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22nd international conference on Artificial Intelligence and soft computing, ICAISC 2023

22nd International Conference on Artificial Intelligence and...

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22nd international conference on Artificial Intelligence and soft computing, ICAISC 2023

ISBN: (纸本)9783031425042

The proceedings contain 48 papers. The special focus in this conference is on international conference on Artificial Intelligence and soft computing. The topics include: Transfer of Knowledge Among Instruments in Automatic Music Transcription;the Geometry of Decision Borders Between Affine Space Prototypes for Nearest Prototype Classifiers;an Interpretable Two-Layered Neural Network Structure–Based on Component-Wise Reasoning;Viscosity Estimation of Water-PVP Solutions from Droplets Using Artificial Neural Networks and Image Processing;pruning Convolutional Filters via Reinforcement Learning with Entropy Minimization;unsupervised Representation Learning: Target Regularization for Cross-Domain Sentiment Classification;decentralized Federated Learning Loop with Constrained Trust Mechanism;Learning Activation Functions for Adversarial Attack Resilience in CNNs;Federated Learning for Human Activity recognition on the MHealth Dataset;on Speeding up the Levenberg-Marquardt Learning Algorithm;image Classification Through Graph Neural Networks and Random Walks;bus Route Classification for Rural Areas Using Graph Convolutional Networks;expansion Rate Parametrization and K-Fold Based Inference with U-Net Neural Networks for Multiclass Medical Image Segmentation;transfer Learning from ImageNet to the Domain of Pigmented Nevi;towards Detecting Freezing of Gait Events Using Wearable Sensors and Genetic Programming;CNN-LSTM Optimized by Genetic Algorithm in Time Series Forecasting: An Automatic Method to Use Deep Learning;authorship Attribution of Literary Texts Using Named Entity Masking and MaxLogit-Based Sequence Classification for Varying Text Lengths;Gates Are Not What You Need in RNNs;generating Image Captions in Polish Using Transformer Architecture;An Hybrid NSGA-II Algorithm for the Bi-objective Mobile Mammography Unit Routing Problem;reinforcement Learning with Brain-Inspired Modulation Improves Adaptation to Environmental Changes;controlled Refresh of the Population in

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Cloud Based Smart Medical Recommendation System Using pattern recognition 1

Cloud Based Smart Medical Recommendation System Using Patter...

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1st IEEE international conference on Networking and Communications, ICNWC 2023

作者： Sujatha, E. Shanthasheela, S. Gopirajan, P.V. Department of Computer Science and Engineering Thandalam Chennai602105 India Velammal Institute of Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai603203 India

ISBN: (纸本)9798350336009

To build smart medical recommendation system the data needs to be collected from laboratories. The system is intended to provide services as control and manage data samples, industry standards, real test results, reports, human resources in the laboratories, machines, tiny instruments, and automation of the workflow. Combining the data from the laboratories and medical institutions enable ease of communication without time delay. The challenging task is that identifying appropriate hardware and software components which provides compatibility in collecting data and their processing steps, repository storage, secured distribution, and the policies used for information representation in the results. In order to automate the entire process of medical recommendation system the metric parameters are used for determining the efficiency and productivity of the lab to enhance the quality of control systems. The vulnerability lies in maintaining traditional way of records in paper and error in the data. The purpose of this project is to develop the process of daily tasks will be computed through the database using pattern recognition techniques. © 2023 IEEE.

关键词： Recommender systems

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22nd international conference on Artificial Intelligence and soft computing, ICAISC 2023

22nd International Conference on Artificial Intelligence and...

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22nd international conference on Artificial Intelligence and soft computing, ICAISC 2023

ISBN: (纸本)9783031425073

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Paper Cuttings pattern Feature Extraction Based on Machine Vision 16

Paper Cuttings Pattern Feature Extraction Based on Machine V...

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16th th international conference on Intelligent computing Technology and Automation, ICICTA 2023

作者： Chen, Jieru Wang, Kunlun Daud, Wan Samiati Andriana W. Mohamad Faculty of Creative Arts Universiti Teknologi Mara Shah Alam Kuala Lumpur40450 Malaysia

ISBN: (纸本)9781643684864

According to the characteristics of exaggeration and deformation of paper cuttings patterns, this paper adopts machine vision to classify and recognize paper cuttings patterns and extract features. Based on the analysis of the characteristics acquired above, the nearest neighbor classifier is used for pattern recognition. Then, the normalized invariant moments are used to train the BP neural network, and a new neural network structure is obtained by combining hollow convolution and feature fusion modules to train the model to learn features. During the process of feature extraction, the improved SIFT algorithm is applied to improve the efficiency of the algorithm through dimensionality reduction, achieving the final feature matching. The experimental test uses a large number of paper cuttings pattern examples, and adopt Libsvm to process actual features. The results show that the improved algorithm can effectively remove noise interference in paper cuttings pattern matching, which greatly improves the accuracy, and the time complexity is better than traditional SIFT algorithm. © 2024 The Authors.

关键词： Feature extraction

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A Novel Efficient Approach for Feature Selection for Enhanced Performance in Breast Cancer Prediction 4th

A Novel Efficient Approach for Feature Selection for Enhance...

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4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Bansal, Arin Lohan, Vishal Kumar Khanna, Munish Agnihotri, Shantanu UPES Dehra Dun Uttarakhand India Galgotias Univ Dankaur India

ISBN: (纸本)9783031837920;9783031837937

Modern research relies on computer-aided diagnostic (CAD) tools for efficient utilisation of time and resources. These technologies help medical practitioners make decisions by swiftly identifying anomalies and providing reliable information. The effective selection of features (characteristics) is crucial for these systems to appropriately categorise high-dimensional biological data. These traits may help diagnose linked medical issues. Biomedical data has many unnecessary or redundant information, making pattern identification difficult. Therefore, a feature selection (FS) technique should be implemented and used to remove these characteristics from the training data. Eliminating irrelevant or uninformative characteristics is the main objective of FS techniques to enhance classification accuracy along with reduction of training and testing time of machine learning (ML) classifiers. In ML-driven CAD systems, the FS phase is crucial to success. Adding efficient features during training may boost ML model performance. This empirical research proposes an FS-based biological data categorisation approach based on integration of Water Wave Optimization (WWO) soft-computing algorithm with efficient ML classifiers. Using the publicly accessible Wisconsin Diagnostic Breast Cancer (WDBC) standard dataset, this study classifies benign and malignant tumours. The 70:30 train-test split approach is implemented and the suggested strategy's competency is assessed using accuracy, sensitivity (Recall), specificity, precision, F1-Score, Kappa Score, Matthews Correlation Coefficient (MCC), and ROC AUC Score. Best accuracy calculated using the proposed method is 97.96%. The suggested clinical decision support system has a good classification performance, making it a useful secondary opinion tool and lowering the workload of experienced medical physicians.

关键词： Feature Selection soft-computing algorithms Medical data Machine Learning Optimization

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CNN-RNN Deep Learning Networks for pattern recognition Problems 2

CNN-RNN Deep Learning Networks for Pattern Recognition Probl...

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2nd international conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Imran, Inas Ismael University of Baghdad College of Education for Women Dept. of Computer Iraq

ISBN: (纸本)9798350335644

Neural networks have remarkably solved many problems. A giant network with a high processing requirement and long training time is being investigated owing to having a massive amount of data. Therefore, parallel processing is essential for practical applications of network training. This study aims to develop the best neural network to accelerate a face recognition system. The standards used to compare tasks were speed and accuracy used to develop an algorithm providing the best performance in a parallel system. Moreover, this study aimed to combine neural networks and parallel programming to develop a swift person recognition system in a camera stream. Video frames were saved, and the images were fed to a neural network built in a parallel structure. This study provides readers with a better understanding of the new and current trends of artificial neural networks (ANN) models, successfully addressing PR issues to permit research emphasis and themes. The system used libraries, such as MPI, graphical computing compute unified device architecture (CUDA) libraries, and the softmax function to provide the required performance with high accuracy and good delay results. The results of this study showed that the parallel system using a conventional neural network (CNN) was 30% better than the serial system for face recognition and that the CNN networks provided the best accuracy among all the other systems. © 2023 IEEE.

关键词： Recurrent neural networks

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An Automatic Key-points Detection and Style Transfer based Method of Articulatory Animation Generation 23

An Automatic Key-points Detection and Style Transfer based M...

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9th international conference on computing and Artificial Intelligence, ICCAI 2023

作者： Liu, Wei Ling, Xufeng Liang, Jingxin Wang, Xiaobo Yang, Jie Shanghai Normal University Tianhua College AI School No. 1661 North Sheng Xin Road China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University 800 Dongchuan Road 200240 China

ISBN: (纸本)9781450399029

The method of generating articulatory animation based on automatic key point detection and style transfer. This paper proposes a system of key-points detection and registration based on image feature space, and uses the GAN for motion transfer, which is used to automatically generate oral and tongue articulatory animation of English. First, by obtaining the standard articulation image of the international Phonetic Alphabet from the public dataset, we train a deep-learning motion model according to the characteristics of the tongue, upper jaw, lower jaw, small tongue, soft palate and other motion features, according to the characteristics of articulatory organ, and automatically detect the key-points in the image feature space. According to the existing MRI articulatory videos, using different styles of articulatory images, a generative model is used to generate end-to-end animation of different styles automatically. For different combinations of consonants and vowels, generate animation of different syllables, and according to different categories of articulatory, such as stress and light tone, adjust the duration of articulatory to form different animation of stress and light tone of the same syllable. And form the animation of words through syllables, and then form the animation of each sentence. Experiments show that this method can simulate realistic articulatory animation according to input phonetic symbols, and has a very positive effect on English articulatory correction. © 2023 Copyright held by the owner/author(s).

关键词： Animation

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Artificial Immune-Based Semi-supervised Learning for Classification 15

Artificial Immune-Based Semi-supervised Learning for Classif...

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15th international conference on Cyber-Enabled Distributed computing and Knowledge Discovery, CyberC 2023

作者： Wang, Yi Hubei University of Economics School of Information Engineering Wuhan430205 China Hubei University of Economics Hubei Internet Finance Information Engineering Technology Research Center Wuhan430205 China

ISBN: (纸本)9798350308693

Recently, artificial immune algorithms have attracted great attention of researchers and been widely used in function optimization, pattern recognition and classification. Traditional artificial immune algorithms for classification are applied to supervised learning problems, which require completely labeled data for training models. However, in many real-world scenarios, it is difficult to obtain all labeled samples. To solve this problem, an efficient semi-supervised artificial immune algorithm for classification tasks is proposed. It employs a clonal selection algorithm to generate memory cells used for classification, which is achieved via selection, cloning, and mutation procedures. Moreover, it utilizes the ensemble learning technique to extend the co-training paradigm and improves classification performance by adding the most confident unlabeled samples into the labeled set. In addition, the theory of learning from noisy examples is adopted to decide whether there are enough newly labeled samples that are used to reduce the negative effects caused by noises. Experimental results show that the proposed method achieves better or comparable performance than well-known semi-supervised and supervised methods on four datasets. © 2023 IEEE.

关键词： pattern recognition

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To Improve the Identification of Images Using LBPH in Machine Learning 1

To Improve the Identification of Images Using LBPH in Machin...

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1st international conference on Intelligent computing and Research Trends, ICRT 2023

作者： Kumar Shukla, Ratnesh Kumar Tripathi, Praveen Kumar, Aman Prakash, Ved Kumar Singh, Dinesh Shambhunath Institute of Engineering & Technology U.P. Prayagraj India Jamia Millia Islamia Faculty of Engineering Dept. of Computer Engineering New Delhi India Kamla Nehru Institute of Engineering & Technology U.P. Sultanpur India

ISBN: (纸本)9798350336771

One of the most significant and extensively studied local texture descriptors is called Local Binary patterns (LBP). In the studies, a comprehensive analysis of the most renowned LBP related procedures is required in order to make sense of them all. Descriptors have been created using multistage convolutional neural networks (CNN) in deep learning. With low-resolution pixels, this work presents an enhanced real-time facial identification system that integrates posture, mood and resolution variations. We produced training and classification datasets. Face detection and recognition is performed using the different algorithm, which gets the face picture. With an updated database, the training and recognition process will resume automatically. The distance between a person and camera can be used for this facial recognition technology to help security forces in system. A surveillance system for airports, bus terminals and other public locations can also be installed to reduce the risk of criminal activity. To lessen the possibility of criminal behaviour, a surveillance system for airports, bus terminals and other public venues can be implemented. © 2023 IEEE.

关键词： Local binary pattern

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EXPLOITING ANNOTATORS' TYPED DESCRIPTION OF EMOTION PERCEPTION TO MAXIMIZE UTILIZATION OF RATINGS FOR SPEECH EMOTION recognition 47

EXPLOITING ANNOTATORS' TYPED DESCRIPTION OF EMOTION PERCEPTI...

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47th IEEE international conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Chou, Huang-Cheng Lin, Wei-Cheng Lee, Chi-Chun Busso, Carlos Univ Texas Dallas Dept Elect & Comp Engn Multimodal Signal Proc MSP Lab Richardson TX 75080 USA Natl Tsing Hua Univ Dept Elect Engn Hsinchu Taiwan

ISBN: (纸本)9781665405409

The decision of ground truth for speech emotion recognition (SER) is still a critical issue in affective computing tasks. Previous studies on emotion recognition often rely on consensus labels after aggregating the classes selected by multiple annotators. It is common for a perceptual evaluation conducted to annotate emotional corpora to include the class "other," allowing the annotators the opportunity to describe the emotion with their own words. This practice provides valuable emotional information, which, however, is ignored in most emotion recognition studies. This paper utilizes easy-accessed natural language processing toolkits to mine the sentiment of these typed descriptions, enriching and maximizing the information obtained from the annotators. The polarity information is combined with primary and secondary annotations provided by individual evaluators under a label distribution framework, creating a complete representation of the emotional content of the spoken sentences. Finally, we train multitask learning SER models with existing learning methods (soft-label, multi-label, and distribution-label) to show the performance of the novel ground truth in the MSP-Podcast corpus.

关键词： Emotion recognition Distribution-label learning soft-label learning Multi-label learning

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