In modern communication scenario of the 5G era, the service quality is the greatest concern for the users. Also, the concept of security can’t be neglected in this case. In the IoT oriented services like vehicle and VANET systems, the security in the presentation layer of the network is required. This work is over the security mechanism of the service storage and fetching the files for service. A new scheme of multi layered file and content encryption has been produced in order to strengthen the security of the file and data to maintain integrity and confidentiality of the IoT enabled services implemented in 5G. The encryption scheme is designed for the password encryption through asymmetric key cryptography (RSA) along with an enhanced concern of internal content or data security with symmetric key (AES-128) cryptography. This encryption system of double layer for a file makes the study unique and differentiable than other security schemes.

The paper projected to study the field of m-learning focusing on investigating the facilities required to initiate an m-learning environment. Facilities and regular practices of conventional learning and e-learning was considered to find the potential facilities for m-learning environment. We used Integrated Tertiary Educational Supply Chain Model framework that stands on conventional education and illustrates the combined form of education supply chain and research supply chain model. Two surveys were conducted to collect data from students and teachers of higher education. The responses from both of the surveys have been presented and later compared with the findings from our studies of the existing learning environments. The significance of this research is in identifying the facilities for a learner and educator centric m-learning environment.

Every software project is unique in its own way. As a consequence, a single software process model cannot be suitable for all types of projects. In the real world, practitioners face different difficulties with the existing process models during development. Still, they cope up with the challenges by tailoring the software development lifecycle according to their needs. Most of these custom-tailored practices are kept inside the walls of the organizations. However, sharing these proven and tested practices as well as acquired knowledge and experience would be highly beneficial for other practitioners as well as researchers. So in this paper, we have presented a software process model which contains the characteristics of both Scrum and Waterfall model and named it “ScrumFall”. This model has been practicing in an Anonymous Software Development Company, Bangladesh to solve the shortcomings of Scrum and Waterfall models. Moreover, we have analyzed the performance and suitability for applying this process model. The result shows that this process model is highly effective for the certain projects.

In this paper, we have tried to identify the prominent factors of Rice production of all the three seasons of the year (Aus, Aman, and Boro) by applying K-Means clustering on climate and soil variables' data warehoused using Fact Constellation schema. For the clustering, the popular machine-learning tool Weka was used whose visualization feature was principally useful to determine the patterns, dependencies, and relationships of rice yield on different climate and soil factors of rice production.

K-means algorithm is one of the most popular algorithms for data clustering. With this algorithm, data of similar types are tried to be clustered together from a large data set with brute force strategy which is done by repeated calculations. As a result, the computational complexity of this algorithm is very high. Several researches have been carried out to minimize this complexity. This paper presents the result of our research, which proposes a modified version of k-means algorithm with an improved technique to divide the data set into specific numbers of clusters with the help of several check point values. It requires less computation and has enhanced accuracy than the traditional k-means algorithm as well as some modified variant of the traditional k-Means.