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Multi-channel CNN to classify Nepali COVID-19 related tweets using hybrid features

Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 2047-2056页

作者： Sitaula, Chiranjibi Shahi, Tej Bahadur Department of Electrical and Computer Systems Engineering Monash University Wellington Rd ClaytonVIC3800 Australia Earth Observation and AI Research Group Department of Infrastructure Engineering The University of Melbourne ParkvilleVIC3010 Australia School of Engineering and Technology Central Queensland University Rockhampton Australia Central Department of Computer Science and Information Technology Tribhuvan University TU Rd Kathmandu Kirtipur44618 Nepal

Because of the current COVID-19 pandemic’s increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected developed countries but also developing countries such as Nepal. These complications can be understood from peoples’ tweets and comments posted online after their felicitous analysis and sentiment classification. Nevertheless, owing to the limited number of tokens and words in each tweet, it is crucial to capture multiple pieces of information associated with them for better understanding. In this study, we, first, represent each tweet by combining both syntactic and semantic information, called hybrid features. The syntactic information is generated from the bag-of-words method, whereas the semantic information is generated from the combination of the fastText-based (ft) and domain-specific (ds) methods. Second, we design a novel multi-channel CNN (MCNN) model, which ensembles the multiple CNNs, to capture multi-scale information for better classification. Last, we evaluate the efficacy of both the proposed feature extraction method and the MCNN model classifying tweets into three sentiment classes (positive, neutral, and negative) on the NepCOV19Tweets dataset, which is the only public COVID-19 tweets dataset in the Nepali language. The evaluation results show that the proposed hybrid features outperform individual feature extraction methods with the highest classification accuracy of 69.7% and the MCNN model outperforms the existing methods with the highest classification accuracy of 71.3% during classification. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： COVID-19

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Toward Privacy-Aware Efficient Federated Graph Attention Network in Smart Cloud 9

Toward Privacy-Aware Efficient Federated Graph Attention Net...

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9th IEEE International Conference on Smart Cloud, SmartCloud 2024

作者： Zhou, Jinhao Su, Zhou Wang, Yuntao Pan, Yanghe Pan, Qianqian Liu, Lizheng Wu, Jun Waseda University Graduate School of Information Production and Systems Fukuoka Japan Xi'an Jiaotong University School of Cyber Science and Engineering China The University of Tokyo School of Engineering Japan Fudan University Academy for Engineering and Technology China

ISBN: (纸本)9798350389500

Federated graph attention networks (FGATs), blending federated learning (FL) with graph attention networks (GAT), present a novel paradigm for collaborative, privacy-conscious graph model training in the smart cloud. FGATs leverage distributed attention mechanisms to enhance graph feature prioritization, improving representation learning while preserving data decentralization. Despite their advancements, FGATs face privacy concerns, such as attribute inference. Our study proposes an efficient privacy-preserving FGAT (PFGAT). We devise an improved multiplication triplet (IMT)-based attention mechanism with a hybrid differential privacy (DP) approach. We invent a novel triplet generation method and a hybrid neighbor aggregation algorithm, specifically designed to respect the distinct traits of neighbor nodes, efficiently secures GAT node embeddings. Evaluations on benchmarks such as Cora, Citeseer, and Pubmed demonstrate PFGAT's ability to safeguard privacy without compromising on efficiency or performance. © 2024 IEEE.

关键词： Blending

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Point-defect-driven flattened polar phonon bands in fluorite ferroelectrics

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npj Computational Materials 2023年第1期9卷 1120-1128页

作者： Pu Ai Fengjun Yan Wen Dong Shi Liu Junlei Zhao Kan-Hao Xue Syed Ul Hasnain Bakhtiar Yilong Liu Qi Ma Ling Miao Mengyuan Hua Guangzu Zhang Shenglin Jiang Wei Luo Qiuyun Fu School of Integrated Circuits Engineering Research Centre for Functional Ceramics of the Ministry of Education&Wuhan National Lab for Optoelectronics&Optical Valley Laboratory Huazhong University of Science and Technology430074 WuhanChina Department of Physics School of ScienceWestlake UniversityHangzhou310024 ZhejiangChina School of Optical and Electronic Information Research Centre&Wuhan National Lab for Optoelectronics&Optical Valley Laboratory Huazhong University of Science and Technology430074 WuhanChina Department of Electrical and Electronic Engineering Southern University of Science and Technology518055 ShenzhenChina Shenzhen Huazhong University of Science and Technology Research Institute 518000 ShenzhenP.R.China

The scale-free ferroelectric polarization of fluorite MO_(2)(M=Hf,Zr)due to flat polar phonon bands are promising for nonvolatile *** are also widely introduced to improve the emergent ***,their roles are still not fully understood at the ***,we report a significant effect of point-defect-driven flattening of polar phonon bands with more polar modes and polarization contribution in doped MO_(2).The polar phonon bands in La-doped MO_(2)(M=Hf,Zr)can be significantly flattened,compared with pure ***,the lower energy barrier with larger polarization of VO-only doped MO_(2) compared with La-doped cases suggest that VO and local lattice distortion should be balanced for high-performance fluorite *** work is believed to bridge the relation between point defects and the generally enhanced induced ferroelectricity in fluorite ferroelectrics at the atomic-level and inspire their further property optimization via defect-engineering.

关键词： bands phonon ferroelectric

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Breast Cancer Diagnosis Using Elephant Herding Optimization and Sparse Autoencoder Through Gene Expression Analysis 15th

Breast Cancer Diagnosis Using Elephant Herding Optimization ...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Kavya, B. Meghana, G. Rajaguru, Harikumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Dayananda Sagar University Bangalore India Department of Computer Science and Engineering PES University RR Campus Bangalore India

ISBN: (纸本)9783031810794

Gene expression analysis is an efficient modality to detect breast cancer. But analysis of the gene expression data is a complicated task due to the involvement of large number of numerical data for each subject. This research work focuses on the development of computerized diagnosis systems for analyzing the gene expression data and classifying the subjects as either normal or breast adenocarcinoma. Sparse autoencoder is used for dimensionality reduction and Elephant Herding Optimization is used for transforming the features. Four different supervised classifiers namely support vector machine, random forest classifier, decision trees, and naive bayes classifier are examined for classification task. Among them, random forest provides the superior balanced accuracy of 86% while it was further enhanced to 92% with the help of sparse autoencoders and elephant herding optimization algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transcription

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Evolution of Cooperation in Snowdrift Game with Multi-Memory Mechanism 42

Evolution of Cooperation in Snowdrift Game with Multi-Memory...

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42nd Chinese Control Conference, CCC 2023

作者： Liu, Yaojun Liu, Xingwen Tashi, Nyima College of Electrical Engineering Southwest Minzu University Lab. of Complex Systems & Intelligent Control Key Lab. of Electronic Information of State Ethnic Affairs Commission Sichuan Chengdu610225 China Engineering Research Center for Tibetan Information Processing School of Information Science and Technology Tibet University Lhasa850000 China

ISBN: (纸本)9789887581543

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural evolution. Since memory can greatly influence the emergence of cooperative behavior, memory-based research on evolutionary games has proliferated in recent years. In the present paper, inspired by the well-known 'Stanford marshmallow experiment' (SME), we proposed a multi-memory mechanism in Snowdrift game (SDG) on spatial lattice networks whose core lies in what follows: Players are divided into two groups according to individual self-control;different memory lengths are set by group and by time in a single realization. The simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary game. More specifically, as memory length increases to an intermediate length, the cooperation level increases as well. For mechanism with short memory length, the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative behavior. These findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Behavioral research

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AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models

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Multimedia Tools and Applications 2025年第4期84卷 1821-1843页

作者： Aydin, Mehmet Barua, Prabal Datta Chadalavada, Sreenivasulu Dogan, Sengul Tuncer, Turker Chakraborty, Subrata Acharya, Rajendra U. Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland Toowoomba Australia School of Engineering University of Southern Queensland Springfield LakesQLD4300 Australia School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2351 Australia Center for Advanced Modelling and Geospatial Information Systems Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia School of Mathematics Physics and Computing University of Southern Queensland Toowoomba Australia

In 2023, Turkiye faced a series of devastating earthquakes and these earthquakes affected millions of people due to damaged constructions. These earthquakes demonstrated the urgent need for advanced automated damage detection models to help people. This study introduces a novel solution to address this challenge through the AttentionPoolMobileNeXt model, derived from a modified MobileNetV2 architecture. To rigorously evaluate the effectiveness of the model, we meticulously curated a dataset comprising instances of construction damage classified into five distinct classes. Upon applying this dataset to the AttentionPoolMobileNeXt model, we obtained an accuracy of 97%. In this work, we have created a dataset consisting of five distinct damage classes, and achieved 97% test accuracy using our proposed AttentionPoolMobileNeXt model. Additionally, the study extends its impact by introducing the AttentionPoolMobileNeXt-based Deep Feature engineering (DFE) model, further enhancing the classification performance and interpretability of the system. The presented DFE significantly increased the test classification accuracy from 90.17% to 97%, yielding improvement over the baseline model. AttentionPoolMobileNeXt and its DFE counterpart collectively contribute to advancing the state-of-the-art in automated damage detection, offering valuable insights for disaster response and recovery efforts. © The Author(s) 2024.

关键词： Image classification

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Research on Vegetable Commodity Pricing and Replenishment Strategy Based on Simulated Annealing Algorithm 3

Research on Vegetable Commodity Pricing and Replenishment St...

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3rd International Signal Processing, Communications and engineering Management Conference, ISPCEM 2023

作者： Shi, Jinfeng Zhang, Rui Feng, Siqi Zhang, Qiang School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China College of Science Wuhan University of Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Systems Science in Metallurgical Process Wuhan China

ISBN: (纸本)9798350307023

In fresh food superstores, the freshness period of most vegetable products is relatively short, and the quality of vegetables will deteriorate due to the increase in storage time and cannot be sold on the next day, so it is very important to determine the total daily replenishment for superstores. This paper discusses the optimization problem of daily replenishment total and pricing decisions. In this paper, firstly, the bivariate correlation coefficient analysis is carried out using SPSS to calculate the correlation coefficients between the total sales volume of each category and the unit price of sales;secondly, the regression model is established to derive the fitting equations and the R2 the test is carried out. For the superstore revenue optimization problem, firstly, the total daily replenishment strategy model of each vegetable category was established, and secondly, the pricing strategy model of each vegetable category was established through the wholesale price and sales unit price. Considering the transportation loss and deterioration of vegetables, the goods will be sold at a discount, this paper establishes the pricing strategy model of undiscounted and discounted sales. Finally, the optimal model is obtained based on the simulated annealing algorithm to ensure revenue maximization. The final maximum returns (RMB) from July 1-7, 2023 are 8091.56, 8102.48, 8097.47, 8114.66, 8166.51, 8115.96, and 8126.11, respectively. © 2023 IEEE.

关键词： Vegetables

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A Four-Valued Ambiguous Logic: Application in Designing Ambiguous Inference System for Control systems

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International Journal of Fuzzy systems 2023年第8期25卷 2921-2938页

作者： Singh, Pritpal Huang, Yo-Ping Quantum Optimization Research Lab Department of Data Science and Analytics Central University of Rajasthan Rajasthan Ajmer305817 India Department of Electrical Engineering National Penghu University of Science and Technology Penghu88046 Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan

Ambiguous set has recently been introduced as a new branch of mathematics and computer science dealing with the nature of ambiguity of uncertain events. There is much scope for the development of new theories based on the principles of the ambiguous set. In this direction, an idea of ambiguous logic is proposed, which can be regarded as a family of four-valued logic. In this study, the limitations of existing theories such as fuzzy logic, intuitionistic fuzzy logic, and neutrosophic logic that are used to deal with uncertainty are first discussed. Then an introduction to the ambiguous set and ambiguous logic is given. In ambiguous logic, there are four membership degrees for each proposition, which can be characterized as true, false, true-ambiguous, and false-ambiguous. We then analyze the membership degrees of propositions using various connectives, including negation, conjunction, and disjunction. In this study, we discuss various laws that support the different algebraic operations for comparing propositions in terms of membership degrees. An approach is presented to define a rule using a proposition consisting of membership degrees, called an ambiguous rule. The modus ponens rule of inference is defined by combining various ambiguous rules, and is called ambiguous inference. Finally, to demonstrate the real-time application of ambiguous logic, an ambiguous inference system is designed that can be used in machine control systems. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy systems Association.

关键词： Fuzzy logic

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Amplitude-Constrained Gerchberg-Saxton (AC-GS) Optimization for Laguerre-Gaussian Beams in Atmospheric-turbulence Channels

Amplitude-Constrained Gerchberg-Saxton (AC-GS) Optimization ...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on information Photonics and Optical Communications, IPOC 2024

作者： Feng, Bianxia Zhao, Tianfeng Wen, Feng Wu, Baojian Qiu, Kun Key Lab of Optical Fiber Sensing and Communications Ministry of Education School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

We propose an amplitude-constrained Gerchberg-Saxton method to effectively compensate for the distorted Laguerre-Gaussian beams. The proposed scheme exhibits the greater adaptability and achieves 5.33% improvement on ... 详细信息

ISBN: (纸本)9798350379266

关键词： Atmospheric turbulence

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Enhanced Object Detection and Classification via Multi-Method Fusion

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Computers, Materials & Continua 2024年第5期79卷 3315-3331页

作者： Muhammad Waqas Ahmed Nouf Abdullah Almujally Abdulwahab Alazeb Asaad Algarni Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073South Korea

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented *** substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled *** address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection *** proposed approach involves the integration ofmultiple methods in a complementary *** process commences with the application of Gaussian filters tomitigate the impact of noise *** images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent *** Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented *** precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms *** Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved *** method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize *** minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall *** proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,***,detection accuracies of 87.2%and 86.6%have been *** ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex *** these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

关键词： BRIEF features saliency map fuzzy c-means object detection object recognition

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