AI-Driven Predictive Analytics for Enhancing Automotive Safety in Financial Risk Assessments in Cloud Data

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 107 - 124

Published: April 4, 2025

Prediction technologies based on AI drive crucial automotive safety and financial risk assessment which results in minimizing losses as well increasing road safety. A Hybrid AI-Econometrics Model merges machine learning algorithms with econometric systems serves the main proposal to estimate quantify economic consequences linked accidents. TensorFlow performs deep operations within framework alongside Statsmodels analyze telematics data, insurance claims data macroeconomic for determining risks of accidents their connected costs. The model uses find patterns before calculating through GARCH (Generalized Autoregressive Conditional Heteroskedasticity) Vector Autoregression provides both accurate predictions understandable make it acceptable various users including insurers governmental agencies industries. research shows better capabilities enhances protective driving environments

Language: Английский

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Transforming Marketing Campaign Effectiveness With AI-Based Consumer Behavior Analytics

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 477 - 490

Published: April 4, 2025

AI-powered content recommendation systems function as the top technological approach in personal marketing through their ability to generate necessary user interactions. Tailored recommendations based on AI processing historical customer data help firms build better involvement with customers for sales operations. Social media and purchase records browsing analyzed by deep learning technology team up collaborative filtering operate established systems. Organizations implement brand provide appropriate products users thus achieving satisfaction loyalty. Companies who create distribution approaches maintain existing consumer relationships boost potential future purchases. Rosefield predicts that current dominant digital remains personalized delivers its best possible campaign results marketers.

Language: Английский

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The Intersection of Consumer Behaviour and Cloud-Driven Online Marketing

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 417 - 434

Published: April 4, 2025

To better understand the behavioral patterns that customers display while making decisions, this research project examines intersection of online marketing and consumer behavior using data-driven approaches. The study starts with preprocessing data, which entails data transformation techniques to ensure attributes used as input for analysis are relevant clean. This also includes process encoding normalizing a range client attributes, such demographics, internet activity, purchasing patterns. Then, feature selection, Recursive Feature Elimination (RFE) algorithm is used. improve model's performance, identifying features have biggest effects eliminating those not crucial. In study, customer classified Support Vector Machines (SVM), sophisticated classification technique can capture complex non-linear relationships in data. Performance metrics like accuracy precision assess support vector machine (SVM) model.

Language: Английский

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Economic Policy Optimization Powered by Advanced AI-Driven Business Intelligence Tools

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 145 - 162

Published: April 4, 2025

Economic policy optimization requires accurate forecasting and data-driven decision-making to navigate complex financial situations. This research combines predictive analytics machine learning models analyse historical economic data project future trends using AI-driven forecasting. The suggested approach uses time series (ARIMA, LSTMs) ensemble techniques increase the accuracy of macroeconomic forecasts, including GDP growth, inflation rates, labor market dynamics. Additionally, AI-powered process real-time indicators dynamically adjust recommendations in response global fluctuations. improves fiscal stability reduces uncertainty by facilitating proactive planning. By integrating projections into Business Intelligence (BI) dashboards, which provide decision-makers with interactive, information, efficacy strategies is further enhanced. In order ensure governance a economy that always evolving.

Language: Английский

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Deep Learning-Powered IoT Solutions for Real-Time Environment Perception and Navigation in Autonomous Vehicles With NLP Features

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 187 - 204

Published: May 1, 2025

Current development of IoT-connected vehicle networks is progressing at a very fast rate which has enabled traffic management and use autonomous cars. In this paper, research questions developed out the Sensor Data Fusion, Feature Importance Techniques, Deep Learning Algorithms are used to improve Traffic Flow Optimization also increasing efficiency Autonomous driving. collected from LiDAR sensor, GPS, video display frame other environment sensors integrated present uniform high accuracy data. By applying approach Random Forest SHAP (SHapley Additive exPlanations), such input-driving factors as speed, density vehicles, climate conditions that have greatest impact on model selected minimize computational load. case flow, Long Short-Term Memory (LSTM) consider temporal dependencies for predictive modelling decision making Convolutional Neural Networks (CNNs) applies features cameras.

Language: Английский

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Predictive Analytics for Risk Reduction in Vehicle Supply Chain Management

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 391 - 404

Published: May 1, 2025

The use of machine learning for customer profile, predictive analytics, and cluster analysis, AI-powered audience segmentation is revolutionizing campaigns to raise awareness car safety. By identifying target demographics, driving patterns, risk variables, this strategy guarantees highly customized marketing campaigns. AI can send safety messages by grouping audiences according concerns using behavioral modeling clustering algorithms. Proactive outreach made possible which forecasts engagement levels accident probability. improving precision marketing, technique that are seen the appropriate people at moment. Additionally, dynamic content adaption automatic campaign optimization AI-driven segmentation, maximizes impact. Through integration data real-time tracking, automated outreach, companies public drive meaningful change.

Language: Английский

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ML Tools for Safety in Automotive Financial Risk Management

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 405 - 422

Published: May 1, 2025

In the rapidly evolving field of automotive finance, managing risk is crucial for maintaining financial stability and security. Machine Learning (ML) tools have demonstrated significant potential in enhancing predictive capabilities management models, enabling more accurate forecasting, real-time monitoring, mitigation strategies. This study explores application an advanced ML method, specifically Deep Neural Networks (DNN), predicting risks industry. The DNN, with its ability to handle complex, non-linear relationships large datasets, integrated Automotive Risk Management Software (ARMS), tool designed dynamic assessment. By leveraging these tools, finance institutions can gain deep insights into market trends, customer behavior, risks, which helps optimizing decisions related credit scoring, loan defaults, asset depreciation.

Language: Английский

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NLP Voice Assistance for IoT Autonomous Vehicles Using ML Algorithms for Seamless Navigation

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 359 - 374

Published: May 1, 2025

The goal of this research project is to ascertain whether or not combining Internet Things (IoT) and Natural Language Processing (NLP) technology could improve the caliber voice-assisted navigation in self-driving cars. system under consideration utilizes machine learning techniques provide smooth an easy-to-understand voice-based interface for car administration. preprocessing step includes speech-to-text conversion, which process converting spoken commands into text additional analysis. preparatory processing another name stage. Utilizing time-series data analysis essential completing feature selection process. Finding important patterns that are necessary navigational judgments requires a study vehicle's sensor data, GPS, speed, ambient inputs. To find trends, required. Sequence models—more especially, Recurrent Neural Networks (RNNs) Long Short-Term Memory (LSTM) networks—are used during classification

Language: Английский

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AI-Based Economic Models for Evaluating Vehicle Safety Costs and Benefits

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 221 - 236

Published: May 1, 2025

This research focuses on artificial intelligent efficient models for the analysis of risks and asset values vehicles applying globalization, feature reduction, time series methods. The concerns increasing pressure to evaluate economic effects protective features in a constantly changing car environment. Normalization normalizes disparate data, creating common framework which cost benefit variables. Defined subspaces eliminate noise unnecessary data by outlining strength predominant thus enabling reduction computational load models. countless look at past present provide future long-standing trends safety paying as well consequences. given provides clear understanding evaluating cost-effectiveness proposed measures, including rates accident insurance cost, costs adoptive technologies.

Language: Английский

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AI-Powered Consumer Insights on Autonomous Vehicle Safety Preferences

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 251 - 266

Published: May 1, 2025

This AI-powered consumer perceptivity independent vehicle safety preferences by applying a combination of advanced data analysis ways. First, outlier discovery styles are employed to identify and manage extreme points that could dispose the results. ensures dataset is clean representative genuine preferences. Next, point selection performed using Chi-square test, which evaluates dependence between categorical variables preferences, allowing identification most significant factors impacting opinions. For bracket, employs Random Forest Classifier, an ensemble literacy system known for its capability handle complex, high- dimensional while minimizing overfitting. The model trained prognosticate grounded on colorful features, similar as technology relinquishment threat aversion. results offer precious manufacturers policymakers aiming align designs with prospects.

Language: Английский

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