Additive Manufacturing (AM) is on the forefront of innovative advance manufacturing techniques leveraging Artificial Intelligence (AI) and machinelearning (ML) to improve processing capabilities. We conducted a liter...
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Additive Manufacturing (AM) is on the forefront of innovative advance manufacturing techniques leveraging Artificial Intelligence (AI) and machinelearning (ML) to improve processing capabilities. We conducted a literature review to survey the current state of the art for AI/ML applications within Material Extrusion AM (MEX-AM). Furthermore, this study explored the intersection of AI applications and use of Carbon Fiber-Reinforced Polymers (CFRP) as a MEX-AM material. We found that while discontinuous CFRPs are covered in several experimental studies, there was a noticeable lack of research on continuous CFRPs among the collected papers. We found that the most common ML Solution for quality issues in MEX-AM was the artificial neural network feed forward supervised learning back propagation (ANN-FFNN-SL-BPN) Solution.
The widespread integration of machinelearning (ML) in software systems has brought forth unprecedented advancements, yet the surge in energy consumption raises ecological concerns. This research addresses the environ...
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ISBN:
(纸本)9798400705915
The widespread integration of machinelearning (ML) in software systems has brought forth unprecedented advancements, yet the surge in energy consumption raises ecological concerns. This research addresses the environmental impact of ML development, focusing on the energy implications of design decisions in ML-based systems. This thesis aims to offer insights into the energy consumption patterns influenced by deployment architecture and training environment. Different case studies on ML-based systems will be conducted to validate and demonstrate the implications of these design choices. The expected outcomes encompass actionable insights, validated through rigorous evaluations, and the development of an energy prediction tool for ML-based system development, to help in the decision-making process. This work contributes to the broader field of Green AI by addressing a critical gap and guiding the transition towards a more sustainable AI landscape.
The Music to Score Conversion (MSC) project focuses on bridging the gap between auditory and visual representations of music. It uses signal processing techniques for the conversion such as pitch estimation, onset det...
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This study employs advanced machinelearning (ML) algorithms to predict and analyze farmer suicides in India using a comprehensive Kaggle dataset spanning 2001 to 2012. When focusing on high accuracy, algorithms like ...
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Advancements in machinelearning and data analytics have transformed the landscape of biomedical decision-making, offering innovative solutions to address the complexities of diagnosing, prognosing, and planning treat...
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In wireless communication system, the most widely used modulation approach is Orthogonal Frequency Division Multiplexing (OFDM) for time fading and frequency selective channels. The channel estimation (CE) process, on...
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Intrusion detection systems are crucial for network security. Verification of these systems is complicated by various factors, including the heterogeneity of network platforms and the continuously changing landscape o...
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ISBN:
(纸本)9798400705045
Intrusion detection systems are crucial for network security. Verification of these systems is complicated by various factors, including the heterogeneity of network platforms and the continuously changing landscape of cyber threats. In this paper, we use automata learning to derive state machines from network-traffic data with the objective of supporting behavioural verification of intrusion detection systems. The most innovative aspect of our work is addressing the inability to directly apply existing automata learning techniques to network-traffic data due to the numeric nature of such data. Specifically, we use interpretable machinelearning (ML) to partition numeric ranges into intervals that strongly correlate with a system's decisions regarding intrusion detection. These intervals are subsequently used to abstract numeric ranges before automata learning. We apply our ML-enhanced automata learning approach to a commercial network intrusion detection system developed by our industry partner, RabbitRun Technologies. Our approach results in an average 67.5% reduction in the number of states and transitions of the learned state machines, while achieving an average 28% improvement in accuracy compared to using expertise-based numeric data abstraction. Furthermore, the resulting state machines help practitioners in verifying system-level security requirements and exploring previously unknown system behaviours through model checking and temporal query checking. We make our implementation and experimental data available online.
We present the Seldonian Toolkit, which enables software engineers to integrate provably safe and fair machinelearning algorithms into their systems. Software systems that use data and machinelearning are routinely ...
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ISBN:
(纸本)9798350322637
We present the Seldonian Toolkit, which enables software engineers to integrate provably safe and fair machinelearning algorithms into their systems. Software systems that use data and machinelearning are routinely deployed in a wide range of settings from medical applications, autonomous vehicles, the criminal justice system, and hiring processes. These systems, however, can produce unsafe and unfair behavior, such as suggesting potentially fatal medical treatments, making racist or sexist predictions, or facilitating radicalization and polarization. To reduce these undesirable behaviors, software engineers need the ability to easily integrate their machine-learning-based systems with domain-specific safety and fairness requirements defined by domain experts, such as doctors and hiring managers. The Seldonian Toolkit provides special machinelearning algorithms that enable software engineers to incorporate such expert-defined requirements of safety and fairness into their systems, while provably guaranteeing those requirements will be satisfied. A video demonstrating the Seldonian Toolkit is available at https://***/wHR- hDm9jX4/.
Pain is a distressing emotional and physical experience caused by tissue damage. Detection of pain is usually through self-reporting, and is not a feasible option for a patient in a coma or with impaired communication...
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IoT device applications are rapidly expanding in the current environment. Today, we can find IoT devices in a variety of industries, including agriculture, home security, entertainment, health, transportation, and edu...
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