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Evolutionary extreme learning machine based on an improved MOPSO algorithm

Neural Computing and Applications 2025年第12期37卷 7733-7750页

作者： Ling, Qinghua Tan, Kaimin Wang, Yuyan Li, Zexu Liu, Wenkai School of Computer Science Jiangsu University of Science and Technology Zhenjiang212003 China

Extreme learning machine (ELM), as a single hidden layer feedforward neural network (SLFN), has attracted extensive attention because of its fast learning speed and high accuracy. However, the random selection of input weights and hidden biases is the main reason that deteriorates the generalization performance and stability of ELM network. In this study, an improved ELM (IMOPSO-ELM) is proposed to enhance the generalization performance and convergence stability of the SLFN by using a multi-objective particle swarm optimization (MOPSO) to determine the input parameters including input weights and hidden biases of the SLFN. Firstly, different from the traditional improved ELM based on single-objective evolutionary algorithm, the proposed algorithm used MOPSO to optimize the input weights and hidden biases of SLFN by considering the two objectives including accuracy on the validation set and the 2-norm of the SLFN output weights. Secondly, in order to improve the diversity and convergence of the solution set obtained by MOPSO, an improved MOPSO (IMOPSO) is proposed. The improved MOPSO uses a new optimal global particle selection strategy, by randomly dividing the population into several subpopulations, each subpopulation uses different particle information in the external archive to guide the subpopulation update, and uses the external archive set as the platform to share the information between sub-swarms. Finally, the experiment on the four regression problems and four classification problems verifies the effectiveness of the approach in improving ELM generalization performance and performance stability. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Extreme learning machine Generalization performance IMOPSO Performance stability

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Deep Learning-based Estimation of Emission Time and Arrival Time in Diffusive Multi-Receiver Molecular Communication

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IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 2025年第2期11卷 257-268页

作者： Cheng, Zhen Liu, Heng Xu, Ziyan Li, Jiaxin Chi, Kaikai School of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China School of Computer Science and Technology Anhui University of Technology Ma’anshan243032 China

Diffusive molecular communication (DMC) utilizes the emission, diffusion and reception of molecules to transmit information. It has promising prospects in the field of drug delivery. The estimation of emission time and arrival time of molecules in DMC system plays important roles in the resource consumption at the receivers. Existing traditional strategies for the derivation of emission time and arrival time mainly focus on known channel state information (CSI). In this paper, we propose a deep learning method for estimating emission time and arrival time of the molecules in DMC system with unknown CSI by using Transformer-based model, respectively. The simulation results show that the emission time and arrival time of molecules can be accurately estimated by the Transformer-based model which exhibits better estimation and generalization abilities than deep neural network (DNN) model. © 2025 IEEE. All rights reserved.

关键词： Deep neural networks

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Adaptive E-Learning Environments: A Methodological Approach to Identifying and Integrating Multi-layered Learning Styles

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SN computer science 2024年第6期5卷 772页

作者： Ramesh, M. Jayashree, R. Department of Computer Science and Applications Faculty of Science and Humanities SRM Institute of Science and Technology Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology

E-learning environments represent digital platforms designed to facilitate online learning experiences. Recognizing the diverse learning preferences of individuals, the need for identifying and integrating multi-layered learning styles within these environments is paramount. Existing approaches often face limitations in accurately capturing and accommodating these intricate learning styles. To address these challenges, this study proposes a novel approach. Firstly, the integration of word embedding-based feature extraction transforms textual data into continuous vector representations, enhancing feature robustness and meaningfulness. Secondly, leveraging deep belief networks (DBNs) allows for the automatic learning of hierarchical data representations, improving model performance by capturing complex patterns. Thirdly, the DBN-based model accommodates multi-layered learning styles, including visual, auditory, kinaesthetic, and read/write preferences, offering personalized recommendations to learners. Lastly, the proposed approach is scalable and generalizable, capable of handling large datasets and diverse educational contexts, thus enhancing the efficacy of e-learning platforms. This innovative approach demonstrates promising advancements in learning style identification within e-learning environments, providing personalized guidance to learners and improving the overall effectiveness of online education. The proposed Deep Belief Network (DBN) model exhibits a significant average increase in accuracy compared to other methods implemented in Python. With an accuracy of 99.5%, the DBN model surpasses the accuracy of the K-Nearest Neighbours (KNN) and Random Forest (RF) models by 25.4 and 3.3%, respectively. This substantial improvement underscores the superiority of the proposed DBN model in learning style identification. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep belief networks E-learning environments Learning style identification Multi-layered learning styles Word embedding

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SMILES2DTA: a CNN-based approach for identifying drug candidates and predicting drug-target binding affinity

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Neural Computing and Applications 2024年第4期37卷 2891-2910页

作者： Mukit, Hasanul Hossain, Sayeed Farabi, Mirza Milan Chowdhury, Mehrab Zaman Pritom, Ahmed Iqbal Rana, Humayan Kabir Department of Computer Science and Engineering Green University of Bangladesh Narayanganj-1461 Dhaka Bangladesh Department of Computer Science The University of Alabama at Birmingham BirminghamAL United States Department of Computer Science and Engineering Islamic University of Technology Gazipur Bangladesh

Computational approaches can speed up the drug discovery process by predicting drug-target affinity, otherwise it is time-consuming. In this study, we developed a convolutional neural network (CNN)-based model named SMILES2DTA (Simplified Molecular Input Line Entry System to Drug-Target Affinity) for predicting the binding affinity between a drug and a target protein. The model utilizes CNNs to process sequences of both drug SMILES and target proteins. SMILES2DTA generates multiple sequences from a single drug SMILES sequence, validates them based on Lipinski’s rule of five, and assesses their binding affinity against a target protein sequence. We evaluated our model using publicly available datasets and compared its performance to state-of-the-art methods. The results showed that SMILES2DTA outperformed the existing methods and demonstrated improved accuracy, mean squared error, and area under the precision-recall curve. SMILES2DTA has the potential to speed up the drug discovery process by reducing the time and cost complexity of identifying effective drugs. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Drug discovery

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Self-supervised Hypergraph Transformer with Alignment and Uniformity for Recommendation

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第3期51卷 292-300页

作者： Yang, XianFeng Liu, Yang School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China School of computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

Graph neural networks have proven their effectiveness for user-item interaction graph collaborative filtering. However, most of the existing recommendation models highly depended on abundant and high-quality datasets and neglected the alignment and uniformity of embedding representation. The noisy and skewed distribution of data from real-world applications is ubiquitous. The alignment and uniformity are crucial for representation learning. In this paper, we propose SHTAU (self-supervised hypergraph transformer with alignment and uniformity for the recommendation) to address the problem. Specifically, we first perform a graph neural network on the user-item interaction graph, then captures the global collaboration relationship between users and items through the hypergraph transformer structure, and use the extracted global information to generate self-supervised signals for data enhancement on the user-item interaction graph to enhance the robustness of the recommendation algorithm. Simultaneously, alignment and uniformity auxiliary task update embeddings from different angles to improve performance. Extensive experiments are conducted on Yelp, Gowalla, MovieLens, Amazon-book and Tmall datasets. The experimental results show that SHTAU has obvious advantages over the baseline methods. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Alignment

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Data Hiding Methods Using Voting Strategy and Mapping Table

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Journal of Internet technology 2024年第3期25卷 365-377页

作者： Chi, Hengxiao Chang, Chin-Chen Lin, Chia-Chen Department of Information Engineering and Computer Science Feng Chia University Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taiwan

With advancements in technology, the study of data hiding (DH) in images has become more and more important. In this paper, we introduce a novel data hiding scheme that employs a voting strategy to predict pixels based on their neighbors, then embeds data into the predicted pixels according to a designed mapping table. To extract the information, it is only necessary to use the voting strategy to predict the pixels again, then to compare the predicted and hidden pixels to extract the secret data with the assistance of the mapping table. Our experimental results demonstrate that the proposed hiding scheme has a high embedding capacity, and preserves a satisfactory visual quality. Additionally, it attains a high peak signal-to-noise ratio (PSNR) even at high embedding capacity. © 2024 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Pixels

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MLMO-HSM: Multi-label Multi-output Hybrid Sequential Model for multi-resident smart home activity recognition

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2313-2325页

作者： Ramanujam, E. Perumal, Thinagaran Department of Computer Science and Engineering National Institute of Technology Silchar Assam 788010 India Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang India

Smart home automation is protective and preventive measures that are taken to monitor elderly people in a non-intrusive manner using simple and pervasive sensors termed Ambient Assistive Living. The smart home produces a large volume of sensor activations to predict an elder’s health status to improve the quality of life and independent living. Machine learning techniques are very familiar and popular in recognizing single resident activities using such a sensor reading. However, multi-resident activities are more complex, and no correlation exists between sensor readings and activities. Recently deep learning and graphical models have been proposed to solve this problem, but it consumes more time to train the model. Moreover, models are primarily executed in parallel, or multi-resident activity recognition has been converted to single for classifying events. This paper proposes a Multi-label Multi-output Hybrid Sequential Model (MLMO-HSM), a feature engineering approach with a hybrid sequential model to recognize the multi-resident activities concurrently in the shortest time. Experimentation has been performed with various machine learning, graphical and deep learning models at the different sizes of the ARAS dataset to validate the efficiency of the proposed model in terms of activity recognition and computation time. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Automation

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COVID-19 Fake News Detection using Deep Learning Model

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Annals of Data science 2024年第6期11卷 2167-2198页

作者： Akhter, Mahabuba Hossain, Syed Md. Minhaz Nigar, Rizma Sijana Paul, Srabanti Kamal, Khaleque Md. Aashiq Sen, Anik Sarker, Iqbal H. Department of Computer Science and Engineering Premier University Chattogram4000 Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram4349 Bangladesh

People may now receive and share information more quickly and easily than ever due to the widespread use of mobile networked devices. However, this can occasionally lead to the spread of false information. Such information is being disseminated widely, which may cause people to make incorrect decisions about potentially crucial topics. This occurred in 2020, the year of the fatal and extremely contagious Coronavirus Disease (COVID-19) outbreak. The spread of false information about COVID-19 on social media has already been labeled as an "infodemic" by the World Health Organization (WHO), causing serious difficulties for governments attempting to control the pandemic. Consequently, it is crucial to have a model for detecting fake news related to COVID-19. In this paper, we present an effective Convolutional Neural Network (CNN)-based deep learning model using word embedding. For selecting the best CNN architecture, we take into account the optimal values of model hyper-parameters using grid search. Further, for measuring the effectiveness of our proposed CNN model, various state-of-the-art machine learning algorithms are conducted for COVID-19 fake news detection. Among them, CNN outperforms with 96.19% mean accuracy, 95% mean F1-score, and 0.985 area under ROC curve (AUC). © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： COVID-19

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Linguistically Differentiating Acts and Recalls of Racial Microaggressions on Social Media

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Proceedings of the ACM on Human-computer Interaction 2024年第CSCW1期8卷 1-36页

作者： Gunturi, Uma Sushmitha Kumar, Anisha Ding, Xiaohan Rho, Eugenia H. Department of Computer Science Virginia Tech United States Department of Computer Science Virginia Tech United States

In this work, we examine the linguistic signature of online racial microaggressions (acts) and how it differs from that of personal narratives recalling experiences of such aggressions (recalls) by Black social media users. We manually curate and annotate a corpus of acts and recalls from in-the-wild social media discussions, and verify labels with Black workshop participants. We leverage Natural Language Processing (NLP) and qualitative analysis on this data to classify (RQ1), interpret (RQ2), and characterize (RQ3) the language underlying acts and recalls of racial microaggressions in the context of racism in the U.S. Our findings show that neural language models (LMs) can classify acts and recalls with high accuracy (RQ1) with contextual words revealing themes that associate Blacks with objects that reify negative stereotypes (RQ2). Furthermore, overlapping linguistic signatures between acts and recalls serve functionally different purposes (RQ3), providing broader implications to the current challenges in content moderation systems on social media. © 2024 Copyright held by the owner/author(s).

关键词： Linguistics

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CCGS-based discriminatory recognition of skeleton-based actions

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Multimedia Tools and Applications 2025年第6期84卷 3161-3174页

作者： Sameem, M Shujah Islam Department of Computer Science College of Computer Sciences & Information Technology King Faisal University Al Ahsa Saudi Arabia

As life forms feature joints, dynamic skeletons have been studied extensively as a potential tool for action detection. As a result of their focus on modelling skeletons, older methods were unable to adequately convey the whole range of motion at more erratic skeletal joints. Despite introducing spatial methods for unstructured data, detecting many simultaneous motions remains challenging. As part of this study, now article proposes a CCGS for skeleton-based action recognition by depicting skeletons organically on joints and therefore attempting to capture the spatiotemporal variation in the data. Let here propose a motion regression to learn the shape statistically from numerous data points since the representation of the joints is crucial to motion convolution. To achieve this sparsity in the underlying CCGS for efficient representation, now give spatial modelling of skeletons and formulate an optimisation problem with a slip-critical joint. Each joint has a strong or weak connection to the other joints in the same frame and to key joints in the action frames that have been processed. The coordinates of the skeleton sequence and the positions of the joints are then sent to the motion-caused graph, which has been tuned, so that features based on Chebyshev, spatial, and angular approximations of the motion may be learned. Also, the article examines the motion features approximation's representation of variation. The results of the experiments show that the recommended joints regression works and the proposed CCGS achieves state-of-the-art performance on the most popular datasets. The present research suggests a CCGS-based method for skeleton-based action recognition that captures spatiotemporal variations by intuitively depicting skeletons on joints. The experimental results suggest that the proposed method obtains state-of-the-art performance, with accuracy enhancements of up to 2% over existing methods on prominent datasets. © The Author(s), under exclusive licenc

关键词： Chebyshev approximation

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