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Design of Intelligent Supermarket Service Robot Based on Raspberry Pi 3

Design of Intelligent Supermarket Service Robot Based on Ras...

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3rd IEEE international conference on Information Technology, Big Data and Artificial intelligence, ICIBA 2023

作者： Han, Xue Wang, Wei Gao, Fei Dalian Neusoft University of Information School of Intelligence & Electronic Engineering Dalian China

ISBN: (纸本)9781665490788

In this paper, an intelligent supermarket service robot with the functions of speech recognition, intelligent following, voice interaction and automatic payment is realized. It is a 1:20 intelligent robot model, in which Raspberry Pi 4B is the main controller responsible for complex digital image processing and STM32F103RCT6 microcontroller is the auxiliary controller responsible for robot movement, obstacle avoidance and communication. The system uses camera to capture video, and realizes object tracking and commodity QR code recognition through OpenCV image recognition technology. Baidu AI is used for speech recognition and speech synthesis to achieve voice interaction with customers. The robot can also be controlled by APP through Bluetooth communication, and obstacle avoidance can be realized through ultrasonic module. The robot recognizes the QR code of goods, calculates the total price of goods, and generates the payment QR code for customers to realize automatic payment. © 2023 IEEE.

关键词： Speech recognition

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Development of a Robust Dataset for Printed Tamil Character recognition 3rd

Development of a Robust Dataset for Printed Tamil Character ...

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3rd international conference on machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Arun, M. Arivazhagan, S. Priyadharshini, R. Ahila Centre for Image Processing and Pattern Recognition Mepco Schlenk Engineering College Tamil Nadu Sivakasi India

ISBN: (纸本)9789819939312

Despite the fact that many character datasets for several languages are publicly available, there are only a very few standardized datasets for Tamil characters. This article presents a subset of the Mepco Tamil Character database, a Tamil font isolated character dataset representing the printed characters. This dataset includes 124 glyphs representing the 247 characters of Tamil language. This dataset is tested for its robustness using multiple experimentations using SVM classifier and is compared against UJTDchar, another dataset available for Tamil language. Also we have verified the robustness of DIGI-Net, CNN architecture for this Tamil character recognition problem using the UJTDchar dataset and the Mepco Tamil Character dataset. We report an accuracy of 90.59% and 97.66% while using SVM and DIGI-Net CNN on our newly created dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Character recognition

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Detect Alzheimer's Disease from Neuroimaging Using Artificial intelligence Techniques 3

Detect Alzheimer's Disease from Neuroimaging Using Artificia...

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3rd international conference on Computing and Information Technology, ICCIT 2023

作者： Alarjani, Maitha King Faisal University College of Computer Science and Information Technology Department of Computer Sciences Al Hofuf36362 Saudi Arabia

ISBN: (纸本)9798350321487

Alzheimer's disease (AD) is the continuous deterioration of cognitive, behavioral, and social skills that affects a person's ability to function independently. It is the most common cause of memory impairment and dementia. Early detection is considered one of the most important factors that helps make plans and prevent the development of symptoms of this disease. The purpose of this work is to compare machine learning (ML) and deep learning (DL) models to detect early warning signs of AD. In addition, a neural network is utilized to extract important features and distinguish between healthy brain imaging and that of those affected by mild cognitive impairment (MCI). A 3D convolutional neural network (3D-CNN) architecture is utilized to compute the features of neuroimaging data. After that, the feature vector is fed to the support vector machines (SVM) for final recognition. The system is assessed using a publicly available ADNI dataset for functional magnetic resonance imaging (fMRI). In the case of a 3D convolutional neural network, a recognition rate of 98.92% and 98.3% is attained on training and testing, respectively. In the case of the SVM model, an accuracy of 97.14% and 97.5% is achieved on training and testing, respectively. © 2023 IEEE.

关键词： Support vector machines

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Using OLAP Cubes as Dataset for Neural Networks: Integrating Business intelligence and Artificial intelligence 3

Using OLAP Cubes as Dataset for Neural Networks: Integrating...

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

作者： Abrarov, Rinat D. Khudaybergenov, Timur A. Urgench branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Department of Software Engineering Urgench Uzbekistan Urgench branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Department of Compyuter Engineering Urgench Uzbekistan

ISBN: (纸本)9798331516321

The integration of OLAP cubes and neural networks represents a significant advancement in business intelligence. OLAP cubes, with their multidimensional data structures, enable efficient analysis across multiple dimensions, helping businesses extract insights from their data. Neural networks excel at learning from large datasets and making accurate predictions. This paper explores how OLAP cubes can be used as datasets for training neural networks. The process includes extracting and preprocessing data to make it suitable for neural network training. The practical applications of this integration in business include improved forecasting, optimization, and strategic decision-making. By combining the analytical capabilities of OLAP cubes with the predictive strengths of neural networks, businesses can achieve more precise and actionable insights. Furthermore, the paper discusses future development and research possibilities in this area, emphasizing the potential for creating better business intelligence solutions. This integration opens new possibilities for enhancing data analysis, making it a promising area for future exploration. © 2024 IEEE.

关键词： Artificial intelligence Business analytics Data integration Data preprocessing Forecasting machine learning Multivariate analysis Neural networks OLAP-cubes Optimization

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3rd international conference on Artificial intelligence, Robotics, and Communication, ICAIRC 2023

3rd International Conference on Artificial Intelligence, Rob...

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3rd international conference on Artificial intelligence, Robotics, and Communication, ICAIRC 2023

ISBN: (纸本)9789819721993

The proceedings contain 33 papers. The special focus in this conference is on Artificial intelligence, Robotics, and Communication. The topics include: Design and Research of UAV Fire Emergency and Intelligent Inspection System in the Valve Hall of Converter Station;Development Overview and Application Prospects of Advanced UUVs;bisporus Mushroom 3D Reconstruction for Digital Twin of Smart Factory;Research on Automatic Music Generation Based on LSTM;specific Emitter Identification Method Based on Aggregation Model with Metric Learning in Complex Field;fake Comment Detection Based on Generative Adversarial Networks;Shipping Risk Assessment Ship Inspection Method Based on Improved YOLOv5;spatiotemporal Prediction of Harmful Algal Blooms Based on Spatiotemporal Attention and Transformer;large-Scale Information Dissemination Algorithm Based on Random Mapping;Smoke Detection in Complex Scenery Using Improved Faster-RCNN;CoT-Enhanced CNN-ViT Fusion with Adaptive Token Learning for Precise Facial Expression recognition;evaluation Method of Cognitive Level Based on machine Learning and Wearable Device;ship Image Segmentation Based on Multi-dimensional Information and Feature Fusion;real-Time 6-DoF Object Pose Estimation Network Based on 2D-3D Coordinate Correspondence;traffic Light Detection Based on Deep Learning;robot Navigation Based on Reinforcement Learning: An Overview;active Control Technology of Supply Chain Intelligent Work Robot Based on Speech Enhancement Technology;research on Security Technology of Power Dispatching Data Network Based on Deep Learning;An Improved RRT Path Planning Method Incorporating Deep Reinforcement Learning for Space Multi-arm Robot;a Classification Denoising Algorithm Based on 3D Point Cloud Curvature;Comparative Analysis of the Effect of KPCA in PSO-GRU Combination Model.

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Suggesting Playlist and Playing Preferred Music Based on Emotion from Facial Expression 3

Suggesting Playlist and Playing Preferred Music Based on Emo...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Akter Asha, Mst. Lima Ahmed Rafi, Muntasir Ahamed, Md. Sazzadur Imran, Md. Hafizul Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350381931

Facial expression is a powerful indicator of human emotion and plays a crucial role in interpersonal communication. The mood of a person or his intention can be analyzed by detecting his expression. For detecting the expression, artificial intelligence and machine learning are very useful for automated detection. By analyzing facial features and expressions, a music recommendation system can predict the user's mood and recommend songs that align with their emotional state. Many researchers have worked on *** proposed system works on 8 moods of humans which are angry, contempt, disgust, fear, happy, neutral, sad, and surprise. This study utilizes a machine learning concept to achieve this goal. The methodology involves data collection, model training using a combination of Convolutional Neural Network (CNN) and VGG16 CNN, and recommending songs from the Spotify music dataset. The results show that both CNN and VGG16 CNN performed well in detecting facial expressions, with CNN achieving 66% accuracy and VGG16 achieving 92% accuracy. This system effectively recommends songs from the Spotify dataset based on the user's mood. © 2024 IEEE.

关键词： machine learning

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Transformer Fault Diagnosis Based on the Improved Sparrow Search Algorithm and Random Forest Feature Selection 3

Transformer Fault Diagnosis Based on the Improved Sparrow Se...

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3rd international conference on Energy and Electrical Power Systems (ICEEPS)

作者： Chen, Xi Ji, Ning Qin, Xue Zhang, Mengmeng Chen, Xueming Jiang, Chenlu Tao, Kai State Grid Jiangsu Elect Power Co Ltd Skill Training Ctr Suzhou Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350375145;9798350375138

To improve the problems such as low relevance, redundant features and the low accuracy of traditional fault diagnosis methods, a transformer fault diagnosis method based on the random forest (RF) feature optimization and improved sparrow search algorithm (ISSA)- support vector machine (SVM) was proposed. First, the transformer fault features were optimized through the RF-based average accuracy reduction method. Then, the traditional sparrow search algorithm (SSA) was improved using the Tent chaos mapping and Levy flight strategy, so that the support vector machine (SVM) parameters could be optimized. The proposed method was compared with the SVM, particle swarm algorithm-support vector machine (PSO-SVM), and sparrow search algorithm-support vector machine (SSA-SVM). The results show that the fault diagnosis rate of ISSA-SVM is 91.67%, which is 11.11%, 8.34% and 2.78% higher than that of SVM, PSO-SVM and SSA-SVM respectively. This result verified the effectiveness of the proposed method in the fault diagnosis of transformer.

关键词： Transformer Fault diagnosis Sparrow search algorithm Support vector machine

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A Study on the Dynamic Intelligent Monitoring of Braking Performance of Cargo Train Based on Artificial intelligence 3

A Study on the Dynamic Intelligent Monitoring of Braking Per...

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3rd international conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2023

作者： Sun, Tao He, Lijuan Wuhan Railway Vocational College of Technology Hubei Wuhan China

ISBN: (纸本)9798350303698

In recent years, artificial intelligence technology has been widely used in many fields and has become the focus of social attention. In this paper, by adopting the YOLOV3 algorithm in the artificial intelligence algorithm, Introducing CIOU regression to optimize loss function, Solve the problem of inconsistent optimization direction of mean square error (MSE) loss, and make the target box regression more stable. At the end of the article, the method proposed in this paper can achieve the identification of Cargo train brake component and achieve a high recognition rate, thereby providing a safe operation of Cargo train driving to avoid driving accidents caused by braking failure. © 2023 IEEE.

关键词： Mean square error

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An Effective machine Learning-based Segmentation and Feature Extraction Technique for Muscular-Disorder 3

An Effective Machine Learning-based Segmentation and Feature...

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

作者： Dhanyal, Somashekhar S. Nandyal, Suvarna BLDEA's V. P. Dr. P. G. Halakatti College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Karnataka Vijayapura586103 India Poojya Doddappa Appa College of Engineering Department of CSE India

ISBN: (纸本)9798350322842

Application of technology for the classification of sports and exercise, Yogasan identification is now common practice for numerous people in modern society especially when dealing with muscular disorders. Over the past three decades, research has focused on motivating and difficult problems related to the usage of computer vision for target recognition and identification. The skeleton parts of the head, neck, arms, hands, legs, and feet are designed and stored as a vector in this research to identify the human yoga asana pose. These skeleton elements are also responsible for head movements, hand postures, and standing postures. Every single interaction between the skeleton joints makes a pose, and every feature is described as a set of moveable joints. The weighted skeletonization-based image segmentation is proposed for the enhanced Yogasan recognition from the selected keyframes, the most relevant keyframes are selected using the Euclidean distance measure. The performance of the proposed method is analyzed by considering the features as well as the training percentage. The accuracy, precision, and sensitivity based on the features of the multi-SVM classifier are 89.020 %, 89.720 %, and 90.855 %, and the training percentage using the multi-SVM classifier is 94.268 %, 93.297 %, and 97.375 %. © 2023 IEEE.

关键词： Support vector machines

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DenseNet-SALD: A Lightweight Diseases and Pests Spatial Attention Classification Model 3

DenseNet-SALD: A Lightweight Diseases and Pests Spatial Atte...

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3rd IEEE international conference on Software Engineering and Artificial intelligence, SEAI 2023

作者： Qin, Zhanghao Chen, Longqiu Wang, Yanan Jiang Nan University College of Artificial Intelligence and Computer Science Wuxi China Tianjin Agricultural University College of Computer and Information Engineering Tianjin China Tianjin University of Commerce College of Science Tianjin China

ISBN: (纸本)9798350337952

Leaf diseases and pests significantly impact crop yields, making it crucial to rapidly and accurately identify plant foliar diseases and implement appropriate control measures. We present a novel DenseNet-SALD-based model for identifying maize diseases and pests to improve classification performance. The proposed model was trained on a balanced dataset, incorporating an attention mechanism, Leaky ReLU activation function, and Dropout regularization to enhance its classification accuracy. The experimental results demonstrate that the proposed model outperforms conventional convolutional neural network models, such as DenseNet and AlexNet, achieving an accuracy of 99.0% and 85.03% for diseases and pests testing, respectively, meeting the primary pests recognition needs. This research provides valuable insights into the application of attention mechanisms in deep learning models for image recognition tasks, which could have potential implications for advancing the classification of corn leaf diseases and pests images. © 2023 IEEE.

关键词： Image recognition

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