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5. A Hybrid Intelligent Framework for Maximising SAG Mill Throughput: An Integration of Expert Knowledge, Machine Learning and Evolutionary Algorithms for Parameter Optimisation

Author

Ghasemi, Zahra, Neshat, Mehdi, Aldrich, Chris, Karageorgos, John, Zanin, Max, Neumann, Frank, and Chen, Lei

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

In mineral processing plants, grinding is a crucial step, accounting for approximately 50 percent of the total mineral processing costs. Semi-autogenous grinding mills are extensively employed in the grinding circuit of mineral processing plants. Maximizing SAG mill throughput is of significant importance considering its profound financial outcomes. However, the optimum process parameter setting aimed at achieving maximum mill throughput remains an uninvestigated domain in prior research. This study introduces a hybrid intelligent framework leveraging expert knowledge, machine learning techniques, and evolutionary algorithms to address this research need. In this study, we utilize an extensive industrial dataset comprising 36743 records and select relevant features based on the insights of industry experts. Following the removal of erroneous data, a comprehensive evaluation of 17 diverse machine learning models is undertaken to identify the most accurate predictive model. To improve the model performance, feature selection and outlier detection are executed. The resultant optimal model, trained with refined features, serves as the objective function within three distinct evolutionary algorithms. These algorithms are employed to identify parameter configurations that maximize SAG mill throughput while adhering to the working limits of input parameters as constraints. Notably, our analysis revealed that CatBoost, as an ensemble model, stands out as the most accurate predictor. Furthermore, differential evolution emerges as the preferred optimization algorithm, exhibiting superior performance in both achieving the highest mill throughput predictions and ensuring robustness in predictions, surpassing alternative methods.

Published
2023

6. Enhanced Genetic Programming Models with Multiple Equations for Accurate Semi-Autogenous Grinding Mill Throughput Prediction

Author

Ghasemi, Zahra, Nesht, Mehdi, Aldrich, Chris, Karageorgos, John, Zanin, Max, Neumann, Frank, and Chen, Lei

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning
Abstract

Semi-autogenous grinding (SAG) mills play a pivotal role in the grinding circuit of mineral processing plants. Accurate prediction of SAG mill throughput as a crucial performance metric is of utmost importance. The potential of applying genetic programming (GP) for this purpose has yet to be thoroughly investigated. This study introduces an enhanced GP approach entitled multi-equation GP (MEGP) for more accurate prediction of SAG mill throughput. In the new proposed method multiple equations, each accurately predicting mill throughput for specific clusters of training data are extracted. These equations are then employed to predict mill throughput for test data using various approaches. To assess the effect of distance measures, four different distance measures are employed in MEGP method. Comparative analysis reveals that the best MEGP approach achieves an average improvement of 10.74% in prediction accuracy compared with standard GP. In this approach, all extracted equations are utilized and both the number of data points in each data cluster and the distance to clusters are incorporated for calculating the final prediction. Further investigation of distance measures indicates that among four different metrics employed including Euclidean, Manhattan, Chebyshev, and Cosine distance, the Euclidean distance measure yields the most accurate results for the majority of data splits.

Published
2023

9. Fake detection in imbalance dataset by Semi-supervised learning with GAN

Author

Bordbar, Jinus, Ardalan, Saman, Mohammadrezaie, Mohammadreza, and Ghasemi, Zahra

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Cryptography and Security
Abstract

As social media continues to grow rapidly, the prevalence of harassment on these platforms has also increased. This has piqued the interest of researchers in the field of fake detection. Social media data, often forms complex graphs with numerous nodes, posing several challenges. These challenges and limitations include dealing with a significant amount of irrelevant features in matrices and addressing issues such as high data dispersion and an imbalanced class distribution within the dataset. To overcome these challenges and limitations, researchers have employed auto-encoders and a combination of semi-supervised learning with a GAN algorithm, referred to as SGAN. Our proposed method utilizes auto-encoders for feature extraction and incorporates SGAN. By leveraging an unlabeled dataset, the unsupervised layer of SGAN compensates for the limited availability of labeled data, making efficient use of the limited number of labeled instances. Multiple evaluation metrics were employed, including the Confusion Matrix and the ROC curve. The dataset was divided into training and testing sets, with 100 labeled samples for training and 1,000 samples for testing. The novelty of our research lies in applying SGAN to address the issue of imbalanced datasets in fake account detection. By optimizing the use of a smaller number of labeled instances and reducing the need for extensive computational power, our method offers a more efficient solution. Additionally, our study contributes to the field by achieving an 81% accuracy in detecting fake accounts using only 100 labeled samples. This demonstrates the potential of SGAN as a powerful tool for handling minority classes and addressing big data challenges in fake account detection., Comment: needed more investigation o final results

Published
2022

12. Multi-objective Semi-supervised Clustering for Finding Predictive Clusters

Author

Ghasemi, Zahra, Khorshidi, Hadi Akbarzadeh, and Aickelin, Uwe

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

This study concentrates on clustering problems and aims to find compact clusters that are informative regarding the outcome variable. The main goal is partitioning data points so that observations in each cluster are similar and the outcome variable can be predicated using these clusters simultaneously. We model this semi-supervised clustering problem as a multi-objective optimization problem with considering deviation of data points in clusters and prediction error of the outcome variable as two objective functions to be minimized. For finding optimal clustering solutions, we employ a non-dominated sorting genetic algorithm II approach and local regression is applied as prediction method for the output variable. For comparing the performance of the proposed model, we compute seven models using five real-world data sets. Furthermore, we investigate the impact of using local regression for predicting the outcome variable in all models, and examine the performance of the multi-objective models compared to single-objective models.

Published
2022

23. Using an integrated social cognition model to predict COVID‐19 preventive behaviours

Author

Lin, Chung‐Ying, Imani, Vida, Majd, Nilofar Rajabi, Ghasemi, Zahra, Griffiths, Mark D, Hamilton, Kyra, Hagger, Martin S, and Pakpour, Amir H

Subjects
Social and Personality Psychology, Psychology, Clinical Research, Prevention, Good Health and Well Being, Betacoronavirus, COVID-19, Cognition, Coronavirus Infections, Humans, Intention, Iran, Pandemics, Pneumonia, Viral, Prospective Studies, SARS-CoV-2, attitude, behaviour change, intention, planning, preventive behaviours, Public Health and Health Services, Sociology, Clinical Psychology, Public health, Clinical and health psychology
Abstract

ObjectivesRates of novel coronavirus disease 2019 (COVID-19) infections have rapidly increased worldwide and reached pandemic proportions. A suite of preventive behaviours have been recommended to minimize risk of COVID-19 infection in the general population. The present study utilized an integrated social cognition model to explain COVID-19 preventive behaviours in a sample from the Iranian general population.DesignThe study adopted a three-wave prospective correlational design.MethodsMembers of the general public (N = 1,718, Mage  = 33.34, SD = 15.77, male = 796, female = 922) agreed to participate in the study. Participants completed self-report measures of demographic characteristics, intention, attitude, subjective norm, perceived behavioural control, and action self-efficacy at an initial data collection occasion. One week later, participants completed self-report measures of maintenance self-efficacy, action planning and coping planning, and, a further week later, measures of COVID-19 preventive behaviours. Hypothesized relationships among social cognition constructs and COVID-19 preventive behaviours according to the proposed integrated model were estimated using structural equation modelling.ResultsThe proposed model fitted the data well according to multiple goodness-of-fit criteria. All proposed relationships among model constructs were statistically significant. The social cognition constructs with the largest effects on COVID-19 preventive behaviours were coping planning (β = .575, p

Published
2020

36. Health-Related Quality of Life Among Iranian Women with Uterine Fibroids: A Cross Sectional Study.

Author

Najafiarab, Hanieh, Farzaneh, Farah, Talebian, Niki, Ghasemi, Zahra, and Talayeh, Maryam

Subjects
CHILDBEARING age, QUALITY of life, IRANIANS, MENSTRUAL cycle, UTERINE fibroids, MENSTRUATION disorders
Abstract

Objective: Uterine fibroids (UFs) are the most common benign tumors in women, and their prevalence varies between 5.4 and 77.0% in reproductive-aged women. Patients with UFs may experience severe symptoms that they can affect different aspects of their lives, including quality of life. This study aimed to investigate the health-related quality of life among Iranian women with UFs. Materials and methods: This cross-sectional study was conducted at Imam Hossein Hospital, Tehran, Iran, between November 2023 and February 2024, Data collection was based on the census method. Uterine Fibroid Symptom and Health-related Quality of Life (UFS-QOL) questionnaire was used to assess symptom severity and health-related quality of life (HRQOL) of women with UFs. Data were analyzed using the SPSS software version 23.0. Results: Overall, 220 patients with a mean age of 43.10±5.01 years were included in the study. Patients had total UFS-QOL score of 64.11±20.35 with the following subscales' scores: symptom severity: (19.00±6.39), concern: (60.79±26.47), activities: (71.76±23.02), energy/mood: (54.39±25.14), control: (66.52±22.82), self-consciousness: (77.63±26.39), and sexual function: (59.40±31.18). Furthermore, patients with multiparity history (P= 0.001), obesity (P<0.001), increased menstrual duration (P<0.001), irregular menstruation (P<0.001), and hyper menorrhea (P<0.001) had lower HRQOL scores. Conclusion: All subscales' scores of HRQOL were over 50 in patients with UF. HRQOL in these patients can be affected by certain factors, such as features of the menstrual cycle, multiparity, and obesity. [ABSTRACT FROM AUTHOR]

Published
2024

37. Systemic and Lung Inflammation and Oxidative Stress Associated With Behavioral Changes Induced by Inhaled Paraquat Are Ameliorated by Carvacrol.

Author

Memarzia, Arghavan, Amin, Fatemeh, Mokhtari-Zaer, Amin, Arab, Zohre, Saadat, Saeideh, Heydari, Mahrokh, Ghasemi, Zahra, Naghdi, Farzaneh, Hosseini, Mahmoud, Boskabady, Mohammad Hossein, and Lau, Christopher

Subjects
LEUCOCYTES, CARVACROL, LYMPHOCYTE count, SUPEROXIDE dismutase, PARAQUAT, PNEUMONIA
Abstract

Paraquat (PQ) is an herbicide toxin that induces injury in different organs. The anti‐inflammatory and antioxidant effects of carvacrol were reported previously. The effects of carvacrol and pioglitazone (Pio) alone and their combination on inhaled PQ‐induced systemic and lung oxidative stress and inflammation as well as behavioral changes were examined in rats. In this study, animals were exposed to saline (control [Ctrl]) or PQ (PQ groups) aerosols. PQ‐exposed animals were treated with 0.03 mg/kg/day dexamethasone (Dexa), 20 and 80 mg/kg/day carvacrol (C‐L and C‐H), 5 mg/kg/day Pio, and Pio+C‐L for 16 days. Inhaled PQ markedly enhanced total and differential white blood cell (WBC) counts, nitric oxide (NO), and malondialdehyde (MDA) levels but decreased catalase (CAT) and superoxide dismutase (SOD) activities and thiol levels both in the bronchoalveolar lavage fluid (BALF) and blood and increased interferon‐gamma (INF‐γ) and interleukin‐10 (IL‐10) levels in the BALF (p < 0.001 for all cases) except lymphocyte count in blood which was not significantly changed. The escape latency and traveled distance were increased in the PQ group. However, the time spent in the target quadrant in the Morris water maze (MWM) test and the duration of time latency in the dark room in the shuttle box test were reduced after receiving an electrical shock (p < 0.05–p < 0.001). Inhaled PQ‐induced changes were significantly improved in carvacrol, Pio, Dexa, and especially in the combination of the Pio+C‐L treated groups (p < 0.05–p < 0.001). Carvacrol and Pio improved PQ‐induced changes similar to Dexa, but ameliorative effects produced by combination treatments of Pio+C‐L were more prominent than Pio and C‐L alone, suggesting a potentiating effect for the combination of the two agents. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Highly sensitive and disposable voltammetric nano-sensor for analysis of Sudan I based on CuCo2O4 nanorods.

Author

Mohammadzadeh Jahani, Peyman, Garkani Nejad, Fariba, Tajik, Somayeh, Dourandish, Zahra, Zaimbashi, Reza, Bazrafkan, Ahlam, Ghasemi, Zahra, and Beitollahi, Hadi

Subjects
ELECTROCHEMICAL sensors, CYCLIC voltammetry, NANORODS, DETECTION limit, STANDARD deviations
Abstract

In this research, CuCo2O4 nanorods (CuCo2O4 NRs) were prepared using a simple hydrothermal technique and characterized by using various techniques. Subsequently, a highly sensitive and simple electrochemical sensor was prepared by modifying a screen-printed graphite electrode with CuCo2O4 NRs (CuCo2O4 NRs/SPGE) and applied for the detection of sudan I. The electrocatalytic oxidation of sudan I was investigated by cyclic voltammetry (CV), differential pulse voltammetry (DPV), chronoamperometry, and linear sweep voltammetry (LSV). The modification of the electrode surface with CuCo2O4 NRs exhibited the highest electro-catalytic activity towards sudan I oxidation, resulting in an improved sensitivity compared to the unmodified electrode. Based on these findings, we analyzed a screen-printed sensor (CuCo2O4 NRs/SPGE) using DPV for sudan I determination. The sensor demonstrated a linear response ranging from 0.04 µM to 560.0 µM, with a limit of detection (LOD) of 0.01 µM and a sensitivity of 0.097 µM/µA. The suitability of the proposed sensor for sudan I determination in real samples was also demonstrated, with good recovery results ranging from 97.6 to 104.0% and a maximum relative standard deviation (RSD) of 3.5%. [ABSTRACT FROM AUTHOR]

Published
2024
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48. An overview of pharmacological effects of Crocus sativous and its constituents.

Author

Saadat, Saeideh, Ghasemi, Zahra, Memarzia, Arghavan, Behrouz, Sepideh, Aslani, Mohammad Reza, and Boskabady, Mohammad Hossein

Subjects
*SAFFRON crocus, *RESPIRATORY organs, *FREE radicals, *LABORATORY animals, *ANTIOXIDANTS, *BLADDER cancer
Abstract

Crocus sativus L. was used for the treatment of a wide range of disorders in traditional medicine. Due to the extensive protective and treatment properties of C. sativus and its constituents in various diseases, the purpose of this review is to collect a summary of its effects, on experimental studies, both in vitro and in vivo. Databases such as PubMed, Science Direct, and Scopus were explored until January 2023 by employing suitable keywords. Several investigations have indicated that the therapeutic properties of C. sativus may be due to its anti-oxidant and anti-inflammatory effects on the nervous, cardiovascular, immune, and respiratory systems. Further research has shown that its petals also have anticonvulsant properties. Pharmacological studies have shown that crocetin and safranal have anti-oxidant properties and through inhibiting the release of free radicals lead to the prevention of disorders such as tumor cell proliferation, atherosclerosis, hepatotoxicity, bladder toxicity, and ethanol induced hippocampal disorders. Numerous studies have been performed on the effect of C. sativus and its constituents in laboratory animal models under in vitro and in vivo conditions on various disorders. This is necessary but not enough and more clinical trials are needed to investigate unknown aspects of the therapeutic properties of C. sativus and its main constituents in different disorders. [ABSTRACT FROM AUTHOR]

Published
2024
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