In November 2021, Dagstuhl seminar 21442 was bringing together researchers and practitioners from various domains such as of databases, automatic testing, and formal methods to build a common ground and to explore pos...
In smaller components of industrial or energy automation systems, such as device controllers of Protection and Control (PAC) systems in smart grids, controller functionality is tightly coupled with the physical device...
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ISBN:
(数字)9781665464543
ISBN:
(纸本)9781665464550
In smaller components of industrial or energy automation systems, such as device controllers of Protection and Control (PAC) systems in smart grids, controller functionality is tightly coupled with the physical device or sensor capabilities. At this level, software is small and therefore easy to maintain and test. However, when multiple controllers are interconnected and higher-level functionality is added, software applications grow exponentially, and ensuring maintainability becomes proportionally challenging. In this paper, we extend the IEC 61499 reference architecture used to develop industrial automation software with the principles of Abstraction Layered Architecture (ALA) that has shown up to 400% improvements in industrial software maintainability. We show that even a light application of abstraction layering on the top, application-level of IEC 61499 applications makes them significantly more readable and slightly more maintainable. More concrete gains in maintainability are expected when abstraction layering is integrated into lower layers.
As a developing country, Sri Lanka needs to go along with cutting-edge technologies. In the beginning phase of this digital advertising, multiple advertisements were displayed on the users' feeds, including advert...
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Rapid urbanization and increasing income levels combined with poor and insufficient road network to accommodate vehicles is causing a major traffic problem in Sri Lanka. Additionally, in urban areas, traffic congestio...
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Recently Smart Home concept has been a popular choice as a solution for emerging security related problems. The primary objective of this research was to create a cyber-threat free fully functioning smart home monitor...
Recently Smart Home concept has been a popular choice as a solution for emerging security related problems. The primary objective of this research was to create a cyber-threat free fully functioning smart home monitoring and anti-theft alarming system with enhanced physical security mechanisms. The focus of this research was to create a holistic and secure smart home system, combining cutting-edge physical security measures. The study introduced novel Intruder Access Prevention methods rooted in human behavior and voice pattern recognition, while also incorporating blockchain and network traffic analysis to safeguard the homeowner's data. Furthermore, a pioneering voice-controlled monitoring mechanism, utilizing protective energy-saving plug technology, was devised to enhance safety within contemporary households. The human behavior recognition and voice recognition-based intruder access prevention system demonstrated over 80% accuracy in intruder prevention, while user data protection mechanism prevents the communication channel from cyber hackings. Further, the smart plug demonstrates reliable and accurate physical environment monitoring with minimum latency. These results underscore the system's significant contribution to home security, marking a noteworthy advancement in the Smart Home concept.
Making the decision to purchase or invest in real estate can be a very crucial process due to its high financial risk. The purchasing decision of residential real estate properties can be even more decisive because, a...
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Loans generate the majority of revenue forbanks and other financial institutions. Loan approval is a critical process in banking organizations because they can only lend to specific people or organizations due to rest...
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ISBN:
(数字)9798350379990
ISBN:
(纸本)9798350391558
Loans generate the majority of revenue forbanks and other financial institutions. Loan approval is a critical process in banking organizations because they can only lend to specific people or organizations due to restricted resources or credit. In order to forecastif a specific individual is eligible for a loan or not, a variety of machine learning techniques are combined with algorithms such as bagging classifiers. These algorithms include logistic regression classifiers, support vector classifiers (SVC), decision trees, and random forest. This system aims to enhance the accuracy and robustness of bank loan approvalpredictions through the implementation of an ensemble machine learning model with 97% accuracy. Leveraging the strengths of Random Forest (RF) and XGBoost (eXtreme Gradient Boosting), this ensemble approach seeks to mitigate the limitations of individual models, providing a more reliable and precise decision-making system for loan approval processes. However, these systems encounter challenges like managing imbalanced datasets and ensuring model interpretability. Furthermore, integrating diverse data sources while maintaining data quality and consistency presents significant difficulties.
In the increasingly competitive outlook of ecommerce, personalized recommendation systems have emerged as pivotal tools to enhance user experience and drive sales. This paper proposes a novel Fashion Recommendation Sy...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
In the increasingly competitive outlook of ecommerce, personalized recommendation systems have emerged as pivotal tools to enhance user experience and drive sales. This paper proposes a novel Fashion Recommendation System (FRS) leveraging deep learning techniques to offer personalized fashion suggestions to users. The system utilizes a combination of convolutional neural networks (CNNs) for image feature extraction. Through a comprehensive analysis of user interactions and preferences, the FRS generates tailored recommendations that align with individual style preferences and current trends. The work proposes a novel approach that utilizes the ResNet50 algorithm to analyze images of fashion items, extracting meaningful features that capture the essence of style, color, and texture. By training the ResNet50 model on vast datasets of fashion images, thereby enhancing its ability to make accurate recommendations. Through the integration of user preferences and historical data, this system can generate personalized fashion recommendations tailored to each user’s unique style and preferences, thus enhancing user satisfaction and engagement. By harnessing the power of deep learning and the ResNet50 algorithm, fashion retailers can offer personalized shopping experiences, increase customer retention, and drive sales.
Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence o...
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The existing advanced machine learning approaches based on Graph Neural Networks (GNN) for efficient traffic engineering (TE) in software Defined Networking (SDN) overlook consideration of link reliability values. Lin...
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