Explainability Feature Bands Adaptive Selection for Hyperspectral Image Classification by Jirui Liu, Jinhui Lan, Yiliang Zeng, Wei Luo, Zhixuan Zhuang, Jinlin Zou

“…Aiming at the problems of poor interpretability of feature classification algorithms for hyperspectral images, multiple feature dimensions, and difficulty in effectively improving classification accuracy, this paper proposes a feature band adaptive selection method for hyperspectral images. …”
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Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions by Rabie Adel El Arab, Omayma Abdulaziz Al Moosa, Mette Sagbakken, Ahmed Ghannam, Fuad H. Abuadas, Joel Somerville, Joel Somerville, Abbas Al Mutair, Abbas Al Mutair, Abbas Al Mutair, Abbas Al Mutair, Abbas Al Mutair

“…These efficiencies allowed nursing teams to devote more time to direct patient care and were associated with reductions in burnout and improved workplace morale.Nursing perceptionsAcross practice settings, nursing students and practicing nurses broadly welcomed AI’s ability to streamline workflows and support decision-making, recognizing its potential to elevate patient care and professional practice.Ethical implicationsSimultaneously, nurses voiced significant ethical concerns—chiefly around safeguarding patient data privacy, mitigating algorithmic bias, and preserving the compassionate, human-centered essence of nursing in an increasingly automated environment.Framework and recommendationsThe Nursing AI Integration Roadmap (NAIIR) was developed, emphasizing transformational education, advanced clinical integration, ethical governance, robust organizational infrastructure, participatory design, and rigorous economic evaluation. …”
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Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review by Platon S. Papageorgiou, Rafail Christodoulou, Panagiotis Korfiatis, Dimitra P. Papagelopoulos, Olympia Papakonstantinou, Nancy Pham, Amanda Woodward, Panayiotis J. Papagelopoulos

“…Innovative segmentation techniques and multimodal imaging models have further enhanced healthcare efficiency by reducing physician workload and improving diagnostic accuracy. …”
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Application of artificial intelligence in modern healthcare for diagnosis of autism spectrum disorder by Abdullah H. Al-Nefaie, Abdullah H. Al-Nefaie, Theyazn H. H. Aldhyani, Theyazn H. H. Aldhyani, Sultan Ahmad, Eidah M. Alzahrani

“…The assessment of these models used a dataset obtained from Kaggle, consisting of 2,940 face images.ResultsThe suggested Inception-V3 model surpassed current transfer learning algorithms, achieving a 98% accuracy rate.DiscussionRegarding performance assessment, the suggested technique demonstrated advantages over the latest models. …”
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Real-Time Acoustic Holography for Target Pattern Acoustic Field Reconstruction With Iterative Unsupervised Learning by Minjie Ma, Xuewei Wang, Yang Li, Jia Wang

“…Under the same network architecture, our newly designed loss function improves energy efficiency to 2.1 times that of the original method.…”
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Synthetic data generation for anomaly detection on table grapes by Ionut M. Motoi, Valerio Belli, Alberto Carpineto, Daniele Nardi, Thomas A. Ciarfuglia

“…We show that the proposed dataset augmentation technique improves the accuracy of an anomaly classifier for table grapes and that the approach can be generalized to other fruit types.…”
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An exploratory study of high-educated poverty through machine learning approach: a case study of East Java, Indonesia by Dias Satria, Risti Permani, Kodrad Winarno, David Kaluge, Citra Rahayu Indraswari, Radityo Putro Handrito

“… Purpose – the purpose of the article is to compare the importance factors for high-educated poverty modeling in the four cultural regions of East Java.  …”
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Optimization of Cleaning and Hygiene Processes in Healthcare Using Digital Technologies and Ensuring Quality Assurance with Blockchain by Semra Tebrizcik, Süleyman Ersöz, Elvan Duman, Adnan Aktepe, Ahmet Kürşad Türker

“…Through the proposed system, cleaning/hygiene processes in patient rooms are conducted securely, contributing to the improvement of quality standards in healthcare services.…”
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Analysis of minimum safe approach distances based on vessels navigation safety domain by Igor Burmaka, Olga Petrychenko, Bohdan Alieksieichuk, Alla Vynohradova

“…The results create a foundation for further improvement of collision avoidance algorithms and contribute to increasing the level of automation in navigation processes and overall maritime safety, especially in conditions of heavy traffic or restricted waterways. …”
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Bearing Fault Diagnosis Grounded in the Multi-Modal Fusion and Attention Mechanism by Jianjian Yang, Haifeng Han, Xuan Dong, Guoyong Wang, Shaocong Zhang

“…The experimental results show that compared to mainstream algorithms such as Vision Transformer, Swin Transformer, and ConvNeXt, the Fusion Attention Network for Bearing Diagnosis (FAN-BD) achieves higher accuracy and robustness in fault diagnosis tasks, providing an efficient and reliable solution for bearing fault diagnosis.The proposed model outperforms ViT, Swin Transformer, ConvNeXt, and CBMA-ViT in terms of classification accuracy, achieving an accuracy of 97.5%. …”
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A robot process automation based mobile application for early prediction of chronic kidney disease using machine learning by Md. Hasan Imam Bijoy, Md. Jueal Mia, Md. Mahbubur Rahman, Mohammad Shamsul Arefin, Pranab Kumar Dhar, Tetsuya Shimamura

“…The XAI technique SHAP and model validation on another CKD dataset highlight the superior prediction capabilities of the proposed ensemble methods compared to traditional classification algorithms. …”
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AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review by Lea Marie Pehrson, Jens Petersen, Nathalie Sarup Panduro, Carsten Ammitzbøl Lauridsen, Jonathan Frederik Carlsen, Sune Darkner, Michael Bachmann Nielsen, Silvia Ingala

“…The algorithms with the highest DSC deployed an encoder–decoder architecture. …”
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Voltage Control Nonlinearity in QZSDMC Fed PMSM Drive System with Grid Filtering by Przemysław Siwek, Konrad Urbanski

“…The structure and control algorithms enabling regulation of the converter’s output voltage in both step-down and step-up modes are presented. …”
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Sex bias consideration in healthcare machine-learning research: a systematic review in rheumatoid arthritis by Coziana Ciurtin, Shruti Turner, Anahita Talwar, Claudia Maw, Georgina Quayle, Thomas N Watt, Sunir Gohil, Emma Duckworth

“…Only three papers assessed bias in model performance by sex disaggregating their results. …”
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Influenza virus genotype to phenotype predictions through machine learning: a systematic review by Laura K. Borkenhagen, Martin W. Allen, Jonathan A. Runstadler

“…We, therefore, offer guidance to overcome these limitations, aid in improving predictive models of previously studied influenza A virus phenotypes, and extend those models to unexplored phenotypes in the ultimate pursuit of tools to enable the characterization of virus isolates across surveillance laboratories.…”
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Predicting emergency department visits for non-traumatic dental-related conditions among Medicaid beneficiaries by Sung Eun Choi, Rindala Fayyad, Daniel Nolte, Christine Riedy, Sharon-Lise Normand

“…We sought to predict future “ED users” for non-traumatic dental-related conditions using machine learning algorithms and identify associated risk factors to help Medicaid outreach programs better target populations in most need of improvement in access to dental care. …”
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Cross-Scene Multi-Object Tracking for Drones: Leveraging Meta-Learning and Onboard Parameters with the New MIDDTD by Chenghang Wang, Xiaochun Shen, Zhaoxiang Zhang, Chengyang Tao, Yuelei Xu

“…Finally, a novel dataset, termed the Multi-Information Drone Detection and Tracking Dataset (MIDDTD), is introduced, containing rich drone-related information and diverse scenes, thereby providing a solid foundation for the validation of cross-scene multi-object tracking algorithms. Experimental results demonstrate that the proposed method improves the IDF1 tracking metric by 1.92% compared to existing state-of-the-art methods, showcasing strong cross-scene adaptability and offering an effective solution for multi-object tracking from a drone’s perspective, thereby advancing theoretical and technical support for related fields.…”
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A comparative analysis of emotion recognition from EEG signals using temporal features and hyperparameter-tuned machine learning techniques by Rabita Hasan, Sheikh Md. Rabiul Islam

“…This study explores improving emotion recognition performance by applying traditional machine learning classifiers and boosting techniques to EEG data from the DEAP dataset. …”
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Securing Communication Channels: An Advanced Steganography Approach With Orthogonal Frequency Division Multiplexing (OFDM) by Ammar Mohammedali Fadhil, Ali Y. Jaber

“…In this work, a robust model is proposed to combine OFDM during information hiding. …”
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The Impact of AI-Personalized Recommendations on Clicking Intentions: Evidence from Chinese E-Commerce by Jiwang Yin, Xiaodong Qiu, Ya Wang

“…When information quality improves, the positive impact of technology acceptance on clicking intention will be enhanced. …”
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