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A Survey on Evolutionary Computation Based Drug Discovery

IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Yu, Qiyuan Lin, Qiuzhen Ji, Junkai Zhou, Wei He, Shan Zhu, Zexuan Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science University of Birmingham Bir-mingham UK Department of Computing The Hong Kong Poly-technic University Hong Kong Hong Kong

Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead com-pound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field. IEEE

关键词： Lead compounds

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Deep Learning Based Approach for Emotion Recognition Using Image-Text Fusion 2

Deep Learning Based Approach for Emotion Recognition Using I...

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2nd International Joint Conference on Information and Communication Engineering, JCICE 2023

作者： Li, Hanyun Liu, Hantao Yu, Peizhen Zhao, Jiacheng Wan, Bing Li, Wenzao Chengdu University of Information Technology Chengdu China Education Informatization and Big Data Center Education Department of Sichuan Province Chengdu China Chengdu Polytechnic School of Software Chengdu China

ISBN: (纸本)9798350325768

To address the problem that traditional emotion recognition methods using only text data fail to adequately explore emotional information, unimodal data lacks comprehensive information and, therefore, fails to sufficiently reflect the true emotional state. Therefore, we propose an emotion recognition model with image-Text fusion. First, the model uses pre-Trained models uses pre-Trained Bidirectional Encoder Representations from Transformers (BERT) and ResNet to extract features from text data and image data, respectively. The final feature representation vectors of images and text are combined using a later fusion approach, and finally, Softmax performs emotion recognition. Comparative experimental results show that our model outperforms both unimodal and other multimodal models on emotion recognition tasks. © 2023 IEEE.

关键词： Emotion Recognition

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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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Computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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Early Fault Detection in Rolling Element Bearings Using Signal Processing Techniques 21

Early Fault Detection in Rolling Element Bearings Using Sign...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Saeed, Ali Akram, M. Usman Khan, Muazzam A. Afsar, M. Belal Quaid-i-Azam University ICESCO Big Data Analytics and Edge Computing Department of Computer Science Islamabad Pakistan National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan Otto-von-Guericke University Faculty of Electrical Engineering and Information Technology Maddeburg Germany

ISBN: (纸本)9798350378078

Many studies show that bearings are the most vulnerable components in low-voltage motors. While advanced bearing diagnostic systems exist, their cost can be a barrier for non-critical machinery due to the potential wait time to see a return on investment. This research explores the feasibility of using basic signal processing techniques on vibration data for bearing condition monitoring. The primary goal is to analyze benchmark data from the Case Western Reserve University (CWRU) dataset and establish a baseline performance for this data-driven approach. The results suggest that, with appropriate signal processing, it's possible to achieve early detection of bearing faults, leading to more efficient time-To-fault identification with higher detection accuracies ranging from 83 % to 100 % for each method. © 2024 IEEE.

关键词： Signal modulation

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Research on the Relationship Between New Energy Vehicles and Dual Carbon in Yangtze River Delta Based on GRA-LSTM Model

Research on the Relationship Between New Energy Vehicles and...

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IEEE Advanced Information technology, Electronic and Automation Control Conference (IAEAC)

作者： Jinxuan Zhou Liang Deng Yi Tong Yan Gao Department of Information Engineering Wuhan Business University Hubei China Major in Data Science and Big Data Technology Major in Internet of Things Engineering Major in Software Engineering the main research direction is Big Data Technology

New energy automobile industry plays an important role in building a green, low-carbon and recycling industrial system. In this paper, the prediction simulation training and prediction accuracy comparison study are carried out with the help of the newly constructed GRA-LSTM model, Biological Neural Network model and the first-order one-variable gray GM (1, 1) model. The LSTM model is created and trained by Learning Rate Decay function. The Learning Rate Decay callback function is set, and the learning rate is gradually reduced during the training process to carry out simulation training, and finally the trained model is used to make predictions, and the time for the Yangtze River Delta region to reach carbon peak is 2028, and the time to reach carbon neutrality is 2060, and at the same time this paper analyzes and finds out that the higher the new energy automobile market ownership is, the shorter the time for carbon peak and carbon neutrality will be.2.

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation 13

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Xunfeng Liu, Songbai Ji, Junkai Ma, Lijia Leung, Victor C. M. Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350308365

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases. © 2024 IEEE.

关键词： Digital elevation model

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CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

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Development of Multi Objective Approach for Validation of PID Controller for Buck Converter 1

Development of Multi Objective Approach for Validation of PI...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Arun Kumar, K. Vegineni, Gopi Chand Suresh, C. Chowdari Marella, Bhagath Chandra Addanki, Sireesha Chimwal, Palak Big Data Architect India Sr.Software Engineer Enrollment and Eligibility Team India Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy Samayapuram621112 India Insights & Data Capgemini Department of Financial Services India System soft Technologies LLC India Bipin Tripathi Kumaon Institute of Technology Department of Electrical Engineering Almora Dwarahat India

ISBN: (纸本)9798331518578

Buck converters are a common converter of DCDC used for high energy conversion and power sources. These regulators are utilized in electronic gear to alter the result voltage because of changes in the heap current and information voltage. Therefore, in order to react to enquiries in real time, an efficient control system is required. In this work, an upgraded Sine Cosine Algorithm (ISCA) with proportional-integral-derivative (PID) controller is proposed for Mult objective optimization of a DC-DC buck converter. Through two distinct modifications, the limitations of the traditional SCA were therefore addressed, leading to the innovative use of nonlinear equations in conjunction with instrumental mechanisms to update the average location. By contrasting its response for frequency, response for increment, uncertainties of parametric, statistics of time-space indispensable error execution, objective capability, and resistance from aggravation with the previous methods-based PID for buck converters of DC-DC, the suggested ISCA-PID technique's robustness was evaluated. The results obtained later revealed that the suggested approach outperformed its algorithmic predecessors in terms of the DC-DC buck converter's remarkably improved transient responsiveness. © 2025 IEEE.

关键词： Buck converter

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Evolutionary Multiobjective Feature Selection Assisted by Unselected Features 13

Evolutionary Multiobjective Feature Selection Assisted by Un...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Duan, Xuan Liu, Songbai Ji, Junkai Li, Lingjie Lin, Qiuzhen Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350308365

To enhance the generalization of multi-objective feature selection (MOFS) in classification, this paper proposes an evolutionary multitasking algorithm, diverging from previous approaches that exclusively target selected features. The algorithm integrates information from both selected and unselected features, introducing a novel objective to minimize the accuracy of unselected features. This objective, combined with the goal of minimizing classification errors for selected features, forms an auxiliary MOFS task. The paper presents a dual-population evolutionary multitasking framework that synergizes the main MOFS task with the auxiliary task. A knowledge transfer mechanism, based on accuracy preferences, seamlessly shares insights from the auxiliary to the main task, aiming to identify improved Pareto feature subsets. Empirical results demonstrate the superior performance of several state-of-the-art multi-objective algorithms within this framework, highlighting significant improvements across diverse datasets. © 2024 IEEE.

关键词： Feature Selection

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Research on software Security Based on DVWA

Research on Software Security Based on DVWA

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Electronic technology, Communication and Information (ICETCI), IEEE International Conference on

作者： Junmei Wang Xinning Liu Department of Software and Big Data Technology Dalian Neusoft University of Information Dalian China

[Purpose] With the popularization of software and the Internet, people pay more and more attention to the security of the Web system. software security vulnerabilities have strong concealment. Once the Web system is attacked, it will cause incalculable losses to enterprises and individual users. How to understand the root causes of various software security vulnerabilities, and how to find software security vulnerabilities in order to improve the security of software systems is a very important topic in software development. [Methods] DVWA (Damn Vulnerable Web App) can help people understand various software security vulnerabilities. By using DVWA, people can understand various types of Web system software security vulnerabilities, as well as different levels of vulnerability prevention methods. [Conclusion] The experimental results show that DVWA provides a testing environment for various Web system security vulnerabilities, covers typical types of vulnerabilities, is easy to understand and use, and can effectively help developers improve the security of Web systems.

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