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A Comprehensive Study: Evaluating Machine Learning Algorithms with Credit Card Transaction Data 3rd

A Comprehensive Study: Evaluating Machine Learning Algorithm...

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Basak, Sarnali Shanto, Shaikh Qutub Department of Computer Science and Engineering Jahangirnagar University Savar Dhaka1342 Bangladesh

ISBN: (纸本)9789819739363

The financial sector places significant emphasis on the detection of credit card fraud, and machine learning techniques have emerged as a promising solution. Nevertheless, questions regarding the interpretability of machine learning models and their resilience against adversarial attacks within the realm of credit card fraud detection persist. This study aims to bridge these gaps and underscore their pivotal role in advancing the field. It involves a comprehensive analysis of a credit card transaction dataset and the development of multiple machine learning models for fraud detection. Python scripts leverage well-known libraries such as Pandas, NumPy, Seaborn, Scikit-Learn, TensorFlow, and Matplotlib for data processing, visualization, and machine learning tasks. For fraud detection, the dataset is partitioned into training and testing sets, and three machine learning models, namely XGBoost, Random Forest, and Support Vector Machine (SVM), are applied. Performance metrics, encompassing accuracy, sensitivity (recall), F1-score, Matthews Correlation Coefficient (MCC), Balanced Classification Rate (BCR), and confusion matrices, are computed and presented. Additionally, a Long Short-Term Memory (LSTM) neural network is deployed for sequence-based fraud detection. This research offers a comprehensive roadmap for analyzing credit card transaction data and constructing machine learning models to detect fraudulent activities, underscoring the importance of data exploration and model evaluation in fraud detection systems. It emphasizes the significance of feature engineering in enhancing the effectiveness of fraud detection models and equips decision-makers with the ability to make informed choices based on model predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Advance Approaches in AI-Powered Website Development for Educational Delivery via Ed-Tech 2

Advance Approaches in AI-Powered Website Development for Edu...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Pandey, Hardik Kaswan, Suresh Bhardwaj, Hrishabh Pal, Priyansh Kumar, Aditya Amita Chandigarh University Department of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350384277

This electronic document is a review paper on website development and the evolution of education into online education. We will discuss the current situation and speculations for the future of education and how can technology help shape it. Some recap of the evolution of education system will also be examined. This comprehensive review explores the dynamic intersection of website development, educational technology, and the evolving landscape of education. The studies encompass a range of topics, from addressing shortcomings in information systems and proposing innovative solutions like web-based alumni databases to examining unfulfilled promises in educational technology and the persistent challenges facing global education. Additionally, experiences in teaching highlevel web development courses during the Web 2.0 era and the increasing financial investment in educational technology are analyzed. The exploration of students' experiences in learning web development sheds light on the complexities and opportunities within these interconnected domains. Together, these diverse perspectives contribute to a nuanced understanding of the ongoing evolution in educational practices, technology integration, and the potential for transformative advancements. © 2024 IEEE.

关键词： Web Design

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Quantum Intelligence: Merging AI and Quantum Computing for Unprecedented Power 2

Quantum Intelligence: Merging AI and Quantum Computing for U...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Kumar, Sonu Simran, S. Singh, Manjit Chandigarh University Department of Computer Science and Engineering Punjab Mohali India

ISBN: (纸本)9798350384277

The result of AI and quantum computing coming together is the novel idea known as Quantum Intelligence (QI). This work explores the possible advantages of combining artificial intelligence (AI) and quantum computing to address difficult problems that have proven to be beyond the capabilities of conventional computer methods. This study offers a thorough grasp of the unique properties of qubits, superposition, and entanglement while examining the fundamental concepts of quantum computing. The current state of artificial intelligence is also examined, with a focus on the difficulties traditional computers have in managing the increasing complexity of computing tasks. The paper's main focus is on Quantum Intelligence, providing an explanation of how quantum algorithms might enhance machine learning procedures and improve artificial intelligence algorithms. The paper also discusses Open Source Libraries, TensorFlow Quantum, and the necessity of quantum programming languages. This abstract discusses the advantages and challenges of integrating quantum computing into AI procedures, emphasizing the necessity for hybrid models and scalable algorithms. We explore the interdependent nature of quantum and classical machine learning models and show how quantum-enhanced algorithms may be able to outperform their classical counterparts in specific application domains. The conclusion of the abstract emphasizes how the development and use of open-source frameworks and tools will fuel the transformative potential of AI and quantum computing together. © 2024 IEEE.

关键词： Quantum computers

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A Novel Graph Clustering Algorithm with Enhanced Security using Power Method and Homomorphic Encryption 1

A Novel Graph Clustering Algorithm with Enhanced Security us...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Jayalatchumy, D. Arunkumar, G. Saravanan, D. Perunthalaivar Kamarajar Institute of Engineering and Technology Department of Computer Science and Engineering Nedungadu Karaikal India Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Madanapalle India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350328042

In recent years, privacy concerns have led to an increasing demand for secure data clustering algorithms that can protect sensitive data while maintaining the accuracy of the clustering results. By using homomorphic encryption to enable safe computation on encrypted data is one interesting strategy. Homomorphic encryption approach that allows addition and multiplication to be performed on encrypted data is the Paillier cryptosystem. In this study, we present an innovative approach for using the power iteration algorithm for clustering with the Paillier cryptosystem. An effective and scalable method for grouping large data sets is the power iteration clustering algorithm. We show that by using the Paillier cryptosystem, we can perform the necessary computations for the power iteration clustering algorithm on encrypted data without revealing any sensitive information. We prove our approach can achieve excellent accuracy while maintaining data privacy by evaluating it on many benchmark datasets. Our results show that the Paillier cryptosystem can be a viable solution for privacypreserving clustering applications. © 2023 IEEE.

关键词： Clustering algorithms

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Does computer-Based Learning Suits Children's Desire to Control Their Own Learning Experience in Ghana?

Does Computer-Based Learning Suits Children's Desire to Cont...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Amponsah, Rebecca Adwoa Doe, Martin Sebuabe, Samuel Yao Asare, Michael Tetteh Arthur, John Kingsley Damoah, Dominic Valley View University Computer Science and Engineering Department Oyibi Ghana

ISBN: (纸本)9798350376838

In Ghana's educational sector, the integration of ICT has changed traditional pedagogical ways hence introducing computer-based learning (CBL) as a powerful tool in controlling children's desire to enhance their learning experience. This paper explores the involvement of CBL with children's intrinsic motivation to manage their own experience in learning. In achieving this, purposive sampling technique was used throughout, and the results obtained was subjected to a simple descriptive statistical analysis. The results show that CBL technologies suit the ability of children to control their learning experiences with immediate feedback, motivation, memory retention, and acquisition of new and general knowledge. © 2024 IEEE.

关键词： Federated learning

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Quantification of Reliability in Object-Oriented Software: Functionality Perspective 1

Quantification of Reliability in Object-Oriented Software: F...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Yadav, Anuj Kumar Gupta, C.L.P. Bansal Institute of Engineering and Technology Department of Computer Science Lucknow India

ISBN: (纸本)9798350373042

Object-oriented design and development are now commonly used in software development environments. Important design concepts including inheritance, coupling, cohesion, and encapsulation are supported by this method. The work in progress aims at offering a functionally-based, robust estimating technique for object-oriented design. Functionality plays a critical part in producing high-quality products within predetermined time and cost limits. This paper introduces an approach for quantifying functionality and reliability, acknowledging the paramount importance of functionality during the design phase. Statistical analyses affirm functionality as a significant factor influencing software reliability. Building on the proposed mapping of object-oriented design features to functionality, a multiple regression equation has been constructed to calculate the functionality of design hierarchies. It is clear that usefulness has a favourable effect on object-oriented systems' dependability. In order to determine dependability based on functionality, an additional multiple regression equation is created. Lastly, experimental trials are used to validate the suggested model. © 2024 IEEE.

关键词： Software reliability

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Bridging the Knowledge Gap: Improving BERT models for answering MCQs by using Ontology-generated synthetic MCQA Dataset 37

Bridging the Knowledge Gap: Improving BERT models for answer...

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37th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2024

作者： Sahil, Sahil Kumar, P. Sreenivasa Department of Computer Science and Engineering Indian Institute of Technology Madras India

BERT-based models possess impressive language understanding capabilities but often lack domain-specific knowledge, limiting their performance on specialised tasks such as medical multiple-choice question answering (MCQA). In this paper, we study how biomedical ontologies, rich repositories of medical knowledge, can be harnessed to enhance BERT-based models for medical MCQA task. Our contributions include OntoMCQA-Gen, a system which leverages different biomedical ontologies to construct BioOntoMCQA, a large synthetic MCQA dataset. OntoMCQA-Gen exploits the subclass-class relationships, definitions of concepts, and also synonym relationships from the ontologies to create this dataset of MCQs automatically. We then use this synthetic dataset to fine-tune various BERT-based models to answer medical MCQs. We evaluated these fine-tuned BERT models on the challenging MedMCQA and MedQA datasets of questions from admission examinations for medical degrees in India and USA, respectively. Our evaluation study on these datasets shows that fine-tuning the BERT-based models on BioOntoMCQA results in significantly improved accuracy scores. BioBERT and PubMedBERT, pretrained on the large medical corpus, have also shown significant improvements with our technique of fine-tuning ontology-generated synthetic data. This finding highlights the effectiveness of incorporating biomedical ontologies to enhance the BERT-based model in the medical domain. Moreover, our results underscore the importance of using ontology-generated data along with model adaptation for specialised domains, contributing to a novel advancement in natural language processing. Copyright © 2024 by the authors.

关键词： Ontology

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Ovulytics: A Machine Learning Approach for Precision Diagnosis of PCOD, PCOD and Infertility 1

Ovulytics: A Machine Learning Approach for Precision Diagnos...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Jha, Tanushree Sirisha, M. Bhargavi, M.S. Bangalore Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331518943

Polycystic Ovary Syndrome (PCDS) and Polycystic Ovarian Disease (PCOD) are common endocrine disorders that affect a significant percentage of women of reproductive age, often leading to complications such as infertility. Early detection and differentiation of these conditions are important for timely intervention and effective management. This research proposes a machine learning-based solution to classify and predict the occurrence of PCDS, PCOD, and PCDS with infertility using Random Forest, Logistic Regression, and XGBoost. Each algorithm is trained and tested to predict the level of presence of PCDS, PCOD, and infertility-related PCDS. Among the models, Random Forest demonstrates superior performance. However, the importance of our work extends beyond performance metrics. This study illustrates how machine learning can be applied to complex, real-world healthcare challenges, such as early diagnosis and differentiation of overlapping conditions like PCDS and PCOD. The ability to leverage machine learning in this context aids earlier detection of these conditions, allowing for timely interventions that can have the potential to prevent further complications, such as infertility, cardiovascular diseases, and metabolic disorders. The potential impact of this research is substantial, offering scalable, non-invasive, and cost-effective diagnostic solutions, thus improving outcomes for millions of women worldwide and transforming reproductive healthcare practices. © 2024 IEEE.

关键词： Logistic regression

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Prediction of the Approval of Bank Loans Using Various Machine Learning Algorithms

Prediction of the Approval of Bank Loans Using Various Machi...

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Rahman, Arifa Tur Purno, Md Rakib Hasan Mim, Samiya Akter Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350310061

Both individuals and businesses now frequently obtain loans from banks, but because banks have limited resources, they carefully consider each applicant before approving a loan. To determine who the loan can be granted to and who would be a better choice for the bank, for this reason, credit institutions go through a protracted phase of verification and identification. This study aims to employ various machine learning techniques to analyze enormous amounts of data in order to detect patterns that could be used to predict whether a loan would be approved for a particular person or organization and determine whether the borrower can afford to repay the loan or not. Another goal of this study is demonstrating how machine learning will give the banking sector a more reliable predictive modeling tool than it currently has. In this work, the data was gathered from 1000 records of applicants with 16 features from the reputed bank in the city of Dhaka, Bangladesh. Finally, the following seven data mining techniques are employed for accuracy, and suggestions are made regarding how to make the loan approval process more effective and affordable for both banks and borrowers by dramatically lowering the time and effort needed: Decision Tree, Gradient Boosting, Random Forest, Naive Bayes, Support Vector Machine, and Logistic Regression are some examples of machine learning techniques. At the conclusion of the study, the Random Forrest method has the highest accuracy. © 2023 IEEE.

关键词： Decision trees

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Preprocessing Big Data using Partitioning Method for Efficient Analysis 1

Preprocessing Big Data using Partitioning Method for Efficie...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Reena, Melbin J Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

Big data collection is the process of gathering unprocessed and unstructured data from disparate sources. As data deluge, the large volume of data collected and integrated consist missing values, outliers, and redundant records. This makes the big dataset insignificant for processing and mining knowledge. Also, it unnecessarily consumes large amount of valuable storage for storing redundant data and meaningless data. The result obtained after applying mining techniques in this insignificant data lead to wrong inferences. This makes it inevitable to preprocess data in order to store and process big dataset effectively and draw correct inferences. When data is preprocessed before analytics the storage consumption is less and computation and communication complexity is reduced. The analytics result is of high quality and the needed time for processing is considerably reduced. Preprocessing data is inevitable for applying any analytics algorithm to obtain valuable pattern. The quality of knowledge mined from large volume of big data depends on the quality of input data used for processing. The major steps in big data preprocessing include data integration from disparate sources, missing value imputation, outlier detection and treatment, and handling redundant data. The process of integration includes steps such as extraction, transformation, and loading. The data extraction step gathers useful data used for analytics and the transformation process organize the collected data in structured format suitable for analytics. The role of load process is to store transformed data into secured storage so that data can be obtained and processed effectively in future. This work provides preprocessing techniques for big data that deals with missing values and outliers and results in obtaining quality data partitions. © 2023 IEEE.

关键词： Data Analytics

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