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International Multidisciplinary Information Technology and engineering Conference (IMITEC)

作者： Mahlori Khoza Moses Olaifa Pius Owolawi Vusi Malele Dept. of Computer Systems Engineering Tshwane University of Technology Tshwane South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

This study proposes a cutting edge Intelligent Doctor Chatbot that improves healthcare delivery and provides patients with easily accessible and customized health related information through the use of Natural language processing (NLP) and artificial intelligence (AI) technologies. These technologies are used by the chatbot to provide accurate medical advice based on symptom analysis and general health guidance in an interactive manner. The doctor chatbot addresses the growing demand for reliable healthcare information and assistance, particularly in situations where access to medical professionals is limited. By leveraging its advanced algorithms and medical knowledge base, the chatbot provides users with quick and accurate answers to their health-related questions. A key feature of the doctor chatbot is that users describe their symptoms and receive preliminary assessments on potential causes and suggested courses of action. It allows people to take an active role in their healthcare management and preventive measures.

关键词： Accuracy System performance Knowledge based systems Medical services Oral communication Chatbots Multilingual History Reliability Medical diagnostic imaging

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Low power implementation of Geometric High-order Decorrelation-based Source Separation on an FPGA board

Low power implementation of Geometric High-order Decorrelati...

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IEEE Cool Chips

作者： Ziquan Qin Kaijie Wei Hideharu Amano Kazuhiro Nakadai Dept. of Information and Computer Science Keio University Yokohama Japan Dept. of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

Open source software for robot audition called HARK aims to make “OpenCV” in audio signal processing, providing comprehensive functions from multichannel audio input to sound localization, sound source separation, and au-tomatic speech recognition. Since each of these HARK modules takes considerable energy when executed on PC, we propose to implement each module on an FPGA board called M-KUBOS connected. Here, we focus on the most computationally expensive function of HARK; the sound source separation, and implement it on a Zynq Ultrascale+ board. More than twice a performance improvement was achieved by using the sound frequency level parallelization in the HLS description compared to the software execution on the Ryzen 3990X64-core server. Power evaluation of the real board showed that the energy consumption is only 1/23.4 of the server.

关键词： Location awareness Energy consumption Source separation Power demand Speech recognition Parallel processing Servers

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Augmented Reality Used in Technical Diagnostics and Maintenance

Augmented Reality Used in Technical Diagnostics and Maintena...

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ELEKTRO

作者： Aleš Janota Peter Holečko Dept. of Control and Information Systems Faculty of Electrical Engineering and IT UNIZA Žilina Slovak Republic

ISBN: (数字)9798350372359

ISBN: (纸本)9798350372366

Integrating digital data and human sensory input in real-world settings offers new possibilities in device diagnostics and maintenance. In this study, we utilized the EcoStruxure Augmented Operator Advisor to develop an augmented reality application for a family house heating system. Our application was designed without access to the real heating system; instead, we simulated its operation with a programmable logic controller. Our final application includes three primary scenes: the heat pump, its indoor unit, and the engine room. Drawing on our practical experience, we discuss the benefits of our approach and the software tools used and summarize our subjective opinions.

关键词： Heating systems Three-dimensional displays Image recognition Heat pumps Two-dimensional displays Time measurement Maintenance

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miRcorrNetPro: Unraveling Algorithmic Insights through Cross-Validation in Multi-Omics Integration for Comprehensive Data Analysis

miRcorrNetPro: Unraveling Algorithmic Insights through Cross...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yazici, Miray Unlu Yousef, Malik Marron, J.S. Bakir-Gungor, Burcu Abdullah Gül University Dept. of Bioengineering Kayseri Turkey Zefat Academic College Dept. of Information Systems Zefat Israel University of North Carolina Dept. of Statistics and Operations Research Chapel HillNC United States Abdullah Gul University Dept. of Computer Engineering Kayseri Turkey

ISBN: (纸本)9798350337488

High throughput -omics technologies facilitate the investigation of regulatory mechanisms of complex diseases. Along this line, scientists develop promising tools and methods to extend our understanding at the molecular and functional levels. To this end, miRcorrNet tool performs integrative analysis of microRNA (miRNA) and gene expression profiles via machine learning (ML) approach to identify significant miRNA groups and their associated target genes. In this study, we propose miRcorrNetPro tool, which extends miRcorrNet by tracking group scoring, ranking and other information through the cross-validation iterations. Heatmap visualizations enable deep novel insights into the collective behavior of clusters of groups in cellular signaling and hence facilitate detection of potential biomarkers for the disease under investigation. Although miRcorrNetPro is designed as a generic tool, here we present our findings and potential miRNA biomarkers for Breast Cancer (BRCA). The miRcorrNetPro tool and all other supplementary files are available at https://***/Miray-Unlu/miRcorrNetPro. © 2023 IEEE.

关键词： Breast Cancer Machine Learning microRNAs Multi-omics Integration

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BIOPHYSICAL SIGNAL PROCESSING FOR AUTOMATIC ANXIETY CLASSIFICATION IN A VIRTUAL REALITY EXPOSURE THERAPY SYSTEM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2024年第2期86卷 73-84页

作者： Jinga, Nicolae Petrescu, Cătălin-Dumitru Mitruț, Oana Moldoveanu, Alin Moldoveanu, Florica Petrescu, Livia Dept. of Computer Science National University of Science and Technology POLITEHNICA Bucharest Romania Dept. of Automatic Control and Systems Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Faculty of Biology University of Bucharest Romania

This paper presents a signal processing framework for automatic anxiety level classification in a virtual reality exposure therapy system. Two types of biophysical data (heart rate and electrodermal activity) were recorded, pre-processed and passed through a feature extraction procedure that provided input for the real-time anxiety level classification algorithm. We showcase the mathematical and engineering techniques behind the procedures and conclude with the challenges encountered in our research and future development ideas. The proposed method provides a good estimate of the level of anxiety while having at the same time a reduced level of complexity, allowing implementation on equipment with limited computing resources. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Classification (of information)

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Anomaly Detection With Quantified Explanations for Multivariate Time Series Networking Data

Anomaly Detection With Quantified Explanations for Multivari...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Marantis, Panagiotis Ramantas, Kostas Verikoukis, Christos Iquadrat Informatica S.L Barcelona Spain University of Patras Dept. of Computer Engineering and Informatics Patras Greece Athena Research Center Athens Greece Industrial Systems Institute Patras Greece

ISBN: (纸本)9798350351255

Next-generation telecommunication networks provide vast amounts of Multivariate Time Series monitoring data during their operation. Using these data to automatically detect anomalous system behavior is of critical importance to operators and Internet Service Providers, in order to analyze the network state and take mitigating actions to resolve possible faults. For the output of this Anomaly Detection process to be trusted, comprehensive and accurate explanations must be provided. These explanations are essential to make critical decisions that affect the network state. In this paper, we present a framework that utilizes reconstruction-based Deep Neural Network models to perform the anomaly detection task, while also using an explainability module that provides explanations of the results. The quality of the explanations is quantified through the use of interpretation labels. In addition, we provide a comparative analysis, performing Anomaly Detection on three different open-source datasets from the mobile networking domain, while testing multiple Deep Learning models and comparing their performance. The presented results show that our framework accurately detects various types of anomalies that can be present in mobile networks, while offering the flexibility to identify and select the model that offers the best performance for each dataset, in both the Anomaly Detection and explainability tasks. © 2024 IEEE.

关键词： DNNs Explainable Anomaly Detection Machine Learning Multivariate Time Series Next-Generation Networks Reconstruction-based Models

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Improving the Traffic engineering of SDN networks by using Local Multi-Agent Deep Reinforcement Learning

Improving the Traffic Engineering of SDN networks by using L...

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IEEE Symposium on Network Operations and Management

作者： José Gómez-delaHiz Jaime Galán-Jiménez Dept. of Computer Systems and Telematics Engineering University of Extremadura Cáceres Spain

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

The flexibility and programmability of Software-Defined Networks (SDN) has allowed the research community to propose new Traffic engineering (TE) techniques to improve their performance. Although the installation of heuristic or optimal solutions in the SDN controller allows to obtain good results in network performance, these are based on historical data that may not be updated to actual traffic variations. Moreover, the research community is exploiting the strength of Deep Reinforcement Learning (DRL) and its capability to learn and adapt to the complexities inherent in networks to propose enhanced routing solutions. However, the nature of DRL can cause a performance degradation during the learning process due to the application of exploration when determining the best policy. For this reason, in this work we propose a Multi-Agent DRL (MADRL) based solution that is able to reduce the Maximum Link Utilization (MLU) of SDN networks only considering the local information of the nodes. Each node has a DRL agent and is able to decide the best routing decision for each flow so that the MLU is minimized. The performance evaluation shows that our approach outperforms the classical shortest path rule based on Dijkstra in 8%.

关键词： Performance evaluation Degradation Routing Deep reinforcement learning Complexity theory Software defined networking

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Enhancing Ship Detection on Satellite Images with Modified DeepLabV3+

Enhancing Ship Detection on Satellite Images with Modified D...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Sharma, Rohit Dahatonde, Shubham Acharjya, Srimanta Kumar Verma, Sourabh Singh Dhaka, Vijaypal Singh Rawat, Ramesh Singh School Of Computing & Intelligent Systems Manipal University Jaipur India C. V Raman Global University Department of Computer Science and engineering Bhubaneswar India Graphic Era deemed to be University Dept. of computer Science and Engg Uttrakhand Deharadun India

ISBN: (纸本)9798350379716

This paper presents a advance approach for ship detection in satellite imagery utilizing a modified DeepLabV3+ architecture, specifically designed to overcome the challenges inherent in such data. The proposed model features an enhanced feature extraction process and a refined atrous spatial pyramid pooling (ASPP) module, which together improve the detection of ships across various sizes and shapes. Comprehensive experiments on publicly available satellite datasets reveal that the modified DeepLabV3+ significantly outperforms existing state-of-the-art methods, achieving an accuracy of 98%. These findings demonstrate the model's robust ability to identify and localize ships in complex maritime settings, offering promising potential for improved maritime situational awareness and operational efficiency. © 2024 IEEE.

关键词： Satellite imagery

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Empowering citizens by a blockchain-Based Robinson list

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International Journal of computers and Applications 2022年第10期44卷 920-928页

作者： Cirillo, Albenzio Dalena, Vito Mauro, Antonio Mogavero, Francesco Pennino, Diego Pizzonia, Maurizio Vitaletti, Andrea Zecchini, Marco Fondazione Ugo Bordoni Roma Italy Roma Tre University–Eng. Dept. Roma Italy Sapienza University of Rome Department of Computer Control and Management Engineering Roma Italy

Robinson lists are adopted in many countries to protect phone subscribers against commercial spam calls. In its essence, they collect the denial of the subscribers to be contacted by marketing operators. Nowadays, all Robinson lists run as centralised services. Namely, citizens have to rely on trusted third parties for the management of their own choices. The Blockchain allows us to envision the development of a decentralised Robinson list in which citizens are finally empowered to autonomously manage their choices. In this paper, we present a proof-of-concept (PoC) of a decentralised Robinson list developed with the Algorand technology. To the best of our knowledge, this is the first attempt to implement a Robinson list on a blockchain technology. The PoC has been designed to support the requirements provided by Fondazione Ugo Bordoni, the manager of the Italian Robinson list. The evaluation of the PoC shows that our decentralised solution provides satisfactory performance, in terms of scalability, latency and costs, when compared to the current centralised service. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Blockchain

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Computationally Efficient Predictive control Based on ANN State-Space Models

Computationally Efficient Predictive Control Based on ANN St...

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IEEE Conference on Decision and control

作者： Jan H. Hoekstra Bence Cseppento Gerben I. Beintema Maarten Schoukens Zsolt Kollár Roland Tóth Dept. of Electrical Engineering at the Eindhoven Control Systems group University of Technology The Netherlands Dept. of Measurement and Information Systems Budapest University of Technology and Economics Hungary Institute for Computer Science and Control Budapest Hungary

Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for the identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinear model predictive control (NMPC), the resulting nonlinear optimization problem is often overly complex due to the size of the network, requires the use of high-order observers to track the states of the ANN model, and the overall control scheme does not exploit the available autograd tools for these models. In this paper, we propose an efficient approach to auto-convert ANN statespace models to linear parameter-varying (LPV) form and solve predictive control problems by successive solutions of linear model predictive problems, corresponding to quadratic programs (QPs). Furthermore, we show how existing deep-learning methods, such as SUBNET that uses a state encoder, enable efficient implementation of MPCs on identified ANN models. Performance of the proposed approach is demonstrated by a simulation study on an unbalanced disc system.

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