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Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms

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Green Chemical engineering 2025年第2期6卷 193-199页

作者： Ao Yang Shirui Sun Lu Qi Zong Yang Kong Jaka Sunarso Weifeng Shen College of Safety Science and Engineering Chongqing University of Science and TechnologyChongqing401331China Beijing Institute of Technology Chongqing Innovation Center Chongqing401135China School of Chemistry and Chemical Engineering Chongqing UniversityChongqing400044China School of Chemistry and Chemical Engineering Yangtze Normal UniversityChongqing408100China Jiangsu Key Laboratory of Advanced Catalytic Materials and Technology School of Petrochemical EngineeringChangzhou UniversityChangzhou213164China School of Engineering and Technology Sunway UniversityNo.5Jalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Sustainable Technologies Faculty of EngineeringComputing and ScienceSwinburne University of TechnologyJalan Simpang TigaKuching93350SarawakMalaysia

This study aims to significantly improve existing quantitative structure-property relationship(QSPR)models for predicting the octanol-water partition coefficient(KOW).This is because accurate predictions of KOW are crucial for assessing the environmental behavior and bioaccumulation potential of *** models have reported determination coefficient(R^(2))values between 0.9451 and 0.9681,and this research seeks to exceed these *** machine learning(ML)models are explored,i.e.,feed-forward neural networks(FNN),extreme gradient boosting(XGBoost),and random forest(RF).Using a dataset of 14,610 solvents(14,580 after data cleaning)and 21 molecular descriptors derived from SMILES representations,we rigorously evaluate these models based on R^(2),mean absolute error(MAE),root mean squared error(RMSE),and mean relative error(MRE).Notably,the best model developed,the XGBoost-based QSPR,demonstrated exceptional performance,exhibiting an impressive R^(2)value of 0.9772,surpassing benchmarks set by prior research ***,shapley additive explanation(SHAP)analysis is also employed for model interpretation,and it is revealed that the top five influential input features include SMR_VSA8,SMR_VSA3,Kappa2,HeavyAtomCount,and fr_*** study not only sets a new benchmark for KOW prediction accuracy but also enhances the interpretability of QSPR models.

关键词： Octanol-water partition coefficient Environmental property QSPR Interpretable model Machine learning

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An Improved Graph-CNN Based State of Health Estimation for Lithium-Ion Batteries 3rd

An Improved Graph-CNN Based State of Health Estimation for L...

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3rd International Joint Conference on Energy, Electrical and Power engineering, CoEEPE 2023

作者： Jin, Tao Hu, Cungang Shen, Weixiang Qin, A.K. School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia School of Electrical Engineering and Automation Anhui University Anhui Hefui China

ISBN: (纸本)9789819739394

State of health (SOH) plays a vital role in the safety, reliability and lifetime of lithium-ion batteries (LIBs). In this paper, a novel SOH estimation method is proposed based on graph convolutional neural network (CNN). First, this method considers the correlation between health features (HFs) and SOH. Second, the selected HFs are connected to form a graph. Third, graph-CNN extracts key features from the graph and performs SOH estimation. The results demonstrate that the proposed SOH estimation method can enhance the estimation accuracy and reduce the computational load. Compared with other deep learning methods, the proposed graph CNN method has high accuracy with robustness against different conditions with the average RMSE below 0.8% on the CALCE dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lithium-ion batteries

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2D Wound Image Segmentation Using Deep Learning Algorithms 2

2D Wound Image Segmentation Using Deep Learning Algorithms

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Sivakumar, Soubraylu Abhiram, V.S.V. Rama, P. Kallakrinda, Vikram Kumar, T. Rajesh Saravanan, P. Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798331541217

The purpose of wound segmentation is to accurately identify and delineate wound areas in images, enabling precise analysis of metrics like size, shape, and spread. This aids healthcare professionals in assessing wound severity and monitoring treatment progress. This research focuses on developing an automated wound image segmentation tool using the Deep learning Algorithms. The objective is to enhance wound assessment accuracy by precisely identifying and segmenting wound areas in digital images. Three models are employed to find the effectiveness in segmenting the wound which includes U- Net, DeepLabV3+ and Mask-CNN. The deep learning models are trained on a massive dataset of wound pictures and masks;they then provide segmented outputs, which are examined for wound characteristics and severity. The U-Net model has obtained better performance in segmenting the wound with an accuracy of 92.3%. This work seeks to incorporate advanced capabilities into a user-friendly interface for practical use in clinical settings. Initial results demonstrate promising segmentation accuracy, with plans for further enhancements in future. © 2024 IEEE.

关键词： Image segmentation

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Comparison of Image Encryption Techniques Using Chaotic Maps and Conventional Cryptographic Techniques 2

Comparison of Image Encryption Techniques Using Chaotic Maps...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Varshney, Aanchal Routh, Pratyusha Sujatha, G. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

In past few years, there has been a great deal of research being done on chaos-based cryptosystems. Chaos-based cryptosystems like Arnold cats map can add security when employed with traditional public key cryptosystems like RSA or El-Gamal, despite the fact that these techniques are not standardized like AES, DES, RSA, etc. Although chaotic-map-based encryption techniques are frequently recommended for picture or video encryption because of their computational efficiency, standard encryption algorithms like DES, AES and RSA have traditionally been the preferred option. The encryption methods like chaos-based picture encryption and conventional cryptographic techniques like AES, DES and RSA are surveyed in this work. There is discussion of the great advancements in image encryption. The comparison is done on the basis of quality of image before and after encryption and decryption using various methods like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Mean Squared Error (MSE). The PSNR, MSE, and SSIM values of chaotic map-based cryptography don't perform well when compared to traditional methods like RSA, DES, and AES. Also the time complexities and the key sizes of all the algorithms have been compared. The conventional techniques consistently show nearly perfect values, highlighting their stronger security and reliability in data protection and encryption. In contrast, the chaotic map-based approaches fall short in terms of these metrics, indicating that the traditional methods are more effective in ensuring robust security. © 2024 IEEE.

关键词： Computational efficiency

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Cherenkov Radiation:A Stochastic Differential Model Driven by Brownian Motions

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Computer Modeling in engineering & Sciences 2023年第4期135卷 155-168页

作者： Qingqing Li Zhiwen Duan Dandan Yang School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan430074China

With the development of molecular imaging,Cherenkov optical imaging technology has been widely *** studies regard the partial boundary flux as a stochastic variable and reconstruct images based on the steadystate diffusion *** this paper,time-variable will be considered and the Cherenkov radiation emission process will be regarded as a stochastic *** on the original steady-state diffusion equation,we first propose a stochastic partial differential *** numerical solution to the stochastic partial differential model is carried out by using the finite element *** the time resolution is high enough,the numerical solution of the stochastic diffusion equation is better than the numerical solution of the steady-state diffusion equation,which may provide a new way to alleviate the problem of Cherenkov luminescent imaging *** addition,the process of generating Cerenkov and penetrating in vitro imaging of 18 F radionuclide inmuscle tissue are also first proposed by GEANT4Monte *** result of the GEANT4 simulation is compared with the numerical solution of the corresponding stochastic partial differential equations,which shows that the stochastic partial differential equation can simulate the corresponding process.

关键词： Cherenkov radiation stochastic partial differential equations numerical approximation and analysis GEANT4 Monte Carlo simulation

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ASYMPTOTICAL STABILITY OF NEUTRAL REACTION-DIFFUSION EQUATIONS WITH PCAS AND THEIR FINITE ELEMENT METHODS

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Acta Mathematica Scientia 2023年第4期43卷 1865-1880页

作者：韩豪张诚坚 School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China

This paper focuses on the analytical and numerical asymptotical stability of neutral reaction-diffusion equations with piecewise continuous ***,for the analytical solutions of the equations,we derive their expressions and asymptotical stability ***,for the semi-discrete and one-parameter fully-discrete finite element methods solving the above equations,we work out the sufficient conditions for assuring that the finite element solutions are asymptotically ***,with a typical example with numerical experiments,we illustrate the applicability of the obtained theoretical results.

关键词： neutral reaction-diffusion equations piecewise continuous arguments asymptotical stability finite element methods numerical experiment

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MFS: A Serverless FaaS Simulator 9

MFS: A Serverless FaaS Simulator

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9th International Conference on Web Research, ICWR 2023

作者： Hanaforoosh, Mahrad Ashtiani, Mehrdad Azgomi, Mohammad Abdollahi Iran University of Science and Technology Trustworthy Computing Laboratory School of Computer Engineering Tehran Iran Iran University of Science and Technology Social Computing Laboratory School of Computer Engineering Tehran Iran

ISBN: (纸本)9798350399691

Serverless computing is one of the concepts that has become a trend these days. Serverless computing facilitates the deployment of software on the cloud, fog, edge, and IoT infrastructures. Serverless computing helps developers implement their codebase without worrying about server management hassles. These are the reasons why the concept of serverless computing is presented in the cloud, the edge, the fog, and the Internet of things. Research on serverless computing, on the other hand, requires extensive hardware equipments. It can also be time consuming. Simulation is needed to overcome these problems. MFS is a simulator based on the Apache Open Whisk architecture that seeks to facilitate the evaluation process of serverless computing researches. Compared to similar researches, this simulator supports more features including containers, heterogeneous processing resources (e.g., CPU, GPU, and TPU), different runtimes, and Apache OpenWhisk architecture. It also calculates and reports metrics such as average response time, utilization of different computing resources, rejection ratio, missed ratio, number of containers used, and costs. The mentioned metrics can be calculated in overall and individually for each physical machine. The status of all functions is also reported at the end of the simulation (e.g., completed, missed, and rejected). © 2023 IEEE.

关键词： Containers

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Feature Selection in Learning to Rank for Listwise Approach using Multiobjective Evolutionary Algorithm

Feature Selection in Learning to Rank for Listwise Approach ...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Amala, K.J. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Information retrieval systems commonly depend on ranking models to prioritize search results, which requires efficient feature selection to improve accuracy. Feature selection improves the model's query relevance ranking by selecting the most relevant features. This unique method avoids diluting the ranking model with unnecessary variables, resulting in more accurate ranks. The proposed approach consists of three separate stages: multi-objective instance selection, feature selection, and ensemble method. By utilizing a listwise methodology, the algorithm directly enhances the optimization of ranking criteria resulting in enhanced performance. The effectiveness of FS-listwise was evaluated using standardized datasets. After conducting experiments, results shown that the proposed method have peak performance than MOFSRank and classical ranking algorithms, such as ListNet and LambdaMART, in terms of Normalized Discounted Cumulative Gain and consistently selecting a minimal yet highly effective feature set. © 2024 IEEE.

关键词： Contrastive Learning

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Smart GPS Based Vehicle Speed Limit Controller on zone identification using Geo-Fencing Algorithm 3

Smart GPS Based Vehicle Speed Limit Controller on zone ident...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Upendra, Modem Reddy, Varun Safa, M. College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Networking and Communications Kattankulathur Chennai603203 India

ISBN: (纸本)9798350381931

In light of growing concerns about road safety and the surge in traffic accidents, this project aspires to an Internet of Things smart GPS-based vehicle speed limit controller system. The primary objective is to elevate road safety by harnessing real-time data and automated control mechanisms to enforce speed limits, thereby diminishing incidents of speeding-related accidents. This project adopts a comprehensive approach, integrating both hardware and software components. Vehicle-mounted sensors, encompassing speed sensors and GPS receivers, continually gather real-time data on vehicle speed and location. Furthermore, road condition data is sourced from multiple outlets, such as weather stations and traffic cameras. This data is wirelessly transmitted to a central control unit, which processes and analyzes it. The control unit deploys machine learning algorithms to evaluate vehicle speed limits against predefined speed limits for specific road segments. Depending on this analysis, it transmits control signals back to the vehicle when necessary. These signals can encompass driver warnings, power limitations, or autonomous speed control. These methodologies collectively enabled the creation of an intelligent and adaptable GPS-based vehicle speed control system, offering the potential to significantly enhance road safety by dynamically enhancing road safety regulating vehicle speeds in different zones, ensuring the safety of people and drivers alike. The Smart GPS-based vehicle speed limit controller system has undergone successful development and testing, both in controlled environments and on public roads. The outcomes manifest a remarkable reduction in incidents of speeding and an enhanced adherence to speed limits, exhibiting an accuracy level exceeding 96%. In real-world scenarios, the system effectively mitigates speeding-related risks, leading to a substantial decline in traffic accidents. Its ability to adapt to changing road conditions and. Its scalability ren

关键词： Internet of things

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Electrothermal Model Based Remaining Charging Time Prediction of Lithium-Ion Batteries against Wide Temperature Range

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Chinese Journal of Mechanical engineering 2024年第2期37卷 330-339页

作者： Rui Xiong Zian Zhao Cheng Chen Xinggang Li Weixiang Shen School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVIC 3122Australia

Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’***,the RCT for a lithiumion battery pack in EVs changes with temperature and other battery *** study proposes an electrothermal model-based method to accurately predict battery ***,a characteristic battery cell is adopted to represent the battery pack,thus an equivalent circuit model(ECM)of the characteristic battery cell is established to describe the electrical behaviors of a battery ***,an equivalent thermal model(ETM)of the battery pack is developed by considering the influence of ambient temperature,thermal management,and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal ***,the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from-20℃to 45℃.The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.

关键词： Electric vehicles Lithium-ion batteries Remaining charging time Electrothermal model

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