Plant diseases are a severe cause of crop losses in the agriculture globally. Detection of diseases in plants is difficult and challenging due to the lack of expert knowledge. Deep learning-based models provide promising ways to identify plant diseases using leaf images. However, need of larger training sets, computational complexity, and overfitting, etc. are the major issues with these techniques that still need to be addressed. In this work, a convolutional neural network (CNN) is developed that consists of smaller number of layers leading to lower computational burden. Some augmentation techniques such as shift, shear, scaling, zoom, and flipping are applied to generate additional samples increasing the training set without actually capturing more images. The CNN model is trained for apple crop using a publicly available dataset PlantVillage to identify Scab, Black rot, and Cedar rust diseases in apple leaves. The rigorous experimental results revealed that the proposed model is well fit to identify apple leaf diseases and achieves 98% classification accuracy. It is also evident from the results that it needs lesser amount of storage and takes smaller execution time than several existing deep CNN models. Altough, there exist several CNN models for crop disease detection with comarable accuracy, but the proposed model needs lower storage and computational resources. Therefore, it is highly suitable for deploying in handheld devices.

The convolutional neural networks (CNN) have shown promising results for various classification problems over the past years. However, selecting various CNN architectures is still challenging as each architecture performs differently with the same dataset. This research aims to evaluate the dependence of brain MRI on various predictive models of CNN based on the complexity of the data for Brain Tumor and Alzheimer’s Disease. Our proposed approach has three parts. First part is the pre-processing of the data which mainly focuses on class balancing and the estimation of data complexity. The second part uses stratified k-fold cross-validation for more reliable results. The last part corresponds to the implementation of four CNN models applying described methods. This paper compares the classification performance of rigorous experimentation on four CNN variants namely S-CNN (CNN trained from scratch), ResNet50, InceptionV3, and Xception over two brain MRI image datasets evaluated with and without the use of Principal Component Analysis (PCA). The work benchmarks CNN models by comparing the average scores of Accuracy, Precision, Recall, F1 score, and AUC score from the stratified five-fold cross-validation.

An accurate amount of fertilizer according to the real-time context is the basis of precision agriculture in terms of sustainability and profitability. Many fertilizers recommendation systems are proposed without considering the real-time context in terms of soil fertility level, crop type, and soil type. The major obstacle in developing the real-time context-aware fertilizer recommendation system is related to the complexity associated with the real-time mapping of soil fertility. Furthermore, the existing methods of determining the real-time soil fertility levels for the recommendation of fertilizer are costly, time-consuming, and laborious. Therefore, to tackle this issue, we propose a machine learning-based fertilizer recommendation methodology according to the real-time soil fertility context captured through the Internet of Things (IoT) assisted soil fertility mapping to improve the accuracy of the fertilizer recommendation system. For real-time soil fertility mapping, an IoT architecture is also proposed to support context-aware fertilizer recommendations. The proposed solution is practically implemented in real crop fields to assess the accuracies of IoT-assisted fertility mapping. The accuracy of IoT-assisted fertility mapping is assessed by comparing the proposed solution with the standard soil chemical analysis method in terms of observing Nitrogen (N), Phosphorous (P), and Potassium (K). The results reveal that the observations by both methods are in line with a mean difference of 0.34, 0.36, and -0.13 for N, P, and K observations, respectively. The context-aware fertilizer recommendation is implemented with the Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbor (KNN) machine learning models to assess the performance of these machine learning models. The evaluation of the proposed solution reveals that the GNB model is more accurate as compared to the machine learning models evaluated, with accuracies of 96% and 94% from training and testing datasets, respectively.

The increasing popularity of mobile phones has led to an abundance of online reviews, making it challenging for consumers to make well-informed purchasing decisions. This study proposes a novel recommendation system-based mobile phone rating classification approach using federated learning and Term Frequency-Inverse Document Frequency (TF-IDF) features. We created a novel dataset by scraping over 13,000 mobile phone reviews from Flipkart’s website. The proposed approach involves the development of a federated deep neural network (FDNN) to classify the newly created Flipkart dataset. This approach includes data cleaning, balancing, TF-IDF feature extraction, and prediction using federated learning. We employ two clients and one server and conduct three rounds of experiments. The experimental results demonstrate that the proposed approach achieved an accuracy of 96.68% on the aggregated server side while maintaining the security of customer data on their local devices. The proposed approach has the potential to assist consumers in making well-informed purchasing decisions and can be extended to other e-commerce platforms with large datasets of online reviews.

Alzheimer’s Disease (AD) is a widespread, chronic, irreversible, and degenerative condition, and its early detection during the prodromal stage is of utmost importance. Typically, AD studies rely on single data modalities, such as MRI or PET, for making predictions. Nevertheless, combining metabolic and structural data can offer a comprehensive perspective on AD staging analysis. To address this goal, this paper introduces an innovative multi-modal fusion-based approach named as Dual-3DM ³ -AD. This model is proposed for an accurate and early Alzheimer’s diagnosis by considering both MRI and PET image scans. Initially, we pre-process both images in terms of noise reduction, skull stripping and 3D image conversion using Quaternion Non-local Means Denoising Algorithm (QNLM), Morphology function and Block Divider Model (BDM), respectively, which enhances the image quality. Furthermore, we have adapted Mixed-transformer with Furthered U-Net for performing semantic segmentation and minimizing complexity. Dual-3DM ³ -AD model is consisted of multi-scale feature extraction module for extracting appropriate features from both segmented images. The extracted features are then aggregated using Densely Connected Feature Aggregator Module (DCFAM) to utilize both features. Finally, a multi-head attention mechanism is adapted for feature dimensionality reduction, and then the softmax layer is applied for multi-class Alzheimer’s diagnosis. The proposed Dual-3DM ³ -AD model is compared with several baseline approaches with the help of several performance metrics. The final results unveil that the proposed work achieves 98% of accuracy, 97.8% of sensitivity, 97.5% of specificity, 98.2% of f-measure, and better ROC curves, which outperforms other existing models in multiclass Alzheimer’s diagnosis.

The Internet of Things (IoT) and the Android operating system have made cutting-edge technology accessible to the general public. These are affordable, easy-to-use, and open-source technology. Android devices connect to different IoT devices such as IoT-enabled cameras, Alexa powered by Amazon, and various other sensors. Due to the escalated growth of Android devices, users are facing cybercrime through their Android devices. This article aims to provide a comprehensive study of the IoT and Android systems. This article classifies different attacks on IoT and Android devices and mitigation strategies proposed by different researchers. The article emphasizes the role of the developer in secure application design. This article attempts to provide a relative analysis of several malware detection methods in the different environments of attacks. This study expands the awareness of certain application-hardening strategies applicable to IoT devices and Android applications and devices. This study will help domain experts and researchers to gain knowledge of IoT systems and Android systems from a security point of view and provide insight into how to design more efficient, robust, and comprehensive solutions. This article discusses different attack vectors and mitigation strategies available to both developers and in the open domain. Certain guidelines are also suggested for application and platform developers, as well as application databases (Google play store), to limit the risk of attack, and users can form their own defense with knowledge regarding keeping hardware and software updated and securing their system with a strong password.