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Employment Status Analysis of Students in Vocational Colleges under the Background of Industry 5.0
No full textThis study analyzes students' employment status in vocational colleges based on Industry 5.0
context. We identify key factors affecting employment rates, and proposes strategies to address
the students’ employment potential. The study integrates theory and practice in teaching,
optimizing content and methods to meet industry needs, and enhancing students’ employability. The study recommends educational and teaching reforms, including the "dual-teacher" model, entrepreneurship education, industry training, and certification, to improve students' adaptability
and competitiveness in the job market. This offers practical guidance for educational institutions and enterprises to enhance the quality of vocational education and meet the talent requirements of modern manufacturing industries
Analysis of Battery Technologies for Use as Battery Management Systems in a Simple Solar Power Plant
No full textA solar power plant is a device that uses solar radiation to generate electricity. However, a
dependable energy storage system is essential to effectively operating solar power plants. This
study aims to examine how battery technology is used in solar power plants as a voltage storage.
Specifically, the performance of lithium-ion and lead acid batteries, known as valve-regulated lead
acid (VRLA), will then be compared. The findings demonstrate that Lithium-ion Batteries are
better at keeping a steady voltage in no-load situations. The highest voltage value was recorded at
13:79 V at a 90° tilt angle and 13:00 Indonesia time. At the same hour, the VRLA Battery recorded
the lowest voltage of 12.08 V with a 0° tilt angle. Under load conditions, the Lithium-ion Battery
performed better with a more moderate voltage drop, achieving the lowest voltage of 12.68 V at
an inclination angle of 165° and 14:00 Indonesia time, compared to the VRLA Battery's lowest
value of 12.08 V under comparable conditions. Thus, Lithium-ion Batteries are thought to be more
efficient and stable than VRLA Batteries in solar power plant applications, particularly in terms of
voltage stability under changing operating conditions. Furthermore, battery selection should still
take into account the initial investment cost and the unique requirements of the solar power
plant system to be deployed
Diabetes Classification Using a Framework Stacking of BiLSTM, Logistic Regression, and XGBoost
No full textDiabetes is a chronic condition that requires accurate and timely diagnosis for effective
management and treatment. This study introduces an innovative approach to diabetes
classification using a stacking framework that combines Bidirectional Long Short-Term
Memory (BiLSTM), Logistic Regression, and XGBoost. The study employed an experimental
approach by implementing the stacking framework. The two base models used were BiLSTM
and Logistic Regression, with BiLSTM achieving an accuracy of 0.9935 and Logistic
Regression reaching 0.9869. The stacking framework with XGBoost as the meta-learner
achieved a perfect accuracy of 1.0. These findings demonstrate the potential of the stacking
framework to improve diabetes classification performance compared to using individual
models alone
Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth Classification
No full textThis study investigates the application of machine learning models to predict plant
growth milestones based on environmental and treatment data. The dataset comprises
categorical variables such as soil type, water frequency, and fertilizer type, alongside numerical
variables including sunlight hours, temperature, and humidity. Preprocessing involved one-hot
encoding for categorical variables and standard scaling for numerical features. The models
employed were Support Vector Machine (SVM), Naive Bayes, and Extreme Learning Machine
(ELM). The baseline SVM model achieved an accuracy of 58.97%, and hyperparameter tuning
using GridSearchCV did not improve this performance, maintaining the accuracy at 58.97%.
The Naive Bayes model achieved an accuracy of 51.28%, while the ELM model had an
accuracy of 43.85%. Among the models, the SVM demonstrated the highest accuracy, though
further improvement is required for practical implementation. The findings underscore the
importance of selecting appropriate machine learning models and optimizing their parameters
to enhance prediction accuracy in agricultural applications. Despite the SVM's superior
performance in this context, continued refinement is essential to address the challenges posed
by predicting plant growth milestones accurately
Phishing Website Detection using Machine Learning
No full textPhishing attacks, a prevalent and significant form of cybercrime, involve attackers masquerading
as reputable entities to deceive individuals into revealing sensitive details such as usernames,
passwords, and credit card information. Deceptive websites are commonly used in these attacks,
appearing legitimate and underscoring the need for individuals and organizations to heighten
their awareness and implement stronger and more advanced detection techniques. By luring
sensitive information through deceptive websites, phishing attacks represent a serious
cybersecurity threat. In this research, the effectiveness of machine learning algorithms,
specifically the Gradient Boosting Classifier, in identifying phishing websites to enhance
accuracy and response time is being assessed
Text to Image Generation Using Machine Learning
No full textA method called text-to-image involves creating images automatically from provided written
descriptions. It contributes significantly to artificial intelligence by tackling the problem of
integrating textual and visual input. One of the usefulness of automatic picture synthesis is the
generation of images using conditional generative models. For this, Generative Adversarial
Networks (GANs) are frequently employed. Using GANs, recent developments in the sector have
made significant progress. An outstanding illustration of deep learning's potential is the
transformation of text into images. It is difficult to create a text-to-image synthesis system that
consistently creates realistic graphics based on predetermined criteria. Many of the existing
algorithms in this field struggle to produce visuals that precisely match the given text. In order to
solve this issue, we carried out a research work where we concentrated on developing the
generative adversarial network (GAN), a deep learning-based architecture. The aim of this
research work is to create a system that allows you to generate images that are semantically
consistent
Comparison of Utility-First CSS Framework
No full textUtility-first CSS frameworks have revolutionized web development by offering predefined utility
classes that streamline the design process and reduce the need for custom CSS. However, selecting
the right framework can be challenging due to the variety of available options. This paper addresses
the problem of choosing between two of the leading utility-first CSS frameworks Tailwind CSS
and Tachyons by providing a comparative analysis based on key factors such as size, load speed,
flexibility, ease of use, and community support. The objective of this research is to identify the
strengths and weaknesses of both frameworks, helping developers make informed decisions based
on project needs. Our methodology involved testing load speeds using Locust for performance
analysis, reviewing community support through GitHub repositories and forums, and assessing the
flexibility and ease of use through practical development tasks. The results revealed that while
both frameworks are robust, Tachyons excels in performance and simplicity due to its smaller size,
whereas Tailwind CSS offers greater customization and flexibility, making it more suitable for
complex projects. The novelty of this research lies in its direct comparison of utility-first
frameworks, highlighting how developer preferences and project requirements play a crucial role
in framework selection. In summary, this study provides valuable insights for developers looking
to optimize web development workflows by selecting the most appropriate CSS framework based
on specific project goals
Mitigating Delay Impacts in Construction Projects: An Evaluation of Causes and Strategies
No full textConstruction plays a crucial role in driving national economic growth for sustainable development.
Nonetheless, delays in many building projects emerged due to various challenges. Identifying and
addressing these delays proved essential for ensuring project success. Timely completion stood as
a key indicator of project success, whereas delays could result in increased costs and disputes
among stakeholders. This study aimed to explore the causes of delays in construction projects and
their remedial measures. Specifically, the research investigated delay factors in construction
projects across Khyber Pakhtunkhwa, including the Machai Hydro Power Project in Mardan, the
Golen Gol Hydro Power Project in Chitral, the KOTO Hydro Power Project in Lower Dir, and the
rehabilitation of the Peshawar to Dara Adam Khel carriageway. Data was collected through
Google Forms, with participants providing valuable responses. The analysis was conducted using
IBM SPSS. Results indicated that land acquisition, client issues, consultant problems, general
factors, and budget conflicts had the most significant impact on delays. This study offered
important insights into the construction industry, highlighting strategies to mitigate these issues
and improve project efficiency
Rethinking Streets: Enhancing Public Spaces and Pedestrian Amenities in Liverpool, Sydney
No full textThis paper undertakes an analysis and provides strategic design recommendations to urbanise Liverpool City Centre, Sydney; by proposing the conversion of the existing fragmented car oriented space into one that is inclusive, pedestrian orientated public domain. Those include problems with car monopolisation and lack of facilities for pedestrians and cyclists, the "Rethinking Streets" project noted. The Complete Street project designs streets for all users, with four modes of transportation accommodated in the same right-of-way. In theory, measures like extending the pedestrian sphere, facilitating linear bike connections and updating streetscape elements as well as parking removal to ease through-traffic could be considered. These enhancements aim to promote a healthier environment, improve connectivity among major attractions, and support economic growth, aligning with Liverpool's vision as a regional hub in Sydney's Metropolitan Pla
Financial Literacy and Saving Attitude among Malaysian Population
No full textThis research was designed to determine whether age, gender, and ethnicity have a significant effect
on the saving attitude. This study aims to assess the financial literacy of Malaysian adults and
identify their wealth-accumulation habits as a precondition for their retirement planning decisionmaking.
It creates a framework based on financial capabilities, availability, accessibility, and
affordability of private retirement plans, as well as individuals' awareness of these requirements.
The findings will help policymakers encourage private retirement planning and the industry create
retirement packages that appeal to a wider range of people. All participants were notified that their
involvement was optional, and their responses would be kept confidential. The aim of the sampling
method was to acquire a representative sample of the population through random selection. Based
on all these findings, it will enable policymakers to incentivize private retirement planning and
help the industry develop retirement packages