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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6841 条记录，以下是71-80 订阅

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Heart Disease Prediction using machine learning Algorithms: A data Science Approach 3

Heart Disease Prediction using Machine Learning Algorithms: ...

引用

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

作者： Murugananthan, V. Rehan, Mahmoud Yehia Emam Selim Srinivasan, R. Kavitha, M. School of Computing Technology Park Malaysia Kuala Lumpur57000 Malaysia Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

The widespread use of data mining is a direct result of the practice's first success in more public arenas like marketing, e-commerce, and retail. Discoveries in healthcare are among them. data is abundant in healthcare systems. In spite of this, there is a lack of efficient analysis tools for uncovering latent connections in data. In particular, this study aims to describe the Nave Bayes and decision tree classifiers used in our research to predict cardiac events. It has been shown, through experimentation on the same dataset, that Decision Tree is more effective than Bayesian classification when it comes to predictive data mining. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

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Macroeconomic data Fusion Method Based on machine learning 3

Macroeconomic Data Fusion Method Based on Machine Learning

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Haixiao, Wang University of California San Diego United States

ISBN: (纸本)9798350354171

The role of data fusion in macroeconomics is very important, but there is a problem of inaccurate prediction of results. Traditional data processing technology cannot solve the problem of macroeconomic data fusion, and the quality of integration is unreasonable. Therefore, this paper proposes a machine learning method for economic data fusion analysis. Firstly, the algebraic method is used to integrate macroeconomic data, and indicators are divided according to the requirements of data fusion to reduce data fusion in the interfering factor. Then, the algebraic method forms a data fusion scheme for the macroeconomy, and comprehensively analyzes the data fusion results. MATLAB simulation shows that under the condition that the fusion quality standard is certain, the accuracy of data fusion of machine learning on macroeconomics, data fusion time is better than traditional data processing techniques. © 2024 IEEE.

关键词： data assimilation

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machine learning Enhanced Smart Sensors for Hand Gesture Recognition 3

Machine Learning Enhanced Smart Sensors for Hand Gesture Rec...

引用

3rd international Symposium on Sensor Technology and Control, ISSTC 2024

作者： Feng, Haoen National University of Singapore College of Design and Engineering Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798331541637

Against the backdrop of highly developed smart sensor technology, machine learning, and multimodal data analysis, hand gesture recognition (HGR) has become an important field in promoting human-computer interaction, health monitoring, and rehabilitation medicine. This paper proposes a smart sensor system based on machine learning enhancement, which combines hardware devices such as Flex sensor, JY901S, camera and smart gloves, as well as machine learning models such as support vector machine (SVM) and long short-term memory network (LSTM), using For processing and analysis of gesture data. By using multi-modal data in static and dynamic gesture classification tasks, experimental results show that collaborative inference of multi-modal data significantly improves the model's classification accuracy compared to single or bi-modal data. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

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Control of Security Breach of Optimization Technique Using machine learning 3

Control of Security Breach of Optimization Technique Using M...

引用

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

作者： Stephen, Vimal Kumar Palanisamy, Ramesh Al-Jufaili, Zaina Rashid Salim Ahmed, Mallika Banu Basheer University of Technology and Applied Sciences Ibra Oman

ISBN: (纸本)9798350369083

Hardware and software can be utilised to lower IP networks' rapidly escalating power consumption. Even if changing to a more efficient transmission medium will still result in the highest savings, in this situation it is the combination of arbitrary power states and an in-depth understanding of traffic matrices that will offer the most savings. An alternative to this approach is a lightweight heuristic approach that is based on a much simpler network model but still allows power-efficient traffic engineering. By training on existing datasets, machine learning algorithms are able to encode and decode a wide variety of data types. In order to reflect and assess the underlying general trends in the population data, the encoded information is utilized. To avoid security breach of the data the study proposed S-GRIDA optimization technique. The proposed model S-GRIDA has provided an accuracy of 98.4% and illustrates that this model works effectively in avoiding security breach in the networks. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

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Big data Analytics and Mining Methods, Applications and Trends 3

Big Data Analytics and Mining Methods, Applications and Tren...

引用

3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Li, Jian Wang, Siyi Wang, Jinxiao Zeng, Dexian Space Engineering University BeiJing China

ISBN: (纸本)9798331506582

In the era of big data information, along with the continuous development and application of 5G, online teaching, machine learning and other high-tech, the amount of information and data in many fields, such as education, scientific research, medical care, communication, e-commerce and so on, is growing geometrically. How to analyse and research these massive data more intelligently and efficiently, and mine the data information with practical application value, is the current key issue in the field of big data technology. Therefore, we need to further study and explore more efficient and intelligent data analysis and mining methods and techniques in order to better use big data technology to support the development of various fields. © 2024 IEEE.

关键词： data Analytics

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Insights into Ocular Disease Recognition: A machine learning Approach 3

Insights into Ocular Disease Recognition: A Machine Learning...

引用

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

作者： Paul, Sosmita Bhatt, Geet Dey, Aditya Sarvajanik College of Engineering and Technology Be in Electronics and Communication Engineering Gujarat Surat India Be in Computer Engineering L D College of Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350375190

This study reviews the potential benefits of machine learning techniques in diagnosing ocular disorders, aiming to address the limitations of traditional diagnostic methods. Various machine learning (ML) algorithms, including Support Vector machine, Decision Tree, K-Nearest Neighbor, Random Forest, Naïve Bayes, and Extra Trees, are compared to determine the effectiveness of each algorithm in identifying eye diseases. Through analysis and testing, the study concludes that Extra Trees (ET) has achieved the highest accuracy rate of 97%. This finding suggests that ET could be the most suitable option for enhancing the accuracy and efficiency of eye disease detection systems. Overall, these research discoveries contribute to the ongoing efforts to improve diagnostic tools for ocular health, highlighting the potential of machine learning (ML) in transforming healthcare. © 2024 IEEE.

关键词： Decision trees

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Promoter Prediction in DNA Classification Using machine learning Algorithms 3

Promoter Prediction in DNA Classification Using Machine Lear...

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Uddin, Mohammad Moin Shiddike, Jasrin Ahmed, Afsana Ahsan, Tanveer International Islamic University Chittagong Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798350396157

The genetic code distinguishing species is stored in DNA (Deoxyribonucleic acid), consisting of four bases: Adenine (A), Cytosine (C), Guanine (G), and Thymine (T). Gene promoters, critical for controlling transcription, are located near transcription start sites. Advances in sequencing technology have accelerated DNA data growth. ML (machine learning) has greatly aided DNA data analysis. This research aims to classify sequences as promoters or non-promoters accurately, achieving high true positive and true negative rates. We collected 106 human promoter and non-promoter data from the University of California, Irvine, ML repository., built a robust promoter classifier using various machine learning models, and utilized tokenization methods like label encoding for feature vectors with different k-mer sizes (3-mer, 6-mer). Combining 6-mer and PCA improved accuracy. Our system successfully distinguishes DNA sequences as promoters or non-promoters with high accuracy and F1-score, around 91% and 93%, automating the identification of complex biological features in sequence data. © 2024 IEEE.

关键词： DNA

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machine learning Methods for Kazakh Morphology: A Comprehensive Overview 3

Machine Learning Methods for Kazakh Morphology: A Comprehens...

引用

3rd IEEE international conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Akhmetov, Iskander Aubakirov, Shakarim Saparov, Timur Mussabayev, Rustam Toleu, Alymzhan Krassovitskiy, Alexander Satbayev University Ai Research Lab Almaty Kazakhstan Kbtu Data Science lab Almaty Kazakhstan

ISBN: (纸本)9798331516321

Kazakh is an agglutinative language, where the sequential attachment of morphemes forms words, each bearing specific grammatical information. The complexity of this morphological structure presents significant challenges for computational linguistics, particularly in natural language processing tasks such as part-of-speech tagging, syntactic parsing, and machine translation. This paper provides a comprehensive review of the most widely used machine learning approaches applied to Kazakh morphology. It compares classical machine learning techniques, such as hidden Markov models and conditional random fields, along with rule-based methods, and recent advances in deep learning, including recurrent neural networks, long short-term memory networks, and transformer-based architectures like BERT. The paper also explores the applicability of data-driven approaches and reviews existing research on Kazakh morphology, offering a comparative analysis based on efficiency, scalability, and accuracy. Finally, future directions are discussed, focusing on expanding annotated datasets and developing models tailored for low-resource languages like Kazakh to improve their representation in natural language processing tasks. © 2024 IEEE.

关键词： Adversarial machine learning

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Revolutionizing Fault Prediction in MetroPT datasets: Enhanced Diagnosis and Efficient Failure Prediction through Innovative data Refinement 3

Revolutionizing Fault Prediction in MetroPT Datasets: Enhanc...

引用

3rd international conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Neamah, Osamah N. Bayir, Raif Graduate Institute Karabuk University Department of Mechatronics Engineering Karabuk Turkey Karabuk University Department of Mechatronics Engineering Karabuk Turkey

ISBN: (纸本)9798350349207

This scientific paper presents groundbreaking advancements in Predictive Maintenance (PdM) within Industry 4.0, employing cutting-edge machine learning classification algorithms for fault prediction and diagnosis in Air Production Unit (APU) systems like MetroPT and MetroPT-3. This research uses data-driven methodologies to optimize feature extraction techniques to enhance fault prediction and improve diagnostic accuracy. A robust and versatile model emerges through comprehensive testing, displaying exceptional potential in fault prediction and diagnosis for complex systems. The paper highlights the significance of enhanced analytical techniques, such as cross-validation, ensuring the reliability and robustness of the model, contributing to refined and accurate fault prediction and diagnosis, all without succumbing to overfitting. This work significantly advances the application of machine learning in predicting malignancy within Industry 4.0, showcasing the promise of these methodologies in fault prediction and diagnosis for intricate systems. © 2024 IEEE.

关键词： machine learning

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Unleashing the Power of Feature Selection Techniques for High-Dimensional Cancer data Classification 3

Unleashing the Power of Feature Selection Techniques for Hig...

引用

3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Sahoo, Chetna Dash, Rasmita University Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350354379

The world has seen a definite increase in the number of Cancer cases which also indicates that there is requirement of techniques for diagnosis of this disease with every possible information available. This study works on deciding upon how to implement feature selection methods on high dimensional cancer data so as to make them suitable for classification. Use of machine learning in medical field can be utilized to resolve the diagnosis process with the only available numeric data that can lead to faster detection and treatment. A comparative study of various normalization and feature selection techniques are included to select the most appropriate methods. datasets are considered of various variety that includes both binary and multiclass classification datasets, so as to make the model acceptable for diverse situations in cancer field that is whether detecting presence of cancer cells, stages of cancer or it's subtypes. Finalized with four normalization and four feature selection techniques. And for classification purpose three machine learning algorithms, which are Logistic Regression, Random Forest and Support Vector machine (SVM) are used to evaluate four genomic datasets. Multiple combinations of these are used to find the best performing algorithm to train the model prepared for classification. © 2024 IEEE.

关键词： Adversarial machine learning

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