The Department of Computer Science and Electrical Engineering (CSEE) offers bachelor’s degrees in computer science, electrical computer engineering, and information technology. We also have master’s degrees in both Electrical Engineering and Computer Science. CSEE participates in the UMKC Interdisciplinary Ph.D. program.

Collections in this community

Recent Submissions

  • Framework for Automatic Identification of Paper Watermarks with Chain Codes 

    Doynov, Plamen (University of Missouri--Kansas City, 2017)
    In this dissertation, I present a new framework for automated description, archiving, and identification of paper watermarks found in historical documents and manuscripts. The early manufacturers of paper have introduced ...
  • Point Cloud Compression and Low Latency Streaming 

    Ainala, Karthik (University of Missouri--Kansas City, 2017)
    With the commoditization of the 3D depth sensors, we can now very easily model real objects and scenes into digital domain which then can be used for variety of application in gaming, animation, virtual reality, immersive ...
  • Context Based Multi-Image Visual Question Answering (VQA) in Deep Learning 

    Peddinti, Sudhakar Reddy (University of Missouri--Kansas City, 2017)
    Image question answering has gained huge popularity in recent years due to advancements in Deep Learning technologies and computer processing hardware which are able to achieve higher accuracies with faster processing ...
  • One-Shot Learning Model for Cancer Diagnosis from Histopathological Images 

    Yarlagadda, Dig Vijay Kumar (University of Missouri--Kansas City, 2017)
    Cancer diagnosis from tissue biomarker scoring is a vital technique used in determining type and grade of cancer. This is a significant part of workload for pathologists, the process is tedious, time consuming, subjective, ...
  • Topic-Based Video Classification and Retrieval Using Machine Learning 

    Vadlamudi, Naga Krishna (University of Missouri--Kansas City, 2017)
    Machine learning has made significant progress for many real-world problems. The Deep Learning (DL) models proposed primarily concentrate on object detection, image classification, and image captioning. However, very ...
  • Study of QoE for DASH at Sub-representational Level 

    Swamy, Deepthi Basavapatna Narayana (University of Missouri--Kansas City, 2017)
    Dynamic Adaptive Streaming over HTTP has become the most commonly used technology in video streaming. It enables the player to adapt to the bitrate of the video while streaming to ensure continuous playback. DASH ...
  • H3DNET: A Deep Learning Framework for Hierarchical 3D Object Classification 

    Patel, Marmikkumar (University of Missouri--Kansas City, 2017)
    Deep learning has received a lot of attention in the fields such as speech recognition and image classification because of the ability to learn multiple levels of features from raw data. However, 3D deep learning is ...
  • Adaptive Routing Using SDN and NFV 

    Parab, Tejas Balkrishna (University of Missouri--Kansas City, 2017)
    In recent times, the primary focus of every ISP is to deliver high quality of service for multimedia applications. Due to ever-increasing network traffic, it is challenging for ISPs to accomplish optimal network ...
  • Advanced Design and Analysis of UWB U-slot Microstrip Patch Using Theory of Characteristic Modes 

    Khan, Mahrukh (University of Missouri--Kansas City, 2017)
    Ultra wide band is rapidly advancing as a high data rate wireless communication technology. As is the case in conventional wireless communication systems, an antenna also plays a very crucial role in UWB systems. ...
  • Numerical Simulation and Performance Optimization of Perovskite Solar Cell 

    Nanduri, Sai Naga Raghuram (University of Missouri--Kansas City, 2017)
    The organic-metal halide perovskite is emerging technology in photo voltaic solar cells. For any solar cell to get the significant efficiency depends on various design parameters such as material thickness, device ...
  • Building a Knowledge Graph for Food, Energy, and Water Systems 

    Gharibi, Mohamed (University of Missouri--Kansas City, 2017)
    A knowledge graph represents millions of facts and reliable information about people, places, and things. Several companies like Microsoft, Amazon, and Google have developed knowledge graphs to better customer experience. ...
  • NCC-EM: A hybrid framework for decision making with missing information 

    Chavakula, Varun (University of Missouri--Kansas City, 2017)
    Accounting for uncertainty is important in any data driven decision making. The popular treatment of uncertainties is to employ classical probability theory by expressing variables as random variables or processes in ...
  • Algorithms on Majority Problem 

    Tarafdar, Rajarshi (University of Missouri--Kansas City, 2017)
    The main idea of the paper to give solutions to the majority problem where we are counting the number of occurrences of the majority element more than half of the total number of the elements in the input set and also ...
  • Improving Cuckoo Hashing with Perfect Hashing 

    Chadalavada, Moulika (University of Missouri--Kansas City, 2017)
    In computer science, the data structure is a systematic way of organizing data such that it can be used efficiently. There are many hashing techniques that aim at storing keys in memory to increase key access efficiency ...
  • Distributed Perimeter Firewall Policy Management Framework 

    Maddumala, Mahesh Nath (University of Missouri--Kansas City, 2017)
    A perimeter firewall is the first line of defense that stops unwanted packets (based on defined firewall policies) entering the organization that deploys it. In the real world, every organization maintains a perimeter ...
  • Silicon on Ferroelectric Insulator Field Effect Transistor (SOFFET): A Radical Alternative to Overcome the Thermionic Limit 

    Es-Sakhi, Azzedin D. (University of Missouri--Kansas City, 2016)
    The path of down-scaling traditional MOSFET is reaching its technological, economic and, most importantly, fundamental physical limits. Before the dead-end of the roadmap, it is imperative to conduct a broad research ...
  • DL-DI: A Deep Learning Framework for Distributed, Incremental Image Classification 

    Maddula, Manikanta (University of Missouri--Kansas City, 2017)
    Deep Learning technologies show promise for dramatic advances in fields such as image classification and speech recognition. Deep Learning (DL) is a class of Machine Learning algorithms that involves learning of multiple ...
  • A Characteristic Mode Analysis of Conductive Nanowires and Microwires Above a Lossy Dielectric Half-Space 

    Kiddle, Daniel S. (University of Missouri--Kansas City, 2017)
    Nanowires possess extraordinary mechanical, thermoelectric and electromagnetic properties which led to their incorporation in a wide variety of applications. The purpose of this study is to investigate the effect of ...
  • A Data Science Approach to Extracting Insights About Cities and Zones Using Open Government Data 

    Gazzaz, Samaa (University of Missouri--Kansas City, 2017)
    In this research, we introduce a system that utilizes open government data and machine learning algorithms to extract meaningful insights about cities and zones in the United States. It is estimated that 4% of the ...
  • Mitigation of Remanence Flux in Power Transformers using Predetermined Method of De-Energization 

    Charlapally, Akhila (University of Missouri--Kansas City, 2017)
    Energization of large power transformers are subject to many transients that may complicate the successful completion of this process and ultimately reduce the expected life of these critical components. The first-time ...

View more