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共 265 条记录，以下是231-240 订阅

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Credit Risk Models using Rule-Based Methods and machine-learning Algorithms 22

Credit Risk Models using Rule-Based Methods and Machine-Lear...

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Proceedings of the 2022 6th international conference on Computer Science and Artificial Intelligence

作者： Maohong Tian Hualin Li Jing Huang Jian Liang Wenbin Bu Bingling Chen Department of Software Engineering and Artificial Intelligence ChongQing Institute of Engineering China Department of Software Engineering and Artificial Intelligence Shu Yi Xin Credit Management CoLTD China Department of Big Data Artificial Intelligence Nanfang College Guangzhou China

ISBN: (纸本)9781450397773

this study applies machine-learning techniques and rule-based methods to construct nonlinear nonparametric models to forecast retail consumer and medium-sized enterprises (SMEs) credit risk. By combining customer transactions and enterprise data from 2018 to 2020 sampled from a major business district in the People’s Republic of China, forecasts were constructed that significantly improved the classification rates of customer and enterprise delinquencies and defaults. Moreover, the time-series patterns of the estimated delinquency rates and credit scores over multiple dimensions produced by this model suggest that aggregated credit risk analytics may have important applications in forecasting systemic risk, which might shed some light on obtaining prospective insights regarding consumer credit that can be gleaned from historical data especially pandemic period.

关键词： Credit risk model 99-00 Credit Score 2021 MSC: 00-01 Rule-based machine learning

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the Effectiveness of CNN-Based Models in Macroscopic Motion Direction Detection

The Effectiveness of CNN-Based Models in Macroscopic Motion ...

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Robotics and Computer Vision (ICRCV), international conference on

作者： Zhiyu Qiu Tianqi Chen Yuxiao Hua Yuki Todo Division of Electrical Engineering and Computer Science Graduate School of Natural Science & Technology Kanazawa University Kanazawa Japan Faculty of Electrical Information and Communication Engineering Kanazawa University Kanazawa Japan

ISBN: (数字)9798331527426

ISBN: (纸本)9798331527433

Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision, offering significant advancements in image and video recognition, classification, and segmentation tasks. Leveraging hierarchical pattern recognition capabilities, CNNs have demonstrated superior performance over traditional machine learning methods. One particularly critical application of CNNs is in motion direction detection, which is essential for enhancing security in surveillance systems, ensuring safe navigation in autonomous vehicles, and providing valuable insights in sports analytics. In this study, our primary goal was to investigate how different types of input data impact the accuracy of motion direction detection. We applied five distinct preprocessing methods to test the effect of input data on accuracy. Furthermore, we conducted experiments in various object and background scenarios to evaluate adaptability of simple 4-layer CNN, EfficientNetBO and ResNet50 in detecting motion direction. Finally, we tested the noise resistance of these three models. the results indicate that CNNs are capable of extracting the macroscopic motion direction of objects in images. Our study highlights the potential of CNN-based models for efficient and effective motion direction detection.

关键词： Resistance Training Computer vision Adaptation models Accuracy Noise Dynamics data models Convolutional neural networks Residual neural networks

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Clustering Social Media data for Bitcoin Price Prediction with Transformer Model 23

Clustering Social Media Data for Bitcoin Price Prediction wi...

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Proceedings of the 2023 6th international conference on machine learning and Natural Language Processing

作者： Yajing Zhi T-H. Hubert Chan Department of Computer Science The University of Hong Kong Hong Kong

ISBN: (纸本)9798400709241

this paper explores the integration of social media data and natural language processing methods, specifically utilizing the Transformer model, to predict Bitcoin price movements. We aim to evaluate the effectiveness of using social media data and the Transformer model in forecasting market trends for Bitcoin. By analyzing social media posts and incorporating them into predictive models, we demonstrate the potential of the Transformer architecture in capturing complex dependencies and patterns within sequential Bitcoin prices. Additionally, different clustering methods are applied to process the original social media data in a rolling manner. the evaluation of Transformer-based models on historical data showcases their predictive performance compared to various social media data clustering approaches. Furthermore, the impact of incorporating outliers of social media data into the Transformer model is explored to improve prediction accuracy. the results of this study demonstrate the potential of clustering on social media data and the Transformer model for forecasting market trends.1

关键词： BERT Bitcoin Clustering Transformer Twitter

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Improving Imbalanced data Classification through Integrated data-Level and Algorithm-Level Techniques

Improving Imbalanced Data Classification Through Integrated ...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Salma El Hajjami Hicham Amelal Abdelkarim El-Hajjami Keshav Kaushik Inam Ullah Khan LabSIV Department of Computer Science Faculty of Science Ibn Zohr University Agadir Morocco Department of Computer Science and Telecommunications INP-ENSEEIHT University of Toulouse France School of Computer Sciences University of Petroleum and Energy Studies Dehradun Uttarakhand India Department of Electronic Engineering School of Engineering and Applied Sciences (SEAS) Isra University Pakistan

Imbalanced class distribution is common issue in machine learning and data mining. It affects various applications like fraud detection, medical diagnosis, and network intrusion detection. the mentioned problem occurs when the majority class greatly outnumbers the minority class, hindering learning systems designed for balanced distribution. Furthermore, presence of overlapping instances between two classes exacerbates problem. this paper introduces a novel method that integrates ensemble and cost-sensitive techniques at algorithm-level with OSBNR approach at data-level to address this challenge. Furtherly, suggested technique is compared with conventional resampling methods, in conjunction with three ensemble algorithms and three cost-sensitive algorithms, to determine optimal combination. Simulation presents OSBNR is having optimal results in terms of processing time and evaluation metrics like G-mean, AUC-ROC, TPR-Recall, precision, Fl-score and FPR. PyOSBNR is simulated as python index package which can easily handle class imbalance in the presence of behavioral noise.

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Miscellaneous EEG Preprocessing and machine learning for Pilots' Mental States Classification: Implications 22

Miscellaneous EEG Preprocessing and Machine Learning for Pil...

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Proceedings of the 6th international conference on Advances in Artificial Intelligence

作者： Ibrahim Mohammad Alreshidi Irene Moulitsas Karl W. Jenkins Centre for Computational Engineering Sciences Cranfield University UK and Machine Learning and Data Analytics Laboratory Digital Aviation Research and Technology Centre (DARTeC) UK Centre for Computational Engineering Sciences Cranfield University UK

ISBN: (纸本)9781450396943

Higher cognitive process efforts may result in mental exhaustion, poor performance, and long-term health issues. An EEG-based methods for detecting a pilot's mental state have recently been created utilizing machine learning algorithms. EEG signals include a significant noise component, and these approaches either ignore this or use a random mix of preprocessing techniques to reduce noise. In the absence of uniform preprocessing procedures for cleaning, it would be impossible to compare the efficacy of machine learning models across research, even if they employ data obtained from the same experiment. In this study, we intend to evaluate how preprocessing approaches affect the performance of machine learning models. To do this, we concentrated on fundamental preprocessing techniques, such as a band-pass filter and independent component analysis. Using a publicly accessible actual physiological dataset gathered from a pilot who was exposed to a variety of mental events, we explore the influence of these preprocessing strategies on two machine learning models, SVMs and ANNs. Our findings indicate that the performance of the models is unaffected by preprocessing techniques. Moreover, our findings indicate that the models were able to anticipate the mental states from merged data collected in two environments. these findings demonstrate the necessity for a standardized methodological framework for the application of machine learning models to EEG inputs.

关键词： machine learning EEG preprocessing Mental states classification

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the Empirical Analysis of machine learning-Based Techniques for Improving Cyber Security

The Empirical Analysis of Machine Learning-Based Techniques ...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Yerrolla Chanti M. Chalapathi Rao Ch. Srinivasa Reddy Mohd. Yaseen Wani Abdullah Samdani Mohit Tiwari School of CS & AI SR University Warangal Telangana India Department of CSE Malla Reddy University Hyderabad Telangana Department of IT Vignan's Institute of Information Technology(A) Duvvada Visakhapatnam School of Law Kashmir University Srinagar Jammu &Kashmir School of Law University of Petroleum & Energy Studies Dehradun Uttarakhand India Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Paschim Vihar Delhi

the absence of training statistics is still the major obstacle among the numerous that tend to cause cyberattack identification difficult to identify. Millions of consumers are still at risk due to an absence of knowledge about website activities and characteristics that could lead to an attack, even when businesses and companies utilize well-known network surveillance technologies. In reality, the majority of attacks happen not since attackers employ sophisticated programming and evasive strategies, yet rather since victims lack the fundamental skills needed to recognize and prevent the *** of these difficulties, machine learning (ML) is demonstrated to fundamentally alter our awareness of the context of cyberattacks. For this research, ML techniques were applied to data from phishing websites to improve cybersecurity by contrasting five techniques and offering knowledge that the broad accessible may employ to prevent common phishing traps. the research outcomes imply that the neural network (NN) technique is the most effective one. Since NNs are built on multi-layer perceptrons, the foundation of intelligence, phishing identification will eventually be automated and transformed into an artificial intelligence endeavor.

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Enhancing Forensic Analysis Using a machine learning-based Approach

Enhancing Forensic Analysis Using a Machine Learning-based A...

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international conference on Advanced Communication Technologies and Networking (CommNet)

作者： Samira Benkerroum Khalid Chougdali National School of Applied Sciences University Ibn Tofail Kenitra Morocco

In recent years, computers or digital devices contribute to the global spread of cyber threats and cyber crimes. these cyberattacks leave some artefacts on the storage of the target device, for this reason they require special treatment, and which will have to be the subject of various investigations in order to study its behavior and analyze and prevent it so that this never happen *** the continued development of digital forensic investigations for the recovery of evidence whether volatile or non-volatile, manual investigations are both time-intensive and laborious. the proposed solution is to use a method to automate manual forensic investigation tasks (forensic analysis) to reduce human effort and improve time *** paper presents a summary of the digital forensic investigation process, we discuss existing ML solutions to automate the analysis ***, the paper proposes an approach based on machine learning where the binary classification was performed using the algorithms K-Nearest Neighbors, Naive Bayes, Random Forest, Support Vector machine, Decision Tree, Logistic Regression, Gradient Boosted Tree, Multi-Layer Perceptron, using CIC-MalMem-2022 dataset to identify *** algorithms’ respective performances were contrasted. the performance metrics Precision, F1-score, Accuracy, Recall, and Area Under the Curve were used to assess the outcomes. Consequently, the Random Forest and Gradient Boosted Tree algorithms demonstrated superior performance, achieving a remarkable accuracy level of 99.98% in the detection of malware through memory scans. the Logistic Regression algorithm exhibited the least favorable performance in analyzing malware using memory data, achieving an accuracy rate of 95.75%. According to the results obtained, many algorithms used have obtained very satisfactory results.

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Detecting Children with Autism Spectrum Disorder Based on Eye-tracking and machine learning

Detecting Children with Autism Spectrum Disorder Based on Ey...

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IEEE international conference on Knowledge Innovation and Invention (ICKII)

作者： Hsin-Yi Lu Yang-Cheng Lin Chia-Hsin Chen Chih-Chung Wang Ia-Wen Han Wen-Lung Liang Department of Rehabilitation Chung-Ho Memorial Hospital Kaohsiung Medical University Kaohsiung Taiwan Department of Industrial Design National Cheng Kung University Tainan Taiwan

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that appears in childhood with varying severity. Unfortunately, current screening methods for ASD often lack sensitivity. While clinical observation-based evaluations are more reliable, they are also time-consuming. An artificial intelligence (AI) model has the potential to analyze eye-tracking data and provide a more efficient and objective assessment of ASD, potentially reducing diagnosis time and improving accuracy. the model helps children with ASD access necessary medical resources more promptly. In the current study, 58 children aged 4 to 6 years were recruited. the children were presented with static images that were divided into five categories based on the number of individuals and interactions. Eye-tracking technology was used to record various metrics, including fixation count, duration, percentage, and time to the first fixation on areas of interest (AOI) during image viewing. Principal Component Analysis (PCA) was implemented for feature selection, and Synthetic Minority Over-sampling Technique was used to generate minority samples. Five classification algorithms (Decision Tree, Random Forest, Logistic Regression, Extreme Gradient Boosting, and Support Vector machine) were applied to classify children with ASD and TD. Both groups of children completed calibration and testing after guidance, and no adverse reactions occurred during the testing process. the results showed that the classification models achieved an SVM accuracy of 83.3%, an AUC of 0.94, an LR accuracy of 83.3%, an AUC of 0.95, a DT accuracy of 77.8%, an AUC of 0.81, an RF accuracy of 88.9%, and an AUC of 0.96, as well as an XGBoost accuracy of 94.4% and an AUC of 0.99 for selected 10 features. Using machine learning methods to analyze eye-tracking data was a useful tool for identifying children with autism and may aid in the diagnostic process.

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FNMD: An Evaluation of machine learning and Deep learning Techniques for Fake News Detection 6th

FNMD: An Evaluation of Machine Learning and Deep Learning Te...

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6th international conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023

作者： Daneshvar, Bahareh Abdi, Asad Hosseini, Seyyed Mahmoud Department of Computing and Mathematics Faculty of Science and Engineering University of Derby Derby United Kingdom Department of Finance and Accounting Faculty of Westminster Business School University of Westminster Westminster United Kingdom

ISBN: (纸本)9783031530814

Fake news proliferation on social media platforms has become alarming because it poses threats to various aspects of society. Fake news encompasses intentionally falsified information designed to mislead readers and manipulate public perception, resulting in low-quality and misleading content. this paper presents a machine learning-based approach for fake news detection using: 1) Sentiment knowledge to calculate a sentence sentiment score as one of the features for sentence-level classification. 2) Statistical and linguistic knowledge to extract required features. the proposed method combines several types of features into a unified feature set to design a more accurate classification system ("True": the fake news;"False": otherwise). thus, to achieve better performance scores, we carried out a performance study of five well-known feature selection techniques and several most famous classifiers to select the most relevant set of features and find an efficient machine learning (ML) classifier, respectively. Alongside traditional machine learning models, we also applied different Deep learning (DL)-based models. Different machine learning and deep learning-based methods are applied to three different datasets including Liar, GossipCop, and PolitiFact, and the results show the integration of machine learning-based classification method and feature selection technique can significantly improve the performance and make the method comparable to other existing methods. Furthermore, DL models, in particular LSTM, exhibited remarkable accuracy thresholds of up to 88% in capturing intricate patterns within textual data. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fake detection

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Enhancing Diabetes Risk Assessment with Ensemble learning Methods 6th

Enhancing Diabetes Risk Assessment with Ensemble Learning M...

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6th international conference on Advanced Informatics for Computing Research, ICAICR 2023

作者： Vyas, Himanshu Aggarwal, Mukul Goyal, Kartik Gupta, Karnika Verma, Kanishk Department of IT KIET Group of Institutions Delhi NCR Ghaziabad India

ISBN: (纸本)9783031840586

Diabetes is a disease which is a burgeoning global health issue, having an impact on millions of individuals and straining healthcare systems worldwide. Rapid and accurate prediction of diabetes is essential for early interventions, lifestyle changes and improved patient outcomes. this research paper explores the field of predictive analytics by harnessing the power of ensemble learning. the study examines the hypothesis that an ensemble model that strategically brings together Gradient Boosting, LightGBM, and AdaBoost classifiers can outperform the predictive ability of individual classifiers. To validate this premise, extensive experiments are conducted that include extensive data preprocessing, hyperparameter tuning, and rigorous model evaluation. the main objective of this research is to provide an innovative, reliable and interpretable diabetes prediction model. We emphasize not only the accuracy of predictions but also the model’s interpretability, a crucial aspect for healthcare professionals to make informed decisions. By fusing multiple base classifiers, the ensemble approach capitalizes on the distinct strengths of each component, enhancing its capacity to decipher complex relationships within the diabetes dataset. the results underscore the remarkable accuracy of the ensemble model, demonstrating its superior performance compared to standalone classifiers. We delve into a comprehensive discussion, elucidating the advantages of ensemble learning in the context of diabetes prediction and its potential to transform clinical practice. this research endeavors to empower healthcare practitioners, researchers, and policymakers with an advanced tool for diabetes risk assessment, thereby fostering a proactive approach to diabetes management. In an era of data driven healthcare, this paper illuminates the path towards harnessing the amalgamation of machine learning algorithms to tackle one of the most pressing public health concerns. By presenting this ensemble lea

关键词： Prediction models

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