Technology Reports of Kansai University

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.

Google Scholar

Submission Deadline

Volume - 66 , Issue 01
20 Jan 2024

Upcoming Publication

Volume - 66 , Issue 01
31 Jan 2024

Aim and Scope

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:

Electrical Engineering and Telecommunication Section:

Electrical Engineering, Telecommunication Engineering, Electro-mechanical System Engineering, Biological Biosystem Engineering, Integrated Engineering, Electronic Engineering, Hardware-software co-design and interfacing, Semiconductor chip, Peripheral equipments, Nanotechnology, Advanced control theories and applications, Machine design and optimization , Turbines micro-turbines, FACTS devices , Insulation systems , Power quality , High voltage engineering, Electrical actuators , Energy optimization , Electric drives , Electrical machines, HVDC transmission, Power electronics.

Computer Science Section :

Software Engineering, Data Security , Computer Vision , Image Processing, Cryptography, Computer Networking, Database system and Management, Data mining, Big Data, Robotics , Parallel and distributed processing , Artificial Intelligence , Natural language processing , Neural Networking, Distributed Systems , Fuzzy logic, Advance programming, Machine learning, Internet & the Web, Information Technology , Computer architecture, Virtual vision and virtual simulations, Operating systems, Cryptosystems and data compression, Security and privacy, Algorithms, Sensors and ad-hoc networks, Graph theory, Pattern/image recognition, Neural networks. Shenyang Jianzhu Daxue Xuebao (Ziran Kexue Ban)/Journal of Shenyang Jianzhu University (Natural Science) General Medicine (ISSN:1311-1817) Chinese Journal of Evidence-Based Medicine Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation Lizi Jiaohuan Yu Xifu/Ion Exchange and Adsorption Kexue Tongbao/Chinese Science Bulletin Dalian Haishi Daxue Xuebao/Journal of Dalian Maritime University

Civil and architectural engineering :

Architectural Drawing, Architectural Style, Architectural Theory, Biomechanics, Building Materials, Coastal Engineering, Construction Engineering, Control Engineering, Earthquake Engineering, Environmental Engineering, Geotechnical Engineering, Materials Engineering, Municipal Or Urban Engineering, Organic Architecture, Sociology of Architecture, Structural Engineering, Surveying, Transportation Engineering.

Mechanical and Materials Engineering :

kinematics and dynamics of rigid bodies, theory of machines and mechanisms, vibration and balancing of machine parts, stability of mechanical systems, mechanics of continuum, strength of materials, fatigue of materials, hydromechanics, aerodynamics, thermodynamics, heat transfer, thermo fluids, nanofluids, energy systems, renewable and alternative energy, engine, fuels, nanomaterial, material synthesis and characterization, principles of the micro-macro transition, elastic behavior, plastic behavior, high-temperature creep, fatigue, fracture, metals, polymers, ceramics, intermetallics.

Chemical Engineering :

Chemical engineering fundamentals, Physical, Theoretical and Computational Chemistry, Chemical engineering educational challenges and development, Chemical reaction engineering, Chemical engineering equipment design and process design, Thermodynamics, Catalysis & reaction engineering, Particulate systems, Rheology, Multifase flows, Interfacial & colloidal phenomena, Transport phenomena in porous/granular media, Membranes and membrane science, Crystallization, distillation, absorption and extraction, Ionic liquids/electrolyte solutions.

Food Engineering :

Food science, Food engineering, Food microbiology, Food packaging, Food preservation, Food technology, Aseptic processing, Food fortification, Food rheology, Dietary supplement, Food safety, Food chemistry.

Physics Section:

Astrophysics, Atomic and molecular physics, Biophysics, Chemical physics, Civil engineering, Cluster physics, Computational physics, Condensed matter, Cosmology, Device physics, Fluid dynamics, Geophysics, High energy particle physics, Laser, Mechanical engineering, Medical physics, Nanotechnology, Nonlinear science, Nuclear physics, Optics, Photonics, Plasma and fluid physics, Quantum physics, Robotics, Soft matter and polymers.

Mathematics Section:

Actuarial science, Algebra, Algebraic geometry, Analysis and advanced calculus, Approximation theory, Boundry layer theory, Calculus of variations, Combinatorics, Complex analysis, Continuum mechanics, Cryptography, Demography, Differential equations, Differential geometry, Dynamical systems, Econometrics, Fluid mechanics, Functional analysis, Game theory, General topology, Geometry, Graph theory, Group theory, Industrial mathematics, Information theory, Integral transforms and integral equations, Lie algebras, Logic, Magnetohydrodynamics, Mathematical analysis.

Latest Articles of

Technology Reports of Kansai University

Journal ID : TRKU-10-12-2020-11359
Total View : 444


Abstract :

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.

Full article
Journal ID : TRKU-09-12-2020-11356
Total View : 434

Title : Analysis of the Time to Peak Modelling

Abstract :

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).

Full article