Plants are valued for their practical purposes (food, medicine, etc.) and are thus considered indispensable. Diseases that attack the plant's leaves might strike at any time among plantings, wreaking havoc on the crop and its economic value. Consequently, the ability to recognize leaf diseases is extremely important in the agricultural sector. Nonetheless, it calls for a lot of work, additional time for planning, and an in-depth understanding of plant pathogens. Both “deep learning (DL) and Machine Learning (ML)” algorithms for diagnosing leaf diseases have been created and evaluated by several researchers, with generally favorable results for both categories of techniques. However, they still have certain issues when it comes to covering large regions to detect leaf diseases. We offer a new method for disease prediction in leaves, which we name Multi-Gradient Deep Convolutional Neural Network with Adaptive Support Vector Machine (M-D-C-A-S). We were able to cover he largest area with the greatest accuracy, and our technique outperformed other more conventional approaches. To evaluate the efficacy of the prediction of leaf diseases, a variety of measures are used, like accuracy, precision, f1-score, and recall. Experiments that predicted plant leaf diseases demonstrated the viability and efficacy of our technique.

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Multicast services are used in emerging multimedia applications based on Quality of Service (QoS) development of multimedia group application and the construction of multicast routing tree is significant. Upper bound on the delay between sender and receiver is one of the common QoS constraints. Delay-constrained routing protocols are used to find paths subject to the delay constraint while efficiently using network resources, also the delay constrained routing problem is difficult because, different constraints can conflict with another. Many of the delay-constrained routing protocols that have been proposed in the literature give priority to cost minimization during the path computing process. With this approach, we propose a new approach for end-to-end delayconstrained routing in a network, which captures the trade-off between cost minimization and the risk level regarding to the delay constraint. We propose a method called Delay-Constrained Routing Agent (DCRA) that implements our approach using a simple and efficient parameterized selection function.