Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review by Natalie S Blencowe, Neil J Smart, George E Fowler, Rhiannon C Macefield, Conor Hardacre, Mark P Callaway

“…This may improve the performance of diagnostic, prognostic and management decisions. While a large amount of work has been undertaken discussing the role of AI little is understood regarding the performance of such applications in the clinical setting. …”
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Fused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and Encryption by Israel Edem Agbehadji, Richard C. Millham, Emmanuel Freeman, Wanqing Wu, Xianbin Zhang

“…Security is one of the increasingly significant issues given advancements in technology that harness data from multiple devices such as the internet of medical devices. While protecting data from unauthorized user access, several techniques are used including fingerprints, passwords, and others. …”
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Spectrum Fitting Approach for Passive Wireless SAW Sensor Interrogation Using Software-Defined Radio by Shihao Wang, Qi Wang, Guopeng Zhu, Lei Liu, Xinning Cao, Tingxin Ren, Yue Zhou, Hao Jin

“…However, conventional frequency estimation methods often rely on iterative algorithms with high computational complexity, limiting their real-time applicability. …”
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Early Warning of Nerve Agent Release in Large Indoor Environments Based on Encoder-Decoder Coupling Physics-Informed Neural Network by Shuobei Sun, Yang Peng, An Wang, Yiwen Xie, Yang Hu, Zhongyu Hou

“…Computational fluid dynamics (CFD) methods provide accurate predictions but struggle with real-time performance, while neural networks offer rapid predictions, though their performance declines with high-dimensional fluid flow. …”
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Enhanced Feature Selection via Hierarchical Concept Modeling by Jarunee Saelee, Patsita Wetchapram, Apirat Wanichsombat, Arthit Intarasit, Jirapond Muangprathub, Laor Boongasame, Boonyarit Choopradit

“…The presented methods are evaluated based on all learned attributes with 10 datasets from the UCI Machine Learning Repository by using three classification algorithms, namely decision trees, support vector machines (SVM), and artificial neural networks (ANN). …”
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Potential of Multi-Source Multispectral vs. Hyperspectral Remote Sensing for Winter Wheat Nitrogen Monitoring by Xiaokai Chen, Yuxin Miao, Krzysztof Kusnierek, Fenling Li, Chao Wang, Botai Shi, Fei Wu, Qingrui Chang, Kang Yu

“…Future work should validate these models using real satellite imagery and explore multi-source data fusion with advanced learning algorithms.…”
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Synergizing Intelligence and Privacy: A Review of Integrating Internet of Things, Large Language Models, and Federated Learning in Advanced Networked Systems by Hongming Yang, Hao Liu, Xin Yuan, Kai Wu, Wei Ni, J. Andrew Zhang, Ren Ping Liu

“…Finally, this review identifies critical open questions and promising future research paths, including ultra-lightweight models, robust algorithms for heterogeneity, machine unlearning, standardized benchmarks, novel FL paradigms, and next-generation security. …”
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Blockchain Approach for Healthcare Using Fog Topology and Lightweight Consensus by Aya Laouamri, Sarra Cherbal, Yacine Mosbah, Chahrazed Benrebbouh, Kamir Kharoubi

“…The architecture and algorithms for signature generation, encryption, emergency state actions and consensus are developed and evaluated through extensive simulations using the NS3 toolkit.Results: The simulation results validate the effectiveness of the proposed approach in improving healthcare IoT systems. …”
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An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining by Charan Kumar Ala, Zefree Lazarus Mayaluri, Aman Kaushik, Nikhat Parveen, Surabhi Saxena, Abu Taha Zamani, Debendra Muduli

“…This study proposes a novel hybrid artificial intelligence (AI) framework that integrates physics informed neural networks (PINNs) with conventional machine learning (ML) algorithms for the accurate prediction and optimization of BIGV. …”
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Evaluation and Early Detection of Downy Mildew of Lettuce Using Hyperspectral Imagery by Songtao Ban, Minglu Tian, Dong Hu, Mengyuan Xu, Tao Yuan, Xiuguo Zheng, Linyi Li, Shiwei Wei

“…These findings provide a scientific basis and practical tools for managing lettuce downy mildew and resistance breeding while laying a foundation for broader applications of hyperspectral imaging in plant pathology.…”
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Assessing Ground Displacement in Oran City, Algeria: A Comparative Analysis of PSInSAR, SBAS, and Combined Method by Kamel Hasni, Bachir Gourine, Boudhane Boubaker, Slimani Mohammed El Amine

“…Advanced interferometric techniques, such as PSInSAR and SBAS provide a pertinent mean to observe this phenomenon by supplying dense displacement measurements for an extended period. However, while these algorithms have proven their effectiveness in some areas, their performance is less consistent in others. …”
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A Spiking Neural Network With Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces by Peiliang Gong, Pengpai Wang, Yueying Zhou, Daoqiang Zhang

“…Recently, energy-efficient spiking neural networks (SNNs) have shown great potential in EEG analysis due to their ability to capture the complex dynamic properties of biological neurons while also processing stimulus information through precisely timed spike trains. …”
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Enhanced supervised classification of seasonal pastures on the Qinghai-Tibet Plateau (1990–2020) using Landsat optimal time window by Panfei Fang, Jia Wang, Haifeng Xu, Ruonan Li, Long Yu, Shaodong Huang, Yuying Liang, Pengfei Zheng

“…The framework leverages Landsat imagery within the optimal time window (Days of Year, DOYs 190–280), incorporates algorithms for automated sample generation and refinement, and employs the Random Forest (RF) classifier to enable fully automated seasonal pasture mapping. …”
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Beyond Linearity: Uncovering the Complex Spatiotemporal Drivers of New-Type Urbanization and Eco-Environmental Resilience Coupling in China’s Chengdu–Chongqing Economic Circle with... by Caoxin Chen, Shiyi Wang, Meixi Liu, Ke Huang, Qiuyi Guo, Wei Xie, Jiangjun Wan

“…A random forest (RF) model, interpreted with Shapley Additive exPlanations (SHAP) and Partial Dependence Plot (PDP) algorithms, explores nonlinear driving mechanisms, while Geographically and Temporally Weighted Regression (GTWR) assesses drivers’ spatiotemporal heterogeneity. …”
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Adaptive Pixel-Level and Superpixel-Level Feature Fusion Transformer for Hyperspectral Image Classification by Wei Huang, Dazhan Zhou, Le Sun, Qiqiang Chen, Junru Yin

“…We compared and analyzed seven advanced HSI classification algorithms, and experiments showed that APSFFT outperforms other state-of-the-art methods.…”
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Advanced Interpretation of Bullet-Affected Chest X-Rays Using Deep Transfer Learning by Shaheer Khan, Nirban Bhowmick, Azib Farooq, Muhammad Zahid, Sultan Shoaib, Saqlain Razzaq, Abdul Razzaq, Yasar Amin

“…Special deep learning algorithms went through a process of optimization before researchers improved their ability to detect and place objects. …”
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Ethical framework for responsible foundational models in medical imaging by Debesh Jha, Gorkem Durak, Abhijit Das, Jasmer Sanjotra, Onkar Susladkar, Suramyaa Sarkar, Ashish Rauniyar, Nikhil Kumar Tomar, Linkai Peng, Sirui Li, Koushik Biswas, Ertugrul Aktas, Elif Keles, Matthew Antalek, Zheyuan Zhang, Bin Wang, Xin Zhu, Xin Zhu, Hongyi Pan, Deniz Seyithanoglu, Alpay Medetalibeyoglu, Vanshali Sharma, Vedat Cicek, Amir A. Rahsepar, Rutger Hendrix, Rutger Hendrix, A. Enis Cetin, Bulent Aydogan, Mohamed Abazeed, Frank H. Miller, Rajesh N. Keswani, Rajesh N. Keswani, Hatice Savas, Sachin Jambawalikar, Daniela P. Ladner, Amir A. Borhani, Concetto Spampinato, Concetto Spampinato, Michael B. Wallace, Michael B. Wallace, Ulas Bagci

“…By establishing clear guidelines for development and deployment, we aim to harness the transformative potential of foundational models while preserving the fundamental principles of medical ethics and patient-centered care.…”
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Machine learning and discriminant analysis model for predicting benign and malignant pulmonary nodules by Zhi Li, Wenjing Zhang, Jinyi Huang, Ling Lu, Dongming Xie, Jinrong Zhang, Jiamin Liang, Yuepeng Sui, Linyuan Liu, Jianjun Zou, Ao Lin, Lei Yang, Fuman Qiu, Zhaoting Hu, Mei Wu, Yibin Deng, Xin Zhang, Jiachun Lu

“…Among them, PNs that remain stable for more than two years or whose pathological results suggest not being lung cancer are considered benign PNs (BPNs), while PNs that conform to the growth pattern of tumors or whose pathological results indicate lung cancer are considered malignant PNs (MPNs). …”
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Artificial intelligence in prostate cancer by Wei Li, Ruoyu Hu, Quan Zhang, Zhangsheng Yu, Longxin Deng, Xinhao Zhu, Yujia Xia, Zijian Song, Alessia Cimadamore, Fei Chen, Antonio Lopez-Beltran, Rodolfo Montironi, Liang Cheng, Rui Chen, Yuanyuan Ji

“…The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. …”
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Using triaxial accelerometry to detect hunts and kills by African wild dogs by James Redcliffe, Scott Creel, Scott Creel, Ben Goodheart, Ben Goodheart, Johnathan Reyes de Merkle, Johnathan Reyes de Merkle, Stephani S. Matsushima, Michelo Mungolo, Ruth Kabwe, Emmanuel Kaseketi, Will Donald, Adrian Kaluka, Clive Chifunte, Matthew S. Becker, Matthew S. Becker, Rory Wilson

“…Here, we used this method and then iteratively improved algorithms by testing them on observed hunts and kills of collared packs. …”
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