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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是861-870 订阅

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Optimising DOS Attacks Using machine learning algorithms and Securing IOT Devices from Attacks

Optimising DOS Attacks Using Machine Learning Algorithms and...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： Rahul Siwal Sivakumar P School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

In today's hyperconnected world, identifying and defending against denial-of-service (DoS) attacks and securing Internet of Things (IoT) devices are increasingly crucial. Recognizing distributed denial-of-service (DDoS) attacks is essential for network security enhancement and IoT device security. This research explores various methods to mitigate the impact of DDoS attacks, aiming to reduce the likelihood of such attacks. We intend to employ machine learning strategies such as the Random Forest Algorithm, ADAboost, Blockchain, Gradient Boost, and Extra Tree Algorithm to prevent malicious network traffic from overloading servers and causing outages. The initiative offers significant advantages, particularly in theory and statistics. We will test our system using the NSL _ KDD dataset, initially training the data and then subjecting it to a series of tests. Data will be presented using tables, graphs.

关键词： Training machine learning algorithms Denial-of-service attack Blockchains Internet of Things Computer crime Protection

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A Comparative Analysis of machine learning algorithms for Predictive Maintenance in Electrical Systems

A Comparative Analysis of Machine Learning Algorithms for Pr...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： Suresh Singh Rupali Gill Rakesh Kumar Anurag Shrivastava Arun Pratap Srivastava Gaurav Thakur Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Centre of Research Impact and Outreach Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Department of Computer Engineering & Applications GLA University Mathura Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Lloyd Institute of Engineering & Technology Greater Noida Department of Civil Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

This research explores machine learning calculations for prescient support in electrical frameworks, centering on Support Vector machines (SVM), Random Forests, Recurrent Neural Networks (RNN), and K-Nearest Neighbors (KNN). Through a comprehensive comparative examination, the study assesses these calculations on a different dataset, considering both classification and relapse errands. Results demonstrate that Random Forest beats other calculations, accomplishing an amazing classification precision of 92%, exactness of 93%, review of 91%, and F1-score of 92%. In relapse assignments, Random Forest shows the most reduced cruel outright mistake (MAE) of 8.5 and cruel squared mistake (MSE) of 120.3, displaying its vigor in anticipating remaining valuable life. SVM illustrates an adjusted execution with a precision of 85% and competitive accuracy and review. RNN, specialized for successive information, accomplishes a precision of 88% and competes closely with SVM. KNN, whereas instinctive, appears to lower execution with a precision of 78%. These discoveries give profitable insights for professionals, directing the choice of machine-learning algorithms custom-made to specific prescient upkeep scenarios in electrical frameworks.

关键词： Support vector machines machine learning algorithms Accuracy Recurrent neural networks Reviews Sensitivity and specificity Prediction algorithms

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Introduction to clustering unsupervised machine learning algorithms applied to power quality disturbances

Introduction to clustering unsupervised machine learning alg...

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Workshop on Metrology for Industry 4.0 and IoT

作者： Marcantonio Catelani Lorenzo Ciani Cristian Garzón Alfonso Francesco Grasso Libero Paolucci Gabriele Patrizi Department of Information Engineering University of Florence Florence Italy

ISBN: (数字)9798350385823

ISBN: (纸本)9798350385830

Unsupervised machine learning algorithms, such as clustering and anomaly detection, work by identifying patterns and anomalies in data without the need for labeled training data. These algorithms are particularly useful in situations where relevant training data is limited or unavailable, making them well-suited for predicting and detecting abnormalities and Power Quality Disturbances (PQD) in electric systems. PQD can have significant impacts on electrical equipment and systems, leading to equipment malfunction, downtime, and potential safety hazards. By applying unsupervised machine learning algorithms to electric data it is possible to gain valuable insights into the underlying patterns and characteristics of different PQDs. For example, clustering algorithms can group similar disturbances together based on their features, helping to identify common causes or sources of disturbances. Anomaly detection algorithms, on the other hand, can automatically flag and alert users to unusual or unexpected disturbances that may indicate potential issues. In this manuscript, a study of the current trends in the field of clustering and anomaly detection unsupervised machine learning (ML) algorithm is presented, dealing with approaches available for PQD detection. Overall, the combination of unsupervised machine learning algorithms and power quality analysis can greatly enhance the ability to predict, monitor, and mitigate the impact of PQD in various industrial settings. By leveraging these advanced technologies, reliability and efficiency of electrical systems can be improved, ultimately leading to a more sustainable and optimized environment.

关键词： Electric potential Technological innovation machine learning algorithms Power quality Clustering algorithms Training data machine learning

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An Experimental Analysis of Multiple Disease Prediction Using machine learning algorithms

An Experimental Analysis of Multiple Disease Prediction Usin...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Shaurya Gupta Karan Pal Deepjyoti Choudhury Dept. of AIML Manipal University Jaipur Jaipur India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

The development of smart healthcare tools is progressing at a rapid pace. It is only a matter of time when the healthcare system all over the world becomes completely automated. This paper contains research conducted on datasets of four different disease being Diabetes, Kidney Disease, Liver Disease and Asthma in a hope to accelerate the growth of machine learning models in the medical world by achieving the most optimal model possible. The models used in the research were Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), K-Neighbour, SVC, AdaBoost and MLP. Random Forest performed best for every dataset achieving mean accuracy score of 87.85% for Diabetes, 95.8% for Kidney disease, 99.68% for Kidney Disease and 95.83% for Asthma.

关键词： Radio frequency Logistic regression machine learning algorithms Static VAr compensators Medical services Diabetes Kidney Random forests Diseases Asthma

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A Research Survey on Predicting Crop Yields and Recommending Fertilizers using machine learning algorithms

A Research Survey on Predicting Crop Yields and Recommending...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Rao Sushant Nihar M. Ranjan Angaj Suyog Rayke Amey Madane Asmita Shinde Shraddha Information Technology Department JSPM’s RSCOE Pune India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Agriculture is a major component of India’s economy. Agricultural production has several factors that influence it, such as temperature, rainfall, soil nutrients, and fertilization, which are essentials in making decisions. Such elements make the agriculture sector a field that requires data collection constantly to analyze changes, trends, and impacts. This paper looks into enhancing fertilizer efficiency for growth and improvement through novel machine learning algorithms like XGBoost and Random Forest. As researchers who sought to apply those algorithms using data-driven methods with modern technologies, we align our work with the new realities of agriculture. The adoption of precision agriculture systems will give farmers valuable insights derived from contextual data, which are bound to lead to more effective outcomes in precision farming. This research will focus on major impacts of technology on agricultural production but, more importantly, towards improving farm efficiency with resilience potential.

关键词： Surveys machine learning algorithms Temperature Production Soil Random forests Crop yield Farming Fertilizers Resilience

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Interpretability of machine learning algorithms for News Category Classification Using XAI

Interpretability of Machine Learning Algorithms for News Cat...

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International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Nafiza Tabassoum Md. Ali Akber Department of Computer Science and Engineering Ahsanullah University Of Science and Technology (AUST) Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Text classification has been a popular research topic for several years. To date, several advanced models have been developed in this field. In today's digital landscape, the proliferation of online news sources necessitates the efficient categorization of user accessibility. To address this situation, machine learning models can be used to automate news category classification based on news headlines and short descriptions. However, we know that machine-learning models act like black boxes. The interpretability of a model provides a clear understanding of how decisions are made, ensuring transparency, and user acceptance. This transparency helps in model validation and effective collaboration between human and computer interactions. Local Interpretable Model-agnostic Explanations (LIME), as an Explainable Artificial Intelligence (XAI) technique, can be implemented to generate interpretable explanations that aid in understanding the reasoning behind specific predictions. Our approach aims not only to predict news categories but also to provide transparent insights into model decisions. Initially, we used Sentence Bidirectional Encoder Representations from Transformers (SBERT) for contextual text embedding purposes, followed by different machine learning models for classification tasks, such as Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and K-Nearest Neighbors (KNN). Notably, RF achieves exceptional accuracy of 91.48 %, surpassing contemporary benchmarks. Finally, LIME elucidates crucial features guiding classification decisions, facilitating model validation, and fostering human-computer collaboration. This study enriches the evolving discourse on interpretable AI models by providing a robust framework for transparent news classification in an era inundated by information.

关键词： Logistic regression machine learning algorithms Explainable AI Computational modeling Collaboration Predictive models Transformers

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Diabetes Mellitus Classification Using machine learning algorithms with Hyperparameter Tuning

Diabetes Mellitus Classification Using Machine Learning Algo...

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International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

作者： Vishnu Vardhana Reddy Karna Viswavardhan Reddy Karna Ravinder Beemagani Aravinda Babu Tummala Satya Veerendra Arigela Varaprasad Janamala Aymen Flah Electronics and Communication Engineeirng Aditya University Surampalem India Department of AIML R V College of Engineering Bengaluru India Dept. of Electrical Engineering Indian Institute of Technology Tirupati India Electronics and Communication Engineeirng Chaitanya Bharati Institute of Technology Hyderabad India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Electrical and Electronics Engineering School of Engineering and Technology Christ University Bangalore Karnataka India Energy Processes Environment and Electrical Systems Unit National Engineering School of Gabes University of Gabes Gabès Tunis Tunisia MEU Research Unit Middle East University Amman Jordan Private Higher School of Applied Sciences and Technology of Gabes University of Gabes Gabès Tunisia ENET Centre VSB—Technical University of Ostrava Ostrava Czech Republic Applied Science Research Center Applied Science Private University Amman Jordan

ISBN: (数字)9798331529987

ISBN: (纸本)9798331529994

Diabetes Mellitus is a prevalent condition globally, marked by elevated blood sugar levels resulting from either insufficient production of insulin or the body cells' inability to respond appropriately to released insulin. For people with diabetes to lead healthy, normal lives, early identification and treatment of the condition are essential. With the need to move away from current traditional procedures, towards a noninvasive methodology, machine learning and data mining technologies can be very useful in the classification of diabetes. Creating an effective machine learning model for the classification of diabetes mellitus was the primary goal of this research. This work is primarily carried out on combined Pima Indian diabetes dataset and German Frankfurt diabetes dataset. The class imbalance issue has been resolved using Synthetic Minority Oversampling Technique. One-hot encoding is applied to convert categorial features to numerical and various single and ensemble classifiers with the best hyperparameters obtained using GridSearchCV method were employed on the pre-processed dataset. With an AUC of 0.98 and maximum accuracy of 98.79%, the Random Forest ensemble technique outperformed the other models, according to the experimental results. As a result, the algorithm might be used to predict diabetes and alert doctors to serious cases that call for emergency care.

关键词： machine learning algorithms Accuracy Medical services Encoding Diabetes Classification algorithms Insulin Random forests Tuning Testing

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Enhancing Diabetes Diagnosis: A Comparative Study of machine learning algorithms with Data Preprocessing Techniques

Enhancing Diabetes Diagnosis: A Comparative Study of Machine...

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Innovations in Communications, Electrical and Computer Engineering (ICICEC), International Conference on

作者： R. Elangovan B. Santhi J. Usha B.K. Ranjith R. Veeramani School of Management SASTRA Deemed University Thanjavur India School of Arts Science and Humanities SASTRA Deemed University Thanjavur India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

The primary aim of this study is to assess the performance of various ML algorithms in diagnosing diabetes depending on various data preprocessing methods. Utilizing the Pima Indian Diabetes Database, the study investigates how data preprocessing such as Min-Max scaling, Z-score normalization and SMOTE (Synthetic Minority Over-sampling Technique) influence model performance in contrast with models trained on raw database. Various machine learning algorithms are employed and the performance are measured using metrics like accuracy, precision, recall and F1-score. This show that Data preparation process is very important in improving the performance of the model as the scaling and resampling enhance model performance as compared to using raw data. This paper, therefore, ensures the need and significance of proper handling of data while building predictive models, especially in the field of medical diagnosis of diseases such as diabetes.

关键词： Technological innovation machine learning algorithms Accuracy Databases Data preprocessing Predictive models Reliability engineering Data models Diabetes Medical diagnosis

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A Comparative Analysis of Lung Cancer Prediction using machine learning algorithms

A Comparative Analysis of Lung Cancer Prediction using Machi...

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Global Conference for Advancement in Technology (GCAT)

作者： Akash Badhan Simarjit Singh Malhi Hassandeep Singh Parampreet Kaur Abhishek Parihar School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

In today’s world, with the human’s lifestyle has totally changed as compared to its ancestors which causes many types of life taking diseases. One of them is Lung cancer. The growth of cancer diseases is a big issue yet to be resolved in today’s world. The easiest way to control cancer is to predict it at its earlier stage. The aim of this paper is to examine the different algorithms of machine learning to predict the lung’s cancer and check the algorithm which offer best accuracy so that a reliable, efficient, and robust model can be designed for early detection of lung cancer which can save life of million human beings. The algorithms we have implemented in the implementation of this research are Decision Tree, KNN, and Random Forest. The parameters that we are considering for comparing the best model are Accuracy, Precision, Recall and F1 score.

关键词： Analytical models machine learning algorithms Accuracy Lung cancer Predictive models Nearest neighbor methods Prediction algorithms Classification algorithms Decision trees Random forests

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AcuLearn - Precision Based learning Analytics Platform Using machine learning algorithms

AcuLearn - Precision Based Learning Analytics Platform Using...

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Emerging Techniques in Computational Intelligence (ICETCI), International Conference on

作者： Poluri Manaswini Sajjad Shaik Rejeti Kartik Sangita Khare Department of Computer Science and Engineering Amrita School Of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

In today's world, the use of data analytics with precision has been extremely important and crucial with the changing times. The creation of virtual environments to facilitate communication and education among the students and teach-ers alike. It allows the creation of personalized education for students. machine learning algorithms, hence can be helpful to recommend students to what courses that can be taken and their likely improvement of the system. This study shows the creation of an website for creating better personalized education for students including course completion predictors and score predictors to help them make decisions proactively. The system shows the random forest regression for score prediction and random forest classifier for course completion to perform the best among all such algorithms.

关键词： machine learning algorithms Databases Scalability Education Linear regression Virtual environments Predictive models Prediction algorithms Real-time systems Random forests

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