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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

IEEE Transactions on Intelligent Transportation systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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Investigating the Feasibility of Using Machine Learning for Real-Time data Analysis

Investigating the Feasibility of Using Machine Learning for ...

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Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Latha B Vijaykumar R. Ghule Geetha G Shweta Singh Vikash Kumar Kharbas Gunveen Ahluwalia Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India School of Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

This paper seeks to determine the feasibility of the usage of device learning for actual-time facts evaluation. system gaining knowledge of can be used to research massive datasets and apprehend styles, in addition to automate selection-making methods. This may be utilized in studying the consequences of digital techniques in real-time and tracking adjustments in patron traits. numerous device studying algorithms, consisting of selection trees, Naïve Bayes, support Vector system (SVM), Logistic Regression, Random wooded area, and Neural Networks, are evaluated if you want to show their overall performance for real-time statistics assessment. A framework based on machine gaining knowledge of techniques is proposed for real-time facts evaluation. Open source datasets are used to evaluate and compare the overall performance of different algorithms. The assessment results show the practicality and effectiveness of the usage of device studying for actual-time records evaluation. In end, the feasibility of using device studying for real-time data analysis is affirmed via this research.

关键词： Performance evaluation Training Support vector machines Privacy data analysis Scalability Vegetation

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Discovering Innovative Applications for Real-Time data Analysis with Machine Learning

Discovering Innovative Applications for Real-Time Data Analy...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Prranjali Jadhav Raja Praveen K N Prateek Aggarwal Sandeep Gautam Preeti Naval D. Nirmala Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

This paper investigates the capability for discovering modern programs for actual-time information analysis the use of system learning. We will take a look at the technology and strategies used in system getting to know and talk their adaptive potential to interpret and analyze dynamically changing facts inputs. Especially, we can examine existing technological methods to understand the capacity of AI to discover creative solutions to complicated challenges. We will also explore capability techniques to contain gadget gaining knowledge of into present real-time records analysis platforms and architectures. Finally, we are able to speak the programs of machine studying in real-time records evaluation and its potential to allow fast and correct decision making. This paper gives a theoretical framework to apprehend and harness the power of device gaining knowledge of to investigate and interpret real-time facts in modern approaches.

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Secure and Privacy-preserving data-sharing Framework based on Blockchain technology for Al-Najaf/Iraq Oil Refinery 19

Secure and Privacy-preserving Data-sharing Framework based o...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Umran, Samir M. Lu, SongFeng Abduljabbar, Zaid Ameen Lu, Zhi Feng, Bingyan Zheng, Lu Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Iraqi Cement State Company Ministry of Industry and Minerals Baghdad10011 Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China University of Basrah College of Education for Pure Sciences Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Industrial Internet Research Institute Wuhan Huazhong Numerical Control Co. Ltd Wuhan430074 China South-Central University for Nationalities College of Computer Science Wuhan430074 China

ISBN: (纸本)9798350346558

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network;provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality. © 2022 IEEE.

关键词： Digital storage

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A Way of Making Smart Health Through Collaborating Machine Learning with the Bibliometrics

A Way of Making Smart Health Through Collaborating Machine L...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： T Vignesh P. S Sruthi T. Mohanraj Gaurav Kumar Akshay Varkale M.Sahaya Sheela Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College TN Department of Computer Science and Business System Nehru Institute of Technology T.M. Palayam Tamil Nadu India Department of Computer Science Engineering Karpagam Academy of Higher Education Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Computer Science & Engineering (CS and E) IES Institute of Technology and Management IES University Bhopal M.P India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

It datasets are widely utilized, shared, and repurposed without providing enough context for understanding the decision-making processes that led to their creation. It is necessary to modify system creation and use procedures to handle the concrete effects of how knowledge for model building is envisioned and used in real-world applications as AI systems assume more and more important roles. Documents are created at every phase of the data generation lifecycle to improve communication, aid in decision-making, and highlight the need of careful data work. By putting out this paradigm, we want to shed light on the choices and duties that are often disregarded while creating datasets, which is a crucial first step in bridging the duty of care gap in AI. This framework provides an essential and important resource for guaranteeing ethical AI activities, and it is in line with current developments in auditing procedures.

关键词： Ethics Decision making Smart healthcare Production Planning Maintenance Stakeholders

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network

arXiv

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arXiv 2024年

作者： Yang, Xinquan Li, Xuguang Luo, Xiaoling Zeng, Leilei Zhang, Yudi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China

Surgical guide plate is an important tool for the dental implant surgery. However, the design process heavily relies on the dentist to manually simulate the implant angle and depth. When deep neural networks have been applied to assist the dentist quickly locates the implant position, most of them are not able to determine the implant depth. Inspired by the video grounding task which localizes the starting and ending time of the target video segment, in this paper, we simplify the implant depth prediction as video grounding and develop a Texture Perceive Implant Depth Prediction Network (TPNet), which enables us to directly output the implant depth without complex measurements of oral bone. TPNet consists of an implant region detector (IRD) and an implant depth prediction network (IDPNet). IRD is an object detector designed to crop the candidate implant volume from the CBCT, which greatly saves the computation resource. IDPNet takes the cropped CBCT data to predict the implant depth. A Texture Perceive Loss (TPL) is devised to enable the encoder of IDPNet to perceive the texture variation among slices. Extensive experiments on a large dental implant dataset demonstrated that the proposed TPNet achieves superior performance than the existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Application of Machine Learning Algorithms for Customer Segmentation in E-Commerce Management

Application of Machine Learning Algorithms for Customer Segm...

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science, technology, engineering and Management (ICSTEM), International Conference on

作者： Rajyalaxmi M C. Vijai Kingshuk Srivastava NallaBala Kalyan B Pravallika Amit Dutt School of Business SR University Warangal Telangana India Department of Commerce and Business Administration Vel Tech Rangarajan Dr.SagunthalaR&amp D Institute of Science and Technology Chennai - 62 India Department of Data Science UPES Dehradun Uttarakhand India Department of Management Studies Sri Venkateswara College of Engineering (Autonomous) Tirupati Andhra Pradesh India Institute of Aeronautical Engineering Hyderabad Telangana India Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

Client segmentation is an essential component of e-commerce operations since it enables more effective marketing and a more satisfying experience for customers. Within the context of e-commerce platforms, this research investigates the functioning of machine literacy algorithms for effective client segmentation. Through the utilization of clustering methods and predictive models, such as k-means clustering, hierarchical clustering, and decision trees, our objective is to categorize guests according to their shopping behavior, demographics, and preferences. In addition, we investigate the incorporation of sophisticated machine literacy methods, such as ensemble styles and deep literacy infrastructures, to improve the delicateness and resilience of client segmentation models. We illustrate the efficacy of machine learningusing empirical evaluation and case studies. This enables us to relate different client components and adapt marketing strategies to fit the individual requirements of each client. In the context of e-commerce enterprises, our findings highlight the value of machine literacy-driven client segmentation in terms of optimizing marketing sweats, enhancing client retention, and maximizing profit.

关键词： Adaptation models Machine learning algorithms Clustering methods Clustering algorithms Predictive models Prediction algorithms Electronic commerce

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A Novel Machine Learning Approach for Fast and Efficient Detection of Mango Leaf Diseases

A Novel Machine Learning Approach for Fast and Efficient Det...

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Computing and Machine Intelligence (ICMI), International Conference on

作者： S M Masfequier Rahman Swapno Sm Nuruzzaman Nobel Md Babul Islam R. Haque V. P. Meena F. Benedetto Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Department of EEE Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Economics Dept SP4TE Laboratory University of ROMA TRE Italy

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Monitoring plantation diseases is a fundamental issue for the agricultural industry. Early diagnosis of plant illnesses dramatically affects food quantity and quality. Here we focus on the case of Mangoes, whose demand is very high. To maximize earnings, mango plant diseases must be managed well. Manual mango leaf disease diagnosis is impractical in today's computerized world due to its high cost, shortage of mango experts, and variable symptoms. In this work, we provide a novel machine Learning-based mango leaf disease classification method. We build a reliable categorization system using 4000 photographs from the Kaggle dataset grouped into 8 sickness categories. Our model reaches a remarkable accuracy of 96.87% throughout the training phase, and an accuracy rate of 93.12% during the validation phase. Finally, using previously unknown data, our model demonstrates an exceptional testing accuracy rate of 94.99 %. Our solution outperforms other existing machine learning detection methods, thus proving to be very effective in enhancing the categorization of Mango Leaf disease. and, more generally, offering improved and enduring food security and safety in the future.

关键词： Training Plant diseases Accuracy Plantations Machine learning Safety Reliability

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Deep Learning Applications for Wind Energy Forecasting in Smart Grids

Deep Learning Applications for Wind Energy Forecasting in Sm...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Hamid Wadood Muhammad Haris Aqib Hassan Muhammad Osama Malik Hamza Yousaf Kaleem Ullah Department of Computer Science University of Alabama at Birmingham Alabama USA Institute for Insight - J Mack Robinson College of Business Georgia State University Atlanta Georgia USA School of computing and mathematical sciences University of Greenwich London United Kingdom Department of Business Analytics University of Tulsa Oklahoma USA Stetson-Hatcher School of Business Mercer University GA USA Department of Energy System Engineering University of Engineering and Technology Peshawar Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Driven by the pressing need for sustainable and reliable energy sources, this paper critically evaluates deep learning's role in advancing wind energy forecasting, highlighting its potential to address current energy demands through improved prediction accuracy. Due to the inherent unpredictability of wind, conventional forecasting models often fall short; however, deep learning models offer enhanced reliability by effectively identifying complex, embedded patterns within wind data. The paper examines techniques such as time-series recurrent neural networks, restricted Boltzmann machines, convolutional neural networks, and autoencoders, each targeting distinct challenges in wind forecasting to strengthen grid integration and ensure energy reliability. Future directions-including hybrid model approaches and model interpretability-are discussed, underscoring deep learning's promise as a powerful tool for optimizing wind energy utilization and supporting a more sustainable, low-carbon energy infrastructure.

关键词： Deep learning Recurrent neural networks Wind energy Predictive models data models Reliability Convolutional neural networks Forecasting Wind forecasting Long short term memory

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