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Applications of machine Learning in Modern Power Systems: A Comprehensive Review 3

Applications of Machine Learning in Modern Power Systems: A ...

引用

3rd international conference on New Energy and Power Engineering, ICNEPE 2023

作者： Mirsaeidi, Sohrab Mendoza, Alejandro Zavaleta Ghaffari, Kimia Yousaf, Muhammad Zain Muttaqi, Kashem M. He, Jinghan Beijing Jiaotong University School of Electrical Engineering Beijing China Beijing Jiaotong University School of Economics and Management Beijing China Hubei University of Automotive Technology School of Electrical and Information Engineering Shiyan China University of Wollongong School of Electrical Comp. and Telecom. Engineering Wollongong Australia

ISBN: (纸本)9798350393224

The demand for electrical power has increased due to population growth, electronic technology advancements, and environmental concerns. This has led to a significant modernization of the power system that incorporates renewable energy sources and electronic components for measurement, communication, and control. However, these changes have added new challenges to the management and control of modern power systems that cannot be overcome using traditional analytical methods. Artificial intelligence (AI), specifically machine Learning (ML) techniques, have recently been deployed by a wide number of researchers from different fields thanks to their adaptability and learning ability at a higher speed. These techniques are adequate for large, non-linear, and multi-variable problems such as modern power systems. Therefore, this paper aims to provide an extensive review of recent ML techniques as well as their usage in modern power systems in terms of power quality, power stability, energy and load forecasting, protection and fault diagnosis, and cybersecurity. Finally, the main challenges associated with the implementation of ML techniques for future modern power systems are pointed out. © 2023 IEEE.

关键词： artificial intelligence machine learning Modern power systems

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Watchlist Challenge: 3rd Open-set Face Detection and Identification 18

Watchlist Challenge: 3rd Open-set Face Detection and Identif...

引用

18th IEEE international Joint conference on Biometrics, IJCB 2024

作者： Kasim, F. Boult, T.E. Mora, R. Biesseck, B. Ribeiro, R. Schlueter, J. Repák, T. Vareto, R. Menotti, D. Schwartz, W.R. Günther, M. University of Zurich Switzerland University of Colorado United States Federal University of Minas Gerais Brazil Federal University of Paraná Brazil Federal Police of Brazil Brazil Dermalog Identification Systems GmbH Germany

ISBN: (纸本)9798350364132

In the current landscape of biometrics and surveillance, the ability to accurately recognize faces in uncontrolled settings is paramount. The Watchlist Challenge addresses this critical need by focusing on face detection and open-set identification in real-world surveillance scenarios. This paper presents a comprehensive evaluation of participating algorithms, using the enhanced UnConstrained College Students (UCCS) dataset with new evaluation protocols. In total, four participants submitted four face detection and nine open-set face recognition systems. The evaluation demonstrates that while detection capabilities are generally robust, closed-set identification performance varies significantly, with models pre-trained on large-scale datasets showing superior performance. However, open-set scenarios require further improvement, especially at higher true positive identification rates, i.e., lower thresholds. © 2024 IEEE.

关键词： Watches

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A GPT-Based Approach for Sentiment Analysis and Bakery Rating Prediction 16th

A GPT-Based Approach for Sentiment Analysis and Bakery Ratin...

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22nd Mexican international conference on Artificial intelligence (MICAI)

作者： Magdaleno, Diego Montes, Martin Estrada, Blanca Ochoa-Zezzatti, Alberto Univ Autonoma Aguascalientes Aguascalientes Aguascalientes Mexico Univ Politecn Aguascalientes Aguascalientes Aguascalientes Mexico Tecnol Univ Aguascalientes Aguascalientes Aguascalientes Mexico UACJ Tecnol Tokyo Japan La Univ Anahuac Ctr Alta Direcc Ingn & Tecnol Naucalpan De Juarez Mexico

ISBN: (纸本)9783031519390;9783031519406

This paper presents a comprehensive approach to predicting the ratings of bakery establishments on diverse online platforms using natural language reviews. The study incorporates Large Language Models (LLMs) and Aspect-Based sentiment analysis to discern nuanced sentiment scores within specific categories. Utilizing advanced machine learning and regression methods, the paper introduces a robust predictive framework that employs LLMs, exemplified by GPT-3.5 Turbo, to accurately infer sentiment from natural language phrases. Framework's effectiveness is demonstrated in accurately forecasting bakery scores based on such sentiment analysis, obtaining a MAE of 0.27, demonstrating low error rates in comparison to other state-of-the-art models. The study also addresses challenges associated with sentiment analysis, including emojis and sarcasm, and underscores LLMs enhanced proficiency in handling intricate linguistic nuances. Taken together, this research highlights the potential of LLMs in constructing a precise predictive model for online platform ratings, offering valuable insights into consumer perceptions.

关键词： Large Language Model GPT Intelligent System Bakeries Bread quality pattern recognition Online reviews

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Feature Fusion Based Convolutional Neural Network for Facial Expression recognition 3

Feature Fusion Based Convolutional Neural Network for Facial...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Hussein, A.H.A. Yamsani, Nagendar Naser, Zamen Latef Almusawi, Muntather Vinod, A. College of Pharmacy Ahl Al Bayt University Karbala Iraq School of Computer Science and Artificial Intelligence SR University Warangal India National University of Science and Technology Dhi Qar Iraq The Islamic University Najaf Iraq Government College of Engineering Department of ECE Dharmapuri India

ISBN: (纸本)9798350317022

Face expressions are straight and significant social signals in communication of people, with another gestures and convey significant nonverbal emotion of cues in relation of interpersonal. The existing techniques has limitations of less recognition accuracy and doesn't fuse features. To overcome these limitations, Feature Fusion based Convolutional Neural Network (FF-CNN) method is proposed to recognize facial expression. The dataset utilized for the research is RAF-DB dataset and proposed method utilizes image branch for extracting middle and huge-level features from entire input image. Patch branch utilized for extracting local features from 16 image patches in actual images. L2 norm-based feature selection is performed towards acquire much discriminatory local features. Proposed method is evaluated by performance metrics of accuracy, precision and recall. The proposed method attained high accuracy of 91.23% which is superior when compared with other existing methods like FF-Artificial Neural Network (FF-ANN), FF-K-Nearest Neighbour (FF-KNN) and FF-Recurrent Neural Network (FF-RNN). © 2023 IEEE.

关键词： Convolutional Neural Network Facial Expression recognition Feature Fusion Image Branch and Patch Branch

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An Ensemble Approach for Facial Behavior Analysis in-the-wild Video

An Ensemble Approach for Facial Behavior Analysis in-the-wil...

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IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Hong-Hai Nguyen Van-Thong Huynh Kim, Soo-Hyung Chonnam Natl Univ Dept Artificial Intelligence Convergence Gwangju South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human emotions recognization contributes to the development of human-computer interaction. The machines understanding human emotions in the real world will significantly contribute to life in the future. This paper introduces the 3rd Affective Behavior Analysis in-the-wild (ABAW3) 2022 challenge. We focused on solving the problem of the Valence-Arousal (VA) estimation and Action Unit (AU) detection. For valence-arousal estimation, we conducted two stages: creating new features from multimodel and temporal learning to predict valence-arousal. First, we make new features;the Gated Recurrent Unit (GRU) and Transformer are combined using a Regular Networks (RegNet) feature, which is extracted from the image. The next step is the GRU combined with local attention to predict valence-arousal. The Concordance Correlation Coefficient (CCC) was used to evaluate the model. The result achieved 0.450 for valence and 0.445 for arousal on the test set, outperforming the baseline method with a corresponding CCC of 0.180 for valence and 0.170 for arousal. We also performed additional experiments on the action unit task with simple transformer blocks. We achieved a score of 49.04 on the test set in terms of F-1 score, which outperforms the baseline method with a corresponding F1 score of 36.50. Our submission to ABAW3 2022 ranks 3rd for both tasks.

关键词： Human computer interaction Sentiment analysis Gold Estimation Logic gates Feature extraction Transformers

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Cortical Circuits of Context Adaptability: Understanding the Neurobehavioral Mechanisms Underlying Flexible Behavior 10th

Cortical Circuits of Context Adaptability: Understanding the...

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10th Biennial international conference on pattern recognition and machine intelligence (PReMI)

作者： Kaman, Sweta Sharma, Ankita Banerjee, Romi IIT Jodhpur Sch Artificial Intelligence & Data Sci Karwar India IIT Jodhpur Sch Liberal Arts Karwar India IIT Jodhpur Dept Comp Sci & Engn Karwar India

ISBN: (纸本)9783031451690;9783031451706

The present pilot study aims to investigate the cortical circuits involved in context adaptability and gain initial insights into the neurobehavioral mechanisms underlying flexible behavior. To accomplish this, we designed a novel first-person perspective picture-based decision-making task, using stick figures and objects, where eight participants were engaged in imagining themselves as stick figures for each depicted scenario. These scenarios were portrayed through objects and emotional contexts of positivity, negativity, and neutrality. Our results showed a significant link (p = 0.020) between the absolute power values of the alpha frequency band (8-12 Hz) and ratings for positive context stimuli in one group. This suggests that alpha power may affect how people rate positive situations. We also found a significant association between the absolute power values of theta (4-7 Hz) and alpha frequency bands for neutral context stimuli in both group 1 (p = 0.023 and p = 0.022) and group 2 (p = 0.047 and p = 0.020). However, we found no significant associations between absolute power values of frequency bands (theta, alpha, beta1, beta2, beta3 and gamma) and ratings for negative stimuli in both the groups (p = 0.099, p = 0.105). Nonetheless, the linear regression models demonstrated strong fits, capturing variance in the data. Findings provide initial insights into emotional processing and decision-making, warranting further research with larger samples.

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Analysis of communication strategy of Guzheng art based on artificial intelligence 3

Analysis of communication strategy of Guzheng art based on a...

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3rd international conference on machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Wang, Jingjing Southwest University of Science and Technology Mianyang621010 China

The current situation of the spread of guzheng art is not good, and the continuous development may lead to the disappearance of the art of guzheng. Therefore, it is necessary to help the art of guzheng to continue to spread in the modern environment. On this basis, this paper will research on the basis of artificial intelligence, mainly introduces the basic concept of artificial intelligence, and then puts forward the idea of coping and the method of thinking. Research shows that artificial intelligence has high application value in the communication of guzheng art. Combining ideas and methods, artificial intelligence can play a role in helping the communication of Guzheng art. Continuous circulation can change the communication status and reverse the elapse of guzheng art. © 2023 The Authors. Published by Elsevier B.V.

关键词： Artificial intelligence

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Fully Automated Pothole Detection and Repair System using Artificial intelligence 3

Fully Automated Pothole Detection and Repair System using Ar...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Hulwan, Dattatray Rane, Milind Dalvi, Anirudha Kulkarni, Anvay Shah, Aayush Bhave, Ameya Arawat, Vhanneshwar Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350322842

India has many areas with poorly maintained roadways. In addition to causing physical problems like back discomfort and injured spines, potholes and uneven roadways can harm automobiles. This work demonstrates a completely autonomous robot that can identify and fix roads overnight at the lowest possible cost utilizing a high accuracy machine learning algorithm. The suggested model makes use of YOLO framework models, which have the highest (Placeholder1) accuracy according to MENG YANG (2020). The testing information is gathered from different automobiles across the nation. To increase the robot's accuracy, the hardware for the system makes use of various sensors that collect inputs and cross-validate the findings. The robot uses LIDAR, IMU, and ultrasonic sensors to determine the depth of potholes and identify structural problems on roads. The suggested model can reduce the cost of fixing a road by 60%. © 2023 IEEE.

关键词： Optical radar

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Artificial intelligence Based Transformation in the Field of Legal Profession 3

Artificial Intelligence Based Transformation in the Field of...

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3rd international conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Kant, Ravi Dixit, Anil Kumar Memoria, Minakshi Chaudhary, Tanupriya Law College Dehradun Uttaranchal University Uttarakhand Dehradun India Uttaranchal Institute of Technology Faulty of Uttaranchal University Uttarakhand Dehradun India Symbiosis University CSE Department Maharashtra Pune India

ISBN: (纸本)9798350303360

This paper explores the impact of the digital age on the legal profession and how it is undergoing significant changes. The Hon'ble Supreme Court is at the forefront of embracing technology to ensure swift justice for litigants. Drawing from reviews, personal correspondence, and best practices, this study reveals a promising future for the legal profession with e-filing, machine simulation, virtual hearings, and blockchain technology. It also examines advancements in foreign courts alongside those made by the Indian Supreme Court, highlighting how technological advancements are reshaping the field. As technology continues to evolve rapidly, we can expect substantial transformations in how legal professionals operate. It is crucial for policymakers and regulatory bodies to establish appropriate guidelines and rules that will shape the destiny of this esteemed profession as it adapts to digitalization's inevitabilities. © 2023 IEEE.

关键词： Blockchain

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Automated recognition Model for Identifying Harmful and Harmless Insects in Crop Management 3

Automated Recognition Model for Identifying Harmful and Harm...

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3rd international conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Al-Shahrani, Amal Alsaedi, Rana Alfadli, Ameera Alahmadi, Taif Alzubaidi, Ohoud Alqthami, Deema Umm Al-Qura University Collage of Computing Dept. of Computer Science and Artificial Intelligence Makkah Saudi Arabia

ISBN: (纸本)9798350367492

Diseases and pests often affect agriculture, causing significant economic losses. Worldwide, pests can cause yield losses of up to 40%. To minimize these losses, detecting and identifying pests as early as possible is crucial. Prior studies have developed detection models to detect harmful or harmless insects. However, there has been no model developed to detect both categories together. To address this issue, we aim to develop a model that can detect and classify both harmful and harmless insects in agricultural environments. We will assess the accuracy of three different methods: YOLO (You Only Look Once) versions 8 and 9, and VGG16 (Visual Geometry Group) on a dataset comprising ten classes, five for harmful insects and five for harmless insects, to determine the most effective approach. The results indicate that YOLOv9 achieved the highest accuracy of 0.972, followed closely by YOLOv8 with 0.969, while VGG16 lagged at 0.849. This suggests that YOLOv9 is the most effective tool among the tested models for detecting and classifying both harmful and harmless insects in agricultural settings. © 2024 IEEE.

关键词： Harmful Harmless Insects VGG-16 YOLO

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