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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

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Role of Fashion in Artificial Intelliegence in Digital Age 3

Role of Fashion in Artificial Intelliegence in Digital Age

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

作者： Rani, Suman Memoria, Minakshi Kumar, Rajiv Chaoudhary, Tanupriya CSE Uttranchal University Dehradun India Uttranchal University CSE Deptt. Dehradun India Symbiosis Institute of Technology Symbiosis International University CSE Deptt. Pune India

ISBN: (纸本)9798350303360

The science of appearance is known as fashion, and it pushes people to desire to look rather than be who they are. One of the many methods for integrating ML in systems for forecasting in the fashion industry is through the use of AI to predict fashion trends. The world of fashion has permeated every aspect of our daily life. The task of trend forecasting requires accurate prediction based on color, pattern, and style. This study offered a method for predicting pattern and attire from photographs posted on Instagram and other social media platforms. This work demonstrates how AI approaches are being used to forecast fashion. AI approaches and conventional techniques are compared. To foresee the trend, experts are now using machine learning and prediction algorithms. This work demonstrates many ways that AI is used in the fashion industry. Implement these applications for AI fashion in the future. AI in fashion employed a variety of methods how to categories fashion photos. © 2023 IEEE.

关键词： Forecasting

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Deep 3D-CNN using Resonance Imaging for Diagnosing Alzheimer's 3

Deep 3D-CNN using Resonance Imaging for Diagnosing Alzheimer...

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

作者： Ayub, Nadish Shah, Syed Zubair Ahmad Assad, Assif Din, Nusrat Mohi Ud Islamic University of Science and Technology Department of Computer Science and Engineering Awantipora India

ISBN: (纸本)9798350320749

Alzheimer's Disease (AD), the most prevalent type of dementia, is an incurable neurological disorder that leads to progressive mental decline. The majority of an AD diagnosis in practise is based on the patient's clinical history and neu- ropsychological information, such as magnetic resource imaging, despite the fact that an exact diagnosis of AD is challenging (MRI). Deep learning's use in recognising AD is the subject of growing research in recent few years. It enables for automatic AD identification using 3D brain MRI images. For AD recognition, the network makes use of a three-dimensional convolutional neural network (3DCNN). It is distinctive in that AD recognition accurately reflects the three-dimensional structure of the brain as a whole. Due to the fact that MRIs are 3D objects and utilise 3D Convolutional Neural Networks (CNNs) since using 3D CNNs the spatial integrity is maintained as compared to other methods that only employ a section of the MRI, such as ROI-based CNNs which works on some specific ROI, 2D slices that uses 2D slices, and 3D Patch-level that use 3D pathes. Four successive processing layers, two fully connected layers, and a classification layer is employed to design the CNN in this study. Each of the three groups in the structure is composed of three layers: a convolutional layer, a pooling layer, and a normalisation layer. The AD Neuroimaging Initiative's MRI data is used to train and evaluate the algorithm. The data used in this work is comprised of 1656 MRI scans of 382 patients. According to the results of the trial, the suggested algorithm had a 99% training accuracy of AD recognition with a validation accuracy of 73%. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Linguistic Test of Chinese and American National Spirit based on Speech recognition and Artificial intelligence 3

Linguistic Test of Chinese and American National Spirit base...

引用

3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Haiyan, Wei Shandong University of Finance and Economics Shandong250014 China

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

In this paper, the subband analysis method is introduced, and a new multi-feature subband hybrid model is proposed. This paper first proposes a method to use the user-confirmed text in the process of human-computer interaction to help improve the accuracy of self-adaptive data machine labeling;the paper selects the typical American 'Social Learning of Western national spiritual education and my country's 'People's Education Edition. The content of national spiritual education in grades 3-6 in Morality and Society was tested in linguistics, and some useful enlightenments for the current national spiritual education in our country were put forward. Then an acoustic confidence method based on the confirmation model is proposed. © 2022 IEEE.

关键词： Linguistics

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EEG Emotion recognition Based on a Novel 4D Feature Fusion and 3D Convolutional Networks 3

EEG Emotion Recognition Based on a Novel 4D Feature Fusion a...

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3rd international conference on Big Data, Artificial intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Gong, Xinhao Gu, Jin Li, Tianrui Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

Emotion recognition based on electroencephalogram (EEG) signals has become an important research field in recent years due to its application in human-computer interaction. Feature fusion is the solution strategy in many pattern recognition works. However, there are few related works on EEG feature fusion. This research field has much room for development. This paper proposes a 4-dimensional representation method of multiple features across channels and frequency bands and 3D convolution neural network (3D-CNN) for EEG emotion recognition. Specifically, we perform two-dimensional electrode position relationship mapping on the extracted one-dimensional sequences of different features. The obtained 2D features are fused to the three-dimensional array to achieve complementarity between features. The 3D features of different frequency bands are then formed into the four-dimensional feature to capture cross-band information. We adopt the subject-independent approach to conduct experiments on the SEED dataset. Compared with the state-of-the-art emotion classification method, the classification performance is improved with an average accuracy of 98.64%. The 4-dimensional representation method combining different features proposed in this paper is proved to be significant in feature extraction and fusion. © 2022 IEEE.

关键词： Emotion recognition

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Designing a Deep Learning Model for recommendation of Drugs on Sentiment Analysis 3

Designing a Deep Learning Model for recommendation of Drugs ...

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3rd IEEE Mysore Sub Section international conference, MysuruCon 2023

作者： Aruna, T.M. Nethravathi, T.L. Muniraju, M. Nitte Meenakshi Institute of Technology Dept of AI-ML Bengaluru India SJCIT Chickballapur Department of Artificial Intelligence and Machine Learning 562101 India

ISBN: (纸本)9798350340358

There has been a rise in creative automation work in recent years, and various applications have been found for machine learning. This essay's focus is on introducing a pharmaceutical recommendation system with the potential to greatly reduce the burden on experts. With the help of a deep learning algorithm, we create a drug recommendation system that utilises patient reviews to predict sentiment through the use of a number of vectorization techniques. Synthetic minority over-sampling method (SMOTE) is used to rectify the effects of unbalanced data on classification accuracy. The evaluation is sorted using a Recurrent Neural Network (RNN), with the weights chosen using an enhanced version of the firefly optimisation algorithm (IFFOA). Predicted feelings were evaluated using a battery of metrics counting precision, recall, f1score, accuracy, and specificity score. The classifier RNN-FCR has a higher accuracy rate (96%) than any other models as a direct result of TF-IDF vectorization. © 2023 IEEE.

关键词： Recurrent neural networks

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Phishing Site Detection Using Logistic Regression and Fine Tuning It Using Various Optimization Parameters 3

Phishing Site Detection Using Logistic Regression and Fine T...

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

作者： Singla, Muskan Gill, Kanwarpartap Singh Chauhan, Rahul Pokhariya, Hemant Singh Banerjee, Deepak Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun24800 India

ISBN: (纸本)9798350319125

Cybercriminals frequently prefer to deceive people into compromising their own security than using exploits or complex assaults to get access to networks. No matter what technological safeguards are in place, this strategy can be very effective. To conceal their actions and get around conventional security measures, economic resources through phishing attempts might leverage encrypted connections, such as HTTPS. This kind of intrusion has been detected and is being prevented by machine learning technologies. Using two different algorithms (Logistic Regression and Multinomial Naive Bayes) to analyse the URLs and various datasets to compare the results with previous research, we suggested a machine learning-based phishing detection system in this paper. The experimental findings show that the suggested models function superbly and have a high success rate in a social environment. © 2023 IEEE.

关键词： Artificial intelligence Logistic Regression machine learning Multinomial Naïve Bayes Phishing Website classification

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Decomposed gate attention graph convolutional networks for skeleton-based action recognition 3

Decomposed gate attention graph convolutional networks for s...

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3rd international conference on Electronic Information Engineering and Data Processing, EIEDP 2024

作者： Sun, Dengdi Zhou, Yuanyin Luo, Bin Ding, Zhuanlian School of Artificial Intelligence Anhui University Hefei230601 China Jianghuai Advance Technology Center Hefei230000 China School of Computer Science and Technology Anhui University Hefei230601 China School of Internet Anhui University Hefei230039 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China

ISBN: (纸本)9781510680531

Graph convolutional networks (GCNs) have garnered significant interest in the field of skeleton-based action recognition in the past few years. In this study, we introduce a novel approach called the Decomposed Gate Excitation Graph Convolutional Network (DGA-GCN) that can extract the features of joint and bones in action sequences from different dimensions for skeleton action recognition. The proposed DGA-GCN comprises an Initial Feature Extraction (IFE) module, a Multi-granularity Timing Analysis (MTA) module, and a Gate Spatial Temporal Attention (GSTA) module. The IFE module is capable of extracting more comprehensive features from the joints, bones, and temporal aspects of the frame. MTC can model global and short-term timing relationships. GSTE calculates the attention rate of bones and joints and then uses it Excite motion-sensitive channels. These three modeling can be integrated into the existing skeleton-based GCN for modeling. Extensive experiments show that HEG-GCN has higher recognition accuracy on NTU RGB+D, NTU RGB+D 120 and Northwestern-UCLA data sets, while ensuring better recognition performance. © 2024 SPIE.

关键词： Convolution

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Defect recognition of small samples in substations based on the domain adaptive YOLOv7 3

Defect recognition of small samples in substations based on ...

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3rd IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Li, Jipan Sun, Suliang Wang, Shuai Guo, Rui Du, Yuyu Wang, Yang Han, Yuankai State Grid Heze Power Supply Company Heze China State Grid Intelligence Technology Co. Ltd. Jinan China

ISBN: (纸本)9798350398410

The computer vision technology based on deep learning provides a new perspective on equipment inspection in power system. The intelligent technologies are now widely used in substations and definitely achieve positive effects, but they also face the problem of poor accuracy due to small samples. To improve the defect recognition in substations, this paper presents an improved YOLOv7 model that additionally includes the domain adaptation module, the SimAM attention module and the auxiliary detection head. The domain adaptation module strengthens the domain invariant features to improve the adaptability to unknown data. The SimAM attention module focuses on the key area and the objects. The auxiliary detection head can reduce the influence of large differences in object scale. The experiments on real-world data demonstrate that the improved model can better deal with the small samples and achieve better performance in the detection rate and the error rate. © 2023 IEEE.

关键词： Deep learning

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A Data Selection Method for Domain-specific machine Translation 3

A Data Selection Method for Domain-specific Machine Translat...

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3rd international conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2023

作者： Ye, Na Jiang, Ling Cai, Dongfeng Shenyang Aerospace University Human-Computer Intelligence Research Center Shenyang China

ISBN: (纸本)9798350302356

Neural machine translation performs well in general domains with large-scale bilingual corpora, but it is not very effective in specific domains with only a small amount of bilingual corpora. Data selection technology is one of the main methods to alleviate the problem of bilingual data scarcity in specific domains by selecting corpus that is close to the domain corpus from large-scale general domain corpora for data expansion. Due to the large scale of general data, traditional dimension reduction methods are inefficient when dealing with massive data. This paper proposes a two-stage data selection strategy, which improves the efficiency of data selection and the data quality at the same time. First, the Inverted File System Product Quantization (IVFPQ) method is used to preliminarily select data, which improves the computational efficiency and the quality of data selection. In order to obtain data with higher quality, on this basis, this paper trains a domain classifier to fully consider the domain characteristics, and further selects higher quality data. Experimental results show that compared with the baseline method, a better domain-specific machine translation model can be trained using the expanded data obtained by this method. © 2023 IEEE.

关键词： Computational efficiency

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E-Commerce Business Model Analysis in Urban Area Using machine Learning 3

E-Commerce Business Model Analysis in Urban Area Using Machi...

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

作者： Kumar, Atul Saxena, Jini Manish Saha, Sonali Aljapurkar, Aditi Ingawale, Satyajit Bagul, Shweta Prakash Brar, Vinaydeep Gupta, Deepak Dr. D. Y. Patil B-School Pune India Riim Pune India Sng Imr Pune India Itm Gwalior India

ISBN: (纸本)9798350319125

In today's highly competitive B2C e-commerce landscape, companies are in a relentless battle to both retain their existing customers and attract those of competitors. The escalating cost of customer acquisition due to increased market saturation and aggressive marketing tactics necessitates a focus on customer retention. The paper outlines a methodology for predicting customer attrition by leveraging machine learning techniques, specifically Support Vector machines (SVM). Coupled with a hybrid recommendation approach tailored for retention strategies, this predictive framework helps e-commerce businesses offer targeted incentives to potential churn customers. Our empirical analysis confirms the effectiveness of the integrated model across various performance indicators such as coverage ratio, hit ratio, and precision rate. The paper further employs the RFM (Recency, Frequency, Monetary) model to segment at-risk customers, aiding in the formulation of more personalized churn prevention tactics. © 2023 IEEE.

关键词： Artificial intelligence Ecommerce machine learning Urban Areas

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