AI-Driven UX/UI Design: Empirical Research and Applications in FinTech

International Journal of Innovative Research in Computer Science & Technology, Journal Year: 2024, Volume and Issue: 12(4), P. 99 - 109

Published: July 1, 2024

This study explores the transformative impact of AI-driven UX/UI design in FinTech sector, examining current practices, user preferences, and emerging trends. Through a mixed-methods approach, including surveys, interviews, case studies, research reveals significant adoption AI technologies design, with 78% surveyed companies implementing such solutions. Personalization emerges as dominant trend, 76% apps utilizing for tailored interfaces. The demonstrates strong correlation between AI-enhanced features improved engagement, incorporating advanced showing 41% increase daily active users. Ethical considerations, data privacy algorithmic bias, are addressed critical challenges implementation. contributes conceptual framework FinTech, synthesizing findings from diverse sources. Future trends, emotional augmented reality integration, explored. concludes that while offers potential enhancing experiences balancing innovation ethical considerations is crucial responsible implementation trust.

Language: Английский

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LLM-Cloud Complete: Leveraging Cloud Computing for Efficient Large Language Model-based Code Completion

Deleted Journal, Journal Year: 2024, Volume and Issue: 5(1), P. 295 - 326

Published: Aug. 8, 2024

This paper introduces LLM-CloudComplete, a novel cloud-based system for efficient and scalable code completion leveraging large language models (LLMs). We address the challenges of deploying LLMs real-time by implementing distributed inference architecture, adaptive resource allocation, multi-level caching mechanisms. Our utilizes pipeline parallelism technique to distribute LLM layers across multiple GPU nodes, achieving near-linear scaling in throughput. propose an allocation algorithm using reinforcement learning optimize utilization under varying workloads. A similarity-based retrieval mechanism is implemented within three-tier reduce computational load improve response times. Additionally, we introduce several latency reduction strategies, including predictive prefetching, incremental generation, sparse attention optimization. Extensive evaluations on diverse programming languages demonstrate that LLM-CloudComplete outperforms existing state-of-the-art systems, 7.4% improvement Exact Match accuracy while reducing 76.2% increasing throughput 320%. ablation studies reveal significant contributions each component overall performance. represents substantial advancement AI-assisted software development, paving way more responsive coding tools. discuss limitations future research directions, privacy-preserving techniques adaptability paradigms.

Language: Английский

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Exploring Machine Learning for Stock Price Prediction and Decision Making

Published: April 19, 2025

Intricate dynamics of the stock market makes its prediction a challenging and daunting activity. In order to create precise predictive models, researchers are employing emerging machine learning models methods. The research starts with collection history, volumes trade other related indicators. Then data is preprocessed feature engineering done, thereby producing useful input representations for models. model employed in SVR model. Grid search CV method utilized discover best possible parameters' values that assists predicting intraday based on recent past data. This respond promptly trends changes, making it optimal short-term momentum trading strategies.

Language: Английский

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AI-Driven drug discovery: Accelerating the development of novel therapeutics in biopharmaceuticals

International Medical Science Research Journal, Journal Year: 2024, Volume and Issue: 4(8), P. 882 - 899

Published: Aug. 23, 2024

Artificial Intelligence (AI) has emerged as a transformative force in drug discovery, revolutionizing the biopharmaceutical industry's approach to developing novel therapeutics. This paper provides comprehensive overview of AI-driven focusing on its applications accelerating development innovative treatments. We examine fundamental AI technologies employed including machine learning algorithms, deep architectures, and natural language processing techniques. The analyzes integration across various stages discovery pipeline, from target identification clinical trial design, highlighting significant improvements efficiency accuracy. explore impact big data discussing challenges opportunities presented by multi-omics integration, electronic health records mining, need for standardization. study also addresses ethical considerations regulatory associated with implementation development. Finally, we present emerging trends prospects biopharmaceuticals, emphasizing importance collaborative ecosystems potential revolutionize personalized medicine. review synthesizes current research industry practices, providing insights into that lie ahead realizing full potential. Keywords: Intelligence, Drug Discovery, Biopharmaceuticals, Machine Learning.

Language: Английский

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Advancements in Deep Learning for Driving Policy and Perception in Autonomous Vehicles

Published: July 18, 2024

This paper systematically discusses the application of reinforcement learning in automatic driving system. Reinforcement frameworks show significant advantages optimizing decision making, predictive perception, path planning, and controller design, exceeding limitations traditional supervised methods. The highlights critical role components such as scene understanding, positioning, map making autonomous systems, which provide reliable environmental awareness through deep sensor fusion technologies to support intelligent decision-making complex urban environments. In addition, innovative approaches safety reduce risk ensure that systems adhere strictly constraints while maximizing expected rewards. These findings an important theoretical practical basis for further improving algorithm robustness, managing multi-agent interactions, integrating ethical considerations future.

Language: Английский

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Machine Learning Integration in Econometric Models

Next generation., Journal Year: 2024, Volume and Issue: 8(1), P. 77 - 77

Published: Nov. 11, 2024

The integration of machine learning (ML) into econometric models represents a transformative advancement in the field econometrics, enabling researchers to tackle complex, high-dimensional datasets while maintaining interpretability and rigor traditional approaches. This research investigates synergies between focusing on how ML techniques can enhance model flexibility, predictive accuracy, causal inference economic analysis. By leveraging methods such as regularization, ensemble learning, deep study explores applications macroeconomic forecasting, policy evaluation, market Furthermore, it addresses challenges balancing with performance, emphasizing need for hybrid frameworks that merge learning's adaptability econometrics' theoretical foundation. findings demonstrate potential ML-enhanced revolutionize policy-making by providing robust, data-driven insights.

Language: Английский

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Developing Models for Analyzing Financial Time Series Data for Investment Strategies

Human computer interaction., Journal Year: 2024, Volume and Issue: 8(1), P. 33 - 33

Published: Nov. 19, 2024

Financial time series data is a cornerstone of investment strategy development, providing critical insights into market trends, asset performance, and risk assessment. This research explores the application advanced statistical machine learning models for analyzing financial to optimize strategies. The study examines various techniques, including autoregressive integrated moving average (ARIMA), GARCH volatility forecasting, recurrent neural networks (RNNs) capturing temporal dependencies in data. By leveraging these models, aims enhance prediction behavior identify profitable opportunities. It also investigates integration feature engineering real-time processing improve model accuracy adaptability. Challenges such as overfitting, non-stationarity, unpredictability markets are addressed, along with importance ethical considerations data-driven decision-making. findings provide actionable effective use offering robust framework optimization.

Language: Английский

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Research on Ice and Snow Tourism Economic Forecasting Method Based on Improved Gated Recurrent Unit Model

Proceedings of the 7th International Conference on Cyber Security and Information Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 388 - 394

Published: Sept. 15, 2024

Language: Английский

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