الصفحة 1
الصفحة 1
Why Context Matters : Applications of Social Network Analysis
Many elements of our society are embedded in network structures in which actors depend on each other as well as the structural context of their actions. This is reflected by the wide use of concepts and terms of social network analysis, such as the concept of the small world, the strength of weak ties, opinion leaders, gatekeepers, viral marketing, terrorist networks, stakeholders and the like. This volume provides a sample of the broad range of research in which social network analysis can be fruitfully applied. Topics addressed include networks of academic hiring, epidemic dynamics of diseases in populations such as HIV/AIDS, flow of information, semantic networks of the internet, relationships in private and public spheres, patent authorship, paper citation, and networks in linguistic as well as political systems.
Methods and Models in Transport and Telecommunications : Cross Atlantic Perspectives
One aspect of the new economy is a transition to a networked society, and the emergence of a highly interconnected, interdependent and complex system of networks to move people, goods and information. An example of this is the in creasing reliance of networked systems (e. g. , air transportation networks, electric power grid, maritime transport, etc. ) on telecommunications and information in frastructure. Many of the networks that evolved today have an added complexity in that they have both a spatial structure , they are located in physical space but also an a spatial dimension brought on largely by their dependence on infor mation technology. They are also often just one component of a larger system of geographically integrated and overlapping networks operating at different spatial levels. An understanding of these complexities is imperative for the design of plans and policies that can be used to optimize the efficiency, performance and safety of transportation, telecommunications and other networked systems. In one sense, technological advances along with economic forces that encourage the clustering of activities in space to reduce transaction costs have led to more efficient network structures.
Instrumaster
Experiments with different neural network structures and algorithms in order to achieve musical note recognition as well as musical instrument recognition, all bundled in a mobile application. It also aims to create the most effective music-learning application that works completely offline, which is hard to find in modern music applications. The paper also explores why the instrument identifying AI is solely based on Multi-Layer Perceptron (MLP) and why the note-identifying AI system was chosen to be a ML system over CNN or other deep-learning trained AI. The paper presents feature extraction methods for audio signals and files and dives deep into the process, such as FFT, MFCCs, Wavelengths, sampling rates, etc. It also touches on Logistic Regression Algorithms, their limitations, and their performance with the different use cases in the application. All these techniques are then compared side by side for maximally added value, making this research paper a good reference for any future developers looking to find optimal neural networks techniques when it comes to audio processing and analysis.
Data Science and Classification
This volume provides new methodological developments in data analysis and classification. A wide range of topics is covered that includes the measurement of similarity and dissimilarity, methods for classification and clustering, network and graph analyses, analysis of symbolic data, and web mining. Apart from structural and theoretical results the book shows how to apply the proposed to a variety of problems, for example in medicine, microarray analysis, social network structures, and music. The combination of new methodological advances with the wide range of real applications collected in this volume is of special value for researchers when choosing the appropriate among newly developed analytical tools for their research problems in classification and data analysis.
