Francisca O. Oladipo, E. Ogbuju, Femi S Alayesanmi, A. Musa
{"title":"The State of the Art in Machine Learning-Based Digital Forensics","authors":"Francisca O. Oladipo, E. Ogbuju, Femi S Alayesanmi, A. Musa","doi":"10.2139/ssrn.3668687","DOIUrl":"https://doi.org/10.2139/ssrn.3668687","url":null,"abstract":"Digital forensics of visual-based evidence from video surveillance systems and forensic photographs holds object detection as a key aspect of the process. Recognizing an instance of object classes over a wide range of image data using computational techniques is one of the areas that has gained continuous attention over the years due to their numerous practical applications. Several algorithms and techniques have been specified for object detection and recognition with Machine Learning gaining more prominence and ensuring the remarkable performance of object detection and recognition systems. This study presents a comprehensive review of the frameworks and applications of Machine Learning in object detection and classification with particular applications to Digital Forensics. The analysis covers a wide range of publications between 2007 and 2019 available in different indexed and non-indexed databases and the candidate papers were selected using certain exclusion criteria proposed in the Kitchenham’s methodology. The study in a bid to streamline future researches categorized digital forensic researches into six knowledge areas and identified the convolutional neural network as a state-of-the-art algorithm for machine learning-based digital forensics.","PeriodicalId":314287,"journal":{"name":"BioRN: Other Computational Biology (Topic)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130779531","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}
{"title":"How to Understand Common Patterns in Big Data: The Case of Human Collective Memory","authors":"S. Frank","doi":"10.2139/ssrn.3309643","DOIUrl":"https://doi.org/10.2139/ssrn.3309643","url":null,"abstract":"Simple patterns often arise from complex systems. For example, human perception of similarity decays exponentially with perceptual distance. The ranking of word usage versus the frequency at which the words are used has a log-log slope of minus one. Recent advances in big data provide an opportunity to characterize the commonly observed patterns of nature. Those observed regularities set the challenge of understanding the mechanistic processes that generate common patterns. This article illustrates the problem with the recent big data analysis of collective memory. Collective memory follows a simple biexponential pattern of decay over time. An initial rapid decay is followed by a slower, longer lasting decay. Candia et al. successfully fit a two stage model of mechanistic process to that pattern. Although that fit is useful, this article emphasizes the need, in big data analyses, to consider a broad set of alternative causal explanations. In this case, the method of signal frequency analysis yields several simple alternative models that generate exactly the same observed pattern of collective memory decay. This article concludes that the full potential of big data will require better methods for developing alternative, empirically testable causal models.","PeriodicalId":314287,"journal":{"name":"BioRN: Other Computational Biology (Topic)","volume":" 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120828268","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}
{"title":"Endemic SIR Model in Random Media with Applications","authors":"A. Swishchuk, M. Svishchuk","doi":"10.2139/ssrn.3135548","DOIUrl":"https://doi.org/10.2139/ssrn.3135548","url":null,"abstract":"We consider an averaging principle for the endemic SIR model in a semi-Markov random media. Under stationary conditions of a semi- Markov media we show that the perturbed endemic SIR model converges to the classic endemic SIR model with averaged coefficients. Numerical toy examples and their interpretations are also presented for two-state Markov and semi-Markov chains. We also discuss two numerical examples involving real data: 1) Dengue Fever Disease (Indonesia and Malaysia (2009)) and 2) Cholera Outbreak in Zimbabwe (2008-2009). Novelty of the paper consists in studying of an endemic SIR model in semi-Markov random media and in implementations and interpretations of the results through numerical toy examples and discussion of numerical examples with real data.","PeriodicalId":314287,"journal":{"name":"BioRN: Other Computational Biology (Topic)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121833201","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}
G. Figueredo, Peer-Olaf Siebers, U. Aickelin, Stephanie J. Foan
{"title":"A Beginners Guide to Systems Simulation in Immunology","authors":"G. Figueredo, Peer-Olaf Siebers, U. Aickelin, Stephanie J. Foan","doi":"10.2139/ssrn.2828463","DOIUrl":"https://doi.org/10.2139/ssrn.2828463","url":null,"abstract":"Some common systems modelling and simulation approaches for immune problems are Monte Carlo simulations, system dynamics, discrete-event simulation and agent-based simulation. These methods, however, are still not widely adopted in immunology research. In addition, to our knowledge, there is few research on the processes for the development of simulation models for the immune system. Hence, for this work, we have two contributions to knowledge. The first one is to show the importance of systems simulation to help immunological research and to draw the attention of simulation developers to this research field. The second contribution is the introduction of a quick guide containing the main steps for modelling and simulation in immunology, together with challenges that occur during the model development. Further, this paper introduces an example of a simulation problem, where we test our guidelines.","PeriodicalId":314287,"journal":{"name":"BioRN: Other Computational Biology (Topic)","volume":"8 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123657294","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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