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3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Athish, V.P. Sree Nandha, S.S. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Dept. of Data Science and Business Systems Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350322842

Liver failure is a serious medical condition that can have life-threatening consequences. The liver is responsible for filtering blood and performing many other important functions. With the rise of machine learning in healthcare, there is an opportunity to use past datasets to predict liver failure early on. The main focus of this research work is to explore the application of supervised machine learning algorithms in predicting liver failure. The study involved pre-processing techniques such as univariate and bivariate analysis, and data visualization to gain better insights into the features of the dataset. Using these techniques, a multi-class classification model was built with machine learning algorithms, and the performance metrics of accuracy, F1 score, and recall were used to compare the algorithms. The results show that machine learning algorithms can accurately predict liver failure and can be an effective tool in healthcare. This study demonstrates the potential of machine learning in predicting liver failure and can contribute to the development of future models in the field. The results of this research study indicates that the proposed algorithm achieved an accuracy of 94.48%. The proposed work will be beneficial to researchers in this field and inspire them to build more advanced and accurate models. © 2023 IEEE.

关键词： Support vector machines

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Deriving Pipeline for Emergency Services Using Natural Language Processing Techniques 11th

Deriving Pipeline for Emergency Services Using Natural Langu...

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11th International Conference on Recent Trends in computing, ICRTC 2022

作者： Anand, Akshat Rajeswari, D. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India

ISBN: (纸本)9789811988240

Communications have a crucial role in times of crisis, particularly in times of emergency. Whenever a region is affected by any disaster, social media sites such as Twitter, etc., are a great way to get information out to those who need it the most. Our paper suggests that in times of crisis, when people are worried about finding the right emergency contacts or are experiencing a transformation in their psychological response, they should manually enter their experience into an app to obtain contact information for the nearest SOS. Alternatively, the proposed application suggests scraping hashtags from around the world with web scraping scripts to retrieve geo-referenced tweets in real time. In order to gather accurate data, Twitter's API or some third-party application can be used in the development of this software. When there is a major crisis, social media user behavior plays a vital role, owing to its efficiency and high reliability with swift data dissemination. Anxiety over finding the right emergency team contact may cause people to lose their cool and become unclear about which authority they should call when an emergency occurs. Eventually, our application bridges the gap between clients in need of help and the approved agency that can really provide that support in different circumstances. In the case of, for example, a lost child or a flooded area, our application helps you get in touch with an organization that can assist you in dealing with the situation. We have used CNN-1-D + LSTM, CNN-1-D + Bi-LSTMs, and CNN-1-D + LSTM + Glove-100-D to get maximum accuracy for the classification model. It is possible to use the prototype solution in a novel way to organize immediate assistance with authorities in comprehensive administration, while integrating Twitter or other social platforms into the application can make it a feasible platform for preventive intervention and catastrophe management. © 2023, The Author(s), under exclusive license to Springer Nature

关键词： Emergency services

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Unifying Genetics and Imaging: MRI-Based Classification of MGMT Genetic Subtypes Using Visual Transformers 18

Unifying Genetics and Imaging: MRI-Based Classification of M...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Babar, Ibrar Tahir, Asjid Haider, Arslan Ur Rehman, Hafeez Alazab, Moutaz National University of Computer and Emerging Sciences Department of Computer Science Peshawar Pakistan Oryx Universal College Department of Computing and Data Science Doha Qatar

ISBN: (纸本)9798350328172

Brain cancer can take many forms, but glioblastoma (GBM) is one of the most aggressive. To treat it effectively, doctors need to know the genetic subtype of a specific part of the tumor called the O-6-methylguanine-DNA-methyltransferase (MGMT) promoter. This information is critical for making treatment decisions and predicting outcomes. However, traditional methods for predicting MGMT status have significant limitations. One of the main reasons for this poor performance is that these methods fail to capture small variations in MRI signatures that are essential for accurately predicting MGMT promoter methylation status. In this work, we propose a two-part approach to predict the MGMT methylation status. First, we use autoencoders to extract features from the MRI images, capturing intricate spatial patterns and representations. Then, we feed these encoded features into a visual transformer architecture to learn long-range dependencies and contextually significant relationships among image regions. This innovative fusion of autoencoders and visual transformers allows us to harness both local and global information present in the MRI data. We utilized a dataset consisting of 1010 3D MRI images from patients with GBM. Half of the patients had MGMT promoter methylation, while the other half did not. The model achieved impressive results, including an overall accuracy score of 0.904, recall score of 0.902, precision score of 0.914, F1 score of 0.906, and specificity score of 0.906. These metrics far surpass the accuracy of conventional imaging techniques. © 2023 IEEE.

关键词： Magnetic resonance imaging

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An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

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Computers, Materials & Continua 2024年第7期80卷 389-425页

作者： Nidhi Parashar Prashant Johri Arfat Ahmad Khan Nitin Gaur Seifedine Kadry School of Computer Science and Engineering Galgotias UniversityGreater Noida203201India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates MEU Research Unit Middle East UniversityAmman11831Jordan Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction *** learning(DL)and machine learning(ML)models effectively deal with such *** research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March *** addition,it analyses the effectiveness of various input parameters considered in crop yield prediction *** conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop *** total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is *** conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research *** study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel *** also discuss the ethical and social impacts of AI on ***,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven ***,thorough research is required to deal with challenges in predicting agricultural output.

关键词： Machine learning crop yield prediction deep learning remote sensing long short-term memory time series prediction systematic literature review

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MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators 31

MQM-APE: Toward High-Quality Error Annotation Predictors wit...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Lu, Qingyu Ding, Liang Zhang, Kanjian Zhang, Jinxia Tao, Dacheng Southeast University China The University of Sydney Australia Southeast University Shenzhen Research Institute China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have shown significant potential as judges for Machine Translation (MT) quality assessment, providing both scores and fine-grained feedback. Although approaches such as GEMBA-MQM (Kocmi and Federmann, 2023a) have shown state-of-the-art performance on reference-free evaluation, the predicted errors do not align well with those annotated by human, limiting their interpretability as feedback signals. To enhance the quality of error annotations predicted by LLM evaluators, we introduce a universal and training-free framework, MQM-APE, based on the idea of filtering out non-impactful errors by Automatically Post-Editing (APE) the original translation based on each error, leaving only those errors that contribute to quality improvement. Specifically, we prompt the LLM to act as (1) evaluator to provide error annotations, (2) post-editor to determine whether errors impact quality improvement and (3) pairwise quality verifier as the error filter. Experiments show that our approach consistently improves both the reliability and quality of error spans against GEMBA-MQM, across eight LLMs in both high- and low-resource languages. Orthogonal to trained approaches, MQM-APE complements translation-specific evaluators such as Tower, highlighting its broad applicability. Further analysis confirms the effectiveness of each module and offers valuable insights into evaluator design and LLMs selection. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Attention boosted individualized regression 24

Attention boosted individualized regression

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Guang Yang Yuan Cao Long Feng Department of Data Science College of Computing City University of Hong Kong Department of Statistics and Actuarial Science School of Computing and Data Science The University of Hong Kong

ISBN: (纸本)9798331314385

Different from classical one-model-fits-all strategy, individualized models allow parameters to vary across samples and are gaining popularity in various fields, particularly in personalized medicine. Motivated by medical imaging analysis, this paper introduces a novel individualized modeling framework for matrix-valued data that does not require additional information on sample similarity for the individualized coefficients. Under our framework, the model individualization stems from an optimal internal relation map within the samples themselves. We refer to the proposed method as Attention boosted Individualized Regression, due to its close connections with the self-attention mechanism. Therefore, our approach provides a new interpretation for attention from the perspective of individualized modeling. Comprehensive numerical experiments and real brain MRI analysis using an ADNI dataset demonstrated the superior performance of our model.

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Extending Whisper for Emotion Prediction Using Word-level Pseudo Labels

Extending Whisper for Emotion Prediction Using Word-level Ps...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Kwok, Chin Yuen Li, Sheng Yip, Jia Qi Chu, Chenhui Kawahara, Tatsuya Chng, Eng Siong Digital Trust Centre Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Japan Kyoto University Japan

ISBN: (纸本)9798350368741

This paper extends Whisper's automatic speech recognition (ASR) capabilities to perform speech-based emotion recognition (SER) by incorporating word-level emotion classification alongside ASR output. We generate four emotion pseudo-labels (neutral, happy, sad, angry) for each word using a pretrained frame-level SER model, and Whisper is fine-tuned for joint ASR and emotion classification at the word level. Sentence-level emotion labels are masked during training to encourage the transformer to use the ASR output for word-level emotion prediction. During inference, word-level predictions are combined with sentence-level predictions through majority voting to generate the final sentence-level label. When evaluated on the IEMOCAP dataset, our method maintains Whisper's ASR word error rate while improving the SER weighted accuracy from 74.4% to 76.4% and the unweighted average recall from 77.1% to 79.0%. © 2025 IEEE.

关键词： automatic speech recognition (ASR) catastrophic forgetting continual learning instruction-tuning speech emotion recognition (SER)

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Sustainable Diffusion-Based Incentive Mechanism for Generative AI-Driven Digital Twins in Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 139-149页

作者： Wen, Jinbo Kang, Jiawen Niyato, Dusit Zhang, Yang Mao, Shiwen Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States

Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs. © 2023 IEEE.

关键词： Smart manufacturing

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Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

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Computers, Materials & Continua 2023年第3期74卷 6335-6349页

作者： Ibrar Amin Saima Hassan Samir Brahim Belhaouari Muhammad Hamza Azam Institute of Computing Kohat University of Science and TechnologyKohat26000K.P.KPakistan College of Science and Engineering Hamad bin Khalifa UniversityDohaQatar Centre of Research in Data Science Computer and Information Sciences DepartmentUniversiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia

Malaria is a lethal disease responsible for thousands of deaths worldwide every *** methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and *** automated diagnosis of diseases is progressively becoming *** deep learning models show high performance in the medical field,it demands a large volume of data for training which is hard to acquire for medical ***,labeling of medical images can be done with the help of medical experts *** recent studies have utilized deep learning models to develop efficient malaria diagnostic system,which showed promising ***,the most common problem with these models is that they need a large amount of data for *** paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer *** proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning *** of the proposed model is evaluated on a publicly available dataset of blood smear images(with malariainfected and normal class)and achieved a classification accuracy of 96.6%.

关键词： Generative adversarial network transfer learning semi-supervised malaria VGG16

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PAIR: Protein-aptamer interaction prediction based on language models and contrastive learning framework

PAIR: Protein-aptamer interaction prediction based on langua...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Jun Yan, Zhiqiang Zeng, Hao Zhu, Zexuan Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350386226

Aptamers are single-stranded DNA or RNA oligonucleotides that selectively bind to specific targets, making them valuable for drug design and diagnostic applications. Identifying the interactions between aptamers and target proteins is crucial for these applications. The systematic evolution of ligands by exponential enrichment process, traditionally used for this purpose, is challenging and time-consuming. The resulting aptamers often suffer from limitations in stability and diversity. Computational approaches have shown promise in aiding the discovery of high-performance aptamers, but existing methods are usually constrained by insufficient training data and limited generalizability. Recently, advancements in pre-training large language models have offered a new avenue to mitigate the dependency on large datasets. In this study, we propose a novel method to predict aptamer-protein interactions using large language models within a contrastive learning framework. Experimental results demonstrate that our method exhibits superior generalization and outperforms existing approaches. This method holds promise as a powerful tool for predicting aptamer-protein interactions. © 2024 IEEE.

关键词： Contrastive Learning

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