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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Abhijna, M.B. Akul, P. Kodipalli, Ashwini Global Academy of Technology Dept. of Information Science and Engineering India Global Academy of Technology Dept. of Computer Science and Engineering India Global Academy of Technology Dept. of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350364866

Trigeminal Neuralgia (TN) is a debilitating chronic pain disorder that significantly diminishes overall well-being, making diagnosis and therapy more challenging. The quick and precise categorization of TN severity is critical to therapy effectiveness. To assess the severity of TN, this study makes use of advanced machine learning techniques and explainable AI (XAI) approaches. We use a range of bagging and boosting strategies, including AdaBoost, Random Forest, Decision Tree, K-Nearest Neighbours (KNN), Gradient Boosting, and Logistic Regression. Random Forest was 95.2% accurate, whereas AdaBoost and Gradient Boosting were 92% and 97.2% in terms of their accuracy. The Decision Tree classifier obtained an accuracy of 84.62%, while K-Nearest Neighbours and Logistic Regression also achieved 92.31% accuracy. F1- score, precision, and recall metrics were used to assess performance on a dataset that included patient demographics and medical histories. XAI techniques, such as LIME and SHAP values, were used to improve the models' readability and transparency, which helped to progress the creation of cutting-edge diagnostic tools and increase clinician and researcher confidence. © 2024 IEEE.

关键词： Logistic regression

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Chinese Grammatical Error Correction via Large Language Model Guided Optimization Training 23rd

Chinese Grammatical Error Correction via Large Language Mod...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Liu, Xiao Li, Ying Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Artificial Intelligence Kunming China

ISBN: (纸本)9789819783663

Pre-trained language model-based methods for Chinese Grammatical Error Correction (CGEC) are categorized into Seq2Seq and Seq2Edit types. However, both Seq2Seq and Seq2Edit models depend on high-quality training data significantly. Considering the strong generation and inference ability of large language models (LLMs), we propose a large language model-guided optimization training method to exploit LLMs to extract error knowledge to optimize the traditional CGEC model training process. On the one hand, we use error types and confusion sets as extra knowledge to guide LLMs to generate diverse pseudo data, thus extending the error distribution of our training data. On the other hand, LLMs are utilized to infer the predicted results from our CGEC models and obtain the re-training data, thus iteratively optimizing our pre-trained CGEC models. Experiments on two benchmark datasets show that our LLMs-guided optimization method with small-scale training data can achieve comparable results with baseline models with large-scale training data. Detailed comparison experiments demonstrate that both the early deviser pseudo data and the later re-training data are extremely useful for traditional CGEC model optimization training, and can benefit from each other. We will release our code and prompts at https://***/SakuraAcedia/llm-cgec-got to facilitate future work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Error correction

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Smart Finance: An overview of artificial intelligence Integration in Fintech 2

Smart Finance: An overview of Artificial Intelligence Integr...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Akre, Parth Dhananjay Pacharaney, Utkarsha Siraskar, Warsha Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Yeshwantrao Chavan College of Engineering Maharashtra Nagpur441110 India

ISBN: (纸本)9798331528713

The role of FinTech in advancing contemporary economies, societies, and technologies is growing. Smart FinTech is the next wave of FinTech that leverages artificial intelligence and data science methodologies to drive major advancements in fields including blockchain, cryptocurrencies, InsurTech, PayTech, BankingTech, TradeTech, LendTech, InsurTech, WealthTech, and RiskTech. An overview of these domains, as well as the artificial intelligence and data science techniques applied, is given in this paper. These techniques include augmentation, optimization, privacy-preserving processing, deep learning, federated learning, and intelligent interactions, complex system methods, mathematical approaches, knowledgeable communication, recognition and responses, and data analytics. This study offers a thorough analysis of smart financial companies, the obstacles they face, the smart FinTech environment, the artificial intelligence and data science methods that make smart FinTech possible, and potential future research areas for the artificial intelligence and data science community. To improve comprehension and analysis, the work makes use of visual aids such as figures and tables. The relevance of data quality, including data augmentation and manipulation, as well as enhancing fairness and decision-support, is emphasized in the study for financial firms. It also covers ethical and explicable FinTech, with a focus on privacy preservation, accountability, openness, and objectivity in FinTech and business designs. The potential for more research and innovation at the heart of analytics, data science, and FinTech is highlighted in the paper's conclusion. © 2024 IEEE.

关键词： Federated learning

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Distributed Ledger Credential Verification System Based on Design Thinking Approach 4

Distributed Ledger Credential Verification System Based on D...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Revathi, K. Priyadharshini, S. Rahul, R. Gnana Vignesh, K. Anantha Varshini, J. Sriram, V. SNS College of Engineering Department of Information Technology Tamilnadu Coimbatore India SNS College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385793

This research study introduces a blockchain-driven solution to revolutionize college credential verification. Addressing the inefficiencies and vulnerabilities of traditional verification methods, the proposed system utilizes blockchain's immutable ledger, smart contracts, and machine learning automation to ensure secure, transparent, and decentralized verification. Results demonstrate its efficacy in fraud prevention, streamlining processes, and providing a reliable, auditable verification platform. This research signifies a transformative shift towards a trustworthy, user-centric credential verification process with implications for students, institutions, and employers seeking streamlined, secure verification of academic records. © 2024 IEEE.

关键词： Distributed ledger

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Secured Banking System Using Decentralized Approach – Block Chain 23th

Secured Banking System Using Decentralized Approach – Block...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Sathyabama, A.R. Katiravan, Jeevaa Suganya, P. V. Raja Information Technology Velammal Engineering College ChennaiTamil Nadu India Artificial Intelligence and Data Science Velammal Engineering College ChennaiTamil Nadu India

ISBN: (纸本)9783031646492

The core functions of centralized payment systems continued to function substantially even after financial transactions were converted from paper to digital records. Nonetheless, the Indian banking sector is currently facing difficulties in upholding openness, increased susceptibility to fraudulent assaults on centralized infrastructure, and growing operational expenses. Because of its decentralization and safety, block chain technology has become a promising game-changer for the financial services industry. One significant development in the financial sector is Near Wallet, a decentralized wallet based on the NEAR Protocol. Near Wallet has an advantage over conventional centralized wallets, including better scalability, cost-effectiveness, transparency, and security. y giving users easy access to fees, terms and conditions, and account balances, Near Wallet promotes transparency in the banking industry. Block chain technology brings a new decentralized structure to payment systems by removing the need for middlemen like banks and using cryptography as its foundation instead. This change could save financial institutions’ operating expenses while also bringing more security and transparency. Parcel, which is referred to as a zero-configuration web build tool, helps to automate the production build optimization process. This covers things like content hashing, automatic code splitting, resizing and optimizing images, and minifying HTML, CSS, and JavaScript. Parcel also optimizes the final build size by implementing tree-shaking. In general, web development processes are made more efficient and streamlined by the use of tools like Parcel. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Transparency

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DocBot Connect: A Conversational Search System Powered by Large Language Models

DocBot Connect: A Conversational Search System Powered by La...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Patil, Ratna Buchkul, Sandesh Ashok Mohite, Ritesh Sanjay Borase, Jayesh Samadhan Khandelwal, Anuj Rawandale, Shitalkumar Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune India Primpri Chinchwad College of Engineering Department of Mechanical Engineering Pune India

ISBN: (纸本)9798350378092

DocBot Connect revolutionizes document retrieval by enabling users to have a natural conversation to search through multiple PDFs. This user-centric system leverages the power of large language models (LLMs) and cutting-edge information retrieval *** Connect boasts a user-friendly interface where users can upload their PDFs and ask questions in plain English. To unlock the deeper meaning within these documents, the system utilizes Google PaLM embeddings. These embeddings capture the essence of the text, allowing for a richer understanding. A critical step involves text segmentation, which meticulously divides the document content into manageable chunks. These segmented portions are then processed by Google PaLM, resulting in a robust embedded representation. This embedded representation forms the foundation for efficient retrieval using a FAISS vector store, enabling lightning-fast information retrieval. To ensure a seamless and contextually aware conversation, DocBot Connect incorporates a ConversationBufferMemory component. This component meticulously stores the conversation history, allowing the system to maintain a coherent dialogue flow. Finally, a ConversationalRetrievalChain, powered by the formidable capabilities of Google PaLM, retrieves relevant information from the vector store. This retrieval process is not solely driven by the user's query but also meticulously considers the established conversational context. DocBot Connect transcends traditional document retrieval systems, offering an intuitive and efficient gateway to access and comprehend information within a multitude of PDFs. This user-friendly approach fosters a more interactive and engaging experience for those navigating the ever-expanding world of document exploration. © 2024 IEEE.

关键词： Gateways (computer networks)

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Blockchain-based Cryptocurrency Security Analysis technology - A Review 7

Blockchain-based Cryptocurrency Security Analysis Technology...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Thakre, Gayatri Saratkar, Saniya Raut, Rohini Chaudhari, Aarti Thute, Trupti Verma, Prateek Datta Meghe Institute of Higher Education And Research Faculty of Engineering And Technology Department of Artificial Intelligence And Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education And Research Faculty of Engineering And Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798350359299

A class of digital currencies known as cryptocurrency is protected by decentralized computer networks for the most part. When Bitcoin was first made available as software that is open-source in 2009, it was the first cryptocurrency. The primary element preserving cryptocurrency security is the Application of block-chain technology. This article examines the security and stability of cryptocurrencies as well as their technological implementation. It also looks at how blockchain technology improves security and its platforms, supporting its findings with empirical case studies. In 2023, social engineering schemes and technological flaws like 51% attacks and faulty smart contracts posed a dual danger to block-chain security for cryptocurrencies. The current study examines how inadequate and underdeveloped the security support is for block-chain technology platforms. Additionally, they look into it and discover that there are significant financial, time, and human resource advantages for the top ten platforms in addition to playing vital roles in providing security assistance. These platforms also provide block-chain users with advantages when choosing consensus algorithms and access to computational resources. Second, cryptocurrency security is guaranteed by encryption. Digital signatures serve to verify the signatory's identity and the transaction on the one hand. However, ownership is confirmed by the hash algorithm's (SHA256) basic idea. These are the basic components with a wide range of applications. Their primary areas of use are digital signatures, message authentication, certificate verification, and security of passwords. © 2024 IEEE.

关键词： Authentication

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Dual Attention and Question Categorization-Based Visual Question Answering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2023年第1期4卷 81-91页

作者： Mishra, Aakansha Anand, Ashish Guha, Prithwijit Department of Computer Science & Engineering Guwahati781039 India Department of Electronics and Electrical Engineering Guwahati781039 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Guwahati Guwahati781039 India

Visual question answering (VQA) aims at predicting an answer to a natural language question associated with an image. This work focuses on two important issues pertaining to VQA, which is a complex multimodal AI task: First, the task of answer prediction in a large output answer space, and second, to obtain enriched representation through cross-modality interactions. This work aims to address these two issues by proposing a dual attention (DA) and question categorization (QC)-based visual question answering model (DAQC-VQA). DAQC-VQA has three main network modules: First, a novel dual attention mechanism that helps toward the objective of obtaining an enriched cross-domain representation of the two modalities;second, a question classifier subsystem for identifying input (natural language) question category. The second module of question categorizer helps in reducing the answer search space;and third, a subsystem for predicting answer depending on the question category. All component networks of DAQC-VQA are trained in an end-to-end manner with a joint loss function. The performance of DAQC-VQA is evaluated on two widely used VQA datasets, viz., TDIUC and VQA2.0. Experimental results demonstrate competitive performance of DAQC-VQA against the recent state-of-the-art VQA models. An ablation analysis indicates that the enriched representation obtained using the proposed dual-attention mechanism helps improve performance. © 2022 IEEE.

关键词： Forecasting

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PolyLite: A Lightweight Transformer With Multi-Scale Attention for Colorectal Polyp Segmentation 15

PolyLite: A Lightweight Transformer With Multi-Scale Attenti...

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15th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2023

作者： Elkarazle, Khaled Raman, Valliappan Chua, Caslon Then, Patrick Faculty of Engineering Sarawak Malaysia Coimbatore Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu India University of Technology Department of Computer Science and Software Engineering Swinburne Melbourne Australia

ISBN: (纸本)9798350316551

The urgent need for early detection and intervention in colorectal cancer highlights the importance of automating colorectal polyp detection. Despite the use of deep learning techniques for polyp detection from medical images, these methods often struggle with issues such as skewed data distribution and class imbalances. In this study, we introduce PolyLite, a lightweight transformer-based model designed to balance performance and efficiency. PolyLite features a custom SegFormer encoder that takes advantage of its lightweight design, and a decoder that uses a multi-scale attention network (MA-NET) to achieve an optimal balance between accuracy and efficiency. To improve accuracy and reduce false negatives, we incorporate a reverse attention layer that effectively counteracts input mask attention during training. This refocuses the model's efforts to better learn non-polyp regions. PolyLite undergoes a two-stage training process, beginning with pre-training on the ImageNet dataset and then fine-tuning on the Kvasir-SEG dataset. We conduct rigorous evaluations on three complex datasets: CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB. Empirical results demonstrate PolyLite's superiority over existing techniques, as evidenced by its performance in terms of Intersection over Union and Dice scores. This validates its effectiveness and scalability in polyp segmentation, making complex and computationally intensive architectures unnecessary. © 2023 IEEE.

关键词： Efficiency

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A Review of Social Media Sentiment Analysis Using Machine Learning To Enhance Stock Market Prediction 2

A Review of Social Media Sentiment Analysis Using Machine Le...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Raut, Rohini Saratkar, Saniya Thakre, Gayatri Thute, Trupti Chaudhari, Aarti Gourshettiwar, Palash Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Predicting stock prices is a critical financial activity that is frequently impacted by sentiment analysis, machine learning, and data mining approaches. This examination looks at a variety of stock price prediction methods, including artificial neural networks, graph-based approaches, time series analysis, and classic machine learning techniques such as Support Vector Machine, K-Nearest Neighbor, and Random Forest. The article assesses the problems and efficacy of various methodologies, including a thorough examination of sentiment analysis in social media to enhance stock market predictions. By examining multiple research, this study provides a complete evaluation of various prediction algorithms and reveals their shortcomings, such as overfitting, non-stationary data, and the complexity of financial transactions. It helps academics and practitioners construct more accurate and dependable stock forecasting models by merging financial data with sentiment analysis. The study also discusses rising trends and new potential for further study, especially hybrid models and advanced deep learning approaches to improve the accuracy of stock forecasts. © 2024 IEEE.

关键词： Prediction models

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