Scene understanding is one of the most challenging areas of research in the fields of robotics and computer vision. Recognising indoor scenes is one of the research applications in the category of scene understanding that has gained attention in recent years. Recent developments in deep learning and transfer learning approaches have attracted huge attention in addressing this challenging area. In our work, we have proposed a fine-tuned deep transfer learning approach using DenseNet201 for feature extraction and a deep Liquid State Machine model as the classifier in order to develop a model for recognising and understanding indoor scenes. We have included fuzzy colour stacking techniques, colour-based segmentation, and an adaptive World Cup optimisation algorithm to improve the performance of our deep model. Our proposed model would dedicatedly assist the visually impaired and blind to navigate in the indoor environment and completely integrate into their day-to-day activities. Our proposed work was implemented on the NYU depth dataset and attained an accuracy of 96% for classifying the indoor scenes.
Maintenance management of stationary combustion engines in the agricultural sector remains largely manual, increasing the risk of unplanned downtime. This study developed a machine learning-based predictive model to anticipate failures within a 60-day horizon, enabling the transi…
Discovery of Association Rules is one of the most common Data Mining techniques. Contrast data mining is a focused data mining research area for discovering interesting contrast patterns that state the significant differences between datasets, i.e., frequent itemsets in one datas…
Rotating machinery plays a critical role in transmission systems, while the scarcity of fault samples and labeled data limits the performance of existing diagnostic methods under few-shot conditions. This paper proposes a few-shot fault diagnosis method for rotating machinery bas…
Timely identification of company financial risks is crucial for investors and regulators. However, existing studies overlook the class imbalance caused by the scarcity of high-risk samples, and the interpretability of deep models is insufficient, making it difficult to meet the p…
Automatic electrocardiogram (ECG) classification using deep learning is sensitive to data leakage, class imbalance and the way multi-segment decisions are aggregated at record level. This study presents a two-channel time–frequency early-fusion pipeline for classifying ECG record…
The classification of malicious network-traffic is critical to cybersecurity. However, to the best of our knowledge, no previous studies have performed a comparative analysis of supervised algorithms for classifying malicious traffic, specifically within the network environment o…