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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Panwar, Amisha Chauhan, Rahul Kumari, Kirti Devliyal, Swati Chanti, Yerrolla Singh, Retinderdeep Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India Chitkara University Institute of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9798350394474

The emergence of Bitcoin as a disruptive force in the business and finance space, starting an extensive examination of its origin, technology foundations, effects on trade, and where it will go from here, has set off a chain reaction. This research paper takes us through the broad range of Bitcoin landscapes, examining its past context, fundamental features, and underpinning technologies for a decentralized framework. The exploration begins with the introduction, which serves as a starting point for an in-depth analysis that explains the background, scope, and significance of the study. Before a dedicated section on Bitcoin, which discusses its roots, developmental evolution and fundamental features that distinguish it from the digital asset field, is an introductory overview of cryptocurrencies. Regulatory complexity, security risks and the risk to business in an evolving cryptocurrencies ecosystem are highlighted by a comprehensive assessment of challenges and concerns. Anticipating the future of Bitcoin occupies the eighth section, where scaling solutions like Segregated Witness (SegWit) and the Lightning Network are discussed. In addition, potential developments which might occur in the Bitcoin landscape such as integration of smart contracts and collaboration with other technologies can also be seen. This research project is intended to provide valuable insight into the current discussion about how bitcoin has fundamentally changed the business landscape and its future development. © 2024 IEEE.

关键词： Smart contract

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Efficient Breast Cancer Classification Using a Hybrid CNN-Attention-Residual Connection (CAR) Model

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SN computer science 2025年第5期6卷 1-16页

作者： Dalai, Sasanka Sekhar Sahu, Bharat Jyoti Ranjan Friday, Ibanga Kpereobong Department of Computer Science & Engineering Institute of Technical Education and Research Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar 751030 India

Breast cancer (BC) remains a ubiquitous malignancy affecting millions of women worldwide, with rising cases in the number of diagnosed cases. Advanced computer-aided diagnosis technology and deep learning (DL) methods have significantly improved early diagnosis, leading to higher survival rates and better treatment outcomes. This study proposes an efficient hybrid DL-based approach that combines a convolutional neural network (CNN), attention mechanism (AM), and residual connections (RC), termed the CAR model. The model uses CNN for custom-designed feature extraction to learn image-specific representations. The AM block focuses on specific image features, and the RC block addresses the vanishing gradient problem mapping the original extracted features and the attended weight features. The CAR model is evaluated for both binary and multi-class breast cancer classification across two benchmark datasets—BUSI and MIAS—using various color schemes (RGB, RGBA, and gray scale) and stratified cross-validation (3-CV, 5-CV, 7-CV). The results demonstrate that gray scale images consistently yield better classification performance compared to RGB and RGBA, achieving accuracies of 98.33% on BUSI and 98.90% on MIAS datasets. These findings underscore the significance of color information in medical imaging and highlight the model's ability to generalize across different cross-validation folds, with 7-CV yielding the best results. The CAR model also outperforms existing state-of-the-art models while maintaining computational efficiency, making it an efficient approach for breast cancer diagnosis. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Attention mechanism (AM) Breast cancer (BC) Convolutional neural network (CNN) Deep learning Image classification Residual connection

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Deep Learning Approaches for Feature Extraction in Big Data Analytics 10

Deep Learning Approaches for Feature Extraction in Big Data ...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Dhaygude, Amol Dattatray Varma, Raj A. Yerpude, Poonam Swarnkar, Suman Kumar Kumar Jindal, Rupesh Rabbi, Fazle University of Washington Department of Data Science Seattle United States Maharashtra Pune India Indian Institute of Technology Dept of Computer Science & Engineering Bhilai India Shri Shankaracharya Institute of Professional Management and Technology Dept of Computer Science and Engineering Raipur India National Institute of Design Dept of Computer Science & Engineering Ahmedabad India Australian Computer Society Australia

ISBN: (纸本)9798350382471

In the context of big data analytics, this study examines the use of algorithms based on deep learning for feature extraction. Traditional methods usually have trouble sifting through the complexity and volume of data to find the important elements. We investigate the application of auto encoders, transformer-based models, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) to address this problem. Our comprehensive review of existing literature compares these deep learning techniques with traditional methods and highlights their adaptability to large-scale datasets. The efficacy and precision of these methodologies are demonstrated by empirical investigations conducted on authentic datasets across a range of disciplines, including but not limited to time-series analysis, picture identification, and natural language processing. Despite challenges like computational requirements and model interpretability, our findings indicate that deep learning-based feature extraction holds significant promise for enhancing big data analytics, leading to valuable insights and discoveries in various fields. © 2023 IEEE.

关键词： Autoencoders Big Data Analytics CNN) Deep Learning Efficiency Feature Extraction Image Recognition Natural Language Processing RNNs Scalability Time-Series Analysis Real-World Datasets Transformer Models

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Machine Learning Techniques for the Prediction of Non-Communicable Diseases

Machine Learning Techniques for the Prediction of Non-Commun...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Lavanya, J.M. Sheela Subbulakshmi, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350334357

Due to the abundance of available data, it is necessary to find intriguing patterns and information. There are numerous tools available now for removing interesting information from raw data. Using machine learning to analyze data is a crucial part of using data effectively. Machine learning methods are utilized for finding patterns in massive datasets and aid decision-making. Having reliable tools for predicting diseases in their early stages is crucial. Machine learning (ML) methods are quite good at predicting diseases. Disease prediction should use machine learning to develop medical data sets. Data sets from medical records have been used in developing disease prediction systems based on machine learning techniques. This paper examines the performance of classification methodologies like Decision Tree, Logistic Regression, Naive Bayes, k-NN, Linear Regression, SVM, Random Forest, K-Means, Gradient Boosting algorithms, LightGBM, CatBoost, XGBoost, and GBM, Dimensionality Reduction Algorithms on different datasets of other diseases such as Kidney, and Ovarian Cancer. The experimental system for evaluating the performance of various algorithms utilizing disease data sets is acquired. Different classification algorithms are used to examine the values of other metrics, like successful precision, F-Measure, the time taken, recall, and classified instances. © 2023 IEEE.

关键词： Decision trees

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Machine Learning based efficient Bankruptcy Prediction Model

Machine Learning based efficient Bankruptcy Prediction Model

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Khemka, Devanshu Kaippada, Ranjeesh Nikhil, Parasu Sai Suseela, S. School of Computer Science Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350305708

Bankruptcy is a severe financial problem for companies that can lead to significant losses for stakeholders, including employees, investors, and creditors. Early bankruptcy prediction is crucial to reduce default risk and avert financial losses. Using the Taiwan Company Bankruptcy Dataset (1999-2009), we investigate the challenge of making an accurate prediction of company failure in this research article. The dataset contains financial ratios and other company characteristics, such as liquidity ratios, solvency ratios, and profitability ratios. In order to predict the likelihood of bankruptcy, this article examines the application of several machine learning techniques, such as SVM, Naive Bayes, Decision Tree and Logistic Regression. This project's primary objective is to develop an efficient and accurate bankruptcy prediction model that financial institutions can use to identify companies that are likely to default on their financial obligations. © 2023 IEEE.

关键词： Bankruptcy Decision Tree Logistic Regression Machine Learning Techniques Random Forest SVM

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HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization of Biomedical Research Articles using Seq2Seq Transformers 23

HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization...

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23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, BioNLP 2024

作者： Malik, Hemang Pradeep, Gaurav Seth, Pratinav Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Data Science & Computer Applications India Dept. of Computer Science & Engineering India

ISBN: (纸本)9798891761308

Lay summarization aims to generate summaries of technical articles for non-experts, enabling easy comprehension for a general audience. The technical language used in research often hinders effective communication of scientific knowledge, making it difficult for non-experts to understand. Automatic lay summarization can enhance access to scientific literature, promoting interdisciplinary knowledge sharing and public understanding. This has become especially important for biomedical articles, given the current global need for clear medical information. Large Language Models (LLMs) (Yao et al., 2024), with their remarkable language understanding capabilities, are ideal for abstractive summarization, helping to make complex information accessible to the public. This paper details our submissions to the BioLaySumm 2024 Shared Task: Lay Summarization of Biomedical Research Articles (Goldsack et al., 2024). We fine-tune and evaluate sequence-to-sequence models like T5 across various training dataset settings and optimization methods such as LoRA (Hu et al., 2021b) for lay summarization. Our submission achieved the 53rd position overall.. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Crime Type and Occurrence Prediction Using Machine Learning 2

Crime Type and Occurrence Prediction Using Machine Learning

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Kavya, T.M. Lavanya, T.N. Ithihas, Maddi Shetty, Namitha R. Amrutha, H.N. Hebbar, Sushruth Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India Department of P.G Studies and Research in Computer Science Kuvempu University Karnataka Shivamogga India

ISBN: (纸本)9798350383867

Nowadays, crime has become a way to get people and people into trouble. Rising crime has fueled unrest at polling stations across the country. Understanding crime patterns is necessary to identify and respond to these criminal activities. This study conducts this crime analysis using crime data obtained from the Kaggle open source which has been used to predict most crimes recently. The main aim of the project is to predict what type of crime is most likely to occur and when and where the crime occurs. This project introduces some machine learning like KNN,SVM to detect various crimes and the accuracy achieved is higher than previous ones. © 2024 IEEE.

关键词： Prediction models

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Hospital Data Security using Blockchain and AI 2

Hospital Data Security using Blockchain and AI

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Peneti, Subhashini Parvathi, S. Rao, Koppula Srinivas Mlr Institute of Technology Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350325799

Data is spread vastly over the web. It is a collection of facts and statistic together used for data analysis, mining etc. Data is additionally used as input for numerous computing algorithms to mine out necessary features. Since information is spread all over the web, it rises a lot of trust issues between various organizations. This questions the security of the data on the web and it's hard to create a cyberspace for this huge amount of data. Providing data security is extremely important in day to day life because there exists a lot of sensitive information, in case it falls into the wrong hands it can cause a lot of problems. Data security is of utmost importance in almost every field like army, hospitals, government offices, colleges, schools etc. In simple words, the process of protecting data from unauthorized access and data corruption is termed as data security. In order to achieve this security, key management, data encryption, hashing and tokenization techniques can be used. Data plays a huge role in any business so it is important to protect your data with the increase in rate of data breach happening. Data security follows the CIA principle. Confidentiality only provides access to authorized users. This can be achieved by implementing access control lists, encryption, strong passwords and two factor authentication. Integrity implies the protection of data from any sort of unauthorized modifications that can disrupt the original data. Digital security can be used to implement integrity. Availability completely focuses on ensuring that all the software and security controls work properly without any interruption. Since the blockchain is a secure chain of individual blocks of transactions there cannot be a chance of hacking or modification of the blocks after it is registered once. Blockchain also provides decentralized identity and other privacy mechanisms. Whereas artificial intelligence helps in enhancing the user security, providing better analytical

关键词： Blockchain

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Offline Recognition Of Handwritten Text Using Combination Of Neural Networks 8

Offline Recognition Of Handwritten Text Using Combination Of...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Maheswari, B. Subha, S. Tulasi Malini, V.K. Ramanan P, Venkada Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396638

Recognition of handwriting is a critical challenge in artificial intelligence having implications for numerous areas such as document analysis, optical character recognition, including the processing of natural languages. In this study, a novel deep neural network technique for offline handwritten textual data recognition is described. Due to the volume of data available today and the numerous algorithmic improvements ongoing, it is now simpler to construct deep neural networks. Recent advancements in GPU accessibility and numerous cloud-based solutions like AWS or Amazon Web Services, as well as Google's cloud computing platform that offer resources to do so have increased the processing power that is required to simulate neural networks. It has shown great promise to achieve high accuracy in handwriting recognition tasks by incorporating a few deep learning approaches, including Region-based Convolutional Neural Networks (RCNN), Convolutional Neural Networks (CNN) along with Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. The Connectionist Temporal Classification (CTC) method is also frequently used to train deep learning models for sequence identification and loss estimation. © 2023 IEEE.

关键词： Long short-term memory

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Credit Card Fraud Detection and Analysis 14

Credit Card Fraud Detection and Analysis

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pawar, Rohan Kathuria, Hardik Praveen Joe, I.R. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

In today's digital world, cybersecurity plays a crucial part. Whenever someone talks about security in the digital world, the main task is to recognize abnormal activity. Whenever someone makes any transaction online, a high percentage of people prefer credit cards. The credit limits we set helps the user make purchases even if they can't afford the product at the current moment. These features can be misused by cyber *** is necessary for credit card providers to identify fraudulent transactions so that customers are not charged unnecessarily. This paper will provide an approach using machine learning algorithms to detect anomalous transactions. © 2023 IEEE.

关键词： Logistic regression

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