peng-Fei Ji, Ya-Juan Zhu, Xue-Yao Ma

“…Future advancements will focus on dynamic tumor microenvironment monitoring, multi-omics integration, improved computational models and algorithms to refine neoantigen prediction, and developing optimized personalized vaccines.…”
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Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation—A Review by Seun Orenuga, Philip Jordache, Daniel Mirzai, Tyler Monteros, Ernesto Gonzalez, Ahmed Madkoor, Rahim Hirani, Raj K. Tiwari, Mill Etienne

“…Despite these advances, ethical considerations, including biases in AI algorithms and data generalizability, pose challenges that must be addressed to optimize AI’s implementation in clinical settings. …”
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Energy Optimisation of Industrial Limestone Grinding Using ANN by Dagmara Kołodziej, Patryk Bałazy, Paweł Knap, Krzysztof Lalik, Damian Krawczykowski

“…In the next phase, black-box optimisation was performed using Bayesian and genetic algorithms to identify optimal mill operating settings. …”
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Investigate the Use of Deep Learning in IoT Attack Detection by Mohamed Saddek Ghozlane, Adlen Kerboua, Smaine Mazouzi, Lakhdar Laimeche

“…This study contributes a comprehensive comparative analysis of deep learning models for IoT security, focusing on the effectiveness of weighted features in improving detection accuracy. …”
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Breaks in the Arctic ice cover: from observations to predictions by A. A. Ershova, L. N. Dyment, T. A. Alekseeva

“…Such information is needed for solving problems of improving models of ice cover dynamics and modeling the interaction between the ocean and the atmosphere at high latitudes. …”
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A Survey on UAV Control with Multi-Agent Reinforcement Learning by Chijioke C. Ekechi, Tarek Elfouly, Ali Alouani, Tamer Khattab

“…Despite significant progress, the field remains fragmented, with a wide variety of algorithms, architectures, and evaluation metrics spread across domains. …”
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Automatic Ladle Tracking with Object Detection and OCR in Steel Melting Shops by Kabil Murugan, Mahinas Senthilmurugan, Venbha V. Senthilkumar, Harshita Velusamy, Karthiga Sekar, Vasanthan Buvanesan, Manikandan Venugopal

“…A ladle tracking system in steel production plants is essential for optimizing the ladle transportation between different processing units. …”
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Developing advanced datadriven framework to predict the bearing capacity of piles on rock by Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom, Ahmed M. Ebid, Fabián Danilo Reyes Silva, José Luis Allauca Palta, José Luis Llamuca Llamuca, Siva Avudaiappan

“…Abstract Developing accurate predictive models for pile bearing capacity on rock is crucial for optimizing foundation design and ensuring structural stability. …”
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New directions in mapping the Earth’s surface with citizen science and generative AI by Linda See, Qingqing Chen, Andrew Crooks, Juan Carlos Laso Bayas, Dilek Fraisl, Steffen Fritz, Ivelina Georgieva, Gerid Hager, Martin Hofer, Myroslava Lesiv, Žiga Malek, Milutin Milenković, Inian Moorthy, Fernando Orduña-Cabrera, Katya Pérez-Guzmán, Dmitry Schepaschenko, Maria Shchepashchenko, Jan Steinhauser, Ian McCallum

“…This paper discusses how generative AI could be harnessed for land cover/land use mapping by enhancing citizen science approaches with multi-modal large language models (MLLMs), including improvements to the spatial awareness of AI.…”
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ML-Based Materials Evaluation in 3D Printing by Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas, Jakub Kopowski

“…Furthermore, by integrating real-time data from sensors during the printing process, ML can continuously monitor and adjust parameters, ensuring optimal material utilization and reducing waste. ML models can identify and correct defects in printed materials by recognizing patterns associated with defects, thus improving the reliability of 3D-printed objects. …”
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Individualized post‐operative prediction of cochlear implantation outcomes in children with prelingual deafness using functional near‐infrared spectroscopy by Zhe Chen, Xue Zhao, Haotian Liu, Yuyang Wang, Zhikai Zhang, Yuxuan Zhang, Yuhe Liu

“…Both classification and individualized regression models were constructed to predict post‐CI behavioral improvement from fNIRS data using support vector machine (SVM) learning algorithms. …”
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Artificial intelligence applications in pediatric ophthalmology: A comprehensive review by Anupama Janardhanan, Meenakshi Ravindran, Allapitchai Fathima, Neelam Pawar

“…The future of AI in pediatric ophthalmology appears bright, with ongoing research aimed at optimizing algorithms, personalizing treatment, and expanding telemedicine capabilities. …”
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Cross-sectional and longitudinal Biomarker extraction and analysis for multicentre FLAIR brain MRI by J. DiGregorio, A. Gibicar, H. Khosravani, P. Jabehdar Maralani, J.-C. Tardif, P.N. Tyrrell, A.R. Moody, A. Khademi

“…Large-scale, automated cross-sectional and longitudinal cerebral biomarker extraction from FLAIR datasets could progress disease characterization, improve disease monitoring, and help to determine optimal intervention times. …”
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Emerging Imaging Technologies in Forensic Medicine: A Systematic Review of Innovations, Ethical Challenges, and Future Directions by Feras Alafer

“…Emerging imaging modalities enhance diagnostic precision and facilitate non-invasive examinations, offering culturally sensitive alternatives to traditional autopsies. However, high costs, algorithmic biases, data security risks, and the lack of standardized forensic imaging protocols present significant challenges. …”
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Advancements in Herpes Zoster Diagnosis, Treatment, and Management: Systematic Review of Artificial Intelligence Applications by Dasheng Wu, Na Liu, Rui Ma, Peilong Wu

“…Classification tasks (85.2%) dominated AI applications, with neural networks, particularly multilayer perceptron and convolutional neural networks being the most frequently used algorithms. AI applications were analyzed across three domains: (1) diagnosis, where mobile deep neural networks, convolutional neural network ensemble models, and mixed-scale attention-based models have improved diagnostic accuracy and efficiency; (2) treatment, where machine learning models, such as deep autoencoders combined with functional magnetic resonance imaging, electroencephalography, and clinical data, have enhanced treatment outcome predictions; and (3) management, where AI has facilitated case identification, epidemiological research, health care burden assessment, and risk factor exploration for postherpetic neuralgia and other complications. …”
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