Tran Ho Xuan Mai , Ngo Ho Anh Khoi , Trinh The Luc, Tran Huynh Khang.
Proceedings: Proceedings of the International Scientific
Conference on the Industrial Revolution 4.0 – Applied to Sustainable
Economic Development Adapting to Climate Change
ISBN: 978-604-965-651-4
At present, artificial intelligence (AI) is one of the most rapidly developing fields in science and technology. In the modern context, AI technologies have become a highly researched area globally, bringing about breakthrough technologies that enhance efficiency and effectiveness across various sectors, including environmental security. Particularly in today's landscape where fire and explosion incidents can happen anywhere, there is concern about environmental pollution caused by hazardous substances released from fires, which come into contact with soil, water, and the air. This can have an impact on human and animal health, leading to potential diseases and adverse effects on the environment. Artificial intelligence (AI) is being widely applied in researching and developing systems for detecting smoke and fires, playing a crucial role in reducing the damages caused by fires to the environment, human beings, and the economy. The "Smoke Detection Dataset" provided by Deep Contractor has been chosen as the primary dataset for this topic. The Decision Tree Classifier algorithm has been selected to build classification models. This algorithm has been applied in many fields of artificial intelligence and has shown significant advancement in recent years. This is also the reason why the Decision Tree Classifier algorithm is chosen as the foundation of the system in this study.
Keywords: Smoke Detection Dataset, Smoke Detection and Recognition, Decision Tree Classifier, Application of Artificial Intelligence to Address Smoke Detection and Recognition, IoT, Application
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Keywords: Smoke Detection Dataset, Smoke Detection and Recognition, Decision Tree Classifier, Application of Artificial Intelligence to Address Smoke Detection and Recognition, IoT, Application
Nguyen Anh Duy, Huynh Vo Huu Tri, Duong Duy Khanh, Ngo Ho Anh Khoi.
Proceedings: Proceedings of the International Scientific
Conference on the Industrial Revolution 4.0 – Applied to Sustainable
Economic Development Adapting to Climate Change
ISBN: 978-604-965-651-4
The increasing prevalence of depression resulting from excessive stress is leading to a rising number of suicide cases among many young people today. Therefore, this is an urgent issue that requires research and implementation of appropriate interventions. This article discusses using a database collected from depression symptoms called Stress-Lysis and applying the Bagging algorithm combined with the evolving machine learning method for data stream processing to analyze and predict stress levels. The goal is to provide timely treatment methods for those affected by the condition. Experimental results demonstrate that this combination achieves favorable outcomes and has been implemented in the development of a prototype electronic platform.
Keywords: Stress-Lysis, stress level prediction, bagging, evolving machine learning, data stream.
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Keywords: Stress-Lysis, stress level prediction, bagging, evolving machine learning, data stream.
DEVELOPMENT EXPERIENCE OF GLASS CLASSIFICATION BY BERNOULI NAIVES BAYES IMPROVED CONTINUOUS LEARNING METHOD
Nguyen Thi Cam Tu , Doan Hoa Minh , Bui Hoang Bac, Ngo Ho Anh Khoi.
Proceedings: Proceedings of the International Scientific
Conference on the Industrial Revolution 4.0 – Applied to Sustainable
Economic Development Adapting to Climate Change
ISBN: 978-604-965-651-4
Artificial intelligence is gradually emerging as a method for optimizing various tasks, offering cost-saving and highly efficient solutions. Nowadays, AI is used as a general term for diverse tasks performed by computers. Fields like machine learning, deep learning, and data science, among others within this scope, are considered part of AI as long as they exhibit the characteristics of artificial intelligence. AI is particularly valuable in predictive analysis, specifically in predicting datasets, and is applied to classification problems. The application of artificial intelligence in solving the glass classification problem aims to categorize and recycle different types of glass. The sliding window method is employed for this classification task as it is the most suitable approach. By classifying glass, this approach contributes to the recycling and reuse of industrial glass, reducing glass waste for the benefit of humanity and limiting environmental pollution.
Keywords: Advanced Machine Learning, Glass Types, classification of glass based on the sliding window approach, deep learning, Bernoulli Naive Bayes.
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Keywords: Advanced Machine Learning, Glass Types, classification of glass based on the sliding window approach, deep learning, Bernoulli Naive Bayes.
Vo Ngoc Truong Duy , Vo Van Phuc , Tran Duy Khang , Ngo Ho Anh Khoi
Proceedings: Proceedings of the International Scientific
Conference on the Industrial Revolution 4.0 – Applied to Sustainable
Economic Development Adapting to Climate Change
ISBN: 978-604-965-651-4
The research is focused on exploring the applications of Artificial Intelligence algorithms in handling diagnostic wine quality data. The article discusses the successful implementation of the Decision Tree algorithm for this purpose. This drives the main research goal, which revolves around integrating the Decision Tree with flexible sliding window techniques that can continuously adapt and update over time. The primary objective of the study is to address the wine quality diagnostic problem. Alongside this goal, there are additional smaller objectives to achieve. The initial step involves studying and researching theoretical foundations and measurement methods, as well as analyzing wine quality. Lastly, the goal of deploying a test application is set, aiming to create a Wine Quality Diagnostic Page. The interface of the page is designed to be user-friendly, intuitive, and informative about the functioning and content of the wine quality diagnostic method.
Keywords: Wine quality diagnosis, Decision Tree algorithm, AI application.
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Keywords: Wine quality diagnosis, Decision Tree algorithm, AI application.
Le Thi My Nhu , Ngo Ho Anh Khoi , Duong Duy Khanh
Proceedings: Proceedings of the International Scientific
Conference on the Industrial Revolution 4.0 – Applied to Sustainable
Economic Development Adapting to Climate Change
ISBN: 978-604-965-651-4
In recent years, the incidence and mortality rates due to cardiovascular diseases have been on the rise globally. This is the primary reason why the main objective of this topic is to investigate techniques aimed at solving the problem of heart disease diagnosis. The research methodology for this topic involves the use of the scientific experimental approach, conducted on the Multilayer Perceptron (MLP) algorithm using the Heart Failure Prediction Dataset as the foundational dataset. This research addresses a highly significant societal issue. If further studied and developed, it has the potential to empower individuals to proactively and effectively prevent heart diseases. The prediction of heart disease has become a crucial field of study, aiding in early detection, risk assessment, and the implementation of preventive measures. This article summarizes several important aspects related to heart disease prediction based on scientific machine learning methods.
Keywords: Heart Failure Prediction Dataset, HEART DISEASE PREDICTION, Multilayer Perceptron, MLP.
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Keywords: Heart Failure Prediction Dataset, HEART DISEASE PREDICTION, Multilayer Perceptron, MLP.
Trieu Vinh Khang , Le Duc Thang , Luc Ha Duy Nguyen, Ngo Ho Anh Khoi
Proceedings: Proceedings of the International Scientific
Conference on the Industrial Revolution 4.0 – Applied to Sustainable
Economic Development Adapting to Climate Change
ISBN: 978-604-965-651-4
In a recovering economic situation after the epidemic, the problem of canceling bookings on a regular basis will cause losses for businesses. The problem of hotel booking cancellation causes many inconveniences for hotels and visitors, affecting revenue and customer's travel experience. The resolution of this issue is very necessary to ensure benefits for both sides and sustainable development of the tourism industry. Solving the problem of hotel cancellation plays an important role in life because it helps to create customer trust and satisfaction. At the same time, it also helps tourism develop sustainably and contribute to the development of the country's economy. The purpose is to provide entities with tools, research, and implementation results to address this problem through an artificial intelligence system, aiming to enhance the reliability of room bookings for both domestic and international businesses in Vietnam and around the world. The research aims to employ artificial intelligence systems to predict customer room booking needs conveniently. This project will help businesses easily understand the requirements of tourists, thus increasing revenue and contributing to the economic development of Vietnam and the global economy.
Keywords: Hotel Reservations Dataset, Hotel Reservation Prediction, Sliding Windows, Machine Learning, AI application.
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Keywords: Hotel Reservations Dataset, Hotel Reservation Prediction, Sliding Windows, Machine Learning, AI application.
Mai Thi My Tien , Tran Thanh Nam , Nguyen Van Linh , Ngo Ho Anh Khoi
Proceedings: Proceedings of the International Scientific
Conference on the Industrial Revolution 4.0 – Applied to Sustainable
Economic Development Adapting to Climate Change
ISBN: 978-604-965-651-4
Currently, the phenomenon of student dropout midway through their education is a concerning issue. This not only directly impacts the lives of students and their families, but also affects higher education institutions and society as a whole. Consequently, this situation leads to various difficulties for students, coupled with a lack of soft skills and life experience, causing many students to turn to part-time jobs. This research aims to construct a machine learning model to predict students' academic outcomes and dropout status. This contributes to identifying causes and effectively addressing the mentioned issues, thereby alleviating burdens on families and society, limiting social problems, boosting economic growth, creating more job opportunities, and enhancing competitiveness and productivity. This dataset is highly valuable for researchers seeking to conduct comparative studies on students' academic achievements and for training in the field of machine learning.
Keywords: Predict Student Dropout Dataset, Predicting Student Dropout using Machine Learning Methods, GaussianNB Classifier, application.
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Keywords: Predict Student Dropout Dataset, Predicting Student Dropout using Machine Learning Methods, GaussianNB Classifier, application.
Nguyen Ngoc Pham , Phan Thi Xuan Trang , Tran Thi Thuy , Ngo Ho Anh Khoi
Proceedings: Proceedings of the International Scientific
Conference on the Industrial Revolution 4.0 – Applied to Sustainable
Economic Development Adapting to Climate Change
ISBN: 978-604-965-651-4
In today's rapidly advancing technological landscape, a significant portion of current technological developments is centered around the field of Artificial Intelligence (AI). Machine Learning, a subfield of AI, applies statistical and mathematical methods to enhance computer performance. AI has made substantial contributions to solving a wide range of problems over the past decade. Particularly, given the current context where distinguishing between different types of mushrooms is not well understood, especially to prevent the consumption of poisonous mushrooms that can have severe health consequences. The chosen algorithm for this project is the MLP (Multi-layer Perceptron) classifier, which has seen significant development in recent years and has widespread applications in various AI domains. This is why it has been selected as the foundation for the system in this project. The research topic aims to utilize this algorithm to construct a system that rapidly predicts whether a mushroom is edible or poisonous, facilitating practical applications. The application of artificial intelligence in the development of a system related to cognitive differentiation holds the promise of reducing the use of toxic mushrooms in daily life.
Keywords: Application of Artificial Intelligence in Solving a Classification Dataset, Advanced Machine Learning for Mushroom Diagnosis, MLP classifier Algorithm.
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Keywords: Application of Artificial Intelligence in Solving a Classification Dataset, Advanced Machine Learning for Mushroom Diagnosis, MLP classifier Algorithm.
Vo Khuong Duy, Bui Thi Diem Trinh, Tran Ngoc Truc Linh, Ngo Ho Anh Khoi
Proceedings: Proceedings of the International Scientific
Conference on the Industrial Revolution 4.0 – Applied to Sustainable
Economic Development Adapting to Climate Change
ISBN: 978-604-965-651-4
Currently, the field of science and technology, particularly Artificial Intelligence (AI), is undergoing significant development. Artificial Intelligence involves computer-based emulation of human intelligence. Machine learning, a subset of AI, employs mathematical methods to enhance computational performance. The application of AI in agriculture is creating opportunities to optimize the selection of suitable plant species, thereby contributing to increased income for farmers and economic development. By applying machine learning techniques to the "Agricultural Crop Dataset," the project has developed an efficient system for predicting appropriate plant species for farmers, driving the practical application of AI in agriculture and establishing a foundation for sustainable economic growth.
Keywords: Machine Learning, AI, sliding windows, plant species, MLP.
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Keywords: Machine Learning, AI, sliding windows, plant species, MLP.
Pham Hoang Minh, Truong Hung Chen, Pham Huynh Thuy An, Ngo Ho Anh Khoi
Proceedings: Proceedings of the International Scientific
Conference on the Industrial Revolution 4.0 – Applied to Sustainable
Economic Development Adapting to Climate Change
ISBN: 978-604-965-651-4
Quality control of milk involves the use of established control measures and testing methods to ensure proper adherence to standards and regulations concerning milk and its products. Testing ensures that dairy products meet the requirements of standards, are acceptable in terms of nutritional content, and adhere to safety standards regarding microbiological factors, heavy metals, pesticide residues, veterinary drug residues, toxins, and more. Therefore, quality checks at various stages of the milk processing chain, from farms to processing facilities and consumers, are crucial. The research methodology for this project involves scientific experimentation, conducted using the Extra Tree Classifier algorithm with evolving method. The scope of the project is not extensive, and the dataset utilized is the Milk Quality Prediction dataset sourced from kaggle.com. The aim of the project is to aid in diagnosing milk quality rapidly and relatively reliably through provided numerical data. This endeavor aims to reduce the prevalence of low-quality milk trading, ultimately contributing to safer and higher quality milk management for consumers.
Keywords: Milk Quality Prediction, Milk Quality Diagnosis, Machine Learning, AI, Extra Tree Classifier, Progressive Learning
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Keywords: Milk Quality Prediction, Milk Quality Diagnosis, Machine Learning, AI, Extra Tree Classifier, Progressive Learning
