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Research on the Analysis and Prediction Model of Health Monitoring data Based on the Internet of things

Research on the Analysis and Prediction Model of Health Moni...

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Signal Processing, Communications and Engineering Management conference (ISPCEM), international

作者： Liling Zhao Zhaomiao Gong School of Nursing Chongqing Medical and Pharmaceutical College China Personnel Division Chongqing Medical and Pharmaceutical College China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

this research centers on the development of an advanced model for analyzing and forecasting elderly health monitoring data, leveraging the Internet of things (IoT) technology. A comprehensive health monitoring system specifically designed for the elderly is established, enabling the real-time acquisition and transmission of daily health metrics through the integration of IoT technologies. During the data analysis phase, an association rule mining algorithm is utilized to identify hidden patterns and correlations within the elderly health data, thereby facilitating the prediction of health outcomes. A subsequent health prediction model is formulated, incorporating data mining and machine learning methodologies, with its effectiveness and reliability rigorously assessed through simulation experiments. the outcomes of these simulations demonstrate a significant enhancement in both the precision and efficiency of the prediction model. this progress provides a solid scientific basis for the health management of the elderly population. Further model simulations confirmed the accuracy and practicality of the developed prediction model, underscoring its extensive potential for application in elderly health management.

关键词： Analytical models data analysis Signal processing algorithms Predictive models Prediction algorithms data models Real-time systems Association rule learning Internet of things Older adults

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Random Forest based Fake Job Detection

Random Forest based Fake Job Detection

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Spandhana Reddy Akiti Akash Bathini Sateesh Kumar Kanagala Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

the intersection of computer science and social sciences leads an increasing problem: fraudulent job ads. their presence can have far-reaching negative ramifications that must be dealt with immediately in order to limit further harm to society. Due to the vast amounts of data accumulated worldwide, effective ways must be found for distinguishing legitimate job advertisements from fraudulent ones. this paper proposes a machine learning approach designed to differentiate between fraudulent and non-fraudulent job postings, offering an automated tool to mitigate the proliferation of fraudulent job listings on the internet. the methodology relies on a range of machine learning classification techniques to analyze web-based job postings, comparing the efficacy of different classifiers in identifying employment scams. the primary objective is to establish a robust model for the detection of fake job posts amidst the overwhelming volume of online listings. this research recommends employing various data mining and categorizing algorithms such as Decision Tree and Support Vector machine, Naive Bayes classifier, Random-forest classifier and Multilayer Perceptron to detect whether an advertisement is genuine. this study has utilized Kaggle data with the proposed classifier enhancing 99.48% accuracy when classifying fake job ads. Additionally, this method provides techniques to detect employment scams to meet applicants' need to protect themselves against scammers.

关键词： Support vector machines Economics Solid modeling machine learning algorithms Accuracy Employment Social sciences

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A Framework for Video Based Sign Language Interpretation Using machine learning and Statistical Methods

A Framework for Video Based Sign Language Interpretation Usi...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Swathi D Susmetha K Sreeji. S S. Mangairkarasi B.E Computer Science and Engineering with Data Science Sathyabama Institute of Science and Technology Semmancheri Chennai TamilNadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Semmancheri Chennai TamilNadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

the Sign Language recognition System has been designed to capture video input, process it to detect hand gestures, and translate these gestures into readable text. the project consists of several key components and steps: Video Processing: Using OpenCV, the system captures frames from the video input. MediaPipe processes these frames to detect and track hand landmarks in real time. OpenCV capabilities allow for efficient frame extraction and basic image processing tasks such as resizing and normalization. Hand Detection and Tracking: MediaPipe pre-trained models identify and track hand movements within the video frames. the accurate detection and tracking of the hand movements are critical for the subsequent recognition of the sign language gestures. Sign Language recognition: the core system is the deep learning model, trained using the TensorFlow and Keras on a dataset of sign language gestures. the model learns to classify the detected hand movements into corresponding sign language characters or words. Convolutional Neural Networks (CNNs) are typically used for task due to their effectiveness in image recognition tasks. Text Display: Once the system recognizes the signs, it converts them into text and displays the output. this can be done through a console output or a graphical user interface (GUI) built with Tkinter. the GUI provides a user friendly experience, allowing users to see the translated text in real-time.

关键词： Hands Deep learning Sign language Translation Tracking Text recognition Speech recognition Streaming media Real-time systems Graphical user interfaces

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Feasibility Study of using MyoB and for learning Electronic Keyboard 25

Feasibility Study of using MyoB and for Learning Electronic ...

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25th international conference on pattern recognition (ICPR)

作者： Mani, Sharmila Rao, Madhav Samsung Res India Bangalore 560037 Karnataka India IIIT Surg & Assist Robot Lab Bangalore 560100 Karnataka India

ISBN: (纸本)9781728188089

learning musical instruments like piano or electronic keyboard on average takes a decade. Currently, learning an instrument requires continuous supervision from a tutor, and it often becomes unrealistic to stay connected with the music tutor for a long time. Online music learning platforms remain unscalable as there is no way for these platforms to verify whether user pressed a key note with the intended finger, which is significant for learning finger based musical instruments. To overcome this, an autonomous system to evaluate and guide in the learning process by continuously tracking finger movements via a non-camera based solution is proposed. Finger press triggers the muscle movements which are detected at the surface of the forearm in the form of surface Electromyography (sEMG) signals. the paper proposes tracking of finger press for 10 seconds on an electronic keyboard using MyoBand [1] wearable device that provided 8 channels of sEMG signals. Eleven time and frequency domain features of sEMG signals were extracted from sEMG signals to classify musical note played by the instrument on corresponding finger press. the feature set was standardized using standard scaler approach, and vector dimensions were reduced by Linear Discriminant Analysis (LDA) method. the resulting reduced dimension data was applied on Random Forest (RF) classifier to report best classification accuracy for our application. Experiments involved single finger press to render a note in the musical instrument, and multiple finger press to define chord sequence on an electronic musical keyboard. Further analysis was performed to maximize the classification accuracy over the number of trials and optimize the position of electrodes for successful identification of musical note played. the proposed method achieves a classification accuracy of 74.25% for 5 musical note played on an electronic keyboard instrument with 4 MyoBand electrodes, and an accuracy of 95.83% with one electrode for identifyi

关键词： Electrodes

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Non-destructive Detection of Moldy Camellia Oleifera Seeds Based on Deep learning and Hyperspectral Imaging

Non-destructive Detection of Moldy Camellia Oleifera Seeds B...

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pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Zhenyu Han Yongli Zhang Guangfei Zhu Xuan Liu Hao Tang Tailin Han School of Electronic and Information Engineering Changchun University of Science and Technology Changchun China Academy of Agricultural Planning and Engineering Key Laboratory of Agro-Products Primary Processing Ministry of Agriculture and Rural Affairs Beijing China School of Information and Communication Engineering Hainan University Haikou China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Camellia oleifera, a woody oil plant of high economic value, faces potential mold growth during storage and transportation, which could significantly impact its sales and processing. therefore, it is crucial to identify and select moldy camellia oleifera seeds. Due to the fact that the mold growth of camellia oleifera seeds typically originates from the inside of the seeds, the mold is not visibly apparent on the surface of the shells in the early stages of mold growth. the existing chemical methods are time-consuming, labor-intensive, and result in sample damage. To address this challenge, this study introduces a non-destructive detection method for assessing the extent of mold growth in camellia oleifera seeds efficiently and effectively. the method is based on deep learning and hyperspectral imaging (HSI) technology. A hyperspectral imaging system was utilized to capture hyperspectral images of camellia oleifera seeds at three degrees of mold. In response to the limitations of traditional machine learning methods in hyperspectral classification, which only focus on learning spectral features and yield unsatisfactory results, this study introduces a deep learning approach based on three-dimensional convolution. this algorithm is capable of extracting features from both spatial and spectral information. the classification performance of the proposed model is compared with machine learning methods that rely solely on spectral information and deep learning methods that use only spatial information from visual images. Based on the dataset constructed in this study, experimental results demonstrate that the proposed method achieves an accuracy of 88.99%, surpassing other methods by at least 18.6%.

关键词： Deep learning Visualization Accuracy Transportation Feature extraction Classification algorithms data mining Surface treatment Hyperspectral imaging Testing

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Skin Disease Classification using machine learning based Proposed Ensemble Model

Skin Disease Classification using Machine Learning based Pro...

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Emerging Technology (INCET), international conference for

作者： Bisahu Ram Sahu Akhilesh Kumar Shrivas Abhinav Shukla Department of IT & CS Dr. C.V. Raman University Kota Bilaspur C.G. India Department of CSIT Guru Ghasidas Vishwavidyalaya Bilaspur C.G. India

Skin disease is a major issue of global health problem affecting a large amount of persons. the advancement of dermatological diseases categorization has grown more accurate in recent years due to the rapid growth of technology and the use of various machine learning techniques. therefore the creation of machine learning methods that can accurately differentiate between the classifications of skin diseases is one of the great importance. this research work focuses on the classification of different kinds of skin diseases using machine learning techniques. In this research, we introduce a novel approach that makes use of four distinct data mining techniques like support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF) and, naive bayes (NB) algorithm. this research work proposed an ensemble model that is combination of SVM, KNN, RF and NB using voting scheme. the proposed model classified the skin disease into five different classes that are Acne, Skin allergy, Nail fungus, Hair loss, and Normal skin. the proposed ensemble model used on skin disease classification that gives better performance over other classifier algorithms. the proposed ensemble model achieved highest 97.33% of accuracy as compared to others.

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ICCBD 2021 - 2021 4th international conference on Computing and Big data

ICCBD 2021 - 2021 4th International Conference on Computing ...

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4th international conference on Computing and Big data, ICCBD 2021

ISBN: (纸本)9781450387194

the proceedings contain 23 papers. the topics discussed include: UDON: unsupervised data selection for biomedical entity recognition;Philippine economic growth: GDP prediction using machine learning algorithms;prediction of information dissemination scale in large-scale online social networks based on machine learning: a comparative analysis;a term selection method combined with sentiment score for twitter sentiment analysis;machine learning-based pipeline for enterprise cross-department conflict data management;analysis on big data based intelligence processing method of electronic reconnaissance satellites;enterprise big data management based on knowledge graph;backpropagation neural network in flexural members: prediction of capacity reduction of beams with cold joints using the angle of inclination and discontinuity location;Covid-19 tweets analysis with topic modeling;and exploratory methods for imbalanced data classification in online recruitment fraud detection: a comparative analysis.

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pattern Discovery in an EEG database of Depression Patients: Preliminary Results

Pattern Discovery in an EEG Database of Depression Patients:...

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international conference on Measurement

作者： Kateřina Hlaváčková-Schindler Christina Pacher Claudia Plant Mykola Lazarenko Milan Paluš Jaroslav Hlinka Aditi Kathpalia Martin Brunovský Data Mining and Machine Learning Research Group Faculty of Computer Science University of Vienna Vienna Austria Department of Complex Systems Institute of Computer Science Czech Academy of Sciences Prague Czechia Clinical Research Programme National Institute of Mental Health Klecany Czechia

the ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolonged intervals of suffering. Symptom alleviation requires 4-6 weeks after starting current antidepressive medication. Based on the data basis of the patients and their EEG before and on the 7th day of treatment we apply data mining, causal discovery and machine learning approaches to discover interactive patterns between patient’s brain regions to separate the treatment responders from non-responders. In this paper we report the preliminary results of our international project "learning Synchronization patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants" ongoing at the University of Vienna, the Czech Academy of Sciences and the National Institute of Mental Health in the Czech Republic.

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Enhancing Engineering Education: the Role of Artificial Intelligence in Personalizing learning and Outcomes

Enhancing Engineering Education: The Role of Artificial Inte...

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Big data Engineering and Education (BDEE), international conference on

作者： Gabriela Dorfman Furman Department of Electrical & Electronic Engineering Shenkar College of Engineering Design and Art Ramat-Gan Israel

ISBN: (数字)9798350388268

ISBN: (纸本)9798350376463

the emergence of artificial intelligence (AI) in educational contexts presents transformative potential for higher engineering education. this paper conducts a comprehensive literature review to explore the role of AI-driven analytics such as personalized learning pathways and adaptive assessment techniques, in personalizing learning experiences and improving educational outcomes for engineering students. through a systematic analysis of existing research, we identify how AI technologies - such as machine learning algorithms, data mining, and natural language processing - can be utilized to tailor educational content, improve learning engagement, and optimize curriculum design. A synthesis of the findings highlights the significant benefits of AI in identifying diverse learning styles, predicting academic performance, and providing real-time feedback, thus fostering a more individualized and effective learning environment. In addition, this article discusses the challenges and ethical considerations associated with implementing AI in education, including data privacy, algorithmic bias, and the need for human oversight to ensure ethical AI deployment. Building upon insights gleaned from a thorough review of published literature, we propose a conceptual framework for integrating AI into engineering education, with the aim of equipping the students with the skills and knowledge required to navigate the complexity of the modern engineering landscape. this theoretical exploration underscores the transformative potential of AI in engineering education and sets a foundation for future empirical research to further investigate and expand upon these benefits and strategies.

关键词： Ethics data privacy Systematics machine learning algorithms learning (artificial intelligence) Prediction algorithms Real-time systems Natural language processing Artificial intelligence Engineering education

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Prediction and detection of diabetes using machine learning 4

Prediction and detection of diabetes using machine learning

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4th international conference on Recent Trends and Applications in Computer Science and Information Technology, RTA-CSIT 2021

作者： Llaha, Olta Rista, Amarildo South East European University Arhiepiskop Angelarij Skopje 1000 North Macedonia Skopje Macedonia

data mining and machine learning have become a vital part of different disease detection and prevention. One of them is diabetes. the purpose of this paper is to evaluate data mining methods and their performances that can be used for analyzing the collected data about the diabetes. We identified the most appropriate data mining methods to analyze the data by comparing them theoretically and practically. Some attributes of this dataset are: Age, Body Mass Index, Insulin, Glucose, etc. Methods are applied on these data to determine their effectiveness in analyzing and preventing diabetes. Evaluations on the data showed that the method with a higher performance is "Decision Tree". this was achieved by some performance measures, such as the number of instances correctly classified, accuracy, precision, recall and F-measure, that has brought better results compared to other methods. We come to the conclusion that the data mining methods and machine learning contribute to the predictions on the possibility of occurrence of the diabetes. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： data mining

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