A Study of University Website Content Classification Using Machine Learning

A university website is a gateway to the institution’s information, products, and services. As websites grow into millions in numbers, it is essential to ensure that the content reflects the needs of its students, staff, and other academic institution as their primary users. This research investigates the development of a new framework that uses machine learning techniques based on webometrics and web usability to classify the web pages of academic websites automatically. The framework briefly introduced how it can help classify web content and eliminate unrelated content and reduce storage space. The findings can also be used to analyse other web-based data to give additional insights that may be beneficial for webometrics studies and identify university website’ characteristics.

Machine Learning Methods for Content - Classification and Categorization

These days online gatherings and web-based media stages have furnished people with the necessary resources to advance their contemplations and put themselves out there free paying little heed to the kind of language used to communicate those thoughts, in certain examples these internet based remarks contain express language which might hurt the peruser. We likewise evaluate the class irregularity issues related with the dataset by utilizing inspecting procedures and misfortune. Models we applied yield high in general exactness with moderately minimal expense. To diminish the adverse consequence of poisonous remark in everyday life we have endeavored to plan a Toxic Language detector.

AUBERT & DUVAL CNS (35NiCr6)

Aubert and Duval CNS (35NiCr6) is a medium-carbon, nickel-chromium, direct hardening alloy steel. It is a medium hardenability steel in the 0.30 to 0.37 mean carbon content classification. This steel is frequently used for water-quenched parts of moderate section size and for oil-quenched parts of small section size. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties. It also includes information on casting. Filing Code: SA-878. Producer or source: Aubert & Duval S.A. (a member of the Eramet Group).

LUCEFIN 50CrMo4 (1.7228)

Lucefin 50CrMo4 is a medium-carbon, chromium-molybdenum direct hardening alloy steel that is also suitable for flame hardening, induction hardening, and nitriding. This steel is a medium hardenability steel in the 0.45 to 0.50 mean carbon content classification. In general, it is used for medium and large size parts requiring high hardness as well as high strength and good toughness. A minimum of 90% martensite in the as-quenched condition is desirable. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties. It also includes information on forming, heat treating, machining, and joining. Filing Code: SA-877. Producer or source: Lucefin S.p.A.

IMPLEMENTATION OF THE PRINCIPLE OF CONTINUATION OF PHYSICAL EDUCATION IN PRIMARY AND PRIMARY SCHOOLS

The curricula of the new Ukrainian school for grades 1-4, grades 5-6, physics programs for grades 7-11 and the content classification between subjects are analyzed. Appropriate methodological improvements are proposed to close the gap between primary and secondary education, in the context of studying certain physical concepts, by improving the adaptation of students in the transition from primary to primary school, in particular, in the transition from certain issues of mathematics, science and others to physics, where the implementation of subject competence. In view of this, it is suggested that primary and secondary school teachers, when studying the components of the content of the school course of physics, follow the generalized plans for their study, as in the school course of physics.

Development of a Job Applicants E-government System Based on Web Mining Classification Methods

Governmental establishments are maintaining historical data for job applicants for future analysis of predication, improvement of benefits, profits, and development of organizations and institutions. In e-government, a decision can be made about job seekers after mining in their information that will lead to a beneficial insight. This paper proposes the development and implementation of an applicant's appropriate job prediction system to suit his or her skills using web content classification algorithms (Logit Boost, j48, PART, Hoeffding Tree, Naive Bayes). Furthermore, the results of the classification algorithms are compared based on data sets called "job classification data" sets. Experimental results indicated that the algorithm j48 had the highest precision (94.80%) compared to other algorithms for the aforementioned dataset.