Plants contribute significantly to the global food supply. Various Plant diseases can result in production losses, which can be avoided by maintaining vigilance. However, manually monitoring plant diseases by agriculture experts and botanists is time-consuming, challenging and error-prone. To reduce the risk of disease severity, machine vision technology (i.e., artificial intelligence) can play a significant role. In the alternative method, the severity of the disease can be diminished through computer technologies and the cooperation of humans. These methods can also eliminate the disadvantages of manual observation. In this work, we proposed a solution to detect tomato plant disease using a deep leaning-based system utilizing the plant leaves image data. We utilized an architecture for deep learning based on a recently developed convolutional neural network that is trained over 18,161 segmented and non-segmented tomato leaf images—using a supervised learning approach to detect and recognize various tomato diseases using the Inception Net model in the research work. For the detection and segmentation of disease-affected regions, two state-of-the-art semantic segmentation models, i.e., U-Net and Modified U-Net, are utilized in this work. The plant leaf pixels are binary and classified by the model as Region of Interest (ROI) and background. There is also an examination of the presentation of binary arrangement (healthy and diseased leaves), six-level classification (healthy and other ailing leaf groups), and ten-level classification (healthy and other types of ailing leaves) models. The Modified U-net segmentation model outperforms the simple U-net segmentation model by 98.66 percent, 98.5 IoU score, and 98.73 percent on the dice. InceptionNet1 achieves 99.95% accuracy for binary classification problems and 99.12% for classifying six segmented class images; InceptionNet outperformed the Modified U-net model to achieve higher accuracy. The experimental results of our proposed method for classifying plant diseases demonstrate that it outperforms the methods currently available in the literature.

It is of great curiosity to observe the effects of prevention methods and the magnitudes of the outbreak including epidemic prediction, at the onset of an epidemic. To deal with COVID-19 Pandemic, an SEIQR model has been designed. Analytical study of the model consists of the calculation of the basic reproduction number and the constant level of disease absent and disease present equilibrium. The model also explores number of cases and the predicted outcomes are in line with the cases registered. By parameters calibration, new cases in Pakistan are also predicted. The number of patients at the current level and the permanent level of COVID-19 cases are also calculated analytically and through simulations. The future situation has also been discussed, which could happen if precautionary restrictions are adopted.

We study the qualitative behavior of a smoking model in which the population is divided into five classes, that is, non-smokers, smokers, people who temporarily quit smoking, people who permanently quit smoking, and people who are associated with illness due to smoking. The global asymptotic stability of the unique positive equilibrium point is presented. More precisely, a graph-theoretic method is used to prove the global stability of the unique positive equilibrium point.

Underwater Wireless Sensor Networks (UWSNs) consist of several sensor nodes deployed underwater and gathering information from the underwater situation. Sometimes during a communication void regions occur when a forwarder node is unable to find the next forwarder node closer to the sink within the transmission ranges which results from its took extra energy consumption. In this research work, we intend schemes for void hole avoidance. First one is, Avoiding Void Hole Adaptive Hop by Hop Vector‐Based Forwarding (AVH‐AHH‐VBF) in an UWSN, and the second, scheme for increasing lifetime and minimizing consumption of energy of the network, Sink Mobility (SM‐AHH‐VBF). Simulation results show that our schemes outperform compared with baseline solution in terms of average Packet Delivery Ratio (PDR), Average Propagation Distance (APD), energy consumption. The simulation results verify the AVH‐AHH‐VBF scheme results is equals to 14% and SM/AHH‐VBF equal to 32% in terms of PDR, AVH‐AHH‐VBF equals to 57% and SM equals to 39% for energy consumption, AVH‐AHH‐VBF had a tradeoff of 63% because of considering two hops and SM equals 20% tradeoff for the average delay and AVH‐AHH‐VBF equals 35% and SM equals 61% improvement for average APD.