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Given the vast diversity of bee species and the limited availability of taxonomy experts, bee species identification has become increasingly important, especially with the rise of apiculture practice. This review systematically explores the application of machine learning (ML) techniques in bee species determination, shedding light on the transformative potential of ML in entomology. Conducting a keyword-based search in the Scopus and Web of Science databases with manual screening resulted in 26 relevant publications. Focusing on shallow and deep learning studies, our analysis reveals a significant inclination towards deep learning, particularly post-2020, underscoring its ability to handle complex, high-dimensional data for accurate species identification. Most studies have utilized images of stationary bees for the determination task, despite the high computational demands from image processing, with fewer studies utilizing the sound and movement of the bees. This emerging field faces challenges in terms of dataset scarcity with limited geographical coverage. Additionally, research predominantly focuses on honeybees, with stingless bees receiving less attention, despite their economic potential. This review encapsulates the state of ML applications in bee species determination. It also emphasizes the growing research interest and technological advancements, aiming to inspire future explorations that bridge the gap between computational science and biodiversity conservation.

Latent toxoplasmosis could induce various hormonal and behavioural perturbations in infected hosts. We aimed to study the latent seroprevalence of Toxoplasma gondii (T. gondii) and the relationship between infection, knowledge and behaviour among 400 pregnant mothers. Plasma samples were tested for the presence of T. gondii IgG antibodies while a structured questionnaire was used to record respondents' socio-demographic characteristics, general information and knowledge on plausible risk factors, symptoms, timing of infection, and preventive knowledge and behaviour regarding toxoplasmosis. The seroprevalence of latent toxoplasmosis among respondents was at 31.8%. This study indicated that 69.5% of them had poor knowledge of toxoplasmosis but most of them (99.8%) practised preventive behaviours. Multiple logistic regression analysis showed that pregnant women with low education levels (aOR: 1.91, 95% CI 1.18, 3.10; p = 0.008) and past medical history (aOR: 2.32, 95% CI 1.32, 4.06; p = 0.003) were both twice as likely to have anti-T. gondii IgG seropositivity. Besides, women who were unsure regarding the transmission mode of the disease via blood transfusion were four times more likely (aOR: 3.93, 95% CI 1.54, 10.01; p = 0.004) to have chronic toxoplasmosis seroprevalence. Women who were unsure regarding the necessities of avoiding stray cats had aOR of 0.42 (95% CI 0.24, 0.71, p = 0.001) for chronic toxoplasmosis seroprevalence. Translating the knowledge on toxoplasmosis into the practice of preventive behaviour via a health education programme is crucial in reducing the risk of disease transmission especially among pregnant women. [ABSTRACT FROM AUTHOR]

Conversational artificial intelligence (AI) is a type of artificial intelligence that uses machine learning techniques to understand and respond to user inputs. This paper presents a conversational chat system that uses an attention mechanism to respond to COVID-19 inquiries. The model is based on the Luong Attention Mechanism’s three scoring methodologies the Dot Attention Mechanism, the General Attention Mechanism, and the Concat Attention Mechanism. The results show that the accuracy of the dot attention mechanism is highest and is 87% when the test questions were obtained directly from the database, as determined by an examination of the results, compared to 38% when the attention mechanism is not used. Furthermore, when the questions are asked with natural variations, human verification accuracy is 63% compared to 16% when the attention mechanism is not used. The research suggests that chatbots can be used everywhere due to their accuracy and accessibility around the clock.

Automatic segmentation is essential for enhancing human activity recognition, especially given the limitations of publicly available datasets that often lack diversity in daily activities. This study introduces a novel segmentation method that utilizes skeleton data for a more accurate and efficient analysis of human actions. By employing an autoencoder, this method extracts representative features and reconstructs the dataset, using the discrepancies between the original and reconstructed data to establish a segmentation threshold. This innovative approach allows for the automatic segmentation of activity datasets into distinct segments. Rigorous evaluations against ground truth across three publicly available datasets demonstrate the method’s effectiveness, achieving impressive average annotation error, precision, recall, and F1-score values of 3.6, 90%, 87%, and 88%, respectively. This illustrates the robustness of the proposed method in accurately identifying change points and segmenting continuous skeleton-based activities as compared to two other state-of-the-art techniques: one based on deep learning and another using the classical time-series segmentation algorithm. Additionally, the dynamic thresholding mechanism enhances the adaptability of the segmentation process to different activity dynamics improving overall segmentation accuracy. This performance highlights the potential of the proposed method to significantly advance the field of human activity recognition by improving the accuracy and efficiency of identifying and categorizing human movements.