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Sweat colorimetric detection system based on OpenMV

Sweat colorimetric detection system based on OpenMV

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machine learning and Computer Application (ICMLCA), international conference on

作者： Yuanyuan Meng Yanxia Chen Chennong Li Yandi Liu Lei Qin Beijing Sensor Key Laboratory Beijing Information Science & Technology University Beijing China College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

the demand for real-time sweat biosensing is increasing in daily health monitoring. Colorimetric detection, as a simple and intuitive method, plays an important role in sweat biosensing. However, achieving an instant and effective colorimetric detection remains a challenge in real-time sweat biosensing for daily health monitoring. Herein, we design a sweat colorimetric detection system based on OpenMV. the colorimetric detection system is capable of sensitively capturing images and quickly recognizing color changes. the colorimetric detection system based on OpenMV provides a simple and effective technology to realize real-time sweat detection and shows great potential as a colorimetric detection system platform for color recognition and image processing. We believe that our colorimetric detection system will have great potential applications and offer effective means for the design of smart biosensors and have great potential in the development of portable and intelligent sweat analysis equipment.

关键词： Image recognition Image color analysis machine learning Computer applications Sensor systems and applications Real-time systems Biosensors data mining Monitoring

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Age Prediction on social media using machine learning and AI 5

Age Prediction on social media using Machine Learning and AI

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5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Khare, Ojasw Agrawal, Kadambari Sharma, Aastha Awasthi, Sandhya Prakash, Ayushi Abes Engineering College Department Of Cse UP Ghaziabad India

ISBN: (纸本)9798350330861

Personalized marketing, age-based demographic analysis, and facial recognition systems are just a few of the many uses for accurate age prediction from facial photos. With the FG-NET dataset, a benchmark dataset frequently used for age estimation tasks, we investigate the usage of deep learning approaches for age prediction in this research. Convolutional neural networks (CNNs) are used in our method to extract meaningful representations from facial photos and forecast the related age labels. We upgrade the pretrained model on the FG-NET dataset using a cutting-edge architecture, such as VGGNet or ResNet, to enhance age prediction accuracy. We adhere to a strict experimental setup to achieve reliable evaluation. We have divided the FG-NET dataset into training, validation, and test sets, making sure that the age groups in each set were distributed equally. To lessen overfitting and increase generalization, we preprocess the photos by aligning the faces, pixel-value normalization, and data augmentation. We use appropriate loss functions, like mean squared error or cross-entropy loss, during the training phase to optimize the network's parameters. In order to improve the performance of the model, we additionally investigate the impact of other hyper parameters, including learning rate, batch size, and regularization *** the model has been trained, we assess its effectiveness using measures like mean absolute error (MAE) and mean squared error (MSE) on the test set. To confirm the efficacy of our strategy, we compare our findings with those of other cutting-edge approaches for age prediction using the FG-NET dataset. Our tests show that age prediction tasks can be successfully completed by deep learning models trained on the FG-NET dataset. Our method advances facial analysis research by reliably determining an individual's age from facial photos, and it creates opportunities for real-world applications in numerous fields. © 2023 IEEE.

关键词： Forecasting

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Analysis of Viral and Non-viral Categories of Video through Social Media Using machine learning Techniques 15th

Analysis of Viral and Non-viral Categories of Video Through ...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Nagashree, B.A. Satheesh Kumar, S. Muthukumaran, V. Joseph, P. Rose Bindu Amrita Vishwa Vidyapeetham Mysuru Campus Mysuru570026 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Mathematics Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9783031889912

People frequently use technological means to collect information from many sources, share it with others on social media, and communicate with friends on these platforms. Social media is therefore a crucial new tool for communication. Collaborative wikis, blogs, and content communities are just a few of the tools and technologies that make up social media. YouTube is the most widely used of these. the influencer’s content gets visible to the users of such platforms. the expansion of social media use has enabled massive and rapid spread ability of content, often called virality. this paper aims to examine the virality of different categories of YouTube videos using machine learning technique, where it plays a major role in analysis of YouTube data such as likes, dislikes, comment count and views. YouTube can serve as a path to virality and fabricate online videos, hence finding the correlation analysis among the videos, which enables us to classify the viral and non-viral video categories, thereby analysing the linear relationship among the features (likes, dislike, views and comment count. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tweets

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Method of Dance Action recognition Based on Path Optimization

Method of Dance Action Recognition Based on Path Optimizatio...

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5th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2024

作者： Wang, Dan School of Music Jianghan University Hubei430056 China

this article aims to construct a dance action recognition model based on path optimization. through trajectory analysis and feature extraction of dance movements, combined with path optimization algorithms to accurately describe dance movement paths, and combined with machine learning or deep learning models for action recognition. After extensive experimental data verification, the model can effectively improve the accuracy and efficiency of dance action recognition, providing a new and reliable solution for dance teaching, digital preservation of dance art, and generation of dance creativity. © 2025 the Authors. Published by Elsevier B.V.

关键词： Deep learning

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Leveraging Sequential pattern Information for Active learning from Sequential data 25

Leveraging Sequential Pattern Information for Active Learnin...

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25th international conference on pattern recognition (ICPR)

作者： Fidalgo-Merino, Raul Gabrielli, Lorenzo Checchi, Enrico European Commiss Joint Res Ctr JRC Ispra Italy CNR Ist Sci & Tecnol Informaz I-56124 Pisa Italy

ISBN: (纸本)9781728188089

this paper presents a novel active learning technique aimed at the selection of sequences for manual annotation from a database of unlabelled sequences. Supervised machine learning algorithms can employ these sequences to build better models than those based on using random sequences for training. the main contribution of the proposed method is the use of sequential pattern information contained in the database to select representative and diverse sequences for annotation. these two characteristics ensure the proper coverage of the instance space of sequences and, at the same time, avoids over-fitting the trained model. the approach, called SPIAL (Sequential pattern Information for Active learning), uses sequential pattern mining algorithms to extract frequently occurring sub-sequences from the database and evaluates how representative and diverse each sequence is, based on this information. the output is a list of sequences for annotation sorted by representativeness and diversity. the algorithm is modular and, unlike most current techniques, it is independent from the sequential machine learning technique that builds the model as SPIAL does not need to know the set of the features taken into account by the training algorithm. Experiments done on well-known benchmarks involving sequential data show that the models trained using SPIAL increase their convergence speed while reducing manual effort by selecting small sets of very informative sequences for annotation. In addition, the computation cost using SPIAL is much lower than for the state-of-the-art algorithms evaluated.

关键词： database systems

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Diabetes Prediction using Support Vector machine

Diabetes Prediction using Support Vector Machine

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Emerging Technology (INCET), international conference for

作者： K. Sagar Sk. Imtiyaz A. Arvindh A. Shiva Nagaraju Ch. Rajendra Prasad P. Kiran Kumar Dept. of ECE SR University Warangal India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Diabetes affects people worldwide, in both developed and developing nations, and is a serious health concern. the Worldwide Diabetes Federation reports that 285 million people globally are living with diabetes at this time, and in the next 20 years, that figure is predicted to rise to 380 million. Scientists are working on a very efficient, low-cost way to identify diabetes, which is vital to treat at an early stage. data mining techniques for accurately predicting diabetes are often tested against the machine learning lab at UCI's Pima Indian diabetes database. In this research, a classifier for diabetes detection using support vector machine (SVM) machine learning technique is proposed. the primary objective is to effectively categorize diabetes from complex medical data. the outcomes of the trial suggest that Support Vector machine has potential for accurate diabetes diagnosis.

关键词： Support vector machines Deep learning databases Predictive models Vectors Diabetes data mining

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MMINN: Management-Model Informed Neural Network with Interpretability and Causal Relationship 5

MMINN: Management-Model Informed Neural Network with Interpr...

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5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Li, Shuang Hou, Si-Ze Yao, Yutong Sun, Yongqi Ding, Bin Xi'an Jiaotong-Liverpool University International Business School Suzhou China Xi'an Jiaotong-Liverpool University Department of Applied Mathematics Suzhou China Xi'an Jiaotong-Liverpool University Data Science and Big Data Technology Suzhou China Xi'an Jiaotong-Liverpool University Department of Financial and Actuarial Mathematics Suzhou China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Deep neural networks have achieved near-human accuracy levels in various classification and prediction tasks, such as in processing image, text, speech, and video data, while the scholars in the management research may have never widely recognized the method. the lack of use may be because the current neural network is still treated as the black box, which lacks the interpretability and the capability to explain the causal relationship in the observational social phenomenon. To extend this method to the management field, this paper puts forward an improved neural network model based on the management theories regarding decision-making strategies. Large-scale datasets of complex project management game simulations are made interpretable by introducing the improved deep learning methods, and causal relationships are explained by incorporating time series methods. this is based on the positivism in social science that the variables in management model should be with the time-series relationships. However, what the result of traditional empirical method based on statistics deduced is the correlation rather than the causality. To improve the methodology in management field, we take a project decision-making simulation game as the research object and conduct experiments using the data collected by Yu [13] on teamwork decision-making. this paper finally constructs a deep learning method based on decision-making strategy, creating a new research paradigm in management. Our method achieves a significant and consistent improvement as compared to other baselines. © 2022 IEEE.

关键词： Decision making

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Coupled learning for Kernel Representation and Graph Tensor in Multi-view Subspace Clustering 5th

Coupled Learning for Kernel Representation and Graph Tensor ...

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5th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Chen, Man-Sheng Wang, Chang-Dong Huang, Dong Lai, Jian-Huang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China

ISBN: (纸本)9783031189098;9783031189104

An increasing amount of attention have been attracted in multi-view subspace clustering, whose impressive performance can be achieved by means of the self-expressive property, under the assumption of linear relations between multi-view data samples. However, most of them fail to recover the nonlinear relations between multi-view data for deeper study, and additionally they are incapable of discovering the comprehensiveness and higher-order correlations among multiple views. To deal with these challenges, we propose a novel model termed Coupled learning for Kernel Representation and Graph Tensor (CLKT) in Multi-view Subspace Clustering, where both nonlinear relations and higher-order correlations among multiple affinity graphs are jointly learned in a unified framework. Optimal solutions of the proposed method can be obtained by an alternative minimizing optimization strategy. Extensive experiments on six real-world datasets indicate the superiority of CLKT compared with the state-of-the-art methods.

关键词： Multi-view clustering Kernel learning Low-rank tensor representation

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An Exploration of the Optimal Feature-Classifier Combinations for Transradial Prosthesis Control 44

An Exploration of the Optimal Feature-Classifier Combination...

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44th Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Douglas, Fraser Gover, Harry Docherty, Cheryl Shields, Gordon Leventi, Konstantina Di Caterina, Gaetano Univ Strathclyde Elect & Elect Engn Dept Glasgow Lanark Scotland Univ British Columbia Human Mot Biomech Lab Sch Biomed Engn Vancouver BC Canada

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Within state-of-the-art gesture-based upper-limb myoelectric prosthesis control, gesture recognition commonly relies on the classification of features extracted from electromyographic (EMG) data gathered from the amputee's residual forearm musculature. Despite best efforts in broadly maximizing gesture recognition accuracy, there does not yet exist a feature-classifier combination accepted as best-practice. In turn, this work hypothesizes that no single feature-classifier combination can consistently maximize accuracy across subjects, positing instead that control schemes should be personalized to the individual. To investigate this hypothesis, the study employed the 40-subject, 49-gesture Ninapro database 2 (DB2) to compare the performance of 7 different historic, more recent, and state-of-the-art feature sets when classified by 5 machine learning algorithms commonly seen within EMG-based pattern recognition literature. the results demonstrate the ability of Linear Discriminant Analysis (LDA) to marginally exceed other more computationally intensive classifiers in terms of mean accuracy, while the feature set which maximized the highest proportion of individuals' accuracies was shown to vary with both classifier choice and gesture count.

关键词： machine learning algorithms databases Gesture recognition Feature extraction Electromyography Linear discriminant analysis Classification algorithms

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Design Amharic Text Sentiment Analysis Model Using machine learning Techniques. in Case of Restaurant Reviews 5

Design Amharic Text Sentiment Analysis Model Using Machine L...

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5th international conference on Information and Communication Technology for Development for Africa, ICT4DA 2023

作者： Gedif, Birku Alemu, Abebe Assefa, Yibeltal Nibret, Shegaw College of Informatics University of Gondar Information Science Ethiopia College of Informatics University of Gondar Computer Science Ethiopia Kombolcha Institute of Technology Wollo University Software Engineering Ethiopia Bahir Dar Institute of Technology Bahir Dar University ICT4D Research Center Ethiopia

ISBN: (纸本)9798350328486

Sentiment analysis is a type of natural language processing for tracking the attitude of the public about a particular product, service or topic. It is also highly challenging as natural language processing research topic, and covers many novel sub-problems. Now business organizations and academics are putting forward their efforts to find the best system for sentiment analysis. the focus of this study was an Amharic unstructured restaurant review on the web. the objective of the paper was to design Amharic text sentiment analysis model using supervised machine learning techniques and evaluate the performance of classifiers. this paper explored the supervised machine learning classification approaches (naive Bayes, support vector machine and k-nearest neighbor) with different feature selection schemes to obtain a sentiment analysis model for domain specific restaurant review dataset at sentence level. the proposed model has the following components: data preparation, preprocessing such as tokenization, normalization, filter stop words, feature extraction and selection to prepare feature vector, polarity classification. Performance analysis carried out on classifiers, based on n-grams proposed. From the results of the experimental studies, all algorithms are known to be highly effective classifiers, and are able to achieve good accuracy in this experiment. the experiments show that Term frequency (TF) and the TF-IDF scheme gives maximum accuracy 80.43 % and 79.49 % respectively for SVM in bigram features. Term frequency and term occurrence also give maximum accuracy 78.37 % and 78.00 % respectively for Naive Bayes classifier at bigram features. TF-IDF also give maximum accuracy 78.00% for KNN at 4-gram. the challenge was opinion holders sometimes give objective text to express their opinion, but the classifier did not identify those facts from opinions. these kinds of complexities of natural languages make sentiment mining systems more challenging and to resolving th

关键词： Sentiment analysis

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