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1st international conference on Intelligent Systems and Applications

作者： Shinde, Swati Borde, Tejas Deo, Aditya Dhamak, Suraj Dungarwal, Shreyas Pimpri Chinchwad Coll Engn Pune 411035 Maharashtra India

ISBN: (纸本)9789811965814;9789811965807

The extensive usage of the Internet to communicate and transfer data might succumb to various network related threats. Intrusions are one such threat, where the client/organization is at a risk of data theft. An intruder is someone who gains unauthorized access to our network or system. A network falling prey to an intrusion might result in loss of valuable data. A solution to intrusions is intrusion detection systems (IDS). This paper provides a comprehensive review of approaches to build IDS. The first section covers a review of the fundamentals of IDS, covering various intrusion types and IDSs, their strengths and their limitations. The next section discusses intrusions in wireless networks, followed by a review of a wireless approach to intrusion detection systems for IEEE 802.11 networks. The next section takes a look at various deep learning and machine learning approaches to intrusion detection systems that are currently being implemented. It summarizes the benchmark datasets that are currently being used to implement models for intrusion detection, followed by the results of a few machine learning models implemented on the NSL-KDD dataset.

关键词： Networks Deep learning Wireless networks machine learning intrusion detection systems

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Advancing Precision in Lung Cancer Subtyping: Integration of machine learning Feature Selection with MLP 7

Advancing Precision in Lung Cancer Subtyping: Integration of...

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7th international conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Yu, Hao Zhan, Siyu Liu, Shuang Guo, Lu Huang, Rui School Of Optoelectronic Science And Engineering University Of Electronic Science And Technology Of China Chengdu China School Of Computer Science And Engineering University Of Electronic Science And Technology Of China Chengdu China Yingcai Experimental College University Of Electronic Science And Technology Of China Chengdu China University Of Electronic Science And Technology Of China Sichuan Provincial People's Hospital Department Of Pulmonary And Critical Care Medicine Chengdu China Hepatobiliary & Pancreatic Cen Sichuan Provincial People's Hospital Chengdu China

ISBN: (纸本)9798350361445

This study focuses on improving the accuracy of lung cancer subtype classification by integrating machine learning feature selection with a deep learning model, namely, a Multi-Layer Perceptron (MLP). Using gene expression data from the TCGA database, we employed threshold variance and chi-square test feature selection, normalized the selected gene features using z-score normalization, and finally applied MLP for deep learning classification. Through comparisons with traditional machine learning models such as KNN, SVM, and Random Forest, we comprehensively examined multiple evaluation metrics, including accuracy, recall, precision, and F1 score. The results indicate that our MLP approach exhibits significant advantages over traditional methods in all aspects, particularly achieving a notable improvement in accuracy ranging from 2.6% to 12.8%. Overall, our deep learning classification method demonstrates outstanding performance in lung cancer subtype tasks, providing robust support for the application of deep learning in the biomedical field. © 2024 IEEE.

关键词： Gene expression

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Water Quality Inversion of UAV Multispectral data Using machine learning 3rd

Water Quality Inversion of UAV Multispectral Data Using Mach...

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3rd international conference on Sustainable Buildings and Structures, ICSBS 2023

作者： Fu, L. Lo, Y. Lu, T.C. Zhang, C. Xi’an Jiaotong-Liverpooliverpool University Jiangsu Suzhou China

ISBN: (纸本)9789819979646

The majority of water quality inversions rely on satellite data with poor spectral resolution. Satellite data is tougher to obtain for a specific date and less timely than UAV data due to transit cycles and weather. This method of inferring water quality from UAV multispectral data is based on the use of machine learning. With high resolution, low flying altitude, low cost, and good performance, UAV multispectral data synchronizes with sampling point water body parameters. Studies on inverting water quality is difficult due to the need for a specific inversion model for each location and set of circumstances. In order to improve water quality inversion results and get around the limitations of linear models, machine learning is being used more and more. For efficient and quick water quality monitoring in Yuandang Lake, use machine learning to invert various water quality indicators, compare the results, and select the appropriate indicators. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Water quality

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MODELDIFF: A Framework for Comparing learning Algorithms 40

MODELDIFF: A Framework for Comparing Learning Algorithms

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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

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

Effect of Shallow Learning Techniques and Feature Selectio...

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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

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

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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

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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...

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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

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Finding Appropriate User Feedback Analysis Techniques for Multiple data Domains 22

Finding Appropriate User Feedback Analysis Techniques for Mu...

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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

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

How Digital Transformation Affects Enterprise Performance: E...

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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

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data protection governance framework: A silver bullet for blockchain-enabled applications

Data protection governance framework: A silver bullet for bl...

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

作者： Karisma, Karisma Tehrani, Pardis Moslemzadeh University of Malaya Malaysia The Centre of Regulatory Studies University of Malaya Malaysia

Blockchain technology is taking centre stage in major industries, ushering in a new era of decentralisation and digitalisation. While blockchain has garnered widespread traction in various sectors, there remain many technological, operational, societal, and legal challenges in deploying blockchain, mainly attributable to its sheer novelty. With ensuing ruminations about data protection concerns and given the real threats posed by the increasing usage of blockchain, scholars have examined these underlying challenges faced by blockchain users and regulators globally. Trust-building issues, such as data protection breaches, warrant our attention due to the negative consequences that may manifest and concretise in any measurable manner, triggering fragmentation of blockchain-based applications and socio-technical assemblages. This paper proposes eight data protection indicators (DPIs) to assess the maturity levels of countries in addressing data protection challenges in the blockchain landscape. The DPIs can contribute to developing institutional and governance frameworks to spur the global diffusion of hard and soft law instruments and establish broader safeguards of blockchain users' data. © 2022 Elsevier B.V.. All rights reserved.

关键词： Blockchain

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