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Decoding of Auditory Imagination Activity Based on machine learning Methods 4

Decoding of Auditory Imagination Activity Based on Machine L...

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4th international conference on Frontiers Technology of Information and Computer, ICFTIC 2022

作者： Ma, Wanly Tang, Xuegang Xia, Qiuqin Xu, Yiwei Gao, Weitong Gu, Jin Southwest Jiaotong University Chengdu China

ISBN: (纸本)9798350321951

the decoding of brain signals has important research value and is also full of challenges. In recent years, the fMRI technology is more and more widely used in the brain signals decoding because of its high spatial resolution and non-invasive characteristic. the existing brain region template provides a powerful tool for brain region decoding research, and the obtained functional connectivity matrix quantifies the correlation between brain regions. the development of pattern recognition technology has created a favorable technical foundation for the study of fMRI data, including a variety of mature machine learning methods such as support vector machine. In this paper, the fMRI data of 24 healthy subjects was measured when they performed the auditory imagination under different sound and scene conditions, then the functional connectivity matrices of brain regions were obtained and to be classified by several pattern recognition methods, such as optimized support vector machine, naive Bayes, and logistic regression. At last, the classification methods were compared according to the classification accuracy. Compared with related works, Methods of data acquisition and the accuracy of the results are better than previous job. the method can help psychologists and neuroscientists perform the brain signal decoding with high efficiency and quality. At the same time, the research is helpful to reveal the neural mechanism of auditory imagination and auditory perception, deepen our understanding of human brain auditory information processing, help the computer to simulate human hearing, and use the correlation of brain signals to help the deaf and mute people to carry out related activities, which has good social benefits. © 2022 IEEE.

关键词： Support vector machines

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4th international conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th international conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249709

the proceedings contain 113 papers. the special focus in this conference is on Applied Technologies. the topics include: Automatic Evaluation of Physiotherapy Activities Using Deep learning Techniques;multivariable Control Approach Applied to the Embryo Incubation Process of Gallus Gallus Domesticus;discovering Visual Deficiencies in Pilots Candidates Using data Mining;an Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital Image Processing and Neural Networks;artificial Intelligence-Based Banana Ripeness Detection;U-Net vs. TransUNet: Performance Comparison in Medical Image Segmentation;access Control through Mask Detection and Estimation of People Capacity in Covered Premises;customer Segmentation in Food Retail Sector: An Approach from Customer Behavior and Product Association Rules;Bibliometric Analysis of Web of Science database STEM Fields in Engineering and Mathematics. Ecuador’s Case Study;smart Antenna Array for Optimal Electromagnetic Energy Capture;image Processing Method to Estimate Water Quality Parameter;use of the Student Engagement as a Strategy to Optimize Online Education, Applying a Supervised machine learning Model Using Facial recognition;crime data Analysis Using machine learning Models;Gene therapy as a Solution to Genetic Diseases through DNA Manipulation;Vulnerability of CAPTCHA Systems Using Bots with Computer Vision Abilities;military Leadership in the Ecuadorian Army;analysis of Key Variables for Ecuadorian Defense Industry Development;the Prioritization of External Security as a Means of Guaranteeing Multidimensional Security and Economic Growth;Comparative Study of Deep learning Algorithms in the Detection of Phishing Attacks Based on HTML and Text Obtained from Web Pages;ground Robot for Search and Rescue Management;virtual Training Module for the Production of Rubber Adhesives through the Production of Cyclopentenol.

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Bug recognition using Hybrid Fuzzy Logic algorithm and Support Vector machine classification 4

Bug Recognition using Hybrid Fuzzy Logic algorithm and Suppo...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Ananthi, K. Balakrishnan, M. Pandi, M. Senthil Madasamy, N. Dr.Mahalingam College of Engineering and Technology Artificial Intelligence &data Science Tamil Nadu Coimbatore642003 India Dr.Mahalingam College of Engineering and Technology Computer Science and Engineering Tamil Nadu Coimbatore642003 India

ISBN: (纸本)9781665474368

In data mining and pattern recognition, the Support Vector machine is an extensively used classification technique. the tuning weight methods and the distance of the metrics used in a Support Vector based classification system are important. the problem of determining the best feature weight values and measuring the distance between neighbors, which affects the Support vector machine's classification accuracy, is a key obstacle. For parameter optimization, a hybrid approach is applied in this paper. To quantify the equalities between thetraining data set and the test data set are observed and the similarity measuring approach known as the fuzzy distance metric is used. Additional work, such as creating a unique fuzzy membership function and membership degrees for each dataset, adds time to the design process for these hybrid systems. To improve the classification model's precision, this automated approach will heuristically explore the optimal bounds of fuzzy membership functions for each dataset. Additionally, looking into the optimal boundaries may decrease error rates and improve accuracy in the proposed system. the experimental results will be implemented into a real-world classification challenge sourced from the UCI machine-learning benchmark library. © 2022 IEEE.

关键词： Support vector machines

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Classification of Flower dataset using machine learning Models 4

Classification of Flower Dataset using Machine Learning Mode...

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4th international conference on Artificial Intelligence and Speech Technology, AIST 2022

作者： Gupta, Tina Arora, Puja Rani, Ritu Jaiswal, Garima Bansal, Poonam Dev, Amita Indira Gandhi Delhi Technical University for Women Computer Science Engineering Delhi India Indira Gandhi Delhi Technical University for Women Center of Excellence - Artificial Intelligence Delhi India Bennet University Computer Science Engineering Noida India Indira Gandhi Delhi Technical University for Women Artificial Intelligence and Data Sciences Delhi India

ISBN: (纸本)9781665499026

Modern day machine learning aims to categorize data based on developed models and predict future outcomes according to these models. Today machine learning finds its application in various fields such as facial recognition, speech recognition, medical diagnosis for example predicting potential heart failure, sentiment analysis, product recommendations etc. this paper proposes 3 classification models to efficiently predict the Iris flower species. the proposed model uses Exploratory data Analysis (EDA) to analyse and pre-process the dataset and the prediction is performed by the three classification models namely- "Logistic Regression, Support Vector machine (SVM) and K-Nearest Neighbours (KNN)". All the proposed models are tested on Iris dataset and achieved maximum accuracy of 96.43, 98.21 and 94.64 percent respectively. this paper provides a thorough analysis of the various supervised machine learning models that are suitable for predicting the species of Iris flower based on the various attributes like sepal width, sepal length, petal width and petal length. © 2022 IEEE.

关键词： Support vector machines

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4th international conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th international conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249778

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Attitude-assisted correction framework for cheerleading based on visual tracking technology 4

Attitude-assisted correction framework for cheerleading base...

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4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Xie, Jin Xianda College of Economics and Humanities Shanghai International Studies University Shanghai200083 China

ISBN: (纸本)9781665401180

Attitude-assisted correction framework for general cheerleading based on visual tracking technology is presented in this paper. In the proposed model, the contributions are focused on the three aspects. (1) A tracking method based on basic diverse examples of learning is proposed. Drift is mitigated by relaxing the selection area of the naive positive sample. (2) the distinction analysis is one of basic pattern recognition research important branches, its essential target is through to the known data sample training and the study, the mentioned ideas are used to construct the novel models. (3) the comprehensive evaluation model based on the data mining framework is discussed, the core fuzzy clustering model is presented. the simulation is also well presented to show the performance of the idea. the future research plan is summarizedin the final forreferences. © 2022 IEEE

关键词： data mining

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Identification Of Medicinal Plants from Leaf Images 4

Identification Of Medicinal Plants from Leaf Images

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4th IEEE Global conference for Advancement in Technology, GCAT 2023

作者： Jhunjhunwala, Parth Dhawas, Riya Sanjay Tiwari, Shristi Sharma, Abhishek Electronics and Communication International Institute of Information Technology Naya Raipur India Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur India International Institute of Information Technology Naya Raipur India

ISBN: (纸本)9798350305258

recognition quest for medicinal herbs is a continuous field of exploitation and is crucial in Ayurveda's industries. Incorrect identification leads to adverse effects. One of the most entrusted classification tools is Support Vector machine (SVM), which deals with high dimensional feature space and is generally a crucial aspect of image data. this paper proposes a comparison of a novel multi-class prediction to segregate plant leaves based on an SVM classifier and deep learning based on Resnet50 and InceptionV3 for leaf classification implemented, each model was individually trained before comparison. the findings from the trained model can be integrated into a web application. the SVM model was built in four stages. Stage 1 involves data preprocessing using techniques like grayscaling, smoothing, thresholding, morphological transformation, and boundary extraction to make the data suitable for SVM-based classification. In Stage 2, shape-based, colour-based, and texture-based features are extracted, and a comma-separated delimiter file is generated that would be fitted in an SVM classification model. Stage 3 and 4 is the model-building and testing stage wherein classification, feature scaling, and parameter tuning is done to obtain optimal hyperparameters for the SVM classifier. Using the transfer learning approach parameters were initialized on the pre-trained Neural networks namely InceptionV3, and ResNet50. When training the models on the relevant dataset, the softmax layer coupled to the dense layer served as the classifier and was used to extract features from the input photos. the last stage involves integrating the solution into a web application. the real-time leaf images can be captured by the user utilizing a camera to obtain plant names from images and upload them to a web interface. the whole framework is compared with the existing frameworks for plant leaf classification. © 2023 IEEE.

关键词： ensemble feature extraction hyperparameters Inception V3 preprocessing Resnet50 SVM Transfer learning

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Auto-Tagging User Traits in the Context of Smartphones 4th

Auto-Tagging User Traits in the Context of Smartphones

引用

4th international conference on Design, Operation and Evaluation of Mobile Communications (MOBILE) Held as Part of the 25th international conference on Human-Computer Interaction (HCII)

作者： Sistla, Srija Jayakumar, Nikitha Smrithi, K. Akhil, G. B. S. Pujari, Nitin, V Pes Univ Banashankari Stage 3 Bangalore 560085 India

ISBN: (纸本)9783031359200;9783031359217

In the past few decades research has been done on the prediction of personality traits of users from social media to enhance users experience while using social media applications. Most often we tend to be more upfront with our smartphones and personalise it to suit our daily moods and emotions. these sensor-rich devices can easily be repurposed to collect rich and extensive records of their users' behaviour. Questionnaire based personality tests pose certain challenges as test takers may not be able to accurately answer the questions due to various personal or professional reasons on a survey. Employing a methodology based in the context of smartphone application usage data is an attempt to increase the accuracy in identifying the user's personality traits as this data will be more closely related to the user's changing behavioural pattern over a set of time periods.

关键词： Human Behaviour machine learning App Usage Usage Stats Big Five Personalities Personality Traits Recursive Feature Elimination Decision Trees Random Forest Gradient Boosting

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the Sentiment Analysis of Small and Medium-sized Investors on the Interactive Communication Platforms Based on Deep learning 4

The Sentiment Analysis of Small and Medium-sized Investors o...

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4th international conference on Big data Economy and Information Management, BDEIM 2023

作者： Miao, Jia Liu, Guangling Luo, Tong Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Tsinghua Shenzhen International Graduate School The Center for Social Governance and Innovation Tsinghua University Shenzhen China *** Beijng China

ISBN: (纸本)9798400716669

In order to alleviate the information disadvantage faced by small and medium-sized investors in capital markets, the Shenzhen Stock Exchange and the Shanghai Stock Exchange introduced interactive communication platforms. the Q&R data from board secretaries on these platforms contains high-density investor sentiment information. To enhance the accuracy of investor sentiment analysis, we propose an integrated deep learning approach that considers both the related news of investor questions and the questions themselves. In this paper, we employ deep learning and rule-based named entity recognition to retrospectively trace news related to investor queries. Combining this with the text of investor inquiries, we utilize the Bert deep learning model for comprehensive sentiment analysis. the proposed method achieves a sentiment analysis accuracy of 90%, demonstrating a g1/48% improvement over considering only investor questions. this innovative approach contributes to both data and methodology in the realm of board secretary Q&R research, providing a reference for future studies. © 2023 ACM.

关键词： Adversarial machine learning

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Human Resource Network Information Recommendation Method Based on machine learning 1

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Wang, Xiao China University of Labor Relations Beijing100048 China

ISBN: (数字)9783031201028

ISBN: (纸本)9783031201011

Human resource network information recommendation method is affected by similarity information, which leads to poor recommendation effect. A human resource network information recommendation method based on machine learning is proposed. the machine learning statistical model is established to filter the noise information. Temporal behavioral preference features were constructed to calculate the average number of information clicks. data preprocessing is accomplished through data format conversion, data cleaning and data specification. the network information resources were retrieved, the pattern matching results were obtained, the loss function based on content association rules was constructed, and the recommendation model was constructed combined with hybrid genetic algorithm. the experimental results show that the highest recommendation accuracy of this method reaches 94%, and the highest recall rate is 0.90, which indicates that the application of research methods to recommend human resources network information has a good effect. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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