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共 6842 条记录，以下是381-390 订阅

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Feature Extraction to Evaluate the Quality of data Using machine learning Technique 3

Feature Extraction to Evaluate the Quality of Data Using Mac...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pratheesh, R. Divya, V. School Of Computing Sciences Vels Institute Of Science Technology & Advanced Studies Chennai India School Of Computing Sciences Vels Institute Of Science Technology & Advanced Studies Department Of Information Technology Chennai India

ISBN: (纸本)9798350318609

data quality analysis is a crucial step in ensuring the reliability and accuracy of data used in machine learning models. The proposed approach utilized with machine learning algorithms to identify outliers in the data. Outliers can indicate errors are anomalies that might affect the quality of the dataset. data Profiling is used to identify patterns and distributions. Missing data Imputation predict the missing values. data Validation and Cleansing, data Duplication Detection, Inconsistency Detection are considered as the primary analysis to be done. The feature extraction process read the sample test dataset, pre-process the dataset by cleaning the data for data duplication, inconsistency through dynamic algorithms principal component analysis (PCA), Linear Discriminant Analysis (LDA) and independent component analysis (ICA) is considered here. The proposed feature extraction data is further opted for pattern validation through isolated forest algorithm. The performance of the system is evaluated using accuracy, precision, and Recall. The LDA model outperforms with accuracy of 99% for the given testset. © 2024 IEEE.

关键词： Feature extraction

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Monthly Rainfall Analysis in Southern Parts of India Using machine learning and Ensemble Methods 3

Monthly Rainfall Analysis in Southern Parts of India Using M...

引用

3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Mohan, Aiswarya Krishna Priya, S. Remya Nair, T. Amrita Vishwa Vidyapeetham School of Computing Dept. of Computer Science and IT Kerala Kochi India

ISBN: (纸本)9798350391565

Rainfall analysis has a vital function for raising awareness of the dangers of excessive rainfall and for boosting the travel and tourism sector, agriculture, and food security in southern India. Because rainfall is influenced by many different causes, it is challenging to forecast. In this research work, four different machine learning techniques are used: Naïve Bayes, KNN, SVM, and LR, and the accuracy for each model is determined. To improve the accuracy of each model, three different ensemble learning techniques are employed: Stacking, Bagging, and Voting Classifier, to analyze the rainfall data in southern parts of India. Given that Bagging attains the maximum accuracy of 99.5%, it is proposed that Bagging is the best model. The study shows that the ensemble model can enhance prediction quality and boost forecasting accuracy in meteorological applications by replacing traditional forecasting instruments. © 2024 IEEE.

关键词： Adversarial machine learning

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Ensemble Hardness Harmonize Classifier for Imbalanced Fault Classification 3

Ensemble Hardness Harmonize Classifier for Imbalanced Fault ...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Lu, Jinyu Yang, Tong Liu, Xinfeng Pan, Weichao Zhang, Yuyao Yin, Yuyan Shandong Jianzhu University Jinan China

ISBN: (纸本)9798350380989

Fault diagnosis is a crucial step to ensure the normal operation of machines and equipment. However, realworld fault data usually presents an imbalanced distribution and low quality, which makes the classification task difficult. To address this problem, this paper delves into the nature of imbalanced classification and finds that in addition to class imbalance, other difficulties such as noise and class overlap also affect the learning of effective classifiers. Therefore, this paper proposes a new imbalanced classification framework that aims to generate powerful integrated classifiers by downsampling and adaptively adjusting the data difficulty. In this paper, extensive experiments are conducted using fault data from oil pumping machines and data from wind blade machine sheets. The results show that the new framework proposed in this paper not only excels in computational efficiency, but also provides robust performance even with highly overlapping fault classes and extremely skewed data distributions. It is important to note that the method in this paper can be easily adapted to existing learning methods such as C4.5, SVM, GBDT, and neural networks to improve their performance on imbalanced fault diagnosis data. This research provides an effective solution for imbalanced classification for practical applications in the field of fault diagnosis. © 2024 IEEE.

关键词： data difficulty adaptation Fault diagnosis imbalanced classification integrated classifiers performance improvement

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A machine learning-Enhanced IoT Wearable Device for Monitoring and Providing Feedback on Stuttering 3

A Machine Learning-Enhanced IoT Wearable Device for Monitori...

引用

3rd international conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Suttarwala, Hussain Kumar, Ritesh Deorankar, Madhavi L. Daulat Sarpate, Dikshendra Shingade, Pallavi Kumar Sharma, Surender More, Vrushali Patkar, Uday Chandrakant Sharma, Deepak K. American Express Senior Software Engineer PhoenixAZ85054 United States Dr. Akhilesh Das Gupta Institute of Professional Studies Dept. of AI and Data Science Delhi New Delhi110053 India Marathwada Mitramandal College of Engineering Dept. of Electronics and Telecommunications Maharashtra Pune411052 India Zeal College of Engineering and Research Dept. of AI and Data Science MaharashtraNarhe Pune411041 India D Y Patil College of Engineering Dept. of AI and Data Science Akurdi Pune411041 India Poornima University Dept. of Electrical Engineering Jaipur India Genba Sopanrao Moze College of Engineering Dept. of AI and Data Science Balewadi Maharashtra Pune411045 India Bharati Vidyapeeth's College of Engineering Department of Computer Engineering Lavale Pune412115 India Savitribai Phule Pune University Dept. of AI and Data Science Maharashtra Pune India

ISBN: (纸本)9798350361391

This study introduces an innovative IoT-enabled wearable device tailored for monitoring and providing feedback on stuttering, leveraging advanced machine learning algorithms to enhance precision and real-time responsiveness. The device integrates an array of hardware and software elements, including sensors, microcontrollers, and communication protocols, to accurately capture speech patterns and movements. Utilizing machine learning techniques such as Support Vector machines (SVM) and Convolutional Neural Networks (CNN), the device performs real-time analysis to identify stuttering instances. Detailed discussions cover the criteria for selecting machine learning models, the training and testing procedures, and the metrics used for performance evaluation. The prototype underwent extensive testing and validation, showcasing high accuracy, sensitivity, and specificity in differentiating stuttered from fluent speech. User feedback emphasized the ergonomic design, user-friendly interface, and real-time feedback features, highlighting its potential to significantly enhance stuttering therapy outcomes. Comparative analysis with existing solutions revealed the device's superior performance, indicating strong potential for clinical application. The study discusses the implications for stuttering therapy, acknowledges the limitations, outlines future research directions, and underscores the device's contributions to personalized and effective stuttering interventions. This research advances the field of assistive technologies in speech therapy and demonstrates the promise of IoT-based wearable devices integrated with machine learning for improving the quality of life for individuals with stuttering disorders. © 2024 IEEE.

关键词： Convolutional neural networks

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Sustainable Security Solutions for IoT: Enhancing Intrusion Detection Using AI and machine learning 3

Sustainable Security Solutions for IoT: Enhancing Intrusion ...

引用

3rd international conference on Sustainable Mobility Applications, Renewables and Technology, SMART 2024

作者： Khan, Inaya Imtiyaz Kanaparthi, Yadidiah Ruchandani, Yash Rizwan, Aliya Department of Computer Science Canadian University Dubai Dubai United Arab Emirates Department of Computer Engineering Manipal University Dubai Dubai United Arab Emirates

ISBN: (纸本)9798331532925

The rapid expansion of the Internet of Things (IoT) has significantly increased the complexity and vulnerability of interconnected systems, posing a challenge to sustainable cybersecurity. With an emphasis on sustainable security practices, this article offers a thorough analysis of machine learning (ML) and artificial intelligence (AI) approaches for intrusion detection systems (IDS) in Internet of Things (IoT) contexts. We examine various AI-ML-based IDS approaches, emphasizing resource-efficient algorithms, energy-saving data processing techniques, and scalable solutions for real-time anomaly detection. Our review includes a comparison of methods based on criteria such as processing efficiency, energy consumption, and integration with existing sustainable practices in IoT ecosystems. The paper highlights the potential of AI-ML techniques to not only improve intrusion detection accuracy but also enhance the sustainability of IoT security through optimized computational resources and eco-friendly approaches. We identify current research gaps and suggest future directions for developing sustainable IDS solutions, including lightweight hybrid classifiers and privacy-preserving techniques that minimize data transfer. © 2024 IEEE

关键词： Network intrusion

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A Comparative Study of Image Classification Models for Terrain Recognition 3

A Comparative Study of Image Classification Models for Terra...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Krishna Kumar, V. Karthika, V. Mohana, T. Mugebur Rahman, A. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

Terrain recognition is vital for various applications such as autonomous navigation, environmental monitoring, military operations, and urban planning. It enables safe navigation for autonomous vehicles, aids in environmental surveillance, assists in military reconnaissance, and supports urban infrastructure development. Recent years have witnessed the augmentation of machine learning models with online feature extraction techniques and self-supervised learning frameworks. However, a notable challenge lies in managing the complexity inherent in processing extensive datasets or numerous features. In this study, this problem is addressed by employing transfer learning for classifying terrains using images. The effectiveness of three widely used pre-trained convolutional neural network models - ResNet50, VGG16, and InceptionV3 - was compared. The findings demonstrate the strong performance of the models, emphasizing the effectiveness of transfer learning in tasks focused on terrain recognition. © 2024 IEEE.

关键词： Self-supervised learning

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Realizing Artificial Intelligence in Healthcare: Applications and Challenges 3

Realizing Artificial Intelligence in Healthcare: Application...

引用

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

作者： Zafar, Beenish Arshad, Rabia Muzzammel, Raheel Department of Computer Science University of Central Punjab Lahore Pakistan

ISBN: (纸本)9798331504861

The integration of artificial intelligence (AI) in healthcare is revolutionizing the clinical procedures in intervention, diagnosis, therapy, amelioration, eradication of diseases and other psychological and neurological diseases. AI has inevitably transformed the health diagnostics, because of the ability to produce improved and accurate results with better accuracy. It has become significant in recent era due to least requirement of equipment and low cost. Moreover, AI related techniques e.g., machine learning (ML) and deep learning (DL) have also played their roles in improving the research and accurate outcomes in healthcare sector. This article presents a brief review about some major techniques of AI in the healthcare sector. Furthermore, some applications of AI relevant to this domain are also presented in this article. ©2024 IEEE.

关键词： Contrastive learning

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Artificial Intelligence in Innovation Research A Bibliometric Perspective 3

Artificial Intelligence in Innovation Research A Bibliometri...

引用

3rd international conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Triana, Ika Arief, M.T.S. Alamsjah, Firdaus Elidjen, Elidjen Bina Nusantara University Bbs Research in Management Jakarta Indonesia Bina Nusantara University Industrial Engineering Department Jakarta Indonesia Bina Nusantara University Business Creation Program Bbs in Management Jakarta Indonesia

ISBN: (纸本)9798350367492

This study explores the intersection of artificial intelligence (AI) and innovation research through bibliometric analysis, aiming to identify key trends, influential authors and pivotal publications within this rapidly evolving field. By systematically examining a large dataset of scholarly articles, we map the intellectual structure and development of AI-driven innovation research over the past decade. The analysis reveals a significant growth in publications, with an emphasis on the application of AI technologies to enhance innovation processes in various industries. Key findings highlight the predominant research themes, such as machine learning, data analytics, and their impact on product development and organizational efficiency. The study also uncovers the geographic distribution of research activities, with leading contributions from North America, Europe, and Asia. Through network analysis, we identify core clusters of collaboration and citation patterns, illustrating the interconnected nature of the research community. This bibliometric perspective provides a comprehensive overview of the state of AI in innovation research, offering valuable insights for scholars, policymakers, and practitioners aiming to leverage AI for future advancements in innovation. © 2024 IEEE.

关键词： Artificial Intelligence data Analytics Innovation Research machine learning Research Trends

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Optimization Strategy of University Library Knowledge Service Based on data Management 3

Optimization Strategy of University Library Knowledge Servic...

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3rd international Joint conference on Information and Communication engineering (JCICE)

作者： Zhou, Yanjun Jianghan Univ Wuhan Peoples R China

ISBN: (纸本)9798350388732;9798350388725

With the rapid development of science and technology, big data technology is profoundly changing all walks of life, and the library management of public libraries is also deeply affected. In the era of big data, how to use advanced technology to innovate the management of public libraries, improve the quality and efficiency of services, and meet the growing information needs of the public is an urgent problem to be solved. This paper mainly analyses the relevant content of library management innovation in public libraries in the era of big data. The study used a questionnaire to investigate the acceptance of teachers and students of the new improved library service system. The reliability of the questionnaire was above 0.7, and the results showed that students' overall evaluation of the library management system was high, with scores above 5.

关键词： library management university library data management book lending

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Predictive Modelling of Critical Vital Signs in ICU Patients by machine learning: An Early Warning System for Improved Patient Outcomes 3

Predictive Modelling of Critical Vital Signs in ICU Patients...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Senthil Pandi, S. Kumaragurubaran, T. Vijay Raj, S.R. Vigneshwaran, R. Rajalakshmi Engineering College Department of CSE Chennai India

ISBN: (纸本)9798350381931

Accurate monitoring of vital signs in an ICU is integral to understanding overall physical well-being for patients. Our research endeavor employed machine learning techniques to construct a predictive classification model utilizing continuous ICU vital sign measurements. The primary aim was to develop an early warning system capable of forecasting whether vital indicators would reach critical values within one hour;our ultimate aim was to enable healthcare professionals, including nurses and doctors, to intervene proactively, preventing emergency situations which could result in organ dysfunction or mortality. Our comprehensive dataset comprises vital sign measurements, lab test results, procedures, and medications from over 50,000 patients collected via rigorous preprocessing procedures like data cleansing, bias correction, feature extraction and selection to produce an insightful dataset with distinguishing attributes. After selecting an algorithmic set that included Decision Trees (DT), Support Vector machines (SVM), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM), to predict critical vital signs in ICU patients one hour in advance - such as Heart Rate, SpO2, Mean Artery Pressure (MAP), Respiratory Rate (RR), and Systolic Blood Pressure (SBP). Our models included Heart Rate prediction as well as respiratory Rate/RR predictions/SBP estimation models. The results of the study demonstrated the efficacy and accuracy of machine learning methods designed to anticipate imminent changes to vital signs. Utilizing such predictive models, healthcare providers can increase their capacity to address potential complications before they occur, ultimately leading to improved patient outcomes in challenging settings. © 2024 IEEE.

关键词： Long short-term memory

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