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All Journal International Journal of Computer and Information System (IJCIS)
Jia, Xuzhong
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Real-time Multimodal Route Optimization and Anomaly Detection for Cross-border Logistics Using Deep Reinforcement Learning Xi, Yue; Jia, Xuzhong; Zhang, Hanqing
International Journal of Computer and Information System (IJCIS) Vol 5, No 2 (2024): IJCIS : Vol 5 - Issue 2 - 2024
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v5i2.209

Abstract

This paper presents a new approach to improve multiple choice and defect detection in cross-border shipments using deep learning (DRL). The design process involves the integration of real-time data from multiple sources to create comprehensive transportation models, including route optimization, cost reduction, and poor research methods. The DRL project is intended to use a multi-agent design to manage complex decision-making processes in a dynamic logistics environment. The hybrid anomaly detection system combines statistics with machine learning techniques to identify and respond to network disruptions. The system's performance was validated using a database including 185,432 shipment records collected over 24 months across the Asia-Pacific transportation system. The experimental results show that the proposed method has achieved 94.5% correct value in choosing the right path and 45% reduction in processing time compared to traditional methods. The negative detection antibody maintains a 96.2% true positive rate with a 1.8% false positive rate. The system's analysis shows that the growth of the needs in the calculation of the growth in the network, indicating the use of good resources in the large deployment. This research supports the state-of-the-art in cross-border business optimization by providing solutions that integrate real-time optimization methods with negative detection and response mechanisms.
Joint Enhancement of Historical News Video Quality Using Modified Conditional GANs: A Dual-Stream Approach for Video and Audio Restoration Jia, Xuzhong; Zhang, Hanqing; Hu, Chenyu; Jia, Guancong
International Journal of Computer and Information System (IJCIS) Vol 5, No 1 (2024): IJCIS : Vol 5 - Issue 1 - 2024
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v5i1.208

Abstract

The preservation and enhancement of historical news video archives represent a critical challenge in digital archiving. This paper proposes a novel dual-stream approach leveraging modified conditional Generative Adversarial Networks (GANs) for simultaneous enhancement of video and audio quality in historical news footage. The framework incorporates parallel processing pathways for video and audio restoration, connected through an innovative feature fusion mechanism. The architecture introduces several key improvements, including temporal-aware processing modules, multi-scale discriminators, and adaptive feature fusion strategies. Comprehensive experiments conducted on a diverse dataset of historical news broadcasts from 1960-2000 demonstrate significant improvements over existing methods, achieving a 35.2% increase in Peak Signal-to-Noise Ratio (PSNR) and 29.8 dB improvement in audio Signal-to-Noise Ratio (SNR). The proposed framework maintains temporal coherence while preserving content authenticity, addressing critical challenges in archival media restoration. Quantitative evaluations show superior performance across multiple quality metrics, while qualitative assessments confirm enhanced perceptual quality and historical accuracy preservation. The experimental results validate the effectiveness of the dual-stream approach in historical news video restoration, establishing a new benchmark for automated archival media enhancement.
Co-Authors Hu, Chenyu Jia, Guancong Yue Xi Zhang, Hanqing