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检索条件"主题词=Machine Learning Algorithms"

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SPEI Based Drought Prediction and Validation Using machine learning Algorithm in Bangladesh from 1981 to 2017

SPEI Based Drought Prediction and Validation Using Machine L...

引用

IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON)

作者： Md. Farhan Mashuq Juthy Akther Krishna Das Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

A natural event known as a drought is defined by a long period of insufficient rainfall and limited access to water supplies, which means there isn't enough water for many uses, including agriculture, industry and domestic consumption. The Standardized Precipitation Evapotranspiration Index (SPEI) is a frequently utilized indicator in drought monitoring. In this work, from 1981 to 2017, temporal variation at Ishwardi upazila was used to evaluate and analyze the SPEI's performance at 1, 3, 6 and 12-month timeframes. An analysis is carried out to see how well machine learning approach such as Support Vector machine (SVM), Random-Forest (RF), Decision-Tree (DT), Extreme Gradient Boosting (XGB) and K-Nearest Neighbors (KNN) work in forecasting drought conditions. The prediction outcomes of all five techniques were assessed using four performance measures: the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Nash-Sutcliffe Model Efficiency Coefficient (NSE). The proposed methods provided good results and can serve as a quick decision-making tool for water management.

关键词： Support vector machines Radio frequency Accuracy machine learning algorithms Droughts Soil moisture Signal processing algorithms Predictive models Nearest neighbor methods Forecasting

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Predictive Analytics in Cardiology: machine learning Techniques for Heart Disease

Predictive Analytics in Cardiology: Machine Learning Techniq...

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Computational Intelligence for Security, Communication and Sustainable Development (CISCSD), International Conference on

作者： Vishal Kumar I B Darshan B S Trupthi Rao Ashwini Kodipalli Dept. of Artificial Intelligence and Data Science Global Academy of Technology Bangalore Karnataka India

ISBN: (数字)9798350365405

ISBN: (纸本)9798350365412

According to recent studies by the WHO, there is a growing prevalence of conditions associated with coronary heart disease, resulting in approximately 17.9 million deaths annually. With an expanding population, early identification and treatment of such issues become increasingly challenging. Nevertheless, advancements in machine learning techniques have shown promising results in addressing these challenges. Hence, the focus of this paper is to develop a machine-learning model for predicting heart disease based on relevant features. To achieve this, the UCI Heart Disease Prediction dataset is utilized, comprising 14 parameters related to heart health. Various machine learning algorithms including Random Forest, Support Vector machine (SVM), Naive Bayes, and Decision Tree are employed in the model's development. Additionally, we explore correlations among the dataset features using established machine-learning techniques to enhance predictive accuracy. The results indicate that Random Forest outperforms other algorithms in terms of both accuracy and computational efficiency. As a decision support tool, this model holds promise for aiding clinical practitioners in their facilities.

关键词： Heart Support vector machines Accuracy machine learning algorithms Computational modeling Training data Vectors Partitioning algorithms Random forests Diseases

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Analysis of Ensemble machine learning Models for Fraud Detection

Analysis of Ensemble Machine Learning Models for Fraud Detec...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Siddharth Chaurasia Satyam Kesharwani Shanu Sharma Swati Sharma Bharti Chugh Dept. of Computer Science and Engineering KIET Group of Institutions Ghaziabad India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Recent developments in electronic payment and e-commerce have led to a rise in digital fraud cases, including credit card fraud. Within the financial services industry, identifying credit card fraud is still a difficult task that can have significant effects on consumers' and financial institutions' reputations. Therefore, it is crucial to implement systems capable of detecting credit card fraud. This research utilizes the European Cardholder Data Collection to assess the efficacy of ensemble machine learning techniques in identifying credit card fraud. The European cardholder data set is subjected to a rigorous assessment and evaluation of series of machine learning models, such as Gradient Boosting, Random Forest and CAT Boost, to detect fraudulent transactions. It has been noted that the dataset is unbalanced, which may indicate that the models' performance is not very ideal. The study suggests using data sampling strategies to achieve a balanced distribution of data across various algorithms that yield optimal outcomes. The main goal is to identify the model that performs best for machine learning classification problems by comparing the performance of different models. The results of this study will support further initiatives to improve credit card transaction security and lower financial losses brought on by fraud.

关键词： Industries machine learning algorithms Europe Forestry Credit cards Fraud Task analysis

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Trust-Based Data Fusion and machine learning for Underwater Sensor Networks

Trust-Based Data Fusion and Machine Learning for Underwater ...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Anita Kanavalli Shilpa Shashikant Chaudhari Bharathi S Department of CSE Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Underwater Wireless Sensor Networks (UWSNs) are essential for a wide range of marine applications, from environmental monitoring to underwater exploration. However, significant obstacles such signal attenuation, multipath propagation, and sensor node mobility make it difficult to sustain flawless communication in dynamic underwater environments. In UWSNs, handover prediction becomes essential for maintaining continuous communication and maximizing the effectiveness of data transfer. This research presents a novel method for precise handover prediction in UWSNs by utilizing machine learning algorithms and real-time maritime data. The system attempts to predict Energy Trust, Environment Trust, and Communication Trust events by examining environmental parameters and past handover events, improving communication efficiency and reliability in dynamic underwater environments.

关键词： Wireless sensor networks Technological innovation machine learning algorithms Data integration Handover Data models Real-time systems Robustness Environmental monitoring Ensemble learning

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Ethical AI with Balancing Bias Mitigation and Fairness in machine learning Models

Ethical AI with Balancing Bias Mitigation and Fairness in Ma...

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Open Innovations Association (FRUCT), Conference of

作者： Khalida Walid Nathim Nada Abdulkareem Hameed Saja Abdulfattah Salih Nada Adnan Taher Hayder Mahmood Salman Dmytro Chornomordenko Alnoor University Nineveh Iraq Al Mansour University College Baghdad Iraq Al Hikma University College Baghdad Iraq Al-Rafidain University College Baghdad Iraq Al-Turath University Baghdad Iraq National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine

ISBN: (数字)9789526524627

ISBN: (纸本)9798331507411

The rapid integration of Artificial Intelligence (AI) into critical domains such as healthcare, finance, and criminal justice has raised significant ethical concerns, particularly around bias and fairness in machine learning models. Despite their potential for improving decision-making processes, these models can perpetuate or even exacerbate existing societal biases. This study aims to investigate approaches to bias mitigation in AI systems, focusing on balancing fairness and performance. A systematic review of 150 research articles published between 2018 and 2023 was conducted, along with experiments on 25 benchmark datasets to evaluate various machine learning algorithms and bias mitigation techniques. Results showed a 23% reduction in bias and an average 17% improvement in nine fairness metrics during model training, though at the cost of up to 9% in overall accuracy. The study highlights the trade-offs between fairness and performance, suggesting that creating AI systems that are both fair and effective remains an ongoing challenge. The findings underscore the need for adaptive frameworks that address bias without significantly compromising model performance. Future research should explore domain-specific adaptations and scalable solutions for integrating fairness throughout the AI development process to ensure more equitable outcomes.

关键词： Ethics Adaptation models machine learning algorithms Costs Prevention and mitigation Finance Collaboration machine learning Medical services Context modeling

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Planetary Defense Using machine learning Early Warning Systems for Hazardous Asteroids

Planetary Defense Using Machine Learning Early Warning Syste...

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Advances in Data Engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： Avala Sandeep Panta Jeevan Reddy T Madhu Perkin Department of Computer Science & Engineering Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Within the dynamic field of planetary defence, machine learning has emerged as a key component that is essential to early warning systems that are tasked with forecasting the orbits and trajectories of potentially dangerous asteroids. Through the application of a broad range of sophisticated algorithms, such as logistic regression, XGBoost, decision trees, k-nearest neighbours (KNN), and multi-layer perceptron (MLP), a group of committed scientists are hard at work developing reliable models that will be crucial in classifying asteroids into different orbit classes. These classifications, particularly the potentially dangerous asteroids and Near-Earth Objects (NEOs), provide us with important information that helps us determine risk levels and develop practical mitigation plans. Thus, the combination of astronomy and artificial intelligence has enabled a significant advancement in our ability to examine the data and features of these celestial planets, providing insight into their mysterious motions and any possible risks. With this innovative method, we may both advance our knowledge of the dynamic universe and improve our capacity to protect Earth from the impending threat of asteroidal collisions. In a time when space travel and the secrets of the cosmos entice us with both wonder and danger, this marriage of science and technology serves as a symbol of human ingenuity and our commitment to safeguarding our home planet.

关键词： Logistic regression machine learning algorithms Asteroids Symbols machine learning Alarm systems Trajectory

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***: Principles and Practices of machine learning Systems Engineering

***: Principles and Practices of Machine Learning Systems En...

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International Conference on Hardware/Software Codesign and System Synthesis (CODES)

作者： Vijay Janapa Reddi Harvard University

ISBN: (数字)9798350356397

ISBN: (纸本)9798350356403

machine learning (ML) has revolutionized numerous fields. This progress has largely been credited to the development of ML algorithms and models, but this focus overshadows the engineering required to effectively deploy, scale, and maintain these models in real-world settings. As machine learning systems become increasingly complex and widespread, the need for a dedicated discipline to address this gap, machine learning Systems Engineering (MLSE), has emerged.

关键词： Maximum likelihood estimation machine learning algorithms machine learning Modeling

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Intelligent System for Intrusion Detection Based on machine learning

Intelligent System for Intrusion Detection Based on Machine ...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Mohamed Ibrahim Ragab Khaled M. Fouad School of Information Technology and Computer Science Nile University Giza Egypt Faculty of Computer Science and Engineering New Mansoura University and Nile University Mansoura Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

In today's digital age, security challenges threaten user privacy as networks face increased vulnerability to malicious attacks due to large data volumes. Intrusion Detection Systems (IDS) play a crucial role in identifying cyber-attacks and protecting system resources and users. This study utilizes machine learning classifiers (MLC) to analyze the NSL-KDD dataset, optimizing by preprocessing to remove irrelevant features. System performance is assessed using four attribute subsets, comparing model accuracy across DoS, Probe, U2L, and R2L attack classes to determine the best algorithm for each class. Using Random Forest with 20 features successfully achieved an accuracy of up to 99 % in intrusion detection.

关键词： Accuracy machine learning algorithms System performance Intrusion detection NSL-KDD Information age Security Probes Intelligent systems Random forests

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Procurement Budget Forecasting of Low-value Consumables in Colleges and Universities based on machine learning

Procurement Budget Forecasting of Low-value Consumables in C...

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Industrial IoT, Big Data and Supply Chain (IIoTBDSC), International Conference on

作者： Fenfang Liu School of Management Wuhan Technology and Business University Wuhan China

ISBN: (数字)9798331539818

ISBN: (纸本)9798331539825

In order to improve the procurement budget forecasting effect of low-value consumables in colleges and universities, this paper applies machine learning algorithm to the procurement budget process. This paper proposes a method of constructing the value index of low-value consumables combined with machine learning. Through the value index of low-value consumables, the due value of low-value consumables in the laboratory can be reflected, and then the low-value consumables with similar value range of low-value consumables value index can be clustered to form a systematic management object set, and the low-value consumables with larger value index can be managed in a targeted manner. Combined with the experimental research, it can be seen that the purchase budget forecasting model of low-value consumables in colleges and universities based on machine learning proposed in this paper has certain effects, and can optimize and improve the management system and system of laboratories in colleges and universities, so as to improve the level and quality of laboratory management in colleges and universities.

关键词： Procurement Systematics machine learning algorithms Supply chains machine learning Predictive models Market research Indexes Forecasting Sustainable development

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machine learning Techniques Reveal the Most Effective and Influencing Gender-based Scan Acquisition Parameters that Contribute to Patient Dose Undergoing Enhanced CT abdomen and pelvis examinations

Machine Learning Techniques Reveal the Most Effective and In...

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International Conference on Developments in eSystems Engineering (DESE)

作者： Entesar Zawam Dalah Meliz Yuvalı Dilber Uzun Ozsahin HQ Diagnostic Imaging Department Dubai Health Dubai UAE College of Medicine and Health Sciences Mohammed Bin Rashid University Dubai Health Dubai UAE Department of Biostatistics Faculty of Medicine Near East University Nicosia Cyprus Operational Research Centre in Healthcare Near East University Nicosia Cyprus Department of Medical Diagnostic Imaging University of Sharjah UAE

ISBN: (数字)9798350368697

ISBN: (纸本)9798350368703

Using machine learning (ML) techniques to determine factors affecting radiation dose for patients undergoing enhanced abdomen and pelvis computed tomography (CT). Factors such as patient characteristics, CT scan acquisition parameters, and patient misalignment were considered as input variables. CT dose indices namely dose-length product (DLP), volumetric CT dose index (CTDI vol ), size-specific dose estimate (SSDE), size-specific estimate based on water equivalent diameter (SSDEWED), and the effective dose (effD) were considered output variables. Herein, 5 different ML algorithms were used, the Logistic Regression (LR), Random forests (RF), Neural Network (NN), k-Nearest Neighbors algorithms (kNN), and Support Vector machine (SVM). Demonstration of the most accurate ML technique to predict the most effective and influencing input variables will be included. Own to our data classification setup we were able to determine the most effective gender-based input variables. Although input variables for males and females were the same, their influence and ranking were different.

关键词： Support vector machines Radio frequency machine learning algorithms Computed tomography Input variables Artificial neural networks Prediction algorithms Classification algorithms Pelvis Abdomen

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