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Error checking is easy and inexpensive in the initial stages as compared to later stages due to when the development cycle precedes the development cost and efforts also increase. UML class model is a key element of modern software methodologies and creates in the initial stage of software development. Therefore, error detection and rectification of the UML class model may save software development costs and time. This paper presents an overview of UML Class model verification approaches and identifies open issues, current research trends, and other improvement areas. This study uses a systematic literature review as an investigation method with six research questions and assesses 65 papers dated January 1997 to December 2020. From 2124 published research papers, 65 papers are selected and distributed into 7 studies. This work provides an analysis of verification approaches and the automation level of proposed approaches. As a result, it is found that the existing UML class model verification methods provide great efforts to check correctness. However, in some situations (when dealing with large and complex models), they consume a significant amount of time and do not support many important features of the UML class model.

Diabetic Retinopathy (DR) is a polygenic disorder issue that affects human eyes. Bruise to the blood vessels of the photosensitive tissue of the retina causes this complication. It’s most frequent in patients who had diabetes for more than ten years. This downside is going on in several individuals worldwide. However, the number of medical practitioners and also the tools needed for the detection of DR are very less for serving the mass population. In this paper, we have proposed DRDNet (Diabetic Retinopathy Diagnosis Network), a neural network framework based on capsule networks (CapsNets) for DR diagnosis. Experiments on a dataset with 1,265 images demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-ofthe-art techniques. The proposed DRDNet performs with an overall accuracy of 80.59% for five class, as compared to the closest competitor with an accuracy of 75.83%. We performed a study on a mixed dataset for two class and found that testing accuracy was 80.59%. We have also done training on a two class model and testing on other unseen datasets. Moreover, we observed that DRDNet has much higher confidence for the predicted probabilities as compared to other state-of-the-art techniques.

This paper provides a comparative study between the performance of Model Reference Adaptive Controller (MRAC) using linearized and nonlinear commercial aircraft models. The linearized and nonlinear flight dynamics of the aircraft were generated using NASA Transport Class Model (TCM). MRAC was developed in pervious study but implemented and integrated with the TCM model to compare its performance on linearized and nonlinear aircraft models. This study focused on comparing the performance of MRAC only with longitudinal model of the aircraft. In addition, the study compares the responses of the angle of attack (AOA) using both models. The simulation tests of the study was conducted under known flight trim conditions. The simulation results showed that MRAC produced comparable responses with linearized and nonlinear aircraft models. Implementation of the MARC on the aircraft models as well as the simulation architectures and test results are all presented in this paper.

One problem of exploiting information captured by an OO system model such as class models is mapping user queries on the model. This is particularly due to the informal character of the user queries. This paper presents an approach based on text mining for identifying and interpreting user queries. This would enable a matching between the interpreted user query and the corresponding entities specified by the system model. Therefore, navigation expressions in OCL can be derived from the system model that have a formal character and are able to answer the user questions against the class model.

This paper proposes LTE radio scheduler analytical models. The motivation behind the models is to evaluate the performance of the complex LTE system analytically, rather than using simulations or real life experiments (which are not always possible to perform) that are too exhaustive and consumes large computation power and time. The models are developed to approximate the system behavior mathematically; it enhances the performance evaluation's processing effort and time significantly compared to traditional simulations. The proposed analytical models are based on the Continuous Time Markov Chain (CTMC) and represent different LTE Time Domain Schedulers (TDS) as well as two different model categories: a single class model and a two class model. In the single class model only a single traffic class is considered with no QoS differentiation. Whereas, the two classes model supports QoS differentiation between the two classes. An extensive analysis have been performed using the aforementioned analytical models and is compared with a well developed simulation model.

Compared with other large-scale warfare simulation systems, wargame, as a traditional type of warfare simulation, has the advantages of low cost, convenience, practicability, etc. This paper discusses the system structure and class model based on “Future: Korea War” which is a product of U.S. “One Small Step” company's Millennium Wars series strategic-level wargames, and designs wargame umpire rule description method based on CLIPS. The “Future: Korea War” system umpire rules could be revised without recompilation of the application software, which enhances the flexibility and expansibility of the wargame umpire rules.

A class model of rational auction negotiation for multi-agent in which time is related. In a creative way, time parameter is introduced into the process of the rational negotiation in multi-agent system. Buyer agents are able to choose several bidding ways like time-dependent tactics, behavior-dependent tactics. As the result, agents can receive different utilities.