There are ongoing researches for the automatic diagnosis of Alzheirer’s disease(AD) based on traditional machine learning techniques. And deep learning-based approaches are becoming popular choice for AD diagnosis. The state-of-the-art techniques that consider multimodal diagnosis have been shown to have accuracy better than manual diagnosis. However, collecting data from different modalities is time consuming and expensive, and some modalities may have radioactive side effects. Our study is confined to structural magnetic resonance imaging (sMRI). The objectives of our attempt are as follows: 1) to increase the accuracy level that is comparable to the state-of-the-art methods; 2) to overcome the overfitting problem; and 3) to analyze proven landmarks of the brain that provide discernible features for AD diagnosis. Here, we focused specifically on both the left and right hippocampus areas. To achieve the objectives, at first, we incorporate ensembles of simple convolutional neural networks (CNNs) as feature extractors and softmax cross-entropy as the classifier. Then, considering the scarcity of data, we deployed a patch-based approach. We have performed our experiment on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We verified the models on the dataset provided by the National Research Center for Dementia (NRCD), Gwangju, South Korea. We manually localized the left and right hippocampus and fed three view patches (TVPs) to the CNN after the preprocessing steps. We achieve 85.55% accuracy on the ADNI dataset and 90.05% accuracy on the NRCD test dataset. Our result is comparable to other state-of-the-art methods.

The integration of cutting-edge technologies such as the Internet of Things (IoT), robotics, and machine learning (ML) has the potential to significantly enhance the productivity and profitability of traditional fish farming. Farmers using traditional fish farming methods incur enormous economic costs owing to labor-intensive schedule monitoring and care, illnesses, and sudden fish deaths. Another ongoing issue is automated fish species recommendation based on water quality. On the one hand, the effective monitoring of abrupt changes in water quality may minimize the daily operating costs and boost fish productivity, while an accurate automatic fish recommender may aid the farmer in selecting profitable fish species for farming. In this paper, we present AquaBot, an IoT-based system that can automatically collect, monitor, and evaluate the water quality and recommend appropriate fish to farm depending on the values of various water quality indicators. A mobile robot has been designed to collect parameter values such as the pH, temperature, and turbidity from all around the pond. To facilitate monitoring, we have developed web and mobile interfaces. For the analysis and recommendation of suitable fish based on water quality, we have trained and tested several ML algorithms, such as the proposed custom ensemble model, random forest (RF), support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), bagging, boosting, and stacking, on a real-time pond water dataset. The dataset has been preprocessed with feature scaling and dataset balancing. We have evaluated the algorithms based on several performance metrics. In our experiment, our proposed ensemble model has delivered the best result, with 94% accuracy, 94% precision, 94% recall, a 94% F1-score, 93% MCC, and the best AUC score for multi-class classification. Finally, we have deployed the best-performing model in a web interface to provide cultivators with recommendations for suitable fish farming. Our proposed system is projected to not only boost production and save money but also reduce the time and intensity of the producer’s manual labor.

Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design.

Credit card fraud has been growing tremendously mainly in recent years. Due to this inconceivable occurrences, along with financial losses of banks, companies, NGOs and personal accounts, the reputation of the organizations is at stake. In this respect to detect fraud, accuracy has become very important to avoid harassing innocent customers. In this paper, we elicit three tree-based classifiers (NB Tree, REP Tree, and Random Tree) regarding the German credit card data set. We have combined this tree classifier with ensemble techniques to get better accuracy. Using the Resampling, Random Committee and Logit Boost, we were able to increase the performance of those classifiers.