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检索条件"主题词=Machine learning algorithms"

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Determining the Critical Factors of Antenatal Care Follow-Up Using Two-Phase machine learning

Determining the Critical Factors of Antenatal Care Follow-Up...

引用

International Conference on Soft Computing and Measurements (SCM)

作者： Igor A. Bessmertny Bidru C. Enkomaryam Adane L. Mamuye Faculty of Software Engineering and Computer Systems ITMO University Saint Petersburg Russia Department of Computer Science University of Gondar Gondar Ethiopia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

Determining the critical factors affecting antenatal visits will greatly contribute to reducing maternal and infant mortality. This study, thus attempted to construct a cluster-based predictive model to determine the critical factors of antenatal care follow-up among women in Ethiopia using two-step machine learning techniques. The 5,990 preprocessed obstetric dataset with 13 selected attributes were used that was collected from the Dabat Research Center at the University of Gondar, Ethiopia. To solve the study problem, six cluster models were developed using K-means, Expectation Maximization and Make Density clustering algorithms, which are well-suited for analyzing health datasets, providing scalability, flexibility, robustness to noise, and the ability to handle uneven cluster sizes. K-means out-formers Density based and Expectation Maximization clustering algorithms with minimum number of sum of square errors (17094) and number of iteration (3). Then, output of the K-means cluster model was used as input to segment the dataset and build a total of 21 predictive models using the tree-based J48 classifier, PART and JRip rule generators while adjusting their parameters. The model for predicting antenatal care follow-up is constructed by induction of the truncated PART rule (accuracy of 99.93%) and extracts attributes of interest such as antenatal care attendance, antenatal care location, antenatal care frequency, current vaccination, woman’s age, and HIV testing to predict antenatal care follow-up in the study area.

关键词： machine learning algorithms Accuracy Scalability Clustering algorithms machine learning Predictive models Prediction algorithms

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Detecting Insider Threats in Cybersecurity Using machine learning

Detecting Insider Threats in Cybersecurity Using Machine Lea...

引用

IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Manikandan S P K Ashok Kumar T Sai Harsha Vardhan G. Thanigaivel B. Venkataramanaiah Vijaya Vardan Reddy S P Department of ECE Saveetha Engineering College Chennai India Department of ECE Bhoj Reddy Engineering College for Women Hyderabad India Department of ECE SRM Institute of Science and Technology Kattankulathur India Department of ECE J. N.N Institute of Engineering Kannigaipair India Department of ECE Vel Tech Rangarajan Dr.sagunthala R&D Institute of science and technology Chennai India Department of EC-ACT R.M.K Engineering College Kavaraipettai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Threats in cyber security increasing now a days. In order to detect insider threats in cyber security environments, this research investigates the creation of a Network Intrusion Detection System (NIDS) that makes use of machine learning techniques. In order to identify suspicious activity and unauthorized access, the suggested system examines network traffic. The NIDS minimizes false positives and achieves high accuracy by using ensemble models, particularly Decision Trees and Logistic Regression, which are optimized by Grid Search. The simulation findings show that, in comparison to independent methods, integrating models produces a more robust and dependable detection mechanism. From Denial-of-Service (DoS) attacks to increasingly sophisticated insider threats, the NIDS continuously demonstrated a high detection accuracy while maintaining a controllable false positive rate. This feature emphasizes how useful machine learning models are for improving NIDS's responsiveness and flexibility. The system's appropriateness for deployment in expansive, dynamic network environments is demonstrated by simulation results, which validate its capacity to expand and sustain performance even in the face of high network traffic. The technique, simulation results, and future directions-such as real-time integration and improved scalability for bigger networks-are covered in the research.

关键词： Logistic regression Accuracy machine learning algorithms Simulation Scalability machine learning Telecommunication traffic Real-time systems Computer crime Regression tree analysis

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machine learning model for predictive maintenance of modern manufacturing assets

Machine learning model for predictive maintenance of modern ...

引用

International Conference on Automation and Computing (ICAC)

作者： Abdu Shaala David Baglee Derek Dixon Faculty of Technology University of Sunderland Sunderland UK

ISBN: (数字)9798350360882

ISBN: (纸本)9798350360899

Predictive maintenance is considered a powerful practice for manufacturing assets health assessment, facilitating the identification of potential failure occurrences. By proactively addressing such failures, manufacturers can avoid unplanned downtime and allocate necessary resources for required maintenance activities. machine learning (ML) methods have emerged as a promising tool for preventing equipment failures in Predictive Maintenance applications. However, the effectiveness of Predictive Maintenance applications is largely determined by the machine learning techniques utilized and the quality of the data utilised. In this research, we adapted the cross-industry standard process for data mining to develop a predictive maintenance model for a unique, large, and complex manufacturing asset, utilizing various machine learning techniques. Specifically, the research incorporate Random Forest, Support Vector machines, K-Nearest Neighbors, eXtreme Gradient Boost, and Logistic Regression algorithms to the asset failure records. Following the fitting of all models, Random Forest emerged as the best-performing model based on the recall parameter. However, the algorithm performance was not satisfactory due to the poor data quality. In addition, an exploratory data analysis process was conducted on the data to derive insights into the failure pattern of the machine.

关键词： machine learning algorithms Computational modeling Predictive models Prediction algorithms Data models Manufacturing Maintenance Data mining Random forests Predictive maintenance

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Research on Keyword Selection and Search Engine Optimization Strategies for Online Marketing Based on machine learning

Research on Keyword Selection and Search Engine Optimization...

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Interactive Intelligent Systems and Techniques (IIST), International Conference on

作者： Suyi Wang Software Engineering Institute of Guangzhou Guangzhou China

ISBN: (数字)9798350374421

ISBN: (纸本)9798350374438

Internet marketing has become an important means of promoting products and services for current enterprises. In the vast amount of online information, how to make their content stand out has become a major challenge for enterprises. Traditional keyword selection and SEO strategies often rely on manual experience and trial and error, which is less efficient and difficult to cope with the increasingly complex market environment. In this regard, keyword selection and SEO strategy development are particularly critical. The purpose of this paper is to investigate machine learning-based keyword selection and search engine optimization strategies for online marketing in order to improve the visibility and influence of companies in cyberspace. This paper first analyzes the limitations and shortcomings of traditional keyword selection methods, and then introduces the application of machine learning in keyword selection, including keyword extraction techniques based on text mining and natural language processing, and keyword prediction models based on user behavior analysis. Through machine learning algorithms, we are able to find the user's search intention and preference more accurately, so as to select keywords that are more in line with the needs of the target audience. This paper proposes SEO optimization strategies based on machine learning, including content optimization, website structure optimization, link building and other aspects. Through machine learning techniques, the evaluation criteria of search engines can be better understood and more scientific and effective SEO strategies can be developed.

关键词： Text mining Analytical models Solid modeling machine learning algorithms machine learning Search engines Predictive models Data models Natural language processing Optimization

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Detection and Classification Model for Respiratory Diseases Using machine learning Techniques

Detection and Classification Model for Respiratory Diseases ...

引用

International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Lili Tanti Budi Triandi Bob Subhan Riza Yan Yang Thanri Juli Iriani Ratih Pusapasari Faculty of Engineering and Computer Science Universitas Potensi Utama Medan Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

In this study, we aim to develop a respiratory disease detection and classification model using machine learning techniques. Respiratory diseases are a significant global health problem, and early detection is critical to improving patient outcomes. In this study, we collected data from multiple sources to analyze and compare the performance of several machine learning algorithms, including kNN, SVM, Gradient Boosting, Adaboost, Neural Network, Random Forest, Naïve Bayes, and Logistic Regression. These models were evaluated based on performance metrics such as Area Under the Curve (AUC), classification accuracy, Fl Score, precision, and recall. The results showed that Random Forest and Naïve Bayes models performed best in terms of accuracy and class separation ability. This study also emphasized the importance of feature analysis and correlation in improving model effectiveness. These findings are expected to contribute to the development of more accurate diagnostic tools for respiratory diseases, as well as support more timely and effective medical interventions.

关键词： Support vector machines Logistic regression Accuracy machine learning algorithms Correlation Pulmonary diseases Neural networks Bayes methods Random forests Medical diagnostic imaging

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Confidentiality Solutions for Distributed machine learning Models

Confidentiality Solutions for Distributed Machine Learning M...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Tummala Venkata Naga Nymisha Ch. Nanda Krishna Kantamneni Mahita Department of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College (JNTUK Affiliation) Vijayawada Andhra Pradesh India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

In modern collaborative environments, the secure transfer of machine learning models between distributed systems is essential for ensuring consistent decision-making and facilitating seamless cooperation. This paper presents a novel approach for securely transferring a machine learning model, selected from various algorithms such as linear regression, K-nearest neighbors (KNN), decision tree, and random forest, across distributed systems. Our method involves encrypting the selected model using the Advanced Encryption Standard (AES) algorithm, while the AES key is encrypted using the ElGamal encryption scheme. This double-layered encryption ensures both the confidentiality of the model and the security of the encryption key during transmission. At the receiver's end, the encrypted model and key are decrypted, enabling predictions to be made using the recovered model. Through this process, the integrity and confidentiality of the transferred model are preserved, thereby mitigating the risk of unauthorized access or tampering. We discuss the implementation details of our approach, including the tools and technologies utilized, as well as the performance evaluation of the encrypted model transfer process. Our findings demonstrate the effectiveness of the proposed method in securely transferring machine learning models between distributed systems, thereby fostering collaboration and knowledge sharing while maintaining data privacy and security. This research contributes to the advancement of secure model transfer techniques in distributed environments, with implications for various domains requiring collaborative machine learning applications.

关键词： machine learning algorithms Computational modeling Collaboration Predictive models Nearest neighbor methods Prediction algorithms Data models

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Enhancing Situational Awareness in Industrial Automation and Cybersecurity Through an Integrated Framework That Leverages Both machine learning and Deep learning Technologies

Enhancing Situational Awareness in Industrial Automation and...

引用

Pattern Recognition and machine learning (PRML), IEEE International Conference on

作者： Xingcheng Lu Kechen Wu Yixuan Chen Nanjing University of Finance and Economics Nanjing China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

With the rapid development of artificial intelligence technology, machine learning and deep learning are widely applied in various fields, showing great potential and value. This article is aimed to explore the integrated application of machine learning with deep learning technologies in industrial automation and cybersecurity situational awareness. Utilizing deep learning technology to facilitate real-time monitoring and early detection of network security threats. This study first reviews the application status of machine learning in industrial safety, including feature selection, model construction, and performance evaluation. Then, the key technologies of deep learning in cybersecurity situational awareness, such as anomaly detection and intrusion identification, are discussed in depth. Furthermore, this paper proposes a framework that integrates machine learning and deep learning to improve their liability and network security protection capabilities of industrial control systems. By conducting experiments, the proposed framework demonstrates its ability to accurately forecast potential equipment failures in industrial settings and promptly detect security threats within the network, offering a novel approach to enhancing industrial automation and safeguarding network security. After the experiment, the accuracy of the improved algorithm is more than 85%, the prediction is more than 75, the regression line is more than 55%, and the modified algorithm is better than XGBost and other algorithms. The concentration of the improved CNN in identifying attack information can reach 95%.

关键词： Deep learning Performance evaluation Automation machine learning algorithms Reviews Network security Prediction algorithms Real-time systems Computer security Protection

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Enhancing Phishing Website Detection Using Ensemble machine learning Models

Enhancing Phishing Website Detection Using Ensemble Machine ...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Himanshu Baliyan A Rama Prasath Department of Computer Applications Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Phishing websites remain a persistent and evolving threat to online security, employing deceptive tactics to lure users into revealing sensitive information such as login credentials, financial data, and personal details. These malicious websites often mimic legitimate ones, making it challenging for users to discern their authenticity. As traditional detection methods struggle to keep pace with increasingly sophisticated phishing techniques, the need for more robust and adaptable solutions is paramount. This research investigates the application of ensemble machine learning models for improved phishing website detection. By combining multiple machine learning algorithms, ensemble models leverage their collective strengths to achieve higher accuracy and generalizability compared to individual models. Our approach incorporates a comprehensive feature extraction process, analyzing various URL and website characteristics such as domain age, URL length, presence of special characters, and HTML and JavaScript features. Through rigorous training and testing on a diverse dataset of legitimate and phishing websites, our ensemble model demonstrated a 91 percent accuracy in identifying phishing attempts, surpassing the performance of individual models like Random Forest, Decision Tree, and Logistic Regression. These findings underscore the potential of ensemble machine learning models as a powerful tool for enhancing phishing website detection. The improved accuracy and adaptability offered by our approach can contribute to the development of more effective security measures, protecting users from online fraud and preserving the integrity of the digital landscape. Future research will explore incorporating additional features and exploring advanced ensemble techniques to further enhance detection capabilities and address emerging phishing threats.

关键词： Uniform resource locators Training Adaptation models Accuracy machine learning algorithms Phishing Computational modeling

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machine learning Based Pomegranate Plant Leaf Disease Features Extraction and Evaluation

Machine Learning Based Pomegranate Plant Leaf Disease Featur...

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International Conference on Advances in Information Technology (ICAIT)

作者： Shivappa M Metagar Gyanappa A. Walikar School of Computer Science and Engineering REVA University Bengaluru India School of Electronics and Communication Engineering REVA University Bengaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Disease prediction in plant life is the maximum essential issues in agricultural discipline by means of the use of image processing and trendy generation. This paper specializes in designing a framework that might apprehend and classify sicknesses on pomegranate vegetation precisely. The framework uses image processing techniques together with image acquisition, image resizing, image enhancement, image segmentation, ROI extraction (area of interest), and characteristic extraction. An images dataset associated with pomegranate leaf disease is utilized to put in force the framework, divided into a training set and a take a look at set. Inside the implementation manner, strategies which include image enhancement and image segmentation are typically used for figuring out ROI and abilities. Image magnificence will then be carried out with the aid of combining a supervised getting to know model with a help vector device. State-of-the-art classification algorithms are used to classify the pomegranate plant leaf based totally on its signs and symptoms, whether or now not its miles health leaf or unhealthy leaf. Again if the plant leaf is in horrific condition then ML algorithms are used to categories the type of disease is there and what sort of percentage of sickness is attached to the plant. The machine learning algorithm is used for feature extraction and evaluation for finding the accurate disease and percent of the disease based on the image processing technique.

关键词： Image segmentation machine learning algorithms Vegetation mapping machine learning Feature extraction Vectors Classification algorithms Data mining Image enhancement Diseases

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Interpretable Software Maintenance and Support Effort Prediction Using machine learning

Interpretable Software Maintenance and Support Effort Predic...

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International Conference on Software Engineering - Companion, ICSE, Companion

作者： Susmita Haldar Luiz Fernando Capretz School of Information Technology Fanshawe College London Ontario Canada Department of Electrical and Computer Engineering Western University London Ontario Canada

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

Software maintenance and support efforts consume a significant amount of the software project budget to operate the software system in its expected quality. Manually estimating the total hours required for this phase can be very time-consuming, and often differs from the actual cost that is incurred. The automation of these estimation processes can be implemented with the aid of machine learning algorithms. The maintenance and support effort prediction models need to be explainable so that project managers can understand which features contributed to the model outcome. This study contributes to the development of the maintenance and support effort prediction model using various tree-based re-gression machine-learning techniques from cross-company project information. The developed models were explained using the state-of-the-art model agnostic technique SHapley Additive Explanations (SHAP) to understand the significance of features from the developed model. This study concluded that staff size, application size, and number of defects are major contributors to the maintenance and support effort prediction models.

关键词： Software maintenance machine learning algorithms Costs Automation Estimation machine learning Predictive models

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