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Credit Card Fraud Identification with ANN and LSTM based models

Credit Card Fraud Identification with ANN and LSTM based mod...

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2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025

作者： Manda, Bala Sumanth Reddy Guruprasad, M.K. Sriram, M.Bhargava Phani Srinivas, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept.Computer Science Amaravati India

ISBN: (纸本)9798331527518

The past few years have seen a sharp escalation in the most concerning and widespread type of credit card theft, which has resulted in significant losses. [1] At the same time, security and authentication technology developments are encouraging because fraudsters are using more advanced methods, including card cloning, phishing, skimming, and data breaches. Among the above, many models Common methods such as LR, SVMs, Forest-based algorithms are also utilized, alongside LightGBM are applied for the purpose of fraud prediction. However, due to their respective deficiencies in handling huge datasets, abstracting hierarchical representations, and engineering new features, most of the above-mentioned models could not fare any better than the accuracy level of deep learning models like ANN. It is well known that ANN-based models require a significant amount of training over larger datasets in order to provide the best results, even if they have the added benefit of improving accuracy with improved performance. Since deep learning methods improve detection while adapting to evolving fraud patterns, the work incorporates them into the Credit card scam detection system. Within the end, The research presented here findings demonstrate that the fight against credit card fraud will need to include ongoing advancements in algorithms and data gathering techniques. The obtained results reveal that the classifiers perform more effective in the processed data than in the original data. From all the algorithms, the ANN-LSTM model presented the best results, attaining an accuracy, precision, recall, specificity and f1-score having values of 98%, 97.69%, 97.75%, 97.69% and 97.72% respectively. © 2025 IEEE.

关键词： Precision engineering

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Cluster Based Classification of Question Independent C Codes 8th

Cluster Based Classification of Question Independent C Codes

引用

8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Vijjapu, Aditya Meti, Aditya H. Rao, Aniruddh Balakrishnan, Roshni M. Pati, Peeta Basa Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031736162

In the field of software development, ensuring the accuracy and quality of code remains a paramount concern. The task of precisely classifying code as correct or incorrect poses inherent challenges. This research introduces a groundbreaking approach that uses clusters constructed from code embeddings generated by CodeBERT to effectively classify code into distinct clusters representing correctness or incorrectness. The model’s ability to grasp intricacies in code semantics and structure leads to a significant reduction in debugging efforts. Consequently, this approach contributes to an overall increase in the reliability and robustness of software systems. CATBOOST algorithm consistently demonstrated high performance with an accuracy of 84% during the experimentation. © IFIP International Federation for Information Processing 2025.

关键词： Software reliability

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Empirical Study on Efficiency of Different Language Modeling Techniques using Masking of Named Entities for Indic Languages 3

Empirical Study on Efficiency of Different Language Modeling...

引用

3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Reddy, Sravan Kumar Sheshadri, Shailashree K Avatapalli, Krishna Likith Gupta, Deepa Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Processing unstructured text in Natural Language Processing (NLP) poses significant challenges for Indic languages, which feature flexible word order, spelling variations, and complex sentence structures. Traditional models often struggle with these complexities, leading to issues such as out-of-vocabulary (OOV) words and increased perplexity. Neural Language Models (NLMs), particularly transformer-based models, address some of these challenges by employing word representations and self-attention mechanisms. However, OOV problems persist, especially with named entities, which are dynamic and vary across domains, making it difficult to create comprehensive lists of names for people, organizations, and locations. To address this, the Masked Entity-Based Language Model (ME-LM) has been introduced, focusing on masking named entities identified through Named Entity Recognition (NER) using pre-trained models like BERT-base-NER and IndicNER. Applied to Indic languages such as Hindi, Kannada, and Telugu for the first time, ME-LM has significantly reduced OOV occurrences by 18.60% to 94.70% and lowered perplexity. Since this is the first application of ME-LM to these languages, no standard benchmark exists for direct comparison, but the results show strong potential for improving named entity handling in these languages. © 2025 The Author(s).

关键词： Convolutional neural networks

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Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud 31

Building a Family of Data Augmentation Models for Low-cost L...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Yue, Yuanhao Wang, Chengyu Huang, Jun Wang, Peng School of Computer Science Fudan University Shanghai China Alibaba Cloud Computing Hangzhou China

ISBN: (纸本)9798891761971

Specializing LLMs in various domain-specific tasks has emerged as a critical step towards achieving high performance. However, the construction and annotation of datasets in specific domains are always very costly. Apart from using superior and expensive closed-source LLM APIs to construct datasets, some open-source models have become strong enough to handle dataset construction in many scenarios. Thus, we present a family of data augmentation models designed to significantly improve the efficiency for model fine-tuning. These models, trained based on sufficiently small LLMs, support key functionalities with low inference costs: instruction expansion, instruction refinement, and instruction-response pair expansion. To fulfill this goal, we first construct an automatic data collection system with seed datasets generated from both public repositories and our in-house datasets. This system leverages powerful LLMs to expand, refine and re-write the instructions and responses, incorporating quality assessment techniques. Following this, we introduce the training process of our models, which effectively distills task-solving and text synthesis abilities from teacher LLMs. Finally, we demonstrate how we integrate these functionalities into a machine learning platform to support low-cost LLM fine-tuning from both dataset preparation and training perspectives for users. Experiments and an application study prove the effectiveness of our approach. ©2025 Association for Computational Linguistics.

关键词： Computational linguistics

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MRI-Based Brain Tumour Detection and Classification Using Random Forest Algorithm 3rd

MRI-Based Brain Tumour Detection and Classification Using Ra...

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3rd International Conference on Intelligent Systems and Sustainable computing, ICISSC 2023

作者： Jareena Begum, D. Chokkalingam, S.P. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819783540

A brain tumour develops when cells in the brain multiply abnormally and out of control. The possibility of fatality makes this growth dangerous. The brain regulates everything from memory to vision to emotions. Recognizing these tumours is a challenging and complex task due to their location, size, and shape. There have been a number of successful attempts to enhance detection. However, the current level of precision is insufficient. This study details a method with a high likelihood of success in identifying brain cancers. The approach is implemented in Python LAB by use of an image segmentation technique and a classifier. One such classifier is the Random Forest Algorithm. The Principal Component Analysis (PCA) and the Discrete Wavelet Transform (DWT) are also employed. The created method is put to the test using a dataset available on the Kaggle platform. The acquired results were approximately 97.68% accurate. A confusion matrix and a comparison of the developed method to other research in the literature are included in the investigation to facilitate the implementation of the proposed strategy in image testing. This Research shows that the offered system is superior to others in terms of detection rate, false positive rate, and recall. Results from this study showed that the developed method was effective at detecting brain tumours due to its high levels of accuracy, precision, and recall. According to the proposed strategy, it is essential to provide professionals who diagnose brain tumours with access to such technology. © The Author(s) 2025.

关键词： Image segmentation

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Network-Centric Malware Detection Using Dynamic Packet Analysis and Hybrid Architectures 1

Network-Centric Malware Detection Using Dynamic Packet Analy...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Raj, B.U. Naveen Utpal Raj, K.B. Srujan, V. Rajagopal, Shinu Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529246

The rapid evolution of malware necessitates advanced detection methodologies to address the limitations of traditional signature-based approaches, which often fail against zero-day malware and polymorphic attacks. This study introduces a dynamic packet analysis framework, leveraging fine-grained network features such as packet length and time-to-live (TTL) for real-time detection. A hybrid architecture, combining Gradient Boosting and Random Forest models, improves detection capabilities to 95.7% with robustness against adversarial attacks. The model's effectiveness in analyzing control flow traces outperforms traditional methods by 26.3% in detecting obfuscated malware and maintains an 83% success rate against adversarial threats. This approach not only sets a new benchmark in adapting to evolving cybersecurity challenges but also provides scalable and resilient malware detection. © 2025 IEEE.

关键词： Zero-day attack

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Small Waste Detection on Land Using fine-tuned YOLOv9 in Unmanned Aerial Vehicle Images 3

Small Waste Detection on Land Using fine-tuned YOLOv9 in Unm...

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3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Sruthi, S. Shreya Shree, S. Katariya, Priyanka Prakash Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Trash collection in inaccessible areas can be labor-intensive and requires excessive resources. Using Unmanned Aerial Vehicles (UAVs) to gather high-resolution imagery for automated environmental monitoring is one such approach that has shown a lot of potential in this arena. However, due to the significant variation in their attributes and the background scenarios, it becomes a challenging task to discover and classify small-sized waste materials that are also highly variable in UAV photos. In this study, deep learning-based different You Only Look Once (YOLO) models have been exploited to enhance UAV-based small object trash detection. A crucial contribution of the research is the evaluation of different novel and state-of-the-art activation functions aiming to address common limitations of legacy ReLU and sigmoid functions along with the effect of data augmentation on the model's performance. The best model proven to be efficient at detecting trash on land after applying Data Augmentation is YOLOv9 with Swish activation function. It achieved the best mean Average Precision (mAP) of 71% approximately which was increased by 4% Furthermore, it is found that the best-performing activation function is Swish due to its model sensitivity and accuracy. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Drones

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Animal Detection and Classification: a Similarity Measure in Realtime Videos 2

Animal Detection and Classification: a Similarity Measure in...

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Sudharshan Duth, P. Tharun, H.P. Srikanta Swamy, N. Bhat, Prajwal Nagaraj Suresh, Archith Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Mysuru India

ISBN: (纸本)9798350367461

Real-time video animal identification has several practical uses in areas including agriculture, security, and wildlife preservation. In this study, we present a method to identify animals in movies shot in their actual habitats in real time. The purpose of this work is to create a YOLO (You Only Look Once) algorithm-based system for real-time animal recognition and segmentation. The technology will examine footage gathered by cameras placed in natural settings in order to identify and isolate individual animals. The YOLO algorithm is a real-time, high-accuracy object recognition system built on deep learning. To recognize and segment animals, the system will use YOLO models that have already been trained and then fine-tune them through transfer learning. Instant segmentation is utilized for pixel-level animal localization and segmentation in images and videos. To help distinguish between distinct species, we have also developed a similarity metric. In order to examine animal properties including color, texture, and form, similarity measures are distance-based (cosine, Jaccard), and feature extraction (image hash) techniques are used for similarity-based image retrieval. © 2024 IEEE.

关键词： Invertebrates

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Loan Approval Prediction Using Credit Card Score Analysis 5th

Loan Approval Prediction Using Credit Card Score Analysis

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Sri Sai Samhitha, J. Sagar, K. Adarsh Haritha, Kundula Dinesh Kumar, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789819788644

Banking sector offers a large variety of financial services out of which the significant amenities include payment services, custodial and safety support, loan application assistance and credit card options. Machine Learning and Data science has a vast application in the field of Banking sector. These domains play significant role in the field of loan prediction which helps in fraud detection, risk management system, maintaining efficiency, transparency and helping in continuous improvement. Due to the increasing rate of loan defaults, it’s a difficult task for the banking authorities to assess the loan requests and deal with the risks of customers defaulting the loan. An automatic loan prediction system is developed using machine learning models where the machine learns and predicts whether to approve the loan or not based on the eligibility criteria. Six prediction models including Logistic Regression Model, Decision tree, Random Forest, Support Vector Machine, K-Nearest Neighbor Classifier and XG-Boost algorithm have been performed. A comparative analysis is carried out between the models where the performance metrics for each model is calculated and compared based on which the best suitable model for predicting the loan approval status is concluded. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Risk management

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Evolving GAN-BERT Architecture for Efficient Text Categorization with Minimal Labeled Data 8th

Evolving GAN-BERT Architecture for Efficient Text Categoriza...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Bagavathi, C. Raakesh, M. Bhanu, Chanamala Vipasyana Reddy, Katheram Venkata Sai Ram Sathwik, Kancharla Rupesh Sai Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783031736162

Categorical text generation is an upcoming field within natural language processing focusing on producing text tailored to specific categories or domains. The goal of categorical text production is to produce text that complies with the requirements and characteristics of the chosen category while also adhering to the grammatical and structural conventions of the language. Text classification works across multiple data categories, and the synthetic data generated usually adheres to a preset pattern and ignores the text’s nuanced emotional content. The proposed approach begins with using BERT to get the text’s deep contextual embeddings. Using these text embeddings, the underlying semantics of the target category can be better understood by adding a task-specific layer that places the sentences in the closest corresponding class. After that, the architecture and settings of the GAN are optimized through evolutionary computing techniques to guarantee that the generated text conforms to the constraints and characteristics specific to each category. © IFIP International Federation for Information Processing 2025.

关键词： Generative adversarial networks

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