no code implementations • 16 Mar 2018 • Sima Siami-Namini, Akbar Siami Namin
Forecasting time series data is an important subject in economics, business, and finance.
no code implementations • 22 May 2019 • Moitrayee Chatterjee, Akbar Siami Namin
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.
no code implementations • 21 Nov 2019 • Faranak Abri, Sima Siami-Namini, Mahdi Adl Khanghah, Fahimeh Mirza Soltani, Akbar Siami Namin
The detection of zero-day attacks and vulnerabilities is a challenging problem.
no code implementations • 21 Nov 2019 • Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin
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.
no code implementations • 11 Apr 2020 • Neda Tavakoli, Sima Siami-Namini, Mahdi Adl Khanghah, Fahimeh Mirza Soltani, Akbar Siami Namin
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.
no code implementations • 14 Jun 2020 • Luis Gutierrez-Espinoza, Faranak Abri, Akbar Siami Namin, Keith S. Jones, David R. W. Sears
Customers represent their satisfactions of consuming products by sharing their experiences through the utilization of online reviews.
no code implementations • 14 Jun 2020 • Shuvalaxmi Dass, Xiaozhen Xue, Akbar Siami Namin
The performance of coverage-based fault localization greatly depends on the quality of test cases being executed.
no code implementations • 8 Oct 2020 • Varsha Nair, Moitrayee Chatterjee, Neda Tavakoli, Akbar Siami Namin, Craig Snoeyink
Our model demonstrated improvement in training time during convolution from $600-700$ ms/step to $400-500$ ms/step.
no code implementations • 8 Oct 2020 • Faranak Abri, Luis Felipe Gutierrez, Akbar Siami Namin, Keith S. Jones, David R. W. Sears
Online reviews play an integral part for success or failure of businesses.
no code implementations • 1 Dec 2020 • Prerit Datta, Natalie Lodinger, Akbar Siami Namin, Keith S. Jones
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
no code implementations • 3 Dec 2020 • Luis Felipe Gutiérrez, Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin
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
no code implementations • 4 Dec 2020 • Faranak Abri, Luis Felipe Gutiérrez, Akbar Siami Namin, David R. W. Sears, Keith S. Jones
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.
no code implementations • 28 Dec 2020 • Luis Felipe Gutiérrez, Faranak Abri, Miriam Armstrong, Akbar Siami Namin, Keith S. Jones
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.
no code implementations • 3 Jun 2021 • Shuvalaxmi Dass, Prerit Datta, Akbar Siami Namin
We propose the use of Hidden Markov Model (HMM) to predict the family of related attacks.
no code implementations • 3 Jun 2021 • Faranak Abri, Luis Felipe Gutierrez, Chaitra T. Kulkarni, Akbar Siami Namin, Keith S. Jones
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.
no code implementations • 17 Dec 2021 • Saroj Gopali, Faranak Abri, Sima Siami-Namini, Akbar Siami Namin
Recently, deep learning techniques have been introduced and explored in the context of time series analysis and prediction.
no code implementations • 29 Jun 2023 • Bipin Chhetri, Saroj Gopali, Rukayat Olapojoye, Samin Dehbash, Akbar Siami Namin
Federated learning is a decentralized machine learning paradigm that allows multiple clients to collaborate by leveraging local computational power and the models transmission.