Background Alzheimer's disease (AD) is a neurological condition that primarily affects older people. Currently available AD drugs are associated with side effects and there is a need to develop natural drugs from plants. Aquilaria is as an endangered medicinal plant genus (commonly called agarwood plants) and various products of Aquilaria plant spp. including resinous heartwood, leaves, bark, and stem have been widely used in various traditional medicine systems. Research on agarwood plants is sparse and only a few previous studies demonstrated their neuroprotective properties in vitro. Owing to the presence of a plethora of secondary metabolites in agarwood plants, it is imperative not only to protect these plants but also evaluate the bioactivity of agarwood phytochemicals. Methods Computational methods such as AutoDock Vina and molecular dynamic (MD) simulations were employed for the docking of 41 selected agarwood compounds with AD-related molecular targets.  Results and Conclusion According to docking data, three compounds aquilarisin, aquilarisinin, aquilarixanthone showed highest binding affinity to selected AD targets compared to their known inhibitors. MD simulation studies revealed that, selected agarwood compounds' protein-ligand complexes showed remarkable structural stability throughout 100 ns simulation. The agarwood chemicals aquilarisin, aquilarisinin, aquilarixanthone, pillion, and agarotetrol are consequently suggested as some of the found hits against AD targets, however, additional experimental validation is required to establish their effectiveness.

Tomato is the most popular and cultivated crop in the world. Nevertheless, the quality and quantity of tomato crops have been declining due to various diseases that afflict tomato crops. Hence, it becomes necessary to detect the disease early to prevent crop damage and increase the yield. The proposed model in this article predicts the infected tomato leaf images (9 classified diseases and also healthy class) obtained from the Plant Village dataset. In this model, Transfer learning was used to extract features from images by VGG16, yielding a high dimension of 25088 features. Overfitting is a commonly anticipated problem because of the higher dimensionality of data. To mitigate this problem, the authors have adopted a novel dimensional reduction-based technique: filter methods, feature extraction techniques like Principal Components Analysis (PCA), and the Boruta feature selection technique of wrapper methods. This adoption enables the proposed model to attain a significantly improved high accuracy of 95.68% and 95.79% in MLP and VGG16, respectively, by reducing its initial dimension on the tomato dataset containing 18160 images across 10 classes.

The multi-input with mixed data modality of the model is based on three folded structure. The first input model is structured by Convolution Network that accepts the images related to the house. The implementation of the network is miniVGGNet design. The network is tested among, which gives a better outcome. The output valued is concatenated eventually with numerical value entry of the same set which is trained and processed by multi-layer perceptron for review the house price of the building. The linear activation is helped to evaluate the predicted value of price after equal dimension merging of convolutional and multi-layer perceptron network.

Computer vision has been demonstrated as state-of-the-art technology in precision agriculture in recent years. In this paper, an Alex net model was implemented to identify and classify cotton leaf diseases. Cotton Dataset consists of 2275 images, in which 1952 images were used for training and 324 images were used for validation. Five convolutional layers of the AlexNet deep learning technique is applied for features extraction from raw data. They were remaining three fully connected layers of AlexNet and machine learning classification algorithms such as Ada Boost Classifier (ABC), Decision Tree Classifier (DTC), Gradient Boosting Classifier (GBC). K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest Classifier (RFC), and Support Vector Classifier (SVC) are used for classification. Three fully connected layers of Alex Net provided the best performance model with a 94.92% F1_score at the training time of about 51min.