Modern cryptosystems allow the use of operation in prime fields with special kind of modules that can speed up the prime field operation: multiplication, squaring, exponentiation. The authors took into account in the optimizations: the CPU architecture and the multiplicity of the degree of the modulus in relation to the machine word width. As example, shown adopted module reduction algorithms hard-coded for modern CPU in special form of pseudo-Mersenne prime used in MAC algorithm Poly1305, - in electronic signature algorithm EdDSA and - in short message encryption algorithm DSTU 9041. These algorithms have been software implemented on both 32-bit and 64-bit platforms and compared with Barrett modular reduction algorithm for different pseudo-Mersenne and generalized-Mersenne modules. Timings for proposed and Barrett algorithms for different modules are presented and discussed.

Many studying systems of gene function work depend on the DNA motif. DNA motifs finding generate a lot of trails which make it complex. Regulation of gene expression is identified according to Transcription Factor Binding Sites (TFBSs). There are different algorithms explained, over the past decades, to get an accurate motif tool. The major problems for these algorithms are on the execution time and the memory size which depend on the probabilistic approaches. Our previous algorithm, called EIMF, is recently proposed to overcome these problems by rearranging data. Because cloud computing involves many resources, the challenge of mapping jobs to infinite computing resources is an NP-hard optimization problem. In this paper, we proposed an Impala framework for solving a motif finding algorithms in single and multi-user based on cloud computing. Also, the comparison between Cloud motif and previous EIMF algorithms is performed in three different motif group. The results obtained the Cloudera motif was a considerable finding algorithms in the experimental group that decreased the execution time and the Memory size, when compared with the previous EIMF algorithms. The proposed MOTIFSM algorithm based on the cloud computing decrease the execution time by 70% approximately in MOTIFSM than EIMF framework. Memory size also is decreased in MOTIFSM about 75% than EIMF.

Predicting crop yields is one of the more difficult tasks in the agriculture sector. A fascinating area of research to estimate agricultural productivity has emerged from recent advancements in information technology for agriculture. Crop yield prediction is a technique for estimating crop production based on a variety of factors, including weather conditions and parameters such as temperature, rainfall, fertilizer, and pesticide use. In the world of agriculture, Data mining techniques are extremely popular. In order to predict the crop production for the following year, data mining techniques are employed and evaluated in the agricultural sector. In this paper, we carried out the comparison between Naive Bayes, K-nearest neighbor, Decision Tree, Random Forest, and K-Means clustering algorithms to predict crop yield in order to determine which method is most effective at doing so. The results show which algorithm is better suitable for this particular purpose by comparing these data mining algorithms for agricultural crop production and determining which algorithm is more successful for this outcome.

The majority of collaborative learning and knowledge sharing (CLKS) platforms are built with numerous communication mediums, team and task management in mind. However, with the CLKS, the Question-Answering (QAs), User profile evaluation based on the quality of answers provided, and feeding of subject or project relevant data are all available. QAs are required for online or offline cooperation between team members or users. To that purpose, this paper presents a web application called CodeUP with features like QA system, Question similarity testing, and user profile rating for boosting communication and cooperation efficiency in CLKS for academic groups and small development teams. CodeUP is intended to be quickly established and step for academic or development groups to collaborate. As the CodeUP application supports the CLKS, it is also an ideal tool for academia and development teams to perform computer supported QA system and knowledge sharing in the sphere of work or study.

Programming language interoperability is highly desirable for a variety of reasons, such as the fact that if a programmer implements specific functionality that has previously been implemented in another language, the software component can simply be reused. Because they are particularly well-suited and efficient at implementing features, certain languages regularly arise to handle issue areas. There are numerous third-party programs available for a variety of languages. When programmers have experience with and preferences for several programming languages, collaboration on complex projects is easier. A range of techniques and methods have been used to handle various cross-language communication challenges. The importance of interoperability and cross-language communication between Java and Python via socket programming is examined in this research article through an empirical model of different execution environment paradigms that can help guide the development of improved approaches for integrating Python libraries with Java without the need for extra libraries or third-party libraries. The interoperability strategy benefits from the quality and availability of Python libraries in Java by cutting down on development time, maintenance needs, general usability, upkeep, and system integration without incurring additional costs. It is versatile to use this interoperability strategy since identical scripts are run in Java client contexts in the same way that they were used in Python. There are different Python modules used in the research article to exemplify and evaluate the expressions, built-in functions, strings, collections, data exploration, statistical data analysis using NumPy, SciPy, and Pandas, and Scikit-Learn for machine learning with linear regression.