Process Mining (PM) and PM tool abilities play a significant role in meeting the needs of organizations in terms of getting benefits from their processes and event data, especially in this digital era. The success of PM initiatives in producing effective and efficient outputs and outcomes that organizations desire is largely dependent on the capabilities of the PM tools. This importance of the tools makes the selection of them for a specific context critical. In the selection process of appropriate tools, a comparison of them can lead organizations to an effective result. In order to meet this need and to give insight to both practitioners and researchers, in our study, we systematically reviewed the literature and elicited the papers that compare PM tools, yielding comprehensive results through a comparison of available PM tools. It specifically delivers tools’ comparison frequency, methods and criteria used to compare them, strengths and weaknesses of the compared tools for the selection of appropriate PM tools, and findings related to the identified papers' trends and demographics. Although some articles conduct a comparison for the PM tools, there is a lack of literature reviews on the studies that compare PM tools in the market. As far as we know, this paper presents the first example of a review in literature in this regard.

The In-Vehicle Coupon Recommendation System is a type of coupon used to represent an idea of different driving scenarios to users. Basically, with the help of presenting the scenarios, the people’s opinion is taken on whether they will accept the coupon or not. The coupons offered in the survey were for Bar, Coffee Shop, Restaurants, and Take Away. The dataset consists of various attributes that capture precise information about the clients to give a coupon recommendation. The dataset is significant to shops to determine whether the coupons they offer are benefi-cial or not, depending on the different characteristics and scenarios of the users. A major problem with this dataset was that the dataset was imbalanced and mixed with missing values. Handling the missing values and imbalanced class problems could affect the prediction results. In the paper, we analysed the impact of four different imputation techniques (Frequent value, mean, KNN, MICE) to replace the missing values and use them to create prediction mod-els. As for models, we applied six classifier algorithms (Naive Bayes, Deep Learning, Logistic Regression, Decision Tree, Random Forest, and Gradient Boosted Tree). This paper aims to analyse the impact of the imputation techniques on the dataset alongside the outcomes of the classifiers to find the most accurate model among them. So that shops or stores that offer coupons or vouchers would get a real idea about their target customers. From our research, we found out that KNN imputation with Deep Learning classifier gave the most accurate outcome for prediction and false-negative rate.

The paper presents the design, simulation and evaluation of an improved obstacle avoidance model for the lawnmower. Studies has shown that there has been few or no work done has on the total minimization of computational time in obstacle avoidances of land mower. Sample image data were collected through a digital camera of high resolution. The obstacle avoidance model was designed using the Unified Modelling Language tools to model the autonomous system from the higher-level perspective of the structural composition of the system. Automata theory was used to model two major components of the system, which are the conversion of the colour image to binary and the obstacle recognizer components by neural network. The model was simulated using the and evaluated using the false acceptance rate and false rejection rate as performance metrics. Results showed that the model obtained False Acceptance Rate and False Rejection Rate values of 0.075 and 0.05 respectively. In addition, the efficiency of the proposed algorithm used in the present work shows that the time taken to avoid obstacles was faster when compared with another existing model.

Scaled agile approaches are increasingly being used by automotive businesses to cope with the complexity of their organizations and products. The development of automotive systems necessitates the use of safe procedures. SafeScrum® is a real example of how agile approaches may be used in the creation of high-reliability systems on a small scale. A framework like SAFe or LeSS does not facilitate the creation of safety-critical systems in large-scale contexts from the start. User stories are a wonderful approach to convey flexible demands, the lifecycle is iterative, and testing is the initial stage in the development process. Scrum plus extra XP approaches may be used to build high-reliability software and certification by the IEC 61508 standard is required for the software. This adds a slew of new needs to the workflow. Scrum's quality assurance measures proved to be inadequate in a recent industry situation. Our study's overarching goal is to provide light on the Scrum development process so that it may be improved for use with life-or-death systems. Our study of the business world was a mixed-methods affair. The findings demonstrated that although Scrum is helpful in ensuring the security of each release, it is less nimble in other respects. The difficulties of prioritization, communication, time constraints, and preparing for and accepting new safety standards were all discussed. In addition, we have had some helpful feedback from the business world, but the generality issue arising from this particular setting has yet to be addressed.

In the recent decades, the automatic veracity verification of rumors is essential, since online social media platforms allow users to post news item or express opinion towards a circulating piece of information without much restriction. The intention of fake news is to make the readers believe in inaccurate information, where the detection of fake news by using content is a difficult task. So, the auxiliary information: user profile, social engagement of the users, and other user’s comments are useful in the detection of fake news. In this manuscript, a novel multi-stage transfer learning approach is introduced for an effective fake news detection, where it utilizes user’s comments as auxiliary information to detect whether the given tweet is true or false. The stances of the response tweets contain opinions on news/rumors are often used for verifying the veracity of the circulating information. In order to devastate the effects of the specific rumors at the earliest, the multi-stage transfer learning approach automatically predict veracity of rumors jointly with the stances of their response tweets. The proposed multi-stage transfer learning is an inductive transfer learning variation that is used to forecast the stance of responses, then to identify fake news. The proposed model’s effectiveness is evaluated on the two-benchmark datasets: semEval-2017 task 8 and PHEME. The proposed model outperformed the existing approaches by obtaining a classification accuracy of 64.30% and 65.30%, an F-measure of 65.95% and 63.90% on semEval-2017 task 8, and PHEME on event-wise datasets.