Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis.

Infection in apple leaves is typically brought on by unanticipated weather conditions such as rain, hailstorms, draughts, and fog. As a direct consequence of this, the farmers suffer a significant loss of productivity. It is essential to be able to identify apple leaf diseases in advance in order to prevent the occurrence of this disease and minimise losses to productivity caused by it. The research offers a bibliometric analysis of the effectiveness of artificial intelligence in diagnosing diseases affecting apple leaves. The study provides a bibliometric evaluation of apple leaf disease detection using artificial intelligence. Through an analysis of broad current developments, publication and citation structures, ownership and cooperation patterns, bibliographic coupling, productivity patterns, and other characteristics, this scientometric study seeks to discover apple diseases. Nevertheless, numerous exploratory, conceptual, and empirical studies have concentrated on the identification of apple illnesses. However, given that disease detection is not confined to a single field of study, there have been very few attempts to create an extensive science map of transdisciplinary studies. In bibliometric assessments, it is important to take into account the growing amount of research on this subject. The study synthesises knowledge structures to determine the trend in the research topic. A scientometric analysis was performed on a sample of 214 documents in the subject of identifying apple leaf disease using a scientific search technique on the Scopus database for the years 2011-2022. In order to conduct the study, the Bibliometrix suite's VOSviewer and the web-based Biblioshiny software were also utilised. Important journals, authors, nations, articles, and subjects were chosen using the automated workflow of the software. Furthermore, citation and co-citation checks were performed along with social network analysis. In addition to the intellectual and social organisation of the meadow, this investigation reveals the conceptual structure of the area. It contributes to the body of literature by giving academics and practitioners a strong conceptual framework on which to base their search for solutions and by making perceptive recommendations for potential future research areas.

Two-Way Feature Extraction for Speech Emotion Recognition Using Deep Learning.

Recognizing human emotions by machines is a complex task. Deep learning models attempt to automate this process by rendering machines to exhibit learning capabilities. However, identifying human emotions from speech with good performance is still challenging. With the advent of deep learning algorithms, this problem has been addressed recently. However, most research work in the past focused on feature extraction as only one method for training. In this research, we have explored two different methods of extracting features to address effective speech emotion recognition. Initially, two-way feature extraction is proposed by utilizing super convergence to extract two sets of potential features from the speech data. For the first set of features, principal component analysis (PCA) is applied to obtain the first feature set. Thereafter, a deep neural network (DNN) with dense and dropout layers is implemented. In the second approach, mel-spectrogram images are extracted from audio files, and the 2D images are given as input to the pre-trained VGG-16 model. Extensive experiments and an in-depth comparative analysis over both the feature extraction methods with multiple algorithms and over two datasets are performed in this work. The RAVDESS dataset provided significantly better accuracy than using numeric features on a DNN.

Direct impact of gonadotropins on glucose uptake and storage in preovulatory granulosa cells: Implications in the pathogenesis of polycystic ovary syndrome.

BACKGROUND: Polycystic ovary syndrome (PCOS) is often associated with higher levels of LH, and arrested ovarian follicular growth. The direct impact of high LH on FSH mediated metabolic responses in PCOS patients is not clearly understood. METHOD: In order to investigate the impact of FSH and LH on glucose metabolism in preovulatory granulosa cells (GCs), we used [U14C]-2 deoxyglucose, D-[U14C]-glucose or 2-NBD glucose to analyse glucose uptake and its incorporation into glycogen. To reproduce the high androgenic potential in PCOS patients, we administered hCG both in vitro and in vivo. The role of IRS-2/PI3K/Akt2 pathway was studied after knockdown with specific siRNA. Immunoprecipitation and specific assays were used for the assessment of IRS-2, glycogen synthase and protein phosphatase 1. Furthermore, we examined the in vivo effects of hCG on FSH mediated glycogen increase in normal and PCOS rat model. HEK293 cells co-expressing FSHR and LHR were used to demonstrate glucose uptake and BRET change by FSH and hCG. RESULTS: In normal human and rat granulosa cells, FSH is more potent than hCG in stimulating glucose uptake, however glycogen synthesis was significantly upregulated only by FSH through increase in activity of glycogen synthase via IRS-2/PI3K/Akt2 pathway. On the contrary, an impaired FSH-stimulated glucose uptake and glycogen synthesis in granulosa cells of PCOS-patients indicated a selective defect in FSHR activation. Further, in normal human granulosa cells, and in immature rat model, the impact of hCG on FSH responses was such that it inhibited the FSH-mediated glucose uptake as well as glycogen synthesis through inhibition of FSH-stimulated IRS-2 expression. These findings were further validated in HEK293 cells overexpressing Flag-LHR and HA-FSHR, where high hCG inhibited the FSH-stimulated glucose uptake. Notably, an increased BRET change was observed in HEK293 cells expressing FSHR-Rluc8 and LHR-Venus possibly suggesting increased heteromerization of LHR and FSHR in the presence of both hCG and FSH in comparison to FSH or hCG alone. CONCLUSION: Our findings confirm a selective attenuation of metabolic responses to FSH such as glucose uptake and glycogen synthesis by high activation level of LHR leading to the inhibition of IRS-2 pathway, resulting in depleted glycogen stores and follicular growth arrest in PCOS patients.