Determinants influencing fishermen’s willingness-to-participate and willingness-to-pay for conservation of small indigenous fishes: a model-based insight from Indian Sundarbans

Small indigenous fishes (SIF) play a crucial role in supporting the livelihoods and nutritional security of the rural population in Southern Asia. However, their abundance and diversity are under threat due to overexploitation and profitable extensive aquaculture, resulting in a sharp decline, particularly in India. Unfortunately, conservation...

Fishing Behavior Detection and Analysis of Squid Fishing Vessel Based on Multiscale Trajectory Characteristics

Accurate fishing activity detection from the trajectories of fishing vessels can not only achieve high-precision fishery management but also ensure the reasonable and sustainable development of marine fishery resources. This paper proposes a new method to detect fishing vessels’ fishing activities based on the defined local dynamic parameters...

Secure Logistic Regression for Vertical Federated Learning

Data island effectively blocks the practical application of machine learning. To meet this challenge, a new framework known as federated learning was created. It allows model training on a large amount of scattered data owned by different data providers. This article presents a parallel solution for computing logistic regression based on...

Comparison of Logistic Regression and Bayesian Networks for Risk Prediction of Breast Cancer Recurrence

Purpose. For individualized follow-up, accurate prediction of locoregional recurrence (LRR) and second primary (SP) breast cancer risk is required. Current prediction models employ regression, but with large data sets, machine-learning techniques such as Bayesian Networks (BNs) may be better alternatives. In this study, logistic regression...

Computer-Aided Detection of Polyps in CT Colonography Using Logistic Regression

We present a computer-aided detection (CAD) system for computed tomography colonography that orders the polyps according to clinical relevance. TheCADsystem consists of two steps: candidate detection and supervised classification. The characteristics of the detection step lead to specific choices for the classification system. The candidates are...