检索结果-内蒙古大学图书馆

International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Julian, Anitha Priya, M.S. Lekshmi Chamundeeswari, V. Vijaya Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

With the growth of Blockchain technology in the second decade of the new millennium, numerous applications throughout the globe have been interested in intriguing new uses. The subject of electronic voting is one of the difficult uses of blockchain technology. Since the election is being conducted offline, security is of the utmost importance. An organization still has full access to the current electoral system’s centralized infrastructure. The entity that controls the database and has complete control over the system is mostly to blame for the issues because it is using antiquated election systems. A significant factor that seriously affects the system is database manipulation. The use of blockchain technology is one of the efficient solutions to this issue. Block chain technology is the best option because it has a decentralized structure and a shared database. This paper explores the potential for using Blockchain technology in e-voting systems to enhance the voting process by addressing issues of trustlessness, privacy, and security with effective face authentication techniques using Haar cascade and Support Vector Machine Algorithms. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Heart Attack Prediction Model Using Stacked Ensembles and Clinical Feature engineering

Comprehensive Heart Attack Prediction Model Using Stacked En...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Jayabalan, Anandhi Uma, R. Padmakala, S. Sri Sai Ram Engineering College Department of Computer Science Engineering Chennai India Saveetha School of Engineering Saveetha Institute Of Medical and Technical Sciences Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331509828

Cardiovascular disease continues to be a predominant cause of mortality globally, requiring precise and effective strategies for early identification. This work examines the application of clinical datasets to explore ensemble models and machine learning for predicting heart attacks. The dataset includes patient information about factors such as age, diabetics, levels of cholesterol, blood pressure, and smoking status. The Label Encoder is utilized to encode categorical variables, while class imbalance is tackled through the application of the Synthetic Minority Over-sampling Technique (SMOTE). The top five features were identified through the application of feature importance in Random Forest analysis. To enhance prediction accuracy, various classification techniques, including Random Forest (RF), Gradient Boosting (GB), XG Boost, and Light GBM, along with an ensemble Voting Classifier, are utilized. The best model performance was attained by intensive hyperparameter optimization using Grid Search CV. The performance of the proposed system was assessed through a stacked ensemble methodology. The Random Forest model revealed an accuracy of 87.25% and a ROC-AVC of 0.91. The results for Gradient Boosting and XG Boost showed accuracies of 85.60% and 86.80%, accompanied by ROC-AUC scores of 0.89 and 0.90, respectively. The ensemble Voting Classifier enhanced performance, reaching an accuracy of 88.15% and a ROC-AUC of 0.92. The findings indicate that utilizing an ensemble method alongside hyperparameter optimization significantly enhances prediction accuracy and facilitates the early identification of heart attack risks. © 2025 IEEE.

关键词： Blood pressure

来源：评论

学校读者我要写书评

暂无评论

A system for crop recommendation to improve the production management

A system for crop recommendation to improve the production m...

引用

International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Ramyadevi, R. Sandeep, K. Girish Sairam, V. Vaisak, P. Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

The increasing world population leads to a heightened demand for sustainable living and nutritious food. The farming industry, which is one of the most profitable in the world, faces challenges in meeting these needs. Integrated Crop Management offers a solution for ecologically sustainable farming that balances quality, quantity, and profitability. By utilizing tools such as weather forecasting, trend analysis, and productivity monitoring, farmers can minimize uncertainty and errors, leading to increased profitability. We propose a solution for farmers that involves utilizing Machine Learning and Data Analytics to manage information related to crop selection, soil type, and production outcomes. This 360-degree approach includes long-term strategies for increased production and provides information on various crop categories and weather predictions to determine the best results based on seasonality. By inputting farm and weather data, farmers can determine the optimal crops to grow for each cycle, predict the return on investment for farm activities. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Information Extraction Using RPA and Generative AI from Unstructured Documents: A Case of Invoices 7th

Information Extraction Using RPA and Generative AI from Unst...

引用

7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Sowjanya, S. Vijaya Chamundeeswari, V. Department of Computer Science Saveetha Engineering College Chennai India

ISBN: (纸本)9783031699856

In the daily operations of both small and large businesses, the handling of voluminous invoices remains an enduring challenge. The manual input and validation of invoice data not only imposes significant labor demands but also serve as ground for potential errors. This creates substantial complications when reconciling balances and cross-verifying information within digital invoices, creating the challenge of meticulous record-keeping. This paper offers an alternative solution for automating data extraction from invoices by leveraging Generative Artificial Intelligence. This also provides businesses with the flexibility to extract data from any format, theme or template while also minimizing human interaction. Getting Generative AI into invoice processing makes it much easier, making operations smoother and cutting down on how much humans must handle. Additionally, RPA takes it a step further by automating the entire end-to-end invoice processing workflow. This combination makes it easy for small and big business alike to easily use the streamline process which seamlessly integrates the user experience and provides enhanced accuracy compared to other methods. The use of NLP by Generative AI increases the understanding and extraction of the required data high precision from the invoices. The outcomes show a notable decrease in processing time and an improvement in accuracy, which eventually results in cost savings and increased operational effectiveness. © IFIP International Federation for Information Processing 2024

关键词： Cost effectiveness

来源：评论

学校读者我要写书评

暂无评论

Enhancing Gaussian Mixture Model Efficiency Through Covariance Matrix Optimization, Singular Value Decomposition, and Block Toeplitz Matrices 9

Enhancing Gaussian Mixture Model Efficiency Through Covarian...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Kanchana, R. Fernandez, F. Mary Harin Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350377972

Gaussian Mixture Models (GMMs) are widely used for clustering, density estimation, and pattern recognition. Despite their versatility, GMMs often face computational challenges, particularly when dealing with large datasets or high-dimensional spaces. To address these issues, an approach is proposed to enhance the efficiency of GMMs by integrating covariance matrix optimization, Singular Value Decomposition (SVD), and Block Toeplitz matrices. The method involves refining the covariance matrix to better capture the underlying data structure, thereby reducing computational complexity. The application of SVD allows for dimensionality reduction and improved numerical stability. Additionally, the use of Block Toeplitz matrices facilitates efficient handling of structured covariance matrices, further accelerating computations. Experimental results demonstrate significant improvements in prediction accuracy and computational speed, providing a more scalable and efficient solution for GMM applications across various domains. © 2024 IEEE.

关键词： Covariance matrix

来源：评论

学校读者我要写书评

暂无评论

Enhancing Accuracy of Cyberstalking Detection with Novel Convolutional Neural Network Comparison with Support Vector Machine 2

Enhancing Accuracy of Cyberstalking Detection with Novel Con...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Chetana, C. Arumugam, S.S. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350384277

The main difference between this research and the Convolutional Neural Network approach is that it aims to improve the accuracy of cyberstalking detection by applying Support Vector Machines algorithm. The datasets are gathered from the different sources on internet and have an alpha value of 0.05. The g-power value of both SVM and CNN is 80%. According to the analysis, group 1 and Group2 are projected to have incidence rate of 92.53%-87.46%. For taking the samples, a BERT feature extraction technique is employed. Using the BERT model for feature extraction, a maximum accuracy of ninety percent is being achieved by utilizing the CNN classifier. The results indicated that there is a significant difference between the two algorithms based on p=0.083 and (p © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Accuracy Comparison of Novel Deep Belief Network Compared over Random Forest in Predicting Uterus Cancer 2

Accuracy Comparison of Novel Deep Belief Network Compared ov...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Reddy, G. Siva Sai Kumar Subramanian, P. Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (纸本)9798350384277

In order to provide an exact comparison between the Novel Deep Belief Networks and the Random Forest based on their ability to detect uterine cancer. Among the groups that are participating in this study, two of them are the Random Forest Algorithm and the Novel Deep Belief Network. Each of these groups seems to have a sample size. A comparison was made between (Group 1) with 10 samples and J48 Decision trees, which resulted in better accuracy. (Group 2) also employed 10 samples at the same time. The study on parameters also includes the following. With a G-Power of 80% and a confidence interval of 95%, the significant findings of the dataset were predicted with the use of a statistical tool designed for the social sciences. Various approaches from the fields of machine learning and Random Forest are *** an accuracy of 86.44%, the Novel Deep Belief Network is much more accurate than the Random Forest, which has an accuracy of 82.18%. This is a significant improvement over the accuracy of the model that was comparison. In the statistical packages for social sciences statistical analysis, it is shown that there exists a statistically significant difference between Random Forest and Novel Deep Belief Network. The value of p = 0.000 (p © 2024 IEEE.

关键词： Machine Learning Naive Bayes Novel Deep Belief Network Random Forest Reproductive Health Support Vector Machine Uterus Cancer

来源：评论

学校读者我要写书评

暂无评论

A Novel Fuzzy Marine White Shark Optimization Based Efficient Routing and Enhancing Network Lifetime in MANET

引用

WIRELESS PERSONAL COMMUNICATIONS 2023年第4期132卷 2363-2385页

作者： Devi, K. Lalitha Bhat, C. Rohith Devi, K. Lalitha KSR College of Engineering Tiruchengode Namakkal India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu 602105 India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

A mobile ad hoc network (MANET) is an independent wireless temporary network established by employing a set of mobile nodes (i.e. laptops, smartphones, iPods, etc.) appropriate for the environment in which the network infrastructures are not fixed. The most common problems faced by MANET are energy efficiency, high energy consumption, low network lifetime as well as high traffic overhead which create an impact on overall network topology. Hence, it is necessary to provide an energy-effective CH election to take steps against such issues. Therefore, this paper proposes a novel model to enhance the network lifetime and energy efficiency by performing a routing strategy in MANET. In this paper, an optimal CH is selected by proposing a novel Fuzzy Marine White Shark optimization (FMWSO) algorithm which is obtained by integrating fuzzy operation with two optimization algorithms namely the marine predator algorithm and white shark optimizer. The proposed approach comprises three diverse stages namely Generation of data, Cluster Generation and CH selection. A novel FMWSO algorithm is proposed in such a way to determine the CH selection in MANET thereby enhancing the network topology, network lifetime and minimizing the overhead rate, and energy consumption. Finally, the performance of the proposed FMWSO approach is compared with various other existing techniques to determine the effectiveness of the system. The proposed FMWSO approach consumes minimum energy of 0.62 mJ which is lower than other approaches.

关键词： MANET Cluster head Fuzzy Marine White Shark Cluster generation Network lifetime Energy consumption

来源：评论

学校读者我要写书评

暂无评论

Enhancing the Accuracy for Predicting Nutritious Food Recommendations for Patients using Ensemble Random Forest over Artificial Neural Network 2

Enhancing the Accuracy for Predicting Nutritious Food Recomm...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Mukund, J. Akilandeswari, A. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha University Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384277

This study uses Innovative Ensemble Random Forest (ERF) and Artificial Neural Network to propose healthy diet to patients based on their health. Data collection and model training are required. This study uses Innovative Ensemble Random Forest (ERF) and Artificial Neural Network (ANN) data mining approaches on two groups. Each group has 10 samples for 20 total. Sample size is calculated using 0.95 G power. The parameters are CI 95, alpha 0.05, and beta 0.002. ERF and ANN should be used to train and assess healthy meal recommendation systems. The Innovative Ensemble Random Forest (ERF) approach outperforms the Artificial Neural Network algorithm with a 95.72 % accuracy and a statistical significance of 0.008 (p © 2024 IEEE.

关键词： Training Quantum computing Artificial neural networks Data collection Market research Data models Data mining

来源：评论

学校读者我要写书评

暂无评论

A Novel Random Forest Classifier With K-Fold Cross Validation for Crime Prediction Using the Chicago Dataset and Comparison with Stochastic Gradient Descent Classifier for Improved Accuracy 2

A Novel Random Forest Classifier With K-Fold Cross Validatio...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Rahaman, Zeenath Nallathamby, Rachel Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

With the use of a Novel Random Forest Classifier with K-Fold Cross Validation and a comparison of the findings with those of a Stochastic Gradient Classifier for the Chicago Crime dataset, the purpose of this endeavour is to improve the accuracy of crime prediction. For the purpose of forecasting the prediction of crimes in Chicago, the Novel Random Forest Classifier with K-Fold Cross Validation and the Stochastic Gradient Classifier are both implemented. The training and testing of these models are carried out using a sample size of 4,763 for each. A G Power test is used, which yields around 80% (the parameters for the G Power test are α=0.05 and power=0.80). A higher level of accuracy is achieved by the Novel Random Forest Classifier with K-fold cross validation (96.17%) in comparison to the stochastic gradient descent (94.73%). Given that the Independent Sample T-Test yields a significance value of p=0.01 (p © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：