{"title":"Vehicle Secrecy Parameters for V2V Communications","authors":"N. Ahn, Dong Hoon Lee","doi":"10.5772/intechopen.89176","DOIUrl":"https://doi.org/10.5772/intechopen.89176","url":null,"abstract":"This paper studies the parameters affecting secrecy capacity in vehicle communication. The vehicle secrecy parameters largely include vehicle driving-related parameters, antenna-related parameters for transmitting and receiving signals, path-related parameters for indirect communication, and noise-related parameters using a fading channel. Although many researches have been conducted on antenna-related parameters and noise-related parameters considered in general wireless communication, relatively little research has been made on parameters caused by the vehicle itself. These vehicle secrecy parameters also imply that secrecy capacity can be varied by the user. In the future, this study will be a very informative topic when trying to perform vehicle communication while maintaining a certain level of security capacity. In the coming autonomous driving era, this research is very necessary and will help to carry out vehicle communications more safely.","PeriodicalId":258328,"journal":{"name":"Intelligent System and Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126101675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Aspragathos, E. Dogkas, P. Konstantinidis, P. Koutmos, Nefeli Lamprinou, V. Moulianitis, G. Paterakis, E. Psarakis, Evangelos Sartinas, K. Souflas, G. Thanellas, G. Tsiourlis, N. Xanthopoulos, P. Xofis
{"title":"From Pillars to AI Technology-Based Forest Fire Protection Systems","authors":"N. Aspragathos, E. Dogkas, P. Konstantinidis, P. Koutmos, Nefeli Lamprinou, V. Moulianitis, G. Paterakis, E. Psarakis, Evangelos Sartinas, K. Souflas, G. Thanellas, G. Tsiourlis, N. Xanthopoulos, P. Xofis","doi":"10.5772/intechopen.86904","DOIUrl":"https://doi.org/10.5772/intechopen.86904","url":null,"abstract":"The importance of forest environment in the perspective of the biodiversity as well as from the economic resources which forests enclose, is more than evident. Any threat posed to this critical component of the environment should be identified and attacked through the use of the most efficient available technological means. Early warning and immediate response to a fire event are critical in avoiding great environmental damages. Fire risk assessment, reliable detection and localization of fire as well as motion planning, constitute the most vital ingredients of a fire protection system. In this chapter, we review the evolution of the forest fire protection systems and emphasize on open issues and the improvements that can be achieved using artificial intelligence technology. We start our tour from the pillars which were for a long time period, the only possible method to oversee the forest fires. Then, we will proceed to the exploration of early AI systems and will end-up with nowadays systems that might receive multimodal data from satellites, optical and thermal sensors, smart phones and UAVs and use techniques that cover the spectrum from early signal processing algorithms to latest deep learning-based ones to achieving the ultimate goal.","PeriodicalId":258328,"journal":{"name":"Intelligent System and Computing","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123446147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Mamyrbayev, N. Mekebayev, M. Turdalyuly, N. Oshanova, Tolga Ihsan Medeni, A. Yessentay
{"title":"Voice Identification Using Classification Algorithms","authors":"O. Mamyrbayev, N. Mekebayev, M. Turdalyuly, N. Oshanova, Tolga Ihsan Medeni, A. Yessentay","doi":"10.5772/intechopen.88239","DOIUrl":"https://doi.org/10.5772/intechopen.88239","url":null,"abstract":"This article discusses the classification algorithms for the problem of personality identification by voice using machine learning methods. We used the MFCC algorithm in the speech preprocessing process. To solve the problem, a comparative analysis of five classification algorithms was carried out. In the first experiment, the support vector method was determined — 0.90 and multilayer perceptron — 0.83, that showed the best results. In the second experiment, a multilayer perceptron with an accuracy of 0.93 was proposed using the Robust scaler method for personal identification. Therefore, to solve this problem, it is possible to use a multi-layer perceptron, taking into account the specifics of the speech signal.","PeriodicalId":258328,"journal":{"name":"Intelligent System and Computing","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121616039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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