Municipal solid waste incineration (MSWI) technology has developed rapidly worldwide. Carbon monoxide (CO) is one of the to be controlled key operating index of such processes. CO emission concentration prediction is a challenge problem duo to its large fluctuation range. A new CO emission concentration prediction method based on concept drift detection using kernel principal component analysis (KPCA) is proposed. The proposed approach includes off-line model construction module, on-line concept drift detection prediction and updating module. First, we construct the LSTM-based CO prediction model using historical data and KPCA-based concept drift detection model for calculating the evaluation index. Then, recursive KPCA is used to adaptive monitor the concept drift of the time-varying process. Finally, based on continuous updating of the historical LSTM mode with the concept drift samples, we achieve higher prediction accuracy. The rationality and validity are verified with the actual data of MSWI processes.
Credit score prediction is a crucial task in financial industry, as it helps lenders and financial institutions evaluate the creditworthiness of borrowers and manage credit risk. In this work, we present a comparative study of deep learning (DL)-based credit score prediction models. To achieve this objective, we compare the performance of DL models against traditional methods in credit scoring. We train and test the models using a real-world dataset of credit histories, containing various features such as credit card balances, payment history, and employment status. Our experimental results show that the hybrid LSTM-GRU model outperform both the LSTM and GRU models in credit score prediction, as well as traditional methods. The hybrid LSTM-GRU model demonstrates higher accuracy and better predictive power, indicating its potential for improving credit scoring models in the financial industry.
Driver behavior plays a crucial role in mitigating traffic accidents. Unlike previous studies focusing on drivers' actual maneuvers, our study examines actions preceding a maneuver or those not manifest in conventional maneuvers. We gathered data from a car-sharing service frequented by university students, scrutinizing the frequency of pedal changes via foot camera images. These data were compared with the pedal depression measurements from Controller Area Network (CAN) bus data to discern instances of potentially reduced safety due to cognitive load. We identified locations with high pedal change frequency, even in the absence of any recorded pedal operation in the CAN bus data. This result suggests the existence of unrecorded driver behaviors that could precipitate traffic accidents. Our findings, therefore, bear substantial implications for enhancing traffic safety measures.
Cardiovascular diseases are the leading cause of death in the world, with arrhythmias being a significant symptom and risk factor. Advancements in technologies such as low-cost and low-power wearable devices, and machine learning techniques for analyzing big volumes of data offer opportunities to address this issue. However, low-cost devices may have limitations, including reduced data quality due to lower sampling rates, bit depth, and the number of leads recorded. These limitations might produce a significant decrease in machine learning models' performance in detecting arrhythmias. This study investigates the impact of data quality reduction on arrhythmia classification using deep neural network models. High-quality ECG data with 12 leads, 500Hz sampling rate, and 32-bits resolution were transformed into low-quality versions with varying leads (from one to six), 100Hz sampling rate, and 8-bits resolution. Training a state-of-the-art deep learning arrhythmia detection model on both high-quality and low-quality datasets revealed a decrease in performance from 95.3% to 93.9% in the worst case, which is concerning given the critical nature of the domain. To mitigate this performance loss, we propose an ensembling method that compensates for 42% of the loss, achieving an accuracy of 94.5% even with the low-quality dataset. The analysis also identifies the leads with the most promising classification performances. These results can aid in making better design decisions when creating cost-effective wearable ECG devices.
In an increasingly digitalized commerce landscape, the proliferation of credit card fraud and the evolution of sophisticated fraudulent techniques have led to substantial financial losses. Automating credit card fraud detection is a viable way to accelerate detection, reducing response times and minimizing potential financial losses. However, addressing this challenge is complicated by the highly imbalanced nature of the datasets, where genuine transactions vastly outnumber fraudulent ones. Furthermore, the high number of dimensions within the feature set gives rise to the “curse of dimensionality”. In this paper, we investigate subspace learning-based approaches centered on One-Class Classification (OCC) algorithms, which excel in handling imbalanced data distributions and possess the capability to anticipate and counter the transactions carried out by yet-tobe-invented fraud techniques. The study highlights the potential of subspace learning-based OCC algorithms by investigating the limitations of current fraud detection strategies and the specific challenges of credit card fraud detection. These algorithms integrate subspace learning into the data description; hence, the models transform the data into a lower-dimensional subspace optimized for OCC. Through rigorous experimentation and analysis, the study validated that the proposed approach helps tackle the curse of dimensionality and the imbalanced nature of credit card data for automatic fraud detection to mitigate financial losses caused by fraudulent activities.
