Segmentation and broad classification of continuous speech

Author(s):  
R.A. Cole ◽  
L. Hou
2020 ◽  
Vol 13 (3) ◽  
pp. 313-318 ◽  
Author(s):  
Dhanapal Angamuthu ◽  
Nithyanandam Pandian

<P>Background: The cloud computing is the modern trend in high-performance computing. Cloud computing becomes very popular due to its characteristic of available anywhere, elasticity, ease of use, cost-effectiveness, etc. Though the cloud grants various benefits, it has associated issues and challenges to prevent the organizations to adopt the cloud. </P><P> Objective: The objective of this paper is to cover the several perspectives of Cloud Computing. This includes a basic definition of cloud, classification of the cloud based on Delivery and Deployment Model. The broad classification of the issues and challenges faced by the organization to adopt the cloud computing model are explored. Examples for the broad classification are Data Related issues in the cloud, Service availability related issues in cloud, etc. The detailed sub-classifications of each of the issues and challenges discussed. The example sub-classification of the Data Related issues in cloud shall be further classified into Data Security issues, Data Integrity issue, Data location issue, Multitenancy issues, etc. This paper also covers the typical problem of vendor lock-in issue. This article analyzed and described the various possible unique insider attacks in the cloud environment. </P><P> Results: The guideline and recommendations for the different issues and challenges are discussed. The most importantly the potential research areas in the cloud domain are explored. </P><P> Conclusion: This paper discussed the details on cloud computing, classifications and the several issues and challenges faced in adopting the cloud. The guideline and recommendations for issues and challenges are covered. The potential research areas in the cloud domain are captured. This helps the researchers, academicians and industries to focus and address the current challenges faced by the customers.</P>


2011 ◽  
pp. 131-140
Author(s):  
Gloria E Phillips-Wren ◽  
Manuel Mora ◽  
Guisseppi Forgionne

Decision support systems (DSSs) have been researched extensively over the years with the purpose of aiding the decision maker (DM) in an increasingly complex and rapidly changing environment (Sprague & Watson, 1996; Turban & Aronson, 1998). Newer intelligent systems, enabled by the advent of the Internet combined with artificial-intelligence (AI) techniques, have extended the reach of DSSs to assist with decisions in real time with multiple informaftion flows and dynamic data across geographical boundaries. All of these systems can be grouped under the broad classification of decision-making support systems (DMSS) and aim to improve human decision making. A DMSS in combination with the human DM can produce better decisions by, for example (Holsapple & Whinston, 1996), supplementing the DM’s abilities; aiding one or more of Simon’s (1997) phases of intelligence, design, and choice in decision making; facilitating problem solving; assisting with unstructured or semistructured problems (Keen & Scott Morton, 1978); providing expert guidance; and managing knowledge. Yet, the specific contribution of a DMSS toward improving decisions remains difficult to quantify.


Author(s):  
Gloria E. Phillips-Wren ◽  
Manuel Mora ◽  
Guisseppi Forgionne

Decision support systems (DSSs) have been researched extensively over the years with the purpose of aiding the decision maker (DM) in an increasingly complex and rapidly changing environment (Sprague & Watson, 1996; Turban & Aronson, 1998). Newer intelligent systems, enabled by the advent of the Internet combined with artificial-intelligence (AI) techniques, have extended the reach of DSSs to assist with decisions in real time with multiple informaftion flows and dynamic data across geographical boundaries. All of these systems can be grouped under the broad classification of decision-making support systems (DMSS) and aim to improve human decision making. A DMSS in combination with the human DM can produce better decisions by, for example (Holsapple & Whinston, 1996), supplementing the DM’s abilities; aiding one or more of Simon’s (1997) phases of intelligence, design, and choice in decision making; facilitating problem solving; assisting with unstructured or semistructured problems (Keen & Scott Morton, 1978); providing expert guidance; and managing knowledge. Yet, the specific contribution of a DMSS toward improving decisions remains difficult to quantify.


Atmospheric pollutants may damage, directly or indirectly, human life and health, other living organisms and complete ecosystems, human artefacts, and climatic conditions. The development of appropriate policies and methods for control of pollution requires, inter alia , an assessment of the routes taken by pollutants or their precursors through the atmosphere. Consideration of these routes leads to a broad classification on a local, regional or global basis associated mainly, but not exclusively, with the terrestrial boundary layer, with the troposphere and with the stratosphere respectively. This may require in some cases the perspective of total biogeochemical cycles, and in any event of the relative importance of man-made and natural sources of materials to be regarded as pollutants.


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