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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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An end-to-end contrastive deep-learning framework for remote physiological signal measurement 3

An end-to-end contrastive deep-learning framework for remote...

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3rd Vision-Based Remote Physiological Signal Sensing Challenge and Workshop, RePSS 2024

作者： Wu, Bingjie Zhou, Menghan Liu, Wei Wang, Xingyao Zheng, Xingjian Xie, Yiping Luo, Chaoqi Zhen, Liangli Singapore Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Electrical Engineering Southwest Jiaotong University Chengdu China

Heart rate measurements based on remote physiological signals could significantly facilitate health monitoring in daily life. However, the ground-truth labels of the physiological signals are expensive and hard to collect. In this paper, we present a contrastive self-supervised learning framework to extract discriminative remote physiological features by leveraging periodic signal priors without ground-truth labels in the pre-training stage. Specifically, a ranking loss and a contrastive learning loss are constructed to extract knowledge with resampling of the video clips. In addition, data augmentation and ensemble learning strategies are designed to fine-tune the pre-trained model and fuse the results to improve the heart rate measurement. Our final solution achieves the 1st place in track 1 of the 3rd Vision-based Remote Physiological Signal Sensing (RePSS) Challenge. © 2024 Copyright for this paper by its authors.

关键词： contactless heart rate measurement Remote photoplethysmography RePSS self-supervised learning

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Tree Regularization for Visual Explanations of Cancer Cell Classification

Tree Regularization for Visual Explanations of Cancer Cell C...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Raza, Muhammad Umair Chen, Jie Wang, Li Nawaz, Adil Leung, Victor C.M. Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China

ISBN: (纸本)9798350386226

The challenge of interpretability remains a significant barrier to adopting deep neural networks in healthcare domains. Although tree regularization aims to align a deep neural network's decisions with a single axis-aligned decision tree, however, relying on one tree for all inputs often leads to sub-optimal performance and interoperability. To address this limitation, we propose an enhanced tree regularization method that integrates a post-hoc visual explainable model such as Grad-CAM. This approach guides the deep model to be well-approximated by decision trees tailored to the salient regions identified by Grad-CAM in the input space. We rigorously validate the effectiveness of this framework on two cancer cell datasets: CNMC, which focuses on acute lymphoblastic leukemia cells, and ISBI2016, which comprises benign and malignant skin lesions. The results demonstrate that the proposed method delivers simpler and more interpretable explanations without compromising accuracy, thereby advancing the interpretability of deep learning models in critical healthcare applications. © 2024 IEEE.

关键词： Cancer cells

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Spatial-spectral attention state-space fusion network for tree carbon estimation using multi-source data

Spatial-spectral attention state-space fusion network for tr...

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2024 International Conference on Remote Sensing and Global Positioning Algorithm, RSGPA 2024

作者： Fu, Yawen Hu, Dong Zhou, Yuxin Sun, Zhixiong Zhang, Shuyu College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China Beijing Institute of Astronautical Systems Engineering Beijing100076 China

Amid rapid industrialization and urbanization, global warming and the urban heat island effect are intensifying. There is an urgent need for rapid and accurate monitoring of carbon storage in urban vegetation to assess and mitigate these environmental issues. While methods exist for estimating above-ground carbon (AGC) stocks in individual trees using deep learning, fewer studies integrate multi-source remote sensing data with these techniques. This study introduces a network that leverages spatial-spectral attention and state-space models, called the spatial-spectral attention state-space fusion network (S4FNet). S4FNet utilizes UAV hyperspectral imaging (HSI) data, light detection and ranging (LiDAR) data, and RGB data to estimate AGC storage in individual trees. This study conducted experiments at two sites, the South and North areas of Shenzhen University’s Yuehai Campus, Shenzhen City, Guangdong Province, China, and compared S4FNet with eight traditional machine learning methods and a deep learning method. The experimental results demonstrate that S4FNet achieves the best performance across all metrics, confirming its excellent capability in predicting AGC stocks using multi-source remote sensing data. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Carbon sequestration

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Foreign Object Recognition Model on Coal Mine Belt Conveyors Based on Improved MobileNetV3 7

Foreign Object Recognition Model on Coal Mine Belt Conveyors...

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7th International Conference on Communication and Information Systems, ICCIS 2023

作者： Hu, Yujia Zhang, Zhiwei College of Computer Science Sichuan University Department of Artificial Intelligence Chengdu China College of Computer Science Xi'an Aeronautical Institute Department of Software Engineering Xi'an China

ISBN: (纸本)9798350330540

Coal mine belt conveyors are commonly used equipment in coal mining enterprises which transport coal from the coal faces using belts. Currently, video image monitoring serves as the predominant approach to identify foreign objects within the transported coal, such as large gangue and rock bolts. In recent years, the utilization of Convolutional Neural Networks has become prevalent in automatically recognizing foreign objects within images. Nevertheless, existing models suffer from issues including low recognition accuracy and slow speed. In response to the aforementioned problems, this paper proposes an improved coal foreign object recognition model based on MobileNetV3. Firstly, the Coordinate Attention mechanism is introduced into MobileNetV3 to fully consider both channel and spatial features. Secondly, the ReLU6 activation function in MobileNetV3 is replaced with the ELU activation function to augment the model's capacity to express information within the negative value range. Finally, superfluous network layers are removed to reduce computational complexity and improve recognition speed. Experimental results demonstrate that the proposed improved MobileNetV3 model achieves an accuracy of 90.8% in recognizing foreign object images, which is 2.5 to 4.0 percentage points higher compared to commonly used neural networks including AlexNet and EfficientNet. Moreover, compared with MobileNetV3, the proposed model reduces the number of parameters and computational complexity by 40.3% and 12.0% respectively. These results indicate that the proposed model reaches higher recognition accuracy while reducing network size and computational load. As a result, it provides a feasible approach for enhancing safety in coal mining enterprises. © 2023 IEEE.

关键词： Attention mechanism Image foreign object recognition MobileNetV3

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Deep Learning in ChatGPT - A Survey 7

Deep Learning in ChatGPT - A Survey

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Hashana, A.M. Jasmine Brundha, P. Ahamed Ayoobkhan, Mohamed Uvaze Fazila, S. Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India Software Engineering New Uzbekistan University Tashkent Uzbekistan

ISBN: (纸本)9798350397284

Abstract-As a subset of machine learning, deep learning makes use of multiple-layer neural networks to learn with available data and make decisions or predictions. A large language model called ChatGPT is based on deep learning, specifically a type of neural network called a transformer. ChatGPT's transformer architecture uses attention mechanisms to focus on the most important parts of the input, allowing it to process and comprehend a large amount of text data. In order for the model to comprehend the context and meaning of natural language text, it is trained on a huge database of text, including articles and books. One of the main importance of using deep learning in ChatGPT is its intelligence to understand relationships and patterns from the input text and generate or predict new text that is homogeneous to the input/training data. Because of this, ChatGPT is able to respond to questions and prompts in a manner that is comparable to that of a human, making it useful for a wide scope of natural language processing missions like translating languages, summarizing texts, and responding to questions. It's worth noting that, while deep learning has been highly effective in ChatGPT, it is not without its limitations. To train, deep learning models can be very complex and require a lot of data and computing power. © 2023 IEEE.

关键词： Network layers

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Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors

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computers, Materials & Continua 2023年第4期75卷 2331-2346页

作者： Hammad Rustam Muhammad Muneeb Suliman A.Alsuhibany Yazeed Yasin Ghadi Tamara Al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic University237 Sangidaehak-ro Siheung-siGyeonggi-do15073Korea

Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports *** have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to *** have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors.

关键词： Genetic algorithm human locomotion activity recognition human–computer interaction human gestures recognition principal hand gestures recognition inertial sensors principal component analysis linear discriminant analysis stochastic neighbor embedding

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Injections Attacks Efficient and Secure Techniques Based on Bidirectional Long Short Time Memory Model

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computers, Materials & Continua 2023年第9期76卷 3605-3622页

作者： Abdulgbar A.R.Farea Gehad Abdullah Amran Ebraheem Farea Amerah Alabrah Ahmed A.Abdulraheem Muhammad Mursil Mohammed A.A.Al-qaness School of Big Data&Software Engineering Chongqing UniversityChongqing401331China Department of Management Science Engineering Dalian University of TechnologyDalian116024China Software College Northeastern UniversityShenyang110169China Department of Information Systems College of Computer and Information ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Management Science and Engineering South China University of TechnologyGuangzhou510641China Department of Computer Engineering and Mathematics University of Rovira i VirgiliTarragonaSpain College of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua321004China

E-commerce,online ticketing,online banking,and other web-based applications that handle sensitive data,such as passwords,payment information,and financial information,are widely *** web developers may have varying levels of understanding when it comes to securing an online *** Query language SQL injection and cross-site scripting are the two vulnerabilities defined by the OpenWeb Application Security Project(OWASP)for its 2017 Top Ten List Cross Site Scripting(XSS).An attacker can exploit these two flaws and launch malicious web-based actions as a result of these *** published articles focused on these attacks’binary *** article described a novel deep-learning approach for detecting SQL injection and XSS *** datasets for SQL injection and XSS payloads are combined into a single *** dataset is labeledmanually into three labels,each representing a kind of *** work implements some pre-processing algorithms,including Porter stemming,one-hot encoding,and the word-embedding method to convert a word’s text into a *** model used bidirectional long short-term memory(BiLSTM)to extract features automatically,train,and test the payload *** payloads were classified into three types by BiLSTM:XSS,SQL injection attacks,and *** outcomes demonstrated excellent performance in classifying payloads into XSS attacks,injection attacks,and non-malicious ***’s high performance was demonstrated by its accuracy of 99.26%.

关键词： Web security SQL injection XSS deep learning RNN LSTM BiLSTM

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EMARec: a sequential recommendation with exponential moving average

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Neural Computing and Applications 2024年第21期36卷 12917-12933页

作者： Chen, Rui Wang, Zonglin Tang, Cundong Zhang, Jianwei Li, Pu Kong, Xiangjie Huang, Min College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450001 China Henan Joint International Research Laboratory of Computer Animation Implementation Technologies Zhengzhou450001 China School of Information Science and Technology Northwest University Xi’an710127 China Faculty of Information and Engineering Xuchang Vocational Technical College Xuchang461000 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China

Capturing dynamic preference features from user historical behavioral data is widely applied to improve the accuracy of recommendations in sequential recommendation tasks. However, existing deep neural network-based sequential recommendation methods often ignore the noise information in user behavioral data that influence recommendation effectiveness, making recommendation models sensitive to noisy data. Additionally, these deep learning-based recommendation models often require a large number of parameters to capture user behavior patterns, leading to higher time complexity and susceptibility to overfitting due to noise fluctuations. To address these issues, in this paper, we apply the moving average concept from the field of time-series analysis to sequential recommendation tasks. The approach smooths sequence data, removes noise, making the data more stable and reliable, increases the model’s insensitivity to noise information, and better understands the evolution of user interests and behavioral patterns. Specifically, in our experiments, the moving average algorithm effectively denoises sequential data with low time complexity. Therefore, we propose a Sequential Recommendation with Exponential Moving Average with MLP architecture that makes the model more competitive in terms of time complexity. Experimental results conducted on two datasets demonstrate that EMARec outperforms state-of-the-art sequential recommendation methods across various common evaluation metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Recommender systems

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SSM-CloudNet: state space model-based cloud and cloud shadow detection network for Sentinel-2 imagery

SSM-CloudNet: state space model-based cloud and cloud shadow...

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2024 International Conference on Remote Sensing and Global Positioning Algorithm, RSGPA 2024

作者： He, Lirong Pu, Pengyu Zhang, Ying Xu, Meng College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China Beijing Institute of Astronautical Systems Engineering Beijing100076 China

Remote sensing images acquired by satellite sensors are inevitably obscured by clouds and cloud shadows, which obscure true ground reflectance information in those areas. Therefore, to ensure the accuracy and reliability of downstream analyses, detecting clouds and their shadows in satellite images is crucial. This paper proposes a state space model-based cloud and cloud shadow detection network for Sentinel-2 imagery, named SSM-CloudNet. This is the first time that a state space model (SSM) is applied to cloud and cloud shadow detection tasks, combining the efficient feature extraction capability of the symmetric encoder-decoder structure with the accuracy of SSM in cloud feature extraction, and providing an innovative approach to this task. Experiments demonstrate that SSM-CloudNet performs exceptionally well across multiple metrics, proving the high effectiveness of the proposed method. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Cloud platforms

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