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Real-Time Revelations through complex Machine Learning Techniques for Environmental Monitoring

Real-Time Revelations through Complex Machine Learning Techn...

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2024 international conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Kumar, Santosh B. Rangaiah, Yagateela Pandu Singh, Navdeep Boob, Nandini Shirish Srinivas, Tummala AL-Zahra Jabbar, Ahmed sabah Abed Department of Computer Science Engineering New Horizon College of Engineering Bangalore India Department of Electronics and Communication Enfineering Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Department of Civil GRIET Bachupally Telangana Hyderabad India Medical Laboratory Technology Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (纸本)9798350355338

Monitoring the environment is a big part of understanding and keeping up with how our world is changing. A new, cutting-edge method called "Real-Time Revelations through complex Machine Learning Techniques for Environmental Monitoring" is described in this study. Its goal is to help people understand the features of the environment in real time. To look at real-time temperature data, the recommended method uses RF, CNNs, and RNNs. We used seven important measures to compare its performance to six standard methods: RMSE, AUC-ROC, F1 Score, Accuracy, Precision, and Recall. The results show that the Proposed Method does better on all performance factors than normal methods. It does a good job because its F1 Score, accuracy, precision, and AUC-ROC are all over 0.9. The fact that error measures like MAE and RMSE have gone down a lot is more proof that real-time observations are accurate and reliable. The algorithm is a very useful tool for making decisions because it can find outliers and give deep insights. It can also be interpreted and changed in many ways. The proposed approach does a good job of giving decision-makers clear information, adapting to changing conditions, and keeping an eye on things in real time. This fixes the problems that come up with watching the surroundings all the time and helps early warning systems. Lastly, "Real-Time Revelations through complex Machine Learning Techniques for Environmental Monitoring" is a great way to get real-time information about the environment that goes above and beyond what was possible with traditional methods. © 2024 ieee.

关键词： Environmental monitoring

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A Comparative Analysis on Fashion Cloth Feature Extraction Methods using Text and Image data 11

A Comparative Analysis on Fashion Cloth Feature Extraction M...

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11th ieee international conference on Advances in computing and Communications, ICACC 2024

作者： Attoor, Lemya Sainudeen Chitra, R. Anusha Bamini, A.M. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350379136

Modern methods of extracting relevant features from images and texts are crucial to the recovery of fashionable garments. In this research, we compare and contrast a number of different approaches to extracting features from images and text. We utilize Generative Pre-trained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) to efficiently process and represent textual descriptions of fashion products for text feature extraction. This allows us to take advantage of GPT's generative capabilities and contextual understanding. In order to extract features from photographs of fashion sketch images, we use a mix of Residual Networks (ResNet) and Visual Geometry Group (VGG) networks. This allows us to pick up complex visual patterns and details. Our method is designed to enhance the accuracy of text and image matching by combining the best features of these state-of-the-art models. Accuracy, recall, F1-score, and retrieval efficiency are some of the assessment metrics that were examined on a wide dataset that included different types of fashion clothing. The hybrid model improves feature extraction capabilities, producing robust and discriminative text and picture representations, according to preliminary findings. More intelligent and user-friendly retrieval systems in digital fashion platforms and e-commerce might be possible thanks to the study's useful insights into the possibilities of advanced deep learning architectures in the field of fashion product retrieval. © 2024 ieee.

关键词： Image matching

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Futures Quantitative Investment With Heterogeneous Continual Graph Neural Network 24

Futures Quantitative Investment With Heterogeneous Continual...

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24th ieee international conference on data Mining, ICDM 2024

作者： Tan, Zhizhong Hu, Min Liu, Bin Yin, Guosheng School of Computer Science and Engineering Macau University of Science and Technolog China School of Finance Southwestern University of Finance and Economics China Center of Statistical Research School of Statistics Southwestern University of Finance and Economics China School of Computing and Data Science The University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798331506681

It is challenging to predict futures prices with traditional econometric models as it necessitates a comprehensive consideration of both historical observations and correlations among various futures. Spatial-temporal graph neural networks (STGNNs) offer a promising approach for modeling such complex spatio-temporal data. Nevertheless, the direct application of STGNNs to high-frequency futures data remains a challenge, as traders must account for both short- and long-term characteristics when making decisions. To capture these distinct timeframes, we leverage additional label information by devising four heterogeneous tasks: price regression, price gap regression, price moving average regression, and change-point detection. To make full use of these labels, we train the model in a continual manner. Traditional continual GNNs define the gradient of losses as parameter importance to overcome the catastrophic forgetting (CF) issue, while this is unsuitable for our heterogeneous tasks with losses in distinct spaces. Therefore, we propose to calculate the parameter importance with mutual information between the original observations and the extracted features. The empirical results based on 49 varieties of commodity futures demonstrate that our model achieves superior performance compared to the leading approaches, highlighting the effectiveness of our approach in modeling the intricate dynamics of the futures market. © 2024 ieee.

关键词： Graph neural networks

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Multi-Class Gait Phase Recognition using Machine Learning Models with Two Training Methods 15

Multi-Class Gait Phase Recognition using Machine Learning Mo...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Mekni, Amal Narayan, Jyotindra Gritli, Hassene Borj Cedria Tunis1164 Tunisia Indian Institute of Technology Guwahati Mechtronics and Robotics Laboratory Department of Mechanical Engineering Assam Guwahati781039 India University of Tunis El Manar le Belvédère Laboratory of Robotics Informatics and Complex Systems RISC Lab-LR16ES07 National Engineering School of Tunis Tunis1002 Tunisia

ISBN: (纸本)9798350370249

Walking is a fundamental human activity, yet understanding its intricacies is crucial for diagnosing and treating various gait abnormalities and musculoskeletal disorders. This study investigates the machine learning-based classification of gait phases into five subphases with two training methods. Using data from 100 individuals obtained from an open-source platform. The gait cycle was divided into the following subphases: Initial Contact, Loading Response, Mid Stance, Terminal Stance, and Pre-Swing. In the first approach, a stratified random sampling method was employed, allocating 80% of data from each subphase for training and 20% for testing. The second method involved training with data from 80% participants and testing with data from 20% participants. Out of different ML algorithms, the Random Forest (RF) achieves the highest performance (99.2%) with similar results across both methods. The k-Nearest Neighbors (k-NN), the Logistic Regression (LR), the Decision Tree (DT), and the Support Vector Machine (SVM) show comparable performance ( 91%) with marginal improvement with the second method. Naive Bayes (NB) has a significantly lower performance ( 77.7%). These findings suggest that RF is a robust and effective algorithm for this task. Furthermore, the study indicates that applying machine learning techniques to datasets divided by subjects presents promising opportunities for creating precise gait analysis systems. This can assist clinicians and researchers in enhancing patient care and gaining deeper insights into human locomotion. © 2024 ieee.

关键词： Adversarial machine learning

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A System for Real-Time Shoulder Angle Measurement Using Computer Vision 4

A System for Real-Time Shoulder Angle Measurement Using Comp...

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4th international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Langote, Meher Saratkar, Saniya Puri, Chetan Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Higher Education and Research Faculty of Engineering and Technology Department of Computer Science Design Maharashtra Wardha India

ISBN: (纸本)9798331529635

Recently, angle measurements have been performed using a goniometer, but the complex motion of shoulder movement has made these measurements intricate. The angle of rotation of the shoulder is particularly difficult to measure from an upright position because of the complicated base and moving axes. This article presents a real-time shoulder angle measurement system utilizing computer vision and machine learning techniques, specifically designed for the rehabilitation of frozen shoulder and related conditions. The system leverages MediaPipe, an open-source framework, to capture and analyze shoulder movements accurately. With its modular architecture, the system processes video feeds in real time, detecting key body points and calculating shoulder angles, thereby providing immediate feedback to patients and therapists. The study evaluates the system's effectiveness in clinical and home-based environments, highlighting its potential to enhance physiotherapy by enabling precise, data-driven treatment plans. This research study also identifies current research gaps, proposes solutions, and explores future enhancements, including environmental adaptation, mobile integration, and the extension to other joints. This study has attempted to estimate the shoulder joint internal/external rotation angle using the combination of pose estimation artificial intelligence (AI), Computer vision, and a machine learning mode. © 2024 ieee.

关键词： Goniometers

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Analysis of Defectives in a Call-Center Process Quality under 2-Dimensional Uncertain Linguistic data by Attribute Control Charts

Analysis of Defectives in a Call-Center Process Quality unde...

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2024 international conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Karasan, Ali Ilbahar, Esra Gundogdu, Fatma Kutlu Yazir, Kubra Olgac, Elifnaz Kaya, Ihsan Industrial Engineering Department Yildiz Technical University Istanbul Turkey Industrial Engineering Department National Defence University Istanbul Turkey The Undersecretariat for Defence Industries Ankara Turkey

ISBN: (纸本)9798331533137

Control charts (CCs) are highly effective indicators for monitoring and controlling process development in manufacturing, as they allow for the tracking of variance. In the context of attribute control charts (ACCs) that is one type of CCs, uncertainty emerges from the combination of linguistic terms (LTs) used for process evaluation and the reluctance of inspectors in making their judgments. The fuzzy set theory (FST) that enables the transmission of the experts' judgment and decision-making processes to a model, therefore facilitating the construction of intricate and dynamic systems is used in this paper. One of the latest versions of the fuzzy sets is Fermatean fuzzy sets (FFS). It allows decision-makers and operators to articulate their judgments across a wider range of decision-making applications that involve subjective data sets derived from human knowledge. That is why Fermatean np chart that is one type of ACCs are developed in this study to reduce information loss by generating more sensitive and refined outputs. The proposed chart is illustrated by a numerical case study from the call center industry. A fuzzy rule-based system is also introduced to interpret sensitive and detailed results when the samples are progressively in-control or out-of-control. The findings offer significant advantages for both managers and operators, as they facilitate the generation of more precise and actionable conclusions. Through that, decision-makers are better equipped to respond to complex changes in the process, ultimately leading to more effective management and operational outcomes. ©2024 ieee.

关键词： Fuzzy set theory

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Visible Light Positioning Method for Indoor Line-of-Sight Scene Based on Time Series Coding 9

Visible Light Positioning Method for Indoor Line-of-Sight Sc...

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9th ieee international conference on data Science in Cyberspace, DSC 2024

作者： Yu, Yonghao Guo, Yongde Zhao, Dawei Fu, Kexue Tang, Yongwei Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong University School of Mechanical Engineering Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education Jinan China

ISBN: (纸本)9798350391367

Existing indoor visible light positioning methods, such as those based on fingerprint database algorithms, are complex to construct and consume significant resources. In response to this issue, the text proposes an end-to-end indoor visible light positioning method that samples continuous signals at a small number of random coordinate positions in indoor environments. Through a deep learning method based on attention mechanism, a nonlinear mapping between the received signal and spatial position coordinates is constructed, and the time series properties in the received signal are encoded as a feature in the data. The method is further improved by combining residual networks to improve accuracy. Through simulation experiments, it has been verified that the deep learning based localization method described in this article can achieve sub meter level accuracy on small sample datasets, and it has been proven that time series features have a significant impact on localization accuracy in indoor visible light positioning algorithms based on deep learning. © 2024 ieee.

关键词： Time series

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Experimental Analysis of Learning Based Approach to Evaluate the Integration of Social Networking Platform with Internet of Things Technology

Experimental Analysis of Learning Based Approach to Evaluate...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Karunakaran, A. Venkatraman, C.B. Suganthi, P. Santhi, G.B. Alok, Narkhede Shalout, Imad S. A. Engineering College Department of Ece Chennai India Sona College of Technology Department of Eee Salem India Sri Sai Ram Institute of Technology Department of Cse Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa university Faculty of engineering Department of Civil Engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Enterprising entrepreneurs are forging a new paradigm for consumer-business interactions by leveraging the convergence of social networking platforms-such as Facebook and Twitter-and the Internet of Things (IoT). This paper aims to assess an integration pipeline's performance using a learning based approach. Our aim is to understand the complex interplay between data that has been collected from IoT and how social media users act using advanced methods in data analytics. Our approach involves gathering data from both SNPs and the Internet of Things (IoT) and pre-processing it as needed before building a model that can be predicted and its performance evaluated. As opposed to the proposed method Elevated Social Networking Integration (ESNI) is a better strategy than the existing Social Networking Integration Analyzer (SNIA), which permits collecting less information through connected devices to enhance decision-making on consumer behavior and encounters. By offering a methodical approach to evaluating the value proposition of combining IoT with SNP, this study adds to the corpus of knowledge. The framework facilitates seamless interaction across diverse social media platforms, optimizing data flow and user experience. Empirical testing demonstrates ESNI's effectiveness in boosting engagement metrics and user satisfaction compared to conventional integration methods. This research highlights ESNI's potential to redefine social networking strategies, providing a robust foundation for future innovations in digital communication and marketing. © 2024 ieee.

关键词： data assimilation

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Research on Tea Disease Model Based on Improved ResNet34 and Transfer Learning

Research on Tea Disease Model Based on Improved ResNet34 and...

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2024 international conference on Intelligent computing and Robotics, ICICR 2024

作者： Ye, Rong Gao, Yanjun He, Yun Shao, Guoqi Gao, Quan Li, Tong College of Food Science and Technology Yunnan Agricultural University Kunming650201 China College of Big Data Yunnan Agricultural University Kunming650201 China The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province Kunming650201 China

ISBN: (纸本)9798350372274

Aiming at the problems of tea leaf disease recognition with complex background and low accuracy in real environment, this study improved ResNet34 first-layer convolution layer model and combined with different migration methods to build tea disease recognition model. To enhance the model's generalization ability, transfer learning is integrated into the ResNet34 model, and the pre-trained parameter weights are transferred to the original model for training, so as to improve the training speed. Then, by comparing the performance of all four models, namely migration, feature extraction, fine-tuning, and new training, The experimental results show that the test accuracy of the fine-tuned ResNet34 model reaches 94.67%, which is 0.41% higher than that of the whole migration model and 6.56% higher than that of the original ResNet34 model. In addition, the modified fine-tuning model reduces the iteration time of each round by 37.21% compared with the original ResNet34 model. The research shows that the improved model has strong robustness to self-built data sets such as sample imbalance and background heterogeneity. The fine-tuning model can reduce the number of parameters and the reduction of calculation, accelerate the convergence rate of the model, and facilitate the deployment of the model to the mobile terminal equipment of intelligent agriculture in the future, providing useful guidance for the intelligent prevention and control of tea diseases and ensuring the food safety of tea. ©2024 ieee.

关键词： Federated learning

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Applying Transformer-Based Neural Networks to Corpus Linguistic data for Predictive Text Generation in Multilingual Environments 6

Applying Transformer-Based Neural Networks to Corpus Linguis...

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6th ieee international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Taufikin Orosoo, Myagmarsuren Rengarajan, Manikandan Fatma, Gulnaz Yuldashev, Dilyorjon Raj, I Infant IAIN Kudus Indonesia Mongolian National University of Education Mongolia Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Tamil Nadu Chennai India University College Al Ardah Jazan University Jazan Saudi Arabia Turin Polytechnic University in Tashkent Automatic Control and Computer Engineering Department Tashkent Uzbekistan K. Ramakrishnan College of Engineering Department of CSE Tamil Nadu Trichy India

ISBN: (纸本)9798350306613

In the subject of corpus linguistics, this study explores the use of transformer-based neural networks to the problems of predictive text production in multilingual contexts. The Transformer architecture presents a viable framework for natural language processing problems because of its reputation for capturing complex patterns and long-range dependencies in sequential data. The research employs a two-phase approach, whereby the Transformer is first pre-Trained using a variety of multilingual corpora. This helps the model learn representations that are independent of language, which makes it easier for it to adapt to various linguistic circumstances. The model is then fine-Tuned using language-specific datasets to improve its ability to produce linguistically nuanced and contextually appropriate text. The study looks into how different model architectures, training methods, and hyperparameters affect how well the suggested multilingual predictive text generation system performs. Evaluation measures are used to evaluate the model's performance in several languages, including linguistic diversity measurements, BLEU scores, and perplexity. The overall accuracy, calculated across all languages, stands at 80.3%, indicating the model's robust cross-lingual generalization and its effectiveness in capturing diverse linguistic nuances The efficacy and generalization of Transformer-based models are improved across a wide range of languages to handle linguistic diversity. © 2024 ieee.

关键词： Corpus Linguistics Cultural Context Integration Multilingual Text Generation Neural Language Models Transformer-Based Neural Networks

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