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Analysis of Human Emotion using Deep Learning

Analysis of Human Emotion using Deep Learning

引用

Applied Artificial intelligence and Computing (ICAAIC), international conference on

作者： Santosh Kumar Smriti Kumari Gupta Yogesh Kumar School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This research introduces an innovative real-time human emotion recognition system that seamlessly integrates computer vision techniques to address the challenges faced by individuals in understanding and recognizing human emotions. By focusing on facial expression recognition, which encompasses emotions such as fear, anger, surprise, sadness, and happiness, the proposed system aims to provide a comprehensive understanding of human emotions. The application of facial expression recognition extends beyond individual interactions to various domains, including security, privacy, and marketing. Businesses use Artificial intelligence (AI) to analyze customer satisfaction by interpreting emotional responses to images or videos. These insights play a crucial role in tailoring products and services to meet customer preferences, thereby enhancing marketing strategies and improving overall customer experience.

关键词： Training Emotion recognition Face recognition Computational modeling Training data Convolutional neural networks Artificial intelligence

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Research on the Application of Artificial intelligence Technology in ADS-B Data Anomaly Detection 3

Research on the Application of Artificial Intelligence Techn...

引用

3rd international conference on Image Processing, Computer Vision and machine Learning, ICICML 2024

作者： Lu, Xianyu Chen, Haitao Guangzhou Civil Aviation College Guangzhou China

ISBN: (纸本)9798350355413

Automatic Dependent Surveillance Broadcast (ADS-B) system is widely used in the field of aerial surveillance because of its accuracy and high efficiency. However, ADS-B system is easy to be spoofed and interfered because it broadcasts signals in plaintext format and lacks data encryption and message authentication mechanism. This article conducts research on this issue. Firstly, it analyzes the structure of the ADS-B system and discusses its data anomaly problems. It summarizes that researchers at home and abroad generally approach the solution to data anomaly problems from three directions: encrypting the data link, improving the ADS-B hardware and software, and analyzing from the perspective of the ADS-B data fields. Then, it particularly points out that artificial intelligence algorithms also build models from the perspective of the ADS-B data fields to complete ADS-B track data anomaly detection, including algorithms based on clustering, data reconstruction, and data temporal correlation. Finally, this paper compares the detection precision, recall rate, and F1 score of several deep learning algorithm models based on data time-related, and finds that they all have excellent ADS-B data anomaly detection effects, with detection precision all above 92.2%, recall rate all above 84.9%, and F1 score all above 86.8%. © 2024 IEEE.

关键词： ADS-B artificial intelligence Aviation surveillance track anomaly detection

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Atrial Fibrillation Dynamic Features recognition using 1-D Convolutional Neural Networks 3

Atrial Fibrillation Dynamic Features Recognition using 1-D C...

引用

3rd IEEE international Biomedical Instrumentation and Technology conference, IBITeC 2023

作者： Ong, Yik Hern Abdul-Kadir, Nurul Ashikin Mahmood, Nasrul Humaimi Wahab, Muhammad Amin Abd Heng, Wei Wei Chan, Weng Howe Embong, Abdul Mutalib Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Bahru Malaysia University of Southampton Malaysia Connected Intelligence Research Group Iskandar Puteri Malaysia Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia Universiti Malaysia Terengganu Pusat Pendidikan Asas Dan Lanjutan Terengganu Malaysia

ISBN: (纸本)9798350302424

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia that requires accurate detection for effective management and treatment. This study aims to propose a simple yet effective one-dimensional convolutional neural network (1D-CNN) for the AF dynamic features recognition which are extracted using a second-order dynamic system (SODS) concept. The SODS concept utilizes a dynamic system approach to capture the short-term behavior of ECG signals. It employs a second-order differential equation to model the ECG signals based on parameters such as the natural frequency (ω), damping coefficient (ξ), and forcing input (u) to enable the characterization and differentiation of ECG signal. The 1D-CNN model was trained and validated with preprocessed ECG signals from the MIT-BIH Atrial Fibrillation and MIT-BIH Normal Sinus Rhythm database. The results demonstrate the efficacy of the SODS and 1D CNN method for AF recognition. The proposed approach achieves high accuracy, with an accuracy of 98.35% using natural frequency, ω feature. © 2023 IEEE.

关键词： Electrocardiograms

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Bio-inspired Sky Polarized Orientation based on the atmospheric polarization symmetry mode and Inertial Sensors 3

Bio-inspired Sky Polarized Orientation based on the atmosphe...

引用

2023 3rd international conference on Computer, Remote Sensing and Aerospace, CRSA 2023

作者： Chen, Zhihe Teng, Lianwei Zhou, Wenzhou He, Xiaofeng Fan, Chen College of Intelligence Science and Technology National University of Defense Technology Changsha China

This paper presents a sky polarized orientation method based on the symmetry of the atmospheric polarization mode and the inertial sensors. Firstly, a method of extracting solar meridians based on canny operator and Hough Transform is proposed by using the symmetry of atmospheric polarization pattern. Then using the horizontal Angle information of the inertial sensor and the ephemeris information of the sun, a method for solving the yaw Angle of the polarized light is designed. Finally, a single axis rotation experiment and a vehicle-mounted dynamic experiment are carried out to verify and compare the proposed method. Compared with the least square method, the positioning accuracy of the single-axis turntable experiment is improved by 22.61%∼73.86%, and that of the vehicle-mounted dynamic experiment is improved by 47.71%, which shows that the method can provide a new orientation means for unmanned vehicles and unmanned aerial vehicles. © Published under licence by IOP Publishing Ltd.

关键词： Hough transforms

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Advanced Face Detection Using SVM and Haar-like Features: A Comprehensive Approach in AI and Image Processing

Advanced Face Detection Using SVM and Haar-like Features: A ...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Devendra Somwanshi Archika Jain Kapil Joshi Vishnu Kant Salil Bharany Bura Vijay Kumar Department of EC Poornima College of Engineering Jaipur India Department of CSE Management & Gramothan (SKIT) Swami Keshvanand Institute of Technology Jaipur India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science and Artificial Intelligence SR University Warangal

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Face detection is a vibrant research field spanning artificial intelligence, image processing, video surveillance, human-machine interaction, identity authentication, machine learning, pattern recognition, and so on. While humans effortlessly recognize faces, computers face challenges due to facial variability. The complexity lies in factors like expressions, lighting changes, and head positions. Thus, solving the problem of face detection has an important academic value and provides important enlightenment. There are many aspects to face detection such as facial expression, head position, changing lighting conditions and many others. To deal with face detection problems various approaches are used such as template matching methods, knowledge-based methods, feature invariant methods, and appearance-based methods. The proposed approach for face detection utilizes Support Vector machine (SVM) and Haar-like Features. It begins by generating an integral image from a binary image measuring 19 x 19 pixels. Following this, the Haar feature extraction method is employed to obtain Haar values from both face and non-face isommages. A set of 200 face and non-face images from MIT database have been used. Features have been reduced five times to make different features sets of images. These features are passed to Support Vector machine as input parameters and processed to classify face and non-face datasets. Support Vector machine is used to create, train and test the neural network. Features are extracted on MATLAB and further processed in SVM using NeuroSolutions tool. The results are evaluated based on two scenarios as varying number of images and features.

关键词： Support vector machines Training Face recognition Lighting machine learning Feature extraction Magnetic heads Face detection MATLAB Faces

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Artificial intelligence and its Road to Altering the World Dynamics

Artificial Intelligence and its Road to Altering the World D...

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Innovation in Technology (INOCON), IEEE international conference for

作者： Sehaj Devyansh Batra Ashish Sharma Computer Science and Engineering Chandigarh University Mohali India Department of Electrical Engineering Chandigarh University Mohali India

Humans have longed for computers to take over and make the monotonous and tedious tasks obsolete. Artificial intelligence(AI) is just the right answers to this problem. There are not one but many sectors that are being manipulated by this ever- growing technology. It is safe to say that each day machines are getting better at understanding humans which is due to the evolution and innovation in this field. In this paper, we have uncovered various sectors in which artificial intelligence along with its various branches like Natural Language Processing (NLP), machine Learning(ML) and Computer Vision have helped the world curate a better place by personalizing every daily use item. This has enhanced the way we live. However, there is a lot of data that is being used in order to bring these technologies to life. We have highlighted the constraints as well as the pleasures that this technology can bring us.

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Multi-scale fusion clustering algorithm for urban travel features 3

Multi-scale fusion clustering algorithm for urban travel fea...

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2023 3rd international conference on Intelligent Traffic Systems and Smart City, ITSSC 2023

作者： Zhang, Zixiang Zhao, Zhilong Deng, Jiaming Zhao, Yingying Xu, Tao Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China School of Computer and Information Engineering Henan University Kaifeng475004 China International Education College Henan University Zhengzhou450046 China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan University Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

ISBN: (纸本)9781510672963

Urban travel data typically encompass various temporal and spatial scales and features. Nonetheless, existing traffic travel feature analysis often focuses on a single spatial scale, which may overlook crucial information and hinder features at different scales. Consequently, this could reduce the accuracy of travel pattern recognition. To solve this problem, we introduce a multi-scale fusion transformation algorithm. In our approach, we first establish a multi-level taxi travel data grid system. Then, we apply the I-Clique clustering algorithm to perform grid clustering at each level of the system to identify taxi travel feature regions of different scales. Subsequently, a multi-scale fusion pyramid model is constructed utilizing the acquired travel feature regions at various scales. Finally, we determine the transformation relationships between scales in the pyramid model, enabling the identification of travel feature areas across both known and unknown scales. This approach facilitates the recognition of travel features within cities at any scale. Experimental results demonstrate that the multi-scale fusion transformation algorithm enhances our understanding of spatial aggregation patterns and the hierarchical structure of geographical entities, offering a novel approach for multi-scale urban traffic analysis. © 2024 SPIE. All rights reserved.

关键词： Clustering algorithms

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A Survey of Advancements in DBSCAN Clustering Algorithms for Big Data

A Survey of Advancements in DBSCAN Clustering Algorithms for...

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Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2020 international conference on

作者： Omkaresh Kulkarni Adnan Burhanpurwala Deparment of Computer Science and Engineering (AIML) VIIT Kondhwa-BK Pune India Department of Artificial Intelligence and Data Science VIIT Kondhwa-BK Pune India

Cluster analysis is an unsupervised machine learning job of grouping objects based on some similarity measure. Among clustering algorithms, DBSCAN (Density Based Spatial Clustering of Application with Noise) contributes to unsupervised machine learning by enabling the clustering of datasets with varying densities, shapes, and sizes. DBSCAN does not require the predefinition of the number of clusters and is able to recognize noiseless arbitrary clusters by using two parameters, minPts and eps. This paper reviews the different DBSCAN algorithms for big data clustering and provides a detailed comparison among the algorithms.

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Face Age Progression with Attribute Manipulation 3rd

Face Age Progression with Attribute Manipulation

引用

3rd international conference on pattern recognition and Artificial intelligence, ICPRAI 2022

作者： Tatikonda, Sinzith Nambiar, Athira Mittal, Anurag Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031090363

The human face is one of the predominant means of person recognition. Human faces are affected by many factors i.e. time, attributes, weather, and other subject-specific variations. Although face aging has been studied in the past, the impact of the aforesaid factors, especially, the effect of attributes on the aging process were unexplored. In this paper, we propose a novel holistic "Face Age progression With Attribute Manipulation" (FAWAM) model that generates face images at different ages while simultaneously varying attributes and other subject specific characteristics. We address the task in a bottom-up manner, considering both age and attributes submodules. For face aging, we use an attribute-conscious face aging model with a pyramidal generative adversarial network that can model age-specific facial changes while maintaining intrinsic subject specific characteristics. For facial attribute manipulation, the age processed facial image is manipulated with desired attributes while preserving other details unchanged, leveraging an attribute generative adversarial network architecture. Our proposed model achieves significant qualitative as well as quantitative performance results. © 2022, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Italian-Chinese Neural machine Translation: results and lessons learnt 23

Italian-Chinese Neural Machine Translation: results and less...

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3rd ACM conference on Information Technology for Social Good, GoodIT 2023

作者： Delnevo, Giovanni Im, Marcus Tse, Rita Lam, Chan-Tong Tang, Su-Kit Salomoni, Paola Pau, Giovanni Ghini, Vittorio Mirri, Silvia Department of Computer Science and Engineering University of Bologna Cesena Italy Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education Macao Polytechnic University China

ISBN: (纸本)9798400701160

Today, access to the Internet provides access to various forms of knowledge like free online lecture series offered by prestigious universities, massive open online courses, films and books, and Wikipedia. In addition, it is possible to join online communities on any topic of interest, get to know people with common interests, exchange thoughts and participate in debates. To enable access to these unprecedented knowledge bases, it is crucial to be able to translate texts into any language known by users. For this reason, machine Translation has been a very active research field for the last thirty years. In this paper, we investigate the task of Chinese-Italian translations by exploiting Neural machine Translation approaches. We trained several deep neural networks starting from two already available datasets containing Chinese-Italian parallel corpora. Then, we compared their performance against some of the most common machine translation services freely available online. In particular, we take advantage of Microsoft Translator, Google Translate, DeepL, and ModernMT. © 2023 Owner/Author.

关键词： Neural machine translation

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