Application of human-in-the-loop hybrid augmented intelligence approach in security inspection system by Ying Huang, Ying Huang, XiaoKan Wang, XiaoKan Wang, Yong Zhang, Yong Zhang, Li Chen, Li Chen, HongJi Zhang

“…A security inspection system exemplifies human-machine collaboration, and enhancing its safety and reliability through advanced technology remains a key research priority. While deep learning has incrementally improved the autonomous capabilities of security inspection equipment for automatic contraband detection, a gap persists between current technological capabilities and practical implementation. …”
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AI augmented edge and fog computing for Internet of Health Things (IoHT) by Deepika Rajagopal, Pradeep Kumar Thimma Subramanian

“…Previous surveys related to healthcare mainly focused on architecture and networking, which left untouched important aspects of smart systems like optimal computing techniques such as artificial intelligence, deep learning, advanced technologies, and services that includes 5G and unified communication as a service (UCaaS). …”
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Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease Classification by Davide Coluzzi, Valentina Bordin, Massimo W. Rivolta, Igor Fortel, Liang Zhan, Alex Leow, Giuseppe Baselli

“…As the leading cause of dementia worldwide, Alzheimer’s Disease (AD) has prompted significant interest in developing Deep Learning (DL) approaches for its classification. …”
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A glimpse into the future: Integrating artificial intelligence for precision HER2‐positive breast cancer management by Xinpei Deng, Yixuan Yan, Zekai Zhan, Jindong Xie, Hailin Tang, Yutian Zou, Jian Tu, Peng Liu

“…Therefore, evaluating patient HER2 status and ascertaining responsiveness to anti‐HER2 therapy is crucial. The advent of deep learning has propelled the artificial intelligence (AI) revolution, leading to an increased applicability of AI in predictive models. …”
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Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT by Chong Jiang, Chunjun Qian, Qiuhui Jiang, Hang Zhou, Zekun Jiang, Yue Teng, Bing Xu, Xin Li, Chongyang Ding, Rong Tian

“…Deep-based radiomic features were extracted from the fusion images using a deep learning model (ResNet18). These features, along with handcrafted radiomics, were utilized to construct a radiomic signature (R-signature) using automatic machine learning in the training and internal validation cohort. …”
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Tongue-LiteSAM: A Lightweight Model for Tongue Image Segmentation With Zero-Shot by Daiqing Tan, Hao Zang, Xinyue Zhang, Han Gao, Ji Wang, Zaijian Wang, Xing Zhai, Huixia Li, Yan Tang, Aiqing Han

“…Objective: Tongue image segmentation is a crucial step in the intelligent recognition of tongue diagnosis in Traditional Chinese Medicine (TCM). Existing deep learning-based tongue image segmentation models face issues such as poor versatility and insufficient expressiveness in zero-shot tasks. …”
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Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses by Ying Xiong, Linpeng Yao, Jinglai Lin, Jiaxi Yao, Qi Bai, Yuan Huang, Xue Zhang, Risheng Huang, Run Wang, Kang Wang, Yu Qi, Pingyi Zhu, Haoran Wang, Li Liu, Jianjun Zhou, Jianming Guo, Feng Chen, Chenchen Dai, Shuo Wang

“…Here we show that the deep learning models can non-invasively predict the likelihood of malignant and aggressive pathology of a renal mass based on preoperative multi-phase CT images.…”
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Critical factors influencing live birth rates in fresh embryo transfer for IVF: insights from cluster ensemble algorithms by Zheng Yu, Xiaoyan Zheng, Jiaqi Sun, Pengfei Zhang, Ying Zhong, Xingyu Lv, Hongwen Yuan, Fanrong Liang, Dexian Wang, Jie Yang

“…By combining feature matrices from NMF, accelerated multiplicative updates for non-negative matrix factorization (AMU-NMF), and the generalized deep learning clustering (GDLC) algorithm. NMFE exhibits superior accuracy and reliability in analyzing the in vitro fertilization and embryo transfer (IVF-ET) dataset. …”
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Detection of Alzheimer Disease in Neuroimages Using Vision Transformers: Systematic Review and Meta-Analysis by Vivens Mubonanyikuzo, Hongjie Yan, Temitope Emmanuel Komolafe, Liang Zhou, Tao Wu, Nizhuan Wang

“…Vision transformers (ViTs) are emerging as promising deep learning models in medical imaging, with potential applications in the detection and diagnosis of AD. …”
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DPD-YOLO: dense pineapple fruit target detection algorithm in complex environments based on YOLOv8 combined with attention mechanism by Cong Lin, Wencheng Jiang, Weiye Zhao, Lilan Zou, Zhong Xue

“…With the development of deep learning technology and the widespread application of drones in the agricultural sector, the use of computer vision technology for target detection of pineapples has gradually been recognized as one of the key methods for estimating pineapple yield. …”
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Artificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a scoping review by Victoria Moglia, Owen Johnson, Gordon Cook, Marc de Kamps, Lesley Smith

“…The most common cancers predicted in the studies were colorectal (n = 9) and pancreatic cancer (n = 9). 16 studies used feature engineering to represent temporal data, with the most common features representing trends. 18 used deep learning models which take a direct sequential input, most commonly recurrent neural networks, but also including convolutional neural networks and transformers. …”
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DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts by Chaonan Ji, Tonio Fincke, Vitus Benson, Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Fabian Gans, Guido Kraemer, Francesco Martinuzzi, David Montero, Karin Mora, Oscar J. Pellicer-Valero, Claire Robin, Maximilian Söchting, Mélanie Weynants, Miguel D. Mahecha

“…Despite recent progress in deep learning to ecosystem monitoring, there is a need for datasets specifically designed to analyse compound heatwave and drought extreme impact. …”
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A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection by Xiaoshuai Cao, Shaojie Zheng, Jincan Zhang, Wenna Chen, Ganqin Du

“…Methods A novel hybrid deep learning approach that combines feature fusion for efficient seizure detection is proposed in this study. …”
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A Heterogeneous Ensemble Learning Method Combining Spectral, Terrain, and Texture Features for Landslide Mapping by Yi He, Hesheng Chen, Qing Zhu, Qing Zhang, Lifeng Zhang, Tao Liu, Wende Li, Huaiyuan Chen

“…Specifically, compared with using only spectral bands, integrating spectral bands, spectral indexes, terrain factors, and texture indexes achieves the highest Recall, Kappa, F1-score, and MIoU in testing areas, and missed alarm (MA) is reduced by 15.56%. Compared with deep learning base classifiers, the constructed heterogeneous ensemble learning demonstrates improvements in Recall ranging from 41.67% to 69.89%, and MA is reduced from 52.17% to 30.11%. …”
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Efficient evidence selection for systematic reviews in traditional Chinese medicine by Yizhen Li, Zhe Huang, Zhongzhi Luan, Shujing Xu, Yunan Zhang, Lin Wu, Darong Wu, Dongran Han, Yixing Liu

“…Methods We integrated an established deep learning model (Evi-BERT combined rule-based method) with Boolean logic algorithms and an expanded retrieval strategy to automatically and accurately select potential evidence with minimal human intervention. …”
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A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security by Kashi Sai Prasad, P Udayakumar, E. Laxmi Lydia, Mohammed Altaf Ahmed, Mohamad Khairi Ishak, Faten Khalid Karim, Samih M. Mostafa

“…Numerous research works were keen to project intelligent network intrusion detection systems (NIDS) to avert the exploitation of IoT data through smart applications. Deep learning (DL) models are applied to perceive and alleviate numerous security attacks against IoT networks. …”
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Empowering Security Operation Center With Artificial Intelligence and Machine Learning—A Systematic Literature Review by Mohamad Khayat, Ezedin Barka, Mohamed Adel Serhani, Farag Sallabi, Khaled Shuaib, Heba M. Khater

“…Various methods, ranging from automated incident response and behavioral analytics to neural networks and deep learning, have been classified and compared. In addition, an in-depth reference architectural model, which is a blueprint for SOC integrating AI and ML into SOCs, is introduced. …”
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Preparing physiotherapists for the future: the development and evaluation of an innovative curriculum by Niki Stolwijk, Anne van Bergen, Evy Jetten, Marjo Maas

“…Areas for improvement were self-directed learning support, and teaching strategies to prompt deep learning. Conclusion The evaluation showed that the guiding principles of PACE were implemented as intended and that the innovation positively contributed to student learning,…”
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A Collaborative and Scalable Geospatial Data Set for Arctic Retrogressive Thaw Slumps with Data Standards by Yili Yang, Heidi Rodenhizer, Brendan M. Rogers, Jacqueline Dean, Ridhima Singh, Tiffany Windholz, Amanda Poston, Stefano Potter, Scott Zolkos, Greg Fiske, Jennifer Watts, Lingcao Huang, Chandi Witharana, Ingmar Nitze, Nina Nesterova, Sophia Barth, Guido Grosse, Trevor Lantz, Alexandra Runge, Luigi Lombardo, Ionut Cristi Nicu, Lena Rubensdotter, Eirini Makopoulou, Susan Natali

“…While numerous RTS studies have published standalone digitisation datasets, the lack of a centralised, unified database has limited their utilisation, affecting the scale of RTS studies and the generalisation ability of deep learning models. To address this, we established the Arctic Retrogressive Thaw Slumps (ARTS) dataset containing 23,529 RTS-present and 20,434 RTS-absent digitisations from 20 standalone datasets. …”
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Awareness and Attitude Toward Artificial Intelligence Among Medical Students and Pathology Trainees: Survey Study by Anwar Rjoop, Mohammad Al-Qudah, Raja Alkhasawneh, Nesreen Bataineh, Maram Abdaljaleel, Moayad A Rjoub, Mustafa Alkhateeb, Mohammad Abdelraheem, Salem Al-Omari, Omar Bani-Mari, Anas Alkabalan, Saoud Altulaih, Iyad Rjoub, Rula Alshimi

“…The majority of respondents (272/394, 69%) were already aware of AI and deep learning in medicine, mainly relying on websites for information on AI, while only 14% (56/394) were aware of AI through medical schools. …”
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