Rice is the primary food for almost half of the world’s population, especially for the people of Asian countries. There is a demand to improve the quality and increase the quantity of rice production to meet the food requirements of the increasing population. Bulk cultivation and quality production of crops need appropriate technology assistance over manual traditional methods. In this work, six popular Deep-CNN architectures, namely AlexNet, VGG-19, VGG-16, InceptionV3, MobileNet, and ResNet-50, are exploited to identify the diseases in paddy plants since they outperform most of the image classification applications. These CNN models are trained and tested with Plant Village dataset for classifying the paddy plant images into one of the four classes namely, Healthy, Brown Spot, Hispa, or Leaf Blast, based on the disease condition. The performance of the chosen architectures is compared with different hyper parameter settings. AlexNet outperformed other convolutional neural networks (CNNs) in this multiclass classification task, achieving an accuracy of 89.4% at the expense of a substantial number of network parameters, indicating the large model size of AlexNet. For developing mobile applications, the ResNet-50 architecture was adopted over other CNNs, since it has a comparatively smaller number of network parameters and a comparable accuracy of 86.1%. A fine-tuned ResNet-50 architecture supported mobile app, “Generic Paddy Plant Disease Detector (GP2D2)” has been developed for the identification of most commonly occurring diseases in paddy plants. This tool will be more helpful for the new generation of farmers in bulk cultivation and increasing the productivity of paddy. This work will give insight into the performance of CNN architectures in rice plant disease detection task and can be extended to other plants too.

By employing an efficient authentication protocol the security of the Cognitive radio Network can be upgraded. This paper portrays a strategy for recognizing the Primary client Emulation Attacks in a CRN using cross layer approach. A few kinds of attacks have been discussed in this paper and an authentication scheme to detect PUEA has been presented. It is identified that the physical layer authentication method can serve as a faster authentication method but with low accuracy in detection.And a cryptography based authentication protocol can serve as a slower authentication method but with high accuracy in detection. In this paper a cross layer confirmation plot that joins both the physical and higher layer authentication is defined. The proposed method can provide good accuracy in detection without sacrificing the speed of the detection.A detecting procedure to detect PUEA in a CRN for a single user has been defined in this paper.

In modern times, multimedia streaming systems that transmit video across a channel primarily use HTTP services as a delivery component. Encoding the video for all quality levels is avoided thanks to fuzzy based encoders' ability to react to network changes. Additionally, the system frequently uses packet priority assignment utilising a linear error model to enhance the dynamic nature of DASH without buffering. Based on a fuzzy encoder, the decision of video quality is made in consideration of the bandwidth available. This is a component of the MPEG DASH encoder. The Fuzzy DASH system seeks to increase the scalability of online video streaming, making it suitable for live video broadcasts through mobile and other devices.

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Medical care is one such region, where WIFI is as yet not utilized as the electromagnetic waves influences patients with sicknesses like neurological problems, diseases and so forth. Accordingly, LIFI can be respected the following large thing, as it represents no gamble to patients and offers more advantages than WIFI, such as faster speeds and a larger spectrum. The only issue that hospitals have while exchanging data through it is ensuring confidentiality. The methodology proposed here leverages Secure Hash Algorithms to give maximum security as a solution to this challenge. The Secure Hash Algorithm is a bonus feature that is mostly utilised for authentication. IoT connects physical devices such as sensors and actuators to networks. The programming routines can be visualised from any location thanks to cloud storage. These algorithms can be employed in a variety of applications, including smart homes, digital technologies, and banking systems. This research presents a model that takes into account a human's heart rate, glucose level, and temperature. In the even to fan emergency, adjacent hospitals are alerted to the patient's condition, allowing them to provide timely and correct care. This will save you from having to go to the hospital. Temperature, blood pressure, heart rate, gas sensor, and fall detection are among the vital signs monitored by the system. An Arduino controller and a GSM900Amodule make up the system design. The monitored values can be supplied via mobile phones, and if an abnormal state is detected, the buzzer is activated, and the information is communicated to the concerned members via the mobile app.