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The objective of my sabbatical leave project was to propose a new scheduling algorithm that extends the current MapReduce model to improve system performance. MapReduce, which has been popularized by Google, is a scalable tool that enables the processing of massive volumes of data.

the page describes the 3 projects Antonios completed: Data Network Traffic Analysis, Gossiping Algorithm Development and Analysis, and ZigBee Networks

In-depth research of theoretical foundations of spatio-temporal databases and their indexing, leading to the development of software.

Topics include: Fundamental wireless concepts: OSI Model, error detection, the ISM Band, modulation, WLAN, FHSS, DSSS, Wireless MANs, Bluetooth ; ZigBee essentials: applications, characteristics, device types, topologies, protocol architecture, and expanded ZigBee PRO features ; Physical layer: includes frequency bands, data rate, channels, data/management services, transmitter power, and receiver sensitivity ; MAC layer: data/management services, MAC layer information base, access methods, and frames ; Network layer: data entities, NIB, device configuration, starting network, addressing, discovery, channel scanning ; Application support sublayer and application layer: includes profiles, cluster format, attributes, device discovery, and binding ; ZigBee network security: includes encryption, trust center, security modes, and security management primitives ; Address assignment and routing techniques ; Alternative technologies: 6lowpan, WirelessHART, and Z-wave.:--pub. desc.

"This thorough primer examines the technological aspects of networking through a practical approach. Readers will gain knowledge of local area networks (LANs), wide area networks (WANs), the Internet, wireless LANs, wireless MANs, voice over IP (VoIP), as well as asynchronous transfer mode (ATM) and network security. Introductory chapters on foundational topics, such as data communications and computer architecture give readers the knowledge base they need to understand more complex networking concepts. This book effectively utilizes a practical approach to networking rather than a strict focus on theory or math, and requires no prior background in communications technology."--BOOK JACKET.

Fingerprinting-based indoor positioning systems require a significant amount of time to set up due to the need for prior, offline signal map creation. We propose a mobile phone-based indoor positioning system that can be set up in a short amount of time in any environment with existing Wi-Fi infrastructure. We introduce interpolation into a fingerprinting-based system to reduce the number of reference points needed, leading to a reduction in signal map creation time. The proposed interpolation method is used in conjunction with a particle filter algorithm to provide an accuracy level comparable to the state-of-the-art. We created signal maps at three separate locations using a 100%, 50%, 20%, and 10% scan coverage in order to evaluate the effectiveness of our interpolation on the localization error on a lower scan percentage. We evaluated our signal maps before and after interpolation using 16 tests which included both motion and stationary tests, as well as tests taken 2 and 3 weeks after the initial data gathering. We show that our interpolation method is able to reduce the effects of a dimensional mismatch between signal map reference point vectors and a test sample vector, as well as reduce the effects of signal map aging. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

The earth’s population is growing at a rapid rate, while the availability of water resources remains limited. Water is required for various purposes, including drinking, agriculture, industry, recreation, and development. Accurate forecasting of river flows can have a significant economic impact, particularly in agricultural water management and planning during water resource scarcity. Developing precise river flow forecasting models can greatly improve the management of water resources in many countries. In this study, we propose a two-phase model for predicting the flow of the Blackwater river located in the South Central United States. In the first phase, we use Multigene Symbolic Regression Genetic Programming (MG-GP) to develop a mathematical model. In the second phase, Particle Swarm Optimization (PSO) is employed to fine-tune the model parameters. Fine-tuning the MG-GP parameters improves the prediction accuracy of the model. The newly fine-tuned model exhibits 96% and 94% accuracy in training and testing cases, respectively © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

The spread of COVID-19 has thrown the world into a panic. We are constantly learning more about the virus every day, from how it spreads to who is more susceptible to becoming infected by different variants. Those with underlying respiratory conditions and other immunocompromised individuals need to be extra cautious regarding the virus. Many researchers have created COVID-19 trackers to detect the spread of COVID-19 around the world and show hot spots where COVID-19 cases are more prevalent. Previous work lacks the consideration of comorbidity as a factor of death rate. This work aims to create an agent-based model to predict comorbidity death rate caused by a health condition in addition to COVID-19. The model is evaluated using the symmetric mean absolute percentage error metric and proved to be very efficient.

High-Throughput DNA and RNA sequencing are revolutionizing precision oncology, enabling personalized therapies such as cancer vaccines designed to target tumor-specific neoepitopes generated by somatic mutations expressed in cancer cells. Identification of these neoepitopes from next-generation sequencing data of clinical samples remains challenging and requires the use of complex bioinformatics pipelines. In this paper, we present GeNeo, a bioinformatics toolbox for genomics-guided neoepitope prediction. GeNeo includes a comprehensive set of tools for somatic variant calling and filtering, variant validation, and neoepitope prediction and filtering. For ease of use, GeNeo tools can be accessed via web-based interfaces deployed on a Galaxy portal publicly accessible at https://neo.engr.uconn.edu/. A virtual machine image for running GeNeo locally is also available to academic users upon request. © Copyright 2023, Mary Ann Liebert, Inc., publishers 2023.

Subcellular localization of messenger RNA (mRNAs) plays a pivotal role in the regulation of gene expression, cell migration as well as in cellular adaptation. Experiment techniques for pinpointing the subcellular localization of mRNAs are laborious, time-consuming and expensive. Therefore, in silico approaches for this purpose are attaining great attention in the RNA community.

As more people rely on smartphones to store sensitive information, the need for robust security measures is all the more pressing. Because traditional one shot authentication methods like PINs and passwords are vulnerable to various attacks, we present a behavioral biometrics based smartphone authentication system using swipes. While previous research focused on a single kind of swipe, our data set features swipes using different fingers and directions collected from 36 users across three sessions. In our system, we experimented with support vector machine (SVM) and random forest (RF) classifiers. We investigated which finger, direction, and classifier provided the best individual swipe authentication results. Then, we analyzed whether fusion of different fingers and directions improved results. The best unimodal result came from a rightward swipe with right thumb using SVM, which resulted in an area under ROC curve (AUC) of 0.936 and an equal error rate (EER) of 0.135. We found that swipes using thumbs offered better performance. Fusion improves results for the most part, and our best result was the combination of a leftward swipe with right thumb and a leftward swipe with left thumb. This combination gave an AUC of 0.969 and EER of 0.081 with the SVM classifier.

The primary goals of this study are to determine if the datasets of positive COVID-19 test cases and CO2 emissions from Connecticut over the span of March 24th, 2020-October 31, 2021 are in any ways correlated. With climate change a prominent issue facing the entire world today, it is important to explore methods of providing records of past patterns of greenhouse gas emissions in order to inform decision making that could reduce future ones. Autoregressive integrated moving average (ARIMA) modeling is also implemented in this paper to provide forecasting based on CO2 emissions in CT starting from 2019. The most significant results from this paper are as follows: the CO2 emission data of transportation sectors including ground transportation, domestics aviation, and international aviation and weekly COVID-19 positive test cases data has a strong relationship during the first 28 weeks of the pandemic with a correlation of -86.34%. The CO2 emissions experienced on average a -22.96% change of pre-pandemic vs during initial quarantine conditions and at most a - 44.48% change when comparing the pre-pandemic mean to the during initial quarantine minimum value. Lastly, the ARIMA model found to have the lowest Akaike information criterion (AIC) was ARIMA (4,0,4). In conclusion, in the event of a collective global pandemic and lockdown conditions, less traveling resulting in a correlated decrease of CO2 emissions. This means that perhaps concentrated efforts on reducing unnecessary travel could help mitigate the levels of carbon dioxide emissions as a more long-term solution to climate change opposed to the pandemic’s short-term example.

This work explores using Probabilistic Context Free Grammars and Artificial Neural Networks as possible machine learning models for classifying introns into major and minor introns. It presents an intron classification framework that combines probabilistic context free grammars and support vector machines. It also assesses the computational prediction power of these two models in comparison to the Position Weight Matrices technique, which is currently the exclusively used model for intron classification. The comparison is done through experimental analysis, and it shows promising results for Probabilistic Context Free Grammars and Artificial Neural Networks. © 2022 IEEE.