| Cyber Security/ NLP |
A Deep Learning Approach for Human-Driven Social Media Account Takeover Detection via Behavioral Text Analysis |
Explores deep models to track behavioral changes in social media content posting |
| Cyber Security/ NLP |
Detecting Human-Led Social Media Account Hijacks Through Authorship Style Drift and Behavioral Cues |
Focuses on how a user's writing style and patterns can change when an account is compromised |
| Fraud Detection/ Cyber Security |
A Robust Machine Learning Framework for Real-Time Credit Card Fraud Detection Using Transaction Behavior Patterns |
Emphasizes real-time detection and behavioral modeling |
| Fraud Detection/ Cyber Security |
Hybrid Ensemble-Based Learning Approach for Enhancing Accuracy in Credit Card Fraudulent Transaction Identification |
Focuses on combining models (e.g., random forest, XGBoost) to improve detection |
| Environmental Forecasting |
Bidirectional Deep Learning Model for Forecasting Urban Air Pollution with Convolutional Temporal Encoding |
Highlights the model design and the urban air quality focus |
| Environmental Forecasting |
Deep Neural Forecasting of Air Pollution Using Convolutional and Recurrent Time Series Features |
Reflects both architectural components and task |
| Text based sentiment analysis |
Evaluating Sentiment Classification Models Using Ensemble Feature Selection on High-Dimensional Text Data |
This work investigates how combining multiple feature selection methods can improve the accuracy and efficiency of various sentiment classifiers when handling complex text datasets. |
| Text based sentiment analysis |
Comparative Study of Machine Learning Models for Sentiment Analysis Using Unified Feature Selection Techniques |
This study compares multiple machine learning classifiers for sentiment prediction by applying a unified ensemble of feature selection approaches to refine input text features. |
| Computer vision/ Image Classification |
Deep Convolutional Neural Network for Image Recognition on MNIST and Fashion-MNIST Datasets |
This project applies a CNN architecture to learn visual patterns from two standard datasets, evaluating performance in recognizing digits and fashion items. |
| Computer vision/ Image Classification |
Optimizing Convolutional Neural Networks for Dual Benchmark Image Classification Tasks |
This study explores CNN design and tuning strategies to improve accuracy and generalization across both MNIST and Fashion-MNIST image datasets. |
| Computer vision/Pattern recognition |
Fusion of Local Texture and Keypoint Features for Enhanced Facial Expression Recognition |
This research proposes a hybrid approach that combines LBP and ORB to improve the accuracy of facial emotion detection through the fusion of complementary visual features. |
| Computer vision/Pattern recognition |
Real-Time Facial Emotion Recognition Using Optimized LBP-ORB Feature Integration |
This work focuses on designing a computationally efficient system for real-time emotion detection by integrating and refining LBP and ORB feature sets. |
| Demographic Prediction/ Face Analysis/Computer vision |
Robust Age and Gender Prediction from Unfiltered Faces Using Deep Visual Encoding |
This study develops deep models that extract stable and discriminative features from real-world facial images to improve demographic attribute prediction accuracy. |
| Demographic Prediction/ Face Analysis/Computer vision |
Learning Demographic Traits from In-the-Wild Faces with Deep Convolutional Classifiers |
This work focuses on using convolutional neural networks to accurately infer age and gender from unconstrained facial images with high visual variability. |
| Medical Image Processing/Computer vision in Healthcare |
Multi-Wavelet Feature Extraction for Accurate Classification of Brain Tumors in MRI Using Support Vector Machines |
This research applies multiple wavelet transforms to extract discriminative features from brain MRI scans and uses SVMs to classify tumor types with high accuracy. |
| Medical Image Processing/Computer vision in Healthcare |
MRI-Based Brain Tumor Detection Using Hybrid Wavelet Analysis and Support Vector Classification |
This study explores a hybrid wavelet approach for extracting tumor-related patterns from MRI images and leverages SVMs for precise tumor categorization |
| Medical Image Enhancement |
Enhancing Diagnostic Quality of X-ray Images Using Total Variation and Homomorphic Filtering Techniques |
This study presents a hybrid enhancement method that improves visual clarity in medical X-rays by combining noise-suppression and illumination correction. |
| Medical Image Enhancement |
Hybrid Image Enhancement for Medical X-rays Using Edge-Preserving Filtering and Frequency-Based Illumination Correction |
This work proposes an effective enhancement approach for X-ray imagery by integrating total variation filtering and homomorphic transformation to improve structural visibility. |
| EEG signal processing/Brain-Computer interfaces |
Deep Convolutional Learning of Spatial-Frequency Patterns for Motor Imagery EEG Classification |
This study introduces a deep CNN model that learns joint spatial and frequency features from EEG signals to enhance classification of motor imagery tasks. |
| EEG signal processing/Brain-Computer interfaces |
Motor Imagery EEG Recognition Using Joint Spatial-Frequency Encoding with Deep Neural Networks |
This work presents a deep learning framework that effectively captures both spatial and spectral dynamics of EEG data to improve motor imagery classification accuracy. |
| Medical image Classification |
Improved Skin Lesion Classification Using Deep Convolutional Networks with Enhanced Regularization |
This work proposes a CNN-based skin lesion classifier that incorporates a novel regularizer to boost model generalization and diagnostic accuracy. |
| Medical image Classification |
Regularization-Driven Deep Learning Framework for Accurate Detection of Skin Lesions |
This study introduces a deep learning model with a custom regularization method to effectively classify dermatological images and reduce overfitting. |
| Natural Language Processing |
Optimized Feature Selection for Extracting High-Quality Answers from Community Forums |
This study explores effective feature selection strategies to automatically extract relevant and high-quality answers from informal online discussions. |
| Natural Language Processing |
Enhanced Answer Extraction from Online Forums Using Selective Linguistic and Contextual Features |
This research presents a method that leverages carefully selected features to improve the accuracy of automatic answer retrieval from web-based community Q&A platforms. |
| Natural Language Processing/ Text summarization |
Summarizing Movie Reviews Using Feature-Driven Opinion Mining Techniques |
This study proposes a method to summarize user movie reviews by extracting opinion-rich features that best represent user sentiment and core themes. |
| Natural Language Processing/ Text summarization |
A Feature-Based Approach for Concise Summarization of Movie Review Texts |
This work explores a feature-centric summarization model that reduces long reviews to meaningful summaries by focusing on key narrative and emotional elements. |
| Cloud based computer vision |
Real-Time Vehicle Image Analytics Using Cloud and Edge Collaborative Systems |
This research investigates a hybrid cloud-edge architecture to enable low-latency, high-throughput vehicle image recognition for smart transportation networks. |
| Cloud based computer vision |
Scalable Vehicle Classification on Distributed Cloud Platforms Using Deep Learning Pipelines |
This study presents a deep learning-based cloud framework for vehicle classification, optimized for horizontal scalability across regional traffic datasets. |
| Video processing/analytics for activity recognition |
Automated Analysis of Public Surveillance Video for Real-Time Threat Detection |
This work presents a surveillance framework that uses intelligent video analytics to detect suspicious activities and threats in public environments in real time. |
| Video processing/analytics for activity recognition |
Intelligent Video Investigation System for Enhancing Public Security Operations |
This study develops an AI-based system to extract, track, and analyze actionable insights from public security footage to support rapid forensic investigation. |
| Network Security/ Anomaly Detection |
Anomaly-Based DDoS Detection Using Statistical Covariance Techniques in Web Traffic Analysis |
This work develops a detection model that uses statistical relationships within traffic data to identify patterns indicative of distributed denial-of-service attacks. |
| Medical Image Analysis/Ophthalmology |
Deep Learning Driven Glaucoma Detection Using Retinal Fundus Imaging |
This work emphasizes a fully automated glaucoma detection system using deep learning techniques applied to fundus images, highlighting the integration of AI in ophthalmic diagnostics. |
| Computer vision/ Healthcare AI |
Intelligent Glaucoma Diagnosis through High-Resolution Fundus Image Classification |
This work focuses on the intelligent classification of glaucoma using high-resolution fundus images, reflecting advanced visual pattern recognition in clinical image processing. |
| Predictive Modeling/Healthcare Informatics |
A Hybrid Machine Learning and Deep Learning Framework for Accurate Prediction of Heart Disease in Clinical Healthcare Systems |
This work reflects a robust and professional phrasing of a research work that combines ML and DL for heart disease prediction, emphasizing accuracy and application in real-world healthcare systems. |
| AI in Healthcare |
Development of an Integrated Deep Learning and Machine Learning Approach for Early Detection of Cardiovascular Diseases Using Electronic Health Records |
This work presents the research as a methodological innovation for early cardiovascular disease prediction using both ML and DL, applied to structured healthcare datasets like EHRs. |
| Cyber Security/ AI |
Integrating Machine Learning and Deep Learning Techniques for Advanced Threat Detection in Cybersecurity Systems |
Emphasizes the development of a multi-layered intelligent defense mechanism that leverages both ML and DL to detect and mitigate modern cyber threats. |
| Network Security/ Intrusion Detection |
A Unified Machine Learning and Deep Learning Strategy for Proactive Cyber Threat Identification and Response |
Conveys the research focus on creating a proactive, unified AI-driven framework that combines the strengths of ML and DL for real-time cyberattack prediction and automated response. |
| Remote Sensing |
Two-Stream Convolutional Fusion Networks for Enhanced Classification of High-Resolution Aerial Scenes in Remote Sensing Applications |
This title emphasizes a deep learning-based dual-stream network architecture designed to improve classification accuracy in high-resolution aerial imagery for geospatial applications. |
| Image Processing/Computer Vision/Environmental Monitoring |
A Deep Fusion-Based Dual-Pathway Architecture for Robust Aerial Scene Understanding from High-Resolution Satellite Imagery |
Highlights a dual-path architecture leveraging deep fusion for extracting and integrating spatial and semantic features from satellite images to enable robust scene understanding. |
| Big data analytics/ Cybercrime detection |
Scalable Feature Extraction and Vulnerability Analysis Framework for Intelligent Crime Detection in Big Data Environments |
Emphasizes a scalable system that performs feature extraction and vulnerability analysis to support accurate and intelligent crime detection across large-scale datasets. |
| Data Mining/ Crime Detection |
Intelligent Analysis of Criminal Patterns Using Feature Extraction and Risk Assessment Techniques in Big Data Platforms |
Focuses on identifying criminal behavior by extracting key features and assessing vulnerabilities using AI-driven techniques applied to big data platforms. |
| Autonmous driving/ Computer vision |
Deep Learning Based Multi-Modal Vehicle Detection Using Fusion of Vision and LiDAR Point Cloud Data |
Highlights a deep learning approach that integrates visual and LiDAR data for precise vehicle detection, with an emphasis on multi-modal sensor fusion. |
| Autonmous driving/ Computer vision |
Robust Vehicle Detection Framework Leveraging Deep Learning on Synchronized Camera and LiDAR Information |
Conveys a robust architecture that utilizes synchronized camera images and LiDAR point clouds for accurate and efficient vehicle detection. |
| Medical Imaging/Machine Learning |
Machine Learning Based Severity Assessment of Pulmonary Conditions Using Lung MRI Imaging Data |
Highlights the use of ML algorithms to analyze lung MRI images for predicting the severity of pulmonary diseases in a clinical setting. |
| Predictive Diagnostics/ Radiology AI |
Automated Prediction of Patient Condition Severity from Lung MRI Scans Using Advanced Machine Learning Techniques |
Focuses on an automated framework that leverages machine learning to estimate disease severity from MRI images of the lungs, enabling early and accurate intervention. |
| Biometrics/Forensic Science |
Convolutional Neural Network Based Enhancement of Latent Fingerprints for Improved Biometric Recognition |
Emphasizes the use of CNNs to enhance the quality of latent fingerprints, improving their usability in biometric authentication and forensic identification. |
| Computer Vision/Biometric Image Processing |
Deep Learning Driven Restoration of Latent Fingerprints Using Convolutional Neural Networks for Forensic Applications |
Highlights a CNN-based framework aimed at restoring and enhancing latent fingerprints to support reliable identification in forensic investigations. |
| User Behavior Modeling |
Personalized Recommendation Using Time-Aware Convolutional Neural Networks for Dynamic User Preference Modeling |
Emphasizes a deep learning-based approach that incorporates temporal dynamics using CNNs to model evolving user preferences for personalized recommendations. |
| Information Retrieval/Intelligent Systems |
Temporal Convolutional Neural Network Framework for Adaptive and Personalized Recommendation Systems |
Presents a temporal-aware CNN framework designed to adapt to user behavior changes over time, enabling more relevant and personalized content delivery |
