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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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3rd international conference on Smart Technologies, Systems and Applications, SmartTech-IC 2022

3rd International Conference on Smart Technologies, Systems ...

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3rd international conference on Smart Technologies, Systems and Applications, SmartTech-IC 2022

ISBN: (纸本)9783031322129

The proceedings contain 37 papers. The special focus in this conference is on Smart Technologies, Systems and Applications. The topics include: Colombian Sign Language Classification Based on Hands Pose and machine learning Techniques;spanish Stylometric Features to Determine Gender and Profession of Ecuadorian Twitter Users;identification of Tropical Dry Forest Transformation from Soundscapes Using Supervised learning;atmospheric Dispersion Prediction for Toxic Gas Clouds by Using machine learning Approaches;facial recognition System with Liveness Detection Integrated into Odoo for Attendance Registration in a Software Laboratory;Classification of Epileptic Seizures Based on CNN and Guided Back-Propagation for Interpretation Analysis;an Approach to the Presumptive Detection of Road Incidents in Cuenca, Ecuador Using the data from the Social Media Twitter and Spanish Natural Language Processing;financial Credit Risk Measurement Using a Binary Classification Model;FPGA-Based Four-Band Multispectral Camera Prototype for Precision Agriculture Applications;data mining Application for the Generation of User Profiles in Serious Games Aimed at Attention and Memory Training;small-Scale Precision Agriculture: A Case Study Applied to a Seed Germination Chamber;diagnosis and Degree of Evolution in a Keratoconus-Type Corneal Ectasia from Image Processing;finite Element Analysis of Uncovered Coplanar Interdigital Electrodes for Capacitive Sensor Applications;algorithm for Medical Diagnostic Support Using machine and Deep learning for Depressive Disorder Based on Electroencephalogram Readings;Effects of a Robotic Lower-Limb Orthosis on Gait Based on the Analysis of sEMG Signals and Kinematic data: Preliminary Results;design and Evaluation of a Prototype of Dual Channel Electrical Stimulator: Application in the Assessment of Current Perception Thresholds to Multiple Stimuli;system for Postural Stability Assessment Based on an Inertial Sensors Network;perspectives on Climate Change

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Smart Crop Cultivation: Harnessing machine and Deep learning for Informed Agricultural Practices 3

Smart Crop Cultivation: Harnessing Machine and Deep Learning...

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Meghani, Smit Vele, Jay Madrecha, Kunal Salunke, Abhijeet Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350375190

Amongst various occupations in India, farming is the most common. It boosts the Indian economy significantly and provides Indians with a wide range of job opportunities. The ideal crop for each type of soil is no longer chosen by farmers. This has an immediate effect on agricultural yields. It is causing serious financial losses for the farmers in the area. Cultivating a certain crop on a particular type of soil now involves several factors to take into account. The optimal crop to plant is determined by taking into account a number of soil specifications. This novel opinion mining method will consider the following parameters: temperature, air humidity, soil pH, nitrogen, potassium, phosphorus, and annual rainfall. The model aids in determining the kind of crop that has to be planted by taking into account the data collected from prior years. In order to support the mission, various algorithms are analyzed and evaluated on multiple fronts, including comparative research and analysis. In addition, the ideal fertilizer will be recommended depending on crop-related environmental factors, temperature, pH level, and soil moisture content. © 2024 IEEE.

关键词： Crops

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SAM based Computational Analysis of Root Mean Square Error for SPES Using LR, RF & ARIMA machine learning Models 3

SAM based Computational Analysis of Root Mean Square Error f...

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3rd international conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2023

作者： Jani, Rudresh Patel, Palak Joshi, Siddharth Vyas, Soham Pandit Deendayal Energy University Department of Electrical Engineering Gujarat Gandhinagar India School of Energy Technology Pandit Deendayal Energy University Department of Electrical Engineering Gandhinagar India Non Executive Consultant Apa Enviornment Pvt. Ltd. Stafford United Kingdom School of Technology Pandit Deendayal Energy University Department of Computer Engineering Gujarat Gandhinagar India

ISBN: (纸本)9798350307399

Renewable energy is crucial in addressing various global challenges like climate change, global warming, human development index, etc. To address these challenges, wind and solar energy are the mature technologies to harness green power. Integrating machine learning into the renewable energy sector can lead to more efficient, reliable, and sustainable energy systems. The work proposed for the solar photovoltaic energy system. The solar photovoltaic system of 500kW AC is simulated in the System Advisor Model (SAM). The data for annual energy generation is fetched to form the dataset. This dataset from SAM contains the hourly energy generation data for a particular year. The comparative analysis is proposed after mining the dataset. The machine learning algorithms are Linear Regression, Random Forest and ARIMA. To evaluate the performance of these algorithms, the statistical measure, namely Root Mean Square Error (RMSE), was used. The comparative analysis shows the effectiveness of the proposed work and its dataset for the machine learning algorithms. © 2023 IEEE.

关键词： Global warming

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A Signal Processing and machine learning Framework for Enhanced TDS Simulation 3

A Signal Processing and Machine Learning Framework for Enhan...

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3rd IEEE international conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Shuvo, Shuvendu Pal Pal Shibazee, Shirshendu Khuki, Arifa Akter Das, Chaitee Paul, Goutam Mita, Mitaly Paul Khulna University of Engineering and Technology Dept. of CE Khulna9203 Bangladesh Bangladesh University of Engineering and Technology Dept. of EEE Dhaka1000 Bangladesh Khulna University of Engineering and Technology Dept. of MSE Khulna9203 Bangladesh Bangabandhu Sheikh Mujibur Rahman Science and Technology University Dept. of Pharmacy Gopalganj8100 Bangladesh Khulna University Dept. of Economics Khulna9208 Bangladesh

ISBN: (纸本)9798331534400

Total dissolved solids (TDS) is one of the most significant indicators of regulated wastewater effluents, and it is critical in assessing and improving the performance of any wastewater treatment facility. The literature review reveals that the pattern of TDS and other water quality parameters generally shows noisy and complicated features;hence, capturing their pattern is tough. This paper proposes a new approach to noise reduction by Ensemble Empirical Mode Decomposition (EEMD), incorporating machine learning algorithms like Artificial Neural Network (ANN) and Support Vector machine (SVM) to improve the accuracy of TDS prediction. The data on temperature, pH, turbidity, and TDS were collected from the Bangabandhu Water Treatment Plant (BWTP) in Khulna, Bangladesh. In the current work, traditional and EEMD-based approaches were developed, and the performances of these were evaluated based on MSE, RMSE, and MAE. It is presented that the EEMD-based approach reduced the error considerably over the traditional models, while a maximum reduction of 89.46% and 91.04% in MSE are achieved in ANN and SVM, respectively. It further suggested that the proposed approaches could be applied to other wastewater treatment plants. Practical deployment of this model will help the operators make real-time decisions in optimized treatment processes. © 2024 IEEE.

关键词： Empirical mode decomposition

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Portable data Collection Unit 3

Portable Data Collection Unit

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3rd international conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Guo, Zhenyan Zhu, Yongcan Bing, Kaiyang He, Jiacheng Xi'an Polytechnic University College of Electronics and Information Xi'an China

ISBN: (纸本)9798350352658

This study aims to address the challenge of inconsistent and difficult-to-unify archiving and analysis of data from live detection and preventive testing of electrical equipment in the intelligent operation and maintenance system of the power grid. To tackle this issue, we propose a portable data aggregation unit. This unit enables seamless integration of live detection and preventive testing instruments, and possesses the capability to extract data from various instruments, regardless of whether they have interfaces or not. Through communication interfaces and protocols of existing instrument devices, the unit collects device data, while for devices without interfaces, deep learning and machine learning algorithms are employed for image differentiation to achieve data recognition and communication. Consequently, collected device data can be transmitted in real-time to a platform server, resolving the problem of devices without interfaces being inaccessible and facilitating online data processing. Additionally, the collected data is utilized for transformer fault diagnosis under multiple feature data conditions. In summary, our research brings substantial improvements to the intelligent operation and maintenance system of the power grid, addressing issues of data recording and analysis inconsistency, and providing crucial support for fault prediction and health management of electrical equipment. © 2024 IEEE.

关键词： Instrument testing

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Optimizing Potato Disease Detection in Pakistan with machine learning: A Comparative Analysis 3

Optimizing Potato Disease Detection in Pakistan with Machine...

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3rd international conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Ahmed, Irfan Khalil, Manahal Ghulam, Safoora Mahmood, Jabar Batool, Khadija Khalid, Nibtal Arshad, Pakiza Dept of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science & Technology Zhejiang University Hangzhou China Dept of Computer Science Int. Institute of Science Art & Technology Gujranwala Pakistan

ISBN: (纸本)9798331504861

The agricultural sector of Pakistan depends heavily on the production of potatoes, however diseases like Bacterial Wilt, Late Blight, and Early Blight are posing a growing danger to this industry since they can negatively affect crop productivity and food security. Detecting these illnesses using traditional methods is time-consuming, labor-intensive, and sometimes it requires specialized knowledge which is not easy for farmers. The implementation of a machine learning (ML) framework for illness identification using the methods of K-Nearest Neighbours (KNN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Support Vector machine (SVM), and Logistic Regression (LR) overcomes these challenges. This study utilizes data mining and preprocessing strategies to improve the accuracy of models and adaptability on a dataset of potato plants’ photos gathered across diverse areas in Pakistan. The models are assessed using performance criteria such computational efficiency, accuracy, precision, recall and F1-score. Random Forest model having 97% accuracy outperforms the other ML models in identifying plant disease. The suggested approach provides a practical, affordable, and effective way to identify diseases in a timely manner, allowing farmers to make informed decisions to lower crop losses. The aim of the study is to highlight how ML has the potential to transform Pakistani agriculture, enhancing food security and promoting long-term economic prosperity. ©2024 IEEE.

关键词： Random forests

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Using machine learning Algorithms to Suggest a Method for Predictive Analysis in data mining 3

Using Machine Learning Algorithms to Suggest a Method for Pr...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Agal, Sanjay Sharma, Priyanka Mohan, Chinnem Rama Madan, Parul Malathy, V. Arri, Harwant Singh Dr. V. R. Godhania College of Engineering and Technology B/h Gujarat Porbandar360578 India Ggdsd College Punjab India Narayana Engineering College Department of Computer Science and Engineering Andhra Pradesh Nellore524004 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India School of Engineering Sr University Department of Ece Telangana Warangal India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (纸本)9798350343274

data mining is frequently utilised in technology & research, but it is also applied in marketing and finances to address problems in those disciplines. By evaluating the actual world, data mining-based decision support systems improve organisational efficiency. Every business frequently experiences a volatile environment as a result of the strains of competitiveness, cost, taxes, etc., The company is dragged farther into a competitive world by privatisation, globalisation, and liberalisation. Proper marketing tactics must be created and put into action if one is to counterbalance the market and endure to accomplish desired results. Throughout its effective data mining technique, the marketing decision-making system reduces the organisational responsibilities associated with analysis and strategic development. In order to quantify the business models for a company, this study effort offered a data mining-based decision support system employing decision trees and synthetic neural networks as a hybrid technique. © 2023 IEEE.

关键词： data mining

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Simsiam Network Based Self-supervised Model for Sign Language recognition 3rd

Simsiam Network Based Self-supervised Model for Sign Langua...

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Kothadiya, Deep R. Bhatt, Chintan M. Rida, Imad Changa India Department of Computer Science and Engineering School of Engineering and Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Laboratoire Biomécanique et Bioingénierie UMR 7338 Centre de Recherches de Royallieu Université de Technologie de Compiègne Compiègne France

ISBN: (纸本)9783031463372

To make a more accurate and robust deep learning model, more labeled data is required. Unfortunately, in many areas, it’s very difficult to manage properly labeled data. Sign language recognition is one of the challenging areas of computer vision, to make a successful deep learning model to recognize sign gestures in real-time, a huge amount of labeled data is needed. Authors have proposed a self-supervised learning approach to address this problem. The proposed architecture used Resnet50 v1 backbone-based simsiam encoder network to learn the similarity between two different images of the same class. Calculated cosine similarity passes to MLP head for further classification. The proposed study uses Indian and American Sign Language detests for simulation. The proposed methodology successfully achieve 74.59% of accuracy. Authors have also demonstrated the impact of other self-supervised deep learning models for sign language recognition. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Opinion mining from Audio Conversation using machine learning tools and BART Transformer 3

Opinion Mining from Audio Conversation using Machine Learnin...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Mathews, Ann Singh, Paras Nath CMR Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350317022

The discipline of opinion mining is expanding rapidly. It gathers subjective data from text and audio sources using Natural Language Processing and text analysis. Finding and extracting subjective data automatically from news stories, blogs, social media posts, customer evaluations, and other sources is the primary objective of opinion mining. Opinions, assessments, appraisals, sentiments, and emotions fall within this category. As consumers increase and data accumulates daily, companies rely on data collection and analytics to track sales, customer insights, and brand reputation. However, voice data, the most direct feedback, is often overlooked. This paper explains how to analyse the sentiment in audio recordings and apply the top three approaches in three different groups of opinions (positive, negative, and neutral) in order to gain a better understanding of how customers evaluate goods and services. An extensive examination of benchmarked datasets, popular feature sets, algorithms, approaches, open-source tools, difficulties, practical applications, and insights into several facets of opinion mining are provided in this research. With 99%+ accuracy for various data sets using Python's BART transformer, these results have both theoretical and practical implications, as well as demonstrating the utility of opinion mining from sound to text converted information in a holistic society. © 2023 IEEE.

关键词： Audio to text BERT and BART Opinion mining Paraphraser Sentiment Analysis Tokenizer

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Calculation and Experimental Verification of Flush Air data System Based on Kriging Model 3rd

Calculation and Experimental Verification of Flush Air Data ...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Zhang, Changrong Shi, Xiaojun Zha, Jun Li, Weiqun Lv, Binbin Wang, Kunming Institute of High Speed Aerodynamics China Aerodynamics Research and Development Cente Mianyang621000 China National Key Laboratory of Aerospace Physics in Fluids China Aerodynamics Research and Development Cente Mianyang621000 China

ISBN: (纸本)9789819624676

Utilizing the Kriging model, a flush air data system has been devised specifically for a flying wing configuration vehicle. This system employs the surface pressure of the aircraft as its input, and outputs key atmospheric parameters, including velocity, angle of attack, and sideslip angle. The intricate mapping relationship between these inputs and outputs is precisely established through the application of the Kriging interpolation method. Experimental data from wind tunnel tests demonstrate the remarkable accuracy of the Kriging model when utilized for flush atmospheric data calculations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wind tunnels

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