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International Conference on Information, Intelligence, systems and Applications (IISA)

作者： Theodoros Tziolas Elpiniki Papageorgiou Konstantinos Papageorgiou Georgios Zardas Alexios Lekidis Papazoglou Panagiotis Dept. of Energy Systems University of Thessaly Gaiopolis Campus Larisa Greece Dept. of Electrical and Computer Engineering University of Thessaly Volos Greece Dept. of Architecture and Computer and Communication Simulation Systems National and Kapodistrian University of Athens Chalkida Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Floods can have significant life-threatening and economic impacts on human societies. Flood forecasting with data-driven methods is a crucial aspect of disaster risk management, providing valuable insights into potential flood events and allowing for timely mitigation measures. This work illustrates the application of a Long Short-Term Memory (LSTM) neural network for short-term forecasting of a river's water level. The water treatment data used for training the LSTM models were gathered from Sperchios river, located in central Greece. Historical time-series data in a half-hour interval of the river's water level, water velocity, and area precipitation were utilized for the development of the proposed scheme. The developed model is deployed in the backend of a web application for half-hour ahead predictions of the river's water level that are visualized in the frontend. The post-analysis of the deployed forecasting model indicates that it consistently delivers forecasts with minimal error and demonstrates high accuracy in recent flood events.

关键词： Training Precipitation Biological system modeling Prevention and mitigation Neural networks Predictive models Rivers Floods Forecasting Long short term memory

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

arXiv

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arXiv 2024年

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

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 explainable 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. Copyright © 2024, The Authors. All rights reserved.

关键词： Scalability

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Towards the Diagnosis of Heart Disease Using an Ensemble Learning Approach

Towards the Diagnosis of Heart Disease Using an Ensemble Lea...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Zheng Zhang Liangliang Xu Yida Bao Sanjeev Baskiyar Dept. of Computer Science & Software Engineering Dept. of Mathematics & Statistics Auburn University Auburn AL United States

Heart disease is one of the leading causes of death worldwide. Understanding the presence of heart disease is crucial for timely intervention and effective management. However, it is still a challenging task to accurately diagnose and treat heart disease. In this paper, we propose an ensemble machine learning model that combines the predictive power of three state-of-the-art machine learning algorithms: BERT (Bidirectional Encoder Representations from Transformers), FT-transformer, and XG-Boost. Through extensive training and evaluation, we assess the performance of our model using established metrics such as accuracy, precision, recall, F1-score, and receiver operating char-acteristic (ROC) curve analysis. The results obtained demonstrate the effectiveness and validity of our machine learning system in predicting heart disease accurately. This paper presents a promising approach that can assist healthcare professionals in making informed decisions and improving patient outcomes, enhances our understanding of heart disease patterns, and contributes to the development of effective diagnostic and treatment approaches.

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A Secure RPL Rank Computation and Distribution Mechanism for Preventing Sinkhole Attack in IoT-based systems

A Secure RPL Rank Computation and Distribution Mechanism for...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Mishra, Alekha Kumar Puthal, Deepak Tripathy, Asis Kumar National Institute of Technology Dept. of Computer Science and Engineering Jamshedpur India Khalifa University Center for Cyber-Physical Systems and Department of EECS Abu Dhabi United Arab Emirates School of IT and Engineering VIT Vellore India

ISBN: (纸本)9781665494274

The sinkhole attack detection mechanisms that have been reported till date are distributed in nature. Defending a sinkhole attack requires protecting the act of forging the rank computation task. This paper propose a secure centralized mechanism to ensure protection of rank computation process against sinkhole nodes in the IoT-Based systems. The expectation maximization algorithm is used to optimally cluster the nodes into a group bearing the same rank. The distribution of ranks to the nodes is protected by the signature of the sink. The experimental results show that the optimal rank computation is secure against sinkhole nodes, however requires additional network communication between nodes and the sink node. © 2023 IEEE.

关键词： Maximum principle

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Performance Evaluation of Quantum-Resistant TLS for Consumer IoT Devices

Performance Evaluation of Quantum-Resistant TLS for Consumer...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Jessica Bozhko Yacoub Hanna Ricardo Harrilal-Parchment Samet Tonyali Kemal Akkaya Dept. of Computer Science Auburn University Auburn USA Dept. of Electrical and Computer Engineering Florida International University Miami USA Dept. of Software Engineering Gumushane University Gumushane Turkey

Post-quantum (PQ) cryptographic algorithms are currently being developed to be able to resist attacks by quantum computers. The practical use of these algorithms for securing networks will depend on their computational and communication efficiency. In particular, this is critical for the security of wireless communications within the context of consumer IoT devices that may have limited computational power and depend on a constrained wireless bandwidth. To this end, there is a need to evaluate the performance of widely used application layer security standards such as transport layer security (TLS) to understand the use of the existing PQ algorithms that are being evaluated by NIST as a replacement to the current cryptographic algorithms. This paper focuses on two widely used IoT standards Bluetooth Low Energy (BLE) and WiFi to find out the optimal performing PQ algorithm for their security when used in end-to-end connections over the Internet. By implementing the capability for IP over BLE and all options of TLS connection establishment, we developed a client-server IoT testbed to measure the efficiency of PQ key encapsulation mechanisms (KEMs) and PQ digital signature algorithms. The test results showed that Kyber512 is the ideal KEM while Falcon-512 and Dilithium2 are the best signatures for BLE and WiFi devices. Based on this outcome, we developed a mechanism for IoT devices with multiple communication interfaces, that dynamically chooses a PQ KEM algorithm based on the MAC layer protocol being used at the time.

关键词： Wireless communication Performance evaluation Encapsulation Heuristic algorithms Resists Internet of Things Cryptography

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Evaluating Datagram Congestion Control Protocol (DCCP) for Real-Time Applications: A Comparative Study with TCP in Multi-Node Networks 10

Evaluating Datagram Congestion Control Protocol (DCCP) for R...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Hassan, Aqib Memon, Vengas Shaikh, Farhan Bashir Alahmari, Saad Iqbal, Mansoor Azam, Farooq School of Computing and Mathematical Sciences University of Greenwich London United Kingdom Universiti Tunku Faculty of Information and Communication Tech. Abdul Rahman Perak Malaysia Northern Border University Department of Computer Science Arar Saudi Arabia University of Wah Department of Computer Science Wah Cantt Pakistan Nust Dept. of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798331532895

The rise in demand for real-time applications, such as live streaming, online gaming, and Internet telephony, has highlighted the necessity for transport protocols that offer low latency and network stability. Traditional protocols, namely Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), have inherent limitations. TCP ensures reliable data delivery but suffers from latency, making it unsuitable for real-time needs, while UDP provides fast data transmission but lacks congestion control, which can lead to network instability. This gap underscores the need for a protocol that combines high-speed data transfer with effective congestion management. To address this issue, the Internet engineering Task Force (IETF) introduced the Datagram Congestion Control Protocol (DCCP), designed to optimize data delivery speed while managing congestion. This study evaluates DCCP's performance against TCP in network scenarios of 8 and 16 nodes using the NS2 simulation environment. By comparing throughput and delay across various bandwidth and latency settings, the study finds that DCCP offers up to 35% higher throughput in 8-node setups and reduces packet loss by 20% in high-bandwidth scenarios. These results suggest that DCCP is well-suited for real-time applications, achieving a crucial balance between fast data transmission and network stability. © 2024 IEEE.

关键词： Congestion control (communication)

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Geographical Self-Organizing Map Clustering in Large-Scale Urban Networks for Perimeter Control 10

Geographical Self-Organizing Map Clustering in Large-Scale U...

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10th International Conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2024

作者： Elouni, Maha Rakha, Hesham A. Menendez, Monica Abdelghaffar, Hossam M. Department of Computer Science Randolph-Macon College AshlandVA United States Charles E. Via Jr. Dept. of Civil and Environmental Engineering Virginia Tech BlacksburgVA United States Division of Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Engineering and Systems Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9789897587030

Traffic congestion in urban areas presents a major challenge to efficient transportation systems. Recent advancements in traffic management provide promising solutions, with perimeter control emerging as a technique to tackle network-wide congestion. However, it is crucial to identify geographically connected homogeneously congested areas for effective implementation. This research explores the application of clustering techniques, particularly geographical self-organizing maps (GeoSOM), to identify spatially connected and homogeneously congested areas within transportation networks. While GeoSOM has found applications across various domains, its adaptation to transportation networks for congestion clustering is novel. This study introduces and implements an adaptation of the GeoSOM algorithm tailored for the large-scale urban environment of downtown Los Angeles. Its performance is assessed through a comparative evaluation with two other clustering algorithms, namely DBSCAN and K-means. The results demonstrate that GeoSOM surpasses other clustering algorithms, exhibiting improvements of up to 43% in traffic density variance, up to 61% in the spatial quantization error, and 15% in the quantization error. This finding demonstrates that the proposed clustering algorithm is effective in identifying a spatially homogeneous congested area within a large-scale transportation network. Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Traffic congestion

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Closed-Loop System Identification of an Unstable Nonlinear System Using Koopman Operator

Closed-Loop System Identification of an Unstable Nonlinear S...

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First International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP)

作者： Benjaset Maneeloke Sudchai Boonto Peerayot Sanposh Yodyium Tipsuwan Natthawut Chinthaned Warit Tanaprasitpattana Dept. of Electrical Engineering Kasetsart University Bangkok Thailand Dept. of Control Systems and Instrumentation Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. of Computer Engineering Kasetsart University Bangkok Thailand

ISBN: (数字)9798331505400

ISBN: (纸本)9798331505417

This paper presents a novel methodology for closed-loop system identification of unstable nonlinear systems using the Koopman operator with Extended Dynamic Mode Decomposition with control (EDMDc). The study highlights the critical role of selecting appropriate observable functions to develop accurate and efficient Koopman models. We demonstrate that the resulting Koopman models exhibit excellent fitting and validation properties and retain the stabilizability of the original nonlinear systems. These models are verified through time and frequency responses under closed-loop control using a Linear Quadratic Regulator (LQR), confirming their effectiveness. Future work will extend this framework to more complex systems and incorporate machine learning techniques to refine the selection of observable functions. This approach aims further to enhance the adaptability and robustness of Koopman-based control strategies.

关键词： Time-frequency analysis Adaptation models Regulators Accuracy Fitting Machine learning Robustness Closed loop systems Complex systems Frequency control

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Cocopal - A Deep Learning Based Intelligent System to Certify and Standardize the Quality of Coconut Based Products 4

Cocopal - A Deep Learning Based Intelligent System to Certif...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Gunawardana, K.H.R. Hemachandra, M.G.S.P. Hettiarachchi, N.M. Deshan, M.P.N. Ganegoda, Devanshi Weerasinghe, Lokesha Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

The procedure of certifying and standardizing the quality of the coconut-based products is done manually in Sri Lanka at precent. It is a time consuming and labor- intensive task and is conducted by experts. In most cases, the quality is decided solely by visual inspections by buyers and suppliers, with no scientific basis. The paper reports the capacity of bringing modern technology solutions such as Artificial Intelligence (AI), Machine Learning (ML), Image Processing (IP), and decentralized storage to aid in the certification and standardization of the quality of raw *** showed that the accuracy of the proposed system is in the 86% to 90% range and showed that this technique could beimproved and used as an alternative to manual techniques. © 2022 IEEE.

关键词： Deep learning

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software Defined Network Based Next Generation Mobile Communication Network Architecture

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第23期18卷 138-148页

作者： Kumar, Sumit Ben Sujin, B. Bhavani, K. Srilatha, Doddi Anand, Ketan Dept. of CSE Haridwar University Uttarakhand Roorkee India University of Technology and Applied Sciences Nizwa Oman Department of Computer Science and Business Systems Panimalar Engineering College Tamil Nadu Chennai India Department of CSE Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Sreenidhi Institute of Science and Technology Telangana Hyderabad India

As mobile networks and network speeds become more prevalent, the demand for marketing strategies increases. The operators are thinking about this, and the development of 5G communication networks is one of their main concerns. As the need for higher transmission speeds increases, 5G networks face challenges of scalability and adaptability. The next-generation mobile network (NMCN-SDN) architecture proposed in this study is based on software-Defined Networks (SDN). A new network model called computer-defined networking allows for dynamic network definition and programming. A network simulator is created to examine the efficiency of the built infrastructure under different network conditions, including throughput, latency, and resource consumption. In this paper, the comparison of end-toend latency between the standard communication architecture and the proposed NMCN-SDN architecture is done. The results show that the proposed architecture has less space in various conditions compared to existing communication architectures in various conditions. © 2024 by the authors of this article.

关键词： 5G end-to-end latency mobile communication resource utilization software-Defined Networks (SDN) throughput

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