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4th international conference on Higher Education learning Methodologies and Technologies Online, HELMeTO 2022

作者： Aversano, Lerina Bernardi, Mario Luca Cimitile, Marta Iammarino, Martina Montano, Debora Verdone, Chiara Department of Engineering University of Sannio Benevento Benevento Italy UnitelmaSapienza University Rome Italy

ISBN: (纸本)9783031297991

this study aims to explore a new feature model and a set of machine learning classifiers to predict student performance by monitoring his/her activities on the Virtual learning Environment (VLE). the features are used for training the classifier to predict the final result of each student. the proposed model is evaluated by using a dataset built at the Open University of London. the results show good performance (80% of accuracy) of the proposed approach compared to other similar studies. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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Performance Evaluation of machine learning Algorithms for Rainfall Prediction Using Dimensionality Reduction Techniques 4

Performance Evaluation of Machine Learning Algorithms for Ra...

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4th international conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Kumari, Sapna Raza, Muhammad Owais Kumari, Arsha Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Dept. of Computer Science Hyderabad Pakistan University of Sufism and Modern Sciences Dept. of Information and Computing Bhitshah Pakistan Mehran University of Engineering & Technology Dept. of Electronics Engineering Jamshoro Pakistan

ISBN: (纸本)9798350335316

In the last few decades, tremendous change is observed in rainfall patterns which are majorly influenced by two major factors 1) climate change and 2) CO2 emission. Erratic rainfall patterns caused catastrophic effects on agriculture and human life in developing countries like Pakistan, where major economic growth is largely dependent on agriculture. the main objective of this study is to evaluate a performance different machine learning algorithms for forecasting rainfall patterns using dimensionality reduction techniques on climate change indicators. For this purpose rainfall data was collected for Pakistan. Principle component analysis (PCA), Pearson correlation, and Greedy search algorithms were used for feature selection and the evolution of models was performed using Root Mean Square error (RMSE), Root Absolute Error (RAE), and Coefficient of determination metrics. Results show that features obtained using the Pearson correlation produced the least error and Bayesian linear regression performed with the highest accuracy followed by Neural Network regression. © 2023 IEEE.

关键词： Climate change

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CNN Design with AlexNet Algorithm for Diagnosis of Diseases in Cassava Leaves 4

CNN Design with AlexNet Algorithm for Diagnosis of Diseases ...

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4th international conference on Expert Clouds and applications, ICOECA 2024

作者： Roshini, Polagani Khajavali, Shaik Snigdha, M L Sneha Harsha, Badugu Deva Srilakshmi, Bandlamudi Gopi, Arepalli Koneru Lakshmaiah Educational Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350385793

Plant diseases play a significant role in reducing crop productivity. Nobody will be able to consume the crops because they have been contaminated with different illnesses. As a result, farmers may face huge losses. Cassava is a staple crop in some nations, so this issue may have severe impact on the economy. Detecting plant diseases manually takes time and is prone to errors. It is not always a reliable method for discovering and avoiding the propagation of plant diseases. In order to overcome these issues, cutting-edge technology like machine learning and deep learning may help with early plant disease identification. the main goal of the work is to use deep learning to image classification in order to accurately identify diseases that especially impact cassava plants. this identification may make it possible to implement early intervention measures like specific application of chemicals or confinement of contaminated crops. Each and every test and training image comes from a rural area in the natural world. Using a specific collection of data, the model has been verified to ascertain its true results. In a nutshell this work uses deep learning methods to combat cassava diseases, supporting agricultural practices and food security. the development of a precise disease detection and prevention model has the potential to significantly increase the capacity for recovery of the cassava crop, improving food production and the quality of life for those whose livelihoods depend on this paramount crop. © 2024 IEEE.

关键词： AlexNet Cassava Deep learning Disease Identification Intervention

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On the evaluation of outlier detection and one-class classification: a comparative study of algorithms, model selection, and ensembles

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DATA MINING AND KNOWLEDGE DISCOVERY 2023年第4期37卷 1473-1517页

作者： Marques, Henrique O. Swersky, Lorne Sander, Jorg Campello, Ricardo J. G. B. Zimek, Arthur Univ Southern Denmark Odense Denmark Univ Alberta Edmonton AB Canada

It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem (Janssens and Postma, in: proceedings of the 18th annual Belgian-Dutch on machine learning, pp 56-64, 2009;Janssens et al. in: proceedings of the 2009 ICMLA international conference on machine learning and applications, IEEE computer Society, pp 147-153, 2009. https://***/10.1109/ICMLA.2009.16). In this paper, we focus on the comparison of one-class classification algorithms with such adapted unsupervised outlier detection methods, improving on previous comparison studies in several important aspects. We study a number of one-class classification and unsupervised outlier detection methods in a rigorous experimental setup, comparing them on a large number of datasets with different characteristics, using different performance measures. In contrast to previous comparison studies, where the models (algorithms, parameters) are selected by using examples from both classes (outlier and inlier), here we also study and compare different approaches for model selection in the absence of examples from the outlier class, which is more realistic for practical applications since labeled outliers are rarely available. Our results showed that, overall, SVDDandGMMare top-performers, regardless of whether the ground truth is used for parameter selection or not. However, in specific application scenarios, other methods exhibited better performance. Combining one-class classifiers into ensembles showed better performance than individual methods in terms of accuracy, as long as the ensemble members are properly selected.

关键词： Semi-supervised learning One-class classification Outlier detection Evaluation Model selection Ensembles

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4th international conference on Inventive Computation and Information Technologies, ICICIT 2022

4th International Conference on Inventive Computation and In...

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4th international conference on Inventive Computation and Information Technologies, ICICIT 2022

ISBN: (纸本)9789811974014

the proceedings contain 68 papers. the special focus in this conference is on Inventive Computation and Information Technologies. the topics include: Jyoti: An Intelligent App for Women Security;performance Analysis of machine learning Algorithms in the Systematic Prediction of Chronic Kidney Disease on an Imbalanced Dataset;robotic Process Automation Powered Admission Management System;implementation of Advanced High Performance Bus to Advanced Peripheral Bus Bridge;the Influence of Digital Financial Literacy on Interest in Investing in the Capital Market of Millennial Generation Students;security Enhancement in IoT with an application of Water Monitoring;decision Support System for Weeding Out Drug Seeking Behavior from Emergency Clinics;analysis of machine learning Algorithm to Predict Symptoms of Diabetes—A Review;sound Mind: Detecting Depression and Anxiety in Humans;a Meta-Classifier Link Prediction Model for False Profile Identification in Facebook;a Survey of Different Identification and Classification Methods for Medicinal Plants;SARS-CoV-2 Mortality Risk Prediction Using machine learning Algorithms to Aid Medical Decision-Making;a Comprehensive Study on Analysis and Prediction of Pollution Data Using machine learning;Utilization of Waste Banks System in the COVID-19 Pandemic;none Shall Pass: A Blockchain-Based Federated Identity Management System;Efficient Big Data Clustering Using Adhoc Fuzzy C Means and Auto-Encoder CNN;covid Analysis Using machine learning Technique;skin Lesion Classification Using machine learning;IOT-Based Weather Monitoring and Weather Prediction Using ML;everTrack: A System to Track History of Used Cars Using Blockchain;Comparison of Various CNN Models for Image Classification;identification of Respiratory Diseases and Diabetes by Non-invasive Method Using IoT.

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Research on Motion Recognition Method in College Dance Teaching Based on Deep learning 6

Research on Motion Recognition Method in College Dance Teach...

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6th international conference on computer Information Science and application Technology, CISAT 2023

作者： Zhao, Qianwen Fan, Mingde Krirk University Bangkok10220 Thailand

ISBN: (纸本)9781510668546

this article proposes a deep learning method for recognizing dance action videos. In this study, a dual path convolutional neural network is proposed to improve the traditional machine learning method. this algorithm can extract pixel information from video images, as well as the differential information of video actions. the Golden Tower algorithm is used to extract the changes in the lattice over time as the change features of convolutional neural networks. this study takes dance action recognition as an example and tests a given dataset. A comparison was made between the existing algorithm Inception V3 network and 3D-CNN network. the test results show that compared with the Inception V3 network, the algorithm proposed in this paper improves Fl by 10.90%. and improves recognition accuracy by 10.85% and 5.27%, respectively. © 2023 SPIE.

关键词： Optical flows

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ICSIMA 2023 - 9th IEEE international conference on Smart Instrumentation, Measurement and applications

ICSIMA 2023 - 9th IEEE International Conference on Smart Ins...

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9th IEEE international conference on Smart Instrumentation, Measurement and applications, ICSIMA 2023

ISBN: (纸本)9798350343380

the proceedings contain 64 papers. the topics discussed include: fast straightforward RR interval extraction based atrial fibrillation detection using Shannon entropy and machine learning classifiers for wearables;augmented reality in education application;scheduling of the battery for peak shaving using model predictive control (MPC);vibration analysis to detect anomalies on railway track using unsupervised machine learning;dorsal vein biometric prototype system using resnet18;development of an iOS application leveraging PalmSens MethodSCRIPT for rapid COVID-19 detection;paddy yield monitoring system;automatic dual-axis solar tracking system for enhancing the performance of a solar photovoltaic panel;optimizing livestock productivity with computer vision-based cow estrus detection in free stall barns using various yolov8 models;and transfer-learning and YOLO V7 hybridized for human cropping for deepfake detection algorithms.

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Agricultural Innovation: Leveraging DenseNet-121 for Superior Paddy Disease Management 4

Agricultural Innovation: Leveraging DenseNet-121 for Superio...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Bhuria, Ruchika Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350387490

Affecting rice crops through different pathogens, paddy diseases can produce symptoms including discolouration and slowed development, therefore affecting production and quality. Respected for its deep learning capacity and dense connection that supports effective feature extraction, the DenseNet121 model was refined for paddy disease classification in order to address this problem. this model is quite good at capturing variability in disease pictures and complicated trends. Accuracy, recall, and F1-score analysis helped to evaluate the DenseNet121 model. High accuracy of the model indicated efficient type of disease diagnosis and reduced false positives. Even although memory scores were somewhat lower, signalling room for improvement in catching all positive occurrences, the Fl-score displayed a great balance between accuracy and recall. this reveals the minimum misclassification capacity of the model in several illness class differences. On a more complicated assessment, however, more complete view of misclassifications across various disease types could come from more insights from the confusion matrix. Even more so is the performance of the model improved by customizing it to certain application criteria-such as giving some diseases top priority or minimizing false positives. © 2024 IEEE.

关键词： Agricultural productivity Crop health Crop yield Dense Convolutional Neural Network (DenseNet-121) Food security machine learning Paddy Sustainable agriculture

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Personality Analysis Based on the Handwriting Shape using Integration of Image Processing and machine learning Algorithm 7

Personality Analysis Based on The Handwriting Shape using In...

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7th international conference on New Media Studies, CONMEDIA 2023

作者： Rosalina Sahuri, Genta Fahmi, Hasanul Moerland, Joe Wesley Mandala, Rila President University Informatics Study Program Indonesia President University Information System Study Program Indonesia President University Magister of Informatics Study Program Indonesia President University Faculty of Computing Indonesia

ISBN: (纸本)9798350307504

In Indonesia, mental health awareness is not as prominent as physical health, and many adolescents have experienced mental health problems. Some people hide their mental and emotional problems by appearing cheerful and peaceful, but research shows that body language and voice tone can reveal a person's inner psychological state. Similarly, handwriting can be analyzed through graphology to reveal personality traits such as honesty, emotional stability, and fears. Graphology is a time-consuming and expensive process, using image processing and machine learning, graphology can be done quickly without human bias. this research aims to predict the personality traits by implement image processing combined with machine learning to analyze handwriting features. Using lazy learner algorithm of K-Nearest Neighbor algorithm, SVM and Naïve Bayes, the application can predict the MBTI personality from handwriting image with an accuracy of 94%, 78%, and 93% respectively. From the experiment, it is shown that the application able to do the analysis of one's handwriting and then predict the personality traits, fully automated using a computer © 2023 IEEE.

关键词： Forecasting

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Covid-19 Detection on X-Ray Image Using Deep learning 4

Covid-19 Detection on X-Ray Image Using Deep Learning

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4th international conference on Intelligent Engineering and Management, ICIEM 2023

作者： Boina, Roja Chaturvedi, Abhay Sharma, Manish Shrivastava, Anurag Kumar, Indradeep Rao, Aln Independent Researcher MorrisvilleNC United States GLA University Department of Electronics and Communication Engineering Uttar Pradesh Mathura281406 India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Aeronautical Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Department of CSE Uttar Pradesh Greater Noida201308 India

ISBN: (纸本)9798350341126

An unexpected pandemic known as COVID-19 struck the entire world in the year 2020. Research in numerous sectors has been prompted to address it as a result of the lack of treatment. Understanding the new variety and developing a vaccination become more challenging as a virus evolves. Numerous nations are impacted by the rise of novel variations. therefore, it is crucial to assess COVID-19's performance in addition to death forecasting. the proposal develops numerous analyses, visualizations, and predictive models that can forecast COVID 19 performance. the goal is to first track data visualization, later Covid 19 data are analyzed and predicted globally to raise awareness by applying machine learning techniques including linear regression, support vector regression, and Holt forecasting method. the objective of this study is to understand how machine learning methods and applications are used for various COVID-19-related tasks and investigations. An analysis of research published in Science Direct, Springer, Hindawi, and MDPI on this topic in 2020 using the search terms COVID-19, machine learning, supervised learning, and unsupervised learning. In total, 16,306 articles were retrieved, but 14 searches from these publications were used in this study. It has been demonstrated that machine learning can be useful for understanding, predicting, and differentiating COVID-19. © 2023 IEEE.

关键词： COVID-19

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