GNNs and ensemble models enhance the prediction of new sRNA-mRNA interactions in unseen conditions by Shani Cohen, Lior Rokach, Isana Veksler-Lublinsky

“…Beyond enhanced performance, our models enable target prediction for species-specific sRNAs, a capability lacking in some existing tools. …”
Get full text

Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks by Md. Rajib Hossain, Mohammed Moshiul Hoque, M. Ali Akber Dewan, Nazmul Siddique, Md. Nazmul Islam, Iqbal H. Sarker

“…This paper presents an authorship classification approach made of Convolution Neural Networks (CNN) comprising four modules: embedding model generation, feature representation, classifier training and classifier testing. …”
Get full text

Predicting emotional responses in interactive art using Random Forests: a model grounded in enactive aesthetics by Xiaowei Chen, Xiaowei Chen, Zainuddin Ibrahim, Azlan Abdul Aziz

“…Predictor variables encompassed sensory stimuli, multimodal interactional features, and immersive environmental cues. Model evaluation was conducted using cross-validation and held-out test sets, applying classification and regression metrics to assess performance.ResultsThe RF model demonstrated the highest predictive accuracy in the domains of cognitive reflection (F1 = 0.746, accuracy = 0.769) and active personalization (F1 = 0.673, accuracy = 0.705), suggesting that these cognitively mediated responses exhibit greater consistency and learnability across participants. …”
Get full text

Design of an Improved Model for Anomaly Detection in CCTV Systems Using Multimodal Fusion and Attention-Based Networks by V. Srilakshmi, Sai Babu Veesam, Mallu Shiva Rama Krishna, Ravi Kumar Munaganuri, Dulam Devee Sivaprasad

“…The proposed models also offer good improvements in the performance of anomaly detection. …”
Get full text

Development and validation of prediction models for special subtype of primary aldosteronism: patients with negative adrenal CT imaging by Hong Zhao, Pan Hu, Min Mao, Xin Li, Ling Wang, Jing Chang

“…This original data was subsequently applied in the construction of a radiomic model, which was combined with clinical data for the final model construction.Results107 radiomic features were extracted from the adrenal scans and 10 features were selected for ML and AI modeling. …”
Get full text

Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare by Kamini Lamba, Shalli Rani, Mohammad Shabaz

“…Thus, an advanced algorithm of explainable artificial intelligence (XAI) has been synergized with hybrid model comprising of DenseNet201 network for extracting the most important features based on the input Magnetic resonance imaging (MRI) data following supervised algorithm, support vector machine (SVM) to distinguish distinct types of brain scans. …”
Get full text

Classification of Apples (<i>Malus × domestica</i> <span style="font-variant: small-caps">borkh.</span>) According to Geographical Origin, Variety and Production Method Using Liqui... by Jule Hansen, Iris Fransson, Robbin Schrieck, Christof Kunert, Stephan Seifert

Get full text

BWFER-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection by Khalid Ijaz, Ikramullah Khosa, Ejaz A. Ansari, Syed Farooq Ali, Asif Hussain, Faran Awais Butt

“…Several concealed object detection models have demonstrated outstanding performance but failed to combat the above-mentioned challenges concurrently. …”
Get full text

Image processing‐based noise‐resilient insulator defect detection using YOLOv8x by Shagor Hasan, Md. Abdur Rahman, Md. Rashidul Islam, Animesh Sarkar Tusher

Get full text

Machine learning modeling of cancer treatment-related cardiac events in breast cancer: utilizing dosiomics and radiomics by Sefika Dincer, Muge Akmansu, Oya Akyol

“…Model performance is assessed by the area under the curve (AUC).ResultsA total of 111 dosiomic and 119 radiomic features were extracted per patient. …”
Get full text

CDMRNet: multimodal meta-adaptive reasoning network with dynamic causal modeling and co-evolution of quantum states by Shengwei Wang, Keda Chen, Mengduo Yu, Pengjiao Zhao, Hui Duan

“…CDMRNet achieves 89.7% accuracy on Visual Genome, improves F1 score to 84.1%, and shows 3.9% performance drop only under modal absence, significantly outperforming state-of-the-art models. …”
Get full text

Optimizing soybean variety selection for the Pan-African Trial network using factor analytic models and envirotyping by Maurício S. Araújo, João P. S. Pavan, André A. Stella, Bruno F. Fregonezi, Natally F. Lima, Erica P. Leles, Michelle F. Santos, Peter Goldsmith, Godfree Chigeza, Brian W. Diers, José B. Pinheiro

“…Three mega-environments were identified, with growing degree days, mean temperature, and photosynthetically active radiation use efficiency being the most associated features for soybean grain yield. To enhance the identification of variety adaptation in these environments, integrating machine learning models with crop growth modeling is essential to assess associations between environmental features and soybean yield.…”
Get full text

Explainable machine learning and online calculators to predict heart failure mortality in intensive care units by An‐Tian Chen, Yuhui Zhang, Jian Zhang

“…LASSO regression determined the shrinkage parameter α = 0.020, and 44 features were chosen for model construction. The linear discriminant analysis (LDA) model showed the best performance, and the accuracy reached 0.8354 in the training cohort and 0.8563 in the testing cohort. …”
Get full text

Design and Modeling of a Reconfigurable Multiple Input, Multiple Output Antenna for 24 GHz Radar Sensors by Mahmoud Shaban

“…A frequency-reconfigurable MIMO antenna with high gain, low mutual coupling and highly suppressed side lobe level (SLL) for applications in 24 GHz ISM band sensing and automotive radar systems was designed, modeled, and simulated. The reconfigurability feature was modeled with the implementation of a varactor diode in the model to alter the frequency in a wide band around 24 GHz. …”
Get full text

A generative model-based coevolutionary training framework for noise-tolerant softsensors in wastewater treatment processes by Yu Peng, Erchao Li

“…The proposed multiobjective evolutionary network optimization with denoising strategy (MENO-D) demonstrated exceptional performance in various experiments. On a water quality prediction dataset, the MENO-D-trained softsensor model achieved the lowest prediction error under 10% and 20% noise interference. …”
Get full text

The construction of HMME-PDT efficacy prediction model for port-wine stain based on machine learning algorithms by Hongxia Yan, Yixin Tan, Fan Qiao, Zhuotong Zeng, Yaqian Shi, Xueqin Zhang, Lu Li, Ting Zeng, Yi Zhan, Ruixuan You, Xinglan He, Rong Xiao, Xiangning Qiu

“…Model performance was assessed using confusion matrix and evaluation metrics. …”
Get full text

Analyzing Tractor Productivity and Efficiency Evolution: A Methodological and Parametric Assessment of the Impact of Variations in Propulsion System Design by Ivan Herranz-Matey

“…This research aims to analyze the evolution of productivity and efficiency in tractors featuring varying propulsion system designs through the development of a parametric modeling approach. …”
Get full text

Machine learning-based prediction model for arteriovenous fistula thrombosis risk: a retrospective cohort study from 2017 to 2024 by Peng Shu, Ling Huang, Xia Wang, Zhuping Wen, Yiqi Luo, Fang Xu

“…Results The study encompassed a cohort of 974 patients, each characterized by 55 clinical data features. Among the six machine learning models evaluated, the Random Forest model demonstrated superior performance, achieving an AUC-ROC of 0.984. …”
Get full text

Aircraft range fuel prediction study based on WPD with IAPO optimized BiLSTM–KAN model by Weizhen Tang, Jie Dai, Yuantai Li

“…In the A320 aircraft model, the MSE, NRMSE, MAPE, and R2 of this model are 40.71, 0.0078, 2.61, and 0.9934, respectively. …”
Get full text

Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction by Zhengbiao Hu, Shuangshan Bu, Kai Wang, Qianqian Cao, Huanhuan Zheng, Jie Yang, Shanshan Chen, Yuemeng Wu, Wenkai Ren, Chenlei He

“…The RF method was applied to select 17 key features for building diagnostic models, including the RF model, support vector machine (SVM), adaptive boosting (ADB), and traditional doctor judgment. …”
Get full text