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49th EPS Conference on Plasma Physics, EPS 2023

作者： Pastore, F. Felici, F. Sauter, O. Bosman, T.O.S.J. Galperti, C. Vincent, B. Pau, A. Poels, Y. Ravensbergen, T. Vu, N.M.T. Lausanne Switzerland DIFFER - Dutch Institute for Fundamental Energy Research Energy Systems and Control Group Eindhoven Netherlands Eindhoven University of Technology Dept. of Mechanical Engineering Control Systems Technology Group Eindhoven Netherlands Eindhoven University of Technology Dept. of Mathematics and Computer Science Data Mining Group Eindhoven Netherlands ITER Organization Route de Vinon-sur-Verdon St. Paul Lez Durance13067 Cedex France

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Devising a Set of Compact and Explainable Spoken Language Feature for Screening Alzheimer's Disease

Devising a Set of Compact and Explainable Spoken Language Fe...

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International Symposium on Chinese Spoken Language Processing

作者： Junan Li Yunxiang Li Yuren Wang Xixin Wu Helen Meng Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong Dept. of Computer Science & Engineering The Chinese University of Hong Kong

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Alzheimer's disease (AD) has become one of the most significant health challenges in an aging society. The use of spoken language-based AD detection methods has gained prevalence due to their scalability due to their scalability. Based on the Cookie Theft picture description task, we devised an explain-able and effective feature set that leverages the visual capabilities of a large language model (LLM) and the Term Frequency-Inverse Document Frequency (TF-IDF) model. Our experimental results show that the newly proposed features consistently outperform traditional linguistic features across two different classifiers with high dimension efficiency. Our new features can be well explained and interpreted step by step which enhance the interpretability of automatic AD screening.

关键词： Visualization Scalability Large language models Linguistics Aging Feature extraction Alzheimer's disease

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Support Chatbot based on ChatGPT for Customer Relationship Management in Educational Institutions 6

Support Chatbot based on ChatGPT for Customer Relationship M...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Mohamed, Islam Samih Abdelsalam, Mohamed Moawad, Ibrahim F. Dept. Business Information Systems Faculty of Commerce and Business Administration Helwan University Cairo Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt

ISBN: (纸本)9798350373875

Chatbot technology is considered one of the latest artificial intelligence tools used to achieve interaction between the organization and the customer. It relies on natural language processing, as it helps the organization know the desires and needs of the customer, in addition to collecting customer data and displaying the services provided by the organization. This research proposes an architectural model for supporting chatbots based on the ChatGPT model. It receives customer interaction inputs, understands questions, and responds quickly and appropriately. In addition, this model was designed and implemented using the Voiceflow platform. This model was named EDUBot to build communication elements and components to customize the structure of the virtual conversation and manage the dialogue. This model can be applied to the Educational City institution as a case study to improve customer service and help the institution manage customer relationships more efficiently. © 2024 IEEE.

关键词： Sales

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ML-Inspired Multiple Relay Selection in UAV-Enabled Untrusted Networks

ML-Inspired Multiple Relay Selection in UAV-Enabled Untruste...

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IEEE Conference on Wireless Communications and Networking

作者： Lefteris Tsipi Emmanouel T. Michailidis Konstantinos Maliatsos Demosthenes Vouyioukas Dept. of Information and Communication Systems Engineering University of the Aegean Samos Greece Dept. of Informatics and Computer Engineering University of West Attica Egaleo Greece Dept. of Digital Systems University of Piraeus Piraeus Greece

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, multiple relay selection (MRS) schemes for untrusted unmanned aerial vehicle (UAV)-enabled networks are proposed. In this context, various machine learning (ML) models are employed to improve secrecy performance by optimally selecting multiple aerial relays from the available ones, all of which are regarded as untrusted. Notably, these ML models can cope with the quickly changing and random positioning of the aerial relays, effectively decoupling the intricate coupling relationship between the secrecy rate, channel coefficients, and inter-node distances. The results indicate that the ML-based MRS schemes obtain sufficient accuracy and better decoupling than a respective exhaustive searching (ES) approach, while also maintaining a lower computational complexity.

关键词： Couplings Accuracy Predictive models Autonomous aerial vehicles Security Ensemble learning Relays Computational complexity Context modeling

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Enhancing G.C.E. Ordinary Level Mathematics Through Adaptive Learning in Sri Lanka

Enhancing G.C.E. Ordinary Level Mathematics Through Adaptive...

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Advancements in Computing (ICAC), International Conference on

作者： P.S.D. Fernando H.P.D.E.R. Amarasekara M.D.B. Rathnakumara B.W. Amarakoon Samantha Rajapaksha Jenny Krishara Dept. of Information Technology Malabe Sri Lanka Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Proficiency in mathematics is essential for both academic and professional success, yet many students find the subject difficult, especially when preparing for important exams like Sri Lanka's General Certificate of Education (G.C.E.) Ordinary Level examination. In Sri Lanka, the students show poor pass rates in Ordinary Level mathematics examination. In order to improve mathematics education for O/L students, this research suggests an innovative mobile application that makes use of adaptive learning strategies. This application features components like a comprehensive knowledge bank that is curriculum-mapped, pre-assessment tools for identifying knowledge shortages, a weaknesses based lesson recommendation for personalized lesson suggestions, a custom developed chat assistant to interact with the knowledge bank, interactive 3D visualizations and justifications of complex concepts, and platforms for problem-solving, writing, and time management skill development. The suggested adaptive learning strategy makes use of multimedia tools and datadriven insights to tailor instruction to each student's unique needs, improving math comprehension and readiness for the O/L examination in Sri Lanka.

关键词： Adaptive learning Visualization Three-dimensional displays Writing Mathematics Natural language processing Mobile applications Problem-solving Stakeholders Research and development

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On Detecting Low-Pass Graph Signals Under Partial Observations

On Detecting Low-Pass Graph Signals Under Partial Observatio...

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Sensor Array and Multichannel Signal Processing Workshop

作者： Hoang-Son Nguyen Hoi-To Wai Dept. of Computer Science & Engineering The Chinese University of Hong Kong Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

The application of graph signal processing (GSP) on partially observed graph signals with missing nodes has gained attention recently. This is because processing data from large graphs are difficult, if not impossible due to the lack of availability of full observations. Many prior works have been developed using the assumption that the generated graph signals are smooth or low pass filtered. This paper treats a blind graph filter detection problem under this context. We propose a detector that certifies whether the partially observed graph signals are low pass filtered, without requiring the graph topology knowledge. As an example application, our detector leads to a pre-screening method to filter out non low pass signals and thus robustify the prior GSP algorithms. We also bound the sample complexity of our detector in terms of the class of filters, number of observed nodes, etc. Numerical experiments verify the efficacy of our method.

关键词： Array signal processing Conferences Low-pass filters Signal processing algorithms Detectors Filtering algorithms Complexity theory

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Remote Sensing–Based UAV Imaging in Heat Pattern Analysis Impact on Climate Change Detection Using Fuzzy Stacked Lasso Elastic-Net Model

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Remote Sensing in Earth systems Sciences 2024年第4期7卷 699-708页

作者： Sailaja, M. Kumar, M. Prema Jyothi, B. Swarna Vanguri, G. L. Narasamba Manjula, S. Priya, D. Divya Department of Computer Science and Engineering Ravindra College of Engineering for Women Andhra Pradesh Kurnool India Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science & amp Engineering Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Information Technology Aditya College of Engineering & amp Technology(A) Andhra Pradesh Surampalem 533437 India Department of Computer Science and Business Systems Nehru Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat islands raise surface temperatures, which has an effect on city dwellers’ health and welfare. Urbanisation-related changes to the land surface, which are especially notable right after sunset, have an impact on radiative forcing. Recently, there has been a surge in the usage of unmanned aerial vehicle (UAV) technology, particularly for precision agriculture and plant phenotyping. This is due to the declining cost and increased accessibility of both UAVs and thermal imaging sensors. This interruption affects subsequent operations like target item categorisation and water quality parameter inversion. The goal of this study was to evaluate the potential for combining machine learning (ML) with unmanned aerial vehicle (UAV)–based imaging technology. In this research, the novel technique in UAV image–based temperature pattern detection of various regions of earth and their sea level rise analysis using image classification by machine learning model. Here, the input is collected as UAV images which have been processed for noise removal and normalisation. These image features have been extracted using convolutional active contour–based kernel component analysis and classified using region-based fuzzy stacked lasso elastic-net model. The classified output shows temperature analysis with sea level rise based on image analysis. The experimental analysis has been carried out for various UAV image dataset in terms of detection accuracy, RMSE, F-measure, mean average precision (MAP), and recall. Proposed technique F-measure of 89%, detection accuracy of 96%, MAP of 94%, recall of 92%, and RMSE of 60%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional active contour Heat pattern detection Kernel component analysis Lasso elastic-net model Remote sensing

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Identification of Cancer Biomarkers for Multi-class Diagnostics through Network Analysis of RNAseq Data of Tumor-Educated Platelets

Identification of Cancer Biomarkers for Multi-class Diagnost...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Toccacieli, Ali Petti, Manuela Sapienza University of Rome MSc Program in Engineering in Computer Science Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9781665468190

Tumor-educated platelets (TEPs) are circulating blood cells with a distinct tumor-driven phenotype and act as carriers and protectors of metastases. To date, some studies have shown that the TEPs transcriptome can be used for cancer diagnostics. The objective of this study is to propose a procedure based on differential gene expression and differential gene co-expression analyses to identify a set of key genes for multi-class cancer diagnostics. To reach this aim, we analyzed RNA-seq data (57736 genes) of 130 subjects, of whom 40 patients with glioblastoma multiforme (GBM), 35 patients with pancreatic adenocarcinoma (PAAD), and 55 healthy donors (HC). We focused our analysis on the subset of differentially expressed genes (DEGs), and we used these genes to build and analyze the differential co-expression networks, identifying the hub genes. With this procedure, we obtained a restricted set of DEGs that maximize the accuracy in classifying patients according to their conditions (GBM, PAAD, or HC). Indeed, we validated our results by comparing the achieved classification accuracy with that resulting from random selections of DEGs and we obtained that genes selected by differential co-expression (DCE) network analysis have greater predictive power than any other set of differentially expressed genes, including using all of them. © 2022 IEEE.

关键词： Platelets

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Forest Fire Prevention and Detection System

Forest Fire Prevention and Detection System

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Power Electronics, computer Applications (ICPECA), IEEE International Conference on

作者： Lasitha Jayakody Chamathka Edirisinghe Tharush Senasinghe Shaminda Dissanayake Narmada Gamage Dinithi Pandithage Jaliya L. Wijayaraja Dept. of Computer Systems Engineering Sri Lanka Institute of Information Malabe Sri Lanka

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires.

关键词： Temperature sensors Technological innovation Wildfires Prevention and mitigation Forestry Sensor phenomena and characterization Sensor systems Real-time systems Gas detectors Resilience

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Local Polynomial Models for Real-Time Tracking of Humidity Sensor Signals 3

Local Polynomial Models for Real-Time Tracking of Humidity S...

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3rd International Conference on Communications, Information, Electronic and Energy systems, CIEES 2022

作者： Nikovski, Plamen Titova, Tanya Mihov, Vasil University of Food Technologies Dept. of Electronics And Electrical Engineering Plovdiv Bulgaria University of Food Technologies Dept. of Automation Information And Control Systems Plovdiv Bulgaria

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

Sensor signal tracking has great practical importance for solving a number of problems such as data fusion and sensor integration in multi-sensor systems (MSS), robotics, fault detection (FD), and diagnosis, etc. The Kalman filter is a widely used algorithm that can be applied in these cases if an appropriate model of the signal is available. The present study shows that under a step change in humidity, the filter can estimate the signal from a humidity sensor by means of a second-order local polynomial model. This gives a reason to expect similar behavior not only for sudden but also for all other changes. The proposed online signal tracking solution is simple to implement and can be used not only for humidity sensors, but also for sensors that measure quantities such as temperature, pressure, level, etc. © 2022 IEEE.

关键词： Polynomials

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