Machine Learning Projects
A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Auto encoders
Time-series forecasting is applied to many areas of smart factories, including machine health monit..
A Parallel Digital VLSI Architecture for Integrated Support Vector Machine Training and Classification
Driver fatigue is a significant factor in a large number of vehicle accidents. Thus, driver drowsine..
Academic and Demographic Cluster Analysis of Engineering Student Success
Contribution: This article uses student semester grade point average (GPA) as a measure of student ..
An Efficient Spam Detection Technique for IoT Devices using Machine Learning
The Internet of Things (IoT) is a group of millions of devices having sensors and actuators linked ..
Boosting-based DDoS Detection in Internet of Things Systems
Distributed denial of service (DDoS) attacks remain challenging to mitigate in existing systems, in..
Can Online Consumer Reviews Signal Restaurant Closure A Deep Learning-Based Time-Series Analysis
Business closure is a critical stage in the lifecycle of any business. Despite a body of literature..
COVID 19 Patient Count Prediction Using LSTM
In December 2019, a pandemic named COVID-19 broke out in Wuhan, China, and in a few weeks, it sprea..
Deep Concatenated Residual Network With Bidirectional LSTM for One Hour Ahead Wind Power Forecasting
This paper presents a deep residual network for improving time-series forecasting models, indispensa..
DeprNet A Deep Convolution Neural Network Framework for Detecting Depression Using EEG
Depression is a common reason for an increase in suicide cases worldwide. Thus, to mitigate the eff..
Disease Inference from Health-Related Questions via Sparse Deep Learning
Automatic disease inference is of importance to bridge the gap between what online health seekers wi..
Drowsy Driver Detection using Representation Learning
Driver fatigue is a significant factor in a large number of vehicle accidents. Thus, driver drowsine..
Early Detection of Alzheimer’s Disease with Blood Plasma Proteins Using Support Vector Machines
The successful development of amyloidbased biomarkers and tests for Alzheimer’s disease (AD) repres..
Ethanol Fuel Demand Forecasting in Brazil Using a LSTM Recurrent Neural Network Approach
Ethanol is a biofuel widely consumed in Brazil, which functions as a substitute for gasoline since ..
Gradient Boosting Feature Selection With Machine Learning Classifiers for Intrusion Detection on Power Grids
Smart grids rely on SCADA (Supervisory Control and Data Acquisition) systems to monitor and control..
Hybrid Deep Learning for Botnet Attack Detection in the Internet of Things Networks
Deep Learning (DL) is an efficient method for botnet attack detection. However, the volume of netwo..