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共 1778 条记录，以下是201-210 订阅

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Localization of False data Injection Attacks in Smart Grids: A Deep learning Framework Based on SAE-CNN 4

Localization of False Data Injection Attacks in Smart Grids:...

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4th international conference on Electrical Engineering and Control Science, IC2ECS 2024

作者： Jiang, Wenlong Gao, Wengen Zhu, Guangyao Sun, Wanjun Anhui Polytechnic University School of Electrical Engineering Wuhu China Hangzhou Jibao Electric Group Co. Ltd Hangzhou China

ISBN: (纸本)9798331510107

the widespread adoption of smart grids has greatly enhanced grid operation and management through automation, real-time monitoring, and data collection capabilities. However, the open and distributed architecture of smart grids exposes them to various cyber threats, particularly False data Injection Attacks (FDIA). FDIAs manipulate sensor data to disrupt grid operations, potentially causing erroneous state estimations and flawed scheduling decisions, which can compromise grid security and stability. To address this challenge, this paper introduces SAE-CNN, a hybrid method combining Sparse Autoencoder (SAE) and Convolutional Neural Network (CNN) for effective detection and localization of false data injection attacks. the approach begins with a sparse autoencoder for feature learning, extracting anomalous patterns from grid operation data to enable efficient compression and anomaly detection. the extracted features are then fed into a convolutional neural network, leveraging its strong spatial pattern recognition capabilities to accurately pinpoint the sources of false data injections. Experimental results demonstrate that the SAE-CNN method achieves high accuracy and robustness in identifying attack nodes, significantly enhancing the smart grid's defense against false data injection attacks. © 2024 IEEE.

关键词： Convolutional neural networks

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the Face Sync - Smart Attendance System 4

The Face Sync - Smart Attendance System

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4th international conference on Expert Clouds and Applications, ICOECA 2024

作者： Asha, P. Bekkem, Chakradhar Reddy Priya, Y Soni Mathivanan, G. Grace, L. K. Joshila Srinivasan, N. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385793

the existing attendance marking in various institutions, including schools and offices, is fraught with various challenges. Traditional methods, such as manual data collection through individual calls, are time-consuming and prone to errors. However, advancements in technology offer automated solutions that streamline the process and ensure accurate record-keeping. In this context, this study proposes the implementation of 'the Face Sync-Smart Attendance Marking System, ' which leverages cutting-edge technologies for face identification and analysis. Developed using deep learning algorithms, this system effectively recognizes faces by analyzing gathered data. the recorded attendance data is then transmitted to the host via email for further processing. the proposed system is implemented using Python programming language and utilizes libraries such as CV2, Face recognition, and Smtplib to achieve its functionality. this innovative approach promises to revolutionize attendance management, enhancing efficiency and reliability across various organizational settings. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis of Bangla Transformation of Sentences Using machine learning 1

引用

4th international conference on Deep learning, Artificial Intelligence and Robotics, ICDLAIR 2022

作者： Das, Rajesh Kumar Sammi, Samrina Sarkar Kobra, Khadijatul Ajmain, Moshfiqur Rahman khushbu, Sharun Akter Noori, Sheak Rashed Haider Daffodil International University Dhaka1341 Bangladesh

ISBN: (数字)9783031303968

ISBN: (纸本)9783031303951

In many languages, various language processing tools have been developed. the work of the Bengali NLP is getting richer day by day. Sentence pattern recognition in Bangla is a subject of attention. Additionally, our motivation was to work on implementing this pattern recognition concept into user-friendly applications. So, we generated an approach where a sentence (sorol, jotil and jougik) can be correctly identified. Our model accepts a Bangla sentence as input, determines the sentence construction type, and outputs the sentence type. the most popular and well-known six supervised machine learning algorithms were used to classify three types of sentence formation: Sorol Bakko (simple sentence), Jotil Bakko (complex sentence) and Jougik Bakko(compound sentence). We trained and tested our dataset, which contains 2727 numbers of data from various sources. We analyzed our dataset and got accuracy, precision, recall, f1-score and confusion matrix. We get the highest accuracy with the decision tree classifier, which is 93.72%. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural language processing systems

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2024 international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024 - Proceedings

2024 International Conference on Emerging Trends in Networks...

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

ISBN: (纸本)9798350353266

the proceedings contain 126 papers. the topics discussed include: towards predictive modelling of recidivism: a case of Namibia correctional services;research and application of commodity recognition algorithm based on machine learning;multi-hop data offloading in heterogeneous ad-hoc UAV networks with deep reinforcement learning;a comprehensive approach to cryptography and security testing in network defense;emotion analysis in smart journals: bridging ML and NLP for holistic mental health monitoring;multi-agent deep reinforcement learning for hybrid motion planning in dynamic environments;advance facial expression detection mechanism using single shot detection;an intelligent technique for the effective prediction of Parkinson disease;beyond traditional methods: exploring deep learning techniques for accurate traffic projection;and an efficient ai based approach for multimedia traffic management in wireless network.

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

4th International Conference on Frontiers in Computing and S...

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

ISBN: (纸本)9789819726103

the proceedings contain 48 papers. the special focus in this conference is on Frontiers in Computing and Systems. the topics include: machine learning-Based Early Epilepsy Diagnosis with Secure EEG data Sharing Using Blockchain;application of Different Decision Tree Classifier for Diabetes Prediction: A machine learning Approach;exploring Electric Vehicle Adoption Research through Bibliometric Analysis and Visualization;optimizing Solar Power Distribution in Microgrids to Reduce Energy Waste;GPA: Uni-directional GRU-Based Traffic Prediction Model for Minimizing Air Pollution;A Real-Time Framework for Automatic Sarcasm Detection Using Proposed Tensor-DNN-50 Algorithm;Unveiling the Art of Music Generation with LSTM;few-Shot learning with Fine-Tuned Language Model for Suicidal Text Detection;a Comparative Analysis of Feature Selection Approaches for Sensor-Based Human Activity recognition;GuideBP: Guided Backpropagation in Multi-output Neural Networks by Channeling Gradients through Weaker Logits;a Study on Users Sentiment from Twitter data and Stock Market During Russia-Ukraine War;illegitimate Comment Filtration Method for Social Media Applications Using Logistic Regression;Unsupervised MTS Anomaly Detection with Variational Autoencoders;SHAPRFs: SHapley Additive eXplanation-Based Random Forests Algorithm for Classification Problems;predicting Suicidal Behavior Among Indian Adults Using Childhood Trauma, Mental Health Questionnaires and machine learning Cascade Ensembles;analyzing Students’ Emotion and Activities in the Classroom: A Rural Education Perspective;MARS: Manual and Automatic Robotic Sanitization on Social Milieu;cervixNet: A Reward-Based Weighted Ensemble Framework for Cervical Cancer Classification;enhancing Graph-Based Representation learning with Adversarial Policy Gradient: A Hyperparameter Analysis;Exploring Label-Specific Feature Weights for Multi-label Feature Selection Using FWMABAC-MFS;CoViT-Net: A Pre-trained Hybrid Vision Transformer for COVID-

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A study on stock market forecasting based on time series with integrated multi-feature LSTM modeling 4

A study on stock market forecasting based on time series wit...

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4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Fu, Pengwei Hainan International College Communication University of China Beijing China

ISBN: (纸本)9798331528676

this paper constructs an effective stock prediction model, which can help investors better understand the market trend and accurately grasp the fluctuation pattern of stock prices, thus guiding them to formulate reasonable investment strategies. Taking Hengrui Medicine as the research object, we collected the stock data, established the feature engineering, set up the index evaluation system for the stock features, and constructed the six features of turnover, average price, relative increase, turnover change volume, increase/decrease amplitude, and amplitude from the aspects of enterprise value, enterprise development potential, and capital stability, respectively. In addition, principal component analysis is used to extract effective information and remove redundant information from the constructed features and original features. the decomposition model, exponential smoothing model, ARIMA and SARIMA are applied to the stock price prediction task of Hengrui Medicine. For the decomposition model, the STL and SEATS decomposition methods are used to decompose the closing rate into seasonal terms, trend-period terms, and residual terms. After that, we tried to integrate machine learning models, such as XGBoost, Adaboost and other Boosting models, and Random Forest and other Bagging models, and formed the meta-model into a Stacking model to evaluate the effect of different methods on stock feature extraction. Finally, we use LSTM model in deep learning field and apply its single-feature and multi-feature input dimensions respectively, and combine the previous models to form an integrated machine learning-multi-feature LSTM stock prediction model, and horizontally compare the traditional and modern methods, and get the conclusion that it has a good fitting effect. ©2024 IEEE.

关键词： Costs

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Lithium-Ion Battery Prognostic and Health Prediction Using machine learning Models 4

Lithium-Ion Battery Prognostic and Health Prediction Using M...

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4th IEEE international conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Nsaka, Ozioma John Malki, Heidar A. University of Houston Dept. of Electrical & Computer Engineering HoustonTX United States University of Houston Dept. of Engineering Technology HoustonTX United States

ISBN: (纸本)9798350395914

Lithium-Ion battery prognostic and health prediction is an essential part of our modern world today. Reliable predictions of the current state and remaining useful life are critical to ensure safe operation of various industrial applications. there exists established and extensively researched battery state prediction models including the equivalent circuit and physics-based models. the emergence and advancements of machine learning algorithms, such as support vector machine (SVM), have led to a new era of data-driven models. In this research, we initially establish a baseline prediction using traditional machine learning algorithms and subsequently employ deep learning algorithms. Our analysis focuses on the battery's degradation pattern, specifically the predication of the State of Health (SOH) prediction and the Remaining Useful Life (RUL), aiming to predict the degradation from a specific threshold cycle to the EOL of the battery. In this study, our aim is to establish a comparable baseline for several machine learning-based prognostic predictions. To overcome this problem, we conduct several experiments on the same dataset using traditional algorithms and as well as neural network models. the classification accuracy aligns with several benchmarks established in the research literature. this research further underscores the viability of using Long Short-Term Memory (LSTM) models for RUL prediction, thereby advancing the role of data-driven models in prognostic and health management for critical engineering applications. © 2024 IEEE.

关键词： Electronic health record

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Vision Assist: Real-Time Guidance System 4

Vision Assist: Real-Time Guidance System

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Mohanraj, E. Devadarshini, D. Manonavaneethan, M. Aravind, S. K.S.Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798331529635

In order to help visually impaired people, this study proposes a machine learning-based system that provides audio feedback, distance calculation, and real-time object detection. the system uses Faster Region-based Convolutional Neural Network (Faster R-CNN), optimized with Compute Unified Device Architecture (CUDA) and CUDA Deep Neural Network library (cuDNN) for GPU-accelerated performance with low latency. Images captured by the rear camera of a smartphone are processed to identify objects and compute their proximity. the system offers audio feedback using the Pyttsx3 text-to-speech engine, alerting users to nearby objects and potential hazards. the system achieved over 99% accuracy in recognizing everyday objects. Parallelized GPU operations ensure efficient real-time object recognition and distance estimation, improving users' safety and mobility. Experimental results demonstrate the system's stability and reliability in assisting visually impaired individuals during navigation. © 2024 IEEE.

关键词： Convolutional neural networks

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Predictive Modelling of Osteoporosis using machine learning Algorithms 4

Predictive Modelling of Osteoporosis using Machine Learning ...

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Dhivya, R. Sagili, Sreenivas Reddy Praveen, R.V.S. Vamsilala, Purushothapatnapu Naga Venkata Sangeetha, A. Suchithra, B. M.Kumarasamy College of Engineering Department of Information Technology Tamilnadu Karur India Rochester Institute of Technology Department of Computer Science RochesterNY14623 United States M/s. Divija Commercial Properties LTIMindtree Telangana Hyderabad500081 India Vignan's Lara Institute of Technology and Science Vadlamudi Andhra Pradesh Guntur district522213 India Thottiam Trichy India Sri Eshwar College of Engineering Department of Cse Coimbatore India

ISBN: (纸本)9798331529635

Osteoporosis implies the deterioration of bones or their denser tissue and the subsequent elevated risk of skeletal fractures constitutes a major health problem today. therefore, early recognition and initiation of treatment should be enacted to enhance the status of patients. this paper aims at presenting an understanding and analysis of the use of these particular methods, and their specificity in the context of osteoporosis modeling using Support Vector machines. With the help of the large number of features, which are clinical and demographic factors, we are to create a stable model that will allow defining the probability of osteoporosis. the SVM algorithm for dealing with high-dimensional data and classification is used for the investigation of the connection between input characteristics and the advance of the condition. It also makes it easier to determine the most relevant factors that could be used in the creation of a predictive instrument that might help in the early detection of the disease and the subsequent improvement of the available treatment plans. It is with this goal in mind that this research aims at contributing to development of further implementation of machine learning approaches in osteological field, thus laying basis for enhancing diagnostic approaches and patients' management. © 2024 IEEE.

关键词： Support vector machines

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

4th International Conference on Frontiers in Computing and S...

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

ISBN: (纸本)9789819726134

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