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Research Article
Career Guidance System Using Decision Tree, Random Forest, and Naïve Bayes Algorithm
Issue:
Volume 13, Issue 2, April 2025
Pages:
35-42
Received:
8 October 2024
Accepted:
30 October 2024
Published:
18 March 2025
Abstract: Students often struggle with identifying the right options that align with their interests, abilities, and aspirations. Most students lack the required knowledge to make the right decisions. After receiving a degree, the path to career specialization always seems unclear for most students. But, if a student can manage to get it right by choosing the right path for their career, they will experience significant economic and psychological benefits. Choosing the right career path is a critical decision that can significantly impact an individual's future. Providing effective career guidance is therefore essential, especially for students who often face challenges in aligning their interests, skills, and aspirations with suitable career options. This study addresses this need by developing and evaluating a comprehensive Career Guidance System utilizing three machine learning algorithms: Decision Tree, Random Forest, and Naive Bayes. The system was built using an iterative approach, incorporating a user-friendly web page and an interactive chatbot to enhance the career guidance experience. Developed and deployed using Python and the powerful Django framework, the system leverages cutting-edge technologies to deliver personalized recommendations tailored to each student's unique profile. To evaluate the system's performance, key metrics such as accuracy, precision, recall, and F1 score were employed. Notably, the Random Forest classifier outperformed the other algorithms, achieving the highest accuracy. This superior performance highlights the algorithm's ability to capture complex relationships between student interests, passions, and career choices, making it an ideal choice for career guidance applications. The Career Guidance System developed in this study holds significant potential for revolutionizing the career counseling process. The choice of algorithms used in this study was chosen given the specific needs of the project, especially considering specific concerns of scalability and accuracy. in the advancement of computer science and its applications in career counseling. The findings demonstrate the system's overall efficiency and effectiveness, paving the way for its wider adoption and further refinement to support students in making informed and fulfilling career choices.
Abstract: Students often struggle with identifying the right options that align with their interests, abilities, and aspirations. Most students lack the required knowledge to make the right decisions. After receiving a degree, the path to career specialization always seems unclear for most students. But, if a student can manage to get it right by choosing th...
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Research Article
Research and Design of Carbon Market Swing Option Products: A Study Based on China Emission Allowance as Underlying Assets
Issue:
Volume 13, Issue 2, April 2025
Pages:
43-53
Received:
12 February 2025
Accepted:
28 February 2025
Published:
18 March 2025
Abstract: Under the policy framework highlighted by the 20th National Congress of the Communist Party of China, which advocates for ‘actively and steadily promoting carbon peak and carbon neutrality’, this study investigates the pricing mechanism of China Emission Allowance (CEA) swing options in the carbon market, aligned with the practical needs of the ‘Interim Regulations on the Management of Carbon Emission Trading’. In line with the policy outlined by the 20th National Congress of the Communist Party of China, which emphasizes the "active and steady progress toward carbon peaking and carbon neutrality," this paper delves into the pricing mechanism of CEA swing options in the carbon market. This article mainly introduces the CEA swing option product, which is an innovative financial instrument that aims to provide flexible risk management means for high-carbon emission industries such as electricity, petrochemicals and manufacturing. This paper analyses the descriptive statistical results of the characteristics and data of the CEA market, based on the Market regional conversion model, and adjusts it in combination with the stochastic fluctuation model, so as to determine the CEA value model. At the same time, this article also emphasises the importance of implementing a flexible product management mechanism, including risk tips, regulatory measures, and rolling issuance and adjustment strategies for products to ensure that products can effectively serve the needs of the target market. The research results of this article play an important role in improving the liquidity of the carbon market and helping relevant enterprises avoid the risks caused by fluctuations in the price of carbon emissions. By introducing swing options, it can not only provide more flexible risk management tools for high-carbon emission enterprises, but also promote the healthy development of the carbon emission trading market and encourage more market entities to participate in energy conservation and emission reduction actions.
Abstract: Under the policy framework highlighted by the 20th National Congress of the Communist Party of China, which advocates for ‘actively and steadily promoting carbon peak and carbon neutrality’, this study investigates the pricing mechanism of China Emission Allowance (CEA) swing options in the carbon market, aligned with the practical needs of the ‘In...
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Research Article
The Effect of Artificial Intelligence (AI) on Customer Satisfaction: A Review of Bangladesh Perspective
Azmat Ullah*
Issue:
Volume 13, Issue 2, April 2025
Pages:
54-60
Received:
4 February 2025
Accepted:
17 February 2025
Published:
31 March 2025
DOI:
10.11648/j.ijsts.20251302.13
Downloads:
Views:
Abstract: Artificial Intelligence (AI) is dynamic and open ended platform for all stakeholders, especially for customers. Customers can easily explore everything in any corner of the world at any time by the support of AI and enjoy on demand customized service 24 hours a day, 7 days a week. The main objective of this study is to clarify the role of Artificial Intelligence (AI) on enhancing customer satisfaction in Bangladesh. This study also evaluates and emphasizes the benefits of artificial intelligence for customer satisfaction that influence consumer engagement on AI-powered systems to boost up the rate of consumer perception and drive to increase the repurchase intention of consumers and challenges of AI for customers as well as organizations that create obstacles for delivering customer service. AI helps to decrease the human involved in various IT related activity with the aid of chatgpt, deepseek, Github, Copilot, Undetectable.ai, YouChat AI and many more systems. AI and virtual assistants enable constant accessibility for consumer questions and task assistance. This approach increases customer satisfaction and builds confidence by ensuring that they can get help whenever they need it and from any location. Additionally, this study makes recommendations about how to properly utilize AI technology to improve both individual and corporate customer satisfactions while avoiding unintended direct and indirect bias, prejudice, and discrimination.
Abstract: Artificial Intelligence (AI) is dynamic and open ended platform for all stakeholders, especially for customers. Customers can easily explore everything in any corner of the world at any time by the support of AI and enjoy on demand customized service 24 hours a day, 7 days a week. The main objective of this study is to clarify the role of Artificia...
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Research Article
The Challenges of Massification in Higher Education in Developing Countries
Issue:
Volume 13, Issue 2, April 2025
Pages:
61-72
Received:
5 March 2025
Accepted:
26 March 2025
Published:
17 April 2025
Abstract: Higher education institutions in developing countries, especially in Africa, face numerous challenges related to the massification of classes due to rapid growth in student enrollment in public universities. This expansion has been encouraged by public authorities, with the support of organizations like the United Nations and UNESCO, to ensure that countries can train qualified professionals and meet their socio-economic needs. However, the increase in student numbers in large classes (with some courses having over 3,000 students) raises significant concerns regarding the quality of education and equity. Issues include resource availability, content reproducibility, study conditions, and access to digital solutions. Providing this type of education requires specialized training conditions and infrastructure, which is often lacking in developing countries. This paper presents the findings of a survey conducted at the University of Lomé, which included 1,800 students and 106 teachers, all of whom experience the challenges associated with large class sizes. The student demographic primarily consists of first-year bachelor’s program entrants. The main objective of the survey is to gather insights and opinions from participants on suitable solutions the University of Lomé can implement to address the challenges of massification when enrollment exceeds 3,000 or 4,000 students. Additionally, the study aims to consider the perspectives of stakeholders in higher education to propose an ICT-based solution for managing large groups effectively. The findings of this research can also be applied to other African universities facing similar challenges and may pave the way for solutions akin to intelligent classrooms for face-to-face courses.
Abstract: Higher education institutions in developing countries, especially in Africa, face numerous challenges related to the massification of classes due to rapid growth in student enrollment in public universities. This expansion has been encouraged by public authorities, with the support of organizations like the United Nations and UNESCO, to ensure that...
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Research Article
Genomic and Epidemiological Surveillance of SARS-CoV-2: Data Analysis from the Central Public Health Laboratory of Alagoas and GISAID Database
Issue:
Volume 13, Issue 2, April 2025
Pages:
80-87
Received:
20 March 2025
Accepted:
31 March 2025
Published:
19 April 2025
Abstract: Genomic and epidemiological surveillance play a critical role in understanding the spread and evolution of SARS-CoV-2 at the regional level. In the state of Alagoas, Brazil, continuous monitoring of viral mutations is essential for assessing transmission dynamics and informing public health policies. The GISAID platform is a valuable resource for genomic data, but challenges related to data access and processing necessitate efficient analytical solutions. This study presents an automated pipeline designed to streamline the retrieval, filtering, and analysis of SARS-CoV-2 sequences from GISAID, with a specific focus on genomic surveillance in Alagoas. Using bioinformatics tools, our approach enables the selection of high-quality sequences based on metadata criteria, improving the accuracy of phylogenetic and epidemiological analyses. Our results demonstrate the successful retrieval and processing of over 90 high-quality SARS-CoV-2 sequences from Alagoas, allowing for the identification of region-specific mutations and their association with emerging variants. Phylogenetic analyses revealed distinct viral lineages circulating in the state, contributing to a deeper understanding of local transmission patterns. Additionally, our approach improved data retrieval efficiency by 40% and reduced processing time by 50% compared to manual methods. This study was conducted in collaboration with the Central Public Health Laboratory of the State of Alagoas, reinforcing the importance of integrating automated bioinformatics tools into regional genomic surveillance efforts. Our study provides a scalable and efficient solution for real-time SARS-CoV-2 monitoring and can be adapted for other viral pathogens, enhancing epidemiological preparedness in Alagoas and beyond.
Abstract: Genomic and epidemiological surveillance play a critical role in understanding the spread and evolution of SARS-CoV-2 at the regional level. In the state of Alagoas, Brazil, continuous monitoring of viral mutations is essential for assessing transmission dynamics and informing public health policies. The GISAID platform is a valuable resource for g...
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