DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction by Yihui Ren, Yu Wang, Wenkai Han, Yikang Huang, Xiaoyang Hou, Chunming Zhang, Dongbo Bu, Xin Gao, Shiwei Sun

“…In this study, we introduce Deep MS Simulator (DMSS), a novel attention-based model tailored for forecasting theoretical spectra in mass spectrometry. …”
Get full text

Crop Classification and Yield Prediction Using Robust Machine Learning Models for Agricultural Sustainability by Abid Badshah, Basem Yousef Alkazemi, Fakhrud Din, Kamal Z. Zamli, Muhammad Haris

“…Two widely used XAI approaches, namely Feature Importance and Local Interpretable Model-Agnostic Explanations (LIME) are used to interpret and explain outcomes of the proposed models. …”
Get full text

Modification of Multilayer Perceptron Using Detection Rate Model for Prediction of Nominal Exchange Rate by Al-Khowarizmi Al-Khowarizmi, Romi Fadillah Rahmat, Michael J Watts, Akrim Akrim, Arif Ridho Lubis, Muhammad Basri

“…An artificial neural network (ANN) is a network of a group of units to be processed which is modeled based on the behavior of human neural networks. …”
Get full text

DINOV2-FCS: a model for fruit leaf disease classification and severity prediction by Chunhui Bai, Chunhui Bai, Chunhui Bai, Lilian Zhang, Lilian Zhang, Lilian Zhang, Lutao Gao, Lutao Gao, Lutao Gao, Lin Peng, Lin Peng, Lin Peng, Peishan Li, Peishan Li, Peishan Li, Linnan Yang, Linnan Yang, Linnan Yang

“…However, the current prediction of disease degree by machine learning methods still faces challenges, including suboptimal accuracy and limited generalizability.MethodsIn light of the growing application of large model technology across a range of fields, this study draws upon the DINOV2 visual large vision model backbone network to construct the DINOV2-Fruit Leaf Classification and Segmentation Model (DINOV2-FCS), a model designed for the classification and severity prediction of diverse fruit leaf diseases. …”
Get full text

Interpretable Machine Learning Models for Predicting Cesarean Delivery in Class III Obese Cohorts by Rachel Bennett, Stephanie L. Pierce, Talayeh Razzaghi

“…Our comparative analysis shows logistic regression to be the most accurate in predicting the need for cesareans in the nulliparous cohort, while random forest outperformed other models in the combined dataset.…”
Get full text

Development of Shear Capacity Prediction Model for FRP-RC Beam without Web Reinforcement by Md. Arman Chowdhury, Zubayer Ibna Zahid, Md. Mashfiqul Islam

“…Available codes and models generally use partially modified shear design equation, developed earlier for steel reinforced concrete, for predicting the shear capacity of FRP-RC members. …”
Get full text

Research on the prediction model of UV spectral water quality parameters based on INFO-LSSVM by Li Chao, Li Wen, Luo Xueke, Yu Zhuofan, Han Yakai, Yu Facheng

“…LSSVM prediction model is effective and provides new re- search value for water quality detection.…”
Get full text

Predicting Mesothelioma Using Artificial Intelligence: A Scoping Review of Common Models and Applications by Malihe Ram MS, Mohammad Reza Afrash PhD, Khadijeh Moulaei PhD, Erfan Esmaeeli, Mohadeseh Sadat Khorashadizadeh, Ali Garavand PhD, Parastoo Amiri PhD, Azam Sabahi PhD

“…Conclusion Artificial intelligence, particularly machine learning models such as neural networks, decision trees, support vector machines, and random forests, holds promise in predicting and managing mesothelioma, potentially enhancing early detection and improving patient outcomes.…”
Get full text

Comparison of Prediction Models for Sonic Boom Ground Signatures Under Realistic Flight Conditions by Jacob Jäschke, Samuele Graziani, Francesco Petrosino, Antimo Glorioso, Volker Gollnick

“…This paper presents a comparative analysis of simplified and high-fidelity sonic boom prediction methods to assess their applicability in the conceptual design of supersonic aircraft. …”
Get full text

A Multi-Head Attention-Based Transformer Model for Predicting Causes in Aviation Incidents by Aziida Nanyonga, Hassan Wasswa, Keith Joiner, Ugur Turhan, Graham Wild

“…To bridge this gap, this study trains and evaluates the performance of a transformer-based model in predicting the likely causes of aviation incidents based on long-input raw text analysis narratives. …”
Get full text

A prediction model for soil heavy metal content based on improved tensor completion by Zhangang Wang, Wenjie Li, Tianhe Yun, Jiaxiang Qi

“…The proposed method estimates heavy metal concentrations at unsampled locations by constructing a prediction model within the Coarse-to-Fine (C2F) framework, leveraging data from sampled points. …”
Get full text

Mechanism-learning prediction model for pitting depth of buried pipeline based on HMOGWO-RF by Fulin SONG, Hong ZHAO, Xingyuan MIAO

“…Conclusion The proposed model has proven effective in accurately predicting the maximum pitting depth of buried pipelines. …”
Get full text

Stock Market Index Prediction Using CEEMDAN-LSTM-BPNN-Decomposition Ensemble Model by John Kamwele Mutinda, Abebe Geletu

“…The model’s predictive accuracy is measured using six metrics: root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE), root mean squared logarithmic error (RMSLE), and R-squared. …”
Get full text

Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model by Tingtao Wu, Lei Xu, Ziwei Pan, Ruinan Cai, Jin Dai, Shuang Yang, Xihao Zhang, Xi Zhang, Nengcheng Chen

“…The spatiotemporal prediction of RZSM refers to the process of estimating its future spatial distribution and temporal variations using predictive models. …”
Get full text

Hybrid neural network models for time series disease prediction confronted by spatiotemporal dependencies by Hamed Bin Furkan, Nabila Ayman, Md. Jamal Uddin

“…The models' predictions were compared using MAPE, and RMSE, as well as graphical representations generated by employed models. …”
Get full text

Alzheimer’s Disease Prediction Using Fisher Mantis Optimization and Hybrid Deep Learning Models by Sameer Abbas, Mustafa Yeniad, Javad Rahebi

Get full text

Normative isokinetic knee strength values and prediction models in non-athletic Chinese adults by Kun Liu, Lulu Yin, Ye Zhang, Gongliang Liu, Ping Fang, Yanhong Ma, Lihua Huang

Subjects: Get full text

Comparing Models and Performance Metrics for Lung Cancer Prediction using Machine Learning Approaches. by Ruqiya, Noman Khan, Saira Khan

“…It optimizes the performance of models for predicting lung cancer. …”
Get full text

Prediction method of sugarcane important phenotype data based on multi-model and multi-task. by Jihong Sun, Chen Sun, Zhaowen Li, Ye Qian, Tong Li

“…The efficacy of generalized sugarcane yield prediction models holds significant implications for global food security. …”
Get full text

The application of artificial intelligence models in predicting the risk of diabetic foot: a multicenter study by Yao Li, Siyuan Zhou, Bichen Ren, Shuai Ju, Xiaoyan Li, Wenqiang Li, Bingzhe Li, Yunmin Cai, Chunlei Chang, Lihong Huang, Zhihui Dong

Subjects: Get full text