<span lang="EN-US">Wireless sensor network (WSN) nodes have high computation limitations, limited communication capabilities, and limited power resources because of the difficulty or impossibility of replacing or recharging the sensor battery. Energy consumption in nodes is a critical issue to consider while developing WSNs. Many routing protocols are proposed for energy conservation as an important goal for improvement. Nonetheless, just delivering energy is not enough to prolong the life of a WSN. Unbalanced energy depletion is a significant problem in WSNs, often resulting in network splits and a reduction in network lifetime, as well as performance retrogression. This article, therefore, proposes a robust protocol called the fuzzy spider monkey optimization routing protocol (FSMORP) to determine the best data path routing for heterogeneous WSNs (HWSNs). In this case, an FSMORP computes the best path across the cluster heads from a sensor to the sink. This work uses the clustering method to organize heterogeneous nodes in HWSNs. The simulation result indicates that the FSMORP considerably enhances data latency reduction, energy balancing, and lifetime maximization for the network.</span>

Recently, the technology become an important part of our live, and it is employed to work together with the Medicine, Space Science, Agriculture, and industry and more else. Stored the information in the servers and cloud become required. It is a global force that has transformed people's lives with the availability of various web applications that serve billions of websites every day. However, there are many types of attack could be targeting the internet, and there is a need to recognize, classify and protect thesis types of attack. Due to its important global role, it has become important to ensure that web applications are secure, accurate, and of high quality. One of the basic problems found on the Web is DDoS attacks. In this work, the review classifies and delineates attack types, test characteristics, evaluation techniques; evaluation methods and test data sets used in the proposed Strategic Strategy methodology. Finally, this work affords guidance and possible targets in the fight against creating better events to overcome the most dangers Cyber-attack types which is DDoS attacks.

Many real-world applications, like multimedia retrieval, confront the difficulty of determining the distance between any two items on Multi-modal data. According to most current Dimension Metrics Learning (DML) techniques, distance metrics may be learned using just one feature type or an aggregated feature space where many features are simply connected. Even though DML has been extensively researched. This study proposes a new framework for online learning, as well as a new classroom learning system for online Multi-modal dimension metric learning (OMDML), that is both efficient and scalable. This paper proposes a low-rank OMDML calculation to reduce the expensive cost of DML on high-dimensional component space, which reduces the computational cost while maintaining extremely competitive or much higher learning accuracy. With the purpose of determining whether or not multi-modular image recovery calculations can be successfully implemented, a large number of experiments are carried out. In most datasets tested, the suggested approach consistently outperforms alternative state-of-the-art algorithms, according to extensive experimental results