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:
Azerbaijan Medical Journal
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery
Interventional Pulmonology
Zhenkong Kexue yu Jishu Xuebao/Journal of Vacuum Science and Technology
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)
Zhonghua yi shi za zhi (Beijing, China : 1980)
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.
The synthetic unit hydrograph method is a popular method for analyzing watershed flood discharge for rivers that do not have observational flood hydrographs. To create flood hydrographs for rivers with no or very few observed flood hydrographs, it is necessary to require data on characteristics or parameters of watershed areas (DAS). The Time to peak Model in this study will consider several parameters including the area of the watershed (A), the length of the main river (L), the length of the river from the center of the watershed to the outlet (Lc), river slope (S), watershed roughness (n), factor the shape of the watershed (Fb), as well as the fractal characteristics of the watershed in the form of river branching ratio (RB) and river length ratio (RL). To get the accuracy of the time to peak model and to get a flood hydrograph that can represent the prototype, it is necessary to do some statistical analysis. The time to peak model predicted using linear regression analysis produced the time to peak equation (Tp) as a function of watershed area (A) and river length from the center of the watershed to outlet (Lc). This equation has a good level of accuracy with a correlation coefficient of 0.893; The coefficient of determination is around 0.797 and Adjusted R2 is around 0.746. The model has also met the requirements of the classical assumptions including linearity test, residual normal test, heteroscedasticity test, autocorrelation test (indicated by the Durbin Watson value of 1.895 with the interpretation of no autocorrelation) and multicollinearity test. Model validation has NSE value of 0.731 (good), RMSE value of 0.482 and MAE value of 0.390 (both RMSE and MAE values are close to zero). Model verification has NSE value of 0.758 (very good), RMSE value of 0.394 and an MAE value of 0.320 (both RMSE and MAE values are close to zero).
