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Issue Date | Title | Author(s) |
2021 | Advanced digital twins for conditions monitoring, examinations, diagnosis and predictive remaining lifecycles based Artificial Intelligence | Hegazy Abdelghany Mohamed Ammar, Mohamed |
10-May-2024 | Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach | Starodubov, D; Danishvar, S; Abu Ebayyeh, AARM; Mousavi, A |
2010 | Ageneric predictive information system for resource planning and optimisation | Tavakoli, Siamak |
2016 | The analytical modelling of collective capability of human networks | Hosseini, Ehsan |
2013 | Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support | Papadopoulou, Theopisti C |
2019 | Automatic recognition of human behaviour in sequential data | Qin, Rui |
13-Dec-2016 | Automatic translation of plant data into management performance metrics: a case for real-time and predictive production control | Mousavi, A; Siervo, HRA |
18-Dec-2020 | Causal Modelling for Predicting Machine Tools Degradation in High Speed Production Process | Angadi, V; Mousavi, A; Bartolome, D; Tellarini, M; Fazziani, M |
2021 | Classification of Small to Medium Size Manufacturing Enterprises in Fluctuating Economic Conditions | Sayad Saravi, Atefeh |
3-Feb-2021 | Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model | Danishvar, M; Danishvar, S; Souza, F; Sousa, P; Mousavi, A |
2011 | A computer-based product classification and component detection for demanufacturing processes | Mousavi, A; Adjapong, PO |
12-Mar-2021 | Correction to: Relaxed rule-based learning for automated predictive maintenance: proof of concept (Algorithms 2020, 13, 219) | Razgon, M; Mousavi, A |
7-Jan-2022 | Cost-Based Decision Support System: A Dynamic Cost Estimation of Key Performance Indicators in Manufacturing | Psarommatis, F; Danishvar, M; Mousavi, A; Kiritsis, D |
28-Jun-2020 | Data-driven versus conventional N<inf>2</inf>O EF quantification methods in wastewater; how can we quantify reliable annual EFs? | Vasilaki, V; Danishvar, S; Mousavi, A; Katsou, E |
2012 | Dealing with uncertain entities in ontology alignment using rough sets | Li, M; Al-Raweshidy, H; Mousavi, A; Qi, M |
2022 | Deep learning for automatic optical inspection and quality evaluation of semiconductor and optoelectronic manufacturing | Abu Ebayyeh, Abd Al Rahman M. |
22-Aug-2022 | Defect detection on optoelectronical devices to assist decision making: A real industry 4.0 case study | Moustris, GP; Kouzas, G; Fourakis, S; Fiotakis, G; Chondronasios, A; Abu Ebayyeh, AARM; Mousavi, A; Apostolou, K; Milenkovic, J; Chatzichristodoulou, Z; Beckert, E; Butet, J; Blaser, S; Landry, O; Müller, A |
2020 | Design, modelling, and control of an ambidextrous robot arm | Mukhtar, Mashood |
2022 | Development of a quality prediction framework for industry 4.0 optoelectronics assembly lines | Markatos, Nikolaos Grigorios |
2021 | Development of Innovation Acceptance Model for Wearable Computing: A Study of Users’ Technology Acceptance in Malaysia | Mohamad Taib, Syakirah |