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- All Subjects: deep learning
- Creators: Computer Science and Engineering Program
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
To bridge the gap between the growing sales industry there is the ability to properly train Millennials so they are successful and stay within their roles longer. By attacking this problem from a university level by strengthening sales programs as well as having employers understand and respond to needs of the Millennial generation, this will create an overall successful Millennial salesperson that will stay with their employer long term.
Strengths and weaknesses of this generation are also important to understand. Millennials are known to be tech-savvy, open-minded, collaborative, and connected, resourceful networkers. They also carry weaknesses and stereotypes of being lazy, lacking communication skills, impatient, entitled, and demanding of feedback and work flexibility. From an employer, they expect a large salary as well as a good culture, manager feedback, a mentor, work-life integration, an employer with a social responsibility mindset, and a sense of purpose.
An analysis of 12 sales programs at various universities across the country helped to understand what is being taught and offered to students as well as commonalities and differences that make a strong sales program. Commonalities among these programs include, about 250+ students, high job placement, sales labs, hosting and competing in sales competitions, and a desire to expand and grow their programs. Unique aspects of various programs were partnerships with the sales industry, hosting fundraisers, student ambassadors for the sales program, CRM courses, and internships and competition requirements.
Primary research was conducted to understand various sales development programs from companies in the sales industry. The 12 companies that participated in this research were from Arizona State University’s Sales Advisory Board. These companies completed a survey that provided detailed information of their onboarding and training process as well as their opinions of Millennial employees.
From this research, recommendations were formed for employers,
• creating a collaborative and innovative culture
• A mentorship program
• work flexibility
• continuous learning
• sense of purpose
As for Arizona State’s Sales Program, recommendations include,
• a mentorship program between Sales Scholars and the Sales Advisory Board
• creating a sales lab
• implementing CRM curriculum in classes
• continued support from the Board and alumni of the sales program
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.
This research paper explores the effects of data variance on the quality of Artificial Intelligence image generation models and the impact on a viewer's perception of the generated images. The study examines how the quality and accuracy of the images produced by these models are influenced by factors such as size, labeling, and format of the training data. The findings suggest that reducing the training dataset size can lead to a decrease in image coherence, indicating that AI models get worse as the training dataset gets smaller. Moreover, the study makes surprising discoveries regarding AI image generation models that are trained on highly varied datasets. In addition, the study involves a survey in which people were asked to rate the subjective realism of the generated images on a scale ranging from 1 to 5 as well as sorting the images into their respective classes. The findings of this study emphasize the importance of considering dataset variance and size as a critical aspect of improving image generation models as well as the implications of using AI technology in the future.