Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery
finding large prime numbers from two prime numbers is a relatively easy task, the problem of factoring the product of two such numbers considered computationally intractable if the primes are carefully selected. This problem called the integer factorization problem. Based on the difficulty of this problem, many public-key cryptosystems developed like RSA, Rabin, and Williams. This cryptosystem together with the development of high-speed computers for the implementation and testing of sophisticated algorithms draws the attention of the researcher to study it. This paper proposed an efficient genetic algorithm for solving the factorization problem. The new efficient features, which are the strategic oscillation (embedded in crossover operation), and exploitation (embedded in the mutation operation), are selected from the meta-heuristic tabu–search. The proposed genetic algorithm has implemented and evaluated against the former algorithms. Test results show that it performs more efficiently in exploring different search space than both algorithms to find the solution
Current depression pre-screening session is usually completed with a tool such as the Depression, Anxiety, and Stress Scale (DASS21) before proceeding with treatment. Development of a device for depression measurement is hoped to overcome the issues related to time management and paper waste during assessment process. The device may also be useful as an assistance to other assessment method in delivering the final diagnosis. Data input and output of DASS21 has been utilized for the system identification technique where analysis comparison was conducted using ARX and transfer function model. The result shows that the transfer function model of order 2 outperform ARX with best fit of 98.1%. A device identifier was developed in order to assess the level of depression by implementing the transfer function into the Arduino code to predict the result from the user input