In today’s real-world, estimation of the level of difficulty of the musical is part of very meaningful musical learning. A musical learner cannot learn without a defined precise estimation. This problem is not very basic but it is complicated up to some extent because of the subjectivity of the contents and the scarcity of the data. In this paper, a lightweight model that generates original music content using deep learning along with generating music based on a specific genre is proposed. The paper discusses a lightweight deep learning-based approach for jazz music generation in MIDI format. In this work, the genre of music chosen is Jazz, and the songs selected are classical numbers composed by various artists. All the songs are in MIDI format and there might be differences in the pace or tone of the music. It is prudential to make sure that the chosen datasets that do not have these kinds of differences and are similar to the final output as desired. A model is trained to take in a part of a music file as input and should produce its continuation. The result generated should be similar to the dataset given as the input. Moreover, the proposed model also generates music using a particular instrument.

Energy efficiency is important for underwater sensor networks. Designing such networks is challenging due to underwater environmental traits that hinder network lifespan extension. Unlike terrestrial protocols, underwater settings require novel protocols due to slower signal propagation. To enhance energy efficiency in underwater sensor networks, ongoing research concentrates on developing innovative solutions. Thus, in this paper, an intelligent bio-inspired autonomous surveillance system using underwater sensor networks is proposed as an efficient method for data communication. The tunicate swarm algorithm is used for the election of the cluster heads by considering different parameters such as energy, distance, and density. Each layer has several clusters, each of which is led by a cluster head that continuously rotates in response to the fitness values of the SNs using the tunicate swarm algorithm. The performance of the proposed protocol is compared with existing methods such as EE-LHCR, EE-DBR, and DBR, and results show the network’s lifespan is improved by the proposed work. Due to the effective fitness parameters during cluster head elections, our suggested protocol may more effectively achieve energy balance, resulting in a longer network lifespan.

The dissemination of news and coverage of events, both domestically and internationally, is now mostly the responsibility of print, web, and broadcast media. Because of the explosion of information available online, verifying the veracity of claims is more difficult than ever. It is extremely difficult to verify the veracity of fake news using conventional data processing techniques without seriously misinforming the public. Indeed, the difficulty for the government and the public is a matter of case by case argument. Several sites were built specifically for this purpose, each using its own unique logic and algorithm to determine which stories were more likely to be true or false. There needs to be a system in place for verifying the veracity of online claims, especially those that garner thousands of shares and clicks before being disproved by credible sources. In this proposed approach to an LSTM-RF classifier was used to determine whether or not a news story was fabricated. The three-step process that makes up the suggested method is as follows: preprocessing; feature extraction and feature selection; and LSTM-RF model training. In preprocessing, you’ll clean the data, get rid of stop words, tokenize it, and stem it. The TF-IDF and Extra Tree Classifier are used for feature selection. The model is trained with LS TM-RF. The proposed method achieves superior results compared to LSTM and RF.

Distributed computing system creates or provides a platform having multiple computing nodes linked in a specified manner. On the basis of literature review of last few decades it has been noticed that most of distributed computing researchers have shown their effort to maintain load balancing between processors ,effective task scheduling and optimizing different parameters affecting execution cost and throughput .With these above scenario an additional parameter “Self reconfiguration of CPU” is also a countable parameter to augment the efficiency of distributed computing system .Through this research paper we want to present new approach of adaptive scheduling algorithm which is the mix output of effective task allocation to processor involved in computing and self-reconfiguration of those processors as per need of computing. By this proposed method we will optimize the execution cost, service rate and maximize the throughput as an outcome of organized processors consist in heterogeneous distributed computing system, resulting provide the considerable enhancement in the performance of Distributed computing environment.

The enormous growth of the World Wide Web in the recent years has made it important to perform resources discovery efficiently. The rapid growth of World Wide Web poses (Doubles in size approximately every eight months) unprecedented scaling challenges for general purpose crawler and search engine. Finding useful information from the web which has a large and distributed structure required efficient search strategies. As ontology plays an important role in providing controlled vocabulary of concepts, each with an explicitly defined and machine process able semantics. In this paper ,we propose the novel concept of intelligent crawling of Ontology based content focused crawling , the new approach that analyses it crawl boundary to find the links that are likely to be the most relevant for the crawl while a boundary irrelevant region of the web. Through our new focused crawling technique we solve the polysemy (refer to word with multiple meaning) and synonymy (refers to multiple word having the same meaning) semantic net problem. Also instead of searching in the whole web, our proposed technique will search in the ontology build by us that is updated periodically after a very short interval than instead of displaying all the information that is not related to the user need, we will display only relevant and related information. Our purposed work give us two fold benefit , firstly only focused result are retrieved which reduce the number of results entreated and secondly, due to focused searching irrelevant result are pruned which reduce the time.