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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Valarmathi, B. Gupta, N. Srinivasa Karthick, V. Chellatamilan, T. Santhi, K. Chalicheemala, Dhanush Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Assistant Systems Engineer Tata Consultancy Services Chennai India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today's digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved. © 2024 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Sensing-Assisted Channel Estimation in ISAC systems 14th

Sensing-Assisted Channel Estimation in ISAC Systems

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14th EAI International Conference on Wireless and Satellite systems, WiSATS 2024

作者： Yin, Rui Shi, Yexin Qi, Wei Chen, Xianfu Wu, Celimuge Ji, Yusheng School of Information and Electrical Engineering Hangzhou City University Hangzhou310018 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Shenzhen CyberAray Network Technology Company Ltd Shenzhen China Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo182-8585 Japan Information Systems Architecture Research Division National Institute of Informatics Tokyo101-8430 Japan

ISBN: (纸本)9783031862021

In future Sixth-Generation (6G) mobile communication, satellite communication is a crucial means of extending mobile networks worldwide. However, in satellite communication systems, the transmission capacity between ground stations and users has become a bottleneck for satellite communication systems. For this issue, Integrated Sensing and Communication (ISAC) technology and Artificial Intelligence (AI), which has been widely studied, has the potential to offer excellent solutions. This paper explores a Uformer-based sensing-assisted communication system that enhances channel estimation accuracy through the design of a Uformer-based channel estimation enhancement algorithm. By formulating a global optimization problem, we seek to improve resource efficiency through finding the optimal resource allocation schemes. Initially, the system utilizes radar data to obtain reconstructed channels and employs communication data for channel estimation. The proposed neural network model then fuses these data into a new and more accurate channel state information. Finally, we present a resource allocation algorithm and validate the performance of all proposed algorithms through simulations. © ICST institute for Computer Sciences, Social informatics and Telecommunications engineering 2025.

关键词： Resource allocation

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Half space object classification via incident angle based fusion of radar and infrared sensors

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Journal of systems engineering and Electronics 2022年第5期33卷 1025-1031页

作者： HE Zhenyu ZHUGE Xiaodong WANG Junxiang YU Shihao XIE Yongjun ZHAO Yuxiong School of Electronic Information and Engineering Beihang UniversityBeijing 100191China Beijing Institute of Electronic System Engineering Beijing 100854China Beijing Institute of Astronautical Systems Engineering Beijing 100076China Shenzhen Institute of Beihang University Shenzhen 518038China School of Software Beihang UniversityBeijing 100191China

In this paper,we introduce an incident angle based fusion method for radar and infrared sensors to improve the recognition rate of complex targets under half space scenarios,e.g.,vehicles on the ground in this *** radar sensors,convolutional operation is introduced into the autoencoder,a“winner-take-all(WTA)”convolutional autoencoder(CAE)is used to improve the recognition rate of the radar high resolution range profile(HRRP).Moreover,different from the free space,the HRRP in half space is more *** order to get closer to the real situation,the half space HRRP is simulated as the *** recognition rate has a growth more than 7%com-pared with the traditional CAE or denoised sparse autoencoder(DSAE).For infrared sensor,a convolutional neural network(CNN)is used for infrared image ***,we com-bine the two results with the Dempster-Shafer(D-S)evidence theory,and the discounting operation is introduced in the fusion to improve the recognition *** recognition rate after fusion has a growth more than 7%compared with a single *** the discounting operation,the accuracy rate has been improved by 1.5%,which validates the effectiveness of the proposed method.

关键词： convolutional autoencoder(CAE) half space high-resolution range profile(HRRP) incident angle based fusion tar-get recognition

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Trusted elements in the digital model of the tunnel

Trusted elements in the digital model of the tunnel

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ITA-AITES World Tunnel Congress, WTC 2024

作者： Paskaleva, Galina Beronneau, Patrick Bednar, Thomas TU Wien Institute of Information Systems Engineering Business Informatics Group Vienna Austria TU Wien Research Unit of Building Physics Institute of Material Technology Building Physics and Building Ecology Vienna Austria

ISBN: (纸本)9781032800424

The application of Building information Modelling (BIM) in Tunnelling involves the generation, co-evolution, and maintenance of digital models. This often requires the collaboration of multiple stakeholders on the same digital model over multiple project phases, which, in turn, necessitates the sharing of responsibility for the same model element among those stakeholders. For example, a niche in the tunnel inner lining may be designed simultaneously by a structural engineer, by a fire safety expert who needs to install a fire hydrant in it, and by a HVAC engineer who needs a ventilation control panel in it. Shared responsibility requires trust. Lack of trust often leads to the duplication of models so that each stakeholder can work on a separate model, free from the influence of the work of others. The consequence is a break-down of model continuity and unsustainable usage of personnel and resources. In this work, we present a method for introducing trusted elements into the digital model of the tunnel. This can be accomplished by anchoring those elements to contractually relevant documents, such as the Employer information Requirement (EIR) according to the ISO 19650 series. Trusted elements can guarantee formally that a certain contractual or technical requirement has been fulfilled, e.g., that the measurement of carbon monoxide for the emergency ventilation system is designed in accordance with ISO 23431. In addition, formal requirements within a trusted element can be tested automatically by the common data environment (CDE) of the project. In case that one stakeholder changes the trusted element so that a requirement is no longer fulfilled, an automatic alert can be issued. We present a standardized workflow for the creation and maintenance of such trusted elements in a digital model on the use case of the design of a ventilation system in a road tunnel. © 2024 The Author(s).

关键词： Tunnels

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LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection 25

LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Qi, Jiaxing Huang, Shaohan Luan, Zhongzhi Yang, Shu Fung, Carol Yang, Hailong Qian, Depei Shang, Jing Xiao, Zhiwen Wu, Zhihui Sino-German Joint Software Institute Beihang University Beijing100191 China The Concordia Institute for Information Systems Engineering Concordia University Quebec Canada The China Mobile Information Technology Center China

ISBN: (纸本)9798350330014

The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-based anomaly detection. These methods face several challenges such as high-dimensional and noisy log data, class imbalance, generalization, and model interpretability. Recently, ChatGPT has shown promising results in various domains. However, there is still a lack of study on the application of ChatGPT for log-based anomaly detection. In this work, we proposed LogGPT, a log-based anomaly detection framework based on ChatGPT. By leveraging ChatGPT's language interpretation capabilities, LogGPT aims to explore the transferability of knowledge from large-scale corpora to log-based anomaly detection. We conduct experiments to evaluate the performance of LogGPT and compare it with three deep learning-based methods on BGL and Spirit datasets. LogGPT shows promising results and has good interpretability. This study provides preliminary insights into prompt-based models, such as ChatGPT, for the log-based anomaly detection task. © 2023 IEEE.

关键词： Anomaly detection

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Data-Filtered Prediction With Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, Mengchu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Kowloon Hong Kong Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China University of Melbourne Cloud Computing and Distributed Systems Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing100191 China CSSC Systems Engineering Research Institute Beijing100036 China Southern Methodist University Department of Computer Science Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in Quality of Service and cost efficiency. However, these data often exhibit nonlinearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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Iterative Design of a Decision Support System for Fall Risk Detection in residential care facilities

Iterative Design of a Decision Support System for Fall Risk ...

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作者： Tschorn, Niklas Keuchel, Maren Müller, Inga Milkov, Sarah Potthoff, Christian Windrath, Dioselina Weber, Yvonne Meister, Sven Burmann, Anja Fraunhofer Institute for Software and Systems Engineering ISST Dortmund / Health Care Informatics Faculty of Health School of Medicine Witten/Herdecke University Speicherstraße 6 Witten Germany Fraunhofer Institute for Software and Systems Engineering ISST Dortmund Germany HS Gesundheit Bochum Bochum Germany Diakonie Michaelshoven Cologne Germany Connext Communication GmbH Paderborn Germany Health Care Informatics Faculty of Health School of Medicine Witten/Herdecke University Witten Germany

Nursing care jobs are predominantly described as burdensome. In particular, the workload and the time available to deal with it are in a state of imbalance. Nurses are very often under time pressure and have to make decisions under psychological, emotional and physical stresses. Machine learning methods and in particular, Decision Support systems (DSS) can be used to support nurses with improved information (e.g. visualization of individual health risks) for decisions that are not easy to make. As falls are one of the most common health problems in care facilities, we will present a concept of a DSS for the prevention of falls that was developed in the PFLIP research project. We explain our user-centered design process based on ISO 9241-210-2019. User feedback is obtained at each phase so that nurses can identify problems at an early stage. The result is a conceptual design, visualized as a click dummy, to identify individual fall risks and predict preventive measures to reduce the risk of falling. © 2024 The Author(s), published by De Gruyter.

关键词： Electronic health record

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques 5

Novel Approach for Enhancing Mental Well-Being Through Machi...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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Reward-Based Energy-Aware Routing Protocols in Wireless Sensor Networks

Reward-Based Energy-Aware Routing Protocols in Wireless Sens...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software and Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Smart cities, industrial automation, healthcare, and environmental monitoring are just a few of the industries that have seen revolutionary changes as a result of Wireless Sensor Networks’ (WSNs) explosive growth. WSNs are significantly hampered by the limited energy resources of their sensor nodes, which are usually battery-powered and placed in inaccessible locations, despite their wide-ranging applications. As a result, energy efficiency is now a crucial component of these networks’ operation and design. Though these protocols frequently fail to adjust to the dynamic and unpredictable nature of WSNs, traditional energy-aware routing methods have been created to decrease energy consumption by choosing the best paths for data transfer. By allowing sensor nodes to learn from their surroundings and make wise routing decisions, the machine learning field of reinforcement learning (RL) provides a viable answer to these problems. In contrast to conventional techniques, RL-based protocols provide continuous energy optimization by dynamically modifying their routing strategies in response to real-time feedback. This paper investigates how to include RL methods—specifically, Q-learning and SARSA—into WSN energy-aware routing protocols. The suggested method creates a more distributed and scalable solution by enabling each node to independently learn and modify its routing choices. The effectiveness of RL-based routing protocols is compared to traditional methods through comprehensive simulations. The findings show that by slowing down the rate at which energy is being used up by each node, RL not only increases energy efficiency but also lengthens the network's operational life. In particular, Q-learning demonstrates a notable improvement in sustaining data delivery rates and network connectivity under various circumstances. Although a little more computationally demanding, the SARSA algorithm offers greater flexibility in situations where network topology changes often. Th

关键词： Multi agent systems

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