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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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A Hybrid Physics-Deep learning Load-Altering Attack Detection and Localization Mechanism 4

A Hybrid Physics-Deep Learning Load-Altering Attack Detectio...

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4th international conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Sayed, Mohammad Ali Arfaoui, Mohamed Amine Atallah, Ribal Debbabi, Mourad Assi, Chadi

ISBN: (纸本)9798350306262

Due to their growing popularity, smart high-wattage devices have rapidly proliferated in the modern power grid. Additionally, the grid has witnessed an increasing integration of Information and Communication Technologies (ICTs) to facilitate the shift towards a more efficient smart grid. this integration, however, has made the smart grid and its subsystems prone to cyber-attacks. One especially dangerous family of attacks is the family of Load-Altering (LA) Attacks that manipulate the demand side of the power grid and cause dangerous instabilities and blackouts. To this end, we develop a Hybrid Physics-Deep learning (DL) LA attack detection and localization mechanism. In this mechanism, the utility collects the generator frequencies and feeds them into the mathematical equations representing the physical behavior of the grid to estimate the load variation patterns on the load buses. these patterns are then fed to a DL algorithm to identify the attacks. the performance of the proposed mechanism is evaluated against several machine learning and DL algorithms and detection models in the literature. Our proposed mechanism proved to be more accurate with an accuracy of 99.75%. © 2024 IEEE.

关键词： Smart power grids

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A Mobile-Based Dynamic Approach to Comparative Study of Some Classification and Regression Techniques 1

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4th international conference on computer and Communication Technologies, IC3T 2022

作者： Maddila, Vijay Souri Sashank, Madipally Sai Krishna Krishnasai, Paleti Vikas, B. Karthika, G. Andhra Pradesh Visakhapatnam530045 India Department of Computer Science and Engineering Design and Manufacturing Indian Institute of Information Technology Tamil Nadu Kancheepuram Chennai India

ISBN: (数字)9789811985638

ISBN: (纸本)9789811985621

With the ever-growing complex world of machine learning algorithms and data processing techniques, the entry requirements for beginners are steadily rising. So, to allow a much lower entry ceiling into the world of machine learning, an Android application is proposed in this study that informs the user about the performance of a few particular supervised algorithms for any given data set. this study informs on three main aspects, namely an Android application that can be developed on flutter as the front end, an API that handles the upload of Comma Separated Values (CSV) file, processing of data, and sending the results back to the front end, and the machine learning pipeline that automates the data preprocessing, model creation, and model evaluation for the classification task. the data preprocessing includes data preparation with SMOTE, missing values filling, Scaling and Transformation of data, Feature Engineering, and Feature Selection. To build models, libraries such as Scikit-Learn, XGBoost, and LightGBM are used. Finally, when the user uses the application, they only need to upload a CSV file with the label of the data set renamed to "Target." As soon as the file is uploaded, the server starts the automation process. Once the results are formed, the view of the application changes to a new screen where the results are displayed as a table. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Extracting Behavior Events from Epidemic Daily Reports by Combining Deep learning and Adversarial Training 4

Extracting Behavior Events from Epidemic Daily Reports by Co...

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4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Lin, Shaofu Zhou, Shiwei Xu, Zhe Chen, Jianhui Faculty of Information Technology Beijing University of Technology Beijing China Beijing University of Technology Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China Beijing University of Technology Beijing Key Laboratory of Mri and Brain Informatics Beijing China Ministry of Education Engineering Research Center of Intelligent Perception and Autonomous Control Beijing China Ministry of Education Engineering Research Center of Digital Community Beijing China

ISBN: (纸本)9798350373233

Extracting emergencies from network information has become an important means of public security. However, existing event extraction methods rely on large-scale annotated corpora and are difficult to be effectively applied to extract epidemic behavior events, which is a brand-new task of event extraction. Firstly, an epidemic provenance model is defined to comprehensively model epidemic information in epidemic daily reports. On this basis, we propose an epidemic extraction model of epidemic behavior events, called CBEventMine, that combines deep learning and adversarial training. A relatively simple model structure of BiLSTM+CRF+sigmoid is adopted to achieve fast convergence of the model on small sample data, and an adversarial learning mechanism is added to alleviate the overfitting problem of the model in few-slot learning. the experimental results demonstrate that the proposed model CBEventMine could effectively extract epidemic event than the existing state-of-the-art methods. © 2023 IEEE.

关键词： Extraction

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learning Neural PDE Solvers with Parameter-Guided Channel Attention 40

Learning Neural PDE Solvers with Parameter-Guided Channel At...

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40th international conference on machine learning

作者： Takamoto, Makoto Alesiani, Francesco Niepert, Mathias NEC Labs Europe Heidelberg Germany Univ Stuttgart Stuttgart Germany

Scientific machine learning (SciML) is concerned with the development of learned emulators of physical systems governed by partial differential equations (PDE). In application domains such as weather forecasting, molecular dynamics, and inverse design, ML-based surrogate models are increasingly used to augment or replace inefficient and often non-differentiable numerical simulation algorithms. While a number of ML-based methods for approximating the solutions of PDEs have been proposed in recent years, they typically do not adapt to the parameters of the PDEs, making it difficult to generalize to PDE parameters not seen during training. We propose a Channel Attention mechanism guided by PDE Parameter Embeddings (CAPE) component for neural surrogate models and a simple yet effective curriculum learning strategy. the CAPE module can be combined with neural PDE solvers allowing them to adapt to unseen PDE parameters. the curriculum learning strategy provides a seamless transition between teacher-forcing and fully autoregressive training. We compare CAPE in conjunction with the curriculum learning strategy using a popular PDE benchmark and obtain consistent and significant improvements over the baseline models. the experiments also show several advantages of CAPE, such as its increased ability to generalize to unseen PDE parameters without large increases inference time and parameter count. An implementation of the method and experiments are available at https://github. com/nec- research/CAPE- ML4Sci.

关键词： Weather forecasting

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Prediction of Stock Price by using machine learning & Big Data Approaches 4

Prediction of Stock Price by using Machine Learning & Big Da...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Balasubramanie, P. Eswaran, Malathi Deepakraj, A.L. Kongu Engineering College Department of Computer Technology Erode India

ISBN: (纸本)9798350300093

Today, social networks are extremely important to every member of society, regardless of their age, community, gender, or professional background. Social networks already connect between 3.4 and 3.6 billion individuals, and by 2025 that number is expected to rise to roughly 4.50 billion. the widespread use of social media platforms makes it possible for numerous e-commerce businesses to spur growth by focusing on certain customer segments. However, it is difficult for businesses to connect with their target audiences because they need to examine enormous amounts of data produced in a social network of people from different age groups and origins. Big Data, which manages both structured and unstructured data, steps in to handle the analysis of these enormous volumes of data. On the basis of the consumer's behavior that satisfies their needs, an algorithm that assesses and divides them is required to be implemented on these data. Two mechanisms are used in this research to offer an ideal answer to this issue. In the first, Word2vec is applied to information that offers the words' numerical representations (vectors), and in the second, K-Means Clustering is used to the numerical values to create a cluster of related words. Companies can determine which stock is more desired or liked by the public by using such clusters. As a result, it is thought that this approach offers businesses a solid resolution. © 2023 IEEE.

关键词： K-means clustering

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proceedings of the 4th international conference on Deep learning theory and applications, DeLTA 2023

Proceedings of the 4th International Conference on Deep Lear...

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proceedings of the 4th international conference on Deep learning theory and applications, DeLTA 2023

ISBN: (纸本)9783031390586

the proceedings contain 31 papers. the special focus in this conference is on Deep learning theory and applications. the topics include: MSDeepNet: A Novel Multi-stream Deep Neural Network for Real-World Anomaly Detection in Surveillance Videos;a Novel Probabilistic Approach for Detecting Concept Drift in Streaming Data;explaining Relation Classification Models with Semantic Extents;phoneme-Based Multi-task Assessment of Affective Vocal Bursts;using Artificial Intelligence to Reduce the Risk of Transfusion Hemolytic Reactions;ALE: A Simulation-Based Active learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP;Exploring ASR Models in Low-Resource Languages: Use-Case the Macedonian Language;facilitating Enterprise Model Classification via Embedding Symbolic Knowledge into Neural Network Models;explainable Abnormal Time Series Subsequence Detection Using Random Convolutional Kernels;improving Primate Sounds Classification Using Binary Presorting for Deep learning;TaxoSBERT: Unsupervised Taxonomy Expansion through Expressive Semantic Similarity;Towards Equitable AI in HR: Designing a Fair, Reliable, and Transparent Human Resource Management application;an Explainable Approach for Early Parkinson Disease Detection Using Deep learning;UMLDesigner: An Automatic UML Diagram Design Tool;graph Neural Networks for Circuit Diagram Pattern Generation;Generative Adversarial Networks for Domain Translation in Unpaired Breast DCE-MRI Datasets;a Survey on Reinforcement learning and Deep Reinforcement learning for Recommender Systems;GAN-Powered Model &Landmark-Free Reconstruction: A Versatile Approach for High-Quality 3D Facial and Object Recovery from Single Images;GAN-Based LiDAR Intensity Simulation;evaluating Prototypes and Criticisms for Explaining Clustered Contributions in Digital Public Participation Processes;towards Exploring Adversarial learning for Anomaly Detection in Complex Driving Scenes;FRLL-Beautified: A Dataset of Fun Selfie

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Apriori Analysis of Deep learning Models on a Multi-class Data Set 4th

Apriori Analysis of Deep Learning Models on a Multi-class Da...

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4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Kosuru, Sai Sathwik Padmanabha Reddy, Y.C.A. Akanksha, Perala Venkata Chaitanya, Kotagiri Parsi, Snehaja Chunduri, Madhu Babu Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Medak Narsapur502313 India Department of Artificial Intelligence & Machine Learning Chaitanya Bharathi Institute of Technology Ganidept Hyderabad500075 India

ISBN: (纸本)9789819751990

Deep learning techniques have large scopes in terms of applications and resource requirements, hence calling for a deep analysis of their behavior. Many studies focused on the application part of these models and there is a relatively lesser discussion about the unit-wise behavior. A multi-class dataset was taken and three different models were trained where their performances of training processes are described in terms of runtime characteristics like learning rate, times, etc. An apriori analysis was conducted where the runtime characteristics are investigated for their similarity with pre-defined knowledge of their architectures, as opposed to the posterior analysis which particularly focuses on observations. the work also serves as a framework for defining the logistics of the model training processes and fine-tunes the parameters devised in future for the maximum accuracy of the model. the results were further converted into graphical representations for enhanced delivery of insights. the study is also novel where, as opposed to previous works, in which performances are only tested, the model is evaluated in a design-specific manner dealing with only architectures and the parameters defining them. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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machine learning-based LFC Modelling for Solar and Wind Power Plant 4

Machine Learning-based LFC Modelling for Solar and Wind Powe...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Sivakumar, P. Prasad, P.Arokiya Kaleeswari, M. Anitha, D. Aswini, A. Tamilvani, P. Rajalakshmi Engineering College Department of Eee Chennai India St.Joseph College of Engineering Department of Eee Chennai India M.P.Nachimuthu M.Jaganathan Engineering College Department of Eee Chennimalai India

ISBN: (纸本)9798350300093

With the increase in global warming, the diminishing supply of fossil fuels, and the high density of carbon dioxide emissions, the world has become more worried about energy generation using alternate sources of energy such as solar, wind, biomass, and tidal energy. When non-linear loads are connected to the grid it produces the issues like voltage instability, frequency fluctuations, and overloading of system components and it affects the overall reliability of system components. Hence frequency regulation is important for the operation of any grid or Renewable Energy Source (RES) that is connected to the load. When the total power supply is lesser than or more than the required load demand, then it produces frequency fluctuations. So, the machine learning-based Load Frequency Control (LFC) method is implemented for counteracting demand fluctuations. When consumer loads are integrated with Distributed Generation (DG) which is comprised of both Solar and wind, the energy demand of the loads will be satisfied by the Renewable Energy Sources during peak hours. In this proposed system, the four areas are considered along with the grid, PV, and wind power plant for Load Frequency Control using machine learning (ML). the proposed model is controlled by Proportional Integral and derivative controller (PID) for the betterment of less oscillatory response. the core focus of the proposed research is to implement modeling of a machine learning (ML) based modified LFC for a four-area model which is penetrated with a green source such as gust energy and photovoltaic energy for minimizing rapid load fluctuations. © 2023 IEEE.

关键词： Global warming

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Research on the application of Artificial Intelligence Technology in Power System Equipment Operation and Maintenance 4

Research on the Application of Artificial Intelligence Techn...

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Tan, Long Wei, Chunming Xu, Chao Qu, Zhongzhi State Grid Heilongjiang Electric Power Research Institute Harbin150000 China

ISBN: (纸本)9798350342710

Artificial intelligence technology is used to analyze various faults in power grid. First, the PTZ camera is used to shoot each device in the power grid, and the captured device image is transmitted to the remote monitoring center. the image of the device is intercepted and preprocessed by the remote monitoring center. A fault detection algorithm based on variational modes is established to extract the fault detection signal of power grid equipment from the image and input it into SVM for learning, so as to achieve the purpose of fault detection and diagnosis of power grid equipment. Simulation results show that the proposed method can accurately identify various faults in power grids under extreme weather conditions such as haze and heavy snow. © 2023 IEEE.

关键词： Artificial intelligence

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A Review of Deep learning based Intrusion Detection Systems 4

A Review of Deep Learning based Intrusion Detection Systems

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Jangra, Rekha Kajal, Abhishek Guru Jambheshwar University of Science and Technology Department of Computer Science and Engineering Haryana Hisar India

ISBN: (纸本)9798350306118

In recent years, proliferation of network attacks and cyber threats has underscored the critical need for robust and efficient Intrusion Detection Systems (IDS) to safeguard digital assets and sensitive information. Deep learning (DL) has emerged as a promising technology for enhancing accuracy and effectiveness of IDS due to its ability to automatically extract intricate patterns and anomalies from large-scale network data. this review paper provides a comprehensive survey of DL-based IDS, offering insights into their strengths, limitations, and recent advancements. Due to the necessity of cyber security, numerous researches have been undertaken to categorize the data moving through the network. Intrusion detection is the prominent method of cyber security to identify the intruders and lessen the network hazard. the present study work has concentrated on the necessity and procedure of IDS using DL mechanisms. Reliability and feasibility of IDS depend on performance and accuracy of deep learning. Present research has conducted a review study of conventional research work and focused on the methodology used during IDS detection and classification using ML and DL. the limitation of conventional research work is also presented in problem statement to propose a better deep learning approach in the future. © 2023 IEEE.

关键词： Intrusion detection

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