AIMC Topic: Algorithms

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IV3TM: Inception V3 enabled bidirectional long short-term memory network for brain tumor classification.

PloS one
A brain tumor is one of the life-threatening neurological conditions affecting millions of people worldwide. Early diagnosis and classification of brain tumor types facilitate prompt treatment, thereby increasing the patient's chances of survival. Th...

Hyperparameter optimization ResNet by improved Beluga Whale Optimization.

PloS one
The parameter values of neural networks will directly affect the performance of the network, so it is very important to choose the appropriate parameter tuning method to improve the performance of the neural network. In this paper, the improved belug...

Self-learning adaptive neuro-fuzzy approximation of robust control behavior in electric power steering systems.

PloS one
Data training algorithms based on Artificial Intelligence (AI) often encounter overfitting, underfitting, or bias issues. This article presents the design of a hybrid self-learning algorithm to address the above challenges. The proposed approach is d...

An efficient cyber-attack detection and classification in IoT networks with high-dimensional feature set using Levenberg-Marquardt optimized feedforward neural network.

PloS one
This paper examines the escalating challenge of detecting cyber-attacks within Internet of Things (IoT) networks, where conventional security measures often falter in addressing the speed and complexity of contemporary threats. In response to the nec...

YOLOv11-MFF: A multi-scale frequency-adaptive fusion network for enhanced CXR anomaly detection.

PloS one
Chest X-ray (CXR) represents one of the most widely utilized clinical diagnostic tools for thoracic diseases. Nevertheless, computer-aided diagnosis based on chest radiographs still faces considerable challenges in anomaly detection. Certain lesions ...

Prediction of postoperative haemorrhage after cerebral tumour surgery using machine learning algorithms.

BMC medical informatics and decision making
BACKGROUND: Traditional diagnostic methods used by neurosurgeons are limited in their ability to address complex interactions. These limitations have necessitated the use of advanced artificial intelligence approaches capable of analyzing multidimens...

Denoising self-supervised learning for disease-gene association prediction.

BMC bioinformatics
Understanding the interplay between diseases and genes is crucial for gaining deeper insights into disease mechanisms and optimizing therapeutic strategies. In recent years, various computational methods have been developed to uncover potential disea...

Enhancing explainability of random survival forests in predicting stent patency risk for malignant colonic obstruction.

BMC gastroenterology
BACKGROUND: This study aims to enhance the explainability and predictive accuracy of the Random Survival Forest (RSF) algorithm in predicting stent patency risk for patients with malignant colonic obstruction.

Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review.

BMC infectious diseases
Sepsis is a life-threatening condition resulting from a dysregulated immune response to infection, often leading to organ failure and death. Early detection is vital, as delays significantly worsen outcomes. In recent years, the integration of artifi...

An optimized bidirectional recurrent neural network for kidney stone detection based on developed bald eagle search method in CT scan images.

Scientific reports
Kidney stone disease is a common syndrome and a recurring one, where it bears a 50% chance of being manifested again within ten years and may lead to serious complications like ureteral obstruction and unbearable pain. If timely intervention is consi...