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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Cheng Tian Song Liu Jinbao Li Guangchen Wang Luyue Kong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Medicine Shandong Medical College Jinan China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Transcriptome prediction from genetic variation data is an important task in the privacy-preserving and biometrics field, which can better protect genomic data and achieve biometric recognition through transcriptome. Many transcriptome prediction methods have achieved good accuracy from genetic variation data. However, these traditional transcriptome prediction methods have the problems of linear assumption, overfitting, expose personal privacy, and extensive manual optimization. To solve these shortcomings, we propose an attention-based transcriptome prediction model from genetic variation named RATPM that improves the accuracy of transcriptome prediction and protects participant genomic data. In RATPM, we introduce and improve the deep learning model with multi-head self-attention into the transcriptome prediction stage of Predixcan, which uncovers the non-linear relationship between genetic variation and transcriptome. Moreover, we introduce a residual attention module to generate attention-aware features and extract more accurate features at different levels from genetic variation. Furthermore, we introduce the BERT pre-training module to encode genetic variation fully utilizing their contextual information. Our research enables scientific institutions to publish only predicted transcriptomic data for biometric purposes, thus protecting the genomic information of the subjects. Finally, we evaluated our model on the 1000 Genomes and Geuvadis projects datasets to compare with other baseline models.

关键词： Privacy Biometrics (access control) Biological system modeling Genomics Predictive models Feature extraction Gene expression

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application of the path optimization method to a discrete spin system

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Physical Review D 2023年第9期108卷 094504-094504页

作者： Kouji Kashiwa Yusuke Namekawa Akira Ohnishi Hayato Takase Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka 811-0295 Japan Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima 730-0053 Japan Yukawa Institute for Theoretical Physics Kyoto University Kyoto 606-8502 Japan

The path optimization method, which is proposed to control the sign problem in quantum field theories with continuous degrees of freedom by machine learning, is applied to a spin model with discrete degrees of freedom. The path optimization method is applied by replacing the spins with dynamical variables, via the Hubbard-Stratonovich transformation, and the sum with the integral. The one-dimensional (Lenz-)Ising model with a complex coupling constant is used as a laboratory for the sign problem in the spin model. The average phase factor is enhanced by the path optimization method, indicating that the method can weaken the sign problem. Our result reproduces the analytic values with controlled statistical errors.

关键词： Optimization problems Hybrid Monte Carlo algorithm

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Biomedical data and ai

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(ai) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new ai based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Detection of Number Plate in Vehicles using Deep Learning based Image Labeler Model

Detection of Number Plate in Vehicles using Deep Learning ba...

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Recent Trends in Electronics and Communication (ICRTEC), International Conference on

作者： Shashi Kant Gupta Surabhi Saxena Alex Khang Bramah Hazela Chandra Kumar Dixit Bhadrappa Haralayya Department of CSE Eudoxia Research University USA Department of Computer Applications PES University Bengaluru India Department of Information Technology AI and Data Scientist University of Science and Technology Vietnam USA Computer Science & Engineering Amity School of Engineering & Technology Lucknow Amity University Uttar Pradesh India Department of Physics Dr. Shakuntala Misra National Rehabilitation University Lucknow UP India Department of MBA Lingaraj Appa Engineering College Bidar Dist-Bidar Karanataka India

Deep learning is playing a vital role in most of the applications being used in today's technological era. There seems to be no application where the use of deep learning concepts has not been used yet. One such application is the detection of number plates of vehicles on roads. The incidence of automobile crashes has greatly expanded along with the population growth. Their needs to be system that could automatically detect the vehicle's number plate whenever necessary. The situation arises when any of the rider does not obey to the traffic rules, or skips the traffic signals or indulges in any other sort of activities such as accidents. In these situations, the rider tries to run away. In order to detect the person, these kind of deep learning models helps us to detect the vehicle. Image labelers on the roadsides. Need of sensors are required to store the data and they need to be deployed on the roadside. These labelers are used to track the vehicles that are not obeying the traffic rules. With the use of sensors, it could be stated that the use of IoT also plays in the deployment of the current work when it comes to real-time. The model initially is trained with numerous number plates. The number plates have various patterns of texts and numbers and it is essential for the deep learning model to get trained. With successful training, the model is able to detect the new number plates. The model proposed in the current paper is compared with the other existing systems and proves to be efficient and cost-efficient than them.

关键词： Deep learning Training Roads Sociology Real-time systems Sensors Labeling

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Prediction Analysis for Diabetic Disease Using Machine Learning Model 22

Prediction Analysis for Diabetic Disease Using Machine Learn...

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22nd OITS International Conference on Information Technology, OCIT 2024

作者： Senthamarai, N. Masood, Jafar Ali Ibrahim Syed Sathya, M. Chakravarthy, N.S. Kalyan Rathore, Bharati Kalith Oli, Mohamed M. Department of Network and Communications SRM Institute of Science and Technology Chennai India Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of IT Nadar Saraswathi College of Engineering and Technology Theni India Center for Data Science AI&ML QIS College of Engineering and Technology Ongole India Faculty of Business and Creative Industries University of South Wales Llantwit Rd PontypriddCF37 1DL United Kingdom School of Bio Sciences and Technology Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331510404

The Diabetes Disease Prediction System is based on predictive modeling, which forecasts the user's disease based on the information that the user inputs to the system, including the number of pregnancies, age, weight, blood pressure, insulin, glucose, and other factors contributing to diabetes. The system evaluates the data supplied by the user as input and outputs the likelihood of the prediction. Nowadays, better production results are key for machine learning activities. research has been conducted on a dataset from the Kaggle data set where single machine learning algorithms have been applied, and recorded results have been compared with results of multialgorithms of machine learning using neural networks. Some major classification algorithms, Random Forest, SVM, and Hybrid model of SVM and Random Forest, have been broadly utilized to anticipate the disease, where different accuracies were obtained. data preprocessing and feature selection were performed before building the models to increase the model's accuracy. Evaluation of the models in the proposed paper is based on the model's accuracy. The hybrid model performed best with 83% accuracy. © 2024 IEEE.

关键词： Random forests

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research on the Rural Community Elderly Care Service Management Systems based on The Internet of Things 6

Research on the Rural Community Elderly Care Service Managem...

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6th International Conference on Electronic Information Technology and computer engineering, EITCE 2022

作者： Zhu, Tingting Wu, Huarui Chen, Cheng Zhu, Huaji School of Computer Science and Communication Engineering JiangSu University ZhenJiang China Big Data and Intelligence Department Research Center of Information Technology Beijing Academy of Agriculture and Forestry Sciences Key Laboratory of Digital Village Technology Ministry of Agriculture and Rural Affairs BeiJing China Industrialization Department Research Center of Information Technology Beijing Academy of Agriculture and Forestry Sciences Key Laboratory of Digital Village Technology Ministry of Agriculture and Rural Affairs BeiJing China

ISBN: (纸本)9781450397148

Under the background of the in-depth implementation of the Rural Revitalization Strategy, the conditions of public service facilities for the elderly have improved significantly in rural areas. However, the problem is that the asymmetry between the intelligent management and construction of the corresponding elderly care services is more prominent. It leads to the lagging management of elderly care services, poor service information, and other problems. In order to solve the above series of problems, we propose an IoT-based rural community elderly care service management system, which can effectively assist community elderly care. The designs of structure, function, and key technologies are discussed in this article. To provide more timely and accurate services for the elderly, the system can realize real-time data collection of behavior, vital signs, and the surrounding environment of the elderly, monitor and warn of abnormal situations. © 2022 Association for Computing Machinery.

关键词： Rural areas

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Shape-aware contrastive deep supervision for esophageal tumor segmentation from CT scans

Shape-aware contrastive deep supervision for esophageal tumo...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jin, Qiangguo Cui, Hui Sun, Changming Huang, Jiapeng Xuan, Ping Xu, Yiyue Wang, Linlin Cao, Leilei Wei, Leyi Su, Ran Northwestern Polytechnical University School of Software Shaanxi China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Csiro Data61 Sydney Australia Shantou University School of Engineering Department of Computer Science Guangdong China Shandong First Medical University Shandong Academy of Medical Sciences Shandong Cancer Hospital and Institute Department of Radiation Oncology Shandong China Zhejiang University Innovation Center of Yangtze River Delta Zhejiang China Shandong University School of Software Shandong China Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China

ISBN: (纸本)9798350337488

Accurate tumor segmentation is crucial for esophageal cancer radiotherapy treatment planning. The low contrast among the esophagus, tumors, and surrounding tissues, and irregular tumor shapes limit the performance of automatic segmentation methods. In this paper, we aim to exploit the irregular shapes of tumors to facilitate accurate segmentation. We propose a simple and pluggable shape-aware contrastive deep supervision network (SCDSNet) with shape-aware regularization and voxel-to-voxel contrastive deep supervision. Specifically, the shape-aware regularization with an uncertainty minimization strategy encourages the precise predictions of an additional shape-aware head. The voxel-to-voxel contrastive deep supervision enhances the multi-scale shape-tumor contrast for better voxel-to-voxel prediction of shapes. The proposed method is simple and highly pluggable, which can easily be extended to other frameworks. Further, we establish a large in-house dataset on esophageal cancer to validate the effectiveness of our proposed method. The quantitative and qualitative experimental results demonstrate the effectiveness of SCDSNet on the esophageal cancer dataset. © 2023 IEEE.

关键词： Deep supervision Esophagus tumor segmentation Shape-aware regularization Voxel-to-voxel contrastive learning

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From Single to Multi-Functional RIS: Architecture, Key Technologies, Challenges, and applications

arXiv

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arXiv 2024年

作者： Ni, Wanli Zheng, ailing Wang, Wen Niyato, Dusit Al-Dhahir, Naofal Debbah, Mérouane The Department of Electronic Engineering Tsinghua University China The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Computer Engineering The University of Texas Dallas United States The KU 6G Research Center Khalifa University of Science and Technology United Arab Emirates

Although reconfigurable intelligent surfaces (RISs) have demonstrated the potential to boost network capacity and expand coverage by adjusting their electromagnetic properties, existing RIS architectures have certain limitations, such as double-fading attenuation and restricted half-space coverage. In this article, we delve into the progressive development from single to multi-functional RIS (MF-RIS) that enables simultaneous signal amplification, reflection, and refraction. We begin by detailing the hardware design and signal model that distinguish MF-RIS from traditional RISs. Subsequently, we introduce the key technologies underpinning MF-RIS-aided communications, along with the fundamental issues and challenges inherent to its deployment. We then outline the promising applications of MF-RIS in the realm of communication, sensing, and computation systems, highlighting its transformative impact on these domains. Lastly, we present simulation results to demonstrate the superiority of MF-RIS in enhancing network performance in terms of spectral efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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An ai-Enabled Framework Within Reach for Enhancing Healthcare Sustainability and Fairness

arXiv

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arXiv 2024年

作者： Huang, Bin Zhao, Changchen Liu, Zimeng Hong, Shenda Zhang, Baochang Lu, Hao Liu, Zhijun Wang, Wenjin Liu, Hui AI Research Center Hangzhou Innovation Institute Beihang University Zhejiang Hangzhou China School of Computer Science Hangzhou Dianzi University China School of Automation Science and Electrical Engineering Beihang University China Institute of Medical Technology Health Science Center of Peking University National Institute of Health Data Science Peking University China Institute of Artificial Intelligence Beihang University China Information Hub Hong Kong University of Science and Technology China Department of Biomedical Engineering City University of Hong Kong China Department of Biomedical Engineering Southern University of Science and Technology China Institute of Medical Information Chinese Academy of Medical Sciences Peking Union Medical College China

Good health and well-being is among key issues in the United Nations 2030 Sustainable Development Goals. The rising prevalence of large-scale infectious diseases and the accelerated aging of the global population are driving the transformation of healthcare technologies. In this context, establishing large-scale public health datasets, developing medical models, and creating decision-making systems with a human-centric approach are of strategic significance. Recently, by leveraging the extraordinary number of accessible cameras, groundbreaking advancements have emerged in ai methods for physiological signal monitoring and disease diagnosis using camera sensors. These approaches, requiring no specialized medical equipment, offer convenient manners of collecting large-scale medical data in response to public health events. Therefore, we outline a prospective framework and heuristic vision for a camera-based public health (CBPH) framework utilizing visual physiological monitoring technology. The CBPH can be considered as a convenient and universal framework for public health, advancing the United Nations Sustainable Development Goals, particularly in promoting the universality, sustainability, and equity of healthcare in low- and middle-income countries or regions. Furthermore, CBPH provides a comprehensive solution for building a large-scale and human-centric medical database, and a multi-task large medical model for public health and medical scientific discoveries. It has a significant potential to revolutionize personal monitoring technologies, digital medicine, telemedicine, and primary health care in public health. Therefore, it can be deemed that the outcomes of this paper will contribute to the establishment of a sustainable and fair framework for public health, which serves as a crucial bridge for advancing scientific discoveries in the realm of ai for medicine (ai4Medicine). Copyright © 2024, The Authors. All rights reserved.

关键词： Electronic health record

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Transforming Business with Generative ai Models applications Trends

Transforming Business with Generative AI Models Applications...

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Artificial Intelligence and Quantum Computation-Based Sensor application (ICaiQSA), International Conference on

作者： Franciskus Antonius Alijoyo L. Chandra Sekhar Reddy V. Selvi Rekha Murugan Shital Kakad A. Balakumar Center for Risk Management and Sustainability Indonesia School of Business and Information Technology STMIK LIKMI Bandung Indonesia Department of CSE (Data Science) CMR College of Engineering & Technology Hyderabad India Department of Computer Science Mother Teresa Women's University Kodaikanal India Department of Commerce and Management Jain Deemed to be University Chris Canadian Degree college Bangalore India Department of AI & DS Vishwakarma Institute of Technology Pune Maharashtra India Department of ECE K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Generative artificial intelligence (ai) has unexpectedly emerged as a transformative generation, with the functionality to revolutionize numerous business sectors by way of developing new and realistic content material from present records. Unlike traditional ai fashions that cognizance in the main on class and reputation obligations, generative ai excels at producing pics, text, tune, and films, which drives innovation and operational performance across a couple of industries. This examine explores the impact of generative ai, evaluating its packages, overall performance metrics, and emerging traits using a complete dataset from Kaggle that includes numerous statistics kinds such as pics, textual content, track, and video. The research technique includes a radical manner of information collection, rigorous preprocessing, and distinct function extraction and engineering. data preprocessing is important, related to the removal of replica records, addressing missing values through imputation techniques, and standardizing records codecs to make certain consistency and reliability. Feature extraction focuses on figuring out key attributes associated with generative ai applications and their effect on commercial enterprise performance. Feature engineering extends this through growing and selecting new metrics to seize the nuanced elements of ai improvements. Classification is completed the usage of both supervised and unsupervised learning techniques to successfully categorize and examine the effect of generative ai on exclusive commercial enterprise procedures. The proposed method achieves an outstanding accuracy of 98%, demonstrating its effectiveness and reliability. Implemented in Python, this approach indicates tremendous ability for boosting innovation and operational efficiency.

关键词： Measurement Industries Technological innovation Quantum computing Generative ai Feature extraction Market research Reliability Unsupervised learning Business

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