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Credit Card Fraud Analysis Using Machine Learning 8th

Credit Card Fraud Analysis Using Machine Learning

8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Charitha, Sree Chowdary, Shivani Rao, Trupthi Kodipalli, Ashwini Kamal, Shoaib Rohini, B.R. Department of Artificial Intelligence and Data Science Global Academy of Technology Bangalore India

ISBN: (纸本)9789819976324

One of the important societal issues affecting the credit card business is fraud. The world has large impact on this problem. The main objectives are to first identify and categorize the different types of credit card theft before reviewing other fraud detection techniques that have been used. We are working to analyze and lesson global fraud. This paper discusses popular phrases used in credit card fraud analysis and highlights significant facts and numbers. Depending on the type of fraud that banks or credit card firms are dealing with, several precautions and processes can be created and implemented. The strategy proposed in this study is likely to be advantageous in a variety of ways, and the method presented employs machine learning methods. The reduction of credit card scam is why it is crucial to use the tactics we have discussed here. In addition, it raises ethical questions when legitimate credit card users are mistakenly labeled as fraudsters. The next step is to apply our model to determine whether or not another transaction is dishonest. Thus, it is important to treat this issue with utmost care. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Retinal Disease Classification: Binary and Multiclass Perspectives 4

Retinal Disease Classification: Binary and Multiclass Perspe...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Lanjewar, Rutuja Nitin Pacharaney, Utkarsha Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India

ISBN: (纸本)9798331523923

Retinal diseases, among which diabetic retinopathy, age-related macular degeneration, and glaucoma account for the greatest number of blindness cases worldwide, affect more than 338 million people. It is essential that early detection leads to timely intervention to preserve vision, but it is limited due to the ever-increasing demand for ophthalmic care and a shortage of specialists. Machine learning-driven systems, especially Convolutional Neural Networks (CNNs), have shown great promise in overcoming these challenges, achieving high accuracy in medical imaging tasks. This paper explores a unified framework for both multiclass and binary classification of retinal diseases, addressing issues like class imbalance and inter-class similarity while leveraging CNNs for robust feature extraction. Techniques like Grad-CAM also increase model interpretability, allowing clinicians to understand and trust AI-driven diagnostic processes. © 2025 IEEE.

关键词： Convolutional neural networks

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Predictive Modeling of Traditional Korean Paper Characteristics Using Machine Learning Approaches (Part 1): Discriminating Manufacturing Origins with Artificial Neural Networks and Infrared Spectroscopy

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Palpu Chongi Gisul/Journal of Korea Technical Association of the Pulp and Paper Industry 2023年第4期55卷 57-69页

作者： Hwang, Sung-Wook Park, Geunyong Kim, Jinho Jeong, Myung-Joon Human Resources Development Center for Big Data-based Glocal Forest Science Kyungpook National University Korea Republic of Department of Wood Science and Technology Kyungpook National University Korea Republic of Department of Wood Science & Technology College of Agricultural Life Science Jeonbuk National University Korea Republic of

This study focuses on machine learning-based approaches in combination with infrared spectroscopy to discriminate the manufacturing origin of Hanji, a traditional Korean paper. Infrared spectra provide useful information about the chemical composition and structural features of Hanji, while principal component analysis and hierarchical clustering extract meaningful patterns related to the manufacturing region. Score plots and hierarchical clustering of the principal components provide enhanced clustering patterns based on manufacturing region by focusing on the spectral region 1800-1200 cm -1 . The clustering patterns are driven by key absorption bands, such as those associated with carboxyl groups, crystalline cellulose, and aromatic rings. In addition, feed-forward neural network classification models that were developed using the spectral data exhibit significant accuracy when classifying the Hanji manufacturing regions. In particular, models utilizing the raw and second derivative spectra in the 1800-1200 cm-1 region exhibit excellent classification performance, indicating the effectiveness of this spectral region for classification purposes. This study demonstrates the effective application of artificial neural networks in conjunction with infrared spectroscopy to characterize and classify Hanji based on its manufacturing region. The results contribute to a better understanding of the unique properties of Hanji and the discovery of new insights from paper cultural artifacts. © 2023 Korean Technical Assoc. of the Pulp and Paper Industry. All rights reserved.

关键词： Infrared spectroscopy

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Two-Stage Guided Constraint Differential Evolution Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1109-1133页

作者： Sung, Tien-Wen Liang, Qiaoxin Hong, Chuanbo Huang, Zeming Li, Wei Nguyen, Trinh-Dong College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam

The development of population intelligence has shown a great trend of a linear surge in recent years, and a large number of intelligent algorithms inspired by biology have been studied. Among them, differential evolution (DE) algorithms have attracted the attention of many researchers because of their simplicity, high efficiency, powerful search capability, etc. DE has strong exploitability and can approach the global optimum faster, but it cannot avoid the phenomenon of search stagnation and premature convergence. To address the drawbacks of DE, the two-stage bootstrap constrained differential evolution algorithm (TSGCDE) is proposed in this paper. In TSGCDE, instead of the traditional one population, the population is divided into two as a way to increase the diversity of the population. Divided into two stages, each stage searches for optimal solutions in different directions in different populations, and the optimal solutions and the bootstrap solutions generated by the populations in the two stages are bounded by each other’s bootstrap, which makes the DE have a more powerful optimal-seeking ability and easier to jump out of the local optimum. To validate the performance of TSGCDE, we conducted experiments on CEC2013 and CEC2014 benchmarks with some well-known algorithm variants, and the experimental results show that our proposed algorithm is competitive. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Swarm intelligence

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CropOptimizer: Intelligent Crop Forecasting and Decision Support System 2

CropOptimizer: Intelligent Crop Forecasting and Decision Sup...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Jadhav, Balasaheb Ligde, Vaidehi Malpani, Rushikesh Lakhotiya, Rushikesh Savale, Phalguni Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350388916

CropOptimizer is an innovative crop forecasting and decision support system designed to revolutionize agricultural practices. Comprising five key modules - crop recommendation, fertilizer suggestion, disease detection, weather forecasting, and market price tracking - CropOptimizer combines modern technologies to help farmers make informed decisions. The crop recommendation module, leveraging XGBoost, uses soil, climate, and nutrient data to select suitable crops that maximize production potential. The fertilizer suggestion module provides specialized guidance based on specific crop and soil characteristics, ensuring effective nutrient management through XGBoost. The disease detection module employs deep learning with ResNet-9 for image processing to identify plant diseases from leaf images, followed by specific treatment recommendations, achieving high accuracy. Weather forecasting offers a 7-day forecast, including rainfall predictions, based on plant type, state, and city through an API. Market price tracking provides live market prices for various crops, enabling farmers to make informed selling decisions. By integrating these components, CropOptimizer increases crop yield and sustainability, fostering a more resilient agricultural ecosystem and improving food security and economic success. © 2024 IEEE.

关键词： Crop rotation

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Analysis of GAN for Melanoma Skin CancerClassification with Dermatologist Recommendation

Analysis of GAN for Melanoma Skin CancerClassification with ...

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2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Saranya, N. Jowin Alfred, C. Rr, Rishikesh Gilbert Idayan, I. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science India

ISBN: (数字)9798350354539

ISBN: (纸本)9798350354539

Melanoma is a type of skin cancer that poses a significant health threat globally, necessitating effective early detection methods. Melanoma arises from the pigment-producing cells(melanocytes) and is known for its higher metastatic potential. Designing a reliable Computer-Aided Diagnosis (CAD) system for accurate melanoma detection is challenging due to the appearance of the nevus. This study investigates the application of a GAN for classification of melanoma. The GAN architecture is employed to learn and generate synthetic melanoma images, enhancing the diversity and richness of the training dataset. We mainly focus on evaluating performance of GAN-enhanced model through comprehensive metrics like specificity, accuracy, the receiver operating characteristic curve (ROC) and area under the curve (AUC). This study sheds light on utility of synthetic data in clinical image analysis and provides insights into the strengths and limitations of utilizing GANs for melanoma skin cancer classification. We also added a dermatologist recommendation feature to our model. It uses computer programs to understand what youneed and then matches you with the best doctor available nearby. This system helps you get the care you need faster and easier, making sure you see a doctor who's a good fit for you and your health needs. This system matches patients with suitable doctors. This matching involves considering factors like doctor specialty and location. © 2024 IEEE.

关键词： Dermatology

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Practical tracking of MIMO uncertain stochastic systems driven by colored noises via active disturbance rejection control

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science China(Information sciences) 2022年第6期65卷 203-217页

作者： Chunwan LV Zhengyong OUYANG Zehao WU Feiqi DENG Mingqing XIAO School of Mathematics and Big Data Foshan University Systems Engineering Institute South China University of Technology Department of Mathematics Southern Illinois University

This study addresses the practical tracking problem for a class of multi-input multi-output(MIMO) uncertain stochastic systems driven by colored noises via the active disturbance rejection control approach. The extended state observer is designed to estimate in real time the unmeasurable states and the stochastic total disturbance of each subsystem, including unknown coupling system dynamics between subsystems, colored noises, and uncertainty caused by partly unknown control parameters. Active disturbance rejection controllers based on the timely estimation of the extended state observer and compensation are then designed, guaranteeing the mean square practical convergence of the tracking errors. Some numerical simulations are performed to validate the effectiveness of the proposed control strategy.

关键词： uncertain stochastic systems active disturbance rejection control extended state observer tracking colored noise

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Cancer Diseases Diagnosis Using Deep Transfer Learning Architectures 1st

Cancer Diseases Diagnosis Using Deep Transfer Learning Archi...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Promy, Tania Ferdousey Joya, Nadia Islam Turna, Tasfia Haque Sukhi, Zinia Nawrin Ashraf, Faisal Bin Uddin, Jia Department of Computer Science and Engineering School of Data Science Brac University Dhaka Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon Korea Republic of

ISBN: (纸本)9783031346187

Cancer is a lethal disease among the diseases in the world. It is clinically known as ‘Malignant Neoplasm’ which is a vast group of diseases that encompasses unmonitored cell expansion. It can begin anywhere in the body such as the breast, skin, liver, lungs, brain, and so on. As reported by the National Institutes of Health (NIH), the projected growth of new cancer cases is forecast at 29.5 million and cancer-related deaths at 16.4 million through 2040. There are many medical procedures to identify the cancer cell, such as mammography, MRI, CT scan, which are common methods for cancer diagnosis. The methods used above have been found to be ineffective and necessitate the development of new and smarter cancer diagnostic technologies. Persuaded by the phenomena of medical image classification using deep learning, our recommended initiative targets to analyze the performance of different deep transfer learning models for cancer cell diagnosis. In this paper, we have used VGG16, Inception V3 and MobileNet V2 deep architectures to diagnosis the breast cancer (KAU-BCMD dataset), lung cancer (IQ-OTH/NCCD dataset) and skin cancerHam10000). Experimental results demonstrate that VGG16 architecture shows comparatively higher accuracy by exhibiting 98.5% of accuracy for breast cancer, 99.90% for lung cancer and 93% for skin cancer dataset. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Diseases

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Beamforming Design for RIS-Aided THz Wideband Communication Systems 24

Beamforming Design for RIS-Aided THz Wideband Communication ...

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Jiang, Yihang Zhou, Ziqin Li, Xiaoyang Gong, Yi Southern University of Science and Technology Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9781665496261

Benefiting from tens of GHz of bandwidth, terahertz (THz) communications has become a promising technology for future 6G networks. However, the conventional hybrid beamforming architecture based on frequency-independent phase-shifters is not able to cope with the beam split effect in THz massive multiple-input multiple-output (MIMO) systems. Despite some work introducing the frequency-dependent phase shifts via the time delay network to mitigate the beam splitting in THz wideband communications, the corresponding issue in reconfigurable intelligent surface (RIS)-aided communications has not been well investigated. In this paper, the beam split effect in THz massive MIMO is quantified by analyzing the array gain loss. A new beamforming architecture has been proposed to mitigate this effect under RIS-aided communications scenarios. Simulations are performed to evaluate the effectiveness of the proposed system architecture in combating the array gain loss. © 2023 IEEE.

关键词： Beamforming

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Toward Sustainable Development Goals (SDGs) with Statistical Modeling: Recursive Bivariate Binary Probit

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IAENG International Journal of Applied Mathematics 2024年第8期54卷 1515-1521页

作者： Ratnasari, Vita Utama, Syirrul Hadi Dani, Andrea Tri Rian Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia the Department of Statistics Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Surabaya Indonesia Statistics Study Program Department of Mathematics Faculty of Mathematics and Natural Sciences Mulawarman University Samarinda Indonesia

Poverty is still a global problem that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. In 2021, West Papua province will have the 2nd most significant percentage of poor people after Papua province, with 21.84% of the poor population. Poor households in West Papua province are dominated by families, with the Head of Household (KRT) working in the agricultural sector at 65.10 %. In this research, joint modelling was carried out between the level of household welfare and the employment sector of the head of the household in the West Papua province. It is suspected that these two variables have endogeneity problems, where one of the response variables becomes a predictor variable in the other equation, so a recursive bivariate binary probit regression model is used. Recursive bivariate binary probit regression parameter estimation uses Maximum Likelihood Estimation (MLE), but the results are not closed form, so it is continued using the Newton-Raphson iteration method. The results of hypothesis testing show that partially, variables that significantly influence the level of household welfare include the variable marital status, KRT formal/informal workers, health complaints, asset ownership status, migration status, number of household members, classification of area of residence (Village/City), age of head of household, and employment sector of head of household. Meanwhile, variables that significantly influence the choice of working in the agricultural sector include the director of household education, classification of area of residence (rural/city), and the age of the head of household. © (2024), (International Association of Engineers). All rights reserved.

关键词： Maximum likelihood estimation

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