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Fulltext Prediction of ground vibration due to mine blasting in a surface lead-zinc mine using machine learning ensemble techniques

Hosseini S, Pourmirzaee R, Armaghani DJ, Sabri Sabri MM

Sci Rep, 2023 Apr 21;13(1):6591.
PMID: 37085660 DOI: 10.1038/s41598-023-33796-7

Ground vibration due to blasting is identified as a challenging issue in mining and civil activities. Peak particle velocity (PPV) is one of the blasting undesirable consequences, which is resulted during emission of vibration in blasted bench. This study focuses on the PPV prediction in the surface mines. In this regard, two ensemble systems, i.e., the ensemble of artificial neural networks and the ensemble of extreme gradient boosting (EXGBoosts) were developed for PPV prediction in one of the largest lead-zinc open-pit mines in the Middle East. For ensemble modeling, several ANN and XGBoost base models were separately designed with different architectures. Then, the validation indices such as coefficient determination (R2), root mean square error (RMSE), mean absolute error (MAE), the variance accounted for (VAF), and Accuracy were used to evaluate the performance of the base models. The five top base models with high accuracy were selected to construct an ensemble model for each of the methods, i.e., ANNs and XGBoosts. To combine the outputs of the top base models and achieve a single result stacked generalization technique, was employed. Findings showed ensemble models increase the accuracy of PPV predicting in comparison with the best individual models. The EXGBoosts was superior method for predicting of the PPV, which obtained values of R2, RMSE, MAE, VAF, and Accuracy corresponding to the EXGBoosts were (0.990, 0.391, 0.257, 99.013(%), 98.216), and (0.968, 0.295, 0.427, 96.674(%), 96.059), for training and testing datasets, respectively. However, the sensitivity analysis indicated that the spacing (r = 0.917) and number of blast-holes (r = 0.839) had the highest and lowest impact on the PPV intensity, respectively.
Fulltext A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network

Marto A, Hajihassani M, Armaghani DJ, Mohamad ET, Makhtar AM

ScientificWorldJournal, 2014;2014:643715.
PMID: 25147856 DOI: 10.1155/2014/643715

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches.
Revealing the nature of cardiovascular disease using DERGA, a novel data ensemble refinement greedy algorithm

Asteris PG, Gavriilaki E, Kampaktsis PN, Gandomi AH, Armaghani DJ, Tsoukalas MZ, et al.

Int J Cardiol, 2024 Jul 03;412:132339.
PMID: 38968972 DOI: 10.1016/j.ijcard.2024.132339

BACKGROUND: The study aimed to determine the most crucial parameters associated with CVD and employ a novel data ensemble refinement procedure to uncover the optimal pattern of these parameters that can result in a high prediction accuracy.
METHODS AND RESULTS: Data were collected from 369 patients in total, 281 patients with CVD or at risk of developing it, compared to 88 otherwise healthy individuals. Within the group of 281 CVD or at-risk patients, 53 were diagnosed with coronary artery disease (CAD), 16 with end-stage renal disease, 47 newly diagnosed with diabetes mellitus 2 and 92 with chronic inflammatory disorders (21 rheumatoid arthritis, 41 psoriasis, 30 angiitis). The data were analyzed using an artificial intelligence-based algorithm with the primary objective of identifying the optimal pattern of parameters that define CVD. The study highlights the effectiveness of a six-parameter combination in discerning the likelihood of cardiovascular disease using DERGA and Extra Trees algorithms. These parameters, ranked in order of importance, include Platelet-derived Microvesicles (PMV), hypertension, age, smoking, dyslipidemia, and Body Mass Index (BMI). Endothelial and erythrocyte MVs, along with diabetes were the least important predictors. In addition, the highest prediction accuracy achieved is 98.64%. Notably, using PMVs alone yields a 91.32% accuracy, while the optimal model employing all ten parameters, yields a prediction accuracy of 0.9783 (97.83%).
CONCLUSIONS: Our research showcases the efficacy of DERGA, an innovative data ensemble refinement greedy algorithm. DERGA accelerates the assessment of an individual's risk of developing CVD, allowing for early diagnosis, significantly reduces the number of required lab tests and optimizes resource utilization. Additionally, it assists in identifying the optimal parameters critical for assessing CVD susceptibility, thereby enhancing our understanding of the underlying mechanisms.
Fulltext Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm

Asteris PG, Gandomi AH, Armaghani DJ, Tsoukalas MZ, Gavriilaki E, Gerber G, et al.

J Cell Mol Med, 2024 Feb;28(4):e18105.
PMID: 38339761 DOI: 10.1111/jcmm.18105

Complement inhibition has shown promise in various disorders, including COVID-19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement-related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID-19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence-based algorithm to predict disease outcome (ICU vs. non-ICU admission). A recently introduced alpha-index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha-index in ranking a substantial number of genetic variants. This approach enables the implementation of well-established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.
Fulltext Genetic justification of severe COVID-19 using a rigorous algorithm

Gavriilaki E, Asteris PG, Touloumenidou T, Koravou EE, Koutra M, Papayanni PG, et al.

Clin Immunol, 2021 May;226:108726.
PMID: 33845193 DOI: 10.1016/j.clim.2021.108726

Recent studies suggest excessive complement activation in severe coronavirus disease-19 (COVID-19). The latter shares common characteristics with complement-mediated thrombotic microangiopathy (TMA). We hypothesized that genetic susceptibility would be evident in patients with severe COVID-19 (similar to TMA) and associated with disease severity. We analyzed genetic and clinical data from 97 patients hospitalized for COVID-19. Through targeted next-generation-sequencing we found an ADAMTS13 variant in 49 patients, along with two risk factor variants (C3, 21 patients; CFH,34 patients). 31 (32%) patients had a combination of these, which was independently associated with ICU hospitalization (p = 0.022). Analysis of almost infinite variant combinations showed that patients with rs1042580 in thrombomodulin and without rs800292 in complement factor H did not require ICU hospitalization. We also observed gender differences in ADAMTS13 and complement-related variants. In light of encouraging results by complement inhibitors, our study highlights a patient population that might benefit from early initiation of specific treatment.