Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%.

Here we report the development of triboelectric nanogenerator (TENG) based self-powered human motion detector with chemically developed Au-g-C₃N₄/ZnO based nanocomposite on common cellulose paper platform. Compared to bare g-C₃N₄, the nanocomposite in the form of hierarchical morphology is found to exhibit higher output voltage owing to the contribution of Au and ZnO in increasing the dielectric constant and surface roughness. While generating power ∼3.5 μW cm⁻² and sensitivity ∼3.3 V N⁻¹, the flexible TENG, is also functional under common biomechanical stimuli to operate as human body movement sensor. When attached to human body, the flexible TENG is found to be sensitive towards body movement as well as the frequency of movement. Finally upon attaching multiple TENG devices to human body, the nature of body movement has been traced precisely using machine learning (ML) techniques. The execution of the learning algorithms like artificial neural network and random forest classifier on the data generated from these multiple sensors can yield an accuracy of 99% and 100% respectively to predict body movement with great deal of precision. The exhibition of superior sensitivity and ML based biomechanical motion recognition accuracy by the hierarchical structure based flexible TENG sensor are the prime novelties of the work.

The number of users of the Internet has been continuously rising, with an estimated 5.1 billion users in 2023, which comprises around 64.7% of the total world population. This indicates the rise of more connected devices to the network. On average, 30,000 websites are hacked daily, and nearly 64% of companies worldwide experience at least one type of cyberattack. As per IDC’s 2022 Ransomware study, two-thirds of global organizations were hit by a ransomware attack that year. This creates the desire for a more robust and evolutionary attack detection and recovery model. One aspect of the study is the bio-inspiration models. This is because of the natural ability of living organisms to withstand various odd circumstances and overcome them with an optimization strategy. In contrast to the limitations of machine learning models with the need for quality datasets and computational availability, bio-inspired models can perform in low computational environments, and their performances are designed to evolve naturally with time. This study concentrates on exploring the evolutionary defence mechanism in plants and understanding how plants react to any known external attacks and how the response mechanism changes to unknown attacks. This study also explores how regenerative models, such as salamander limb regeneration, could build a network recovery system where services could be automatically activated after a network attack, and data could be recovered automatically by the network after a ransomware-like attack. The performance of the proposed model is compared to open-source IDS Snort and data recovery systems such as Burp and Casandra.