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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13394 条记录，以下是111-120 订阅

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machine learning Approach for Handling Imbalanced Students' Performance data 15

Machine Learning Approach for Handling Imbalanced Students' ...

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sujatha, E. Divya, S. Sakthivelan, R.G. Gopirajan, P.V. Thandalam Chennai India Chennai India Department of Computer Science and Engineering Gopalan College of Engineering and Management Bengaluru India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur campus Chennai India

ISBN: (纸本)9798331300579

Imbalanced student performance data in educational institutions is crucial for any machine learning prediction model. It affects the efficiency of classifiers and challenges the sampling methods for having a more significant number of features. The proposed model was designed to predict the student's performance by comparing the results with popular similar models. The Kaggle dataset having 386 rows and 33 columns of student data, was used in this study. In addition, the Portugal database was considered for experimental analysis, containing 394 students and 19 attributes. It proves that the Random Forest classifier yields the highest accuracy at 84.15% in handling imbalanced datasets. The results show that the SVM-SMOTE is higher accuracy as, 94.76%, than the other sampling methods in predicting the student's performance with various features. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

来源：评论

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machine learning in Action: Supervised learning Models for Classifying Credit Card Fraud

Machine Learning in Action: Supervised Learning Models for C...

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Pendalwar, Rishab Verma, Aditya Patil, Ratna Vishwakarma Institute of Information Technology Dept. of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350378092

Financial institutions face a relentless battle against fraudulent credit card transactions. machine learning (ML) offers a promising approach for tackling this challenge. This paper explores six supervised machine learning models (Support Vector machine (SVM), Random Forest, Naive Bayes, K-Nearest Neighbours (KNN), XGBoost, and LIGHT GBM) for detecting credit card fraud using a benchmark dataset. We explore the impact of dimensionality reduction with Principal Component Analysis (PCA) and data balancing with Generative Adversarial Networks (GANs) on imbalanced class distributions. The assessment relies on metrics such as accuracy, precision, recall, and F1-score. The results indicate that Random Forest demonstrates the best overall performance, especially when paired with GAN-based data balancing techniques. This study highlights the importance of addressing class imbalance and exploring dimensionality reduction techniques for enhancing credit card fraud detection capabilities. © 2024 IEEE.

关键词： Generative adversarial networks

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A Xgboost Optimized Ensemble Model for Remaining useful Life Prediction of Aircraft Turbofan Engines 6

A Xgboost Optimized Ensemble Model for Remaining useful Life...

引用

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

作者： Sharma, Vaasudev Dagar, Riyansh Sharanya, S. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (纸本)9798350306613

Proactive maintenance for the optimized lifecycle of equipment is now effortless via condition monitoring and fault diagnosis. Recognition and avoidance of a state of failure help to achieve solidity and reliability. Artificial Intelligence and machine learning (ML) forecast such situations and aid in the prevention of economic and safety emergencies. This work reviews such ML algorithms and ML models that help diagnose faults in Turbofan engines via measurement of the Remaining Useful Life parameter. This proposed an ensemble machine-learning model optimized by Extreme Gradient Boosting. The approach is then evaluated via different metrics and their performance is measured to current innovative methods. The system is useful for all processes that require high reliability quality and efficiency and reduces unplanned downtime, maintenance costs and asset quality. © 2024 IEEE.

关键词： CMAPSS Ensemble Model machine learning Predictive Maintenance Remaining useful life (RUL) Turbofan Engine

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machine learning for Malware Detection: A Comprehensive Review of Visual, Textual and Network Traffic Features

Machine Learning for Malware Detection: A Comprehensive Revi...

引用

2024 international conference on computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Jolly, Jasmine Ananthanarayanan, V. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Campus Coimbatore641112 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore641112 India

ISBN: (纸本)9798331510299

IoT devices, constrained by limited resources and weak security measures, are highly vulnerable to malware at- tacks. This review examines malware detection methods using textual, visual, and network traffic features, with experiments fo- cused on network traffic data. Feature extraction techniques such as correlation analysis, eXtreme Gradient Boosting(XGBoost), and the Firefly algorithm were applied to machine learning models, including logistic regression, random forest, Adaptive Boosting(AdaBoost), and perceptron. Results highlight the poten- tial of these approaches for resource-constrained environments. © 2024 IEEE.

关键词： Adaptive boosting

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Unmasking Frame Duplication: Comprehensive Approach to Detection in Digital Video Using machine learning Algorithms and Signal Processing Techniques

Unmasking Frame Duplication: Comprehensive Approach to Detec...

引用

2024 international conference on Advances in data Engineering and Intelligent computing Systems, ADICS 2024

作者： Praddipraj, N.N. Praveen Kumar, B. Sreeji, S. Sathyabama Institute Of Science And Technology Dept. Of Computer Science And Engineering Tamilnadu Chennai600119 India

ISBN: (纸本)9798350364828

Our research proposes a comprehensive approach to identify duplicate frames in digital videos. It integrates machine learning and signal processing techniques for effective identification. The process begins with pre-processing, enhancing video quality through temporal filtering and noise reduction. Next, feature extraction captures statistical and structural information from each frame. Deep neural networks and other machine learning methods are used to classify frames as original or duplicated using these features. To enhance detection accuracy, signal processing methods like block-based and key point-based approaches are applied. Block-based methods divide frames into segments for region comparison, while key point-based methods identify and match key points between frames. Our method's effectiveness and robustness are demonstrated through experiments on various digital videos. This combination of machine learning and signal processing offers a comprehensive solution to uncover frame duplication, ensuring video content's integrity and authenticity. © 2024 IEEE.

关键词： learning algorithms

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Psychological Mental Health Analysis using NLP and machine learning

Psychological Mental Health Analysis using NLP and Machine L...

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Anjali, Kumari Negi, Hritik Nautiyal, Rishabh Bijalwan, Saksham Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350378092

This research explores real-time psychological analysis using pre-store information and Natural Language Processing (NLP). It aims to enhance the precision of psychological evaluation by analyzing language patterns in live conversation, utilizing NLP and machine learning techniques. The study addresses mental health challenges, focusing on stress prediction among university students, workers and introverted individuals. Additionally, it delves into depression identification through version NLP techniques and machine learning algorithms. The comparative analysis evaluates the effectiveness of approaches for discerning depression for discerning depressive states from textual data. The research contributes to understanding live text patterns and has implications for personalized mental health interventions, considering ethical considerations in technological advancements. © 2024 IEEE.

关键词： Adversarial machine learning

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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...

引用

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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5G User Equipment (UE) Positioning and Localization Estimation using machine learning

5G User Equipment (UE) Positioning and Localization Estimati...

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Survase, Sanket Diwan, Raman Lal, Kumari Nidhi Kumar, Shishupal VNIT Nagpur Dept. of Computer Science and Engineering Nagpur India IIIT Nagpur Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350378092

The emergence of 5G technology has ushered in a new era of connectivity, making communications faster and cheaper. One of the main challenges of 5G networks is accuracy and efficiency, which are important for many applications such as driver less cars, smart cities and virtual reality. This research paper explores the integration of machine learning techniques to improve the accuracy and reliability of 5G infrastructure. The plan uses rich data generated by 5G networks, combining various parameters such as signal strength, flight time and angle of arrival. machine learning algorithms are used to process this data and create good models for the correct location. The system is designed to adapt to dynamic and complex urban environments where traditional methods often encounter limitations. The main contribution of this research includes the development of a new machine learning-based distribution algorithm suitable for 5G networks, a comprehensive evaluation of performance comparison with the existing system, and evaluation of recommendations for feasibility and implementation. The results showed significant improvements in accuracy and reliability, demonstrating the potential of machine learning to revolutionize the 5G workplace. © 2024 IEEE.

关键词： Adversarial machine learning

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Realizing Quantum Kernel Models at Scale with Matrix Product State Simulation 24

Realizing Quantum Kernel Models at Scale with Matrix Product...

引用

2024 international conference for High Performance computing, Networking, Storage and Analysis

作者： Metcalf, Mekena Andres-Martinez, Pablo Fitzpatrick, Nathan HSBC Holdings Plc Innovat & Ventures San Francisco CA 94111 USA Quantinuum Cambridge England

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

data representation in quantum state space offers an alternative function space for machine learning tasks. However, benchmarking these algorithms at a practical scale has been limited by ineffective simulation methods. We develop a quantum kernel framework using a Matrix Product State (MPS) simulator and employ it to perform a classification task with 165 features and 6400 training data points, well beyond the scale of any prior work. We make use of a circuit ansatz on a linear chain of qubits with increasing interaction distance between qubits. We assess the MPS simulator performance on CPUs and GPUs and, by systematically increasing the qubit interaction distance, we identify a crossover point beyond which the GPU implementation runs faster. We show that quantum kernel model performance improves as the feature dimension and training data increases, which is the first evidence of quantum model performance at scale.

关键词： Tensors Scalability Qubit Training data Graphics processing units machine learning data models Complexity theory Kernel Integrated circuit modeling

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Optimal Key Generation using Support Vector machine for Health Records

Optimal Key Generation using Support Vector Machine for Heal...

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2024 international conference on Emerging Innovations and Advanced computing, INNOCOMP 2024

作者： Himanshu Punhani, Akash SRM Institute of Science and Technology Department of Computer Science and Engineering Delhi-NCR Campus Ghaziabad201204 India

ISBN: (纸本)9798350376470

As the digital age continues to progress, the demand for advanced data privacy techniques has become increasingly vital. With the rise of large-scale collection of data and the proliferation of machine learning algorithms, ensuring privacy while extracting valuable insights has become a challenging situation. This research paper proposes an approach that leverages machine learning-based techniques to achieve scalable privacy preservation, while also providing an optimal key generation mechanism for data encryption and decryption. The proposed approach is evaluated on a real-world healthcare dataset, and the experimental results demonstrate that the proposed approach provides a significant improvement in privacy preservation, while maintaining the performance and accuracy of the system. Healthcare data is among most sensitive one for this scenario as this data encompasses a vast array of information related to patient health, medical conditions, treatments, outcomes, and more. Leveraging this data in a responsible and privacy-preserving manner holds tremendous potential for improving healthcare outcomes, advancing medical research, and enhancing the overall quality of patient care. © 2024 IEEE.

关键词： Electronic health record

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