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CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

引用

intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Gotte Ranjith Kumar Shubham Deepak Banerjee School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The abstract provides a concise overview of a study that focuses on predicting hair breakage levels using a CNN-KNN hybrid model. The study evaluates the model's performance across five Breakage Degrees (BDs) by utilizing precision, recall, and F1-score metrics. Among these degrees, BD2 stands out with precision, recall, and F1-score values of 94.20%, indicating the model's ability to accurately classify hair damage levels between 21% and 40%. The support values, which indicate the number and proportion of instances in each BD, demonstrate a well-balanced distribution across all categories, ensuring the model's robust training. Researchers used a dataset of 10,050 images to reach an impressive 94.02% accuracy in predicting hair breakage levels caused by Telogen Effluvium in the research. Moreover, the model achieves an impressive overall accuracy of 98%, showcasing its effectiveness in distinguishing between various degrees of hair breakage severity. This high accuracy level highlights the model's potential application in clinical settings for dermatological diagnostics and personalized treatment planning in hair care management. The abstract underscores the CNN-KNN hybrid approach as a promising methodology for automated hair health assessment, providing healthcare professionals with a dependable tool to improve diagnostic accuracy and treatment efficacy. By incorporating machine learning techniques, specifically convolutional and k-nearest neighbor algorithms, the study contributes to the advancement of computational methods in dermatology and cosmetology. The results suggest practical implications for enhancing patient care through early detection and intervention of hair-related conditions, ultimately supporting better health outcomes and quality of life for individuals affected by hair breakage concerns.

关键词： Hair Training Measurement Accuracy Dermatology Medical services Machine learning Predictive models Planning Reliability

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Ensemble Deep learning Approach for Identification of DDOS Attack 1st

Ensemble Deep Learning Approach for Identification of DDOS A...

引用

1st international conference on Advances in Artificial Intelligence and Machine learning in Big data Processing, AAIMB 2023

作者： Balakrishnan, C. kumar, V. S. Prassana Department of Computer Science and Engineering S.A.Engineering College Chennai India

ISBN: (纸本)9783031730641

Numerous IoT web apps have been hindered by multiple safety hazards and network intrusions as a result of the home control dataset's on going enhancement. Automating your home has always been centred on safety and the recognition of DDOS Threats. It is essentially possible to make use of numerous IoT web-based resources through input of a set of data for the home's automation or by tapping on a hyperlink in the application itself. The cloud manages the aforementioned difficulties in the concept of the Edge of Things. An attacker can create various web strikes. By embedding executable instructions or injecting illicit software inside a DDOS threat, many simultaneous intricate models are employed to improve system reliability and the ease of maintaining data disclosure. By accurately recognising malicious automated homes information set, it is crucial to enhance the steadfast reliability and integrity of IOT web apps. The site of harmful home automation information set detection according to character-centered material categorization emphasises will be investigated using an artificial learning algorithm system in this work. The fact that hazardous buzz phrases are exceptional home automation information set, it is possible to divide the equipment in an ordinary intelligent home setting into four classes: Class 1 is very high traffic reliability, Class 2 is high traffic certainty, Class 3 is medium traffic accuracy, and Class 4 is low traffic stability. According on the test outcomes, our suggested neural network identification algorithm is actually appropriate for high-accuracy tasks such as categorization. When compared to different categorization designs, the model's accuracy rate is over %. Considerable speculative and empirical advantages for web safety studies can be derived from the use of deep learning to aggregate home automation information to identify Web visitor's goals, which opens up novel possibilities for clever safety studies. © The Author(s), under ex

关键词： Deep learning

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Video Content Analysis and Classification Based on Human Activity Recognition 1st

Video Content Analysis and Classification Based on Human Act...

引用

1st international conference on Computational Technologies and Electronics, ICCTE 2023

作者： Hari, K.C. Pokharel, Manish Shrestha, Sushil Department of Computer Science and Engineering School of Engineering Kathmandu University Dhulikhel Nepal

ISBN: (纸本)9783031819346

Video content analysis holds significant potential in computer vision tasks for the automated understanding and interpretation of human motion and activities. This paper aims to introduce an intelligent framework to recognize human motion and activities within video data. By leveraging advanced techniques in feature extraction, temporal modeling and deep learning, a comprehensive approach that aims to accurately categorize and interpret various human activities is presented in this study. The combination of Convolution layers and Bidirectional Long Short-Term Memory Layers is used to extract the spatial and temporal features of the video enabling the detection of motion patterns and activity sequences. Video classification and Activity Recognition (VCHAR) is developed and trained using the University of Central Florida 101 Classes (UCF101) dataset. This dataset consists of 101 classes of activities such as archery, surfing, typing, swing and so on. The model is tested on day-to-day human activities videos. The study provides the classification of videos based on the activities within the video. The result of this study is compared with previous benchmark results and it showed better accuracy of 87.67%. The finding of this study contributes to improving surveillance security, sports activity, intrusion detection and robotics. Sport media companies, automation Industry, education sector and researchers will be benefitted from this type of study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Empirical study to Select a Place to stay in NCR Area Using data Mining and Machine learning

Empirical Study to Select a Place to Stay in NCR Area Using ...

引用

3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Jain, Nitin Chauhan, Surendra Singh Goel, Ajay Rastogi, Ashish Kumar Raj, Alok Agrawal, Priyanka Chandigarh University Mohali India Srm University Haryana Sonipat India Bhai Gurdas Institute of Engineering & Technology Punjab Sangrur-148001 India Iilm University Greater Noida India XenonStack Pvt. Ltd. Punjab Mohali India XenonStack Pvt. Ltd. Punjab Mohali India

ISBN: (纸本)9798331542375

From starting of the human civilization, the peoples are traveling to one place to another place for different kinds of works such as business, tourism etc. and they are need accommodation to stay there. Before the 21st century, there was a limited option to search the places for accommodation. In the digital world there are options are increasingly in comparison with the previous time. Now if someone going for a vacation for a short term, he or she can find a hotel or lodge for the same, but when students migrate one place to another or a person transfer to another city or place than he need to find a suitable place to stay for a long time. In this research paper, we try to find the best solution for this kind of problem. Here we discuss the result with some predetermined parameters. These parameters set and trained on the collected data from different type of people from different geolocations. After it, the collected data is fed to the K-Means algorithm. This algorithm is used for the making the clusters. After it by using REst API, the output of K-Means algorithms is shown on a Map. © 2025 IEEE.

关键词： K-means clustering

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Workshop on Machine learning in Smart Mobility 21st

Workshop on Machine Learning in Smart Mobility

引用

21st international conference on intelligent data engineering and automated learning

作者： Ferreira, Sara Cardoso, Henrique Lopes Rossetti, Rosaldo J. F. Artificial Intelligence & Comp Sci Lab LIACC Porto Portugal Res Ctr Terr Transports & Environm CITTA Porto Portugal Univ Porto Fac Engn Porto Portugal

The workshop on Machine learning in Smart Mobility (MLSM) was co-located with the 21st international conference on intelligent data engineering and automated learning—IDEAL 2020, held online on November 4–6. © ... 详细信息

ISBN: (纸本)9783030623647;9783030623654

The workshop on Machine learning in Smart Mobility (MLSM) was co-located with the 21^st international conference on intelligent data engineering and automated learning—IDEAL 2020, held online on November 4–6. © 2020, Springer Nature Switzerland AG.

关键词： Machine learning

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Automatic and intelligent Recommendations to Support students' Self-Regulation 21

Automatic and Intelligent Recommendations to Support Student...

引用

21st IEEE international conference on Advanced learning Technologies (ICALT)

作者： Afzaal, Muhammad Nouri, Jalal Zia, Aayesha Papapetrou, Panagiotis Fors, Uno Wu, Yongchao Li, Xiu Weegar, Rebecka Stockholm Univ Dept Comp & Syst Sci Stockholm Sweden

ISBN: (纸本)9781665441063

In this paper, we propose a counterfactual explanations-based approach to provide an automatic and intelligent recommendation that supports student's self-regulation of learning in a data-driven manner, aiming to improve their performance in courses. Existing work under the fields of learning analytics and AI in education predict students' performance and use the prediction outcome as feedback without explaining the reasons behind the prediction. Our proposed approach developed an algorithm that explains the root causes behind student's performance decline and generates data-driven recommendations for action. The effectiveness of the proposed predictive model that constitutes the intelligent recommendations is evaluated, with results demonstrating high accuracy.

关键词： learning analytics Counterfactual Explanations intelligent Recommendations Self-Regulation Artificial Intelligence

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On Random-Forest-Based Prediction Intervals 1

引用

21st international conference on intelligent data engineering and automated learning

作者： Calvino, Aida Univ Complutense Madrid Dept Stat & Data Sci Madrid Spain

ISBN: (数字)9783030623623

ISBN: (纸本)9783030623616;9783030623623

In the context of predicting continuous variables, many proposals in the literature exist dealing with point predictions. However, these predictions have inherent errors which should be quantified. Prediction intervals (PI) are a great alternative to point predictions, as they permit measuring the uncertainty of the prediction. In this paper, we review Quantile Regression Forests and propose five new alternatives based on them, as well as on classical random forests and linear and quantile regression, for the computation of PIs. Moreover, we perform several numerical experiments to evaluate the performance of the reviewed and proposed methods and extract some guidelines on the method to choose depending on the size of the data set and the shape of the target variable.

关键词： Quantile Regression Coverage probability Skewness Kurtosis Machine learning

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automated information aggregation for scaling scale-resistant services 06

Automated information aggregation for scaling scale-resistan...

引用

21st IEEE/ACM international conference on automated Software engineering, ASE 2006

作者： Gross, Philip Kaiser, Gail Columbia University Department of Computer Science New York NY 10027

ISBN: (纸本)0769525792

Machine learning provides techniques to monitor system behavior and predict failures from sensor data. However, such algorithms are "scale resistant" - high computational complexity and not parallelizable. The problem then becomes identifying and delivering the relevant subset of the vast amount of sensor data to each monitoring node, despite the lack of explicit "relevance" labels. The simplest solution is to deliver only the "closest" data items under some distance metric. We demonstrate a better approach using a more sophisticated architecture: a scalable data aggregation and dissemination overlay network uses an influence metric reflecting the relative influence of one node's data on another, to efficiently deliver a mix of raw and aggregated data to the monitoring components, enabling the application of machine learning tools on real-world problems. We term our architecture Level of Detail after an analogous computer graphics technique. © 2006 IEEE.

关键词： learning systems

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learning User Comfort and Well-Being Through Smart Devices 21st

Learning User Comfort and Well-Being Through Smart Devices

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21st international conference on intelligent data engineering and automated learning

作者： Sousa, David Silva, Fabio Analide, Cesar Univ Minho ALGORITMI Ctr Dept Informat Braga Portugal Politecn Porto ESTG CIICESI Felgueiras Portugal

ISBN: (纸本)9783030623616;9783030623623

This article aims to provide a large-scale study, without geographical restrictions, on how people's habits can influence their comfort and well-being. In this sense, sensing techniques are used, through smart devices, such as the smartphone, with the main objective of collecting information about the user and the environment that surrounds him. The collected data are subsequently processed, and several models of deep learning are built that aim to predict the well-being and comfort in the different environments in which a person is inserted. However, due to the pandemic, the main focus has been changed and the main objective is to understand if it is possible to predict comfort and well-being in the quarantine.

关键词： Comfort Deep learning Supervised learning Well-being

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MongoDB scheme analysis 21

MongoDB scheme analysis

引用

21st IEEE international conference on intelligent engineering Systems (INES)

作者： Vokorokos, Liberios Uchnar, Matus Balaz, Anton Tech Univ Kosice Dept Comp & Informat Letna 9 Kosice 04200 Slovakia

ISBN: (纸本)9781479976775

The aim of this work is to create a web administration interface for the MongoDB database to analyze the stored data. The application design was based on the needs of potential users and similar solutions that already exist. The created tool consists of a console application that performs the necessary analysis and a web application that visualizes the results. Console application can also be used separately and can be integrated into other tools. The created application is faster than existing solutions, has more features and has been tested by automated testing and by real users.

关键词： Bigdata data Analysis MongoDB NoSQL Web Application

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