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A survey on textual emotion cause extraction in social networks

Digital Communications and Networks 2025年第2期11卷 524-536页

作者： Sancheng Peng Lihong Cao Guojun Wang Zhouhao Ouyang Yongmei Zhou Shui Yu Laboratory of Language Engineering and Computing Center for Linguistics and Applied LinguisticsGuangdong University of Foreign StudiesGuangzhou510006China School of English Education Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China South China University of Technology Guangzhou510006China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science University of Technology SydneySydneyNSW 2007Australia

With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their *** and more people are used to commenting on a certain hot spot in SNs,resulting in a large amount of texts containing *** Emotion Cause Extraction(TECE)aims to automatically extract causes for a certain emotion in texts,which is an important research issue in natural language *** is different from the previous tasks of emotion recognition and emotion *** addition,it is not limited to the shallow-level emotion classification of text,but to trace the emotion *** this paper,we provide a survey for ***,we introduce the development process and classification of ***,we discuss the existing methods and key factors for ***,we enumerate the challenges and developing trend for TECE.

关键词： Text Emotion Emotion cause Machine learning Deep learning

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MedAccess HBPF: A Privacy-Preserving Hybrid-Blockchain Framework for Secure and Efficient Cloud-Based Electronic Health Record Sharing

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SN computer science 2024年第8期5卷 1-10页

作者： Duggegowda, Divyashree Ramamoorthy, Umadevi School of Science and Computer Studies CMR University Bengaluru India

Electronic Health Record (EHR) sharing is essential for enhancing the quality of healthcare services, particularly in organ donation systems where timely access to patient data is crucial. However, centralized cloud-based EHR systems are susceptible to privacy breaches and unauthorized access. This paper presents the MedAccess Hybrid-Blockchain Privacy Framework (HBPF), a novel approach that combines the scalability of cloud storage with the security and immutability of blockchain technology. Our framework employs a hybrid blockchain architecture consisting of a Record Chain (RecChain) Blockchain for storing immutable transaction records and an Audit Chain (AuditChain) Blockchain for managing sensitive operations and overseeing the RecChain. The framework also incorporates a Three-Party Key Exchange Protocol (TPKE) protocol enabling fine-grained access control and ensuring that only authorized healthcare providers can access specific EHRs. Performance evaluation demonstrates that the MedAccess HBPF framework achieves significant improvements over existing models. Specifically, it reduces storage costs by 50% compared to BCAACPC, and maintains low computational costs, starting at 20 ms and only slightly increasing to about 25 ms, making it suitable for large-scale deployment. These results highlight the framework's effectiveness in maintaining high computational efficiency and low communication overhead, making it a viable solution for modern healthcare systems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Access control Cloud computing Electronic health records Hybrid-blockchain Organ donation Privacy-preserving

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Improving agility in projects using machine learning algorithm

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Multimedia Tools and Applications 2024年第38期83卷 85987-86005页

作者： Varun, Janani Karthika, R.A. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

All the software products developed will need testing to ensure the quality and accuracy of the product. It makes the life of testers much easier when they can optimize on the effort spent and predict defects for the upcoming modules in the Agile era. The functionality being discussed in this paper is to predict the defects using Random Forest Algorithm. Predictive analytics draws on information from the past to create forecasts about the outcomes of future events. Product team always have the difficulty in delivering the product as per schedule. As we are in the agile era, the requirement keeps changing and team is unsure on upcoming releases. Prediction helps the team to focus on the complex and error prone modules in upcoming releases. The Predictive analytics model designed, can predict defects with an accuracy rate of 88% with the help of historical data. By predicting, testers can focus on the module where there are a greater number of defects predicted by the model and left shift the delivery. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Predictive analytics

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Hybrid Artificial Neural Network Activation Function to Reduce Water Wastage in Agricultural Irrigation

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IEEE Access 2025年 13卷 93302-93322页

作者： Jawad, Baraa H. Alwesabi, Ola A. Abdullah, Nibras Mohammed, Ahmed Abed University Sains Malaysia School of Computer Science Peneng 11800 Malaysia Arab Open University Faculty of Computer Studies Saudi Arabia University of Al-Qadisiyah College of Computer Science and Information Technology Diwaniyah Iraq

Most nations rely heavily on agriculture to provide employment. In recent years, as a result of a growing global population's increased demand for food and water, numerous nations are consuming excessive amounts of already scarce freshwater resources. A large amount of water is used for irrigation. The purpose of this study is to find a way to reduce the amount of water lost during the irrigation process. The only practical solutions to the above problems are precision farming and smart irrigation. The study proposes a hybrid activation function based on the Artificial Neural Network algorithm. This function is used to classify the need for irrigation in various crops and predict the best time of the day for watering. The achievement of this is done by utilizing a hybrid activation function known as TanElu, which combines the Tanh and ELU functions. This function is used to classify the need for irrigation and predict the best time to water crops. The overall accuracy achieved by the proposed model was 98.24% and 97.31% in irrigation classification and prediction of the best irrigation time, respectively, which is a significant improvement compared to previous studies. Besides, execution time was 7.88 and 8.28 seconds in irrigation classification and prediction of best irrigation time respectively. The results indicate that the proposed hybrid activation function outperforms the single activation functions. Thus, the proposed model can significantly reduce water loss during the irrigation process and represents an encouraging step toward sustainable agriculture and water conservation. © 2013 IEEE.

关键词： Crops

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Autism spectrum disorder prediction system using machine learning and deep learning

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International Journal of Applied Systemic studies 2024年第2期11卷 159-173页

作者： Sharma, Anshu Tanwar, Poonam School of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Haryana Faridabad121002 India

Autism spectrum disease (ASD) is a neuro developmental illness that is both complicated and degenerative. A majority of known approaches use autism detection observation schedule (ADOS), pattern recognition, etc. to detect ASD with a small dataset, resulting in high accuracy but low generality. In this research, an ASD detection hybrid model is presented which is works on two different types of datasets. Firstly, behavioural datasets which works on logistic regression technique and secondly, facial dataset which works on based on convolutional neural network (CNN) Classifier in order to predict whether a person is suffering from autism or not. The suggested hybrid model which works on behavioural and facial dataset of a person beats state-of-the-art approaches in terms of accuracy, according to simulation findings. The proposed hybrid model had an average accuracy of 88% for the logistic regression model while it achieved an accuracy of 82.76% for the CNN model. © 2024 Inderscience Enterprises Ltd.

关键词： Classification (of information)

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On the complexity of computing Markov perfect equilibrium in general-sum stochastic games

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National science Review 2023年第1期10卷 288-301页

作者： Xiaotie Deng Ningyuan Li David Mguni Jun Wang Yaodong Yang Center on Frontiers of Computing Studies School of Computer Science Peking University Center for Multi-Agent Research Institute for AI Peking University Huawei UK Computer Science University College London

Similar to the role of Markov decision processes in reinforcement learning,Markov games(also called stochastic games) lay down the foundation for the study of multi-agent reinforcement learning and se quential agent *** introduce approximate Markov perfect equilibrium as a solution to the computational problem of finite-state sto chastic games repeated in the infinite horizon and prove its *** solution concept preserves the Markov perfect property and opens up the possibility for the success of multi-agent reinforcement learning algorithms on static two-player games to be extended to multi-agent dynamic games,expanding the reign of the PPAD-complete class.

关键词： Markov game multi-agent reinforcement learning Markov perfect equilibrium PPAD-completeness stochastic game

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Web API Recommendation via Combining Adaptive Multichannel Graph Representation and xDeepFM Quality Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3218-3232页

作者： Cao, Buqing Qing, Yueying Zhou, Dong Xie, Xiang Kang, Guosheng Liu, Jianxun Fletcher, Kenneth K. Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou510420 China University of Massachusetts Computer Science BostonMA02125-3393 United States

With the increasing number of Web services, how to provide developers with Web APIs that meet their Mashup requirements accurately and efficiently has become a challenge. Even though the existing methods show improvements in service recommendation, the efficiency and accuracy (ACC) of them still need to be improved due to their limited representation in fuzing network topology and node feature of Web service, and the neglected higher-order feature interactions of Web service. To address this problem, this article proposes a Web APIs recommendation method via combining adaptive multichannel (AMC) graph representation and eXtreme deep factorization machine (xDeepFM) quality prediction. In this method, firstly, specific embedding and shared embedding in Web API node isomorphic network are extracted from the nodes' feature space, topology space, and the combination of the two spaces. Then, attention mechanism is used to adaptively learn the importance weight of each embedding. Next, these embeddings are adaptively integrated to generate the multichannel graph representation of Web APIs for service classification. Finally, aiming at the Web APIs in the service cluster, it utilizes xDeepFM to model and mine the complex feature interactions and predict and rank the scores of Web APIs for Mashup creation. The experimental results on the real datasets of ProgrammableWeb show that compared with DeepFM, wide and deep learning (WDL), FM supported neural network (FNN), neural factorization machine (NFM), and mixed logistic regression (MLR), the method proposed in this article has an average improvement in AUC of 2.3%, 7.9%, 8.0%, 9.6%, and 13.3%. © 2020 IEEE.

关键词： Network topology

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OSSIoT: An ontology-based Operational Security model for Social Internet of Things using Machine Learning Techniques

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第10期51卷 1440-1453页

作者： Kumar, K.S. Santhosh Hanumanthappa, J. Prakash, S.P. Shiva Krinkin, Kirill Department of Studies in Computer Science University of Mysore Karnataka Mysuru India Department of Information Science and Engineering JSS Science and Technology University Karnataka Mysuru India School of Computer Science and Engineering Constructor University Bremen Germany

The Social Internet of Things (SIoT) is an innovative fusion of IoT and smart devices that enable them to establish dynamic relationships. Securing sensitive data in a smart environment requires a model to determine the relationships between devices through object profiling and ontological models. To address this need, we have proposed an ontology-based operational security model for the SIoT. In our approach, the interpolation method is used to establish relationships, while fiduciary relationships are employed to detect threats. Furthermore, the encryption of heterogeneous device data, coupled with the implementation of an operation-based intrusion detection system, proves highly effective in identifying potential *** relationships are identified as intruders and validated using machine learning techniques. Encrypting heterogeneous device data, along with an operation-based intrusion detection system, efficiently identifies threats in the ever-evolving dynamic nature of the SIoT environment. the encryption of heterogeneous device data, coupled with an operation-based intrusion detection system, effectively identifies threats. Invalid relationships are promptly identified as attackers and machine learning techniques are used to validate relationships encryption and machine learning stand as indispensable tools in the endeavor to secure sensitive information within the realm of the SIoT. The suggested model outperformed the results of the current model, as evidenced by its average accuracy of 85.67%, precision of 90.37%, recall of 92.06%, and F1-score of 91.03% when compared to the existing model. © (2024), (International Association of Engineers). All rights reserved.

关键词： Network intrusion

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Beyond the Hype: Empirical Evaluation of Cryptocurrency Unicorn Success 58

Beyond the Hype: Empirical Evaluation of Cryptocurrency Unic...

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58th Hawaii International Conference on System sciences, HICSS 2025

作者： Mukherjee, Arghya Moore, Tyler Tandy School of Computer Science College of Engineering & Computer Science The University of Tulsa United States School of Cyber Studies College of Engineering & Computer Science The University of Tulsa United States

ISBN: (纸本)9780998133188

Thousands of cryptocurrency coins and tokens have been introduced in recent years, with each purporting to offer a unique take on disrupting traditional financial instruments. Most fail to attract significant investment, but some grow quite valuable for at least a short time. This paper focuses on so-called "crypto unicorns", which reach a market capitalization of at least $1 billion at some point during their lifetimes. 37 coins and 139 tokens have reached unicorn status. However, only 15 coins and 35 tokens retain market capitalizations exceeding $1 billion at end of our study, with 6 coins and 31 tokens falling below $100 million. We empirically examine the factors that influence the relative success or failure of crypto unicorns. Using regression analysis, we find that bitcoin price, the type of service offered by the coin or token, having an ICO and social media activity all affect success. © 2025 IEEE computer Society. All rights reserved.

关键词： Blockchain

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Realizing quantum imaginary time evolution in experiment

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science China(Physics,Mechanics & Astronomy) 2024年第4期67卷 167-168页

作者： Xiao Yuan Center on Frontiers of Computing Studies Peking UniversityBeijing 100871China School of Computer Science Peking UniversityBeijing 100871China

NISQ,or Noisy Intermediate-Scale Quantum computing,represents a pivotal and the state-of-the-art quantum technology[1].Characterized by intermediate-scale quantum devices that are beyond classical computational capabilities yet fall short of the error-correction thresholds required for fault-tolerant quantum computing,NISQ devices exhibit both promise and *** devices hold immense potential in solving certain computational problems that surpass classical capabilities,with wide applications in optimization,machine learning,and quantum chemistry.

关键词： quantum imaginary correction

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