Search Results - (( developing skills validation algorithm ) OR ( java implication based algorithm ))

Job position prediction based on skills and experience using machine learning algorithm / Ezaryf Hamdan by Hamdan, Ezaryf

“…The proposed system integrates data processing modules to preprocess and analyse LinkedIn job posting pages, extracting crucial information such as job titles, company names, years of experience, qualifications, and skills. Text preprocessing ensures consistent data representation and facilitates validation. …”
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Development and validation of a problem-solving instrument (multiple-choice questions) for computational thinking among trainee teachers in the Klang Valley, Malaysia by Abdul Hamed, Ahmad Sarji, Wong, Su Luan, Abdul Zani, Mohd Zariat

“…This informs the subsequent quantitative phase, involving the development and validation of the MCQ tool. Key objectives include evaluating content validity, difficulty and discrimination indices, and construct validity. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Capability requirements on fraud prevention in the Nigeria public sector: perception of forensic accountants and auditors by Lateef, Saheed Ademola

“…(PLS-SEM)version 3.2.3). Six hypotheses are developed in the study. Three were tested through the use of the PLS-SEM Algorithm and Bootstrapping techniques regarding their internal consistency reliability, convergent validity, discriminant validity, and direct relationships. …”
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A Data Mining-Based Model for Assessing Guangzhou's Higher Vocational Colleges 'New Energy Automobile Majors' Vocational Skills by He, Ling, Hamid, Hashima

“…For the purpose of measuring the real-world abilities of students participating in Renewable Energy (RE) vehicle programs at the HVC in Guangzhou, China, this study develops a unique model. The approach employs algorithms for data mining to enhance the accuracy and accessibility of results through the use of Random Forest (RF) and Generalized Additive Models (GAM) in a layering architecture. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

From Employees to Entrepreneurs: A Qualitative Exploration of Career Transitions in Ghana by Gerhana, Yana Aditia

“…The main challenges faced by Higher Education Institutions (HEIs) in Indonesia in the context of Industrial Revolution (IR) 4.0 lie in the development of knowledge- intensive skills and the development of outcome-oriented curricula. …”
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BLAZEBREAKER: GAMIFY THE FLAMES by SAID MOHD NAJIB, ELIAS

“…Furthermore, it discusses the potential impact of VR simulations on knowledge retention, decision-making skills, and overall safety awareness. The practical implementation of the VR simulation involved in-depth research from fire safety experts and real-world testing to validate its effectiveness. …”
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Let’s collaborate’: Malaysian TVET-engineering institution and industry partnership by U. S., Subri, N. E., Sohimi, H. M., Affandi, S. M., Noor, F. A. N, Yunus

“…TVET Institutions must play an important role in fulfilling the demands from the industry and developing innovative skills for students. Future research is suggested to test the model on a wider range of TVET respondents to see its appropriateness.…”
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An Artificial Intelligence-Based Knowledge Management System for Outcome-Based Education Implementing in Higher Education Institutions by Gerhana, Yana Aditia

“…The main challenges faced by Higher Education Institutions (HEIs) in Indonesia in the context of Industrial Revolution (IR) 4.0 lie in the development of knowledge intensive skills and the development of outcome-oriented curricula. …”
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Development of predictive modeling and deep learning classification of taxi trip tolls by Al-Shoukry, Suhad, M. Jawad, Bushra Jaber, Zalili, Musa, Sabry, Ahmad H.

“…The combined training and validation data is next pre-processed, which involves tasks such as cleaning and developing new features skills. …”
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Development of a Neural-Fuzzy Model for Machinability Data Selection in Turning Process by Kong, Hong Shim

“…Object oriented approach is applied as the development process to enhance reusability. The results from the model have been validated and compared against machining data of Machining Data Handbook from Metcut Research Associate. …”
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Development of a novel natural frequencies prediction tool for laminated composite plates using integrated artificial neural network (ANN) - simulink MATLAB / Mohd Arif Mat Norman by Mat Norman, Mohd Arif

“…The prediction tool utilises an Artificial Neural Network (ANN) with a two-layer feed-forward algorithm and ten hidden layers, using Levenberg-Marquardt as the training algorithm. …”
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DEVELOPMENT OF PREDICTIVE MODELING AND DEEP LEARNING CLASSIFICATION OF TAXI TRIP TOLLS by Al-Shoukry S., Jawad B.J.M., Musa Z., Sabry A.H.

“…The combined training and validation data is next pre-processed, which involves tasks such as cleaning and developing new features skills. …”

Modelling and optimisation of assembly line balancing problem with resource constraint by Nur Hairunnisa, Kamarudin

“…For the optimisation purpose, this research introducesGenetic Algorithm (GA) with two new crossovers. The crossovers are developed using a ranking approach and known as rank-based crossover type I and type II (RBC-I and RBCII). …”
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Query disambiguation approach using triple-filter by Agaie, Aliyu Isah

“…Extensive experimental evaluations were conducted in order to validate the proposed approach, using the developed prototype. …”
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Dielectric spectroscopy of palm olein during batch deep frying and their relation with degradation parameters by Ibrahim, Nur Ul Atikah, Abd Aziz, Samsuzana, Hashim, Norhashila, Jamaludin, Diyana, Al-Khaled, Al-Fadhl Yahya Khaled

“…Partial least square (PLS) analysis produced good prediction of TPC and FFA with the application of genetic algorithm (GA). Model developed for prediction of TPC and FFA yielded highly significant correlation with R2 of 0.91 and 0.95, respectively and both had root mean square error in cross‐validation (RMSECV) of 1.06%. …”
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