The identification of plant species is fundamental for the effective study and management of biodiversity. In a manual identification process, different characteristics of plants are measured as identification keys which are examined sequentially and adaptively to identify plant species. However, the manual process is laborious and time-consuming. Recently, technological development has called for more efficient methods to meet species’ identification requirements, such as developing digital-image-processing and pattern-recognition techniques. Despite several existing studies, there are still challenges in automating the identification of plant species accurately. This study proposed designing and developing an automated real-time plant species identification system of medicinal plants found across the Borneo region. The system is composed of a computer vision system that is used for training and testing a deep learning model, a knowledge base that acts as a dynamic database for storing plant images, together with auxiliary data, and a front-end mobile application as a user interface to the identification and feedback system. For the plant species identification task, an EfficientNet-B1-based deep learning model was adapted and trained/tested on a combined public and private plant species dataset. The proposed model achieved 87% and 84% Top-1 accuracies on a test set for the private and public datasets, respectively, which is more than a 10% accuracy improvement compared to the baseline model. During real-time system testing on the actual samples, using our mobile application, the accuracy slightly dropped to 78.5% (Top-1) and 82.6% (Top-5), which may be related to training data and testing conditions variability. A unique feature of the study is the provision of crowdsourcing feedback and geo-mapping of the species in the Borneo region, with the help of the mobile application. Nevertheless, the proposed system showed a promising direction toward real-time plant species identification system.

The current pronunciation scoring based on Goodness of Pronunciation (GOP) uses posterior probabilities of the Acoustic Models. Such algorithms suffer from generalization since they are utilized to determine a score metric for each phoneme rather than on the completeness or comparison with the ideal utterance of the words. In this paper, a novel method is proposed for computing scores calculated using combined scores of prosodic, fluency, completeness, and accuracy. This is achieved using context-aware GOP in conjugation with dynamic time warping (DTW) matching of the pitch contours of a weighted average of the context tokens found in the audio file that is rich in mispronounced phonemes. The proposed work gives flexibility in tuning the results according to different speech aspects based on a single hyperparameter. The results achieved are encouraging and have been validated on the speechocean762 dataset, where Automatic Speech Recognition (ASR) model has been trained on the Librispeech dataset. The resultant mean error of the proposed approach is 3.38% and the value of the correlation coefficient achieved is 0.652.

A fundamental challenge in bioinformatics or clinical research is the inability to accurately identify important information (Embi and Payne 2009). As such, diagnostics is an active and evolving fi...

Renewable energy can help India’s economy and society. Solar energy is everywhere and can be used anywhere, making it popular. Solar energy’s drawbacks are weather and environmental dependencies and solar radiation variations. Solar Radiation Forecasting (SRF) reduces this drawback. SRF eliminates solar power generation variations, grid overvoltage, reverse current, and islanding. Short-term solar radiation forecasts improve photovoltaic (PV) power generation and grid connection. Previous promising SRF studies often fail to generalize to new data. A biogeography-based optimization artificial neural network (BBO-ANN) model for SRF is proposed in this work. 5-year and 6-year data are used to train and validate the model. The data was collected from India’s Jaipur Rajasthan weather station from 2014 to 2019. This work used biogeography-based optimization (BBO) to optimize and adjust the inertia weight of artificial neural networks (ANN) during training. The BBO-ANN model developed in this study had a Mean Absolute Percentage Error (MAPE) of 3.55%, which is promising compared to previous SRF studies. The BBO-ANN SRF model introduced in this work can generalize well to new data because it was able to produce equally accurate autumn and winter forecasts despite the great climatic variation that occurs during the summer and spring.

Globally, the industrial landscape is witnessing a significant transformation with the emergence of Industry 5.0, marking a new era characterized by seamless convergence of digital technologies, physical systems, and human expertise. This shift hinges on the dynamic interplay between big data analytics (BDA) and artificial intelligence (AI), becoming the cornerstone of intelligent decision-making in Industry 5.0. Accordingly, this study explores the profound impact of integrating BDA and AI in Industry 5.0, emphasizing the pivotal roles of data acquisition, storage, and processing. Additionally, it examines how AI improves human decision-making across various industrial sectors like manufacturing, retail, automotive, energy grid management, and healthcare, showcasing real-world case studies. Moreover, the chapter addresses the challenges associated with managing large-scale data and offers innovative solutions. It concludes by looking ahead, outlining promising areas for future research at the intersection of BDA and AI to foster well-informed decision-making in Industry 5.0. Request access from your librarian to read this chapter's full text.