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检索条件"任意字段=Proceedings of the 6th International Conference on Algorithms, Computing and Systems"

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Application of Remote Sensing to Assess the Ability to Absorb Carbon Dioxide of Green Areas in thu Dau Mot City, Binh Duong Province, Vietnam 6th

Application of Remote Sensing to Assess the Ability to Absor...

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6th international conference on Intelligent computing and Optimization, ICO 2023

作者： Nguyen, Huynh Anh Tuyet Nguyen, Hien than Dang, Trung thanh Thu Dau Mot University Binh Duong Province Thủ Dầu Một Viet Nam

ISBN: (纸本)9783031503290

Climate change has been causing certain impacts on the environment and public health, especially in large urban areas, where the population density is dense and the green area is not enough. Trees play an important role in climate regulation because of their ability to absorb CO2 generated from city activities. the study was conducted to evaluate the CO2 absorption of green areas in thu Dau Mot City, Binh Duong province, where has many large urban and residential areas, by using remote sensing technology and field investigation. Total load of CO2 absorbed by green space of thu Dau Mot city is about 874 thousands tons, including 698 thousands tons from high vegetation areas (accounting for 80%) and 176 thousand tons from low vegetation areas (making up 20%). Hoa Phu is the suburban ward that has the highest load of CO2 absorbed by green space while the central ward of Phu Cuong is the lowest one. the economic value of CO2 stored in urban green area of thu Dau Mot city is about 15 millions USD, equivalent to 346 billions VND. this value will increase every year due to the growing of tree biomass. this research proves that trees have a great role in absorbing CO2 arising from human activities and therefore it is necessary for authorities of thu Dau Mot city as well as other places to promote the planting, caring and protecting of green spaces as much as possible to protect the environment and community’s health. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Remote sensing

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Software Log Classification in Telecommunication Industry 6

Software Log Classification in Telecommunication Industry

引用

6th international conference on Computer Science and Engineering, UBMK 2021

作者： Ülkü, Onur Gözüacik, Necip Tanberk, Senem Aydm, Muhammed Ali Zaim, Abdul Halim Department of Computer Engineering Istanbul University Cerrahpaşa Istanbul Turkey Department of Computer Engineering Bahcesehir University Istanbul Turkey International R&D Orion Innovation Turkey Istanbul Turkey Department of Computer Engineering Istanbul Commerce University Istanbul Turkey

ISBN: (纸本)9781665429085

Software system admins depend on log data for understanding system beliavior, monitoring anomalies, tracking software bugs, and malfunctioning detection. Log analysis based on machine learning techniques enables to transform of raw logs into meaningful information that helps the DevOps team and administrators to solve problems. AI ensures to group similar logs together and keeps periodic logs more organized and sorted, allowing us to get to where we need to look faster. In this paper, we present a log classification system on log data generated by VoIP (Voice over Internet Protocol) soft-switch product. In this way, we targeted to detect the problem, direct it to the relevant department, allocate resources, and solve software bugs faster and more efficiently. Machine learning algorithms such as Linear Classifiers, Support Vector Machines, Decision Tree, Random Forest, Boosting, K-Nearest Neighbors, and Multilayer Perceptron are used for log classification. © 2021 IEEE

关键词： Decision trees

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Distributed Spatial-Temporal Demand and Topology Aware Resource Provisioning for Edge Cloud Services 6

Distributed Spatial-Temporal Demand and Topology Aware Resou...

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6th international conference on Fog and Mobile Edge computing(FMEC)

作者： Vu San Ha Huynh Radenkovic, Milena Wang, Ning Univ Nottingham Nottingham NG8 1BB England Univ Surrey Inst Commun Syst ICS 5GIC & 6GIC Guildford GU2 7XH Surrey England

ISBN: (纸本)9781665458702

Current edge cloud providers offer a wide range of on-demand private and public cloud services for customers. Predictive demand monitoring and supply optimisation are necessary to deliver truly elastic distributed edge cloud services with resizable resource and compute capacity to adapt to dynamically changing customer requirements. However, current state-of-the-art monitoring and provisioning systems remain reactive which often results in over or under service provisioning, incurring unnecessary costs for customers or deterioration in the quality of service for the end-user. this paper proposes an adaptive protocol, ARPP, that enables distributed real-time demand monitoring and automatic resource provision based on the dynamically changing spatial-temporal workload patterns. ARPP leverages distributed predictive analytics and deep reinforcement learning at the edges to predict the dynamically changing spatial-temporal demand and allocate the appropriate amount of resources at the right times and right locations. We show that ARPP outperforms benchmark and state of the art algorithms across a range of criteria in the face of dynamically changing mobile real-world topologies and user interest patterns.

关键词： Elastic edge cloud services network resource supply-demand

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proceedings - 2019 6th international conference on Image and Signal Processing and their Applications, ISPA 2019

Proceedings - 2019 6th International Conference on Image and...

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6th international conference on Image and Signal Processing and their Applications, ISPA 2019

ISBN: (纸本)9781728131566

the proceedings contain 93 papers. the topics discussed include: novel four-dimensional chaotic oscillator for Sub1GHz chaos-based communication systems;new measurement method for respiration and heartbeat estimation using IR-UWB radar;integration of network coding in a multi-source multi-relay cooperative wireless network;PAPR reduction in MIMO (2x2)-FBMC-OQAM systems using attenuating QAM symbols;deep reinforcement learning for real-world anomaly detection in surveillance videos;enhanced face recognition system based on deep CNN;experimental design and analysis of sound event detection systems: case studies;and vibration signal analysis for bearing fault diagnostic of asynchronous motor using HT-DWT technique.

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Machine Learning in the Last-Mile Delivery: Modified Q-Learning for the TSP 19th

Machine Learning in the Last-Mile Delivery: Modified Q-Learn...

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19th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Ilin, Vladimir Simić, Dragan Veličković, Marko Garunović, Nemanja Saulić, Nenad Faculty of Technical Sciences University of Novi Sad Trg Dositeja Obradovića 6 Novi Sad21000 Serbia

ISBN: (纸本)9783031750120

the increasing demands of e-commerce have increased the need for efficient last-mile delivery solutions, presenting logistical challenges such as traffic congestion and high operational costs. this paper explores the application of machine learning (ML) techniques to optimise last-mile delivery, focusing on Q-learning algorithm to solve the Travelling Salesman Problem (TSP). A case study is presented, where modified Q-learning is applied to the TSP, demonstrating its potential to optimise delivery routes and reduce costs. the proposed model is implemented in the MATLAB environment and it is tested on symmetric and asymmetric small and medium-sized instances from TSPLIB as well as on the real-life TSP instances. Experimental results indicate that ML-based model demonstrates better results on average in most instances compared to Basic Q-learning, Double Q-learning, and SARSA algorithms. this paper contributes to the growing body of knowledge on ML applications in logistics, offering insights into future research directions and practical implementations in last-mile delivery. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Traffic congestion

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Centralized and Decentralized Federated Learning in Autonomous Swarm Robots: Approaches, algorithms, Optimization Criteria and Challenges : the Sixth Edition of international conference on Pattern Analysis and Intelligent systems (PAIS’24)

Centralized and Decentralized Federated Learning in Autonomo...

引用

international conference on Pattern Analysis and Intelligent systems (PAIS)

作者： Aicha Hafid Riadh Hocine Lahcene Guezouli Mohamed. R Abdessemed Dept. Math and Computer Science University of Batna2 Batna Algeria

ISBN: (数字)9798350350265

ISBN: (纸本)9798350350272

Fault tolerance and energy consumption optimization are critical issues in swarm robotics. this study examines recent approaches to address these challenges, focusing on a comparative analysis between Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL). CFL requires a centralized access point for model aggregation, while DFL eliminates the need for a central server, enabling aggregation at each node according to a specific architecture. the analysis of the results reveals that other approaches, such as Hybrid Federated Learning (HFL), more effectively meet the needs of intelligent agents (swarm robots). this effectiveness is particularly enhanced when HFL is combined with Deep Reinforcement Learning (DRL), resulting in Deep Hybrid Federated Reinforcement Learning (DHFRL). the results demonstrate that, although DFL eliminates the necessity of a central server, hybrid approaches are more efficient, especially when combined with Deep Reinforcement Learning (DRL), thus forming Deep Hybrid Federated Reinforcement Learning (DHFRL).

关键词： Federated learning Swarm robotics Focusing Deep reinforcement learning Servers Intelligent agents Pattern analysis

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On the Discrete Concavity of Packet Reception Ratio Utility Functions

On the Discrete Concavity of Packet Reception Ratio Utility ...

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Circuits, systems, Communications and Computers (CSCC), international conference on

作者： Evangelos D. Spyrou Alkiviadis Tromaras Vassilios Kappatos Evangelos Bekiaris Hellenic Institute of Transport Centre for Research and Technology Hellas 6th Km Charilaou - Thermis Thessaloniki Greece

the wireless channel often shows high complexity in the attempt to analyze theoretically the network layer parameters. A parameter of this layer is the Packet Reception Ratio (PRR). the PRR is a practical metric, which when analyzed theoretically, exhibits high complexity. Its optimization results in high throughput and a reduction in the interference and collisions of transmissions. In theory, PRR is described with a sigmoid function, which conveys a very hard problem to solve. In this paper, we will reduce this complexity by transforming PRR and transmission power function to a discrete concave-up one; thus, allowing fast algorithms, which locate the globally optimal solutions, to be applied. We do this transformation utilizing networking terms, which come into play to reduce the complexity of a PRR optimization process.

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Deep Learning for Videoconferencing: A Brief Examination of Speech to Text and Speech Synthesis 6

Deep Learning for Videoconferencing: A Brief Examination of ...

引用

6th international conference on Computer Science and Engineering, UBMK 2021

作者： Tanberk, Senem Dagli, Volkan Gürkan, Mustafa Kagan International R&D Orion Innovation Turkey Istanbul Turkey

ISBN: (纸本)9781665429085

Integrating Al-powered applications into video conferencing systems is furtlier expected to blow up in various industrial scenarios. In this modern era of the video conferencing industry, deep learning techniques are revolutionizing to improve the quality of communication in use cases such as resolution improvement, background noise reduction, video compression, face alignment, transcription, and speech synthesis. this paper reviews the latest works on deep learning-based transcription and speech synthesis methods. they are classified into three categories: Speech to text, text to speech, and speech to text to speech. We included experimental studies conducted in two specific methods in a speech to text and speech synthesis. Experimental results on various test scenarios of two state-of-the-art pre-trained models are also analyzed. Finally, the future development trend of A I - powered video conferencing system is predicted. © 2021 IEEE

关键词： Speech recognition

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Design of a Smart Home Door Security System with Face Detection and Smart Bell using ESP32-CAM

Design of a Smart Home Door Security System with Face Detect...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Nurhusna Salsabila Apri Siswanto Luhur Bayuaji Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Fac. Data Science and Information Technology INTI International University Malaysia

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

this research introduces the design and implementation of an advanced IoT-based home security system aimed at improving both convenience and safety for homeowners. the system integrates a smart doorbell with a mobile application, enabling real-time alerts for guest arrivals. Key features include facial recognition for automated door unlocking and notification of unrecognized visitors. When an unknown face is detected, the system sends an image along with a notification to the homeowner via a Telegram bot. the architecture incorporates the ESP32-CAM microcontroller for face detection, a solenoid lock for automatic door access, and an ultrasonic sensor to detect visitor presence. Extensive testing under various environmental conditions was conducted to assess the system's performance, particularly in terms of face recognition accuracy and notification delivery. the results indicate that the system significantly enhances home security by ensuring timely notifications and automating door access for verified individuals. While the system performs reliably in well-lit environments, challenges remain in low-light conditions. Software testing confirmed the robustness of the underlying logic. this innovative IoT solution overcomes the limitations of traditional security systems, offering a more secure, user-friendly, and efficient alternative for modern home security.

关键词： Software testing Face recognition System performance Smart homes Chatbots Real-time systems Software reliability Security Face detection Solenoids

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Movie Subtitle Document Classification Using Unsupervised Machine Learning Approach 6

Movie Subtitle Document Classification Using Unsupervised Ma...

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6th IEEE international conference on computing, Communication and Automation, ICCCA 2021

作者： Hasan, Md. Mehedi Dip, Sadia Tamim Kamruzzaman, T.M. Akter, Sonia Salehin, Imrus Daffodil International University Department of Cse Dhaka Bangladesh

ISBN: (纸本)9781665414739

Since the evolution of digital and online text content, automatic document classification has become a significant research issue. there is a most commonly used machine learning approach to improve this task: an unsupervised approach, where no human interaction or labelling documents are required at any point throughout the whole procedure. this study addressed an approach for movie subtitle document classification using an unsupervised machine learning technique. the dataset has been created, collecting almost 500 English movie subtitle files based on the popular movies of IMDB. Two feature extraction methods have been used and combined with unsupervised machine learning algorithms and a dimension reduction technique has been used to reduce the dimensionality of this work. As unsupervised machine learning techniques, we used Bisecting K-Means, K-Means and Agglomerative Hierarchical Clustering Algorithm;Average link, Single Link and Double link. We assessed that K-means and Bisecting k-means are the best performers of the unsupervised techniques in the term of cluster quality. We addressed the reason for the outliers of the training set and recommended using unsupervised techniques to improve predefining categories and labelling the textual documents in the training set. © 2021 IEEE.

关键词： Learning algorithms

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