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NearReal Time Profiling of Fine Scale Environmental Proxies Using Mobile Sensors along Kathmandu Road Lines
... Prediction of the average temperature for the next time periods based on the weather conditions and pollution was done using deep learning with the RNN model. The data is converted to an appropriate format and then transformed into a supervised learning problem [12]. Dataset is then split to prepare train and test sets. ...
... Prediction of the average temperature for the next time periods based on the weather conditions and pollution was done using deep learning with the RNN model. The data is converted to an appropriate format and then transformed into a supervised learning problem [12]. Dataset is then split to prepare train and test sets. ...
Nepal, containing a rugged elevation ranging from less than 100 meters to over 8000 meters and having various climates varying from tropical to alpine and perpetual snow, has a great potential for the study of the highly varying environment and weather proxies. Fine spatio-temporal-scale measurements of such data using sufficiently distributed automatic weather stations are essential for such study. Such datasets are still beyond the reach of the country, limiting only very coarse-scale point measurements of weather and environmental data over most of the major cities. In this study, we try to explore the advantage of current information technology in the profiling of very fine spatio-temporal-scale weather and environmental pollution data along some major road line of the country using mobile sensors.
Our database provides other extended applications like in air quality modeling, city weather and pollution mapping, and big data warehousing of the resourceful dataset for further researches in this field. For analysis purposes of the spatio-temporal data, preliminary steps of data mining such as removal of duplicate data and outliers and handling of missing data are followed by various supervised and unsupervised machine learning algorithms to map some interesting features and environmental proxies. The data were applied to statistical mathematical methods for knowing their characteristics.
Malicious URLs play a very important role in today’s critical scam and attacks. They are harmful to every aspect of the usage of computers. Identification and detection of these malicious URL are very crucial. Malignant codes are synchronized with malicious software by invaders or hackers. Malicious content can be like Trojan horses, worms, backdoors, etc.; detection of these URLs is done previously by the usage of blacklists and whitelists. Blacklist itself cannot be sufficient to check the malicious URLs because they suffer from a shortage in the capacity in terms of newly created malicious URLs. These conventional approaches shortfalls by effectively dealing with evolving technologies and web searching mechanisms. In recent years, systems have been explored and evolved with the increasing research attention on enhancing the ability to detect malicious URLs. In this research paper, an innovative classification method was proposed to solve the difficulties encountered in malicious URL detection by using the existing mechanisms. The proposed classification model is based on high-performance machine learning methods which not only takes the syntactic essence of the URL into consideration but also the semantic and lexical meaning of these dynamically changing URLs. It is expected that the proposed approach will overcome the drawbacks of the existing techniques. A comparative analysis of Logistic regression, Support Vector Machine, and Naïve Bayes classification has also been performed. The tests of computer simulation have developed SVM with greater accuracy than logistic regression and Naive Bayes. Support Vector Machine has been obtained with an accuracy of 85.35%.
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