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Chinese Automation Congress (CAC)

作者： Yan Zhang Sujia Fu Yanyun Qu School of Mathematical Sciences Guizhou Normal University Guiyang China School of Big Data and Computer Science Guizhou Normal University Guiyang China Computer Science Department Xiamen University Xiamen China

Research on 3D representation has sprung up recently. However, most existing view-based methods lack interpretability and few researchers explore to mine high-order correlation for multi-view features aggregate in 3D shape representation. This paper presents a tensor multi-task model learning, T-MTL, which transforms the original multi-view classification problem into constructing a common space in which the multi-view representation can be compared. The proposed method can construct a tensor structure in the classifier weight subspace with multiple viewpoints and use a tensor low-rank constraint and Bregman discrepancy to capture high-order correlations between multi-view images and 3D models. In the classifier weight subspace, our method makes the inter-class divergence larger and the intra-class divergence smaller. Moreover, the proposed model can improve recognition performance further by unifying multiple visual features in a flexible manner. Abundant experiments on these challenging benchmark datasets show that our method achieves comparable results to the deep learning models approach.

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A Clustering Algorithm Based on Document Embedding to Identify Clinical Note Templates

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Annals of data science 2021年第3期8卷 497-515页

作者： Tang, Chunlei Plasek, Joseph Michael Xiong, Yun Zhang, Zhikun Bates, David Westfall Zhou, Li Division of General Internal Medicine and Primary Care Harvard Medical School Brigham and Women’s Hospital BostonMA02115 United States Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai201203 China

This paper proposes a novel unsupervised document embedding based clustering algorithm to generate clinical note templates. We adapted Charikar’s SimHash to embed each clinical document into a vector representation. We modified the traditional K-means algorithm to merge any two clusters with centroids when they are very close. Under the K-means paradigm, our algorithm designates the cluster representative corresponding to the document vector closest to the centroid as the prototype template. On a corpus of clinical notes, we evaluated the feasibility of utilizing our algorithm at the individual author level. The corpus contains 1,063,893 clinical notes corresponding to 19,146 unique providers between January 2011 and July 2016. Our algorithm achieved more than 80% precision and runs in O(n) time complexity. We further validated our algorithm using human annotators who reported it is able to efficiently detect a real clinical document that can represent the other documents in the same cluster at both the department level and the individual clinician level. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Embeddings

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Hierarchical Bayesian Modeling for FVC Prediction in Pulmonary Fibrosis and Low-Cost Spirometer Incorporating Internet of Things

Hierarchical Bayesian Modeling for FVC Prediction in Pulmona...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： Sumithra M Sundari. S V. Deepa Nagaraj P Dhiliphan Rajkumar T S Magesh Kumar Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering School of Engineering and Technology Jeppiaar University Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Lung disease remains a significant public health concern in India, with a rising prevalence attributed to various environmental and lifestyle factors. Recent statistics reveal that chronic obstructive pulmonary disease (COPD) affects approximately 57.8 million people in India, while pulmonary fibrosis accounts for a substantial portion of respiratory-related morbidity and mortality nationwide. Despite the growing prevalence, accurate lung function diagnosis and monitoring remain challenging, particularly in resource-constrained settings. This study presents a novel approach utilizing hierarchical Bayesian modelling in conjunction with low-cost IoT-based spirometry technology for predicting forced vital capacity (FVC) in pulmonary fibrosis patients. Leveraging real-time data from the Indian population,[7] [8] where respiratory diseases are prevalent, our research underscores the critical need for reliable and accessible spirometry measurements for effective disease management. We begin by elucidating the epidemiological landscape of lung disease in India, highlighting the alarming rates of COPD and pulmonary fibrosis. Recent studies suggest that when integrated into clinical practice, spirometry data can significantly improve diagnostic accuracy and treatment outcomes for respiratory conditions. Leveraging data from the Indian population, where respiratory diseases are prevalent, our research underscores the critical need for reliable and accessible spirometry measurements for effective disease management. Our proposed methodology integrates the YFS201 flow sensor with an ESP8266 microcontroller, providing a cost-effective solution for real-time measurement of airflow. Through hierarchical Bayesian modeling, we demonstrate the capability of our approach to accurately predict FVC in pulmonary fibrosis patients, thereby facilitating early detection and personalized treatment strategies. By incorporating spirometry data into clinical practice, our research aims to empow

关键词： Spirometry Adaptation models Accuracy Lungs Predictive models data models Real-time systems Bayes methods Monitoring Context modeling

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Concept, Technology, Barriers To Smart Village: A Systematic Literature Review

Concept, Technology, Barriers To Smart Village: A Systematic...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Joy Nashar Utamajaya Meyliana Harjanto Prabowo Achmad Nizar Hidayanto Arief Ramadhan Computer Science Department BINUS Graduate Program - Computer Science Department Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia

Smart villages that utilize digital technology are a solution to assist rural community services in meeting their needs, especially for developing countries. However, financial problems, namely the high cost of infrastructure, the size of the informal economy and governance are challenges in limiting the government’s aspirations regarding the smart village concept. The presentation of this article examines the state of smart village development in developing countries, which includes conceptual understanding, motivations, drivers and barriers to smart villages. The purpose of this study is to find answers to research questions by reviewing several selected study articles. The method used was to search for selected studies and there were 60 selected studies identified from a search of five literature databases which initially contained 3394 articles. The results of the data were analyzed using synthetic analysis and thematic analysis. The results of this study found data related to Concept, Technology, Barriers To Smart Village which came from several articles by answering questions from the research, namely RQ1: How many articles from the 10 most populous countries discussed Smart Village, RQ2: What are the basic dimensions of Smart Village, RQ3: What new technologies are discussed in Smart Village digitization, RQ4: What are the Challenges in Smart Village. The results of this SLR can also later be used as a reference for further researchers to find out the background of research related to smart villages. The conclusion obtained in this research is the end of the explanation of this article concludes that there are still many shortcomings in this research and it is hoped that in the future many studies will be able to explore other aspects such as regulations and others that are not discussed in the article so that it is expected to be a reference for further researchers to examine information related to smart villages. in order to find an ideal and up-to-date con

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Big data-Intelligence Analytics for Energy Optimization in IoT-Enabled Smart Home Devices

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Yihong Song, Qiang Zhaoqing University Big Data Engineering Technology Center China Zhaoqing University School of Computer Science and Software China

This article explores the integration of Artificial Intelligence (AI) and Big data Analytics to optimize energy consumption in IoT-enabled smart home devices. It presents a robust analytical framework that leverages Variational Autoencoders (VAEs) for feature extraction and Differential Evolution (DE) for optimizing energy management parameters. data was gathered from various IoT devices, including energy usage patterns, occupancy data, and environmental conditions. The results show a notable 40% reduction in energy consumption, leading to annual cost savings of up to $300 per household. Moreover, user satisfaction increased by 25%, with participants reporting heightened awareness and engagement in energy conservation. The study highlights how the proposed framework efficiently identifies common usage patterns and optimizes energy distribution while preserving user comfort. These findings reinforce the potential of AI-driven analytics in improving energy efficiency in smart homes, demonstrating that advanced algorithms not only support energy conservation but also promote active user participation in sustainability efforts. © 2025 IEEE.

关键词： Energy utilization

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Enhancing Intrusion Detection Systems Using Ensemble Machine Learning Techniques

Enhancing Intrusion Detection Systems Using Ensemble Machine...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Isaac Adedoyin Oyebade Isaac Oluwafemi Elesemoyo Mercy Oka Ebute Abidemi Emmanuel Adeniyi Joseph Bamidele Awotunde Akpan Caleb Solomon Agbotiname Lucky Imoize Kehinde Amudat Odulana Department of Computer Science Bowen University Iwo Nigeria Department of Computer Engineering Elizade University Ilara-Mokin Nigeria Department of Accounting Anderson School of Management University of New Mexico USA Department of Computer Science University of Ilorin Ilorin Nigeria Department of Electrical and Electronics Engineering University of Lagos Akoka Lagos Nigeria School of Physics Engineering and Computer Science (Data Science and Analytics) University of Hertfordshire

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Intrusion detection systems are an essential part of the current cybersecurity environment, and IDS devices are intended to scan the activity of the network and systems to identify possible improper actions or violations. IDSs work in analyzing the traffic of networks and the traffic of networks and the behavior of systems, distinguishing suspicious information and signs of violation of legal access. This capability is critical for protecting information systems’ characteristics, such as integrity, confidentiality, and availability. Useful data was collected from Kaggle data archives, and several base models such as K-Nearest Neighbor, Random Forest, Logistic Regression, and Decision Tree classifier will be used. Their predictions are combined using a Voting Classifier. The models are trained and evaluated based on accuracy, precision, recall, and F1-Score. Results show that the Voting classifier achieved an accuracy of 99.78%, with a precision of 99.67% and recall of 99.92% for intrusion detection. The Logistic Regression and K-Nearest Neighbor models achieved accuracies of 95.37% and 99.52%, respectively, while having a precision of 94.93% and 99.51%, respectively. The Decision Tree and Random Forest models achieved accuracies of 99.76% and 99.86%, respectively, while having a precision of 99.75% and 99.83%, respectively. These findings suggest ensemble techniques significantly enhance intrusion detection, making networks safer.

关键词： Logistic regression Accuracy Sensitivity Intrusion detection Telecommunication traffic Nearest neighbor methods Classification algorithms Decision trees Object recognition Random forests

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Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics

Novel NBA Fantasy League driven by Engineered Team Chemistry...

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IEEE International Conference on Big data

作者： Ganesh Arkanath Nishad Gupta Hasan Kurban Parichit Sharma K R Madhavan Elham Khorasani Buxton Mehmet M. Dalkilic Computer Science Department Indiana University Bloomington IN US Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Data Science Program Indiana University Bloomington IN US Computer Science Department University of Illinois Springfield IL US

Fantasy Sports has a current market size of ${\$}$27B and is expected to grow more than ${\$}$84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent players actually played in a team. Using individual player and team statistics, models can be trained to predict an outcome. But fans are left wanting more. To achieve a more realistic outcome, aspects of what makes live teams win need to be included: (1) transforming player statistics to reflect their relative importance with respect to a player position; (2) team chemistry (TC). In this work, we show a novel characterization of relative position statistics and a new description of TC. Drawn from the NBA’s API, we form a data set to determine whether a fantasy team makes the playoffs using almost two dozen features, including TC. Various Machine Learning models are trained on this data and the best-performing model is offered to the users through a web service. Users can not only inspect fantasy teams and their TC but can also simulate their match-ups with existing 2023 NBA teams and utilize performance visualizations to help improve their team creation process. Our web service can be accessed at https://***/fantasyleague/, and the source code can be found at https://***/gany-15/nbafan.

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Enhancing Medical ASR Accuracy through the Integration of T5-Based Small Language Models and Error Correction Mechanisms

Enhancing Medical ASR Accuracy through the Integration of T5...

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Engineering Informatics, IEEE

作者： Nischay Kondai Abhishek Devapangu Sai Kartik Hosur Pratik Raj Nitya Tiwari K S Nataraj Data Science & AI IIIT Dharwad Dharwad India School of Electrical and Computer Science IIT Bhubaneswar Bhubaneswar India Electronics and Communication Engg. IIIT Dharwad Dharwad India

ISBN: (数字)9798331505776

ISBN: (纸本)9798331505783

A key challenge in healthcare is the time spent on documentation, which hinders patient care. Automatic Speech Recognition (ASR) technology automates this process and enhances efficiency and access to medical data. Medical ASR systems face significant challenges in accurately understanding the complexities of healthcare conversations, including various accents, dialects, and specialized terminology. Fine-tuning ASR systems with domain-specific medical speech datasets can improve performance, but the challenge of acquiring large and high-quality datasets limits accuracy. To tackle this issue, we propose an ASR output error correction method using a T5-based language model, which integrates the strengths of large language models with error correction training to improve ASR performance. The model was fine-tuned using a diverse dataset comprising synthetic text, synthetic speech, and natural speech data. Synthetic text data was generated to simulate common transcription errors, producing $\mathbf{3 0, 0 0 0}$ pairs of incorrect and correct text. For performance evaluation, synthetic speech recordings were created from 5,000 text samples using edge text-to-speech synthesizer. Additionally, natural speech data was sourced from the Medical Speech, Transcription, and Intent dataset, consisting of $\mathbf{6, 6 0 2}$ speech recordings focused on medical symptoms. Experiments were conducted to assess the model’s accuracy and Word Error Rate (WER) across these datasets. The T5-based error correction model significantly improved ASR performance, achieving an accuracy of $91.23 \%$ and a Word Error Rate (WER) of just $1.20 \%$ on synthetic text data, while also enhancing WER to $3.03 \%$ for synthetic speech and $\mathbf{3. 6 4 \%}$ for natural speech after targeted training. To evaluate the effectiveness of our models for Indian doctors and their specific context, we recorded prescription speech from six Indian doctors. The experiments on these recordings demonstrated that tar

关键词： Training Accuracy Error analysis Natural languages Medical services Oral communication data models Error correction Recording Automatic speech recognition

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PSO Based ADRC for Altitude Test Facility Intake Pressure Control

PSO Based ADRC for Altitude Test Facility Intake Pressure Co...

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2023 China Automation Congress, CAC 2023

作者： Li, Chuanjian Zhang, Hehong Zhai, Chao Dan, Zhihong Zhang, Song Wang, Xin Zhang, Zhizhou College of Computer and Data Science Fuzhou University Fuzhou China School of Automation China University of Geosciences Wuhan China Sichuan Gas Turbine Establishment Aero Engine Corporation of China Mianyang China School of Aeronautics and Astronautics Sun Yat-Sen University Shenzhen China

ISBN: (纸本)9798350303759

Significant disturbances are imposed on the altitude test facility pressure control system during the aeroengines' transition state tests, which degrades the control performance and subsequently reduces the accuracy and reliability of engine tests and evaluation. To tackle this problem, we propose the utilization of an active disturbance rejection control (ADRC) based controller. Additionally, we introduce a particle swarm optimization (PSO) algorithm to optimize and fine-tune the parameters of the ADRC controller for the simulation system of intake environmental pressure. Meanwhile, a component-level modeling approach is employed to establish a simulation platform for altitude test facility pressure, and proposed method is validated through experiments conducted on the platform. The results clearly indicate that the PSO-ADRC significantly improves the performance of pressure control. © 2023 IEEE.

关键词： Controllers

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Machine learning strategies for small sample size in materials science

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science China Materials 2025年第2期68卷 387-405页

作者： Qiuling Tao JinXin Yu Xiangyu Mu Xue Jia Rongpei Shi Zhifu Yao Cuiping Wang Haijun Zhang Xingjun Liu School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhenShenzhen 518055China School of Computer Science&Technology Harbin Institute of TechnologyShenzhenShenzhen 518055China Advanced Institute for Materials Research(WPI-AIMR) Tohoku UniversitySendai 980-8577Japan Department of Materials Science and Engineering College of MaterialsXiamen UniversityXiamen 361005China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyShenzhenShenzhen 518055China

Machine learning (ML) has been widely used todesign and develop new materials owing to its low computational cost and powerful predictive capabilities. In recentyears, the shortcomings of ML in materials science have gradually emerged, with a primary concern being the scarcity ofdata. It is challenging to build reliable and accurate ML modelsusing limited data. Moreover, the small sample size problemwill remain long-standing in materials science because of theslow accumulation of material data. Therefore, it is importantto review and categorize strategies for small-sample learningfor the development of ML in materials science. This reviewsystematically sorts the research progress of small-samplelearning strategies in materials science, including ensemblelearning, unsupervised learning, active learning, and transferlearning. The directions for future research are proposed, including few-shot learning, and virtual sample *** importantly, we emphasize the significance of embedding material domain knowledge into ML and elaborate on thebasic idea for implementing this strategy.

关键词： material design machine learning small sample size few-shot learning material domain knowledge

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