检索结果-内蒙古大学图书馆

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Mohanty, Sanjukta Acharya, Arup Abhinna School of Computer Engineering KIIT Deemed to Be University Bhubaneswar751024 India

The overwhelming growth and popularity of web motivated the cyber attackers to develop fraudulent web sites and execute numerous attacking strategies to trick the user revealing sensitive information, installing malware automatically through drive-by-download attack, stealing the identity and money etc. Most of the attacking strategies are spreading through compromised URL (Uniform Resource Locator) and it greatly influences the internet performance. Blacklisting approach is adopted to mitigate these issues, but the demerits of this approach is unable to identify the zero-day attacking patterns and is computationally expensive as it has to search the URLs from a large pool of database. So, to plan an effective detection framework for identifying suspicious web sites is a tedious task. To overcome these issues, we have proposed a suspicious URL detection technique for selecting the most influential significant features for classifying the URL as safe or malignant with the help of multivariate filter-based feature selection technique (MFBFST) of machine learning. The redundant features are eliminated by correlation feature selection technique and the significance of relevant attributes are tested with statistical t-test to obtain the most significant features that has more impact on the prediction of malicious websites. The relevant features obtained from MFBFST are used for evaluating the machine learning algorithms like Bagging, Adaboost, GBoost and kNN (k Nearest Neighbour) to create an efficient model for predicting the malicious web sites efficiently. To assess the effectiveness of MFBFST in the enhancement of prediction of classification results we have evaluated the classifier with and without considering the FST also tested the scalability issues by considering two publicly available datasets. Our implementation results demonstrated that with utilizing the CFS, the machine learning algorithms accomplished the highest classification accuracy of 97% in dataset I

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

MODELDIFF: A Framework for Comparing learning Algorithms 40

MODELDIFF: A Framework for Comparing Learning Algorithms

引用

40th international conference on machine learning

作者： Shah, Harshay Park, Sung Min Ilyas, Andrew Madry, Aleksander MIT 77 Massachusetts Ave Cambridge MA 02139 USA

We study the problem of (learning) algorithm comparison, where the goal is to find differences between models trained with two different learning algorithms. We begin by formalizing this goal as one of finding distinguishing feature transformations, i.e., input transformations that change the predictions of models trained with one learning algorithm but not the other. We then present MODELDIFF, a framework that leverages data-models (Ilyas et al., 2022) to compare learning algorithms based on how they use training data. We demonstrate MODELDIFF through three case studies, comparing models trained with/without data augmentation, with/without pre-training, and with different SGD hyperparameters. Our code is available at https://***/MadryLab/modeldiff.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

machine learning based Approximate Query Processing for Women Health Analytics

Machine Learning based Approximate Query Processing for Wome...

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Parvathi, A.J. Gopika, H. Suresh, Jasna Sree, Sapa Laasya Harikumar, Sandhya Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

Good health and well being is one of the most essential targets of the Sustainable Development Goals (SDGs). This paper primarily focuses on Preventive and Diagnostic care of Women Health because even today, women are disadvantaged by discrimination in many societies especially in rural sectors. Two main health issues, fetal abnormality in pregnant women and cervical cancer in women are analyzed so that the doctors and patients can be given early signals to take proactive measures. As per National Health Portal of India, around 1.7 million birth defects occur in India every year. So Antenatal Care(ANC) should be given utmost importance during Pregnancy in a woman's life. Cervical cancer is another issue prevalent amongst women, especialy over the age of 30. It's critical to catch it early and eliminate any risks that come with it. Here arises the need to develop a system that can analyze and predict the aforementioned anomalies at an early stage. This work proposes approximate query processing using different dynamic machine learning algorithms to analyze and predict the abnormalities. Further, a web application is built to facilitate the stakeholders, especially doctors, to interact with the system and iteratively query the system to understand the relationships amongst the various data variables and get appropriate predictions about fetal anomaly from CTG scans as well as presence of cervical cancer from various demographic information, habits, and historic medical records. Approximate Query Processing is accomplished using Correlation analysis, linear, and logistic regression algorithms in a dynamic, interactive, and iterative manner. © 2022 Elsevier B.V.. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

machine learning and data mining assisted petroleum reservoir engineering: a comprehensive review

引用

international JOURNAL OF OIL GAS AND COAL TECHNOLOGY 2022年第4期30卷 359-387页

作者： Purbey, Rupali Parijat, Harshwardhan Agarwal, Divya Mitra, Devarati Agarwal, Rakhi Pandey, Rakesh Kumar Dahiya, Anil Kumar DIT Univ Sch Engn & Technol Dehra Dun 248009 Uttarakhand India DIT Univ Sch Comp Data Sci Res Grp Dehra Dun 248009 Uttarakhand India

The oil and gas industry faces several challenges associated with managing massive datasets and extracting relevant information. The machine learning tools have proven to be significantly valuable for analysing complex, heterogeneous data and produce quicker and more reliable outcomes even on large-scales. machine learning and data mining tools have been applied in several aspects of the upstream oil and gas industry, such as exploration, drilling, reservoir engineering, and production forecasting. This review has been explicitly focused on machine learning and data mining implementations in reservoir engineering, including reservoir characterisation and performance prediction, well test analysis, well logging and formation evaluation, and enhanced oil recovery operations. The commonly used statistical measures for classification and regression models have been discussed as well. The observations from the review have led to suitable suggestions that shall enrich the research in this area. [Received: July 29, 2021;Accepted: October 30, 2021]

关键词： machine learning data mining prediction model neural network statistical measure petroleum reservoir engineering performance prediction well test enhanced oil recovery

来源：评论

学校读者我要写书评

暂无评论

Effect of Shallow learning Techniques and Feature Selection for Breast Cancer Detection 2nd

Effect of Shallow Learning Techniques and Feature Selectio...

引用

2nd international conference on Emerging Electronics and Automation, E2A 2022

作者： Deb, Bitanuka Choudhury, Nurul Amin Soni, Badal Department of Computer Science and Engineering National Institute of Technology Assam Silchar788010 India

ISBN: (纸本)9789819968541

Breast cancer is among the most prevalent cancers in women and one of the highest reason for women’s fatality rates. Most of the works in breast cancer detection are done either using deep learning models or heavily concentrated neural networks that require high computational machine and large datasets. This paper incorporates four shallow machine learning models, i.e., Naive Bayes (Both Gaussian and Categorical), K-Nearest Neighbor (KNN), and Decision Tree (DT) for detecting breast cancer. A state-of-art breast cancer dataset named–Breast Cancer Wisconsin has been incorporated for model training, testing, and validation. data pre-processing and feature engineering has been done to make the raw data meaningful and robust for efficient model classification. An extensive data exploration and visualization is carried out for added understanding and correlation. On testing the different models, Gaussian Naive Bayes and Decision Tree managed to achieve the best accuracies with a highest accuracy of 97.36% and 96.61%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

LOOPED TRANSFORMERS ARE BETTER AT learning learning ALGORITHMS 12

LOOPED TRANSFORMERS ARE BETTER AT LEARNING LEARNING ALGORITH...

引用

12th international conference on learning Representations, ICLR 2024

作者： Yang, Liu Lee, Kangwook Nowak, Robert D. Papailiopoulos, Dimitris University of Wisconsin Madison United States

Transformers have demonstrated effectiveness in in-context solving data-fitting problems from various (latent) models, as reported by Garg et al. (2022). However, the absence of an inherent iterative structure in the transformer architecture presents a challenge in emulating the iterative algorithms, which are commonly employed in traditional machine learning methods. To address this, we propose the utilization of looped transformer architecture and its associated training methodology, with the aim of incorporating iterative characteristics into the transformer architectures. Experimental results suggest that the looped transformer achieves performance comparable to the standard transformer in solving various data-fitting problems, while utilizing less than 10% of the parameter count. © 2024 12th international conference on learning Representations, ICLR 2024. All rights reserved.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Finding Appropriate User Feedback Analysis Techniques for Multiple data Domains 22

Finding Appropriate User Feedback Analysis Techniques for Mu...

引用

44th ACM/IEEE international conference on Software engineering - New Ideas and Emerging Results (ICSE-NIER)

作者： Devine, Peter Univ Auckland Auckland New Zealand

ISBN: (纸本)9781665495981

Software products now have more users than ever. This means more people to please, more use-cases to consider, and more requirements to fulfill. These users can then write feedback on software in any number of public or private online repositories. Many tools have been proposed for classifying, embedding, clustering, and characterizing this feedback in aid of generating requirements from it. I am investigating which techniques and machine learning models are most appropriate for enabling these analyses across multiple feedback platforms and data domains.

关键词： Analytical models machine learning Software data models Software engineering

来源：评论

学校读者我要写书评

暂无评论

Smart Agriculture in the Era of Big data: IoTAssisted Pest Forecasting and Resource Optimization for Sustainable Farming 2

Smart Agriculture in the Era of Big Data: IoTAssisted Pest F...

引用

2nd IEEE international conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Kumar, Deepak Pawar, Priyanka Pramod Meesala, Mohan Kumar Pareek, Piyush Kumar Addula, Santosh Reddy Shwetha, K.S. Department of Information Technology University of the Cumberlands WilliamsburgKY United States Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9798331504960

The goal was to facilitate quick development and revitalisation of rural areas by utilising IoT knowledge to accomplish smart agriculture against the backdrop of big data. Depleted soil fertility, increased pest attacks, and diseases are some of the climate-related risks that the agricultural sector is battling. Predicting Utilising directly sensed environmental conditions as input, a machine learning model incorporating Random Forest and the Catboost algorithm determines, based on the current environmental conditions, whether pest populations are present or not. Next, Hybrid Weighted Ant Colony Optimisation (HWACO) is used to fine-tune the Catboost algorithm. This method evaluates the machine learning model's accuracy and precision in making predictions using test data. Predictions have been made using data from 2028 to 2022, a span of five years. Weekly forecasts are produced by the pest prediction model. With high F-measure, Precision, and Recall, the weekly predictions have an overall accuracy of 93%. Over time, the precision of the pest forecast steadily rises. Predictions for the next week are made by averaging all weather data from the past seven days. For immediate defences against pests, the weekly forecasts are crucial. The findings support sustainable farming methods and food security by showing that the device can maximise crop yields while decreasing resource consumption. © 2024 IEEE.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Visual-Semantic Alignment for Few-shot Remote Sensing Scene Classification 24

Visual-Semantic Alignment for Few-shot Remote Sensing Scene ...

引用

16th international conference on machine learning and Computing (ICMLC)

作者： Li, Haojun Li, Linjia Luo, Wei South China Agr Univ Pazhou Lab Guangzhou Peoples R China

ISBN: (纸本)9798400709234

We propose a few-shot learning approach that aligns visual and semantic features in an embedding feature space to alleviate the shortage of training (or reference) data in remote sensing scene classification (RSSC). Specifically, the self-supervised learning is first employed to improve the expressive ability of the learned feature, which could effectively enhance the features' generalizability. Meanwhile, we align the image feature and its corresponding class-semantic feature, which is obtained by feeding the class name to a language model such as BERT, to increase the image feature's discriminability. By systematically integrating the self-supervised learning and visual-semantic alignment with the backbone network, our approach could achieve image features with good generalizability and discriminability. Experiments on UCMerced LandUse, NWPU-RESISC45, and AID benchmarks validate the feasibility of our approach and verify its improved few-shot classification performance in RSSC.

关键词： Remote sensing scene classification Few-shot learning Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

How Digital Transformation Affects Enterprise Performance: Evidence from machine Algorithms 24

How Digital Transformation Affects Enterprise Performance: E...

引用

5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Zhang, Ying Fu, Rui Chen, Feng Xihua University Sichuan Yibin China

ISBN: (纸本)9798400709999

Digital transformation of enterprises, as one of the core issues in modern enterprise management, has become an important scenario for computer applications. This article empirically analyzes how digital transformation affects enterprise performance based on data from Chinese A-share listed companies from 2011 to 2022, using machine learning methods from artificial intelligence technology to explore the predictive capability of digital transformation on enterprise performance. The research results indicate a significant positive correlation between digital transformation and enterprise performance in Chinese companies, and that machine learning methods can effectively handle the relationships between variables, improving the accuracy of enterprise performance predictions. This study provides theoretical and practical support for how enterprises can improve their performance through the application of computer technology during the process of digital transformation. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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：