Matching Items (1,500)
Filtering by
- Creators: Marine Biological Laboratory Archives
ContributorsHuettner, Alfred F. (Alfred Francis), b. 1884 (Creator) / Marine Biological Laboratory Archives (Publisher)
Created1929
ContributorsHuettner, Alfred F. (Alfred Francis), b. 1884 (Creator) / Marine Biological Laboratory Archives (Publisher)
ContributorsHuettner, Alfred F. (Alfred Francis), b. 1884 (Creator) / Marine Biological Laboratory Archives (Publisher)
Created1918
ContributorsHuettner, Alfred F. (Alfred Francis), b. 1884 (Creator) / Marine Biological Laboratory Archives (Publisher)
Created1930
Description
This reports investigates the general day to day problems faced by small businesses, particularly small vendors, in areas of marketing and general management. Due to lack of man power, internet availability and properly documented data, small business cannot optimize their business. The aim of the research is to address and find a solution to these problems faced, in the form of a tool which utilizes data science. The tool will have features which will aid the vendor to mine their data which they record themselves and find useful information which will benefit their businesses. Since there is lack of properly documented data, One Class Classification using Support Vector Machine (SVM) is used to build a classifying model that can return positive values for audience that is likely to respond to a marketing strategy. Market basket analysis is used to choose products from the inventory in a way that patterns are found amongst them and therefore there is a higher chance of a marketing strategy to attract audience. Also, higher selling products can be used to the vendors' advantage and lesser selling products can be paired with them to have an overall profit to the business. The tool, as envisioned, meets all the requirements that it was set out to have and can be used as a stand alone application to bring the power of data mining into the hands of a small vendor.
ContributorsSharma, Aveesha (Author) / Ghazarian, Arbi (Thesis advisor) / Gaffar, Ashraf (Committee member) / Bansal, Srividya (Committee member) / Arizona State University (Publisher)
Created2016
Description
A well-defined Software Complexity Theory which captures the Cognitive means of algorithmic information comprehension is needed in the domain of cognitive informatics & computing. The existing complexity heuristics are vague and empirical. Industrial software is a combination of algorithms implemented. However, it would be wrong to conclude that algorithmic space and time complexity is software complexity. An algorithm with multiple lines of pseudocode might sometimes be simpler to understand that the one with fewer lines. So, it is crucial to determine the Algorithmic Understandability for an algorithm, in order to better understand Software Complexity. This work deals with understanding Software Complexity from a cognitive angle. Also, it is vital to compute the effect of reducing cognitive complexity. The work aims to prove three important statements. The first being, that, while algorithmic complexity is a part of software complexity, software complexity does not solely and entirely mean algorithmic Complexity. Second, the work intends to bring to light the importance of cognitive understandability of algorithms. Third, is about the impact, reducing Cognitive Complexity, would have on Software Design and Development.
ContributorsMannava, Manasa Priyamvada (Author) / Ghazarian, Arbi (Thesis advisor) / Gaffar, Ashraf (Committee member) / Bansal, Ajay (Committee member) / Arizona State University (Publisher)
Created2016
Description
Driver distraction research has a long history spanning nearly 50 years, intensifying in the last decade. The focus has always been on identifying the distractive tasks and measuring the respective harm level. As in-vehicle technology advances, the list of distractive activities grows along with crash risk. Additionally, the distractive activities become more common and complicated, especially with regard to In-Car Interactive System. This work's main focus is on driver distraction caused by the in-car interactive System. There have been many User Interaction Designs (Buttons, Speech, Visual) for Human-Car communication, in the past and currently present. And, all related studies suggest that driver distraction level is still high and there is a need for a better design. Multimodal Interaction is a design approach, which relies on using multiple modes for humans to interact with the car & hence reducing driver distraction by allowing the driver to choose the most suitable mode with minimum distraction. Additionally, combining multiple modes simultaneously provides more natural interaction, which could lead to less distraction. The main goal of MMI is to enable the driver to be more attentive to driving tasks and spend less time fiddling with distractive tasks. Engineering based method is used to measure driver distraction. This method uses metrics like Reaction time, Acceleration, Lane Departure obtained from test cases.
ContributorsJahagirdar, Tanvi (Author) / Gaffar, Ashraf (Thesis advisor) / Ghazarian, Arbi (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2015
Description
Text Classification is a rapidly evolving area of Data Mining while Requirements Engineering is a less-explored area of Software Engineering which deals the process of defining, documenting and maintaining a software system's requirements. When researchers decided to blend these two streams in, there was research on automating the process of classification of software requirements statements into categories easily comprehensible for developers for faster development and delivery, which till now was mostly done manually by software engineers - indeed a tedious job. However, most of the research was focused on classification of Non-functional requirements pertaining to intangible features such as security, reliability, quality and so on. It is indeed a challenging task to automatically classify functional requirements, those pertaining to how the system will function, especially those belonging to different and large enterprise systems. This requires exploitation of text mining capabilities. This thesis aims to investigate results of text classification applied on functional software requirements by creating a framework in R and making use of algorithms and techniques like k-nearest neighbors, support vector machine, and many others like boosting, bagging, maximum entropy, neural networks and random forests in an ensemble approach. The study was conducted by collecting and visualizing relevant enterprise data manually classified previously and subsequently used for training the model. Key components for training included frequency of terms in the documents and the level of cleanliness of data. The model was applied on test data and validated for analysis, by studying and comparing parameters like precision, recall and accuracy.
ContributorsSwadia, Japa (Author) / Ghazarian, Arbi (Thesis advisor) / Bansal, Srividya (Committee member) / Gaffar, Ashraf (Committee member) / Arizona State University (Publisher)
Created2016
Description
A lot of research can be seen in the field of social robotics that majorly concentrate on various aspects of social robots including design of mechanical parts and their move- ment, cognitive speech and face recognition capabilities. Several robots have been developed with the intention of being social, like humans, without much emphasis on how human-like they actually look, in terms of expressions and behavior. Fur- thermore, a substantial disparity can be seen in the success of results of any research involving ”humanizing” the robots’ behavior, or making it behave more human-like as opposed to research into biped movement, movement of individual body parts like arms, fingers, eyeballs, or human-like appearance itself. The research in this paper in- volves understanding why the research on facial expressions of social humanoid robots fails where it is not accepted completely in the current society owing to the uncanny valley theory. This paper identifies the problem with the current facial expression research as information retrieval problem. This paper identifies the current research method in the design of facial expressions of social robots, followed by using deep learning as similarity evaluation technique to measure the humanness of the facial ex- pressions developed from the current technique and further suggests a novel solution to the facial expression design of humanoids using deep learning.
ContributorsMurthy, Shweta (Author) / Gaffar, Ashraf (Thesis advisor) / Ghazarian, Arbi (Committee member) / Gonzalez-Sanchez, Javier (Committee member) / Arizona State University (Publisher)
Created2017
ContributorsMarine Biological Laboratory Archives (Publisher) / Arizona Board of Regents (Publisher)
Created1979-09-11 to 1979-09-13