The challenge of accurately estimating effort for software development projects is critical for project managers (PM) and researchers. A common issue they encounter is missing data values in datasets, which complicates effort estimation (EE). While several models have been introduced to address this issue, none have proven entirely effective. The Analogy-Based Effort Estimation (ABEE) model is the most widely used approach, relying on historical data for estimation. However, the common practice of deleting cases or cells with missing observations results in a reduction of statistical power and negatively impacts the performance of ABEE, leading to inefficiencies and biases. This study employ... More >

Nowadays, state estimation is widely used in fields such as autonomous driving and drone navigation. However, in practical applications, it is difficult to obtain accurate target motion models and noise covariance.This leads to a decrease in the estimation accuracy of traditional Kalman filters. To address this issue, this paper proposes an adaptive model free state estimation method based on attention parameter learning module. This method combines Transformer's encoder with Long Short Term Memory Network (LSTM), and obtains the system's operational characteristics through offline learning of measurement data without modeling the system dynamics and measurement characteristics. In addition,... More >

In recent years, deep learning techniques have been increasingly applied to the detection of remote sensing images. However, the substantial size variation and dense distribution of objects in these images present significant challenges to detection algorithms. Current methods often suffer from low efficiency, missed detections, and inaccurate bounding boxes. To address these issues, this paper presents an improved YOLO algorithm, YOLOv7-bw, designed for efficient remote sensing image detection, thereby advancing object detection applications in the remote sensing industry. YOLOv7-bw enhances the original SPPCSPC pooling pyramid network by incorporating a Bi-level Routing Attention module, w... More >