A survey and analysis of intrusion detection models based on cse-cic-ids2018 big data
JL Leevy, TM Khoshgoftaar - Journal of Big Data, 2020 - Springer
The exponential growth in computer networks and network applications worldwide has been
matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 …
matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 …
Combining multiple feature-ranking techniques and clustering of variables for feature selection
Feature selection aims to eliminate redundant or irrelevant variables from input data to
reduce computational cost, provide a better understanding of data and improve prediction …
reduce computational cost, provide a better understanding of data and improve prediction …
Canola and soybean oil price forecasts via neural networks
X Xu, Y Zhang - Advances in Computational Intelligence, 2022 - Springer
Forecasts of commodity prices are vital issues to market participants and policy-makers.
Those of cooking section oil are of no exception, considering its importance as one of main …
Those of cooking section oil are of no exception, considering its importance as one of main …
Detecting cybersecurity attacks using different network features with lightgbm and xgboost learners
CSE-CIC-IDS2018 is an intrusion detection dataset containing roughly 16,000,000 normal
and anomalous instances, with about 17% of these instances representing attack traffic. Our …
and anomalous instances, with about 17% of these instances representing attack traffic. Our …
Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network
Y Wang, Y Chen, N Yang, L Zheng, N Dey… - Applied Soft …, 2019 - Elsevier
Hepatic granuloma develops in the early stage of liver cirrhosis which can seriously injury
liver health. At present, the assessment of medical microscopic images is necessary for …
liver health. At present, the assessment of medical microscopic images is necessary for …
Detecting cybersecurity attacks across different network features and learners
Abstract Machine learning algorithms efficiently trained on intrusion detection datasets can
detect network traffic capable of jeopardizing an information system. In this study, we use the …
detect network traffic capable of jeopardizing an information system. In this study, we use the …
Line spectral frequency-based features and extreme learning machine for voice activity detection from audio signal
H Mukherjee, SM Obaidullah, KC Santosh… - International Journal of …, 2018 - Springer
Voice activity detection (VAD) refers to the task of identifying vocal segments from an audio
clip. It helps in reducing the computational overhead as well elevate the recognition …
clip. It helps in reducing the computational overhead as well elevate the recognition …
A lazy learning-based language identification from speech using MFCC-2 features
H Mukherjee, SM Obaidullah, KC Santosh… - International Journal of …, 2020 - Springer
Developing an automatic speech recognition system for multilingual countries like India is a
challenging task due to the fact that the people are inured to using multiple languages while …
challenging task due to the fact that the people are inured to using multiple languages while …
Limiting the collection of ground truth data for land use and land cover maps with machine learning algorithms
Land use and land cover (LULC) classification maps help understand the state and trends of
agricultural production and provide insights for applications in environmental monitoring …
agricultural production and provide insights for applications in environmental monitoring …
Segmentation and analysis of CT images for bone fracture detection and labeling
Computed tomography (CT) images are a crucial resource for assessing the severity and
prognosis of bone injuries caused by trauma or accident. Fracture detection in long bones is …
prognosis of bone injuries caused by trauma or accident. Fracture detection in long bones is …