Navin Chhibber

Multi-Granular Attention-Driven Reinforcement Learning Framework for Web Intelligent Enhancement Systems

Navin Chhibber, Deepak Singh, Anokh Kishore, Nikita Chawla et autres

From the past few years, web intelligent enhancement systems increasingly rely on heterogeneous and dynamic web data to deliver personalized, context-aware services. However, traditional machine learning, deep learning, and reinforcement learning models often struggle with semantic understanding, adaptability, and scalability in continuously …

Multi-Granular Attention-Driven Reinforcement Learning Framework for Web Intelligent Enhancement Systems

Navin Chhibber, Deepak Singh, Anokh Kishore, Nikita Chawla et autres

From the past few years, web intelligent enhancement systems increasingly rely on heterogeneous and dynamic web data to deliver personalized, context-aware services. However, traditional machine learning, deep learning, and reinforcement learning models often struggle with semantic understanding, adaptability, and scalability in continuously …

Predictive Analytics in E-Commerce for CustomerBehavior Forecasting using hybrid Ret-DNN withXGBoost Model

Degala Pushpa Sri, Mayank Atreya, Lakshmi. H, Navin Chhibber et autres

In recent years, electronic (E) commerce services have rapidly increased in the daily lives of people, which helpsthem to purchase products online. However, retail platforms have struggled to understand customer behavior and make it difficult to predict their future purchases. To overcome …

TMR-GGNN: Credit Card Fraud Detection based on Time-Aware Multi-Relational Guided Graph Neural Network

Rohit Tewari, Shubhankar Shilpi, Navin Chhibber, Devendra Singh Parmar et autres

In recent years, credit card fraud detection has faced significant challenges due to highly imbalanced data, evolving fraud patterns, and complex relational structures among transaction entities. To address these issues, this research proposes a novel framework called Timeaware Multi Relational Guided Graph …

TMR-GGNN: Credit Card Fraud Detection based on Time-Aware Multi-Relational Guided Graph Neural Network

Rohit Tewari, Shubhankar Shilpi, Navin Chhibber, Devendra Singh Parmar et autres

In recent years, credit card fraud detection has faced significant challenges due to highly imbalanced data, evolving fraud patterns, and complex relational structures among transaction entities. To address these issues, this research proposes a novel framework called Timeaware Multi Relational Guided Graph …

in (code pays fourni par la source)

Predictive Analytics in E-Commerce for CustomerBehavior Forecasting using hybrid Ret-DNN withXGBoost Model

Degala Pushpa Sri, Mayank Atreya, Lakshmi. H, Navin Chhibber et autres

In recent years, electronic (E) commerce services have rapidly increased in the daily lives of people, which helpsthem to purchase products online. However, retail platforms have struggled to understand customer behavior and make it difficult to predict their future purchases. To overcome …

in (code pays fourni par la source)

Multi-Granular Attention-Driven Reinforcement Learning Framework for Web Intelligent Enhancement Systems

Navin Chhibber, Deepak Singh, Anokh Kishore, Nikita Chawla et autres

From the past few years, web intelligent enhancement systems increasingly rely on heterogeneous and dynamic web data to deliver personalized, context-aware services. However, traditional machine learning, deep learning, and reinforcement learning models often struggle with semantic understanding, adaptability, and scalability in continuously …

us, in (code pays fourni par la source)

Improved Light Gradient Boosting Decision Tree for Demand Forecasting Prediction in smart retail system based on Machine Learning

Navin Chhibber

Stock Market Price Prediction using Neural Prophet with Deep Neural Network

Navin Chhibber, Sunil Khemka, Neha Tyagi, Rohit Tewari et autres

Stock market price prediction is a significant interdisciplinary research domain that depends at the intersection of finance, statistics, and economics. Forecasting Accurately predicting stock prices has always been a focal point for various researchers. However, existing statistical approaches for time-series prediction often …

Stock Market Price Prediction using Neural Prophet with Deep Neural Network

Navin Chhibber, Sunil Khemka, Neha Tyagi, Rohit Tewari et autres

Stock market price prediction is a significant interdisciplinary research domain that depends at the intersection of finance, statistics, and economics. Forecasting Accurately predicting stock prices has always been a focal point for various researchers. However, existing statistical approaches for time-series prediction often …

us, in (code pays fourni par la source)

Cyber Threat Detection and Vulnerability Assessment System using Generative AI and Large Language Model

Keerthi Kumar. M, Swarun Kumar Joginpelly, Sunil Khemka, Lakshmi R et autres

Background: Cyber-attacks have evolved rapidly in recent years, many individuals and business owners have been affected by cyber-attacks in various ways. Cyber-attacks include various threats such as ransomware, malware, phishing, and Denial of Service (DoS)-related attacks. Challenges: Traditional models such as Generative …

Cyber Threat Detection and Vulnerability Assessment System using Generative AI and Large Language Model

Keerthi Kumar. M, Swarun Kumar Joginpelly, Sunil Khemka, Lakshmi R et autres

Background: Cyber-attacks have evolved rapidly in recent years, many individuals and business owners have been affected by cyber-attacks in various ways. Cyber-attacks include various threats such as ransomware, malware, phishing, and Denial of Service (DoS)-related attacks. Challenges: Traditional models such as Generative …