الصفحة 8
الصفحة 8
Advances in Data Mining : Theoretical aspects and applications ; 7th Industrial Conference, ICDM 2007, Leipzig, Germany, July 14-18, 2007, Proceedings
The book range from aspects of classification and prediction, clustering, Web mining, data mining in medicine, applications of data mining, time series and frequent pattern mining, and association rule mining.
Advances in computer systems architecture ; Vol. 3740 ; 10th Asia-Pacific conference, ACSAC 2005, Singapore, October 24-26, 2005, Proceedings
The papers are organized in topical sections on energy efficient and power aware techniques, methodologies and architectures for application-specific systems, processor architectures and microarchitectures, high-reliability and fault-tolerant architectures, compiler and OS for emerging architectures, data value predictions, reconfigurable computing systems and polymorphic architectures, interconnect networks and network interfaces, parallel architectures and computation models, hardware-software partitioning, verification, and testing of complex architectures, architectures for secured computing, simulation and performance evaluation, architectures for emerging technologies and applications, and memory systems hierarchy and management
Advances in computer systems architecture ; 12th Asia-Pacific Conference, ACSAC 2007, Seoul, Korea, August 23-25, 2007, Proceedings
Covering hardware and software techniques for state-of-the-art multicore and multithreaded architectures.
Advances in air pollution modeling for environmental security ; Proceedings of the NATO Advanced Research Workshop Advances in Air Pollution Modeling for Environmental Security, Borovetz, Bulgaria, 8-12 May 2004
Includes selected papers from the NATO ARW held at Borovetz (Bulgaria), in the period 8-12 May, 2004. This book talks about improving the abilities of air pollution models to calculate reliable predictions of the pollution levels in a given domain and in real time by using adequate description of the physical and chemical processes.
Advancement of Deep Learning and its Applications in Object Detection and Recognition
In just the past five years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, etc. This book presents an introduction to deep learning and various applications of deep learning such as recommendation systems, text recognition, diabetic retinopathy prediction of breast cancer, prediction of epilepsy, sentiment, fake news detection, software defect prediction and protein function prediction.
Advanced Wired and Wireless Networks
ADVANCED WIRED AND WIRELESS NETWORKS brings the reader a sample of recent research efforts representative of advances in the areas of recognized importance for the future Internet, In Part I, we bring ad-hoc networking closer to the reality of practical use. The focus is on more advanced scalable routing suitable for large networks, directed flooding useful in information dissemination networks, as well as self-configuration and security issues important in practical deployments. Part II illustrates the efforts towards development of advanced mobility support techniques (beyond traditional "mobile phone net") and Mobile IP technologies. The issues range from prediction based mobility support, through context transfer during Mobile IP handoff, to service provisioning platforms for heterogeneous networks. The focus of the final section concerns the performance of networks and protocols. Furthermore this section illustrates researchers’ interest in protocol enhancement requests for improved performance with advanced networks, reliable and efficient multicast methods in unreliable networks, and composite scheduling in programmable/active networks where computing resources equal network performance as transmission bandwidth.
Advanced Techniques in Knowledge Discovery and Data Mining
This explosion is a result of the growing use of electronic media. But what is data mining (DM)? A Web search using the Google search engine retrieves many (really many) definitions of data mining. We include here a few interesting ones. One of the simpler definitions is: “As the term suggests, data mining is the analysis of data to establish relationships and identify patterns” [1]. It focuses on identifying relations in data. Our next example is more elaborate: An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis .
Advanced data mining and applications ; Vol. 4093 : 2nd International Conference, ADMA 2006, Xi'an, China, August 14-16, 2006, Proceedings
This book constitutes the refereed proceedings of the Second International Conference on Advanced Data Mining and Applications, ADMA 2006, held in Xi'an, China in August 2006. The 41 revised full papers and 74 revised short papers presented together with 4 invited papers were carefully reviewed and selected from 515 submissions. The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, text mining, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, security and privacy issues, spatial data mining, and streaming data mining.
Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well.
Adaptive Business Intelligence
In the modern information era, managers must recognize the competitive opportunities represented by decision-support tools. Adaptive business intelligence systems combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address the fundamental questions: What is likely to happen in the future? And what is the best decision right now? Adaptive Business Intelligence includes elements of data mining, predictive modeling, forecasting, optimization, and adaptability.
Adapting the built environment for climate change : Design principles for climate emergencies
Adapting the Built Environment for Climate Change: Design Principles for Climate Emergencies analyzes several scenarios and proposes various adaptation strategies for climate emergencies (heat waves, wildfires, floods, and storms). Divided into three themes, the book offers an organized vision of a complex and multi-factor challenge. It covers climatic resilience and building refurbishment, implications for service life prediction and maintainability, and climate adaptation in the maintenance and management of buildings. Sections cover infrastructure materials, climate emergency adaptation and building adaptation to heat waves, wildfires, floods and storms.
A proposed model for predicting financial Loss of private conventional and Islamic banks in Syria
This study aimed to find a model consisting of a set of financial ratios in which each ratio has its own weight that indicate its importance to predict probability of financial loss of conventional and Islamic banks in Syria. The early prediction warns the concerned parties that they can intervene and take corrective actions before the collapses of bank. To achieve this ratios of conventional and Islamic Syrian banks were analyzed using Binary logistic regression from the period of 2011-2020 The statistical results show that the logistic regression model is accurate to predict the probability of a financial loss in conventional banks about 82.2%, 81.3%, 80.1%, 78% before 90 days ,180 days, 270 days, one year respectively. We can generally use five variables (Non-performing debt, return on equity, size, growth rate and financing portfolio ratio) in bank's financial loss prediction, but for Islamic banks, no significant values were shown so we can’t find logistic regression model is accurate for Islamic banks.
A Graph-Theoretic Approach to Enterprise Network Dynamics
This monograph treats the application of numerous graph-theoretic algorithms to a comprehensive analysis of dynamic enterprise networks. Network dynamics analysis yields valuable information about network performance, efficiency, fault prediction, cost optimization, indicators and warnings.
