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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1863 条记录，以下是241-250 订阅

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Research on Intelligent Auxiliary Review of Power Grid 3D Design Based on BP Neural Network 3

Research on Intelligent Auxiliary Review of Power Grid 3D De...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Fan, Dongmei Yan, Wendou Liang, Yi Cheng, Zhi Guangzhou Power Supply Bureau Guangdong Power Grid Co. Ltd. Guangzhou102200 China

ISBN: (纸本)9798350319125

BP neural network, or back propagation neural network, is a machine learning algorithm widely used in pattern recognition, function approximation, signal processing and other fields. In recent years, with the expansion of power grid scale and the increase of complexity, BP neural network plays an increasingly important role in the three-dimensional processing of power grid. This paper mainly discusses the application of BP neural network in intelligent assistance, evaluation and optimization of power grid. © 2023 IEEE.

关键词： 3D model based on BP neural network computer Grid design power system

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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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Hybrid Deep learning and Ensemble Methods for Alzheimer’s Detection from EEG Signals 3

Hybrid Deep Learning and Ensemble Methods for Alzheimer’s D...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Anjali, Mokara Sundar, G. Naveen Meghana, Yetukuri gana bala Sreeja, Beereddy Narmadha Thangavel, Senthil Kumar Karunya Institute of technology and sciences Coimbatore India Division of AIML Karunya Institute of technology and sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798331532420

Alzheimer's disease (AD) is a progressive neurological disorder characterised by aberrant behaviour, memory loss, and cognitive impairment. Electroencephalography (EEG) is an efficient method that provides valuable information on brain activity and can be used to predict AD. The application of EEG data for AD classification is examined in this abstract, with particular attention paid to the study of frequency bands, and machine learning (ML) *** algorithms classifies AD with excellent levels of accuracy, indicating possibility of EEG-based methods for early disease identification. In order to classify AD in its early stages, this research proposes a unique deep learning model based on convolutional neural networks (CNNs) and signal processing techniques. The proposed model aims to classify patients into two categories: AD positive and negative. At the early stage the dataset was preprocessed using techniques such as label encoding and noise removal. Empirical mode decomposition (EMD) and variational mode decomposition (VMD) are the techniques applied in signal processing. The classification is done using Deep learning (DL) and machine learning (ML) methods are used to develop the best model. ML algorithms namely Stacking classifier, Gradient Boosting, Stacknet, AdaBoost, and DL algorithms like Convolutional Neural Networks (CNN) and Deep Neural Network (DNN) architectures have shown promise in AD classification. Random Forest and AdaBoost excel in ensemble learning, while CNN and DNN models capture intricate patterns in EEG data, further enhancing the accuracy of diagnosis. In conclusion, EEG data offer non-invasive and economical method for classification of AD and contain useful information. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparative Analysis of Fake Comments Posted in Twitter Using Different machine learning Models 1

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3rd international conference on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Jaiswal, Vivek Suri, Akshat Vishvakarma, Akanksha Sabherwal, Suruchi Alam, Mahboob Computer Science Department JSS Academy of Technical Education Uttar Pradesh Noida India Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (数字)9789819748600

ISBN: (纸本)9789819748594

The introduction of the World Wide Web and the fast assumption of online social media and the entertainment stages (like Facebook, Twitter, LinkedIn, etc.) prepared for data dissemination that has never been seen in the mankind's set of experiences earlier. Customers are creating and exchanging more data than at any other time in recent memory thanks to the continued use of virtual entertainment platforms, some of which are delusional and have no application in the real world. It is a difficult task to classify a written article as false or misleading using a computer. Without a doubt, before making a determination on the veracity of an article, even a specialist in a given field needs to consider numerous points of view. In this work, we suggest using a machine learning approach for content article classification that is automated. Differentiate the real from the phony. Using those characteristics, we train a variety of alternative machine learning estimation methods using diverse ensemble procedures and evaluate their performance on datasets. The exploratory assessment supports our suggested ensemble learner approach's higher-level presentation as compared to individual learners. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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Analysis of Cognitive Aspects in Online Education Amid the COVID-19: A machine learning Approach 3rd

Analysis of Cognitive Aspects in Online Education Amid the C...

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3rd international conference on Emerging Trends in Expert applications and Security, ICE-TEAS 2024

作者： Gupta, Rakhi Siddharth Tumrate, Charanjeet Singh Department of English JECRC University Vidhani India Department of Computer Science JECRC University Vidhani India Department of Civil Engineering Manipal University Jaipur Jaipur India Multiscale Simulation and Research Centre Manipal University Jaipur Jaipur India

ISBN: (纸本)9789819739905

The student’s academic response analysis during the COVID-19 pandemic is vital in comprehending their cognitive aspects towards the online teaching methodology adopted by the educational centres. This study provides the application of machine learning classification techniques to highlight the student’s response towards the online teaching pedagogy. The student response data considered is analysed using machine learning based random forest and gradient boosting machine classification techniques. The considered data is divided into two cases to increase the variation and importance accuracy in classification outcome. Based on the feature importance values obtained, less time spent on the self-study and the online lectures were the main reasons for the low performance among the students. Moreover, the various indoor stress busting activities played a huge role in safeguarding the mental health of students during pandemic. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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The gap between theoretical computational theories and application models 3

The gap between theoretical computational theories and appli...

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3rd international conference on Signal Processing and machine learning, CONF-SPML 2023

作者： Yang, Chengge College of Arts and Sciences Cornell University IthacaNY14850 United States

This paper examined two key generalization assumptions in the theoretical and computational fields and discussed three dominant pre-training models from multiple computer science concentrations. Then, this paper illustrated the increasingly severe gap between theoretical research and model applications for current machine learning studies. This paper discussed four advanced concepts in academia regarding possible solutions for the gap. The researchers developed these concepts to redirect future machine learning work by helping the academia re-understand the concept of "learning"and its appropriate implementation in the machine learning field. Finally, one possible reason for such a gap to exist is explained, and based on the interpretation of advanced concepts, possible solutions the academia can try to implement are also discussed. © Published under licence by IOP Publishing Ltd.

关键词： machine learning

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Quantum machine learning Techniques based on Nurturing Agri-Ontology Framework in Agricultural Science 3

Quantum Machine Learning Techniques based on Nurturing Agri-...

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

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Bennet Prabhu, A. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Drbccc Hindu College Department of Computer Applications Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Kone Elevator India Technology & Engineering Centre Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

The absence of a consistent ontological framework often impedes efficient knowledge organization and sharing within the agricultural community, despite the quantity of data created in agricultural research. The lack of defined vocabulary and conceptual frameworks hinders data integration, agricultural research, and innovation by preventing interoperability between databases and systems. To address these issues, this research work proposes the Nurturing Agri-Ontology Framework (NAOF), which offers a systematic approach to creating and updating agricultural ontologies. Ontologies that faithfully portray agricultural ideas and connections can be more easily created with the help of NAOF, which combines ideas from knowledge management, agricultural science, and ontology engineering. NAOF aims to ensure that the ontologies produced reflect the wide range of viewpoints and terminology used in agricultural research by encouraging cooperation among stakeholders, ontology engineers, and domain specialists. In this study, the improvement of knowledge organization, sharing and integration in the agricultural area is shown with the application of NAOF. Using this research study, scientists can build ontologies that improve agricultural decision-making, data interoperability, and the discovery of new information. Using quantum machine learning techniques, a strong foundation of knowledge can be laid for several agricultural applications, such as precision agriculture, crop management, and the formulation of agricultural policies. Ontology coverage, interoperability, scalability, and usability are established evaluation parameters that are used to assess the efficacy of NAOF in this research work. The analysis shows that NAOF effectively solves the problems and creates a better agricultural knowledge ecosystem by thoroughly evaluating it using these standards. © 2024 IEEE.

关键词： Ontology

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application of machine learning Techniques for Software Anomaly Detection 3

Application of Machine Learning Techniques for Software Anom...

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3rd international conference on Applied Mathematics and computer Science, ICAMCS 2023

作者： Gancheva, Veska Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350324266

A rising variety of platforms and software programs have leveraged repository-stored datasets and remote access in recent years. As a result, datasets are more vulnerable to malicious attacks. As a result, network security has grown in importance as a research topic. The usage of intrusion detection systems is a well-known strategy for safeguarding computer networks. This paper proposes an anomaly detection method that blends rule-based and machine-learning-based methods. In order to construct the appropriate rules, a genetic algorithm is utilized. Principal component analysis is used to extract the relevant features aimed to improve the performance. The suggested method is validated experimentally using the KDD Cup 1999 dataset, which meets the requirement of using appropriate data. The proposed method is applied to detect and analyze four types of attacks in a well-known benchmark dataset: Neptune, Ipsweep, Pod, and Teardrop, utilizing Support Vector machine, Decision Tree, and Naive Bayes algorithms. After testing the characteristics specified in the training phase, the data is classified into attack categories and normal behavior during the machine learning phase. © 2023 IEEE.

关键词： Network security

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Integrating ARIMA with machine learning for temperature prediction in major cities in China 3

Integrating ARIMA with machine learning for temperature pred...

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3rd international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2022

作者： Boyan, Li Loskot, Pavel Zhejiang University-University of Illinois Urbana-Champaign Institute Zhejing University China

ISBN: (纸本)9781510661301

The changes in temperature may arise risks in many industries. To solve this problem, the National Meteorological Center and Dalian Commodity Exchange jointly compiled a temperature index which includes 5 cities. Therefore, forecasting time series temperature data in those cities is an important subject. Traditionally, we use statistic method ARIMA to predict the next lags of time series. With the advancement in computational power of computers and the introduction of more advanced machine learning algorithms, this paper develops a method by integrating ARIMA with machine learning to analyze and forecast time series data. The empirical studies conducted show that integrating ARIMA with Long Short-Term Memory outperforms that with Support Vector Regression, or Random Forest in their prediction accuracy. © 2023 SPIE.

关键词： Long short-term memory

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Domain-Specific Sentiment Analysis of Tweets Using machine learning Methods 3rd

Domain-Specific Sentiment Analysis of Tweets Using Machine ...

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3rd international conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2023

作者： Sefara, Tshephisho Joseph Rangata, Mapitsi Roseline Council for Scientific and Industrial Research Pretoria South Africa

ISBN: (纸本)9783031488573

Most general sentiment analysers degrade quality when tested on Tweets in the broadcast domain. This domain covers both radio and television broadcast. This paper proposes domain-specific data in the broadcast domain. Furthermore, it proposes the use of machine learning methods for the sentiment analysis of tweets in this domain. Data were collected from Twitter using Twitter application programming interfaces. The data were preprocessed, and most special characters and emoticons were not removed, as sentiment analysis involves the use of opinions and emotions which are expressed using emoticons and other characters. The data were automatically labelled using a pre-trained sentiment analyser to enable the use of supervised learning on the data. Two supervised machine learning methods, such as XGBoost and multinomial logistic regression (MLR), are trained and evaluated on the data. The performance of the models was affected by two factors;limited data and the use of a general sentiment analyser to label the data in a specific domain. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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