Dataset

Analysing Structural Failure Of Bridges Employing Bridge Parameters And Stochastic Modelling

Structural Failure In Pertaining To Bridges Is Extremely Challenging To Assess.. Stochastic And Statistical Computing Is Being Explored To Derive Conclusive Decisions Where Human Intervention Is Difficult In Time And Resource Constrained Si...

A Review On Statistical Models For Anomaly Detection Using Metro Turnout Data

Metro Railway Systems Are Among The Most Important Modes Of Urban Transportation, Providing Safe, Reliable, And Efficient Mobility For Millions Of Passengers Every Day. One Of The Most Critical Components Of A Metro Railway Network Is The T...

Enhanced Deepfake Detection Using ResNet50 And Facial Landmark Analysis

This Research Focuses On Accuracy Enhancement In The Detection Of Deepfakes Using The ResNet50 Algorithm Designed Through Deep Learning. It Analyzes Anomalies In Artificial Facial Images. Materials And Methods: The Two Implemented Deep Lear...

Data Leakage Detection And Intelligent Data Preprocessing System

Data Leakage Is One Of The Most Critical Challenges In Machine Learning Systems, Leading To Unrealistic Model Performance And Poor Generalization In Real-world Applications. Leakage Occurs When Information From Outside The Training Dataset...

Deep Fake Detection And Authenticity Verification

Deepfake Technology Threatens Information Integrity By Enabling The Creation Of Highly Realistic Synthetic Media That Can Mislead Viewers, Manipulate Public Opinion, And Compromise Biometric Authentication Systems. Existing Detection Models...

AN INTELLIGENT MULTI-ALGORITHM MACHINE LEARNING SYSTEM FOR SKIN DISEASE CLASSIFICATION AND PREDICTION

This Paper Proposes An Interpretable And Explainable Multi-class Skin Lesion Classification Model For The HAM10000 Dataset. Timely Detection Of Skin Cancer Is Important But Difficult Due To Class Imbalance And Uninterpretability Of Current...

TweetSense: Emotion Detection From Twitter Data Using Natural Language Processing

This Research Investigates The Application Of Natural Language Processing (NLP) Models For Sentiment Analysis Of Twitter Data, A Domain That Has Gained Immense Importance In The Era Of Digital Communication.[1] Twitter, As A Microblogging P...

Identifying Credit Card Frauds Employing Deep Learning

With Increased Internet Usage, Online Transactions Have Been On The Rise. One Of The Most Prevalent Problems Faced Is Credit Cards Frauds. While Web Applications And Mailing Services Are Heavily Spammed, The Upsurge Of Handheld Mobile Devic...

GAN FOR FINGERPRINT IMAGE GENERATION

Multimodal Emotion Recognition Using Visual-Text Fusion With ResNet-50 And SVM On The MELD Dataset

This Paper Presents A Multimodal Emotion Recognition Framework That Integrates Visual And Textual Modalities To Improve The Accuracy And Robustness Of Emotion Classification Systems. The Visual Modality Is Processed Using A Pre-trained ResN...

A Comparative Study Of SVD And NMF For Semantic Text Clustering Using KMeans

The Exponential Growth Of Unstructured Text Data, Such As News Articles, Social Media Posts, And Online Discussions, Has Created The Need For Effective Methods Of Semantic Organisation. Text Clustering Plays A Vital Role In This Context By...

Intelligent SMS Spam Filtering Under Adversarial Conditions Using Random Forests

The Growing Volume Of Unsolicited And Deceptive SMS Messages Poses A Critical Threat To Mobile Users, Necessitating The Development Of Accurate And Resilient Spam Detection Systems. While Machine Learning (ML)-based Models Have Been Widely...

BENCHMARKING DEEP LEARNING MODELS FOR AMERICAN SIGN LANGUAGE RECOGINITION A COMPARATIVE STUDY ON WLASL

This Paper Presents A Comprehensive Comparative Analysis Of Recent State-of-the-art Deep Learning Models Developed For American Sign Language (ASL) Recognition, With A Focus On Those Benchmarked Using The WLASL Dataset. The Rising Demand Fo...

Brain Tumer Detection Using Machine Learning

Brain Tumors Result From The Abnormal And Uncontrolled Growth Of Cells. If Left Untreated During The Early Stages, They Can Become Life-threatening. Although Numerous Significant Advancements Have Been Achieved In This Field, Ensuring Accur...

Brain Tumor Disease Detection Using Federated Learning With FedAvg

Federated Learning (FL) Has Emerged As A Critical Paradigm For Collaborative Model Training In Privacy-constrained Domains, Particularly In Healthcare. This Study Presents A Comprehensive FedAvg-based Framework For Brain Tumor Detection Fro...