An Efficient and Hybrid Deep Learning-Driven Model to Enhance Security and Performance of Healthcare Internet of Things by Muhammad Babar, Muhammad Usman Tariq, Basit Qureshi, Zabeeh Ullah, Fahim Arif, Zahid Khan

“…Secondly, a security module based on Bidirectional Long Short-Term Memory (BiLSTM) is implemented to recognize various forms of attacks within the H-CIoT network. …”
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A novel rotation and scale-invariant deep learning framework leveraging conical transformers for precise differentiation between meningioma and solitary fibrous tumor by Mohamed T. Azam, Hossam Magdy Balaha, Akshitkumar Mistry, Khadiga M. Ali, Bret C. Mobley, Nalin Leelatian, Sanjay Bhatia, Murat Gokden, Norman Lehman, Mohammed Ghazal, Ayman El-Baz, Dibson D. Gondim

“…Meningiomas, the most prevalent tumors of the central nervous system, can have overlapping histopathological features with solitary fibrous tumors (SFT), presenting a significant diagnostic challenge. …”
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Cationic substitution, dynamical stability, thermal stability, electronic and thermoelectric properties in 2D dialkali metal monoxides via DFT and ML approach by S. Chellaiya, Thomas Rueshwin, R. D. Eithiraj

“…Based on the phonon studies, both 1T-KNaO and 1T-KRbO are dynamically unstable with a slightly visible imaginary frequency. …”
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Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies by Md. Kabin Hasan Kanchon, Mahir Sadman, Kaniz Fatema Nabila, Ramisa Tarannum, Riasat Khan

“…A custom dataset has been constructed in this research comprising approximately 506 samples and 22 features utilizing the Moodle learning management system (LMS), successfully categorizing students into their respective learning styles. …”
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Hybrid Deep Learning Approach for Accurate Detection and Multiclass Classification of Broken Conductor Faults in Power Distribution Systems by Firas Saadoon Mohammed Al-Jumaili, Mustafa Onat

“…Moreover, we use an appropriate DenseNet pre-trained model to extract features and estimate the topology state with reliable data representation. …”
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Statistical analysis and machine learning of TMS-induced MEPs for predicting poststroke motor impairment and performance by Chun-Ren Phang, Shintaro Uehara, Sachiko Kodera, Akiko Yuasa, Shin Kitamura, Yohei Otaka, Akimasa Hirata

“…A linear support vector machine (SVM) was trained using leave-one-subject-out cross-validation to predict the motor outcomes. …”
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Identification of anoikis-related molecular patterns and the novel risk model to predict prognosis, tumor microenvironment infiltration and immunotherapy response in bladder cancer by Luochen Zhu, Feng Xiao, Yi Hou, Shenjun Huang, Yanyan Xu, Xiaohong Guo, Xinwei Dong, Chunlu Xu, Xiaolei Zhang, Haijuan Gu

“…Nonetheless, the specific mechanisms among anoikis, the clinical prognosis and tumor microenvironment (TME) of bladder cancer (BLCA) are insufficiently understood.MethodBLCA patients were classified into different anoikis subtypes based on the expression of candidate anoikis-related genes (ARGs), and differences in the clinicopathological features, TME, immune cell infiltration, and immune checkpoints between two anoikis subtypes were analyzed. …”
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XSShield: A novel dataset and lightweight hybrid deep learning model for XSS attack detection by Gia-Huy Luu, Minh-Khang Duong, Trong-Phuc Pham-Ngo, Thanh-Sang Ngo, Dat-Thinh Nguyen, Xuan-Ha Nguyen, Kim-Hung Le

“…To address these challenges, in this paper, we propose a novel framework that automatically collects web resources, efficiently extracts informative features, and constructs an up-to-date XSS attack dataset, which is then used to train a machine learning-based XSS detection model. …”
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Transfer Learning Empowered Multiple‐Indicator Optimization Design for Terahertz Quasi‐Bound State in the Continuum Biosensors by Shengfeng Wang, Bingwei Liu, Xu Wu, Zuanming Jin, Yiming Zhu, Linjie Zhang, Yan Peng

“…The two‐stage transfer learning method pre‐trains on low‐dimensional datasets to extract shared features, followed by fine‐tuning on complex, high‐dimensional tasks. …”
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Developing a Transparent Anaemia Prediction Model Empowered With Explainable Artificial Intelligence by Muhammad Sajid Farooq, Muhammad Hassan Ghulam Muhammad, Oualid Ali, Zahid Zeeshan, Muhammad Saleem, Munir Ahmad, Sagheer Abbas, Muhammad Adnan Khan, Taher M. Ghazal

“…Actionable insights that are enabled by transparent AI models (white boxes) based on the explainable AI methodologies reveal the rationales of the prediction, clarify the features that are responsible for them, and help clinicians and healthcare providers. …”
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Development of a Solar-Powered Edge Processing Perimeter Alert System with AI and LoRa/LoRaWAN Integration for Drone Detection and Enhanced Security by Mateo Mejia-Herrera, Juan Botero-Valencia, José Ortega, Ruber Hernández-García

“…The system incorporates a PDM microphone based on an Arduino Nano 33 BLE with a trained model to identify a drone or a UH-60 from an audio spectrogram to demonstrate its functionality. …”
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Integrating deep learning and clinical characteristics for early prediction of endometrial cancer using multimodal ultrasound imaging: a multicenter study by Cuiyan Lin, Wanming Chen, Jichuang Lai, Jieyi Huang, Xiaolu Ye, Sijia Chen, Xinmin Guo, Yichun Yang

“…While traditional diagnostic methods rely on clinical evaluation and imaging, there is growing interest in leveraging artificial intelligence, particularly deep learning (DL), to enhance diagnostic accuracy.MethodsThis study developed a DL-based predictive model integrating multimodal ultrasound features and clinical risk factors to improve early EC diagnosis. …”
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Research on High-Accuracy, Lightweight, Superfast Model for Nitrogen Diagnosis and Plant Growth in Lettuce (<i>Lactuca sativa</i> L.) by Xuyang Li, Iftikhar Hussain Shah, Xiaohao Gong, Muhammad Azam, Wu Jinhui, Pengli Li, Yidong Zhang, Qingliang Niu, Liying Chang

“…Nondestructive techniques have the ability to handle complex relationships in the data, enabling more accurate and efficient nitrogen diagnosis. Collected spectral features were combined with chemometrics, and a lettuce nitrogen regression diagnostic model was trained. …”
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Recognition of Serious Infections in the Elderly Visiting the Emergency Department: The Development of a Diagnostic Prediction Model (ROSIE) by Thomas Struyf, Lisa Powaga, Marc Sabbe, Nicolas Léonard, Ivan Myatchin, Ben Van Calster, Jos Tournoy, Frank Buntinx, Laurens Liesenborghs, Jan Y. Verbakel, Ann Van den Bruel

“…Clinical information and blood samples were collected at inclusion by a trained study nurse. A prediction model for <i>any serious infection</i> was developed based on ten candidate predictors that were further reduced to four ad interim using a penalized Firth multivariable logistic regression model. …”
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Metabolomics Profiling of Epicardial Adipose Tissue: MESA and the Rotterdam Study by Ian J. Neeland, Fang Zhu, Goncalo Graca, Anastasios Lymperopoulos, Gianluca Iacobellis, Ali Farzaneh, Daniel Bos, Mohsen Ghanbari, Jeffrey J. Goldberger, Maryam Kavousi, Philip Greenland

“…Conclusions From >20 000 metabolomic features, 1,5‐anhydrosorbitol, glycoproteins, phospholipids, and atherogenic dyslipidemia markers emerged as significant markers of EAT. …”
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SageNet: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation by Fan Zhang, Yifang Luo, Bohua Li, Ruihan Cao, Wenjin Peng, Joel Meyers, Paul R. Shapiro

“…The dual capability of learning both physical and artificial features of the numerical GW spectra establishes SageNet as a robust alternative to exact numerical methods. …”
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Fatigue trajectories by wearable remote monitoring of breast cancer patients during radiotherapy by Angela Barillaro, Chiara Feoli, Adriano Tramontano, Marco Comerci, Mara Caroprese, Renato Cuocolo, Oscar Tamburis, Mario Petrazzuoli, Maria Anna D’Arienzo, Antonio Farella, Caterina Oliviero, Stefania Clemente, Laura Cella, Mario Magliulo, Manuel Conson, Roberto Pacelli

“…Several Machine Learning (ML) methods were trained to characterize RIF on the basis of HR and STP collected data. …”
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Longitudinal analysis of Covid-19 infection trends and in-hospital mortality across six pandemic waves in Tunisia by Manel Ben Fredj, Amel Gara, Meriem Kacem, Wafa Dhouib, Donia Ben Hassine, Cyrine Bennasrallah, Imen Zemni, Nissaf Ben Alaya, Sondes Derouiche, Amani Maatouk, Ines Bouanene, Hela Abroug, Asma Belguith Sriha, Group investigators

“…The data were collected using a questionnaire manually completed by well-trained residents. The data were globally analyzed across all hospitalized patients and then compared based on the different waves. …”
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Bangladeshi Vehicle Classification and Detection Using Deep Convolutional Neural Networks With Transfer Learning by Farid, Proshanta Kumer Das, Monirul Islam, Ebna Sina

“…We have collected data from real-world highway conditions in Dhaka city and labelled 54,556 objects from 5,460 images based on 16 Bangladeshi on-road vehicle classes. …”
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Semantic classification of Indonesian consumer health questions by Raniah Nur Hanami, Rahmad Mahendra, Alfan Farizki Wicaksono

“…This framework facilitates a deeper understanding of the role played by word-based features in the model’s decision-making process. …”
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