5 research outputs found
ΠΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠ° Π΄Π»Ρ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΡΠ΅ΠΌΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π΄Π°Π½Π½ΡΡ ΠΈ Π·Π°ΠΏΡΡΠΊΠ° Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ ΡΠ΅ΡΠ΅ΠΉ
Π Π°Π±ΠΎΡΠ° ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠΈ, ΠΏΡΠ΅Π΄Π½Π°Π·Π½Π°ΡΠ΅Π½Π½ΠΎΠΉ Π΄Π»Ρ ΡΠ°Π±ΠΎΡΡ Ρ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΠΌΠΈ ΡΠ΅ΡΡΠΌΠΈ ΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π΄Π°Π½Π½ΡΡ
. ΠΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠ° ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π° Π² ΠΎΠ±Π»Π°ΡΡΠΈ Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠ΅Π²ΡΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ².The work is devoted to the development of a library designed to work with neural networks and data processing. The library can be used in the field of deep learning of neural network algorithms
Human-Centered Explainable Artificial Intelligence for Anomaly Detection in Quality Inspection: A Collaborative Approach to Bridge the Gap Between Humans and AI
In the quality inspection industry, the use of Artificial Intelligence (AI) continues to advance to produce safer and faster autonomous systems that can perceive, learn, decide, and act independently. As observed by the researcher interacting with the local energy company over a one-year period, these AI systemsβ performance is limited by the machineβs current inability to explain its decisions and actions to human users. Especially in energy companies, eXplainable-AI (XAI) is critical to achieve speed, reliability, and trustworthiness with human inspection workers. Placing humans alongside AI will establish a sense of trust that augments the individualβs capabilities at the workplace. To achieve such an XAI system centered around humans, it is necessary to design and develop more explainable AI models. Incorporating these XAI systems centered around human workers in the inspection industry brings a significant shift in conducting visual inspections. Adding this explainability factor to the AI intelligent inspection systems makes the decision-making process more sustainable and trustworthy by bringing a collaborative approach. Currently, there is a lack of trust between the inspection workers and AI, creating uncertainty among inspection workers about the use of the existing AI models. To address this gap, the purpose of this qualitative research study was to explore and understand the need for human-centered XAI systems to detect anomalies in quality inspection in energy industries