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Credit risk assessment has gained importance in recent years due to global financial crisis and credit crunch. Financial institutions therefore seek the support of credit rating agencies to predict the ability of creditors to meet financial persuasions. The purpose of this paper is to construct neural network (NN) and fuzzy support vector machine (FSVM) classifiers to discriminate good creditors from bad ones and identify a best classifier for credit risk assessment.

This study uses artificial neural network, the most popular AI technique used in the field of financial applications for classification and prediction and the new machine learning classification algorithm, FSVM to differentiate good creditors from bad. As membership value on data points influence the classification problem, this paper presents the new FSVM model. The instances membership is computed using fuzzy c-means by evolving a new membership. The FSVM model is also tested on different kernels and compared and the classifier with highest classification accuracy for a kernel is identified.

The paper identifies a standard AI model by comparing the performances of the NN model and FSVM model for a credit risk data set. This work proves that that FSVM model performs better than back propagation-neural network.

The proposed model can be used by financial institutions to accurately assess the credit risk pattern of customers and make better decisions.

This paper has developed a new membership for data points and has proposed a new FCM-based FSVM model for more accurate predictions.

The most commonly used approaches for cluster validation are based on indices but the majority of the existing cluster validity indices do not work well on data sets of different complexities. The purpose of this paper is to propose a new cluster validity index (ARSD index) that works well on all types of data sets.

The authors introduce a new compactness measure that depicts the typical behaviour of a cluster where more points are located around the centre and lesser points towards the outer edge of the cluster. A novel penalty function is proposed for determining the distinctness measure of clusters. Random linear search-algorithm is employed to evaluate and compare the performance of the five commonly known validity indices and the proposed validity index. The values of the six indices are computed for all nc ranging from (nc _min, nc _max) to obtain the optimal number of clusters present in a data set. The data sets used in the experiments include shaped, Gaussian-like and real data sets.

Through extensive experimental study, it is observed that the proposed validity index is found to be more consistent and reliable in indicating the correct number of clusters compared to other validity indices. This is experimentally demonstrated on 11 data sets where the proposed index has achieved better results.

The originality of the research paper includes proposing a novel cluster validity index which is used to determine the optimal number of clusters present in data sets of different complexities.

The purpose of this paper is to investigate consumers’ evaluation of non-focal overlay images appearing closer than the focal point (e.g. a transparent brand logo appearing in front of an online news article).

Three experiments identify factors on both task-side and image-side that influence consumers’ liking of non-focal overlay images.

The findings show that study participants evaluate the non-focal overlay image more favorably when they are engaged in a primary task that is challenging rather than unchallenging, and when the primary task and the non-focal overlay images require different processing modes (e.g. a conceptual primary task paired with a perceptual image) rather than similar processing modes (e.g. a conceptual primary task paired with a conceptual image).

A caveat is that Experiment 1 lacked a baseline condition. Another limitation is that we conducted all three experiments in a controlled laboratory environment, without real-world marketing stimuli. Therefore, further research should be conducted in a field setting to validate how extensively our theoretical insights apply to real-world marketing contexts. Future research may replicate the findings on various platforms such as YouTube and The Wall Street Journal to provide immediate, readily applicable suggestions to online marketers.

The current research provides marketers with a framework for identifying optimal vehicles for the marketing message. Transparent overlay ads can bolster or damage later evaluations of the advertised objects. Online marketers, in their desire to persuade consumers to perceive products positively, must consider what types of activities consumers are pursuing at a target website, what kinds of activities the website promotes and how meaningful are the images.

The current work extends to the work on fluency effects and persuasion knowledge model, both of which have typically shown that subtle exposure to marketing communications positively affects subsequent judgments about products and brands. The findings extend this line of evidence by demonstrating that marketing communications may exert even greater influence when the primary task requires greater cognitive processing.

The purpose of this paper is to propose a data mining approach for mining valuable markets for online customer relationship management (CRM) marketing strategy. The industry of coffee shops in Taiwan is employed as an empirical case study in this research.

Via a proposed data mining approach, the study used fuzzy clustering algorithm and Apriori algorithm to analyze customers for obtaining more marketing and purchasing knowledge of online CRM systems.

The research found three hard markets and one fuzzy market. Furthermore, the study discovered two association rules and two fuzzy association rules.

However, industry of coffee shops has been always a fast-growing and competitive business around the world. Thus, marketing strategy is important for this industry. The results and the proposed data mining approach of this research can be used in the industry of coffee shop or other retailers for their online CRM marketing systems.

The purpose of this study is to analyze the relationship between spiritual leadership, quality of work life (QWL), ethical behavior, and its implication to organizational citizenship behavior (OCB).

The study design is the quantitative method as an explanatory research with the purpose to explain the phenomena or pattern of correlation between the concepts (Solimun et al., 2017). Focus on this study is the employees in three private hospitals managed by foundations or religious-based institutions as research areas, namely (1) Adventist Hospital; (2) Pancaran Kasih Hospital; (3) Siti Maryam Hospital, Manado, Indonesia. The population in this study was all the employees, and the sample size is 150 respondents (using Slovin’s Formula), by simple random sampling. The analytical method used to test the hypothesis of the research was structural equation modeling (SEM) covariance based called partial least square (PLS).

The spiritual leadership has the direct effect QWL. The spiritual leadership has the direct effect on ethical behavior. The spiritual leadership does not have the direct effect on OCB. QWL has the direct effect on OCB. The ethical behavior has the direct effect on OCB. The spiritual leadership has the indirect effect on OCB, with QWL and ethical behavior as mediation variables.

This is one of few research studies comprehensively investigating the relationship between spiritual leadership (as exogenous variable), with QWL and ethical behavior (as intervening variable) and OCB (as endogenous variable). Based on the views and opinions of some of the mentioned writers, we assume that spiritual leadership has a relationship with OCB mediated by QWL or ethical behavior.

Reports on a new methodology for formation of virtual (“extended”) machining cells using generic capability patterns termed “resource elements”. Resource elements are used to uniquely describe the processing requirements of the component mix and dynamically match them to the processing capabilities of the machining shop. The virtual cell formation methodology is based on four steps: component requirement analysis and generation of processing alternatives; definition of virtual cell capability boundaries; machine tool selection; and system evaluation. The proposed methodology facilitates the dynamic formation of virtual manufacturing structures by providing accurate assessment of the component processing requirements and their matching with the available capabilities of the existing manufacturing facilities.

The decomposition of production facilities into efficient cells is one of the areas attracting increasing research attention due to the performance benefits which cellular manufacturing offers. One of the problems associated with cell formation is the restrictive character of the existing task formalization models resulting in most cases from the use of single machine tool routeings in representing the component requirements. In contrast, the industrial reality provides a more complex picture with multiple choice of processing routeings in terms of available machine alternatives which need to be considered in order to achieve an “optimum” cellular decomposition of manufacturing facilities. Presents a facility decomposition approach based on multiple choice of processing alternatives for each component. The decision making is based on a generic description of the component processing routeings using unique machine capability patterns ‐ “resource elements”. Manufacturing cells are formed using concurrent fuzzy clustering methodology and a validation procedure for selection of the “optimum” facility partition.

This paper aims to propose a multi-agent approach to adaptive control of fixed-wing unmanned aerial vehicles (UAVs) tracking a moving ground target. The approach implies that the UAVs in a single group must maintain preset phase shift angles while rotating around the target so as to evaluate the target’s movement more accurately. Thus, the controls should ensure that the UAV swarm follows a moving circular path whose center is the target while also attaining and maintaining a circular formation of a specific geometric shape; and the formation control system is capable of self-tuning because the UAV dynamics is uncertain.

This paper considers two interaction architectures: an open-chain where each UAV only interacts with its neighbors; and a cooperative leader, where the leading UAV is involved in attaining the formation. The cooperative controllers are self-tuned by fuzzy model reference adaptive control (MRAC).

Using open-chain decentralized architecture allows to have an unlimited number of aircraft in a formation, which is in line with the swarm behavior concept. The approach was tested for efficiency and performance in various scenarios using complete nonlinear flying-wing UAV models equipped with configured standard autopilot models.

Assume the target follows a rectilinear trajectory at a constant speed. The speed is supposed to be known in advance. Another assumption is that the weather is windless.

In contrast to known studies, this one uses Lyapunov guidance vector fields that are direction- and magnitude-nonuniform. The overall cooperative controller structure is based on a decentralized and centralized consensus.

The purpose of this paper is to investigate the potential quality of Kamut® (triticum turgidum turanicum) as an ancient relative of modern durum wheat for food preparation and Egyptian consumption.

The methodology included in this paper is based on quality evaluation of Kamut wheat of the Dashure-Fayume geographical origin physically, chemically and technologically compared to Beni Suef 1, Beni Suef 3 and Suhag 3, the most dominant durum varieties in Egypt. After that, producing a specific end product (traditional couscous) regarded the critical quality aspects in Kamut wheat.

The results obtained showed that Kamut grains had higher physical properties indicating higher milling yield potential. Besides, Kamut flour was remarkable with higher protein and oil content. The use of a farinograph for assessing the rheological properties of Kamut dough has proven a useful quality for its measured characteristics compared to the Egyptian durum varieties. The good physical and rheological properties, coupled with high protein content, validated that Kamut is a valuable addition to the Egyptian diet and suited for the production of pasta and/or couscous. The sensory attributes of traditional couscous were significantly (p < 0.05) highly acceptable to the panelists.

These results lead to valuable addition and improvement of the Egyptian diet which consider The Sustainable Agricultural Development Strategy (SADS) towards 2030 in Egypt based on achieving higher rates of food security in strategic goods in regard to improve food quality and safety, especially Kamut wheat which produces high quality grains without artificial fertilizers and pesticides.

Background: Compliance of patients with self-care practices is the mainstay of measures to manage diabetes. This study explored self-care practices of type 2 diabetes patients receiving insulin treatment in North-East Ethiopia.

Methods: The study employed an interpretive phenomenological approach using purposive sampling. The data were collected from 24 (11 males and 13 females) participants (July 2019 to January 2020) using in-depth interviews till theoretical saturation. The data were analyzed thematically and organized using QDA Miner Lite v2.0.8.

Results: The findings were categorized into: labeling diabetes, self-care maintenance experiences, recognition of symptoms, and mitigating mechanisms. The self-care maintenance practice of patients was linked with diet input preferences and the effectiveness level of insulin. What guides the self-care behavior was the patients’ preferentiality of strictly adhering to their preferred dietary inputs. Barley and wheat were the most common preferential and non-preferential inputs, respectively. The patients strictly adhered to insulin treatment because they found it effective. The most common hyperglycemia symptoms to be managed by taking an additional dose of insulin, were frequent urination, increased thirst, and their consequence (dehydration). Excessive sweating (initial), shivering (middle), and falling (final), respectively in severity, were the most common symptoms of hypoglycemia which were perceived to be treated with sweet snacks.

Originality: To our knowledge, this is the first research in Ethiopia to investigate the self-care experiences of type 2 diabetes patients receiving insulin using an interpretive phenomenological approach.