In recent years, web crawling has gained a significant attention due to the drastic advancements in the World Wide Web. Web Search Engines have the issue of retrieving massive quantity of web documents. One among the web crawlers is the focused crawler, that intends to selectively gather web pages from the Internet. But the efficiency of the focused crawling can easily be affected by the environment of web pages. In this view, this paper presents an Automated Word Embedding with Parameter Tuned Deep Learning (AWE-PTDL) model for focused web crawling. The proposed model involves different processes namely pre-processing, Incremental Skip-gram Model with Negative Sampling (ISGNS) based word embedding, bidirectional long short-term memory-based classification and bird swarm optimization based hyperparameter tuning. The SGNS training desires to go over the complete training data to pre-compute the noise distribution before performing Stochastic Gradient Descent (SGD) and the ISGNS technique is derived for the word embedding process. Besides, the cosine similarity is computed from the word embedding matrix to generate a feature vector which is fed as input into the Bidirectional Long Short-Term Memory (BiLSTM) for the prediction of website relevance. Finally, the Birds Swarm Optimization-Bidirectional Long Short-Term Memory (BSO-BiLSTM) based classification model is used to classify the webpages and the BSO algorithm is employed to determine the hyperparameters of the BiLSTM model so that the overall crawling performance can be considerably enhanced. For validating the enhanced outcome of the presented model, a comprehensive set of simulations are carried out and the results are examined in terms of different measures. The Automated Word Embedding with Parameter Tuned Deep Learning (AWE-PTDL) technique has attained a higher harvest rate of 85% when compared with the other techniques. The experimental results highlight the enhanced web crawling performance of the proposed model over the recent state of art web crawlers.

The pipeline leakage detection and leak localization trouble is a highly demanding and dangerous issue. Underground pipelines are a critical mode of transporting enormous fluid volumes (e.g., water) across extended distances. Solving this problem will save the country much money and resources, but it will also protect the environment. On the other hand, present leak detection technologies are insufficient for monitoring underground pipelines due to the extreme subterranean environmental conditions. This study proposes a hybrid wireless sensor network based on TDR (time domain reflectometry) and magnetic induction for monitoring underground pipelines to solve these problems. In this scenario, TDR is deployed beneath an MI-based wireless sensor network. TDR precisely locates the leak and dramatically decreases the amount of time required for inspection. We offer a wireless sensor network based on MI technology for low-cost, real-time leak detection in subsurface pipes. MISE-PIPE identifies leaks by integrating data from a range of different types of sensors installed within and around underground pipelines. Ad-hoc WSNs are used to measure pressure. (WDNs) is a hot topic that has piqued researchers' interest in recent years. Time and accuracy are critical components of leak localization, as they substantially impact the human population and economy. Statistical classifiers acting in the residual space are offered as a general method for leak localization. Classifiers are trained on leak data from all network nodes, taking demand uncertainty, sensor preservative noise, and leak magnitude on the account. Following leak identification and localization, all monitoring data is forwarded to the CH using the K-means clustering method, which serves two critical functions: optimal clustering and prolonging the Network Lifetime (NL) and preserving the QoS. The clustering method is optimized using the K-Means approach .