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Malaria is predominant in many subtropical nations with little health-monitoring infrastructure. To forecast malaria and condense the disease's impact on the population, time series prediction models are necessary. The conventional technique of detecting malaria disease is for certified technicians to examine blood smears visually for parasite-infected RBC (red blood cells) underneath a microscope. This procedure is ineffective, and the diagnosis depends on the individual performing the test and his/her experience. Automatic image identification systems based on machine learning have previously been used to diagnose malaria blood smears. However, so far, the practical performance has been insufficient. In this paper, we have made a performance analysis of deep learning algorithms in the diagnosis of malaria disease. We have used Neural Network models like CNN, MobileNetV2, and ResNet50 to perform this analysis. The dataset was extracted from the National Institutes of Health (NIH) website and consisted of 27,558 photos, including 13,780 parasitized cell images and 13,778 uninfected cell images. In conclusion, the MobileNetV2 model outperformed by achieving an accuracy rate of 97.06% for better disease detection. Also, other metrics like training and testing loss, precision, recall, fi-score, and ROC curve were calculated to validate the considered models. [ABSTRACT FROM AUTHOR]

Artificial intelligence is an emerging technology that revolutionizes human lives. Despite the fact that this technology is used in higher education, many professors are unaware of it. In this current scenario, there is a huge need to arise, implement information bridge technology, and enhance communication in the classroom. Through this paper, the authors try to predict the future of higher education with the help of artificial intelligence. This research article throws light on the current education system the problems faced by the subject faculties, students, changing government rules, and regulations in the educational sector. Various arguments and challenges on the implementation of artificial intelligence are prevailing in the educational sector. In this concern, we have built a use case model by using a student assessment data of our students and then built a synthesized using generative adversarial network (GAN). The dataset analyzed, visualized, and fed to different machine learning algorithms such as logistic Regression (LR), linear discriminant analysis (LDA), K-nearest neighbors (KNN), classification and regression trees (CART), naive Bayes (NB), support vector machines (SVM), and finally random forest (RF) algorithm and achieved a maximum accuracy of 58%. This article aims to bridge the gap between human lecturers and the machine. We are also concerned about the psychological emotions of the faculty and the students when artificial intelligence takes control. [ABSTRACT FROM AUTHOR]

In this work, the known and unknown structural as well as biological properties of 3-(2-aminoethyl) indole (tryptamine) were interpreted using molecular spectroscopy (FT-IR, FT-Raman, NMR and UV–Visible) and cheminformatic tools. The supportive drug-related information was gained by analysing the obtained data which will be useful for the drug chemist for the pharmaceutical research. The important biological properties of the present chemical species satisfied the Lipinski five rules and it was opt to fabricate complex antibiotic compounds. The acquired charge potential load for creating antibiotic strain on compositional parts was keenly observed from the obtained data and it was evaluated by the vibrational analysis and Mulliken charge profile. From the NMR data, the chemical nodal points were noted and their movement around the molecule was carefully monitored. The degenerate and non-degenerate energy profile of orbital interaction system was studied and the link of chemical reactivity path was identified. The significance of excited electronic transitions among non-bonding molecular orbital system was justified and their transitional energy coefficient was determined. The toxicity level was checked from the chirality characteristics and enantiomer structure obtained from vibrational circular dichroism profile. [ABSTRACT FROM AUTHOR]

In this study, an attempt has been made to study an image retrieval technique based on the combination of Haar wavelet transformation using lifting scheme and the colour histogram (CH) called lifting wavelet-based colour histogram. The colour feature is described by the CH, which is translation and rotation invariant. The Haar wavelet transformation is used to extract the texture features and the local characteristics of an image, to increase the accuracy of the retrieval system. The lifting scheme reduces the processing time to retrieve images. The experimental results indicate that the proposed technique outperforms the other schemes, in terms of the average precision, the average recall and the total average precision/recall. [ABSTRACT FROM AUTHOR]