Call centers with a postponed callback offer

We study a call center model with a postponed callback option. A customer at the head of the queue whose elapsed waiting time achieves a given threshold receives a voice message mentioning the option to be called back later. This callback option differs from the traditional ones found in the literature where the callback offer is given at customer’s arrival. We approximate this system by a two-dimensional Markov chain, with one dimension being a unit of a discretization of the waiting time. We next show that this approximation model converges to the exact one. This allows us to obtain explicitly the performance measures without abandonment and to compute them numerically otherwise. From the performance analysis, we derive a series of practical insights and recommendations for a clever use of the callback offer. In particular, we show that this time-based offer outperforms traditional ones when considering the waiting time of inbound calls

Spartan Daily, April 25, 1994

Volume 102, Issue 54https://scholarworks.sjsu.edu/spartandaily/8554/thumbnail.jp

Spartan Daily, November 21, 1997

Volume 109, Issue 60https://scholarworks.sjsu.edu/spartandaily/9207/thumbnail.jp

Superpixel labeling for medical image segmentation

openNowadays, most methods for image segmentation consider images in a pixel- wise manner, which is a huge job and also time-consuming. On the other hand, superpixel labeling can make the segmentation task easier in some aspects. First, superpixels carry more information than pixels because they usually follow the edges present in the image. Furthermore, superpixels have perceptual meaning, and finally, they can be very useful in computationally demanding problems, since by mapping pixels to superpixels we are reducing the complexity of the problem. In this thesis, we propose to do superpixel-wise labeling on two med- ical image datasets including ISIC Lesion Skin and Chest X-ray, then we feed them to the U-Net Convolutional Neural Network (CNN) DoubleU-Net and Dual-Aggregation Transformer (DuAT) network to segment our images in term of superpixels. Three different methods of labeling are used in this thesis: Su- perpixel labeling, Extended Superpixel Labeling (Distance-base Labeling), and Random Walk Superpixel labeling. The Superpixel labeled ground truths are used just for training. For the evaluation, we consider the original image and also the original binary ground truth. We considered four different superpixel algorithms, namely Simple Linear Iterative Clustering (SLIC), Felsenszwalb Hut- tenlocher (FH), QuickShift (QS) , and Superpixels Extracted via Energy-Driven Sampling (SEEDS). We evaluate the segmentation result with metrics such as Dice Coefficient, Precision, Intersection Over Union (IOU), and Sensitivity. Our results show the accuracy of 0.89 and 0.95 percent in dice coefficient for skin lesion and chest X-ray datasets respectively. Key Words: Superpixels, Medical Images, U-Net, DoubleU-Net, Image seg- mentation, CNN, DuAT, SEEDS.Nowadays, most methods for image segmentation consider images in a pixel- wise manner, which is a huge job and also time-consuming. On the other hand, superpixel labeling can make the segmentation task easier in some aspects. First, superpixels carry more information than pixels because they usually follow the edges present in the image. Furthermore, superpixels have perceptual meaning, and finally, they can be very useful in computationally demanding problems, since by mapping pixels to superpixels we are reducing the complexity of the problem. In this thesis, we propose to do superpixel-wise labeling on two med- ical image datasets including ISIC Lesion Skin and Chest X-ray, then we feed them to the U-Net Convolutional Neural Network (CNN) DoubleU-Net and Dual-Aggregation Transformer (DuAT) network to segment our images in term of superpixels. Three different methods of labeling are used in this thesis: Su- perpixel labeling, Extended Superpixel Labeling (Distance-base Labeling), and Random Walk Superpixel labeling. The Superpixel labeled ground truths are used just for training. For the evaluation, we consider the original image and also the original binary ground truth. We considered four different superpixel algorithms, namely Simple Linear Iterative Clustering (SLIC), Felsenszwalb Hut- tenlocher (FH), QuickShift (QS) , and Superpixels Extracted via Energy-Driven Sampling (SEEDS). We evaluate the segmentation result with metrics such as Dice Coefficient, Precision, Intersection Over Union (IOU), and Sensitivity. Our results show the accuracy of 0.89 and 0.95 percent in dice coefficient for skin lesion and chest X-ray datasets respectively. Key Words: Superpixels, Medical Images, U-Net, DoubleU-Net, Image seg- mentation, CNN, DuAT, SEEDS

The Johnsonian Spring Edition Jan. 29, 1992

The Johnsonian is the weekly student newspaper of Winthrop University. It is published during fall and spring semesters with the exception of university holidays and exam periods. We have proudly served the Winthrop and Rock Hill community since 1923.https://digitalcommons.winthrop.edu/thejohnsonian1990s/1058/thumbnail.jp

Strengthening Medicaid: Challenges States Must Address As The Public Health Emergency Ends

Medicaid is an essential program that provides health services for millions of people who otherwise could not afford them. Medicaid improves health outcomes for recipients, improves their financial stability, provides access to potentially life-saving healthcare, creates thousands of jobs that bolster our local economies, and helps reduce economic and racial disparities in health insurance and healthcare access. While Medicaid improves the health and lives of recipients and benefits the healthcare system and the US economy, Medicaid systems for enrollment, renewal/redetermination, and using Medicaid coverage need improvement. All people who meet Medicaid eligibility criteria are guaranteed coverage. However, many who are eligible struggle to enroll in and maintain Medicaid coverage. Barriers to obtaining and renewing coverage and accessing services often make it challenging and time-consuming to navigate the system. Many who successfully enrolled face further dissatisfaction and stress as Medicaid leaves their needs unaddressed. Research shows that Medicaid recipients experience many barriers to accessing quality healthcare

Growing Old in America: Expectations vs. Reality

Presents survey results on indicators of old age, "felt age," and the upsides and downsides of growing older, by age, gender, income, and race/ethnicity. Highlights gaps between perceptions of younger adults and the self-reported experiences of seniors