Deep learning-based carotid plaque vulnerability classification with multicentre contrast-enhanced ultrasound video: a comparative diagnostic study by Yanli Guo, Hongxia Zhang, Yang Guang, Wen He, Bin Ning, Chen Yin, Mingchang Zhao, Fang Nie, Pintong Huang, Rui-Fang Zhang, Qiang Yong, Jianjun Yuan, Yicheng Wang, Lijun Yuan, Litao Ruan, Tengfei Yu, Haiman Song, Yukang Zhang

“…Objectives The aim of this study was to evaluate the performance of deep learning-based detection and classification of carotid plaque (DL-DCCP) in carotid plaque contrast-enhanced ultrasound (CEUS).Methods and analysis A prospective multicentre study was conducted to assess vulnerability in patients with carotid plaque. …”
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Machine learning using scRNA-seq Combined with bulk-seq to identify lactylation-related hub genes in carotid arteriosclerosis by Gaoyan Liu, Ye Song, Shanxue Yin, Bo Zhang, Peng Han

“…A diagnostic model was constructed by combining 10 machine learning algorithms and 101 algorithms, SOD1, DDX42 and PDLIM1 as core genes. …”
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Moth walls: shedding light on moth biodiversity by Joseph J. Bowden, Avalon Owens, Kayla Brown, Robert W. Harding, Marianne Graversen, Maxim Larrivée, Kent McFarland, Tyler A. Miller, Jamie Warren, Jodi O. Young

“…The addition of automation and machine learning algorithms could further contribute to the capture and processing of detections across our growing network. …”
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A Versatile, Machine-Learning-Enhanced RF Spectral Sensor for Developing a Trunk Hydration Monitoring System in Smart Agriculture by Oumaima Afif, Leonardo Franceschelli, Eleonora Iaccheri, Simone Trovarello, Alessandra Di Florio Di Renzo, Luigi Ragni, Alessandra Costanzo, Marco Tartagni

“…Thanks to the flexibility of the system’s architecture, which embeds a Linux operating system, we can easily embed machine learning (ML) algorithms and predictive models for information detection. …”
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FinSafeNet: securing digital transactions using optimized deep learning and multi-kernel PCA(MKPCA) with Nyström approximation by Ahmad Raza Khan, Shaik Shakeel Ahamad, Shailendra Mishra, Mohd Abdul Rahim Khan, Sunil Kumar Sharma, Abdullah AlEnizi, Osama Alfarraj, Majed Alowaidi, Manoj Kumar

“…These improvements further help the model to detect the relevant features in transaction data that may present a threat to the security of the system. …”
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Artificial Intelligence: The Latest Advances in the Diagnosis of Bladder Cancer by Satyendra Singh, Ram Mohan Shukla

“…This review explores the current state and potential of AI technologies, including machine learning algorithms, deep learning networks, and computer vision, in enhancing the diagnostic process for bladder cancer. …”
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Research Progress and Prospect of Multi-robot Collaborative SLAM in Complex Agricultural Scenarios by MA Nan, CAO Shanshan, BAI Tao, KONG Fantao, SUN Wei

“…Firstly, the development of enhanced data fusion algorithms will facilitate improved integration of sensor information, leading to greater accuracy and robustness of the system. …”
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Mitigating malicious denial of wallet attack using attribute reduction with deep learning approach for serverless computing on next generation applications by Amal K. Alkhalifa, Mohammed Aljebreen, Rakan Alanazi, Nazir Ahmad, Sultan Alahmari, Othman Alrusaini, Ali Alqazzaz, Hassan Alkhiri

“…The primary purpose of the MMDoWA-ARDL approach is to propose a novel framework that effectively detects and mitigates malicious attacks in serverless environments using an advanced deep-learning model. …”
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Grid Search Based Hyperparameter-Tuned Deep Learning Model for Osteoporosis Diagnosis with Bi-Cubic Interpolation of X-Ray Images by Ruhul Amin, Md.Shamim Reza, Dewan Ahmed Muhtasim, Jungpil Shin, Md. Maniruzzaman, Md.Mahfujul Hasan

“…Even with the widespread use of deep learning (DL) and machine learning (ML) algorithms, the early diagnosis of osteoporosis patients can still be improved. …”
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Artificial Intelligence in the Diagnostic Use of Transcranial Doppler and Sonography: A Scoping Review of Current Applications and Future Directions by Giuseppe Miceli, Maria Grazia Basso, Elena Cocciola, Antonino Tuttolomondo

“…This review included 41 studies on the application of artificial intelligence (AI) in neurosonology in the diagnosis and monitoring of vascular and parenchymal brain pathologies. Machine learning, deep learning, and convolutional neural network algorithms have been effectively utilized in the analysis of TCD and TCCD data for several conditions. …”
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A network toxicology and machine learning approach to investigate the mechanism of kidney injury from melamine and cyanuric acid co-exposure by Zhan Wang, Zhaokai Zhou, Zihao Zhao, Junjie Zhang, Shengli Zhang, Luping Li, Yingzhong Fan, Qi Li

“…Potential target proteins were identified using ChEMBL, STITCH, and GeneCards databases, and hub genes were screened using three machine learning algorithms: LASSO regression, Random Forest, and Molecular Complex Detection. …”
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Fuzzy Rule-Based Combination Model for the Fire Pixel Segmentation by Alberto Lopez-Alanis, Hector de-la-Torre-Gutierrez, Arturo Hernandez-Aguirre, Maria T. Orvananos-Guerrero

“…The proposed approach automatically learns the optimal set of fuzzy operators and rules for fire detection to construct a combined model. …”
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Artificial intelligence in the diagnosis of cerebrovascular diseases using magnetic resonance imaging: A scoping review by Yituo Wang, Zeru Zhang, Ying Peng, Silu Chen, Shuai Zhou, Jiqiang Liu, Song Gao, Guangming Zhu, Cong Han, Bing Wu

“…Both classical machine learning and deep learning were widely used. The evaluation metrics varied according to the five main algorithm tasks of classification, detection, segmentation, estimation, and generation. …”
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An inductive learning-based method for predicting drug-gene interactions using a multi-relational drug-disease-gene graph by Jian He, Yanling Wu, Linxi Yuan, Jiangguo Qiu, Menglong Li, Xuemei Pu, Yanzhi Guo

“…Computational analysis can accurately detect drug-gene interactions (DGIs) cost-effectively. …”
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A multi-robot collaborative manipulation framework for dynamic and obstacle-dense environments: integration of deep learning for real-time task execution by Afnan Ahmed Adil, Saber Sakhrieh, Jinane Mounsef, Noel Maalouf

“…Path planning is achieved through a sampling-based algorithm that is integrated with the LiDAR data to facilitate precise obstacle avoidance and localization. …”
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Diagnostic Accuracy of Machine Learning-Assisted MRI for Mild Cognitive Impairment in Parkinson’s Disease: A Systematic Review and Meta-Analysis by Feng Zhang, Liangqing Guo, Lin Liu, Xiaochun Han

“…We systematically searched for studies that applied machine learning algorithms to MRI data for diagnosing PD with mild cognitive impairment (PD-MCI). …”
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Intelligent Grinding System for Medium/Thick Plate Welding Seams in Construction Machinery Using 3D Laser Measurement and Deep Learning by Qifeng Liu, Rencheng Zheng, Pengchao Li, Chao Liu, Deyuan Mi, Jian Wang, Wenli Xie

“…This study makes use of 3D line laser measurement technology and deep learning algorithms in tandem, which perform automated 3D measurement and analysis to extract key parameters of the weld seam, in conjunction with deep learning algorithms applied on image data of the weld seam for the automatic classification, positioning, and segmentation of the weld seam. …”
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The Emerging Role of Artificial Intelligence in Dermatology: A Systematic Review of Its Clinical Applications by Ernesto Martínez-Vargas, Jeaustin Mora-Jiménez, Sebastian Arguedas-Chacón, Josephine Hernández-López, Esteban Zavaleta-Monestel

“…Conclusions: AI has demonstrated robust clinical potential in dermatology, particularly in cancer detection and workflow optimization. However, further studies are required to address challenges such as algorithmic bias, data privacy, and regulatory oversight. …”
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Myocarditis Diagnosis Using Semi-Supervised Generative Adversarial Network and Differential Evolution by Haifeng Gui, Na Zhang

“…To minimize reliance on hyperparameters, the Random Key method is employed, optimized using the DE algorithm. The efficacy of the model is demonstrated on the Z-Alizadeh Sani myocarditis dataset, with further validation achieved through experiments on the EMIDEC dataset, assessing transfer learning (TL) effects. …”
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Conjecture Interaction Optimization Model for Intelligent Transportation Systems in Smart Cities Using Reciprocated Multi-Instance Learning for Road Traffic Management by Abdullah Faiz Al Asmari, Ahmed Almutairi, Fayez Alanazi, Tariq Alqubaysi, Ammar Armghan

“…The MIL algorithm integrates multi-instance learning into the interaction labelling process, enhancing the routing prediction by differentiating between reliable and erroneous data. …”
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