Machine learning method for simulation of adsorption separation: Comparisons of model’s performance in predicting equilibrium concentrations

Yin, Guanwei; Alazzawi, Fouad Jameel Ibrahim; Mironov, S N; Reegu, Faheem Ahmad; El-Shafay, A.S.; Rahman, Md. Lutfor; Su, Chia‐Hung; Lu, Yi-Ze; Nguyen, Hoang Chinh

doi:10.1016/j.arabjc.2021.103612

Cited by 40 publications

(4 citation statements)

References 57 publications

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“…e in-depth data search strategy to be followed to get the more specific and trending research topics can be found in the domain of healthcare using blockchain interoperability [43,44].…”

Section: Study Limitations Scopus Database Has Been Used In Thismentioning

confidence: 99%

Interoperability Requirements for Blockchain-Enabled Electronic Health Records in Healthcare: A Systematic Review and Open Research Challenges

Reegu

Abas

Jabbari

et al. 2022

Security and Communication Networks

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A distributed ledger system blockchain proves to be worthy in the domain of healthcare due to its enormous applications and benefits. Peer-to-peer devolved transactions in an allocated way make the blockchain an efficient and modern tool to be utilized in healthcare as a solution to problems and challenges. The traditional healthcare system utilizes classical approaches to manage and maintain the EHR and cross-domain implementation. Therefore, a systematic investigation is necessary to find the current research trends, challenges, and solutions to implement blockchain to address the challenges. The motivation of this study is to pave the way for future research to find more problem-specific solutions by implementing blockchain to make the healthcare system more robust. The presented systematic survey provides the visualization and graphical representation of current methodologies, challenges, and future directions. The bibliometric analysis has been performed on the published studies in Scopus from 2017 to 2021. The publication published in the first three months of 2021 in the domain of healthcare using blockchain has also been reviewed to find the latest trends in the blockchain. The study covers multiple challenges in the presented systematic literature review, especially the interoperability of blockchain-based systems in the healthcare domain. The presented study results show the top trending research topics, top-ranked authors, and top institutes focusing on blockchain around the world. The proposed study provides a baseline for future challenges and solutions related to blockchain implementation in healthcare.

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“…e in-depth data search strategy to be followed to get the more specific and trending research topics can be found in the domain of healthcare using blockchain interoperability [43,44].…”

Section: Study Limitations Scopus Database Has Been Used In Thismentioning

confidence: 99%

Interoperability Requirements for Blockchain-Enabled Electronic Health Records in Healthcare: A Systematic Review and Open Research Challenges

Reegu

Abas

Jabbari

et al. 2022

Security and Communication Networks

Self Cite

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“…Guanwei Yin et al (2021) explored different ML models-Multi-Layer Perceptron (MLP), Passive Aggressive Regression, and Decision Tree Regressor-to predict dye adsorption from aqueous solutions. Among these, the Decision Tree Regressor emerged as the most effective, suggesting its suitability for correlating adsorption equilibrium data due to high accuracy and low error rates (R 2 = 0.99) [59]. Chen Zhao et al (2024) demonstrated how multivalent ions like Ca 2+ , K + , Na + , and Mg 2+ impact the adsorption of azo dyes using ML models coupled with Density Functional Theory (DFT).…”

Section: Machine Learning-assisted Optimization For Improving the Ads...mentioning

confidence: 99%

Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Haematoxylum campechianum Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process

Gamboa,

Abatal,

Lima

et al. 2024

IJMS

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This work aimed to describe the adsorption behavior of Congo red (CR) onto activated biochar material prepared from Haematoxylum campechianum waste (ABHC). The carbon precursor was soaked with phosphoric acid, followed by pyrolysis to convert the precursor into activated biochar. The surface morphology of the adsorbent (before and after dye adsorption) was characterized by scanning electron microscopy (SEM/EDS), BET method, X-ray powder diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) and, lastly, pHpzc was also determined. Batch studies were carried out in the following intervals of pH = 4–10, temperature = 300.15–330.15 K, the dose of adsorbent = 1–10 g/L, and isotherms evaluated the adsorption process to determine the maximum adsorption capacity (Qmax, mg/g). Kinetic studies were performed starting from two different initial concentrations (25 and 50 mg/L) and at a maximum contact time of 48 h. The reusability potential of activated biochar was evaluated by adsorption–desorption cycles. The maximum adsorption capacity obtained with the Langmuir adsorption isotherm model was 114.8 mg/g at 300.15 K, pH = 5.4, and a dose of activated biochar of 1.0 g/L. This study also highlights the application of advanced machine learning techniques to optimize a chemical removal process. Leveraging a comprehensive dataset, a Gradient Boosting regression model was developed and fine-tuned using Bayesian optimization within a Python programming environment. The optimization algorithm efficiently navigated the input space to maximize the removal percentage, resulting in a predicted efficiency of approximately 90.47% under optimal conditions. These findings offer promising insights for enhancing efficiency in similar removal processes, showcasing the potential of machine learning in process optimization and environmental remediation.

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“…Dolayısıyla, RMSE'in mümkün olduğunca küçük, r 2 değerinin ise mümkün olduğunca büyük olması beklenmektedir. Bu çalışmada, 'Diamonds' veri kümesi için en uygun regresyon modelini belirlerken, RMSE ve r 2 ölçütlerinin ikisini bir arada değerlendirmek tercih edilmiştir [11].…”

Section: Veri Kümesi üZerinde Regresyon Modelleriunclassified

Pırlanta fiyat tahmini için regresyon modellerinin karşılaştırmalı analizi

ASİL

Alptekin

2022

NÖHÜ Müh. Bilim. Derg.

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Bilişim dünyasındaki gelişmeler ve artan veri hacmi ve çeşitliliği ile birlikte, hayatımıza büyük veri kavramı girmiş ve beraberinde birçok zorluğu da peşinde getirmiştir. Verinin işlenebilirliği büyük bir önem kazanmış ve güncel kullanılan bazı veri işleme yöntemlerinin performansı yetersiz gelmeye başlamıştır. Büyük veri analizinde yapay zekâ ve makine öğrenmesi teknikleri kullanılarak bu sorunlar çözülmeye çalışılmakta ve gün geçtikçe daha etkin çözümler bulan algoritmalar önerilmeye devam edilmektedir. Bu çalışmanın amacı, iyi bilinen ve sıklıkla kullanılan regresyon algoritmalarını bir veri kümesi üzerinde çalıştırmak ve performans sonuçlarını karşılaştırmalı olarak sunmaktır. Makalede pırlantaların kesimi, rengi, berraklığı ve fiyatı gibi özellikleri barındıran açık bir veri kümesi kullanılmıştır. Verilerin ön işlemesi yapılmış, tanımlayıcı analiz gerçekleştirilmiş ve fiyatlarının tahmini için 12 farklı regresyon modeli, hem ilkel hem de optimize edilmiş halleriyle çalıştırılmıştır. Algoritmalar RMSE (Root Mean Squared Error) değerlerine göre karşılaştırılmış ve sonuçlar yorumlanmıştır.

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Machine learning method for simulation of adsorption separation: Comparisons of model’s performance in predicting equilibrium concentrations

Cited by 40 publications

References 57 publications

Interoperability Requirements for Blockchain-Enabled Electronic Health Records in Healthcare: A Systematic Review and Open Research Challenges

Interoperability Requirements for Blockchain-Enabled Electronic Health Records in Healthcare: A Systematic Review and Open Research Challenges

Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Haematoxylum campechianum Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process

Pırlanta fiyat tahmini için regresyon modellerinin karşılaştırmalı analizi

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