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Dynamic Approach to the Analysis of Financial Structure: Overcoming the Bank-Based vs Market-Based Dichotomy 16

Dynamic Approach to the Analysis of Financial Structure: Ove...

引用

16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Krinichansky, Konstantin Grineva, Natalia Financial University under the Government of the Russian Federation Institute of Financial Studies Department of Financial Markets and Financial Engineering Moscow Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350337907

A conceptual approach to the typology of countries' financial development has been developed, which will allow government authorities to make more accurate macroeconomic forecasts and apply a strategic approach to economic development planning. Modeling was conducted to analyze the impact of macroeconomic indicators, dummy variables of economic crises, and the technological component variable on indicators of financial development and variables characterizing the structure of the financial system. © 2023 IEEE.

关键词： Commerce

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Improving the Quality of Diabetic data with Large Language Model-driven Cleaning Techniques

Improving the Quality of Diabetic Data with Large Language M...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Biradar, Divya Dattangire, Rahul Vaidya, Ruchika Inti, NagaSuryaShivani University of Texas at Arlington Computer Science ArlingtonTX76013 United States Data Engineering HoustonTX77002 United States Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Wardha442001 India University of Texas at Arlington Computer and Information Science ArlingtonTX76013 United States

ISBN: (纸本)9798331539948

The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by looking up and dealing with the inaccuracies encountered during the project like missing or duplicated records, outliers, and data that will not add any value to the project. The 'Diabetes' data set from Kaggle is divided into implementing the combined approach data-cleaning process such as removing duplicate entries, and emptying fields by a Large Language Model, to improve the accuracy is assessed. Three components will increase the reliability of the collected data: 1. Gateway, 2. Storage 3. Cleaning. The outcomes demonstrated the LLMbased approach's effective impact in correcting and consistent data within the healthcare dataset. © 2024 IEEE.

关键词： Electronic health record

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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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machine learning-Based Security for Cloud Computing Challenges and Implications 3

Machine Learning-Based Security for Cloud Computing Challeng...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Dattangire, Rahul Burle, Rushikesh Biradar, Divya Dewangan, Leelkanth Data Engineering Independent Researcher HoustonTX77002 United States Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Wardha442001 India University of Texas at Arlington Computer Science ArlingtonTX76013 United States G H Raisoni College of Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350364569

Cloud computing"is a computer model that provides end users with quantifiable, scalable, and on-demand services. These days, almost every organization uses computer technology extensively for infrastructure, cost-cutting, advanced platforms, data processing, data analytics, and other uses. End users can use any online application from anywhere to access the cloud service providers' (CSP) services. It is imperative to guarantee the security of cloud infrastructure, and a multitude of research employing various technologies is used to provide more accurate and potent defenses against cloud threats. machine learning has shown to be a more useful technology in recent years for protecting cloud infrastructures. machine learning algorithms are trained to generate models that mechanize the process of recognizing cloud threats more precisely than any other technology by using a range of actual datasets as training grounds. A couple of the most recent research that has worked on machine learning to defend against cloud attacks are examined in this paper. © 2024 IEEE.

关键词： Adversarial machine learning

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Structured multi-track accompaniment arrangement via style prior modelling 24

Structured multi-track accompaniment arrangement via style p...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jingwei Zhao Gus Xia Ziyu Wang Ye Wang Institute of Data Science NUS and Integrative Sciences and Engineering Programme NUS Graduate School Machine Learning Department MBZUAI and Computer Science Department NYU Shanghai Computer Science Department NYU Shanghai and Machine Learning Department MBZUAI School of Computing NUS and Institute of Data Science NUS and Integrative Sciences and Engineering Programme NUS Graduate School

ISBN: (纸本)9798331314385

In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges. Such challenges include maintaining track cohesion, ensuring long-term coherence, and optimizing computational efficiency. In this paper, we introduce a novel system that leverages prior modelling over disentangled style factors to address these challenges. Our method presents a two-stage process: initially, a piano arrangement is derived from the lead sheet by retrieving piano texture styles; subsequently, a multi-track orchestration is generated by infusing orchestral function styles into the piano arrangement. Our key design is the use of vector quantization and a unique multi-stream Transformer to model the long-term flow of the orchestration style, which enables flexible, controllable, and structured music generation. Experiments show that by factorizing the arrangement task into interpretable sub-stages, our approach enhances generative capacity while improving efficiency. Additionally, our system supports a variety of music genres and provides style control at different composition hierarchies. We further show that our system achieves superior coherence, structure, and overall arrangement quality compared to existing baselines.

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Lung Cancer Classification Using CNN with data Augmentation

Lung Cancer Classification Using CNN with Data Augmentation

引用

IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： V Karunakaran N Akshaya B Harismita S Atchaya Artificial Intelligence and Machine Learning Sri Eshwar College of Engineering Coimbatore India Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Lung cancer remains a leading cause of cancer-related deaths globally. This study investigates the application of Convolutional Neural Networks (CNNs) for the classification of lung cancer using the IQ-OTH/NCCD lung cancer dataset. The study compares the performance of CNN models with and without data augmentation techniques. Experimental results demonstrate that CNN models with data augmentation exhibit superior performance, achieving higher accuracy, precision, F1-score, and recall compared to models trained without data augmentation. These findings highlight the importance of data augmentation in improving the accuracy and robustness of deep learning models for lung cancer classification, ultimately contributing to early detection and improved patient outcomes.

关键词： Measurement Accuracy Lung cancer data augmentation data models Robustness Convolutional neural networks Intelligent systems Standards Medical diagnostic imaging

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Handwritten Signature Verification using CNN

Handwritten Signature Verification using CNN

引用

Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Rutuja Nitin Lanjewar Radha Wasudeo Wande Swapnil Gundewar Faculty of Engineering and Technology Artificial Intelligence and Data Science Sawangi Maharashtra India Faculty of Engineering and Technology Artificial Intelligence and Machine Learning Sawangi Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

A signature is a special kind of a sign made or given by a person on an instrument or document to indicate that he or she fully understands everything written on the document, agrees with them or being bound to fulfill the obligations stated on the document. It is used to verify the genuineness of the writing, to indicate origin and to constrain the signer to the provisions thus provided in the writing. Thus, the field of signature verification is not only valuable for the sphere of commercial banking, used for tools against frauds and approval of transactions, but also for a lot of other fields. Employment of this method is widely seen in legal, financial and governmental sectors to avoid forgery of numerous contracts agreements, legal instruments and other related documents. This paper concentrates on the offline handwritten signature verification using CNN, an advanced deep learning architecture which is famous for its capability to work on image data. CNNs are more appropriate for this task concerning the following characteristics: they are capable to draw out significant features from the province of the images of signatures, and the features like the strokes, texture, and general structure of signatures and so on with the high classification accuracy of genuine and forged signatures. This approach makes the signature verification more efficient and accurate and provides a better and automated solution for many applications for which signature authentication is most essential.

关键词： Deep learning Handwriting recognition Accuracy Recurrent neural networks Instruments Writing Real-time systems Convolutional neural networks Optimization Residual neural networks

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A Review on Web data Mining to Detect Online Spread of Terrorism Activity 2

A Review on Web Data Mining to Detect Online Spread of Terro...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Jha, Saurav Kumar Pacharaney, Utkarsha Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine learning Maharashtra Wardha India

ISBN: (纸本)9798331505745

This research study outlines the significant contribution that web data mining can make in discovering and combating the dissemination of online terrorism. With terrorist groups using the internet more and more to deliver propaganda, recruit, and organize, there is a pressing need for efficient monitoring and analysis of web content. This article discusses different data mining methods that can be used to identify and study patterns suggestive of terrorist activity throughout networked digital environments. Specifically, the embedding of machine learning algorithms, i.e., Support Vector machines (SVM), Random Forest and Gradient Boosting, is investigated with regard to its suitability to differentiate between benign and harmful online content. These algorithms can process large volumes of data, where features are extracted, including URL structures, domain ages, and keyword frequencies, to detect anticipate threats. According to the article, feature extraction plays a crucial role in improving detection system accuracy, enabling real-time surveillance of anomalous online activities. In addition, the review discusses the problems that researchers and law enforcement agencies have been encountering in using web data mining for counter-terrorism purposes. Ethical concerns around privacy and civil liberties are addressed and technical challenges, including the quality of data and the dynamic character of online content, are elaborated. Scenarios in which false positives arising in detection systems may occur are also considered, with emphasis on the need for ongoing algorithm and technique refinement. © 2025 IEEE.

关键词： Crop protection

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A Review on Web data Mining to Detect Online Spread of Terrorism Activity

A Review on Web Data Mining to Detect Online Spread of Terro...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Saurav Kumar Jha Utkarsha Pacharaney Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Wardha Maharashtra India Department of Artificial Intelligence and Machine learning Faculty of Engineering and Technology Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

关键词： Support vector machines Uniform resource locators machine learning algorithms Reviews Law enforcement Terrorism Feature extraction Real-time systems data mining Internet of Things

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Enhancing Career Development: An AI-Driven Mentorship Platform for Student-Mentor Connections

Enhancing Career Development: An AI-Driven Mentorship Platfo...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Charanya P Nidhish M Prajesh U R Saktheeswar K Sanjana S Pillai Department of Artificial Intelligence and Machine Learning Sri Eshwar College of Engineering Coimbatore India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Thunai is an AI-powered mentorship platform that connects users with personalized mentors for career guidance. Key features include automated calendar booking, video communication with privacy-enhancing dynamic emojis, and AI-driven mentor matching aligned with mentee goals. The AI assistant, GURU, supports task management, resource recommendations, and LinkedIn-based evaluations to foster growth. Thunai offers subscription plans and partnerships with educational and mental health organizations, enhancing engagement, leadership development, and networking. Leveraging AI technologies like sentiment analysis and feedback, Thunai improves mentor matching, user satisfaction, and mentee engagement for career and emotional well-being.

关键词： Sentiment analysis Technological innovation engineering profession Organizations Mental health Solids Real-time systems Mentoring Artificial intelligence Monitoring

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