Applying Architectural Analysis for Current Software Systems: A Case Study of KFC and Pizza Hut Online Food Ordering Systems in Malaysia

The main aim of this study is to discover the ability in analyzing, criticizing and providing suggestion in improving the selected important properties of a software application using architectural analysis dimensions. The researchers selected KFC and Pizza Hut online food ordering systems in Malaysia for the case study purpose. These two selected systems are critically analyzed using seven architectural dimensions such as goals of analysis, scope of analysis, primary architectural concern being analyzed, level of formality of architectural models, type of analysis, level of automation, system stakeholders who are interested in analysis. The finding suggests that there are some characteristics provided by Pizza Hut system which are better than KFC system. Furthermore, details of the findings and discussion are highlighted from seven different aspects of analysis which have been carefully studied and very well analyzed on two popular online food ordering systems

A proposed memory-based collaborative filtering technique based on a new similarity and MADM methods (CF-NSMA) for improving the recommendation accuracy

The collaborative filtering (CF), as one of the most widely used and most successful approaches to provide service of recommendations, provides users with a set of recommendations related to what they need (their interests). These recommendations will be generated based on the correlation among the users’ preferences such as ratings and behaviour. Nevertheless, the number of users and items available on the Internet has increased dramatically, and most of the users do not give enough ratings for the items. Moreover, this vast growth has made the user-item rating matrix very large and sparse. This is considered a problem in the current traditional memory-based CF recommender system because the similarity calculation process between users/items becomes very difficult or may lead to locating unsuccessful neighbours which in turn to a weak recommendation. Therefore, formulating a right similarity method to identify the successful neighborhoods is a one key of memory-based CF. Similarly, the prediction method has the same level of importance in the process of improving the CF accuracy. Unfortunately, most studies on improving the accuracy of conventional CF systems have focused solely on enhancing the similarity measure. In contrast, improving the prediction method has been somewhat neglected. Consequently, the prediction method is still an open area for improvement to get better candidate items ranking and in turn increase the accuracy of CF. In the prediction process, the system predicts a user score for each item in the candidate set and promotes the highest-rated items as recommendations. This process of evaluating and ranking candidate items is therefore quite significant to the performance accuracy of the CF. Therefore, in this work, a new memory-based Collaborative Filtering (CF) technique is proposed to address the issue of sparsity data and improve the accuracy of recommendations, it is called CF-NSMA technique. The proposed technique consists of three main steps: 1- Constructing a new normalized matrix to overcome the sparsity issue; 2- Formulating a new similarity measure, based on adopting the fairness and the proportion of common rating factors to locate the accurate neighbours; 3- Applying the MADM method to get better evaluating and ranking list of candidate items. These phases were carefully designed and implemented to solve the issues that were mentioned earlier. Moreover, to assess the accuracy of CF-NSMA technique, several experiments were conducted using a public dataset (MovieLens 100K, MovieLens 1M benchmark datasets). The evaluation process was performed to measure the accuracy of the proposed technique using Mean Absolute Error (MAE) to measure the prediction accuracy and Precision, Recall and F-measure to measure the performance accuracy. These selected metrics are considered as the most common metrics to be used in an accuracy evaluation process of the CF techniques. The result of the experiments revealed that the accuracy of the proposed technique is better compared to the common base memory-based CF methods. The prediction accuracy percentage in terms of MAE was around 0.76 and 0.74 via 100K and 1M datasets, respectively. While, the improvement of the CF-NSMA technique in terms of performance accuracy was around more than three-fold in term precision, around four-fold in term of recall, and around three-fold in term of F-measure. In conclusion, this work contributes significantly to the field of improving the accuracy of memory-based CF by developing the critical phases of traditional memory-based CF, including re-representing the rating matrix, formulating a new similarity method and replacing the prediction method with the MADM method. Furthermore, MADM successfully minimizes the negative effect of the prediction method in evaluating and ranking the candidate items and significantly improves the accuracy of memory-based CF. Therefore, the primary objectives of this research were achieved

Collaborative Filtering Similarity Measures: Revisiting

This paper discussed the most commonly used similarity measures in Collaborative Filtering (CF) recommender system. In addition, the author introduced some recommendations related to CF system quality improvement which should be considered in the process of formulating similarity measure that may lead to alleviating the issue of data sparsity and some existing measures shortcomings. Generally, CF approach is one of the most widely used and most successful methods for the recommendation system, such as e-commerce. CF system introduced items to the user based on his/her previous ratings and the ratings of his/her neighbors. Therefore, the most important stage in CF system is locating the successful neighbor. Nevertheless, the sparsity of data is the major issue faced by the memory-based CF. The reason behind this is that many of the users rated a few number of items from the huge number of available items. This has encouraged many researchers to provide solutions. One of these solutions was by proposing or updating similarities measures take in considerations the global information preference, all ratings provided by users, the size of common ratings, and so on. In this work, the researcher discussed these measures alongside with their limitations. In addition, the researcher also listed some advicesthat are important in the process of locating successful neighbors, which may help researchers to improve the quality of CF system

A Developed Collaborative Filtering Similarity Method to Improve the Accuracy of Recommendations under Data Sparsity

This paper presented a new similarity method to improve the accuracy of traditional Collaborative Filtering (CF) method under sparse data issue. CF provides the user with items, that what they need, based on analyses the preferences of users who have a strong correlation to him/her preference. However, the accuracy is influencing by the method that use to find neighbors. Pearson correlation coefficient and Cosine measures, as the most widely used methods, depending on the rating of only co-rated items to find the correlations between users. Consequently, these methods have lack of ability in addressing the sparsity. This paper presented a new proposed similarity method based on the global user preference to address the sparsity issue and improve the accuracy of recommendation. Thus, the novelty of this method is the ability to solve the similarity issue with a capability of finding the relationship among non-correlated users. Furthermore, to determine the right neighbors during the process of computing the similarity between a pair of users, the developed method considered two main factors (fairness and proportion of co-rated). The MovieLens 100K benchmark dataset is used to evaluate the developed method accuracy. The experiments’ result showed that the accuracy of the developed method is improved compared to the traditional CF similarity methods using a specific common CF evaluation metrics

Collaborative Filtering Recommender System: Overview and Challenges

This paper is to present an overview of Collaborative Filtering (CF) recommender system and show the major CF challenges. In general, the recommendation systems are the best way to help users to overcome the information overload issue. The CF approach is one of the most widely used and most successful methods in the recommendation system, such as e-commerce. This paper introduced a brief description about recommender’s approaches which are: content-Based, collaborative filtering and hybrid approach. Next, defined the main challenges which have clearly impact on the performance and accuracy of CF recommender system. The major finding of this paper is the CF main problems: Data sparsity, Cold-star, and Scalability. By presenting of these challenges the quality of recommendations can be improved by proposing new methods. The paper ends with conclusion summarizes the limitations of the existing methods and recommendations

An improved memory-based collaborative filtering method based on the TOPSIS technique.

This paper describes an approach for improving the accuracy of memory-based collaborative filtering, based on the technique for order of preference by similarity to ideal solution (TOPSIS) method. Recommender systems are used to filter the huge amount of data available online based on user-defined preferences. Collaborative filtering (CF) is a commonly used recommendation approach that generates recommendations based on correlations among user preferences. Although several enhancements have increased the accuracy of memory-based CF through the development of improved similarity measures for finding successful neighbors, there has been less investigation into prediction score methods, in which rating/preference scores are assigned to items that have not yet been selected by a user. A TOPSIS solution for evaluating multiple alternatives based on more than one criterion is proposed as an alternative to prediction score methods for evaluating and ranking items based on the results from similar users. The recommendation accuracy of the proposed TOPSIS technique is evaluated by applying it to various common CF baseline methods, which are then used to analyze the MovieLens 100K and 1M benchmark datasets. The results show that CF based on the TOPSIS method is more accurate than baseline CF methods across a number of common evaluation metrics

Environmental Risks in Supply Chain: Recommendations and Directions for Future Research

This paper explores the environmental risks in supply chains and provides recommendations for subsequent studies. Typically, the environmental responsibility is a core part of sustainability which focuses attention directly to protecting and restoring the environment. Furthermore, environmental awareness is essential to design globally distributed supply chain networks. Therefore, the aim of this research is to explore the environmental risks and to identify the available solutions along with a projection of future trends. The basic research steps have been followed to complete this work. The Findings of this research describes the environmental risks that arises during supply chains operations. Furthermore, recommended solutions have been highlighted to address the identified risks. The Blockchain technology is regarded as a suitable solution for the environmental risks issue. The motivation for recommending this technology is its capabilities to contribute effectively towards greening the supply chain operations

Reducing the throughput time for patient flow in emergency department: simulation and modelling overview

Satisfaction of patient considered as a main issue of quality of service in the healthcare sector. Typically, this satisfaction depends on the services quality provided by hospitals. Emergency Department (ED), as a critical department in the hospital, has a complicated registration system that may lead to increase the patient throughput time. Thus, to minimize this growing in the throughput time, numerous simulation and modelling, in the literature, have been developed and introduced. However, the throughput time in ED still represent in issue need for improvement to increase the ED performance. Therefore, in this paper, the main objective is providing an overview related to the characteristics and significance of current simulation and model techniques. As a result, in the ED realistically, integrating Agent-Based Simulation (ABS), Desecrate Event Simulation (DES), and System Dynamic (SD) techniques has been preferred as the solution to modelling the patient flow in ED and in turn may lead to decrease the throughput time. The proactive and independent characteristics of aforementioned techniques can contribute to the good representation the patients flow and their throughput time in ED

Eco-design based on collaborative filtering recommender system

Eco-design Collaborative Filtering Recommender System is an approach to assist designers in producing a green product. Collaborative Filtering (CF) approach is the most commonly used and most successful approaches for the systems of recommendation. In eco-design (Ecological Design), several studies focused on the implementation of eco strategies to reduce the products’ environmental impact. While the raw materials of the product are even more important in order to design a product to preserve the environment. Therefore, in this paper, the researcher employ the CF to develop a new eco-design method to provide a set of raw materials to assist the designers at early stage to preserve the environment. CF system is able to overcome the information overload issue by analyzing the past behavior of its users. It’s very simple and effective way to assist eco-designer to identify the best options from alternatives. CF system introduce a set of recommendations to the product designers through comparing the new product with the existing products in data base based on products’ information. Next, determine the most similar products and rank them based on its environmental impact. Then, the components of products which have low environment impact will be provided to the eco-designers as a recommendations. An assumed example of eco-design will be used to explanation the proposed method. Further research can be conducted on this proposed method by implementing it with real dataset to generalize its performance

New usability guidelines with implementation ways of mobile learning application based on mobile learning usability attributes

Objectives: This work aims to propose new improvements to mobile learning application. These improvements are presented by proposing new guidelines of mobile learning application along with their implementation ways and propose mapping each proposed guideline with mobile learning usability attributes. Methods/Statistical Analysis: The researchers review the existing usability guidelines for mobile learning application in related work in order to identify the current challenges. Proposed solutions are presented to solve the identified challenges by producing new guidelines along with their implementation ways. Also, the researcher proposed mapping these guidelines with mobile usability learning attributes to ensure that proposed guidelines covers the usability issues of mobile learning. Moreover, the proposed improvements are evaluated using expert usability review method to measure its acceptance. Findings: Expert usability review method is used to evaluate the proposed guidelines, proposed implementation way of guidelines, and proposed mapping guidelines with usability mobile for mobile learning application. The researchers used quantitative approach for the data collection purpose. Evaluation result shows that 81% of the participated experts’ opinions are agreed with the proposed improvements of the usability guidelines based on produced guidelines, proposed ways to implement each guideline, and proposed mapping the guidelines with mobile learning usability attributes. Followed by 11% of the experts’ opinion are undecided and only 7% of the experts’ opinion are disagree the proposed improvement of the usability. Application/Improvements: From findings, it is obvious that the proposed improvements can be used for developing mobile learning application with meeting usability standards efficiently. New guideline can be added in order to handle new issues that might be appeared in the future