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dc.contributor.authorOmondi, Kevin Ochieng’
dc.date.accessioned2017-11-22T11:50:18Z
dc.date.available2017-11-22T11:50:18Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/11071/5663
dc.descriptionThesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore Universityen_US
dc.description.abstractCompetence in the provision of security to the civilians in Kenya has generally deteriorated and hence negatively affecting the public trust accorded to security agencies. Indeed, the police to civilian ratio is low and this has affected the institutions of learning as they have become new attack grounds for the terrorists. Institutions of learning have suffered the worst since they are expected to be accountable of their own security in many cases. As a result, many institutions of learning use available security agencies, most of which employ outdated and less efficient means of implementing security. Examples of commonly used physical security techniques include the use of security guards, perimeter walls, some places use turnstiles as well as CCTVs. The inefficiencies that comes along with these security measures has still however exposed these institutions to great dangers of insecurity. This study proposes the use of sound classification to enhance physical security. The solution relies on the integration of the possible solutions of the artificial neural networks (ANN) in sound classification to detect sound variations in the leaning institutions. It is expected that decisions made through classification assist security personnel on the ground to tighten the physical security. The solution offers automatic analysis of the recorded sound from the environment, compares it to the stored dataset which has urban sounds and the score labels displayed on the output screens for the security personnel to help them enhance the available physical security. The usage of scientific research methodology through experimentation ensured that the sounds were captured, the dataset sounds were collected and trained for comparison to take place and finally results validated to prove the theory. The system proved an accuracy percentage of 78%, and the efficiency, user friendliness and reliability were al passed.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectPhysical Securityen_US
dc.subjectSecurity Personnelen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectSoundsen_US
dc.titleSound analysis prototype to enhance physical security in academic institutionsen_US
dc.typeThesisen_US


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