AIMC Topic: Algorithms

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Decoding basic emotional states through integration of an fNIRS-based brain-computer interface with supervised learning algorithms.

PloS one
Automated detection of emotional states through brain-computer interfaces (BCIs) offers significant potential for enhancing user experiences and personalizing services across domains such as mental health, adaptive learning and interactive entertainm...

Robustness evaluation against corruptions for Optical Diffraction Tomography-based classifiers.

Computers in biology and medicine
Optical Diffraction Tomography (ODT) is a promising technique for three-dimensional imaging, but practical use demands rigorous robustness testing due to real-world noise factors. Despite the growing importance of machine learning safety, robustness ...

CoBdock-2: enhancing blind docking performance through hybrid feature selection combining ensemble and multimodel feature selection approaches.

Journal of computer-aided molecular design
Identifying orthosteric binding sites and predicting small molecule affinities remains a key challenge in virtual screening. While blind docking explores the entire protein surface, its precision is hindered by the vast search space. Cavity detection...

Early breast cancer detection via infrared thermography using a CNN enhanced with particle swarm optimization.

Scientific reports
Breast cancer remains the most prevalent cause of cancer-related mortality among women worldwide, with an estimated incidence exceeding 500,000 new cases annually. Timely diagnosis is vital for enhancing therapeutic outcomes and increasing survival p...

A preprocessing method based on 3D U-Net for abdomen segmentation.

Computers in biology and medicine
Deep learning methods have made significant progress in the field of biomedical automatic segmentation but still open to developments, especially due to the insufficient use of preprocessing methods. In this study, a pre-processing step is proposed b...

Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography.

BMC pulmonary medicine
BACKGROUND: Pulmonary nodules seen by computed tomography (CT) can be benign or malignant, and early detection is important for optimal management. The existing manual methods of identifying nodules have limitations, such as being time-consuming and ...

Deep learning algorithm for identifying osteopenia/osteoporosis using cervical radiography.

Scientific reports
Due to symptomatic gait imbalance and a high incidence of falls, patients with cervical disease-including degenerative cervical myelopathy-have a significantly increased risk of fragility fractures. To prevent such fractures in patients with cervical...

A deep learning approach for heart disease detection using a modified multiclass attention mechanism with BiLSTM.

Scientific reports
Heart disease remains the leading cause of death globally, mainly caused by delayed diagnosis and indeterminate categorization. Many of traditional ML/DL methods have limitations of misclassification, similar features, less training data, heavy compu...

Accurate and real-time brain tumour detection and classification using optimized YOLOv5 architecture.

Scientific reports
The brain tumours originate in the brain or its surrounding structures, such as the pituitary and pineal glands, and can be benign or malignant. While benign tumours may grow into neighbouring tissues, metastatic tumours occur when cancer from other ...

Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms.

Scientific reports
Right ventricular dysfunction (RVD) is strongly associated with increased mortality in patients with acute pulmonary embolism (PE), making its early detection crucial. Identifying RVD risk factors rapidly, accurately, and economically within the acut...