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CIBIP-AI: Development of California Invasive Bird Identification and Prediction Artificial Intelligence System 3

CIBIP-AI: Development of California Invasive Bird Identifica...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Chen, Kayla Shao, Claire Harker Sch San Jose CA 95129 USA Lynbrook High Sch San Jose CA USA

ISBN: (纸本)9798350372977;9798350372984

Invasive species are detrimental to native wildlife and pose a major threat to biodiversity. In California alone, municipalities and conservation authorities are estimated to spend approximately $50.8 million per year on invasive species. Effective management of bio-invasions depends on reliable identifying the invasive threats and predicting areas at risk of invasion. Yet, there is no efficient solution to address the issue. This paper proposes the first California Invasive Bird Identification and Prediction AI System (CIBIP-AI). The CIBIP-AI system utilizes a deep learning solution to automatically identify and predict two California invasive birds, the mute swan and brown-headed cowbird. The CIBIP-AI system uses a hybrid deep learning architecture based on VGG16 and ResNet50 for identification. The CIBIP-AI system can also predict the geographical areas at risk of invasion based on a set of the birds' physiology features and microclimate conditions. The CIBIP-AI database is created for storing data and continuous training. The initial experimental results show that the classification accuracy for the image input reached 96%. The audio input was more challenging to classify, with 73% accuracy. After applying knowledge-based filtering and common pattern extraction, the accuracy was improved to 77%. The video was split into multiple images and audio and input into the hybrid models. The classification accuracy of the hybrid models reached 97%. The work shows that the proposed CIBIP-AI system provides significant aid to the early detection and prevention of invasive bird species.

关键词： Invasive species artificial intelligence deep learning ecology mute swan brown-headed cowbird

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learning from patterns: Information Retrieval and Visualisation Issues Between Bioimage Informatics and Digital Humanities 3rd

Learning from Patterns: Information Retrieval and Visualisat...

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3rd international and Interdisciplinary conference on Image and Imagination, IMG 2021

作者： Cazzaro, Irene Dipartimento di Architettura Alma Mater Studiorum Università di Bologna Bologna Italy

ISBN: (纸本)9783031259050

The large amount of data generated in different fields, among which bioimage informatics and digital humanities, is increasingly requiring appropriate automatic processing techniques, such as computer vision, data mining and particular visualisation tools, to extract information out of complexity and to clearly display it. This has led, in digital humanities, to the use of pattern recognition techniques similar to those applied in biology, chemistry and medical studies, but where patterns to be analysed and segmented are extracted from texts, images, audio-visual and online media rather than from cells and tissues. Regularities can be recognised through machine learning, based on artificial neural networks that are modelled, to some extent, after the brain’s structure, showing a variety of analogies between natural and artificial world. These processes can also add information to 3D models for cultural heritage: data mining technologies allow information retrieval from archives and repositories, as well as the comparison of data in order to better understand the context of – and relationships between – works of art, thus producing knowledge enhancement. Various tools to describe complexity are here analysed not only for their educational aim, but also for their heuristic value, allowing new discoveries and connections between different disciplines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Visualization

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3rd international conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

3rd International Conference on Emerging Trends in Expert Ap...

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3rd international conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

ISBN: (纸本)9789819739905

The proceedings contain 36 papers. The special focus in this conference is on Emerging Trends in Expert Applications and Security. The topics include: Advancements and Perspectives in Digital Image Watermarking;Analysis of Cognitive Aspects in Online Education Amid the COVID-19: A machine learning Approach;survey on IoT Enabled Smart Tourism in India: Indian Tourism Service Dimensions;AI-Driven Prediction of Medicinal Property Presence in Plants Cultivated with Nutrient-Enriched Manure;deep learning-Based Multi-class Classification of Diabetic Retinopathy Utilizing Transfer learning with MobileNet Architecture;sentiment Classification of Multidomain Reviews Using machine learning Models;Anomaly Detection in ECG Signals Through Unsupervised machine learning: A Novel Approach Using Hybrid Autoencoders for Medical data Analysis;an Insight on Role of Artificial Intelligence for Detection of Parkinson’s Disease;a Generative Study of Employing Convolutional Neural Networks for Plant Categorization;ensembling of Transfer learning for Enhanced Precision Agriculture in Plant Disease Classification;a Novel Fast-Charging Approach and an Adaptive Control of Drives in Electric Vehicle Applications;bibliometric Review of Artificial Intelligence in Credit Risk;smart Fields, Smart Yields: Technologies Driving Precision Agriculture Revolution a Survey;integrating Handwritten Digit recognition with learning Management Systems for Evaluated Answer Scripts;virtual Education in Management Institutions: Investigating Its Influence on Student Satisfaction and Academic Achievements in Higher Education;conflict Between Artificial Intelligence and Traditional Designers: How Artificial Intelligence Transforms the Design Process;enhancing License Plate recognition with Dehazing and machine learning;an Analysis of the Increasing Trend of Digital Transformation of Business in India: Case Study Approach;salutem Officia: Health Services Application.

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Comparison Analysis of Forecasting Accuracy for Electricity Consumption Using Extreme learning machine and Backpropagation Algorithms 3

Comparison Analysis of Forecasting Accuracy for Electricity ...

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3rd FORTEI-international conference on Electrical Engineering, FORTEI-ICEE 2024

作者： Miefthawati, Nanda Putri Akbar, Sukma Aini, Zulfatri Sutoyo Department of Electrical Engineering State Islamic University of Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (纸本)9798331542207

Accurate calculations are essential in forecasting. Therefore, it is important to select the appropriate forecasting method, which can be determined by testing the accuracy level of the forecasting results using the MAPE value. The purpose of this research is to analyze the comparison of the accuracy of electricity consumption forecasts using the Extreme learning machine and Backpropagation algorithms. The data used are monthly electricity consumption data as input pattern designs. The input data will be divided into two parts: training data and testing data, followed by simulations using the Extreme learning machine and Backpropagation algorithms to obtain the 2022 forecast results. The accuracy of each algorithm's forecast results will then be calculated using the MAPE value. The research results indicate that the Extreme learning machine algorithm has a MAPE value of 4.67%, where the error rate is higher than the Backpropagation algorithm, which has only 2.27%. From the algorithm testing stages, the most accurate forecasting result for electricity consumption was obtained using Backpropagation, as it has the lowest MAPE value, indicating the highest accuracy and lowest error rate. © 2024 IEEE.

关键词： Adversarial machine learning

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Academic Performance Prediction of At-Risk Students using machine learning Techniques 3

Academic Performance Prediction of At-Risk Students using Ma...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Tk, Shoukath Midhunchakkravarthy Lincoln University College Faculty of Computer Science and Multimedia Malaysia

ISBN: (纸本)9798350399264

Imparting quality education is a top priority of higher education institutions. Leaving at risk students unattended would adversely impact the quality of education. Predicting the academic performance of students is one of the basis for improving the quality of education, as it would help the institution to take early and timely measures to support the low-performing students and improve their performance. At the same time, it has identified that the prediction of student performance considered as challenging one due to large dataset in educational environment. However, Educational data mining (EDM) using machine learning techniques is an emerging research area, which allows the researchers to effectively process and evaluate the educational data collected from different sources. In this paper, we propose the use of support vector machine (SVM) on an imbalanced dataset to predict the academic performance of students. © 2023 IEEE.

关键词： Students

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Application of text mining technology in power data prediction 3

Application of text mining technology in power data predicti...

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2022 3rd international conference on Computer Information and Big data Applications, CIBDA 2022

作者： Guo, Jian Guo, Wu College of Architecture and Civil Engineering Qiqihar University Qiqihar China College of Communication and Electronic Engineering Qiqihar University Qiqihar China

ISBN: (纸本)9783800758760

This paper analyses the main research methods of power text mining technology in detail, and discusses the research hotspots of power text named entity recognition and named entity relationship extraction based on machine learning and deep learning. On this basis, the limited Boltzmann machine is further used to predict the power data. The test results show that the analysis of power text features in this paper is accurate, can effectively analyse the key text information, and the prediction results meet the requirements. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Character recognition

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Comprehensive Analysis of Feature Extraction Methods for EEG-based Emotion Classification 3

Comprehensive Analysis of Feature Extraction Methods for EEG...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Sivaranjani, S. Prithiga, V. Ragavi, S. Reena, S. Sanchana Sri, S. M. Kumarasamy College of Engineering Department of Ece Karur India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Emotion recognition from EEG signals is widely applicable and a rapidly emerging field in human-computer interaction, mental health analysis, and affective computing. The main problem of addressing a novel enhanced method for EEG-based emotion classification using the Discrete Wavelet Transform (DWT) to extract its features into delta, theta, alpha, beta, and gamma frequency components, and essential time-frequency features could be extracted that can improve the accuracy of the classification. The features are fed into the machine learning classifiers, namely CNN, RNN, SVM, LDA, and Decision Trees, in order to obtain a classification accuracy of 96%. The experimental results indicate a progressive increase in accuracy and consistent reduction in loss over 20 epochs, showing the effectiveness of DWT-based feature extraction. The study evaluates model robustness by using key performance metrics like accuracy, sensitivity, and specificity. It shall improve in future, including optimized wavelet parameters, refinement in architectures of the neural network, real-time processing improvement, and multimodal data fusion to be incorporated for enhancement of system performance. © 2025 IEEE.

关键词： data fusion

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Research on Disease Prediction Based on Daily data Analysis of the Elderly Based on Neural Network Algorithms 3

Research on Disease Prediction Based on Daily Data Analysis ...

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3rd IEEE international conference on Electrical Engineering, Big data and Algorithms, EEBDA 2024

作者： Lei, Liu Guangdong Open University Guangdong Polytechnic Institute Guangzhou China

ISBN: (纸本)9798350380989

In recent years, with the gradual arrival of an aging society, the health status of the elderly has attracted widespread attention. Against this background, this study aims to explore the potential value of these data for disease prediction by collecting and analyzing daily data of the elderly based on neural network algorithms. First, by using technical means such as smart sensing devices and mobile applications, we collected multi-dimensional data on the elderly's daily life, including but not limited to physiological indicators, activity levels, social interactions, and sleep patterns. These data are considered potential disease predictors because they provide comprehensive information about the physical condition and lifestyle of older adults. Second, we apply the collected data to a neural network algorithm, which has powerful learning and pattern recognition capabilities. By training models, neural networks can learn potential patterns and regularities from large amounts of complex data, thereby providing strong support for disease prediction. We pay special attention to the superiority of neural networks in processing multi-source heterogeneous data to more comprehensively understand the multi-faceted information of the elderly's health status. Through experiments and verification, we evaluate the performance of neural network algorithms in disease prediction in the elderly. This study explores disease prediction based on the daily data of the elderly based on neural network algorithms, aiming to provide scientific basis for establishing a more intelligent and accurate elderly health management system to promote the health and well-being of the elderly. © 2024 IEEE.

关键词： data analysis elderly health neural networks

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3rd EAI international conference on IoT and Big data Technologies for Health Care, IotCARE 2022

3rd EAI International Conference on IoT and Big Data Technol...

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3rd EAI international conference on IoT and Big data Technologies for Health Care, IotCARE 2022

ISBN: (纸本)9783031335440

The proceedings contain 23 papers. The special focus in this conference is on IoT and Big data Technologies for Health Care. The topics include: Personalized Recommendation Method of Maternal and Child Health Education Resources Based on Association Rule mining Algorithm;methods of Integrating Ideological and Political Education into Health Management in Colleges and Universities Based on Internet of Things Technology;data mining of Psychological Tendency and Health of Ideological and Political Students in Higher Vocational Tourism English Courses;intelligent Imaging Method of Nuclear Magnetic Resonance Medical Devices Based on Compression Sensing;evaluation of Post Fitness of Employees in Health Care Enterprises Based on Big data;design of Telemedicine and Health Care System Based on Embedded Technology;anomaly Detection Method of Healthcare Internet of Things Gateway Supporting Edge Computing;research on Fast Encryption of Electronic Health Record data Based on Privacy Protection;research on Secure Storage of Healthcare data in the Environment of Internet of Things;research on Cloud Health Privacy Information Protection Algorithm Based on data mining;architecture of Wide Area Health Monitoring System;internet of Things Technologies in Healthcare for People with Hearing Impairments;artificial Intelligence-Based Early Warning Method for Abnormal Operation and Maintenance data of Medical and Health Equipment;abnormal Signal recognition Method of Wearable Sensor Based on machine learning;data Security mining Method for Social Media Users’ Mental Health Status Test Based on machine learning Algorithm;early Warning Method of College Students Mental Subhealth Based on Internet of Things.

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3rd international conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

3rd International Conference on Emerging Trends in Expert Ap...

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3rd international conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

ISBN: (纸本)9789819737444

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