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Technologies Behind the Smart Grid and Internet of Things: A System Survey

computers, Materials & Continua 2023年第6期75卷 5049-5072页

作者： Kuldeep Sharma Arun Malik Isha Batra A.S.M.Sanwar Hosen Md Abdul Latif Sarker Dong Seog Han Department of Computer Science and Engineering Lovely Professional UniversityPunjabIndia Department of Artificial Intelligence and Big Data Woosong UniversityDaejeon34606Korea Center for ICT&Automotive Convergence Kyungpook National UniversityDaegu41566Korea School of Electronics Engineering Kyungpook National UniversityDaegu41566Korea

Electric smart grids enable a bidirectional flow of electricity and information among power system *** proper monitoring and con-trolling of power quality,reliability,scalability and flexibility,there is a need for an environmentally friendly system that is transparent,sustainable,cost-saving,energy-efficient,agile and *** paper provides an overview of the emerging technologies behind smart grids and the internet of *** dependent variables are identified by analyzing the electricity consumption patterns for optimal utilization and planning preventive maintenance of their legacy assets like power distribution transformers with real-time parameters to ensure an uninterrupted and reliable power *** addition,the paper sorts out challenges in the traditional or legacy electricity grid,power generation,transmission,distribution,and revenue management challenges such as reduc-ing aggregate technical and commercial loss by reforming the existing manual or semi-automatic techniques to fully smart or automatic *** article represents a concise review of research works in creating components of the smart grid like smart metering infrastructure for postpaid as well as in prepaid mode,internal structure comparison of advanced metering methods in present scenarios,and communication systems.

关键词： Electricity consumption bidirectional advanced meter infrastructure energy internet of things smart grid smart meter

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Augmenting IoT Intrusion Detection System Performance Using Deep Neural Network

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computers, Materials & Continua 2023年第1期74卷 1351-1374页

作者： Nasir Sayed Muhammad Shoaib Waqas Ahmed Sultan Noman Qasem Abdullah M.Albarrak Faisal Saeed Department of Computer Science Islamia College PeshawarPeshawar25120Pakistan Department of Computer Science CECOS University of IT and Emerging SciencesPeshawar25000Pakistan Department of Electrical Engineering HITEC University TaxilaTaxila47080Pakistan Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK

Due to their low power consumption and limited computing power,Internet of Things(IoT)devices are difficult to ***,the rapid growth of IoT devices in homes increases the risk of *** detection systems(IDS)are commonly employed to prevent *** systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate *** have been made in the past to detect new attacks using machine learning and deep learning techniques,however,these efforts have been *** this paper,we propose two deep learning models to automatically detect various types of intrusion attacks in IoT ***,we experimentally evaluate the use of two Convolutional Neural Networks(CNN)to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 *** accomplish this goal,the network stream data were initially converted to twodimensional images,which were then used to train the neural network *** also propose two baseline models to demonstrate the performance of the proposed ***,both models achieve high accuracy in detecting the majority of these nine attacks.

关键词： Internet of things intrusion detection system deep learning convolutional neural network supervised learning

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The Quest for Fairness: A Comparative Study of Accuracy in AI Hiring Systems 3

The Quest for Fairness: A Comparative Study of Accuracy in A...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： D'Souza, Jason Kadam, Vedant Shinde, Pranali Saxena, Kumkum Thadomal Shahani Engineering College Information Technology Department Mumbai India Thadomal Shahani Engineering College Artificial Intelligence and Data Science Department Mumbai India Thadomal Shahani Engineering College Computer Engineering Department Mumbai India

ISBN: (纸本)9798350302288

Artificial Intelligence and Machine Learning based models are increasingly being used in developing models for automating recruitment systems in various Multinational Companies as well as small-scale and developing companies and industries. With the advent of Generative Artificial Intelligence models, many companies are turning towards automatic and AI-based hiring software. J.P. Morgan uses HireVue, Deloitte incorporates Hiretual for talent sourcing and engagement, IBM incorporates *** for automated interview scheduling and many more. But in all these systems a major factor to consider is the biasness in the models and how to detect them and efficiently mitigate them. Some models unwillingly sometimes amplify the biases in the models and results in faulty and biased predictions which can lead to the inaccurate matching of profiles to the job description and result in wayward hiring processes. The biasness can be based on the parameters such as gender, address, etc., or biasness transferred from the programmer into the model while creating the model which is called unconscious bias. The models implemented in the research are Random Forest, Decision Tree, KNN Tree, AdaBoost, and XGBoost. The accuracies for the models Random Forest, Decision Tree, KNN Tree, AdaBoost, and XGBoost were 0.80, 0.81, 0.81, 0.78, and 0.84 respectively. © 2023 IEEE.

关键词： Decision trees

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Automatically Choosing Selection Operator Based on Semantic Information in Evolutionary Feature Construction 20th

Automatically Choosing Selection Operator Based on Semantic...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Zhang, Hengzhe Chen, Qi Xue, Bing Banzhaf, Wolfgang Zhang, Mengjie Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington6140 New Zealand Department of Computer Science and Engineering Michigan State University East Lansing48824 United States

ISBN: (纸本)9789819970216

In recent years, genetic programming-based evolutionary feature construction has shown great potential in various applications. However, a critical challenge in applying this technique is the need to select an appropriate selection operator with great care. To tackle this issue, this paper introduces a novel approach that leverages the Thompson sampling technique to automatically choose the optimal selection operator based on semantic information of genetic programming models gathered during the evolutionary process. The experimental results on a standard symbolic regression benchmark containing 37 datasets show that the proposed adaptive operator selection algorithm outperforms expert-designed operators, demonstrating the effectiveness of the adaptive operator selection algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Genetic programming

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Emergency Vehicle Detection Using Deep ConvNet2D and computer Vision 2

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Poongavanam, Elumalaivasan Prathik, A. Munirathinam, T. Kayalvizhi, V. Rajesh, R. Mahavishnu, C. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Shreenivasa Engineering College Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350328202

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases. © 2024 IEEE.

关键词： Real time systems

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Real-time road occupancy and traffic measurements using unmanned aerial vehicle and fundamental traffic flow diagrams

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Personal and Ubiquitous Computing 2023年第5期27卷 1669-1680页

作者： Ahmed, Afzal Outay, Fatima Farooq, Muhammad Umer Saeed, Sehrish Adnan, Muhammad Ismail, Muhammad Ali Qadir, Adnan Department of Urban & Infrastructure Engineering NED University of Engineering & Technology Karachi Pakistan Zayed University Dubai United Arab Emirates Department of Computer Science and Information Technology NED University of Engineering & Technology Karachi Pakistan Exascale Open Data Analytics Lab NED University of Engineering & Technology Karachi Pakistan Hasselt University Diepenbeek Belgium

The technologies and sensors developed for standard traffic streams often fail to accurately measure the heterogeneous traffic with no lane discipline. This research proposes an efficient framework to measure traffic flow parameters in real-time using an unmanned aerial vehicle (UAV). The proposed framework in this research estimates road area occupancy from the images extracted from the video recorded using a UAV by applying the image segmentation technique. The road area occupancy is converted into traffic density using an occupancy-density model developed for the local heterogeneous traffic. A speed-density fundamental diagram is developed, which estimates the average speed corresponding to the measured traffic density. The fundamental relation between flow rate, average speed, and density is applied to compute traffic flow rate using the estimated average speed and density. The proposed framework is computationally less demanding in comparison with other computer vision and artificial intelligence–based techniques. The proposed framework accurately measured road area occupancy with an average accuracy of 97.6%. Traffic density was measured with an average accuracy of 88.8%. Similarly, the average speed and flow rate were measured with an average accuracy of 85.4% and 84.2%, respectively. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： Image segmentation

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Exploring Deep Convolutional Neural Networks: A Grad-CAM Enhanced Comparative Study for Automated COVID-19 Diagnosis from Chest X-Ray Images 3

Exploring Deep Convolutional Neural Networks: A Grad-CAM Enh...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Reza, Md. Sazid Jahangir Kabir, Mir Md. Rakib Mollah, Md. Abdur Nahin, Nazmul Islam Data Science and Innovation Td School Department of Computer Science & Engineering India University of Technology Sydney Sydney Australia Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350319125

The emergence of COVID-19 in late 2019 sparked a global health crisis. Identifying infections quickly is key to providing proper care and limiting transmission. The Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test, a widely recognized primary diagnostic method for detecting COVID-19, faces challenges associated with cost and compatibility in resource-constrained environments. This issue is resolved by chest X-ray imaging since it is non-invasive and inexpensive. In this domain, research often utilizes pre-trained models on relatively small datasets of chest X-ray images, limiting the potential for optimal generalization and accuracy achievable with larger training sets. Also, these research works limit the exploration of crucial information on salient regions within CXR images, constraining the depth of analysis and interpretation. This study addresses current research challenges through a detailed comparative analysis of convolutional neural network (CNN) architectures, including pre-trained models like Inception V3, Xception, DenseNet-121, VGG-19, and a Custom CNN developed from scratch. The evaluation focuses on their effectiveness in detecting COVID-19 using a relatively vast and comprehensive dataset. The evaluation focuses on assessing the efficacy of these models in detecting COVID-19 across binary-class and multi-class classifications within a large dataset, offering valuable insights into their overall effectiveness. Grad-CAM is employed in this study, which visualizes picture salient areas affecting deep neural network predictions and improves model interpretability. The heatmaps reveal the model's decision-making processes, making their behavior more understandable to medical practitioners. With resulting accuracies of 99.13% and 97.66% in binary-class and multi-class classification, Inception V3 demonstrated the most optimal performance. This proposed approach surpasses existing benchmark methods, underscoring its superiority. © 2023 IEEE.

关键词： chest X-ray CNN COVID-19 Deep Learning DenseNet-121 Grad-CAM Inception V3 ResNet-50 RT-PCR VGG-19 Xception

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A Queuing Model for Software-Defined Networking in Cloud and Multi-Controller Environments 6

A Queuing Model for Software-Defined Networking in Cloud and...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Teboub, Islem Aissani, Amar Merazka, Fatiha Usthb University Riima Laboratory Department of Artificial Intelligence And Data Sciences Computer Engineering Faculty Algiers16111 Algeria Usthb University Lisic Lab. Department of Telecommunications Electrical Engineering Faculty Algiers16111 Algeria

ISBN: (纸本)9798350350265

Enterprises are increasingly adopting Software-Defined Networking (SDN) for advanced infrastructure management, cost reduction, and adaptation to technological challenges. Cloud-based SDN controllers play a pivotal role in ensuring flexibility, security, and scalability. The transition from a single-controller to a multi-controller paradigm in cloud networks not only improves security and availability but also reinforces the overall resilience of SDN. Our scientific study aims to propose a novel approach for deploying SDN networks and to introduce a queueing model for this proposed concept. The work focuses on modeling SDN networks using Markovian stochastic processes, taking into consideration the availability criterion through the multiplicity of SDN controllers at the control layer, specifically utilizing queuing theory. The proposed model brings improvement and optimization compared to existing ones. The evaluation of Quality of Service (QoS), based on response time measurements, has demonstrated the efficiency of the model. Additionally, observing the model through the conducted experiments has led us to conclude that scalability is ensured concerning the multiplicity of controllers. Our model has successfully addressed the multi-criteria constraint and ensured an optimal combination that incorporates cloud concepts, QoS, and multi-controllers. © 2024 IEEE.

关键词： Quality of service

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Evaluating the Effectiveness of Deep Learning Models in Network Intrusion Detection

Evaluating the Effectiveness of Deep Learning Models in Netw...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Kumar, Gaurav Gupta, Pankaj Yadav, Ganesh Kumar Verma, Richa Bhati, Jai Prakash Bhakuni, Vihan Singh IIMT College of Engineering Department of CSE Uttar Pradesh Greater Noida India Manager Data Engineer Discover Financial Services United States JSS Academy of Technical Education Department of Computer Science and Engineering UP Noida201301 India Computer Science and Engineering BBDITM Lucknow India Greater Noida India Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9798331542344

Network security is now a significant concern in the current digital era since the increasing volume of cyber-attacks in day-to-day life has become a potential threat for the integrity and privacy of data. Most traditional intrusion detection systems face problems when it comes to detecting new and sophisticated attacks. The paper goes ahead to elaborate on how the concepts of machine learning and deep learning can be utilized to enhance the process of detecting and classifying network attacks. For instance, we experimented on the UNSW-NB15 dataset with different models: Decision Tree, Random Forest, SVM, KNN, MLP, and DNN. We have proved through our results that such kinds of models can considerably enhance the accuracy and robustness of IDS. With an average accuracy of 94.91%, the Decision Tree model took second place as the Deep Neural Network was closely followed. We will outline performance metrics based on the respective strengths and weaknesses of the models. This research is used to construct more effective and reliable network intrusion detection systems, which lead further into advancements of cybersecurity. © 2024 IEEE.

关键词： Network intrusion

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Detection of Misinformation Related to Pandemic Diseases Using Machine Learning 7th

Detection of Misinformation Related to Pandemic Diseases Usi...

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7th EAI International Conference on Robotics and Networks, ROSENET 2023

作者： Naeem, Javaria Gül, Ömer Melih Parlak, Ismail Burak Karpouzis, Kostas Kadry, Seifedine Nimer Salman, Yücel Batu Department of Computer Engineering Bahçeşehir University İstanbul Turkey Department of Computer Engineering Galatasaray University İstanbul Turkey Panteion Uninversity of Social and Political Sciences Kallithea Greece Department of Applied Data Science Noroff College Kristiansand Norway Ajman University Ajman United Arab Emirates Department of Software Engineering Bahcesehir University İstanbul Turkey

ISBN: (纸本)9783031644948

The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies SVM, decision tree, logistic regression, and neural networks to perform in-depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. After applying ensemble learning, accuracy reached 91% for Facebook content, 79% for Instagram, 80% for Twitter, and 95% for YouTube. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

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