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2022 international conference on machine learning and Knowledge engineering, MLKE 2022

作者： Chen, Shihao Shanghai American School Pudong Campus Shanghai Links Executive Community Shanghai China

ISBN: (纸本)9781665495677

data-driven is an important method of social sciences as the huge amount of cross-media data. Adolescents are at a critical stage of psychological maturity and are most susceptible to the influence of their surroundings and to psychological and behavioral problems. Therefore, adolescent mental health education has become an important part of campus work. The huge amount of cross-media data provides new ways and possibilities for adolescent mental health education, and also poses new challenges for data management and statistics. In this paper, we analyzed the applicability of monitoring methods for adolescent mental health in the context of big data. © 2022 IEEE.

关键词： Information management

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Sentiment analysis of Twitter data during Farmers' Protest in India through machine learning 2

Sentiment analysis of Twitter data during Farmers' Protest i...

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2nd international conference on Computer Science and Software engineering (CSASE)

作者： Singh, Abhiraj Kalra, Nidhi Singh, Amritpal Sharma, Seemu Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala Punjab India

ISBN: (纸本)9781665426329

One of the most essential analysis methods is sentiment analysis using machine learning and tweets as data. The difficulty of performing sentiment analysis of important events was addressed in this work. The farmers' protest, a popular topic on social media for months, was the centre of this article. We can examine the depth of this protest and analyze the data based on various people's viewpoints utilizing data acquired in the form of tweets connected to farmer protests using Twitter APL We investigated various strategies for performing sentiment analysis on datasets we produced by gathering and cleaning data from Twitter using popular protest hashtags. We focused on Naive Bayesian network classifiers, Support Vector machines, and Logistic Regression for machine learning. When working with vast amounts of text data, we can conclude that Naive Bayes is more efficient in Sentiment Analysis than Support Vector machine and Logistic Regression.

关键词： Sentiment Analysis Naive Bayes (NB) Support vector machines (SVM) Logistic Regression Farmers Protest Application Programming Interface (API) machine learning (ML)

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Improvement of Human Activity Recognition (HAR) Performance by Utilizing LSTM in the Structure of Progressive learning 12

Improvement of Human Activity Recognition (HAR) Performance ...

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12th international Scientific conference on Computer Science, COMSCI 2024

作者： Hakim, Ahmed A. M. Yildiz, Solen Kumbay Yilmaz, Atila Saeed, Mohamedalfateh T. M. Hacettepe University Department of Electrical and Electronic Engineering Ankara Turkey Istanbul University-Cerrahpaşa Department of Electrical and Electronic Engineering Istanbul Turkey

ISBN: (纸本)9798350392166

Human Activity Recognition is a crucial area of research in many applications. However, training deep neural network-based models can be expensive and time-consuming. This paper introduces a progressive learning-based approach designed to not only reduce dimensionality but also maintain high accuracy. The proposed method replaces extensive sensor data with four extracted features, derived using a CNN-LSTM-based model trained on the WISDM dataset comprising six activity classes. The extracted features are fed into machine learning algorithms and achieve 97.73% accuracy. © 2024 IEEE.

关键词： Active learning

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Meteorological data Based Detection of Stroke Using machine learning Techniques 33rd

Meteorological Data Based Detection of Stroke Using Machine ...

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33rd international conference on Artificial Neural Networks and machine learning (ICANN)

作者： Marc, Anastasia-Daria Ploscar, Andreea Alina Coroiu, Adriana Mihaela Babes Bolyai Univ Cluj Napoca Romania

ISBN: (纸本)9783031723520;9783031723537

Using meteorological data, this study compares machine learning approaches such as K-Nearest Neighbors, Support Vector machine, and Artificial Neural Networks for detecting days with a greater probability of stroke incidence in the region of Transylvania, Romania. Being the first to address this problem in Romania, the study contributes to previous research by employing machine learning approaches and applying them to meteorological data that also includes air fronts. Furthermore, the study was conducted on data which was collected in a ten-year span (2013-2022). Because the initial dataset had a substantial class imbalance, having the positive class represent 1/20 of the whole dataset, the proposed approaches include dimensionality reduction and clustering techniques. According to the obtained results, the best-performing model is the Support Vector machine, having an accuracy of 63%, a precision of 70%, and a recall of 63%.

关键词： Stroke machine learning Classification Subarachnoid Hemorrhage Weather Air Front

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Early Fire Detection and Alert System using Modified Inception-v3 under Deep learning Framework

Early Fire Detection and Alert System using Modified Incepti...

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2022 international conference on machine learning and data engineering, icmlde 2022

作者： Biswas, Anuraag Ghosh, Swarup Kr Ghosh, Anupam Sister Nivedita University Kolkata700156 India Netaji Shubash Engineering College Kolkata700152 India

ADSI-2019 report states that there were approximately eleven thousand fire accidents reported across India in 2019. In this article, a deep learning model has been developed for fire and smoke detection from images. In this context, we have modified the Inception Convolution Neural Network of the third version (known as Inception-V3) model which is commonly applied for image classification and object discovery tasks for this work. The modified Inception-V3 has been applied on the fire image dataset including smoke for fire image detection. A new optimization function has been incorporated into this model which reduces the computational cost in an efficient way. We have compared the results such as deep learning-based models as well as early fire detection. In this study, the Inception v3 based model yielded the best result with the least number of false positives as compared to the early investigations. © 2023 The Authors. Published by Elsevier B.V.

关键词： Fires

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Deep Reinforcement learning Based Reliable data Transmission Scheme for Internet of Underwater Things in 5G and beyond Networks 2

Deep Reinforcement Learning Based Reliable Data Transmission...

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2nd international conference on machine learning and data engineering, icmlde 2023

作者： Consul, Prakhar Budhiraja, Ishan Garg, Deepak School of Computer Science Engineering and Technology Bennett University Uttar Pradesh India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh India School of Computer Science and Artificial Intelligence SR University Anandsagar Hansaparthy Hanumakomda Telagana India

The Internet of Underwater Things (IoUT) is an interconnected communication ecosystem for underwater devices in maritime environments. IoUT devices range from seabed sensors to ships and boats in oceans which help in diverse practical applications of underwater information ranging from marine transportation, navigation, and underwater exploration to disaster prevention with intelligent monitoring. There exist significant obstacles within the present architecture of the IoUT. This article proposes three distinct methodologies aimed at enhancing the robustness and reliability of the communication architecture for 5G and future networks. Firstly, the employment of the Deep Reinforcement learning approach is advocated for the identification of anomalies and faulty sensors. Secondly, energy consumption is identified as a crucial factor for IoUT devices. To address this concern, the integration of renewable energy sources, namely oxygen rechargeable batteries, with nano-generators is employed. This integration allows for the replenishment of the batteries through the harnessing of kinetic energy from water. Thirdly, the process by which information can be efficiently transmitted from IoUT sensors located on the seabed to the final terrestrial network is delineated, with an emphasis on minimizing the number of hops required. Additionally, a case study is presented, focusing on the mitigation of interference within the IoUT. Lastly, the article delves into the various research challenges and unresolved matters that persist within the realm of IoUT. © 2024 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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Unbalanced Credit Card Fraud Detection data: A machine learning-Oriented Comparative Study of Balancing Techniques

Unbalanced Credit Card Fraud Detection Data: A Machine Learn...

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2022 international conference on machine learning and data engineering, icmlde 2022

作者： Gupta, Palak Varshney, Anmol Khan, Mohammad Rafeek Ahmed, Rafeeq Shuaib, Mohammed Alam, Shadab Department of Computer Engineering and Applications GLA University UP Mathura India Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India College of Computer Science and IT Jazan University Jazan Saudi Arabia

The number of individuals who use credit cards has increased dramatically in recent decades, as has the volume of credit card fraud transactions. Consequently, banks and credit card companies must be able to classify fraudulent credit card transactions so that clients do not have to pay for products they did not purchase. data Science can easily tackle such challenges, and the value of machine learning methodologies cannot be emphasized. The study demonstrates how to model utilizing multiple classifiers and data balance using machine learning approaches to learning about Credit Card Fraud Detection. The data has been observed as an imbalanced dataset that could have inferred not much optimal performance of models. The experimentation on the imbalanced data has been done and observed that XGBoost has yielded good performance with 0.91 precision score and 0.99 accuracy score. The different sampling techniques have been carried out in procedure so as to enhance the scores in terms of precision, recall, f1-score, and accuracy. The Random Oversampling technique has come out to be the best suited technique over the imbalance data and yields 0.99 precision and 0.99 accuracy score, when applied on the best model i.e., XGBoost. The models are then used to compare the results of all of the classifiers employed, resulting in varied conclusions and further research. While working on the study, many data balancing procedures such as oversampling, under sampling, and SMOTE are used, with XGBoost beating residual algorithms with a 99% accuracy score and precision score when Random Over-Sampling is considered. The research suggested the use of data sampling techniques to balance data over the algorithms that show best results under the imbalanced data scenarios, to conclude the best possible performance of the model for fraudulent activities classification. © 2023 The Authors. Published by Elsevier B.V.

关键词： Decision trees

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Robust Object Segmentation using 3D Mesh Models and Self-Supervised learning 2

Robust Object Segmentation using 3D Mesh Models and Self-Sup...

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2nd international conference on machine learning and data engineering, icmlde 2023

作者： Joshi, Amey Kurchania, Hrishitaa Harikrishnan, R. Symbiosis Institute of Technology Pune Campus Symbiosis International Deemed University Pune412115 India

The proposed method presents an innovative solution that addresses the challenges of unlabeled data, robust performance in unknown environments, and foreground-background differentiation in object segmentation. The objective of this work is to improve object segmentation tasks with an unannotated pool of data, derive model structure to demystify and educate large AI models by using knowledge distillation method, implementing framework with self-supervised learning analyse impacting and driving factors. Proposed method handles unlabeled data effectively, improving accuracy and generalization in diverse scenarios. It demonstrates remarkable reliability in unknown environments, ensuring consistent and accurate object segmentation in complex visual contexts. By incorporating novel techniques, the approach overcomes the longstanding problem of achieving consistent and non-trivial solutions for object segmentation, offering a comprehensive and effective solution for computer vision and image analysis. The literature review reveals that the DINO model using a ViT-B/8 backbone achieves an impressive 80.1% accuracy in linear regression, while the DINO model with a ViT-B/16 backbone attains the highest k-NN accuracy at 78.3%. The approach integrates deep neural networks for object segmentation with Kalman filtering for non-linear state estimation. Specifically, depth information obtained from point clouds is directly incorporated into the Unscented Kalman filtering, allowing for efficient and accurate performance. A one-parameter procedure is employed to identify and eliminate irregularities in point clouds, significantly improving the performance of segmenting objects. By incorporating self-supervised learning with distillation, the method effectively predicts and addresses the challenge of refining trivial solutions, leading to improved performance in viewpoint and illumination scenarios. Decision-making is deferred until a global view of the entire frame can be established

关键词： Kalman filters

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Prediction of Load Capacities of Closed-Ended Piles Using Boosting machine learning Methods 5th

Prediction of Load Capacities of Closed-Ended Piles Using Bo...

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5th international conference on New Developments in Soil Mechanics and Geotechnical engineering (ICNDSMGE-ZM)

作者： Karakas, S. Ulker, M. B. C. Taskin, G. Istanbul Univ Cerrahpasa Dept Civil Engn TR-34320 Istanbul Turkiye Istanbul Tech Univ Inst Disaster Management Earthquake Engn Program Istanbul Turkiye

ISBN: (纸本)9783031201745;9783031201721;9783031201714

In this study, a novel data-driven model is developed using boosting-type machine learning algorithms with the aim of predicting the ultimate load-bearing capacities of closed-ended piles. A comprehensive database is gathered using the full-scale load test data with four features. Special boosting type machine learning methods are trained and tested with the database. Once predictions are made, a newly developed machine learning algorithm called Shapley method is utilized to decide the effectiveness of the selected features in predicting pile capacities. Results indicate that the pile cross-section area and length features are sufficient to achieve accurate predictions covering the parameters on the pile side and the CPT-based tip resistance is the only parameter needed on the soil side. While different boosting methods result in different levels of accuracy in predicting the load bearing capacities of closed-ended piles, it is generally possible to determine the minimum number of features necessary to satisfy a high goodness of fit. In the end, optimum number of features are determined in the prediction process using the Shapley method through the boosting algorithms giving us a valuable prediction tool for estimating the bearing capacity of closed-ended piles.

关键词： Boosting algorithms Closed-ended piles CPT test Load-bearing capacity machine learning Shapley method

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Diagnosis of cognitive and motor disorders levels in stroke patients through explainable machine learning based on MRI

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MEDICAL PHYSICS 2024年第3期51卷 1763-1774页

作者： Wang, Meng Lin, Yi Gu, Feifei Xing, Wenyu Li, Boyi Jiang, Xue Liu, Chengcheng Li, Dan Li, Ying Wu, Yi Ta, Dean Fudan Univ Ctr Biomed Engn Sch Informat Sci & Technol Shanghai Peoples R China Fudan Univ Huashan Hosp Dept Rehabil Med Shanghai Peoples R China Fudan Univ Huashan Hosp Dept Radiol Shanghai Peoples R China Fudan Univ Acad Engn & Technol Shanghai Peoples R China Fudan Univ Dept Elect Engn Sch Informat Sci & Technol Shanghai Peoples R China Fudan Univ Ctr Biomed Engn Sch Informat Sci & Technol 2005 Songhu Rd Shanghai Peoples R China

BackgroundGlobally, stroke is the third most significant cause of disability. A stroke may produce motor, sensory, perceptual, or cognitive disorders that result in disability and affect the likelihood of recovery, affecting a person's ability to function. Evaluation post-stroke is critical for optimal stroke *** methods for classifying the clinical disorders of cognitive and motor in stroke patients use assessment and interrogative measures, which are time-consuming, complex, and labor-intensive. In response to the current situation, this study develops an algorithm to automatically classify motor and cognitive disorders in stroke patients by 3D brain MRI to assist physicians in ***, radiomics and fusion features are extracted from the OAx T2 Propeller of 3D brain MRI. Then, we use 14 machine learning models and one model ensemble method to predict Fugl-Meyer and MMSE levels of stroke patients. Next, we evaluate the models using accuracy, recall, f1-score, and area under the curve (AUC). Finally, we employ SHAP to explain the output of the *** best predictive models come from Random Forest (RF) Classifier with fusion features in cognitive classification and Linear Discriminant Analysis (LDA) with radiomics features in motor classification. The highest accuracies are 92.0 and 82.5% for cognitive and motor *** brain maps can classify the cognitive and motor disorders of stroke patients. Radiomics features demonstrate its merits. The proposed algorithms with MRI images can efficiently assist physicians in diagnosing the cognitive and motor disorders of stroke patients in clinical practice. Additionally, this lessens labor costs, improves diagnostic effectiveness, and avoids the subjective difference that comes with manual assessment.

关键词： Fugl-Meyer machine learning MMSE radiomics features rd international conference on learning Representations (ICLR 2015) stroke

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