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检索条件"主题词=Machine Learning Algorithms"

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Hybrid Model Based on machine learning for the Prediction of Consumer Credit Delinquency in the Banking Sector of Peru

Hybrid Model Based on Machine Learning for the Prediction of...

引用

Intelligent Robotics and Systems (ISoIRS), International Symposium on

作者： Nicole Kraenau Mariano Silva Pedro Castañeda Faculty of Information Systems Engineering Peruvian University of Applied Sciences Lima Peru

ISBN: (数字)9798350364798

ISBN: (纸本)9798350364804

The delinquency rate among clients of banking institutions in Peru has increased exponentially in recent years, due to the lack of early detection of potentially delinquent clients, mainly due to the use of inadequate prediction techniques for the identification of delinquent clients. This causes profitability to be reduced, credit risk to increase and the country's economy to be unstable. Previously, different solutions were generated to prevent non-payment, however these studies were not applied in the Peruvian environment and did not cover the personal and financial variables necessary to improve the detection of delinquent clients. In this work, a delinquency prediction system is proposed using classification algorithms such as logistic regression and Random Forest, with the aim of improving and automating the early detection of delinquent clients and counteracting the increase in delinquency, so that banks can of Peru can reduce their financial losses due to non-payment by delinquent clients, and prevent the granting of consumer loans to clients who have a high probability of delinquency. After validating the performance of the algorithm using key indicators, it was obtained that the results are superior to the compared algorithms, thus showing a precision of 90 percent, a recall of 95 percent and an accuracy of 91 percent.

关键词： machine learning algorithms Accuracy Profitability Banking Predictive models Prediction algorithms Robustness

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Generation of Lower Limb Exoskeleton Joint Angles from sEMG Inputs using machine learning

Generation of Lower Limb Exoskeleton Joint Angles from sEMG ...

引用

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

作者： Rithu R. Sreeja Kochuvila Dept. of Electronics and Communication Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

The field of biomechanics has shown tremendous progress in prosthetics with the emergence of artificial intelligence. Among the forefront technologies developed for a social cause, lower limb exoskeletons (LLE) combined with robotics, machine and deep learning techniques is the pioneer research area. The conversion of noisy, random surface electromyography (sEMG) signals to smooth continuous joint angles is important in lower limb prosthetics for the safety as well as comfortability of the wearer. Some recent technologies developed have been concentrated only on knee and hip angles with sEMG taken from different leg muscles. An LLE with 3 degrees of freedom on each leg is considered in this work. The sEMG signals are considered from only four lower limb muscles. These sEMG signals are trained using different machine learning algorithms to predict joint angles. The comparison of the models is carried out using regression plots and various performance indices. It is found that random forest algorithm performed better in predicting the joint angles.

关键词： Legged locomotion Limbs machine learning algorithms Exoskeletons Muscles Prediction algorithms Safety Robots Random forests Prosthetics

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Intrusion Detection Using machine learning for Security and Privacy in Humanitarian Aid System

Intrusion Detection Using Machine Learning for Security and ...

引用

IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Nurul Amirah Mashudi Nurul Taqiah Ab Aziz Wan Fariza Wan Abdul Rahman Norulhusna Ahmad Norliza Mohd Noor Faculty of Artificial Intelligence Universiti Teknologi Malaysia Kuala Lumpur Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi Mara Kelantan Malaysia

ISBN: (数字)9798350340204

ISBN: (纸本)9798350340211

Intrusion detection systems (IDS) are crucial for ensuring the integrity and security of humanitarian aid systems to secure sensitive data. However, deploying IDS in humanitarian aid systems can be challenging due to limited infrastructure, lack of cybersecurity expertise, and data complexity. This study proposes several machine learning algorithms, including Gaussian Naïve Bayes, decision tree, random forest, support vector machine, logistic regression, gradient boosting, and artificial neural network, on the KDDCUP-99 dataset. The results showed excellent performance, with decision tree and gradient boosting achieving 100% accuracy, random forest and support vector machine obtaining 99% accuracy, and artificial neural network achieving 99.84% accuracy rate. This study also compares the proposed models with the state-of-the-art methods to provide a performance benchmark for the IDS. Future works can focus on ensemble and deep learning models to improve the performance of IDS in humanitarian aid systems.

关键词： Support vector machines Deep learning Logistic regression Accuracy machine learning algorithms Humanitarian activities Intrusion detection Boosting Decision trees Random forests

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Evaluation and Prediction Algorithm of Students' Comprehensive Quality Based on Big Data and machine learning

Evaluation and Prediction Algorithm of Students' Comprehensi...

引用

Telecommunications and Power Electronics (TELEPE), International Conference on

作者： Yuzhu Han Shuai Wang Lixia Zhou Weifang Engineering Vocational College Qingzhou Shandong China

ISBN: (数字)9798350369212

ISBN: (纸本)9798350380002

The purpose of this study is to explore the evaluation and prediction algorithm of students' comprehensive quality based on big data and machine learning technology. This paper designs a multi-task neural network (MNN) model, which combines several evaluation indexes, such as academic performance, social activities, health status, etc., to comprehensively evaluate students' comprehensive quality level. Through the experimental verification of real student data, it is found that MNN model has better performance than the traditional benchmark model in predicting students' comprehensive quality, showing lower Mean absolute error (MAE) and mean-squared error (MSE). In addition, this study also analyzes the feature importance of the model, and finds that study time and social activities are the two factors that have the greatest influence on the comprehensive quality evaluation. The experimental results also show that our model has good performance stability and generalization ability on different data subsets. This study provides new ideas and methods for students' evaluation in the field of education, and provides an important reference for schools and education management departments to formulate personalized education policies.

关键词： machine learning algorithms Education machine learning Predictive models Big Data Benchmark testing Prediction algorithms Data models Stability analysis Multi-layer neural network

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PFO PUF: A Lightweight Parallel Feed Obfuscation PUF Resistant to machine learning Attacks

PFO PUF: A Lightweight Parallel Feed Obfuscation PUF Resista...

引用

International Test Conference in Asia (ITC-Asia)

作者： Zhengfeng Huang Yankun Lin Fansheng Zeng Jingchang Bian Zhao Yang Huaguo Liang Yingchun Lu Liang Yao Xiaoqing Wen Tianming Ni School of Microelectronics Hefei University of Technology Hefei China School of Integrated Circuits University of Anhui Hefei China Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan School of Integrated Circuits Anhui Polytechnic University Wuhu China

ISBN: (数字)9798331540333

ISBN: (纸本)9798331540340

Arbiter Physically Unclonable Functions (APUFs) are hardware security primitives that leverage manufacturing process variation to generate security keys. They can produce exponential challenge-response pairs (CRPs) with minimal hardware overhead. However, the symmetric nature of linear additive functions makes them vulnerable to modeling attacks rooted in machine learning. To address this issue, this paper introduces a novel design called Parallel Feed Obfuscation PUF (PFO PUF). In this approach, the intermediate decision signals from the lower APUF are used as a concealed challenge for the upper APUF, enhancing the overall nonlinearity of the dual-APUF. Additionally, obfuscation modules are employed to determine the weights of the intermediate decision signals from both the upper and lower APUFs, protecting the actual response. Experimental results demonstrate that the proposed PFO PUF effectively withstands four advanced machine learning attack algorithms, including Logistic Regression (LR), Support Vector machine (SVM), Deep Feedforward Neural Network (DFNN), and Efficient CANDECOMP/PARAFAC Tensor Regression Network (ECPTRN). The prediction accuracy of these four algorithms is consistently below 66.30%. Compared with other enhanced structures based on APUF, PFO-PUF only uses 493 LUTs and has lower resource overhead.

关键词： Support vector machines Resistance machine learning algorithms Tensors machine learning Resists Prediction algorithms

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Exploring Reinforcement learning to Aid Tor Latency Performance

Exploring Reinforcement Learning to Aid Tor Latency Performa...

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Cybersecurity Conference (SVCC), Silicon Valley

作者： Kelei Zhang Sang-Yoon Chang Computer Science University of Colorado Colorado Springs Colorado Springs CO Department of Informatics Fort Hays State University Hays KS

ISBN: (数字)9798350383140

ISBN: (纸本)9798350383157

This paper explores the novel application of reinforcement learning to enhance Tor’s latency performance. We utilize on-the-fly training model that employs the Q-learning algorithm. The model begins with the relay selection probabilities from the Tor consensus file and progressively trains to improve its ability to construct low-latency circuits. The model’s probabilistic policy is dynamically updated based on real-time consensus file updates, aiming to adapt to the ever-changing Tor network.

关键词： Training Adaptation models Q-learning machine learning algorithms Circuits Probabilistic logic Real-time systems

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Design of a Deep learning-Based Autonomous Vehicle Environmental Perception and Decision-Making System

Design of a Deep Learning-Based Autonomous Vehicle Environme...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Hulin Jin Zhiran Jin Yong-Guk Kim School of Computer Science and Technology Anhui University Hefei China Foothill Preparatory School Temple City California USA Department of Computer Engineering Sejong University seoul South Korea

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Advances in computing power and sensor technology have propelled substantial advancements in autonomous vehicles, particularly in high-precision positioning, environmental perception, and intelligent decision-making. This study introduces a novel deep learning-based environmental perception and decision-making system for autonomous vehicles aimed at mitigating the challenges encountered in current studies. The proposed system employs advanced machine learning algorithms and deep neural network technology to rapidly and accurately adjust to a variety of intricate driving scenarios. The system integrates a range of sensors, encompassing cameras, radar, and Lidar, to thoroughly detect objects in the vehicle's environment and enhance driving safety and efficiency. Detailed methodologies, encompassing sensor fusion techniques and model construction, are delineated. Simulation results attest to the system's efficacy in precisely perceiving the environment and making astute driving decisions. Through the comprehensive analysis, the proposed system demonstrates superior adaptability, generalization capabilities, and performance relative to traditional rule-based control methods, thereby offering significant potential for enhancing the safety and efficiency of autonomous driving.

关键词： Adaptation models machine learning algorithms Laser radar Simulation Decision making Artificial neural networks Sensor fusion Cameras Safety Autonomous vehicles

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Development of an Application for Detecting Lung Cancer Using machine learning

Development of an Application for Detecting Lung Cancer Usin...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Abha Marathe Sahil Makadi Pratik Meshram Amey Mahulkar Ayush Maurya Krishna Mhaske Department of Electronics and Telecommunication Engineering Vishwakarma Institute of Technology Pune India Department of Computer Science Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

Lung cancer continues to be a major contributor to cancer-related deaths globally, making it necessary to bring improvements in reliable and accurate detection methods. This study discusses lung cancer detection which uses an open source dataset about patient and 4 ML algorithms-Random Forest (RF), Support Vector machines (SVM), Decision Tree (DT), and Logistic Regression (LR). To reduce the bias data shuffling was performed initially to randomize the order of tuples. Categorical variables changed to numerical variables with use of factorization for accurate algorithmic processing. Feature selection techniques like Mean Decrease in Impurity (MDI), Recursive Feature Elimination (RFE), and Random Forest Mean Decrease (RFMD) were used to reduce computation load without compromising performance. The study deeply evaluates model performance on the basis of sensitivity, specificity, accuracy, and detection rate. Among the algorithms, RF demonstrated superior accuracy when combined with the selected features. Visualizations using importance scores and confusion matrices provided deeper insights into the models, which aids in interpreting the findings. Finally, an app was developed with selected 8 features and RF to predict the lung cancer. This research demonstrates the vital role of ML in lung cancer diagnosis and its potential of early detection and treatment. The findings of research contributed in advancing the healthcare and addresses the global challenges of lung cancer.

关键词： Radio frequency Support vector machines Accuracy machine learning algorithms Sensitivity Computational modeling Lung cancer Prediction algorithms Random forests Load modeling

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A Novel Deep learning Model for Automated Golf Swing Pattern Recognition in Video Footage

A Novel Deep Learning Model for Automated Golf Swing Pattern...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Chahak Goswami Niyati Goswami Priyanka Israni Carroll Senior High School Southlake TX USA Computer Science Major UNC-Chapel Hill NC USA Independent AI-ML Researcher Ahmedabad Gujarat India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

The automated classification of golf swing events is a crucial procedure for analyzing swings using video analysis. However, there has been a lack of initiative in applying machine learning (ML) algorithms to accurately determine and classify the various stages of a golf swing. This paper proposes a Customized Convolutional Neural Network (CNN) to classify the golf swing phases into eight primary phases, encompassing the pre and post-swing events, based on open source Kaggle dataset. The frames from the videos are extracted and converted into structured data along with the player’s data details. The combined data is pre-processed using Linear discriminant analysis. The proposed customized CNN model is compared with the ML models like Support Vector machines and the CatBoost. The proposed CNN model outperforms by achieving an accuracy of 84.60%. Furthermore, the proposed approaches can separate all the swing phases only based on video data. This suggests that machine learning-based swing classification techniques may be utilized in different motion-analysis settings by splitting motion phases with more flexibility in video analytics in real time.

关键词： Support vector machines Accuracy machine learning algorithms Visual analytics Streaming media Transformers Data models Real-time systems Linear discriminant analysis Convolutional neural networks

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machine learning-Driven Exploration and Stages Classification of Liver Cirrhosis: A Comprehensive Approach

Machine Learning-Driven Exploration and Stages Classificatio...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Veeraswamy Ammisetty UmaSankar Pechetti Viswanath Veera Krishna Maddinala K John Bunyan Department of Computer Science &amp Engineering Koneru Lakshmaiah Educational Foundation Guntur Andhra Pradesh India Department of CSE Sri Vasavi Engineering College (A) Tadepalligudem A.P Department of CSE Sri Vasavi Engineering College Tadepalligudem Andhra Pradesh India Department CB&DS Bapatla Engineering College Bapatla Andhra Pradesh India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Liver cirrhosis, a progressive liver disease, necessitates accurate and timely staging for optimal patient management. This study proposes a novel approach combining exploratory data analysis (EDA) with machine learning (ML) to enhance liver cirrhosis classification. By leveraging clinical and laboratory data, EDA will identify key predictors of disease progression. Subsequently, KNN, XGBoost, Random Forest, and Gradient Boosting algorithms will be applied to classify disease stages with precision. The integration of EDA and ML aims to improve risk stratification, treatment decisions, and overall patient outcomes in liver cirrhosis management.

关键词： Data analysis machine learning algorithms Liver diseases Predictive models Nearest neighbor methods Boosting Prediction algorithms Feature extraction Random forests Diseases

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