One of the major problems of e-commerce globally is the selling and buying of goods among the parties over the Internet in which the traders may not trust their partners. Cash on delivery allows customers to pay in cash when the product is delivered to their home or a location they choose. This is sometimes called a payment system because customers receive goods before making a payment. This paper investigates a critical verification process issue in the cash on delivery system. In particular, we propose a multi shippers mechanism, which consists of blockchain technology, smart contracts and hyper-ledger fabric platform to achieve distributed and trustworthy verification across participants in the decentralized markets. Our proposed mechanism is given to not only ensure the benefits of the seller but also prevent shipper’s fraudulent. The solution leverages the consistency and robustness of decentralized markets where trust is flexible and effectively controlled. To demonstrate the application and implementation of the proposed framework, we conduct several case studies on real-world transaction datasets from a local computer retailer. We also provide our sources codes for further reproducibility and development. Our conclusion is that the continued integration of multi-shipper mechanism and blockchain technology in the decentralized markets will cause significant transformations across several disciplines.

In emerging economies, with the explosion of e-commerce, payment methods have increasingly enhanced security. However, Cash-on-Delivery (COD) payment method still prevails in cash-based economies. Although COD allows consumers to be more proactive in making payments, it still appears to be vulnerable by the appearance of a third party (shipping companies). In this paper, we proposed a payment system based on “smart contract” implemented on top of blockchain technology to minimize risks for parties. The platform consists of a set of rules that each party must follow including specific delivery time and place, cost of delivery, mortgage money; thereby, forcing parties to be responsible for their tasks in order to complete the contract. We also provided a detailed implementation to illustrate the efficiency of our model.

The adoption of decentralized cryptocurrency plat-forms is growing fast, thanks to the implementation of Blockchain technology and smart contracts. It encourages the novel frame-works in a wide range of applications including finance and payment methods such as cash on delivery. However, a large number of smart contracts developed for cash on delivery suffer from fraudulent transactions which enable malicious participants to break the signed contracts without sufficient penalties. A shipper will involve in the system and place a mortgage to ensure reliability. A buyer also pledges an amount of money when making the order. Our process not only ensures the interests of a seller but also prevents a fraud shipper. The penalties will be made in two scenarios: (i) the buyer refuses to receive the commodities without any reliable reasons; and (ii) the shipper attempts to make any modification on the delivered goods during transportation. To help developers create more secure and reliable cash on delivery system, we introduce double smart contracts, a framework rooted in Blockchain technology and Ethereum, to tackle those mentioned problems. We also contribute our solution as an open source software that developers can easily add to their implementation to enhance functionality.

There has been a lot of research on the use of deep neural networks in forecasting time series and chaotic time series data. However, there exist very few works on multi-step ahead forecasting in chaotic time series using deep neural networks. Several strategies that deal with multi-step-ahead forecasting problem have been proposed in literature: recursive (or iterated) strategy, direct strategy, a combination of both the recursive and direct strategies, called DirRec, the Multiple-Input Multiple-Output (MIMO) strategy, and the fifth strategy, called DirMO which combines Direct and MIMO strategies. This paper aims to propose a new deep learning model for chaotic time series forecasting: LSTM-based stacked autoencoder and answer the research question: which strategy for multi-step ahead forecasting using LSTM-based stacked autoencoder yields the best performance for chaotic time series. We evaluated and compared in terms of two performance criteria: Root-Mean-Square Error (RMSE) and Mean-Absolute-Percentage Error (MAPE). The experimental results on synthetic and real-world chaotic time series datasets reveal that MIMO strategy provides the best predictive accuracy for chaotic time series forecasting using LSTM-based stacked autoencoder.

Recommender systems have undergone more than three decades of continuous development, evolving from early collaborative filtering techniques and matrix factorization to the integration of deep learning methods. In recent years, ranking prediction in collaborative filtering has attracted particular attention, as it directly impacts personalization quality in domains such as e-commerce, social media, and multimedia services. Among various approaches, Graph Neural Networks have emerged as a dominant paradigm, owing to their inherent ability to model high-order user–item relationships. This survey provides a systematic and up-to-date review of graph-based collaborative filtering models for ranking prediction, covering the period 2020–2025 and analyzing 80 representative works. We classify existing methods along multiple dimensions, including graph learning techniques, model architectures, auxiliary learning strategies, recommendation scenarios, and application domains. Furthermore, we summarize evaluation protocols and benchmark datasets to provide practical guidance for future experimentation. Finally, we discuss major challenges—such as data sparsity, cold start, oversmoothing, fairness, and scalability—and outline promising research directions, including self-supervised learning, multimodal modeling, and the integration of large language models into recommender systems. This work aims to serve both researchers and practitioners as a comprehensive reference and a roadmap for advancing the field of GNN-based ranking prediction in collaborative filtering.