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Keyword: LSTM Model
Optimized Component based Selection using LSTM Model by Integrating Hybrid MVO-PSO Soft Computing Technique
Research focused on training and testing of dataset after Optimizing Software Component with the help of deep neural network mechanism. Optimized components are selected for training and testing to improve the accuracy at the time of software selection. Selected components are required to be attuned and accommodating as per requirement. Soft computing mechanism such as…
Read More Enhancing the Network Anomaly Detection using CNN-Bidirectional LSTM Hybrid Model and Sampling Strategies for Imbalanced Network Traffic Data
The cybercriminal utilized the skills and freely available tools to breach the networks of internet-connected devices by exploiting confidentiality, integrity, and availability. Network anomaly detection is crucial for ensuring the security of information resources. Detecting abnormal network behavior poses challenges because of the extensive data, imbalanced attack class nature, and the abundance of features in…
Read More CNN-LSTM Based Model for ECG Arrhythmias and Myocardial Infarction Classification
ECG analysis is commonly used by medical practitioners and cardiologists for monitoring cardiac health. A high-performance automatic ECG classification system is a challenging area because there is difficulty in detecting and clustering various waveforms in the signal, especially in the manual analysis of electrocardiogram (ECG) signals. In this paper, an accurate (ECG) classification and monitoring…
Read More Optimizing the Performance of Network Anomaly Detection Using Bidirectional Long Short-Term Memory (Bi-LSTM) and Over-sampling for Imbalance Network Traffic Data
Cybercriminal exploits integrity, confidentiality, and availability of information resources. Cyberattacks are typically invisible to the naked eye, even though they target a wide range of our digital assets, such as internet-connected smart devices, computers, and networking devices. Implementing network anomaly detection proves to be an effective method for identifying these malicious activities. The traditional anomaly…
Read More An Ensemble of Voting- based Deep Learning Models with Regularization Functions for Sleep Stage Classification
Sleep stage performs a vital role in people’s daily lives in the detection of sleep-related diseases. Conventional automated sleep stage classifier models are not efficient due to the complexity linked to the design of mathematical models and extraction of hand-engineering features. Further, quick oscillations amongst sleep stages frequently lead to indistinct feature extraction, which might…
Read More A Hybrid NMF-AttLSTM Method for Short-term Traffic Flow Prediction
In view of the current short-term traffic flow prediction methods that fail to fully consider the spatial correlation of traffic flow, and fail to make full use of historical data features, resulting in low prediction accuracy and poor robustness. Therefore, in paper, combining Non-negative Matrix Factorization (NMF) and LSTM model Based on Attention Mechanism (AttLSTM),…
Read More Differential Evolution based Hyperparameters Tuned Deep Learning Models for Disease Diagnosis and Classification
With recent advancements in medical filed, the quantity of healthcare care data is increasing at a faster rate. Medical data classification is considered as a major research topic and numerous research works have been already existed in the literature. Presently, deep learning (DL) models offers an efficient method for developing a dedicated model to determine…
Read More Artificial Bee Colony-Optimized LSTM for Bitcoin Price Prediction
In recent years, deep learning has been widely used for time series prediction. Deep learning model that is most often used for time series prediction is LSTM. LSTM is widely used because of its excellence in remembering very long sequences. However, doing training on models that use LSTM requires a long time. Trying from one…
Read More Integrating Diacritics Restoration and Question Classification into Vietnamese Question Answering System
This paper presents a solution for question answering system for Vietnamese language by integrating diacritics restoration and question classification via deep learning approach. It could be said that this will be the first research integrating two phases into Vietnamese question answering system. Question classification has a critical role in the question answering system. However if…
Read More Smart Meter Data Analysis for Electricity Theft Detection using Neural Networks
The major problem in electric utility is Electrical Theft, which is harmful to electric power suppliers and causes economic loss. Detecting and controlling electrical theft is a challenging task that involves several aspects like economic, social, regional, managerial, political, infrastructural, literacy rate, etc. Numerous methods were proposed formerly for detecting electricity theft. However, the previous…
Read More Malware Classification Based on System Call Sequences Using Deep Learning
Malware has always been a big problem for companies, government agencies, and individuals because people still use it as a primary tool to influence networks, applications, and computer operating systems to gain unilateral benefits. Until now, malware detection with heuristic and signature-based methods are still struggling to keep up with the evolution of malware. Machine…
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