Multi-Transformer-Based Ensemble Embedding Model for Enhanced Vector Search in NoSQL Database: A Comparative Statistical and Performance Analysis

Transformer-based embedding models are widely used for similarity search as they are reliable and efficient for capturing semantic similarity. This study uses all-MiniLM-L6-v2, paraphrase-MiniLM-L6-v2 and all-distilroberta-v1 transformer-based embedding models to find the similarity search for Wikipedia documents. All three transformer models are ensembled for enhanced semantic search, and Principal Component Analysis (PCA) is applied to ensure smooth assembly of a different dimensionality model. To understand the strength of the proposed transformer models, 2,000 Wikipedia documents were arbitrarily selected and converted into vectors before storing them in MongoDB. The ground truth of the proposed transformer-based models was examined using 996 TREC questions. The all-MiniLM-L6-v2 and paraphrase-MiniLM-L6-v2 consume less memory than all-distilroberta-v1 model. However, the ensemble process abruptly increased the memory usage to 924.79 MB, higher than individual models. Following that, the average execution time for each query increased to 0.1031 seconds. Beneficially, the ensemble+PCA attained higher precision@10 and recall, resulting in a higher F1 score with an average of 0.5094. The error analysis method indicates that the ensemble+PCA approach significantly improved the semantic search with a higher relevant rate to the raised query. Furthermore, ensemble-based PCA methods are recommended for large dataset handling and are suitable for real-time applications

Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification

A. lumbricoides infection affects up to 1/3 of the world population (approximately 1.4 billion people worldwide). It has been estimated that 1.5 billion cases of infection globally and 65,000 deaths occur due to A. lumbricoides. Generally, allied health classifies parasite egg type by using on microscopy-based methods that are laborious, are limited by low sensitivity, and require high expertise. However, misclassification may occur due to their heterogeneous experience. For their reason, computer technology is considered to aid humans. With the benefit of speed and ability of computer technology, image recognition is adopted to recognize images much more quickly and precisely than human beings. This research proposes deep learning for A. lumbricoides’s egg image recognition to be used as a prototype tool for parasite egg detection in medical diagnosis. The challenge is to recognize 3 types of eggs of A. lumbricoides with the optimal architecture of deep learning. The results showed that the classification accuracy of the parasite eggs is up to 93.33%. This great effectiveness of the proposed model could help reduce the time-consuming image classification of parasite egg

Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification

A. lumbricoides infection affects up to 1/3 of the world population (approximately 1.4 billion people worldwide). It has been estimated that 1.5 billion cases of infection globally and 65,000 deaths occur due to A. lumbricoides. Generally, allied health classifies parasite egg type by using on microscopy-based methods that are laborious, are limited by low sensitivity, and require high expertise. However, misclassification may occur due to their heterogeneous experience. For their reason, computer technology is considered to aid humans. With the benefit of speed and ability of computer technology, image recognition is adopted to recognize images much more quickly and precisely than human beings. This research proposes deep learning for A. lumbricoides’s egg image recognition to be used as a prototype tool for parasite egg detection in medical diagnosis. The challenge is to recognize 3 types of eggs of A. lumbricoides with the optimal architecture of deep learning. The results showed that the classification accuracy of the parasite eggs is up to 93.33%. This great effectiveness of the proposed model could help reduce the time-consuming image classification of parasite egg.</jats:p

Heterogeneous ensemble machine learning to predict the asiaticoside concentration in centella asiatica urban

This study proposes a novel heterogeneous ensemble machine learning methodology to predict the concentration of asiaticoside in Centella asiatica (CA-CA) in the context of the lack of an effective prediction method capable of accurately estimating its quantity based on various growing environmental factors. The accurate prediction of the asi-aticoside concentration in CA-CA holds great significance in optimizing cultivation practices and improving the efficacy of the derived medicinal products. The presented approach aims to address this crucial need by employing a diverse ensemble of machine learning techniques. The proposed model integrates several machine learning tech-niques, including the standard long short-term memory (LSTM), gated recurrent unit (GRU), convolutional long short-term memory (ConvLSTM), and attention-based LSTM, by utilizing a differential evolution algorithm to optimize the ensemble model's weights. The developed model is called the heterogeneous ensemble machine learning model (He-ML). Experimental results demonstrate that the He-ML achieves an im-pressive root-mean-square error (RMSE) value of 4.76, which is up to 12.48 % lower than the RMSE. The findings highlight the advantages of employing an ensemble model over a single model, as the ensemble model achieves an RMSE value that is 14.67 % lower than that of the individual machine learning model. The utilization of differential evolution as the decision fusion strategy provides a notable improvement over the unweighted average approach. As a result, the RMSE value achieved is 8.46 % lower than that obtained with the unweighted average (UWA) technique

A Portable Hybrid Photovoltaic Thermal Application: Shape-Stabilised Phase-Change Material with Metal Flakes for Enhanced Heat Transfer

Photovoltaic&ndash;thermal (PVT) applications have been widely studied in recent years, though commercialisation has become critical due to their operational characteristics and size. In this study, a portable PVT system was developed for mobilisation with assistance from an organic phase-change material (PCM). Two different PCM composites were developed using the PCM with charcoal (PCM + C) and charcoal and metal flakes (PCM + C + M). Considering the portability of the PVT system, conventional metal-container-based PCM storage units were avoided, and the shape-stabilised PCMs (SS-PCMs) were fitted directly on the back surface of the PV module. Further, a serpentine copper tube was placed on the SS-PCMs to extract heat energy for hot water applications. It was found that PVPCM+C+M exhibited a higher cooling rate, with peak reductions of 24.82 &deg;C and 4.19 &deg;C compared to the PVnoPCM and PVPCM+C, respectively. However, PVPCM+C exhibited a higher outlet water temperature difference of 11.62 &deg;C. Secondly, an increase of more than 0.2 litres per minute showed a declining trend in cooling in the PV module. Considering the primary concern of electrical power generation, it was concluded that PVPCM+C+M is suitable for PVT mobilisation applications, owing to it having shown the highest thermal cooling per 190 g of PCM and a 1-Watt (TCPW) cooling effect of 2.482 &deg;C. In comparison, PVPCM+C achieved a TCPW cooling effect of 1.399 &deg;C