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Network Emulator Based on Openstack for the Implementation of a Virtual Data Network Laboratory in Higher Education Institutions 1

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Terrel-Villanueva, Vasty Burga-Durango, Daniel Tello-Saenz, Carlos Peruvian Univ Appl Sci Lima Peru Sao Paulo State Univ Sao Paulo Brazil

ISBN: (数字)9783031832079

ISBN: (纸本)9783031832062;9783031832079

Nowadays, networks are indispensable tools for the development of new technologies such as cloud computing, big data, the Internet of Things, among others. Therefore, it is necessary to explore new tools that allow improving the understanding and practical skills of students in the field of networks. This document presents a web application based on OpenStack, which allows for the emulation of physical network laboratories through the virtualization of components, as it partitions physical resources to provide access to more users. For this, some Openstack services were used in conjunction with VirtualBox. Additionally, validation was conducted through end users, and the results obtained showed that 94% of expert teachers and 95.8% of surveyed students highlighted the utility of the application, while 78% of teachers and 91.5% of surveyed students emphasized the usability of the developed application. These results demonstrate the potential of the application to be implemented in a virtual laboratory of a networking course and take advantage of the available technological resources.

关键词： emulator networking openstack virtual laboratory

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A Hybrid Quantum Machine Learning Model for Multi-class Classifier 4th

A Hybrid Quantum Machine Learning Model for Multi-class Clas...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Mondal, Bhaskar Kumar, Mandeep Natl Inst Technol Patna Patna Bihar India

ISBN: (纸本)9783031837951;9783031837968

Quantum machine learning has gained attention due to the computational capabilities of quantum computers in solving complex problems that are challenging for classical computers. To increase processing power and efficiency, it integrates conventional machine learning approaches with quantum computing. The proposed model provide a hybrid quantum multi-class classifier that utilises quantum properties like superposition and entanglement. The Proposed model utilizes a unitary operation on a single qubit for state preparation, demonstrated on the IBMQX platform, and implemented on a quantum simulator for classification tasks. The protocol designed a quantum circuit for multi-class classification which is feasible to handle multiple classes. The experimental study of proposed model on iris dataset shows a promising accuracy of 96.26%, indicating the effectiveness of the QMCC model. The result obtained through qubit measurements, showcasing the effectiveness of the proposed model.

关键词： Quantum entanglement quantum machine learning multi-class classification

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Kernel Cutset-Type Possibility C-Means Algorithm for Gaussian Granularity 4th

Kernel Cutset-Type Possibility C-Means Algorithm for Gaussia...

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4th International Conference on Artificial Intelligence Logic and Applications

作者： Tang, Yiming Dong, Yajie Chen, Rui Gao, Jianwei Li, Shujie Wu, Xi Hefei Univ Technol Sch Comp & Informat Anhui Prov Key Lab Affect Comp & Adv Intelligent Hefei 230601 Anhui Peoples R China

ISBN: (纸本)9789819603534;9789819603541

With the development of various disciplines, most of the traditional clustering algorithms for numerical data cannot adapt to more complex datasets. The introduction of granular clustering breaks through the bottleneck of this research field. Previous studies mainly focus on interval, triangular and trapezoidal granular data to carry out granular clustering. However, for Gaussian granularity, there is little involvement. Aiming at such problem, in this study, we propose the Kernel Cutset-type Possibility C-Means (KC-PCM) algorithm of granular clustering for Gaussian granularity. Firstly, a new granularity weight is designed for each Gaussian granularity with normalized granularity quality based upon the principle of justifiable granularity. Secondly, the Gaussian kernel distance is employed instead of Euclidean distance to measure the distance between granular data. Thirdly, the KC-PCM algorithm is proposed based on the granularity weight and the Gaussian kernel distance. Lastly, we evaluate and compare the performance of some related granular clustering algorithms on artificial and UCI datasets. It is validated that the KC-PCM algorithm can effectively solve the overlapping problem of cluster centers in the PCM algorithm series. By contrast with the related granular clustering algorithm, the cluster center obtained by KC-PCM is closer to the actual cluster center, and the obtained reconstruction error is smaller, and the polygonal convergence trend of KC-PCM is more significant. As a consequence, the performance of KC-PCM is superior over other algorithms.

关键词： Clustering Granular Computing Fuzzy C-Means Gaussian Granularity Gaussian Kernel Distance

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Deep Learning Forecasting of Photovoltaics Output Using Digital Twin Data 5th

Deep Learning Forecasting of Photovoltaics Output Using Digi...

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5th Southern African Conference for Artificial Intelligence Research

作者： Vilakazi, Nomfundo van Zyl, Terence Univ Johannesburg Inst Intelligent Syst Johannesburg South Africa

ISBN: (纸本)9783031782541;9783031782558

Solar power is an accessible form of renewable energy, especially in South Africa, where solar radiance is concentrated. However, this source of energy is not constant in supply. Its stochastic nature can lead to fluctuations in the voltage and frequency in the energy grid. Accurate forecasting of the availability of solar power is crucial for operational efficiency and distribution of power. Machine learning models provide a powerful forecasting tool for solar photovoltaic power. Due to the inaccessibility of accurate and measured solar irradiance data, this study uses a digital twin to generate meteorological attributes to predict the solar output power of the University of Johannesburg solar photovoltaic plant based in Auckland Park, Johannesburg. The synthetic dataset contains hourly data over a calendar year. The deep learning algorithms that were used for prediction are the Long-Short Term Memory (LSTM) and Recurrent Neural Network. The findings showed that the LSTM is the best predictor using the Root Mean Squared Error (RMSE), Mean Squared Error (MSE) and Mean Absolute Error (MAE) performance metrics. This study is crucial as it supports the Sustainable Development Goals (SDG) aim to reduce carbon emissions, provide clean energy and improve access to electricity.

关键词： photovoltaics digital twin power prediction

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Task Scheduling in Distributed Real-Time Systems Using Hybrid Model Based on ACO-GA 4th

Task Scheduling in Distributed Real-Time Systems Using Hybri...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Sharma, Anchal Sharma, Sangeeta Thakur, Sanat Kumar, Ankush Mahato, Dharmendra Prasad Natl Inst Technol Hamirpur Hamirpur 177005 Himachal Prades India

ISBN: (纸本)9783031837821;9783031837838

Task scheduling in distributed real-time systems (DRTS) is a complex problem due to the heterogeneous nature of processors and the need for efficient resource utilization. This paper presents a novel hybrid model combining Ant Colony Optimization (ACO) and Genetic Algorithm (GA) to address the task scheduling problem. The proposed model aims to minimize system cost and response time while maximizing system reliability by leveraging the exploration capabilities of ACO and the solution refinement strength of GA. The hybrid algorithm operates in two phases: the first phase uses ACO to generate initial task assignments by dynamically exploring the search space based on pheromone trails and heuristic information. In the second phase, GA refines these solutions through crossover and mutation operators optimizing the task allocation further. Experimental results demonstrate that the proposed ACO-GA hybrid model significantly improves task scheduling performance compared to standalone algorithms by achieving better resource utilization, by reducing execution time, and by enhancing the system reliability. This approach is especially effective for real-time applications where optimal task assignment is critical to system performance.

关键词： Task scheduling Distributed real-time systems Ant Colony Optimizations Genetic Algorithm Response time optimization

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Decision Making in the Intelligent Distribution Centers of the Industry 4.0 Era: A Structural Approach 5th

Decision Making in the Intelligent Distribution Centers of t...

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5th 5th Olympus - International Conference on Supply Chain

作者： Efthymiou, Orestis Ponis, Stavros T. Natl Tech Univ Athens Sch Mech Engn Heroon Polytech 9 Athens 15780 Greece

ISBN: (纸本)9783031693434;9783031693441

Nowadays, the growing need for retailers to compete with their products across cost, quality and service dimensions on international markets has given rise to the need of developing more efficient and sustainable warehousing management strategies and processes. Traditional warehouses were designed to serve mainly one type of customers, usually stores, and at specific times during the day, while contemporary Distribution Centers (DCs) need to be able to serve all types of retail channels, such as e-commerce, stores and click-and-collect orders, that are received continuously within the day and have to be handled as soon as possible. Even though the topic of warehouse management and performance has attracted significant attention during the past decades, literature is mainly focused on facility location problems, inventory control and warehouse process design and management, with the emphasis being put on the load/material routing and order picking processes in the warehouse, due to them being demanding, labor intensive and costly. When it comes to decision making in DCs, be that operational or administrative, literature is rather limited. This paper provides the necessary focus on all the day-to-day issues that a DC director needs to come to terms with, in order to run DC operations successfully. In addition, these issues are addressed in the context of Industry 4.0, taking under consideration the managerial changes and organizational transformations that the emerging Industry 5.0 dictates or requires from contemporary DC management, which have the employee and the environment at the epicenter. The principal research question addressed in this paper, is how DC management can cope with these changes and effectively support the decision-making process with a structured set of planning, controlling, operational and administrative decisions. In that direction, this paper attempts to provide a set of guidelines and a proposed road map for DC management, which will effecti

关键词： management decision support distribution centers warehouse management Industry 4.0 Industry 5.0

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Infusing Human Feedback into Intermediate Prompting Steps of Large Language Models 8th

Infusing Human Feedback into Intermediate Prompting Steps of...

引用

2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Li, Wangfan Gendron, Claire Toxtli, Carlos Clemson Univ Human AI Empowerment Lab Clemson SC 29631 USA

ISBN: (纸本)9783031856273;9783031856280

This paper explores the implications for the problem-solving performance of large language models (LLM) when utilizing augmented intelligence by infusing human reasoning into the generated intermediate steps before a model outputs a result. We propose a framework that includes steps for injecting human reasoning and feedback into the prompting steps of large language models, using three different categories of human feedback, namely Substitution, N-Shot Learning, and Conversational, aiming to improve the model's problem-solving ability. In order to test the framework, we conducted a user study with participants who edited the intermediate steps to align with their reasoning. The results of revised prompts are compared against their base performance on the ARC, DROP, and WinoGrande datasets. Our findings reveal that the injection of human reasoning steps can boost a large language model's problem-solving accuracy regarding the test benchmarks, particularly regarding the N-Shot Learning approach. We aim to shed light on the different applications of infusing human feedback into the output of pre-trained models.

关键词： LLM Crowd-sourcing Human-in-the-Loop

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Enhancing Security in Software-Defined Networks: A Machine Learning-Driven Hybrid Intrusion Detection System with Optimized Feature Selection 4th

Enhancing Security in Software-Defined Networks: A Machine L...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Logeswari, G. Roselind, J. Deepika Chakraborty, Sparsh Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9783031837890;9783031837906

Software Defined Networking (SDN) has emerged as a revolutionary approach to network management offering enhanced flexibility and control. However with the rise of SDN networks have become increasingly susceptible to sophisticated cyber-attacks. Intrusion Detection Systems (IDS) play a vital role in safeguarding SDN infrastructures by identifying and mitigating potential threats in real-time. To address the evolving nature of these threats the proposed IDS model integrates advanced feature selection and classification techniques combining the strengths of information Gain (IG), Genetic Algorithm (GA) and an ensemble of classifiers to improve detection accuracy and reduce computational complexity. The proposed IDS for SDN enhances detection accuracy by combining IG and GA (IG2A) in a hybrid feature selection process. IG ranks features based on their ability to distinguish between different classes while GA optimizes the selection of feature subsets through evolutionary techniques. This integrated IG2A approach selects the most relevant features to reduce computational complexity and improve classification accuracy. Following feature selection the system employs a Soft voting-based Ensemble Classification (SEC) technique. The ensemble consists of four classifiers: Random Forest (RF) k-Nearest Neighbors (KNN) Naive Bayes (NB) and Support Vector Machine (SVM). By combining the outputs of these classifiers using soft voting the model enhances prediction robustness and accuracy. Experimental results show that the IG2 A-SEC model outperforms traditional feature selection methods. The IG2 A-SEC achieved 96.31% accuracy 94.62% precision 92.56% recall and 93.57% F-measure demonstrating its superior performance on the InSDN dataset.

关键词： Intrusion detection system software-defined networks information gain genetic algorithm machine learning soft-voting ensemble classification

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Cyber-Attack Detection in Healthcare Systems Based on Modified -Recurrent Neural Network Approach 4th

Cyber-Attack Detection in Healthcare Systems Based on Modifi...

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4th International Conference on Artificial Intelligence and Knowledge Processing

作者： Chintale, Pradeep Sure, Tharun Anand Reddy Quazi, Fardin Desaboyina, Gopi Najana, Madhavi Buddiga, Pranitha SEI Investment Co Downingtown PA 19335 USA ServiceNow 933 Heartleaf Dr Leander TX USA

ISBN: (纸本)9783031734762;9783031734779

The association of operating systems, and networks in healthcare environment increases risk of data security due to cyber-attacks. However, previous cyber-attack detection model fails to detect attacks in healthcare system accurately and needs more resources. To overcome this problem, a Modified Recurrent Neural Network (RNN) is employed for cyber-attack detection and to classify attacks as per labels. TON-Internet of Things (TON-IoT), Intensive Care Unit (ICU), Extensive Care Unit-Internet of Health Things (ECU-IoHT), Washington University in St. Louis Enhanced Healthcare Monitoring System (WUSTL-EHMS) datasets are preprocessed by one-hot encoding method and min-max normalization technique. After that, features are selected using XGBoost Regressor and irrelevant features are eliminated by Recursive Feature Elimination (RFE) algorithm. Finally, accurate detection of cyber-attacks is performed by proposed approach. The experimental analysis indicates promising results for detection of cyber-attacks in healthcare system with accuracy of 99.2%, recall of 99.1%, precision of 99.1%, and F1-score of 99% which is superior than existing detection methods like LightGBM and Transformer, Extreme Learning Machine (ELM) and Bayesian optimization, Ensemble Learning, Directed Acyclic Graph-Long Short-Term Memory (DAG-LSTM) and Quantum Deep Neural Network (QDNN).

关键词： Cyber-attack detection Healthcare System Modified Recurrent Neural Network One-Hot encoding Recursive Feature Elimination

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Online Car-Hailing Order Matching Method Based on Demand Clustering and Reinforcement Learning 5th

Online Car-Hailing Order Matching Method Based on Demand Clu...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Gao, Huifei Chen, Tian Hao, Jingxiang Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China Southern Univ Sci & Technol Inst Future Networks Shenzhen Peoples R China Linkinsense Co Ltd Hefei Peoples R China

ISBN: (纸本)9789819770007;9789819770014

The development of online car-hailing platforms has addressed the limitations of traditional taxis in meeting passengers' personalized travel needs. However, the online car-hailing model encounters challenges such as the expense of detours for drivers and the optimization of order matching. We propose a car-hailing order-matching approach based on passenger demand clustering and reinforcement learning to tackle these issues. This approach leverages Mean-Shift clustering to categorize passengers with similar pickup and drop-off locations into clusters. Subsequently, employing the reinforcement learning A2C algorithm, we optimize order matching between online car-hailing and passenger clusters to maximize driver utilization while minimizing detour costs. This enhances the quality of ride services for passengers. Experimental findings demonstrate that compared to alternative algorithms, our proposed order-matching method, which integrates passenger demand clustering and reinforcement learning algorithm, diminishes driver detour costs, augments driver revenue by approximately 20.4% and elevates order response rates by approximately 12.5%.

关键词： online car-hailing passenger clustering order-matching reinforcement learning

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