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Investigating the Use of Neural Networks for AI-Based Image recognition Systems 3

Investigating the Use of Neural Networks for AI-Based Image ...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sakhare, Nitin N. Mondal, Dipannita Vigenesh, M. Geetha, B. Dhabliya, Dharmesh Purohit, Ashok Kumar Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Dr. D.Y.Patil College of Engineering and Innovation Artificial Intelligence and Data Science Department Talegaon India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Mangalayatan University Department of Commerce Aligarh India

ISBN: (纸本)9798350319125

This paper examines the usage of synthetic intelligence (AI) to broaden photo recognition structures through neural networks. Strategies along with supervised gaining knowledge, unsupervised gaining knowledge, deep studying, and convolutional neural networks are investigated, as are their benefits and barriers. The paper appears at many demanding situations involved in neural networks for photo recognition, including the need for categorized information, the magnificence imbalance trouble, and the scalability issue. It additionally describes several methods to improve accuracy and velocity, combining different architectures and photograph pre-processing techniques. Eventually, the paper discusses the ability packages and future research directions of AI-based total image popularity structures. © 2023 IEEE.

关键词： combining directions imbalance networks velocity

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

Proceedings - 2023 3rd International Conference on Innovativ...

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

ISBN: (纸本)9798350303360

The proceedings contain 146 papers. The topics discussed include: realization of SISO and MIMO-based VLC for future communication systems;face mask detection using CNN model and transfer learning;age based hybrid recommendation system using machine learning;a wide analysis of loss functions for image classification using convolution neural network;IoT-enabled smart waste management: leveraging the power of IoT for sustainable solutions;the tracking system for e-healthcare employs IoT and machine learning techniques;time series forecasting of the Microsoft corporation stock price;reliability prediction of solar bridge cooking device utilizing structure function approach;role of artificial intelligence in thyroid disorder;real time theft detection using YOLOv5 object detection model;and cyber-crime prevention and techniques: a comprehensive survey.

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Explainable Artificial intelligence Methods in Text Classification machine Learning Models for Investigating Human Behavior in Digital Environments 3

Explainable Artificial Intelligence Methods in Text Classifi...

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3rd IEEE international conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024

作者： Gafarov, Fail M. Gafarova, Viliuza R. Ustin, Pavel N. Institute of Computational Mathematics and Information Technologies Kazan Federal University Kazan Russia Institute of Applied Semiotics Tatarstan Academy of Sciences Kazan Russia Institute of Pedagogy and Psychology Kazan Federal University Kazan Russia

ISBN: (纸本)9798331516321

Text classification, also commonly known as text categorization, is the process of assigning labels or tags to textual data. To solve this problem different models are used, based on machine learning algorithms, as well as deep learning methods based on neural networks of different architectures (recurrent, convolutional, transformers, etc.). But most complex machine learning and deep learning models are black boxes because they contain a large number of parameters and have complex architectures that make them uninterpretable. This paper proposes a framework to generate explanations in binary text classification task for an investigation of subject's behavior in the digital environment. Our framework utilizes five machine learning models (Random Forest, K-nearest neighbors, Decision Tree, Logistic Regression, Gradient Boosting) and a fine-tuned BERT-based classifier and an explainability module using LIME, SHAP and Integrated Gradients to generate explanations for each prediction. The main objective of this paper is to show how machine learning-based natural language processing methods enhanced by Explainable AI methods can be applied in psychological domain. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on the Performance of Commercial Artificial intelligence Model for Internet Audio Streaming Media's Lyrics recognition 3

Research on the Performance of Commercial Artificial Intelli...

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3rd international conference on Electronic Communication and Artificial intelligence, IWECAI 2022

作者： Tang, Kaizhen Guanghua Cambridge International School Shanghai201315 China

ISBN: (纸本)9781665479974

In the second AI boom from the 1980s to 1990s, speech recognition was one of the most representative breakthroughs at that time. Let the computer understand every sentence and word people say, which is the goal that scientists strive to pursue on the first day of the birth of artificial intelligence. On the other hand, Internet audio streaming media is developing so fast. The user is increasing geometrically. There are massive demands for lyrics. However, a lot of songs do not yet have official lyrics from their uploaders. Thus, this research aims to study the commercial Artificial intelligence's performance to automatically generate lyrics from the songs. Therefore, a series of experiments to test the recognition rate of lyrics by three commercial artificial intelligence models-Watson from IBM, Tencent Cloud from Tencent, and I Fly Rec from I Fly Technology have been done. The results show that I Fly Rec has the best performance among the three models. © 2022 IEEE.

关键词： Speech recognition

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Bio-inspired Swarm intelligence: A Flocking Project with Group Object recognition 3

Bio-inspired Swarm Intelligence: A Flocking Project with Gro...

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3rd international conference on Consumer Electronics and Computer Engineering, ICCECE 2023

作者： Tianbo, Song Weijun, Hu Jiangfeng, Cai Weijia, Liu Quan, Yuan Kun, He Ira A. Fulton Schools of Engineering Arizona State University Tempe United States Saint Petersburg State University Faculty of Applied Mathematics and Control Processes Saint Petersburg Russia Lomonosov Moscow State University Department of Chemistry Moscow Russia Allen School of Engineering and Computing Trine University Angola United States The Open University of China Faculty of Engineering Hangzhou China College of Computing Illinois Institute of Technology Chicago United States

ISBN: (纸本)9798350331578

With the help of the adaptive gain control algorithm and the flocking SWARM algorithm with adversarial agents, the project described in this article makes the entire system communication-free while maintaining the same functionality and performance. It outlines the entirety of this biologically inspired swarm intelligence's thought process, from inception to conclusion (Flocking algorithm). The entire project is dependable and practically impenetrable under a variety of conditions. The neighbor observation for group conjecture is then strengthened by the use of the adaptive gain control technique and a partial Kalman filter. After the system has been stabilized, use the built-in camera of the SWARM drone to introduce the Object recognition method in computer vision. As a result, the system enters the real world from the simulation level. © 2023 IEEE.

关键词： Object recognition

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Research on Gait Feature Fusion Method of OpenPose and GEI Based on Convolutional Network 3

Research on Gait Feature Fusion Method of OpenPose and GEI B...

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3rd international conference on Neural Networks, Information and Communication Engineering, NNICE 2023

作者： Yuan, Jia Hao Wuhan University of Technology Department of Computer and Artificial Intelligence Wuhan430070 China

ISBN: (纸本)9798350335972

Gait recognition has the advantages of low resolution, easy acquisition but not easy to imitate, and difficult to camouflage, etc. It is one of the biometric recognition technologies with the most development potential at long distances. However, factors such as viewing angle, clothing, and walking with weights in gait data can make gait recognition performance poor. Convolutional neural network is one of the best deep learning techniques, which has the ability to fit features and extract typical features. Therefore, this paper proposes a gait recognition method based on a multi-layer convolutional neural network. This method is based on the OpenPose module to obtain the skeletal key point set of the human body in the gait cycle and the gait energy image (GEI) corresponding to the gait cycle, autonomously learn the characteristics of the target in a gait cycle, and use the triplet loss function to optimize the network. The recognition accuracy of the method above is 92.40% in a single view and 90.25% in a cross view after testing on typical gait dataset CASIA-B, which is robust to several uncertain factors. © 2023 IEEE.

关键词： Convolutional neural networks

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Hybridization Methodology for Predicting ORE from Mines to End User using machine Learning Technique 3

Hybridization Methodology for Predicting ORE from Mines to E...

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3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Prakash, S.P. Kumar, N Ram Vinu, J. Mohanasharan, K. Saabareesh, M. Bannari Amman Instuite of Technology Department of Electronics and Communication Erode Tamilnadu Sathyamangalam India Bannari Amman Instuite of Technology Department of Artificial Intelligence and Data Science Erode Tamilnadu Sathyamangalam India

ISBN: (纸本)9798350370867

Iron ore is the most important iron ore in India and it is often lost on the way from the mine to its final destination. Manual sorting of many types of iron ore is difficult and time consuming. We deploy the most advanced RFID technology, along with weighbridges and GPS tracking systems, to solve this problem. Instead of using F-CNN, Iron ore grades were classified using a preliminary object recognition algorithm (YOLO V5). One crucial technical job in mining and excavation is locating mineral reserves. Conventional methods, however, are laborious and time-consuming. The concept makes use of machine learning capabilities to provide proactive, data-driven mine-to-end-user logistics, which minimizes environmental impact, lowers costs, and increases efficiency. Thus, datasets pertaining to mineral reserves were classified more accurately through the application of data augmentation and transfer learning. Comparative trials showed that using transfer learning may effectively counteract the improvement of categorization performance, up to and including 94%. This is a new concept for mining problems that can be solved by the above mentioned method. © 2023 IEEE.

关键词： Iron ores

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Unsupervised Discovery of Recurring Spoken Terms Using Diagonal patterns 10th

Unsupervised Discovery of Recurring Spoken Terms Using Diago...

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

作者： Sudhakar, P. Rao, K. Sreenivasa Mitra, Pabitra Indian Inst Technol Adv Technol Dev Ctr Kharagpur 721302 India Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 India

ISBN: (纸本)9783031451690;9783031451706

Spoken term discovery is a challenging task when a lot of spoken content is generated without annotation. The spoken term discovery task accomplished by pattern matching techniques resolves the challenge by directly capturing the resemblance of the spoken terms at the acoustic feature level. Despite feasibility, the pattern-matching approach generates more false alarms during the discovery task due to fluctuations that arise in natural speech;hence degradation in the performance was observed. In the proposed approach, the challenge that arises due to the variability is addressed in two stages. In the first stage, the RASTA-PLP spectrogram was used as an acoustic feature representation that reduces the variabilities among similar spoken contents. In the second stage, the novel Diagonal pattern Search method unconstrainedly computes the pattern resemblance between the identical spoken terms at the segmental level. The proposed approach was evaluated using the IITKGP-SDUC speech corpus and inferred that a 10.11% improvement in the accuracy was achieved compared to other state-of-the-art systems in the spoken term discovery task.

关键词： spoken term discovery Diagonal pattern Search pattern matching diagonal similarity

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Optimized Detection of Continuous Gravitational-Wave Signals using Convolutional Neural Network 3

Optimized Detection of Continuous Gravitational-Wave Signals...

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3rd international conference on Artificial intelligence and Signal Processing, AISP 2023

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Niranjani, V. Parvathy, K. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Dr NGP Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350320749

Identifying gravitational waves produced by binary black hole mergers has sparked an unheard-of revolution in physics and astronomy. However, due to the low magnitudes of gravitational wave signals and the inevitability of detector noise, proper processing of gravitational wave data is in high demand. In this research, we propose a deep learning technique termed convolution neural network based on a set of time-series data from '***/datasets/andradaolteanu/g2net-gravitational-wave-dataset' that contains simulated gravitational waves with noise. This work uses a convolution neural network and Fourier transformation-based gravitational wave signal recognition model (CNN). According to experimental findings, the frequency-domain signal graph describes the gravitational wave signal's features more accurately than the time-domain signal. The signal recognition accuracy has been improved using CNN's convolutional processing. © 2023 IEEE.

关键词： Gravity waves

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Sensorimotor Coordinated Multi-UAV Coverage Path Planning 33

Sensorimotor Coordinated Multi-UAV Coverage Path Planning

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33rd IEEE international conference on Robot and Human Interactive Communication (IEEE RO-MAN) - Embracing Human-Centered HRI

作者： Liu, Siyuan Fang, Hongyu Deng, Ziyang Luo, Dingsheng Peking Univ Sch Intelligence Sci & Technol Natl Key Lab Gen Artificial Intelligence Key Lab Machine PerceptMoE Beijing 100871 Peoples R China

ISBN: (纸本)9798350375039;9798350375022

We propose a novel on-line multi-UAV coverage path planning method for 3D reconstruction. UAVs play homogeneous roles in most existing multi-UAV path planning methods, where their flexibility is not fully exploited. The fact that UAVs can explore with different missions and strategies at different heights simultaneously is not well considered. Therefore, we give UAVs different identities by presenting a global-local pattern, where global UAV locates the interesting regions while local UAV explores these regions in succession. Leveraging the mechanism of sensorimotor coordination, our strategy adjusts the paths of local UAV on-line according to the input from global UAV in order to gather more information and save time. In addition, our method is compatible with real-time SLAM systems. Simulation shows that our strategy takes less time than other strategies based on area-division, and indicates that it gathers more information for better reconstruction.

关键词： Motion planning

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