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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Sharma, Rahul Kumar Dagur, Arvind School of Computing Science & Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819771776

Sentiment analysis is a subset of NLP and has encountered an outstanding change with the introduction of new approaches to increase its precision and performance. Neural networks and transformers in deep learning are very widely used techniques in sentiment analysis. This paper suggests the most recent research techniques that are currently used in sentiment analysis and also examines the difficulty and limitations of recently used techniques in sentiment analysis. This paper also provides directions for future studies. The increasing effectiveness of deep learning algorithms in sentiment analysis is an important finding from this survey. To analyze text data and determine the underlying sentiment, neural networks, and transformers have shown extraordinary potential. They are especially helpful in traversing the nuances of human language, which present difficulties for traditional machine learning techniques. Despite these developments, there are still difficulties with reliably identifying irony and sarcasm in sentiment analysis tools. To mitigate these issues, future research should concentrate on devising more robust sentiment analysis strategies capable of accurately interpreting sarcasm and irony. Additionally, efforts should be directed toward developing diverse datasets to ensure sentiment analysis techniques do not favor certain demographics or viewpoints. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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An EETR Approach for Therapeutic Response Prediction Using Gene Expression and Drug Properties 3rd

An EETR Approach for Therapeutic Response Prediction Using G...

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3rd International Conference on computing and Communication Networks, ICCCN 2023

作者： Selvi Rajendran, P. Jawahar, Janiel Computer Science and Engineering School of Computing Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9789819726707

In recent years, the role of computational methods such as machine learning and deep learning has evolved to help better understand an individual’s response to drugs. Through advancements in the discipline of precision medicine, cancer therapies are made based on an individual's pharmacogenomics data. In this way, doctors can make informed decisions about patient outcomes and reduce treatment costs. Beyond these advancements, there is enough void to precisely predict drug response. A drug’s response to therapy is dependent on a number of factors such as the drug’s physicochemical properties, pharmacokinetics, metabolism, protein, mutation, somatic variation, environment, and much more. Studying the correlation of more of these factors and their association with the drug is one of the promising ways to bridge the gap in improving results. Extra tree regression is an ensemble-based algorithm and is less prone to overfitting than other ensemble learning algorithms particularly when low bias and reduced overfitting are desired, and computational efficiency is important. In this paper, we have applied the drug’s physicochemical properties to predict the Inhibition values using an enhanced extra tree regression (EETR) algorithm. The EETR algorithm prediction outperformed both the random forest and original ETR algorithm in terms of both MSE and R2 score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Cross-graph Knowledge Exchange for Personalized Response Generation in Dialogue Systems

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IEEE Internet of Things Journal 2025年第10期12卷 13769-13789页

作者： Dong, Yuezhou Qin, Ke Ke, Pei Liang, Shuang Luo, Guangchun National Key Laboratory of Intelligent Collaborative Computing China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Electronic Science and Technology of China National Key Laboratory of Intelligent Collaborative Computing Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

Recent advancements in language models have greatly improved dialogue systems, but they still face challenges in generating personalized responses that are consistent with the user's persona and dialogue context. Existing approaches typically model dialogue context and persona information together in a unified manner, but they lack fine-grained differentiation between the two, leading to inaccurate user modeling. This misalignment hinders the ability of dialogue systems to produce personalized responses. In this work, we propose CKE (Cross-Graph Knowledge Exchange), a novel algorithm designed to enhance personalized response generation in dialogue systems. CKE constructs separate dialogue user graphs for each party in the dialogue, representing both their dialogue context and persona information. These graphs are then utilized to perform cross-graph structured knowledge aggregation, where the aggregation is under supervised by both its own and the other party's persona and dialogue context, providing richer, more accurate representations. Furthermore, CKE introduces a hybrid prompt template that combines both discrete and continuous elements, improving the language model's ability to leverage graph-structured information. The experimental results demonstrate that CKE significantly outperforms existing baseline methods in generating more coherent, contextually appropriate, and personalized responses in dialogue systems. © 2014 IEEE.

关键词： Knowledge graph

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Performance investigation of MEA-1DMA2P solution for CO2 capture: A rate-based modelling, optimization, and cost analysis

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Journal of Industrial and engineering Chemistry 2025年 147卷 179-200页

作者： Afkhamipour, Morteza Shamsi, Mohammad Seifi, Ebad Hekmatiyan, Abbas Ansarpour, Meisam N.Borhani, Tohid National Iranian Gas Company South Pars Gas Complex Asaluyeh Iran Process Engineering Department Faculty of Chemical Engineering Tarbiat Modares University Tehran Iran Department of Chemical Engineering Faculty of Engineering Arak University Arak Iran School of Engineering Centre for Engineering Innovation and Research School of Engineering Computing and Mathematical Sciences University of Wolverhampton United Kingdom

This study presents a framework for evaluating the performance of MEA-1DMA2P solution in CO2 absorption for a structured packed bed by integrating a rate-based model with statistical optimization. The developed model is validated with experimental data. Sensitivity analysis identified mass transfer correlations as critical factors influencing CO2 concentration profiles, with kinetic models having a less significant impact. Operating parameters showed that CO2 removal efficiency increases with higher amine flow rate, column pressure, and amine temperature but decreases with higher CO2 gas concentration. Efficiency notably declines when the amine temperature exceeds 333 K. Statistical optimization was used to analyze the combined influence of operating parameters on CO2 removal performance. The response values for statistical optimization were obtained using the rate-based model, identifying the ranking of parameter impacts: amine flow rate had the most significant influence, followed by CO2 partial pressure, blended amine temperature, and CO2 loading of blended amine. Cost analysis was also done by scaling up the absorption system using an MEA-1DMA2P solution. The cost of CO2 capture based on the absorber column was estimated as 40.35 $/tonne of CO2, which is acceptable compared to the literature and other solvents and processes for CO2 capture. © 2024 The Korean Society of Industrial and engineering Chemistry

关键词： Sensitivity analysis

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Cost-Effective Edge Data Caching with Failure Tolerance and Popularity Awareness

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IEEE Transactions on Mobile computing 2025年第6期24卷 5357-5369页

作者： Luo, Ruikun Zhang, Zujia He, Qiang Xu, Mengxi Chen, Feifei Dai, Xiaohai Wu, Song Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology China Deakin University School of Information Technology GeelongVIC3125 Australia

In the mobile edge computing environment, caching data in edge storage systems can significantly reduce data retrieval latency for users while saving the costs incurred by cloud-edge data transmissions for app vendors. Existing edge data caching (EDC) methods prioritize popular data and aim to minimize users' data retrieval latency and system storage costs jointly. However, these EDC methods often rely on the assumption that data popularity always follows certain distributions. As a result, they cannot properly adapt to the fluctuations in data popularity due to user mobility or unexpected increases in user demands. Meanwhile, unlike cloud data centers, complex and fragile edge servers are more likely to experience physical failures or network outages, presenting new challenges for EDC strategies. Specifically, when an edge server fails or experiences an outage, cached data may become temporarily unavailable, leading to increased latency as requests are redirected to alternative servers or the cloud. In this paper, to enable uncertainty-aware edge data caching (uEDC), we first model the problem as a robust optimization problem and propose an optimal algorithm named uEDC-B to find the optimal uEDC solution. To address the high computational complexity of uEDC-B, we introduce an approximate algorithm named uEDC-L based on linear decision rules. Theoretical analysis and extensive experiments on a real-world dataset demonstrate that the proposed methods outperform two state-of-the-art approaches in handling the uncertainties in data popularity and edge server failure with a significant performance improvement of 59.27% in data retrieval latency and 55.07% in data caching cost. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Some Considerations for the Preservation of Endangered Languages Using Low-Resource Machine Translation 37th

Some Considerations for the Preservation of Endangered La...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Kho, Alastair Pham, Duc-Son Soon, Susannah Chan, Kit Yan School of Electrical Engineering Computing and Mathematical Sciences Curtin University BentleyWA Australia

ISBN: (纸本)9789819603473

While widespread languages remain actively prevalent in digital mediums, endangered languages such as Indigenous Australian languages, are often scarce in textual resources and lack a substantial digital presence. This diminishes the survivability of their language and cultural heritages, making language preservation initiatives important. Neural Machine Translation (NMT) efforts for low-resource languages can help accelerate the digitisation and preservation of such languages by further enabling translation, information access, and second-language acquisition. This study explores the challenges and considerations with low-resource machine translation (MT), specifically focusing on primarily oral Indigenous Australian languages with minimally available textual resources. Additionally, we explore the existing challenges in the search for quality data and ethical research considerations in approaching Indigenous Cultural Intellectual Property (ICIP). As NMT performance often scales with the quality and quantity of multilingual corpora, we explore promising alternatives such as leveraging large language models (LLMs) to tackle severely low-resource MT as a few-shot prompting translation task. By employing a data imputation approach inspired by Continuous Bag-of-Words (CBOW) to strengthen a prompt’s contextual relevancy, we enhance translations generated by LLMs, achieving a chrF score of 37.3 on imputed data, compared to a baseline of 31.6 with GPT-3.5, and 39.3 compared to a baseline of 38.3 on GPT-4. Through our work, we hope to establish a foundation for future efforts in preserving Indigenous Australian languages. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computer aided language translation

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meMIA: Multilevel Ensemble Membership Inference Attack

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 93-106页

作者： Ullah, Najeeb Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore University of Nebraska-Lincoln School of Computing LincolnNE68588 United States

Leakage of private information in machine learning models can lead to breaches of confidentiality, identity theft, and unauthorized access to personal data. Ensuring the safe and trustworthy deployment of AI systems necessitates addressing privacy concerns to prevent unintentional disclosure and discrimination. One significant threat, membership inference (MI) attacks, exploit vulnerabilities in target learning models to determine if a given sample was part of the training set. However, the effectiveness of existing MI attacks is often limited by the number of classes in the dataset or the need for diverse multilevel adversarial features to exploit overfitted models. To enhance MI attack performance, we propose meMIA, a novel framework based on stacked ensemble learning. meMIA integrates embeddings from a neural network (NN) and a long short-term memory (LSTM) model, training a subsequent NN, termed the meta-model, on the concatenated embeddings. This method leverages the complementary strengths of NN and LSTM models;the LSTM captures order differences in confidence scores, while the NN discerns probability distribution differences between member and nonmember samples. We extensively evaluate meMIA on seven benchmark datasets, demonstrating that it surpasses current state-of-the-art MI attacks, achieving accuracy up to 94.6% and near-perfect recall. meMIA's superior performance, especially on datasets with fewer classes, underscores the urgent need for robust defenses against privacy attacks in machine learning, contributing to the safer and more ethical use of AI technologies. © 2024 IEEE.

关键词： Information leakage

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Label-specific multi-label text classification based on dynamic graph convolutional networks

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Soft computing 2025年第3期29卷 1897-1909页

作者： Yan, Yaoyao Liu, Fang‘ai Liu, Kenan Xu, Weizhi Zhuang, Xuqiang Information Science and Engineering School Shandong Normal University Jinan250358 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Multi-label text classification is a key task in natural language processing, aiming to assign each text to multiple predefined categories simultaneously. Existing neural network models usually learn the same text representation for different labels, which limits the effectiveness of the models in capturing deep semantics and distinguishing between similar labels;moreover, these models tend to ignore inter-label correlation, leading to loss of information. To overcome these limitations, we propose a novel label-specific dynamic graph convolutional network (LDGCN). This network combines convolutional operations and BiLSTM to model text sequences and obtains label-specific text representations through a label attention mechanism. In addition, LDGCN improves the dynamic graph convolutional network by utilizing statistical label co-occurrence and label reconstruction maps to effectively capture inter-label dependencies and adaptive interactions between label-specific semantic components. Extensive experiments on the RCV1, AAPD, and EUR-Lex datasets show that our model achieves 96.92%, 86.30%, and 81.42% on the P@1 metrics, respectively, and demonstrates a significant advantage in dealing with tail labels. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Semantics

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AFuNet: an attention-based fusion network to classify texts in a resource-constrained language

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Neural computing and Applications 2025年第9期37卷 6725-6748页

作者： Hossain, Md. Rajib Hoque, Mohammed Moshiul Dewan, M. Ali Akber Hoque, Enamul Siddique, Nazmul Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada School of Information Technology York University TorontoONM3J 1P3 Canada School of Computing Engineering and Intelligent Systems Ulster University Coleraine United Kingdom

In the era of widespread Internet use and extensive social media interaction, the digital realm is accumulating vast amounts of unstructured text data. This unstructured data often contain undesirable information, necessitating time-consuming manual classification efforts. An intelligent text classification system capable of automatically categorizing digitized texts based on semantic meaning is crucial. However, this task is particularly challenging for low-resource languages like Bengali due to a shortage of annotated corpora, issues with out-of-vocabulary words, lack of domain-specific hyperparameter tuning, limited ability to extract generalized text features, and class imbalances within the corpus. AFuNet: an attention-based fusion network to classify texts in a resource-constrained language. AFuNet undergoes a comprehensive four-phase experimental process, including baseline model evaluation and hyperparameter tuning, late fusion and model selection, attention-based early fusion and model identification, and an ablation study with impact analysis. Fine-tuned based on five Bengali text classification corpora, AFuNet achieves impressive accuracies: 96.60 ± 0.2 (BTCC11), 85.37 ± 0.2 (OSBC), 97.35 ± 0.2 (BARD), 93.74 ± 0.2 (IndicNLP), and 96.51 ± 0.2 (ProthomAlo). In comparison with previous state-of-the-art models on these corpora, AFuNet demonstrates significant accuracy improvements ranging from 0.54% to 4.49%, showcasing its effectiveness in advancing text classification capabilities for the Bengali language. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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Optimizing DDoS Attack Detection Using Machine Learning 2

Optimizing DDoS Attack Detection Using Machine Learning

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Aggarwal, Saransh Behera, Bhagrajyoti Singh, Murari Kumar Sharma, Ajeet Kumar School of Computing Science and Engineering Sharda University Greater Noida India School of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9798331530389

The increase in the Distributed Denial of Service attack (DDoS) leads to a significant threat to the network security. Inability to timely and accurately detect DDoS attacks disrupts services offered by companies and the government causing financial losses. Additionally, prolonged DDoS attacks can spoil user experience and degrade company reputation. The recent literature reveals various techniques for detecting DDoS attacks, including pre-trained machine learning and deep learning algorithms. This research paper addresses this challenge by tweaking random forest algorithms for maximum accuracy using CICDDoS2019 dataset. The methodology includes collecting of dataset, data processing, feature scaling and training multiple machine learning algorithms to identify maximum accuracy for detecting DDoS attacks. The result shows improvement of accuracy and precision of machine learning models used. Future work may include training the model for other types of attacks, real time implementation of the model developed, developing a hybrid approach for detection and protection from DDoS attack. © 2025 IEEE.

关键词： Network security

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