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:
The quantum Cryptography is a latter – day appliance in physics and information security, which employs the design of protocols to availing from quantum mechanics externals to assuredness the security of key distribution. in this work by using polarization and phase coding, all these are based on computer programs to discuss, analyze, compute the probability of error and the average of information received, compute the employee distance in polarization and phase coding by using parallel quantum cryptography. It was shown that the probability of error in parallel quantum cryptography is less than that for the single quantum cryptography. The results that the bit rate in the first one is larger than the other, because the noise in detector has more effect in wave length (1300 nm) than (1550 nm) and the relation between the optical length (distance) and the probability of correction. This relation is linear between them. There is another new coming cryptography method, which employs four mathematical processes with their tables, so hereupon been boosting the aptness of cryptography and the information security.
The popularity of Short Message Services (SMS) created a propitious environment for spamming. SMS spam filters are not, unfortunately, as easy to develop as email filters because of the rigid size of text messages and messages being laden with noisy elements, such as slangs and symbols. These inhibit effective training and classification of machine learning algorithms deployed for spam filtering. This study, therefore, proposes an enhanced SMS spam filter model that selects the best features from a text pre-process module, based on lexicography and semantic dictionaries, to normalize and expand incoming messages with the view of minimizing the noise element and combating the brevity of short text messages. A hybrid SMS spam filter model, which comprised of text pre-processing section, feature selection section and machine training and classification section, was developed. The model was simulated on the Scikit-learn library of the python programming platform. Evaluation was done using confusion matrix. Wilcoxon Signed-Ranks Test was used to determine the superiority of the proposed technique. A combination of ten machine learning algorithms was employed for validation. The study concluded that incorporation of feature selection techniques to normalized and expanded SMS messages size enhanced the performance of machine learning algorithms in the classification of SMS messages as either ham or spam.