News Image Captioning via Separate Attention on Entity Categories by Sonali Ajankar, Tanima Dutta

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Implementation of an AI English-Speaking Interactive Training System Using Multi-Model Neural Networks by Ching-Ta Lu, Yen-Yu Lu, Yi-Ru Lu, Ying-Chen Pan, Yu-Chun Liu

“…Eye CNN is used to recognize whether the user falls asleep during the learning process and as a reference for continuing the lesson. …”
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A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers by Hyoseob Noh, Yong Sung Park, Il Won Seo

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An Enhanced Human Evolutionary Optimization Algorithm for Global Optimization and Multi-Threshold Image Segmentation by Liang Xiang, Xiajie Zhao, Jianfeng Wang, Bin Wang

“…However, existing threshold image-segmentation methods suffer from the problem of easily falling into locally optimal thresholds, resulting in poor image segmentation. …”
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TransTab: A transformer-based approach for table detection and tabular data extraction from scanned document images by Yongzhou Wang, Wenliang Lv, Weijie Wu, Guanheng Xie, BiBo Lu, ChunYang Wang, Chao Zhan, Baishun Su

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Segment Anything Model (SAM) and Medical SAM (MedSAM) for Lumbar Spine MRI by Christian Chang, Hudson Law, Connor Poon, Sydney Yen, Kaustubh Lall, Armin Jamshidi, Vadim Malis, Dosik Hwang, Won C. Bae

“…These results demonstrated the feasibility of “zero-shot” DL models to segment lumbar spine MRI. While performance falls short of recent models, these zero-shot models offer key advantages in not needing training data and faster adaptation to other anatomies and tasks. …”
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Automated derivation of diagnostic criteria for lung cancer using natural language processing on electronic health records: a pilot study by Andrew Houston, Sophie Williams, William Ricketts, Charles Gutteridge, Chris Tackaberry, John Conibear

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Studying and enhancing the methods for distance teaching of computer science in Kazakh secondary school students during the pandemic by M. M. Zhamankarin, A. M. Sivinskiy, M. K. Aitkenova, M. B. Zhanibekov

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Power distribution and forecasting using a probabilistic and systematic data processing model for renewable resources by Hammad Alnuman, Ghulam Abbas, Amr Yousef

“…Current forecasting techniques often fall short, struggling to effectively handle unexpected spikes or changes in demand, which can lead to inefficiencies and even system instability. …”
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Quadruple Deep Q-Network-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicles by Dingyi Guo, Guangyin Lei, Huichao Zhao, Fang Yang, Qiang Zhang

“…Additionally, this study highlights that alternating updates between two Q-networks in DDQN helps avoid local optima, further enhancing performance, especially when greedy strategies tend to fall into suboptimal choices. The conclusions suggest that QDQN is an effective and robust approach for optimizing energy management in PHEVs, offering superior energy efficiency over traditional reinforcement learning methods. …”
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JEDAN: Joint Euclidean Distance and Autoencoder Network for Robust Out-of-Distribution Detection in Radar-Based Hand Gesture Recognition by Muhammad Ghufran Janjua, Kevin Kaiser, Thomas Stadelmayer, Stephan Schoenfeldt, Vadim Issakov

“…Traditional autoencoders often fall short in OOD detection because they prioritize minimizing reconstruction error over forming distinct clusters in the latent space. …”
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Enhancing Crowd Safety at Hajj: Real-Time Detection of Abnormal Behavior Using YOLOv9 by Amani A. Alsabei, Tahani M. Alsubait, Hosam H. Alhakami

“…Faced with the challenges of monitoring dense crowds, existing methodologies often fall short in accuracy, efficiency, and cost-effectiveness. …”
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Papillary Thyroid Carcinoma Semantic Segmentation Using Multi-Scale Adaptive Convolutional Network With Dual Decoders by Thanat Payatsuporn, Pittipol Kantavat, Nichthida Tangnuntachai, Nopporn Tipparawong, Waratchanok Techapapa, Boonserm Kijsirikul, Somboon Keelawat

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PHILOSOPHICAL AND ANTHROPOLOGICAL IMPORTANCE OF DEVELOPMENT OF ARTIFICIALLY CREATED INTELLIGENT SYSTEMS by Yu. D. Gensitskiy

“…The problems of machine learning as technology transformation M2M were analysed. …”
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Artificial Intelligence in Medical Education: A Practical Guide for Educators by Nivritti Gajanan Patil, Nga Lok Kou, Daniel T. Baptista‐Hon, Olivia Monteiro

“…The fast pace of generative AI development poses challenges, particularly for less tech‐savvy teachers or those who delay learning about these tools, leaving them at risk of falling behind. …”
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Potential Biomarkers for Predicting the Risk of Developing Into Long COVID After COVID‐19 Infection by Zhiyong Hou, Yu Ming, Jun Liu, Zhong Wang

“…Differentially expressed genes (DEGs) falling under COVID‐19 and long COVID were identified with R packages, and contemporaneously conducted module detection was performed with the Modular Pharmacology Platform (http://112.86.129.72:48081/). …”
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Optimizing Energy and Spectral Efficiency in Mobile Networks: A Comprehensive Energy Sustainability Framework for Network Operators by Luis Mata, Marco Sousa, Pedro Vieira, Maria Paula Queluz, Antonio Rodrigues

“…For example, the results show that using higher order modulation coding schemes reduces the probability of a BS falling into class D by 50%. These findings provide practical insights for developing more sustainable network operations.…”
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A Hybrid MIL Approach Leveraging Convolution and State-Space Model for Whole-Slide Image Cancer Subtyping by Dehui Bi, Yuqi Zhang

“…Under the weakly supervised multiple instance learning (MIL) paradigm, existing techniques frequently fall short in simultaneously capturing local tissue textures and long-range contextual relationships. …”
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Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance by Pablo Neirz, Hector Allende, Carolina Saavedra

“…Traditional variable selection techniques, such as filter and wrapper methods, often fall short in capturing these complexities. Our methodology, grounded in decision trees but extendable to other machine learning models, was rigorously evaluated across various data scenarios. …”
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PROACTIVE APPROACH IN TAX RISK MANAGEMENT: DATA ANALYSIS TECHNIQUES TO IDENTIFY HIGH-RISK TAXPAYERS by Funda KARAKOYUN

“…With this process, which has been accelerating for decades, statistical models that will reduce tax loss and evasion of states and increase efficiency in collection have been diversified in the field of machine learning. While innovations in software programs with digitalisation transformation reduce the manual workload of the tax administration, machine learning algorithms are used with experts employed in the field in continuously developing risk analysis studies. …”
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