In this research, a new fuzzy streaming data classification method is presented. Fuzzy data classification is one of the most challenging areas in data analysis due to high-volume and sophisticated computation engaged in data analysis techniques. However, in some other areas such as mathematical programming, great achievements are obtained in modeling of systems measured by fuzzy data. So, in this research classification based on data envelopment analysis (DEA) is selected to handle fuzzy data classification. On the other hand, the proposed method can be applied for dealing with streaming data by using an updating schema. This feature makes the proposed method suitable for non-stationary environment in which data pattern varies over the time. Experiments using simulated data demonstrate the capability of the proposed method in dealing with uncertain and non-stationary environment.

The Counter-intuitive behavior which means that the permanent de ection of the elastic-plastic beam come to rest in opposite direction of the impulsive loading، normally appears and disappears abruptly، in certain small ranges of the loading and structural parameters. One of the most important issues in the study of this phenomenon is the determination of the in uence of the different parameters on this behavior. This present paper studied the e ects of hardening in the counter-intuitive dynamic behavior of the elastic-plastic pin-ended beams under impulsive loading، by developing the proposed Galerkin's numerical model and by presenting a novel algorithm. Galerkin's method as well as Finite element code ANSYS/LS-DYNA were applied to study this phenomenon and the results of these two methods have been compared. In order to regard the hardening e ects in Galerkin's method، a new algorithm was proposed. To test the validity of our proposed algorithm، the response of the elastic-perfectly plastic beam was rstly studied، and the hardening e ects on the counter-intuitive response was investigated afterwards. The displacementtime history curves of mid-span of the beam were studied in detail and the region of the occurrence of the counterintuitive behavior was determined. Furthermore، using the nite element code، energy diagrams of the beam were also investigated. It was been found out that the counter-intuitive response is a phenomenon which is very sensitive to loading so that it may appear with a little change in the amount of loading. The results also showed that when considering the hardening e ects، both methods predicted two continuous and distinct regions of loading for the occurrence of this phenomenon where the rst had a narrower band and occurred in the tight region of the impulsive loading while the next one had a wider band and occurred in the vast region of the impulsive loading. In addition، our investigations on the energy diagrams showed that، this anomalous behavior would occur in the proper proportions of kinetic to internal energy.

Although lot streaming scheduling is an active research field, lot streaming flexible flow lines problems have received far less attention than classical flow shops. This paper deals with scheduling jobs in lot streaming flexible flow line problems. The paper mathematically formulates the problem by a mixed integer linear programming model. This model solves small instances to optimality. Moreover, a novel artificial bee colony optimization is developed. This algorithm utilizes five effective mechanisms to solve the problem. To evaluate the algorithm, it is compared with adaptation of four available algorithms. The statistical analyses showed that the proposed algorithm significantly outperformed the other tested algorithms.

The Today’s digital world computations are tremendously difficult and always demands for essential requirements to significantly process and store enormous size of datasets for wide variety of applications. Since the volume of digital world data is enormous, this is mostly generated unstructured data with more velocity at beyond the limits and double day by day. In last decade, many organizations have been facing major problems to handling and process massive chunks of data, which could not be processed efficiently due to lack of enhancements on existing and conventional technologies. In this paper address, how to overcome these problems as efficiently by using the most recent and world primary powerful data processing tool, which is hadoop clean open source and one of the core component called Map Reduce, but which has few performance issues. This paper main goal is address and overcome the limitations and weaknesses of Map Reduce with Apache Spark.

The integration of the internet with digital games has catalyzed significant transformations in the production, distribution, and consumption within the gaming industry. A notable catalyst in this evolution is the advent of live-streaming platforms, particularly Twitch. This study employs qualitative meta-analysis to scrutinize 19 scientific studies, aiming to synthesize a comprehensive interpretation of Twitch's role and influence in the digital gaming arena. Our analysis reveals Twitch's multifaceted impact: it is a driving force in the digital economy, facilitating new job creation, diverse revenue streams for industry participants, and a fertile ground for innovation and ideation. Twitch has revolutionized the landscape for game designers, producers, and developers, fostering independence and enhancing the quality and quantity of independent game productions. Moreover, it extends the lifespan of games in the market, resurrects interest in older titles, and assists consumers in making informed purchasing decisions. Importantly, Twitch serves as an educational platform, offering insights into specialized gaming industry skills, including design and programming. These roles and functions position Twitch as a pivotal player in the dynamic and burgeoning digital gaming industry.

The classification of fuzzy uncertain data is considered one of the most challenging issues in data analysis. In spite of the significance of fuzzy data in mathematical programming, the development of the analytical methods of fuzzy data is slow. Therefore, the current study proposes a new fuzzy data classification method based on fuzzy data envelopment analysis (DEA) which can handle streaming data. The new method is tested by simulated data and the results indicate its effectiveness in facing uncertain data and variable conditions.