This study sought to develop and test a machine learning based prototype of a smart home security system for Kampala Metropolitan area in Uganda. The researchers used a Design Science Research (DSR) method to execute ...
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ISBN:
(数字)9798350387902
ISBN:
(纸本)9798350387919
This study sought to develop and test a machine learning based prototype of a smart home security system for Kampala Metropolitan area in Uganda. The researchers used a Design Science Research (DSR) method to execute the project. The researchers identified a problem- which was high level of insecurity and crimes around homes in Kampala and its metropolitan areas, defined objectives, collected requirements, designed and developed the artifact, then demonstrated, evaluated, and communicated the artifact to stakeholders. The team created a working prototype of a home-security system that was later tested, demonstrated, validated among potential users. Practically, this project was implemented in a simulated environment using Tinkercad Arduino software. The developed system prototype was simulated on screen and tested on at least one home to ensure its effectiveness in reducing unauthorized entry, robbery, and other forms of crimes that are widely prevalent in Kampala today. One limitation of the project was that Arduino board used was quite limited on the number of pins that were available for use. The initial project idea had featured a temperature sensor to that was intended to regulate the temperature in the home by making use of the fan. However, the researchers did not implement this due to the limited pins and the capacity of the Arduino Uno R3 board that was used. Higher capacity industrial tools could provide a more realistic design of a similar system. The project simulated an environment of a smart home security system that can be used to detect and reduce crime. This study is part of a wider project to test ML and IoT systems in building secure smart homes. The authors envisage the application on more advanced ML algorithms and techniques in improving the system.
Drastic increase of cardiovascular disease has led to a lot of adult's death. As per 'News 18' very year, 12 million young people in India die from heart disease. In most of these cases people experience c...
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The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today’s world, e-commerce websites are transitioning ...
The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today’s world, e-commerce websites are transitioning from traditional search-driven methods to customized and intuitive approaches via product suggestions. Product recommendation systems are vital in e-commerce, from bringing new business to retaining existing ones. Our three-part recommendation system is designed so that new users have a great and engaging experience as the Product Popularity -Based System shows them carefully chosen products that are in demand. Collaborative Filtering Recommendations are highly personalized recommendations given to people who have already made their first purchases based on their prior actions and preferences. The K-Means Clustering-Based Recommendation System uses textual clustering analysis to deliver contextually relevant recommendations. We use a variety of evaluation metrics, such as click-through rates, user engagement, and the Silhouette Score, to assess the effectiveness and accuracy of our recommendation systems. Our findings show significant increases in user engagement, conversion rates, and relevant recommendations. Our findings demonstrate the transformative power of well-designed recommendation systems, which improve user experiences and retention and provide invaluable solutions for businesses entering the e-commerce space. This paper provides an in-depth examination of the multifaceted landscape of e-commerce recommendations, shedding light on their far-reaching implications for customer acquisition and retention in this dynamic digital era.
Personal Protective Equipment (PPE) regulations require construction workers to wear safety helmets to ensure site safety. However, monitoring PPE compliance consistently in fast-paced, dynamic construction environmen...
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Markerless-based is the method in Augmented Reality (AR) to augmentation 3D objects in the frame. One of the challenges in markerless AR is to extract features as input data training on markerless AR. One of algorithm...
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Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and...
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ISBN:
(数字)9798350362541
ISBN:
(纸本)9798350362558
Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM).
A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine ...
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The number of woven fabric on the island of Timor is very large and varied, making it difficult to distinguish between types and origins. Many woven fabric motifs appear similar but represent different types. Therefor...
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ISBN:
(数字)9798331517601
ISBN:
(纸本)9798331517618
The number of woven fabric on the island of Timor is very large and varied, making it difficult to distinguish between types and origins. Many woven fabric motifs appear similar but represent different types. Therefore, this classification was used to perform pattern recognition of woven fabric in the system. The process allowed the system’s ability to be tested by applying the pattern recognition algorithm to woven images. In this context, the feature extraction method used was gray level co-occurrence matrix (GLCM), while the method used for classification was artificial neural network (ANN). The results showed an accuracy of $87.5 \%$ in the system. Finally, GUI system was developed, enabling tests to be performed on woven images for the identification of Timor Weaving image patterns.
The Internet of Things (IoT) is rethinking the model for building the smart cities, and this paper makes just such a step in that direction. It then examines various ways of cutting urban pollution. Under increasing p...
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Based on one of the fundamental principles of the educational model of the European Higher Education Area concerning the development of the capacity to achieve lifelong learning, this project has been developed in whi...
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