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Found 17 papers, 0 papers with code
A Survey on Blockchain-Based Federated Learning and Data Privacy
Federated learning is a decentralized machine learning paradigm that allows multiple clients to collaborate by leveraging local computational power and the models transmission.
A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models
Recently, deep learning techniques have been introduced and explored in the context of time series analysis and prediction.
Attack Prediction using Hidden Markov Model
We propose the use of Hidden Markov Model (HMM) to predict the family of related attacks.
Toward Explainable Users: Using NLP to Enable AI to Understand Users' Perceptions of Cyber Attacks
The results of the open card sorting study showed a large amount of inter-participant variation making the research team wonder how the consequences of security attacks were comprehended by the participants.
Phishing Detection through Email Embeddings
In this paper, we crafted a set of phishing and legitimate emails with similar indicators in order to investigate whether these cues are captured or disregarded by email embeddings, i. e., vectorizations.
Predicting Emotions Perceived from Sounds
This paper conducts an experiment through which several mainstream and conventional machine learning algorithms are developed to study the prediction of emotions perceived from sounds.
A Concern Analysis of FOMC Statements Comparing The Great Recession and The COVID-19 Pandemic
It is important and informative to compare and contrast major economic crises in order to confront novel and unknown cases such as the COVID-19 pandemic.
Computational Engineering, Finance, and Science General Economics Economics
Cyber-Attack Consequence Prediction
We compare the performance through various machine learning models employing word vectors obtained using both tf-idf and Doc2Vec models.
Cryptography and Security Human-Computer Interaction
Fast Fourier Transformation for Optimizing Convolutional Neural Networks in Object Recognition
Our model demonstrated improvement in training time during convolution from $600-700$ ms/step to $400-500$ ms/step.
Fake Reviews Detection through Analysis of Linguistic Features
Online reviews play an integral part for success or failure of businesses.
Detection of Coincidentally Correct Test Cases through Random Forests
The performance of coverage-based fault localization greatly depends on the quality of test cases being executed.
Fake Reviews Detection through Ensemble Learning
Customers represent their satisfactions of consuming products by sharing their experiences through the utilization of online reviews.
Clustering Time Series Data through Autoencoder-based Deep Learning Models
In particular, deep learning techniques are capable of capturing and learning hidden features in a given data sets and thus building a more accurate prediction model for clustering and labeling problem.
The Performance of Machine and Deep Learning Classifiers in Detecting Zero-Day Vulnerabilities
The detection of zero-day attacks and vulnerabilities is a challenging problem.
A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM
The major question is that whether the gates incorporated in the LSTM architecture already offers a good prediction and whether additional training of data would be necessary to further improve the prediction.
Deep Reinforcement Learning for Detecting Malicious Websites
Phishing is the simplest form of cybercrime with the objective of baiting people into giving away delicate information such as individually recognizable data, banking and credit card details, or even credentials and passwords.
Forecasting Economics and Financial Time Series: ARIMA vs. LSTM
Forecasting time series data is an important subject in economics, business, and finance.
