Enhancing cardiovascular disease classification in ECG spectrograms by using multi-branch CNN.

Cardiovascular disease (CVD) is caused by the abnormal functioning of the heart which results in a high mortality rate across the globe. The accurate and early prediction of various CVDs from the electrocardiogram (ECG) is vital for the prevention of deaths caused by CVD. Artificial intelligence (AI) is used to categorize and accurately predict various CVDs.

Effective cardiac disease classification using FS-XGB and GWO approach.

Globally, cardiovascular diseases (CVDs) are a leading cause of death; however, their impact can be greatly mitigated by early detection and treatment. Machine learning (ML)-based algorithms that use features extracted from electrocardiogram (ECG) signals are known to provide good accuracy in predicting various CVDs. Thus, in order to build more effective and efficient machine learning models, it is necessary to extract significant features from ECGs.

An improved real time image detection system for elephant intrusion along the forest border areas.

Human-elephant conflict is a major problem leading to crop damage, human death and injuries caused by elephants, and elephants being killed by humans. In this paper, we propose an automated unsupervised elephant image detection system (EIDS) as a solution to human-elephant conflict in the context of elephant conservation. The elephant's image is captured in the forest border areas and is sent to a base station via an RF network.