SPAM-XAI confusion matrix using PC1 dataset. by Mohd Mustaqeem (19106494)

“…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. …”

Analysis PC1 AU-ROC curve. by Mohd Mustaqeem (19106494)

“…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. …”

Proposed method approach. by Muhammad Usman Tariq (11022141)

“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”

LSTM model performance. by Muhammad Usman Tariq (11022141)

“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”

Descriptive statistics. by Muhammad Usman Tariq (11022141)

“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”

CNN-LSTM Model performance. by Muhammad Usman Tariq (11022141)

“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”

MLP Model performance. by Muhammad Usman Tariq (11022141)

“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”

RNN Model performance. by Muhammad Usman Tariq (11022141)

“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”

CNN Model performance. by Muhammad Usman Tariq (11022141)

“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”

Bi-directional LSTM Model performance. by Muhammad Usman Tariq (11022141)

“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”

Early Parkinson’s disease identification via hybrid feature selection from multi-feature subsets and optimized CatBoost with SMOTE by Subhashree Mohapatra (17387852)

“…The proposed framework leverages a strong categorical boosting (CatBoost) algorithm optimized using Grid Search Optimization (GSO). …”

Minimal Dateset. by Hongwei Yue (574068)

“…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”

Loss Function Comparison. by Hongwei Yue (574068)

“…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”

Comparative Results of Different Models. by Hongwei Yue (574068)

“…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”

Loss Function Comparison. by Hongwei Yue (574068)

“…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”

Overall Framework of the PSO-KM Model. by Hongwei Yue (574068)

“…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”

Overall Framework of the PSO-KM Model. by Hongwei Yue (574068)

“…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”

Data_Sheet_1_Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery.docx by Jinzhang Li (9221930)

The pseudocode outlines the proposed method for scoring the similarity between two protein sequences. by Belén Otero-Carrasco (21262360)

Supplementary file 1_Development of a venous thromboembolism risk prediction model for patients with primary membranous nephropathy based on machine learning.docx by Lian Li (49049)

“…Objective<p>This study utilizes real-world data from primary membranous nephropathy (PMN) patients to preliminarily develop a venous thromboembolism (VTE) risk prediction model with machine learning. …”