Evolutionary intelligent agents for e-commerce: Generic preference detection with feature analysis

Product recommendation and preference tracking systems have been adopted extensively in e-commerce businesses. However, the heterogeneity of product attributes results in undesired impediment for an efficient yet personalized e-commerce product brokering. Amid the assortment of product attributes, there are some intrinsic generic attributes having significant relation to a customer’s generic preference. This paper proposes a novel approach in the detection of generic product attributes through feature analysis. The objective is to provide an insight to the understanding of customers’ generic preference. Furthermore, a genetic algorithm is used to find the suitable feature weight set, hence reducing the rate of misclassification. A prototype has been implemented and the experimental results are promising

Intelligent Product Brokering for E-Commerce: An Incremental Approach to Unaccounted Attribute Detection

This research concentrates on designing generic product-brokering agent to understand user preference towards a product category and recommends a list of products to the user according to the preference captured by the agent. The proposed solution is able to detect both quantifiable and non-quantifiable attributes through a user feedback system. Unlike previous approaches, this research allows the detection of unaccounted attributes that are not within the ontology of the system. No tedious change of the algorithm, database, or ontology is required when a new product attribute is introduced. This approach only requires the attribute to be within the description field of the product. The system analyzes the general product descriptions field and creates a list of candidate attributes affecting the user’s preference. A genetic algorithm verifies these candidate attributes and excess attributes are identified and filtered off. A prototype has been created and our results show positive results in the detection of unaccounted attributes affecting a user

SAFE: Secure-Roaming Agents for E-commerce

The development of the Internet has made a powerful impact on the concept of commerce. E-commerce, a new way to conduct business, is gaining more and more popularity. Despite its rapid growth, there are limitations that hinder the expansion of e-commerce. The primary concern for most people when talking about on-line shopping is security. Due to the open nature of the Internet, personal financial details necessary for on-line shopping can be stolen if sufficient security mechanism is not put in place. How to provide the necessary assurance of security to consumers remains a question mark despite various past efforts. Another concern is the lack of intelligence. The Internet is an ocean of information depository. It is rich in content but lacks the necessary intelligent tools to help one locate the correct piece of information. Intelligent agent, a piece of software that can act on behalf of its owner intelligently, is designed to fill this gap. However, no matter how intelligent an agent is, if it remains on its owner’s machine and does not have any roaming capability, its functionality is limited. With the roaming capability, more security concerns arise. In response to these concerns, SAFE, Secure roaming Agent For E-commerce, is designed to provide secure roaming capability to intelligent agents

Secure agent data integrity shield

In the rapidly expanding field of E-Commerce, mobile agent is the emerging technology that addresses the requirement of intelligent filtering/processing of information. This paper will address the area of mobile agent data integrity protection. We propose the use of Secure Agent Data Integrity Shield (SADIS) as a scheme that protects the integrity of data collected during agent roaming. With the use of a key seed negotiation protocol and integrity protection protocol, SADIS protects the secrecy as well as the integrity of agent data. Any illegal data modification, deletion, or insertion can be detected either by the subsequent host or the agent butler. Most important of all, the identity of each malicious host can be established. To evaluate the feasibility of our design, a prototype has been developed using Java. The result of benchmarking shows improvement both in terms of data and time efficiency

Agent fabrication and its implementation for agent-based electronic commerce

In the last decade, agent-based e-commerce has emerged as a potential role for the next generation of e-commerce. How to create agents for e-commerce applications has become a serious consideration in this field. This paper proposes a new scheme named agent fabrication and elaborates its implementation in multi-agent systems based on the SAFER (Secure Agent Fabrication, Evolution & Roaming) architecture. First, a conceptual structure is proposed for software agents carrying out e-commerce activities. Furthermore, agent module suitcase is defined to facilitate agent fabrication. With these definitions and facilities in the SAFER architecture, the formalities of agent fabrication are elaborated. In order to enhance the security of agent-based e-commerce, an infrastructure of agent authorization and authentication is integrated in agent fabrication. Our implementation and prototype applications show that the proposed agent fabrication scheme brings forth a potential solution for creating agents in agent-based e-commerce applications

Multiorder neurons for evolutionary higher-order clustering and growth

This letter proposes to use multiorder neurons for clustering irregularly shaped data arrangements. Multiorder neurons are an evolutionary extension of the use of higher-order neurons in clustering. Higher-order neurons parametrically model complex neuron shapes by replacing the classic synaptic weight by higher-order tensors. The multiorder neuron goes one step further and eliminates two problems associated with higher-order neurons. First, it uses evolutionary algorithms to select the best neuron order for a given problem. Second, it obtains more information about the underlying data distribution by identifying the correct order for a given cluster of patterns. Empirically we observed that when the correlation of clusters found with ground truth information is used in measuring clustering accuracy, the proposed evolutionary multiorder neurons method can be shown to outperform other related clustering methods. The simulation results from the Iris, Wine, and Glass data sets show significant improvement when compared to the results obtained using self-organizing maps and higher-order neurons. The letter also proposes an intuitive model by which multiorder neurons can be grown, thereby determining the number of clusters in data

Henri Temianka Correspondence; (gruan-an)

This collection contains material pertaining to the life, career, and activities of Henri Temianka, violin virtuoso, conductor, music teacher, and author. Materials include correspondence, concert programs and flyers, music scores, photographs, and books.https://digitalcommons.chapman.edu/temianka_correspondence/3742/thumbnail.jp