Designing a knowledge-based system (KBS) to study consumer purchase intention: the impact of digital influencers in Pakistan

PurposeThe purpose of this study is to present a complete framework that defines the link between choices and decision criteria based on existing research on digital influencers (DIs) connected to consumer purchase intentions. The primary goal of this article is to assess the effect of DIs on customer purchase intentions via the creation of an integrated knowledge-based system (KBS).Design/methodology/approachThe suggested KBS is based on the fuzzy analytic hierarchy process (AHP), which creates a link between DI elements and their overall effect on consumer purchase intentions.Findings With the help of a KBS, the performance of DIs may be evaluated. It demonstrates the link between choices connected to factors and decision criteria of various variables, demonstrating the beneficial effect of DIs in molding customer purchase intentions in the organic skincare industry.Practical implicationsThe proposed KBS would aid marketing managers and decision makers in assessing the effect of DIs on customer purchase intentions. This research would also give decision makers with extensive information on influencer marketing and crucial elements that have a significant effect on customer purchase intentions.Originality/valueThis is the first research to employ the fuzzy AHP methodology and KBS in relation to influencers' effect. No prior research has targeted the organic skincare industry to assess the effect of Internet influencers on consumer purchase intentions. Furthermore, the KBS offers a holistic and complete way to studying influencers' effect on cost per impression (CPI) by establishing a linkage between choices and decision criteria.

A Knowledge-Based System for Children’s Music Teaching Strategies Based on the Inheritance of Local Music Culture in Southern Jiangsu

In order to analyze the feasibility of integrating Sunan music into children’s music teaching through research, a knowledge-based system for children’s music teaching strategies based on case-based reasoning is proposed. The system can collect sufficient data and information, the exact orientation of piano learning, and the exact choice of teaching modes by questionnaires. Research data show that the vast majority of children like music teaching, and they like 76% more than they do not like, and more parents who express an indifferent attitude to music are 84% more than those who feel important. This experimental study shows that most teachers hold a positive attitude toward the development and use of folk music education resources in kindergartens, and parents do not have enough knowledge about folk music. The infiltration of excellent local culture in children’s music activities can improve students’ cultural literacy, let the new generation understand the tradition and the classics, and let children understand the local culture of southern Jiangsu and inherit the excellent culture of the Chinese nation through the understanding, feeling, and understanding of the local culture of southern Jiangsu. This is the new idea of local cultural heritage in southern Jiangsu and also the responsibility of kindergarten education.

Big knowledge-based semantic correlation for detecting slow and low-level advanced persistent threats

Targeted cyber attacks, which today are known as Advanced Persistent Threats (APTs), use low and slow patterns to bypass intrusion detection and alert correlation systems. Since most of the attack detection approaches use a short time-window, the slow APTs abuse this weakness to escape from the detection systems. In these situations, the intruders increase the time of attacks and move as slowly as possible by some tricks such as using sleeper and wake up functions and make detection difficult for such detection systems. In addition, low APTs use trusted subjects or agents to conceal any footprint and abnormalities in the victim system by some tricks such as code injection and stealing digital certificates. In this paper, a new solution is proposed for detecting both low and slow APTs. The proposed approach uses low-level interception, knowledge-based system, system ontology, and semantic correlation to detect low-level attacks. Since using semantic-based correlation is not applicable for detecting slow attacks due to its significant processing overhead, we propose a scalable knowledge-based system that uses three different concepts and approaches to reduce the time complexity including (1) flexible sliding window called Vermiform window to analyze and correlate system events instead of using fixed-size time-window, (2) effective inference using a scalable inference engine called SANSA, and (3) data reduction by ontology-based data abstraction. We can detect the slow APTs whose attack duration is about several months. Evaluation of the proposed approach on a dataset containing many APT scenarios shows 84.21% of sensitivity and 82.16% of specificity.

Farm-n-Pedia: Expert mobile agricultural knowledge-based system for Indian Farmers

Efficiency in farming productivity and optimum utilization of resources in the agriculture sector in developing countries is a challenge that can be addressed with technological advancement. There is also a strong need to work on the farming community’s engagement to make better farming decisions. This study aims to create an expert knowledge-based system (KBS) in a mobile application to help Indian farmers improve their agricultural practices and increase crop productivity. A prototype mobile application, ‘Farm-n-Pedia,’ is designed and used to fulfill the farmers’ informational and engagement needs. It provides a tool for agriculture management using a single platform. The expert KBS incorporates a crowdsourcing system as part of the knowledge base and interface design. The mobile application enables the users to access the worldwide information they want, get personalized expert guidance, interact with the local agrarian community, know about the latest farming techniques and technology, crowdsource data collection and increase agricultural productivity.