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arXiv 2024年

作者： Wu, Jialong Wang, Zhenglin Zhang, Linhai Lai, Yilong He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King’s College London United Kingdom The Alan Turing Institute United Kingdom

Key-Value (KV) cache has become a bottleneck of LLMs for long-context generation. Despite the numerous efforts in this area, the optimization for the decoding phase is generally ignored. However, we believe such optimization is crucial, especially for long-output generation tasks based on the following two observations: (i) Excessive compression during the prefill phase which requires specific full context, impairs the comprehension of the reasoning task;(ii) Deviation of heavy hitters1 occurs in the reasoning tasks with long outputs. Therefore, SCOPE, a simple yet efficient framework that separately performs KV cache optimization during the prefill and decoding phases, is introduced. Specifically, the KV cache during the prefill phase is preserved to maintain the essential information, while a novel strategy based on sliding is proposed to select essential heavy hitters for the decoding phase. Memory usage and memory transfer are further optimized using adaptive and discontinuous strategies. Extensive experiments on LONGGENBENCH show the effectiveness and generalization of SCOPE and its compatibility as a plug-in to other prefill-only KV compression methods. 2 Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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A Reliability-constrained Association Rule Mining Method for Explaining Machine Learning Predictions on Continuity of Asthma Care

A Reliability-constrained Association Rule Mining Method for...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Tong, Yao Wang, Yongli Zhang, Qibin Zhang, Zhenxiang Chen, Gang Zhengzhou University Computer and Artificial Intelligence School Zhengzhou China University of Washington Biomedical Informatics and Medical Education Department Seattle United States Nanjing University of Technology School of Computer Science and Engineering Nanjing China Zhengzhou University School of Nursing and Health Zhengzhou China

ISBN: (纸本)9781665468190

Continuity of care has been shown to possess numerous health benefits for asthma. However, the use of methodology to predetermine the level of care continuity has been underinvestigated. We recently built a machine learning model to predict the continuity of care for diagnosed asthma patients at the University of Washington Medicine. As there is a consensus on the un-explainable featured nowadays by black-box machine learning, which impedes the clinical deployment of our model. To tackle this issue, we proposed a reliability-constrained association rule mining method, called RC-ARM, to automatically explain the predictions of any machine learning model. First, we introduced the belief function to build a reliability-constrained rule framework. Then, the two-step reliable association rule mining algorithms were developed to generate reliable explanation rules for the machine learning model's predictions. At last, clinical intervention suggestions regarding each mined rule were embedded for further understanding. The results showed that the proposed method could explain all (110/110) predictions of our machine learning model for asthma patients with low levels of continuity of care. This semantic-fused method sheds light on black-box models and encourages clinical experts to embrace the benefits of machine learning without any prior concern about its lack of explainability. © 2022 IEEE.

关键词： Association rules

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Stock Price Forecasting in Finance with Artificial Neural Networks

Stock Price Forecasting in Finance with Artificial Neural Ne...

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Information engineering (ACIE), IEEE Asia Conference on

作者： Turgut Daloğlu Garibe Osi Murat Aslan Mehmet Beşkirli Cizre District Directorate of National Education Şırnak University Şırnak Türkiye Department of Computer Engineering Şırnak University Şırnak Türkiye Departmen of Medical Education and Informatics Faculty of Medicine Karamanoğlu Mehmetbey University Karaman Türkiye

ISBN: (数字)9798331528409

ISBN: (纸本)9798331528416

This paper examines the applications of Artificial Neural Networks (ANNs) in the field of finance, particularly in stock price forecasting. Artificial neural networks are mathematical models that mimic biological nervous systems and are used to solve complex problems such as financial data analysis. Stock price forecasting is an important topic for investors and finance professionals. Artificial neural networks have been widely used in this field due to their ability to process large amounts of financial data, learn complex relationships and predict future price movements. This study deals with stock price forecasting with artificial neural networks using historical stock prices, economic indicators and other financial data. Neural networks attempt to predict future price movements by learning the complex relationships between these data. Stock price forecasting with artificial neural networks can offer a more flexible and adaptive approach than traditional statistical methods. However, the success of the model depends on factors such as the quality of the data set used, network architecture and training parameters. This study was conducted to understand the potential of artificial neural networks in finance and to evaluate their effectiveness in stock price forecasting. The purpose of this study is to explore how artificial neural networks can be used in financial data analysis, and in particular their potential in stock price forecasting. The study is designed to understand the advantages of applying ANN in finance, to evaluate the stock price forecasting performance of this technology and to provide a new perspective that can influence future investment strategies.

关键词： Training Accuracy Statistical analysis Finance Training data Predictive models Data models Forecasting Long short term memory Testing

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A Multi-Modal Behavior Quantitative Analysis Model for Autism Early Screening

A Multi-Modal Behavior Quantitative Analysis Model for Autis...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Lei, Jiayi Zhang, E. She, Yingying Wang, Xin Liao, Yuhan Hu, Bin Wu, Hang Yang, Minqiang Tian, Jiajia Wang, Yong School of Informatics Xiamen University China University of Kansas Medical Center Department of Occupational Therapy Education United States School of Film National Institute for Data Science in Health and Medicine Xiamen University China School of Computer Science and Technology Beijing Institute of Technology China Together Education Institute China School of Information Science and Engineering Lanzhou University China Fujian Medical University Union Hospital Pediatric Department China

ISBN: (纸本)9798350337020

Human-computer Interaction (HCI) and Machine Learning (ML) technologies have potential for the behavioral screening of autistic children but how to design a tool and analyse behavior reliably is challenging. Based on psychophysiological computation, this paper proposes an interactive behavior perception analytical model for autism screening. We presented the multi-scenario reactive behavior paradigms that designed based on the atypical characteristics of autistic children. We recorded the eye movement data and facial data of 91 participants, and performed multi-modal feature extraction, used machine learning to train classification model. We conducted comparative experiments, and the experimental results verified the advantages of multi-scenario paradigms and multi-modal feature groups, which indicates that our analysis methods and screening models are effective and reliable and have real research significance. © 2023 IEEE.

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN computer Science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Construction of Influenza Early Warning Model Based on Combinatorial Judgment Classifier:A Case Study of Seasonal Influenza in Hong Kong

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Current Medical Science 2022年第1期42卷 226-236页

作者： Zi-xiao WANG James NTAMBARA Yan LU Wei DAI Rui-jun MENG Dan-min QIAN Department of Medical Informatics School of MedicineNantong UniversityNantong 226001China Department of Computer Science College of Engineering and Computing SciencesNew York Institute of TechnologyNew York 10023USA Department of Computer Science College of Overseas EducationNanjing University of Posts and TelecommunicationsNanjing 210023China Department of Epidemiology School of Public HealthNantong UniversityNantong 226019China Artificial Intelligence Laboratory Center De Montfort University of LeicesterLeicester LE1 9BHUnited Kingdom

Objective:The annual influenza epidemic is a heavy burden on the health care system,and has increasingly become a major public health problem in some areas,such as Hong Kong(China).Therefore,based on a variety of machine learning methods,and considering the seasonal influenza in Hong Kong,the study aims to establish a Combinatorial Judgment Classifier(CJC)model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning.

关键词： influenza prediction data-driven Support Vector Machine Discriminant Analysis Ensemble Classifier

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Leaf Disease Segmentation Using Uunet++ Architecture 3rd

Leaf Disease Segmentation Using Uunet++ Architecture

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3rd International Conference on Trends in Electronics and Health informatics, TEHI 2023

作者： Farooqui, Nafees Akhter Mishra, Amit Kumar Ray, Kanad Mallik, Saurav Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India Karnataka Bengaluru India Department of Physics Amity School of Applied Sciences Amity University Rajasthan Rajasthan Jaipur India Amity Cognitive Computing and Brain Informatics Centre Amity University Rajasthan Rajasthan303002 India Department of Environmental Health Harvard T H Chan School of Public Health BostonMA02115 United States Pharmacology & Toxicology University of Arizona TucsonAZ85721 United States

ISBN: (纸本)9789819739363

Segmentation of leaves is an initial step in image-based plant phenotyping, which has received more attention recently. Leaf segmentation shows features of the leaf and growth stages at the leaf level. Due to the significant overlapping between leaves and fluctuating environmental circumstances, such as intensity change and blur due to wind, segmentation of plant tissues like leaves is a most important issue in plant phenotyping. The leaf segmentation work has more complicated problems, such as leaf surface, genotypes, length, structure, and thickness variability. This study proposes an ultra-modern deep gaining knowledge of architectures: UUNet++, a convolutional neural network for advance segmentation. A vital assessment is done using computer Vision for Plant Phenotyping dataset (CVPPP). The plant phenotyping is performed on tobacco and Arabidopsis leaf of RGB images from plant village dataset. The proposed model needs less space as compared to the main UNet architecture. In contrast to typical deep learning image sets, this dataset contains a limited number of samples. Despite that, we achieve exceptional results in leaf segmentation, specifically in distinguishing the interior of leaves from the binary segmentation of the entire leaf. Furthermore, studies are necessary to accurately quantify the number of leaves. Without reducing performance measures, the disease classification inferences time is extremely efficient and accurate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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Research on optimization strategy of medical data information security and privacy 9

Research on optimization strategy of medical data informatio...

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9th International Conference on Electrical engineering, computer Science and informatics, EECSI 2022

作者： Abdulshaheed, Haider Rasheed Mohammed Al-Juboori, Sura Abdulmunem Al Sayed, Intisar A.M. Barazanchi, Israa Al Gheni, Hassan Muwafaq Jaaz, Zahraa A. Baghdad College of Economic Sciences University Computer Engineering Techniques Department Baghdad Iraq Ministry of Higher Education and Scientific Research Baghdad Iraq Ashur University College-Medical Instrumentation Techniques Engineering Baghdad Iraq College of Computing and Informatics UNITEN Malaysia Al-Mustaqbal University College Computer Techniques Engineering Department Hillah 51001 Iraq College of Science-AINahrain University Baghdad College of Computing and Informatics UNITEN Computer Department Malaysia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

Due to the rapid development of information technology and network technology, personal medical data information is increasingly challenged by privacy breaches and security issues at all life cycle stages. In this paper, we analyze the risk patterns of privacy information leakage of personal medical data in the comprehensive lifecycle of medical information in the context of the importance of hospital information system construction and discuss the risk factors of medical data privacy security in hospital information system based on the installation of the vulnerability scanning system, establishment of perfect access control mechanism, installation of the intrusion detection system (IDS) and data backup. The protection strategy of medical data privacy security under the hospital information system is discussed. The change strategy can physically separate the use and storage of patients personal information and use different algorithms to encrypt privacy information hierarchically. The hospital departments protect the security of patients personal privacy information from the source according to the mechanism of hierarchical grading and on-demand access without affecting the hospital business. © 2022 Institute of Advanced engineering and Science (IAES).

关键词： Data security Hospital information system Medical privacy leak Privacy protection Privacy security

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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Sustainable Emerging Innovations in engineering and Technology (ICSEIET), International Conference on

作者： Maylia Fatmawati Mega Novita Nugroho Dwi Saputro Alok Singh Chauhan Noora Qotrun Nada Amit Sinha Bambang Agus Herlambang Shruti Gupta Department of Informatics Faculty of Engineering and Informatics Universitas PGRI Semarang Semarang Indonesia Postgraduate Program of Science Education Universitas PGRI Semarang Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India Department of Computer Science & Engineering ABES Engineering College Ghaziabad Uttar Pradesh India

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks.

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