Search Results for author:
Found 26 papers, 2 papers with code
ExplainableDetector: Exploring Transformer-based Language Modeling Approach for SMS Spam Detection with Explainability Analysis
In this work, we employ optimized and fine-tuned transformer-based Large Language Models (LLMs) to solve the problem of spam message detection.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+3
A Data-Driven Predictive Analysis on Cyber Security Threats with Key Risk Factors
We collected the dataset from victims and non-victims of cyberattacks based on socio-demographic features.
An Explainable Transformer-based Model for Phishing Email Detection: A Large Language Model Approach
In this research paper, we present an optimized, fine-tuned transformer-based DistilBERT model designed for the detection of phishing emails.
Agricultural Recommendation System based on Deep Learning: A Multivariate Weather Forecasting Approach
Agriculture plays a fundamental role in driving economic growth and ensuring food security for populations around the world.
Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis
Finally, the outcomes of various tree-based single classifiers are compared with those of stacking and voting ensemble classifiers.
Exploring a Hybrid Deep Learning Framework to Automatically Discover Topic and Sentiment in COVID-19 Tweets
COVID-19 has created a major public health problem worldwide and other problems such as economic crisis, unemployment, mental distress, etc.
AI Potentiality and Awareness: A Position Paper from the Perspective of Human-AI Teaming in Cybersecurity
This position paper explores the broad landscape of AI potentiality in the context of cybersecurity, with a particular emphasis on its possible risk factors with awareness, which can be managed by incorporating human experts in the loop, i. e., "Human-AI" teaming.
A Dynamic Topic Identification and Labeling Approach of COVID-19 Tweets
Twitter is one of the most influential social media services, which has seen a dramatic increase in its use from the epidemic.
Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks
In total 36 classification models, including four classification models (CNN, LSTM, SVM, SGD) and three embedding techniques with 100, 200 and 250 embedding dimensions, are trained with optimized hyperparameters and tested on three benchmark datasets (BACC-18, BAAD16 and LD).
Emotion Classification in a Resource Constrained Language Using Transformer-based Approach
A Bengali emotion corpus consists of 6243 texts is developed for the classification task.
CyberLearning: Effectiveness Analysis of Machine Learning Security Modeling to Detect Cyber-Anomalies and Multi-Attacks
Detecting cyber-anomalies and attacks are becoming a rising concern these days in the domain of cybersecurity.
An Efficient K-means Clustering Algorithm for Analysing COVID-19
Based on this proposed method, we have determined health care quality clusters of countries utilizing the COVID-19 datasets.
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies
Cybersecurity has recently gained considerable interest in today's security issues because of the popularity of the Internet-of-Things (IoT), the considerable growth of mobile networks, and many related apps.
Predicting Individual Substance Abuse Vulnerability using Machine Learning Techniques
Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol.
SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviews
In addition, a comparative analysis of the proposed technique with other machine learning algorithms presented.
A Survey on the Use of AI and ML for Fighting the COVID-19 Pandemic
Recent studies showed that AI and ML can be used to fight against the COVID-19 pandemic.
A Rule Based Expert System to Assess Coronary Artery Disease under Uncertainty
The coronary artery disease (CAD) involves narrowing and damaging the major blood vessels has become the most life threating disease in the world especially in south Asian reason.
Quantitative Methods Applications
Crime Prediction Using Spatio-Temporal Data
Research can help society to prevent and solve crime activates.
BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model
This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones.
CalBehav: A Machine Learning based Personalized Calendar Behavioral Model using Time-Series Smartphone Data
Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar.
AppsPred: Predicting Context-Aware Smartphone Apps using Random Forest Learning
Random Forest learning is one of the most popular machine learning techniques to build a multi-class prediction model.
E-MIIM: An Ensemble Learning based Context-Aware Mobile Telephony Model for Intelligent Interruption Management
Decision tree is the most popular machine learning classification technique that is used in existing context-aware mobile intelligent interruption management (MIIM) model to overcome such issues.
A Machine Learning based Robust Prediction Model for Real-life Mobile Phone Data
In this paper, we address these issues and present a robust prediction model for real-life mobile phone data of individual users, in order to improve the prediction accuracy of the model.
Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus
Extracting knowledge from such data can be useful to predict diabetic patients.
Research Issues in Mining User Behavioral Rules for Context-Aware Intelligent Mobile Applications
Context-awareness in smart mobile applications is a growing area of study, because of it's intelligence in the applications.
An Improved Naive Bayes Classifier-based Noise Detection Technique for Classifying User Phone Call Behavior
In order to improve the classification accuracy, we effectively identify noisy instances from the training dataset by analyzing the behavioral patterns of individuals.
