In the paper, we are enhancing the accuracy of the fuel consumption prediction model with Machine Learning to minimize Fuel Consumption. This will lead to an economic improvement for the business and satisfy the domain needs. We propose a machine learning model to predict vehicle fuel consumption. The proposed model is based on the Support Vector Machine algorithm. The Fuel Consumption estimation is given as a function of Mass Air Flow, Vehicle Speed, Revolutions Per Minute, and Throttle Position Sensor features. The proposed model is applied and tested on a vehicle's On-Board Diagnostics Dataset. The observations were conducted on 18 features. Results achieved a higher accuracy with an R-Squared metric value of 0.97 than other related work using the same Support Vector Machine regression algorithm. We concluded that the Support Vector Machine has a great effect when used for fuel consumption prediction purposes. Our model can compete with other Machine Learning algorithms for the same purpose which will help manufacturers find more choices for successful Fuel Consumption Prediction models.

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Due to environmental changes, including global warming, climatic changes, ecological impact, and dangerous diseases like the Coronavirus epidemic. Since coronavirus is a hazardous disease that causes many deaths, government of Egypt undertook many strict regulations, including lockdowns and social distancing measures. These circumstances have affected agricultural experts' presence to help farmers or advise on solving agricultural problems. For helping this issue, this work focused on improving support for farmers on the major field crops in Egypt Retrieving solutions corresponding to farmer query. For our work, we have mainly focused on detecting the semantic similarity between large agriculture dataset and user queries using Latent Semantic Analysis (LSA) based on Term Frequency Weighting and Inverse Document Frequency (TF-IDF) method. In this research paper, we apply SVM MapReduce classifier as a framework for paralleling and distributing the work on the dataset to classify the dataset. Then we apply different approaches for computing the similarity of sentences. We presented a system based on semantic similarity methods and support vector machine algorithm to detect the similar complaints of the user query. Finally, we run different experiments to evaluate the performance and efficiency of the proposed system as the system performs approximately 77.8%~94.8% in F-score measure. The experimental results show that the accuracy of SVM classifier is approximately 88.68%~89.63% and noted the leverage of SVM classification to the semantic similarity measure between sentences.

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It is very difficult for human beings to manually summarize large documents of text. Text summarization solves this problem. Nowadays, Text summarization systems are among the most attractive research areas. Text summarization (TS) is used to provide a shorter version of the original text and keeping the overall meaning. There are various methods that aim to find out well-formed summaries. One of the most commonly used methods is the Latent Semantic Analysis (LSA). In this review, we present a comparative study among almost algorithms based on Latent Semantic Analysis (LSA) approach. General TermsNatural Language Processing (NLP).