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共 4173 条记录，以下是331-340 订阅

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An Approach for Securing Data Access with Blockchain Technology and machine learning 4

An Approach for Securing Data Access with Blockchain Technol...

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4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Ahire, Nilesh B. Chaudhari, Sarthak A. Jagtap, Varunraje J. Shinde, Prathamesh S. Kapale, Namdev D. Sanjivani College of Engineering Savitribai Phule Pune University Department of Electronics and Computer Engineering Pune India

ISBN: (纸本)9798350393248

the quantity of data is quickly expanding as a direct result of the increasing number of devices that are linked to the internet. Big data offers a wide range of potential uses and benefits, but it also poses a number of challenges that need to be surmounted before the quality of the services that are provided can be improved. these problems are associated with the security and integrity of data, analytics, the organization of large amounts of data, and acumen. Big data will see tremendous expansion as a consequence of the methods it has made available for decentralized data administration, the reconciliation of digital property, and the sharing of data through the internet of things. It is very difficult for unauthorized parties to access the data that is kept on the blockchain network due to the fact that the data is encrypted and the network is decentralized. this article provides some insightful and in-depth discussion on the applications of big data. these results might be investigated using machine learning techniques that are driven by the context in which the data was collected. this study investigates the classification of data via the use of machine learning and blockchain techniques, as well as various selection procedures, query strategies, application development, and safety measures. through the use of blockchain technology, problems with the protection of highly secure data may be overcome. this paper presents a comprehensive investigation of the relationships that exist between blockchain technology and machine learning. In addition, we will investigate a variety of challenges that may be encountered when using the blockchain technology to the process of machine learning. © 2023 IEEE.

关键词： Blockchain

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A Network application Fault Repair Method Based on Reinforcement learning 4

A Network Application Fault Repair Method Based on Reinforce...

引用

4th international conference on machine learning and computer application, ICMLCA 2023

作者： Liu, Wenbo Gao, Yan Chen, Ming Department of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Chinese Institute of Electronics Beijing China

ISBN: (纸本)9798400709449

With the continuous expansion of enterprise networks and the growing emphasis on security requirements, many enterprises have implemented multi-level security equipment (MLSE) within their networks. However, this deployment has led to network application failures and reduced overall network availability. this paper conducts an analysis of the underlying mechanisms responsible for such network application failures in MLSE enterprise networks. Subsequently, a Fault Location and Recovery (FDR) method is proposed, utilizing Q-learning path search with an experience storage (ESQL) mechanism. To validate the proposed approach, a prototype MLSE enterprise network system is constructed using NFV technology, and a series of verification experiments are performed on the FDR method. the experimental results demonstrate that the proposed approach successfully reproduces the failure phenomena and accurately locates and expedites the repair of network application failures through the FDR method. © 2023 ACM.

关键词： Reinforcement learning

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Particle Swarm Optimization of Deep learning Models for the Determination of Vehicle Hijacking Tweets 4

Particle Swarm Optimization of Deep Learning Models for the ...

引用

4th international conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Patel, Taahir A. Nyirenda, Clement N. University of the Western Cape Department of Computer Science Cape Town South Africa

ISBN: (纸本)9798350353266

the problem of vehicle hijacking is increasingly prevalent in many parts of the world, including South Africa, causing travelers to live in constant fear of becoming victims. To address this, this work employs the usage of social media data to identify hijacking incidents in urban environments by using the city of Cape Town in South Africa as the case study. More specifically, the incorporated data is obtained from the Twitter social media platform. However, this form of data tends to be informal and unstructured. Hence, this led to the issue of data relevance, which was addressed by using machine learning algorithms to classify tweets. Two deep learning algorithms were employed: a Convolutional Neural Network (CNN), and a Multi-layer Feed-forward Neural Network (MLFNN). However, due to model complexity, Particle Swarm Optimization (PSO) was also implemented to produce the model variations, PSOCNN and PSOMLFNN. It was found that the PSO-optimized models outperformed the standard non-PSO models. the PSOMLFNN approach was also identified to be the best-performing model amongst the employed selection by obtaining an accuracy of 96.15%. Hence, it was successfully employed along with the Tweet data to produce vehicle hijacking maps. In future work, further machine learning and optimization algorithms will be tested to identify the best-performing algorithms for this task as well as extend the hijacking maps to a mobile application for drivers on South African roads. © 2024 IEEE.

关键词： Tweets

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Investigation the Impact of Features on Malicious Traffic Identification Based on Different machine learning Algorithms Combined with Dimensionality Reduction 4

Investigation the Impact of Features on Malicious Traffic Id...

引用

4th international conference on machine learning and computer application, ICMLCA 2023

作者： Wang, Runze Department of Internet of Things Hebei University of Technology Tianjin China

ISBN: (纸本)9798400709449

With the Internet's rapid evolution, the incidence of cyberattacks has surged significantly. Employing machine learning to precisely detect and thwart malicious network traffic has emerged as a novel and effective solution for safeguarding computer networks. this research program centers on the identification of suitable machine learning models and the meticulous curation of data features. Within this study, a total of 13 features, encompassing conventional timestamps, the volume of traffic packets in data streams, and their associated sizes, are extracted as key features following the dataset's traffic packet consolidation process. three algorithms such as Random Forest, Decision Tree and Support Vector machine were chosen for training and testing the dataset. In addition, Principle Component Analysis dimensionality reduction is performed for these 13 features to determine the effect on the accuracy of the results before and after the dimensionality reduction process. the final result is that the Random Forest algorithm achieves best processing power, but produces large fluctuations in the accuracy in one dimension. In the face of large-scale network traffic analysis, the random forest model should be preferred as the machine learning model, while ensuring that the dimension is greater than one dimension after dimensionality reduction. © 2023 ACM.

关键词： Decision trees

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Towards Redefining the Reproducibility in Quantum Computing: A Data Analysis Approach on NISQ Devices 4

Towards Redefining the Reproducibility in Quantum Computing:...

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4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Senapati, Priyabrata Wang, Zhepeng Jiang, Weiwen Humble, Travis S Fang, Bo Xu, Shuai Guan, Qiang Kent State University Department of Computer Science United States George Mason University Department of Electrical and Computer Engineering United States George Mason University Quantum Science and Engineering Center United States Quantum Science Center Oak Ridge National Laboratory United States Hpc Group Pacific Northwest National Laboratory United States Case Western Reserve University Department of Computer and Data Sciences United States

ISBN: (纸本)9798350343236

Although the building of quantum computers has kept making rapid progress in recent years, noise is still the main challenge for any application to leverage the power of quantum computing. Existing works addressing noise in quantum devices proposed noise reduction when deploying a quantum algorithm to a specified quantum computer. the reproducibility issue of quantum algorithms has been raised since the noise levels vary on different quantum computers. Importantly, existing works largely ignore the fact that the noise of quantum devices varies as time goes by. therefore, reproducing the results on the same hardware will even become a problem. We analyze the reproducibility of quantum machine learning (QML) algorithms based on daily model training and execution data collection. Our analysis shows a correlation between our QML models' test accuracy and quantum computer hardware's calibration features. We also demonstrate that noisy simulators for quantum computers are not a reliable tool for quantum machine learning applications. © 2023 IEEE.

关键词： machine learning

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Survivability Prediction from Multiomics Cancerous Data with 1DCNN-LSTM-Attention Mechanism 4

Survivability Prediction from Multiomics Cancerous Data with...

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4th international conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Hazarika, Ambika Kumar, Ansuman Halde, Anindya North-Eastern Hill University Dept.of Computer Application Meghalaya India

ISBN: (纸本)9798350389128

the possibilities for survivability prediction to enhance patient treatment and outcomes is gaining attention in the field of health care. A human being's biological composition can be fully understood by utilizing multiomics data, which combines various biological information from different omics viz., genomics, transcriptomics, proteomics, and metabolomics. Considering the increasing popularity of Deep learning (DL) algorithms as a means for recognizing significant patterns in complex and multi-dimensional information, in this study, we propose a novel method for predicting survivability using multiomics data by utilizing one dimensional convolutional neural network(1DCNN),Long Short-Term Memory (LSTM) networks and attention mechanism. Preprocessing the multiomics data using 1DCNN to extract relevant features and integration of the omics data is the first step in our approach. then, in order to extract the complex patterns and temporal relationships present in multiomics data, we use LSTM networks. Moreover, in order to improve model understanding and concentrate on informative characteristics, we include an attention mechanism into LSTM output. Experimental results of the proposed method when compared to LSTM and other machine learning classifiers utilized here, indicate that LSTM with attention mechanism performs better in terms of accuracy and other performance evaluation measure (precision, recall and F1-score)on three different types of cancerous multiomics data viz., Breast, Kidney, and Lung. © 2024 IEEE.

关键词： Lung cancer

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Proposing the application of LSTM Models to Create Music 4

Proposing the Application of LSTM Models to Create Music

引用

4th international conference on Electrical Engineering and Informatics, ICon EEI 2024

作者： Tan, Nghia Duong Anh, Bang Nguyen thanh, Xuyen Do Bui Phuc, Lam Nguyen Tan Hanoi Viet Nam VinBigdata Hanoi Viet Nam Le Quy Don High School for the Gifted Vung Tau Viet Nam

ISBN: (纸本)9798331540791

In recent years, artificial intelligence (AI) and more specifically machine learning (ML) have emerged as a demonstration of the 4th industrial revolution. AI is present in all areas of human life from economics, education, and healthcare to housework, entertainment, and even the military. Outstanding applications in AI development come from many fields to solve different problems of life. the paper aims to learn and apply machine learning in sound processing for entertainment. In the paper, we use the long short-term memory (LSTM) model to create music. To perform the model, we have to collect data and preprocess to standardize the model training data. We then build a deep learning (DL) model using the LSTM network and train it with optimization parameters to create new, unique, and creative music. As a result, an automatic music generation model does not require human intervention and can generate highly rated music tracks based on the QoE factor as 5.15;3.65;and 3.7 points for tracks 1, 2, and 3, respectively. the results show that the AI model has the potential to be applied in many areas of life. © 2024 IEEE.

关键词： Long short-term memory

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A machine learning Plus-Features Based Approach for Optimal Asset Allocation 23

A Machine Learning Plus-Features Based Approach for Optimal ...

引用

4th ACM international conference on AI in Finance (ICAIF)

作者： Ramirez, Domingo Suarez, Fernando Pena, Jose-Manuel Larre, Omar Cifuentes, Arturo Fintual Adm Gen Fondos SA Santiago Chile CLAPES UC Santiago Chile

ISBN: (纸本)9798400702402

Within the asset management industry, the portfolio selection problem stands as one of the most important challenges. the approaches that experts have taken to address this problem have ranged from intuition-based decisions to sophisticated optimization frameworks. In this paper, we introduce a novel portfolio optimization methodology for investors with a medium- to long-term horizon focusing on public markets via index funds. Our method marries a Markowitz-inspired framework with a Conditional-Value-at-Risk (CVaR) constraint, and employs synthetic data generated through a Modified Conditional Generative Adversarial Network (CTGAN) approach that incorporates contextual information, specifically, the U.S. Treasury yield curve. the synthetic data generation algorithm captures the essential structure of the training dataset, while the CVaR-based optimization leads to portfolios with very good out-of-sample performance. this study emphasizes the merits of integrating contextual information and demonstrates the generative networks superiority over traditional approaches based solely on historical data. this innovative approach lays the groundwork for further advancements in asset allocation strategies fueled by synthetic data generating processes.

关键词： asset allocation portfolio optimization synthetic data machine learning features contextual information GAN CTGAN neural networks

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Differential diagnosis of radiation encephalopathy and post-radiation brain tumor recurrence by machine learning models based on contrast-enhanced MRI 4

Differential diagnosis of radiation encephalopathy and post-...

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4th international conference on Bioinformatics and Intelligent Computing (BIC)

作者： Li, Te Wang, Lingfei Wang, Xi Sun, Hong Zhejiang Police Vocat Acad Dept Secur & Precaut Hangzhou Peoples R China Hangzhou Med Coll Dept Oncol 903 Hosp PLA Joint Logist Support Force Xi Hu Affiliated Hosp Hangzhou Peoples R China

ISBN: (纸本)9798400716645

Purpose: Discriminating radiation encephalopathy (REP) from brain tumor recurrence is often difficult. this study aims to develop and validate an approach to distinguish REP from post-radiation brain tumor recurrence using machine learning. Methods: this study involved 102 patients diagnosed and treated in our institution between 2020 and 2023. A total of 2153 radiomics features were extracted from contrast-enhanced MRI by using 3D-Slicer software and Pycharm platform. Nine diagnostic models were built and compared based on three selection methods and three classification algorithms. the sensitivity, specificity, accuracy, and area under curve (AUC) of each model were calculated, and based on these the optimal model was chosen. Results: the least absolute shrinkage and selection operator with correlation (LASSO+corr) was chosen as the optimal selection method, which selected 17 important radiomics features. the most promising model was a combination of LASSO+corr as the selection method and logistic regression (LR) as the classification algorithm. the combination models of LASSO+corr with LR, random forest (RF) and support vector machine (SVM) showed sensitivities of 0.88, 0.86 and 0.97, and specificities of 0.98, 0.93 and 0.90 with AUC of 0.9805, 0.9452 and 0.9743, respectively. Conclusion: Radiomics-based machine learning has potential to be utilized in differentiating REP from recurrent brain tumor after radiotherapy accurately.

关键词： Radiation encephalopathy (REP) brain tumor radiomics machine learning differential diagnosis

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machine learning Techniques Using the Rapid Miner Tool for Solar Production Forecasting 4

Machine Learning Techniques Using the Rapid Miner Tool for S...

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4th international conference on Digital Age and Technological Advances for Sustainable Development, ICDATA 2023

作者： Ledmaoui, Younes El Fahli, Asmaa Elmaghraoui, Adila El Aroussi, Mohamed Saadane, Rachid Chehri, Abdellah Hassania School of Public Works Casablanca Laboratory Engineering System Morocco Mohammed Vi Polytechnic University Benguerir Morocco Mathematics and Computer Science KingstonONK7K 7B4 Canada

ISBN: (纸本)9798350311112

the Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBOOST), Support Vector machine (SVM), and Random Forest (RF) machine learning (ML) algorithms are used in this research to give a comparative study of solar energy production forecasting in Morocco. the models were developed, trained, and then assessed. In this paper, two metrics were employed to evaluate the models' performance: Mean absolute error (MAE), and root mean squared error (RMSE). the models' performances show that ANN is the most reliable predictive model for energy forecasting in similar scenarios. © 2023 IEEE.

关键词： Solar energy

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