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This paper aims to introduce recently an unregulated unsupervised algorithm focused on voice activity detection by data clustering maximum margin, i.e. support vector machine. The algorithm for clustering K-mean used to solve speech behaviour detection issues was later applied, the application, therefore, did not permit the identification of voice detection. This is critical in demands for speech recognition.

Here, the authors find a voice activity detection detector based on a report provided by a K-mean algorithm that permits sliding window detection of voice and noise. However, first, it needs an initial detection pause. The machine initialized by the algorithm will work on health-care infrastructure and provides a platform for health-care professionals to detect the clear voice of patients.

Timely usage discussion on many histories of NOISEX-92 var reveals the average non-speech and the average signal-to-noise ratios hit concentrations which are higher than modern voice activity detection.

Research work is original.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Should the polygraph test be used in industry? The considerations involved are presented and assessed against practical examples.

Provides details of the different systems that can be used in order to provide maximum security in the workplace. Sets out the systems under the headings of access control, intrusion detection and visual systems. Concludes with a summary to be used as workplace security guidelines.

The purpose of this paper is to investigate a set of preventive measures required for mitigating fire risks in big box retail facilities.

The paper identifies the potential sources of ignition and fuel in big box retail facilities. It describes the variety of hazardous situations commonly found in such facilities worldwide. The paper then endeavors to discuss the series of fire protection challenges that could be faced during fire emergencies. It also explores the challenges of evacuation and rescue in such mega store facilities.

Mega stores, distribution centers and large retail stores are amongst the most challenging occupancies from a fire protection perspective. Fires can occur in big box retail facilities at any time and from a number of causes. These facilities represent a type of occupancy that poses considerable challenges to both fixed fire suppression systems and fire departments in cases of fire emergencies. The paper also describes the responsibilities of building management staff towards their employees and the public. Facility managers should always seek proactive measures to reduce the risk of fire and fire spread in big box retail facilities. These measure include providing sufficient number and capacity of exits; clear exit access; efficient smoke detection systems; voice communication systems; and efficient automatic sprinkler systems.

This paper serves to increase the awareness about fire and its effects in mega store facilities. The paper provides practical value to property directors and facility managers responsible for the daily operations of mega store facilities and for surveyors inspecting such properties.