Python
A Novel Integrated Approach for Stock Prediction Based on Modal Decomposition Technology and Machine Learning
Advancing Fake News Detection: Hybrid Deep Learning With FastText and Explainable AI
Deep Learning Algorithms for Cyber-Bulling Detection in Social Media Platforms
Deep Learning Model for Driver Behavior Detection in Cyber-Physical System-Based Intelligent Transport Systems
Diagnosis of Liver Disease using ANN and MLAlgorithms with Hyperparameter Tuning
The aim of this project is to develop a system for the diagnosis of liver disease using Artificial Neural Networks (ANN) and Machine Learning (ML) algorithms with hyperparameter tuning. The project focuses on leveraging advanced models and optimization techniques to enhance predictive capabilities, aiding in the early detection and effective management of liver disease.
Enhancing Smishing Detection A Deep Learning Approach for Improved Accuracy and Reduced False Positives
The aim of this work is to explore and develop advanced methods for enhancing the detection and prevention of smishing attacks. This involves utilizing cutting-edge technologies such as machine learning, artificial intelligence, and behavioral analysis to identify and block fraudulent SMS messages, protecting users from financial and personal data theft. The goal is to create more effective, real-time detection systems to mitigate the growing threat of smishing attack
Object Detection Method Using Image and Number of Objects on Image as Label
To develop an object detection model using YOLOv8 to address the limitations of existing methods and improve detection accuracy, robustness, and efficiency. The aim is to design a system that reduces the dependency on extensive labelling while ensuring adaptability to unseen environments. The model will utilize YOLOv8’s capabilities to process data efficiently and deliver high-performance results for diverse applications in object detection.
Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning
Predicting Market Performance Using Machine and Deep Learning Techniques
The aim of this study is to evaluate the effectiveness of various machine learning and deep learning algorithms, including LSTM networks, ARIMA models, and traditional machine learning techniques, for forecasting market prices. We analyze the performance of these models on stock historical datasets and compare their predictive accuracy to determine the most suitable approach for real-time market analysis. This research seeks to provide insights into the predictability of markets and support informed decision-making for investors
Real-Time Plant Disease Dataset Development and Detection of Plant Disease Using Deep Learning
Rule-Based With Machine Learning IDS for DDoS Attack Detection in Cyber-Physical Production Systems (CPPS)
