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Emotion recognition from EEG signals using machine learning model 5

Emotion recognition from EEG signals using machine learning ...

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5th international conference on Multimedia, Signal Processing and Communication Technologies (IMPACT)

作者： Akshay, K. R. Sundar, Sumod Shanir, Muhammed P. P. TKM Coll Engn Ind Instrumentat & Control Engn Kollam Kerala India TKM Coll Engn Ctr Artificial Intelligence Kollam Kerala India TKM Coll Engn Dept Elect & Elect Engn Kollam Kerala India

ISBN: (纸本)9781665476478

the objective of this work is to identify emotion from EEG signals, that represent the brain activity of individuals. With the rapid advancement of machine learning algorithms and numerous real-world applications of brain-computer interface for regular people, emotion categorization from EEG data has recently gained a lot of attention. Researchers previously have little knowledge of the specific interactions between distinct EEG characteristics and various emotional states. Using EEG-based emotion recognition, the computer may be able to understand the emotional state of the user. this work is executed with DEAP dataset with 32 channels for EEG recording, it achieves a better classification accuracy with the random forest machine learning (ML) algorithm. In the subject-specific experiment an average best accuracy of 91.26% and in the subject-dependent experiment, the best accuracy of 78.5% is obtained for the random forest classifier. the proposed method is superior to other studies when compared to those using a four-class problem because those have only achieved the best accuracy of 71.43%.

关键词： Random forest Kfold ML EEG emotion recognition

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Smart Potato Disorders Diagnostic System Based on Fuzzy K-Nearest Neighbor 5

Smart Potato Disorders Diagnostic System Based on Fuzzy K-Ne...

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5th international conference on Computing and Informatics (IEEE ICCI)

作者： Rayan, Zeina Samir, Sara Abdelfattah, Doaa Salem, Abdel-Badeeh M. Ain Shams Univ Fac Comp & Informat Sci Comp Sci Dept Cairo Egypt

ISBN: (纸本)9781665499736;9781665499729

Towards smart agriculture, machine learning techniques are now used for different things in agriculture. One of these things is plant diagnosis. this paper aims to establish a smart system to diagnose the diseases of the potato plant with less number of symptoms from the user that appeared on the plant through knowledge discovery (data mining process) techniques, and provide the decision support to the farmer when farmer needs to know the treatment for the potato plant. the proposed model achieved an accuracy of 97%.

关键词： Smart Agriculture Artificial Intelligence machine learning Disorder Classification Fuzzy KNN Intelligent System

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Research on intelligent elderly care service information system based on machine learning 5

Research on intelligent elderly care service information sys...

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5th international conference on Internet of things, Automation and Artificial Intelligence, IoTAAI 2023

作者： Chen, Qi Sheng, Nan Guangzhou Academy of Fine Arts Guangdong Guangzhou510006 China Lancaster University Lancashire LancasterLA1 4YW United Kingdom

ISBN: (纸本)9798400716485

the aging of the population is becoming more and more serious, and intelligent pension is becoming more and more important. Today, machine learning and artificial intelligence are widely used in all walks of life, and facial recognition based on machine learning is applied to the pension service system, which will make the pension system intelligent. the elderly identity verification through face recognition has the characteristics of direct and convenient, not easy to lose. In recent years, with the research and application of machine learning in the field of artificial intelligence such as computer vision, face recognition as an important part of computer vision technology has also been greatly developed, all kinds of face recognition algorithms based on machine learning emerge endlessly, and the effect is far higher than the traditional method. this paper designs and implements an artificial intelligence elderly care service information system based on machine learning, and studies the face recognition technology of intelligent elderly care system. the paper carried out feasibility analysis and demand analysis of the system research, discussed the functions to be designed and implemented in the research, analyzed the work flow of the research, and according to the work flow, the research was divided into five functional modules: image acquisition, face detection, feature extraction, identity recognition and result output. the machine learning model YOLO is used for face detection, ERT algorithm is used for face alignment, and 128-dimensional feature vector is extracted by residual neural network to realize face recognition. the input and output of real-time images are realized by OpenCV. the function of the algorithm is tested on the flw data set, and the accuracy of the algorithm can reach more than 98%. It realizes the research of face recognition system in artificial intelligence elderly care service information system based on machine learning, which can achi

关键词： Face recognition

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5th international conference on Computing and data Science

5th International Conference on Computing and Data Science

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5th international conference on Computing and data Science, CONF-CDS 2023

the proceedings contain 11 papers. the topics discussed include: restoration and enhancement optimization of blurred images based on SRGAN;real-time gesture-based control of UAVs using multimodal fusion of FMCW radar and vision;tunneling towards efficiency: a survey of design and optimization strategies for tunnel FETs in ultralow power applications;orthopedic patient analysis using machine learning techniques;transformer-based network with difference information aware attention module;visual object detection using audio data;automating bird species classification: a deep learning approach with CNNs;source and drain formation in FinFETs;Identification of language from multi-lingual dataset using classification algorithms;analysis of power system load forecasting based on neural networks;and virtual human speech emotion recognition based on multi-channel CNN: MFCC, LPC, and F0 features.

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AI-Driven Optimization for Urban and Vertical Agriculture Planning: A Multi-Model Approach 5

AI-Driven Optimization for Urban and Vertical Agriculture Pl...

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5th international conference on Electrical, Communication and Computer Engineering, ICECCE 2024

作者： Sayad, Ismail El Sidhu, Harjobanpreet Singh Kundan, Montek Glaros, Alesandros Pizzirani, Stefania School of Computing University of the Fraser Valley AbbotsfordBC Canada Food and Agriculture Institute University of the Fraser Valley AbbotsfordBC Canada

ISBN: (纸本)9798331529437

Urban agriculture has emerged as a critical strategy for enhancing food security, mitigating urban heat islands, and promoting community well-being in densely populated areas. However, the complexity of urban environments poses significant challenges for effective planning and implementation. this paper presents an AI-driven framework to optimize urban and vertical agriculture planning by leveraging advanced machine learning models, including Artificial Neural Networks (ANN), Spiking Neural Networks (SNN), and Large Language Models (LLM). the proposed framework integrates diverse dataset that includes as socioeconomic data, geographic information, and urban zoning regulations to provide actionable insights for decision-makers. the Spiking Neural Network model demon-strated superior predictive accuracy in identifying optimal sites for urban agriculture by effectively handling complex patterns and temporal dynamics in the data. Additionally, the integration of an LLM-powered chatbot into a user-friendly web application enhances interactivity and supports real-time decision-making, guiding users through the prediction process with context-specific recommendations. Experimental results validate the robustness and scalability of the framework across various urban settings, demonstrating its potential to transform urban agriculture practices by providing precise, data-driven recommendations. the findings of this study highlight the transformative potential of AI in urban planning and agriculture, offering a novel approach to fostering sustainable urban development and food security. Future research will focus on expanding the dataset, refining model performance, and enhancing the application's capabilities to support more complex user queries. © 2024 IEEE.

关键词： Agricultural Technology Artificial Intelligence (AI) Artificial Neural Networks (ANN) data mining machine learning Spiking Neural Networks (SNN) Sustainability Urban Agriculture Urban Planning Vertical Farming

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4th international conference on Recent Trends in machine learning, IoT, Smart Cities, and Applications, ICMISC 2023

4th International Conference on Recent Trends in Machine Lea...

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4th international conference on Recent Trends in machine learning, IoT, Smart Cities, and Applications, ICMISC 2023

ISBN: (纸本)9789819994410

the proceedings contain 69 papers. the special focus in this conference is on Recent Trends in machine learning. the topics include: Implementation of Dual-Band Dielectric Resonator Antenna for 5G Applications;defect Detection in Metal Surfaces Using Computer Vision;liver Cirrhosis Prediction Using machine learning Classification Techniques;a Recent Survey on Risk Factors Affecting the Blood Pressure in India;Real-Time Monitoring System for Breakdown Analysis and OEE in the Wire Drawing Industry;tooth Sensitivity Device—Detection and Diagnosing of Sensitivity in the Dental Pulp;recognition of Skin Cancer;ioT-Based Smart Street Lighting Surveillance System;rock Segmentation of Real Martian Scenes Using Dual Attention Mechanism-Based U-Net;IAAS: IoT-Based Automatic Attendance System with Photo Face recognition in Smart Campus;hardware Implementation of Moving Object Detection Using Background Subtraction Algorithm;crime pattern Identification and Prediction Using machine learning;IMICE: An Improved Missing data Imputation Using machine learning;analyzing Students’ Opinion on E-learning—Indian Students’ Perspective;rash Driving Detection and Alerting System;Logistic-Based OVA-CNN Model for Alzheimer’s Disease Detection and Prediction Using MR Images;Comparative Study of CNNs for Camouflaged Object Detection;3D Avatar Reconstruction Using Multi-level Pixel-Aligned Implicit Function;Helmet Detection Using YOLO-v5 and Paddle OCR for Embedded Systems;defogNet: A Residual Network for Removal of Fog Using Weighted Combination Loss;Text-to-Image Generation Model with DNN Architecture and Computer Vision for Embedded Devices Using Quantization Technique;one-Shot learning for Archaeological Site data Using Deep Neural Network on Embedded Systems;enhanceNet: A Deep Neural Network for Low-Light Image Enhancement with Image Restoration;intelligent Prediction of Cardiac Abnormality.

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Brain Controlled Communication system using machine learning techniques of Computer Vision 5

Brain Controlled Communication system using Machine Learning...

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5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Sabharwal, Munish Sultania, Shantam Jindal, Harsh Galgotias University Greater Noida India Wissen Technology Banglore India Chandigarh University Dept. Of Computer Science And Engineering Mohali India

ISBN: (纸本)9798350330861

Nowadays computers have become a necessity for all computers have made a great leap for us and with the help of that we are able to move to a golden age of Artificial Intelligence. Artificial Intelligence or A.I has huge advantages and potentials for making the lives easy and more accessible. However, we as a society have to keep on innovating and developing new innovations and products to help different aspects of the community. the proposed system uses a brain-controlled interface, eye tracking along with a camera and screen for communication and training. the system requires input in the form of an image captured with the help of a camera. After the image is taken as an input, it will be used to perform eye-tracking and eye-blink detection. Both of these actions will be performed by employing a machine learning algorithm. the algorithm will process a morse code entered by the user, and then will enable the speech functionality using built-in speakers. thus, rendering the system speak out whatever the user wanted to convey. the proposed system follows a coordinated approach for eye pattern detection and mapping. It detects the gazing spot of the eyes, and calculates the midpoint of the eye gaze. As a result, it predicts the eye blink pattern. this eye pattern recognition system is proposed to help out people with disabilities who find it difficult to communicate. © 2023 IEEE.

关键词： Cameras

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Sentimental Analysis of Movie Reviews Using NLP Techniques 5

Sentimental Analysis of Movie Reviews Using NLP Techniques

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Basi Reddy, A. Suresh Reddy, B.V. Nandam, Anjana Devin Naresh, K. Depuruu, Sivakumar Sakthivel, M. School of Computing Mohan Babu University A.P Tirupati India Koneru Lakshmaiah Education Foundation Dept. Of. Cse A.P Vaddeswaram India A.P Tirupati India

ISBN: (纸本)9798350321425

the goal of natural language processing is to build machines that can comprehend text or speech input and respond to it in a way that is similar to how people do. To accomplish this, various Natural Language Processing (NLP) tasks are employed to analyze human language data, aiding computers in understanding the information it consumes. these tasks include sentiment analysis, voice recognition, part-of-speech tagging, and natural language generation. Sentiment analysis that detects emotions such as happiness, frustration, rage, and sadness goes beyond polarity. To do this, advanced machine learning techniques or lexicon collections of words and the emotions arouse are used. In this study, the Internet Movie database (IMDB) Reviews dataset is subjected to sentiment analysis to determine if a user's review is favourable or unfavourable based on the supplied details. Utilizing data from the real world, the proposed approach's correctness is assessed. © 2023 IEEE.

关键词： machine learning

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Optimizing Transformer Based on High-Performance Optimizer for Predicting Employment Sentiment in American Social Media Content 5

Optimizing Transformer Based on High-Performance Optimizer f...

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5th international conference on machine learning and Computer Application, ICMLCA 2024

作者： Wang, Feiyang Bao, Qiaozhi Wang, Zixuan Chen, Yanlin Information Networking Instutite Carnegie Mellon University SunnyvaleCA94085 United States North Carolina State University Department of Statistics RaleighNC27695 United States College of Engineering Carnegie Mellon University Mountain ViewCA94035 United States Tandon School of Engineering New York University BrooklynNY11201 United States

ISBN: (纸本)9798331530334

this article improves the Transformer model based on swarm intelligence optimization algorithm, aiming to predict the emotions of employment related text content on American social media. through text preprocessing, feature extraction, and vectorization, the text data was successfully converted into numerical data and imported into the model for training. the experimental results show that during the training process, the accuracy of the model gradually increased from 49.27% to 82.83%, while the loss value decreased from 0.67 to 0.35, indicating a significant improvement in the performance of the model on the training set. According to the confusion matrix analysis of the training set, the accuracy of the training set is 86.15%. the confusion matrix of the test set also showed good performance, with an accuracy of 82.91 %. the accuracy difference between the training set and the test set is only 3.24%, indicating that the model has strong generalization ability. In addition, the evaluation of polygon results shows that the model performs well in classification accuracy, sensitivity, specificity, and area under the curve (AVC), with a Kappa coefficient of 0.66 and an F -measure of 0.80, further verifying the effectiveness of the model in social media sentiment analysis. the improved model proposed in this article not only improves the accuracy of sentiment recognition in employment related texts on social media, but also has important practical significance. this social media based data analysis method can not only capture social dynamics in a timely manner, but also promote decision-makers to pay attention to public concerns and provide data support for improving employment conditions. © 2024 IEEE.

关键词： data accuracy

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Research on vehicle spare parts demand forecast based on XGBoost-LightGBM 23

Research on vehicle spare parts demand forecast based on XGB...

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5th international conference on pattern recognition and Intelligent Systems, PRIS 2023

作者： Zhu, Qianqian Yang, Liu Liu, Yingnan Automotive Data of China Co. Ltd. China Automotive Technology and Research Center Co.Ltd. Tianjin China

ISBN: (纸本)9781450399968

Vehicle spare parts demand forecasting is crucial for optimizing inventory and improving maintenance efficiency. this study aims to explore a vehicle spare parts demand forecasting method based on the fusion of XGBoost and LightGBM models to enhance prediction accuracy and precision. In this paper, we first collected a large amount of historical spare parts demand data and associated feature data, followed by data preprocessing and feature engineering. then, we constructed individual machine learning models as well as the XGBoost-LightGBM fusion model, and performed parameter tuning and optimization using the Optuna framework. Experimental results demonstrate that both XGBoost and LightGBM models achieve favorable performance in spare parts demand forecasting, but the fusion of these two models further enhances prediction accuracy. the fusion model exhibits lower MAPE values compared to individual models on the test set, confirming its superiority and effectiveness. this method leverages the strengths of both models and improves prediction accuracy through weight fusion, offering practical significance in achieving accurate spare parts demand forecasting, optimizing inventory, and improving maintenance efficiency. Future research can explore alternative machine learning algorithms and feature engineering methods to further enhance the accuracy and reliability of vehicle spare parts forecasting. © 2023 ACM.

关键词： Vehicles

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