CS 765-01: Foundations of Neurocomputation

This course is designed to help you develop a solid understanding of neural network algorithms and architectures. At the end of this course you should be able to read and critically evaluate most neural network papers published in major journals, (e.g. IEEE Transaction on Neural Networks, Neural Networks, and Neural Computation). IN addition, you should be able to implement a broad range of network architectures and learning algorithms for a variety of applications

Biliary stones: an atypical cause of abdominal pain in paediatric age group

OBJECTIVE: To identify Paediatric patients with biliary stone disease presenting to a tertiary care hospital in order to determine the etiology, presentation and management. METHODS: Retrospective study of all cases of ultrasonographically proven biliary stones under the age of 15 years from January 1988 to December 2008. Data included their risk factors, complications, management and outcome. RESULTS: Total 32 patients were identified with biliary stones, treated in the hospital. Mean age at presentation was 8.25 +/- 3.33 years. Sixteen patients underwent cholecystectomy. CONCLUSION: Paediatric cholelithiasis is an atypical and under-diagnosed cause of abdominal pain in childhood. True prevalence of the disease may be higher than reported. Appropriate surgical intervention is required in patients with symptomatic and complicated biliary lithiasis

Smart Energy Management System for Minimizing Electricity Cost and Peak to Average Ratio in Residential Areas with Hybrid Genetic Flower Pollination Algorithm

Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous regarding appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, ourproposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC

Predictive Analysis and Comparison of Various Models on Esports Competitions

eSports has emerged as a popular genre for players and viewers, promoting a global industry in entertainment. The study of eSports has grown to resolve the need for data driven feedback, which focuses on assessment, strategy, and prediction of cyber-athletes. The focus of this project is to create and compare various models to predict the likely winner for professional games based on the data recorded from various eSports tournament matches. Pro-games have the top industry and audience attention but are restricted in number. The project is dominant on Deep Learning and Machine Learning, where the predictions are made using the model that we will build. This project can play a big part in gauging which model is most suitable for predicting the results of a match