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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..
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..
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..
Interpretable Machine Learning for COVID 19 An Empirical Study on Severity Prediction Task
The black-box nature of machine learning models hinders the deployment of some high-accuracy medica..
Machine Learning Applied in SARS CoV 2 COVID 19 Screening using Clinical Analysis Parameters
COVID-19 was considered a pandemic by the World Health Organization. Since then, world governments ..
Optimizing Survival Analysis of XG Boost for Ties to Predict Disease Progression of Breast Cancer
Objective: Some excellent prognostic models based on survival analysis methods for breast cancer ha..