Brain tumor detection with integrating traditional and computational intelligence approaches across diverse imaging modalities-Challenges and future directions

A Batool, YC Byun - Computers in Biology and Medicine, 2024 - Elsevier
Brain tumor segmentation and classification play a crucial role in the diagnosis and
treatment planning of brain tumors. Accurate and efficient methods for identifying tumor …

[HTML][HTML] A federated learning approach to breast cancer prediction in a collaborative learning framework

MF Almufareh, N Tariq, M Humayun, B Almas - Healthcare, 2023 - mdpi.com
Breast cancer continues to pose a substantial worldwide public health concern,
necessitating the use of sophisticated diagnostic methods to enable timely identification and …

Intrusion detection using machine learning techniques: an experimental comparison

KA Tait, JS Khan, F Alqahtani, AA Shah… - 2021 International …, 2021 - ieeexplore.ieee.org
Due to an exponential increase in the number of cyber-attacks, the need for improved
Intrusion Detection Systems (IDS) is apparent than ever. In this regard, Machine Learning …

Electroencephalography (EEG) based neonatal sleep staging and detection using various classification algorithms

HA Siddiqa, M Irfan, S Abbasi… - … Materials & Continua, 2023 - research.birmingham.ac.uk
Automatic sleep staging of neonates is essential for monitoring their brain development and
maturity of the nervous system. EEG based neonatal sleep staging provides valuable …

Single-Channel EEG data analysis using a multi-branch CNN for neonatal sleep staging

HA Siddiqa, Z Tang, Y Xu, L Wang, M Irfan… - IEEE …, 2024 - ieeexplore.ieee.org
Neonatal sleep staging is crucial for understanding infant brain development and assessing
neurological health. This study explores the optimal electrode configuration to reduce …

[HTML][HTML] Recurrent nonsymmetric deep auto encoder approach for network intrusion detection system

R Kalpana - Measurement: Sensors, 2022 - Elsevier
An important part of network security is a network intrusion detection system (NIDS). In the
face of the need for new networks, there are issues regarding the feasibility of traditional …

Ensemble-based effective diagnosis of thyroid disorder with various feature selection techniques

T Akhtar, S Arif, Z Mushtaq, SO Gilani… - … Conference of Smart …, 2022 - ieeexplore.ieee.org
Thyroid illness is characterized by the abnormal growth of thyroid tissue on the thyroid
gland's periphery. Hyperthyroidism and hypothyroidism are the two most common types of …

[PDF][PDF] Scalable Anomaly Detection Frameworks for Network Traffic Analysis in cybersecurity using Machine Learning Approaches

M Gopalsamy - Int. J. Curr. Eng. Technol, 2022 - researchgate.net
The capacity to detect facts or observations that differ from what is normally thought of by
domain experts is crucial for many contemporary applications. These outliers may be …

A Comparative Performance Analysis of Machine Learning Models for Intrusion Detection Classification.

A Hussain, A Khatoon, A Aslam… - … of Cybersecurity (2579 …, 2024 - search.ebscohost.com
The importance of cybersecurity in contemporary society cannot be inflated, given the
substantial impact of networks on various aspects of daily life. Traditional cybersecurity …

Методика обнаружения аномалий и кибератак на основе интеграции методов фрактального анализа и машинного обучения

ИВ Котенко, ИБ Саенко, ОС Лаута… - Информатика и …, 2022 - mathnet.ru
В современных сетях передачи данных для постоянного мониторинга сетевого
трафика и обнаружения в нем аномальной активности, а также идентификации и …