Search Results - (( waste detection method algorithm ) OR ( data visualisation learning algorithm ))
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Visualisation System of COVID-19 Data in Malaysia
Published 2021“…Pandemics are highly unlikely events, therefore, we need a system to understand the statistics about the pandamic. Machine learning algorithms can analyse the data and then we can plan for handling the pandamic. …”
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Visualisation System of COVID-19 Data in Malaysia
Published 2021“…Pandemics are highly unlikely events, therefore, we need a system to understand the statistics about the pandamic. Machine learning algorithms can analyse the data and then we can plan for handling the pandamic. …”
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The efficacy of personal computer (PC) AI image enhancer software on low and high contrast PA chest radiograph: an experimental study
Published 2026“…Conclusions: The findings highlight the effectiveness of AI software deep learning in visualising anatomical elements in CXR. …”
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Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization
Published 2021“…Hybrid combinations of feature selection, classification and visualisation using machine learning (ML) methods have the potential for enhanced understanding and 30-day mortality prediction of patients with cardiovascular disease using population-specific data. …”
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An Embedded Machine Learning-Based Spoiled Leftover Food Detection Device for Multiclass Classification
Published 2024“…In conclusion, the work demonstrates a novel method for using machine learning algorithms to classify, identify, and predict the contamination level of leftover cooked food, contributing to reducing food waste generated primarily by Malaysians…”
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Sales prediction for Adha Station by using predictive analytics
Published 2025“…This research presented a technique for projecting sales utilising current data through a machine learning algorithm. The CRISP-DM approach was employed to execute the project across the phases of business understanding, data preparation, modelling, assessment, and deployment. …”
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Optimizing E-commerce inventory management using a machine-learning approach
Published 2025“…The results were visualised to generate actionable insights, enabling data-driven decisions. …”
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RGB and RGNIR image dataset for machine learning in plastic waste detection
Published 2025“…The increasing volume of plastic waste is an environmental issue that demands effective sorting methods for different types of plastic. …”
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Fault detection and diagnosis for gas density monitoring using multivariate statistical process control
Published 2011“…Therefore, an efficient fault detection and diagnosis algorithm needs to be developed to detect faults that are present in a process and pinpoint the cause of these detected faults. …”
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PCB defect detection system using run-length encoding
Published 2017“…The secondary objective is to design run-length encoding algorithm able to detect and classify the defect of printed circuit board (PCB). …”
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A survey of deep learning for lung disease detection on medical images: state-of-the-art, taxonomy, issues and future directions
Published 2020“…The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. …”
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Real-time anomaly detection using clustering in big data technologies / Riyaz Ahamed Ariyaluran Habeeb
Published 2019“…Ultimately, the processed data has been visualised by using Matplot and stored via HBase. …”
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A collaborative filtering approach using machine learning and business intelligence: a critical review
Published 2025“…Specifically, we discovered that combining deep learning techniques with reinforcement learning algorithms enhanced recommendation reliability by 35% while improving user engagement measures by 25%. …”
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The summer heat of cryptojacking season : Detecting cryptojacking using heatmap and fuzzy
Published 2022“…For machine learning-based detection, it's important to find important features in a minimal amount of data. …”
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