Enhancements of minimax access-point setup optimisation approach for IEEE 802.11 WLAN

As a flexible and cost-efficient internet access network, the IEEE 802.11 wireless local-area network (WLAN) has been broadly deployed around the world. Previously, to improve the IEEE 802.11n WLAN performance, we proposed the four-step minimax access-point (AP) setup optimisation approach: 1) link throughputs between the AP and hosts in the network field are measured manually; 2) the throughput estimation model is tuned using the measurement results; 3) the bottleneck host suffering the least throughput is estimated using this model; 4) the AP setup is optimised to maximise the throughput of the bottleneck host. Unfortunately, this approach has drawbacks: 1) a lot of manual throughput measurements are necessary to tune the model; 2) the shift of the AP location is not considered; 3) IEEE 802.11ac devices at 5 GHz are not evaluated, although they can offer faster transmissions. In this paper, we present the three enhancements: 1) the number of measurement points is reduced while keeping the model accuracy; 2) the coordinate of the AP setup is newly adopted as the optimisation parameter; 3) the AP device with IEEE 802.11ac at 5 GHz is considered with slight modifications. The effectiveness is confirmed by extensive experiments in three network fields

DeepCrop: Deep learning-based crop disease prediction with web application

Agriculture plays a significant role in every nation's economy by producing crops. Plant disease identification is one of the most important aspects of maintaining an agriculturally developed nation. The timely and efficient detection of plant diseases is essential for a healthy and productive agricultural sector and to prevent wasting money and other resources. Various diseases that could affect a plant cause crop farmers to lose a substantial sum yearly. Deep learning can play a crucial role in helping farmers prevent crop failure by early disease detection in plant leaves. In the experiment, we examined CNN, VGG-16, VGG-19 and ResNet-50 models on plant-village 10000 image dataset to detect crop infection and got the accuracy rate of 98.60%, 92.39%, 96.15%, and 98.98% for CNN, VGG-16, VGG-19 and ResNet-50 respectively. The study indicates that ResNet-50 outperforms the other models with an accuracy of 98.98%. So, the ResNet50 model was chosen to be developed into a smart web application for real-life crop disease prediction. The proposed web application aims to assist farmers in identifying diseases of plants by analyzing photos of the plant leaves. The proposed application uses the ResNet50 transfer learning model at its heart to distinguish healthy and infected leaves and classify the present disease type. The goal is to help farmers save resources and prevent economic loss by detecting plant diseases early and applying the appropriate treatment