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In this research, three different evolutionary algorithms (EAs), namely, particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE), are integrated with the adaptive neuro-fuzzy inference system (ANFIS) model. The developed hybrid models are proposed to forecast rainfall time series. The capability of the proposed evolutionary hybrid ANFIS was compared with the conventional ANFIS in forecasting monthly rainfall for the Pahang watershed, Malaysia. To select the optimal model, sixteen different combinations of six different lag attributes taking into account the effect of monthly, seasonal, and annual history were considered. The performances of the forecasting models were assessed using various forecasting skill indicators. Moreover, an uncertainty analysis of the developed forecasting models was performed to evaluate the ability of the hybrid ANFIS models. The bound width of 95% confidence interval (d-factor) and the percentage of observed samples which was enveloped by 95% forecasted uncertainties (95PPU) were used for this purpose. The results indicated that all the hybrid ANFIS models performed better than the conventional ANFIS and for all input combinations. The obtained results showed that the models with best input combinations had the (95PPU and d-factor) values of (91.67 and 1.41), (91.03 and 1.41), (89.74 and 1.42), and (88.46 and 1.43) for ANFIS-PSO, ANFIS-GA, ANFIS-DE, and the conventional ANFIS, respectively. Based on the 95PPU and d-factor, it is concluded that all hybrid ANFIS models have an acceptable degree of uncertainty in forecasting monthly rainfall. The results of this study proved that the hybrid ANFIS with an evolutionary algorithm is a reliable modeling technique for forecasting monthly rainfall.

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ML (mobile learning) has extended e-learning to a new paradigm of “anywhere, anytime learning”. The use of 3G and 4G Phones (high-speed data transfer) will be represented as a great opportunity for both learners and teachers to meet together, access and exchange information in virtual spaces whilst on the move. The aim of this work is to design a mobile learning framework for Students in higher education. Thus, this will provide better quality of teaching. Additionally, Mobile learning (M-learning) has turned into a critical instructive innovation part in advanced education. M-learning makes it workable for students to learn, team up, and impart thoughts among each other as much as web innovation and improvements will allow. In any case, M-learning acknowledgment by learners and instructors is basic to the occupations of M-learning frameworks. Attitudes towards M-learning innovation is an imperative method to measuring whether or not learners and instructors are prepared to utilize M-learning. Such  attitudes will serve to distinguish qualities and shortcomings and encourage the advancement of the innovation foundation. We will investigate students and instructors' state of the arts in using M-learning in higher institutes for some of Private and Public universities of Ministry of higher education. As a result of student feedback regarding M-learning methods of teaching, a suitable framework for M-learning is proposed by reviewing many other frameworks and also by the analysis of results of a survey that asked many students and staff in higher education fields.

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The quality evaluation of software, e.g., defect measurement, gains significance with higher use of software applications. Metric measurements are considered as the primary indicator of imperfection prediction and software maintenance in various empirical studies of software products. However, there is no agreement on which metrics are compelling quality indicators for novel development approaches such as Aspect Oriented Programming (AOP). AOP intends to enhance programming quality, by providing new and novel constructs for the development of systems, for example, point cuts, advice and inter-type relationships. Hence, it is not evident if quality pointers for AOP can be derived from direct expansions of traditional OO measurements. Then again, investigations of AOP do regularly depend on established coupling measurements. Notwithstanding the late reception of AOP in empirical studies, coupling measurements have been adopted as useful markers of flaw inclination in this context. In this paper we will investigate the state of the art metrics for measurement of Aspect Oriented systems development.