Adedeji B. Badiru

Olufemi A. Omitaomu

Recent global anxiety indicates that more focus needs to be directed at economic issues related to industry. Conventional techniques often do not adequately embrace the integrated global factors that affect unique industries and industry focused computational tools have not been readily available. Until now. Computational Economic Analysis for Engineering and Industry presents direct computational tools, techniques, models, and approaches for economic analysis with a specific focus on industrial and engineering processes. Here are just a few of the topics you'll find: New economic analysis models and techniques Tent-shaped cash flows Industrial economic analysis Project-based economic measures Profit ratio analysis Equity break-even point Utility based analysis Project-balance analysis Customized ENGINEA software tool Engineering conversion factors The authors supply downloadable software, ENGINEA, that allows you to easily perform the various techniques outlined in the text, such as investment justification, breakeven analysis, and replacement analysis. Providing a high-level presentation of economic analysis of the unique aspects of industrial processes, they integrate mathematical models, optimization, computer analysis, and managerial decision processes. A comprehensive treatment of economic analysis considering the specific needs of industry, the book is a pragmatic alternative to conventional economic analysis books.

Computational Economic Analysis for Engineering and Industry

Mohamed Medhat Gaber

João Gama

Ranga Raju Vatsavai

Nitesh V. Chawla

Auroop R. Ganguly

This book contains thoroughly refereed extended papers from the Second International Workshop on Knowledge Discovery from Sensor Data, Sensor-KDD 2008, held in Las Vegas, NV, USA, in August 2008. The 12 revised papers presented together with an invited paper were carefully reviewed and selected from numerous submissions. The papers feature important aspects of knowledge discovery from sensor data, e.g., data mining for diagnostic debugging; incremental histogram distribution for change detection; situation-aware adaptive visualization; WiFi mining; mobile sensor data mining; incremental anomaly detection; and spatiotemporal neighborhood discovery for sensor data.

Second International Workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Selected Papers

Knowledge Discovery from Sensor Data

Olufemi A. Omitaomu

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