Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features by Haoru Wang MD, Ling He MD, Xin Chen MD, Shuang Ding MD, Mingye Xie MD, Jinhua Cai MD

“…A predictive model for bone marrow metastasis was then developed using the support vector machine algorithm based on the selected radiomics features. The performance of the radiomics model was evaluated using the area under the curve (AUC), 95% confidence interval (CI), accuracy, sensitivity, and specificity. …”
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Advancing EGFR mutation subtypes prediction in NSCLC by combining 3D pretrained ConvNeXt, radiomics, and clinical features by Peng Hao, Yinghong Yu, Chan-Tao Huang, Fang Zhou, Yi-Kai Xu, Jiancheng Yang, Jiancheng Yang, Jun Xu

“…The instances were randomly divided into training, validation, and test sets. Feature selection was performed, and XGBoost was used to create solo models and combined models to predict the presence of EGFR and subtypes mutations. …”
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Microstrip Patch Antenna Design Using a Four-Layer Feed Forward Artificial Neural Network Trained by Levenberg-Marquardt Algorithm by Jitu Prakash Dhar, Maodudul Hasan, Eisuke Nishiyama, Ichihiko Toyoda

“…The ANN contains a multi-layered network architecture that learns and generalizes complex patterns through the LM algorithm and weight optimization based on the datasets without any feature extraction like Deep Neural Network (DNN). …”
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CFNN for Identifying Poisonous Plants by Israa Mohammed Hassoon, Shaymaa Akram Hantoosh

“…Combination of shape features and statistical features are extracted from leaf then fed to cascade-forward neural network which used TRAINLM function for training. 500 samples of leaf images are used, 250 samples are poisonous, the remaining 250 samples are non-poisonous.300 samples used in training, 200 samples for testing. …”
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Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer by Pu Zhou, Pu Zhou, Hongyan Qian, Pengfei Zhu, Jiangyuan Ben, Jiangyuan Ben, Guifang Chen, Qiuyi Chen, Lingli Chen, Jia Chen, Ying He, Ying He

“…These patients were randomly assigned to training and validation cohorts. Tumor regions of interest (ROI) were delineated, and radiomics features were extracted. …”
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Integrating radiomics features and CT semantic characteristics for predicting visceral pleural invasion in clinical stage Ia peripheral lung adenocarcinoma by Fengnian Zhao, Yunqing Zhao, Zhaoxiang Ye, Qingna Yan, Haoran Sun, Guiming Zhou

“…Methods A total of 537 patients diagnosed with clinical stage Ia LA underwent resection and were stratified into training and validation cohorts at a ratio of 7:3. …”
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Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study by Enock Adjei Agyekum, Wentao Kong, Doris Nti Agyekum, Eliasu Issaka, Xian Wang, Yong-zhen Ren, Gongxun Tan, Xuan Jiang, Xiangjun Shen, Xiaoqin Qian

“…A US-based GCN model was built using US deep learning features. …”
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Fourier Features and Machine Learning for Contour Profile Inspection in CNC Milling Parts: A Novel Intelligent Inspection Method (NIIM) by Manuel Meraz Méndez, Juan A. Ramírez Quintana, Elva Lilia Reynoso Jardón, Manuel Nandayapa, Osslan Osiris Vergara Villegas

“…A feed-forward neural network is employed to classify contour profiles based on quality properties. Experimental evaluations involving 60 machined calibration pieces, resulting in 356 images for training and testing, demonstrate the accuracy and computational efficiency of the proposed NIIM for profile line tolerance inspection. …”
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Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer by Minghan Jiang, Minghan Jiang, Zeyang Miao, Run Xu, Mengyao Guo, Xuefeng Li, Guanwu Li, Peng Luo, Su Hu, Su Hu

“…ObjectiveThis study aimed to develop MRI-based radiomics machine learning models for predicting adverse pathological prognostic features in prostate cancer and to explore the feasibility of integrating radiomics with clinical characteristics to improve preoperative risk stratification, addressing the limitations of conventional clinical models.MethodsA retrospective cohort of 137 prostate cancer patients between January 2021 and April 2023 with preoperative MRI and postoperative pathology data was divided into adverse-feature-positive (n=85) and negative (n=52) groups. …”
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Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning Using Graph Neural Networks and Transfer Learning by Johannes Pauli, Maximilian Hoffmann, Ralph Bergmann

“…Second, the pretrained model is then integrated into the GNN model by either using fine-tuning, i.e., the parameters of the pretrained model are further trained, or feature extraction, i.e., the parameters of the pretrained model are converted to constants. …”
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Clinical features and long-term outcomes of children with pneumococcal meningitis in China: a 10-year single-centre retrospective analysis by Yixuan Li, Binglin Jian, Xuekai Kang, Mengjie Zhang, Lingyun Guo, Xue Ning, Liang Zhu, Tianming Chen, Bing Hu, ShuPing Liu, Haijuan Xiao, Xin Guo, Wenya Feng, Zhenzhen Dou, Linlin Liu, Qinjing Li, Bing Liu, Gang Liu

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YogiCombineDeep: Enhanced Yogic Posture Classification Using Combined Deep Fusion of VGG16 and VGG19 Features by Arun Kumar Rajendran, Sibi Chakkaravarthy Sethuraman

“…Then, the collected features are combined and entered into classifiers to train and assess the outcome of yoga posture classification. …”
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Glacier surface melt monitoring using Sentinel-1 SAR backscattering coefficient and polarimetric decomposition features at Greenland ice sheet by Huimin Jiao, Gang Li, Zhuoqi Chen, Xiao Cheng

“…Then, an ensemble decision tree model is trained using the different feature values of both SAR features and RGZ categories to determine the glacier melt status. …”
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The Diagnostic Value of EEG Wave Trains for Distinguishing Immature Absence Seizures and Sleep Spindles: Evidence from the WAG/Rij Rat Model by Olga S. Sushkova, Alexei A. Morozov, Alexandra V. Gabova, Karine Yu. Sarkisova

“…<b>Results:</b> The criteria for diagnosis of the immature form of epileptic discharges and sleep spindles have been developed based on the analysis of wave-train activity with the construction of AUC diagrams (area under the curve diagrams). …”
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Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach by David Agustriawan, Adithama Mulia, Marlinda Vasty Overbeek, Vincent Kurniawan, Jheno Syechlo, Moeljono Widjaja, Muhammad Imran Ahmad

“…ResultsAmong the models evaluated, the highest observed accuracy was achieved using 139 gene features without oversampling, resulting in 98% accuracy for White patients and 97% for African American patients, based on 388 training samples and 92 testing samples. …”
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Efficient deep learning model for classifying lung cancer images using normalized stain agnostic feature method and FastAI-2 by Pranshu Saxena, Sanjay Kumar Singh, Mamoon Rashid, Sultan S. Alshamrani, Mrim M. Alnfiai

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A Hybrid Deep Learning-ViT Model and A Meta-Heuristic Feature Selection Algorithm for Efficient Remote Sensing Image Classification by Bilal Ahmed, Syed Rameez Naqvi, Tallha Akram, Lu Peng, Fahdah Almarshad

“…In this study, we introduced XNANet, a self-attention-based CNN network for image classification. Bayesian optimization has been used to initialize the hyperparameters of the proposed model to improve training on the radiographic images. …”
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Comparison of external load and physical performance of professional soccer players between a cup match and a league match: A preliminary study by Luiz Ricardo Mendes de Sousa Silva, Luciano Bernardes Leite, Pedro Miguel Forte, Victor Neiva Lavorato, José E. Teixeira, Ricardo Ferraz, Leôncio Lopes Soares, Eduardo Mendonça Pimenta, Guilherme de Azambuja Pussieldi, Christian Emanuel Torres Cabido, Mario Norberto Sevilio de Oliveira Junior, Christiano Eduardo Veneroso

“…Understanding these differences can help coaches optimise training and game strategies tailored to the specific demands of each competition type. …”
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Predicting Index Trend Using Hybrid Neural Networks with a Focus on Multi-Scale Temporal Feature Extraction in the Tehran Stock Exchange by Mohammad Osoolian, Ali Nikmaram, Mahdi Karimi

“…The dataset used in this study consists of daily historical data pertaining to the TSE Composite Index, covering a substantial period from the year 1998 to 2022, which has been meticulously gathered, preprocessed, and subsequently partitioned into distinct sets for training and validation purposes. Within the framework of this hybrid neural network model, a sophisticated approach is adopted to harness multiscale temporal features derived from the input data, enabling the generation of highly accurate predictions regarding the future trends of the index. …”
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A single HIIT session does not alter blood sphingolipid levels in healthy young adults: The SphingoHIIT randomized controlled trial by Thomas Angst, Nadia Weber, Seraina Fischer, Joëlle Lehmann, Denis Infanger, Tony Teav, Fabian Schwendinger, Lukas Streese, Timo Hinrichs, Ilaria Croci, Christian Schmied, Hector Gallart-Ayala, Christoph Höchsmann, Karsten Koehler, Henner Hanssen, Arno Schmidt-Trucksäss, Julijana Ivanisevic, Justin Carrard

“…Introduction: Sphingolipids and ceramides have been identified as critical drivers of cardiometabolic diseases. Ceramide-based scores were developed, predicting cardiometabolic risk independently of and beyond low-density lipoprotein cholesterol. …”
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