FW-S3KIFCM: Feature Weighted Safe-Semi-Supervised Kernel-Based Intuitionistic Fuzzy C-Means Clustering Method by Shirin Khezri, Nasser Aghazadeh, Mahdi Hashemzadeh, Amin Golzari Oskouei

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Predicting Deterioration in Patients With Normotensive Acute Pulmonary Embolism Using Clinical‐Imaging Features: A Multicenter Prospective Cohort Study by Yizhuo Gao, Shibo Wei, Yuchen Liu, Zhenglun Yu, Shanshan Zhan, Binze Yang, Chuanxin Qi, Shougang Qi, Minggang Wang, Dong Jia

“…This study aims to develop and validate a novel score for deterioration prediction using clinical‐imaging features. Methods This is multicenter, prospective observational cohort study (AOAPECT [Adverse Outcomes in Acute Pulmonary Embolism patients using Computed Tomography pulmonary angiography] cohort, NCT05098769). …”
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Experimental Study of Trajectory Features for the Recognition of Low-Flying Low-Speed Radar Targets Using Passive Coherent Radar Systems by V. L. Dao, A. A. Konovalov, M. H. Le

“…Specific characteristics of the trajectory parameters of target classes were built using computer statistical modeling in the MatLab environment. …”
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Prediction of Neoadjuvant Chemoradiotherapy Sensitivity in Patients With Esophageal Squamous Cell Carcinoma Using CT-Based Radiomics Combined With Clinical Features by Xindi Li, Jigang Dong, Baosheng Li, Ouyang Aimei, Yahong Sun, Xia Wu, Wenjuan Liu, Ruobing Li, Zhongyuan Li, Yu Yang

“…Objective: This study aimed to establish a predictive model, based on computed tomography (CT) radiomics features and clinical parameters, to predict sensitivity to nCRT in patients with ESCC pre-treatment. …”
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MSKFaceNet: A Lightweight Face Recognition Neural Network for Low-Power Devices by Peng Zhang, Qinghua Ma, Yi Li, Min Cui

“…In recent years, the rapid development of lightweight convolutional neural networks (CNNs) and lightweight vision transformers (ViTs) has led to significant progress in the field of mobile computing. However, deploying facial recognition models on low-power devices (with power consumption below 10 watts) remains challenging. …”
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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

“…Classifying emotions based on EEG signals is really important for enhancing our interactions with computers, monitoring mental health and creating applications in affective computing field. …”
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The value of 18F-FDG PET/CT combined with 3D quantitative technology and clinicopathological features in predicting prognosis of NSCLC by Yuling Su, Siwen Qiu, Jinyu Wang

“…ObjectiveTo investigate the value of Fluorine-18 Fluorodeoxyglucose (18F-FDG) Positron Emission Tomography/Computed Tomography (PET/CT) combined with 3D quantitative technology and clinicopathological features in predicting the prognosis of non-small cell lung cancer (NSCLC).MethodsA retrospective review was performed for patients who underwent PET/CT and curative resection of NSCLC between January 2016 and June 2019 in our hospital. …”
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The Relationship between Pathological Features and 18F-FDG PET/CT that Changed the Surgeon's Decision as Neoadjuvant Therapy in Breast Cancer by Akay Edizsoy, Ahmet Dağ, Pınar Pelin Özcan, Zehra Pınar Koç

“…Materials and Methods The demographic features and treatment plans of 151 cases who were diagnosed with any stage of breast cancer were evaluated. …”
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Studies of the impact of environmental conditions and varietal features of sweet cherry on the accumulation of vitamin C in fruits by using the regression analysis method by Іryna IVANOVA, Мarina SERDYUK, Vira MALKINA, Tetiana TYMOSHCHUK, Marharyta VOROVKA, Ivan MRYNSKYI, Anastasiia ADAMOVYCH

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A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning by Yan Chen, Miaolin Yu, Haochong Wei, Huanxing Qi, Yiming Qin, Xiaochun Hu, Rongxing Jiang

“…Additionally, the execution speed and high computational resource demands of complex prediction models make them difficult to deploy on edge computing nodes such as wind farms. …”
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Remote sensing image fusion based on real time image smoothing and image similarity by Yanfang Hou, Kaixuan Guo, Xueyan Bi

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Features of liver damage in patients with coronavirus disease (COVID-19) with pneumonia in relation to indicators of inflammation taking into account oxygen dependence by O. V. Riabokon, I. O. Kuliesh

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Wind during terrestrial laser scanning of trees: Simulation-based assessment of effects on point cloud features and leaf-wood classification by W. Albert, H. Weiser, H. Weiser, R. Tabernig, R. Tabernig, B. Höfle, B. Höfle

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Anatomical Characteristics Predict Response to Transcranial Direct Current Stimulation (tDCS): Development of a Computational Pipeline for Optimizing tDCS Protocols by Giulia Caiani, Emma Chiaramello, Marta Parazzini, Eleonora Arrigoni, Leonor J. Romero Lauro, Alberto Pisoni, Serena Fiocchi

“…A computational approach was used to map the electric field distribution over the brain tissues of realistic head models reconstructed from MRI images of twenty-three subjects, including adults and children of both genders. …”
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Computational analysis of heat transfer for hybrid nanofluid flow within a wavy lid-driven cavity with entropy generation and non-uniform heating by Muhammad Fawad Malik, Yasir Ul Umair Bin Turabi, Mohsan Raza, Muhammad Imran, Taseer Muhammad

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Low complexity radar signal classification based on spectrum shape by Liang YIN, Rui LIN, Xiaolei WANG, Yuliang YAO, Lin ZHOU, Yuan HE

“…In order to solve the problems of high computational complexity, low recognition accuracy of low signal to noise ratio (SNR) environment and low fidelity of simulation data in radar signal modulation recognition, a low complexity radar signal classification algorithm based on spectrum shape was proposed.Signal spectrum was normalized, feature parameters were extracted by spectrum sampling method, and then machine learning classification model was trained.The test results of the data generated by the radar signal source show that the classification accuracy of Barker code, Frank code, LFM code, BPSK, QPSK modulation and conventional radar signals is more than 90% (SNR≥3 dB).The algorithm has low computational complexity, can adapt to the change of signal parameters, and has good generalization.…”
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A Lightweight Detection Method for Meretrix Based on an Improved YOLOv8 Algorithm by Zhongxu Tian, Sifan Hou, Xiaoxue Yue, Xuewen Hu

“…The proposed enhancements include the following: replacing the original backbone network of YOLOv8 with a Reversible Columnar Network (RevColNet) to reduce feature redundancy and computational load; upgrading the C2f modules in both the backbone and neck networks to C2f-Faster to optimize feature fusion strategies and improve fusion efficiency; and incorporating a Dynamic Head (DyHead) to enhance feature extraction and detection accuracy by adaptively adjusting the detection head structure. …”
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Voxelwise characterization of noise for a clinical photon-counting CT scanner with a model-based iterative reconstruction algorithm by Luigi Masturzo, Patrizio Barca, Luca De Masi, Daniela Marfisi, Antonio Traino, Filippo Cademartiri, Marco Giannelli

“…Abstract Background Photon-counting detector (PCD) technology has the potential to reduce noise in computed tomography (CT). This study aimed to carry out a voxelwise noise characterization for a clinical PCD-CT scanner with a model-based iterative reconstruction algorithm (QIR). …”
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RTL-Net: real-time lightweight Urban traffic object detection algorithm by Zhiqing Cui, Jiahao Yuan, Haibin Xu, Yamei Wei, Zhenglong Ding

“…Experimental results demonstrate that the proposed algorithm achieves a significant 43.9% reduction in parameters and an 18.9% decrease in computational complexity. …”
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A Lightweight Network With Embedded Soft Constraints on Approximate Spectral Features for Real-Time Water Body Segmentation in Remote Sensing Images by Qingqing Cao, Boya Zhao, Zijin Li, Fangfang Zhang, Yuanfeng Wu

“…With only 0.22 million parameters and a computational cost of 0.32 GFLOPs, the inference time per image is 6.45 ms. …”
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