High-throughput screening of CO2 cycloaddition MOF catalyst with an explainable machine learning model by Xuefeng Bai, Yi Li, Yabo Xie, Qiancheng Chen, Xin Zhang, Jian-Rong Li

“…The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding. 12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100 °C and 1 bar within one day using the model, and 239 potentially efficient catalysts were discovered. …”
Get full text

Three-Dimensional Structural Modelling and Characterization of Sodalite Material Network concerning the Irregularity Topological Indices by Shahid Zaman, Muhammad Salman, Asad Ullah, Shahzad Ahmad, Mohammed Salaheldeen Abdelgader Abas

Get full text

Predictive modeling of soil profiles for precision agriculture: a case study in safflower cultivation environments by Megha Sharma, Shailendra Goel, Ani A. Elias

“…Calcium, sand, soil organic carbon, phosphorous, potassium, and sodium were found to be most influential in classifying the representative TE. Random Forest model was found to be the best performing with average prediction accuracy above 85% in all test settings which reached 100% in some. …”
Get full text

When and why does motor preparation arise in recurrent neural network models of motor control? by Marine Schimel, Ta-Chu Kao, Guillaume Hennequin

“…It is unclear why these patterns arise: while they have been proposed to seed an initial neural state from which the movement unfolds, recent experiments have uncovered the presence and necessity of ongoing inputs during movement, which may lessen the need for careful initialization. Here, we modeled the motor cortex as an input-driven dynamical system, and we asked what the optimal way to control this system to perform fast delayed reaches is. …”
Get full text

A hybrid deep learning model for sentiment analysis of COVID-19 tweets with class balancing by Md. Alamin Talukder, Md. Ashraf Uddin, Suman Roy, Partho Ghose, Smita Sarker, Ansam Khraisat, Mohsin Kazi, Md Momtazur Rahman, Musawer Hakimi

“…Additionally, to mitigate class imbalance, Random OverSampling (ROS) is employed, leading to significant improvements in model performance. Before applying ROS, the model exhibited lower accuracy and inconsistent performance across sentiment categories. …”
Get full text

A predictive analytics approach with Bayesian-optimized gentle boosting ensemble models for diabetes diagnosis by Behnaz Motamedi, Balázs Villányi

“…Machine learning (ML) based models have garnered attention in the realm of predictive healthcare, with ensemble methods, in particular, bolstering algorithms to improve classification performance. …”
Get full text

Optimized ANN Model for Predicting Buckling Strength of Metallic Aerospace Panels Under Compressive Loading by Shahrukh Khan, Saiaf Bin Rayhan, Md Mazedur Rahman, Jakiya Sultana, Gyula Varga

“…Using regression metrics, performance was benchmarked against classical machine learning models such as CatBoost, XGBoost, LightGBM, random forest, decision tree, and k-nearest neighbors. …”
Get full text

Automated weed and crop recognition and classification model using deep transfer learning with optimization algorithm by K. Gopalakrishnan, R. Sivaraj, M. Vijayakumar

“…The ShuffleNetV2 approach is exploited in the AWRC-DLMLO method to ascertain feature vector. Next, the lemurs optimization algorithm (LOA) is applied to increase the hyperparameter and fine-tune the DL technique, further enhancing its performance. …”
Get full text

Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models by Abir Das, Saurabh Singh, Jaejeung Kim, Tariq Ahamed Ahanger, Anil Audumbar Pise

“…The accurate classification of electroencephalogram (EEG) data is crucial for enhancing BCI performance. The BCI architecture processes electroencephalography signals through three critical stages: data pre-processing, feature extraction, and classification. …”
Get full text

Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization by Waqar Ashiq, Samra Kanwal, Adnan Rafique, Muhammad Waqas, Tahir Khurshaid, Elizabeth Caro Montero, Alicia Bustamante Alonso, Imran Ashraf

“…The novel hybrid model integrates deep learning (DL) and transformer models for automatic feature extraction, combined with machine learning algorithms (MLAs) for classification. …”
Get full text

Building electrical consumption patterns forecasting based on a novel hybrid deep learning model by Nasser Shahsavari-Pour, Azim Heydari, Farshid Keynia, Afef Fekih, Aylar Shahsavari-Pour

“…Specifically, the proposed model comprises three key components: (i) a mutual information-based feature selection method to identify the most significant input variables influencing energy consumption; (ii) a variational mode decomposition (VMD) approach to decompose the original energy consumption signal into intrinsic mode functions (IMFs), capturing relevant trends and eliminating noise; and (iii) a long short-term memory (LSTM) neural network to perform time-series forecasting of the target energy consumption values. …”
Get full text

A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases by G. Sambasivam, G. Prabu kanna, Munesh Singh Chauhan, Prem Raja, Yogesh Kumar

“…A dataset consisting of around 36,000 labelled images of cassava leaves, afflicted by diseases such as Cassava Brown Streak Disease, Cassava Mosaic Disease, Cassava Green Mottle, Cassava Bacterial Blight, and healthy leaves, was used to train these models. Further the images were pre-processed by converting them into grayscale, reducing noise using Gaussian filter, obtaining the region of interest using Otsu binarization, Distance transformation, as well as Watershed technique followed by employing contour-based feature selection to enhance model performance. …”
Get full text

Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi‐Sensor System with Foundation Models by Gianmarco Gabrieli, Matteo Manica, Joris Cadow‐Gossweiler, Patrick W. Ruch

“…However, personalized and portable sensor systems are typically unsuitable for the generation of extensive data sets, thereby limiting the ability to train large models in the chemical sensing realm. Foundation models have demonstrated unprecedented zero‐shot learning capabilities on various data structures and modalities, in particular for language and vision. …”
Get full text

Model of superior semicircular canal dehiscence: asymmetrical vestibular dysfunction induces reversible balance impairment by Sean S. Hong, P. Ashley Wackym, P. Ashley Wackym, Damian J. Murphy, Eran Peci, Matthew Y. Kiel, Aaron Tucker, Nicolas L. Carayannopoulos, Shrivaishnavi C. Chandrasekar, Nikhil Suresh, Umut A. Utku, Justin D. Yao, Justin D. Yao, Todd M. Mowery, Todd M. Mowery

“…A feature that is unique in this model is that spontaneous resurfacing of the dehiscence occurs via osteoneogenesis without a subsequent intervention. …”
Get full text

Development and validation of a machine learning model for predicting pulmonary metastasis in hepatocellular carcinoma patients by Gangfeng Zhu, Qiang Yi, Rui Xu, Yi Xie, Siying Chen, Yipeng Song, Yi Xiang, Xiangcai Wang, Li Huang

“…Eight machine learning models were then developed and evaluated using validation cohorts for predictive performance. …”
Get full text

Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution by Jialu Sui, Xianping Ma, Xiaokang Zhang, Man-On Pun, Hao Wu

“…Recently, the denoising diffusion probabilistic model (DDPM) has shown promising performance in image reconstructions by overcoming problems inherent in generative models, such as oversmoothing and mode collapse. …”
Get full text

Revolutionizing Mental Health Sentiment Analysis With BERT-Fuse: A Hybrid Deep Learning Model by Md. Mithun Hossain, Sanjara, Md. Shakil Hossain, Sudipto Chaki, Md. Saifur Rahman, A. B. M. Shawkat Ali

“…An ablation study highlights the contributions of key model components to its performance. BERT-Fuse shows promise as a scalable, high-precision system for mental health detection with an impressive average test accuracy of 97.05%. …”
Get full text

A study on the risk prediction model for venous thromboembolism in orthopedic inpatients based on machine learning by Bo Zhang, Yumei Qin, Liandi Jiu, Chunming Qin, Jiangbo Wang, Haiqing Zhao

“…The SHapley Additive exPlanation (SHAP) method was used to rank the feature importance and explain the final model.ResultsThrough a comprehensive evaluation and comparison of eight different machine learning models, the results clearly indicate that the XGBoost model outperforms the others across all performance metrics, achieving the highest accuracy of 0.828 and AUROC of 0.931, significantly surpassing the other models, particularly in prediction accuracy and discriminative ability. …”
Get full text

A Clinical Risk Prediction Model for Depressive Disorders Based on Seven Machine Learning Algorithms by Jin W, Chen S, Wang M, Lin P

“…Univariate logistic regression analysis (p< 0.1) was initially performed to identify potential predictors, followed by feature selection using the Boruta and LASSO algorithms. …”
Get full text

A Novel Model for Authority and Access Delegation Utilizing Self-Sovereign Identity and Verifiable Credentials by Spela Cucko, Muhamed Turkanovic

“…Despite its positive features, SSI focuses predominantly on direct interactions between two independent entities. …”
Get full text