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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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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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A data mining Method Based on Natural Language Processing and machine learning 4

A Data Mining Method Based on Natural Language Processing an...

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4th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

作者： Tang, Zhihang Lin, Rong Zhou, Zikai Tang, Zipei Hunan Institute of Engineering School of Computer and Communication Xiangtan China Ningbo University of Finance and Economics School of Finance and Information Ningbo China

ISBN: (纸本)9798350308082

the data source and data collection method are described, and the results of initial data are presented. And, the method of word segmentation and stop words removal in the preprocessing process is used to clean the data to obtain the data text before clustering. then, the application scenarios and main characteristics of K-means algorithm are introduced. the algorithm process and calculation methods of the traditional K means algorithm and the improved dichotomous K-means algorithm are introduced in two sections, finally, the experimental text clustering effects of the two algorithms are tested through experiments, and the experimental results are briefly analyzed. It is found that the improved dichotomous K means clustering algorithm has more stable clustering results, and can get more accurate keywords. © 2023 IEEE.

关键词： K-means clustering

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4th international conference on Recent Innovations in Computing, ICRIC 2021

4th International Conference on Recent Innovations in Comput...

引用

4th international conference on Recent Innovations in Computing, ICRIC 2021

ISBN: (纸本)9789811688911

the proceedings contain 45 papers. the special focus in this conference is on Recent Innovations in Computing. the topics include: learning-Based Techniques for Assessing Zero-Day Attacks and Vulnerabilities in IoT;a Review of Credit Card Fraud Detection Techniques;an Intensive Literature Review on machine Translation Using Neural Approach;cyber Security and IoT: Attacks and Security Countermeasures;scope and Challenges of Blockchain Technology;web Usage mining—Process, Tools and Practices;e-Health Systems for Mother and Child Care Domain: A Systematic Review;hierarchical Clustering Based Characterization of Protein database Using Molecular Dynamic Simulation;model to Predict the Crop Cost Using machine learning and Read dataset;a Survey on Scalable Resource Allocation in Cloud Computing;computational Intelligence Techniques for Big data Analytics: A Contemplative Perspective;recent Techniques for Sentiment Analysis on Medical data to Predict Depression: A Review;deep learning in IoT and Edge/Fog Integrated Environments: A Review;challenges in recognition of Kashmiri Script;plantaeK: A Leaf database of Native Plants of Jammu and Kashmir;plummeting Makespan by Proficient Workflow Scheduling in Cloud;web Recommendation by Exploiting User Profile and Collaborative Filtering;Feature Extraction from Radiology Images for Visual Question Answering System Using CNN and BiLSTM Model;superpixels-Guided Background Modeling Approach for Foreground Detection;a Review on Leaf-Based Plant Disease Detection Systems Using machine learning;a Review on machine learning Techniques for the Diagnosis of Cancer;Usability Improvements in E-Governance Application with Simple & Usable Interface Using Citizen-Satisfaction-Model-of-Performance-Related-Pay (CSMPRP);License Plate Detection and recognition Using OpenCV–Python;corpus Creation for Anaphora Resolution in Punjabi Language.

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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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4th international conference on Recent Innovations in Computing, ICRIC 2021

4th International Conference on Recent Innovations in Comput...

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4th international conference on Recent Innovations in Computing, ICRIC 2021

ISBN: (纸本)9789811682476

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Modern theoretical Tools for Understanding and Designing Next-generation Information Retrieval System 22

Modern Theoretical Tools for Understanding and Designing Nex...

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15th ACM international conference on Web Search and data mining (WSDM)

作者： Xu, Da Ruan, Chuanwei Walmart Labs Sunnyvale CA 94086 USA Instacart San Francisco CA USA

ISBN: (纸本)9781450391320

In the relatively short history of machine learning, the subtle balance between engineering and theoretical progress has been proved critical at various stages. the most recent wave of AI has brought to the IR community powerful techniques, particularly for pattern recognition. While many benefits from the burst of ideas as numerous tasks become algorithmically feasible, the balance is tilting toward the application side. the existing theoretical tools in IR can no longer explain, guide, and justify the newly-established methodologies. With no choices, we have to bet our design on black-box mechanisms that we only empirically understand. the consequences can be suffering: in stark contrast to how the IR industry has envisioned modern AI making life easier, many are experiencing increased confusion and costs in data manipulation, model selection, monitoring, censoring, and decision making. this reality is not surprising: without handy theoretical tools, we often lack principled knowledge of the pattern recognition model's expressivity, optimization property, generalization guarantee, and our decision-making process has to rely on over-simplified assumptions and human judgments from time to time. Facing all the challenges, we started researching advanced theoretical tools emerging from various domains that can potentially resolve modern IR problems. We encountered many impactful ideas and made several independent publications emphasizing different pieces. Time is now to bring the community a systematic tutorial on how we successfully adapt those tools and make significant progress in understanding, designing, and eventually productionize impactful IR systems. We emphasize systematicity because IR is a comprehensive discipline that touches upon particular aspects of learning, causal inference analysis, interactive (online) decision-making, etc. It thus requires systematic calibrations to render the actual usefulness of the imported theoretical tools to serve IR problem

关键词： Information retrieval theory machine learning pattern recognition Decision making Causal inference Bandit Reinforcement learning

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Education data Analysis using Ensemble Models 4

Education Data Analysis using Ensemble Models

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

作者： Janet, B. Joshua Arul Kumar, R. Mitra, Anisha Titus, S. Department of Computer Application NIT Tiruchirappalli Tiruchirappalli India Department of Mechatronics Engineering Rane Polytechnic Technical Campus Tiruchirappalli India Department of EEE K.Ramakrishnan College of Engg Tiruchirappalli India

ISBN: (纸本)9781665401180

In this evolving era of machine learning, educational data mining has immense potential in transforming the pedagogy methods. In higher educational institutes, improvement of students' performance is an important aspect for NIRF parameters such as Teaching and learning. It is a crucial task to identify significant attributes present in the education dataset that contribute most to academic performance prediction. Such work helps educational stakeholders in developing better methods which will improve the effectiveness of Teaching and learning. In this paper, various classifier models along with ensemble learning models have been used to compare performance of the students. the novelty of this work is to observe the effects of feature engineering on the classification accuracy of the model when applied to the educational dataset. the results obtained show that the Ensemble algorithms perform better than the base algorithms when making student grade predictions. © 2022 IEEE

关键词： Students

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

Auto-Tagging User Traits in the Context of Smartphones

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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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Novel Framework for Quality Crop Predictions Using data mining and Soft Computing Techniques

Novel Framework for Quality Crop Predictions Using Data Mini...

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2023 international conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Maurya, Rajesh Kumar Jain, Himani Sharma, Tarun Kumar Sharma, Surbhi Dublish, Mani Abes Engineering College Department of Computer Applications NH-9 Near Crossing Republic Ghaziabad India

ISBN: (纸本)9798350322309

data mining (DM) and Soft Computing (SC) are a vital computational approach that offers good competence of flexible agricultural data processing systems to solve farmer's problems. Recently, soft computing has emerged as a powerful technique for solving and analyzing complex real-world problems. this article suggests an approach of smart crop predictions is presented through DM &SC in the field of agricultural quality crop prediction. A five-level framework is proposed namely 1) Collection of data from different repositories, 2) Pre-processing of data, 3) Appropriate Classifier Selection, 4) Prediction and Estimation 5) Draw AUC & ROC curve. Method proposed here focuses on analyzing agricultural yield, soil for crop, rainfall required based on chemical property of soil. Agricultural data analysis and cataloging is one of the best applications of new computing tools such as soft Computing, machine learning (ML) approach, became a burning area for the reason that of the massive development of farming data. DM&SC approaches for accomplishing applied research will give effective answers for this type of problem. ML is a working tool to study multiple learners and combine their assessments for accomplishing greater forecasting accurateness. In this investigation, we had recapitulated ML methods which can be applied as an essential tool by the farmer and agriculture scientist for timely prediction of crop production. © 2023 IEEE.

关键词： Crops

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