This study aims to optimize Deep Feedforward Neural Networks (DFNNs) training using nature-inspired optimization algorithms, such as PSO, MTO, and its variant called MTOCL. We show how these algorithms efficiently update the weights of DFNNs when learning from data. We evaluate the performance of DFNN fused with optimization algorithms using three Wisconsin breast cancer datasets, Original, Diagnostic, and Prognosis, under different experimental scenarios. The empirical analysis demonstrates that MTOCL is the most performing in most scenarios across the three datasets. Also, MTOCL is comparable to past weight optimization algorithms for the original dataset, and superior for the other datasets, especially for the challenging Prognostic dataset.

Shill Bidding (SB) is still a predominant auction fraud because it is the toughest to identify due to its resemblance to the standard bidding behavior. To reduce losses on the buyers' side, we develop an example-incremental classification model that detects fraudsters from incoming auction transactions. Thousands of auctions occur every day in a commercial site, and to process the continuous rapid data flow, we design a batch-based incremental classification algorithm that addresses the imbalanced and non-linear learning. We train the proposed algorithm incrementally with several SB training batches and concurrently assess the performance of the new learned models with unseen batches.

The 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2018) was held at Concordia University in Montreal, Canada, June 25–28, 2018. This report summarizes the The 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2018) was held at Concordia University in Montreal, Canada, June 25–28, 2018.  IEA/AIE 2018 continued the tradition of emphasizing on applications of applied intelligent systems to solve real-life problems in all areas including engineering, science, industry, automation a robotics, business and finance, medicine and biomedicine, bioinformatics, cyberspace, and human-machine interactions.

Although shill bidding is a common auction fraud, it is however very tough to detect. Due to the unavailability and lack of training data, in this study, we build a high-quality labeled shill bidding dataset based on recently collected auctions from eBay. Labeling shill biding instances with multidimensional features is a critical phase for the fraud classification task. For this purpose, we introduce a new approach to systematically label the fraud data with the help of the hierarchical clustering CURE that returns remarkable results as illustrated in the experiments.

In the last three decades, we have seen a significant increase in trading goods and services through online auctions. However, this business created an attractive environment for malicious moneymakers who can commit different types of fraud activities, such as Shill Bidding (SB). The latter is predominant across many auctions but this type of fraud is difficult to detect due to its similarity to normal bidding behaviour. The unavailability of SB datasets makes the development of SB detection and classification models burdensome. Furthermore, to implement efficient SB detection models, we should produce SB data from actual auctions of commercial sites. In this study, we first scraped a large number of eBay auctions of a popular product. After preprocessing the raw auction data, we build a high-quality SB dataset based on the most reliable SB strategies. The aim of our research is to share the preprocessed auction dataset as well as the SB training (unlabelled) dataset, thereby researchers can apply various machine learning techniques by using authentic data of auctions and fraud.

Multi-Attribute Reverse Auctions (MARAs) are excellent protocols to automate negotiation among sellers. Eliciting the buyer0s preferences and determining the winner are both challenging problems for MARAs. To solve these problems, we propose two algorithms namely MAUT* and CP-net*, which are respectively the improvement of the Multi-Attribute Utility Theory (MAUT) and constrained CP-net. The buyers can now express conditional, qualitative as well as quantitative preferences over the item attributes. To evaluate the performance in time of the proposed algorithms, we conduct an experimental study on several problem instances. The results favor MAUT* in most of the cases.