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SipSmart: Elevating Palates with machine learning for Wine Quality Prediction 6

SipSmart: Elevating Palates with Machine Learning for Wine Q...

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6th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Talwandi, Navjot Singh Khare, Shanu Yadav, Anup Lal Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350306613

this study explores the request of Mechanism Education (ML) techniques for predicting wine quality, aiming to enhance the understanding and precision in assessing the characteristics of different wine varieties. the dataset utilized encompasses diverse oenological parameters, such as acidity, residual sugar, alcohol content, and more. Various ML algorithms, including regression and classification models, were employed to analyze and predict wine quality based on these features. the results demonstrate the effectiveness of ML in discerning patterns and providing accurate predictions, offering valuable insights for both connoisseurs and the wine *** classification poses a formidable challenge, exacerbated by the elusive criteria that define a good-tasting wine. Certification processes currently rely on subjective sensory analysis conducted by human tasters. To address this, our study aims to predict wine quality, offering a more objective approach. We leveraged a dataset featuring various physicochemical properties of red and white variantsof Portuguese 'Vinho Verde' wine, sourced from Kaggle, and utilized Google Colab for analysis. machine learning algorithms were employed to discern ex- ceptional or subpar wine qualities. data preprocessing involved identifying and handling missing values, while One-Hot encoding transformed categorical values into numerical ones. Feature Scaling normalized the range of independent variables. Our predictive models, including Logistic Regression, Decision Tree Classifier, Random Forest Classifier, and Extra Trees Classifier, underwent training and evaluation. Comparisons of accuracyand precision guided the selection of the most effective machine learning algorithm. this process enables us to predict wine quality on a scale of 0-10 based on a given set of features. the results contribute to the refinement of wine quality assessment, offering a more standardized and data-driven approach than traditional sensory analysis © 2024

关键词： Classification Models data mining Feature Analysis machine learning Oenological Parameters Predictive Analytics Regression Models Wine Quality

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Multilingual Speech Identification Framework (MSIF) A Novel Approach in Language Identification 10th

Multilingual Speech Identification Framework (MSIF) A Novel ...

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10th Biennial international conference on pattern recognition and machine Intelligence (PReMI)

作者： Sawalkar, Swapnil Roy, Pinki Sipna Coll Engn & Technol Amravati Amravati MS India Natl Inst Technol Silchar Silchar Assam India

ISBN: (纸本)9783031451690;9783031451706

Multilingual language detection is the process of automatically identifying the language(s) present in a given speech corpus that may contain multiple languages. Several approaches have been proposed for multilingual speech corpus detection, including statistical methods, machine learning algorithms, and deep learning models. these models have difficulty determining specific language, especially when dealing with biased towards certain accents, dialects, or languages, and reduce the accuracy of the model. Hence a novel framework named "Multilingual Speech Identification Framework (MSIF)" is developed to solve this problem by data augmentation and increase the accuracy of language identification. there is a limited amount of datasets available for languages except English makes it difficult to train the Indian regional language. So the proposed framework uses a novel Superintendence Neuvised Network, which combines GAN and CNN for data augmentation and transfer learning for feature extraction. the existing multilingual models have been implemented to identify the languages but these models were not able to detect dialect variations because these model does not utilize the attention mechanism. For this reason, the proposed model uses a novel Duel Atenuative memory network, which integrates a Generalized self-attention mechanism with bi-LSTM to understand dialect variations thereby providing better language detection in the Indian regional language.

关键词： Speech corpus back translation transfer learning attention mechanism deep learning multilingual language detection

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A Customer Churn Prediction Using CSL-Based Analysis for ML Algorithms: the Case of Telecom Sector 6th

A Customer Churn Prediction Using CSL-Based Analysis for ML ...

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6th international conference on Innovative Computing and Communication, ICICC 2023

作者： Lavanya, Kampa Aasritha, Juluru Jahnavi Sai Garnepudi, Mohan Krishna Chellu, Vamsi Krishna Department of Computer Science and Engineering University College of Sciences Acharya Nagarjuna University Andhra Pradesh Guntur India Andhra Pradesh Mylavaram India

ISBN: (纸本)9789819940707

the loss of customers is a serious issue that needs to be addressed by all major businesses. Companies, especially in the telecommunications industry, are trying to find ways to predict customer churn because of the direct impact on revenue. therefore, it is important to identify the causes of customer churn to take measures to decrease it. Customer churn occurs when a company loses customers because of factors such as the introduction of new offerings by rivals or disruptions in service. Under these circumstances, customers often decide to end their subscription. Predicting the likelihood of a customer defecting by analyzing their past actions, current circumstances, and demographic data is the focus of customer churn predictive modeling. Predicting customer churn is a well-studied problem in the fields of data mining and machine learning. A common method for dealing with this issue is to employ classification algorithms to study the behaviors of both churners and non-churners. However, the current state-of-the-art classification algorithms are not well aligned with commercial goals because the training and evaluation phases of the models do not account for the actual financial costs and benefits. Different types of misclassification errors have different costs, so cost-sensitive learning (CSL) methods for learning on data have been proposed over the years. In this work, we present the CSL version of various machine learning methods for Telecom Customer Churn Predictive Model. Furthermore, also adopted feature selection strategies along with CSL in real-time telecom dataset from the UCI repository. the proposed combination of CSL with ML, the results outperforms the state-of-the-art machine learning techniques in terms of prediction accuracy, precision, sensitivity, area under the ROC curve, and F1-score. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： data mining

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IProceedings of the 4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

IProceedings of the 4th International Conference on Artifici...

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4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

ISBN: (纸本)9798400708831

the proceedings contain 200 papers. the topics discussed include: heat-aware graph data placement strategy for NVM;parameters estimation of photovoltaic models via an improved differential evolution algorithm;study on the extraction of law enforcement relationships in administrative law enforcement instrument data;online algorithm for exploring a grid polygon with two robots;research on gesture recognition method by improving dung beetle algorithm to optimize BP neural network;an improved algorithm for frequent sequence pattern mining based on PrefixSpan-ComplexPrefixSpan;machine learning-based research on reserve prediction of natural-gas-hydrates;enhancing coal mine safety monitoring algorithm using graph computing techniques;reverse distillation support vector data description for unsupervised anomaly detection;and few-shot object counting model based on self-support matching and attention mechanism.

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A Multimodal Framework for Automated Teaching Quality Assessment of One-to-many Online Instruction Videos 26

A Multimodal Framework for Automated Teaching Quality Assess...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Pan, Yueran Wu, Jiaxin Ju, Ran Zhou, Ziang Gu, Jiayue Zeng, Songtian Yuan, Lynn Li, Ming Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China Duke Kunshan Univ Ctr Teaching & Learning Kunshan Jiangsu Peoples R China Duke Kunshan Univ Data Sci Res Ctr Kunshan Jiangsu Peoples R China Fumi Hlth & Technol LLC Hunan Fujian Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In the post-pandemic era, online courses have been adopted universally. Manually assessing online course teaching quality requires significant time and professional pedagogy experience. To address this problem, we design an evaluation protocol and propose a multimodal machine learning framework1 for automated teaching quality assessment of one-to-many online instruction videos. Our framework evaluates online teaching quality from five aspects, namely Clarity, Classroom interaction, Technical management of online teaching, Empathy, and Time management. Our method includes mid-level behavior feature extraction, high-level interpretable feature extraction, and supervised learning prediction. Our automated multimodal teaching quality assessment system achieves comparable performance to human annotators on our one-to-many online instruction videos. For binary classification, the best average accuracy of five aspects is 0.898. For regression, the best average means square error is 0.527 on a 0-10 scale.

关键词： Teaching Quality Assessment Multimodal Behavior Coding Interptretable Feature Extraction Speaker Diarization Emotion recognition

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Building a Heroin Abuse Prediction Model: A Generalized machine learning Approach 6th

Building a Heroin Abuse Prediction Model: A Generalized Mach...

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6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

作者： Kumari, Divya Swetapadma, Aleena New Horizon College of Engineering Bangalore560103 India KIIT University Odisha Bhubaneswar751024 India

ISBN: (纸本)9783031530814

Aims: In the modern era, substance abuse is a global problem. CDC’s (Centre for Disease Control) National Center for Health Statistics reported in July 2021 that, more than 93000 drug overdose deaths occurred alone in the United States. Design: A cohort study. Setting: the drug consumption data is collected online in United Kingdom. Total 2000 records are present, out of which, 1885 records are considered for the work after initial data processing. the dataset has the drug consumption output outcome of 18 drugs, heroin is one of them. Measurements: In this work, an intelligent approach has been proposed for detecting abuse of one of the most illicit substances - heroin. A random forest-based machine learning model is proposed which can predict heroin abused individuals with very high accuracy. For the abuse prediction, various supervised machine learning methods are applied, and their performance is compared. these algorithms are applied to different sets of features. Among the applied five algorithms, feature importance score is calculated for logistic regression, ensemble learning (gradient boosting) and random forest. the feature importance score for each feature is calculated for all applied algorithms k-nearest neighbour and naïve bayes. the ranking of all features is done based on the obtained score. Findings: It is found in the study that the heroin-abuse dataset collected from UCI, two-classification based on random forest (RF) and gradient boosting (GB) achieved more than 90% accuracy as well as more than 90% sensitivity, specificity, precision, and f-score. the proposed model gave the best accuracy 94.697% for heroin-use detection. Conclusions: the assessment of the proposed framework on all possible feature sets shows that the framework works well for heroin abuse prediction as different give satisfactory results. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Demography

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Yoga Pose recognition Using Deep learning 6

Yoga Pose Recognition Using Deep Learning

引用

6th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Kulkarni, Prachi Gawai, Shailesh Bhabad, Siddhi Patil, Abhilasha Choudhari, Shraddha JSPM's Rajashri Shahu College of Engineering Department of Information Technology Pune India

ISBN: (纸本)9798350306613

Yoga pose detection holds significant importance in various aspects of the yoga practice and its integration with technology. the importance of yoga lies in its ability to promote physical health, mental well-being, stress reduction, improved focus, emotional balance, resilience, spiritual growth, and a holistic approach to life. With the increasing popularity of yoga, there is a growing need for technological advancements to support practitioners and instructors in monitoring and refining their practice. the paper begins by outlining the significance of automated yoga pose detection, highlighting the potential benefits it offers in providing real-Time feedback, enhancing self-correction, and optimizing performance. It explores the existing literature on computer vision and machine learning techniques applied to human pose estimation and their applicability to yoga pose detection. Based on a thorough review of state-of-the-Art methodologies, the research paper proposes a yoga pose detection that combines multiple modalities, including RGB images, depth maps, and skeletal joint data. the proposed system leverages deep learning algorithms, such as convolutional neural networks (CNNs) and long short-Term memory (LSTM), to precisely recognize and continuously monitor yoga poses. Moreover, the paper discusses the challenges associated with pose variation, occlusion, and complex body movements within yoga practice. It explores strategies for data augmentation, model optimization, and performance evaluation to ensure robustness and accuracy of the proposed detection system. the practical implications of the research are discussed, emphasizing the potential for widespread adoption of yoga pose detection systems in various settings, including yoga studios, fitness centers, and home practice environments. the paper concludes by outlining future research directions and the potential for integrating the proposed system with emerging technologies, such as augmented reality (AR)

关键词： asanas CNN deep learning human pose detection LSTM machine learning Media pipe pose prediction yoga pose yoga pose recognition

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An Adaptive Image Compression Algorithm Based on Deep learning 6

An Adaptive Image Compression Algorithm Based on Deep Learni...

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6th international conference on Artificial Intelligence and pattern recognition, AIPR 2023

作者： Liang, Yanxia Zhao, Meng Lu, Guangyue Jiang, Jing He, Hua Jia, Tong Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

In recent years, deep artificial neural networks have received wide attention because it surpasses the effect of traditional methods by parameter fitting under the condition of data convergence. On the other hand, limited transmission bandwidth and storage capacity lead to necessity of image compression in communications. In this paper, we propose an adaptive image compression algorithm with variable compression rate and learning rate based on the neural network in deep learning. this algorithm can meet various requirements in different situations by adjusting the parameters reasonably. Experiment results show the feasibility, flexibility and effectiveness of our proposed algorithm. Lower errors are obtained compared to other algorithms under the same compression rate. © 2023 ACM.

关键词： Image compression

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Autoencoder Attractors for Uncertainty Estimation 26

Autoencoder Attractors for Uncertainty Estimation

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Da Cruz, Steve Dias Taetz, Bertram Stifter, thomas Stricker, Didier IEE SA Bissen Luxembourg Univ Kaiserslautern Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI Darmstadt Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

the reliability assessment of a machine learning model's prediction is an important quantity for the deployment in safety critical applications. Not only can it be used to detect novel sceneries, either as out-of-distribution or anomaly sample, but it also helps to determine deficiencies in the training data distribution. A lot of promising research directions have either proposed traditional methods like Gaussian processes or extended deep learning based approaches, for example, by interpreting them from a Bayesian point of view. In this work we propose a novel approach for uncertainty estimation based on autoencoder models: the recursive application of a previously trained autoencoder model can be interpreted as a dynamical system storing training examples as attractors. While input images close to known samples will converge to the same or similar attractor, input samples containing unknown features are unstable and converge to different training samples by potentially removing or changing characteristic features. the use of dropout during training and inference leads to a family of similar dynamical systems, each one being robust on samples close to the training distribution but unstable on new features. Either the model reliably removes these features or the resulting instability can be exploited to detect problematic input samples. We evaluate our approach on several dataset combinations as well as on an industrial application for occupant classification in the vehicle interior for which we additionally release a new synthetic dataset.

关键词： Training Visualization Uncertainty Computational modeling Estimation Training data Safety

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Adversarial Attack on Semantic Segmentation Preprocessed with Super Resolution 26

Adversarial Attack on Semantic Segmentation Preprocessed wit...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Lim, Gyeongsup Kim, Minjae Hur, Junbeom Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Computer vision tasks, such as image classification, semantic segmentation, and super resolution, are broadly utilized in many applications. Recent studies revealed that machine learning-based models for the computer vision tasks are vulnerable to adversarial attacks. Since the adversarial attack can disturb the computer vision models in real-world systems, many countermeasures have been proposed against the adversarial attacks, such as denoising, resizing, and machine learning-based super resolution model as a preprocessing. Recently, a prior work demonstrated that the super resolution model as a preprocessing can be vulnerable to the adversarial attack targeted to the preprocessing itself, only when the perturbation is inactive before the preprocessing. However, we also found that the perturbation before the preprocessing can be another serious threat if the super resolution model is used for a mitigation of adversarial attacks. In this paper, we propose Layered Adversary Generation (LAG) that generates the adversarial example by recursively injecting noises to clean image in white-box environment. We then show that LAG is effective to attack a semantic segmentation model even if the super resolution models with/without two countermeasures as auxiliary methods such as resizing and denoising are adopted to mitigate the adversarial attacks. Furthermore, we demonstrate that LAG is transferable across other super resolution models. Lastly, we discuss our attack method in gray-box and black-box environments, and suggests a mitigation for robust preprocessing.

关键词： Computer vision Computational modeling Semantic segmentation Perturbation methods Superresolution Noise reduction pattern recognition

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