Weather and rainfall are important factors for human life. By depends upon the rainfall agriculture, horticulture harvesting, and goods transportation. These all are goods and supply chain processes. If in supply chain process breaks any point, Ultimately, the farmer will get the loss. Timely predicting rainfall helps the farmers and agriculture and horticulture stock manage to maintain people require India’s coastal area. For these reasons, this paper proposes the Ensemble Models (Catboost, Boost). Most of the authors are working on rainfall prediction using statistical models. Using statistical models to analyze and predict a huge amount of data is very difficult, depending upon the features. But using Ensemble models is likely to boost up the elements, and apply the classification to prediction makes it easy. This paper discusses and Compares the statistical decision tree model with ensemble models to find out the difference between the characteristics of algorithms and how they impact the timely predict the rainfall.
Mobile Ad Hoc Networks (MANET) is the framework for social networking with a realistic framework. In the MANET environment, based on the query, information is transmitted between the sender and receiver. In the MANET network, the nodes within the communication range are involved in data transmission. Even the nodes that lie outside of the communication range are involved in the transmission of relay messages. However, due to the openness and frequent mobility of nodes, they are subjected to the vast range of security threats in MANET. Hence, it is necessary to develop an appropriate security mechanism for the data MANET environment for data transmission. This paper proposed a security framework for the MANET network signature escrow scheme. The proposed framework uses the centralised Software Defined Network (SDN) with an ECC cryptographic technique. The developed security framework is stated as Escrow Elliptical Curve Cryptography SDN (EsECC_SDN) for attack detection and classification. The developed EsECC-SDN was adopted in two stages for attack classification and detection: (1) to perform secure data transmission between nodes SDN performs encryption and decryption of the data; and (2) to detect and classifies the attack in the MANET hyper alert based Hidden Markov Model Transductive Deep Learning. Furthermore, the EsECC_SDN is involved in the assignment of labels in the transmitted data in the database (DB). The escrow handles these processes, and attacks are evaluated using the hyper alert. The labels are assigned based on the k-medoids attack clustering through label assignment through a transductive deep learning model. The proposed model uses the CICIDS dataset for attack detection and classification. The developed framework EsECC_SDN's performance is compared to that of other classifiers such as AdaBoost, Regression, and Decision Tree. The performance of the 6666 CMC, 2023, vol.74, no.3 proposed EsECC_SDN exhibits ∼3% improved performance compared with conventional techniques.
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